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  Subjects -> COMPUTER SCIENCE (Total: 2007 journals)
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COMPUTER SCIENCE (1169 journals)                  1 2 3 4 5 6 | Last

Showing 1 - 200 of 872 Journals sorted alphabetically
3D Printing and Additive Manufacturing     Full-text available via subscription   (Followers: 14)
Abakós     Open Access   (Followers: 4)
ACM Computing Surveys     Hybrid Journal   (Followers: 24)
ACM Journal on Computing and Cultural Heritage     Hybrid Journal   (Followers: 9)
ACM Journal on Emerging Technologies in Computing Systems     Hybrid Journal   (Followers: 13)
ACM Transactions on Accessible Computing (TACCESS)     Hybrid Journal   (Followers: 3)
ACM Transactions on Algorithms (TALG)     Hybrid Journal   (Followers: 16)
ACM Transactions on Applied Perception (TAP)     Hybrid Journal   (Followers: 6)
ACM Transactions on Architecture and Code Optimization (TACO)     Hybrid Journal   (Followers: 9)
ACM Transactions on Autonomous and Adaptive Systems (TAAS)     Hybrid Journal   (Followers: 7)
ACM Transactions on Computation Theory (TOCT)     Hybrid Journal   (Followers: 12)
ACM Transactions on Computational Logic (TOCL)     Hybrid Journal   (Followers: 4)
ACM Transactions on Computer Systems (TOCS)     Hybrid Journal   (Followers: 18)
ACM Transactions on Computer-Human Interaction     Hybrid Journal   (Followers: 15)
ACM Transactions on Computing Education (TOCE)     Hybrid Journal   (Followers: 6)
ACM Transactions on Design Automation of Electronic Systems (TODAES)     Hybrid Journal   (Followers: 1)
ACM Transactions on Economics and Computation     Hybrid Journal  
ACM Transactions on Embedded Computing Systems (TECS)     Hybrid Journal   (Followers: 4)
ACM Transactions on Information Systems (TOIS)     Hybrid Journal   (Followers: 21)
ACM Transactions on Intelligent Systems and Technology (TIST)     Hybrid Journal   (Followers: 8)
ACM Transactions on Interactive Intelligent Systems (TiiS)     Hybrid Journal   (Followers: 4)
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)     Hybrid Journal   (Followers: 10)
ACM Transactions on Reconfigurable Technology and Systems (TRETS)     Hybrid Journal   (Followers: 7)
ACM Transactions on Sensor Networks (TOSN)     Hybrid Journal   (Followers: 9)
ACM Transactions on Speech and Language Processing (TSLP)     Hybrid Journal   (Followers: 10)
ACM Transactions on Storage     Hybrid Journal  
ACS Applied Materials & Interfaces     Full-text available via subscription   (Followers: 25)
Acta Automatica Sinica     Full-text available via subscription   (Followers: 3)
Acta Universitatis Cibiniensis. Technical Series     Open Access  
Ad Hoc Networks     Hybrid Journal   (Followers: 11)
Adaptive Behavior     Hybrid Journal   (Followers: 11)
Advanced Engineering Materials     Hybrid Journal   (Followers: 26)
Advanced Science Letters     Full-text available via subscription   (Followers: 9)
Advances in Adaptive Data Analysis     Hybrid Journal   (Followers: 8)
Advances in Artificial Intelligence     Open Access   (Followers: 16)
Advances in Calculus of Variations     Hybrid Journal   (Followers: 2)
Advances in Catalysis     Full-text available via subscription   (Followers: 5)
Advances in Computational Mathematics     Hybrid Journal   (Followers: 15)
Advances in Computer Science : an International Journal     Open Access   (Followers: 15)
Advances in Computing     Open Access   (Followers: 2)
Advances in Data Analysis and Classification     Hybrid Journal   (Followers: 52)
Advances in Engineering Software     Hybrid Journal   (Followers: 26)
Advances in Geosciences (ADGEO)     Open Access   (Followers: 11)
Advances in Human Factors/Ergonomics     Full-text available via subscription   (Followers: 26)
Advances in Human-Computer Interaction     Open Access   (Followers: 21)
Advances in Materials Sciences     Open Access   (Followers: 16)
Advances in Operations Research     Open Access   (Followers: 11)
Advances in Parallel Computing     Full-text available via subscription   (Followers: 7)
Advances in Porous Media     Full-text available via subscription   (Followers: 4)
Advances in Remote Sensing     Open Access   (Followers: 40)
Advances in Science and Research (ASR)     Open Access   (Followers: 6)
Advances in Technology Innovation     Open Access   (Followers: 4)
AEU - International Journal of Electronics and Communications     Hybrid Journal   (Followers: 8)
African Journal of Information and Communication     Open Access   (Followers: 8)
African Journal of Mathematics and Computer Science Research     Open Access   (Followers: 4)
Air, Soil & Water Research     Open Access   (Followers: 9)
AIS Transactions on Human-Computer Interaction     Open Access   (Followers: 6)
Algebras and Representation Theory     Hybrid Journal   (Followers: 1)
Algorithms     Open Access   (Followers: 11)
American Journal of Computational and Applied Mathematics     Open Access   (Followers: 4)
American Journal of Computational Mathematics     Open Access   (Followers: 4)
American Journal of Information Systems     Open Access   (Followers: 5)
American Journal of Sensor Technology     Open Access   (Followers: 4)
Anais da Academia Brasileira de Ciências     Open Access   (Followers: 2)
Analog Integrated Circuits and Signal Processing     Hybrid Journal   (Followers: 7)
Analysis in Theory and Applications     Hybrid Journal   (Followers: 1)
Animation Practice, Process & Production     Hybrid Journal   (Followers: 5)
Annals of Combinatorics     Hybrid Journal   (Followers: 3)
Annals of Data Science     Hybrid Journal   (Followers: 11)
Annals of Mathematics and Artificial Intelligence     Hybrid Journal   (Followers: 8)
Annals of Pure and Applied Logic     Open Access   (Followers: 2)
Annals of Software Engineering     Hybrid Journal   (Followers: 13)
Annual Reviews in Control     Hybrid Journal   (Followers: 6)
Anuario Americanista Europeo     Open Access  
Applicable Algebra in Engineering, Communication and Computing     Hybrid Journal   (Followers: 2)
Applied and Computational Harmonic Analysis     Full-text available via subscription   (Followers: 1)
Applied Artificial Intelligence: An International Journal     Hybrid Journal   (Followers: 13)
Applied Categorical Structures     Hybrid Journal   (Followers: 2)
Applied Clinical Informatics     Hybrid Journal   (Followers: 2)
Applied Computational Intelligence and Soft Computing     Open Access   (Followers: 12)
Applied Computer Systems     Open Access   (Followers: 2)
Applied Informatics     Open Access  
Applied Mathematics and Computation     Hybrid Journal   (Followers: 33)
Applied Medical Informatics     Open Access   (Followers: 10)
Applied Numerical Mathematics     Hybrid Journal   (Followers: 5)
Applied Soft Computing     Hybrid Journal   (Followers: 15)
Applied Spatial Analysis and Policy     Hybrid Journal   (Followers: 5)
Architectural Theory Review     Hybrid Journal   (Followers: 3)
Archive of Applied Mechanics     Hybrid Journal   (Followers: 5)
Archive of Numerical Software     Open Access  
Archives and Museum Informatics     Hybrid Journal   (Followers: 131)
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 4)
Artifact     Hybrid Journal   (Followers: 2)
Artificial Life     Hybrid Journal   (Followers: 7)
Asia Pacific Journal on Computational Engineering     Open Access  
Asia-Pacific Journal of Information Technology and Multimedia     Open Access   (Followers: 1)
Asian Journal of Computer Science and Information Technology     Open Access  
Asian Journal of Control     Hybrid Journal  
Assembly Automation     Hybrid Journal   (Followers: 2)
at - Automatisierungstechnik     Hybrid Journal   (Followers: 1)
Australian Educational Computing     Open Access   (Followers: 1)
Automatic Control and Computer Sciences     Hybrid Journal   (Followers: 4)
Automatic Documentation and Mathematical Linguistics     Hybrid Journal   (Followers: 5)
Automatica     Hybrid Journal   (Followers: 11)
Automation in Construction     Hybrid Journal   (Followers: 6)
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (Followers: 9)
Basin Research     Hybrid Journal   (Followers: 5)
Behaviour & Information Technology     Hybrid Journal   (Followers: 52)
Biodiversity Information Science and Standards     Open Access  
Bioinformatics     Hybrid Journal   (Followers: 298)
Biomedical Engineering     Hybrid Journal   (Followers: 15)
Biomedical Engineering and Computational Biology     Open Access   (Followers: 14)
Biomedical Engineering, IEEE Reviews in     Full-text available via subscription   (Followers: 18)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 34)
Briefings in Bioinformatics     Hybrid Journal   (Followers: 45)
British Journal of Educational Technology     Hybrid Journal   (Followers: 131)
Broadcasting, IEEE Transactions on     Hybrid Journal   (Followers: 10)
c't Magazin fuer Computertechnik     Full-text available via subscription   (Followers: 2)
CALCOLO     Hybrid Journal  
Calphad     Hybrid Journal  
Canadian Journal of Electrical and Computer Engineering     Full-text available via subscription   (Followers: 14)
Capturing Intelligence     Full-text available via subscription  
Catalysis in Industry     Hybrid Journal   (Followers: 1)
CEAS Space Journal     Hybrid Journal   (Followers: 1)
Cell Communication and Signaling     Open Access   (Followers: 2)
Central European Journal of Computer Science     Hybrid Journal   (Followers: 5)
CERN IdeaSquare Journal of Experimental Innovation     Open Access   (Followers: 1)
Chaos, Solitons & Fractals     Hybrid Journal   (Followers: 3)
Chemometrics and Intelligent Laboratory Systems     Hybrid Journal   (Followers: 14)
ChemSusChem     Hybrid Journal   (Followers: 7)
China Communications     Full-text available via subscription   (Followers: 7)
Chinese Journal of Catalysis     Full-text available via subscription   (Followers: 2)
CIN Computers Informatics Nursing     Full-text available via subscription   (Followers: 11)
Circuits and Systems     Open Access   (Followers: 15)
Clean Air Journal     Full-text available via subscription   (Followers: 2)
CLEI Electronic Journal     Open Access  
Clin-Alert     Hybrid Journal   (Followers: 1)
Cluster Computing     Hybrid Journal   (Followers: 1)
Cognitive Computation     Hybrid Journal   (Followers: 4)
COMBINATORICA     Hybrid Journal  
Combustion Theory and Modelling     Hybrid Journal   (Followers: 14)
Communication Methods and Measures     Hybrid Journal   (Followers: 12)
Communication Theory     Hybrid Journal   (Followers: 20)
Communications Engineer     Hybrid Journal   (Followers: 1)
Communications in Algebra     Hybrid Journal   (Followers: 3)
Communications in Partial Differential Equations     Hybrid Journal   (Followers: 3)
Communications of the ACM     Full-text available via subscription   (Followers: 55)
Communications of the Association for Information Systems     Open Access   (Followers: 19)
COMPEL: The International Journal for Computation and Mathematics in Electrical and Electronic Engineering     Hybrid Journal   (Followers: 3)
Complex & Intelligent Systems     Open Access   (Followers: 1)
Complex Adaptive Systems Modeling     Open Access  
Complex Analysis and Operator Theory     Hybrid Journal   (Followers: 2)
Complexity     Hybrid Journal   (Followers: 6)
Complexus     Full-text available via subscription  
Composite Materials Series     Full-text available via subscription   (Followers: 9)
Computación y Sistemas     Open Access  
Computation     Open Access  
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 and Structural Biotechnology Journal     Open Access   (Followers: 2)
Computational and Theoretical Chemistry     Hybrid Journal   (Followers: 9)
Computational Astrophysics and Cosmology     Open Access   (Followers: 1)
Computational Biology and Chemistry     Hybrid Journal   (Followers: 11)
Computational Chemistry     Open Access   (Followers: 2)
Computational Cognitive Science     Open Access   (Followers: 2)
Computational Complexity     Hybrid Journal   (Followers: 4)
Computational Condensed Matter     Open Access  
Computational Ecology and Software     Open Access   (Followers: 9)
Computational Economics     Hybrid Journal   (Followers: 9)
Computational Geosciences     Hybrid Journal   (Followers: 15)
Computational Linguistics     Open Access   (Followers: 22)
Computational Management Science     Hybrid Journal  
Computational Mathematics and Modeling     Hybrid Journal   (Followers: 8)
Computational Mechanics     Hybrid Journal   (Followers: 4)
Computational Methods and Function Theory     Hybrid Journal  
Computational Molecular Bioscience     Open Access   (Followers: 2)
Computational Optimization and Applications     Hybrid Journal   (Followers: 7)
Computational Particle Mechanics     Hybrid Journal   (Followers: 1)
Computational Research     Open Access   (Followers: 1)
Computational Science and Discovery     Full-text available via subscription   (Followers: 2)
Computational Science and Techniques     Open Access  
Computational Statistics     Hybrid Journal   (Followers: 14)
Computational Statistics & Data Analysis     Hybrid Journal   (Followers: 30)
Computer     Full-text available via subscription   (Followers: 89)
Computer Aided Surgery     Hybrid Journal   (Followers: 5)
Computer Applications in Engineering Education     Hybrid Journal   (Followers: 8)
Computer Communications     Hybrid Journal   (Followers: 10)
Computer Engineering and Applications Journal     Open Access   (Followers: 5)
Computer Journal     Hybrid Journal   (Followers: 9)
Computer Methods in Applied Mechanics and Engineering     Hybrid Journal   (Followers: 22)
Computer Methods in Biomechanics and Biomedical Engineering     Hybrid Journal   (Followers: 12)
Computer Methods in the Geosciences     Full-text available via subscription   (Followers: 2)
Computer Music Journal     Hybrid Journal   (Followers: 18)
Computer Physics Communications     Hybrid Journal   (Followers: 6)
Computer Science - Research and Development     Hybrid Journal   (Followers: 8)
Computer Science and Engineering     Open Access   (Followers: 19)
Computer Science and Information Technology     Open Access   (Followers: 13)
Computer Science Education     Hybrid Journal   (Followers: 14)
Computer Science Journal     Open Access   (Followers: 21)

        1 2 3 4 5 6 | Last

Journal Cover Cognitive Computation
  [SJR: 0.692]   [H-I: 19]   [4 followers]  Follow
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 1866-9964 - ISSN (Online) 1866-9956
   Published by Springer-Verlag Homepage  [2355 journals]
  • Mood Impact on Automaticity of Performance: Handwriting as Exemplar
    • Authors: Clara Rispler; Gil Luria; Allon Kahana; Sara Rosenblum
      Abstract: Abstract The goal of this study was to assess how existing handwriting research can contribute to understanding how moods impact the automatic processing of handwriting performance. We based our hypotheses on extensive research connecting mood with cognitive functions, because handwriting production was shown to be an automated cognitive task impacted by cognitive load. As far as we know, no previous research has examined the direct relationship between affect and handwriting (transcription and text generation when writing by hand). Specifically, evidence exists only for a general relationship between affect and writing (using written words to express ideas or opinions). In this experiment, 62 participants were divided into three mood groups (positive, negative, and neutral). Mood manipulation was conducted according to accepted methods of memory recall and film induction and was evaluated using the PANAS scale. Online measurements of the participants’ handwriting were captured with a tablet and electronic pen. Results showed that the strokes in the negative mood manipulation were shorter in duration and shorter in width and height. The findings presented in this article make a twofold contribution to the cognitive and biologically inspired computational studies: by integrating the study of affect with the study of cognition and by exploring additional objective performance-based evaluation of functional capabilities with the aid of a computerized device. Practical implications are discussed, as are ideas for further research.
      PubDate: 2018-01-10
      DOI: 10.1007/s12559-017-9540-y
  • Discriminative Deep Belief Network for Indoor Environment Classification
           Using Global Visual Features
    • Authors: Nabila Zrira; Haris Ahmad Khan; El Houssine Bouyakhf
      Abstract: Abstract Indoor environment classification, also known as indoor environment recognition, is a highly appreciated perceptual ability in mobile robots. In this paper, we present a novel approach which is centered on biologically inspired methods for recognition and representation of indoor environments. First, global visual features are extracted by using the GIST descriptor, and then we use the subsequent features for training the discriminative deep belief network (DDBN) classifier. DDBN employs a new deep architecture which is based on restricted Boltzmann machines (RBMs) and the joint density model. The back-propagation technique is used over the entire classifier to fine-tune the weights for an optimum classification. The acquired experimental results validate our approach as it performs well both in the real-world and in synthetic datasets and outperforms the Convolution Neural Networks (ConvNets) in terms of computational efficiency.
      PubDate: 2018-01-01
      DOI: 10.1007/s12559-017-9534-9
  • Parkinson’s Disease and Aging: Analysis of Their Effect in Phonation and
           Articulation of Speech
    • Authors: T. Arias-Vergara; J. C. Vásquez-Correa; J. R. Orozco-Arroyave
      Pages: 731 - 748
      Abstract: Abstract Parkinson’s disease (PD) is a neurological disorder that affects the communication ability of patients. There is interest in the research community to study acoustic measures that provide objective information to model PD speech. Although there are several studies in the literature that consider different characteristics of Parkinson’s speech like phonation and articulation, there are no studies including the aging process as another possible source of impairments in speech. The aim of this work is to analyze the vowel articulation and phonation of Parkinson’s patients compared with respect to two groups of healthy people: (1) young speakers with ages ranging from 22 to 50 years and (2) people with ages matched with respect to the Parkinson’s patients. Each participant repeated the sustained phonation of the five Spanish vowels three times and those utterances per speaker are modeled by using phonation and articulation features. Feature selection is applied to eliminate redundant information in the features space, and the automatic discrimination of the three groups of speakers is performed using a multi-class Support Vector Machine (SVM) following a one vs. all strategy, speaker independent. The results are compared to those obtained using a cognitive-inspired classifier which is based on neural networks (NN). The results indicate that the phonation and articulation capabilities of young speakers clearly differ from those exhibited by the elderly speakers (with and without PD). To the best of our knowledge, this is the first paper introducing experimental evidence to support the fact that age matching is necessary to perform more accurate and robust evaluations of pathological speech signals, especially considering diseases suffered by elderly people, like Parkinson’s. Additionally, the comparison among groups of speakers at different ages is necessary in order to understand the natural change in speech due to the aging process.
      PubDate: 2017-12-01
      DOI: 10.1007/s12559-017-9497-x
      Issue No: Vol. 9, No. 6 (2017)
  • A Bayesian Assessment of Real-World Behavior During Multitasking
    • Authors: Jeroen H.M. Bergmann; Joan Fei; David A Green; Amir Hussain; Newton Howard
      Pages: 749 - 757
      Abstract: Abstract Multitasking is common in everyday life, but its effect on activities of daily living is not well understood. Critical appraisal of performance for both healthy individuals and patients is required. Motor activities during meal preparation were monitored in healthy individuals with a wearable sensor network during single and multitask conditions. Motor performance was quantified by the median frequencies (f m) of hand trajectories and wrist accelerations. The probability that multitasking occurred based on the obtained motor information was estimated using a Naïve Bayes Model, with a specific focus on the single and triple loading conditions. The Bayesian probability estimator showed task distinction for the wrist accelerometer data at the high and low value ranges. The likelihood of encountering a certain motor performance during well-established everyday activities, such as preparing a simple meal, changed when additional (cognitive) tasks were performed. Within a healthy population, the probability of lower acceleration frequency patterns increases when people are asked to multitask. Cognitive decline due to aging or disease might yield even greater differences.
      PubDate: 2017-12-01
      DOI: 10.1007/s12559-017-9500-6
      Issue No: Vol. 9, No. 6 (2017)
  • Motor Imagery EEG Classification Based on Kernel Hierarchical Extreme
           Learning Machine
    • Authors: Lijuan Duan; Menghu Bao; Song Cui; Yuanhua Qiao; Jun Miao
      Pages: 758 - 765
      Abstract: Abstract As connections from the brain to an external device, Brain-Computer Interface (BCI) systems are a crucial aspect of assisted communication and control. When equipped with well-designed feature extraction and classification approaches, information can be accurately acquired from the brain using such systems. The Hierarchical Extreme Learning Machine (HELM) has been developed as an effective and accurate classification approach due to its deep structure and extreme learning mechanism. A classification system for motor imagery EEG signals is proposed based on the HELM combined with a kernel, herein called the Kernel Hierarchical Extreme Learning Machine (KHELM). Principle Component Analysis (PCA) is used to reduce the dimensionality of the data, and Linear Discriminant Analysis (LDA) is introduced to push the features away from different classes. To demonstrate the performance, the proposed system is applied to the BCI competition 2003 Dataset Ia, and the results are compared with those from state-of-the-art methods; we find that the accuracy is up to 94.54%.
      PubDate: 2017-12-01
      DOI: 10.1007/s12559-017-9494-0
      Issue No: Vol. 9, No. 6 (2017)
  • Online Extreme Learning Machine with Hybrid Sampling Strategy for
           Sequential Imbalanced Data
    • Authors: Wentao Mao; Mengxue Jiang; Jinwan Wang; Yuan Li
      Pages: 780 - 800
      Abstract: Abstract In real applications of cognitive computation, data with imbalanced classes are used to be collected sequentially. In this situation, some of current machine learning algorithms, e.g., support vector machine, will obtain weak classification performance, especially on minority class. To solve this problem, a new hybrid sampling online extreme learning machine (ELM) on sequential imbalanced data is proposed in this paper. The key idea is keeping the majority and minority classes balanced with similar sequential distribution characteristic of the original data. This method includes two stages. At the offline stage, we introduce the principal curve to build confidence regions of minority and majority classes respectively. Based on these two confidence zones, over-sampling of minority class and under-sampling of majority class are both conducted to generate new synthetic samples, and then, the initial ELM model is established. At the online stage, we first choose the most valuable ones from the synthetic samples of majority class in terms of sample importance. Afterwards, a new online fast leave-one-out cross validation (LOO CV) algorithm utilizing Cholesky decomposition is proposed to determine whether to update the ELM network weight at online stage or not. We also prove theoretically that the proposed method has upper bound of information loss. Experimental results on seven UCI datasets and one real-world air pollutant forecasting dataset show that, compared with ELM, OS-ELM, meta-cognitive OS-ELM, and OSELM with SMOTE strategy, the proposed method can simultaneously improve the classification performance of minority and majority classes in terms of accuracy, G-mean value, and ROC curve. As a conclusion, the proposed hybrid sampling online extreme learning machine can be effectively applied to the sequential data imbalance problem with better generalization performance and numerical stability.
      PubDate: 2017-12-01
      DOI: 10.1007/s12559-017-9504-2
      Issue No: Vol. 9, No. 6 (2017)
  • An Interval Neutrosophic Projection-Based VIKOR Method for Selecting
    • Authors: Junhua Hu; Li Pan; Xiaohong Chen
      Pages: 801 - 816
      Abstract: Abstract Mobile healthcare applications are emerging as an innovative and practical technology resource to provide automated and efficient medical services online. However, vagueness and uncertainty commonly exist during the process of selecting doctors online and few studies have addressed these problems. In this paper, we employed interval neutrosophic sets (INSs) to process evaluation information. We proposed and normalized an improved projection measurement for INSs to overcome the shortcomings in extant projection measurements. Additionally, we presented a projection-based difference measure combined with the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method to establish a projection-based VIKOR method. Moreover, we introduced transition functions to transform online evaluation information into INSs and employed the maximizing deviation method to obtain weights for criteria. It is a practical problem for patients to choose a suitable doctor on a mobile healthcare application. This problem can be solved by applying the proposed method. Finally, we verified the validity of the method by comparison with several existing methods, and then we conducted sensitivity analysis to demonstrate the method’s reliability. The doctor selection problem can be effectively solved by the projection-based VIKOR method for INSs. Our comparison indicates that the proposed method is more appropriate than other methods, and the final ranking results are more precise than the actual ranking list found online. INSs are effective in describing vagueness and uncertainty in multi-criteria decision-making problems. The method proposed in this paper was viable and valid for use in doctor selection processes with information expressed by INSs.
      PubDate: 2017-12-01
      DOI: 10.1007/s12559-017-9499-8
      Issue No: Vol. 9, No. 6 (2017)
  • Optimization of Non-rigid Demons Registration Using Cuckoo Search
    • Authors: Sayan Chakraborty; Nilanjan Dey; Sourav Samanta; Amira S. Ashour; C. Barna; M. M. Balas
      Pages: 817 - 826
      Abstract: Abstract Video processing including registration has a significant role in surveillance and real-time applications. Image registration is considered a compulsory step in video registration for numerous aspects. One of the major challenges in image registration is to determine the optimal parameters during the registration process. Bio-inspired computational including natural and artificial cognitive systems can be employed to define the optimal solutions. The present work proposed a comprehensive automatic non-rigid video set registration algorithm using Demons algorithm. For optimal velocity smoothing kernels, the demons registration is optimized using cuckoo search (CS) algorithm, where there are no previous studies that have optimized demons algorithm using CS algorithm. A comparison between the CS algorithm and the particle swarm optimization (PSO)-based demons registration is conducted to evaluate the proposed system performance. Thus, the correlation coefficient is taken as a fitness function. The obtained results using CS show a minor increment of the optimized fitness value compared to PSO-based framework value. The proposed CS-based approach reports faster convergence rate than the PSO-based approach.
      PubDate: 2017-12-01
      DOI: 10.1007/s12559-017-9508-y
      Issue No: Vol. 9, No. 6 (2017)
  • Multi-Criteria Decision-Making Method Based on Distance Measure and
           Choquet Integral for Linguistic Z-Numbers
    • Authors: Jian-qiang Wang; Yong-xi Cao; Hong-yu Zhang
      Pages: 827 - 842
      Abstract: Abstract Z-numbers are a new concept considering both the description of cognitive information and the reliability of information. Linguistic terms are useful tools to adequately and effectively model real-life cognitive information, as well as to characterize the randomness of events. However, a form of Z-numbers, in which their two components are in the form of linguistic terms, is rarely studied, although it is common in decision-making problems. In terms of Z-numbers and linguistic term sets, we provided the definition of linguistic Z-numbers as a form of Z-numbers or a subclass of Z-numbers. Then, we defined some operations of linguistic Z-numbers and proposed a comparison method based on the score and accuracy functions of linguistic Z-numbers. We also presented the distance measure of linguistic Z-numbers. Next, we developed an extended TODIM (an acronym in Portuguese of interactive and multi-criteria decision-making) method based on the Choquet integral for multi-criteria decision-making (MCDM) problems with linguistic Z-numbers. Finally, we provided an example concerning the selection of medical inquiry applications to demonstrate the feasibility of our proposed approach. We then verified the applicability and superiority of our approach through comparative analyses with other existing methods. Illustrative and comparative analyses indicated that the proposed approach was valid and feasible for different decision-makers and cognitive environments. Furthermore, the final ranking results of the proposed approach were closer to real decision-making processes. Linguistic Z-numbers can flexibly characterize real cognitive information as well as describe the reliability of information. This method not only is a more comprehensive reflection of the decision-makers’ cognition but also is more in line with expression habits. The proposed method inherited the merits of the classical TODIM method and considers the interactivity of criteria; therefore, the proposed method was effective for dealing with real-life MCDM problems. Consideration about bounded rational and the interactivity of criteria made final outcomes convincing and consistent with real decision-making.
      PubDate: 2017-12-01
      DOI: 10.1007/s12559-017-9493-1
      Issue No: Vol. 9, No. 6 (2017)
  • Learning Word Representations for Sentiment Analysis
    • Authors: Yang Li; Quan Pan; Tao Yang; Suhang Wang; Jiliang Tang; Erik Cambria
      Pages: 843 - 851
      Abstract: Abstract Word embedding has been proven to be a useful model for various natural language processing tasks. Traditional word embedding methods merely take into account word distributions independently from any specific tasks. Hence, the resulting representations could be sub-optimal for a given task. In the context of sentiment analysis, there are various types of prior knowledge available, e.g., sentiment labels of documents from available datasets or polarity values of words from sentiment lexicons. We incorporate such prior sentiment information at both word level and document level in order to investigate the influence each word has on the sentiment label of both target word and context words. By evaluating the performance of sentiment analysis in each category, we find the best way of incorporating prior sentiment information. Experimental results on real-world datasets demonstrate that the word representations learnt by DLJT2 can significantly improve the sentiment analysis performance. We prove that incorporating prior sentiment knowledge into the embedding process has the potential to learn better representations for sentiment analysis.
      PubDate: 2017-12-01
      DOI: 10.1007/s12559-017-9492-2
      Issue No: Vol. 9, No. 6 (2017)
  • A Novel Technique for Detecting Plagiarism in Documents Exploiting
           Information Sources
    • Authors: Mansi Sahi; Vishal Gupta
      Pages: 852 - 867
      Abstract: Abstract Plagiarism takes place when we use any person’s work without giving due acknowledgment. There are several fields where the text similarity is involved like web document retrieval, information mining, and searching related articles. Several approaches have been introduced for detecting plagiarism in the text documents based on the syntactic structure of the text, string similarity, fingerprinting, semantic meaning underlying the text, etc. The basic limitation of plagiarism detection systems these days is that they fail to detect tough cases of plagiarism. The proposed plagiarism detection approach is the hybrid of semantic and syntactic similarity between the text documents. This novel approach exploits linguistic information sources non-linearly using the lexical database for finding the relatedness between text documents. The proposed approach uses semantic knowledge to perform cognitive-inspired computing. The framework is capable of detecting intelligent plagiarism cases like a verbatim copy, paraphrasing, rewording in a sentence, and sentence transformation. The approach has been evaluated on the standard PAN-PC-11 dataset. The experiments show that our technique has outperformed other strong baseline techniques in terms of precision, recall, F-measure, and plagiarism detection (PlagDet) score.
      PubDate: 2017-12-01
      DOI: 10.1007/s12559-017-9502-4
      Issue No: Vol. 9, No. 6 (2017)
  • Evaluating Integration Strategies for Visuo-Haptic Object Recognition
    • Authors: Sibel Toprak; Nicolás Navarro-Guerrero; Stefan Wermter
      Abstract: Abstract In computational systems for visuo-haptic object recognition, vision and haptics are often modeled as separate processes. But this is far from what really happens in the human brain, where cross- as well as multimodal interactions take place between the two sensory modalities. Generally, three main principles can be identified as underlying the processing of the visual and haptic object-related stimuli in the brain: (1) hierarchical processing, (2) the divergence of the processing onto substreams for object shape and material perception, and (3) the experience-driven self-organization of the integratory neural circuits. The question arises whether an object recognition system can benefit in terms of performance from adopting these brain-inspired processing principles for the integration of the visual and haptic inputs. To address this, we compare the integration strategy that incorporates all three principles to the two commonly used integration strategies in the literature. We collected data with a NAO robot enhanced with inexpensive contact microphones as tactile sensors. The results of our experiments involving every-day objects indicate that (1) the contact microphones are a good alternative to capturing tactile information and that (2) organizing the processing of the visual and haptic inputs hierarchically and in two pre-processing streams is helpful performance-wise. Nevertheless, further research is needed to effectively quantify the role of each identified principle by itself as well as in combination with others.
      PubDate: 2017-12-28
      DOI: 10.1007/s12559-017-9536-7
  • Multiple Attribute Decision-Making Methods Based on the Expected Value and
           the Similarity Measure of Hesitant Neutrosophic Linguistic Numbers
    • Authors: Jun Ye
      Abstract: Abstract The existing neutrosophic linguistic decision-making approach uses only one neutrosophic linguistic number (NLN) to express its evaluation value of an attribute in decision making. Sometimes, it may not reflect exactly what decision makers mean due to the ambiguity and indeterminacy of their cognitions to complex decision-making problems. In this situation, decision makers might hesitate among several NLNs to express their opinions. To deal with the issue, this paper defines hesitant neutrosophic linguistic numbers (HNLNs), the expected value of HNLN and proposes the generalized distance and similarity measure between two HNLN sets based on the least common multiple cardinality for HNLNs. Then, multiple attribute decision-making (MADM) methods are established based on the expected value and the similarity measure under a HNLN environment. In the proposed decision-making methods, the evaluation values of alternatives over attributes provided by decision makers are HNLNs, and then all the alternatives are ranked by the expected values of HNLNs and the similarity measure values between each alternative and the ideal alternative (ideal solution) to select the best one. An actual example on the selection problem of manufacturing alternatives is provided to demonstrate the applicability of the developed decision-making approaches. The decision results of manufacturing alternatives and the comparative analysis indicate that the proposed methods are effective and superior to existing ones. The MADM methods based on the expected value and the similarity measure can effectively deal with MADM problems with HNLN information and are more objective and more useful than the existing ones.
      PubDate: 2017-12-27
      DOI: 10.1007/s12559-017-9535-8
  • Emotional Human-Machine Conversation Generation Based on Long Short-Term
    • Authors: Xiao Sun; Xiaoqi Peng; Shuai Ding
      Abstract: Abstract With the rise in popularity of artificial intelligence, the technology of verbal communication between man and machine has received an increasing amount of attention, but generating a good conversation remains a difficult task. The key factor in human-machine conversation is whether the machine can give good responses that are appropriate not only at the content level (relevant and grammatical) but also at the emotion level (consistent emotional expression). In our paper, we propose a new model based on long short-term memory, which is used to achieve an encoder-decoder framework, and we address the emotional factor of conversation generation by changing the model’s input using a series of input transformations: a sequence without an emotional category, a sequence with an emotional category for the input sentence, and a sequence with an emotional category for the output responses. We perform a comparison between our work and related work and find that we can obtain slightly better results with respect to emotion consistency. Although in terms of content coherence our result is lower than those of related work, in the present stage of research, our method can generally generate emotional responses in order to control and improve the user’s emotion. Our experiment shows that through the introduction of emotional intelligence, our model can generate responses appropriate not only in content but also in emotion.
      PubDate: 2017-12-26
      DOI: 10.1007/s12559-017-9539-4
  • Extreme Learning Machines for VISualization+R: Mastering Visualization
           with Target Variables
    • Authors: Andrey Gritsenko; Anton Akusok; Stephen Baek; Yoan Miche; Amaury Lendasse
      Abstract: Abstract The current paper presents an improvement of the Extreme Learning Machines for VISualization (ELMVIS+) nonlinear dimensionality reduction method. In this improved method, called ELMVIS+R, it is proposed to apply the originally unsupervised ELMVIS+ method for the regression problems, using target values to improve visualization results. It has been shown in previous work that the approach of adding supervised component for classification problems indeed allows to obtain better visualization results. To verify this assumption for regression problems, a set of experiments on several different datasets was performed. The newly proposed method was compared to the ELMVIS+ method and, in most cases, outperformed the original algorithm. Results, presented in this article, prove the general idea that using supervised components (target values) with nonlinear dimensionality reduction method like ELMVIS+ can improve both visual properties and overall accuracy.
      PubDate: 2017-12-22
      DOI: 10.1007/s12559-017-9537-6
  • Hierarchical Convolutional Neural Networks for EEG-Based Emotion
    • Authors: Jinpeng Li; Zhaoxiang Zhang; Huiguang He
      Abstract: Abstract Traditional machine learning methods suffer from severe overfitting in EEG-based emotion reading. In this paper, we use hierarchical convolutional neural network (HCNN) to classify the positive, neutral, and negative emotion states. We organize differential entropy features from different channels as two-dimensional maps to train the HCNNs. This approach maintains information in the spatial topology of electrodes. We use stacked autoencoder (SAE), SVM, and KNN as competing methods. HCNN yields the highest accuracy, and SAE is slightly inferior. Both of them show absolute advantage over traditional shallow models including SVM and KNN. We confirm that the high-frequency wave bands Beta and Gamma are the most suitable bands for emotion reading. We visualize the hidden layers of HCNNs to investigate the feature transformation flow along the hierarchical structure. Benefiting from the strong representational learning capacity in the two-dimensional space, HCNN is efficient in emotion recognition especially on Beta and Gamma waves.
      PubDate: 2017-12-16
      DOI: 10.1007/s12559-017-9533-x
  • Implicit Heterogeneous Features Embedding in Deep Knowledge Tracing
    • Authors: Haiqin Yang; Lap Pong Cheung
      Abstract: Abstract Deep recurrent neural networks have been successfully applied to knowledge tracing, namely, deep knowledge tracing (DKT), which aims to automatically trace students’ knowledge states by mining their exercise performance data. Two main issues exist in the current DKT models: First, the complexity of the DKT models increases the tension of psychological interpretation. Second, the input of existing DKT models is only the exercise tags representing via one-hot encoding. The correlation between the hidden knowledge components and students’ responses to the exercises heavily relies on training the DKT models. The existing rich and informative features are excluded in the training, which may yield sub-optimal performance. To utilize the information embedded in these features, researchers have proposed a manual method to pre-process the features, i.e., discretizing them based on the inner characteristics of individual features. However, the proposed method requires many feature engineering efforts and is infeasible when the selected features are huge. To tackle the above issues, we design an automatic system to embed the heterogeneous features implicitly and effectively into the original DKT model. More specifically, we apply tree-based classifiers to predict whether the student can correctly answer the exercise given the heterogeneous features, an effective way to capture how the student deviates from others in the exercise. The predicted response and the true response are then encoded into a 4-bit one-hot encoding and concatenated with the original one-hot encoding features on the exercise tags to train a long short-term memory (LSTM) model, which can output the probability that a student will answer the exercise correctly on the corresponding exercise. We conduct a thorough evaluation on two educational datasets and demonstrate the merits and observations of our proposal.
      PubDate: 2017-12-15
      DOI: 10.1007/s12559-017-9522-0
  • Very Fast Semantic Image Segmentation Using Hierarchical Dilation and
           Feature Refining
    • Authors: Qingqun Ning; Jianke Zhu; Chun Chen
      Abstract: Abstract With the rapid development of deep learning techniques, semantic image segmentation has been considerably improved recently, which is viewed as the key problem of scene understanding in computer vision. These advances are built upon the capability of complex architectures for deep neural network. In this paper, we present a novel deep neural network architecture designed for semantic image segmentation. In order to improve the segmentation accuracy, we introduce a novel hierarchical dilation block to effectively enlarge the size of receptive field and enable multi-scale processing in fully convolutional neural network. Moreover, we exploit the technique of bypass and intermediate supervision to capture the context information during upsampling and refining coarse features. We have conducted extensive experiments on several popular semantic segmentation testbeds, including Cityscapes, CamVid, Kitti, and Helen facial datasets. The experimental results demonstrate that our proposed approach runs two times faster than the state-of-the-art method. Our full system is able to obtain realtime inference performance on 1080P images using a PC with single GPU. It executes a network forwarding at 200fps in our experiment while retaining high accuracy. Our proposed approach not only runs faster than the existing realtime methods but also performs on par with them.
      PubDate: 2017-12-05
      DOI: 10.1007/s12559-017-9530-0
  • Cognitive Fusion of Thermal and Visible Imagery for Effective Detection
           and Tracking of Pedestrians in Videos
    • Authors: Yijun Yan; Jinchang Ren; Huimin Zhao; Genyun Sun; Zheng Wang; Jiangbin Zheng; Stephen Marshall; John Soraghan
      Abstract: Abstract In this paper, we present an efficient framework to cognitively detect and track salient objects from videos. In general, colored visible image in red-green-blue (RGB) has better distinguishability in human visual perception, yet it suffers from the effect of illumination noise and shadows. On the contrary, the thermal image is less sensitive to these noise effects though its distinguishability varies according to environmental settings. To this end, cognitive fusion of these two modalities provides an effective solution to tackle this problem. First, a background model is extracted followed by a two-stage background subtraction for foreground detection in visible and thermal images. To deal with cases of occlusion or overlap, knowledge-based forward tracking and backward tracking are employed to identify separate objects even the foreground detection fails. To evaluate the proposed method, a publicly available color-thermal benchmark dataset Object Tracking and Classification in and Beyond the Visible Spectrum is employed here. For our foreground detection evaluation, objective and subjective analysis against several state-of-the-art methods have been done on our manually segmented ground truth. For our object tracking evaluation, comprehensive qualitative experiments have also been done on all video sequences. Promising results have shown that the proposed fusion-based approach can successfully detect and track multiple human objects in most scenes regardless of any light change or occlusion problem.
      PubDate: 2017-12-04
      DOI: 10.1007/s12559-017-9529-6
  • Learning Optimal Seeds for Ranking Saliency
    • Authors: Huiling Wang; Lixiang Xu; Xiaofeng Wang; Bin Luo
      Abstract: Abstract A variety of methods have been developed for visual saliency analysis, and it is a challenge to detect the most important scene from the input image. In this paper, to improve the shortage that the spatial connectivity of every node in model only via the k-regular graph and the idealistic boundary prior assumption is used in graph-based manifold ranking, we present a new optimal seed method to get saliency map. First, we evaluate the salience value of each region by global contrast-based spatial and color feature. Second, the salience values of the first stage are used to optimize the background and foreground queries (seeds); meanwhile, we tackle boundary cues from hierarchical graph to optimize background seeds. Then, we derive each stage saliency measure by the classical manifold ranking after obtaining optimal seeds. Finally, the final saliency map is obtained by combining the saliency results of two stages. Our algorithm is tested on the five public datasets and compared with nine state-of-the-art methods; the quantitative evaluation indicates that our method is effective and efficient. Our method can handle complex images with different details and can produce more accurate saliency maps than other state-of-the-art approaches.
      PubDate: 2017-12-01
      DOI: 10.1007/s12559-017-9528-7
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