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19th International Conference on Web Information Systems and Applications, WISA 2022

作者： Li, Jianuo Zhao, Feng Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9783031203084

Semantic reasoning techniques based on knowledge graphs have been widely studied since they were proposed. Previous studies are mostly based on closed-world assumptions, which cannot reason about unknown facts. To this end, we propose the Two-Stage Temporal Reasoning Model (TSTR) for reasoning about future facts. In the first stage, probability of future facts occurring is reasoned using repeated information in history. In the second stage, the semantics of the neighborhood nodes are aggregated using the structural encoder and the temporal information is captured using the temporal encoder. The predicted probabilities are obtained by the decoder. Finally, the candidate entity probabilities of the two-stage reasoning are weighted to achieve the prediction of the two-stage fusion. We tested the performance of the TSTR on public datasets and the results demonstrated the effectiveness of the TSTR. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Recurrent neural networks

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Owl: Differential-Based Side-Channel Leakage Detection for CUDA Applications

Owl: Differential-Based Side-Channel Leakage Detection for C...

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International Conference on Dependable Systems and Networks (DSN)

作者： Yu Zhao Wenjie Xue Weijie Chen Weizhong Qiang Deqing Zou Hai Jin National Engineering Research Center for Big Data Technology and System Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Jinyinhu Laboratory Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350341058

ISBN: (纸本)9798350341065

Over the past decade, various methods for detecting side-channel leakage have been proposed and proven to be effective against CPU side-channel attacks. These methods are valuable in assisting developers to identify and patch side-channel vulnerabilities. Nevertheless, recent research has revealed the feasibility of exploiting side-channel vulnerabilities to steal sensitive information from GPU applications, which are beyond the reach of previous side-channel detection methods. Therefore, in this paper, we conduct an in-depth examination of various GPU features and present Owl, a novel side-channel detection tool targeting CUDA applications on NVIDIA GPUs. Owl is designed to detect and locate side-channel leakage in various types of CUDA applications. When tracking the execution of CUDA applications, we design a hierarchical tracing scheme and extend the A-DCFG (Attributed Dynamic Control Flow Graph) to address the massively parallel execution in CUDA, ensuring Owl's detection scalability. After completing the initial assessment and filtering, we conduct statistical tests on the differences in program traces to determine whether they are indeed caused by input variations, subsequently facilitating the positioning of side-channel leaks. We evaluate Owl's capability to detect side-channel leaks by testing it on Libgpucrypto, PyTorch, and nvJPEG. Meanwhile, we verify that our solution effectively handles a large number of threads. Owl has successfully identified hundreds of leaks within these applications. To the best of our knowledge, we are the first to implement side-channel leakage detection for general CUDA applications.

关键词： Target tracking Statistical analysis Filtering Scalability Graphics processing units Side-channel attacks Feature extraction

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Mobile Sensing in the COVID-19 Era:A Review

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Health data science 2022年第1期2022卷 56-68页

作者： Zhiyuan Wang Haoyi Xiong Mingyue Tang Mehdi Boukhechba Tabor E.Flickinger Laura E.Barnes School of Engineering and Applied Science University of VirginiaCharlottesvilleUSA Big Data Lab Baidu ResearchBaidu Inc.BeijingChina Department of Medicine University of VirginiaCharlottesvilleVirginiaUSA

Background. During the COVID-19 pandemic, mobile sensing and data analytics techniques have demonstrated their capabilitiesin monitoring the trajectories of the pandemic, by collecting behavioral, physiological, and mobility data on individual,neighborhood, city, and national scales. Notably, mobile sensing has become a promising way to detect individuals’ infectiousstatus, track the change in long-term health, trace the epidemics in communities, and monitor the evolution of viruses andsubspecies. Methods. We followed the PRISMA practice and reviewed 60 eligible papers on mobile sensing for monitoringCOVID-19. We proposed a taxonomy system to summarize literature by the time duration and population scale under mobilesensing studies. Results. We found that existing literature can be naturally grouped in four clusters, including remote detection,long-term tracking, contact tracing, and epidemiological study. We summarized each group and analyzed representative workswith regard to the system design, health outcomes, and limitations on techniques and societal factors. We further discussed theimplications and future directions of mobile sensing in communicable diseases from the perspectives of technology andapplications. Conclusion. Mobile sensing techniques are effective, efficient, and flexible to surveil COVID-19 in scales of timeand populations. In the post-COVID era, technical and societal issues in mobile sensing are expected to be addressed toimprove healthcare and social outcomes.

关键词： neighborhood summarized directions

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UniCodec: Unified Audio Codec with Single Domain-Adaptive Codebook

arXiv

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arXiv 2025年

作者： Jiang, Yidi Chen, Qian Ji, Shengpeng Xi, Yu Wang, Wen Zhang, Chong Yue, Xianghu Zhang, Shiliang Li, Haizhou National University of Singapore Singapore Tongyi Speech Lab Shanghai Jiao Tong University China Shenzhen Research Institute of Big Data Shenzhen China School of Data Science The Chinese University of Hong Kong Shenzhen China

The emergence of audio language models is empowered by neural audio codecs, which establish critical mappings between continuous waveforms and discrete tokens compatible with language model paradigms. The evolutionary trends from multi-layer residual vector quantizer to single-layer quantizer are beneficial for language-autoregressive decoding. However, the capability to handle multi-domain audio signals through a single codebook remains constrained by inter-domain distribution discrepancies. In this work, we introduce UniCodec, a unified audio codec with a single codebook to support multi-domain audio data, including speech, music, and sound. To achieve this, we propose a partitioned domain-adaptive codebook method and domain Mixture-of-Experts strategy to capture the distinct characteristics of each audio domain. Furthermore, to enrich the semantic density of the codec without auxiliary modules, we propose a self-supervised mask prediction modeling approach. Comprehensive objective and subjective evaluations demonstrate that UniCodec achieves excellent audio reconstruction performance across the three audio domains, outperforming existing unified neural codecs with a single codebook, and even surpasses state-of-the-art domain-specific codecs on both acoustic and semantic representation capabilities1 Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

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data-driven production optimization using particle swarm algorithm based on the ensemble-learning proxy model

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Petroleum science 2023年第5期20卷 2951-2966页

作者： Shu-Yi Du Xiang-Guo Zhao Chi-Yu Xie Jing-Wei Zhu Jiu-Long Wang Jiao-Sheng Yang Hong-Qing Song School of Civil and Resource Engineering University of Science and Technology BeijingBeijing100083China National&Local Joint Engineering Lab for Big Data Analysis and Computer Technology Beijing100190China CNPC International Hong Kong Limited Abu DhabiUnited Arab Emirates Computer Network Information Center Chinese Academy of SciencesBeijing065007China China National Petroleum Corporation Langfang065007HebeiChina

Production optimization is of significance for carbonate reservoirs,directly affecting the sustainability and profitability of reservoir *** physics-based numerical simulations suffer from insufficient calculation accuracy and excessive time consumption when performing production *** establish an ensemble proxy-model-assisted optimization framework combining the Bayesian random forest(BRF)with the particle swarm optimization algorithm(PSO).The BRF method is implemented to construct a proxy model of the injectioneproduction system that can accurately predict the dynamic parameters of producers based on injection data and production *** the help of proxy model,PSO is applied to search the optimal injection pattern integrating Pareto front *** experimental testing,the proxy model not only boasts higher prediction accuracy compared to deep learning,but it also requires 8 times less time for *** addition,the injection mode adjusted by the PSO algorithm can effectively reduce the gaseoil ratio and increase the oil production by more than 10% for carbonate *** proposed proxy-model-assisted optimization protocol brings new perspectives on the multi-objective optimization problems in the petroleum industry,which can provide more options for the project decision-makers to balance the oil production and the gaseoil ratio considering physical and operational constraints.

关键词： Production optimization Random forest The Bayesian algorithm Ensemble learning Particle swarm optimization

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A Hybrid Approach for Many-Objective Feature Selection in Intrusion Detection on Windows Operating Systems

A Hybrid Approach for Many-Objective Feature Selection in In...

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2023 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, ChileCon 2023

作者： Francisco, J. Benitez Pinto-Roa, Diego P. Miguel, Garcia-Torres Parameshachari, B.D. Universidad Autónoma de Asunción Facultad de Ciencias y Tecnologías Asunción Paraguay Universidad Nacional de Asunción Facultad Politécnica Campus San Lorenzo Paraguay Pablo de Olavide University Data Science and Big Data Lab SevilleES-41013 Spain Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (纸本)9798350369533

The exponential increase in devices connected to the Internet has rendered them vulnerable to various types of cyberattacks, disrupting their proper functioning. As a result, it is crucial to have reliable Intrusion Detection Systems (IDS) that can identify malicious activities on the network. However, the unpredictable nature of network behavior and the large volume of data to be audited pose significant challenges. This work aims to address these challenges by eliminating redundant data features, identifying a subset of relevant and representative features that enhance the anomaly detection performance of an IDS using many-objective optimization algorithms. In this work, we have analyzed the recent TON IoT dataset, which collects monitoring data on devices using Windows and Linux operating systems. The NSGA-II, NSGA-III, RVEA, and MOEA/D were used as many-objective feature selection algorithms. Our computational simulations identified good subsets of features that showed better classification performance and accuracy in comparison to the complete set of features. Notably, the results obtained using the NSGA-II stood out from the others. © 2023 IEEE.

关键词： Intrusion detection

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Learning to Accelerate Approximate Methods for Solving Integer Programming via Early Fixing

arXiv

引用

arXiv 2022年

作者： Li, Longkang Wu, Baoyuan The School of Data Science The Chinese University of Hong Kong Shenzhen China Secure Computing Lab of Big Data Shenzhen Research Institute of Big Data Shenzhen China

Integer programming (IP) is an important but challenging problem. Approximate methods have shown promising performance on solving the IP problem. However, we observed that a large fraction of variables solved by some iterative approximate methods fluctuate around their final converged discrete states in very long iterations. It implies that these approximate methods could be significantly accelerated by early fixing these fluctuated variables to their converged states, while not significantly harming the solution quality. To this end, we propose an innovative framework of learning to early fix variables along with the approximate method. Specifically, we formulate the early fixing process as a Markov decision process, and train it using imitation learning, where a policy network evaluates the posterior probability of each free variable concerning its discrete candidate states in each block of iterations. Extensive experiments on three typical IP applications are conducted, including constrained linear programming, MRF energy minimization and sparse adversarial attack, covering moderate and large-scale IP problems. The results demonstrate that our method could not only significantly accelerate the previous approximate method up to over 10 times in most cases, but also produce similar or even better solutions. The implementation of our method is publicly available at https://***/SCLBD/Accelerated-Lpbox-ADMM. © 2022, CC BY-NC-ND.

关键词： Integer programming

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X-Stream: A Flexible, Adaptive Video Transformer for Privacy-Preserving Video Stream Analytics

X-Stream: A Flexible, Adaptive Video Transformer for Privacy...

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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Dou Feng Lin Wang Shutong Chen Lingching Tung Fangming Liu National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Paderborn University Germany National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Guangxi University China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Peng Cheng Laboratory China

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Video stream analytics (VSA) systems fuel many exciting applications that facilitate people’s lives, but also raise critical concerns about exposing too much individuals’ privacy. To alleviate these concerns, various frameworks have been presented to enhance the privacy of VSA systems. Yet, existing solutions suffer two limitations: (1) being scenario-customized, thus limiting the generality of adapting to multifarious scenarios, (2) requiring complex, imperative programming, and tedious process, thus largely reducing the usability of such systems. In this paper, we present X-Stream, a privacy-preserving video transformer that achieves flexibility and efficiency for a large variety of VSA tasks. X-Stream features three major novel designs: (1) a declarative query interface that provides a simple yet expressive interface for users to describe both their privacy protection and content exposure requirements, (2) an adaptation mechanism that dynamically selects the most suitable privacy-preserving techniques and their parameters based on the current video context, and (3) an efficient execution engine that incorporates optimizations for multi-task deduplication and inter-frame inference. We implement X-Stream and evaluate it with representative VSA tasks and public video datasets. The results show that X-Stream achieves significantly improved privacy protection quality and performance over the state-of-the-art, while being simple to use.

关键词： Privacy Transforms Streaming media Transformers Multitasking Real-time systems Task analysis

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PDA-GCN: Predicting Piwi-interacting RNA-Disease Associations Based on Graph Convolution Network

PDA-GCN: Predicting Piwi-interacting RNA-Disease Association...

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IEEE International Conference on Bioinformatics and Computational Biology (ICBCB)

作者： Honglin Su Hui Gao Trusted Cloud Computing and Big Data Engineering Lab University of Electronic Science and Technology of China Sichuan China

Piwi-interacting RNAs (piRNAs) play a significant role in multiple complex human diseases. In order to understand disease pathogenesis at the molecular level, identifying potential associations between piRNAs and diseases accurately is very critical. The computational methods provide a cost-reducing strategy for biological wet experiments. Several computational methods have been proposed, however, most of them still adopted traditional machine learning methods. It is still a challenge for them to deal with the complex and non-linear data. Identifying associations between piRNA-disease is a link prediction task in the heterogeneous network constructed by piRNAs, diseases, and the existing verified associations between them. In this study, a computational method named PDA-GCN is proposed based on Graph Convolution Network (GCN). GCN performance well in aggregate complex information hidden in the heterogeneous network. PDA-GCN adopt 2-layer GCNs to extract features form three aspects. Experiment results show that PDA-GCN achieves superior performance comparing with state-of-the-art methods.

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DCSim: Computing and Networking Integration based Container Scheduling Simulator for data Centers

arXiv

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arXiv 2024年

作者： Hu, Jinlong Rao, Zhizhe Liu, Xingchen Deng, Lihao Dong, Shoubin Guangdong Key Lab of Multimodal Big Data Intelligent Analysis School of Computer Science and Engineering South China University of Technology Guangzhou China

The increasing prevalence of cloud-native technologies, particularly containers, has led to the widespread adoption of containerized deployments in data centers. The advancement of deep neural network models has increased the demand for container-based distributed model training and inference, where frequent data transmission among nodes has emerged as a significant performance bottleneck. However, traditional container scheduling simulators often overlook the influence of network modeling on the efficiency of container scheduling, primarily concentrating on modeling computational resources. In this paper, we focus on a container scheduling simulator based on collaboration between computing and networking within data centers. We propose a new container scheduling simulator for data centers, named DCSim. The simulator consists of several modules: a data center module, a network simulation module, a container scheduling module, a discrete event-driven module, and a data collection and analysis module. Together, these modules provide heterogeneous computing power modeling and dynamic network simulation capabilities. We design a discrete event model using SimPy to represent various aspects of container processing, including container requests, scheduling, execution, pauses, communication, migration, and termination within data centers. Among these, lightweight virtualization technology based on Mininet is employed to construct a software-defined network. An experimental environment for container scheduling simulation was established, and functional and performance tests were conducted on the simulator to validate its scheduling simulation capabilities. Copyright © 2024, The Authors. All rights reserved.

关键词： Software testing

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