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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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Fatigue detection using a pressure sensor chair

Fatigue detection using a pressure sensor chair

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2011 IEEE International Conference on Granular Computing, GrC 2011

作者： Nakane, Hisataka Toyama, Jun Kudo, Mineichi Laboratory for Pattern Recognition and Machine Learning Hokkaido University Sapporo Japan

ISBN: (纸本)9781457703713

Fatigue is considered a contributing factor to accidents and illnesses at work. Hence, detecting fatigue by routinely monitoring an individual's person health is useful in preventing accidents and illnesses. There are many conventional methods for detecting fatigue;However, these methods require users to monitor the extent of their fatigue with gauges and instruments that can restrict users' actions. On the other hand, fatigue detection using posturography can mitigate this problem. This study focuses on posturography in seated position, because sitting is quite a common action in daily life, and posturography in seated position can be measured without restricting the users' movement. Posturography in seated position is measured when subjects sit on a chair attached to a pressure sensor sheet. We formed an assumption that the more tired a person is, the more rapidly the COP (center of pressure) converges toward a point. Based on this assumption, we investigated the difference between postural sway in seated position as subjects went from experiencing a condition of fatigue to non-fatigue. Experimental result showed that the COP for left-right axis for subjects experiencing fatigue converged earlier than for those experiencing non-fatigue. © 2011 IEEE.

关键词： Pressure sensors

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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.

关键词： Qubits

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Robust human pose estimation from corrupted images with partial occlusions and noise pollutions

Robust human pose estimation from corrupted images with part...

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2011 IEEE International Conference on Granular Computing, GrC 2011

作者： Lu, Guoliang Kudo, Mineichi Toyama, Jun Laboratory of Pattern Recognition and Machine Learning Graduate School of Information Science and Technology Hokkaido University Sapporo 060-0814 Japan

ISBN: (纸本)9781457703713

Robust human pose estimation from the given visual observations has attracted many attentions in the past two decades. However, this problem is still challenging due to the suituation that observations are often corrupted with partial occlusions or noise pollutions or both in real-world applications. In this paper, we propose to estimate human pose by using robust silhouette matching in original rectangle-coordinate space. In addition, human action model is employed to determinate reasonable matching results. Experimental results on robustness sequence of Weizman dataset reveal that our proposed approach can estimate human pose robustly and reasonably when pose observations are corrupted with partial occlusions or noise pollutions. © 2011 IEEE.

关键词： Noise pollution

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

Internet of Things (The Netherlands)

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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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Fatigue detection using a pressure sensor chair

Fatigue detection using a pressure sensor chair

引用

IEEE International Conference on Granular Computing (GRC)

作者： Hisataka Nakane Jun Toyama Mineichi Kudo Laboratory for Pattern Recognition and Machine Learning Hokkaido University Sapporo Japan

Fatigue is considered a contributing factor to accidents and illnesses at work. Hence, detecting fatigue by routinely monitoring an individual's person health is useful in preventing accidents and illnesses. There are many conventional methods for detecting fatigue; However, these methods require users to monitor the extent of their fatigue with gauges and instruments that can restrict users' actions. On the other hand, fatigue detection using posturography can mitigate this problem. This study focuses on posturography in seated position, because sitting is quite a common action in daily life, and posturography in seated position can be measured without restricting the users' movement. Posturography in seated position is measured when subjects sit on a chair attached to a pressure sensor sheet. We formed an assumption that the more tired a person is, the more rapidly the COP (center of pressure) converges toward a point. Based on this assumption, we investigated the difference between postural sway in seated position as subjects went from experiencing a condition of fatigue to non-fatigue. Experimental result showed that the COP for left-right axis for subjects experiencing fatigue converged earlier than for those experiencing non-fatigue.

关键词： Fatigue Position measurement Accidents Monitoring Laboratories Muscles

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DNLM-MA-P: A Parallelization of the Deceived Non Local Means Filter with Moving Average and Symmetric Weighting

DNLM-MA-P: A Parallelization of the Deceived Non Local Means...

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2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018

作者： Calderon, Saul Castro, Jorge Zurnbado, Manuel Pattern Recognition and Machine Learning Group School of Computing Costa Rica Institute of Technology Costa Rica Advanced Computing Laboratory Costa Rica National High Technology Center Costa Rica

ISBN: (纸本)9781538675069

This paper presents a novel computational optimization of the deceived non local means filter using moving average and symmetric weighting. The proposed optimization is compared with different approaches that reduce the computational cost of the deceived non local means filter. Furthermore, the impact of parallelizing different optimization approaches is assessed by evaluating the execution time and scalability in Xeon Phi KNL architecture. The proposed optimization for the sequential implementation achieved a 90x speedup, while its parallelized implementation yielded a speedup of up to 1662x. © 2018 IEEE.

关键词： Image denoising

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Colorectal Segmentation Using Multiple Encoder-Decoder Network in Colonoscopy Images

Colorectal Segmentation Using Multiple Encoder-Decoder Netwo...

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Artificial Intelligence and Knowledge Engineering (AIKE)

作者： Quang Nguyen Sang-Woong Lee Pattern Recognition and Machine Learning Laboratory Gachon University Seongnam South Korea

ISBN: (纸本)9781538695562;9781538660843

Colorectal cancer is the third most common cancer which causes of cancer-related deaths. Therefore, early diagnosis of polyps by colonoscopy could result in successful treatment. Diagnosis of polyps in colonoscopy videos is a challenging task due to variations in the size and shape of polyps. In this paper, we propose a polyp segmentation method based on the encoder-decoder network. Performance of the method is enhanced by two strategies, we perform a novel database augmentation method for colonoscopy images in the training phase. Besides, in the test phase, we perform an effective prediction by combining multi-model to compare the probability of each image that is produced by the network. Evaluation of the proposed method using the ETIS-LariPolypDB database shows that our proposed method outperforms state-of-the-art results.

关键词： Image segmentation Training Convolution Testing Colonoscopy Databases Tumors

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CMQA: A Dataset of Conditional Question Answering with Multiple-Span Answers 29

CMQA: A Dataset of Conditional Question Answering with Multi...

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29th International Conference on Computational Linguistics, COLING 2022

作者： Ju, Yiming Wang, Weikang Zhang, Yuanzhe Zheng, Suncong Liu, Kang Zhao, Jun National Laboratory of Pattern Recognition Institute of Automation CAS Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Machine Learning Platform Department Tencent China

Forcing the answer of the Question Answering (QA) task to be a single text span might be restrictive since the answer can be multiple spans in the context. Moreover, we found that multi-span answers often appear with two characteristics when building the QA system for a real-world application. First, multi-span answers might be caused by users lacking domain knowledge and asking ambiguous questions, which makes the question need to be answered with conditions. Second, there might be hierarchical relations among multiple answer spans. Some recent span-extraction QA datasets include multi-span samples, but they only contain unconditional and parallel answers, which cannot be used to tackle this problem. To bridge the gap, we propose a new task: conditional question answering with hierarchical multi-span answers, where both the hierarchical relations and the conditions need to be extracted. Correspondingly, we introduce CMQA, a Conditional Multiple-span Chinese Question Answering dataset to study the new proposed task. The final release of CMQA consists of 7,861 QA pairs and 113,089 labels, where all samples contain multi-span answers, 50.4% of samples are conditional, and 56.6% of samples are hierarchical. CMQA can serve as a benchmark to study the new proposed task and help study building QA systems for real-world applications. The low performance of models drawn from related literature shows that the new proposed task is challenging for the community to solve. CMQA can be accessed at https://***/juyiming/CMQA. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Domain Knowledge

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Robust human pose estimation from corrupted images with partial occlusions and noise pollutions

Robust human pose estimation from corrupted images with part...

引用

IEEE International Conference on Granular Computing (GRC)

作者： Guoliang Lu Mineichi Kudo Jun Toyama Laboratory of pattern recognition and machine learning Graduate School of Information Science and Technology Hokkaido University Sapporo Japan

Robust human pose estimation from the given visual observations has attracted many attentions in the past two decades. However, this problem is still challenging due to the situation that observations are often corrupted with partial occlusions or noise pollutions or both in real-world applications. In this paper, we propose to estimate human pose by using robust silhouette matching in original rectangle-coordinate space. In addition, human action model is employed to determinate reasonable matching results. Experimental results on robustness sequence of Weizman dataset reveal that our proposed approach can estimate human pose robustly and reasonably when pose observations are corrupted with partial occlusions or noise pollutions.

关键词： Estimation Legged locomotion Humans Testing Computational modeling Feature extraction Robustness

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