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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Junwei Tang Zilong Xu Luyao Ye Tao Peng Ruhan He Xinrong Hu Wenlong Tian Hubei Provincial Engineering Research Center for Intelligent Textile and Fashion Wuhan Textile University School of Computer Science and Artificial Intelligence Wuhan Textile University School of Computer Science University of South China School of Physical and Mathematical Sciences Nanyang Technological University Singapore

Android malware detection has become research hotspot in mobile security. When security service providers obtain feature information from target samples, they may involve user privacy information such as identity and behavioral habits, creating an urgent need for a distributed machine learning model to carry out malicious application detection with privacy protection. However, the information exchange between clients and servers in basic federated learning still poses risks of model data leakage, and the likelihood of inferring privacy information from the original feature data is high. Therefore, This paper propose a privacy-enhanced federated learning system called PE-FedAvg for distributed Android malware detection. Firstly, Gaussian noise is added to the feature data uploaded from the client to the server, satisfying differential privacy characteristics while retaining data distribution for privacy protection. Secondly, an improved homomorphic encryption algorithm based on modular repeated squaring is used to encrypt and transmit the gradient weight information exchanged between federated learning clients and servers, preventing model-targeted attacks. By conducting the experiments on the Drebin and Malgenome datasets, our design has demonstrated exceptional performance compared to the state-of-the-art work, achieving accuracy rates of 98.61% and 99.3%, respectively. This achievement highlights that our approach not only enhances the security of the federated learning system, but also more effectively accomplishes the detection of Android malware.

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Estimation of Comfort or Discomfort State During Sleep Using Lower Limb Movement Features

Estimation of Comfort or Discomfort State During Sleep Using...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Shuzhen Zhou Hazuki Masuda Kazutaka Yamamoto Kenta Fuke Shima Okada Yusuke Sakaue Taizo Aoki Hiroshi Maruyama Graduate School of Science and Engineering Ritsumeikan University Shiga Japan Department of Robotics Ritsumeikan University Shiga Japan Global Innovation Research Organization Ritsumeikan University Shiga Japan Product Analysis Center Panasonic Holdings Corporation Osaka Japan

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

Sleep is a daily and essential activity. A higher occurrence of mid-sleep awakenings, increased body movements, and changes in sleep stages have been reported due to high-temperature/humidity and low-temperature environments. Thus, strategies for adjusting the thermal environment are necessary for comfortable sleep. One of the methods to improve thermal comfort and sleep quality is using airflow devices that control the in-bed environment. However, it might be challenging to achieve a satisfactory thermal environment due to the problem of overcooling. So, real-time control of temperature and humidity conditions and the sleeper's perceived level of comfort or discomfort is required to achieve perfect conditions for good sleep. In this study, we hypothesized that body movements during sleep appear not only with sleep depth transitions but also in response to changes in the uncomfortable in-bed temperature and humidity condition and that characteristics of pleasantness and uncomfortableness are reflected in the waveform of foot acceleration and angular acceleration, enabling real-time assessment of comfort and discomfort states based on the body movement waveform during sleep. Based on these assumptions, an experiment was conducted with five subjects who were asked to sleep in a comfortable sleeping environment created by using a blower device to supply air from the foot and in an environment without airflow. The videos of their sleep were recorded, and lower limb acceleration and angular acceleration were measured during sleep under two conditions: pleasant and unpleasant temperatures. To clarify the acceleration characteristics at each sleep stage and estimate the comfort and discomfort state, we referred to the FLACC behavior scale, commonly used to assess pain and discomfort. Originally developed to assess the condition of infants, it can also be used for individuals who cannot communicate. By assigning to the face (F), lower limb (L), and activity (A) parts of the

关键词： Accelerometers Temperature measurement Limbs Accuracy Temperature Estimation Real-time systems Physiology Standards Videos

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An Adaptive Decision-Making Approach for Better Selection of Blockchain Platform for Health Insurance Frauds Detection with Smart Contracts: Development and Performance Evaluation 14

An Adaptive Decision-Making Approach for Better Selection of...

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14th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023

作者： Kaafarani, Rima Ismail, Leila Zahwe, Oussama ICCS-Lab Computer Science Department American University of Culture and Education Beirut1507 Lebanon Laboratory School of Computing and Information Systems The University of Melbourne Melbourne Australia Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Abu Dhabi United Arab Emirates National Water and Energy Center United Arab Emirates University Abu Dhabi United Arab Emirates

Blockchain technology has piqued the interest of businesses of all types, while consistently improving and adapting to business requirements. Several blockchain platforms have emerged, making it challenging to select a suitable one for a specific type of business. This paper presents a classification of over one hundred blockchain platforms. We develop smart contracts for detecting healthcare insurance frauds using the top two blockchain platforms selected based on our proposed decision-making map approach which selects the top suitable platforms for healthcare insurance frauds detection application. Our classification shows that the largest percentage of platforms can be used for all types of application domains, the second biggest percentage for financial services, and a small number is to develop applications in specific domains. Our decision-making map and performance evaluations reveal that Hyperledger Fabric surpassed Neo in all metrics for detecting healthcare insurance frauds. © 2023 Elsevier B.V.. All rights reserved.

关键词： Smart contract

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L-CAD: language-based colorization with any-level descriptions using diffusion priors 23

L-CAD: language-based colorization with any-level descriptio...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Zheng Chang Shuchen Weng Peixuan Zhang Yu Li Si Li Boxin Shi School of Artificial Intelligence Beijing University of Posts and Telecommunications National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University and National Engineering Research Center of Visual Technology School of Computer Science Peking University International Digital Economy Academy

Language-based colorization produces plausible and visually pleasing colors under the guidance of user-friendly natural language descriptions. Previous methods implicitly assume that users provide comprehensive color descriptions for most of the objects in the image, which leads to suboptimal performance. In this paper, we propose a unified model to perform language-based colorization with any- level descriptions. We leverage the pretrained cross-modality generative model for its robust language understanding and rich color priors to handle the inherent ambiguity of any-level descriptions. We further design modules to align with input conditions to preserve local spatial structures and prevent the ghosting effect. With the proposed novel sampling strategy, our model achieves instance-aware colorization in diverse and complex scenarios. Extensive experimental results demonstrate our advantages of effectively handling any-level descriptions and outperforming both language-based and automatic colorization methods. The code and pretrained models are available at: https://***/changzheng123/L-CAD.

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A Few-Shot Network Flow Attack Classification via Graph Contrastive Learning

A Few-Shot Network Flow Attack Classification via Graph Cont...

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IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)

作者： Binbin Gel Bo Li Xudong Mou Jun Zhao Xudong Liu School of Computer Science and Engineering Beihang University Beijing China Zhongguancun Laboratory Beijing China Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing Beijing China

ISBN: (数字)9798350376982

ISBN: (纸本)9798350376999

Accurately identifying network attacks is crucial for maintaining network security. However, these attacks are often hide within massive volumes of network traffic, posing significant challenges for traditional detection methods. Supervised learning approaches require substantial labeled data and struggle to adapt to unknown attack types, while unsupervised methods face difficulties in accurately pinpointing specific attack categories. To address these limitations, we propose a novel fewshot learning model for network flow attack classification based on graph contrastive learning. Our model leverages contrastive learning to enhance feature representation and generalization capabilities, enabling high-accuracy attack detection even with limited training data. Specifically, we first construct a multi- graph representation of network traffic and segment the data into snapshots. Then, we perform graph data augmentation within each snapshot to generate augmented sample pairs, which are used to pre-train the model via contrastive learning. Finally, we fine-tune the model parameters to achieve multi-class attack classification, leveraging the learned feature representations to identify various attack types, even those unseen during training. Experimental results demonstrate that our model exhibits excellent generalization ability and achieves high attack detection performance, even with limited training data.

关键词： Training Adaptation models Supervised learning Training data Contrastive learning Telecommunication traffic Data augmentation

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Research on Higher-Order Operator Transformation Algorithms for Syntax Transformation-Based Model Checking

Research on Higher-Order Operator Transformation Algorithms ...

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IEEE International Conference on Automation, Electronics and Electrical engineering (AUTEEE)

作者： Ji Liu Jie Liu Lingyun Luo Fei Yang Mingxing Liu Yu Tu School of Computer Science University of South China Hengyang Hunan China Hunan Engineering Research Center of Software Evaluation and Testing for Intellectual Equipment School of Computer Science University of South China Hengyang Hunan China China Nuclear Power Institute Research and Design Institute Chengdu Sichuan China

ISBN: (数字)9798350377033

ISBN: (纸本)9798350377040

Model checking technology addresses the limitations of traditional testing methods, which cannot cover all possible system states. Syntax transformation-based model checking methods convert source programs into equivalent forms and leverage existing model checking tools for verification, providing adaptability to various syntactic structures. The L2C language, widely used in safety-critical domains, lacks effective model checking support for its advanced features, particularly higher-order operators. To address this issue, this paper proposes a syntax transformation-based higher-order operator model checking method that converts L2C programs into equivalent Lustre programs. The method employs the ANTLR tool for lexical analysis, syntax analysis, abstract syntax tree generation, and simplification, enabling effective model checking of higher-order operators in the L2C language.

关键词： Computational modeling Semantics Symbols Syntactics Model checking Stability analysis Iterative algorithms Thermal stability Tree traversal Testing

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Counting Answers to Unions of Conjunctive Queries: Natural Tractability Criteria and Meta-Complexity

arXiv

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arXiv 2023年

作者： Focke, Jacob Goldberg, Leslie Ann Roth, Marc Živný, Stanislav CISPA Helmholtz Center for Information Security Saarbrücken Germany Department of Computer Science University of Oxford United Kingdom School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom

We study the problem of counting answers to unions of conjunctive queries (UCQs) under structural restrictions on the input query. Concretely, given a class C of UCQs, the problem #UCQ(C) provides as input a UCQ Ψ ∈ C and a database D and the problem is to compute the number of answers of Ψ in D. Chen and Mengel [PODS’16] have shown that for any recursively enumerable class C, the problem #UCQ(C) is either fixed-parameter tractable or hard for one of the parameterised complexity classes W[1] or #W[1]. However, their tractability criterion is unwieldy in the sense that, given any concrete class C of UCQs, it is not easy to determine how hard it is to count answers to queries in C. Moreover, given a single specific UCQ Ψ, it is not easy to determine how hard it is to count answers to Ψ. In this work, we address the question of finding a natural tractability criterion: The combined conjunctive query of a UCQ Ψ = φ1 ∨···∨φ is the conjunctive query ∧(Ψ) = φ1∧···∧φ. We show that under natural closure properties of C, the problem #UCQ(C) is fixed-parameter tractable if and only if the combined conjunctive queries of UCQs in C, and their contracts, have bounded treewidth. A contract of a conjunctive query is an augmented structure, taking into account how the quantified variables are connected to the free variables — if all variables are free, then a conjunctive query is equal to its contract;in this special case the criterion for fixed-parameter tractability of #UCQ(C) thus simplifies to the combined queries having bounded treewidth. Finally, we give evidence that a closure property on C is necessary for obtaining a natural tractability criterion: We show that even for a single UCQ Ψ, the meta problem of deciding whether #UCQ({Ψ}) can be solved in time O(|D|d) is NP-hard for any fixed d ≥ 1. Moreover, we prove that a known exponential-time algorithm for solving the meta problem is optimal under assumptions from fine-grained complexity theory. As a corollary of our reductio

关键词： Query processing

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

SSRN

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SSRN 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. © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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A Priority-Aware AI-Generated Content Resource Allocation Method for Multi-UAV Aided Metaverse

A Priority-Aware AI-Generated Content Resource Allocation Me...

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Zhang, Zheng Wang, Jingjing Chen, Jianrui Fang, Zhengru Jiang, Chunxiao Han, Zhu School of Cyber Science and Technology Beihang University Beijing China City University of Hong Kong Department of Computer Science Hong Kong Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China University of Houston Department of Electrical and Computer Engineering HoustonTX United States

ISBN: (纸本)9798350368369

With the advancement of large model technologies, AI -generated content is gradually emerging as a mainstream method for content creation. The metaverse, as a key application scenario for the next-generation communication technologies, heavily depends on advanced content generation technologies. Nevertheless, the diverse types of metaverse applications and their stringent real-time requirements constrain the full potential of AIGC technologies within this environment. In order to tackle with this problem, we construct a priority-aware multi-UAV aided metaverse system and formulate it as a Markov decision process (MDP). We propose a diffusion-based reinforcement learning algorithm to solve the resource allocation problem and demonstrate its superiority through enough comparison and ablation experiments. © 2025 IEEE.

关键词： Drones

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Detecting and Corrupting Convolution-based Unlearnable Examples

arXiv

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arXiv 2023年

作者： Li, Minghui Wang, Xianlong Yu, Zhifei Hu, Shengshan Zhou, Ziqi Zhang, Longling Zhang, Leo Yu School of Software Engineering Huazhong University of Science and Technology China School of Cyber Science and Engineering Huazhong University of Science and Technology China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security China School of Computer Science and Technology Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia

Convolution-based unlearnable examples (UEs) employ class-wise multiplicative convolutional noise to training samples, severely compromising model performance. This fire-new type of UEs have successfully countered all defense mechanisms against UEs. The failure of such defenses can be attributed to the absence of norm constraints on convolutional noise, leading to severe blurring of image features. To address this, we first design an Edge Pixel-based Detector (EPD) to identify convolution-based UEs. Upon detection of them, we propose the first defense scheme against convolution-based UEs, COrrupting these samples via random matrix multiplication by employing bilinear INterpolation (COIN) such that disrupting the distribution of class-wise multiplicative noise. To evaluate the generalization of our proposed COIN, we newly design two convolution-based UEs called VUDA and HUDA to expand the scope of convolution-based UEs. Extensive experiments demonstrate the effectiveness of detection scheme EPD and that our defense COIN outperforms 11 state-of-the-art (SOTA) defenses, achieving a significant improvement on the CIFAR and ImageNet datasets. Copyright © 2023, The Authors. All rights reserved.

关键词： Convolution

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