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DRL-Based Time-Varying Workload Scheduling With Priority and Resource Awareness

IEEE Transactions on Network and Service Management 2025年第3期22卷 2838-2852页

作者： Liu, Qifeng Fan, Qilin Zhang, Xu Li, Xiuhua Wang, Kai Xiong, Qingyu Chongqing University School of Big Data and Software Engineering Chongqing400044 China Chongqing University Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education Chongqing400044 China Nanjing University School of Electronic Science and Engineering Nanjing210023 China Haihe Laboratory of Information Technology Application Innovation Tianjin300072 China Harbin Institute of Technology School of Computer Science and Technology Weihai264209 China Shandong Key Laboratory of Industrial Network Security Weihai264209 China

With the proliferation of cloud services and the continuous growth in enterprises' demand for dynamic multi-dimensional resources, the implementation of effective strategy for time-varying workload scheduling has become increasingly significant. In this paper, we propose a deep reinforcement learning (DRL)-based method for time-varying workload scheduling, aiming to allocate resources efficiently across servers in the cluster. Specifically, we integrate a classifier and queue scorer to construct a priority queue that exploits temporal resource utilization patterns across different workload classes. Then, we design parallel graph attention layers to capture the dimensional features and temporal dynamics of cloud server cluster. Moreover, we propose a DRL algorithm to generate scheduling strategies that can adapt to dynamic environments. Validation on real-world traces from Google cluster demonstrates that our method outperforms existing approaches in key metrics of cloud server cluster management. © 2004-2012 IEEE.

关键词： Cloud platforms

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Brain-inspired artificial intelligence research: A review

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science China(Technological sciences) 2024年第8期67卷 2282-2296页

作者： WANG GuoYin BAO HuaNan LIU Qun ZHOU TianGang WU Si HUANG TieJun YU ZhaoFei LU CeWu GONG YiHong ZHANG ZhaoXiang HE Sheng Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and TelecommunicationsChongqing 400065China Key Laboratory of Cyberspace Big Data Intelligent Security Chongqing University of Posts and TelecommunicationsChongqing 400065China College of Computer and Information Science Chongqing Normal UniversityChongqing 401331China State Key Laboratory of Brain and Cognitive Science Institute of BiophysicsChinese Academy of SciencesBeijing 100101China School of Psychological and Cognitive Sciences Peking UniversityBeijing 100871China State Key Laboratory of Multimedia Information Processing School of Computer SciencePeking UniversityBeijing 100871China Department of Computer Science School of ElectronicsInformation and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240China Faculty of Electronic and Information Engineering Xi’an Jiaotong UniversityXi’an 710049China The Center for Research on Intelligent Perception and Computing Institute of AutomationChinese Academy of SciencesBeijing 100190China Institute of Biophysics Chinese Academy of SciencesBeijing 100101China

Artificial intelligence(AI) systems surpass certain human intelligence abilities in a statistical sense as a whole, but are not yet the true realization of these human intelligence abilities and behaviors. There are differences, and even contradictions, between the cognition and behavior of AI systems and humans. With the goal of achieving general AI, this study contains a review of the role of cognitive science in inspiring the development of the three mainstream academic branches of AI based on the three-layer framework proposed by David Marr, and the limitations of the current development of AI are explored and analyzed. The differences and inconsistencies between the cognition mechanisms of the human brain and the computation mechanisms of AI systems are analyzed. They are found to be the cause of the differences and contradictions between the cognition and behavior of AI systems and humans. Additionally, eight important research directions and their scientific issues that need to focus on braininspired AI research are proposed: highly imitated bionic information processing, a large-scale deep learning model that balances structure and function, multi-granularity joint problem solving bidirectionally driven by data and knowledge, AI models that simulate specific brain structures, a collaborative processing mechanism with the physical separation of perceptual processing and interpretive analysis, embodied intelligence that integrates the brain cognitive mechanism and AI computation mechanisms,intelligence simulation from individual intelligence to group intelligence(social intelligence), and AI-assisted brain cognitive intelligence.

关键词： artificial intelligence cognitive science brain science intelligence science large language model

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Securing electronics Gadgets using Blockchain based Framework 2

Securing Electronics Gadgets using Blockchain based Framewor...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Prakash, P. N. Senthil Lanitha, B. Sundaram, Karthic Vellore Institute of Technology School of Computer Science and Engineering Chennai India Karpagam Academy of Higher Education Department of Artificial Intelligence and Data Science Coimbatore India KPR Institute of Engineering and Technology Department of Information Technology Coimbatore India

ISBN: (纸本)9798331534950

The usage of electronic gadgets has grown dramatically in the current digital era. Devices like mobile phones and smart watches are unavoidable and has ingrained themselves into every person's daily routine. Almost everyone owns at least one portable electronic device and these devices are usually expensive. Due to this widespread use, there is also an increase in the number of lost or stolen cases reported. In a recent study, it has been revealed that the number of devices lost is mostly because of misplacing in public places like home, office, public transport. Such incidents will not only cause financial loss but it may also lead to data breaches stored in the portable devices. Hence there is a need for a secure eco-system which helps to identify the genuine owners of electronic devices. A blockchain-based framework is deployed on an Ethereum-based network and adopts the Proof of Stake (PoS) mechanism to protecting gadgets. Further, it is designed using the smart contract defined using solidity programming language. The system enables the owner's details to be recorded upon purchase of a device. The complete specification details of the devices along with owner details is contained in a block and then appended to network. Later on, if the owner wants to resale the device, then the transfer of ownership information is stored in a block and added to the network. The proposed system provides an appropriate interface to check the genuine owner. The proposed framework has been implemented using solidity language, Remix IDE and Sepolia testnet. Obtained results highlights the suitability of the method and its performance in terms of gas consumption and execution latency. © 2024 IEEE.

关键词： Ethereum

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KSKV:Key-Strategy for Key-Value data Collection with Local Differential Privacy

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computer Modeling in engineering & sciences 2024年第6期139卷 3063-3083页

作者： Dan Zhao Yang You Chuanwen Luo Ting Chen Yang Liu Artificial Intelligence Development Research Center Institute of Scientific and Technical Information of ChinaBeijing100038China Industry Development Department NSFOCUS Inc.BeijingChina School of Information Science and Technology Beijing Forestry UniversityBeijing100083China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu610054China Institute of Computing Technology China Academy of Railway Sciences Corporation LimitedBeijing10081China

In recent years,the research field of data collection under local differential privacy(LDP)has expanded its focus fromelementary data types to includemore complex structural data,such as set-value and graph ***,our comprehensive review of existing literature reveals that there needs to be more studies that engage with key-value data *** studies would simultaneously collect the frequencies of keys and the mean of values associated with each ***,the allocation of the privacy budget between the frequencies of keys and the means of values for each key does not yield an optimal utility *** the importance of obtaining accurate key frequencies and mean estimations for key-value data collection,this paper presents a novel framework:the Key-Strategy Framework forKey-ValuedataCollection under ***,theKey-StrategyUnary Encoding(KS-UE)strategy is proposed within non-interactive frameworks for the purpose of privacy budget allocation to achieve precise key frequencies;subsequently,the Key-Strategy Generalized Randomized Response(KS-GRR)strategy is introduced for interactive frameworks to enhance the efficiency of collecting frequent keys through group-anditeration *** strategies are adapted for scenarios in which users possess either a single or multiple key-value ***,we demonstrate that the variance of KS-UE is lower than that of existing *** claims are substantiated through extensive experimental evaluation on real-world datasets,confirming the effectiveness and efficiency of the KS-UE and KS-GRR strategies.

关键词： Key-value local differential privacy frequency estimation mean estimation data perturbation

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Enhanced Learned Query Driven Cardinality Estimator with Active Learning

Enhanced Learned Query Driven Cardinality Estimator with Act...

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2023 International Conference on Algorithms, Computing and data Processing, ACDP 2023

作者： Tang, Yiheng Zhan, Siyu Zhou, Li School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Panzhihua Institute of Ecological and Environmental Protection Panzhihua China

ISBN: (纸本)9798350326680

Recent research has shown the excellent per-formance of using machine learning algorithms to optimize database query optimizer, especially the cardinality estimator. Cardinality estimation is a crucial component of query op-timization, as it directly affects the accuracy of cost model and play a critical role in evaluating query execution plans. Learned query-driven cardinality estimation methods aim to capture query features and data distribution, learning the regression from query to cardinality, resulting in more pre-cise cardinality predictions. However, in dynamic production environments, the performance of supervised models can significantly decrease due to data change operations such as insertion, deletion, and update. To address this, We introduce an active learning based model updating strategy that enhances the accuracy of the learned query-driven cardinality estimator, while reducing the model update time in dynamic data change scenarios. We selected two classic, learned query-driven cardinality estimation methods (LW-NN and MSCN) and tested the impact of our proposed model updating strategy on them in the PostgreSQ L database. The experimental results show that our method achieved an effective balance between model accuracy and updating time. This indicates that our method can promote the application of learned query-driven cardinality estimator in the dynamic production deployment environment. © 2023 IEEE.

关键词： database systems

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Cross-view Contrastive Unification Guides Generative Pretraining for Molecular Property Prediction 24

Cross-view Contrastive Unification Guides Generative Pretrai...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Lin, Junyu Zheng, Yan Chen, Xinyue Ren, Yazhou Pu, Xiaorong He, Jing Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Faculty of Medicine Biomedical Sciences The University of Queensland BrisbaneQLD Australia

ISBN: (纸本)9798400706868

Multi-view based molecular properties prediction learning has received widely attention in recent years in terms of its potential for the downstream tasks in the field of drug discovery. However, the consistency of different molecular view representations and the full utilization of complementary information among them in existing multi-view molecular property prediction methods remain to be further explored. Furthermore, most current methods focus on generating global level representations at the graph level with information from different molecular views (e.g., 2D and 3D views) assuming that the information can be corresponded to each other. In fact it is not unusual that for example the conformation change or computational errors may lead to discrepancies between views. To addressing these issues, we propose a new Cross-View contrastive unification guides Generative Molcular pre-trained model, call MolCVG. We first focus on common and private information extraction from 2D graph views and 3D geometric views of molecules, Minimizing the impact of noise in private information on subsequent strategies. To exploit both types of information in a more refined way, we propose a cross-view contrastive unification strategy to learn cross-view global information and guide the reconstruction of masked nodes, thus effectively optimizing global features and local descriptions. Extensive experiments on real-world molecular data sets demonstrate the effectiveness of our approach for molecular property prediction task. © 2024 ACM.

关键词： Contrastive Learning

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Emerging Technologies in Online Learning with New Trends and Analysis

Emerging Technologies in Online Learning with New Trends and...

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electronics, Computing, Communication and Control Technology (ICECCC), International Conference on

作者： Raja Marappan Saraswatikaniga R School of Computer Science and Engineering (SCOPE) Vellore Institute of Technology Chennai Computer Science and Engineering (Business Analytics) School of Computer Science and Engineering Vellore Institute of Technology Chennai

ISBN: (数字)9798350371802

ISBN: (纸本)9798350371819

To better understand the state of e-learning and direct future research, guidance work is required. Recent research indicates that e-learning environments have been classified under many labels. The primary motivation behind adding these additional subtopics is to provide a more recent description of e-learning in this area. Micro-credentials, artificial intelligence, virtual reality, big data, blended, mobile, and cloud e-learning, the Internet of Things, gamification, and online video will be analyzed as emerging technologies in online education. The purpose of this research is to provide clarity around the idea of online education. E-learning also emphasizes expanding students' horizons. New adversarial ideas emerge daily in online classrooms. These novel ideas spread fast throughout society and became crucial teacher resources. Recent online education settings are being employed as interconnected platforms. Fundamentally, they believe in the potential of online education. The percentage of applications of the recent trends are analyzed as follows: Micro-credentials:15-20%, AI:20-25%, Big data: 20-25%, Blended Learning: 30-40%, Mobile Learning: 25-30%, Cloud E-Learning:30-35%, Internet of Things (IoT): 5-10%, Gamification: 15-20%.

关键词： Cloud computing Electric potential Education Virtual reality Big data Market research Mobile learning

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Cyclone Chronicles: A Comprehensive Survey of Advancements in Cyclone Prediction Techniques 2

Cyclone Chronicles: A Comprehensive Survey of Advancements i...

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2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Raj, Abhineet Tiwari, Tanmay Madurwar, Ayush Sandosh, S. Integrated Mtech in CSE specialization In Business Analytics Vellore Institute of Technology Chennai India School Of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350340235

Tropical cyclones represent formidable natural phenomena, demanding constant progress in prediction methodologies. The survey paper amalgamates diverse insights from recent research, offering an extensive overview of the state of tropical cyclone prediction. This study analyzes the stat-of-the-art approaches, such as Support Vector Machine and polynomial regression models to achieve pre cise cyclone tracking. Additionally, this study examines the critical role of satellite imagery in automation, shedding light on the automated techniques employed. Furthermore, this study explores the emerging applications of Generative Adversarial Networks (GANs) for learning physical characteristics within cyclone data distribution, enhancing predictive capabilities. It also addresses challenges like rapid intensification, presenting prototype visualization tools and bias correction techniques as innovative solutions. The survey underscores the significance of machine learning models and ensemble forecasting in elevating the precision of cyclone predictions. © 2023 IEEE.

关键词： Bias correction Convolutional Neural Networks Deep learning Ensemble forecasting Generative Adversarial Networks Machine learning Satellite imagery Tropical cyclone prediction Weather forecasting

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Nanoimprint lithography-assisted block copolymer self-assembly for hyperfine fabrication of magnetic patterns based on L10-FePt nanoparticles

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science China Chemistry 2025年第5期68卷 2027-2034页

作者： Jie Zhang Yi Chen Guijun Li Zelin Sun Zhengong Meng Wai-Yeung Wong Department of Applied Biology and Chemical Technology and Research Institute for Smart Energy The Hong Kong Polytechnic University The Hong Kong Polytechnic University Shenzhen Research Institute Division of Integrative Systems and Design and Department of Electronic and Computer EngineeringThe Hong Kong University of Science and Technology School of Flexible Electronics (Future Technology) and Institute of Advanced Materials (IAM) Nanjing Tech University

L10-FePt-type bit-patterned media has provided a promising alternative for ultrahigh-density magnetic recording systems in the current digital era, but rapid fabrication of magnetic patterns with hyperfine bit islands is still challenging, especially with the target for miniaturization and scalable production simultaneously. Herein, Fe,Pt-containing block copolymers were utilized as single-source precursors for solution-processable patterning and subsequent generation of the demanding magnetic FePt dots by in situ pyrolysis. High-throughput nanoimprint lithography was initially employed to fabricate the predefined bit cells precisely,and then the intrinsic self-assembly of phase-separated block copolymers further drove the formation of accurate bit *** from the synergistic effect of top-down lithographic approach and bottom-up self-assembly, the customizable patterns could be achieved for large-scale mass production in targeted areas, but high-density isolated dots could also be accurately aligned along the patterned features after subsequent self-assembly. This reliable strategy would provide a good avenue to precisely construct ultrahigh-density magnetic data storage devices.

关键词： organometallic precursor magnetic nanoparticles block copolymer self-assembly nanoimprint lithography

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A Proficient Approach in Crop Recommendation System using Gradient Boosting Machine Technique 3

A Proficient Approach in Crop Recommendation System using Gr...

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3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Kumar, Kandukuri Joseph Reddy, N Praneeth Kumar Vasu, Andhavarapu Sandosh, S. Business Analytics Vellore Institute of Technology Integrated Mtech in CSE Specialization Chennai India Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350343632

The Crop Recommendation System (CRS), a powerful data-driven tool, has revolutionized farming practices by delivering precise crop recommendations. Decision Trees and Logistic Regression are employed as initial models to assess crop choices and further enhance accuracy with Gradient Boosting Machine (GBM), which consistently outperforms other methods. CRS meticulously processes essential factors, including Nitrogen (N), Phosphorus (P), Potassium (K), Temperature, Humidity, Rainfall, and pH levels. These inputs lead to highly personalized recommendations, tailored to specific farming contexts. CRS significantly boosts agricultural productivity, providing farmers with insights for optimal crop selection, increased yields, and efficient resource allocation. It promotes sustainability by encouraging environmentally responsible farming practices. In modern agriculture, CRS emerges as a pivotal asset, contributing to productivity and global food security. Its ability to guide farmers toward sustainable practices positions it as a driving force for positive change in the agricultural sector, offering a path toward a more sustainable and food-secure future. © 2023 IEEE.

关键词： Agricultural Decision Support System Crop Modeling data-driven Agriculture Machine Learning Algorithms Precision Farming Remote Sensing Technologies

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