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SAMOC: Enabling Atomic Invocations for Cross-chain Crowdsourcing Testing DApps in Industrial Control Through Trusted Smart Community and Lock Mechanism 23

SAMOC: Enabling Atomic Invocations for Cross-chain Crowdsour...

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23rd IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2024

作者： Huang, Weiguo Ding, Yong Li, Jun Wang, Yujue Liang, Hai Yang, Changsong Guilin University of Electronic Technology Guangxi Key Laboratory of Cryptography and Information Security School of Computer Science and Information Security Guilin China Hkct Institute for Higher Education Lion Rock Labs of Cyberspace Security Institute of Cyberspace Technology 999077 China Guilin University of Electronic Technology Guangxi Engineering Research Center of Industrial Internet Security and Blockchain Guilin China China Industrial Control Systems Cyber Emergency Response Team Beijing China Hangzhou Innovation Institute of Beihang University Hangzhou China

Crowdsourcing testing leverages extensive user participation to identify and fix potential issues in industrial control software, ensuring its security and reliability. Decentralized applications (DApps) can utilize blockchain and smart contract technologies to ensure the decentralization, transparency, and tamper-resistance of industrial control software testing. However, testing efforts conducted via DApps are typically confined to a single blockchain. Coordinating and scheduling DApps deployed on different blockchains has become a critical challenge. Moreover, DApps need to verify the credibility of crowdsourcing participants to ensure the reliability of testing. In order to address these issues, this paper proposes SAMOC, an atomic cross-chain system based on the Atomic-Oracle chain, which aims to realize atomic cross-chain invocations for DApps. SAMOC can realize atomic cross-chain invokes, and the intelligent community deployed on the Atomic-Oracle chain calculates the reputation of users' accounts across multiple chains through Oracle. This paper also designs a corresponding incentive mechanism to ensure that the auditing of cross-chain invokes is honest and trustworthy. Experimental results show that the cross-chain invokes of the proposed SAMOC scheme have lower latency compared to AtomCI. © 2024 IEEE.

关键词： Software testing

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Balanced Contrastive Learning for Long-Tailed Visual Recognition

arXiv

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arXiv 2022年

作者： Zhu, Jianggang Wang, Zheng Chen, Jingjing Chen, Yi-Ping Phoebe Jiang, Yu-Gang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University China Shanghai Collaborative Innovation Center on Intelligent Visual Computing China Department of Computer Science and Information Technology La Trobe University Australia

Real-world data typically follow a long-tailed distribution, where a few majority categories occupy most of the data while most minority categories contain a limited number of samples. Classification models minimizing cross-entropy struggle to represent and classify the tail classes. Although the problem of learning unbiased classifiers has been well studied, methods for representing imbalanced data are under-explored. In this paper, we focus on representation learning for imbalanced data. Recently, supervised contrastive learning has shown promising performance on balanced data recently. However, through our theoretical analysis, we find that for long-tailed data, it fails to form a regular simplex which is an ideal geometric configuration for representation learning. To correct the optimization behavior of SCL and further improve the performance of long-tailed visual recognition, we propose a novel loss for balanced contrastive learning (BCL). Compared with SCL, we have two improvements in BCL: class-averaging, which balances the gradient contribution of negative classes;class-complement, which allows all classes to appear in every mini-batch. The proposed balanced contrastive learning (BCL) method satisfies the condition of forming a regular simplex and assists the optimization of cross-entropy. Equipped with BCL, the proposed two-branch framework can obtain a stronger feature representation and achieve competitive performance on long-tailed benchmark datasets such as CIFAR-10-LT, CIFAR-100-LT, ImageNet-LT, and iNaturalist2018. Our code is available at this URL. Copyright © 2022, The Authors. All rights reserved.

关键词： Benchmarking

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FedDRC: A Robust Federated Learning-based Android Malware Classifier under Heterogeneous Distribution

FedDRC: A Robust Federated Learning-based Android Malware Cl...

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International Conference on computer Supported Cooperative Work in Design

作者： Changnan Jiang Chunhe Xia Mengyao Liu Chen Chen Huacheng Li Tianbo Wang Pengfei Li Beijing Key Lab. of Network Technology Beihang University Beijing China Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing Guangxi Normal University Guilin China School of Cyber Science and Technology Beihang University Beijing China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China SNationa Innovation Center of Intelligent and Connected Vehicles Beijing China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

In the traditional centralized Android malware classification framework, privacy concerns exist due to collected users’ apps containing sensitive information. A new classification framework based on Federated Learning (FL) has emerged to protect privacy. However, significant spatiotemporal heterogeneity exists in the distribution of Android malware samples in different clients. It presents a huge challenge to existing FL schemes, as trained local models differ significantly, resulting in slower model convergence and lower classification accuracy. To bridge this gap, we propose FedDRC, a robust FL-based Android malware classifier. First, we design a functional semantic embedding mechanism of API features, FSEM, using word embedding to improve the robustness of the model to the time heterogeneity of the client’s samples. Secondly, we use the idea of Information Bottleneck (IB) and transfer learning to design a robust local model, PAMIB, to deal with the model degradation caused by the space heterogeneity of the distribution of client samples. Extensive experiments on the Androzoo dataset show that FedDRC has the best robustness for Android malware classification tasks in various heterogeneity distribution settings: fastest convergence and best classification accuracy.

关键词： Degradation Privacy Accuracy Federated learning Transfer learning Semantics Malware

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Multi-Modality is All You Need for Transferable Recommender Systems

arXiv

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arXiv 2023年

作者： Li, Youhua Du, Hanwen Ni, Yongxin Zhao, Pengpeng Guo, Qi Yuan, Fajie Zhou, Xiaofang ShanghaiTech University Shanghai China Shanghai Innovation Center for Processor Technologies SHIC China Westlake University Hangzhou China School of Computer Science and Technology Soochow University Suzhou China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China The Hong Kong University of Science and Technology Hong Kong

ID-based Recommender Systems (RecSys), where each item is assigned a unique identifier and subsequently converted into an embedding vector, have dominated the designing of RecSys. Though prevalent, such ID-based paradigm is not suitable for developing transferable RecSys and is also susceptible to the cold-start issue. In this paper, we unleash the boundaries of the ID-based paradigm and propose a Pure Multi-Modality based Recommender system (PMMRec), which relies solely on the multi-modal contents of the items (e.g., texts and images) and learns transition patterns general enough to transfer across domains and platforms. Specifically, we design a plug-and-play framework architecture consisting of multimodal item encoders, a fusion module, and a user encoder. To align the cross-modal item representations, we propose a novel next-item enhanced cross-modal contrastive learning objective, which is equipped with both inter- and intra-modality negative samples and explicitly incorporates the transition patterns of user behaviors into the item encoders. To ensure the robustness of user representations, we propose a novel noised item detection objective and a robustness-aware contrastive learning objective, which work together to denoise user sequences in a self-supervised manner. PMMRec is designed to be loosely coupled, so after being pre-trained on the source data, each component can be transferred alone, or in conjunction with other components, allowing PMMRec to achieve versatility under both multi-modality and single-modality transfer learning settings. Extensive experiments on 4 sources and 10 target datasets demonstrate that PMMRec surpasses the state-of-the-art recommenders in both recommendation performance and transferability. Our code and dataset is available at: https://***/ICDE24/PMMRec. Copyright © 2023, The Authors. All rights reserved.

关键词： Recommender systems

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Robustness and Privacy for Green Learning under Noisy labels

Robustness and Privacy for Green Learning under Noisy Labels

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： De Li Tiange Xia Qiyu Li Xianxian Li Jinyan Wang Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China Guangxi Key Lab of Multi-Source Information Mining & Security Guangxi Normal University Guilin China School of Computer Science and Engineering Guangxi Normal University Guilin China Faculty of Education Guangxi Normal University Guilin China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

As a new paradigm of machine learning, green learning has achieved performance comparable to deep learning in vision tasks, knowledge graph learning, and modeling graph structures. Compared with deep learning, green learning has the advantages of a low carbon footprint, lightweight model and logical transparency. In this paper, to enhance the robustness of green learning, we study the performance of the green model when the training dataset labels are noisy. Through theoretical analysis and experimental verification, we found that noisy labels will not only reduce the performance of green learning, but also amplify the risk of privacy leaks of training dataset members. Therefore, we propose a robust green learning to approach the above two threats. Specifically, we design a multi-stage label-consistency sample selection method to filter out noisy labels in the training dataset by taking advantage of the characteristics of unsupervised representation learning in green learning. In order to further improve the stable classification performance of the model, we propose a feature-level class-balanced data augmentation method in the feature decision learning stage to solve the class imbalance problem caused by sample selection. Finally, a large number of experiments on multiple noisy datasets show that the robust green learning method proposed in this paper can not only enhance the generalization of the model under noisy training labels, but also alleviate the risk of member privacy information leakage.

关键词： Training Deep learning Representation learning Data privacy Privacy Robustness Data models

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A Survey on the Efficiency, Reliability, and Security of Data Query in blockchain Systems

SSRN

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SSRN 2022年

作者： Zhang, Qizhi He, Yale Lai, Ruilin Hou, Zhihao Zhao, Gansen School of Computer Science South China Normal University Shipai Campus Guangdong Guangzhou51063 China Key Lab on Cloud Security and Assessment technology of Guangzhou Guangdong Guangzhou51063 China SCNU & VeChina Joint Lab on BlockChain Technology and Application Guangdong Guangzhou51063 China

With the rise of digital currencies, blockchain systems work for storing data efficiently and securely, creating a decentralized and tamper-proof digital platform. However, less works focus on data backtracking and supporting for many complex query functions. Therefore, we explore blockchain queries from both a technical and an application standpoint. Current methods to deliver are based on using distributed databases, data indexing structures, and various cryptographic algorithms to implement rich, verifiable, and secure query functions. As a result, the purpose of this survey is to provide some guidance on blockchain-related query technology. We analyze the issues of query efficiency, query reliability, and privacy protection of queries in blockchain. In addition we summarize some classic scenarios of query schemes in blockchain and discuss future research challenges that still need to be addressed by blockchain query technology. © 2022, The Authors. All rights reserved.

关键词： blockchain

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Modeling and performance analysis of moving V2V 5G NR systems

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e-Prime - Advances in Electrical Engineering, Electronics and Energy 2024年 9卷

作者： Rjab, Mohamed Chamkhia, Hela Omri, Aymen Bouallegue, Seifeddine Bouallegue, Ridha National Engineering School of Tunis University of Tunis El Manar Tunisia Innovation of Communicant & Cooperative Mobiles (Innov'Com) Lab (SUP'COM) Tunis Tunisia Hela Chamkhia is with Computer Science and Engineering Department Qatar University Doha Qatar Seifeddine Bouallegue is with College of Computing and Information Technology University of Doha for Science and Technology Qatar

Vehicle-to-vehicle (V2V) communication is a promising solution for enhancing network performance. This paper presents the modeling and performance analysis of moving V2V fifth generation new radio (5G NR) systems using Poisson point process (PPP) and Matern cluster process (MCP). A new mode selection scheme, the hybrid communication scheme (HCS), is proposed to improve V2V 5G NR system performance. The HCS employs side link (SL) communications, a predefined average threshold of SL received signal strength (RSS), and a minimum residence time for V2V mode. We derive expressions for the threshold RSS, minimum residence time, and ergodic capacity to evaluate HCS performance. Monte Carlo simulations using Matlab confirm the analytical results and demonstrate the advantages of the proposed scheme over related approaches in the literature. © 2024 The Author(s)

关键词： Hybrid communication scheme Side link V2V

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Novel few-shot learning based fuzzy feature detection algorithms

Novel few-shot learning based fuzzy feature detection algori...

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International Conference on Data science and Advanced Analytics (DSAA)

作者： Yun Luo Liangfu Lu Xudong Cui Yan Du Yingying Bi Limin Zhu Christy Jie Liang Faculty of Engineering and Information Technology University of Technology Sydney Australia ICSL Institute of Innovation Science and Sustainability (IISS) Federation University Melbourne Australia School of Mathematics Tianjin University Tianjin China Department of AI Ding-Beijing Intelligent Technology Beijing China Tianjin Key Lab of Retinal Functions and Diseases Tianjin Medical University Tianjin China School of Computer Science University of Technology Sydney Sydney Australia

The Internet of Things (IoT) has significantly enhanced various aspects of our daily lives, including security, health, education, and energy efficiency, among others. Within the realm of IoT, image classification stands as a pivotal technique that has achieved notable success in domains such as facial recognition within security and scene recognition in transportation for traffic analysis. Nonetheless, the challenge emerges when tackling classification tasks with only limited labeled samples available for each category. Conventional machine learning techniques often struggle to attain satisfactory classification results under such circumstances. To address this issue, the concept of few-shot learning has emerged, aiming to achieve effective classification using only a small number of labeled samples. State-of-the-art few-shot learning models have introduced novel frameworks to tackle this problem. However, the inherent ambiguity and uncertainty within data often hinder the performance of classification methods. To overcome this limitation, this paper proposes the integration of fuzzy learning with few-shot learning in the context of feature extraction. The objective is to mitigate data fuzziness and enhance model performance. Leveraging a fuzzy extraction algorithm, we introduce fuzzy prototype networks and a fuzzy graph neural network with fuzzy reasoning. These frameworks are designed to analyze noisy and uncertain data, utilizing convolutional neural networks for feature extraction and applying fuzzy reasoning to capture ambiguity representations for features within each fuzzy set. The SoftMax function is then normalized to serve as a feature weight, effectively constraining the original feature vector. The effectiveness and efficiency of our proposed model are demonstrated through experimental evaluations conducted on various public datasets. The results showcase the model’s capability in addressing the challenges posed by limited labeled data and data uncertai

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HeGTa: Leveraging Heterogeneous Graph-enhanced Large Language Models for Few-shot Complex Table Understanding

arXiv

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arXiv 2024年

作者： Jin, Rihui Li, Yu Qi, Guilin Hu, Nan Li, Yuan-Fang Chen, Jiaoyan Wang, Jianan Chen, Yongrui Min, Dehai Bi, Sheng School of Computer Science and Engineering Southeast University China Key Laboratory of New Generation Artificial Intelligence Technology and its Interdisciplinary Applications Southeast University Ministry of Education China Monash University Australia The University of Manchester United Kingdom Alibaba Group China Law and Innovation Lab Law School Southeast University China State Key Laboratory for Novel Software Technology Nanjing University China

Table Understanding (TU) has achieved promising advancements, but it faces the challenges of the scarcity of manually labeled tables and the presence of complex table structures. To address these challenges, we propose HeGTa, a heterogeneous graph (HG)-enhanced large language model (LLM) designed for few-shot TU tasks. This framework aligns structural table semantics with the LLM’s parametric knowledge through soft prompts and instruction tuning. It also addresses complex tables with a multi-task pre-training scheme by incorporating novel multi-granularity self-supervised HG pretext tasks. We empirically demonstrate the effectiveness of HeGTa, showing that it outperforms the SOTA for few-shot complex TU on several benchmarks. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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blockchain based Power Transaction Asynchronous Settlement System 91

Blockchain based Power Transaction Asynchronous Settlement S...

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91st IEEE Vehicular technology Conference, VTC Spring 2020

作者： Ai, Songpu Hu, Diankai Zhang, Tong Jiang, Yunpeng Rong, Chunming Cao, Junwei Research Institute of Information Technology Tsinghua University Beijing China Co. Ltd. Qingdao University of Technology Blockchain Innovation Lab School of Information and Control Engineering Qingdao China University of Stavanger Department of Electrical Engineering and Computer Science Stavanger Norway

ISBN: (纸本)9781728152073

The popularization and rapid development of distributed energy becomes a trend of the times. Distributed energy prosumers should be able to trade with each other to reduce losses, increase efficiency, flexibility and economy. The traditional centralized power settlement scheme is not suitable for the utilization on the situation of distributed energy transaction settlement. Energy internet as the next generation energy system integrating cuttingedge information technologies with energy system could realize peerto- peer energy services. The distributed interactive concept of the energy trading is highly consistent with the principle of blockchain. In this paper, aiming at the problems of information disunity, trust system difficult to establish, power deviation waste and cost advance caused by power pre-sale, a power transaction asynchronous settlement system for microgrid is proposed based on blockchain technology. The experiment results illustrate that the system obtains promising performance by reasonable set grid structure which could meet the requirements of practical applications. © 2020 IEEE.

关键词： blockchain

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