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32nd ACM International Conference on Multimedia, MM 2024

作者： Liu, Fangyi Ye, Mang Du, Bo National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan China

ISBN: (纸本)9798400706868

Person re-identification (ReID) is crucial in video surveillance, aiming to match individuals across different camera views while cloth-changing person re-identification (CC-ReID) focuses on pedestrians changing attire. Many existing CC-ReID methods overlook generalization, crucial for universality across cloth-consistent and cloth-changing scenarios. This paper pioneers exploring the cloth-generalized person re-identification (CG-ReID) task and introduces the Cloth-aware Augmentation (CaAug) strategy. Comprising domain augmentation and feature augmentation, CaAug aims to learn identity-relevant features adaptable to both scenarios. Domain augmentation involves creating diverse fictitious domains and simulating various clothing scenarios. Supervising features from different cloth domains enhances robustness and generalization against clothing changes. Additionally, for feature augmentation, element exchange introduces diversity concerning clothing changes. Regularizing the model with these augmented features strengthens resilience against clothing change uncertainty. Extensive experiments on cloth-changing datasets demonstrate the efficacy of our approach, consistently outperforming state-of-the-art methods. © 2024 ACM.

关键词： Feature extraction

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Devil is in Details: Locality-Aware 3D Abdominal CT Volume Generation for Self-Supervised Organ Segmentation 24

Devil is in Details: Locality-Aware 3D Abdominal CT Volume G...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Wang, Yuran Wan, Zhijing Qiu, Yansheng Wang, Zheng National Engineering Research Center for Multimedia Software Hubei Key Laboratory of Multimedia and Network Communication Engineering Institute of Artificial Intelligence School of Computer Science Wuhan University Wuhan China

ISBN: (纸本)9798400706868

In the realm of medical image analysis, self-supervised learning (SSL) techniques have emerged to alleviate labeling demands, while still facing the challenge of training data scarcity owing to escalating resource requirements and privacy constraints. Numerous efforts employ generative models to generate high-fidelity, unlabeled 3D volumes across diverse modalities and anatomical regions. However, the intricate and indistinguishable anatomical structures within the abdomen pose a unique challenge to abdominal CT volume generation compared to other anatomical regions. To address the overlooked challenge, we introduce the Locality-Aware Diffusion (Lad), a novel method tailored for exquisite 3D abdominal CT volume generation. We design a locality loss to refine crucial anatomical regions and devise a condition extractor to integrate abdominal priori into generation, thereby enabling the generation of large quantities of high-quality abdominal CT volumes essential for SSL tasks without the need for additional data such as labels or radiology reports. Volumes generated through our method demonstrate remarkable fidelity in reproducing abdominal structures, achieving a decrease in FID score from 0.0034 to 0.0002 on AbdomenCT-1K dataset, closely mirroring authentic data and surpassing current methods. Extensive experiments demonstrate the effectiveness of our method in self-supervised organ segmentation tasks, resulting in an improvement in mean Dice scores on two abdominal datasets effectively. These results underscore the potential of synthetic data to advance self-supervised learning in medical image analysis. © 2024 ACM.

关键词： Self-supervised learning

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A Theoretical Analysis of Backdoor Poisoning Attacks in Convolutional Neural networks 41

A Theoretical Analysis of Backdoor Poisoning Attacks in Conv...

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41st International Conference on Machine Learning, ICML 2024

作者： Li, Boqi Liu, Weiwei School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan China

The rising threat of backdoor poisoning attacks (BPAs) on Deep Neural networks (DNNs) has become a significant concern in recent years. In such attacks, the adversaries strategically target a specific class and generate a poisoned training set. The neural network (NN), well-trained on the poisoned training set, is able to predict any input with the trigger pattern as the targeted label, while maintaining accurate outputs for clean inputs. However, why the BPAs work remains less explored. To fill this gap, we employ a dirty-label attack and conduct a detailed analysis of BPAs in a two-layer convolutional neural network. We provide theoretical insights and results on the effectiveness of BPAs. Our experimental results on two real-world datasets validate our theoretical findings. Copyright 2024 by the author(s)

关键词： Convolutional neural networks

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PSAbot: A Chatbot System for the Analysis of Posts on Stack Overflow 35

PSAbot: A Chatbot System for the Analysis of Posts on Stack ...

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35th IEEE International Conference on Software engineering Education and Training, CSEE and T 2023

作者： Shau, An-Chi Liang, Yan-Cih Hsieh, Wan-Jung Lin, Xiang-Ling Ma, Shang-Pin National Taiwan Ocean University Department of Computer Science and Engineering Taiwan National Yang Ming Chiao Tung University Institute of Computer Science and Engineering Taiwan National Yang Ming Chiao Tung University Institute of Network Engineering Taiwan

ISBN: (纸本)9798350322026

With the progressive development of technology, programming learners have significantly increased. However, the lack of human tutors and the rapidly updating information cause the learners to spend a considerable amount of time browsing and filtering authentic online resources, and decrease learning efficiency. Although many coding websites and programming communities can provide credible advice, it is still a challenge for learners to figure out their accurate questions. Therefore, we devised a Chatbot system, named PSAbot, to consider the above issue. PSAbot supports keyword extraction and analysis for multiple posts to guide the users through questions. PSAbot applies word embedding, sentence similarity, LDA (Latent Dirichlet Allocation) topic modeling, and weighting functions to help filter out redundant information and decrease the time cost of browsing, and further improve the learning efficiency. The conducted experiments show that about 80% of the Top1 answers recommended by PSAbot can largely meet the user expectations. © 2023 IEEE.

关键词： Data integration

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Invited: Caching in Automated Data Centric Vehicles for Edge Computing Scenarios 23

Invited: Caching in Automated Data Centric Vehicles for Edge...

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Proceedings of the 60th Annual ACM/IEEE Design Automation Conference

作者： Nora Sperling Alex Bendrick Dominik Stöhrmann Rolf Ernst Institute of Computer and Network Engineering TU Braunschweig Braunschweig Germany

ISBN: (纸本)9798350323481

With the current trend towards large data volumes, state-of-the-art communication and data management solutions based on the publish/subscribe pattern reach the network limits, hampering the deployment of advanced edge and cloud computing services. A main reason is the established publisher-centric distribution mechanism that over-utilizes network resources. We suggest a network and subscriber-centric adaptive data caching communication scheme supported by dynamic network management and configuration, which has great potential to solve the data distribution challenges. We show that state-of-the-art publish/subscribe middlewares like Data Distribution Service (DDS) can transparently be combined with such software caching.

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Conservative Design with High-Performance COTS Architectures - Beyond Traditional Approaches

Conservative Design with High-Performance COTS Architectures...

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International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Robin Hapka Rolf Ernst Institute of Computer and Network Engineering TU Braunschweig Germany

ISBN: (数字)9798350361230

ISBN: (纸本)9798350361247

Safety-critical industrial systems are subject to many stringent requirements, including non-functional design constraints that are essential for robustness, reliability, and safety. System timing is often treated as an afterthought, even though data age, race conditions, or missed deadlines can affect functional safety. This challenge has been addressed in real-time system design, but the situation has worsened in recent system designs. Many applications require high performance to achieve behavioral safety goals, i.e., safety of intended functionality, using complex heterogeneous architectures with multi-level memory, interconnected chiplets, and accelerators based on the latest technologies. Machine learning applications in factory automation, robotics, and autonomous systems are important examples. The timing of such architectures is heavily influenced by the physical effects of temperature control and chip variations, including aging, leading to even more complex chip-specific software execution timing. The paper shows that identical chips differ in their timing behavior, and that most of the timing variation of a single chip can be effectively modeled using additive white Gaussian noise. We explain how such timing behavior can be exploited in high-assurance design for safety and reliability, following established reliability analysis, test, and redundancy techniques.

关键词： Temperature distribution Redundancy Reliability engineering Real-time systems Timing Safety Temperature control Manufacturing automation System analysis and design Standards

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Theoretical study of aromaticity reversal in dimethyldihydropyrene derivatives

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Chinese Journal of Structural Chemistry 2024年第5期43卷 8-10页

作者： Fangwen Peng Zhen Luo Yingjin Ma Haibo Ma School of Chemistry and Chemical Engineering Nanjing UniversityNanjing210023China Computer Network Information Center Chinese Academy of SciencesBeijing100190China Qingdao Institute for Theoretical and Computational Sciences School of Chemistry and Chemical EngineeringShandong UniversityQingdaoShandong266237China

Aromaticity has always been an intriguing chemistry topic,which is considered to have close relations with materials'stability and chemical reactivity due to special energy *** conjugated cycles,Hückel's rule,proposed in 1931,suggests that annulenes having[4n+2]and[4n]πelectrons present aromatic and antiaromatic characters with closed and open electronic configurations,respectively[1].

关键词： Hückel aromatic reactivity

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Malicious Traffic Detection with Noise Labels Based on Cross-Modal Consistency

IEEE Networking Letters

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IEEE networking Letters 2024年第2期6卷 148-151页

作者： Yuan, Qingjun Niu, Weina Wang, Yongjuan Gou, Gaopeng Lu, Bin Henan Key Laboratory of Network Cryptography Technology Zhengzhou450001 China Institute for Cyber Security School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518110 China Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China

To train robust malicious traffic identification models under noisy labeled datasets, a number of learning with noise labels approaches have been introduced, among which parallel training methods have been proved to be effective. Parallel training methods tend to select samples with disagreement to mitigate the risk of self-control degradation. However, it also introduces noisy knowledge into training. In this letter, we try to avoid introducing noisy knowledge by enhancing the consistency of the representations of parallel networks. Meanwhile, the two networks are heterogeneous and introduce information from different modalities, thus mitigating the risk of self-control degradation from multiple perspectives. © 2019 IEEE.

关键词： Feature extraction

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An overview on IRS-enabled sensing and communications for 6G: architectures, fundamental limits, and joint beamforming designs

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Science China(Information Sciences) 2025年第5期68卷 170-193页

作者： Xianxin SONG Yuan FANG Feng WANG Zixiang REN Xianghao YU Ye ZHANG Fan LIU Jie XU Derrick Wing Kwan NG Rui ZHANG Shuguang CUI School of Science and Engineering (SSE) Shenzhen Future Network of Intelligence Institute (FNii-Shenzhen)and Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Department of Electrical Engineering City University of Hong Kong School of Information Engineering Guangdong University of Technology Key Laboratory of Wireless-Optical Communications Chinese Academy of Sciences School of Information Science and Technology University of Science and Technology of China Computer School Beijing Information Science and Technology University National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University School of Electrical Engineering and Telecommunications University of New South Wales School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Department of Electrical and Computer Engineering National University of Singapore

This study presents an overview on intelligent reflecting surface(IRS)-enabled sensing and communication for the forthcoming sixth-generation(6G) wireless networks, in which IRSs are strategically deployed to proactively reconfigure wireless environments to improve both sensing and communication(S&C) performance. First, we exploit a single IRS to enable wireless sensing in the base station's(BS's) non-line-of-sight(NLoS) area. In particular, we present three IRS-enabled NLoS target sensing architectures with fully-passive, semi-passive, and active IRSs, respectively. We compare their pros and cons by analyzing the fundamental sensing performance limits for target detection and parameter estimation. Next, we consider a single IRS to facilitate integrated sensing and communication(ISAC), in which the transmit signals at the BS are used for achieving both S&C functionalities, aided by the IRS through reflective beamforming. We present joint transmit signal and receiver processing designs for realizing efficient ISAC, and jointly optimize the transmit beamforming at the BS and reflective beamforming at the IRS to balance the fundamental performance tradeoff between S&C. Furthermore, we discuss multi-IRS networked ISAC, by particularly focusing on multi-IRS-enabled multi-link ISAC, multi-region ISAC, and ISAC signal routing, respectively. Finally, we highlight various promising research topics in this area to motivate future work.

关键词： integrated sensing and communication (ISAC) intelligent reflecting surface (IRS) non-line-of-sight (NLoS) sensing sensing and communication tradeoff

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Algorithmic Innovations in Machine Learning for Drug Discovery and Pharmaceutical Innovations 2

Algorithmic Innovations in Machine Learning for Drug Discove...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Sangeetha, M. Harin Fernandez, F. Mary Ganesh Ramachandran, A. Saravanan, S.K. Bhanumathi, M. University of Technology and Applied Sciences Department of Computer Engineering Nizwa Oman Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science Chennai India Saveetha School of Engineering Simats BigData and Network Security Department of Computers Science and Engineering Tamil Nadu Chennai India Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350384277

Algorithmic advancements in machine learning have transformed drug discovery and pharmaceutical innovation. This study examines how machine learning might speed medication development and pharmaceutical innovation. Data from genomes, chemistry, and clinical trials is collected to start the research. Careful data cleansing, normalization, and feature engineering prepare the data for model building. Random Forest, SVM, NN, Decision Tree, and Logistic Regression are tested for prediction. ROC AUC results show that Random Forest is the most accurate model at 86%. Ensemble approaches have drug discovery potential. Beyond accuracy, models are assessed for precision, false positive, and false negative rates. Drug discovery relies on precision to measure positive predictions. The Random Forest model makes the most accurate positive predictions at 0.87. However, false positives and negatives also matter in drug discovery. The Decision Tree model contains the falsest positives (35), underlining the need to reduce them to save resources. The Neural network approach reduces false negatives to 12, ensuring good medication candidates are not overlooked. The results show that different machine learning models have trade-offs, underscoring the necessity for a personalized strategy depending on objectives and restrictions. Algorithmic breakthroughs in pharmaceutical innovation reduce medication development time by 40% and cost by 25%, according to the study. © 2024 IEEE.

关键词： Drug discovery

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