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  Subjects -> MATHEMATICS (Total: 1040 journals)
    - APPLIED MATHEMATICS (83 journals)
    - GEOMETRY AND TOPOLOGY (23 journals)
    - MATHEMATICS (770 journals)
    - MATHEMATICS (GENERAL) (43 journals)
    - NUMERICAL ANALYSIS (23 journals)
    - PROBABILITIES AND MATH STATISTICS (98 journals)

MATHEMATICS (770 journals)                  1 2 3 4 | Last

Showing 1 - 200 of 538 Journals sorted alphabetically
Abakós     Open Access   (Followers: 5)
Abhandlungen aus dem Mathematischen Seminar der Universitat Hamburg     Hybrid Journal   (Followers: 4)
Academic Voices : A Multidisciplinary Journal     Open Access   (Followers: 2)
Accounting Perspectives     Full-text available via subscription   (Followers: 7)
ACM Transactions on Algorithms (TALG)     Hybrid Journal   (Followers: 16)
ACM Transactions on Computational Logic (TOCL)     Hybrid Journal   (Followers: 3)
ACM Transactions on Mathematical Software (TOMS)     Hybrid Journal   (Followers: 6)
ACS Applied Materials & Interfaces     Hybrid Journal   (Followers: 38)
Acta Applicandae Mathematicae     Hybrid Journal   (Followers: 1)
Acta Mathematica     Hybrid Journal   (Followers: 12)
Acta Mathematica Hungarica     Hybrid Journal   (Followers: 2)
Acta Mathematica Scientia     Full-text available via subscription   (Followers: 5)
Acta Mathematica Sinica, English Series     Hybrid Journal   (Followers: 6)
Acta Mathematica Vietnamica     Hybrid Journal  
Acta Mathematicae Applicatae Sinica, English Series     Hybrid Journal  
Advanced Science Letters     Full-text available via subscription   (Followers: 12)
Advances in Applied Clifford Algebras     Hybrid Journal   (Followers: 4)
Advances in Calculus of Variations     Hybrid Journal   (Followers: 6)
Advances in Catalysis     Full-text available via subscription   (Followers: 5)
Advances in Complex Systems     Hybrid Journal   (Followers: 9)
Advances in Computational Mathematics     Hybrid Journal   (Followers: 21)
Advances in Decision Sciences     Open Access   (Followers: 3)
Advances in Difference Equations     Open Access   (Followers: 3)
Advances in Fixed Point Theory     Open Access   (Followers: 8)
Advances in Geosciences (ADGEO)     Open Access   (Followers: 17)
Advances in Linear Algebra & Matrix Theory     Open Access   (Followers: 10)
Advances in Materials Science     Open Access   (Followers: 17)
Advances in Mathematical Physics     Open Access   (Followers: 7)
Advances in Mathematics     Full-text available via subscription   (Followers: 15)
Advances in Nonlinear Analysis     Open Access  
Advances in Numerical Analysis     Open Access   (Followers: 7)
Advances in Operations Research     Open Access   (Followers: 12)
Advances in Porous Media     Full-text available via subscription   (Followers: 5)
Advances in Pure and Applied Mathematics     Hybrid Journal   (Followers: 8)
Advances in Pure Mathematics     Open Access   (Followers: 9)
Advances in Science and Research (ASR)     Open Access   (Followers: 6)
Aequationes Mathematicae     Hybrid Journal   (Followers: 2)
African Journal of Educational Studies in Mathematics and Sciences     Full-text available via subscription   (Followers: 7)
African Journal of Mathematics and Computer Science Research     Open Access   (Followers: 5)
Afrika Matematika     Hybrid Journal   (Followers: 1)
Air, Soil & Water Research     Open Access   (Followers: 13)
AKSIOMA Journal of Mathematics Education     Open Access   (Followers: 2)
Al-Jabar : Jurnal Pendidikan Matematika     Open Access   (Followers: 1)
Algebra and Logic     Hybrid Journal   (Followers: 7)
Algebra Colloquium     Hybrid Journal   (Followers: 4)
Algebra Universalis     Hybrid Journal   (Followers: 2)
Algorithmic Operations Research     Open Access   (Followers: 5)
Algorithms     Open Access   (Followers: 11)
Algorithms Research     Open Access   (Followers: 1)
American Journal of Computational and Applied Mathematics     Open Access   (Followers: 8)
American Journal of Mathematical Analysis     Open Access  
American Journal of Mathematics     Full-text available via subscription   (Followers: 6)
American Journal of Operations Research     Open Access   (Followers: 6)
An International Journal of Optimization and Control: Theories & Applications     Open Access   (Followers: 11)
Anadol University Journal of Science and Technology B : Theoritical Sciences     Open Access  
Analele Universitatii Ovidius Constanta - Seria Matematica     Open Access  
Analysis and Applications     Hybrid Journal   (Followers: 1)
Analysis and Mathematical Physics     Hybrid Journal   (Followers: 6)
Analysis Mathematica     Full-text available via subscription  
Analysis. International mathematical journal of analysis and its applications     Hybrid Journal   (Followers: 3)
Annales Mathematicae Silesianae     Open Access   (Followers: 2)
Annales mathématiques du Québec     Hybrid Journal   (Followers: 4)
Annales Universitatis Mariae Curie-Sklodowska, sectio A – Mathematica     Open Access   (Followers: 1)
Annales Universitatis Paedagogicae Cracoviensis. Studia Mathematica     Open Access  
Annali di Matematica Pura ed Applicata     Hybrid Journal   (Followers: 1)
Annals of Combinatorics     Hybrid Journal   (Followers: 4)
Annals of Data Science     Hybrid Journal   (Followers: 13)
Annals of Discrete Mathematics     Full-text available via subscription   (Followers: 7)
Annals of Mathematics     Full-text available via subscription   (Followers: 2)
Annals of Mathematics and Artificial Intelligence     Hybrid Journal   (Followers: 12)
Annals of PDE     Hybrid Journal  
Annals of Pure and Applied Logic     Open Access   (Followers: 4)
Annals of the Alexandru Ioan Cuza University - Mathematics     Open Access  
Annals of the Institute of Statistical Mathematics     Hybrid Journal   (Followers: 1)
Annals of West University of Timisoara - Mathematics     Open Access  
Annals of West University of Timisoara - Mathematics and Computer Science     Open Access   (Followers: 1)
Annuaire du Collège de France     Open Access   (Followers: 6)
ANZIAM Journal     Open Access   (Followers: 1)
Applicable Algebra in Engineering, Communication and Computing     Hybrid Journal   (Followers: 2)
Applications of Mathematics     Hybrid Journal   (Followers: 3)
Applied Categorical Structures     Hybrid Journal   (Followers: 4)
Applied Computational Intelligence and Soft Computing     Open Access   (Followers: 14)
Applied Mathematics     Open Access   (Followers: 4)
Applied Mathematics     Open Access   (Followers: 8)
Applied Mathematics & Optimization     Hybrid Journal   (Followers: 10)
Applied Mathematics - A Journal of Chinese Universities     Hybrid Journal   (Followers: 1)
Applied Mathematics and Nonlinear Sciences     Open Access  
Applied Mathematics Letters     Full-text available via subscription   (Followers: 4)
Applied Mathematics Research eXpress     Hybrid Journal   (Followers: 1)
Applied Network Science     Open Access   (Followers: 3)
Applied Numerical Mathematics     Hybrid Journal   (Followers: 5)
Applied Spatial Analysis and Policy     Hybrid Journal   (Followers: 6)
Arab Journal of Mathematical Sciences     Open Access   (Followers: 4)
Arabian Journal of Mathematics     Open Access   (Followers: 2)
Archive for Mathematical Logic     Hybrid Journal   (Followers: 3)
Archive of Applied Mechanics     Hybrid Journal   (Followers: 6)
Archive of Numerical Software     Open Access  
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 6)
Arkiv för Matematik     Hybrid Journal   (Followers: 1)
Armenian Journal of Mathematics     Open Access   (Followers: 1)
Arnold Mathematical Journal     Hybrid Journal   (Followers: 1)
Artificial Satellites     Open Access   (Followers: 25)
Asia-Pacific Journal of Operational Research     Hybrid Journal   (Followers: 3)
Asian Journal of Algebra     Open Access   (Followers: 1)
Asian-European Journal of Mathematics     Hybrid Journal   (Followers: 3)
Australian Mathematics Teacher, The     Full-text available via subscription   (Followers: 7)
Australian Primary Mathematics Classroom     Full-text available via subscription   (Followers: 5)
Australian Senior Mathematics Journal     Full-text available via subscription   (Followers: 2)
Automatic Documentation and Mathematical Linguistics     Hybrid Journal   (Followers: 5)
Axioms     Open Access   (Followers: 1)
Baltic International Yearbook of Cognition, Logic and Communication     Open Access   (Followers: 1)
Basin Research     Hybrid Journal   (Followers: 5)
BIBECHANA     Open Access   (Followers: 2)
Biomath     Open Access  
BIT Numerical Mathematics     Hybrid Journal   (Followers: 1)
Boletim Cearense de Educação e História da Matemática     Open Access  
Boletim de Educação Matemática     Open Access  
Boletín de la Sociedad Matemática Mexicana     Hybrid Journal  
Bollettino dell'Unione Matematica Italiana     Full-text available via subscription   (Followers: 2)
British Journal of Mathematical and Statistical Psychology     Full-text available via subscription   (Followers: 20)
Bruno Pini Mathematical Analysis Seminar     Open Access  
Buletinul Academiei de Stiinte a Republicii Moldova. Matematica     Open Access   (Followers: 13)
Bulletin des Sciences Mathamatiques     Full-text available via subscription   (Followers: 4)
Bulletin of Dnipropetrovsk University. Series : Communications in Mathematical Modeling and Differential Equations Theory     Open Access   (Followers: 3)
Bulletin of Mathematical Sciences     Open Access   (Followers: 1)
Bulletin of Symbolic Logic     Full-text available via subscription   (Followers: 2)
Bulletin of the Australian Mathematical Society     Full-text available via subscription   (Followers: 2)
Bulletin of the Brazilian Mathematical Society, New Series     Hybrid Journal  
Bulletin of the Iranian Mathematical Society     Hybrid Journal  
Bulletin of the London Mathematical Society     Hybrid Journal   (Followers: 3)
Bulletin of the Malaysian Mathematical Sciences Society     Hybrid Journal  
Calculus of Variations and Partial Differential Equations     Hybrid Journal  
Canadian Journal of Mathematics / Journal canadien de mathématiques     Hybrid Journal  
Canadian Journal of Science, Mathematics and Technology Education     Hybrid Journal   (Followers: 20)
Canadian Mathematical Bulletin     Hybrid Journal  
Carpathian Mathematical Publications     Open Access   (Followers: 1)
Catalysis in Industry     Hybrid Journal   (Followers: 1)
CEAS Space Journal     Hybrid Journal   (Followers: 2)
CHANCE     Hybrid Journal   (Followers: 5)
Chaos, Solitons & Fractals     Hybrid Journal   (Followers: 3)
Chaos, Solitons & Fractals : X     Open Access  
ChemSusChem     Hybrid Journal   (Followers: 8)
Chinese Annals of Mathematics, Series B     Hybrid Journal  
Chinese Journal of Catalysis     Full-text available via subscription   (Followers: 2)
Chinese Journal of Mathematics     Open Access  
Clean Air Journal     Full-text available via subscription   (Followers: 1)
Cogent Mathematics     Open Access   (Followers: 2)
Cognitive Computation     Hybrid Journal   (Followers: 3)
Collectanea Mathematica     Hybrid Journal  
COMBINATORICA     Hybrid Journal  
Combinatorics, Probability and Computing     Hybrid Journal   (Followers: 4)
Combustion Theory and Modelling     Hybrid Journal   (Followers: 15)
Commentarii Mathematici Helvetici     Hybrid Journal  
Communications in Advanced Mathematical Sciences     Open Access  
Communications in Combinatorics and Optimization     Open Access  
Communications in Contemporary Mathematics     Hybrid Journal  
Communications in Mathematical Physics     Hybrid Journal   (Followers: 4)
Communications On Pure & Applied Mathematics     Hybrid Journal   (Followers: 4)
Complex Analysis and its Synergies     Open Access   (Followers: 3)
Complex Variables and Elliptic Equations: An International Journal     Hybrid Journal  
Composite Materials Series     Full-text available via subscription   (Followers: 9)
Compositio Mathematica     Full-text available via subscription  
Comptes Rendus Mathematique     Full-text available via subscription  
Computational and Applied Mathematics     Hybrid Journal   (Followers: 4)
Computational and Mathematical Methods in Medicine     Open Access   (Followers: 2)
Computational and Mathematical Organization Theory     Hybrid Journal   (Followers: 2)
Computational Complexity     Hybrid Journal   (Followers: 4)
Computational Mathematics and Modeling     Hybrid Journal   (Followers: 10)
Computational Mechanics     Hybrid Journal   (Followers: 5)
Computational Methods and Function Theory     Hybrid Journal  
Computational Optimization and Applications     Hybrid Journal   (Followers: 8)
Computers & Mathematics with Applications     Full-text available via subscription   (Followers: 11)
Concrete Operators     Open Access   (Followers: 5)
Confluentes Mathematici     Hybrid Journal  
Contributions to Game Theory and Management     Open Access  
COSMOS     Hybrid Journal  
Cryptography and Communications     Hybrid Journal   (Followers: 13)
Cuadernos de Investigación y Formación en Educación Matemática     Open Access  
Cubo. A Mathematical Journal     Open Access  
Current Research in Biostatistics     Open Access   (Followers: 8)
Czechoslovak Mathematical Journal     Hybrid Journal   (Followers: 1)
Demographic Research     Open Access   (Followers: 15)
Demonstratio Mathematica     Open Access  
Dependence Modeling     Open Access  
Design Journal : An International Journal for All Aspects of Design     Hybrid Journal   (Followers: 31)
Developments in Clay Science     Full-text available via subscription   (Followers: 1)
Developments in Mineral Processing     Full-text available via subscription   (Followers: 3)
Dhaka University Journal of Science     Open Access  
Differential Equations and Dynamical Systems     Hybrid Journal   (Followers: 4)
Differentsial'nye Uravneniya     Open Access  
Digital Experiences in Mathematics Education     Hybrid Journal  
Discrete Mathematics     Hybrid Journal   (Followers: 8)
Discrete Mathematics & Theoretical Computer Science     Open Access  
Discrete Mathematics, Algorithms and Applications     Hybrid Journal   (Followers: 2)
Discussiones Mathematicae - General Algebra and Applications     Open Access  
Discussiones Mathematicae Graph Theory     Open Access   (Followers: 2)
Diskretnaya Matematika     Full-text available via subscription  
Dnipropetrovsk University Mathematics Bulletin     Open Access  
Doklady Akademii Nauk     Open Access  
Doklady Mathematics     Hybrid Journal  

        1 2 3 4 | Last

Similar Journals
Journal Cover
Algorithms
Journal Prestige (SJR): 0.217
Citation Impact (citeScore): 1
Number of Followers: 11  

  This is an Open Access Journal Open Access journal
ISSN (Print) 1999-4893
Published by MDPI Homepage  [222 journals]
  • Algorithms, Vol. 12, Pages 176: Online EEG Seizure Detection and
           Localization

    • Authors: Amirsalar Mansouri, Sanjay P. Singh, Khalid Sayood
      First page: 176
      Abstract: Epilepsy is one of the three most prevalent neurological disorders. A significant proportion of patients suffering from epilepsy can be effectively treated if their seizures are detected in a timely manner. However, detection of most seizures requires the attention of trained neurologists—a scarce resource. Therefore, there is a need for an automatic seizure detection capability. A tunable non-patient-specific, non-seizure-specific method is proposed to detect the presence and locality of a seizure using electroencephalography (EEG) signals. This multifaceted computational approach is based on a network model of the brain and a distance metric based on the spectral profiles of EEG signals. This computationally time-efficient and cost-effective automated epileptic seizure detection algorithm has a median latency of 8 s, a median sensitivity of 83%, and a median false alarm rate of 2.9%. Hence, it is capable of being used in portable EEG devices to aid in the process of detecting and monitoring epileptic patients.
      Citation: Algorithms
      PubDate: 2019-08-23
      DOI: 10.3390/a12090176
      Issue No: Vol. 12, No. 9 (2019)
       
  • Algorithms, Vol. 12, Pages 177: Simple K-Medoids Partitioning Algorithm
           for Mixed Variable Data

    • Authors: Weksi Budiaji, Friedrich Leisch
      First page: 177
      Abstract: A simple and fast k-medoids algorithm that updates medoids by minimizing the total distance within clusters has been developed. Although it is simple and fast, as its name suggests, it nonetheless has neglected local optima and empty clusters that may arise. With the distance as an input to the algorithm, a generalized distance function is developed to increase the variation of the distances, especially for a mixed variable dataset. The variation of the distances is a crucial part of a partitioning algorithm due to different distances producing different outcomes. The experimental results of the simple k-medoids algorithm produce consistently good performances in various settings of mixed variable data. It also has a high cluster accuracy compared to other distance-based partitioning algorithms for mixed variable data.
      Citation: Algorithms
      PubDate: 2019-08-24
      DOI: 10.3390/a12090177
      Issue No: Vol. 12, No. 9 (2019)
       
  • Algorithms, Vol. 12, Pages 178: Adaptive-Size Dictionary Learning Using
           Information Theoretic Criteria

    • Authors: Bogdan Dumitrescu, Ciprian Doru Giurcăneanu
      First page: 178
      Abstract: Finding the size of the dictionary is an open issue in dictionary learning (DL). We propose an algorithm that adapts the size during the learning process by using Information Theoretic Criteria (ITC) specialized to the DL problem. The algorithm is built on top of Approximate K-SVD (AK-SVD) and periodically removes the less used atoms or adds new random atoms, based on ITC evaluations for a small number of candidate sub-dictionaries. Numerical experiments on synthetic data show that our algorithm not only finds the true size with very good accuracy, but is also able to improve the representation error in comparison with AK-SVD knowing the true size.
      Citation: Algorithms
      PubDate: 2019-08-25
      DOI: 10.3390/a12090178
      Issue No: Vol. 12, No. 9 (2019)
       
  • Algorithms, Vol. 12, Pages 179: A Fast Particle-Locating Method for the
           Arbitrary Polyhedral Mesh

    • Authors: Zongyang Li, Yefei Wang, Le Wang
      First page: 179
      Abstract: A fast particle-locating method is proposed for the hybrid Euler–Lagrangian models on the arbitrary polyhedral mesh, which is of essential importance to improve the computational efficiency by searching the host cells for the tracked particles very efficiently. A background grid, i.e., a uniform Cartesian grid with a grid spacing much smaller than computational mesh, is constructed over the whole computational domain. The many-to-many mapping relation between the computational mesh and the background grid is then specified through a recursive tetrahedron neighbor searching procedure, after the tetrahedral decomposition of computational cells and a mapping inverse operation. Finally, the host cell is straightforwardly identified by the point-in-cell test among the optional elements determined based on the mapping relation. The proposed method is checked on three meshes with different types of the cells and compared with the existing methods in the literatures. The results reveal that the present method is highly efficient and easy to implement on the arbitrary polyhedral mesh.
      Citation: Algorithms
      PubDate: 2019-08-26
      DOI: 10.3390/a12090179
      Issue No: Vol. 12, No. 9 (2019)
       
  • Algorithms, Vol. 12, Pages 180: Nearest Embedded and Embedding Self-Nested
           Trees

    • Authors: Romain Azaïs
      First page: 180
      Abstract: Self-nested trees present a systematic form of redundancy in their subtrees and thus achieve optimal compression rates by directed acrylic graph (DAG) compression. A method for quantifying the degree of self-similarity of plants through self-nested trees was introduced by Godin and Ferraro in 2010. The procedure consists of computing a self-nested approximation, called the nearest embedding self-nested tree, that both embeds the plant and is the closest to it. In this paper, we propose a new algorithm that computes the nearest embedding self-nested tree with a smaller overall complexity, but also the nearest embedded self-nested tree. We show from simulations that the latter is mostly the closest to the initial data, which suggests that this better approximation should be used as a privileged measure of the degree of self-similarity of plants.
      Citation: Algorithms
      PubDate: 2019-08-29
      DOI: 10.3390/a12090180
      Issue No: Vol. 12, No. 9 (2019)
       
  • Algorithms, Vol. 12, Pages 181: A FEAST Algorithm for the Linear Response
           Eigenvalue Problem

    • Authors: Zhongming Teng, Linzhang Lu
      First page: 181
      Abstract: In the linear response eigenvalue problem arising from quantum chemistry and physics, one needs to compute several positive eigenvalues together with the corresponding eigenvectors. For such a task, in this paper, we present a FEAST algorithm based on complex contour integration for the linear response eigenvalue problem. By simply dividing the spectrum into a collection of disjoint regions, the algorithm is able to parallelize the process of solving the linear response eigenvalue problem. The associated convergence results are established to reveal the accuracy of the approximated eigenspace. Numerical examples are presented to demonstrate the effectiveness of our proposed algorithm.
      Citation: Algorithms
      PubDate: 2019-08-29
      DOI: 10.3390/a12090181
      Issue No: Vol. 12, No. 9 (2019)
       
  • Algorithms, Vol. 12, Pages 182: A Novel Hybrid Genetic-Whale Optimization
           Model for Ontology Learning from Arabic Text

    • Authors: Rania M. Ghoniem, Nawal Alhelwa, Khaled Shaalan
      First page: 182
      Abstract: Ontologies are used to model knowledge in several domains of interest, such as the biomedical domain. Conceptualization is the basic task for ontology building. Concepts are identified, and then they are linked through their semantic relationships. Recently, ontologies have constituted a crucial part of modern semantic webs because they can convert a web of documents into a web of things. Although ontology learning generally occupies a large space in computer science, Arabic ontology learning, in particular, is underdeveloped due to the Arabic language’s nature as well as the profundity required in this domain. The previously published research on Arabic ontology learning from text falls into three categories: developing manually hand-crafted rules, using ordinary supervised/unsupervised machine learning algorithms, or a hybrid of these two approaches. The model proposed in this work contributes to Arabic ontology learning in two ways. First, a text mining algorithm is proposed for extracting concepts and their semantic relations from text documents. The algorithm calculates the concept frequency weights using the term frequency weights. Then, it calculates the weights of concept similarity using the information of the ontology structure, involving (1) the concept’s path distance, (2) the concept’s distribution layer, and (3) the mutual parent concept’s distribution layer. Then, feature mapping is performed by assigning the concepts’ similarities to the concept features. Second, a hybrid genetic-whale optimization algorithm was proposed to optimize ontology learning from Arabic text. The operator of the G-WOA is a hybrid operator integrating GA’s mutation, crossover, and selection processes with the WOA’s processes (encircling prey, attacking of bubble-net, and searching for prey) to fulfill the balance between both exploitation and exploration, and to find the solutions that exhibit the highest fitness. For evaluating the performance of the ontology learning approach, extensive comparisons are conducted using different Arabic corpora and bio-inspired optimization algorithms. Furthermore, two publicly available non-Arabic corpora are used to compare the efficiency of the proposed approach with those of other languages. The results reveal that the proposed genetic-whale optimization algorithm outperforms the other compared algorithms across all the Arabic corpora in terms of precision, recall, and F-score measures. Moreover, the proposed approach outperforms the state-of-the-art methods of ontology learning from Arabic and non-Arabic texts in terms of these three measures.
      Citation: Algorithms
      PubDate: 2019-08-29
      DOI: 10.3390/a12090182
      Issue No: Vol. 12, No. 9 (2019)
       
  • Algorithms, Vol. 12, Pages 183: An Intelligent Warning Method for
           Diagnosing Underwater Structural Damage

    • Authors: Kexin Li, Jun Wang, Dawei Qi
      First page: 183
      Abstract: A number of intelligent warning techniques have been implemented for detecting underwater infrastructure diagnosis to partially replace human-conducted on-site inspections. However, the extensively varying real-world situation (e.g., the adverse environmental conditions, the limited sample space, and the complex defect types) can lead to challenges to the wide adoption of intelligent warning techniques. To overcome these challenges, this paper proposed an intelligent algorithm combing gray level co-occurrence matrix (GLCM) with self-organization map (SOM) for accurate diagnosis of the underwater structural damage. In order to optimize the generative criterion for GLCM construction, a triangle algorithm was proposed based on orthogonal experiments. The constructed GLCM were utilized to evaluate the texture features of the regions of interest (ROI) of micro-injury images of underwater structures and extracted damage image texture characteristic parameters. The digital feature screening (DFS) method was used to obtain the most relevant features as the input for the SOM network. According to the unique topology information of the SOM network, the classification result, recognition efficiency, parameters, such as the network layer number, hidden layer node, and learning step, were optimized. The robustness and adaptability of the proposed approach were tested on underwater structure images through the DFS method. The results showed that the proposed method revealed quite better performances and can diagnose structure damage in underwater realistic situations.
      Citation: Algorithms
      PubDate: 2019-08-30
      DOI: 10.3390/a12090183
      Issue No: Vol. 12, No. 9 (2019)
       
  • Algorithms, Vol. 12, Pages 184: Fault Diagnosis of Rolling Bearing Using
           Multiscale Amplitude-Aware Permutation Entropy and Random Forest

    • Authors: Yinsheng Chen, Tinghao Zhang, Wenjie Zhao, Zhongming Luo, Kun Sun
      First page: 184
      Abstract: A rolling bearing is an important connecting part between rotating machines. It is susceptible to mechanical stress and wear, which affect the running state of bearings. In order to effectively identify the fault types and analyze the fault severity of rolling bearings, a rolling bearing fault diagnosis method based on multiscale amplitude-aware permutation entropy (MAAPE) and random forest is proposed in this paper. The vibration signals of rolling bearings to be analyzed are decomposed into different coarse-grained time series by using the coarse-graining procedure in multiscale entropy, highlighting the fault dynamic characteristics of vibration signals at different scales. The fault features contained in the coarse-grained time series at different time scales are extracted by using amplitude-aware permutation entropy’s sensitive characteristics to signal amplitude and frequency changes to form fault feature vectors. The fault feature vector set is used to establish the random forest multi-classifier, and the fault type identification and fault severity analysis of rolling bearings is realized through random forest. In order to demonstrate the feasibility and effectiveness of the proposed method, experiments were fully conducted in this paper. The experimental results show that multiscale amplitude-aware permutation entropy can effectively extract fault features of rolling bearings from vibration signals, and the extracted feature vectors have high separability. Compared with other rolling bearing fault diagnosis methods, the proposed method not only has higher fault type identification accuracy, but also can analyze the fault severity of rolling bearings to some extent. The identification accuracy of four fault types is up to 96.0% and the fault recognition accuracy under different fault severity reached 92.8%.
      Citation: Algorithms
      PubDate: 2019-09-04
      DOI: 10.3390/a12090184
      Issue No: Vol. 12, No. 9 (2019)
       
  • Algorithms, Vol. 12, Pages 185: Consensus Tracking by Iterative Learning
           Control for Linear Heterogeneous Multiagent Systems Based on
           Fractional-Power Error Signals

    • Authors: Yu-Juan Luo, Cheng-Lin Liu, Guang-Ye Liu
      First page: 185
      Abstract: This paper deals with the consensus tracking problem of heterogeneous linear multiagent systems under the repeatable operation environment, and adopts a proportional differential (PD)-type iterative learning control (ILC) algorithm based on the fractional-power tracking error. According to graph theory and operator theory, convergence condition is obtained for the systems under the interconnection topology that contains a spanning tree rooted at the reference trajectory named as the leader. Our algorithm based on fractional-power tracking error achieves a faster convergence rate than the usual PD-type ILC algorithm based on the integer-order tracking error. Simulation examples illustrate the correctness of our proposed algorithm.
      Citation: Algorithms
      PubDate: 2019-09-05
      DOI: 10.3390/a12090185
      Issue No: Vol. 12, No. 9 (2019)
       
  • Algorithms, Vol. 12, Pages 186: Semi-Supervised Manifold Alignment Using
           Parallel Deep Autoencoders

    • Authors: Fayeem Aziz, Aaron S. W. Wong, Stephan Chalup
      First page: 186
      Abstract: The aim of manifold learning is to extract low-dimensional manifolds from high-dimensional data. Manifold alignment is a variant of manifold learning that uses two or more datasets that are assumed to represent different high-dimensional representations of the same underlying manifold. Manifold alignment can be successful in detecting latent manifolds in cases where one version of the data alone is not sufficient to extract and establish a stable low-dimensional representation. The present study proposes a parallel deep autoencoder neural network architecture for manifold alignment and conducts a series of experiments using a protein-folding benchmark dataset and a suite of new datasets generated by simulating double-pendulum dynamics with underlying manifolds of dimensions 2, 3 and 4. The dimensionality and topological complexity of these latent manifolds are above those occurring in most previous studies. Our experimental results demonstrate that the parallel deep autoencoder performs in most cases better than the tested traditional methods of semi-supervised manifold alignment. We also show that the parallel deep autoencoder can process datasets of different input domains by aligning the manifolds extracted from kinematics parameters with those obtained from corresponding image data.
      Citation: Algorithms
      PubDate: 2019-09-06
      DOI: 10.3390/a12090186
      Issue No: Vol. 12, No. 9 (2019)
       
  • Algorithms, Vol. 12, Pages 187: Using Graph Partitioning for Scalable
           Distributed Quantum Molecular Dynamics

    • Authors: Hristo N. Djidjev, Georg Hahn, Susan M. Mniszewski, Christian F. A. Negre, Anders M. N. Niklasson
      First page: 187
      Abstract: The simulation of the physical movement of multi-body systems at an atomistic level, with forces calculated from a quantum mechanical description of the electrons, motivates a graph partitioning problem studied in this article. Several advanced algorithms relying on evaluations of matrix polynomials have been published in the literature for such simulations. We aim to use a special type of graph partitioning to efficiently parallelize these computations. For this, we create a graph representing the zero–nonzero structure of a thresholded density matrix, and partition that graph into several components. Each separate submatrix (corresponding to each subgraph) is then substituted into the matrix polynomial, and the result for the full matrix polynomial is reassembled at the end from the individual polynomials. This paper starts by introducing a rigorous definition as well as a mathematical justification of this partitioning problem. We assess the performance of several methods to compute graph partitions with respect to both the quality of the partitioning and their runtime.
      Citation: Algorithms
      PubDate: 2019-09-07
      DOI: 10.3390/a12090187
      Issue No: Vol. 12, No. 9 (2019)
       
  • Algorithms, Vol. 12, Pages 188: A Compendium of Parameterized Problems at
           Higher Levels of the Polynomial Hierarchy

    • Authors: Ronald de Haan, Stefan Szeider
      First page: 188
      Abstract: We present a list of parameterized problems together with a complexity classification of whether they allow a fixed-parameter tractable reduction to SAT or not. These problems are parameterized versions of problems whose complexity lies at the second level of the Polynomial Hierarchy or higher.
      Citation: Algorithms
      PubDate: 2019-09-09
      DOI: 10.3390/a12090188
      Issue No: Vol. 12, No. 9 (2019)
       
  • Algorithms, Vol. 12, Pages 189: Parameterised Enumeration for Modification
           Problems

    • Authors: Nadia Creignou, Raïda Ktari, Arne Meier, Julian-Steffen Müller, Frédéric Olive, Heribert Vollmer
      First page: 189
      Abstract: Recently, Creignou et al. (Theory Comput. Syst. 2017), introduced the class DelayFPT into parameterised complexity theory in order to capture the notion of efficiently solvable parameterised enumeration problems. In this paper, we propose a framework for parameterised ordered enumeration and will show how to obtain enumeration algorithms running with an FPT delay in the context of general modification problems. We study these problems considering two different orders of solutions, namely, lexicographic order and order by size. Furthermore, we present two generic algorithmic strategies. The first one is based on the well-known principle of self-reducibility and is used in the context of lexicographic order. The second one shows that the existence of a neighbourhood structure among the solutions implies the existence of an algorithm running with FPT delay which outputs all solutions ordered non-decreasingly by their size.
      Citation: Algorithms
      PubDate: 2019-09-09
      DOI: 10.3390/a12090189
      Issue No: Vol. 12, No. 9 (2019)
       
  • Algorithms, Vol. 12, Pages 190: Parallelism Strategies for Big Data
           Delayed Transfer Entropy Evaluation

    • Authors: Jonas R. Dourado, Jordão Natal de Oliveira Júnior, Carlos D. Maciel
      First page: 190
      Abstract: Generated and collected data have been rising with the popularization of technologies such as Internet of Things, social media, and smartphone, leading big data term creation. One class of big data hidden information is causality. Among the tools to infer causal relationships, there is Delay Transfer Entropy (DTE); however, it has a high demanding processing power. Many approaches were proposed to overcome DTE performance issues such as GPU and FPGA implementations. Our study compared different parallel strategies to calculate DTE from big data series using a heterogeneous Beowulf cluster. Task Parallelism was significantly faster in comparison to Data Parallelism. With big data trend in sight, these results may enable bigger datasets analysis or better statistical evidence.
      Citation: Algorithms
      PubDate: 2019-09-09
      DOI: 10.3390/a12090190
      Issue No: Vol. 12, No. 9 (2019)
       
  • Algorithms, Vol. 12, Pages 191: Feedback-Based Integration of the Whole
           Process of Data Anonymization in a Graphical Interface

    • Authors: Bernhard Meindl, Matthias Templ
      First page: 191
      Abstract: The interactive, web-based point-and-click application presented in this article, allows anonymizing data without any knowledge in a programming language. Anonymization in data mining, but creating safe, anonymized data is by no means a trivial task. Both the methodological issues as well as know-how from subject matter specialists should be taken into account when anonymizing data. Even though specialized software such as sdcMicro exists, it is often difficult for nonexperts in a particular software and without programming skills to actually anonymize datasets without an appropriate app. The presented app is not restricted to apply disclosure limitation techniques but rather facilitates the entire anonymization process. This interface allows uploading data to the system, modifying them and to create an object defining the disclosure scenario. Once such a statistical disclosure control (SDC) problem has been defined, users can apply anonymization techniques to this object and get instant feedback on the impact on risk and data utility after SDC methods have been applied. Additional features, such as an Undo Button, the possibility to export the anonymized dataset or the required code for reproducibility reasons, as well its interactive features, make it convenient both for experts and nonexperts in R—the free software environment for statistical computing and graphics—to protect a dataset using this app.
      Citation: Algorithms
      PubDate: 2019-09-10
      DOI: 10.3390/a12090191
      Issue No: Vol. 12, No. 9 (2019)
       
  • Algorithms, Vol. 12, Pages 192: Coarsely Quantized Decoding and
           Construction of Polar Codes Using the Information Bottleneck Method

    • Authors: Syed Aizaz Ali Shah, Maximilian Stark, Gerhard Bauch
      First page: 192
      Abstract: The information bottleneck method is a generic clustering framework from the field of machine learning which allows compressing an observed quantity while retaining as much of the mutual information it shares with the quantity of primary relevance as possible. The framework was recently used to design message-passing decoders for low-density parity-check codes in which all the arithmetic operations on log-likelihood ratios are replaced by table lookups of unsigned integers. This paper presents, in detail, the application of the information bottleneck method to polar codes, where the framework is used to compress the virtual bit channels defined in the code structure and show that the benefits are twofold. On the one hand, the compression restricts the output alphabet of the bit channels to a manageable size. This facilitates computing the capacities of the bit channels in order to identify the ones with larger capacities. On the other hand, the intermediate steps of the compression process can be used to replace the log-likelihood ratio computations in the decoder with table lookups of unsigned integers. Hence, a single procedure produces a polar encoder as well as its tailored, quantized decoder. Moreover, we also use a technique called message alignment to reduce the space complexity of the quantized decoder obtained using the information bottleneck framework.
      Citation: Algorithms
      PubDate: 2019-09-10
      DOI: 10.3390/a12090192
      Issue No: Vol. 12, No. 9 (2019)
       
  • Algorithms, Vol. 12, Pages 193: Combining Satellite Images and Cadastral
           Information for Outdoor Autonomous Mapping and Navigation: A
           Proof-of-Concept Study in Citric Groves

    • Authors: Joaquín Torres-Sospedra, Patricio Nebot
      First page: 193
      Abstract: The development of robotic applications for agricultural environments has several problems which are not present in the robotic systems used for indoor environments. Some of these problems can be solved with an efficient navigation system. In this paper, a new system is introduced to improve the navigation tasks for those robots which operate in agricultural environments. Concretely, the paper focuses on the problem related to the autonomous mapping of agricultural parcels (i.e., an orange grove). The map created by the system will be used to help the robots navigate into the parcel to perform maintenance tasks such as weed removal, harvest, or pest inspection. The proposed system connects to a satellite positioning service to obtain the real coordinates where the robotic system is placed. With these coordinates, the parcel information is downloaded from an online map service in order to autonomously obtain a map of the parcel in a readable format for the robot. Finally, path planning is performed by means of Fast Marching techniques using the robot or a team of two robots. This paper introduces the proof-of-concept and describes all the necessary steps and algorithms to obtain the path planning just from the initial coordinates of the robot.
      Citation: Algorithms
      PubDate: 2019-09-11
      DOI: 10.3390/a12090193
      Issue No: Vol. 12, No. 9 (2019)
       
  • Algorithms, Vol. 12, Pages 194: Unsteady State Lightweight Iris
           Certification Based on Multi-Algorithm Parallel Integration

    • Authors: Liu Shuai, Liu Yuanning, Zhu Xiaodong, Zhang Kuo, Ding Tong, Li Xinlong, Wang Chaoqun
      First page: 194
      Abstract: Aimed at the one-to-one certification problem of unsteady state iris at different shooting times, a multi-algorithm parallel integration general model structure is proposed in this paper. The iris in the lightweight constrained state affected by defocusing, deflection, and illumination is taken as the research object, the existing algorithms are combined into the model structure effectively, and a one-to-one certification algorithm for lightweight constrained state unsteady iris was designed based on multi-algorithm integration and maximum trusted decision. In this algorithm, a sufficient number of iris internal feature points from the unstable state texture were extracted as effective iris information through the image processing layer composed of various filtering processing algorithms, thereby eliminating defocused interference. In the feature recognition layer, iris deflection interference was excluded by the improved methods of Gabor and Hamming and Haar and BP for the stable features extracted by the image processing layer, and two certification results were obtained by means of parallel recognition. The correct number of certifications for an algorithm under a certain lighting condition were counted. The method with the most correct number was set as the maximum trusted method under this lighting condition, and the results of the maximum trusted method were taken as the final decision, thereby eliminating the effect of illumination. Experiments using the JLU and CASIA iris libraries under the prerequisites in this paper show that the correct recognition rate of the algorithm can reach a high level of 98% or more, indicating that the algorithm can effectively improve the accuracy of the one-to-one certification of lightweight constrained state unsteady iris. Compared with the latest architecture algorithms, such as CNN and deep learning, the proposed algorithm is more suitable for the prerequisites presented in this paper, which has good environmental inclusiveness and can better improve existing traditional algorithms’ effectiveness through the design of a parallel integration model structure.
      Citation: Algorithms
      PubDate: 2019-09-12
      DOI: 10.3390/a12090194
      Issue No: Vol. 12, No. 9 (2019)
       
  • Algorithms, Vol. 12, Pages 195: An Intelligent Artificial Neural Network
           Modeling of a Magnetorheological Elastomer Isolator

    • Authors: Shiping Zhao, Yong Ma, Dingxin Leng
      First page: 195
      Abstract: Recently, magnetorheological elastomer (MRE) has been paid increasingly attention for vibration mitigation devices with the benefits of low power cost, fail safe performances, and fast responses. To make full use of the striking advantages of MRE device, a highly precise model should be developed to predict its dynamic performances. In the work, an MRE isolator in shear–squeeze mixed mode is developed and tested under dynamic loadings. The nonlinear performances in various displacement amplitude and currents are shown. An artificial neural network model with a back-propagation algorithm is proposed to characterize the nonlinear hysteresis of MRE isolator for its implementation in vibration control applications. This model utilized the displacement, velocity, and applied current as inputs and output force as output. The results show that the proposed model has high modeling accuracy and can well portray the complicated behaviors of MRE isolator with different excitations, which shows a fundamental basis for structural vibration control.
      Citation: Algorithms
      PubDate: 2019-09-16
      DOI: 10.3390/a12090195
      Issue No: Vol. 12, No. 9 (2019)
       
  • Algorithms, Vol. 12, Pages 196: Faster and Better Nested Dissection Orders
           for Customizable Contraction Hierarchies

    • Authors: Lars Gottesbüren, Michael Hamann, Tim Niklas Uhl, Dorothea Wagner
      First page: 196
      Abstract: Graph partitioning has many applications. We consider the acceleration of shortest path queries in road networks using Customizable Contraction Hierarchies (CCH). It is based on computing a nested dissection order by recursively dividing the road network into parts. Recently, with FlowCutter and Inertial Flow, two flow-based graph bipartitioning algorithms have been proposed for road networks. While FlowCutter achieves high-quality results and thus fast query times, it is rather slow. Inertial Flow is particularly fast due to the use of geographical information while still achieving decent query times. We combine the techniques of both algorithms to achieve more than six times faster preprocessing times than FlowCutter and even faster queries on the Europe road network. We show that, using 16 cores of a shared-memory machine, this preprocessing needs four minutes.
      Citation: Algorithms
      PubDate: 2019-09-16
      DOI: 10.3390/a12090196
      Issue No: Vol. 12, No. 9 (2019)
       
  • Algorithms, Vol. 12, Pages 197: Compression Challenges in Large Scale
           Partial Differential Equation Solvers

    • Authors: Sebastian Götschel, Martin Weiser
      First page: 197
      Abstract: Solvers for partial differential equations (PDEs) are one of the cornerstones of computational science. For large problems, they involve huge amounts of data that need to be stored and transmitted on all levels of the memory hierarchy. Often, bandwidth is the limiting factor due to the relatively small arithmetic intensity, and increasingly due to the growing disparity between computing power and bandwidth. Consequently, data compression techniques have been investigated and tailored towards the specific requirements of PDE solvers over the recent decades. This paper surveys data compression challenges and discusses examples of corresponding solution approaches for PDE problems, covering all levels of the memory hierarchy from mass storage up to the main memory. We illustrate concepts for particular methods, with examples, and give references to alternatives.
      Citation: Algorithms
      PubDate: 2019-09-17
      DOI: 10.3390/a12090197
      Issue No: Vol. 12, No. 9 (2019)
       
  • Algorithms, Vol. 12, Pages 198: Correspondence between Multilevel Graph
           Partitions and Tree Decompositions

    • Authors: Michael Hamann, Ben Strasser
      First page: 198
      Abstract: We present a mapping between rooted tree decompositions and node separator based multilevel graph partitions. Significant research into both tree decompositions and graph partitions exists. We hope that our result allows for an easier knowledge transfer between the two research avenues.
      Citation: Algorithms
      PubDate: 2019-09-17
      DOI: 10.3390/a12090198
      Issue No: Vol. 12, No. 9 (2019)
       
  • Algorithms, Vol. 12, Pages 199: Fairness in Algorithmic Decision-Making:
           Applications in Multi-Winner Voting, Machine Learning, and Recommender
           Systems

    • Authors: Yash Raj Shrestha, Yongjie Yang
      First page: 199
      Abstract: Algorithmic decision-making has become ubiquitous in our societal and economic lives. With more and more decisions being delegated to algorithms, we have also encountered increasing evidence of ethical issues with respect to biases and lack of fairness pertaining to algorithmic decision-making outcomes. Such outcomes may lead to detrimental consequences to minority groups in terms of gender, ethnicity, and race. As a response, recent research has shifted from design of algorithms that merely pursue purely optimal outcomes with respect to a fixed objective function into ones that also ensure additional fairness properties. In this study, we aim to provide a broad and accessible overview of the recent research endeavor aimed at introducing fairness into algorithms used in automated decision-making in three principle domains, namely, multi-winner voting, machine learning, and recommender systems. Even though these domains have developed separately from each other, they share commonality with respect to decision-making as an application, which requires evaluation of a given set of alternatives that needs to be ranked with respect to a clearly defined objective function. More specifically, these relate to tasks such as (1) collectively selecting a fixed number of winner (or potentially high valued) alternatives from a given initial set of alternatives; (2) clustering a given set of alternatives into disjoint groups based on various similarity measures; or (3) finding a consensus ranking of entire or a subset of given alternatives. To this end, we illustrate a multitude of fairness properties studied in these three streams of literature, discuss their commonalities and interrelationships, synthesize what we know so far, and provide a useful perspective for future research.
      Citation: Algorithms
      PubDate: 2019-09-18
      DOI: 10.3390/a12090199
      Issue No: Vol. 12, No. 9 (2019)
       
  • Algorithms, Vol. 12, Pages 147: An Optimized Differential Step-Size LMS
           Algorithm

    • Authors: Alexandru-George Rusu, Silviu Ciochină, Constantin Paleologu, Jacob Benesty
      First page: 147
      Abstract: Adaptive algorithms with differential step-sizes (related to the filter coefficients) are well known in the literature, most frequently as “proportionate” algorithms. Usually, they are derived on a heuristic basis. In this paper, we introduce an algorithm resulting from an optimization criterion. Thereby, we obtain a benchmark algorithm and also another version with lower computational complexity, which is rigorously valid for less correlated input signals. Simulation results confirm the theory and outline the performance of the algorithms. Unfortunately, the good performance is obtained by an important increase in computational complexity. Nevertheless, the proposed algorithms could represent useful benchmarks in the field.
      Citation: Algorithms
      PubDate: 2019-07-24
      DOI: 10.3390/a12080147
      Issue No: Vol. 12, No. 8 (2019)
       
  • Algorithms, Vol. 12, Pages 148: Variational Calculus Approach to Optimal
           Interception Task of a Ballistic Missile in 1D and 2D Cases

    • Authors: Dariusz Horla
      First page: 148
      Abstract: The paper presents the application of variational calculus to achieve the optimal design of the open-loop control law in the process of anti-ballistic missile interception task. It presents the analytical results in the form of appropriate Euler–Lagrange equations for three different performance indices, with a simple model of the rocket and the missile, based on the conservation of momentum principle. It also presents the software program enabling rapid simulation of the interception process with selected parameters, parametric analysis, as well as easy potential modification by other researchers, as it is written in open code as m-function of Matlab.
      Citation: Algorithms
      PubDate: 2019-07-26
      DOI: 10.3390/a12080148
      Issue No: Vol. 12, No. 8 (2019)
       
  • Algorithms, Vol. 12, Pages 149: Mapping a Guided Image Filter on the HARP
           Reconfigurable Architecture Using OpenCL

    • Authors: Thomas Faict, Erik H. D’Hollander, Bart Goossens
      First page: 149
      Abstract: Intel recently introduced the Heterogeneous Architecture Research Platform, HARP. In this platform, the Central Processing Unit and a Field-Programmable Gate Array are connected through a high-bandwidth, low-latency interconnect and both share DRAM memory. For this platform, Open Computing Language (OpenCL), a High-Level Synthesis (HLS) language, is made available. By making use of HLS, a faster design cycle can be achieved compared to programming in a traditional hardware description language. This, however, comes at the cost of having less control over the hardware implementation. We will investigate how OpenCL can be applied to implement a real-time guided image filter on the HARP platform. In the first phase, the performance-critical parameters of the OpenCL programming model are defined using several specialized benchmarks. In a second phase, the guided image filter algorithm is implemented using the insights gained in the first phase. Both a floating-point and a fixed-point implementation were developed for this algorithm, based on a sliding window implementation. This resulted in a maximum floating-point performance of 135 GFLOPS, a maximum fixed-point performance of 430 GOPS and a throughput of HD color images at 74 frames per second.
      Citation: Algorithms
      PubDate: 2019-07-27
      DOI: 10.3390/a12080149
      Issue No: Vol. 12, No. 8 (2019)
       
  • Algorithms, Vol. 12, Pages 150: Defacement Detection with Passive
           Adversaries

    • Authors: Francesco Bergadano, Fabio Carretto, Fabio Cogno, Dario Ragno
      First page: 150
      Abstract: A novel approach to defacement detection is proposed in this paper, addressing explicitly the possible presence of a passive adversary. Defacement detection is an important security measure for Web Sites and Applications, aimed at avoiding unwanted modifications that would result in significant reputational damage. As in many other anomaly detection contexts, the algorithm used to identify possible defacements is obtained via an Adversarial Machine Learning process. We consider an exploratory setting, where the adversary can observe the detector’s alarm-generating behaviour, with the purpose of devising and injecting defacements that will pass undetected. It is then necessary to make to learning process unpredictable, so that the adversary will be unable to replicate it and predict the classifier’s behaviour. We achieve this goal by introducing a secret key—a key that our adversary does not know. The key will influence the learning process in a number of different ways, that are precisely defined in this paper. This includes the subset of examples and features that are actually used, the time of learning and testing, as well as the learning algorithm’s hyper-parameters. This learning methodology is successfully applied in this context, by using the system with both real and artificially modified Web sites. A year-long experimentation is also described, referred to the monitoring of the new Web Site of a major manufacturing company.
      Citation: Algorithms
      PubDate: 2019-07-29
      DOI: 10.3390/a12080150
      Issue No: Vol. 12, No. 8 (2019)
       
  • Algorithms, Vol. 12, Pages 151: Soft Iterative Decoding Algorithms for
           Rateless Codes in Satellite Systems

    • Authors: Meixiang Zhang, Satya Chan, Sooyoung Kim
      First page: 151
      Abstract: The satellite system is one of the most efficient means for broadcasting due to its wide service coverage as well as the fact that it can provide high data rate services by using high frequency bands. However, there are a number of problems in the satellite system, such as a long round trip delay (RTD) and heterogeneity of the channel conditions of the earth stations. Even though utilizing adaptive coding and modulation (ACM) is almost mandatory for the satellite systems using high frequency bands due to the serious rain fading, the long RTD makes it difficult to quickly respond to channel quality information, resulting in a decrease in the efficiency of ACM. A high heterogeneity of earth stations caused by a wide service coverage also makes it difficult to apply a uniform transmission mode, and thus satellite systems require receiver-dependent transmission modes. A rateless code can be an effective means to compensate for these disadvantages of satellite systems compared to terrestrial wireless systems. This paper presents soft iterative decoding algorithms for efficient application of rateless codes in satellite systems and demonstrates that rateless codes can be effectively used for hybrid automatic repeat request schemes.
      Citation: Algorithms
      PubDate: 2019-07-29
      DOI: 10.3390/a12080151
      Issue No: Vol. 12, No. 8 (2019)
       
  • Algorithms, Vol. 12, Pages 152: Bicriteria Vehicle Routing Problem with
           Preferences and Timing Constraints in Home Health Care Services

    • Authors: Syrine Roufaida Ait Haddadene, Nacima Labadie, Caroline Prodhon
      First page: 152
      Abstract: Home Healthcare (HHC) is an emerging and fast-expanding service sector that gives rise to challenging vehicle routing and scheduling problems. Each day, HHC structures must schedule the visits of caregivers to patients requiring specific medical and paramedical services at home. These operations have the potential to be unsuitable if the visits are not planned correctly, leading hence to high logistics costs and/or deteriorated service level. In this article, this issue is modeled as a vehicle routing problem where a set of routes has to be built to visit patients asking for one or more specific service within a given time window and during a fixed service time. Each patient has a preference value associated with each available caregiver. The problem addressed in this paper considers two objectives to optimize simultaneously: minimize the caregivers’ travel costs and maximize the patients’ preferences. In this paper, different methods based on the bi-objective non-dominated sorting algorithm are proposed to solve the vehicle routing problem with time windows, preferences, and timing constraints. Numerical results are presented for instances with up to 73 clients. Metrics such as the distance measure, hyper-volume, and the number of non-dominated solutions in the Pareto front are used to assess the quality of the proposed approaches.
      Citation: Algorithms
      PubDate: 2019-07-30
      DOI: 10.3390/a12080152
      Issue No: Vol. 12, No. 8 (2019)
       
  • Algorithms, Vol. 12, Pages 153: γ-Graphs of Trees

    • Authors: Stephen Finbow, Christopher M. van Bommel
      First page: 153
      Abstract: For a graph G = ( V , E ) , the γ -graph of G, denoted G ( γ ) = ( V ( γ ) , E ( γ ) ) , is the graph whose vertex set is the collection of minimum dominating sets, or γ -sets of G, and two γ -sets are adjacent in G ( γ ) if they differ by a single vertex and the two different vertices are adjacent in G. In this paper, we consider γ -graphs of trees. We develop an algorithm for determining the γ -graph of a tree, characterize which trees are γ -graphs of trees, and further comment on the structure of γ -graphs of trees and its connections with Cartesian product graphs, the set of graphs which can be obtained from the Cartesian product of graphs of order at least two.
      Citation: Algorithms
      PubDate: 2019-07-30
      DOI: 10.3390/a12080153
      Issue No: Vol. 12, No. 8 (2019)
       
  • Algorithms, Vol. 12, Pages 154: A Survey of Convolutional Neural Networks
           on Edge with Reconfigurable Computing

    • Authors: Mário P. Véstias
      First page: 154
      Abstract: The convolutional neural network (CNN) is one of the most used deep learning models for image detection and classification, due to its high accuracy when compared to other machine learning algorithms. CNNs achieve better results at the cost of higher computing and memory requirements. Inference of convolutional neural networks is therefore usually done in centralized high-performance platforms. However, many applications based on CNNs are migrating to edge devices near the source of data due to the unreliability of a transmission channel in exchanging data with a central server, the uncertainty about channel latency not tolerated by many applications, security and data privacy, etc. While advantageous, deep learning on edge is quite challenging because edge devices are usually limited in terms of performance, cost, and energy. Reconfigurable computing is being considered for inference on edge due to its high performance and energy efficiency while keeping a high hardware flexibility that allows for the easy adaption of the target computing platform to the CNN model. In this paper, we described the features of the most common CNNs, the capabilities of reconfigurable computing for running CNNs, the state-of-the-art of reconfigurable computing implementations proposed to run CNN models, as well as the trends and challenges for future edge reconfigurable platforms.
      Citation: Algorithms
      PubDate: 2019-07-31
      DOI: 10.3390/a12080154
      Issue No: Vol. 12, No. 8 (2019)
       
  • Algorithms, Vol. 12, Pages 155: A Rigid Motion Artifact Reduction Method
           for CT Based on Blind Deconvolution

    • Authors: Yuan Zhang, Liyi Zhang
      First page: 155
      Abstract: In computed tomography (CT), artifacts due to patient rigid motion often significantly degrade image quality. This paper suggests a method based on iterative blind deconvolution to eliminate motion artifacts. The proposed method alternately reconstructs the image and reduces motion artifacts in an iterative scheme until the difference measure between two successive iterations is smaller than a threshold. In this iterative process, Richardson–Lucy (RL) deconvolution with spatially adaptive total variation (SATV) regularization is inserted into the iterative process of the ordered subsets expectation maximization (OSEM) reconstruction algorithm. The proposed method is evaluated on a numerical phantom, a head phantom, and patient scan. The reconstructed images indicate that the proposed method can reduce motion artifacts and provide high-quality images. Quantitative evaluations also show the proposed method yielded an appreciable improvement on all metrics, reducing root-mean-square error (RMSE) by about 30% and increasing Pearson correlation coefficient (CC) and mean structural similarity (MSSIM) by about 15% and 20%, respectively, compared to the RL-OSEM method. Furthermore, the proposed method only needs measured raw data and no additional measurements are needed. Compared with the previous work, it can be applied to any scanning mode and can realize six degrees of freedom motion artifact reduction, so the artifact reduction effect is better in clinical experiments.
      Citation: Algorithms
      PubDate: 2019-07-31
      DOI: 10.3390/a12080155
      Issue No: Vol. 12, No. 8 (2019)
       
  • Algorithms, Vol. 12, Pages 156: A Collocation Method for the Numerical
           Solution of Nonlinear Fractional Dynamical Systems

    • Authors: Francesca Pitolli
      First page: 156
      Abstract: Fractional differential problems are widely used in applied sciences. For this reason, there is a great interest in the construction of efficient numerical methods to approximate their solution. The aim of this paper is to describe in detail a collocation method suitable to approximate the solution of dynamical systems with time derivative of fractional order. We will highlight all the steps necessary to implement the corresponding algorithm and we will use it to solve some test problems. Two Mathematica Notebooks that can be used to solve these test problems are provided.
      Citation: Algorithms
      PubDate: 2019-07-31
      DOI: 10.3390/a12080156
      Issue No: Vol. 12, No. 8 (2019)
       
  • Algorithms, Vol. 12, Pages 157: In Search of the Densest Subgraph

    • Authors: András Faragó, Zohre R. Mojaveri
      First page: 157
      Abstract: In this survey paper, we review various concepts of graph density, as well as associated theorems and algorithms. Our goal is motivated by the fact that, in many applications, it is a key algorithmic task to extract a densest subgraph from an input graph, according to some appropriate definition of graph density. While this problem has been the subject of active research for over half of a century, with many proposed variants and solutions, new results still continuously emerge in the literature. This shows both the importance and the richness of the subject. We also identify some interesting open problems in the field.
      Citation: Algorithms
      PubDate: 2019-08-02
      DOI: 10.3390/a12080157
      Issue No: Vol. 12, No. 8 (2019)
       
  • Algorithms, Vol. 12, Pages 158: A Fast Randomized Algorithm for the
           Heterogeneous Vehicle Routing Problem with Simultaneous Pickup and
           Delivery

    • Authors: Napoleão Nepomuceno, Ricardo Barboza Saboia, Plácido Rogério Pinheiro
      First page: 158
      Abstract: In the vehicle routing problem with simultaneous pickup and delivery (VRPSPD), customers demanding both delivery and pickup operations have to be visited once by a single vehicle. In this work, we propose a fast randomized algorithm using a nearest neighbor strategy to tackle an extension of the VRPSPD in which the fleet of vehicles is heterogeneous. This variant is an NP-hard problem, which in practice makes it impossible to be solved to proven optimality for large instances. To evaluate the proposal, we use benchmark instances from the literature and compare our results to those obtained by a state-of-the-art algorithm. Our approach presents very competitive results, not only improving several of the known solutions, but also running in a shorter time.
      Citation: Algorithms
      PubDate: 2019-08-03
      DOI: 10.3390/a12080158
      Issue No: Vol. 12, No. 8 (2019)
       
  • Algorithms, Vol. 12, Pages 159: Compaction of Church Numerals

    • Authors: Isamu Furuya, Takuya Kida
      First page: 159
      Abstract: In this study, we address the problem of compaction of Church numerals. Church numerals are unary representations of natural numbers on the scheme of lambda terms. We propose a novel decomposition scheme from a given natural number into an arithmetic expression using tetration, which enables us to obtain a compact representation of lambda terms that leads to the Church numeral of the natural number. For natural number n, we prove that the size of the lambda term obtained by the proposed method is O ( ( slog 2 n ) ( log n / log log n ) ) . Moreover, we experimentally confirmed that the proposed method outperforms binary representation of Church numerals on average, when n is less than approximately 10,000.
      Citation: Algorithms
      PubDate: 2019-08-08
      DOI: 10.3390/a12080159
      Issue No: Vol. 12, No. 8 (2019)
       
  • Algorithms, Vol. 12, Pages 160: A Novel Virtual Sample Generation Method
           to Overcome the Small Sample Size Problem in Computer Aided Medical
           Diagnosing

    • Authors: Mohammad Wedyan, Alessandro Crippa, Adel Al-Jumaily
      First page: 160
      Abstract: Deep neural networks are successful learning tools for building nonlinear models. However, a robust deep learning-based classification model needs a large dataset. Indeed, these models are often unstable when they use small datasets. To solve this issue, which is particularly critical in light of the possible clinical applications of these predictive models, researchers have developed approaches such as virtual sample generation. Virtual sample generation significantly improves learning and classification performance when working with small samples. The main objective of this study is to evaluate the ability of the proposed virtual sample generation to overcome the small sample size problem, which is a feature of the automated detection of a neurodevelopmental disorder, namely autism spectrum disorder. Results show that our method enhances diagnostic accuracy from 84%–95% using virtual samples generated on the basis of five actual clinical samples. The present findings show the feasibility of using the proposed technique to improve classification performance even in cases of clinical samples of limited size. Accounting for concerns in relation to small sample sizes, our technique represents a meaningful step forward in terms of pattern recognition methodology, particularly when it is applied to diagnostic classifications of neurodevelopmental disorders. Besides, the proposed technique has been tested with other available benchmark datasets. The experimental outcomes showed that the accuracy of the classification that used virtual samples was superior to the one that used original training data without virtual samples.
      Citation: Algorithms
      PubDate: 2019-08-09
      DOI: 10.3390/a12080160
      Issue No: Vol. 12, No. 8 (2019)
       
  • Algorithms, Vol. 12, Pages 161: Distributed Centrality Analysis of Social
           Network Data Using MapReduce

    • Authors: Ranjan Kumar Behera, Santanu Kumar Rath, Sanjay Misra, Robertas Damaševičius, Rytis Maskeliūnas
      First page: 161
      Abstract: Analyzing the structure of a social network helps in gaining insights into interactions and relationships among users while revealing the patterns of their online behavior. Network centrality is a metric of importance of a network node in a network, which allows revealing the structural patterns and morphology of networks. We propose a distributed computing approach for the calculation of network centrality value for each user using the MapReduce approach in the Hadoop platform, which allows faster and more efficient computation as compared to the conventional implementation. A distributed approach is scalable and helps in efficient computations of large-scale datasets, such as social network data. The proposed approach improves the calculation performance of degree centrality by 39.8%, closeness centrality by 40.7% and eigenvalue centrality by 41.1% using a Twitter dataset.
      Citation: Algorithms
      PubDate: 2019-08-09
      DOI: 10.3390/a12080161
      Issue No: Vol. 12, No. 8 (2019)
       
  • Algorithms, Vol. 12, Pages 162: Distributed Balanced Partitioning via
           Linear Embedding †

    • Authors: Kevin Aydin, MohammadHossein Bateni, Vahab Mirrokni
      First page: 162
      Abstract: Balanced partitioning is often a crucial first step in solving large-scale graph optimization problems, for example, in some cases, a big graph can be chopped into pieces that fit on one machine to be processed independently before stitching the results together, leading to certain suboptimality from the interaction among different pieces. In other cases, links between different parts may show up in the running time and/or network communications cost, hence the desire to have small cut size. We study a distributed balanced-partitioning problem where the goal is to partition the vertices of a given graph into k pieces so as to minimize the total cut size. Our algorithm is composed of a few steps that are easily implementable in distributed computation frameworks such as MapReduce. The algorithm first embeds nodes of the graph onto a line, and then processes nodes in a distributed manner guided by the linear embedding order. We examine various ways to find the first embedding, for example, via a hierarchical clustering or Hilbert curves. Then we apply four different techniques including local swaps, and minimum cuts on the boundaries of partitions, as well as contraction and dynamic programming. As our empirical study, we compare the above techniques with each other, and also to previous work in distributed graph algorithms, for example, a label-propagation method, FENNEL and Spinner. We report our results both on a private map graph and several public social networks, and show that our results beat previous distributed algorithms: For instance, compared to the label-propagation algorithm, we report an improvement of 15–25% in the cut value. We also observe that our algorithms admit scalable distributed implementation for any number of partitions. Finally, we explain three applications of this work at Google: (1) Balanced partitioning is used to route multi-term queries to different replicas in Google Search backend in a way that reduces the cache miss rates by ≈ 0.5 % , which leads to a double-digit gain in throughput of production clusters. (2) Applied to the Google Maps Driving Directions, balanced partitioning minimizes the number of cross-shard queries with the goal of saving in CPU usage. This system achieves load balancing by dividing the world graph into several “shards”. Live experiments demonstrate an ≈ 40 % drop in the number of cross-shard queries when compared to a standard geography-based method. (3) In a job scheduling problem for our data centers, we use balanced partitioning to evenly distribute the work while minimizing the amount of communication across geographically distant servers. In fact, the hierarchical nature of our solution goes well with the layering of data center servers, where certain machines are closer to each other and have faster links to one another.
      Citation: Algorithms
      PubDate: 2019-08-10
      DOI: 10.3390/a12080162
      Issue No: Vol. 12, No. 8 (2019)
       
  • Algorithms, Vol. 12, Pages 163: Idea of Using Blockchain Technique for
           Choosing the Best Configuration of Weights in Neural Networks

    • Authors: Alicja Winnicka, Karolina Kęsik
      First page: 163
      Abstract: The blockchain technique is becoming more and more popular due to its advantages such as stability and dispersed nature. This is an idea based on blockchain activity paradigms. Another important field is machine learning, which is increasingly used in practice. Unfortunately, the training or overtraining artificial neural networks is very time-consuming and requires high computing power. In this paper, we proposed using a blockchain technique to train neural networks. This type of activity is important due to the possible search for initial weights in the network, which affect faster training, due to gradient decrease. We performed the tests with much heavier calculations to indicate that such an action is possible. However, this type of solution can also be used for less demanding calculations, i.e., only a few iterations of training and finding a better configuration of initial weights.
      Citation: Algorithms
      PubDate: 2019-08-10
      DOI: 10.3390/a12080163
      Issue No: Vol. 12, No. 8 (2019)
       
  • Algorithms, Vol. 12, Pages 164: Equisum Partitions of Sets of Positive
           Integers

    • Authors: Roger B. Eggleton
      First page: 164
      Abstract: Let V be a finite set of positive integers with sum equal to a multiple of the integer b . When does V have a partition into b parts so that all parts have equal sums' We develop algorithmic constructions which yield positive, albeit incomplete, answers for the following classes of set V , where n is a given positive integer: (1) an initial interval { a ∈ ℤ + : a ≤ n } ; (2) an initial interval of primes { p ∈ ℙ : p ≤ n } , where ℙ is the set of primes; (3) a divisor set { d ∈ ℤ + : d n } ; (4) an aliquot set { d ∈ ℤ + : d n ,   d < n } . Open general questions and conjectures are included for each of these classes.
      Citation: Algorithms
      PubDate: 2019-08-11
      DOI: 10.3390/a12080164
      Issue No: Vol. 12, No. 8 (2019)
       
  • Algorithms, Vol. 12, Pages 165: Algorithmic Matching Attacks on Optimally
           Suppressed Tabular Data

    • Authors: Kazuhiro Minami, Yutaka Abe
      First page: 165
      Abstract: The objective of the cell suppression problem (CSP) is to protect sensitive cell values in tabular data under the presence of linear relations concerning marginal sums. Previous algorithms for solving CSPs ensure that every sensitive cell has enough uncertainty on its values based on the interval width of all possible values. However, we find that every deterministic CSP algorithm is vulnerable to an adversary who possesses the knowledge of that algorithm. We devise a matching attack scheme that narrows down the ranges of sensitive cell values by matching the suppression pattern of an original table with that of each candidate table. Our experiments show that actual ranges of sensitive cell values are significantly narrower than those assumed by the previous CSP algorithms.
      Citation: Algorithms
      PubDate: 2019-08-11
      DOI: 10.3390/a12080165
      Issue No: Vol. 12, No. 8 (2019)
       
  • Algorithms, Vol. 12, Pages 166: MapReduce Algorithm for Variants of
           Skyline Queries: Skyband and Dominating Queries

    • Authors: Md. Anisuzzaman Siddique, Hao Tian, Mahboob Qaosar, Yasuhiko Morimoto
      First page: 166
      Abstract: The skyline query and its variant queries are useful functions in the early stages of a knowledge-discovery processes. The skyline query and its variant queries select a set of important objects, which are better than other common objects in the dataset. In order to handle big data, such knowledge-discovery queries must be computed in parallel distributed environments. In this paper, we consider an efficient parallel algorithm for the “K-skyband query” and the “top-k dominating query”, which are popular variants of skyline query. We propose a method for computing both queries simultaneously in a parallel distributed framework called MapReduce, which is a popular framework for processing “big data” problems. Our extensive evaluation results validate the effectiveness and efficiency of the proposed algorithm on both real and synthetic datasets.
      Citation: Algorithms
      PubDate: 2019-08-13
      DOI: 10.3390/a12080166
      Issue No: Vol. 12, No. 8 (2019)
       
  • Algorithms, Vol. 12, Pages 167: LMI Pole Regions for a Robust
           Discrete-Time Pole Placement Controller Design

    • Authors: Danica Rosinová, Mária Hypiusová
      First page: 167
      Abstract: Herein, robust pole placement controller design for linear uncertain discrete time dynamic systems is addressed. The adopted approach uses the so called “D regions” where the closed loop system poles are determined to lie. The discrete time pole regions corresponding to the prescribed damping of the resulting closed loop system are studied. The key issue is to determine the appropriate convex approximation to the originally non-convex discrete-time system pole region, so that numerically efficient robust controller design algorithms based on Linear Matrix Inequalities (LMI) can be used. Several alternatives for relatively simple inner approximations and their corresponding LMI descriptions are presented. The developed LMI region for the prescribed damping can be arbitrarily combined with other LMI pole limitations (e.g., stability degree). Simple algorithms to calculate the matrices for LMI representation of the proposed convex pole regions are provided in a concise way. The results and their use in a robust controller design are illustrated on a case study of a laboratory magnetic levitation system.
      Citation: Algorithms
      PubDate: 2019-08-13
      DOI: 10.3390/a12080167
      Issue No: Vol. 12, No. 8 (2019)
       
  • Algorithms, Vol. 12, Pages 168: Cyclotomic Trace Codes

    • Authors: Dean Crnković, Andrea Švob, Vladimir D. Tonchev
      First page: 168
      Abstract: A generalization of Ding’s construction is proposed that employs as a defining set the collection of the sth powers ( s ≥ 2 ) of all nonzero elements in G F ( p m ) , where p ≥ 2 is prime. Some of the resulting codes are optimal or near-optimal and include projective codes over G F ( 4 ) that give rise to optimal or near optimal quantum codes. In addition, the codes yield interesting combinatorial structures, such as strongly regular graphs and block designs.
      Citation: Algorithms
      PubDate: 2019-08-13
      DOI: 10.3390/a12080168
      Issue No: Vol. 12, No. 8 (2019)
       
  • Algorithms, Vol. 12, Pages 169: Structural Analysis and Application of
           Non-Standard Components Based on Genetic Algorithm

    • Authors: Zhao Lei, Hu Lai, Zhang Hua, Chen Hua
      First page: 169
      Abstract: Aiming at the problems of low efficiency, heavy quality, and high cost of traditional components, it is necessary to study a design and analysis method of non-standard components. Taking the non-standard parts-turret loading and -unloading device as the carrier, the key parts of the non-standard parts are extracted for structural design and the multi-objective mathematical model and modal theory model are established. The optimization analysis of the key parts is carried out by genetic algorithm. Finally, the optimization results are compared and simulated by ANSYS Workbench. The results show that: in this case, the genetic algorithm optimized data with other data, the overall quality difference is 4.1%. The first six order modal values in the optimized results are in the range of 68 Hz to 130 Hz, which provides a basis for similar research in the future.
      Citation: Algorithms
      PubDate: 2019-08-15
      DOI: 10.3390/a12080169
      Issue No: Vol. 12, No. 8 (2019)
       
  • Algorithms, Vol. 12, Pages 170: Protograph LDPC Code Design for
           Asynchronous Random Access

    • Authors: Federico Clazzer, Balázs Matuz, Sachini Jayasooriya, Mahyar Shirvanimoghaddam, Sarah J. Johnson
      First page: 170
      Abstract: This work addresses the physical layer channel code design for an uncoordinated, frame- and slot-asynchronous random access protocol. Starting from the observation that collisions between two users yield very specific interference patterns, we define a surrogate channel model and propose different protograph low-density parity-check code designs. The proposed codes are both tested in a setup where the physical layer is abstracted, as well as on a more realistic channel model, where finite-length physical layer simulations of the entire asynchronous random access scheme, including decoding, are carried out. We find that the abstracted physical layer model overestimates the performance when short blocks are considered. Additionally, the optimized codes show gains in supported channel traffic, a measure of the number of terminals that can be concurrently accommodated on the channel, of around 17% at a packet loss rate of 10 − 2 w.r.t. off-the-shelf codes.
      Citation: Algorithms
      PubDate: 2019-08-15
      DOI: 10.3390/a12080170
      Issue No: Vol. 12, No. 8 (2019)
       
  • Algorithms, Vol. 12, Pages 171: An Application of Manifold Learning in
           Global Shape Descriptors

    • Authors: Fereshteh S. Bashiri, Reihaneh Rostami, Peggy Peissig, Roshan M. D’Souza, Zeyun Yu
      First page: 171
      Abstract: With the rapid expansion of applied 3D computational vision, shape descriptors have become increasingly important for a wide variety of applications and objects from molecules to planets. Appropriate shape descriptors are critical for accurate (and efficient) shape retrieval and 3D model classification. Several spectral-based shape descriptors have been introduced by solving various physical equations over a 3D surface model. In this paper, for the first time, we incorporate a specific manifold learning technique, introduced in statistics and machine learning, to develop a global, spectral-based shape descriptor in the computer graphics domain. The proposed descriptor utilizes the Laplacian Eigenmap technique in which the Laplacian eigenvalue problem is discretized using an exponential weighting scheme. As a result, our descriptor eliminates the limitations tied to the existing spectral descriptors, namely dependency on triangular mesh representation and high intra-class quality of 3D models. We also present a straightforward normalization method to obtain a scale-invariant and noise-resistant descriptor. The extensive experiments performed in this study using two standard 3D shape benchmarks—high-resolution TOSCA and McGill datasets—demonstrate that the present contribution provides a highly discriminative and robust shape descriptor under the presence of a high level of noise, random scale variations, and low sampling rate, in addition to the known isometric-invariance property of the Laplace–Beltrami operator. The proposed method significantly outperforms state-of-the-art spectral descriptors in shape retrieval and classification. The proposed descriptor is limited to closed manifolds due to its inherited inability to accurately handle manifolds with boundaries.
      Citation: Algorithms
      PubDate: 2019-08-16
      DOI: 10.3390/a12080171
      Issue No: Vol. 12, No. 8 (2019)
       
  • Algorithms, Vol. 12, Pages 172: Practical Access to Dynamic Programming on
           Tree Decompositions

    • Authors: Max Bannach, Sebastian Berndt
      First page: 172
      Abstract: Parameterized complexity theory has led to a wide range of algorithmic breakthroughs within the last few decades, but the practicability of these methods for real-world problems is still not well understood. We investigate the practicability of one of the fundamental approaches of this field: dynamic programming on tree decompositions. Indisputably, this is a key technique in parameterized algorithms and modern algorithm design. Despite the enormous impact of this approach in theory, it still has very little influence on practical implementations. The reasons for this phenomenon are manifold. One of them is the simple fact that such an implementation requires a long chain of non-trivial tasks (as computing the decomposition, preparing it, …). We provide an easy way to implement such dynamic programs that only requires the definition of the update rules. With this interface, dynamic programs for various problems, such as 3-coloring, can be implemented easily in about 100 lines of structured Java code. The theoretical foundation of the success of dynamic programming on tree decompositions is well understood due to Courcelle’s celebrated theorem, which states that every MSO-definable problem can be efficiently solved if a tree decomposition of small width is given. We seek to provide practical access to this theorem as well, by presenting a lightweight model checker for a small fragment of MSO 1 (that is, we do not consider “edge-set-based” problems). This fragment is powerful enough to describe many natural problems, and our model checker turns out to be very competitive against similar state-of-the-art tools.
      Citation: Algorithms
      PubDate: 2019-08-16
      DOI: 10.3390/a12080172
      Issue No: Vol. 12, No. 8 (2019)
       
  • Algorithms, Vol. 12, Pages 173: Long Short-Term Memory Neural Network
           Applied to Train Dynamic Model and Speed Prediction

    • Authors: Zhen Li, Tao Tang, Chunhai Gao
      First page: 173
      Abstract: The automatic train operation system is a significant component of the intelligent railway transportation. As a fundamental problem, the construction of the train dynamic model has been extensively researched using parametric approaches. The parametric based models may have poor performances due to unrealistic assumptions and changeable environments. In this paper, a long short-term memory network is carefully developed to build the train dynamic model in a nonparametric way. By optimizing the hyperparameters of the proposed model, more accurate outputs can be obtained with the same inputs of the parametric approaches. The proposed model was compared with two parametric methods using actual data. Experimental results suggest that the model performance is better than those of traditional models due to the strong learning ability. By exploring a detailed feature engineering process, the proposed long short-term memory network based algorithm was extended to predict train speed for multiple steps ahead.
      Citation: Algorithms
      PubDate: 2019-08-16
      DOI: 10.3390/a12080173
      Issue No: Vol. 12, No. 8 (2019)
       
  • Algorithms, Vol. 12, Pages 174: A Novel Blind Restoration and
           Reconstruction Approach for CT Images Based on Sparse Representation and
           Hierarchical Bayesian-MAP

    • Authors: Yunshan Sun, Liyi Zhang, Yanqin Li, Juan Meng
      First page: 174
      Abstract: Computed tomography (CT) image reconstruction and restoration are very important in medical image processing, and are associated together to be an inverse problem. Image iterative reconstruction is a key tool to increase the applicability of CT imaging and reduce radiation dose. Nevertheless, traditional image iterative reconstruction methods are limited by the sampling theorem and also the blurring of projection data will propagate unhampered artifact in the reconstructed image. To overcome these problems, image restoration techniques should be developed to accurately correct a wide variety of image degrading effects in order to effectively improve image reconstruction. In this paper, a blind image restoration technique is embedded in the compressive sensing CT image reconstruction, which can result in a high-quality reconstruction image using fewer projection data. Because a small amount of data can be obtained by radiation in a shorter time, high-quality image reconstruction with less data is equivalent to reducing radiation dose. Technically, both the blurring process and the sparse representation of the sharp CT image are first modeled as a serial of parameters. The sharp CT image will be obtained from the estimated sparse representation. Then, the model parameters are estimated by a hierarchical Bayesian maximum posteriori formulation. Finally, the estimated model parameters are optimized to obtain the final image reconstruction. We demonstrate the effectiveness of the proposed method with the simulation experiments in terms of the peak signal to noise ratio (PSNR), and structural similarity index (SSIM).
      Citation: Algorithms
      PubDate: 2019-08-16
      DOI: 10.3390/a12080174
      Issue No: Vol. 12, No. 8 (2019)
       
  • Algorithms, Vol. 12, Pages 175: A Distributed Hybrid Community Detection
           Methodology for Social Networks

    • Authors: Konstantinos Georgiou, Christos Makris, Georgios Pispirigos
      First page: 175
      Abstract: Nowadays, the amount of digitally available information has tremendously grown, with real-world data graphs outreaching the millions or even billions of vertices. Hence, community detection, where groups of vertices are formed according to a well-defined similarity measure, has never been more essential affecting a vast range of scientific fields such as bio-informatics, sociology, discrete mathematics, nonlinear dynamics, digital marketing, and computer science. Even if an impressive amount of research has yet been published to tackle this NP-hard class problem, the existing methods and algorithms have virtually been proven inefficient and severely unscalable. In this regard, the purpose of this manuscript is to combine the network topology properties expressed by the loose similarity and the local edge betweenness, which is a currently proposed Girvan–Newman’s edge betweenness measure alternative, along with the intrinsic user content information, in order to introduce a novel and highly distributed hybrid community detection methodology. The proposed approach has been thoroughly tested on various real social graphs, roundly compared to other classic divisive community detection algorithms that serve as baselines and practically proven exceptionally scalable, highly efficient, and adequately accurate in terms of revealing the subjacent network hierarchy.
      Citation: Algorithms
      PubDate: 2019-08-17
      DOI: 10.3390/a12080175
      Issue No: Vol. 12, No. 8 (2019)
       
  • Algorithms, Vol. 12, Pages 145: A Study on Sensitive Bands of EEG Data
           under Different Mental Workloads

    • Authors: Hongquan Qu, Zhanli Fan, Shuqin Cao, Liping Pang, Hao Wang, Jie Zhang
      First page: 145
      Abstract: Electroencephalogram (EEG) signals contain a lot of human body performance information. With the development of the brain–computer interface (BCI) technology, many researchers have used the feature extraction and classification algorithms in various fields to study the feature extraction and classification of EEG signals. In this paper, the sensitive bands of EEG data under different mental workloads are studied. By selecting the characteristics of EEG signals, the bands with the highest sensitivity to mental loads are selected. In this paper, EEG signals are measured in different load flight experiments. First, the EEG signals are preprocessed by independent component analysis (ICA) to remove the interference of electrooculogram (EOG) signals, and then the power spectral density and energy are calculated for feature extraction. Finally, the feature importance is selected based on Gini impurity. The classification accuracy of the support vector machines (SVM) classifier is verified by comparing the characteristics of the full band with the characteristics of the β band. The results show that the characteristics of the β band are the most sensitive in EEG data under different mental workloads.
      Citation: Algorithms
      PubDate: 2019-07-22
      DOI: 10.3390/a12070145
      Issue No: Vol. 12, No. 7 (2019)
       
  • Algorithms, Vol. 12, Pages 146: Hybrid MU-MIMO Precoding Based on K-Means
           User Clustering

    • Authors: Razvan-Florentin Trifan, Andrei-Alexandru Enescu, Constantin Paleologu
      First page: 146
      Abstract: Multi-User (MU) Multiple-Input-Multiple-Output (MIMO) systems have been extensively investigated over the last few years from both theoretical and practical perspectives. The low complexity Linear Precoding (LP) schemes for MU-MIMO are already deployed in Long-Term Evolution (LTE) networks; however, they do not work well for users with strongly-correlated channels. Alternatives to those schemes, like Non-Linear Precoding (NLP), and hybrid precoding schemes were proposed in the standardization phase for the Third-Generation Partnership Project (3GPP) 5G New Radio (NR). NLP schemes have better performance, but their complexity is prohibitively high. Hybrid schemes, which combine LP schemes to serve users with separable channels and NLP schemes for users with strongly-correlated channels, can help reduce the computational burden, while limiting the performance degradation. Finding the optimum set of users that can be co-scheduled through LP schemes could require an exhaustive search and, thus, may not be affordable for practical systems. The purpose of this paper is to present a new semi-orthogonal user selection algorithm based on the statistical K-means clustering and to assess its performance in MU-MIMO systems employing hybrid precoding schemes.
      Citation: Algorithms
      PubDate: 2019-07-23
      DOI: 10.3390/a12070146
      Issue No: Vol. 12, No. 7 (2019)
       
  • Algorithms, Vol. 12, Pages 222: A Reinforcement Learning Method for a
           Hybrid Flow-Shop Scheduling Problem

    • Authors: Han, Guo, Su
      First page: 222
      Abstract: The scheduling problems in mass production, manufacturing, assembly, synthesis, and transportation, as well as internet services, can partly be attributed to a hybrid flow-shop scheduling problem (HFSP). To solve the problem, a reinforcement learning (RL) method for HFSP is studied for the first time in this paper. HFSP is described and attributed to the Markov Decision Processes (MDP), for which the special states, actions, and reward function are designed. On this basis, the MDP framework is established. The Boltzmann exploration policy is adopted to trade-off the exploration and exploitation during choosing action in RL. Compared with the first-come-first-serve strategy that is frequently adopted when coding in most of the traditional intelligent algorithms, the rule in the RL method is first-come-first-choice, which is more conducive to achieving the global optimal solution. For validation, the RL method is utilized for scheduling in a metal processing workshop of an automobile engine factory. Then, the method is applied to the sortie scheduling of carrier aircraft in continuous dispatch. The results demonstrate that the machining and support scheduling obtained by this RL method are reasonable in result quality, real-time performance and complexity, indicating that this RL method is practical for HFSP.
      Citation: Algorithms
      PubDate: 2019-10-23
      DOI: 10.3390/a12110222
      Issue No: Vol. 12, No. 11 (2019)
       
  • Algorithms, Vol. 12, Pages 223: (Hyper)Graph Embedding and Classification
           via Simplicial Complexes

    • Authors: Alessio Martino, Alessandro Giuliani, Antonello Rizzi
      First page: 223
      Abstract: This paper investigates a novel graph embedding procedure based on simplicial complexes. Inherited from algebraic topology, simplicial complexes are collections of increasing-order simplices (e.g., points, lines, triangles, tetrahedrons) which can be interpreted as possibly meaningful substructures (i.e., information granules) on the top of which an embedding space can be built by means of symbolic histograms. In the embedding space, any Euclidean pattern recognition system can be used, possibly equipped with feature selection capabilities in order to select the most informative symbols. The selected symbols can be analysed by field-experts in order to extract further knowledge about the process to be modelled by the learning system, hence the proposed modelling strategy can be considered as a grey-box. The proposed embedding has been tested on thirty benchmark datasets for graph classification and, further, we propose two real-world applications, namely predicting proteins’ enzymatic function and solubility propensity starting from their 3D structure in order to give an example of the knowledge discovery phase which can be carried out starting from the proposed embedding strategy.
      Citation: Algorithms
      PubDate: 2019-10-25
      DOI: 10.3390/a12110223
      Issue No: Vol. 12, No. 11 (2019)
       
  • Algorithms, Vol. 12, Pages 224: A QUBO Model for the Traveling Salesman
           Problem with Time Windows

    • Authors: Christos Papalitsas, Theodore Andronikos, Konstantinos Giannakis, Georgia Theocharopoulou, Sofia Fanarioti
      First page: 224
      Abstract: This work focuses on expressing the TSP with Time Windows (TSPTW for short) as a quadratic unconstrained binary optimization (QUBO) problem. The time windows impose time constraints that a feasible solution must satisfy. These take the form of inequality constraints, which are known to be particularly difficult to articulate within the QUBO framework. This is, we believe, the first time this major obstacle is overcome and the TSPTW is cast in the QUBO formulation. We have every reason to anticipate that this development will lead to the actual execution of small scale TSPTW instances on the D-Wave platform.
      Citation: Algorithms
      PubDate: 2019-10-25
      DOI: 10.3390/a12110224
      Issue No: Vol. 12, No. 11 (2019)
       
  • Algorithms, Vol. 12, Pages 225: Enhancing Backtracking Search Algorithm
           using Reflection Mutation Strategy Based on Sine Cosine

    • Authors: Chong Zhou, Shengjie Li, Yuhe Zhang, Zhikun Chen, Cuijun Zhang
      First page: 225
      Abstract: Backtracking Search Algorithm (BSA) is a younger population-based evolutionary algorithm and widely researched. Due to the introduction of historical population and no guidance toward to the best individual, BSA does not adequately use the information in the current population, which leads to a slow convergence speed and poor exploitation ability of BSA. To address these drawbacks, a novel backtracking search algorithm with reflection mutation based on sine cosine is proposed, named RSCBSA. The best individual found so far is employed to improve convergence speed, while sine and cosine math models are introduced to enhance population diversity. To sufficiently use the information in the historical population and current population, four individuals are selected from the historical or current population randomly to construct an unit simplex, and the center of the unit simplex can enhance exploitation ability of RSCBSA. Comprehensive experimental results and analyses show that RSCBSA is competitive enough with other state-of-the-art meta-heuristic algorithms.
      Citation: Algorithms
      PubDate: 2019-10-28
      DOI: 10.3390/a12110225
      Issue No: Vol. 12, No. 11 (2019)
       
  • Algorithms, Vol. 12, Pages 226: Multiple-Attribute Decision Making ELECTRE
           II Method under Bipolar Fuzzy Model

    • Authors: Shumaiza, Muhammad Akram, Ahmad N. Al-Kenani
      First page: 226
      Abstract: The core aim of this paper is to provide a new multiple-criteria decision making (MCDM) model, namely bipolar fuzzy ELimination and Choice Translating REality (ELECTRE) II method, by combining the bipolar fuzzy set with ELECTRE II technique. It can be used to solve the problems having bipolar uncertainty. The proposed method is established by defining the concept of bipolar fuzzy strong, median and weak concordance as well as discordance sets and indifferent set to define two types of outranking relations, namely strong outranking relation and weak outranking relation. The normalized weights of criteria, which may be partly or completely unknown for decision makers, are calculated by using an optimization technique, which is based on maximizing deviation method. A systematic iterative procedure is applied to strongly outrank as well as weakly outrank graphs to determine the ranking of favorable actions or alternatives or to choose the best possible solution. The implementation of the proposed method is presented by numerical examples such as selection of business location and to chose the best supplier. A comparative analysis of proposed ELECTRE II method is also presented with already existing multiple-attribute decision making methods, including Technique for the Order of Preference by Similarity to an Ideal Solution (TOPSIS) and ELECTRE I under bipolar fuzzy environment by solving the problem of business location.
      Citation: Algorithms
      PubDate: 2019-10-29
      DOI: 10.3390/a12110226
      Issue No: Vol. 12, No. 11 (2019)
       
  • Algorithms, Vol. 12, Pages 227: Facial Expression Recognition Based on
           Auxiliary Models

    • Authors: Yingying Wang, Yibin Li, Yong Song, Xuewen Rong
      First page: 227
      Abstract: In recent years, with the development of artificial intelligence and human–computer interaction, more attention has been paid to the recognition and analysis of facial expressions. Despite much great success, there are a lot of unsatisfying problems, because facial expressions are subtle and complex. Hence, facial expression recognition is still a challenging problem. In most papers, the entire face image is often chosen as the input information. In our daily life, people can perceive other’s current emotions only by several facial components (such as eye, mouth and nose), and other areas of the face (such as hair, skin tone, ears, etc.) play a smaller role in determining one’s emotion. If the entire face image is used as the only input information, the system will produce some unnecessary information and miss some important information in the process of feature extraction. To solve the above problem, this paper proposes a method that combines multiple sub-regions and the entire face image by weighting, which can capture more important feature information that is conducive to improving the recognition accuracy. Our proposed method was evaluated based on four well-known publicly available facial expression databases: JAFFE, CK+, FER2013 and SFEW. The new method showed better performance than most state-of-the-art methods.
      Citation: Algorithms
      PubDate: 2019-10-31
      DOI: 10.3390/a12110227
      Issue No: Vol. 12, No. 11 (2019)
       
  • Algorithms, Vol. 12, Pages 228: Blended Filter-Based Detection for
           Thruster Valve Failure and Control Recovery Evaluation for RLV

    • Authors: Hongqiang Sun, Shuguang Zhang
      First page: 228
      Abstract: Security enhancement and cost reduction have become crucial goals for second-generation reusable launch vehicles (RLV). The thruster is an important actuator for an RLV, and its control normally requires a valve capable of high-frequency operation, which may lead to excessive wear or failure of the thruster valve. This paper aims at developing a thruster fault detection method that can deal with the thruster fault caused by the failure of the thruster valve and play an emergency role in the cases of hardware sensor failure. Firstly, the failure mechanism of the thruster was analyzed and modeled. Then, thruster fault detection was employed by introducing an angular velocity signal, using a blended filter, and determining an isolation threshold. In addition, to support the redundancy management of the thruster, an evaluation method of the nonlinear model-based numerical control prediction was proposed to evaluate whether the remaining fault-free thruster can track the attitude control response performance under the failure of the thruster valve. The simulation results showed that the method is stable and allowed for the effective detection of thruster faults and timely evaluation of recovery performance.
      Citation: Algorithms
      PubDate: 2019-11-01
      DOI: 10.3390/a12110228
      Issue No: Vol. 12, No. 11 (2019)
       
  • Algorithms, Vol. 12, Pages 229: Special Issue on “Algorithm Engineering:
           Towards Practically Efficient Solutions to Combinatorial Problems”

    • Authors: Mattia D’Emidio, Daniele Frigioni
      First page: 229
      Abstract: The purpose of this special issue of Algorithms was to attract papers presenting original research in the area of algorithm engineering. In particular, submissions concerning the design, analysis, implementation, tuning, and experimental evaluation of discrete algorithms and data structures, and/or addressing methodological issues and standards in algorithmic experimentation were encouraged. Papers dealing with advanced models of computing, including memory hierarchies, cloud architectures, and parallel processing were also welcome. In this regard, we solicited contributions from all most prominent areas of applied algorithmic research, which include but are not limited to graphs, databases, computational geometry, big data, networking, combinatorial aspects of scientific computing, and computational problems in the natural sciences or engineering.
      Citation: Algorithms
      PubDate: 2019-11-03
      DOI: 10.3390/a12110229
      Issue No: Vol. 12, No. 11 (2019)
       
  • Algorithms, Vol. 12, Pages 230: The Inapproximability of k-DominatingSet
           for Parameterized AC 0 Circuits †

    • Authors: Wenxing Lai
      First page: 230
      Abstract: Chen and Flum showed that any FPT-approximation of the k-Clique problem is not in para- AC 0 and the k-DominatingSet (k-DomSet) problem could not be computed by para- AC 0 circuits. It is natural to ask whether the f ( k ) -approximation of the k-DomSet problem is in para- AC 0 for some computable function f. Very recently it was proved that assuming W [ 1 ] ≠ FPT , the k-DomSet problem cannot be f ( k ) -approximated by FPT algorithms for any computable function f by S., Laekhanukit and Manurangsi and Lin, seperately. We observe that the constructions used in Lin’s work can be carried out using constant-depth circuits, and thus we prove that para- AC 0 circuits could not approximate this problem with ratio f ( k ) for any computable function f. Moreover, under the hypothesis that the 3-CNF-SAT problem cannot be computed by constant-depth circuits of size 2 ε n for some ε > 0 , we show that constant-depth circuits of size n o ( k ) cannot distinguish graphs whose dominating numbers are either ≤k or > log n 3 log log n 1 / k . However, we find that the hypothesis may be hard to settle by showing that it implies NP ⊈ NC 1 .
      Citation: Algorithms
      PubDate: 2019-11-04
      DOI: 10.3390/a12110230
      Issue No: Vol. 12, No. 11 (2019)
       
  • Algorithms, Vol. 12, Pages 231: A GA-SA Hybrid Planning Algorithm Combined
           with Improved Clustering for LEO Observation Satellite Missions

    • Authors: Xiangyu Long, Shufan Wu, Xiaofeng Wu, Yixin Huang, Zhongcheng Mu
      First page: 231
      Abstract: This paper presents a space mission planning tool, which was developed for LEO (Low Earth Orbit) observation satellites. The tool is focused on a two-phase planning strategy with clustering preprocessing and mission planning, where an improved clustering algorithm is applied, and a hybrid algorithm that combines the genetic algorithm with the simulated annealing algorithm (GA–SA) is given and discussed. Experimental simulation studies demonstrate that the GA–SA algorithm with the improved clique partition algorithm based on the graph theory model exhibits higher fitness value and better optimization performance and reliability than the GA or SA algorithms alone.
      Citation: Algorithms
      PubDate: 2019-11-04
      DOI: 10.3390/a12110231
      Issue No: Vol. 12, No. 11 (2019)
       
  • Algorithms, Vol. 12, Pages 232: Comparison and Interpretation Methods for
           Predictive Control of Mechanics

    • Authors: Timothy Sands
      First page: 232
      Abstract: Objects that possess mass (e.g., automobiles, manufactured items, etc.) translationally accelerate in direct proportion to the force applied scaled by the object’s mass in accordance with Newton’s Law, while the rotational companion is Euler’s moment equations relating angular acceleration of objects that possess mass moments of inertia. Michel Chasles’s theorem allows us to simply invoke Newton and Euler’s equations to fully describe the six degrees of freedom of mechanical motion. Many options are available to control the motion of objects by controlling the applied force and moment. A long, distinguished list of references has matured the field of controlling a mechanical motion, which culminates in the burgeoning field of deterministic artificial intelligence as a natural progression of the laudable goal of adaptive and/or model predictive controllers that can be proven to be optimal subsequent to their development. Deterministic A.I. uses Chasle’s claim to assert Newton’s and Euler’s relations as deterministic self-awareness statements that are optimal with respect to state errors. Predictive controllers (both continuous and sampled-data) derived from the outset to be optimal by first solving an optimization problem with the governing dynamic equations of motion lead to several controllers (including a controller that twice invokes optimization to formulate robust, predictive control). These controllers are compared to each other with noise and modeling errors, and the many figures of merit are used: tracking error and rate error deviations and means, in addition to total mean cost. Robustness is evaluated using Monte Carlo analysis where plant parameters are randomly assumed to be incorrectly modeled. Six instances of controllers are compared against these methods and interpretations, which allow engineers to select a tailored control for their given circumstances. Novel versions of the ubiquitous classical proportional-derivative, “PD” controller, is developed from the optimization statement at the outset by using a novel re-parameterization of the optimal results from time-to-state parameterization. Furthermore, time-optimal controllers, continuous predictive controllers, and sampled-data predictive controllers, as well as combined feedforward plus feedback controllers, and the two degree of freedom controllers (i.e., 2DOF). The context of the term “feedforward” used in this study is the context of deterministic artificial intelligence, where analytic self-awareness statements are strictly determined by the governing physics (of mechanics in this case, e.g., Chasle, Newton, and Euler). When feedforward is combined with feedback per the previously mentioned method (provenance foremost in optimization), the combination is referred to as “2DOF” or two degrees of freedom to indicate the twice invocation of optimization at the genesis of the feedforward and the feedback, respectively. The feedforward plus feedback case is augmented by an online (real time) comparison to the optimal case. This manuscript compares these many optional control strategies against each other. Nominal plants are used, but the addition of plant noise reveals the robustness of each controller, even without optimally rejecting assumed-Gaussian noise (e.g., via the Kalman filter). In other words, noise terms are intentionally left unaddressed in the problem formulation to evaluate the robustness of the proposed method when the real-world noise is added. Lastly, mismodeled plants controlled by each strategy reveal relative performance. Well-anticipated results include the lowest cost, which is achieved by the optimal controller (with very poor robustness), while low mean errors and deviations are achieved by the classical controllers (at the highest cost). Both continuous predictive control and sampled-data predictive control perform well at both cost as well as errors and deviations, while the 2DOF controller performance was the best overall.
      Citation: Algorithms
      PubDate: 2019-11-04
      DOI: 10.3390/a12110232
      Issue No: Vol. 12, No. 11 (2019)
       
  • Algorithms, Vol. 12, Pages 233: Comparison of Satellite Repeat Shift Time
           for GPS, BDS, and Galileo Navigation Systems by Three Methods

    • Authors: Yanxi Yang, Jinguang Jiang, Mingkun Su
      First page: 233
      Abstract: The characteristic of the satellite repeat shift time can reflect the status of the satellite operation, and is also one of the key factors of the sidereal filtering multipath correction. Although some methods have been developed to calculate the repeat shift time, few efforts have been made to analyze and compare the performance of this feature for the GPS (Global Positioning System), BDS (BeiDou System), and Galileo in depth. Hence, three methods used for calculating the repeat shift time are presented, and used to compare and analyze the three global systems in depth, named the broadcast ephemeris method (BEM), correlation coefficient method (CCM), and aspect repeat time method (ARTM). The experiment results show that the repeat shift time of each satellite is different. Also, the difference between the maximum and minimum varies from different systems. The maximum difference is about 25 s for the BDS IGSO (Inclined Geosynchronous Orbit) and the minimum is merely 10 s for the GPS system. Furthermore, for the same satellite, the shift time calculated by the three methods is almost identical, and the maximum difference is only about 7 s between the CCM and the ARTM method for the BDS MEO (Medium Earth Orbit) satellite. Although the repeat shift time is different daily for the same satellite and the same method, the changes are very small. Moreover, in terms of the STD (Standard Deviation) of the BS (between satellites) and MS (mean shift for the same satellite), the GPS system is the best, the performance of the BDS system is medium, and the Galileo performs slightly worse than the GPS and BDS.
      Citation: Algorithms
      PubDate: 2019-11-05
      DOI: 10.3390/a12110233
      Issue No: Vol. 12, No. 11 (2019)
       
  • Algorithms, Vol. 12, Pages 234: Complex Neutrosophic Hypergraphs: New
           Social Network Models

    • Authors: Anam Luqman, Muhammad Akram, Florentin Smarandache
      First page: 234
      Abstract: A complex neutrosophic set is a useful model to handle indeterminate situations with a periodic nature. This is characterized by truth, indeterminacy, and falsity degrees which are the combination of real-valued amplitude terms and complex-valued phase terms. Hypergraphs are objects that enable us to dig out invisible connections between the underlying structures of complex systems such as those leading to sustainable development. In this paper, we apply the most fruitful concept of complex neutrosophic sets to theory of hypergraphs. We define complex neutrosophic hypergraphs and discuss their certain properties including lower truncation, upper truncation, and transition levels. Furthermore, we define T-related complex neutrosophic hypergraphs and properties of minimal transversals of complex neutrosophic hypergraphs. Finally, we represent the modeling of certain social networks with intersecting communities through the score functions and choice values of complex neutrosophic hypergraphs. We also give a brief comparison of our proposed model with other existing models.
      Citation: Algorithms
      PubDate: 2019-11-06
      DOI: 10.3390/a12110234
      Issue No: Vol. 12, No. 11 (2019)
       
  • Algorithms, Vol. 12, Pages 235: Exploring an Ensemble of Methods that
           Combines Fuzzy Cognitive Maps and Neural Networks in Solving the Time
           Series Prediction Problem of Gas Consumption in Greece

    • Authors: Konstantinos I. Papageorgiou, Katarzyna Poczeta, Elpiniki Papageorgiou, Vassilis C. Gerogiannis, George Stamoulis
      First page: 235
      Abstract: This paper introduced a new ensemble learning approach, based on evolutionary fuzzy cognitive maps (FCMs), artificial neural networks (ANNs), and their hybrid structure (FCM-ANN), for time series prediction. The main aim of time series forecasting is to obtain reasonably accurate forecasts of future data from analyzing records of data. In the paper, we proposed an ensemble-based forecast combination methodology as an alternative approach to forecasting methods for time series prediction. The ensemble learning technique combines various learning algorithms, including SOGA (structure optimization genetic algorithm)-based FCMs, RCGA (real coded genetic algorithm)-based FCMs, efficient and adaptive ANNs architectures, and a hybrid structure of FCM-ANN, recently proposed for time series forecasting. All ensemble algorithms execute according to the one-step prediction regime. The particular forecast combination approach was specifically selected due to the advanced features of each ensemble component, where the findings of this work evinced the effectiveness of this approach, in terms of prediction accuracy, when compared against other well-known, independent forecasting approaches, such as ANNs or FCMs, and the long short-term memory (LSTM) algorithm as well. The suggested ensemble learning approach was applied to three distribution points that compose the natural gas grid of a Greek region. For the evaluation of the proposed approach, a real-time series dataset for natural gas prediction was used. We also provided a detailed discussion on the performance of the individual predictors, the ensemble predictors, and their combination through two well-known ensemble methods (the average and the error-based) that are characterized in the literature as particularly accurate and effective. The prediction results showed the efficacy of the proposed ensemble learning approach, and the comparative analysis demonstrated enough evidence that the approach could be used effectively to conduct forecasting based on multivariate time series.
      Citation: Algorithms
      PubDate: 2019-11-06
      DOI: 10.3390/a12110235
      Issue No: Vol. 12, No. 11 (2019)
       
  • Algorithms, Vol. 12, Pages 236: Stability Analysis of Jacobian-Free
           Newton’s Iterative Method

    • Authors: Abdolreza Amiri, Alicia Cordero, Mohammad Taghi Darvishi, Juan R. Torregrosa
      First page: 236
      Abstract: It is well known that scalar iterative methods with derivatives are highly more stable than their derivative-free partners, understanding the term stability as a measure of the wideness of the set of converging initial estimations. In multivariate case, multidimensional dynamical analysis allows us to afford this task and it is made on different Jacobian-free variants of Newton’s method, whose estimations of the Jacobian matrix have increasing order. The respective basins of attraction and the number of fixed and critical points give us valuable information in this sense.
      Citation: Algorithms
      PubDate: 2019-11-06
      DOI: 10.3390/a12110236
      Issue No: Vol. 12, No. 11 (2019)
       
  • Algorithms, Vol. 12, Pages 237: What Do a Longest Increasing Subsequence
           and a Longest Decreasing Subsequence Know about Each Other'

    • Authors: Elizabeth J. Itskovich, Vadim E. Levit
      First page: 237
      Abstract: As a kind of converse of the celebrated Erdős–Szekeres theorem, we present a necessary and sufficient condition for a sequence of length n to contain a longest increasing subsequence and a longest decreasing subsequence of given lengths x and y, respectively.
      Citation: Algorithms
      PubDate: 2019-11-07
      DOI: 10.3390/a12110237
      Issue No: Vol. 12, No. 11 (2019)
       
  • Algorithms, Vol. 12, Pages 238: Microscopic Object Recognition and
           Localization Based on Multi-Feature Fusion for In-Situ Measurement In Vivo
           

    • Authors: Shi-Xian Yan, Peng-Fei Zhao, Xin-Yu Gao, Qiao Zhou, Jin-Hai Li, Jie-Peng Yao, Zhi-Qiang Chai, Yang Yue, Zhong-Yi Wang, Lan Huang
      First page: 238
      Abstract: Microscopic object recognition and analysis is very important in micromanipulation. Micromanipulation has been extensively used in many fields, e.g., micro-assembly operation, microsurgery, agriculture, and biological research. Conducting micro-object recognition in the in-situ measurement of tissue, e.g., in the ion flux measurement by moving an ion-selective microelectrode (ISME), is a complex problem. For living tissues growing at a rate, it remains a challenge to accurately recognize and locate an ISME to protect living tissues and to prevent an ISME from being damaged. Thus, we proposed a robust and fast recognition method based on local binary pattern (LBP) and Haar-like features fusion by training a cascade of classifiers using the gentle AdaBoost algorithm to recognize microscopic objects. Then, we could locate the electrode tip from the background with strong noise by using the Hough transform and edge extraction with an improved contour detection method. Finally, the method could be used to automatically and accurately calculate the relative distance between the two micro-objects in the microscopic image. The results show that the proposed method can achieve good performance in micro-object recognition with a recognition rate up to 99.14% and a tip recognition speed up to 14 frames/s at a resolution of 1360 × 1024. The max error of tip positioning is 6.10 μm, which meets the design requirements of the ISME system. Furthermore, this study provides an effective visual guidance method for micromanipulation, which can facilitate automated micromanipulation research.
      Citation: Algorithms
      PubDate: 2019-11-07
      DOI: 10.3390/a12110238
      Issue No: Vol. 12, No. 11 (2019)
       
  • Algorithms, Vol. 12, Pages 239: A Hybrid Ontology-Based Recommendation
           System in e-Commerce

    • Authors: Márcio Guia, Rodrigo Rocha Silva, Jorge Bernardino
      First page: 239
      Abstract: The growth of the Internet has increased the amount of data and information available to any person at any time. Recommendation Systems help users find the items that meet their preferences, among the large number of items available. Techniques such as collaborative filtering and content-based recommenders have played an important role in the implementation of recommendation systems. In the last few years, other techniques, such as, ontology-based recommenders, have gained significance when reffering better active user recommendations; however, building an ontology-based recommender is an expensive process, which requires considerable skills in Knowledge Engineering. This paper presents a new hybrid approach that combines the simplicity of collaborative filtering with the efficiency of the ontology-based recommenders. The experimental evaluation demonstrates that the proposed approach presents higher quality recommendations when compared to collaborative filtering. The main improvement is verified on the results regarding the products, which, in spite of belonging to unknown categories to the users, still match their preferences and become recommended.
      Citation: Algorithms
      PubDate: 2019-11-08
      DOI: 10.3390/a12110239
      Issue No: Vol. 12, No. 11 (2019)
       
  • Algorithms, Vol. 12, Pages 240: Tensor-Based Algorithms for Image
           Classification

    • Authors: Stefan Klus, Patrick Gelß
      First page: 240
      Abstract: Interest in machine learning with tensor networks has been growing rapidly in recent years. We show that tensor-based methods developed for learning the governing equations of dynamical systems from data can, in the same way, be used for supervised learning problems and propose two novel approaches for image classification. One is a kernel-based reformulation of the previously introduced multidimensional approximation of nonlinear dynamics (MANDy), the other an alternating ridge regression in the tensor train format. We apply both methods to the MNIST and fashion MNIST data set and show that the approaches are competitive with state-of-the-art neural network-based classifiers.
      Citation: Algorithms
      PubDate: 2019-11-09
      DOI: 10.3390/a12110240
      Issue No: Vol. 12, No. 11 (2019)
       
  • Algorithms, Vol. 12, Pages 241: Fingerprints Classification through Image
           Analysis and Machine Learning Method

    • Authors: Huong Thu Nguyen, Long The Nguyen
      First page: 241
      Abstract: The system that automatically identifies the anthropometric fingerprint is one of the systems that interact directly with the user, which every day will be provided with a diverse database. This requires the system to be optimized to handle the process to meet the needs of users such as fast processing time, almost absolute accuracy, no errors in the real process. Therefore, in this paper, we propose the application of machine learning methods to develop fingerprint classification algorithms based on the singularity feature. The goal of the paper is to reduce the number of comparisons in automatic fingerprint recognition systems with large databases. The combination of using computer vision algorithms in the image pre-processing stage increases the calculation time, improves the quality of the input images, making the process of feature extraction highly effective and the classification process fast and accurate. The classification results on 3 datasets with the criteria for Precision, Recall, Accuracy evaluation and ROC analysis of algorithms show that the Random Forest (RF) algorithm has the best accuracy (≥96.75%) on all 3 databases, Support Vector Machine (SVM) has the best results (≥95.5%) 2 / 3 databases.
      Citation: Algorithms
      PubDate: 2019-11-11
      DOI: 10.3390/a12110241
      Issue No: Vol. 12, No. 11 (2019)
       
  • Algorithms, Vol. 12, Pages 242: On Neighborhood Structures and Repair
           Techniques for Blocking Job Shop Scheduling Problems

    • Authors: Julia Lange, Frank Werner
      First page: 242
      Abstract: The job shop scheduling problem with blocking constraints and total tardiness minimization represents a challenging combinatorial optimization problem of high relevance in production planning and logistics. Since general-purpose solution approaches struggle with finding even feasible solutions, a permutation-based heuristic method is proposed here, and the applicability of basic scheduling-tailored mechanisms is discussed. The problem is tackled by a local search framework, which relies on interchange- and shift-based operators. Redundancy and feasibility issues require advanced transformation and repairing schemes. An analysis of the embedded neighborhoods shows beneficial modes of implementation on the one hand and structural difficulties caused by the blocking constraints on the other hand. The applied simulated annealing algorithm generates good solutions for a wide set of benchmark instances. The computational results especially highlight the capability of the permutation-based method in constructing feasible schedules of valuable quality for instances of critical size and support future research on hybrid solution techniques.
      Citation: Algorithms
      PubDate: 2019-11-12
      DOI: 10.3390/a12110242
      Issue No: Vol. 12, No. 11 (2019)
       
  • Algorithms, Vol. 12, Pages 243: An Improved Genetic Algorithm with
           Adaptive Variable Neighborhood Search for FJSP

    • Authors: Xiaolin Gu, Ming Huang, Xu Liang
      First page: 243
      Abstract: For solving the complex flexible job-shop scheduling problem, an improved genetic algorithm with adaptive variable neighborhood search (IGA-AVNS) is proposed. The improved genetic algorithm first uses a hybrid method combining operation sequence (OS) random selection with machine assignment (MA) hybrid method selection to generate the initial population, and it then groups the population. Each group uses an improved genetic operation for global search, then the better solutions from each group are stored in the elite library, and finally, the adaptive local neighborhood search is used in the elite library for detailed local searches. The simulation experiments are carried out by three sets of international standard examples. The experimental results show that the IGA-AVNS algorithm is an effective algorithm for solving flexible job-shop scheduling problems.
      Citation: Algorithms
      PubDate: 2019-11-14
      DOI: 10.3390/a12110243
      Issue No: Vol. 12, No. 11 (2019)
       
  • Algorithms, Vol. 12, Pages 244: A Novel Multi-Objective Five-Elements
           Cycle Optimization Algorithm

    • Authors: Chunling Ye, Zhengyan Mao, Mandan Liu
      First page: 244
      Abstract: Inspired by the mechanism of generation and restriction among five elements in Chinese traditional culture, we present a novel Multi-Objective Five-Elements Cycle Optimization algorithm (MOFECO). During the optimization process of MOFECO, we use individuals to represent the elements. At each iteration, we first divide the population into several cycles, each of which contains several individuals. Secondly, for every individual in each cycle, we judge whether to update it according to the force exerted on it by other individuals in the cycle. In the case of an update, a local or global update is selected by a dynamically adjustable probability P s ; otherwise, the individual is retained. Next, we perform combined mutation operations on the updated individuals, so that a new population contains both the reserved and updated individuals for the selection operation. Finally, the fast non-dominated sorting method is adopted on the current population to obtain an optimal Pareto solution set. The parameters’ comparison of MOFECO is given by an experiment and also the performance of MOFECO is compared with three classic evolutionary algorithms Non-dominated Sorting Genetic Algorithm II (NSGA-II), Multi-Objective Particle Swarm Optimization algorithm (MOPSO), Pareto Envelope-based Selection Algorithm II (PESA-II) and two latest algorithms Knee point-driven Evolutionary Algorithm (KnEA) and Non-dominated Sorting and Local Search (NSLS) on solving test function sets Zitzler et al’s Test suite (ZDT), Deb et al’s Test suite (DTLZ), Walking Fish Group (WFG) and Many objective Function (MaF). The experimental results indicate that the proposed MOFECO can approach the true Pareto-optimal front with both better diversity and convergence compared to the five other algorithms.
      Citation: Algorithms
      PubDate: 2019-11-14
      DOI: 10.3390/a12110244
      Issue No: Vol. 12, No. 11 (2019)
       
  • Algorithms, Vol. 12, Pages 200: A Machine Learning Approach to Algorithm
           Selection for Exact Computation of Treewidth

    • Authors: Borislav Slavchev, Evelina Masliankova, Steven Kelk
      First page: 200
      Abstract: We present an algorithm selection framework based on machine learning for the exact computation of treewidth, an intensively studied graph parameter that is NP-hard to compute. Specifically, we analyse the comparative performance of three state-of-the-art exact treewidth algorithms on a wide array of graphs and use this information to predict which of the algorithms, on a graph by graph basis, will compute the treewidth the quickest. Experimental results show that the proposed meta-algorithm outperforms existing methods on benchmark instances on all three performance metrics we use: in a nutshell, it computes treewidth faster than any single algorithm in isolation. We analyse our results to derive insights about graph feature importance and the strengths and weaknesses of the algorithms we used. Our results are further evidence of the advantages to be gained by strategically blending machine learning and combinatorial optimisation approaches within a hybrid algorithmic framework. The machine learning model we use is intentionally simple to emphasise that speedup can already be obtained without having to engage in the full complexities of machine learning engineering. We reflect on how future work could extend this simple but effective, proof-of-concept by deploying more sophisticated machine learning models.
      Citation: Algorithms
      PubDate: 2019-09-20
      DOI: 10.3390/a12100200
      Issue No: Vol. 12, No. 10 (2019)
       
  • Algorithms, Vol. 12, Pages 201: GASP: Genetic Algorithms for Service
           Placement in Fog Computing Systems

    • Authors: Claudia Canali, Riccardo Lancellotti
      First page: 201
      Abstract: Fog computing is becoming popular as a solution to support applications based on geographically distributed sensors that produce huge volumes of data to be processed and filtered with response time constraints. In this scenario, typical of a smart city environment, the traditional cloud paradigm with few powerful data centers located far away from the sources of data becomes inadequate. The fog computing paradigm, which provides a distributed infrastructure of nodes placed close to the data sources, represents a better solution to perform filtering, aggregation, and preprocessing of incoming data streams reducing the experienced latency and increasing the overall scalability. However, many issues still exist regarding the efficient management of a fog computing architecture, such as the distribution of data streams coming from sensors over the fog nodes to minimize the experienced latency. The contribution of this paper is two-fold. First, we present an optimization model for the problem of mapping data streams over fog nodes, considering not only the current load of the fog nodes, but also the communication latency between sensors and fog nodes. Second, to address the complexity of the problem, we present a scalable heuristic based on genetic algorithms. We carried out a set of experiments based on a realistic smart city scenario: the results show how the performance of the proposed heuristic is comparable with the one achieved through the solution of the optimization problem. Then, we carried out a comparison among different genetic evolution strategies and operators that identify the uniform crossover as the best option. Finally, we perform a wide sensitivity analysis to show the stability of the heuristic performance with respect to its main parameters.
      Citation: Algorithms
      PubDate: 2019-09-21
      DOI: 10.3390/a12100201
      Issue No: Vol. 12, No. 10 (2019)
       
  • Algorithms, Vol. 12, Pages 202: Evolutionary Algorithms in Health
           Technologies

    • Authors: Sai Ho Ling, Hak Keung Lam
      First page: 202
      Abstract: Health technology research brings together complementary interdisciplinary research skills in the development of innovative health technology applications. Recent research indicates that artificial intelligence can help achieve outstanding performance for particular types of health technology applications. An evolutionary algorithm is one of the subfields of artificial intelligence, and is an effective algorithm for global optimization inspired by biological evolution. With the rapidly growing complexity of design issues, methodologies and a higher demand for quality health technology applications, the development of evolutionary computation algorithms for health has become timely and of high relevance. This Special Issue intends to bring together researchers to report the recent findings in evolutionary algorithms in health technology.
      Citation: Algorithms
      PubDate: 2019-09-24
      DOI: 10.3390/a12100202
      Issue No: Vol. 12, No. 10 (2019)
       
  • Algorithms, Vol. 12, Pages 203: Data-Driven Predictive Modeling of
           Neuronal Dynamics Using Long Short-Term Memory

    • Authors: Benjamin Plaster, Gautam Kumar
      First page: 203
      Abstract: Modeling brain dynamics to better understand and control complex behaviors underlying various cognitive brain functions have been of interest to engineers, mathematicians and physicists over the last several decades. With the motivation of developing computationally efficient models of brain dynamics to use in designing control-theoretic neurostimulation strategies, we have developed a novel data-driven approach in a long short-term memory (LSTM) neural network architecture to predict the temporal dynamics of complex systems over an extended long time-horizon in future. In contrast to recent LSTM-based dynamical modeling approaches that make use of multi-layer perceptrons or linear combination layers as output layers, our architecture uses a single fully connected output layer and reversed-order sequence-to-sequence mapping to improve short time-horizon prediction accuracy and to make multi-timestep predictions of dynamical behaviors. We demonstrate the efficacy of our approach in reconstructing the regular spiking to bursting dynamics exhibited by an experimentally-validated 9-dimensional Hodgkin-Huxley model of hippocampal CA1 pyramidal neurons. Through simulations, we show that our LSTM neural network can predict the multi-time scale temporal dynamics underlying various spiking patterns with reasonable accuracy. Moreover, our results show that the predictions improve with increasing predictive time-horizon in the multi-timestep deep LSTM neural network.
      Citation: Algorithms
      PubDate: 2019-09-24
      DOI: 10.3390/a12100203
      Issue No: Vol. 12, No. 10 (2019)
       
  • Algorithms, Vol. 12, Pages 204: Simulation on Cooperative Changeover of
           Production Team Using Hybrid Modeling Method

    • Authors: Xiaodong Zhang, Yiqi Wang, Bingcun Xu
      First page: 204
      Abstract: In the multi-variety and small-quantity manufacturing environment, changeover operation occurs frequently, and cooperative changeover method is often used as a way to shorten the changeover time and balance the workload. However, more workers and tasks will be affected by cooperative changeover. As such, the effectiveness of the cooperative changeover is dependent on other factors, such as the scope of cooperation and the proportion of newly introduced products. For this reason, this paper proposes a hybrid modeling method to support the simulation study of the production team's cooperative changeover strategies under various environments. Firstly, a hybrid simulation modeling method consisting of multi-agent systems and discrete events is introduced. Secondly, according to the scope of cooperation, this paper puts forward four kinds of cooperative changeover strategies. This paper also describes the cooperative line-changing behavior of operators. Finally, based on the changeover strategies, the proposed simulation method is applied to a production cell. Four production scenarios are considered according to the proportion of newly introduced part. The performance of various cooperative strategies in different production scenarios is simulated, and the statistical test results show that the optimal or satisfactory strategy can be determined in each production scenario. Additionally, the effectiveness and practicability of the proposed modeling method are verified.
      Citation: Algorithms
      PubDate: 2019-09-24
      DOI: 10.3390/a12100204
      Issue No: Vol. 12, No. 10 (2019)
       
  • Algorithms, Vol. 12, Pages 205: Real-Time Conveyor Belt Deviation
           Detection Algorithm Based on Multi-Scale Feature Fusion Network

    • Authors: Chan Zeng, Junfeng Zheng, Jiangyun Li
      First page: 205
      Abstract: The conveyor belt is an indispensable piece of conveying equipment for a mine whose deviation caused by roller sticky material and uneven load distribution is the most common failure during operation. In this paper, a real-time conveyor belt detection algorithm based on a multi-scale feature fusion network is proposed, which mainly includes two parts: the feature extraction module and the deviation detection module. The feature extraction module uses a multi-scale feature fusion network structure to fuse low-level features with rich position and detail information and high-level features with stronger semantic information to improve network detection performance. Depthwise separable convolutions are used to achieve real-time detection. The deviation detection module identifies and monitors the deviation fault by calculating the offset of conveyor belt. In particular, a new weighted loss function is designed to optimize the network and to improve the detection effect of the conveyor belt edge. In order to evaluate the effectiveness of the proposed method, the Canny algorithm, FCNs, UNet and Deeplab v3 networks are selected for comparison. The experimental results show that the proposed algorithm achieves 78.92% in terms of pixel accuracy (PA), and reaches 13.4 FPS (Frames per Second) with the error of less than 3.2 mm, which outperforms the other four algorithms.
      Citation: Algorithms
      PubDate: 2019-09-26
      DOI: 10.3390/a12100205
      Issue No: Vol. 12, No. 10 (2019)
       
  • Algorithms, Vol. 12, Pages 206: Self-Improving Generative Artificial
           Neural Network for Pseudorehearsal Incremental Class Learning

    • Authors: Diego Mellado, Carolina Saavedra, Steren Chabert, Romina Torres and Rodrigo Salas
      First page: 206
      Abstract: Deep learning models are part of the family of artificial neural networks and, as such, they suffer catastrophic interference when learning sequentially. In addition, the greater number of these models have a rigid architecture which prevents the incremental learning of new classes. To overcome these drawbacks, we propose the Self-Improving Generative Artificial Neural Network (SIGANN), an end-to-end deep neural network system which can ease the catastrophic forgetting problem when learning new classes. In this method, we introduce a novel detection model that automatically detects samples of new classes, and an adversarial autoencoder is used to produce samples of previous classes. This system consists of three main modules: a classifier module implemented using a Deep Convolutional Neural Network, a generator module based on an adversarial autoencoder, and a novelty-detection module implemented using an OpenMax activation function. Using the EMNIST data set, the model was trained incrementally, starting with a small set of classes. The results of the simulation show that SIGANN can retain previous knowledge while incorporating gradual forgetfulness of each learning sequence at a rate of about 7% per training step. Moreover, SIGANN can detect new classes that are hidden in the data with a median accuracy of 43 % and, therefore, proceed with incremental class learning.
      Citation: Algorithms
      PubDate: 2019-10-01
      DOI: 10.3390/a12100206
      Issue No: Vol. 12, No. 10 (2019)
       
  • Algorithms, Vol. 12, Pages 207: Recommending Links to Control Elections
           via Social Influence

    • Authors: Federico Corò, Gianlorenzo D’Angelo, Yllka Velaj
      First page: 207
      Abstract: Political parties recently learned that they must use social media campaigns along with advertising on traditional media to defeat their opponents. Before the campaign starts, it is important for a political party to establish and ensure its media presence, for example by enlarging their number of connections in the social network in order to assure a larger portion of users. Indeed, adding new connections between users increases the capabilities of a social network of spreading information, which in turn can increase the retention rate and the number of new voters. In this work, we address the problem of selecting a fixed-size set of new connections to be added to a subset of voters that, with their influence, will change the opinion of the network’s users about a target candidate, maximizing its chances to win the election. We provide a constant factor approximation algorithm for this problem and we experimentally show that, with few new links and small computational time, our algorithm is able to maximize the chances to make the target candidate win the elections.
      Citation: Algorithms
      PubDate: 2019-10-01
      DOI: 10.3390/a12100207
      Issue No: Vol. 12, No. 10 (2019)
       
  • Algorithms, Vol. 12, Pages 208: A Convex Optimization Algorithm for
           Electricity Pricing of Charging Stations

    • Authors: Jing Zhang, Xiangpeng Zhan, Taoyong Li, Linru Jiang, Jun Yang, Yuanxing Zhang, Xiaohong Diao, Sining Han
      First page: 208
      Abstract: The problem of electricity pricing for charging stations is a multi-objective mixed integer nonlinear programming. Existing algorithms have low efficiency in solving this problem. In this paper, a convex optimization algorithm is proposed to get the optimal solution quickly. Firstly, the model is transformed into a convex optimization problem by second-order conic relaxation and Karush–Kuhn–Tucker optimality conditions. Secondly, a polyhedral approximation method is applied to construct a mixed integer linear programming, which can be solved quickly by branch and bound method. Finally, the model is solved many times to obtain the Pareto front according to the scalarization basic theorem. Based on an IEEE 33-bus distribution network model, simulation results show that the proposed algorithm can obtain an exact global optimal solution quickly compared with the heuristic method.
      Citation: Algorithms
      PubDate: 2019-10-01
      DOI: 10.3390/a12100208
      Issue No: Vol. 12, No. 10 (2019)
       
  • Algorithms, Vol. 12, Pages 209: A Finite Regime Analysis of Information
           Set Decoding Algorithms

    • Authors: Marco Baldi, Alessandro Barenghi, Franco Chiaraluce, Gerardo Pelosi, Paolo  Santini
      First page: 209
      Abstract: Decoding of random linear block codes has been long exploited as a computationally hard problem on which it is possible to build secure asymmetric cryptosystems. In particular, both correcting an error-affected codeword, and deriving the error vector corresponding to a given syndrome were proven to be equally difficult tasks. Since the pioneering work of Eugene Prange in the early 1960s, a significant research effort has been put into finding more efficient methods to solve the random code decoding problem through a family of algorithms known as information set decoding. The obtained improvements effectively reduce the overall complexity, which was shown to decrease asymptotically at each optimization, while remaining substantially exponential in the number of errors to be either found or corrected. In this work, we provide a comprehensive survey of the information set decoding techniques, providing finite regime temporal and spatial complexities for them. We exploit these formulas to assess the effectiveness of the asymptotic speedups obtained by the improved information set decoding techniques when working with code parameters relevant for cryptographic purposes. We also delineate computational complexities taking into account the achievable speedup via quantum computers and similarly assess such speedups in the finite regime. To provide practical grounding to the choice of cryptographically relevant parameters, we employ as our validation suite the ones chosen by cryptosystems admitted to the second round of the ongoing standardization initiative promoted by the US National Institute of Standards and Technology.
      Citation: Algorithms
      PubDate: 2019-10-01
      DOI: 10.3390/a12100209
      Issue No: Vol. 12, No. 10 (2019)
       
  • Algorithms, Vol. 12, Pages 210: Laplacian Eigenmaps Dimensionality
           Reduction Based on Clustering-Adjusted Similarity

    • Authors: Honghu Zhou, Jun Wang
      First page: 210
      Abstract: Euclidean distance between instances is widely used to capture the manifold structure of data and for graph-based dimensionality reduction. However, in some circumstances, the basic Euclidean distance cannot accurately capture the similarity between instances; some instances from different classes but close to the decision boundary may be close to each other, which may mislead the graph-based dimensionality reduction and compromise the performance. To mitigate this issue, in this paper, we proposed an approach called Laplacian Eigenmaps based on Clustering-Adjusted Similarity (LE-CAS). LE-CAS first performs clustering on all instances to explore the global structure and discrimination of instances, and quantifies the similarity between cluster centers. Then, it adjusts the similarity between pairwise instances by multiplying the similarity between centers of clusters, which these two instances respectively belong to. In this way, if two instances are from different clusters, the similarity between them is reduced; otherwise, it is unchanged. Finally, LE-CAS performs graph-based dimensionality reduction (via Laplacian Eigenmaps) based on the adjusted similarity. We conducted comprehensive empirical studies on UCI datasets and show that LE-CAS not only has a better performance than other relevant comparing methods, but also is more robust to input parameters.
      Citation: Algorithms
      PubDate: 2019-10-04
      DOI: 10.3390/a12100210
      Issue No: Vol. 12, No. 10 (2019)
       
  • Algorithms, Vol. 12, Pages 211: Approximating the Temporal Neighbourhood
           Function of Large Temporal Graphs

    • Authors: Pierluigi Crescenzi, Clémence Magnien, Andrea Marino
      First page: 211
      Abstract: Temporal networks are graphs in which edges have temporal labels, specifying their starting times and their traversal times. Several notions of distances between two nodes in a temporal network can be analyzed, by referring, for example, to the earliest arrival time or to the latest starting time of a temporal path connecting the two nodes. In this paper, we mostly refer to the notion of temporal reachability by using the earliest arrival time. In particular, we first show how the sketch approach, which has already been used in the case of classical graphs, can be applied to the case of temporal networks in order to approximately compute the sizes of the temporal cones of a temporal network. By making use of this approach, we subsequently show how we can approximate the temporal neighborhood function (that is, the number of pairs of nodes reachable from one another in a given time interval) of large temporal networks in a few seconds. Finally, we apply our algorithm in order to analyze and compare the behavior of 25 public transportation temporal networks. Our results can be easily adapted to the case in which we want to refer to the notion of distance based on the latest starting time.
      Citation: Algorithms
      PubDate: 2019-10-10
      DOI: 10.3390/a12100211
      Issue No: Vol. 12, No. 10 (2019)
       
  • Algorithms, Vol. 12, Pages 212: Addendum: Mircea-Bogdan Radac and Timotei
           Lala. Learning Output Reference Model Tracking for Higher-order Nonlinear
           Systems with Unknown Dynamics. Algorithms 2019, 12, 121

    • Authors: Radac, Lala
      First page: 212
      Abstract: The authors would like to mention that their paper is an extended version of the IEEE conference paper [...]
      Citation: Algorithms
      PubDate: 2019-10-10
      DOI: 10.3390/a12100212
      Issue No: Vol. 12, No. 10 (2019)
       
  • Algorithms, Vol. 12, Pages 213: Multimodal Dynamic Journey-Planning

    • Authors: Kalliopi Giannakopoulou, Andreas Paraskevopoulos, Christos Zaroliagis
      First page: 213
      Abstract: In this paper, a new model, known as the multimodal dynamic timetable model (DTM), is presented for computing optimal multimodal journeys in schedule-based public transport systems. The new model constitutes an extension of the dynamic timetable model (DTM), which was developed originally for a different setting (unimodal journey-planning). Multimodal DTM demonstrates a very fast query algorithm that meets the requirement for real-time response to best journey queries, and an ultra-fast update algorithm for updating the timetable information in case of delays of scheduled-based vehicles. An experimental study on real-world metropolitan networks demonstrates that the query and update algorithms of Multimodal DTM compare favorably with other state-of-the-art approaches when public transport, including unrestricted—with respect to departing time—traveling (e.g., walking and electric vehicles) is considered.
      Citation: Algorithms
      PubDate: 2019-10-13
      DOI: 10.3390/a12100213
      Issue No: Vol. 12, No. 10 (2019)
       
  • Algorithms, Vol. 12, Pages 214: Exploiting Sparse Statistics for a
           Sequence-Based Prediction of the Effect of Mutations

    • Authors: Mihaly Mezei
      First page: 214
      Abstract: Recent work showed that there is a significant difference between the statistics of amino acid triplets and quadruplets in sequences of folded proteins and randomly generated sequences. These statistics were used to assign a score to each sequence and make a prediction whether a sequence is likely to fold. The present paper extends the statistics to higher multiplets and suggests a way to handle the treatment of multiplets that were not found in the set of folded proteins. In particular, foldability predictions were done along the line of the previous work using pentuplet statistics and a way was found to combine the quadruplet and pentuplets statistics to improve the foldability predictions. A different, simpler, score was defined for hextuplets and heptuplets and were used to predict the direction of stability change of a protein upon mutation. With the best score combination the accuracy of the prediction was 73.4%.
      Citation: Algorithms
      PubDate: 2019-10-14
      DOI: 10.3390/a12100214
      Issue No: Vol. 12, No. 10 (2019)
       
  • Algorithms, Vol. 12, Pages 215: Backstepping Adaptive Neural Network
           Control for Electric Braking Systems of Aircrafts

    • Authors: Zhang, Lin
      First page: 215
      Abstract: This paper proposes an adaptive backstepping control algorithm for electric braking systems with electromechanical actuators (EMAs). First, the ideal mathematical model of the EMA is established, and the nonlinear factors are analyzed, such as the deformation of the reduction gear. Subsequently, the actual mathematical model of the EMA is rebuilt by combining the ideal model and the nonlinear factors. To realize high performance braking pressure control, the backstepping control method is adopted to address the mismatched uncertainties in the electric braking system, and a radial basis function (RBF) neural network is established to estimate the nonlinear functions in the control system. The experimental results indicate that the proposed braking pressure control strategy can improve the servo performance of the electric braking system. In addition, the hardware-in-loop (HIL) experimental results show that the proposed EMA controller can satisfy the requirements of the aircraft antilock braking systems.
      Citation: Algorithms
      PubDate: 2019-10-15
      DOI: 10.3390/a12100215
      Issue No: Vol. 12, No. 10 (2019)
       
  • Algorithms, Vol. 12, Pages 216: Adaptive Clustering via Symmetric
           Nonnegative Matrix Factorization of the Similarity Matrix

    • Authors: Paola Favati, Grazia Lotti, Ornella Menchi, Francesco Romani
      First page: 216
      Abstract: The problem of clustering, that is, the partitioning of data into groups of similar objects, is a key step for many data-mining problems. The algorithm we propose for clustering is based on the symmetric nonnegative matrix factorization (SymNMF) of a similarity matrix. The algorithm is first presented for the case of a prescribed number k of clusters, then it is extended to the case of a not a priori given k. A heuristic approach improving the standard multistart strategy is proposed and validated by the experimentation.
      Citation: Algorithms
      PubDate: 2019-10-17
      DOI: 10.3390/a12100216
      Issue No: Vol. 12, No. 10 (2019)
       
  • Algorithms, Vol. 12, Pages 217: Can People Really Do Nothing' Handling
           Annotation Gaps in ADL Sensor Data

    • Authors: Alaa E. Abdel Hakim, Wael Deabes
      First page: 217
      Abstract: In supervised Activities of Daily Living (ADL) recognition systems, annotating collected sensor readings is an essential, yet exhaustive, task. Readings are collected from activity-monitoring sensors in a 24/7 manner. The size of the produced dataset is so huge that it is almost impossible for a human annotator to give a certain label to every single instance in the dataset. This results in annotation gaps in the input data to the adopting learning system. The performance of the recognition system is negatively affected by these gaps. In this work, we propose and investigate three different paradigms to handle these gaps. In the first paradigm, the gaps are taken out by dropping all unlabeled readings. A single “Unknown” or “Do-Nothing” label is given to the unlabeled readings within the operation of the second paradigm. The last paradigm handles these gaps by giving every set of them a unique label identifying the encapsulating certain labels. Also, we propose a semantic preprocessing method of annotation gaps by constructing a hybrid combination of some of these paradigms for further performance improvement. The performance of the proposed three paradigms and their hybrid combination is evaluated using an ADL benchmark dataset containing more than 2.5 × 10 6 sensor readings that had been collected over more than nine months. The evaluation results emphasize the performance contrast under the operation of each paradigm and support a specific gap handling approach for better performance.
      Citation: Algorithms
      PubDate: 2019-10-17
      DOI: 10.3390/a12100217
      Issue No: Vol. 12, No. 10 (2019)
       
  • Algorithms, Vol. 12, Pages 218: A New Coding Paradigm for the Primitive
           Relay Channel

    • Authors: Marco Mondelli, S. Hamed Hassani, Rüdiger Urbanke
      First page: 218
      Abstract: We consider the primitive relay channel, where the source sends a message to the relay and to the destination, and the relay helps the communication by transmitting an additional message to the destination via a separate channel. Two well-known coding techniques have been introduced for this setting: decode-and-forward and compress-and-forward. In decode-and-forward, the relay completely decodes the message and sends some information to the destination; in compress-and-forward, the relay does not decode, and it sends a compressed version of the received signal to the destination using Wyner–Ziv coding. In this paper, we present a novel coding paradigm that provides an improved achievable rate for the primitive relay channel. The idea is to combine compress-and-forward and decode-and-forward via a chaining construction. We transmit over pairs of blocks: in the first block, we use compress-and-forward; and, in the second block, we use decode-and-forward. More specifically, in the first block, the relay does not decode, it compresses the received signal via Wyner–Ziv, and it sends only part of the compression to the destination. In the second block, the relay completely decodes the message, it sends some information to the destination, and it also sends the remaining part of the compression coming from the first block. By doing so, we are able to strictly outperform both compress-and-forward and decode-and-forward. Note that the proposed coding scheme can be implemented with polar codes. As such, it has the typical attractive properties of polar coding schemes, namely, quasi-linear encoding and decoding complexity, and error probability that decays at super-polynomial speed. As a running example, we take into account the special case of the erasure relay channel, and we provide a comparison between the rates achievable by our proposed scheme and the existing upper and lower bounds.
      Citation: Algorithms
      PubDate: 2019-10-18
      DOI: 10.3390/a12100218
      Issue No: Vol. 12, No. 10 (2019)
       
  • Algorithms, Vol. 12, Pages 219: On Finding Two Posets that Cover Given
           Linear Orders

    • Authors: Ivy Ordanel, Proceso Fernandez, Henry Adorna
      First page: 219
      Abstract: The Poset Cover Problem is an optimization problem where the goal is to determine a minimum set of posets that covers a given set of linear orders. This problem is relevant in the field of data mining, specifically in determining directed networks or models that explain the ordering of objects in a large sequential dataset. It is already known that the decision version of the problem is NP-Hard while its variation where the goal is to determine only a single poset that covers the input is in P. In this study, we investigate the variation, which we call the 2-Poset Cover Problem, where the goal is to determine two posets, if they exist, that cover the given linear orders. We derive properties on posets, which leads to an exact solution for the 2-Poset Cover Problem. Although the algorithm runs in exponential-time, it is still significantly faster than a brute-force solution. Moreover, we show that when the posets being considered are tree-posets, the running-time of the algorithm becomes polynomial, which proves that the more restricted variation, which we called the 2-Tree-Poset Cover Problem, is also in P.
      Citation: Algorithms
      PubDate: 2019-10-19
      DOI: 10.3390/a12100219
      Issue No: Vol. 12, No. 10 (2019)
       
  • Algorithms, Vol. 12, Pages 220: Freeway Traffic Congestion Reduction and
           Environment Regulation via Model Predictive Control

    • Authors: Juan Chen, Yuxuan Yu, Qi Guo
      First page: 220
      Abstract: This paper proposes a model predictive control method based on dynamic multi-objective optimization algorithms (MPC_CPDMO-NSGA-II) for reducing freeway congestion and relieving environment impact simultaneously. A new dynamic multi-objective optimization algorithm based on clustering and prediction with NSGA-II (CPDMO-NSGA-II) is proposed. The proposed CPDMO-NSGA-II algorithm is used to realize on-line optimization at each control step in model predictive control. The performance indicators considered in model predictive control consists of total time spent, total travel distance, total emissions and total fuel consumption. Then TOPSIS method is adopted to select an optimal solution from Pareto front obtained from MPC_CPDMO-NSGA-II algorithm and is applied to the VISSIM environment. The control strategies are variable speed limit (VSL) and ramp metering (RM). In order to verify the performance of the proposed algorithm, the proposed algorithm is tested under the simulation environment originated from a real freeway network in Shanghai with one on-ramp. The result is compared with fixed speed limit strategy and single optimization method respectively. Simulation results show that it can effectively alleviate traffic congestion, reduce emissions and fuel consumption, as compared with fixed speed limit strategy and classical model predictive control method based on single optimization method.
      Citation: Algorithms
      PubDate: 2019-10-21
      DOI: 10.3390/a12100220
      Issue No: Vol. 12, No. 10 (2019)
       
  • Algorithms, Vol. 12, Pages 221: Image Deblurring under Impulse Noise via
           Total Generalized Variation and Non-Convex Shrinkage

    • Authors: Fan Lin, Yingpin Chen, Yuqun Chen, Fei Yu
      First page: 221
      Abstract: Image deblurring under the background of impulse noise is a typically ill-posed inverse problem which attracted great attention in the fields of image processing and computer vision. The fast total variation deconvolution (FTVd) algorithm proved to be an effective way to solve this problem. However, it only considers sparsity of the first-order total variation, resulting in staircase artefacts. The L1 norm is adopted in the FTVd model to depict the sparsity of the impulse noise, while the L1 norm has limited capacity of depicting it. To overcome this limitation, we present a new algorithm based on the Lp-pseudo-norm and total generalized variation (TGV) regularization. The TGV regularization puts sparse constraints on both the first-order and second-order gradients of the image, effectively preserving the image edge while relieving undesirable artefacts. The Lp-pseudo-norm constraint is employed to replace the L1 norm constraint to depict the sparsity of the impulse noise more precisely. The alternating direction method of multipliers is adopted to solve the proposed model. In the numerical experiments, the proposed algorithm is compared with some state-of-the-art algorithms in terms of peak signal-to-noise ratio (PSNR), structural similarity (SSIM), signal-to-noise ratio (SNR), operation time, and visual effects to verify its superiority.
      Citation: Algorithms
      PubDate: 2019-10-22
      DOI: 10.3390/a12100221
      Issue No: Vol. 12, No. 10 (2019)
       
 
 
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