Subjects -> ENGINEERING (Total: 2918 journals)
    - CHEMICAL ENGINEERING (259 journals)
    - CIVIL ENGINEERING (255 journals)
    - ELECTRICAL ENGINEERING (182 journals)
    - ENGINEERING (1464 journals)
    - ENGINEERING MECHANICS AND MATERIALS (476 journals)
    - HYDRAULIC ENGINEERING (60 journals)
    - INDUSTRIAL ENGINEERING (101 journals)
    - MECHANICAL ENGINEERING (121 journals)

ENGINEERING (1464 journals)                  1 2 3 4 5 6 7 8 | Last

Showing 1 - 200 of 1205 Journals sorted alphabetically
3 Biotech     Open Access   (Followers: 9)
3D Research     Hybrid Journal   (Followers: 22)
AAPG Bulletin     Hybrid Journal   (Followers: 11)
Abstract and Applied Analysis     Open Access   (Followers: 4)
Aceh International Journal of Science and Technology     Open Access   (Followers: 9)
ACS Nano     Hybrid Journal   (Followers: 448)
Acta Geotechnica     Hybrid Journal   (Followers: 7)
Acta Metallurgica Sinica (English Letters)     Hybrid Journal   (Followers: 10)
Acta Nova     Open Access   (Followers: 1)
Acta Polytechnica : Journal of Advanced Engineering     Open Access   (Followers: 4)
Acta Scientiarum. Technology     Open Access   (Followers: 3)
Acta Universitatis Cibiniensis. Technical Series     Open Access   (Followers: 1)
Active and Passive Electronic Components     Open Access   (Followers: 8)
Adaptive Behavior     Hybrid Journal   (Followers: 9)
Adsorption     Hybrid Journal   (Followers: 5)
Advanced Energy and Sustainability Research     Open Access   (Followers: 7)
Advanced Engineering Forum     Full-text available via subscription   (Followers: 14)
Advanced Engineering Research     Open Access  
Advanced Journal of Graduate Research     Open Access   (Followers: 4)
Advanced Quantum Technologies     Hybrid Journal   (Followers: 1)
Advanced Science     Open Access   (Followers: 13)
Advanced Science Focus     Free   (Followers: 7)
Advanced Science Letters     Full-text available via subscription   (Followers: 13)
Advanced Science, Engineering and Medicine     Partially Free   (Followers: 11)
Advanced Synthesis & Catalysis     Hybrid Journal   (Followers: 20)
Advanced Theory and Simulations     Hybrid Journal   (Followers: 5)
Advances in Catalysis     Full-text available via subscription   (Followers: 8)
Advances in Complex Systems     Hybrid Journal   (Followers: 12)
Advances in Engineering Software     Hybrid Journal   (Followers: 31)
Advances in Fuel Cells     Full-text available via subscription   (Followers: 20)
Advances in Fuzzy Systems     Open Access   (Followers: 5)
Advances in Geosciences (ADGEO)     Open Access   (Followers: 22)
Advances in Heat Transfer     Full-text available via subscription   (Followers: 30)
Advances in Human Factors/Ergonomics     Full-text available via subscription   (Followers: 27)
Advances in Magnetic and Optical Resonance     Full-text available via subscription   (Followers: 10)
Advances in Natural Sciences : Nanoscience and Nanotechnology     Open Access   (Followers: 36)
Advances in Operations Research     Open Access   (Followers: 14)
Advances in OptoElectronics     Open Access   (Followers: 6)
Advances in Physics Theories and Applications     Open Access   (Followers: 21)
Advances in Polymer Science     Hybrid Journal   (Followers: 53)
Advances in Porous Media     Full-text available via subscription   (Followers: 6)
Advances in Remote Sensing     Open Access   (Followers: 58)
Advances in Science and Research (ASR)     Open Access   (Followers: 8)
Aerobiologia     Hybrid Journal   (Followers: 4)
Aerospace Systems     Hybrid Journal   (Followers: 10)
African Journal of Science, Technology, Innovation and Development     Hybrid Journal   (Followers: 8)
AIChE Journal     Hybrid Journal   (Followers: 38)
Ain Shams Engineering Journal     Open Access   (Followers: 7)
Al-Nahrain Journal for Engineering Sciences     Open Access  
Al-Qadisiya Journal for Engineering Sciences     Open Access   (Followers: 2)
AL-Rafdain Engineering Journal     Open Access   (Followers: 3)
Alexandria Engineering Journal     Open Access   (Followers: 3)
AMB Express     Open Access   (Followers: 1)
American Journal of Applied Sciences     Open Access   (Followers: 27)
American Journal of Engineering and Applied Sciences     Open Access   (Followers: 12)
American Journal of Engineering Education     Open Access   (Followers: 20)
American Journal of Environmental Engineering     Open Access   (Followers: 16)
American Journal of Industrial and Business Management     Open Access   (Followers: 31)
Annals of Civil and Environmental Engineering     Open Access   (Followers: 3)
Annals of Combinatorics     Hybrid Journal   (Followers: 3)
Annals of Pure and Applied Logic     Open Access   (Followers: 6)
Annals of Regional Science     Hybrid Journal   (Followers: 10)
Annals of Science     Hybrid Journal   (Followers: 10)
Annual Journal of Technical University of Varna     Open Access   (Followers: 1)
Antarctic Science     Hybrid Journal   (Followers: 1)
Applicable Algebra in Engineering, Communication and Computing     Hybrid Journal   (Followers: 3)
Applicable Analysis: An International Journal     Hybrid Journal   (Followers: 2)
Applications in Energy and Combustion Science     Open Access   (Followers: 2)
Applications in Engineering Science     Open Access   (Followers: 1)
Applied Catalysis A: General     Hybrid Journal   (Followers: 8)
Applied Catalysis B: Environmental     Hybrid Journal   (Followers: 22)
Applied Clay Science     Hybrid Journal   (Followers: 6)
Applied Computational Intelligence and Soft Computing     Open Access   (Followers: 16)
Applied Engineering Letters     Open Access   (Followers: 4)
Applied Magnetic Resonance     Hybrid Journal   (Followers: 4)
Applied Nanoscience     Open Access   (Followers: 11)
Applied Network Science     Open Access   (Followers: 3)
Applied Numerical Mathematics     Hybrid Journal   (Followers: 6)
Applied Physics Research     Open Access   (Followers: 7)
Applied Sciences     Open Access   (Followers: 6)
Applied Spatial Analysis and Policy     Hybrid Journal   (Followers: 6)
Arab Journal of Basic and Applied Sciences     Open Access  
Arabian Journal for Science and Engineering     Hybrid Journal   (Followers: 5)
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 6)
Archives of Thermodynamics     Open Access   (Followers: 13)
Arctic     Open Access   (Followers: 7)
Arid Zone Journal of Engineering, Technology and Environment     Open Access   (Followers: 2)
Arkiv för Matematik     Hybrid Journal   (Followers: 1)
ArtefaCToS : Revista de estudios sobre la ciencia y la tecnología     Open Access   (Followers: 1)
Asia-Pacific Journal of Science and Technology     Open Access  
Asian Engineering Review     Open Access  
Asian Journal of Applied Science and Engineering     Open Access   (Followers: 2)
Asian Journal of Applied Sciences     Open Access   (Followers: 2)
Asian Journal of Biotechnology     Open Access   (Followers: 9)
Asian Journal of Control     Hybrid Journal  
Asian Journal of Technology Innovation     Hybrid Journal   (Followers: 7)
Assembly Automation     Hybrid Journal   (Followers: 2)
ATZagenda     Hybrid Journal  
ATZextra worldwide     Hybrid Journal  
AURUM : Mühendislik Sistemleri ve Mimarlık Dergisi = Aurum Journal of Engineering Systems and Architecture     Open Access   (Followers: 1)
Australasian Journal of Engineering Education     Hybrid Journal   (Followers: 3)
Australasian Physical & Engineering Sciences in Medicine     Hybrid Journal   (Followers: 1)
Australian Journal of Multi-Disciplinary Engineering     Hybrid Journal   (Followers: 2)
Autocracy : Jurnal Otomasi, Kendali, dan Aplikasi Industri     Open Access  
Automotive and Engine Technology     Hybrid Journal  
Automotive Experiences     Open Access  
Automotive Innovation     Hybrid Journal   (Followers: 1)
Avances en Ciencias e Ingenierías     Open Access  
Avances: Investigación en Ingeniería     Open Access   (Followers: 6)
Balkan Region Conference on Engineering and Business Education     Open Access   (Followers: 2)
Bangladesh Journal of Scientific and Industrial Research     Open Access  
Basin Research     Hybrid Journal   (Followers: 6)
Batteries     Open Access   (Followers: 11)
Batteries & Supercaps     Hybrid Journal   (Followers: 7)
Bautechnik     Hybrid Journal   (Followers: 3)
Bell Labs Technical Journal     Hybrid Journal   (Followers: 29)
Beni-Suef University Journal of Basic and Applied Sciences     Open Access   (Followers: 3)
Beyond : Undergraduate Research Journal     Open Access  
Bhakti Persada : Jurnal Aplikasi IPTEKS     Open Access  
Bharatiya Vaigyanik evam Audyogik Anusandhan Patrika (BVAAP)     Open Access   (Followers: 1)
Bilge International Journal of Science and Technology Research     Open Access   (Followers: 1)
Biointerphases     Open Access   (Followers: 1)
Biomaterials Science     Full-text available via subscription   (Followers: 14)
Biomedical Engineering     Hybrid Journal   (Followers: 15)
Biomedical Engineering and Computational Biology     Open Access   (Followers: 13)
Biomedical Engineering Letters     Hybrid Journal   (Followers: 5)
Biomedical Engineering: Applications, Basis and Communications     Hybrid Journal   (Followers: 5)
Biomedical Microdevices     Hybrid Journal   (Followers: 8)
Biomedical Science and Engineering     Open Access   (Followers: 7)
Biomicrofluidics     Open Access   (Followers: 7)
Biotechnology Progress     Hybrid Journal   (Followers: 44)
Black Sea Journal of Engineering and Science     Open Access  
Botswana Journal of Technology     Full-text available via subscription   (Followers: 1)
Boundary Value Problems     Open Access   (Followers: 1)
Brazilian Journal of Science and Technology     Open Access   (Followers: 2)
Bulletin of Canadian Petroleum Geology     Full-text available via subscription   (Followers: 13)
Bulletin of Engineering Geology and the Environment     Hybrid Journal   (Followers: 15)
Bulletin of the Crimean Astrophysical Observatory     Hybrid Journal  
Cahiers Droit, Sciences & Technologies     Open Access   (Followers: 1)
Calphad     Hybrid Journal   (Followers: 2)
Canadian Geotechnical Journal     Hybrid Journal   (Followers: 30)
Canadian Journal of Remote Sensing     Full-text available via subscription   (Followers: 50)
Carbon Resources Conversion     Open Access   (Followers: 3)
Carpathian Journal of Electronic and Computer Engineering     Open Access  
Case Studies in Engineering Failure Analysis     Open Access   (Followers: 6)
Case Studies in Thermal Engineering     Open Access   (Followers: 8)
Catalysis Communications     Hybrid Journal   (Followers: 7)
Catalysis Letters     Hybrid Journal   (Followers: 3)
Catalysis Reviews: Science and Engineering     Hybrid Journal   (Followers: 9)
Catalysis Science and Technology     Hybrid Journal   (Followers: 13)
Catalysis Surveys from Asia     Hybrid Journal   (Followers: 4)
Catalysis Today     Hybrid Journal   (Followers: 8)
CEAS Space Journal     Hybrid Journal   (Followers: 6)
Cell Reports Physical Science     Open Access  
Cellular and Molecular Neurobiology     Hybrid Journal   (Followers: 2)
Central European Journal of Engineering     Hybrid Journal  
Chaos : An Interdisciplinary Journal of Nonlinear Science     Hybrid Journal   (Followers: 3)
Chaos, Solitons & Fractals     Hybrid Journal   (Followers: 3)
Chaos, Solitons & Fractals : X     Open Access   (Followers: 1)
Chinese Journal of Catalysis     Full-text available via subscription   (Followers: 3)
Chinese Journal of Engineering     Open Access   (Followers: 2)
Chinese Journal of Population, Resources and Environment     Open Access  
Chinese Science Bulletin     Open Access   (Followers: 1)
Ciencia e Ingenieria Neogranadina     Open Access  
Ciencia en su PC     Open Access   (Followers: 1)
Ciencia y Tecnología     Open Access  
Ciencias Holguin     Open Access   (Followers: 2)
CienciaUAT     Open Access   (Followers: 1)
Cientifica     Open Access  
CIRP Annals - Manufacturing Technology     Hybrid Journal   (Followers: 11)
CIRP Journal of Manufacturing Science and Technology     Hybrid Journal   (Followers: 14)
City, Culture and Society     Hybrid Journal   (Followers: 27)
Clay Minerals     Hybrid Journal   (Followers: 9)
Coal Science and Technology     Full-text available via subscription   (Followers: 4)
Coastal Engineering     Hybrid Journal   (Followers: 14)
Coastal Engineering Journal     Hybrid Journal   (Followers: 9)
Coastal Engineering Proceedings : Proceedings of the International Conference on Coastal Engineering     Open Access   (Followers: 2)
Coastal Management     Hybrid Journal   (Followers: 30)
Coatings     Open Access   (Followers: 4)
Cogent Engineering     Open Access   (Followers: 3)
Cognitive Computation     Hybrid Journal   (Followers: 3)
Color Research & Application     Hybrid Journal   (Followers: 4)
COMBINATORICA     Hybrid Journal  
Combustion Theory and Modelling     Hybrid Journal   (Followers: 17)
Combustion, Explosion, and Shock Waves     Hybrid Journal   (Followers: 20)
Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering     Open Access  
Communications in Numerical Methods in Engineering     Hybrid Journal   (Followers: 2)
Components, Packaging and Manufacturing Technology, IEEE Transactions on     Hybrid Journal   (Followers: 28)
Composite Interfaces     Hybrid Journal   (Followers: 10)
Composite Structures     Hybrid Journal   (Followers: 334)
Composites Part A : Applied Science and Manufacturing     Hybrid Journal   (Followers: 275)
Composites Part B : Engineering     Hybrid Journal   (Followers: 311)
Composites Part C : Open Access     Open Access   (Followers: 3)
Composites Science and Technology     Hybrid Journal   (Followers: 245)
Comptes Rendus : Mécanique     Open Access   (Followers: 2)
Computation     Open Access   (Followers: 1)
Computational Geosciences     Hybrid Journal   (Followers: 20)
Computational Optimization and Applications     Hybrid Journal   (Followers: 11)
Computer Applications in Engineering Education     Hybrid Journal   (Followers: 6)
Computer Science and Engineering     Open Access   (Followers: 20)

        1 2 3 4 5 6 7 8 | Last

Similar Journals
Journal Cover
Cognitive Computation
Journal Prestige (SJR): 0.908
Citation Impact (citeScore): 4
Number of Followers: 3  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1866-9964 - ISSN (Online) 1866-9956
Published by Springer-Verlag Homepage  [2658 journals]
  • Idempotent Computing Rules and Novel Comparative Laws for Hesitant Fuzzy
           Cognitive Information and Their Application to Multiattribute Decision
           Making

    • Free pre-print version: Loading...

      Abstract: Hesitant fuzzy cognitive information provides an effective and convenient form for expressing human subjective cognition about the research object, i.e., hesitant fuzzy sets (HFSs). Studies on decision making with HFSs have become an important branch in decision theory, in which operational laws of hesitant fuzzy elements (HFEs) play a core role in the solution. However, current HFE computational laws have the disadvantages of subjectivity and dimensional problems. Therefore, how to define an objective HFE operational rule without dimensional problems is an open issue. This paper introduces an idempotent HFE computing rule to overcome current disadvantages. The weighted mean of HFEs under the developed computing rule is further discussed. In addition, a novel comparison law between HFEs is proposed. The property of idempotence is introduced to provide an intuitive integrated result of HFEs. To decrease the integrated HFEs dimensions, the sliding window model is utilized. Fundamental mathematical properties of the developed operations are discussed. Furthermore, the normal weighted means of HFEs are extended by using the developed idempotent computing rules. Finally, a novel comparison law for comparing HFEs is designed, which is further used to provide a multiattribute decision procedure. Additive idempotent is developed as a special and intuitive property for the HFE additive operation. Normal weighted means of HFEs, including arithmetic and geometric means, are correspondingly derived. Numerical examples have shown that the proposed HFE operational laws are valid, which can effectively decrease the dimensions of integrated results. The developed idempotent computing rules provide a novel HFE algebra structure, which includes the additive operation, multiplicative operation, scalar multiplication and power operation. By using the sliding window model, the developed idempotent computing rules can effectively reduce the integrated HFEs dimensions. The strength of the developed computational model is that integrating two identical pieces of cognitive information produces the same result. In addition, the modified HFE comparison law can overcome the drawback of current comparison laws, and a much more reasonable comparison result can be obtained.
      PubDate: 2021-10-12
       
  • neurolib: A Simulation Framework for Whole-Brain Neural Mass Modeling

    • Free pre-print version: Loading...

      Abstract: neurolib is a computational framework for whole-brain modeling written in Python. It provides a set of neural mass models that represent the average activity of a brain region on a mesoscopic scale. In a whole-brain network model, brain regions are connected with each other based on biologically informed structural connectivity, i.e., the connectome of the brain. neurolib can load structural and functional datasets, set up a whole-brain model, manage its parameters, simulate it, and organize its outputs for later analysis. The activity of each brain region can be converted into a simulated BOLD signal in order to calibrate the model against empirical data from functional magnetic resonance imaging (fMRI). Extensive model analysis is made possible using a parameter exploration module, which allows one to characterize a model’s behavior as a function of changing parameters. An optimization module is provided for fitting models to multimodal empirical data using evolutionary algorithms. neurolib is designed to be extendable and allows for easy implementation of custom neural mass models, offering a versatile platform for computational neuroscientists for prototyping models, managing large numerical experiments, studying the structure–function relationship of brain networks, and for performing in-silico optimization of whole-brain models.
      PubDate: 2021-10-12
       
  • Consensus Building in Multi-criteria Group Decision-Making with
           Single-Valued Neutrosophic Sets

    • Free pre-print version: Loading...

      Abstract: In order to obtain high satisfaction from experts, the consensus reaching process (CRP) is an essential requirement for dealing with multi-criteria group decision making (MCGDM) problems. Single-valued neutrosophic number (SVNN) is an effective tool to describe the uncertainty of the expert cognition. Thus, we develop a consensus reaching model for single-valued neutrosophic MCGDM in this paper. First, each expert makes his/her judgment on each alternative with respect to multiple criteria by SVNNs, and the group solution is obtained by the generalized Shapley single-valued neutrosophic Choquet integral (GS-SVNCI) operator to consider the correlations among elements comprehensively. Second, the projection-based consensus measure is proposed to reflect the agreement between the individual and collective opinions. Then, a threshold value is used to determine the CRP whether to be executed based on the expert’s consensus level. If yes, the feedback mechanism provides the experts with personalized adjustment advices based on their psychic utility to group pressure. Finally, we illustrate the feasibility of the proposed consensus model by an example and analyze the superiority by comparing with some existing MCGDM methods and different CRP models. The developed consensus model can consider interrelationships between experts, which is more effective and reasonable to obtain the collective resolution. Further, the consensus measure based on the projection can comprehensively reflect the closeness between the individual and collective opinions. In addition, the personalized adjustment advices considering the experts’ psychic utility to group pressure improve their acceptance of these advices.
      PubDate: 2021-10-11
       
  • An Improved Neuro-fuzzy Generalized Predictive Control of
           Ultra-supercritical Power Plant

    • Free pre-print version: Loading...

      Abstract: A generalized predictive control method based on neuro-fuzzy network (NFN-GPC) is presented for a 1000-MW ultra-supercritical (USC) power plant to improve control performance. First, to decrease the nonlinearity, local linear models are elaborately constructed for approximating the studied system by virtue of neuro-fuzzy network (NFN). Second, a compensation mechanism is delicately developed to further increase the accuracy of local models. Through gauss function and B-spline function, the memberships of local models which represent the weights of local regions are determined. Finally, based on the obtained models, a multi-variable generalized predictive controller is designed to realize the optimal control over the whole operating region combined with the membership of the current neuro-fuzzy network. This scheme closely connects engineering with artificial intelligence; compared with traditional generalized predictive control, the merit of proposed NFN-GPC is that it can capture the details over the whole operating range which can get more accurate and faster control effects. The simulation results show that the proposed neuro-fuzzy generalized predictive control method can achieve the satisfactory performance even in the case of strong coupling and nonlinearity. In conclusion, the proposed method is an effective long-term approach to control the USC power plant.
      PubDate: 2021-10-08
       
  • The Effect of Fatigue on the Performance of Online Writer Recognition

    • Free pre-print version: Loading...

      Abstract: The performance of biometric modalities based on things done by the subject, like signature and text-based recognition, may be affected by the subject’s state. Fatigue is one of the conditions that can significantly affect the outcome of handwriting tasks. Recent research has already shown that physical fatigue produces measurable differences in some features extracted from common writing and drawing tasks. It is important to establish to which extent physical fatigue contributes to the intra-person variability observed in these biometric modalities and also to know whether the performance of recognition methods is affected by fatigue. In this paper, we assess the impact of fatigue on intra-user variability and on the performance of signature-based and text-based writer recognition approaches encompassing both identification and verification. Several signature and text recognition methods are considered and applied to samples gathered after different levels of induced fatigue, measured by metabolic and mechanical assessment and also by subjective perception. The recognition methods are dynamic time warping and multi-section vector quantization, for signatures, and allographic text-dependent recognition for text in capital letters. For each fatigue level, the identification and verification performance of these methods is measured. Signature shows no statistically significant intra-user impact, but text does. On the other hand, performance of signature-based recognition approaches is negatively impacted by fatigue, whereas the impact is not noticeable in text-based recognition, provided long enough sequences are considered.
      PubDate: 2021-10-03
       
  • Decoding Premovement Patterns with Task-Related Component Analysis

    • Free pre-print version: Loading...

      Abstract: Noninvasive brain–computer interface (BCI)-based electroencephalograms (EEGs) have made great progress in cognitive activities detection. However, the decoding of premovements from EEG signals remains a challenge for noninvasive BCI. This work aims to decode human intention (movement or rest) before movement onset from EEG signals. We propose to decode premovement patterns from movement-related cortical potential activities with task-related component analysis and canonical correlation patterns (TRCA+CCPs). Specifically, we first optimize the MRCP data with the spatial filter TRCA. CCPs are then extracted from the optimized signals. The extracted CCPs are classified with the linear discriminated analysis classifier. We applied the classification in a sliding window, which changes from readiness potential (RP section) to movement-monitoring potential (MMP section). The classification result on event-related desynchronization (ERD) indicates that the motor cortex becomes active as the limbs move. When applying classification between elbow flexion and rest, the proposed TRCA+CCP method achieves an accuracy of 0.9001±0.0997 in the RP section. The previous methods, discriminative canonical pattern matching + common spatial pattern (DCPM+CSP) and the optimized DCPM+CSP method, exhibit accuracy values of 0.7827± 0.1276 and 0.8141±0.1295 for the RP section, respectively. Compared with these methods, the proposed TRCA+CCP method achieves higher average accuracy in the RP section. The proposed TRCA+CCP method can decode the patterns in the RP section efficiently, which indicates that the premovement patterns in EEG signals can be decoded before execution of the movement. The system is expected to assist movement detection in ERD analysis.
      PubDate: 2021-10-02
       
  • Online Handwriting, Signature and Touch Dynamics: Tasks and Potential
           Applications in the Field of Security and Health

    • Free pre-print version: Loading...

      Abstract: Advantageous property of behavioural signals (e.g. handwriting), in contrast to morphological ones (e.g. iris, fingerprint, hand geometry), is the possibility to ask a user to perform many different tasks. This article summarises recent findings and applications of different handwriting/drawing tasks in the field of security and health. More specifically, it is focused on on-line handwriting and hand-based interaction, i.e. signals that utilise a digitizing device (specific devoted or general-purpose tablet/smartphone) during the realization of the tasks. Such devices permit the acquisition of on-surface dynamics as well as in-air movements in time, thus providing complex and richer information when compared to the conventional “pen and paper” method. Although the scientific literature reports a wide range of tasks and applications, in this paper, we summarize only those providing competitive results (e.g. in terms of discrimination power) and having a significant impact in the field.
      PubDate: 2021-10-01
       
  • Training Affective Computer Vision Models by Crowdsourcing Soft-Target
           Labels

    • Free pre-print version: Loading...

      Abstract: Emotion detection classifiers traditionally predict discrete emotions. However, emotion expressions are often subjective, thus requiring a method to handle compound and ambiguous labels. We explore the feasibility of using crowdsourcing to acquire reliable soft-target labels and evaluate an emotion detection classifier trained with these labels. We hypothesize that training with labels that are representative of the diversity of human interpretation of an image will result in predictions that are similarly representative on a disjoint test set. We also hypothesize that crowdsourcing can generate distributions which mirror those generated in a lab setting. We center our study on the Child Affective Facial Expression (CAFE) dataset, a gold standard collection of images depicting pediatric facial expressions along with 100 human labels per image. To test the feasibility of crowdsourcing to generate these labels, we used Microworkers to acquire labels for 207 CAFE images. We evaluate both unfiltered workers and workers selected through a short crowd filtration process. We then train two versions of a ResNet-152 neural network on soft-target CAFE labels using the original 100 annotations provided with the dataset: (1) a classifier trained with traditional one-hot encoded labels and (2) a classifier trained with vector labels representing the distribution of CAFE annotator responses. We compare the resulting softmax output distributions of the two classifiers with a 2-sample independent t-test of L1 distances between the classifier’s output probability distribution and the distribution of human labels. While agreement with CAFE is weak for unfiltered crowd workers, the filtered crowd agree with the CAFE labels 100% of the time for happy, neutral, sad, and “fear + surprise” and 88.8% for “anger + disgust.” While the F1-score for a one-hot encoded classifier is much higher (94.33% vs. 78.68%) with respect to the ground truth CAFE labels, the output probability vector of the crowd-trained classifier more closely resembles the distribution of human labels (t = 3.2827, p = 0.0014). For many applications of affective computing, reporting an emotion probability distribution that accounts for the subjectivity of human interpretation can be more useful than an absolute label. Crowdsourcing, including a sufficient filtering mechanism for selecting reliable crowd workers, is a feasible solution for acquiring soft-target labels.
      PubDate: 2021-09-27
       
  • A Novel Multi-attribute Group Decision-Making Method Based on q-Rung Dual
           Hesitant Fuzzy Information and Extended Power Average Operators

    • Free pre-print version: Loading...

      Abstract: Multi-attribute group decision-making (MAGDM) is one of the most active research fields in modern social cognition and decision science theory. An obvious challenge in MAGDM problems is that it is not easy to appropriately describe decision-makers’ (DMs’) cognitive information, which is because the cognition of DMs is usually diverse and contains a lot of uncertainties and fuzziness. The recently proposed q-rung dual hesitant fuzzy set (q-RDHFS), encompassing diverse cognition and providing wide information space, has been proved to be effective to depict DMs’ cognitive information in the MAGDM process. However, existing operations and aggregation operators of q-RDHFSs still have limitations, which result in the weakness of existing q-RDHFS-based MAGDM methods. Therefore, this paper aims at introducing new operational rules and aggregation operators to deal with q-rung dual hesitant fuzzy information. To this end, we first propose a wide range of generalized operations for q-rung dual hesitant fuzzy elements (q-RDHFEs) based on Archimedean t-norm and t-conorm. Second, we put forward some new aggregation operators by generalizing the recently invented extended power average (EPA) operator into q-RDHFSs. Existing literature has revealed the powerfulness and flexibility of the EPA operator over the classical power average operator. Finally, a new MAGDM approach based on the proposed operators is developed. Our proposed method can effectively handle MAGDM problems with q-rung dual hesitant fuzzy cognitive information. Some numerical examples are conducted to demonstrate the validity of the new MAGDM method. Further, we conduct parameter analysis and comparative analysis to prove the flexibility and superiority of our proposed MAGDM method, respectively. In a word, this paper contributes to a new q-rung dual hesitant fuzzy MAGDM method, which absorbs the advantages of EPA operator and Archimedean operations. This method can be applied to describe complex cognitive information and solving realistic MAGDM problems effectively.
      PubDate: 2021-09-25
       
  • Real-Time Lane Detection by Using Biologically Inspired Attention
           Mechanism to Learn Contextual Information

    • Free pre-print version: Loading...

      Abstract: Background State-of-the-art lane detection methods have achieved prominent performance in complex scenarios, but many limits have also existed. For example, only a fixed number of lanes can be detected, and the cost of detection time is unaffordable in many cases. Methods Inspired by human vision, attention mechanism makes network learning more concerned features. In this paper, we propose a real-time lane detection method by using attention mechanism. The network proposed consists of three modules: an encoder module that extracts the feature of lanes; the instance feature maps of lanes are predicted by two decoder modules, namely binary decoder and embeddable decoder. In the encoder, we use the biologically inspired attention to extract features, which contain many details of the target area. The correlation between the features obtained from the convolutions and that extracted by the attention is established to learn the contextual information. In the decoder, the contextual information is fused with the features from up-sampling, to compensate for the lost detailed information. Binary decoder classifies all the pixels into lane or background. Embeddable decoder obtains the distinguishable lanes. And then, the outputs of the binary decoder serve as one of the inputs to the embeddable decoder to guiding the generation of exact pixel points on the lanes. Results Comparative experiments on two benchmarks (TuSimple and Caltech lanes datasets) show that the proposed method is independent of lane number and lane pattern. It can handle an indefinite number of lanes and run at 10ms in the TuSimple dataset. Conclusions Experiments verify that our method outperforms a lot of state-of-the-art methods while maintaining a real-time performance.
      PubDate: 2021-09-24
       
  • Recognizing Emotion Cause in Conversations

    • Free pre-print version: Loading...

      Abstract: We address the problem of recognizing emotion cause in conversations, define two novel sub-tasks of this problem, and provide a corresponding dialogue-level dataset, along with strong transformer-based baselines. The dataset is available at https://github.com/declare-lab/RECCON. Recognizing the cause behind emotions in text is a fundamental yet under-explored area of research in NLP. Advances in this area hold the potential to improve interpretability and performance in affect-based models. Identifying emotion causes at the utterance level in conversations is particularly challenging due to the intermingling dynamics among the interlocutors. We introduce the task of Recognizing Emotion Cause in CONversations with an accompanying dataset named RECCON, containing over 1,000 dialogues and 10,000 utterance cause/effect pairs. Furthermore, we define different cause types based on the source of the causes, and establish strong Transformer-based baselines to address two different sub-tasks on this dataset. Our transformer-based baselines, which leverage contextual pre-trained embeddings, such as RoBERTa, outperform the state-of-the-art emotion cause extraction approaches on our dataset. We introduce a new task highly relevant for (explainable) emotion-aware artificial intelligence: recognizing emotion cause in conversations, provide a new highly challenging publicly available dialogue-level dataset for this task, and give strong baseline results on this dataset.
      PubDate: 2021-09-13
       
  • Binary Chimp Optimization Algorithm (BChOA): a New Binary Meta-heuristic
           for Solving Optimization Problems

    • Free pre-print version: Loading...

      Abstract: Chimp optimization algorithm (ChOA) is a newly proposed meta-heuristic algorithm inspired by chimps’ individual intelligence and sexual motivation in their group hunting. The preferable performance of ChOA has been approved among other well-known meta-heuristic algorithms. However, its continuous nature makes it unsuitable for solving binary problems. Therefore, this paper proposes a novel binary version of ChOA and attempts to prove that the transfer function is the most important part of binary algorithms. Therefore, four S-shaped and V-shaped transfer functions, as well as a novel binary approach, have been utilized to investigate the efficiency of binary ChOAs (BChOA) in terms of convergence speed and local minima avoidance. In this regard, forty-three unimodal, multimodal, and composite optimization functions and ten IEEE CEC06-2019 benchmark functions were utilized to evaluate the efficiency of BChOAs. Furthermore, to validate the performance of BChOAs, four newly proposed binary optimization algorithms were compared with eighteen novel state-of-the-art algorithms. The results indicate that both the novel binary approach and V-shaped transfer functions improve the efficiency of BChOAs in a statistically significant way.
      PubDate: 2021-09-13
       
  • Three-way Bayesian Confirmation in Classifications

    • Free pre-print version: Loading...

      Abstract: Bayesian confirmation provides a practical approach to reasoning about the truth of hypotheses based on the observation of evidence. It has been applied in many topics closely related to cognitive computing, such as decision makings and problem solving, especially those involving learning and reasoning based on the descriptions of objects. This paper investigates the application of Bayesian confirmation into classification which is a basis in many cognitive computing topics. Bayesian confirmation measures are adopted to evaluate the degree to which a description of objects (i.e., a piece of evidence) confirms the belongingness of the objects to a given class (i.e., a hypothesis). Accordingly, a description space is divided into three regions of confirmatory, disconfirmatory, and neutral descriptions, formulating a three-way Bayesian confirmation model. Based on a sequence of description spaces induced by either attributes or attribute–value pairs, the neutral regions can be further refined, which leads to a sequential model. Furthermore, with a discussion on constructing meaningful trisections of attributes or attribute–value pairs according to their utility, we present a three-level three-way Bayesian confirmation framework where each level focuses on one set in a trisection. Due to their different utility levels, the three sets in a trisection are used in different appropriate ways in constructing the three levels. Bayesian confirmation provides a meaningful perspective of evaluating descriptions in the context of classifications. This work may bring new insights into research on related topics such as decision makings, three-level analysis, rough set theory, and concept analysis.
      PubDate: 2021-09-13
       
  • MTFFNet: a Multi-task Feature Fusion Framework for Chinese Painting
           Classification

    • Free pre-print version: Loading...

      Abstract:   Different artists have their unique painting styles, which can be hardly recognized by ordinary people without professional knowledge. How to intelligently analyze such artistic styles via underlying features remains to be a challenging research problem. In this paper, we propose a novel multi-task feature fusion architecture (MTFFNet), for cognitive classification of traditional Chinese paintings. Specifically, by taking the full advantage of the pre-trained DenseNet as backbone, MTFFNet benefits from the fusion of two different types of feature information: semantic and brush stroke features. These features are learned from the RGB images and auxiliary gray-level co-occurrence matrix (GLCM) in an end-to-end manner, to enhance the discriminative power of the features for the first time. Through abundant experiments, our results demonstrate that our proposed model MTFFNet achieves significantly better classification performance than many state-of-the-art approaches. In this paper, an end-to-end multi-task feature fusion method for Chinese painting classification is proposed. We come up with a new model named MTFFNet, composed of two branches, in which one branch is top-level RGB feature learning and the other branch is low-level brush stroke feature learning. The semantic feature learning branch takes the original image of traditional Chinese painting as input, extracting the color and semantic information of the image, while the brush feature learning branch takes the GLCM feature map as input, extracting the texture and edge information of the image. Multi-kernel learning SVM (supporting vector machine) is selected as the final classifier. Evaluated by experiments, this method improves the accuracy of Chinese painting classification and enhances the generalization ability. By adopting the end-to-end multi-task feature fusion method, MTFFNet could extract more semantic features and texture information in the image. When compared with state-of-the-art classification method for Chinese painting, the proposed method achieves much higher accuracy on our proposed datasets, without lowering speed or efficiency. The proposed method provides an effective solution for cognitive classification of Chinese ink painting, where the accuracy and efficiency of the approach have been fully validated.
      PubDate: 2021-09-10
       
  • Multimodal Emotion Distribution Learning

    • Free pre-print version: Loading...

      Abstract: Background Emotion recognition is an interesting and challenging problem and has attracted much attention in recent years. To more accurately express emotions, emotion distribution learning (EDL) introduces the emotion description degree to form an emotion distribution at a fine granularity, which is used to describe the fusion of multiple basic emotions at different levels. Challenge Existing EDL research has shown a strong representation ability on emotion recognition, but all studies are based on unimodal information, meaning the results may be one-sided. Method As the first pioneering investigation of multimodal emotion distribution learning, we present a corresponding learning method named MEDL. First, for each modality, we learn an emotion distribution and obtain the corresponding label correlation matrix. Second, we constrain the consistency of label correlation matrices between different modalities to utilize modal complementarity. Finally, the final emotion distribution is achieved based on a simple decision fusion strategy. Results and Conclusions The experimental results demonstrate that our proposal performs better than some state-of-the-art multimodal emotion recognition methods and unimodal emotion distribution learning methods.
      PubDate: 2021-09-08
       
  • Causal Asymmetry Analysis in the View of Concept-Cognitive Learning by
           Incremental Concept Tree

    • Free pre-print version: Loading...

      Abstract: Causal asymmetry is an important feature in the description of causality, and it has attracted wide attention in the field of physics and philosophy. It hypothesizes that there is a pervasive and fundamental bias in humans’ understanding of physical causation. However, how to express the causal asymmetry in computer science is still an open, interesting, and important issue. In this paper, we propose a solution to this issue by introducing an incremental concept tree (ICT) representation. The ICT is a structure description method originated from the concept tree and attribute topology methods in the field of concept cognitive learning. It focuses on figuring the cognitive process of human being and has been applied to casual analysis. Firstly, we introduce the concept of “causal asymmetry” into the field of concept-cognitive learning according to the internal unity of attribute topology and causality. Secondly, an Incremental concept tree is designed to represent the incremental evolution of the concepts as time arrows on the basis of attribute topology. Finally, we perform an experimental analysis of the Acute Inflammations data to illustrate the feasibility of the proposed algorithm in visualizing causal asymmetry and compare the ICT to the other structural representations. The experimental results show that the ICT is a promising tool for figuring out the casual asymmetry in the view of concept cognitive learning.
      PubDate: 2021-09-08
       
  • Separable Reversible Data Hiding Based on Integer Mapping and MSB
           Prediction for Encrypted 3D Mesh Models

    • Free pre-print version: Loading...

      Abstract: Reversible data hiding in encrypted domain (RDH-ED) technology can embed data into cover media without exposing the original content to third parties. In addition, the recipient can recover the cover media losslessly after extracting the embedded data. Image-based RDH-ED has been widely studied, but RDH-ED based on 3D meshes has obtained few research results due to the complex data structure and irregular geometric structure of 3D meshes. With the widespread application of 3D meshes, the research on 3D meshes has attracted extensive research from researchers in recent years. In this paper, we propose a reversible data hiding for encrypted 3D meshes based on integer mapping and most significant bit (MSB) prediction. The content owner divides all vertices into “embedded” sets and “reference” sets and then maps floating-point coordinates to integers. After calculating the MSB prediction error of the “embedded” sets, the encryption technology is performed. Then, additional data can be embedded through the MSB replacement strategy. According to different permissions, legal recipients can obtain the original meshes, the additional data or both of them by using the proposed separable method. Higher embedding capacity is achieved by adopting MSB embedding strategy, and perfect recovery of the original meshes is achieved by using ring prediction scheme. The experimental results show that the proposed method has greater embedding capacity compared with the state-of-the-art method.
      PubDate: 2021-09-04
       
  • A New GAN-Based Approach to Data Augmentation and Image Segmentation for
           Crack Detection in Thermal Imaging Tests

    • Free pre-print version: Loading...

      Abstract: As a popular nondestructive testing (NDT) technique, thermal imaging test demonstrates competitive performance in crack detection, especially for detecting subsurface cracks. In thermal imaging test, the temperature of the crack area is higher than that of the non-crack area during the NDT process. By extracting the features of the thermal image sequences, the temperature curve of each spatial point is employed for crack detection. Nevertheless, the quality of thermal images is influenced by the noises due to the complex thermal environment in NDT. In this paper, a modified generative adversarial network (GAN) is employed to improve the image segmentation performance. To improve the feature extraction ability and alleviate the influence of noises, a penalty term is put forward in the loss function of the conventional GAN. A data preprocessing method is developed where the principle component analysis algorithm is adopted for feature extraction. The data argumentation technique is utilized to guarantee the quantity of the training samples. To validate its effectiveness in thermal imaging NDT, the modified GAN is applied to detect the cracks on the eddy current pulsed thermography NDT dataset.
      PubDate: 2021-09-04
       
  • Words, Tweets, and Reviews: Leveraging Affective Knowledge Between
           Multiple Domains

    • Free pre-print version: Loading...

      Abstract: Three popular application domains of sentiment and emotion analysis are: 1) the automatic rating of movie reviews, 2) extracting opinions and emotions on Twitter, and 3) inferring sentiment and emotion associations of words. The textual elements of these domains differ in their length, i.e., movie reviews are usually longer than tweets and words are obviously shorter than tweets, but they also share the property that they can be plausibly annotated according to the same affective categories (e.g., positive, negative, anger, joy). Moreover, state-of-the-art models for these domains are all based on the approach of training supervised machine learning models on manually annotated examples. This approach suffers from an important bottleneck: Manually annotated examples are expensive and time-consuming to obtain and not always available. In this paper, we propose a method for transferring affective knowledge between words, tweets, and movie reviews using two representation techniques: Word2Vec static embeddings and BERT contextualized embeddings. We build compatible representations for movie reviews, tweets, and words, using these techniques, and train and evaluate supervised models on all combinations of source and target domains. Our experimental results show that affective knowledge can be successfully transferred between our three domains, that contextualized embeddings tend to outperform their static counterparts, and that better transfer learning results are obtained when the source domain has longer textual units than the target domain.
      PubDate: 2021-09-02
       
  • Classification-level and Class-level Complement Information Measures Based
           on Neighborhood Decision Systems

    • Free pre-print version: Loading...

      Abstract: Information measures in neighborhood decision systems underlie information processing and uncertainty measurement, especially regarding neighborhood rough sets. Their constructions on covering structuring and neighborhood counting can acquire theoretical extensions and practical compactness but become difficult and rare when comparing the sample counting approach and its defects. In terms of three-way information measures (i.e., information entropy, conditional entropy, and mutual information), classification-level and class-level complement information measures are established by extending equivalence decision systems to neighborhood decision systems; thus, the construction on nonrepetitive neighborhoods becomes ingenious, and the study of criss-cross structuring and granulation properties becomes valuable. At the classification level, neighborhood-complement information measures are proposed by imitation; they perfectly expand existing complement information measures, and thus, they offer an extended isomorphism regarding systematicity. At the class level, neighborhood-complement information measures are determined by decomposition to generate a hierarchical isomorphism, and they also induce equivalence-complement information measures to yield a degenerate isomorphism. Then, granulation nonmonotonicity and monotonicity of neighborhood-complement information measures are revealed at these two levels, and their uncertainty mechanisms are analyzed deeply by three-level granular structures. Finally, all complement measures are calculated programmatically, and their relationships and nonmonotonicity or monotonicity are effectively verified by virtue of table examples and data experiments. In summary, systematically, there are four criss-cross modes of three-way complement information measures based on two knowledge granulations and two decision levels; the variably extended and degenerated isomorphisms and hierarchically decomposed and integrated isomorphisms are thoroughly uncovered, the granulation nonmonotonicity and monotonicity are deeply mined, and all achievements are found to have good application prospects for feature reduction and rule induction in machine learning.
      PubDate: 2021-09-02
       
 
JournalTOCs
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Email: journaltocs@hw.ac.uk
Tel: +00 44 (0)131 4513762
 


Your IP address: 3.236.232.99
 
Home (Search)
API
About JournalTOCs
News (blog, publications)
JournalTOCs on Twitter   JournalTOCs on Facebook

JournalTOCs © 2009-