Article
Computer Science, Artificial Intelligence
Panagiotis Tzirakis, Jiaxin Chen, Stefanos Zafeiriou, Bjorn Schuller
Summary: This paper proposes an emotion recognition system that utilizes raw text, audio, and visual information, achieving state-of-the-art results in text, visual, and multimodal domains through the use of Deep Neural Networks and other techniques.
INFORMATION FUSION
(2021)
Article
Computer Science, Artificial Intelligence
Ross Harper, Joshua Southern
Summary: Automatic prediction of emotion has the potential to revolutionize human-computer interaction. This study presents an end-to-end deep learning model for classifying emotional valence from unimodal heartbeat time series. The proposed Bayesian framework models uncertainty over these predictions and provides a probabilistic procedure for decision-making. The benchmarking results show a peak classification accuracy of 90 percent, laying the foundation for real-world applications of affective computing.
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
(2022)
Article
Computer Science, Information Systems
Anamaria Radoi, Andreea Birhala, Nicolae-Catalin Ristea, Liviu-Cristian Dutu
Summary: The study presents an end-to-end neural network architecture that aggregates temporal audio and video information in an asynchronous setting to determine the emotional state of a subject. Feature descriptors for audio and video representations are extracted using simple Convolutional Neural Networks for real-time processing. The approach provides a natural augmentation technique to address the challenge of collecting annotated training data.
Article
Computer Science, Information Systems
Jian Shi, Ge Sun, Jinyu Zhang, Zhihui Wang, Haojie Li
Summary: In this paper, a weakly supervised attribute location module (ALM) is proposed to effectively detect facial regions with only image-level attribute labels, and improve face attribute recognition using region-based local features. Moreover, a bottom-up skip connection structure is introduced to enhance attribute-specific region location with low-level spatial information supplements. Extensive experiments demonstrate the superior performance of the proposed method on LFWA and CelebA datasets.
MULTIMEDIA SYSTEMS
(2023)
Article
Computer Science, Hardware & Architecture
Van Thong Huynh, Hyung-Jeong Yang, Guee-Sang Lee, Soo-Hyung Kim
Summary: This work introduces an approach for emotion recognition in videos by combining visual, audio, and language information, utilizing a lightweight feature extractor, attention strategy, and adaptive loss. The use of temporal convolutional network, attention mechanism, and adaptive loss during training significantly improves the performance in emotion recognition on a large dataset.
Article
Computer Science, Artificial Intelligence
Jia Wen Li, Rong Jun Chen, Shovan Barma, Fei Chen, Sio Hang Pun, Peng Un Mak, Lei Jun Wang, Xian Xian Zeng, Jin Chang Ren, Hui Min Zhao
Summary: Researchers propose a method based on brain rhythm sequencing and asymmetric features to recognize emotions during self-isolation. The results show that this method achieves high accuracy in emotion recognition, especially in resource-limited situations. Further investigation reveals the individual characteristics of emotion recognition, suggesting the inclusion of subject-dependent properties.
COGNITIVE COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
Yang Li, Wenming Zheng, Lei Wang, Yuan Zong, Zhen Cui
Summary: In this paper, a novel EEG emotion recognition method inspired by neuroscience is proposed. The method utilizes spatial and temporal neural network models to learn discriminative spatial-temporal EEG features. Experimental results demonstrate that the proposed method achieves state-of-the-art performance in emotion recognition.
IEEE TRANSACTIONS ON AFFECTIVE COMPUTING
(2022)
Article
Psychology, Developmental
Kenn L. Dela Cruz, Caroline M. Kelsey, Xin Tong, Tobias Grossmann
Summary: The current longitudinal study examined maternal facial emotion recognition and infant affect-based attention using eye-tracking at different ages. The results showed consistent maternal responses to angry facial expressions, indicating a trait-like response to social threat among mothers. However, neither maternal responses to happy or fearful facial expressions nor infants' responses to all three facial emotions showed such consistency, suggesting the changeable nature of facial emotion processing, especially in infants. The study also found dynamic changes in infants' attention to negative emotions and limited evidence for developmental continuity in processing negative emotions and the bidirectional interplay of infant affect-biased attention and maternal facial emotion recognition.
INFANT BEHAVIOR & DEVELOPMENT
(2023)
Article
Computer Science, Information Systems
Richard Orjesek, Roman Jarina, Michal Chmulik
Summary: This study proposes a deep neural network-based solution for automatic music emotion recognition, which can extract emotion features directly from raw audio waveform and achieve high regression accuracy on the DEAM dataset.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
A. Aruna Gladys, V. Vetriselvi
Summary: Emotion is a natural state of mind created by physiological changes in response to internal or external stimuli. Emotions influence decision-making, which in turn shapes behavior and character. Recognizing and managing emotions is an essential part of emotional intelligence, but it requires expertise in psychology and can be time-consuming. Computational recognition of emotional intelligence offers new possibilities for exploring this field.
Article
Psychology, Multidisciplinary
Lillian Dollinger, Petri Laukka, Lennart Bjorn Hogman, Tanja Banziger, Irena Makower, Hakan Fischer, Stephan Hau
Summary: Nonverbal emotion recognition accuracy (ERA) is essential for successful communication, and two different training programs focusing on multimodal expressions and micro expressions respectively were evaluated. Results showed that the training program focusing on multimodal expressions was more effective in improving overall ERA, while the one focusing on micro expressions was more effective in improving micro expression ERA specifically. Transfer effects of the training programs were not observed, and participants with lower baseline ERA showed more improvements.
FRONTIERS IN PSYCHOLOGY
(2021)
Article
Computer Science, Artificial Intelligence
Tejas Kashinath, Twisha Jain, Yash Agrawal, Tanvi Anand, Sanjay Singh
Summary: This paper presents a model based on deep neural networks that can automatically detect and convert tables into an editable or searchable format. By combining computer vision and machine learning, this model facilitates document digitization and extraction of data for decision-making in fields like healthcare and finance.
APPLIED SOFT COMPUTING
(2022)
Article
Psychology, Experimental
Marena S. Manierka, Rachel Rezaei, Samantha Palacios, Sarah M. Haigh, Jeffrey J. Hutsler
Summary: The study found that fluctuations in individuals' mood state can impact the recognition of specific facial expressions, with increased positive mood improving recognition of scared expressions but worsening recognition of happy expressions. This suggests that minor mood fluctuations in a neurotypical population affect emotion recognition and should be taken into consideration by researchers and clinicians assessing FER skills.
Article
Chemistry, Multidisciplinary
Sung-Woo Byun, Ju-Hee Kim, Seok-Pil Lee
Summary: This paper proposes a method of emotion recognition using speech and text data to improve accuracy, feeding acoustic feature vectors and embedding vectors into deep learning models to calculate probabilities for predicted output classes, demonstrating more accurate performance than previous research.
APPLIED SCIENCES-BASEL
(2021)
Article
Computer Science, Artificial Intelligence
Khan Mustaqeem, Abdulmotaleb El Saddik, Fahd Saleh Alotaibi, Nhat Truong Pham
Summary: This article proposes a Deep Echo-State-Network (DeepESN) system for emotion recognition using a dilated convolutional neural network and multi-headed attention mechanism. The proposed system achieves high recognition rates on two public speech corpora, EMO-DB and RAVDESS, outperforming the State-of-The-Art (SOTA). The system also requires less computational time. Rating: 8/10.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Hao Yang, Min Wang, Zhengfei Yu, Hang Zhang, Jinshen Jiang, Yun Zhou
Summary: In this paper, a novel method called CSTTA is proposed for test time adaptation (TTA), which utilizes confidence-based optimization and sample reweighting to better utilize sample information. Extensive experiments demonstrate the effectiveness of the proposed method.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Jin Liu, Ju-Sheng Mi, Dong-Yun Niu
Summary: This article focuses on a novel method for generating a canonical basis for decision implications based on object-induced operators (OE operators). The logic of decision implication based on OE operators is described, and a method for obtaining the canonical basis for decision implications is given. The completeness, nonredundancy, and optimality of the canonical basis are proven. Additionally, a method for generating true premises based on OE operators is proposed.
KNOWLEDGE-BASED SYSTEMS
(2024)
Review
Computer Science, Artificial Intelligence
Kun Bu, Yuanchao Liu, Xiaolong Ju
Summary: This paper discusses the importance of sentiment analysis and pre-trained models in natural language processing, and explores the application of prompt learning. The research shows that prompt learning is more suitable for sentiment analysis tasks and can achieve good performance.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Xiangjun Cai, Dagang Li
Summary: This paper presents a new decomposition mechanism based on learned decomposition mapping. By using a neural network to learn the relationship between original time series and decomposed results, the repetitive computation overhead during rolling decomposition is relieved. Additionally, extended mapping and partial decomposition methods are proposed to alleviate boundary effects on prediction performance. Comparative studies demonstrate that the proposed method outperforms existing RDEMs in terms of operation speed and prediction accuracy.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Xu Wu, Yang Liu, Jie Tian, Yuanpeng Li
Summary: This paper proposes a blockchain-based privacy-preserving trust management architecture, which adopts federated learning to train task-specific trust models and utilizes differential privacy to protect device privacy. In addition, a game theory-based incentive mechanism and a parallel consensus protocol are proposed to improve the accuracy of trust computing and the efficiency of consensus.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Zaiyang Yu, Prayag Tiwari, Luyang Hou, Lusi Li, Weijun Li, Limin Jiang, Xin Ning
Summary: This study introduces a 3D view-based approach that effectively handles occlusions and leverages the geometric information of 3D objects. The proposed method achieves state-of-the-art results on occluded ReID tasks and exhibits competitive performance on holistic ReID tasks.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Yongliang Shi, Runyi Yang, Zirui Wu, Pengfei Li, Caiyun Liu, Hao Zhao, Guyue Zhou
Summary: Neural implicit representations have gained attention due to their expressive, continuous, and compact properties. However, there is still a lack of research on city-scale continual implicit dense mapping based on sparse LiDAR input. In this study, a city-scale continual neural mapping system with a panoptic representation is developed, incorporating environment-level and instance-level modeling. A tailored three-layer sampling strategy and category-specific prior are proposed to address the challenges of representing geometric information in city-scale space and achieving high fidelity mapping of instances under incomplete observation.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Ruihan Hu, Zhi-Ri Tang, Rui Yang, Zhongjie Wang
Summary: Mesh data is crucial for 3D computer vision applications worldwide, but traditional deep learning frameworks have struggled with handling meshes. This paper proposes MDSSN, a simple mesh computation framework that models triangle meshes and represents their shape using face-based and edge-based Riemannian graphs. The framework incorporates end-to-end operators inspired by traditional deep learning frameworks, and includes dedicated modules for addressing challenges in mesh classification and segmentation tasks. Experimental results demonstrate that MDSSN outperforms other state-of-the-art approaches.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Buliao Huang, Yunhui Zhu, Muhammad Usman, Huanhuan Chen
Summary: This paper proposes a novel semi-supervised conditional normalizing flow (SSCFlow) algorithm that combines unsupervised imputation and supervised classification. By estimating the conditional distribution of incomplete instances, SSCFlow facilitates imputation and classification simultaneously, addressing the issue of separated tasks ignoring data distribution and label information in traditional methods.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Deeksha Varshney, Asif Ekbal, Erik Cambria
Summary: This paper focuses on the neural-based interactive dialogue system that aims to engage and retain humans in long-lasting conversations. It proposes a new neural generative model that combines step-wise co-attention, self-attention-based transformer network, and an emotion classifier to control emotion and knowledge transfer during response generation. The results from quantitative, qualitative, and human evaluation show that the proposed models can generate natural and coherent sentences, capturing essential facts with significant improvement over emotional content.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Junchen Ye, Weimiao Li, Zhixin Zhang, Tongyu Zhu, Leilei Sun, Bowen Du
Summary: Modeling multivariate time series has long been a topic of interest for scholars in various fields. This paper introduces MvTS, an open library based on Pytorch, which provides a unified framework for implementing and evaluating these models. Extensive experiments on public datasets demonstrate the effectiveness and universality of the models reproduced by MvTS.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Reham R. Mostafa, Ahmed M. Khedr, Zaher Al Aghbari, Imad Afyouni, Ibrahim Kamel, Naveed Ahmed
Summary: Feature selection is crucial in classification procedures, but it faces challenges in high-dimensional datasets. To overcome these challenges, this study proposes an Adaptive Hybrid-Mutated Differential Evolution method that incorporates the mechanics of the Spider Wasp Optimization algorithm and the concept of Enhanced Solution Quality. Experimental results demonstrate the effectiveness of the method in terms of accuracy and convergence speed, and it outperforms contemporary cutting-edge algorithms.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Ti Xiang, Pin Lv, Liguo Sun, Yipu Yang, Jiuwu Hao
Summary: This paper introduces a Track Classification Model (TCM) based on marine radar, which can effectively recognize and classify shipping tracks. By using a feature extraction network with multi-feature fusion and a dataset production method to address missing labels, the classification accuracy is improved, resulting in successful engineering application in real scenarios.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Zhihao Zhang, Yuan Zuo, Chenghua Lin, Junjie Wu
Summary: This paper proposes a novel unsupervised context-aware quality phrase mining framework called LMPhrase, which is built upon large pre-trained language models. The framework mines quality phrases as silver labels using a parameter-free probing technique on the pre-trained language model BERT, and formalizes the phrase tagging task as a sequence generation problem by fine-tuning on the Sequence to-Sequence pre-trained language model BART. The results of extensive experiments show that LMPhrase consistently outperforms existing competitors in two different granularity phrase mining tasks.
KNOWLEDGE-BASED SYSTEMS
(2024)
Article
Computer Science, Artificial Intelligence
Kemal Buyukkaya, M. Ozan Karsavuran, Cevdet Aykanat
Summary: The study aims to investigate the hybrid parallelization of the Stochastic Gradient Descent (SGD) algorithm for solving the matrix completion problem on a high-performance computing platform. A hybrid parallel decentralized SGD framework with asynchronous inter-process communication and a novel flexible partitioning scheme is proposed to achieve scalability up to hundreds of processors. Experimental results on real-world benchmark datasets show that the proposed algorithm achieves 6x higher throughput on sparse datasets compared to the state-of-the-art, while achieving comparable throughput on relatively dense datasets.
KNOWLEDGE-BASED SYSTEMS
(2024)