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International Workshop on Quality of Service

作者： Yanyan Wang Jia Liu Shen-Huan Lyu Zhihao Qu Bin Tang Baoliu Ye Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University China College of Computer Science and Software Engineering Hohai University China State Key Laboratory for Novel Software Technology Nanjing University China

ISBN: (数字)9798350350128

ISBN: (纸本)9798350350135

With the proliferation of RFID-enabled applications, multiple readers are required for the complete coverage of numerous tags in a large-scale RFID system. In this scenario, we sometimes pay more attention to a subset of tags instead of all, which are referred to as key tags. In this paper, we study an under-investigated problem key tag distribution identification, which aims to identify which key tags are beneath which readers. This is crucial for efficiently managing specific items of interest, which can quickly pinpoint key tags and help RFID readers covering these tags collaborate to improve the tag inventory efficiency. Since key tags typically make up a small part of all tags, it is time consuming to deal with all tags in the traditional way. We propose a protocol called Kadept that identifies the key tag distribution by using a sophisticatedly designed filter that teases out key tags as well as assigns each of them a singleton slot for response. With this design, a great number of trivial (non-key) tags will keep silent and free up bandwidth resources for key tags, and each key tag is sorted in a collision-free way and can be identified with only 1-bit response, which significantly improves the time efficiency. We theoretically analyze how can we optimize protocol parameters of Kadept and conduct extensive simulations under different tag distribution scenarios. Compared with the state-of-the-art, Kadept can improve the time efficiency by a factor of 3.7×, when the ratio of key tags to all tags is 0.1.

关键词： Analytical models Protocols Quality of service Bandwidth Radiofrequency identification

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High-Efficiency Generative Adversarial Network Model for Chemical Process Fault Diagnosis

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IFAC-PapersOnLine 2022年第7期55卷 732-737页

作者： Ruoshi Qin Jinsong Zhao State Key Laboratory of Chemical Engineering Department of Chemical Engineering Tsinghua University Beijing China Beijing Key Laboratory of Industrial Big Data System and Application Tsinghua University Beijing China

Fault diagnosis techniques are essential to ensure the long-term reliability of industrial process systems. Current deep learning methods mainly rely on a large quantity of training data. Generative Adversarial Network (GAN) model has started to be utilized for diagnostic problems with small sample size and data imbalance in recent years, but the fault diagnosis performance heavily depends on the experience of the model builder. In this work, a high-efficiency GAN model (HGAN) is proposed for chemical process fault diagnosis. HGAN integrates the advantages of Wasserstein GAN and Auxiliary Classifier GAN to promote the generating model training stability and the discriminative model training efficiency with Bayesian optimization. Experiments on the benchmark Tennessee Eastman process under the circumstance of small samples show that the presented model can achieve a satisfactory fault diagnosis accuracy without the assistance of a redundant deep neural network classifier and with reduced effort in model tuning.

关键词： Auxiliary Classifier Generative Adversarial Network Wasserstein distance Gradient penalty Bayesian optimization Fault diagnosis Tennessee Eastman process

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Revealing the real-time diversity and abundance of small mammals by using an Intelligent Animal Monitoring System(IAMS)

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Integrative Zoology 2022年第6期17卷 1121-1135页

作者： Xifu YANG Liliang HAN Yong WANG Cong GUO Zhibin ZHANG State Key Laboratory of Integrated Management of Pest Insects and Rodents in Agriculture Institute of ZoologyChinese Academy of SciencesBeijingChina Tsinghua-Qingdao Big Data Engineering Research Center QingdaoChina Key Laboratory of Agro-Ecological Processes in Subtropical Region Institute of Subtropical AgricultureChinese Academy of SciencesChangshaChina College of Life Science Sichuan UniversityChengduChina CAS Center for Excellence in Biotic Interactions University of Chinese Academy of SciencesBeijingChina

It is challenging to reveal the real-time spatio-temporal change of diversity and abundance of animals in natural systems by using traditional *** rapid advancement of new technologies such as the Internet of Things,artiﬁcial intelligence,and big-data processing,provide opportunities for developing novel technologies for moni-toring biodiversity and population abundance of animals with high efﬁcacy and *** this study,by using a recently developed Intelligent Animal Monitoring System,named“Vector Intelligent Monitoring System(VIMS)”,we investigated the real-time diversity and abundance of small mammals in the Banruosi forest,Dujiangyan region,southwest *** make a comparison of the VIMS with traditional methods,we also surveyed the diversity and abundance of small mammals using wired live *** to live traps,the VIMS has several advantages such as automatic data collection,intelligent identiﬁcation of species,data visualization,whole-day and all-weather op-eration,little disturbance to animals,real-time monitoring,and is capable of revealing more small mammal ***,the VIMS also has several disadvantages over live traps such as lower trapping efﬁciency and being more expensive than live *** results suggest that the VIMS can be a complementary method to traditional ones in monitoring the real-time spatio-temporal change of diversity and abundance of small mammals(especially rare species).In addition,the VIMS is useful in monitoring other small animals like small carnivores,birds,amphibians,and reptiles.

关键词： artiﬁcial intelligence(AI) Internet of Things(IoT) live trap small mammal species diversity

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Logical Relation Modeling and Mining in Hyperbolic Space for Recommendation 40

Logical Relation Modeling and Mining in Hyperbolic Space for...

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40th IEEE International Conference on data engineering, ICDE 2024

作者： Tan, Yanchao Lv, Hang Zhou, Zihao Guo, Wenzhong Xiong, Bo Liu, Weiming Chen, Chaochao Wang, Shiping Yang, Carl College of Computer and Data Science Fuzhou University Fuzhou China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Institute for Artificial Intelligence University of Stuttgart Stuttgart Germany College of Computer Science Zhejiang University Hangzhou China Emory University Department of Computer Science Atlanta United States

ISBN: (纸本)9798350317152

The sparse interactions between users and items have aggravated the difficulty of their representations in recommender systems. Existing methods leverage tags to alleviate the sparsity problem but ignore prevalent logical relations among items and tags (e.g., membership, hierarchy, and exclusion), which can be leveraged to enhance the accuracy of modeling user preferences and conducting recommendations. To this end, we propose to extract logical relations among item tags from existing tag taxonomies and exploit the individual strengths of the Poincaré and the Lorentz models in hyperbolic space for logical relation modeling towards enhanced recommendations. Moreover, we find that the logical relations directly extracted from existing tag taxonomies can be inaccurate and coarse. Therefore, we further devise innovative consistency-based and granularity-based weighting mechanisms based on user behavior patterns for data-driven logical relation mining that can be jointly optimized along with recommendations in an end-to-end fashion. Extensive experiments on four real-world benchmark datasets show drastic performance gains brought by our proposed framework, which constantly achieves an average of 8.25% improvement over state-of-the-art competitors regarding both Recall and NDCG metrics. Insightful case studies further demonstrate that our automatically refined logical relations are highly accurate and interpretable. © 2024 IEEE.

关键词： Recommender systems

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MECNet: Multi-Scale Exposure-Consistency Learning via Fourier Transform for Exposure Correction

MECNet: Multi-Scale Exposure-Consistency Learning via Fourie...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Gaofei Qiao Zhibin Zhang Liqiang He Key Laboratory of Wireless Networks and Mobile Computing Engineering Research Center of Ecological Big Data Ministry of Education Inner Mongolia University Hohhot China Geomechanica Inc Canada

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In the real world, due to various challenging lighting conditions such as low light, underexposure, and overexposure, captured images often exhibit undesirable appearances. Given that images with different exposure levels require different correction processes, a single neural network struggles to produce satisfactory results. We propose a coarse-to-fine exposure correction model for learning exposure consistency representation to address underexposure and overexposure issues. Building upon the bilateral activation mechanism, we introduce the Fourier transform to capture global information and fuse it with locally extracted information through convolution to achieve superior feature representation. Additionally, we employ Laplacian pyramids to decompose the source image into different spatial frequency bands, then the image details are enhanced by denoising high-frequency layers. Experimental results on the MSEC and SICE datasets demonstrate the superiority of our proposed method over current state-of-the-art approaches. Our code will be made available on GitHub.

关键词： Representation learning Laplace equations Fourier transforms Image color analysis Plants (biology) Noise reduction Neural networks Lighting Feature extraction Software development management

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Demand Response Scheme: Achieving Sustainability in Distributed data Processing 9

Demand Response Scheme: Achieving Sustainability in Distribu...

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9th IEEE International Conference on data Science in Cyberspace, DSC 2024

作者： Zhai, Xueying Peng, Yunfeng Guo, Xiuping Zhang, Liang Zhang, Wei University of Science and Technology Beijing School of Computer and Communication Engineering Beijing China Beijing University of Posts and Telecommunications School of Economics and Management Beijing China Guizhou Institute of Technology Guizhou Key Laboratory of Electric Power Big Data Guizhou China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China

ISBN: (纸本)9798350391367

The rapid development of Internet of Things (IoT) technology has driven the intelligent transformation of various industries, including manufacturing, agriculture, and healthcare, etc., significantly enhancing their management capabilities and decision-making efficiency. data analysis is crucial, serving as both the core technology of this transformation and a major driving force for advancing industry intelligence. However, the increasing volume of data has led to high energy consumption during data analysis, posing significant challenges to environmental and economic sustainability. Integrating renewable energy sources (RESs) is an effective way to reduce greenhouse gas emissions due to their low-carbon and renewable nature. Additionally, the spatiotemporal variability and complementarity of RES generation and grid electricity prices provide favorable conditions for distributed data processing. Therefore, a two-stage demand response scheme is proposed to reduce carbon emissions and electricity costs by prioritizing data processing in data centers (DCs) where RES is abundant or electricity prices are low, based on RES generation and electricity price signals. Simulation results show that the proposed scheme reduces 36.37% carbon emissions and 20.49% electricity costs compared to the benchmark algorithm, indicating its potential for both environmental and economic optimization. © 2024 IEEE.

关键词： Low emission

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Multi-Turn Jailbreaking Large Language Models via Attention Shifting 39

Multi-Turn Jailbreaking Large Language Models via Attention ...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Du, Xiaohu Mo, Fan Wen, Ming Gu, Tu Zheng, Huadi Jin, Hai Shi, Jie China National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security China Cluster and Grid Computing Lab School of Computer Science and Technology HUST China JinYinHu Laboratory China Huawei International China

ISBN: (纸本)157735897X

Large Language Models (LLMs) have achieved significant performance in various natural language processing tasks but also pose safety and ethical threats, thus requiring red teaming and alignment processes to bolster their safety. To effectively exploit these aligned LLMs, recent studies have introduced jailbreak attacks based on multi-turn dialogues. These attacks aim to prompt LLMs to generate harmful or biased content by guiding them through contextual content. However, the underlying reasons for the effectiveness of multi-turn jailbreaks remain unclear. Existing attacks often focus on optimizing queries and escalating toxicity to construct dialogues, lacking a thorough analysis of the inherent vulnerabilities of LLMs. In this paper, we first conduct an in-depth analysis of the differences between single-turn and multi-turn jailbreaks and find that successful multi-turn jailbreaks can effectively disperse the attention of LLMs on keywords associated with harmful behaviors, especially in historical responses. Based on this, we propose ASJA, a new multi-turn jailbreak approach by shifting the attention of LLMs, specifically by iteratively fabricating the dialogue history through a genetic algorithm to induce LLMs to generate harmful content. Extensive experiments on three LLMs and two datasets show that our approach surpasses existing approaches in jailbreak effectiveness, the stealth of jailbreak prompts, and attack efficiency. Our work emphasizes the importance of enhancing the robustness of LLMs’ attention mechanism in multi-turn dialogue scenarios for a better defense strategy. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Identifying Scientific and Technical“Unicorns”

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Journal of data and Information Science 2021年第2期6卷 96-115页

作者： Lucy L.Xu Miao Qi Fred Y.Ye Jiangsu Key Laboratory of Data Engineering and Knowledge Service School of Information ManagementNanjing UniversityNanjing 210023China University Library Nantong UniversityNantong 226019China

Purpose:Using the metaphor of"unicorn,"we identify the scientific papers and technical patents characterized by the informetric feature of very high citations in the first ten years after publishing,which may provide a new pattern to understand very high impact works in science and ***/methodology/approach:When we set CT as the total citations of papers or patents in the first ten years after publication,with CT≥5,000 for scientific"unicorn"and CT≥500 for technical"unicorn,"we have an absolute standard for identifying scientific and technical"unicorn"***:We identify 165 scientific"unicorns"in 14,301,875 WoS papers and 224 technical"unicorns"in 13,728,950 DII patents during 2001–*** 50%of"unicorns"belong to biomedicine,in which selected cases are individually *** rare"unicorns"increase following linear model,the fitting data show 95%confidence with the RMSE of scientific"unicorn"is 0.2127 while the RMSE of technical"unicorn"is *** limitations:A"unicorn"is a pure quantitative consideration without concerning its quality,and"potential unicorns"as CT≤5,000 for papers and CT≤500 for patents are left in future *** implications:Scientific and technical"unicorns"provide a new pattern to understand high-impact works in science and ***"unicorn"pattern supplies a concise approach to identify very high-impact scientific papers and technical ***/value:The"unicorn"pattern supplies a concise approach to identify very high impact scientific papers and technical patents.

关键词： Unicorn Scientific paper Technical patent Citation analysis Patent analysis

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A Survey of Internet of Things in Healthcare 29th

A Survey of Internet of Things in Healthcare

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29th International Conference on Computational and Experimental engineering and Sciences, ICCES 2023

作者： Chen, Shuwen Wang, Jiaji Song, Zhijun School of Physics and Information Engineering Jiangsu Second Normal University Nanjing211200 China School of Computing and Mathematical Sciences University of Leicester LeicesterLE1 7RH United Kingdom Jiangsu Province Engineering Research Center of Basic Education Big Data Application Nanjing211200 China State Key Laboratory of Millimeter Waves Southeast University Nanjing210096 China

ISBN: (纸本)9783031449468

With the rapid development of computers, Internet of Things (IoT) technology is becoming more and more closely integrated with the healthcare sector. This article introduces two major applications of IoT in healthcare: hospital information management and remote healthcare. In digitizing the healthcare industry, it is a great challenge to avoid the leakage of private medical data. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Internet of things

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Fast One-Stage Unsupervised Domain Adaptive Person Search

arXiv

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arXiv 2024年

作者： Cui, Tianxiang Wang, Huibing Peng, Jinjia Deng, Ruoxi Fu, Xianping Wang, Yang The College of Information and Science Technology Dalian Maritime University China The College School of Cyber Security and Computer Hebei University China The College of Computer Science and Artificial Intelligence Wenzhou University China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology China

Unsupervised person search aims to localize a particular target person from a gallery set of scene images without annotations, which is extremely challenging due to the unexpected variations of the unlabeled domains. However, most existing methods dedicate to developing multi-stage models to adapt domain variations while using clustering for iterative model training, which inevitably increases model complexity. To address this issue, we propose a Fast One-stage Unsupervised person Search (FOUS) which complementary integrates domain adaptaion with label adaptaion within an end-to-end manner without iterative clustering. To minimize the domain discrepancy, FOUS introduced an Attention-based Domain Alignment Module (ADAM) which can not only align various domains for both detection and ReID tasks but also construct an attention mechanism to reduce the adverse impacts of low-quality candidates resulting from unsupervised detection. Moreover, to avoid the redundant iterative clustering mode, FOUS adopts a prototype-guided labeling method which minimizes redundant correlation computations for partial samples and assigns noisy coarse label groups efficiently. The coarse label groups will be continuously refined via label-flexible training network with an adaptive selection strategy. With the adapted domains and labels, FOUS can achieve the state-of-the-art (SOTA) performance on two benchmark datasets, CUHK-SYSU and PRW. The code is available at https://***/whbdmu/FOUS © 2024, CC0.

关键词： Iterative methods

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