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7th IEEE International Conference on Data Science in Cyberspace, DSC 2022

作者： Liu, Tong Wang, Zhongru Zhang, Yuntao Liu, Zhehui Fang, Binxing Pang, Zhengyuan Ministry of Education Key Laboratory of Trustworthy Distributed Computing and Service Beijing China Chinese Academy of Cyberspace Studies Beijing China North China University of Technology School of Information Science and Technology Beijing100144 China Beijing DigApis Technology Co. Ltd Beijing100871 China JiangSu DigApis Technology Co. Ltd Jiangsu Nantong226000 China

ISBN: (数字)9781665474801

ISBN: (纸本)9781665474801

At present, program vulnerabilities occur frequently, which seriously threatens the security of cyberspace. Automated vulnerability discovery technology has attracted more and more attention because of its efficiency and universality. In the current field of vulnerability discovery, it is common to combine fuzzing with symbolic execution technology. Symbolic execution technology is often used to solve the input of complex paths and help fuzzing improve the program coverage, so as to detect vulnerabilities better. However, the symbolic execution has no goals in the process of hybrid execution exploration path, it is easy to cause the execution path to deviate from the expected target points. And generating the test case of the corresponding path requires frequently calling the solver for solution, while the current solver has low efficiency and poor solvability for nonlinear operation. In order to solve the above problems, we propose an automated vulnerability discovery system based on hybrid execution. In the system, we propose the symbolic execution guidance algorithm based on dynamic and static combination to guide the symbolic execution to solve the input reaching the target points, so as to avoid exploring in useless paths and avoid consuming a lot of time and computing resources. In addition we hook some nonlinear functions to optimize the nonlinear function constraint solving, so as to improve the hybrid execution efficiency. We have conducted extensive experiments on the RHG 2019 challenge dataset and the RHG 2021 challenge dataset. The experimental results show the effectiveness and scalability of the system. © 2022 IEEE.

关键词： Efficiency

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Internet of Digital Twin: Framework, Applications and Enabling Technologies

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IEEE Communications Surveys and Tutorials 2025年

作者： Liu, Ruhan Luan, Tom H. Qu, Youyang Xiang, Yong Gao, Longxiang Zhao, Dongmei Deakin University School of Info Technology MelbourneVIC3125 Australia Xi'an Jiaotong University School of Cyber Science and Engineering Shaanxi Xi'an710049 China Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Jinan China McMaster University Department of Electrical and Computer Engineering HamiltonON Canada

Intelligent physical systems, such as smart vehicles and robotic arms, are increasingly integrated into both industrial and everyday applications. However, the systems typically face hardware limitations that constrain their computational capacities. Digital twin systems offer a solution by creating real-time digital replicas of physical systems that enhance computational efficiency, overcoming physical limitations. Moreover, multiple digital twins that hold complementary knowledge can conveniently collaborate to share information and computational resources, further improving the performance of physical systems by forming an Internet of Digital Twin (IoDT). This paper presents a comprehensive investigation of the digital twin network, tracing the evolution of digital twins and providing a classification of the key technologies, functional frameworks, and application domains of IoDT. This paper delves into the IoDT communication framework by studying the fundamental communication modes of IoDT, exploring its integration with advanced technologies such as edge computing, blockchain, 5G/6G networks, and machine learning to facilitate data transmission, interaction, and omni-directional sensing. By offering a broad perspective, the paper aims to deepen stakeholders' understanding of current research and potential future developments, encouraging further exploration of IoDT technologies and their evolution. © 1998-2012 IEEE.

关键词： 5G mobile communication systems

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Localized Adaptive Channel and Power Selection With TinyML (LACPSA) in Dense IEEE 802.11 WLANs

Localized Adaptive Channel and Power Selection With TinyML (...

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IEEE International Conference on Smart Internet of Things (SmartIoT)

作者： Khalid Ibrahim Qureshi Cheng Lu Ruoheng Luo Muhammad Ali Lodhi Lei Wang Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province School of Software Dalian University of Technology Dalian China Guangdong-Hong Kong-Macao Joint Laboratory for Emotion Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen MSU-BIT University China

ISBN: (数字)9798350366440

ISBN: (纸本)9798350366457

In dense IEEE 802.11 WLANs, optimized channel allocation and transmission power control are crucial for miti- gating interference and congestion. Traditional approaches face significant challenges due to limited channels and dynamic traffic patterns. To address these issues, we propose a novel scheme wherein TinyML models are directly integrated into access points (APs) for adaptive channel optimization and transmission power control. This distributed approach empowers APs to optimize settings autonomously based on their local information, reducing reliance on central controllers and enhancing scalability. Leveraging compact TinyML models enables rapid decision- making, allowing APs to adapt to network changes promptly. By dynamically adjusting channel optimization and transmission power based on signal strength, interference levels, and traffic load, interference can be minimized, thereby improving network performance. Through extensive simulations, we demonstrate a significant reduction in interference compared to traditional approaches. Our proposed distributed solution enhances network resilience and flexibility, making it more suitable for real-world deployment scenarios where conditions change rapidly. The integration of TinyML into the network presents a promising approach for future WLAN optimization efforts.

关键词： Adaptation models Wireless LAN Tiny machine learning Power control Interference Channel allocation Traffic control Optimization IEEE 802.11 Standard Load modeling

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Multimodal Dialogue Emotion Recognition Based on Label Optimization and Coarse-Grained Assisted Fine-Grained

Multimodal Dialogue Emotion Recognition Based on Label Optim...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhibo Zhang Zhenyu Yang Yan Huang Baojie Xu Wenyue Hu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Multimodal dialogue emotion recognition integrates data from multiple modalities to accurately identify emotional states in conversations. However, differences in expression and information density across modalities complicate the fusion of features. Traditional methods may introduce redundant information from other utterances, reducing the accuracy of emotion recognition. Existing one-hot labels often fail to capture the full range of emotional expressions, leading to biased results. To address these issues, we propose a model that fuses different modalities within the same utterance to avoid redundancy. It employs a progressive classification process, refining emotion recognition from coarse to fine granularity. Additionally, we use emotion polarity probabilities as weights for fine-grained classification and introduce a multimodal information-rich label that considers both the data and their interactions. Experiments on IEMOCAP and MELD datasets demonstrate the model’s effectiveness, significantly improving dialog emotion recognition accuracy. Our code is available at https://***/r/LOCG-188E.

关键词： Emotion recognition Accuracy Fuses Refining Termination of employment Speech recognition Signal processing Speech processing Optimization Faces

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HiCoCS: High Concurrency Cross-Sharding on Permissioned Blockchains

arXiv

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arXiv 2025年

作者： Yang, Lingxiao Dong, Xuewen Wan, Zhiguo Lu, Di Zhang, Yushu Shen, Yulong School of Computer Science and Technology Xidian University Engineering Research Center of Blockchain Technology Application and Evaluation Ministry of Education Shaanxi Key Laboratory of Blockchain and Secure Computing Xi’an710071 China Zhejiang Lab Zhejiang Hangzhou311121 China School of Computer Science and Technology Xidian University Shaanxi Key Laboratory of Network and System Security Xi’an710071 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Jiangsu Nanjing210016 China

As the foundation of the Web3 trust system, blockchain technology faces increasing demands for scalability. Sharding emerges as a promising solution, but it struggles to handle highly concurrent cross-shard transactions (CSTxs), primarily due to simultaneous ledger operations on the same account. Hyperledger Fabric, a permissioned blockchain, employs multi-version concurrency control for parallel processing. Existing solutions use channels and intermediaries to achieve cross-sharding in Hyperledger Fabric. However, the conflict problem caused by highly concurrent CSTxs has not been adequately resolved. To fill this gap, we propose HiCoCS, a high concurrency cross-shard scheme for permissioned blockchains. HiCoCS creates a unique virtual sub-broker for each CSTx by introducing a composite key structure, enabling conflict-free concurrent transaction processing while reducing resource overhead. The challenge lies in managing large numbers of composite keys and mitigating intermediary privacy risks. HiCoCS utilizes virtual sub-brokers to receive and process CSTxs concurrently while maintaining a transaction pool. Batch processing is employed to merge multiple CSTxs in the pool, improving efficiency. We explore composite key reuse to reduce the number of virtual sub-brokers and lower system overhead. Privacy preservation is enhanced using homomorphic encryption. Evaluations show that HiCoCS improves cross-shard transaction throughput by 3.5-20.2 times compared to the baselines. Copyright © 2025, The Authors. All rights reserved.

关键词： Concurrency control

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Improving Chinese Spelling Check by Character Pronunciation Prediction: The Effects of Adaptivity and Granularity

Improving Chinese Spelling Check by Character Pronunciation ...

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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Li, Jiahao Wang, Quan Mao, Zhendong Guo, Junbo Yang, Yanyan Zhang, Yongdong University of Science and Technology of China Hefei China MOE Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Beijing China People's Daily Online Co. Beijing China People's Public Security University of China Beijing China

Chinese spelling check (CSC) is a fundamental NLP task that detects and corrects spelling errors in Chinese texts. As most of these spelling errors are caused by phonetic similarity, effectively modeling the pronunciation of Chinese characters is a key factor for CSC. In this paper, we consider introducing an auxiliary task of Chinese pronunciation prediction (CPP) to improve CSC, and, for the first time, systematically discuss the adaptivity and granularity of this auxiliary task. We propose SCOPE which builds on top of a shared encoder two parallel decoders, one for the primary CSC task and the other for a fine-grained auxiliary CPP task, with a novel adaptive weighting scheme to balance the two tasks. In addition, we design a delicate iterative correction strategy for further improvements during inference. Empirical evaluation shows that SCOPE achieves new state-of-the-art on three CSC benchmarks, demonstrating the effectiveness and superiority of the auxiliary CPP task. Comprehensive ablation studies further verify the positive effects of adaptivity and granularity of the task. Code and data used in this paper are publicly available at https://***/jiahaozhenbang/SCOPE. © 2022 Association for Computational Linguistics.

关键词： Computational linguistics

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Towards Planning Package Delivery Paths by Exploiting the Crowd of Private Cars

Towards Planning Package Delivery Paths by Exploiting the Cr...

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IEEE International Conference on High Performance computing and Communications (HPCC)

作者： Hejing Nie Yiping Wen Tiancai Li Sipeng OuYang Muyang Tian School of Computer Science and Engineering Hunan University of Science and Technology China Hunan Key Laboratory for Service computing and Novel Software Technology Hunan University of Science & Technology

Crowdsourced delivery can provide cost-effective deliveries and it is becoming increasingly important in practice. This paper considers a package delivery problem which exploits relays of private cars to transport packages via their commuter routes. Specifically, we propose a method of planning package delivery paths based on the commuter routes of private cars. Using the commuter routes and time constraints as the reference, it can find optimal delivery paths and assign drivers of private cars to transport packages. The simulation experiment results show the validity and effectiveness of such method.

关键词： Product delivery Planning Automobiles Time factors Relays Task analysis

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An Efficient Authentication Protocol for Brand Cosmetics Anti-Counterfeiting system

An Efficient Authentication Protocol for Brand Cosmetics Ant...

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

作者： Xiangwei Meng Qingchun Yu Wei Lang Yufeng Liang Zisang Xu Kuanching Li Nanjing University of Aeronautics and Astronautics Nanjing China Hunan University of Science and Technology Xiangtan China Hunan Key Laboratory for Service computing and Novel Software Technology Hunan University of Science and Technology Xiangtan China Changsha University of Science and Technology Changsha China

The counterfeit brand cosmetics made available on the market have always been a widespread concern. Traditional anti-counterfeiting schemes usually focus on the authenticity of brand cosmetics. However, verifying the identity of the sales counter is also a challenging issue to be solved. In this paper, we propose a new authentication protocol for brand cosmetics anti-counterfeiting system which is used for checking the identity of sales counter and the authenticity of brand cosmetics. The sales counter authenticated by the anticounterfeiting server obtains a cosmetic authentication confirmation about the authenticity of the brand cosmetic. Besides, the security of proposed protocol is proved by informally analysis. Performance evaluation shows that the proposed protocol is efficiency.

关键词：

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Cross-Modal Representation Reconstruction for Zero-Shot Classification

Cross-Modal Representation Reconstruction for Zero-Shot Clas...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Yu Wang Shenjie Zhao Key Laboratory of Embedded System and Service Computing Ministry of Education P. R. China Mojie Technology (Ningbo)

ISBN: (纸本)9781728176055;9781728176062

Zero-shot learning (ZSL) aims to recognize novel classes without training samples through transferring knowledge from seen classes, based on the assumption that both the seen and unseen classes share a latent semantic space. Previous works either focus on directly learning various mapping functions between visual space and semantic space, or searching a latent common subspace to alleviate semantic gap between different modalities. However, few methods directly learn modality-invariant representations for ZSL. In this paper, we propose a Cross-Modal Representation Reconstruction (CM-RR) framework to bridge the semantic gap between visual features and semantic attributes, as well as introducing a novel regularizer for automatically feature selection. Moreover, an iterative optimization process based on the ALM (Augmented Lagrangian Method) algorithm with the alternating direction strategy is developed to solve the proposed formulation. Extensive experiments on four benchmark datasets show the effectiveness of the proposed approach, and even the performance surpasses some deep learning based methods.

关键词： Training Visualization Semantics Signal processing algorithms Signal processing Feature extraction Iterative algorithms

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CacheAlarm: Monitoring Sensitive Behaviors of Android Apps Using Cache Side Channel

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IEEE Transactions on Dependable and Secure computing 2025年

作者： Tian, Jianwen Ma, Haoyu Gao, Debin Kuang, Xiaohui Academy of Military Sciences Institute of System Engineering China Beijing Jiaotong University School of Cyberspace Science and Technology Beijing100044 China Beijing Jiaotong University Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing100045 China Singapore Management University School of Computing and Information Systems Singapore

Malware attack has been a serious threat to the security and privacy of both individual and corporation users of the Android platform. Business entities seek to protect themselves by means of monitoring privacy-related sensitive behaviors conducted on company-issued Android devices. However, due to Android's own access control and privacy protection policies, this is difficult to be done with third-party apps using only normal privileges. Existing works proposed using side-channel readings from leaky APIs and system virtual files to speculate runtime app behaviors, which could be unreliable due to future system updates (that ban exploited resources), hardware jittering, etc. In this paper, we argue that a more traditional side-channel attack strategy, namely the CPU-cache-based side channel, could be exploited in the benign scenario of app behavior surveillance. Specifically, we propose CacheAlarm, a sensitive app behavior monitor and foreground app identification system, which works by measuring cache side-channel readings of selected methods within the Android framework, and conducted in-lab and in-the-wild user studies to compare the effectiveness of our scheme against SideNet, a previous Android app behavior surveillance scheme using API-based side channels. Results of the studies suggested that CacheAlarm outperforms SideNet on the accuracy of detecting sensitive behaviors in addition to gaining the capability of detecting apps running at foreground of the user device. © 2025 IEEE. All rights reserved.

关键词： Android malware

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