Article
Computer Science, Information Systems
Yi Yang, Qiang Gao, Yu Song, Xiaolin Song, Zemin Mao, Junjie Liu
Summary: With the development of sensor technology and learning algorithms, multimodal emotion recognition has become a popular research topic. Existing studies have mainly focused on emotion recognition in normal individuals, but there is a greater need for emotion recognition in deaf individuals who cannot express emotions through words. This paper proposes a method using deep belief network (DBN) to classify emotions based on electroencephalograph (EEG) and facial expressions in deaf subjects. The results show a classification accuracy of 99.92% with feature selection in deaf emotion recognition.
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
(2022)
Article
Computer Science, Artificial Intelligence
Fatemeh Hasanzadeh, Mohsen Annabestani, Sahar Moghimi
Summary: This paper introduces a fuzzy parallel cascades (FPC) model for predicting the continuous subjective emotional appraisal of music using time-varying spectral content of EEG signals. The FPC model outperformed other models in estimating the valence and arousal of musical excerpts, with the lowest RMSE of 0.082. The analysis also confirmed the role of frontal channels in emotion recognition.
APPLIED SOFT COMPUTING
(2021)
Article
Multidisciplinary Sciences
Valerie F. Reyna
Summary: The framework presented in the article explores how misinformation influences decision-making, emphasizing the importance of cognitive representations of gist. It suggests a shift in science communication towards achieving insight and values, in addition to disseminating factual information.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2021)
Article
Engineering, Biomedical
B. Indira Priyadarshini, D. Krishna Reddy
Summary: An optimized Adaptive Neuro Fuzzy Inference System (OANFIS) classifier is proposed in this paper to automatically detect seizures, aiming to increase classifier accuracy while reducing computational complexity. By selecting optimal features using the Binary Particle Swarm Optimization (BPSO) algorithm, the proposed system achieves a classification accuracy of 99.25% and consumes only 2.018 mu W power.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2021)
Article
Psychology, Multidisciplinary
Jie Chen, Yulin Zhang, Guozhen Zhao
Summary: The study focuses on the acquisition and evaluation of the Qingdao Preschooler Facial Expression (QPFE) set, which features images of emotion expressions of 54 Chinese preschoolers. The set includes pictures of six basic emotions, five positive emotions, and a neutral expression, evaluated by 43 adult raters online. The data contributes to research on children's emotion expressions and positive emotions.
FRONTIERS IN PSYCHOLOGY
(2021)
Article
Computer Science, Theory & Methods
Maryam Ahmady, Seyed Saeid Mirkamali, Bahareh Pahlevanzadeh, Elnaz Pashaei, Ali Asghar Rahmani Hosseinabadi, Adam Slowik
Summary: Facial expression recognition is an important aspect of emotional computing. This paper proposes a fuzzy-based approach that combines different types of features to improve the recognition rate of facial expressions. Experimental results show the effectiveness of the proposed method.
FUZZY SETS AND SYSTEMS
(2022)
Article
Computer Science, Theory & Methods
Maryam Ahmady, Seyed Saeid Mirkamali, Bahareh Pahlevanzadeh, Elnaz Pashaei, Ali Asghar Rahmani Hosseinabadi, Adam Slowik
Summary: The article introduces a fuzzy-based approach for facial expression recognition that incorporates different types of features, and demonstrates its effectiveness and superiority through experimental results.
FUZZY SETS AND SYSTEMS
(2022)
Article
Engineering, Electrical & Electronic
Yi Yang, Qiang Gao, Xiaolin Song, Yu Song, Zemin Mao, Junjie Liu
Summary: This study proposes a multimodal continuous emotion recognition method based on facial expressions and EEG signals for deaf subjects. The results show that EEG signals are more effective in continuous emotion recognition compared to facial expressions, and multimodality can improve performance. The neural activities of deaf subjects are closely related to processing different emotions.
IEEE SENSORS JOURNAL
(2021)
Article
Automation & Control Systems
Mahmut Dirik
Summary: Emotion recognition from facial images is an important research area. This paper proposes an emotion recognition model based on adaptive neuro-fuzzy inference system optimized with particle swarm optimization, achieving a prediction accuracy of 99.6% on the MUG database.
INTERNATIONAL JOURNAL OF FUZZY SYSTEMS
(2022)
Article
Psychology, Multidisciplinary
Shinnosuke Ikeda
Summary: This study examined the effects of social anxiety and social sensitivity on the accuracy of emotion inference from masked facial expressions in a Japanese sample. The results showed that wearing a mask made it difficult to identify the emotions of sadness and fear, happy and neutral expressions remained unaffected, and angry expressions were read more accurately. Furthermore, the ability to infer emotions from facial expressions and social sensitivity were found to have different effects on the accuracy of emotion inference from facial expressions with a mask.
CURRENT PSYCHOLOGY
(2023)
Article
Chemistry, Multidisciplinary
Vladimir Kosonogov, Ekaterina Kovsh, Elena Vorobyeva
Summary: This study examined event-related potentials during recognition of dynamic facial expressions, revealing typical ERP and predicting accuracy of participants who recognize emotions quickly based on the amplitude of posterior P2 and LPP.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Information Systems
Zehao Lin, Jiahui She, Qiu Shen
Summary: This paper proposes a method called real emotion seeker (RES) to recalibrate compound facial expressions by incorporating subjective implicit knowledge through Bayesian inference and posterior distribution. The recalibrated annotation, combined with one-hot label, guides more realistic prediction and significantly improves accuracy in facial expression recognition.
MULTIMEDIA SYSTEMS
(2023)
Review
Transportation
Matthew Vechione, Ruey Long Cheu
Summary: This study investigates the adaptation of Fuzzy Inference System (FIS) model for mandatory lane changing decisions and compares its performance with Adaptive FIS (AFIS) and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) models on a test data set. Results suggest that an ANFIS model is recommended for mandatory lane changes due to its higher overall correct decision rate.
JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS
(2022)
Article
Mathematics, Applied
Aditya Khamparia, Rajat Jain, Poonam Rani, Deepak Gupta, Ashish Khanna, Oscar Castill
Summary: The study aims to design a system for diagnosing COVID-19 using ANFIS, and comparative analysis reveals that ANFIS model outperforms fuzzy systems in accuracy.
APPLIED AND COMPUTATIONAL MATHEMATICS
(2021)
Article
Psychology, Experimental
Elizabeth Gregory, James W. Tanaka, Xiaoyi Liu
Summary: This study investigated holistic gist perception of facial expressions within a single glance. Results showed facilitation effects for congruent angry expressions and interference effects for incongruent happy and angry expressions at the shortest exposure duration. These findings suggest that holistic gist perception of facial expressions cannot be overridden by selective attention.
COGNITION & EMOTION
(2021)
Article
Computer Science, Artificial Intelligence
Rui Lv, Dingheng Wang, Jiangbin Zheng, Zhao-Xu Yang
Summary: In this paper, the authors investigate tensor decomposition for neural network compression. They analyze the convergence and precision of tensor mapping theory, validate the rationality of tensor mapping and its superiority over traditional tensor approximation based on the Lottery Ticket Hypothesis. They propose an efficient method called 3D-KCPNet to compress 3D convolutional neural networks using the Kronecker canonical polyadic (KCP) tensor decomposition. Experimental results show that 3D-KCPNet achieves higher accuracy compared to the original baseline model and the corresponding tensor approximation model.
Article
Computer Science, Artificial Intelligence
Xiangkun He, Zhongxu Hu, Haohan Yang, Chen Lv
Summary: In this paper, a novel constrained multi-objective reinforcement learning algorithm is proposed for personalized end-to-end robotic control with continuous actions. The approach trains a single model using constraint design and a comprehensive index to achieve optimal policies based on user-specified preferences.
Article
Computer Science, Artificial Intelligence
Zhijian Zhuo, Bilian Chen, Shenbao Yu, Langcai Cao
Summary: In this paper, a novel method called Expansion with Contraction Method for Overlapping Community Detection (ECOCD) is proposed, which utilizes non-negative matrix factorization to obtain disjoint communities and applies expansion and contraction processes to adjust the degree of overlap. ECOCD is applicable to various networks with different properties and achieves high-quality overlapping community detection.
Article
Computer Science, Artificial Intelligence
Yizhe Zhu, Chunhui Zhang, Jialin Gao, Xin Sun, Zihan Rui, Xi Zhou
Summary: In this work, the authors propose a Contrastive Spatio-Temporal Distilling (CSTD) approach to improve the detection of high-compressed deepfake videos. The approach leverages spatial-frequency cues and temporal-contrastive alignment to fully exploit spatiotemporal inconsistency information.
Review
Computer Science, Artificial Intelligence
Laijin Meng, Xinghao Jiang, Tanfeng Sun
Summary: This paper provides a review of coverless steganographic algorithms, including the development process, known contributions, and general issues in image and video algorithms. It also discusses the security of coverless steganography from theoretical analysis to actual investigation for the first time.
Article
Computer Science, Artificial Intelligence
Yajie Bao, Tianwei Xing, Xun Chen
Summary: Visual question answering requires processing multi-modal information and effective reasoning. Neural-symbolic learning is a promising method, but current approaches lack uncertainty handling and can only provide a single answer. To address this, we propose a confidence based neural-symbolic approach that evaluates NN inferences and conducts reasoning based on confidence.
Article
Computer Science, Artificial Intelligence
Anh H. Vo, Bao T. Nguyen
Summary: Interior style classification is an interesting problem with potential applications in both commercial and academic domains. This project proposes a method named ISC-DeIT, which combines data-efficient image transformer architectures and knowledge distillation, to address the interior style classification problem. Experimental results demonstrate a significant improvement in predictive accuracy compared to other state-of-the-art methods.
Article
Computer Science, Artificial Intelligence
Shashank Kotyan, Danilo Vasconcellos Vargas
Summary: This article introduces a novel augmentation technique called Dynamic Scanning Augmentation to improve the accuracy and robustness of Vision Transformer (ViT). The technique leverages dynamic input sequences to adaptively focus on different patches, resulting in significant changes in ViT's attention mechanism. Experimental results demonstrate that Dynamic Scanning Augmentation outperforms ViT in terms of both robustness to adversarial attacks and accuracy against natural images.
Article
Computer Science, Artificial Intelligence
Hiba Alqasir, Damien Muselet, Christophe Ducottet
Summary: The article proposes a solution to improve the learning process of a classification network by providing shape priors, reducing the need for annotated data. The solution is tested on cross-domain digit classification tasks and a video surveillance application.
Article
Computer Science, Artificial Intelligence
Dexiu Ma, Mei Liu, Mingsheng Shang
Summary: This paper proposes a method using neural dynamics solvers to solve infinity-norm optimization problems. Two improved solvers are constructed and their effectiveness and superiority are demonstrated through theoretical analysis and simulation experiments.
Article
Computer Science, Artificial Intelligence
Francesco Gregoretti, Giovanni Pezzulo, Domenico Maisto
Summary: Active Inference is a computational framework that uses probabilistic inference and variational free energy minimization to describe perception, planning, and action. cpp-AIF is a header-only C++ library that provides a powerful tool for implementing Active Inference for Partially Observable Markov Decision Processes through multi-core computing. It is cross-platform and improves performance, memory management, and usability compared to existing software.
Article
Computer Science, Artificial Intelligence
Zelin Ying, Dawei Cheng, Cen Chen, Xiang Li, Peng Zhu, Yifeng Luo, Yuqi Liang
Summary: This paper proposes a novel stock market trends prediction framework called SMART, which includes a self-supervised stock technical data sequence embedding model S3E. By training with multiple self-supervised auxiliary tasks, the model encodes stock technical data sequences into embeddings and uses the learned sequence embeddings for predicting stock market trends. Extensive experiments on China A-Shares market and NASDAQ market prove the high effectiveness of our model in stock market trends prediction, and its effectiveness is further validated in real-world applications in a leading financial service provider in China.
Article
Computer Science, Artificial Intelligence
Hao Li, Hao Jiang, Dongsheng Ye, Qiang Wang, Liang Du, Yuanyuan Zeng, Liu Yuan, Yingxue Wang, C. Chen
Summary: DHGAT1, a dynamic hyperbolic graph attention network, utilizes hyperbolic metric properties to embed dynamic graphs. It employs a spatiotemporal self-attention mechanism and weighted node representations, resulting in excellent performance in link prediction tasks.
Article
Computer Science, Artificial Intelligence
Jiehui Huang, Zhenchao Tang, Xuedong He, Jun Zhou, Defeng Zhou, Calvin Yu-Chian Chen
Summary: This study proposes a progressive learning multi-scale feature blending model for image deraining tasks. The model utilizes detail dilation and texture extraction to improve the restoration of rainy images. Experimental results show that the model achieves near state-of-the-art performance in rain removal tasks and exhibits better rain removal realism.
Article
Computer Science, Artificial Intelligence
Lizhi Liu, Zilin Gao, Yinhe Wang, Yongfu Li
Summary: This paper proposes a novel discrete-time interconnected model for depicting complex dynamical networks. The model consists of nodes and edges subsystems, which consider the dynamic characteristic of both nodes and edges. By designing control strategies and coupling modes, the stabilization and synchronization of the network are achieved. Simulation results demonstrate the effectiveness of the proposed methods.
