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IEEE International Conference on Computer-Aided Design

作者： Yihao Yang Pengfei Qiu Chunlu Wang Yu Jin Qiang Gao Xiaoyong Li DongSheng Wang Gang Qu Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education Beijing China Tsinghua University Beijing China Zhongguancun Laboratory Beijing China University of Maryland USA

Performance Monitoring Unit (PMU) is a common hardware module in modern processors that monitors the processor's architectural and microarchitectural events (PMU events) for CPU performance analysis and optimization. Vendors publish PMU events in documents such as Intel's Software Development Manual (SDM) and ARM processor technical reference manuals. In this paper, we report our findings that these documented PMU events are only a very small portion of the PMU event space. We define hidden PMU events as those that can be triggered in the instruction's execution but are not documented by the vendors. The hidden PMU events may not be as useful as the documented ones for CPU performance analysis. However, they might introduce security vulnerabilities. We develop an automated tool to traverse all the possible PMU events during the execution of each valid instruction to locate the hidden PMU events. On six Intel processors with different micro-architectures, where there are about 307 documented PMU core events on average, our tool finds an average of 17,361 hidden PMU events. We further demonstrate the security implications in both defense and attack of these hidden PMU events. Our experimental results show that up to 6,613 hidden PMU events on the i7-6700 can be used to detect transient execution attacks and 1,192 hidden PMU events can be exploited for side-channel attacks.

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Reinforcement learning based time-domain mutual interference avoidance for automotive radar

Reinforcement learning based time-domain mutual interference...

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IET International Radar Conference (IRC 2023)

作者： He Xiao Jianping Wang Runlong Li Yuan He Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Beijing People's Republic of China Faculty of EEMCS Delft University of Technology Delft Netherlands

ISBN: (数字)9781837240982

Due to the extensive usage of automotive radars on vehicles, mutual interference among radars on the road is becoming considerable. To address this, we propose a time domain strategy based on deep reinforcement learning (DRL). This approach helps avoid mutual interference for automotive radars in the time domain without extra communications. The numerical simulation results demonstrate that the proposed approach can avoid interference as effectively as frequency hopping. Moreover, the time domain strategy has more advantages than frequency hopping when encountering dynamic interference.

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Feature-Adaptive and Data-Scalable In-Context Learning

arXiv

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arXiv 2024年

作者： Li, Jiahao Wang, Quan Zhang, Licheng Jin, Guoqing Mao, Zhendong 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

In-context learning (ICL), which promotes inference with several demonstrations, has become a widespread paradigm to stimulate LLM capabilities for downstream tasks. Due to context length constraints, it cannot be further improved in spite of more training data, and general features directly from LLMs in ICL are not adaptive to the specific downstream task. In this paper, we propose a feature-adaptive and data-scalable in-context learning framework (FADS-ICL), which can leverage task-adaptive features to promote inference on the downstream task, with the supervision of beyond-context samples. Specifically, it first extracts general features of beyond-context samples via the LLM with ICL input form one by one, and introduces a task-specific modulator to perform feature refinement and prediction after fitting a specific downstream task. We conduct extensive experiments on FADS-ICL under varying data settings (4∼128 shots) and LLM scale (0.8∼70B) settings. Experimental results show that FADS-ICL consistently outperforms previous state-of-the-art methods by a significant margin under all settings, verifying the effectiveness and superiority of FADS-ICL. For example, under the 1.5B and 32 shots setting, FADS-ICL can achieve +14.3 average accuracy from feature adaptation over vanilla ICL on 10 datasets, with +6.2 average accuracy over the previous state-of-the-art method, and the performance can further improve with increasing training data. Code and data are publicly available at https://***/jiahaozhenbang/FADS-ICL. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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Fundamentalists, Integrationists, & Transformationists: An Empirical Theory of Men Software Engineers’ Orientations in Gender Inequalities

Fundamentalists, Integrationists, & Transformationists: An E...

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IEEE/ACM International Conference on Software Engineering: Software Engineering in Society Track (ICSE-SEIS)

作者： Yi Wang Xinyue Zhang Wei Wang Beijing University of Posts and Telecommunications Beijing P.R. China Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) MoE Beijing P.R. China

Professional software development is an occupation dominated by men software engineers. Recently, researchers have been investigating gender inequality in the software development profession from multiple perspectives. While most of the extant literature on gender inequality in software development takes the perspective of female developers, the vast majority of the software development workforce–men software engineers–receives much less attention. In this article, we reported on a quantitative interview study aiming at developing empirical understandings of men software engineers’ orientations in gender inequalities. Through analyzing interview narratives from 21 professional men software engineers, plenty of findings emerged, enabling us to build a theoretical framework characterizing three different types of orientations in gender inequalities. In our theory, men software engineers’ orientation in gender issues could be conceptualized as fundamentalists, integrationists, and transformationists. We introduced each type’s characteristics from three aspects of individuals’ social cognition, which are belief, attitude, and action. We further discussed the implications and the limitations of the study.

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Tree-IDS: An Incremental Intrusion Detection System for Connected Vehicles

Tree-IDS: An Incremental Intrusion Detection System for Conn...

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Conference on Local Computer Networks (LCN)

作者： Yuxin Zhao Zixiang Bi Guosheng Xu Chenyu Wang Guoai Xu Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Beijing China School of Computer Science and Technology Harbin Institute of Technology Shenzhen Shenzhen China

The rapid development of Internet of Vehicles technology has led to the continuous upgrading of the functions of connected vehicles. While connected vehicles bring convenience to people’s life, there are also many security threats. Connected vehicles not only have intra-vehicle networks communication, but also communicate with the external network. The diversity of communication methods makes the attack surface wider, and some new attacks are constantly emerging. In order to ensure vehicle security, This paper focuses on the attacks that are vulnerable to vehicles, and proposes an incremental intrusion detection system, which can not only detect attacks, but also incrementally learn new types of attacks. Experimental results illustrate that the proposed system can incrementally learn new attacks and avoid catastrophic forgetting problems, and can detect various types of known attacks with 99.99% accuracy on the Car-Hacking Dataset and 99.37% accuracy on the CICIDS2017.

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A Local Interpretability Model-Based Approach for Black-Box Adversarial Attack 8th

A Local Interpretability Model-Based Approach for Black-Box ...

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8th International Conference on Data Mining and Big Data, DMBD 2023

作者： Duan, Yuanjie Zuo, Xingquan Huang, Hai Wu, Binglin Zhao, Xinchao School of Computer Science Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing China School of Science Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9789819708437

Deep learning models are vulnerable to adversarial examples due to their fragility. Current black-box attack methods typically add perturbations to the whole example, and added perturbations may be large and easily detected by human eyes. This study proposes a Local Interpretable Model-agnostic Explanations (LIME)-based approach for black-box adversarial Attack (LIME-Attack). The approach can reduce the size of perturbations via adding perturbations in discriminative regions of an example. First, LIME is used to interpret a black-box model to obtain discriminative regions of an example. Then, the gradient information of the example is estimated by a derivative-free optimization method (Nature Evolution Strategy). Utilizing the gradient information, two white-box attack methods are adapted to generate perturbations, which are added in discriminative regions of the example to form an adversarial example. LIME-Attack is applied to several typical neural network models. Experiments show that it can achieve a high attack success rate with perturbation size 10%–30% lower than that of comparative methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Lime

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Experimental Demonstration of 2D-Trellis Coded Modulation PAM8 for 50G PON Based on 10G-class O-Band DML 27

Experimental Demonstration of 2D-Trellis Coded Modulation PA...

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27th OptoElectronics and Communications Conference/International Conference on Photonics in Switching and computing, OECC/PSC 2022

作者： Cui, Han Liu, Shuangyue Lu, Yueming Qiao, Yaojun School of Information and Communication Engineering State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing100876 China School of Cyberspace Security Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Ministry of Education Beijing100876 China

We present the first experimental demonstration of using 2D-TCM-PAM8 for a 50Gb/s PON system based on 10G-class O-band DML. Compared to PAM6, the receiver sensitivity of 2D-TCM-PAM8 is improved by 1.1 dB over 20-km tr... 详细信息

ISBN: (数字)9784885523366

ISBN: (纸本)9784885523366

关键词： Viterbi algorithm

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ModalSync: Synchronizing User Behavior with Multimodal Features for Multimodal Pre-training Recommendation 25

ModalSync: Synchronizing User Behavior with Multimodal Featu...

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Companion Proceedings of the ACM on Web Conference 2025

作者： Shiqin Liu Chaozhuo Li Minjun Zhao Litian Zhang Jiajun Bu College of Computer Science and Technology Zhejiang University Hangzhou China Key Laboratory of Trustworthy Distributed Computing and Service (MoE) Beijing University of Posts and Telecommunications Beijing China School of Cyber Science and Technology Beihang University Beijing China

ISBN: (纸本)9798400713316

In the continually evolving landscape of online platforms, integrating multimodal information into recommender systems offers a promising avenue to enhance our understanding of user preferences and product insights. Traditional models primarily rely on user-item interactions, but the advent of multimodal systems utilizes additional data modalities-text, images, audio, and video-to enhance recommendation accuracy. However, existing multimodal recommendation architectures often fail to fully exploit the potential synergy between multimodal feature extraction and recommendation processes, leading to domain bias and false positives. In this work, we introduce ModalSync, a novel multimodal pre-training framework designed to synchronize multimodal features with user behaviors, closely aligning with human perceptual processes. Unlike previous approaches that utilize pre-trained generic encoders, ModalSync incorporates a pre-training method that integrates both unsupervised and supervised strategies, thereby fostering a harmonious relationship between interaction graphs and multimodal data. Our framework uniquely addresses domain bias by infusing recommendation-specific interaction data into the feature extraction process and reduces false positives by directing encoder attention towards crucial attributes. Furthermore, ModalSync introduces a staged co-training module that strategically adjusts the training dynamics of the feature extractors and GNNs, promoting an effective and seamless fusion of multimodal information. Extensive experiments across three public datasets demonstrate that ModalSync significantly outperforms existing methods, achieving state-of-the-art results.

关键词： graph neural network

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Intellicise Wireless Networks From Semantic Communications: A Survey, Research Issues, and Challenges

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IEEE Communications Surveys and Tutorials 2024年第3期27卷 1-1页

作者： Zhang, Ping Xu, Wenjun Liu, Yiming Qin, Xiaoqi Niu, Kai Cui, Shuguang Shi, Guangming Qin, Zhijin Xu, Xiaodong Wang, Fengyu Meng, Yue Dong, Chen Dai, Jincheng Yang, Qianqian Sun, Yaping Gao, Dahua Gao, Hui Han, Shujun Song, Xiaodan State Key Laboratory of Network and Switching Technology Beijing University of Posts and Telecommunications Beijing China Shenzhen China Peng Cheng Laboratory Shenzhen China Department of Electronic Engineering Tsinghua University Beijing China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China Ministry of Education Key Laboratory of Universal Wireless Communications Beijing University of Posts and Telecommunications Beijing China College of Information Science and Electronic Engineering Zhejiang University Hangzhou China School of Artificial Intelligence Xidian University Xi’an China Ministry of Education Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Beijing China Guangzhou Institute of Technology Xidian University Guangzhou China

Information and communication technology (ICT) has been an essential part of modern society. However, the current communication systems are not sufficient to meet the demands of emerging applications. Intellicise (intelligent and concise) wireless networks, with their inherent characteristics of intelligence-endogenous and primitive-concise, have been proposed as a promising research direction. In this paper, we focus on intellicise wireless networks from semantic communication (SemCom). We present a comprehensive framework of intellicise wireless networks, including components such as brain for intellicise wireless networks (BIWN), intellicise signal processing, intellicise information transmission, intellicise network organization, and intellicise service bearing. We also investigate the enabling technologies and driving factors of intellicise wireless networks. Subsequently, we introduce the applications of intellicise wireless networks and envision new services. Finally, we outline the challenges of implementing intellicise wireless networks from a broad perspective and discuss possible solutions. IEEE

关键词： Mobile telecommunication systems

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Security-Critical Components Recognition Algorithm for Complex Heterogeneous Information Systems

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Computers, Materials & Continua 2021年第8期68卷 2579-2595页

作者： Jinxin Zuo Yueming Lu Hui Gao Tong Peng Ziyv Guo Tong An Enjie Liu School of Cyberspace Security Beijing University of Posts and TelecommunicationsBeijing100876China Key Laboratory of Trustworthy Distributed Computing and Service(BUPT) Ministry of EducationBeijing100876China School of Information and Communication Engineering Beijing University of Posts and TelecommunicationsBeijing100876China University of Bedfordshire Institute for Research in Applicable Computing(IRAC)LutonUK

With the skyrocketing development of technologies,there are many issues in information security quantitative evaluation(ISQE)of complex heterogeneous information systems(CHISs).The development of CHIS calls for an ISQE model based on security-critical components to improve the efficiency of system security evaluation *** this paper,we summarize the implication of critical components in different filed and propose a recognition algorithm of security-critical components based on threat attack tree to support the ISQE *** evaluation model establishes a framework for ISQE of CHISs that are updated ***,with the support of asset identification and topology data,we sort the security importance of each asset based on the threat attack tree and obtain the security-critical components(set)of the ***,we build the evaluation indicator tree of the evaluation target and propose an ISQE algorithm based on the coefficient of variation to calculate the security quality value of the ***,we present a novel indicator measurement uncertainty aiming to better supervise the performance of the proposed *** results show the advantages of the proposed algorithm in the evaluation of CHISs.

关键词： Complex heterogeneous information system security-critical component threat attack tree information security quantitative evaluation

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