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8th International Conference on computer and Communication systems, ICCCS 2023

作者： Qian, Hong Wu, Wei Zhu, Qi Wang, Fei Zhao, Jinwei Zheng, Yan School of Computer Science and Technology University of Science and Technology of China Hefei China Jiangnan Institute of Computing Technology System Software Research Department Wuxi China Tsinghua University Department of Computer Science and Technology Beijing China National Research Center of Parallel Computer Engineering and Technology Beijing China

ISBN: (纸本)9781665456128

The Sunway family supercomputers have achieved a series of remarkable achievements. However, the toolchains provided by them are not perfect, which has brought great challenges to the development of high-performance application software. In this paper, a profiling and optimizing tool is proposed to assist people to analyze and optimize the performance of their programs. SWPFOPLD is independent of the application program and gathers the runtime performance data through the PMUs, an automatically hot functions rearrange optimization based on the performance data is furtherly accomplished. The evaluation shows that SWPFOPLD can be easily and effectively used to analyze and optimize the performance of the application programs. © 2023 IEEE.

关键词： Supercomputers

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Improved Demonstration-Knowledge Utilization in Reinforcement Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第5期5卷 2139-2150页

作者： Liu, Yanyu Zeng, Yifeng Ma, Biyang Pan, Yinghui Gao, Huifan Zhang, Yuting Xiamen University Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision Department of Automation Xiamen361102 China Northumbria University Department of Computer and Information Sciences NewcastleNE1 8ST United Kingdom Minnan Normal University Department of Computer Science Zhangzhou363000 China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China

Reinforcement learning (RL) has made great success in recent years. Generally, the learning process requires a huge amount of interaction with the environment before an agent can achieve acceptable performance. This motivates many techniques, such as incorporating prior knowledge which is usually presented as experts' demonstration, and using a probability distribution to represent state-and-action values, to accelerate the learning process. The methods perform well when the prior knowledge is genuinely correct and no much change occurs to the learning environment. However, the requirement is not perfectly realistic in many complex applications. The demonstration knowledge may not reflect the true environment and even be full of noise. In this article, we introduce a dynamic distribution merging method to improve knowledge utilization in a general RL algorithm, namely Q-learning. The new method adapts a normal distribution to represent state-action values and merges the prior and learned knowledge in a discriminative way. We theoretically analyze the new learning method and demonstrate its empirical performance over multiple problem domains. © 2020 IEEE.

关键词： Merging

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A Study of Data Augmentation for Imbalance Problem in Network Traffic 3

A Study of Data Augmentation for Imbalance Problem in Networ...

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3rd International Conference on Blockchain Technology and Information Security, ICBCTIS 2023

作者： Wang, Xu Sun, Shimin Han, Li School of Computer Science and Engineering Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Autonomous Intelligent Technology and System School of Software Tiangong University Tianjin China School of Computer Science and Engineering Tianjin University of Technology National Engineering Laboratory for Computer Virus Prevention and Control Technology Tianjin China

ISBN: (纸本)9798350340402

The complexity and diversity of smart grid systems increase the likelihood of anomalies in communications between devices in the system, and how these anomalies are detected is critical to the security of the grid system. However, the distribution of traffic types in the grid is often highly unbalanced, with the total amount of normal traffic data often far outweighing the anomalous data. This imbalance compromises the effectiveness of the detection model and poses a significant threat to the overall security of the grid This paper improves on traditional conditional generation adversarial neural networks and designs a new approach to overcome this obstacle and enhance the security of smart grid systems. In the generator part, the spatial features of the data are captured by convolutional neural networks, and the temporal features of the data are captured by gated recurrent units. By integrating these two components into the generative network we can generate synthetic data with both temporal and spatial features, thus enhancing the minority class samples. By incorporating the augmented minority data into the training process, the proposed approach enables the detection model to understand the distribution of features of the minority anomaly samples more accurately. This improved learning accuracy translates into improved model performance, resulting in higher detection rates for Previously under-represented anomalies. In addition, our approach effectively addresses the inherent bias towards traffic types with higher flows typically observed in asymmetric datasets, thus providing a more equitable and comprehensive security solution for smart grid systems. © 2023 IEEE.

关键词： Anomaly detection

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An EnKF-LSTM Assimilation Algorithm for Crop Growth Model

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2024年第2期2卷 372-380页

作者： Zhou, Siqi Wang, Ling Liu, Jie Tang, Jinshan Harbin Institution of Technology Department of Computer Science and Technology Harbin150001 China Harbin Institute of Technology Department of Computer Science and Technology Harbin150001 China National Key Laboratory of Smart Farming Technology and System Heilongjiang Harbin150000 China George Mason University Department of Health Administration and Policy College of Public Health FairfaxVA22033 United States

Accurate and timely prediction of crop growth is of great significance to ensure crop yields, and researchers have developed several crop models for the prediction of crop growth. However, there are large differences between the simulation results obtained by the crop models and the actual results;thus, in this article, we proposed to combine the simulation results with the collected crop data for data assimilation so that the accuracy of prediction will be improved. In this article, an EnKF-LSTM data assimilation method for various crops is proposed by combining an ensemble Kalman filter and long short-term memory (LSTM) neural network, which effectively avoids the overfitting problem of the existing data assimilation methods and eliminates the uncertainty of the measured data. The verification of the proposed EnKF-LSTM method and the comparison of the proposed method with other data assimilation methods were performed using datasets collected by sensor equipment deployed on a farm. © 2024 IEEE.

关键词： Crops

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Fine-Grained software Vulnerability Detection via Neural Architecture Search 28th

Fine-Grained Software Vulnerability Detection via Neural Ar...

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28th International Conference on Database systems for Advanced Applications, DASFAA 2023

作者： Du, Qianjin Kuang, Xiaohui Li, Xiang Zhao, Gang The Department of Computer Science and Technology Tsinghua University Beijing100084 China National Key Laboratory of Science and Technology on Information System Security Beijing100084 China

ISBN: (纸本)9783031306778

Vulnerability detection methods based on the deep learning have achieved remarkable performance improvements compared to traditional methods. Current deep learning-based detectors mostly use a single RNN or its variants (i.e., LSTM or GRU) to detect vulnerabilities. However, vulnerability detection is a multi-domain problem. Different types of vulnerabilities have different characteristics. Using a single neural network cannot perform well in all types of vulnerability detection tasks. Manually designing a matching neural network for each type of vulnerability detection task not only requires a lot of trials and computational resources but also highly relies on the knowledge and design experience of experts. To address this issue, in this paper, we propose a novel fine-grained vulnerability detection framework named A-DARTS, which is capable of searching for well-performed neural network architectures for different vulnerability detection tasks by introducing neural network architecture search (NAS) techniques. Specifically, we design a more efficient search space to ensure superior neural network architectures can be found by the search algorithm. Besides, we propose an adaptive differentiable search algorithm to search for superior neural network architectures. Experimental results show that searched models consistently outperform all baseline models and achieve significant performance improvements. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Long short-term memory

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OblivTime: Oblivious and Efficient Interval Skyline Query Processing Over Encrypted Time-Series Data

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IEEE Transactions on Services Computing 2025年第03期18卷 1602-1617页

作者： Ouyang, Huajie Zheng, Yifeng Wang, Songlei Hua, Zhongyun Harbin Institute of Technology School of Computer Science and Technology Guangdong Shenzhen518055 China The Hong Kong Polytechnic University Department of Electrical and Electronic Engineering Hong Kong Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518055 China

Time-series data is prevalent in many applications like smart homes, smart grids, and healthcare. And it is now increasingly common to store and query time-series data in the cloud. Despite the benefits, data privacy concerns in such outsourced services are pressing, making it imperative to embed privacy assurance mechanisms from the outset. Most existing related works have been focused on querying for different types of aggregate statistics. In this paper, we instead focus on the secure support for advanced interval skyline queries, which allow to identify time series that are not dominated by any other time series within a query time interval. This is valuable for time-series data analytics in applications like remote health monitoring (e.g., identifying patients with high heart rates in a certain week). We present OblivTime, a new system framework for oblivious and efficient interval skyline query processing over encrypted time-series data. OblivTime is built from a synergy of time-series data analytics, lightweight cryptography, and GPU parallel computing, achieving stronger security guarantees and lower online query latency over the state-of-the-art prior work. Extensive experiments demonstrate that OblivTime can achieve up to 666\times speedup in online query latency over the state-of-the-art prior work. © 2008-2012 IEEE.

关键词： Structured Query Language

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Gene Expression Variability Linked to Chromatin Clique Configurations and cis-Regulatory Elements 24

Gene Expression Variability Linked to Chromatin Clique Confi...

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Proceedings of the 2024 8th International Conference on Computational Biology and Bioinformatics

作者： Gatis Melkus Andrejs Sizovs Sandra Silina Peteris Rucevskis Lelde Lace Edgars Celms Juris Viksna Research Laboratory of System Modeling and Software Technologies Institute of Mathematics and Computer Science University of Latvia Riga Latvia

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BHJO: A Novel Hybrid Metaheuristic Algorithm Combining the Beluga Whale, Honey Badger, and Jellyfish Search Optimizers for Solving Engineering Design Problems

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computer Modeling in Engineering & sciences 2024年第10期141卷 219-265页

作者： Farouq Zitouni Saad Harous Abdulaziz S.Almazyad Ali Wagdy Mohamed Guojiang Xiong Fatima Zohra Khechiba Khadidja Â Kherchouche Department of Computer Science and Information Technology Kasdi Merbah UniversityP.O.Box 30000OuarglaAlgeria Department of Computer Science College of Computing and InformaticsUniversity of SharjahP.O.Box 27272SharjahUnited Arab Emirates Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11937Jordan Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China

Hybridizing metaheuristic algorithms involves synergistically combining different optimization techniques to effectively address complex and challenging optimization *** approach aims to leverage the strengths of multiple algorithms,enhancing solution quality,convergence speed,and robustness,thereby offering a more versatile and efficient means of solving intricate real-world optimization *** this paper,we introduce a hybrid algorithm that amalgamates three distinct metaheuristics:the Beluga Whale Optimization(BWO),the Honey Badger Algorithm(HBA),and the Jellyfish Search(JS)*** proposed hybrid algorithm will be referred to as *** this fusion,the BHJO algorithm aims to leverage the strengths of each *** this hybridization,we thoroughly examined the exploration and exploitation capabilities of the BWO,HBA,and JS metaheuristics,as well as their ability to strike a balance between exploration and *** meticulous analysis allowed us to identify the pros and cons of each algorithm,enabling us to combine them in a novel hybrid approach that capitalizes on their respective strengths for enhanced optimization *** addition,the BHJO algorithm incorporates Opposition-Based Learning(OBL)to harness the advantages offered by this technique,leveraging its diverse exploration,accelerated convergence,and improved solution quality to enhance the overall performance and effectiveness of the hybrid ***,the performance of the BHJO algorithm was evaluated across a range of both unconstrained and constrained optimization problems,providing a comprehensive assessment of its efficacy and applicability in diverse problem ***,the BHJO algorithm was subjected to a comparative analysis with several renowned algorithms,where mean and standard deviation values were utilized as evaluation *** rigorous comparison aimed to assess the performance of the BHJOalgorithmabout its counterparts,shedd

关键词： Global optimization hybridization of metaheuristics beluga whale optimization honey badger algorithm jellyfish search optimizer chaotic maps opposition-based learning

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Exploring Global and Local Information for Anomaly Detection with Normal Samples

Exploring Global and Local Information for Anomaly Detection...

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2023 IEEE International Conference on systems, Man, and Cybernetics, SMC 2023

作者： Xu, Fan Wang, Nan Zhao, Xibin School of Computer Science and Technology University of Science and Technology of China Hefei230026 China School of Software Beijing Jiaotong University Beijing100044 China Key Laboratory for Information System Security Beijing100084 China

ISBN: (纸本)9798350337020

Anomaly detection aims to detect data that do not conform to regular patterns, and such data is also called outliers. The anomalies to be detected are often tiny in proportion, containing crucial information, and are suitable for application scenes like intrusion detection, fraud detection, fault diagnosis, e-commerce platforms, et al. However, in many realistic scenarios, only the samples following normal behavior are observed, while we can hardly obtain any anomaly information. To address such problem, we propose an anomaly detection method GALDetector which is combined of global and local information based on observed normal samples. The proposed method can be divided into a three-stage method. Firstly, the global similar normal scores and the local sparsity scores of unlabeled samples are computed separately. Secondly, potential anomaly samples are separated from the unlabeled samples corresponding to these two scores and corresponding weights are assigned to the selected samples. Finally, a weighted anomaly detector is trained by loads of samples, then the detector is utilized to identify else anomalies. To evaluate the effectiveness of the proposed method, we conducted experiments on three categories of real-world datasets from diverse domains, and experimental results show that our method achieves better performance when compared with other state-of-the-art methods. © 2023 IEEE.

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Poster Abstract: Ripple: Large-Scale Service and Configuration Management in the Cloud 25

Poster Abstract: Ripple: Large-Scale Service and Configurati...

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25th ACM International Middleware Conference Demos, Posters and Doctoral Symposium, MIDDLEWARE 2024

作者： Ji, Shuping Tang, Zhen Wang, Wei Li, Hui Yao, Jianguo Jacobsen, Hans-Arno Institute of Software Chinese Academy of Sciences Beijing China Shanghai Jiao Tong University Jinan Inspur Data Technology Co. Ltd Shanghai China University of Toronto Toronto Canada Key Laboratory of System Software CAS State Key Laboratory of Computer Science Institute of Software at CAS University of CAS Beijing China University of CAS Nanjing College Nanjing China Nanjing Institute of Software Technology Nanjing China

ISBN: (纸本)9798400713545

Microservice architectures backed by container technology have been widely used in many real-world cloud-native applications. The number of containers in cloud data centers continues to increase, expected to reach several million soon. At this scale, existing service and configuration management solutions have limited efficiency, scalability and robustness. To overcome these limitations, we propose Ripple, which uniquely combines several existing and some novel features including consistent hashing-based workload distribution, dynamic destination list-based and client-assisted message delivery, incremental update, and adaptive load balancing. The experiments show that our solution greatly outperforms its counterparts. © 2024 Copyright held by the owner/author(s).

关键词： Efficiency

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