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arXiv 2024年

作者： Shi, Minjia Wang, Xuan An, Junmin Kim, Jon-Lark The Key Laboratory of Intelligent Computing Signal Processing Ministry of Education School of Mathematical Sciences Anhui University Hefei230601 China State Key Laboratory of integrated Service Networks Xidian University Xi’an710071 China Department of Mathematics Sogang University Seoul Korea Republic of

We introduce the notion of logarithmically concave (or log-concave) sequences in Coding Theory. A sequence a0, a1, . . ., an of real numbers is called log-concave if a2i ≥ ai−1ai+1 for all 1 ≤ i ≤ n−1. A natural sequence of positive numbers in coding theory is the weight distribution of a linear code consisting of the nonzero values among Ai’s where Ai denotes the number of codewords of weight i. We call a linear code log-concave if its nonzero weight distribution is log-concave. Our main contribution is to show that all binary general Hamming codes of length 2r − 1 (r = 3 or r ≥ 5), the binary extended Hamming codes of length 2r (r ≥ 3), and the second order Reed-Muller codes R(2, m) (m ≥ 2) are all log-concave while the homogeneous and projective second order Reed-Muller codes are either log-concave, or 1-gap log-concave. Furthermore, we show that any MDS [n, k] code over Fq satisfying 3 ≤ k ≤ n/2 + 3 is log-concave if q ≥ q0(n, k) which is the larger root of a quadratic polynomial. Hence, we expect that the concept of log-concavity in coding theory will stimulate many interesting *** Codes 94B15 © 2024, CC BY.

关键词： Turbo codes

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Facial StO2-based Stress Recognition using Automatic Graph Generation and Dual-Stream GNN

Facial StO2-based Stress Recognition using Automatic Graph G...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yingjuan Jia Hanpu Wang Xinyu Liu Tong Chen College of Electronic and Information Engineering Southwest University China Chongqing Key Laboratory of Generic Technology and System of Service Robots Southwest University China Institute of Legal Psychology and Intelligent Computing Southwest University China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

The recognition of stress responses in health, resilience, and psychopathology holds significant scientific importance. The biological information carried by the facial tissue oxygen saturation (StO2) is a useful indicator for stress recognition. Although graph-based methods have achieved state-of-the-art (SOTA), there is room for improvement. This study proposes an automatic graph generation method for facial StO2, replacing manual methods and enhancing the information of graphs. To further enhance the capability of graphs’ representation, a dimensionality reduction strategy for nodes is proposed. The strategy is implemented based on an novel objective function that can increase the inter-class distance and reduce the intra-class distance. After obtaining highly representative graphs, a dual-stream network integrating GAT and GCN is designed to extract stress-related features from the graphs, ultimately achieving the SOTA recognition accuracy.

关键词： Dimensionality reduction Accuracy Face recognition Manuals Feature extraction Linear programming Biology Convolutional neural networks Stress Resilience

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Joint Extraction of Entities and Relations by Adversarial Training and Mixup Data Augmentation 7

Joint Extraction of Entities and Relations by Adversarial Tr...

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7th International Conference on Computer and Communications, ICCC 2021

作者： Chen, Hongjing Lu, Gang Wu, Xiaojun Zhang, Yumei SNNU Key Laboratory of Intelligent Computing and Service Technology For Folk Song Ministry of Culture and Tourism Shaanxi Normal University School of Computer Science Xi'an China

ISBN: (纸本)9781665409506

Joint extraction of entities and relations is an important task for building a knowledge graph and information extraction. However, small interference in the text can greatly change the semantics of words and sentences in natural language, thereby affecting the model's prediction results. To solve the sensitivity of text to noise, in this paper, we present a method, called BERT of Adversarial Training and Sentence Mixup Data Augmentation (BERT-AT-SMDA), which use the BERT pre-training model to obtain the embedding vector representation of the text and then to incorporate the FGM adversarial training strategy into the fine-tuning of Bert to construct adversarial samples. Both the adversarial sample and the original sample into the encoder to training. Then, to prevent the over-fitting phenomenon and the additional interference caused by the instability of adversarial training, we introduce the mixup data augmentation method after obtaining the hidden layer of the encoder output, which performs a linear interpolation between the output hidden layer. In addition, we also study the effect of linear interpolation at different positions on the model's joint extraction of entities and relations. We conduct experiments on a public dataset produced by the distant supervision method. The experimental results show that our method is effective for the joint extraction method based on tagging scheme. Not only improves the accuracy of entity and relation extraction, and ensures the robustness and generalization of the model. © 2021 IEEE.

关键词： Interpolation

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Expanding self-orthogonal codes over a ring Z4 to self-dual codes and unimodular lattices

arXiv

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arXiv 2024年

作者： Shi, Minjia Tao, Sihui Hong, Jihoon Kim, Jon-Lark The Key Laboratory of Intelligent Computing Signal Processing Ministry of Education School of Mathematical Sciences Anhui University Anhui Hefei230601 China State Key Laboratory of Integrated Service Networks Xidian University Xi’an710071 China The Department of Mathematics Sogang University Seoul Korea Republic of

Self-dual codes have been studied actively because they are connected with mathematical structures including block designs and lattices and have practical applications in quantum error-correcting codes and secret sharing schemes. Nevertheless, there has been less attention to construct self-dual codes from self-orthogonal codes with smaller dimensions. Hence, the main purpose of this paper is to propose a way to expand any self-orthogonal code over a ring Z4 to many self-dual codes over Z4. We show that all self-dual codes over Z4 of lengths 4 to 8 can be constructed this way. Furthermore, we have found five new self-dual codes over Z4 of lengths 27, 28, 29, 33, and 34 with the highest Euclidean weight 12. Moreover, using Construction A applied to our new Euclidean-optimal self-dual codes over Z4, we have constructed a new odd extremal unimodular lattice in dimension 34 whose kissing number was not previously known. © 2024, CC0.

关键词： Crystal lattices

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KF-TMIAF: An Efficient key-Frames-Based Temporal Modeling of Images and AU Features structure for Masked Facial Expression Recognition

KF-TMIAF: An Efficient Key-Frames-Based Temporal Modeling of...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Huijie Gu Hanpu Wang Shiwei He Mengyan Li Jianmeng Zhou Tong Chen College of Electronic and Information Engineering Southwest University Chongqing China Chongqing Key Laboratory of Generic Technology and System of Service Robots SWU China Institute of Legal Psychology and Intelligent Computing Southwest University Chongqing China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Masked Facial Expression refers to facial expression that people deliberately make which do not correspond to their true emotions. The purpose of masked facial expression is to conceal one’s genuine feelings. Precisely identifying masked facial expression aids in revealing the genuine emotions, and finds applications in diagnosis of mental illness. However, the masked facial expression sequences have redundant information, temporal modeling is difficult, and the expressive ability of AU features needs to be improved. To solve the existing problems, we first propose a key frame Selection and Data Augmentation (KS-DA) method to obtain the key frame in the sequence. In addition, we propose a Global Feature Relation Block (GFR) to aggregate global information and combine it with Temporal Convolutional Networks (TCN) to better realize temporal modeling. Finally, we propose an Adaptive Weight Generation Module (Ada-WGM) to form different weights from adaptation, allowing for better extraction of the temporal changes in AU features. Finally, the experiments have shown that the proposed method has improved the mixed expression classification task (36 task) by 15.23% and the experienced emotions classification task (6E task) by 17.77%, respectively, and obtain sort results.

关键词： Training Gold Image recognition Face recognition Computational modeling Video sequences Feature extraction Data augmentation Computational efficiency Convolutional neural networks

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Unsupervised Multivariate Time Series Anomaly Detection by Feature Decoupling in Federated Learning Scenarios

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： He, Yifan Ding, Xi Tang, Yateng Guan, Jihong Zhou, Shuigeng Fudan University School of Computer Science Shanghai200433 China Shanghai Key Laboratory of Intelligent Information Processing Shanghai200433 China Tencent Weixin Group Guangzhou510308 China Tongji University Department of Computer Science and Technology Shanghai201804 China Ministry of Education Key Laboratory of Embedded System and Service Computing Tongji University Shanghai200092 China

Anomalies are usually regarded as data errors or novel patterns previously unseen, which are quite different from most observed data. Accurate detection of anomalies is crucial in various application scenarios. This paper focuses on unsupervised anomaly detection from Multivariate Time Series (MTS), as real-world data collected from sources such as wearable devices, medical equipment and industrial machines typically manifest as MTS and are often unlabeled. Anomaly detection in MTS represents a data-driven challenge that traditionally requires substantial centralized data for training models. However, in practice, data are frequently distributed among multiple institutions, with privacy concerns restricting unrestricted access. To address these issues, we introduce Feature Decoupling Federated Learning (FDFL), an approach designed to collaboratively train a representation learning network over multiple clients for unsupervised anomaly detection in MTS. Unlike previous methods that simply integrate MTS anomaly detection algorithms with federated learning strategies, FDFL specifically addresses heterogeneity among clients by decoupling the representation network into shared and private branches through a contrastive learning mechanism. This method aggregates shared parameters during each federated round while maintaining client-specific private parameters locally. Additionally, we develop a self-attention block that integrates the representations derived from both shared and private parameters to reconstruct MTS and identify anomalies based on reconstruction errors. Extensive experiments conducted on three publicly available datasets demonstrate that FDFL outperforms existing algorithms in most cases, highlighting the effectiveness and superiority of our proposed method in MTS anomaly detection. © 2020 IEEE.

关键词： Federated learning

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Behavioral Unicity: On the Limits of Anonymized Social Behavior Metadata 1st

Behavioral Unicity: On the Limits of Anonymized Social Beh...

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1st Conference on Blockchain and Web3.0 Technology Innovation and Application, BWTAC 2024

作者： Tang, Hao Wang, Cheng Tongji University Shanghai201804 China The Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai201804 China Shanghai Artificial Intelligence Laboratory Shanghai200030 China Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai200092 China

ISBN: (纸本)9789819794119

Social behavior metadata has altered business models and human lifestyles. However, the inclusion of personal information in social behavior metadata poses risks of identity exposure for users. Under the requirements of modern data management laws, service providers now employ anonymization techniques when collecting user behavior metadata, which aims to break the link between personal identifiers and behavioral attributes for privacy protection. This paper conducts experiments on an open-source dataset of anonymized social behavior metadata and finds that even with privacy protection techniques such as data generalization, there remains a high risk of reidentification. Specifically, four social behavior metadata records are sufficient to reidentify users from the anonymized social behavior metadata, achieving a unicity of 90.10%. This indicates that the current widespread collection of social behavior metadata brings a high risk of privacy breaches. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Anonymity

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MixSA: Training-free Reference-based Sketch Extraction via Mixture-of-Self-Attention

arXiv

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arXiv 2025年

作者： Yang, Rui Wu, Xiaojun He, Shengfeng The School of Computer Sciences Shaanxi Normal University Xi'An China The School of Computer Science The Key Laboratory of Intelligent Computing and Service Technology for Folk Song of the Ministry of Culture and Tourism Shaanxi Normal University Xi'An710119 China The School of Computing and Information Systems Singapore Management University Singapore

Current sketch extraction methods either require extensive training or fail to capture a wide range of artistic styles, limiting their practical applicability and versatility. We introduce Mixture-of-Self-Attention (MixSA), a training-free sketch extraction method that leverages strong diffusion priors for enhanced sketch perception. At its core, MixSA employs a mixture-of-self-attention technique, which manipulates self-attention layers by substituting the keys and values with those from reference sketches. This allows for the seamless integration of brushstroke elements into initial outline images, offering precise control over texture density and enabling interpolation between styles to create novel, unseen styles. By aligning brushstroke styles with the texture and contours of colored images, particularly in late decoder layers handling local textures, MixSA addresses the common issue of color averaging by adjusting initial outlines. Evaluated with various perceptual metrics, MixSA demonstrates superior performance in sketch quality, flexibility, and applicability. This approach not only overcomes the limitations of existing methods but also empowers users to generate diverse, high-fidelity sketches that more accurately reflect a wide range of artistic expressions. Copyright © 2025, The Authors. All rights reserved.

关键词： Image texture

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A Fine-Grained Anomaly Detection Method Fusing Isolation Forest and Knowledge Graph Reasoning 19th

A Fine-Grained Anomaly Detection Method Fusing Isolation For...

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19th International Conference on Web Information Systems and Applications, WISA 2022

作者： Xu, Jie Zhou, Jiantao College of Computer Science Engineering Research Center of Ecological Big Data Ministry of Education National and Local Joint Engineering Research Center of Mongolian Intelligent Information Processing Technology Inner Mongolia Cloud Computing and Service Software Engineering Laboratory Inner Mongolia Social Computing and Data Processing Key Laboratory Inner Mongolia Discipline Inspection and Supervision Big Data Key Laboratory Inner Mongolia Big Data Analysis Technology Engineering Laboratory Inner Mongolia University Hohhot China

ISBN: (纸本)9783031203084

Anomaly detection aims to find outliers data that do not conform to expected behaviors in a specific scenario, which is indispensable and critical in current safety environments related studies. However, when performing outlier detection on a large-scale multidimensional dataset, most of the traditional methods make no distinctions between classes of outliers and lack the reasoning ability and the explainability of the analysis results, which leads to the low accuracy of global outlier detection and the inability to effectively identify local outliers. In this paper, we propose a fine-grained anomaly detection method which well utilizes isolation forest and knowledge graph reasoning tactics. First, a better extracting data feature method and a more reasonable weighted strategy are used to find global outliers based on the traditional isolation forest algorithm, and then we construct a custom rule base according to the in-depth research and analysis of global abnormal data, and at last the ontology knowledge is reasoned based on such rule base to achieve the detection of local outliers. Through analysis of extensive experiment results, our method could effectively discover more abnormal data without big loss of time costs, has strong generalization, and correspondingly improves the performance for abnormal detection. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Anomaly detection

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Adaptive ELM-Based Time-Constrained Control of Uncertain Robotic Manipulators With Minimum Learning Computation

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IEEE Transactions on Emerging Topics in computing 2025年

作者： Jin, Xiaozheng Hou, Yuhan Wu, Xiaoming Chi, Jing Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Shandong Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Jinan250013 China Qilu Institute of Technology School of Intelligent Manufacturing and Control Engineering Shandong Jinan250200 China Shandong University of Finance and Economics School of Computing and Artificial Intelligence Shandong Jinan250014 China

This paper studies the extreme learning machine (ELM)-based time-constrained trajectory tracking control of a class of robotic manipulator systems with model/input uncertainties, matched and mismatched disturbances. A uniform robust exact differentiator (URED) is developed to construct a multi-layer virtual control framework of the robotic manipulator system. Under the control framework, adaptive ELM-based control strategies are proposed to suppress the influence of the uncertainties and disturbances layer-by-layer, combining with the minimum learning parameter (MLP) technique which is employed to reduce the calculation complexity of ELM parameters. The significance of this study is that general model/input uncertainties, matched and mismatched disturbances can be effectively compensated within a limited time, so that bounded trajectory tracking of the robotic manipulator can be achieved avoiding the complexity explosion issue. The time-constrained stability of the closed-loop robotic error systems is proved through Lyapunov stability theory. Finally, comparative simulations are employed to display the feasibility and superiority of the designed robust adaptive ELM-based control schemes of a robotic manipulator system. © 2013 IEEE.

关键词： Robust control

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