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47th International Conference on Telecommunications and Signal Processing, TSP 2024

作者： Mekruksavanich, Sakorn Phaphan, Wikanda Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand King Mongkut's University of Technology North Bangkok Research Group in Statistical Learning and Inference Faculty of Applied Science Department of Applied Statistics Bangkok Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Faculty of Applied Science Department of Mathematics Bangkok Thailand

ISBN: (纸本)9798350365597

Everyday actions like scratching one's nose or resting the chin on one's hand may facilitate the spread of germs and diseases. Detecting these gestures holds the potential for innovative health monitoring and disease prevention applications. However, the identification of face-touching poses challenges due to the variety of human gestures and the limitations in accuracy associated with wearable sensors. This study introduces an original deep-learning system to identify face-touching gestures using standard smartwatches' inertial measurement unit sensors. The system on the smartwatch captures and pre-processes multi-channel time-series data from the accelerometer to generate robust input features. We utilize a benchmark, the Face Touching dataset, to evaluate the recognition performance of deep learning networks, including our proposed network. Our approach proposes a hybrid deep residual network architecture tailored for sequence-based gesture classification using signals from the smartwatch sensors. In our experiments, the developed deep learning framework achieves an impressive F1-score of 97.53% in detecting face-touch gestures. The suggested system takes a step forward in advancing the practical applications of face-touch gesture detection to smartwatch-based health sensing and disease prevention. © 2024 IEEE.

关键词： Network architecture

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Region-aware neural graph collaborative filtering for personalized recommendation

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International Journal of Digital Earth 2022年第1期15卷 1446-1462页

作者： Shengwen Li Renyao Chen Chenpeng Sun Hong Yao Xuyang Cheng Zhuoru Li Tailong Li Xiaojun Kang State Key Laboratory of Biogeology and Environmental Geology China University of GeosciencesWuhanPeople’s Republic of China School of Computer Science China University of GeosciencesWuhanPeople’s Republic of China School of Future Technology China University of GeosciencesWuhanPeople’s Republic of China National Engineering Research Center of Geographic Information System China University of GeosciencesWuhanPeople’s Republic of China

Personalized recommender systems have been widely deployed in various scenarios to enhance user experience in response to the challenge of information ***,personalized recommendation models based on graph structure have advanced greatly in predicting user ***,geographical region entities that reflect the geographical context of the items is not being utilized in previous works,leaving room for the improvement of personalized *** study proposes a region-aware neural graph collaborative filtering(RA-NGCF)model,which introduces the geographical regions for improving the prediction of user *** approach first characterizes the relationships between items and users with a user-item-region ***,a neural network model for the region-aware graph is derived to capture the higher-order interaction among users,items,and ***,the model fuses region and item vectors to infer user *** on real-world dataset results show that introducing region entities improves the accuracy of personalized *** study provides a new approach for optimizing personalized recommendation as well as a methodological reference for facilitating geographical regions for optimizing spatial applications.

关键词： Collaborative filtering neural graph collaborative filtering geographical region personalized recommendation graph convolution networks

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Learning Optimal "Pigovian Tax" in Sequential Social Dilemmas

arXiv

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arXiv 2023年

作者： Hua, Yun Gao, Shang Li, Wenhao Jin, Bo Wang, Xiangfeng Zha, Hongyuan School of Computer Science and Technology East China Normal University Shanghai200092 China School of Data Science The Chinese University of Hong Kong Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Shenzhen China School of Software Engineering Tongji University Shanghai201804 China School of Computer Science and Technology East China Normal University Shanghai200062 China

In multi-agent reinforcement learning, each agent acts to maximize its individual accumulated rewards. Nevertheless, individual accumulated rewards could not fully reflect how others perceive them, resulting in selfish behaviors that undermine global performance. The externality theory, defined as "the activities of one economic actor affect the activities of another in ways that are not reflected in market transactions," is applicable to analyze the social dilemmas in MARL. One of its most profound non-market solutions, "Pigovian Tax", which internalizes externalities by taxing those who create negative externalities and subsidizing those who create positive externalities, could aid in developing a mechanism to resolve MARL's social dilemmas. The purpose of this paper is to apply externality theory to analyze social dilemmas in MARL. To internalize the externalities in MARL, the Learning Optimal Pigovian Tax method (LOPT), is proposed, where an additional agent is introduced to learn the tax/allowance allocation policy so as to approximate the optimal "Pigovian Tax" which accurately reflects the externalities for all agents. Furthermore, a reward shaping mechanism based on the approximated optimal "Pigovian Tax" is applied to reduce the social cost of each agent and tries to alleviate the social dilemmas. Compared with existing state-of-the-art methods, the proposed LOPT leads to higher collective social welfare in both the Escape Room and the Cleanup environments, which shows the superiority of our method in solving social dilemmas. Copyright © 2023, The Authors. All rights reserved.

关键词： Taxation

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Spectral Extrema of Bipartite Graphs: Forbidden an Even Cycle of Specified Length

SSRN

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SSRN 2024年

作者： Feng, Zhimin Luo, Xiaobing Wei, Wei School of Mathematics and Statistics Xinyang Normal University Xinyang China School of Computer Technology and Software Engineering Wuhan Polytechnic Wuhan China Center of Intelligent Computing and Applied Statistics School of Mathematics Physics and Statistics Shanghai University of Engineering Science Shanghai201620 China

A graph is called $H$-free if the graph has no subgraph being isomorphic to $H$. The classical Tur\'an problem aims to determine the maximum size of an $H$-free graph with given order. In 2010, Nikiforov introduced the spectral Tur\'an-type problem: Determine the maximum spectral radius of an $H$-free graph with given order. He also presented the following conjecture: For sufficiently large $\nu$ and for $k\geq 2$, the graph $K_k\vee (\overline{K_{\nu-k-2}}\cup K_2)$ is the unique $\nu$-vertex $\{C_{2k+1}, C_{2k+2}\}$-free graph having the largest spectral radius and $K_k\vee \overline{K_{\nu-k}}$ is the unique $\nu$-vertex $C_{2k+2}$-free graph having the largest spectral radius. Recently, this conjecture was confirmed by Cioab\u{a}, Desai and Tait. In this contribution, we resolve the Nikiforov's even cycle conjecture for bipartite graphs. Let $\nu$ and $k$ be two integers satisfying $\nu\geq 1$ if $k=1,$ and $\nu \geq \left(\frac{7^22^{24}k\left(2k-1\right)^2(k+1)^{20}} {(2k-3)^2}\right)^{\frac{k+1}{k-1}}$ if $k\geq 2$. Then we show that the complete bipartite graph $K_{k,\nu-k}$ uniquely maximizes the spectral radius over the set of $C_{2k+2}$-free bipartite graphs of order $\nu$. © 2024, The Authors. All rights reserved.

关键词： Graph theory

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DCNet: Weakly Supervised Saliency Guided Dual Coding Network for Visual Sentiment Recognition 26

DCNet: Weakly Supervised Saliency Guided Dual Coding Network...

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26th European Conference on Artificial Intelligence, ECAI 2023

作者： Zhang, Xinyue Xiang, Jing Zhang, Hanxiu Wu, Chunwei Wang, Hailing Cao, Guitao School of Computer Science and Technology East China Normal University Shanghai China Shanghai Institute of Ai for Education East China Normal University Shanghai China MoE Engineering Research Center of SW/HW Co-design Technology and Application East China Normal University Shanghai China

ISBN: (纸本)9781643684369

Visual sentiment recognition is a challenging task with scientific significance in probing vision-processing mechanisms. Recent approaches mainly focused on using overall images or precise annotations to learn emotional representations, yet neglected to capture abstract semantics from regional information, or led to a heavy annotation burden. In this paper, we propose an end-to-end weakly supervised framework, called Dual Coding Network (DCNet), which models a dual coding process for both shallow features and high-level regional information. On the one hand, with the help of the fine-grained module (FG), visual features (e.g. texture features) are utilized to enhance the learning of distinguished representation. On the other hand, the DCNet innovatively leverages saliency information to imitate the neural decoding of perceived visual sentiment contents in human brain activity. Specifically, the saliency information guides the generation of sentiment-specific pseudo affective maps (SAMG), which serve as weak annotations. Then the DCNet couples fine-grained features with pseudo affective maps, and obtains semantic vectors for final sentiment prediction. Extensive experiments show that the proposed DCNet outperforms the state-of-the-art performance on five benchmark datasets. © 2023 The Authors.

关键词： Network coding

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Measuring Encrypted DNS Service with TLS1.3 Support over IPv6

Measuring Encrypted DNS Service with TLS1.3 Support over IPv...

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IEEE Symposium on computers and Communications (ISCC)

作者： Liang Jiao Jianqiang Li Wenxiu Zhang Tianyu Cui Yi Zhou Yujia Zhu Qingyun Liu Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security University of Chinese Academy of Sciences National Computer Network Emergency Response Technical Team/Coordination Center of China Shandong Branch National Computer Network Emergency Response Technical Team/Coordination Center School of Information Science and Engineering University of Jinan Zhongguancun Laboratory

ISBN: (数字)9798350354232

ISBN: (纸本)9798350354249

The Encrypted Domain Name System (DNS) and Encrypted Server Name Indication (ESNI) are recently proposed to enhance network security and privacy protection; we refer to these schemes collectively as domain name encryption technologies. Previous research has shown that the destination IP address accessed by the user cannot be associated with common web services such as websites because a large number of websites are hosted through cloud or CDN over IPv6. However, encrypted DNS, as an internet infrastructure service, is typically deployed independently by the service provider rather than hosted through cloud or CDN. In this paper, we propose a method to discover the unique service provider of encrypted DNS resolvers on a large-scale encrypted traffic with TLS1.3 support over IPv6. The model utilizes a Siamese Network to determine whether two IPv6 resolver addresses belong to the same service provider of encrypted DNS, even if the DNS query is protected by ESNI. Through a comprehensive analysis of two real-world datasets, which include encrypted DNS data and common web data, we find that the implementation of TLS1.3, especially ESNI, does not impact the association of encrypted DNS server addresses. Our model achieves an accuracy rate of 95.29%.

关键词： Measurement Cloud computing Privacy Correlation Web services Network security Domain Name System Cryptography Servers Protection

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Learning with Noisy Labels Using Collaborative Sample Selection and Contrastive Semi-Supervised Learning

arXiv

引用

arXiv 2023年

作者： Miao, Qing Wu, Xiaohe Xu, Chao Ji, Yanli Zuo, Wangmeng Guo, Yiwen Meng, Zhaopeng College of Intelligence and Computing Tianjin University Tianjin300072 China School of Computer Science and Technology Harbin Institute of Technology Harbin150006 China Center for Future Media School of Computer Science and Engineering UESTC Chengdu611731 China China

Learning with noisy labels (LNL) has been extensively studied, with existing approaches typically following a framework that alternates between clean sample selection and semi-supervised learning (SSL). However, this approach has a limitation: the clean set selected by the Deep Neural Network (DNN) classifier, trained through self-training, inevitably contains noisy samples. This mixture of clean and noisy samples leads to misguidance in DNN training during SSL, resulting in impaired generalization performance due to confirmation bias caused by error accumulation in sample selection. To address this issue, we propose a method called Collaborative Sample Selection (CSS), which leverages the large-scale pre-trained model CLIP. CSS aims to remove the mixed noisy samples from the identified clean set. We achieve this by training a 2-Dimensional Gaussian Mixture Model (2D-GMM) that combines the probabilities from CLIP with the predictions from the DNN classifier. To further enhance the adaptation of CLIP to LNL, we introduce a co-training mechanism with a contrastive loss in semi-supervised learning. This allows us to jointly train the prompt of CLIP and the DNN classifier, resulting in improved feature representation, boosted classification performance of DNNs, and reciprocal benefits to our Collaborative Sample Selection. By incorporating auxiliary information from CLIP and utilizing prompt fine-tuning, we effectively eliminate noisy samples from the clean set and mitigate confirmation bias during training. Experimental results on multiple benchmark datasets demonstrate the effectiveness of our proposed method in comparison with the state-of-the-art approaches. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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TPE-H2MWD:an exact thumbnail preserving encryption scheme with hidden Markov model and weighted diffusion

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Frontiers of Information Technology & Electronic engineering 2023年第8期24卷 1169-1180页

作者： Xiuli CHAI Xiuhui CHEN Yakun MA Fang ZUO Zhihua GAN Yushu ZHANG School of Artificial Intelligence Henan Engineering Research Center for Industrial Internet of ThingsHenan UniversityZhengzhou450046China Henan Key Laboratory of Cyberspace Situation Awareness Zhengzhou450001China School of Software Intelligent Data Processing Engineering Research Center of Henan ProvinceInstitute of Intelligent Network SystemHenan UniversityKaifeng475004China College of Computer Science and Technology Nanjing University of Aeronautics and AstronauticsNanjing211106China

With the substantial increase in image transmission,the demand for image security is ***-like images can be obtained by conventional encryption schemes,and although the security of the images can be guaranteed,the noise-like images cannot be directly previewed and *** on the rank-then-encipher method,some researchers have designed a three-pixel exact thumbnail preserving encryption(TPE2)scheme,which can be applied to balance the security and availability of images,but this scheme has low encryption *** this paper,we introduce an efficient exact thumbnail preserving encryption ***,blocking and bit-plane decomposition operations are performed on the plaintext *** zigzag scrambling model is used to change the bit positions in the lower four bit ***,an operation is devised to permute the higher four bit planes,which is an extended application of the hidden Markov ***,according to the difference in bit weights in each bit plane,a bit-level weighted diffusion rule is established to generate an encrypted image and still maintain the same sum of pixels within the *** results show that the proposed scheme improves the encryption efficiency and can guarantee the availability of images while protecting their privacy.

关键词： Hidden Markov model Weighted diffusion Balance between usability and privacy Image encryption

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Deep Learning Networks for Human Knee Abnormality Detection Based on Surface EMG Signals 47

Deep Learning Networks for Human Knee Abnormality Detection ...

引用

47th International Conference on Telecommunications and Signal Processing, TSP 2024

作者： Mekruksavanich, Sakorn Phaphan, Wikanda Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand Research Group in Statistical Learning and Inference King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Applied Statistics Bangkok Thailand Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut's University of Technology North Bangkok Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9798350365597

Early knee problem management relies on precise identification and classification of abnormalities. Surface electromyography (sEMG) and goniometer signals offer non-invasive screening for muscle activity and joint angle patterns, yet their complexity poses challenges in extracting critical diagnostic information. This paper proposes a novel deep-learning method using sEMG and goniometer data for knee abnormality diagnosis. The proposed ResNeXt model, employing CNNs and multi-kernel modules, is evaluated on the UCIEMG dataset. Experimental results demonstrate ResNeXt's superior accuracy, precision, recall, and F1-score compared to baseline models (CNN and LSTM). Res NeXt achieves the best performance with combined EMG and goniometer data, reaching 96.37% accuracy and 93.77% F1-score, with fewer trainable parameters, indicating computational efficiency. The findings indicate ResNeXt's effectiveness in identifying knee abnormalities using biosensor data, particularly sEM G and goniometer signals, aiding early disease detection and treatment. © 2024 IEEE.

关键词： Learning systems

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GradedDAG: An Asynchronous DAG-based BFT Consensus with Lower Latency

GradedDAG: An Asynchronous DAG-based BFT Consensus with Lowe...

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Reliable Distributed Systems

作者： Xiaohai Dai Zhaonan Zhang Jiang Xiao Jingtao Yue Xia Xie Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China School of Computer Science and Technology Hainan University China

To enable parallel processing, the Directed Acyclic Graph (DAG) structure is introduced to the design of asyn-chronous Byzantine Fault Tolerant (BFT) consensus protocols, known as DAG-based BFT. Existing DAG-based BFT protocols operate in successive waves, with each wave containing three or four Reliable Broadcast (RBC) rounds to broadcast data, resulting in high latency due to the three communication steps required in each RBC. For instance, Tusk, a state-of-the-art DAG-based BFT protocol, has a good-case latency of 7 communication steps and an expected worst latency of 21 communication steps. To reduce latency, we propose GradedDAG, a new DAG-based BFT consensus protocol based on our adapted RBC called Graded RBC (GRBC) and the Consistent Broadcast (CBC), with each wave consisting of only one GRBC round and one CBC round. Through GRBC, a replica can deliver data with a grade of 1 or 2, and a non-faulty replica delivering the data with grade 2 can ensure that more than 2/3 of replicas have delivered the same data. Meanwhile, through CBC, data delivered by different non-faulty replicas must be identical. In each wave, a block in the GRBC round will be elected as the leader. If a leader block has been delivered with grade 2, it and all its ancestor blocks can be committed. GradedDAG offers a good-case latency of 4 communication steps and an expected worst latency of 7.5 communication steps, significantly lower than the state-of-the-art. Experimental results demonstrate GradedDAG's feasibility and efficiency.

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