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SIR-HCL: Semantic-Inconsistency Reasoning and Hybrid Contrastive Learning for Efficient Cross-Emotion Anomaly Detection

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IEEE Transactions on Cognitive and Developmental Systems 2025年

作者： Liu, Xin Chen, Qiyan Cheung, Yiu-Ming Peng, Shu-Juan Huaqiao University Department of Computer Science Xiamen361021 China Hong Kong Baptist University Department of Computer Science SAR Hong Kong Hong Kong Xiamen Key Laboratory of Computer Vision and Pattern Recognition Xiamen361021 China Huaqiao University Fujian Key Laboratory of Big Data Intelligence and Security Xiamen361021 China Huaqiao University Department of Artificial Intelligence Xiamen China Fujian Province University Key Laboratory of Computer Vision and Machine Learning Huaqiao University Xiamen361021 China

Cross-emotion anomaly detection is an emerging and challenging research topic in cognitive analysis field, which aims at identifying the abnormal emotion pair whose semantic patterns are inconsistent across different emotional modalities. To the best of our knowledge, this topic has yet to be well studied, which could potentially benefit lots of valuable cognitive applications such as autistic children diagnosis and criminal deception detection. To this end, this paper proposes an efficient cross-emotion anomaly detection approach via semanticinconsistency reasoning and hybrid contrastive learning (SIR-HCL), which is the first attempt to detect the anomalous emotional pairs across the audio-visual emotions. First, the proposed framework utilizes dual-branch network to obtain the deep emotional features in each modality, and then employs the shared residual block to derive the semantically compatible features. Subsequently, an efficient hybrid contrastive learning approach is designed to enlarge the semantic-inconsistency among abnormal emotional pair with different affective classes, while enhancing the semantic-consistency and increasing the feature correlation between normal emotional pair from the same affective class. At the same time, an efficient bidirectional learning scheme is employed to significantly improve the data utilization and a two-component Beta Mixture Model is adaptively utilized to reason the anomalous emotion pairs. Extensive experiments evaluated on two benchmark datasets show that the proposed SIR-HCL method can well detect the anomalous emotional pairs across audio-visual emotional data, and brings substantial improvements over the state-of-the-art competing methods. © 2016 IEEE.

关键词： Contrastive Learning

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Interaction Confidence Attention for Human–Object Interaction Detection

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International Journal of computer vision 2025年 1-20页

作者： Zhang, Hong-Bo Lin, Wang-Kai Su, Hang Lei, Qing Liu, Jing-Hua Du, Ji-Xiang Department of Computer Science and Technology Huaqiao University Xiamen361021 China Fujian Key Laboratory of Big Data Intelligence and Security Huaqiao University Xiamen361021 China Xiamen Key Laboratory of Computer Vision and Pattern Recognition Huaqiao University Xiamen361021 China Fujian Province University Xiamen361021 China

In human–object interaction (HOI) detection task, ensuring that interactive pairs receive higher attention weights while reducing the weight of non-interaction pairs is imperative for enhancing HOI detection accuracy. Guiding attention learning is also a key aspect of existing transformer-based algorithms. To tackle this challenge, this study proposes a novel approach termed Interaction Confidence Score Learning Attention (ICSLA), which introduces weakening and augmentation operations into the original attention weight calculation and feature extraction processes. In ICSLA, feature learning is coupled with confidence score learning, simultaneously. Leveraging ICSLA, a new and universal decoder is devised, establishing a transformer-based one-stage HOI detection architecture. Experimental results demonstrate the effectiveness of the proposed method in improving HOI detection accuracy, offering valuable insights for further optimization of attention mechanisms. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Augmentation operation Human–object interaction detection Interaction confidence score learning attention Weakening operation

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Deep Learning in Palmprint recognition-A Comprehensive Survey

arXiv

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arXiv 2025年

作者： Gao, Chengrui Yang, Ziyuan Jia, Wei Leng, Lu Zhang, Bob Teoh, Andrew Beng Jin College of Computer Science Sichuan University Chengdu610065 China Singapore School of Computer and Information Hefei University of Technology Hefei China Jiangxi Provincial Key Laboratory of Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang China Pattern Analysis and Machine Intelligence Group Department of Computer and Information Science University of Macau Taipa China School of Electrical and Electronic Engineering College of Engineering Yonsei University Seoul Korea Republic of

Palmprint recognition has emerged as a prominent biometric technology, widely applied in diverse scenarios. Traditional handcrafted methods for palmprint recognition often fall short in representation capability, as they heavily depend on researchers' prior knowledge. Deep learning (DL) has been introduced to address this limitation, leveraging its remarkable successes across various domains. While existing surveys focus narrowly on specific tasks within palmprint recognition-often grounded in traditional methodologies-there remains a significant gap in comprehensive research exploring DL-based approaches across all facets of palmprint recognition. This paper bridges that gap by thoroughly reviewing recent advancements in DL-powered palmprint recognition. The paper systematically examines progress across key tasks, including region-of-interest segmentation, feature extraction, and security/privacy-oriented challenges. Beyond highlighting these advancements, the paper identifies current challenges and uncovers promising opportunities for future research. By consolidating state-of-the-art progress, this review serves as a valuable resource for researchers, enabling them to stay abreast of cutting-edge technologies and drive innovation in palmprint recognition. Copyright © 2025, The Authors. All rights reserved.

关键词： Biometrics

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A new nano-scale authentication architecture for improving the security of human-computer interaction systems based on quantum computing

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Analog Integrated Circuits and Signal Processing 2025年第1期124卷 1-15页

作者： Ahmadpour, Seyed-Sajad Zohaib, Muhammad Navimipour, Nima Jafari Misra, Neeraj Kumar Rasmi, Hadi Salahov, Huseyn Avval, Danial Bakhshayeshi Navin, Ahmad Habibizad Hosseinzadeh, Mehdi Department of Computer Engineering Faculty of Engineering and Natural Sciences Kadir Has University Istanbul Turkey Department of Electrical and Electronics Engineering Faculty of Engineering and Natural Sciences Kadir Has University Istanbul Turkey Future Technology Research Center National Yunlin University of Science and Technology Douliou Taiwan School of Electronics Engineering VIT-AP University Amaravathi India Department of Computer Engineering Dezful Branch Islamic Azad University Dezful Iran Department of Information Systems Engineering Sakarya University Sakarya Turkey Department of Computer Engineering Tabriz Branch Islamic Azad University Tabriz Iran Pattern Recognition and Machine Learning Laboratory School of Computing Gachon University Seongnam Republic of Korea

Human-computer Interaction (HCI) is an interdisciplinary area of study focusing on the interaction of users and computers by scheming interactive computer interfaces. In addition, HCI systems need security to confirm user authentication, which is a crucial issue in these systems. Hence, user authentication is vital, allowing only authorized users to access data. Authentication is critical to the digital world since it provides security and safety for digital data. Moreover, a digital signature is an authentication method to confirm the accuracy and reliability of digital documents or communications. In addition, designing the circuit based on the complementary metal-oxide semiconductor (CMOS) technology can affect the security and safety of digital data due to the excessive heat dissipation of circuits. On the other hand, quantum-dot cellular automata (QCA) and reversible logic as alternative technologies to CMOS address these problems. Since QCA and reversible logic circuits have minimal energy dissipation, which is considered nearly zero, approaching these technologies proves extremely difficult for any hacker. This work presents an effective structure for the authenticator and human-computer interaction using QCA and IBM quantum computing with Qiskit simulations. The proposed structure has outperformed current circuits in terms of area, cell count, and latency. The paper demonstrates the QCA reversible logic layout of the proposed HCI authenticator and integrates IBM quantum computing simulations using Qiskit for validation. The implementation and testing of results are performed utilizing QCADesigner-2.0.3 and Qiskit simulation tools. The accuracy and efficiency of the proposed design are validated through simulation-derived comparison values, and energy dissipation simulations prove that the suggested circuit dissipates minimal energy.

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H-TERF: A hybrid approach combining fuzzy multi-criteria decision-making techniques and enhanced random forest to improve WBAN-IoT

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Internet of Things (The Netherlands) 2025年 32卷

作者： Khoshvaght, Parisa Tanveer, Jawad Rahmani, Amir Masoud Mohammadi, Mohammad Mehranzadeh, Amin Lansky, Jan Hosseinzadeh, Mehdi Institute of Research and Development Duy Tan University Da Nang Viet Nam School of Engineering & Technology Duy Tan University Da Nang Viet Nam Department of Computer Science and Engineering Sejong University Seoul 05006 South Korea Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Department of Computer Engineering Dezful Branch Islamic Azad University Dezful Iran Department of Computer Science and Mathematics Faculty of Economic Studies University of Finance and Administration Prague Czech Republic Pattern Recognition and Machine Learning Laboratory School of Computing Gachon University Seongnam South Korea

The Internet of Things (IoT) technology today has grown rapidly compared to the last few years, and the use of this technology has increased the quality of service to users day by day. The various applications of IoT have caused the attention of this innovation to enhance among different organizations. One of the important challenges of the IoT is routing, which can affect having a stable network. In this research, a hybrid approach called H-TERF (Hybrid TOPSIS and Enhanced Random Forest) is proposed for achieving efficient routing in IoT networks, specifically in Wireless Body Area Networks (WBAN). This method initially cluster nodes by using the DBSCAN clustering algorithm to optimize intra-cluster communication. Then, for routing, the nodes are ranked using the Fuzzy TOPSIS and Fuzzy AHP. This ranking is determined by several criteria, including the remaining energy of nodes, node memory, and throughput. Additionally, to manage more complex criteria such as node historical records and traffic rate, the initial ranking by the TOPSIS approach, along with the other mentioned criteria, is fed into an enhanced random forest model to identify the optimal path. This hybrid method enhances network performance in terms of lifespan, efficiency, delay, and packet delivery ratio. The outcomes of the simulation show that the suggested method surpasses existing approaches and is highly effective for application in IoT and WBAN networks. For example, the performance improvement of the proposed approach over the F-EVM, DECR, and DHH-EFO approaches in energy consumption was 20.62%, 25.85%, and 32.57%, respectively. © 2025 Elsevier B.V.

关键词： DBSCAN clustering Enhanced random forest Fuzzy AHP Fuzzy TOPSIS Internet of Things

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Numerical integration methods for simulation of mass-spring-damper systems

Numerical integration methods for simulation of mass-spring-...

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26th Annual International Symposium on computer and Information science, ISCIS 2011

作者： Özgüz, Mete Eskil, M. Taner Department of Computer Science and Engineering Pattern Recognition and Machine Intelligence Laboratory ISIK University Istanbul Turkey

The dynamics of a face are often implemented as a system of connected particles with various forces acting upon them. Animation of such a system requires the approximation of velocity and position of each particle through numerical integration. There are many numerical integrators that are commonly used in the literature. We conducted experiments to determine the suitability of numerical integration methods in approximating the particular dynamics of mass-spring-damper systems. Among Euler, semi-implicit Euler, Runge-Kutta and Leapfrog, we found that simulation with Leapfrog numerical integration characterizes a mass-spring-damper system best in terms of the energy loss of the overall system. © 2012 Springer-Verlag London Limited.

关键词： Energy dissipation

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Palmprint verification using SIFT majority voting

Palmprint verification using SIFT majority voting

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26th Annual International Symposium on computer and Information science, ISCIS 2011

作者： Abeysundera, H. Pasindu Eskil, M. Taner Department of Computer Science and Engineering Pattern Recognition and Machine Intelligence Laboratory ISIK University Istanbul Turkey

In this paper we illustrate the implementation of a robust, real-time biometric system for identity verification based on palmprint images. The palmprint images are preprocessed to align the major axes of hand shapes and to extract the palm region. We extract features using Scale Invariant Feature Transform (SIFT). Classification of individual SIFT features is done through KNN. The class of the hand image is decided by a majority based voting among its classified SIFT features. We demonstrate on the CASIA and PolyU datasets that the proposed system achieves authentication accuracy comparable to other state of the art algorithms. © 2012 Springer-Verlag London Limited.

关键词： Anthropometry

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Semi-automatic adaptation of high-polygon wireframe face models through inverse perspective projection

Semi-automatic adaptation of high-polygon wireframe face mod...

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26th Annual International Symposium on computer and Information science, ISCIS 2011

作者： Benli, Kristin S. Aǧdoǧan, Didem Özgüz, Mete Eskil, M. Taner Department of Computer Science and Engineering Pattern Recognition and Machine Intelligence Laboratory ISIK University Istanbul Turkey

Precise registration of a generic 3D face model with a subject's face is a critical stage for model based analysis of facial expressions. In this study we propose a semi-automatic model fitting algorithm to fit a high-polygon wireframe model to a single image of a face. We manually mark important landmark points both on the wireframe model and the face image. We carry out an initial alignment by translating and scaling the wireframe model. We then translate the landmark vertices in the 3D wireframe model so that they coincide with inverse perspective projections of image landmark points. The vertices that are not manually labeled as landmark are translated with a weighted sum of vectorial displacement of k neighboring landmark vertices, inversely weighted by their 3D distances to the vertex under consideration. Our experiments indicate that we can fit a high-polygon model to the subject's face with modest computational complexity. © 2012 Springer-Verlag London Limited.

关键词： Geometry

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Design and Implementation of a vision- and Grating-Sensor-Based Intelligent Unmanned Settlement System

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2022年第2期3卷 254-264页

作者： Zhang, Hong-Bo Zhou, Yi-Zhong Dong, Li-Jia Lei, Qing Du, Ji-Xiang Department of Computer Science and Technology Huaqiao University Xiamen361000 China Xiamen Key Laboratory of Computer Vision and Pattern Recognition The Fujian Key Laboratory of Big Data Intelligence and Security Huaqiao University Xiamen361000 China

In this article, a new vision- and grating-sensor-based intelligent unmanned settlement (IUS) system is proposed for convenience stores to automatically recognize the shopping behavior of customers, record their identities, and generate invoices. First, we design a new IUS architecture, which includes a shelf module and exit module. To achieve automatic settlement for each customer, a shopping event detection method is proposed. In this method, a vision-based human pose estimation algorithm is used to detect a human form standing in front of a shelf. The hand actions of each customer are detected by a grating sensor, and an image recognition method based on a convolutional neural network (CNN) is applied to recognize the items in the hands of customers. To reduce the image annotation workload, we propose a semisupervised training method for the recognition network. Based on hand action detection and item recognition, a shopping event recognition method is designed for the system, and a facial image of the customer corresponding to each shopping behavior is captured. Finally, each detected shopping event is added to the invoice of the corresponding customer via a facial recognition method. To verify the effectiveness of the proposed IUS system, we have built a handheld item image dataset and a shopping event dataset for an unmanned convenience store. The experimental results show that the proposed system can accurately recognize shopping behaviors and generate invoices. © 2020 IEEE.

关键词： Sales

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Tile selection method based on error minimization for photomosaic image creation

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Frontiers of computer science 2021年第3期15卷 165-172页

作者： Hongbo ZHANG Xin GAO Jixiang DU Qing LEI Lijie YANG Department of Computer Science and Technology Huaqiao UniversityXiamen 361021China Fujian Key Laboratory of Big Data Intelligence and Security Huaqiao UniversityXiamen 361021China Xiamen Key Laboratory of Computer Vision and Pattern Recognition Huaqiao UniversityXiamen 361021China School of Computer Science and Technology Harbin Institute of TechnologyShenzhen 518055China

Photomosaic images are composite images composed of many small images called *** its overall visual effect,a photomosaic image is similar to the target image,and photomosaics are also called“montage art”.Noisy blocks and the loss of local information are the major obstacles in most methods or programs that create photomosaic *** solve these problems and generate a photomosaic image in this study,we propose a tile selection method based on error minimization.A photomosaic image can be generated by partitioning the target image in a rectangular pattern,selecting appropriate tile images,and then adding them with a weight *** on the principles of montage art,the quality of the generated photomosaic image can be evaluated by both global and local *** the proposed framework,via an error function analysis,the results show that selecting a tile image using a global minimum distance minimizes both the global error and the local error ***,the weight coefficient of the image superposition can be used to adjust the ratio of the global and local ***,to verify the proposed method,we built a new photomosaic creation dataset during this *** experimental results show that the proposed method achieves a low mean absolute error and that the generated photomosaic images have a more artistic effect than do the existing approaches.

关键词： photomosaic image tile image target image error minimization mean absolute error

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