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  Subjects -> MATHEMATICS (Total: 874 journals)
    - APPLIED MATHEMATICS (71 journals)
    - GEOMETRY AND TOPOLOGY (19 journals)
    - MATHEMATICS (647 journals)
    - MATHEMATICS (GENERAL) (41 journals)
    - NUMERICAL ANALYSIS (19 journals)
    - PROBABILITIES AND MATH STATISTICS (77 journals)

MATHEMATICS (647 journals)                  1 2 3 4 | Last

Showing 1 - 200 of 538 Journals sorted alphabetically
Abakós     Open Access   (Followers: 3)
Abhandlungen aus dem Mathematischen Seminar der Universitat Hamburg     Hybrid Journal   (Followers: 2)
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: 4)
ACM Transactions on Mathematical Software (TOMS)     Hybrid Journal   (Followers: 6)
ACS Applied Materials & Interfaces     Full-text available via subscription   (Followers: 21)
Acta Applicandae Mathematicae     Hybrid Journal   (Followers: 1)
Acta Mathematica     Hybrid Journal   (Followers: 11)
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: 5)
Acta Mathematica Vietnamica     Hybrid Journal  
Acta Mathematicae Applicatae Sinica, English Series     Hybrid Journal  
Advanced Science Letters     Full-text available via subscription   (Followers: 5)
Advances in Applied Clifford Algebras     Hybrid Journal   (Followers: 3)
Advances in Calculus of Variations     Hybrid Journal   (Followers: 2)
Advances in Catalysis     Full-text available via subscription   (Followers: 5)
Advances in Complex Systems     Hybrid Journal   (Followers: 7)
Advances in Computational Mathematics     Hybrid Journal   (Followers: 15)
Advances in Decision Sciences     Open Access   (Followers: 4)
Advances in Difference Equations     Open Access   (Followers: 1)
Advances in Fixed Point Theory     Open Access   (Followers: 5)
Advances in Geosciences (ADGEO)     Open Access   (Followers: 10)
Advances in Linear Algebra & Matrix Theory     Open Access   (Followers: 1)
Advances in Materials Sciences     Open Access   (Followers: 16)
Advances in Mathematical Physics     Open Access   (Followers: 6)
Advances in Mathematics     Full-text available via subscription   (Followers: 10)
Advances in Numerical Analysis     Open Access   (Followers: 4)
Advances in Operations Research     Open Access   (Followers: 11)
Advances in Porous Media     Full-text available via subscription   (Followers: 4)
Advances in Pure and Applied Mathematics     Hybrid Journal   (Followers: 5)
Advances in Pure Mathematics     Open Access   (Followers: 4)
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: 5)
African Journal of Mathematics and Computer Science Research     Open Access   (Followers: 4)
Afrika Matematika     Hybrid Journal   (Followers: 1)
Air, Soil & Water Research     Open Access   (Followers: 7)
AKSIOMA Journal of Mathematics Education     Open Access   (Followers: 1)
Algebra and Logic     Hybrid Journal   (Followers: 3)
Algebra Colloquium     Hybrid Journal   (Followers: 4)
Algebra Universalis     Hybrid Journal   (Followers: 2)
Algorithmic Operations Research     Full-text available via subscription   (Followers: 5)
Algorithms     Open Access   (Followers: 9)
Algorithms Research     Open Access   (Followers: 1)
American Journal of Biostatistics     Open Access   (Followers: 9)
American Journal of Computational and Applied Mathematics     Open Access   (Followers: 3)
American Journal of Mathematical Analysis     Open Access  
American Journal of Mathematics     Full-text available via subscription   (Followers: 7)
American Journal of Operations Research     Open Access   (Followers: 5)
American Mathematical Monthly     Full-text available via subscription   (Followers: 6)
An International Journal of Optimization and Control: Theories & Applications     Open Access   (Followers: 7)
Analele Universitatii Ovidius Constanta - Seria Matematica     Open Access   (Followers: 1)
Analysis     Hybrid Journal   (Followers: 2)
Analysis and Applications     Hybrid Journal   (Followers: 1)
Analysis and Mathematical Physics     Hybrid Journal   (Followers: 4)
Analysis Mathematica     Full-text available via subscription  
Annales Mathematicae Silesianae     Open Access  
Annales mathématiques du Québec     Hybrid Journal   (Followers: 4)
Annales UMCS, 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: 3)
Annals of Data Science     Hybrid Journal   (Followers: 8)
Annals of Discrete Mathematics     Full-text available via subscription   (Followers: 6)
Annals of Mathematics     Full-text available via subscription  
Annals of Mathematics and Artificial Intelligence     Hybrid Journal   (Followers: 6)
Annals of Pure and Applied Logic     Open Access   (Followers: 2)
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  
Annuaire du Collège de France     Open Access   (Followers: 5)
Applicable Algebra in Engineering, Communication and Computing     Hybrid Journal   (Followers: 2)
Applications of Mathematics     Hybrid Journal   (Followers: 1)
Applied Categorical Structures     Hybrid Journal   (Followers: 2)
Applied Computational Intelligence and Soft Computing     Open Access   (Followers: 12)
Applied Mathematics     Open Access   (Followers: 3)
Applied Mathematics     Open Access   (Followers: 4)
Applied Mathematics & Optimization     Hybrid Journal   (Followers: 4)
Applied Mathematics - A Journal of Chinese Universities     Hybrid Journal  
Applied Mathematics Letters     Full-text available via subscription   (Followers: 1)
Applied Mathematics Research eXpress     Hybrid Journal   (Followers: 1)
Applied Network Science     Open Access  
Applied Numerical Mathematics     Hybrid Journal   (Followers: 5)
Applied Spatial Analysis and Policy     Hybrid Journal   (Followers: 4)
Arab Journal of Mathematical Sciences     Open Access   (Followers: 2)
Arabian Journal of Mathematics     Open Access   (Followers: 2)
Archive for Mathematical Logic     Hybrid Journal   (Followers: 1)
Archive of Applied Mechanics     Hybrid Journal   (Followers: 4)
Archive of Numerical Software     Open Access  
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 4)
Arkiv för Matematik     Hybrid Journal   (Followers: 1)
Arnold Mathematical Journal     Hybrid Journal   (Followers: 1)
Artificial Satellites : The Journal of Space Research Centre of Polish Academy of Sciences     Open Access   (Followers: 18)
Asia-Pacific Journal of Operational Research     Hybrid Journal   (Followers: 3)
Asian Journal of Algebra     Open Access   (Followers: 1)
Asian Journal of Current Engineering & Maths     Open Access  
Asian-European Journal of Mathematics     Hybrid Journal   (Followers: 2)
Australian Mathematics Teacher, The     Full-text available via subscription   (Followers: 7)
Australian Primary Mathematics Classroom     Full-text available via subscription   (Followers: 2)
Australian Senior Mathematics Journal     Full-text available via subscription   (Followers: 1)
Automatic Documentation and Mathematical Linguistics     Hybrid Journal   (Followers: 5)
Axioms     Open Access  
Baltic International Yearbook of Cognition, Logic and Communication     Open Access  
Basin Research     Hybrid Journal   (Followers: 3)
BIBECHANA     Open Access  
BIT Numerical Mathematics     Hybrid Journal  
BoEM - Boletim online de Educação Matemática     Open Access  
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: 1)
British Journal of Mathematical and Statistical Psychology     Full-text available via subscription   (Followers: 21)
Bruno Pini Mathematical Analysis Seminar     Open Access  
Buletinul Academiei de Stiinte a Republicii Moldova. Matematica     Open Access   (Followers: 7)
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: 1)
Bulletin of Mathematical Sciences     Open Access   (Followers: 1)
Bulletin of the Brazilian Mathematical Society, New Series     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 Science, Mathematics and Technology Education     Hybrid Journal   (Followers: 17)
Carpathian Mathematical Publications     Open Access   (Followers: 1)
Catalysis in Industry     Hybrid Journal   (Followers: 1)
CEAS Space Journal     Hybrid Journal  
CHANCE     Hybrid Journal   (Followers: 5)
Chaos, Solitons & Fractals     Hybrid Journal   (Followers: 3)
ChemSusChem     Hybrid Journal   (Followers: 7)
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: 2)
Cogent Mathematics     Open Access   (Followers: 2)
Cognitive Computation     Hybrid Journal   (Followers: 4)
Collectanea Mathematica     Hybrid Journal  
College Mathematics Journal     Full-text available via subscription   (Followers: 2)
COMBINATORICA     Hybrid Journal  
Combustion Theory and Modelling     Hybrid Journal   (Followers: 13)
Commentarii Mathematici Helvetici     Hybrid Journal   (Followers: 1)
Communications in Contemporary Mathematics     Hybrid Journal  
Communications in Mathematical Physics     Hybrid Journal   (Followers: 1)
Communications On Pure & Applied Mathematics     Hybrid Journal   (Followers: 3)
Complex Analysis and its Synergies     Open Access   (Followers: 2)
Complex Variables and Elliptic Equations: An International Journal     Hybrid Journal  
Complexus     Full-text available via subscription  
Composite Materials Series     Full-text available via subscription   (Followers: 9)
Comptes Rendus Mathematique     Full-text available via subscription   (Followers: 1)
Computational and Applied Mathematics     Hybrid Journal   (Followers: 2)
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: 8)
Computational Mechanics     Hybrid Journal   (Followers: 4)
Computational Methods and Function Theory     Hybrid Journal  
Computational Optimization and Applications     Hybrid Journal   (Followers: 7)
Computers & Mathematics with Applications     Full-text available via subscription   (Followers: 5)
Concrete Operators     Open Access   (Followers: 4)
Confluentes Mathematici     Hybrid Journal  
COSMOS     Hybrid Journal  
Cryptography and Communications     Hybrid Journal   (Followers: 14)
Cuadernos de Investigación y Formación en Educación Matemática     Open Access  
Cubo. A Mathematical Journal     Open Access  
Czechoslovak Mathematical Journal     Hybrid Journal   (Followers: 1)
Demographic Research     Open Access   (Followers: 12)
Demonstratio Mathematica     Open Access  
Dependence Modeling     Open Access  
Design Journal : An International Journal for All Aspects of Design     Hybrid Journal   (Followers: 29)
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: 2)
Discrete Mathematics     Hybrid Journal   (Followers: 7)
Discrete Mathematics & Theoretical Computer Science     Open Access  
Discrete Mathematics, Algorithms and Applications     Hybrid Journal   (Followers: 2)
Discussiones Mathematicae Graph Theory     Open Access   (Followers: 1)
Doklady Mathematics     Hybrid Journal  
Duke Mathematical Journal     Full-text available via subscription   (Followers: 1)
Edited Series on Advances in Nonlinear Science and Complexity     Full-text available via subscription  
Electronic Journal of Graph Theory and Applications     Open Access   (Followers: 2)
Electronic Notes in Discrete Mathematics     Full-text available via subscription   (Followers: 2)
Elemente der Mathematik     Full-text available via subscription   (Followers: 3)
Energy for Sustainable Development     Hybrid Journal   (Followers: 9)
Enseñanza de las Ciencias : Revista de Investigación y Experiencias Didácticas     Open Access  
Ensino da Matemática em Debate     Open Access  
Entropy     Open Access   (Followers: 5)
ESAIM: Control Optimisation and Calculus of Variations     Full-text available via subscription   (Followers: 1)
European Journal of Combinatorics     Full-text available via subscription   (Followers: 4)
European Journal of Mathematics     Hybrid Journal   (Followers: 1)
European Scientific Journal     Open Access   (Followers: 2)
Experimental Mathematics     Hybrid Journal   (Followers: 3)
Expositiones Mathematicae     Hybrid Journal   (Followers: 2)
Facta Universitatis, Series : Mathematics and Informatics     Open Access  
Fasciculi Mathematici     Open Access  
Finite Fields and Their Applications     Full-text available via subscription   (Followers: 4)
Fixed Point Theory and Applications     Open Access   (Followers: 1)
Formalized Mathematics     Open Access   (Followers: 2)
Foundations and Trends® in Econometrics     Full-text available via subscription   (Followers: 4)

        1 2 3 4 | Last

Journal Cover Algorithms
  [SJR: 0.357]   [H-I: 17]   [9 followers]  Follow
    
  This is an Open Access Journal Open Access journal
   ISSN (Print) 1999-4893
   Published by MDPI Homepage  [151 journals]
  • Algorithms, Vol. 10, Pages 36: DNA Paired Fragment Assembly Using Graph
           Theory

    • Authors: J. Quiroz-Ibarra, Guillermo Mallén-Fullerton, Guillermo Fernández-Anaya
      First page: 36
      Abstract: DNA fragment assembly requirements have generated an important computational problem created by their structure and the volume of data. Therefore, it is important to develop algorithms able to produce high-quality information that use computer resources efficiently. Such an algorithm, using graph theory, is introduced in the present article. We first determine the overlaps between DNA fragments, obtaining the edges of a directed graph; with this information, the next step is to construct an adjacency list with some particularities. Using the adjacency list, it is possible to obtain the DNA contigs (group of assembled fragments building a contiguous element) using graph theory. We performed a set of experiments on real DNA data and compared our results to those obtained with common assemblers (Edena and Velvet). Finally, we searched the contigs in the original genome, in our results and in those of Edena and Velvet.
      PubDate: 2017-03-24
      DOI: 10.3390/a10020036
      Issue No: Vol. 10, No. 2 (2017)
       
  • Algorithms, Vol. 10, Pages 37: A Spatial-Temporal-Semantic Neural Network
           Algorithm for Location Prediction on Moving Objects

    • Authors: Fan Wu, Kun Fu, Yang Wang, Zhibin Xiao, Xingyu Fu
      First page: 37
      Abstract: Location prediction has attracted much attention due to its important role in many location-based services, such as food delivery, taxi-service, real-time bus system, and advertisement posting. Traditional prediction methods often cluster track points into regions and mine movement patterns within the regions. Such methods lose information of points along the road and cannot meet the demand of specific services. Moreover, traditional methods utilizing classic models may not perform well with long location sequences. In this paper, a spatial-temporal-semantic neural network algorithm (STS-LSTM) has been proposed, which includes two steps. First, the spatial-temporal-semantic feature extraction algorithm (STS) is used to convert the trajectory to location sequences with fixed and discrete points in the road networks. The method can take advantage of points along the road and can transform trajectory into model-friendly sequences. Then, a long short-term memory (LSTM)-based model is constructed to make further predictions, which can better deal with long location sequences. Experimental results on two real-world datasets show that STS-LSTM has stable and higher prediction accuracy over traditional feature extraction and model building methods, and the application scenarios of the algorithm are illustrated.
      PubDate: 2017-03-24
      DOI: 10.3390/a10020037
      Issue No: Vol. 10, No. 2 (2017)
       
  • Algorithms, Vol. 10, Pages 38: An Asynchronous Message-Passing Distributed
           Algorithm for the Generalized Local Critical Section Problem

    • Authors: Sayaka Kamei, Hirotsugu Kakugawa
      First page: 38
      Abstract: This paper discusses the generalized local version of critical section problems including mutual exclusion, mutual inclusion, k-mutual exclusion and l-mutual inclusion. When a pair of numbers (li, ki) is given for each process Pi, it is the problem of controlling the system in such a way that the number of processes that can execute their critical sections at a time is at least li and at most ki among its neighboring processes and Pi itself. We propose the first solution for the generalized local (li, Ni + 1)-critical section problem (i.e., the generalized local li-mutual inclusion problem). Additionally, we show the relationship between the generalized local (li, ki)-critical section problem and the generalized local ( Ni + 1 − ki, Ni + 1 − li)-critical section problem. Finally, we propose the first solution for the generalized local (li, ki)-critical section problem for arbitrary (li, ki), where 0 ≤ li < ki + Ni + 1 for each process Pi.
      PubDate: 2017-03-24
      DOI: 10.3390/a10020038
      Issue No: Vol. 10, No. 2 (2017)
       
  • Algorithms, Vol. 10, Pages 39: From Intrusion Detection to an Intrusion
           Response System: Fundamentals, Requirements, and Future Directions

    • Authors: Shahid Anwar, Jasni Mohamad Zain, Mohamad Fadli Zolkipli, Zakira Inayat, Suleman Khan, Bokolo Anthony, Victor Chang
      First page: 39
      Abstract: In the past few decades, the rise in attacks on communication devices in networks has resulted in a reduction of network functionality, throughput, and performance. To detect and mitigate these network attacks, researchers, academicians, and practitioners developed Intrusion Detection Systems (IDSs) with automatic response systems. The response system is considered an important component of IDS, since without a timely response IDSs may not function properly in countering various attacks, especially on a real-time basis. To respond appropriately, IDSs should select the optimal response option according to the type of network attack. This research study provides a complete survey of IDSs and Intrusion Response Systems (IRSs) on the basis of our in-depth understanding of the response option for different types of network attacks. Knowledge of the path from IDS to IRS can assist network administrators and network staffs in understanding how to tackle different attacks with state-of-the-art technologies.
      PubDate: 2017-03-27
      DOI: 10.3390/a10020039
      Issue No: Vol. 10, No. 2 (2017)
       
  • Algorithms, Vol. 10, Pages 40: Fuzzy Random Walkers with Second Order
           Bounds: An Asymmetric Analysis

    • Authors: Georgios Drakopoulos, Andreas Kanavos, Konstantinos Tsakalidis
      First page: 40
      Abstract: Edge-fuzzy graphs constitute an essential modeling paradigm across a broad spectrum of domains ranging from artificial intelligence to computational neuroscience and social network analysis. Under this model, fundamental graph properties such as edge length and graph diameter become stochastic and as such they are consequently expressed in probabilistic terms. Thus, algorithms for fuzzy graph analysis must rely on non-deterministic design principles. One such principle is Random Walker, which is based on a virtual entity and selects either edges or, like in this case, vertices of a fuzzy graph to visit. This allows the estimation of global graph properties through a long sequence of local decisions, making it a viable strategy candidate for graph processing software relying on native graph databases such as Neo4j. As a concrete example, Chebyshev Walktrap, a heuristic fuzzy community discovery algorithm relying on second order statistics and on the teleportation of the Random Walker, is proposed and its performance, expressed in terms of community coherence and number of vertex visits, is compared to the previously proposed algorithms of Markov Walktrap, Fuzzy Walktrap, and Fuzzy Newman–Girvan. In order to facilitate this comparison, a metric based on the asymmetric metrics of Tversky index and Kullback–Leibler divergence is used.
      PubDate: 2017-03-30
      DOI: 10.3390/a10020040
      Issue No: Vol. 10, No. 2 (2017)
       
  • Algorithms, Vol. 10, Pages 41: RST Resilient Watermarking Scheme Based on
           DWT-SVD and Scale-Invariant Feature Transform

    • Authors: Yunpeng Zhang, Chengyou Wang, Xiao Zhou
      First page: 41
      Abstract: Currently, most digital image watermarking schemes are affected by geometric attacks like rotation, scaling, and translation (RST). In the watermark embedding process, a robust watermarking scheme is proposed against RST attacks. In this paper, three-level discrete wavelet transform (DWT) is applied to the original image. The three-level low frequency sub-band is decomposed by the singular value decomposition (SVD), and its singular values matrix is extracted for watermarking embedding. Before the watermarking extraction, the keypoints are selected by scale-invariant feature transform (SIFT) in the original image and attacked image. By matching the keypoints in two images, the RST attacks can be precisely corrected and the better performance can be obtained. The experimental results show that the proposed scheme achieves good performance of imperceptibility and robustness to common image processing and malicious attacks, especially geometric attacks.
      PubDate: 2017-03-30
      DOI: 10.3390/a10020041
      Issue No: Vol. 10, No. 2 (2017)
       
  • Algorithms, Vol. 10, Pages 42: RGloVe: An Improved Approach of Global
           Vectors for Distributional Entity Relation Representation

    • Authors: Ziyan Chen, Yu Huang, Yuexian Liang, Yang Wang, Xingyu Fu, Kun Fu
      First page: 42
      Abstract: Most of the previous works on relation extraction between named entities are often limited to extracting the pre-defined types; which are inefficient for massive unlabeled text data. Recently; with the appearance of various distributional word representations; unsupervised methods for many natural language processing (NLP) tasks have been widely researched. In this paper; we focus on a new finding of unsupervised relation extraction; which is called distributional relation representation. Without requiring the pre-defined types; distributional relation representation aims to automatically learn entity vectors and further estimate semantic similarity between these entities. We choose global vectors (GloVe) as our original model to train entity vectors because of its excellent balance between local context and global statistics in the whole corpus. In order to train model more efficiently; we improve the traditional GloVe model by using cosine similarity between entity vectors to approximate the entity occurrences instead of dot product. Because cosine similarity can convert vector to unit vector; it is intuitively more reasonable and more easily converge to a local optimum. We call the improved model RGloVe. Experimental results on a massive corpus of Sina News show that our proposed model outperforms the traditional global vectors. Finally; a graph database of Neo4j is introduced to store these relationships between named entities. The most competitive advantage of Neo4j is that it provides a highly accessible way to query the direct and indirect relationships between entities.
      PubDate: 2017-04-17
      DOI: 10.3390/a10020042
      Issue No: Vol. 10, No. 2 (2017)
       
  • Algorithms, Vol. 10, Pages 43: Reliable Portfolio Selection Problem in
           Fuzzy Environment: An mλ Measure Based Approach

    • Authors: Yuan Feng, Li Wang, Xinhong Liu
      First page: 43
      Abstract: This paper investigates a fuzzy portfolio selection problem with guaranteed reliability, in which the fuzzy variables are used to capture the uncertain returns of different securities. To effectively handle the fuzziness in a mathematical way, a new expected value operator and variance of fuzzy variables are defined based on the m λ measure that is a linear combination of the possibility measure and necessity measure to balance the pessimism and optimism in the decision-making process. To formulate the reliable portfolio selection problem, we particularly adopt the expected total return and standard variance of the total return to evaluate the reliability of the investment strategies, producing three risk-guaranteed reliable portfolio selection models. To solve the proposed models, an effective genetic algorithm is designed to generate the approximate optimal solution to the considered problem. Finally, the numerical examples are given to show the performance of the proposed models and algorithm.
      PubDate: 2017-04-18
      DOI: 10.3390/a10020043
      Issue No: Vol. 10, No. 2 (2017)
       
  • Algorithms, Vol. 10, Pages 44: Revised Gravitational Search Algorithms
           Based on Evolutionary-Fuzzy Systems

    • Authors: Danilo Pelusi, Raffaele Mascella, Luca Tallini
      First page: 44
      Abstract: The choice of the best optimization algorithm is a hard issue, and it sometime depends on specific problem. The Gravitational Search Algorithm (GSA) is a search algorithm based on the law of gravity, which states that each particle attracts every other particle with a force called gravitational force. Some revised versions of GSA have been proposed by using intelligent techniques. This work proposes some GSA versions based on fuzzy techniques powered by evolutionary methods, such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO) and Differential Evolution (DE), to improve GSA. The designed algorithms tune a suitable parameter of GSA through a fuzzy controller whose membership functions are optimized by GA, PSO and DE. The results show that Fuzzy Gravitational Search Algorithm (FGSA) optimized by DE is optimal for unimodal functions, whereas FGSA optimized through GA is good for multimodal functions.
      PubDate: 2017-04-21
      DOI: 10.3390/a10020044
      Issue No: Vol. 10, No. 2 (2017)
       
  • Algorithms, Vol. 10, Pages 45: An Efficient Sixth-Order Newton-Type Method
           for Solving Nonlinear Systems

    • Authors: Xiaofeng Wang, Yang Li
      First page: 45
      Abstract: In this paper, we present a new sixth-order iterative method for solving nonlinear systems and prove a local convergence result. The new method requires solving five linear systems per iteration. An important feature of the new method is that the LU (lower upper, also called LU factorization) decomposition of the Jacobian matrix is computed only once in each iteration. The computational efficiency index of the new method is compared to that of some known methods. Numerical results are given to show that the convergence behavior of the new method is similar to the existing methods. The new method can be applied to small- and medium-sized nonlinear systems.
      PubDate: 2017-04-25
      DOI: 10.3390/a10020045
      Issue No: Vol. 10, No. 2 (2017)
       
  • Algorithms, Vol. 10, Pages 46: An Improved Multiobjective Particle Swarm
           Optimization Based on Culture Algorithms

    • Authors: Chunhua Jia, Hong Zhu
      First page: 46
      Abstract: In this paper, we propose a new approach to raise the performance of multiobjective particle swam optimization. The personal guide and global guide are updated using three kinds of knowledge extracted from the population based on cultural algorithms. An epsilon domination criterion has been employed to enhance the convergence and diversity of the approximate Pareto front. Moreover, a simple polynomial mutation operator has been applied to both the population and the non-dominated archive. Experiments on two series of bench test suites have shown the effectiveness of the proposed approach. A comparison with several other algorithms that are considered good representatives of particle swarm optimization solutions has also been conducted, in order to verify the competitive performance of the proposed algorithm in solve multiobjective optimization problems.
      PubDate: 2017-04-25
      DOI: 10.3390/a10020046
      Issue No: Vol. 10, No. 2 (2017)
       
  • Algorithms, Vol. 10, Pages 47: Trust in the Balance: Data Protection Laws
           as Tools for Privacy and Security in the Cloud

    • Authors: Darra Hofman, Luciana Duranti, Elissa How
      First page: 47
      Abstract: A popular bumper sticker states: “There is no cloud. It’s just someone else’s computer.” Despite the loss of control that comes with its use, critical records are increasingly being entrusted to the cloud, generating ever-growing concern about the privacy and security of those records. Ultimately, privacy and security constitute an attempt to balance competing needs: privacy balances the need to use information against the need to protect personal data, while security balances the need to provide access to records against the need to stop unauthorized access. The importance of these issues has led to a multitude of legal and regulatory efforts to find a balance and, ultimately, to ensure trust in both digital records and their storage in the cloud. Adding a particular challenge is the fact that distinct jurisdictions approach privacy differently and an in-depth understanding of what a jurisdiction’s laws may be, or even under what jurisdiction particular data might be, requires a Herculean effort. And yet, in order to protect privacy and enhance security, this effort is required. This article examines two legal tools for ensuring the privacy and security of records in the cloud, data protection laws, and data localization laws, through the framework of “trust” as understood in archival science. This framework of trust provides new directions for algorithmic research, identifying those areas of digital record creation and preservation most in need of novel solutions.
      PubDate: 2017-04-27
      DOI: 10.3390/a10020047
      Issue No: Vol. 10, No. 2 (2017)
       
  • Algorithms, Vol. 10, Pages 48: Adaptive Mutation Dynamic Search Fireworks
           Algorithm

    • Authors: Xi-Guang Li, Shou-Fei Han, Liang Zhao, Chang-Qing Gong, Xiao-Jing Liu
      First page: 48
      Abstract: The Dynamic Search Fireworks Algorithm (dynFWA) is an effective algorithm for solving optimization problems. However, dynFWA easily falls into local optimal solutions prematurely and it also has a slow convergence rate. In order to improve these problems, an adaptive mutation dynamic search fireworks algorithm (AMdynFWA) is introduced in this paper. The proposed algorithm applies the Gaussian mutation or the Levy mutation for the core firework (CF) with mutation probability. Our simulation compares the proposed algorithm with the FWA-Based algorithms and other swarm intelligence algorithms. The results show that the proposed algorithm achieves better overall performance on the standard test functions.
      PubDate: 2017-04-28
      DOI: 10.3390/a10020048
      Issue No: Vol. 10, No. 2 (2017)
       
  • Algorithms, Vol. 10, Pages 49: Multivariate Statistical Process Control
           Using Enhanced Bottleneck Neural Network

    • Authors: Khaled Bouzenad, Messaoud Ramdani
      First page: 49
      Abstract: Monitoring process upsets and malfunctions as early as possible and then finding and removing the factors causing the respective events is of great importance for safe operation and improved productivity. Conventional process monitoring using principal component analysis (PCA) often supposes that process data follow a Gaussian distribution. However, this kind of constraint cannot be satisfied in practice because many industrial processes frequently span multiple operating states. To overcome this difficulty, PCA can be combined with nonparametric control charts for which there is no assumption need on the distribution. However, this approach still uses a constant confidence limit where a relatively high rate of false alarms are generated. Although nonlinear PCA (NLPCA) using autoassociative bottle-neck neural networks plays an important role in the monitoring of industrial processes, it is difficult to design correct monitoring statistics and confidence limits that check new performance. In this work, a new monitoring strategy using an enhanced bottleneck neural network (EBNN) with an adaptive confidence limit for non Gaussian data is proposed. The basic idea behind it is to extract internally homogeneous segments from the historical normal data sets by filling a Gaussian mixture model (GMM). Based on the assumption that process data follow a Gaussian distribution within an operating mode, a local confidence limit can be established. The EBNN is used to reconstruct input data and estimate probabilities of belonging to the various local operating regimes, as modelled by GMM. An abnormal event for an input measurement vector is detected if the squared prediction error (SPE) is too large, or above a certain threshold which is made adaptive. Moreover, the sensor validity index (SVI) is employed successfully to identify the detected faulty variable. The results demonstrate that, compared with NLPCA, the proposed approach can effectively reduce the number of false alarms, and is hence expected to better monitor many practical processes.
      PubDate: 2017-04-29
      DOI: 10.3390/a10020049
      Issue No: Vol. 10, No. 2 (2017)
       
  • Algorithms, Vol. 10, Pages 50: Hierarchical Parallel Evaluation of a
           Hamming Code

    • Authors: Shmuel Klein, Dana Shapira
      First page: 50
      Abstract: The Hamming code is a well-known error correction code and can correct a single error in an input vector of size n bits by adding logn parity checks. A new parallel implementation of the code is presented, using a hierarchical structure of n processors in logn layers. All the processors perform similar simple tasks, and need only a few bytes of internal memory.
      PubDate: 2017-04-30
      DOI: 10.3390/a10020050
      Issue No: Vol. 10, No. 2 (2017)
       
  • Algorithms, Vol. 10, Pages 51: Adaptive Vector Quantization for Lossy
           Compression of Image Sequences

    • Authors: Raffaele Pizzolante, Bruno Carpentieri, Sergio De Agostino
      First page: 51
      Abstract: In this work, we present a scheme for the lossy compression of image sequences, based on the Adaptive Vector Quantization (AVQ) algorithm. The AVQ algorithm is a lossy compression algorithm for grayscale images, which processes the input data in a single-pass, by using the properties of the vector quantization to approximate data. First, we review the key aspects of the AVQ algorithm and, subsequently, we outline the basic concepts and the design choices behind the proposed scheme. Finally, we report the experimental results, which highlight an improvement in compression performances when our scheme is compared with the AVQ algorithm.
      PubDate: 2017-05-09
      DOI: 10.3390/a10020051
      Issue No: Vol. 10, No. 2 (2017)
       
  • Algorithms, Vol. 10, Pages 52: Searchable Data Vault: Encrypted Queries in
           Secure Distributed Cloud Storage

    • Authors: Geong Poh, Vishnu Baskaran, Ji-Jian Chin, Moesfa Mohamad, Kay Win Lee, Dharmadharshni Maniam, Muhammad Z’aba
      First page: 52
      Abstract: Cloud storage services allow users to efficiently outsource their documents anytime and anywhere. Such convenience, however, leads to privacy concerns. While storage providers may not read users’ documents, attackers may possibly gain access by exploiting vulnerabilities in the storage system. Documents may also be leaked by curious administrators. A simple solution is for the user to encrypt all documents before submitting them. This method, however, makes it impossible to efficiently search for documents as they are all encrypted. To resolve this problem, we propose a multi-server searchable symmetric encryption (SSE) scheme and construct a system called the searchable data vault (SDV). A unique feature of the scheme is that it allows an encrypted document to be divided into blocks and distributed to different storage servers so that no single storage provider has a complete document. By incorporating the scheme, the SDV protects the privacy of documents while allowing for efficient private queries. It utilizes a web interface and a controller that manages user credentials, query indexes and submission of encrypted documents to cloud storage services. It is also the first system that enables a user to simultaneously outsource and privately query documents from a few cloud storage services. Our preliminary performance evaluation shows that this feature introduces acceptable computation overheads when compared to submitting documents directly to a cloud storage service.
      PubDate: 2017-05-09
      DOI: 10.3390/a10020052
      Issue No: Vol. 10, No. 2 (2017)
       
  • Algorithms, Vol. 10, Pages 53: Application of Gradient Descent Continuous
           Actor-Critic Algorithm for Bilateral Spot Electricity Market Modeling
           Considering Renewable Power Penetration

    • Authors: Huiru Zhao, Yuwei Wang, Mingrui Zhao, Chuyu Sun, Qingkun Tan
      First page: 53
      Abstract: The bilateral spot electricity market is very complicated because all generation units and demands must strategically bid in this market. Considering renewable resource penetration, the high variability and the non-dispatchable nature of these intermittent resources make it more difficult to model and simulate the dynamic bidding process and the equilibrium in the bilateral spot electricity market, which makes developing fast and reliable market modeling approaches a matter of urgency nowadays. In this paper, a Gradient Descent Continuous Actor-Critic algorithm is proposed for hour-ahead bilateral electricity market modeling in the presence of renewable resources because this algorithm can solve electricity market modeling problems with continuous state and action spaces without causing the “curse of dimensionality” and has low time complexity. In our simulation, the proposed approach is implemented on an IEEE 30-bus test system. The adequate performance of our proposed approach—such as reaching Nash Equilibrium results after enough iterations of training are tested and verified, and some conclusions about the relationship between increasing the renewable power output and participants’ bidding strategy, locational marginal prices, and social welfare—is also evaluated. Moreover, the comparison of our proposed approach with the fuzzy Q-learning-based electricity market approach implemented in this paper confirms the superiority of our proposed approach in terms of participants’ profits, social welfare, average locational marginal prices, etc.
      PubDate: 2017-05-10
      DOI: 10.3390/a10020053
      Issue No: Vol. 10, No. 2 (2017)
       
  • Algorithms, Vol. 10, Pages 54: Extending the Applicability of the MMN-HSS
           Method for Solving Systems of Nonlinear Equations under Generalized
           Conditions

    • Authors: Ioannis Argyros, Janak Sharma, Deepak Kumar
      First page: 54
      Abstract: We present the semilocal convergence of a multi-step modified Newton-Hermitian and Skew-Hermitian Splitting method (MMN-HSS method) to approximate a solution of a nonlinear equation. Earlier studies show convergence under only Lipschitz conditions limiting the applicability of this method. The convergence in this study is shown under generalized Lipschitz-type conditions and restricted convergence domains. Hence, the applicability of the method is extended. Moreover, numerical examples are also provided to show that our results can be applied to solve equations in cases where earlier study cannot be applied. Furthermore, in the cases where both old and new results are applicable, the latter provides a larger domain of convergence and tighter error bounds on the distances involved.
      PubDate: 2017-05-12
      DOI: 10.3390/a10020054
      Issue No: Vol. 10, No. 2 (2017)
       
  • Algorithms, Vol. 10, Pages 55: Erratum: Ahmad, F., et al. A Preconditioned
           Iterative Method for Solving Systems of Nonlinear Equations Having Unknown
           Multiplicity. Algorithms 2017, 10, 17

    • Authors: Fayyaz Ahmad, Toseef Bhutta, Umar Shoaib, Malik Ullah, Ali Alshomrani, Shamshad Ahmad, Shahid Ahmad
      First page: 55
      Abstract: n/a
      PubDate: 2017-05-12
      DOI: 10.3390/a10020055
      Issue No: Vol. 10, No. 2 (2017)
       
  • Algorithms, Vol. 10, Pages 56: Clustering Using an Improved Krill Herd
           Algorithm

    • Authors: Qin Li, Bo Liu
      First page: 56
      Abstract: In recent years, metaheuristic algorithms have been widely used in solving clustering problems because of their good performance and application effects. krill herd algorithm (KHA) is a new effective algorithm to solve optimization problems based on the imitation of krill individual behavior, and it is proven to perform better than other swarm intelligence algorithms. However, there are some weaknesses yet. In this paper, an improved krill herd algorithm (IKHA) is studied. Modified mutation operators and updated mechanisms are applied to improve global optimization, and the proposed IKHA can overcome the weakness of KHA and performs better than KHA in optimization problems. Then, KHA and IKHA are introduced into the clustering problem. In our proposed clustering algorithm, KHA and IKHA are used to find appropriate cluster centers. Experiments were conducted on University of California Irvine (UCI) standard datasets, and the results showed that the IKHA clustering algorithm is the most effective.
      PubDate: 2017-05-17
      DOI: 10.3390/a10020056
      Issue No: Vol. 10, No. 2 (2017)
       
  • Algorithms, Vol. 10, Pages 57: A Prediction of Precipitation Data Based on
           Support Vector Machine and Particle Swarm Optimization (PSO-SVM)
           Algorithms

    • Authors: Jinglin Du, Yayun Liu, Yanan Yu, Weilan Yan
      First page: 57
      Abstract: Precipitation is a very important topic in weather forecasts. Weather forecasts, especially precipitation prediction, poses complex tasks because they depend on various parameters to predict the dependent variables like temperature, humidity, wind speed and direction, which are changing from time to time and weather calculation varies with the geographical location along with its atmospheric variables. To improve the prediction accuracy of precipitation, this context proposes a prediction model for rainfall forecast based on Support Vector Machine with Particle Swarm Optimization (PSO-SVM) to replace the linear threshold used in traditional precipitation. Parameter selection has a critical impact on the predictive accuracy of SVM, and PSO is proposed to find the optimal parameters for SVM. The PSO-SVM algorithm was used for the training of a model by using the historical data for precipitation prediction, which can be useful information and used by people of all walks of life in making wise and intelligent decisions. The simulations demonstrate that prediction models indicate that the performance of the proposed algorithm has much better accuracy than the direct prediction model based on a set of experimental data if other things are equal. On the other hand, simulation results demonstrate the effectiveness and advantages of the SVM-PSO model used in machine learning and further promises the scope for improvement as more and more relevant attributes can be used in predicting the dependent variables.
      PubDate: 2017-05-17
      DOI: 10.3390/a10020057
      Issue No: Vol. 10, No. 2 (2017)
       
  • Algorithms, Vol. 10, Pages 58: A Flexible Pattern-Matching Algorithm for
           Network Intrusion Detection Systems Using Multi-Core Processors

    • Authors: Chun-Liang Lee, Tzu-Hao Yang
      First page: 58
      Abstract: As part of network security processes, network intrusion detection systems (NIDSs) determine whether incoming packets contain malicious patterns. Pattern matching, the key NIDS component, consumes large amounts of execution time. One of several trends involving general-purpose processors (GPPs) is their use in software-based NIDSs. In this paper, we describe our proposal for an efficient and flexible pattern-matching algorithm for inspecting packet payloads using a head-body finite automaton (HBFA). The proposed algorithm takes advantage of multi-core GPP parallelism and single-instruction multiple-data operations to achieve higher throughput compared to that resulting from traditional deterministic finite automata (DFA) using the Aho-Corasick algorithm. Whereas the head-body matching (HBM) algorithm is based on pre-defined DFA depth value, our HBFA algorithm is based on head size. Experimental results using Snort and ClamAV pattern sets indicate that the proposed algorithm achieves up to 58% higher throughput compared to its HBM counterpart.
      PubDate: 2017-05-24
      DOI: 10.3390/a10020058
      Issue No: Vol. 10, No. 2 (2017)
       
  • Algorithms, Vol. 10, Pages 59: Contradiction Detection with
           Contradiction-Specific Word Embedding

    • Authors: Luyang Li, Bing Qin, Ting Liu
      First page: 59
      Abstract: Contradiction detection is a task to recognize contradiction relations between a pair of sentences. Despite the effectiveness of traditional context-based word embedding learning algorithms in many natural language processing tasks, such algorithms are not powerful enough for contradiction detection. Contrasting words such as “overfull” and “empty” are mostly mapped into close vectors in such embedding space. To solve this problem, we develop a tailored neural network to learn contradiction-specific word embedding (CWE). The method can separate antonyms in the opposite ends of a spectrum. CWE is learned from a training corpus which is automatically generated from the paraphrase database, and is naturally applied as features to carry out contradiction detection in SemEval 2014 benchmark dataset. Experimental results show that CWE outperforms traditional context-based word embedding in contradiction detection. The proposed model for contradiction detection performs comparably with the top-performing system in accuracy of three-category classification and enhances the accuracy from 75.97% to 82.08% in the contradiction category.
      PubDate: 2017-05-24
      DOI: 10.3390/a10020059
      Issue No: Vol. 10, No. 2 (2017)
       
  • Algorithms, Vol. 10, Pages 60: Correction: A No Reference Image Quality
           Assessment Metric Based on Visual Perception. Algorithms 2016, 9, 87

    • Authors: Yan Fu, Shengchun Wang
      First page: 60
      Abstract: We would like to make the following change to our article [1]. [...]
      PubDate: 2017-05-26
      DOI: 10.3390/a10020060
      Issue No: Vol. 10, No. 2 (2017)
       
  • Algorithms, Vol. 10, Pages 61: Design and Implementation of a Multi-Modal
           Biometric System for Company Access Control

    • Authors: Elisabetta Stefani, Carlo Ferrari
      First page: 61
      Abstract: This paper is about the design, implementation, and deployment of a multi-modal biometric system to grant access to a company structure and to internal zones in the company itself. Face and iris have been chosen as biometric traits. Face is feasible for non-intrusive checking with a minimum cooperation from the subject, while iris supports very accurate recognition procedure at a higher grade of invasivity. The recognition of the face trait is based on the Local Binary Patterns histograms, and the Daughman’s method is implemented for the analysis of the iris data. The recognition process may require either the acquisition of the user’s face only or the serial acquisition of both the user’s face and iris, depending on the confidence level of the decision with respect to the set of security levels and requirements, stated in a formal way in the Service Level Agreement at a negotiation phase. The quality of the decision depends on the setting of proper different thresholds in the decision modules for the two biometric traits. Any time the quality of the decision is not good enough, the system activates proper rules, which ask for new acquisitions (and decisions), possibly with different threshold values, resulting in a system not with a fixed and predefined behaviour, but one which complies with the actual acquisition context. Rules are formalized as deduction rules and grouped together to represent “response behaviors” according to the previous analysis. Therefore, there are different possible working flows, since the actual response of the recognition process depends on the output of the decision making modules that compose the system. Finally, the deployment phase is described, together with the results from the testing, based on the AT&T Face Database and the UBIRIS database.
      PubDate: 2017-05-27
      DOI: 10.3390/a10020061
      Issue No: Vol. 10, No. 2 (2017)
       
  • Algorithms, Vol. 10, Pages 62: Influence Factors Analysis on the Modal
           Characteristics of Irregularly-Shaped Bridges Based on a Free-Interface
           Mode Synthesis Algorithm

    • Authors: Hanbing Liu, Mengsu Zhang, Xianqiang Wang, Shuai Tian, Yubo Jiao
      First page: 62
      Abstract: In order to relieve traffic congestion, irregularly-shaped bridges have been widely used in urban overpasses. However, the analysis on modal characteristics of irregularly-shaped bridges is not exhaustive, and the effect of design parameters on modal characteristics will be deeply investigated in future studies. In this paper, a novel strategy based on a free-interface mode synthesis algorithm is proposed to evaluate the parameters’ effect on the modal characteristics of irregularly-shaped bridges. First, a complicated, irregularly-shaped bridge is divided into several substructures based on its properties. Then, the modal characteristics of the overall structure can be obtained, only by a few low-order modal parameters of each substructure, using a free-interface mode synthesis method. A numerical model of a typical irregularly-shaped bridge is employed to verify the effectiveness of the proposed strategy. Simulation results reveal that the free-interface mode synthesis method possesses favorable calculation accuracy for analyzing the modal characteristics of irregularly-shaped bridges. The effect of design parameters such as ramp curve radius, diaphragm beam stiffness, cross-section feature, and bearing conditions on the modal characteristics of an irregularly-shaped bridge is evaluated in detail. Analysis results can provide references for further research into and the design of irregularly-shaped bridges.
      PubDate: 2017-05-28
      DOI: 10.3390/a10020062
      Issue No: Vol. 10, No. 2 (2017)
       
  • Algorithms, Vol. 10, Pages 63: Development of Filtered Bispectrum for EEG
           Signal Feature Extraction in Automatic Emotion Recognition Using
           Artificial Neural Networks

    • Authors: Prima Purnamasari, Anak Ratna, Benyamin Kusumoputro
      First page: 63
      Abstract: The development of automatic emotion detection systems has recently gained significant attention due to the growing possibility of their implementation in several applications, including affective computing and various fields within biomedical engineering. Use of the electroencephalograph (EEG) signal is preferred over facial expression, as people cannot control the EEG signal generated by their brain; the EEG ensures a stronger reliability in the psychological signal. However, because of its uniqueness between individuals and its vulnerability to noise, use of EEG signals can be rather complicated. In this paper, we propose a methodology to conduct EEG-based emotion recognition by using a filtered bispectrum as the feature extraction subsystem and an artificial neural network (ANN) as the classifier. The bispectrum is theoretically superior to the power spectrum because it can identify phase coupling between the nonlinear process components of the EEG signal. In the feature extraction process, to extract the information contained in the bispectrum matrices, a 3D pyramid filter is used for sampling and quantifying the bispectrum value. Experiment results show that the mean percentage of the bispectrum value from 5 × 5 non-overlapped 3D pyramid filters produces the highest recognition rate. We found that reducing the number of EEG channels down to only eight in the frontal area of the brain does not significantly affect the recognition rate, and the number of data samples used in the training process is then increased to improve the recognition rate of the system. We have also utilized a probabilistic neural network (PNN) as another classifier and compared its recognition rate with that of the back-propagation neural network (BPNN), and the results show that the PNN produces a comparable recognition rate and lower computational costs. Our research shows that the extracted bispectrum values of an EEG signal using 3D filtering as a feature extraction method is suitable for use in an EEG-based emotion recognition system.
      PubDate: 2017-05-30
      DOI: 10.3390/a10020063
      Issue No: Vol. 10, No. 2 (2017)
       
  • Algorithms, Vol. 10, Pages 64: Expanding the Applicability of Some High
           Order Househölder-Like Methods

    • Authors: Sergio Amat, Ioannis Argyros, Miguel Hernández-Verón, Natalia Romero
      First page: 64
      Abstract: This paper is devoted to the semilocal convergence of a Househölder-like method for nonlinear equations. The method includes many of the studied third order iterative methods. In the present study, we use our new idea of restricted convergence domains leading to smaller γ -parameters, which in turn lead to the following advantages over earlier works (and under the same computational cost): larger convergence domain; tighter error bounds on the distances involved, and at least as precise information on the location of the solution.
      PubDate: 2017-05-31
      DOI: 10.3390/a10020064
      Issue No: Vol. 10, No. 2 (2017)
       
  • Algorithms, Vol. 10, Pages 65: Seismic Signal Compression Using
           Nonparametric Bayesian Dictionary Learning via Clustering

    • Authors: Xin Tian, Song Li
      First page: 65
      Abstract: We introduce a seismic signal compression method based on nonparametric Bayesian dictionary learning method via clustering. The seismic data is compressed patch by patch, and the dictionary is learned online. Clustering is introduced for dictionary learning. A set of dictionaries could be generated, and each dictionary is used for one cluster’s sparse coding. In this way, the signals in one cluster could be well represented by their corresponding dictionaries. A nonparametric Bayesian dictionary learning method is used to learn the dictionaries, which naturally infers an appropriate dictionary size for each cluster. A uniform quantizer and an adaptive arithmetic coding algorithm are adopted to code the sparse coefficients. With comparisons to other state-of-the art approaches, the effectiveness of the proposed method could be validated in the experiments.
      PubDate: 2017-06-07
      DOI: 10.3390/a10020065
      Issue No: Vol. 10, No. 2 (2017)
       
  • Algorithms, Vol. 10, Pages 66: A New Approach to Image-Based Estimation of
           Food Volume

    • Authors: Hamid Hassannejad, Guido Matrella, Paolo Ciampolini, Ilaria Munari, Monica Mordonini, Stefano Cagnoni
      First page: 66
      Abstract: A balanced diet is the key to a healthy lifestyle and is crucial for preventing or dealing with many chronic diseases such as diabetes and obesity. Therefore, monitoring diet can be an effective way of improving people’s health. However, manual reporting of food intake has been shown to be inaccurate and often impractical. This paper presents a new approach to food intake quantity estimation using image-based modeling. The modeling method consists of three steps: firstly, a short video of the food is taken by the user’s smartphone. From such a video, six frames are selected based on the pictures’ viewpoints as determined by the smartphone’s orientation sensors. Secondly, the user marks one of the frames to seed an interactive segmentation algorithm. Segmentation is based on a Gaussian Mixture Model alongside the graph-cut algorithm. Finally, a customized image-based modeling algorithm generates a point-cloud to model the food. At the same time, a stochastic object-detection method locates a checkerboard used as size/ground reference. The modeling algorithm is optimized such that the use of six input images still results in an acceptable computation cost. In our evaluation procedure, we achieved an average accuracy of 92 % on a test set that includes images of different kinds of pasta and bread, with an average processing time of about 23 s.
      PubDate: 2017-06-10
      DOI: 10.3390/a10020066
      Issue No: Vol. 10, No. 2 (2017)
       
  • Algorithms, Vol. 10, Pages 67: Research on Misalignment Fault Isolation of
           Wind Turbines Based on the Mixed-Domain Features

    • Authors: Yancai Xiao, Yujia Wang, Huan Mu, Na Kang
      First page: 67
      Abstract: The misalignment of the drive system of the DFIG (Doubly Fed Induction Generator) wind turbine is one of the important factors that cause damage to the gears, bearings of the high-speed gearbox and the generator bearings. How to use the limited information to accurately determine the type of failure has become a difficult study for the scholars. In this paper, the time-domain indexes and frequency-domain indexes are extracted by using the vibration signals of various misaligned simulation conditions of the wind turbine drive system, and the time-frequency domain features—energy entropy are also extracted by the IEMD (Improved Empirical Mode Decomposition). A mixed-domain feature set is constructed by them. Then, SVM (Support Vector Machine) is used as the classifier, the mixed-domain features are used as the inputs of SVM, and PSO (Particle Swarm Optimization) is used to optimize the parameters of SVM. The fault types of misalignment are classified successfully. Compared with other methods, the accuracy of the given fault isolation model is improved.
      PubDate: 2017-06-10
      DOI: 10.3390/a10020067
      Issue No: Vol. 10, No. 2 (2017)
       
  • Algorithms, Vol. 10, Pages 68: An Easily Understandable Grey Wolf
           Optimizer and Its Application to Fuzzy Controller Tuning

    • Authors: Radu-Emil Precup, Radu-Codrut David, Alexandra-Iulia Szedlak-Stinean, Emil M. Petriu, Florin Dragan
      First page: 68
      Abstract: This paper proposes an easily understandable Grey Wolf Optimizer (GWO) applied to the optimal tuning of the parameters of Takagi-Sugeno proportional-integral fuzzy controllers (T-S PI-FCs). GWO is employed for solving optimization problems focused on the minimization of discrete-time objective functions defined as the weighted sum of the absolute value of the control error and of the squared output sensitivity function, and the vector variable consists of the tuning parameters of the T-S PI-FCs. Since the sensitivity functions are introduced with respect to the parametric variations of the process, solving these optimization problems is important as it leads to fuzzy control systems with a reduced process parametric sensitivity obtained by a GWO-based fuzzy controller tuning approach. GWO algorithms applied with this regard are formulated in easily understandable terms for both vector and scalar operations, and discussions on stability, convergence, and parameter settings are offered. The controlled processes referred to in the course of this paper belong to a family of nonlinear servo systems, which are modeled by second order dynamics plus a saturation and dead zone static nonlinearity. Experimental results concerning the angular position control of a laboratory servo system are included for validating the proposed method.
      PubDate: 2017-06-10
      DOI: 10.3390/a10020068
      Issue No: Vol. 10, No. 2 (2017)
       
  • Algorithms, Vol. 10, Pages 69: Cross-Language Plagiarism Detection System
           Using Latent Semantic Analysis and Learning Vector Quantization

    • Authors: Anak Agung Putri Ratna, Prima Dewi Purnamasari, Boma Anantasatya Adhi, F. Astha Ekadiyanto, Muhammad Salman, Mardiyah Mardiyah, Darien Jonathan Winata
      First page: 69
      Abstract: Computerized cross-language plagiarism detection has recently become essential. With the scarcity of scientific publications in Bahasa Indonesia, many Indonesian authors frequently consult publications in English in order to boost the quantity of scientific publications in Bahasa Indonesia (which is currently rising). Due to the syntax disparity between Bahasa Indonesia and English, most of the existing methods for automated cross-language plagiarism detection do not provide satisfactory results. This paper analyses the probability of developing Latent Semantic Analysis (LSA) for a computerized cross-language plagiarism detector for two languages with different syntax. To improve performance, various alterations in LSA are suggested. By using a linear vector quantization (LVQ) classifier in the LSA and taking into account the Frobenius norm, output has reached up to 65.98% in accuracy. The results of the experiments showed that the best accuracy achieved is 87% with a document size of 6 words, and the document definition size must be kept below 10 words in order to maintain high accuracy. Additionally, based on experimental results, this paper suggests utilizing the frequency occurrence method as opposed to the binary method for the term–document matrix construction.
      PubDate: 2017-06-13
      DOI: 10.3390/a10020069
      Issue No: Vol. 10, No. 2 (2017)
       
  • Algorithms, Vol. 10, Pages 70: An Improved Brain-Inspired Emotional
           Learning Algorithm for Fast Classification

    • Authors: Ying Mei, Guanzheng Tan, Zhentao Liu
      First page: 70
      Abstract: Classification is an important task of machine intelligence in the field of information. The artificial neural network (ANN) is widely used for classification. However, the traditional ANN shows slow training speed, and it is hard to meet the real-time requirement for large-scale applications. In this paper, an improved brain-inspired emotional learning (BEL) algorithm is proposed for fast classification. The BEL algorithm was put forward to mimic the high speed of the emotional learning mechanism in mammalian brain, which has the superior features of fast learning and low computational complexity. To improve the accuracy of BEL in classification, the genetic algorithm (GA) is adopted for optimally tuning the weights and biases of amygdala and orbitofrontal cortex in the BEL neural network. The combinational algorithm named as GA-BEL has been tested on eight University of California at Irvine (UCI) datasets and two well-known databases (Japanese Female Facial Expression, Cohn–Kanade). The comparisons of experiments indicate that the proposed GA-BEL is more accurate than the original BEL algorithm, and it is much faster than the traditional algorithm.
      PubDate: 2017-06-14
      DOI: 10.3390/a10020070
      Issue No: Vol. 10, No. 2 (2017)
       
  • Algorithms, Vol. 10, Pages 71: Bayesian and Classical Estimation of
           Stress-Strength Reliability for Inverse Weibull Lifetime Models

    • Authors: Qixuan Bi, Wenhao Gui
      First page: 71
      Abstract: In this paper, we consider the problem of estimating stress-strength reliability for inverse Weibull lifetime models having the same shape parameters but different scale parameters. We obtain the maximum likelihood estimator and its asymptotic distribution. Since the classical estimator doesn’t hold explicit forms, we propose an approximate maximum likelihood estimator. The asymptotic confidence interval and two bootstrap intervals are obtained. Using the Gibbs sampling technique, Bayesian estimator and the corresponding credible interval are obtained. The Metropolis-Hastings algorithm is used to generate random variates. Monte Carlo simulations are conducted to compare the proposed methods. Analysis of a real dataset is performed.
      PubDate: 2017-06-21
      DOI: 10.3390/a10020071
      Issue No: Vol. 10, No. 2 (2017)
       
  • Algorithms, Vol. 10, Pages 5: Efficient Algorithms for the Maximum Sum
           Problems

    • Authors: Sung Bae, Tong-Wook Shinn, Tadao Takaoka
      First page: 5
      Abstract: We present efficient sequential and parallel algorithms for the maximum sum (MS) problem, which is to maximize the sum of some shape in the data array. We deal with two MS problems; the maximum subarray (MSA) problem and the maximum convex sum (MCS) problem. In the MSA problem, we find a rectangular part within the given data array that maximizes the sum in it. The MCS problem is to find a convex shape rather than a rectangular shape that maximizes the sum. Thus, MCS is a generalization of MSA. For the MSA problem, O ( n ) time parallel algorithms are already known on an ( n , n ) 2D array of processors. We improve the communication steps from 2 n − 1 to n, which is optimal. For the MCS problem, we achieve the asymptotic time bound of O ( n ) on an ( n , n ) 2D array of processors. We provide rigorous proofs for the correctness of our parallel algorithm based on Hoare logic and also provide some experimental results of our algorithm that are gathered from the Blue Gene/P super computer. Furthermore, we briefly describe how to compute the actual shape of the maximum convex sum.
      PubDate: 2017-01-04
      DOI: 10.3390/a10010005
      Issue No: Vol. 10, No. 1 (2017)
       
  • Algorithms, Vol. 10, Pages 6: Using Force-Field Grids for Sampling
           Translation/Rotation of Partially Rigid Macromolecules

    • Authors: Mihaly Mezei
      First page: 6
      Abstract: An algorithm is presented for the simulation of two partially flexible macromolecules where the interaction between the flexible parts and rigid parts is represented by energy grids associated with the rigid part of each macromolecule. The proposed algorithm avoids the transformation of the grid upon molecular movement at the expense of the significantly lesser effect of transforming the flexible part.
      PubDate: 2017-01-04
      DOI: 10.3390/a10010006
      Issue No: Vol. 10, No. 1 (2017)
       
  • Algorithms, Vol. 10, Pages 7: Backtracking-Based Iterative Regularization
           Method for Image Compressive Sensing Recovery

    • Authors: Lingjun Liu, Zhonghua Xie, Jiuchao Feng
      First page: 7
      Abstract: This paper presents a variant of the iterative shrinkage-thresholding (IST) algorithm, called backtracking-based adaptive IST (BAIST), for image compressive sensing (CS) reconstruction. For increasing iterations, IST usually yields a smoothing of the solution and runs into prematurity. To add back more details, the BAIST method backtracks to the previous noisy image using L2 norm minimization, i.e., minimizing the Euclidean distance between the current solution and the previous ones. Through this modification, the BAIST method achieves superior performance while maintaining the low complexity of IST-type methods. Also, BAIST takes a nonlocal regularization with an adaptive regularizor to automatically detect the sparsity level of an image. Experimental results show that our algorithm outperforms the original IST method and several excellent CS techniques.
      PubDate: 2017-01-06
      DOI: 10.3390/a10010007
      Issue No: Vol. 10, No. 1 (2017)
       
  • Algorithms, Vol. 10, Pages 8: Modeling Delayed Dynamics in Biological
           Regulatory Networks from Time Series Data

    • Authors: Emna Ben Abdallah, Tony Ribeiro, Morgan Magnin, Olivier Roux, Katsumi Inoue
      First page: 8
      Abstract: Background: The modeling of Biological Regulatory Networks (BRNs) relies on background knowledge, deriving either from literature and/or the analysis of biological observations. However, with the development of high-throughput data, there is a growing need for methods that automatically generate admissible models. Methods: Our research aim is to provide a logical approach to infer BRNs based on given time series data and known influences among genes. Results: We propose a new methodology for models expressed through a timed extension of the automata networks (well suited for biological systems). The main purpose is to have a resulting network as consistent as possible with the observed datasets. Conclusion: The originality of our work is three-fold: (i) identifying the sign of the interaction; (ii) the direct integration of quantitative time delays in the learning approach; and (iii) the identification of the qualitative discrete levels that lead to the systems’ dynamics. We show the benefits of such an automatic approach on dynamical biological models, the DREAM4(in silico) and DREAM8 (breast cancer) datasets, popular reverse-engineering challenges, in order to discuss the precision and the computational performances of our modeling method.
      PubDate: 2017-01-09
      DOI: 10.3390/a10010008
      Issue No: Vol. 10, No. 1 (2017)
       
  • Algorithms, Vol. 10, Pages 9: Elite Opposition-Based Social Spider
           Optimization Algorithm for Global Function Optimization

    • Authors: Ruxin Zhao, Qifang Luo, Yongquan Zhou
      First page: 9
      Abstract: The Social Spider Optimization algorithm (SSO) is a novel metaheuristic optimization algorithm. To enhance the convergence speed and computational accuracy of the algorithm, in this paper, an elite opposition-based Social Spider Optimization algorithm (EOSSO) is proposed; we use an elite opposition-based learning strategy to enhance the convergence speed and computational accuracy of the SSO algorithm. The 23 benchmark functions are tested, and the results show that the proposed elite opposition-based Social Spider Optimization algorithm is able to obtain an accurate solution, and it also has a fast convergence speed and a high degree of stability.
      PubDate: 2017-01-08
      DOI: 10.3390/a10010009
      Issue No: Vol. 10, No. 1 (2017)
       
  • Algorithms, Vol. 10, Pages 10: Estimating the Local Radius of Convergence
           for Picard Iteration

    • Authors: Ştefan Măruşter
      First page: 10
      Abstract: In this paper, we propose an algorithm to estimate the radius of convergence for the Picard iteration in the setting of a real Hilbert space. Numerical experiments show that the proposed algorithm provides convergence balls close to or even identical to the best ones. As the algorithm does not require to evaluate the norm of derivatives, the computing effort is relatively low.
      PubDate: 2017-01-09
      DOI: 10.3390/a10010010
      Issue No: Vol. 10, No. 1 (2017)
       
  • Algorithms, Vol. 10, Pages 11: Acknowledgement to Reviewers of Algorithms
           in 2016

    • Authors: Algorithms Editorial Office
      First page: 11
      Abstract: The editors of Algorithms would like to express their sincere gratitude to the following reviewers for assessing manuscripts in 2016.[...]
      PubDate: 2017-01-10
      DOI: 10.3390/a10010011
      Issue No: Vol. 10, No. 1 (2017)
       
  • Algorithms, Vol. 10, Pages 12: Coupled Least Squares Identification
           Algorithms for Multivariate Output-Error Systems

    • Authors: Wu Huang, Feng Ding
      First page: 12
      Abstract: This paper focuses on the recursive identification problems for a multivariate output-error system. By decomposing the system into several subsystems and by forming a coupled relationship between the parameter estimation vectors of the subsystems, two coupled auxiliary model based recursive least squares (RLS) algorithms are presented. Moreover, in contrast to the auxiliary model based recursive least squares algorithm, the proposed algorithms provide a reference to improve the identification accuracy of the multivariate output-error system. The simulation results confirm the effectiveness of the proposed algorithms.
      PubDate: 2017-01-12
      DOI: 10.3390/a10010012
      Issue No: Vol. 10, No. 1 (2017)
       
  • Algorithms, Vol. 10, Pages 13: A Fault Detection and Data Reconciliation
           Algorithm in Technical Processes with the Help of Haar Wavelets Packets

    • Authors: Paolo Mercorelli
      First page: 13
      Abstract: This article is focused on the detection of errors using an approach that is signal based. The proposed algorithm considers several criteria: soft, hard and very hard recognition error. After the recognition of the error, the error is replaced. In this sense, different strategies for data reconciliation are associated with the proposed criteria error detection. Algorithms in several industrial software platforms are used for detecting errors of sensors. Computer simulations confirm the validation of the presented applications. Results with actual sensor measurements in industrial processes are presented.
      PubDate: 2017-01-14
      DOI: 10.3390/a10010013
      Issue No: Vol. 10, No. 1 (2017)
       
  • Algorithms, Vol. 10, Pages 14: Kernel Clustering with a Differential
           Harmony Search Algorithm for Scheme Classification

    • Authors: Yu Feng, Jianzhong Zhou, Muhammad Tayyab
      First page: 14
      Abstract: This paper presents a kernel fuzzy clustering with a novel differential harmony search algorithm to coordinate with the diversion scheduling scheme classification. First, we employed a self-adaptive solution generation strategy and differential evolution-based population update strategy to improve the classical harmony search. Second, we applied the differential harmony search algorithm to the kernel fuzzy clustering to help the clustering method obtain better solutions. Finally, the combination of the kernel fuzzy clustering and the differential harmony search is applied for water diversion scheduling in East Lake. A comparison of the proposed method with other methods has been carried out. The results show that the kernel clustering with the differential harmony search algorithm has good performance to cooperate with the water diversion scheduling problems.
      PubDate: 2017-01-14
      DOI: 10.3390/a10010014
      Issue No: Vol. 10, No. 1 (2017)
       
  • Algorithms, Vol. 10, Pages 15: Toward Personalized Vibrotactile Support
           When Learning Motor Skills

    • Authors: Olga Santos
      First page: 15
      Abstract: Personal tracking technologies allow sensing of the physical activity carried out by people. Data flows collected with these sensors are calling for big data techniques to support data collection, integration and analysis, aimed to provide personalized support when learning motor skills through varied multisensorial feedback. In particular, this paper focuses on vibrotactile feedback as it can take advantage of the haptic sense when supporting the physical interaction to be learnt. Despite each user having different needs, when providing this vibrotactile support, personalization issues are hardly taken into account, but the same response is delivered to each and every user of the system. The challenge here is how to design vibrotactile user interfaces for adaptive learning of motor skills. TORMES methodology is proposed to facilitate the elicitation of this personalized support. The resulting systems are expected to dynamically adapt to each individual user’s needs by monitoring, comparing and, when appropriate, correcting in a personalized way how the user should move when practicing a predefined movement, for instance, when performing a sport technique or playing a musical instrument.
      PubDate: 2017-01-16
      DOI: 10.3390/a10010015
      Issue No: Vol. 10, No. 1 (2017)
       
  • Algorithms, Vol. 10, Pages 16: Length-Bounded Hybrid CPU/GPU Pattern
           Matching Algorithm for Deep Packet Inspection

    • Authors: Yi-Shan Lin, Chun-Liang Lee, Yaw-Chung Chen
      First page: 16
      Abstract: Since frequent communication between applications takes place in high speed networks, deep packet inspection (DPI) plays an important role in the network application awareness. The signature-based network intrusion detection system (NIDS) contains a DPI technique that examines the incoming packet payloads by employing a pattern matching algorithm that dominates the overall inspection performance. Existing studies focused on implementing efficient pattern matching algorithms by parallel programming on software platforms because of the advantages of lower cost and higher scalability. Either the central processing unit (CPU) or the graphic processing unit (GPU) were involved. Our studies focused on designing a pattern matching algorithm based on the cooperation between both CPU and GPU. In this paper, we present an enhanced design for our previous work, a length-bounded hybrid CPU/GPU pattern matching algorithm (LHPMA). In the preliminary experiment, the performance and comparison with the previous work are displayed, and the experimental results show that the LHPMA can achieve not only effective CPU/GPU cooperation but also higher throughput than the previous method.
      PubDate: 2017-01-18
      DOI: 10.3390/a10010016
      Issue No: Vol. 10, No. 1 (2017)
       
  • Algorithms, Vol. 10, Pages 17: A Preconditioned Iterative Method for
           Solving Systems of Nonlinear Equations Having Unknown Multiplicity

    • Authors: Fayyaz Ahmad, Toseef Bhutta, Umar Sohaib, Malik Zaka Ullah, Ali Alshomrani, Shamshad Ahmad, Shahid Ahmad
      First page: 17
      Abstract: A modification to an existing iterative method for computing zeros with unknown multiplicities of nonlinear equations or a system of nonlinear equations is presented. We introduce preconditioners to nonlinear equations or a system of nonlinear equations and their corresponding Jacobians. The inclusion of preconditioners provides numerical stability and accuracy. The different selection of preconditioner offers a family of iterative methods. We modified an existing method in a way that we do not alter its inherited quadratic convergence. Numerical simulations confirm the quadratic convergence of the preconditioned iterative method. The influence of preconditioners is clearly reflected in the numerically achieved accuracy of computed solutions.
      PubDate: 2017-01-18
      DOI: 10.3390/a10010017
      Issue No: Vol. 10, No. 1 (2017)
       
  • Algorithms, Vol. 10, Pages 18: Imperialist Competitive Algorithm with
           Dynamic Parameter Adaptation Using Fuzzy Logic Applied to the Optimization
           of Mathematical Functions

    • Authors: Emer Bernal, Oscar Castillo, José Soria, Fevrier Valdez
      First page: 18
      Abstract: In this paper we are presenting a method using fuzzy logic for dynamic parameter adaptation in the imperialist competitive algorithm, which is usually known by its acronym ICA. The ICA algorithm was initially studied in its original form to find out how it works and what parameters have more effect upon its results. Based on this study, several designs of fuzzy systems for dynamic adjustment of the ICA parameters are proposed. The experiments were performed on the basis of solving complex optimization problems, particularly applied to benchmark mathematical functions. A comparison of the original imperialist competitive algorithm and our proposed fuzzy imperialist competitive algorithm was performed. In addition, the fuzzy ICA was compared with another metaheuristic using a statistical test to measure the advantage of the proposed fuzzy approach for dynamic parameter adaptation.
      PubDate: 2017-01-23
      DOI: 10.3390/a10010018
      Issue No: Vol. 10, No. 1 (2017)
       
  • Algorithms, Vol. 10, Pages 19: Pressure Control for a Hydraulic Cylinder
           Based on a Self-Tuning PID Controller Optimized by a Hybrid Optimization
           Algorithm

    • Authors: Ru Wang, Chao Tan, Jing Xu, Zhongbin Wang, Jingfei Jin, Yiqiao Man
      First page: 19
      Abstract: In order to improve the performance of the hydraulic support electro-hydraulic control system test platform, a self-tuning proportion integration differentiation (PID) controller is proposed to imitate the actual pressure of the hydraulic support. To avoid the premature convergence and to improve the convergence velocity for tuning PID parameters, the PID controller is optimized with a hybrid optimization algorithm integrated with the particle swarm algorithm (PSO) and genetic algorithm (GA). A selection probability and an adaptive cross probability are introduced into the PSO to enhance the diversity of particles. The proportional overflow valve is installed to control the pressure of the pillar cylinder. The data of the control voltage of the proportional relief valve amplifier and pillar pressure are collected to acquire the system transfer function. Several simulations with different methods are performed on the hydraulic cylinder pressure system. The results demonstrate that the hybrid algorithm for a PID controller has comparatively better global search ability and faster convergence velocity on the pressure control of the hydraulic cylinder. Finally, an experiment is conducted to verify the validity of the proposed method.
      PubDate: 2017-01-23
      DOI: 10.3390/a10010019
      Issue No: Vol. 10, No. 1 (2017)
       
  • Algorithms, Vol. 10, Pages 20: Computing a Clique Tree with the Algorithm
           Maximal Label Search

    • Authors: Anne Berry, Geneviève Simonet
      First page: 20
      Abstract: The algorithm MLS (Maximal Label Search) is a graph search algorithm that generalizes the algorithms Maximum Cardinality Search (MCS), Lexicographic Breadth-First Search (LexBFS), Lexicographic Depth-First Search (LexDFS) and Maximal Neighborhood Search (MNS). On a chordal graph, MLS computes a PEO (perfect elimination ordering) of the graph. We show how the algorithm MLS can be modified to compute a PMO (perfect moplex ordering), as well as a clique tree and the minimal separators of a chordal graph. We give a necessary and sufficient condition on the labeling structure of MLS for the beginning of a new clique in the clique tree to be detected by a condition on labels. MLS is also used to compute a clique tree of the complement graph, and new cliques in the complement graph can be detected by a condition on labels for any labeling structure. We provide a linear time algorithm computing a PMO and the corresponding generators of the maximal cliques and minimal separators of the complement graph. On a non-chordal graph, the algorithm MLSM, a graph search algorithm computing an MEO and a minimal triangulation of the graph, is used to compute an atom tree of the clique minimal separator decomposition of any graph.
      PubDate: 2017-01-25
      DOI: 10.3390/a10010020
      Issue No: Vol. 10, No. 1 (2017)
       
  • Algorithms, Vol. 10, Pages 21: Concurrent vs. Exclusive Reading in
           Parallel Decoding of LZ-Compressed Files

    • Authors: Sergio Agostino, Bruno Carpentieri, Raffaele Pizzolante
      First page: 21
      Abstract: Broadcasting a message from one to many processors in a network corresponds to concurrent reading on a random access shared memory parallel machine. Computing the trees of a forest, the level of each node in its tree and the path between two nodes are problems that can easily be solved with concurrent reading in a time logarithmic in the maximum height of a tree. Solving such problems with exclusive reading requires a time logarithmic in the number of nodes, implying message passing between disjoint pairs of processors on a distributed system. Allowing concurrent reading in parallel algorithm design for distributed computing might be advantageous in practice if these problems are faced on shallow trees with some specific constraints. We show an application to LZC (Lempel-Ziv-Compress)-compressed file decoding, whose parallelization employs these computations on such trees for realistic data. On the other hand, zipped files do not have this advantage, since they are compressed by the Lempel–Ziv sliding window technique.
      PubDate: 2017-01-28
      DOI: 10.3390/a10010021
      Issue No: Vol. 10, No. 1 (2017)
       
  • Algorithms, Vol. 10, Pages 22: Evaluation of Diversification Techniques
           for Legal Information Retrieval

    • Authors: Marios Koniaris, Ioannis Anagnostopoulos, Yannis Vassiliou
      First page: 22
      Abstract: “Public legal information from all countries and international institutions is part of the common heritage of humanity. Maximizing access to this information promotes justice and the rule of law”. In accordance with the aforementioned declaration on free access to law by legal information institutes of the world, a plethora of legal information is available through the Internet, while the provision of legal information has never before been easier. Given that law is accessed by a much wider group of people, the majority of whom are not legally trained or qualified, diversification techniques should be employed in the context of legal information retrieval, as to increase user satisfaction. We address the diversification of results in legal search by adopting several state of the art methods from the web search, network analysis and text summarization domains. We provide an exhaustive evaluation of the methods, using a standard dataset from the common law domain that we objectively annotated with relevance judgments for this purpose. Our results: (i) reveal that users receive broader insights across the results they get from a legal information retrieval system; (ii) demonstrate that web search diversification techniques outperform other approaches (e.g., summarization-based, graph-based methods) in the context of legal diversification; and (iii) offer balance boundaries between reinforcing relevant documents or sampling the information space around the legal query.
      PubDate: 2017-01-29
      DOI: 10.3390/a10010022
      Issue No: Vol. 10, No. 1 (2017)
       
  • Algorithms, Vol. 10, Pages 23: An Architectural Based Framework for the
           Distributed Collection, Analysis and Query from Inhomogeneous Time Series
           Data Sets and Wearables for Biofeedback Applications

    • Authors: James Lee, David Rowlands, Nicholas Jackson, Raymond Leadbetter, Tomohito Wada, Daniel James
      First page: 23
      Abstract: The increasing professionalism of sports persons and desire of consumers to imitate this has led to an increased metrification of sport. This has been driven in no small part by the widespread availability of comparatively cheap assessment technologies and, more recently, wearable technologies. Historically, whilst these have produced large data sets, often only the most rudimentary analysis has taken place (Wisbey et al in: “Quantifying movement demands of AFL football using GPS tracking”). This paucity of analysis is due in no small part to the challenges of analysing large sets of data that are often from disparate data sources to glean useful key performance indicators, which has been a largely a labour intensive process. This paper presents a framework that can be cloud based for the gathering, storing and algorithmic interpretation of large and inhomogeneous time series data sets. The framework is architecture based and technology agnostic in the data sources it can gather, and presents a model for multi set analysis for inter- and intra- devices and individual subject matter. A sample implementation demonstrates the utility of the framework for sports performance data collected from distributed inertial sensors in the sport of swimming.
      PubDate: 2017-02-01
      DOI: 10.3390/a10010023
      Issue No: Vol. 10, No. 1 (2017)
       
  • Algorithms, Vol. 10, Pages 24: Problems on Finite Automata and the
           Exponential Time Hypothesis

    • Authors: Henning Fernau, Andreas Krebs
      First page: 24
      Abstract: We study several classical decision problems on finite automata under the (Strong) Exponential Time Hypothesis. We focus on three types of problems: universality, equivalence, and emptiness of intersection. All these problems are known to be CoNP-hard for nondeterministic finite automata, even when restricted to unary input alphabets. A different type of problems on finite automata relates to aperiodicity and to synchronizing words. We also consider finite automata that work on commutative alphabets and those working on two-dimensional words.
      PubDate: 2017-02-05
      DOI: 10.3390/a10010024
      Issue No: Vol. 10, No. 1 (2017)
       
  • Algorithms, Vol. 10, Pages 25: An On-Line Tracker for a Stochastic Chaotic
           System Using Observer/Kalman Filter Identification Combined with Digital
           Redesign Method

    • Authors: Tseng-Hsu Chien, Yeong-Chin Chen
      First page: 25
      Abstract: This is the first paper to present such a digital redesign method for the (conventional) OKID system and apply this novel technique for nonlinear system identification. First, the Observer/Kalman filter Identification (OKID) method is used to obtain the lower-order state-space model for a stochastic chaos system. Then, a digital redesign approach with the high-gain property is applied to improve and replace the observer identified by OKID. Therefore, the proposed OKID combined with an observer-based digital redesign novel tracker not only suppresses the uncertainties and the nonlinear perturbations, but also improves more accurate observation parameters of OKID for complex Multi-Input Multi-Output systems. In this research, Chen’s stochastic chaotic system is used as an illustrative example to demonstrate the effectiveness and excellence of the proposed methodology.
      PubDate: 2017-02-15
      DOI: 10.3390/a10010025
      Issue No: Vol. 10, No. 1 (2017)
       
  • Algorithms, Vol. 10, Pages 26: Analysis and Improvement of Fireworks
           Algorithm

    • Authors: Xi-Guang Li, Shou-Fei Han, Chang-Qing Gong
      First page: 26
      Abstract: The Fireworks Algorithm is a recently developed swarm intelligence algorithm to simulate the explosion process of fireworks. Based on the analysis of each operator of Fireworks Algorithm (FWA), this paper improves the FWA and proves that the improved algorithm converges to the global optimal solution with probability 1. The proposed algorithm improves the goal of further boosting performance and achieving global optimization where mainly include the following strategies. Firstly using the opposition-based learning initialization population. Secondly a new explosion amplitude mechanism for the optimal firework is proposed. In addition, the adaptive t-distribution mutation for non-optimal individuals and elite opposition-based learning for the optimal individual are used. Finally, a new selection strategy, namely Disruptive Selection, is proposed to reduce the running time of the algorithm compared with FWA. In our simulation, we apply the CEC2013 standard functions and compare the proposed algorithm (IFWA) with SPSO2011, FWA, EFWA and dynFWA. The results show that the proposed algorithm has better overall performance on the test functions.
      PubDate: 2017-02-17
      DOI: 10.3390/a10010026
      Issue No: Vol. 10, No. 1 (2017)
       
  • Algorithms, Vol. 10, Pages 27: Fragile Watermarking for Image
           Authentication Using the Characteristic of SVD

    • Authors: Heng Zhang, Chengyou Wang, Xiao Zhou
      First page: 27
      Abstract: Digital image authentication has become a hot topic in the last few years. In this paper, a pixel-based fragile watermarking method is presented for image tamper identification and localization. By analyzing the left and right singular matrices of SVD, it is found that the matrix product between the first column of the left singular matrix and the transposition of the first column in the right singular matrix is closely related to the image texture features. Based on this characteristic, a binary watermark consisting of image texture information is generated and inserted into the least significant bit (LSB) of the original host image. To improve the security of the presented algorithm, the Arnold transform is applied twice in the watermark embedding process. Experimental results indicate that the proposed watermarking algorithm has high security and perceptual invisibility. Moreover, it can detect and locate the tampered region effectively for various malicious attacks.
      PubDate: 2017-02-17
      DOI: 10.3390/a10010027
      Issue No: Vol. 10, No. 1 (2017)
       
  • Algorithms, Vol. 10, Pages 28: Mining Domain-Specific Design Patterns: A
           Case Study †

    • Authors: Vassiliki Gkantouna, Giannis Tzimas
      First page: 28
      Abstract: Domain-specific design patterns provide developers with proven solutions to common design problems that arise, particularly in a target application domain, facilitating them to produce quality designs in the domain contexts. However, research in this area is not mature and there are no techniques to support their detection. Towards this end, we propose a methodology which, when applied on a collection of websites in a specific domain, facilitates the automated identification of domain-specific design patterns. The methodology automatically extracts the conceptual models of the websites, which are subsequently analyzed in terms of all of the reusable design fragments used in them for supporting common domain functionalities. At the conceptual level, we consider these fragments as recurrent patterns consisting of a configuration of front-end interface components that interrelate each other and interact with end-users to support certain functionality. By performing a pattern-based analysis of the models, we locate the occurrences of all the recurrent patterns in the various website designs which are then evaluated towards their consistent use. The detected patterns can be used as building blocks in future designs, assisting developers to produce consistent and quality designs in the target domain. To support our case, we present a case study for the educational domain.
      PubDate: 2017-02-21
      DOI: 10.3390/a10010028
      Issue No: Vol. 10, No. 1 (2017)
       
  • Algorithms, Vol. 10, Pages 29: Stable Analysis of Compressive Principal
           Component Pursuit

    • Authors: Qingshan You, Qun Wan
      First page: 29
      Abstract: Compressive principal component pursuit (CPCP) recovers a target matrix that is a superposition of low-complexity structures from a small set of linear measurements. Pervious works mainly focus on the analysis of the existence and uniqueness. In this paper, we address its stability. We prove that the solution to the related convex programming of CPCP gives an estimate that is stable to small entry-wise noise. We also provide numerical simulation results to support our result. Numerical results show that the solution to the related convex program is stable to small entry-wise noise under board condition.
      PubDate: 2017-02-21
      DOI: 10.3390/a10010029
      Issue No: Vol. 10, No. 1 (2017)
       
  • Algorithms, Vol. 10, Pages 30: Towards Efficient Positional Inverted Index
           †

    • Authors: Petr Procházka, Jan Holub
      First page: 30
      Abstract: We address the problem of positional indexing in the natural language domain. The positional inverted index contains the information of the word positions. Thus, it is able to recover the original text file, which implies that it is not necessary to store the original file. Our Positional Inverted Self-Index (PISI) stores the word position gaps encoded by variable byte code. Inverted lists of single terms are combined into one inverted list that represents the backbone of the text file since it stores the sequence of the indexed words of the original file. The inverted list is synchronized with a presentation layer that stores separators, stop words, as well as variants of the indexed words. The Huffman coding is used to encode the presentation layer. The space complexity of the PISI inverted list is O ( ( N − n ) ⌈ log 2 b N ⌉ + ( ⌊ N − n α ⌋ + n ) × ( ⌈ log 2 b n ⌉ + 1 ) ) where N is a number of stems, n is a number of unique stems, α is a step/period of the back pointers in the inverted list and b is the size of the word of computer memory given in bits. The space complexity of the presentation layer is O ( − ∑ i = 1 N ⌈ log 2 p i n ( i ) ⌉ − ∑ j = 1 N ′ ⌈ log 2 p j ′ ⌉ + N ) with respect to p i n ( i ) as a probability of a stem variant at position i, p j ′ as the probability of separator or stop word at position j and N ′ as the number of separators and stop words.
      PubDate: 2017-02-22
      DOI: 10.3390/a10010030
      Issue No: Vol. 10, No. 1 (2017)
       
  • Algorithms, Vol. 10, Pages 31: Optimization-Based Approaches to Control of
           Probabilistic Boolean Networks

    • Authors: Koichi Kobayashi, Kunihiko Hiraishi
      First page: 31
      Abstract: Control of gene regulatory networks is one of the fundamental topics in systems biology. In the last decade, control theory of Boolean networks (BNs), which is well known as a model of gene regulatory networks, has been widely studied. In this review paper, our previously proposed methods on optimal control of probabilistic Boolean networks (PBNs) are introduced. First, the outline of PBNs is explained. Next, an optimal control method using polynomial optimization is explained. The finite-time optimal control problem is reduced to a polynomial optimization problem. Furthermore, another finite-time optimal control problem, which can be reduced to an integer programming problem, is also explained.
      PubDate: 2017-02-22
      DOI: 10.3390/a10010031
      Issue No: Vol. 10, No. 1 (2017)
       
  • Algorithms, Vol. 10, Pages 32: A New Quintic Spline Method for Integro
           Interpolation and Its Error Analysis

    • Authors: Feng-Gong Lang
      First page: 32
      Abstract: In this paper, to overcome the innate drawbacks of some old methods, we present a new quintic spline method for integro interpolation. The method is free of any exact end conditions, and it can reconstruct a function and its first order to fifth order derivatives with high accuracy by only using the given integral values of the original function. The approximation properties of the obtained integro quintic spline are well studied and examined. The theoretical analysis and the numerical tests show that the new method is very effective for integro interpolation.
      PubDate: 2017-03-03
      DOI: 10.3390/a10010032
      Issue No: Vol. 10, No. 1 (2017)
       
  • Algorithms, Vol. 10, Pages 33: Large Scale Implementations for Twitter
           Sentiment Classification

    • Authors: Andreas Kanavos, Nikolaos Nodarakis, Spyros Sioutas, Athanasios Tsakalidis, Dimitrios Tsolis, Giannis Tzimas
      First page: 33
      Abstract: Sentiment Analysis on Twitter Data is indeed a challenging problem due to the nature, diversity and volume of the data. People tend to express their feelings freely, which makes Twitter an ideal source for accumulating a vast amount of opinions towards a wide spectrum of topics. This amount of information offers huge potential and can be harnessed to receive the sentiment tendency towards these topics. However, since no one can invest an infinite amount of time to read through these tweets, an automated decision making approach is necessary. Nevertheless, most existing solutions are limited in centralized environments only. Thus, they can only process at most a few thousand tweets. Such a sample is not representative in order to define the sentiment polarity towards a topic due to the massive number of tweets published daily. In this work, we develop two systems: the first in the MapReduce and the second in the Apache Spark framework for programming with Big Data. The algorithm exploits all hashtags and emoticons inside a tweet, as sentiment labels, and proceeds to a classification method of diverse sentiment types in a parallel and distributed manner. Moreover, the sentiment analysis tool is based on Machine Learning methodologies alongside Natural Language Processing techniques and utilizes Apache Spark’s Machine learning library, MLlib. In order to address the nature of Big Data, we introduce some pre-processing steps for achieving better results in Sentiment Analysis as well as Bloom filters to compact the storage size of intermediate data and boost the performance of our algorithm. Finally, the proposed system was trained and validated with real data crawled by Twitter, and, through an extensive experimental evaluation, we prove that our solution is efficient, robust and scalable while confirming the quality of our sentiment identification.
      PubDate: 2017-03-04
      DOI: 10.3390/a10010033
      Issue No: Vol. 10, No. 1 (2017)
       
  • Algorithms, Vol. 10, Pages 34: A Novel, Gradient Boosting Framework for
           Sentiment Analysis in Languages where NLP Resources Are Not Plentiful: A
           Case Study for Modern Greek

    • Authors: Vasileios Athanasiou, Manolis Maragoudakis
      First page: 34
      Abstract: Sentiment analysis has played a primary role in text classification. It is an undoubted fact that some years ago, textual information was spreading in manageable rates; however, nowadays, such information has overcome even the most ambiguous expectations and constantly grows within seconds. It is therefore quite complex to cope with the vast amount of textual data particularly if we also take the incremental production speed into account. Social media, e-commerce, news articles, comments and opinions are broadcasted on a daily basis. A rational solution, in order to handle the abundance of data, would be to build automated information processing systems, for analyzing and extracting meaningful patterns from text. The present paper focuses on sentiment analysis applied in Greek texts. Thus far, there is no wide availability of natural language processing tools for Modern Greek. Hence, a thorough analysis of Greek, from the lexical to the syntactical level, is difficult to perform. This paper attempts a different approach, based on the proven capabilities of gradient boosting, a well-known technique for dealing with high-dimensional data. The main rationale is that since English has dominated the area of preprocessing tools and there are also quite reliable translation services, we could exploit them to transform Greek tokens into English, thus assuring the precision of the translation, since the translation of large texts is not always reliable and meaningful. The new feature set of English tokens is augmented with the original set of Greek, consequently producing a high dimensional dataset that poses certain difficulties for any traditional classifier. Accordingly, we apply gradient boosting machines, an ensemble algorithm that can learn with different loss functions providing the ability to work efficiently with high dimensional data. Moreover, for the task at hand, we deal with a class imbalance issues since the distribution of sentiments in real-world applications often displays issues of inequality. For example, in political forums or electronic discussions about immigration or religion, negative comments overwhelm the positive ones. The class imbalance problem was confronted using a hybrid technique that performs a variation of under-sampling the majority class and over-sampling the minority class, respectively. Experimental results, considering different settings, such as translation of tokens against translation of sentences, consideration of limited Greek text preprocessing and omission of the translation phase, demonstrated that the proposed gradient boosting framework can effectively cope with both high-dimensional and imbalanced datasets and performs significantly better than a plethora of traditional machine learning classification approaches in terms of precision and recall measures.
      PubDate: 2017-03-06
      DOI: 10.3390/a10010034
      Issue No: Vol. 10, No. 1 (2017)
       
  • Algorithms, Vol. 10, Pages 35: A Geo-Clustering Approach for the Detection
           of Areas-of-Interest and Their Underlying Semantics

    • Authors: Evaggelos Spyrou, Michalis Korakakis, Vasileios Charalampidis, Apostolos Psallas, Phivos Mylonas
      First page: 35
      Abstract: Living in the “era of social networking”, we are experiencing a data revolution, generating an astonishing amount of digital information every single day. Due to this proliferation of data volume, there has been an explosion of new application domains for information mined from social networks. In this paper, we leverage this “socially-generated knowledge” (i.e., user-generated content derived from social networks) towards the detection of areas-of-interest within an urban region. These large and homogeneous areas contain multiple points-of-interest which are of special interest to particular groups of people (e.g., tourists and/or consumers). In order to identify them, we exploit two types of metadata, namely location-based information included within geo-tagged photos that we collect from Flickr, along with plain simple textual information from user-generated tags. We propose an algorithm that divides a predefined geographical area (i.e., the center of Athens, Greece) into “tile”-shaped sub-regions and based on an iterative merging procedure, it aims to detect larger, cohesive areas. We examine the performance of the algorithm both in a qualitative and quantitative manner. Our experiments demonstrate that the proposed geo-clustering algorithm is able to correctly detect regions that contain popular tourist attractions within them with very promising results.
      PubDate: 2017-03-18
      DOI: 10.3390/a10010035
      Issue No: Vol. 10, No. 1 (2017)
       
 
 
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