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Application-Oriented Cloud Workload Prediction:A Survey and New Perspectives

Tsinghua science and Technology 2025年第1期30卷 34-54页

作者： Binbin Feng Zhijun Ding Key Laboratory of Embedded System and Service Computing Ministry of EducationDepartment of Computer Science and TechnologyTongji UniversityShanghai 201804China Key Laboratory of Embedded System and Service Computing Ministry of EducationTongji UniversityDepartment of Computer Science and TechnologyTongji UniversityShanghai 201804China Shanghai Artificial Intelligence Laboratory Shanghai 200232China

Workload prediction is critical in enabling proactive resource management of cloud *** workload prediction is valuable for cloud users and providers as it can effectively guide many practices,such as performance assurance,cost reduction,and energy consumption ***,cloud workload prediction is highly challenging due to the complexity and dynamics of workloads,and various solutions have been proposed to enhance the prediction *** paper aims to provide an in-depth understanding and categorization of existing solutions through extensive literature *** existing surveys,for the first time,we comprehensively sort out and analyze the development landscape of workload prediction from a new perspective,i.e.,application-oriented rather than prediction methodologies per ***,we first introduce the basic features of workload prediction,and then analyze and categorize existing efforts based on two significant characteristics of cloud applications:variability and ***,we also investigate how workload prediction is applied to resource ***,open research opportunities in workload prediction are highlighted to foster further advancements.

关键词： cloud computing workload prediction resource management artificial intelligence for IT operations(AIOps)

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The Life Cycle of Knowledge in Big Language Models:A Survey

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Machine Intelligence Research 2024年第2期21卷 217-238页

作者： Boxi Cao Hongyu Lin Xianpei Han Le Sun Chinese Information Processing Laboratory Beijing 100190China State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 101408China

Knowledge plays a critical role in artificial ***,the extensive success of pre-trained language models(PLMs)has raised significant attention about how knowledge can be acquired,maintained,updated and used by language *** the enormous amount of related studies,there is still a lack of a unified view of how knowledge circulates within language models throughout the learning,tuning,and application processes,which may prevent us from further understanding the connections between current progress or realizing existing *** this survey,we revisit PLMs as knowledge-based systems by dividing the life circle of knowledge in PLMs into five critical periods,and investigating how knowledge circulates when it is built,maintained and *** this end,we systematically review existing studies of each period of the knowledge life cycle,summarize the main challenges and current limitations,and discuss future directions.

关键词： Pre-trained language model knowledge acquisition knowledge representation knowledge probing knowledge editing knowledge application

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GTE: learning code AST representation efficiently and effectively

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science China(Information sciences) 2025年第3期68卷 393-394页

作者： Yihao QIN Shangwen WANG Bo LIN Kang YANG Xiaoguang MAO College of Computer Science and Technology National University of Defense Technology Key Laboratory of Software Engineering for Complex Systems National University of Defense Technology

With the development of deep learning in recent years, code representation learning techniques have become the foundation of many software engineering tasks such as program classification [1] and defect detection. Earlier approaches treat the code as token sequences and use CNN, RNN, and the Transformer models to learn code representations.

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Large-Scale Multi-Objective Optimization Algorithm Based on Weighted Overlapping Grouping of Decision Variables

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computer Modeling in Engineering & sciences 2024年第7期140卷 363-383页

作者： Liang Chen Jingbo Zhang Linjie Wu Xingjuan Cai Yubin Xu Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan University of Science and TechnologyTaiyuan030024China School of State Key Laboratory of Novel Software Technology Nanjing UniversityNanjing210008China

The large-scale multi-objective optimization algorithm(LSMOA),based on the grouping of decision variables,is an advanced method for handling high-dimensional decision ***,in practical problems,the interaction among decision variables is intricate,leading to large group sizes and suboptimal optimization effects;hence a large-scale multi-objective optimization algorithm based on weighted overlapping grouping of decision variables(MOEAWOD)is proposed in this ***,the decision variables are perturbed and categorized into convergence and diversity variables;subsequently,the convergence variables are subdivided into groups based on the interactions among different decision *** the size of a group surpasses the set threshold,that group undergoes a process of weighting and overlapping ***,the interaction strength is evaluated based on the interaction frequency and number of objectives among various decision *** decision variable with the highest interaction in the group is identified and disregarded,and the remaining variables are then reclassified into ***,the decision variable with the strongest interaction is added to each *** minimizes the interactivity between different groups and maximizes the interactivity of decision variables within groups,which contributed to the optimized direction of convergence and diversity exploration with different *** was subjected to testing on 18 benchmark large-scale optimization problems,and the experimental results demonstrate the effectiveness of our *** with the other algorithms,our method is still at an advantage.

关键词： Decision variable grouping large-scale multi-objective optimization algorithms weighted overlapping grouping direction-guided evolution

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A buffer overflow detection and defense method based on RiSC-V instruction set extension

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Cybersecurity 2024年第2期7卷 28-43页

作者： Chang Liu Yan-Jun Wu Jing-Zheng Wu Chen Zhao Intelligent Software Research Center Institute of SoftwareChinese Academy of SciencesBejingChina University of Chinese Academy of Sciences BeijingChina State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijingChina

Buffer overflow poses a serious threat to the memory security of modern operating *** overwrites the con-tents of other memory areas by breaking through the buffer capacity limit,destroys the system execution environ-ment,and provides implementation space for various system attacks such as program control flow *** makes it a wide range of harms.A variety of security technologies have been proposed to deal with system security problems including buffer *** example,No eXecute(NX for short)is a memory management technology commonly used in Harvard *** can refuse the execution of code which residing in a specific memory,and can effectively suppress the abnormal impact of buffer overflow on control ***,in recent years,it has also been used in the field of system security,deriving a series of solutions based on NX technology,such as ExecShield,DEP,StackGuard,***,these security solutions often rely too much on the processor archi-tecture so that the protection coverage is insufficient and the accuracy is *** in the emerging system architecture field represented by RiSC-V,there is still a lack of effective solutions for buffer overflow *** the continuous rapid development of the system architecture,it is urgent to develop defense methods that are applicable to different system application environments and oriented to all executable memory spaces to meet the needs of system security ***,we propose BOP,A new system memory security design method based on RISC-V extended instructions,to build a RISC-V buffer overflow detection and defense system and deal with the buffer overflow threat in *** to this method,NX technology can be combined with program control flow analysis,and Nx bit mechanism can be used to manage the executability of memory space,so as to achieve a more granular detection and defense of buffer overflow attacks that may occur in RISC-V system

关键词： RISc-V Operating system security Buffer overflow Control flow hijacking Nx bit Xibop

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Learning to represent code semantics

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science China(Information sciences) 2025年

作者： Fang LIU Ge LI Qianhui ZHAO Li ZHANG State Key Laboratory of Complex & Critical Software Environment School of Computer Science & Engineering Beihang University Key Laboratory of High Confidence Software Technologies (Peking University) Ministry of Education School of Computer Science Peking University

Code semantic learning serves as the basis of many program analysis tasks. Researchers have paid much effort to build robust and effective code representation models over the years. One line of work focuses on introducing the code structure into the representations. To further improve the robustness of the code representation, approaches based on compiler intermediate representations(IRs) are proposed. However, these IR-based models suffer from heavy computational costs and memory overhead. How to represent program semantics effectively and efficiently still remains a challenge. To this end, we propose EECS, an effective and efficient code semantic representation approach based on compiler IRs and a hybrid attention mechanism. For input representation, to address the unlimited vocabulary size issue in IR, we propose a variable identification strategy to allocate each register variable to a new ID that can represent their relative positions. Besides, we also extract the data flow information among the code blocks. Then we build a hierarchical multi-layer Transformer encoder to capture the data dependency information as well as the code semantics through a hybrid attention mechanism. To enable EECS to learn code semantics and functionality better, we optimize three objectives jointly during the training *** results on three code semantic understanding tasks show that EECS performs better than the state-of-the-art techniques, demonstrating the remarkable capability of EECS on program semantics understanding.

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TSA-IPFE: A Secure Solution for Authorization Revocation and Dynamic Identity Assignment in IoT

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IEEE Internet of Things Journal 2025年第12期12卷 20288-20300页

作者： Yang, Haoxuan Peng, Changgen State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China

Inner Product Functional Encryption (IPFE) offers strong privacy protection for smart devices by outputting only the results of function computations, minimizing data leakage. This makes it well-suited for privacy-preserving operations in edge cloud environments, however, the edge cloud increases the complexity of the information authorization system, and traditional static authorization methods hinder collaboration between smart devices. Moreover, existing IPFE schemes fail to revoke computational authorizations when a smart device switches edge clouds. To address these challenges, we propose the Two-Step Authentication Inner Product Functional Encryption scheme (TSA-IPFE), specifically designed for the three-tier »cloud-edge cloud-smart device» architecture. TSA-IPFE enables dynamic authorization management through a two-step identity-matching process. This process revokes computational authorizations as devices move between edge clouds. It also facilitates dynamic authorization by setting up identity validation processes at different network locations, enabling seamless collaboration between smart devices from different vendors within the same cloud architecture. Finally, we prove the semantic security and anonymity of TSA-IPFE through a simulation-based proof, utilizing indistinguishable transformations in the dual system, a comparison with other algorithms demonstrates the efficiency of TSA-IPFE on a common platform. © 2014 IEEE.

关键词： Cloud platforms

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Few-shot node classification via local adaptive discriminant structure learning

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Frontiers of computer science 2023年第2期17卷 135-143页

作者： Zhe XUE Junping DU Xin XU Xiangbin LIU Junfu WANG Feifei KOU Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia School of Computer ScienceBeijing University of Posts and TelecommunicationsBeijing 100876China

Node classification has a wide range of application scenarios such as citation analysis and social network *** many real-world attributed networks,a large portion of classes only contain limited labeled *** of the existing node classification methods cannot be used for few-shot node *** train the model effectively and improve the robustness and reliability of the model with scarce labeled samples,in this paper,we propose a local adaptive discriminant structure learning(LADSL)method for few-shot node *** aims to properly represent the nodes in the attributed graphs and learn a metric space with a strong discriminating power by reducing the intra-class variations and *** conducted on various attributed networks datasets demonstrate that LADSL is superior to the other methods on few-shot node classification task.

关键词： few-shot learning node classification graph neural network adaptive structure learning attention strategy

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Open Grounded Planning: Challenges and Benchmark Construction 62

Open Grounded Planning: Challenges and Benchmark Constructio...

引用

62nd Annual Meeting of the Association for Computational Linguistics, ACL 2024

作者： Guo, Shiguang Deng, Ziliang Lin, Hongyu Lu, Yaojie Han, Xianpei Sun, Le Chinese Information Processing Laboratory China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Key Laboratory of System Software Chinese Academy of Sciences China

ISBN: (纸本)9798891760943

The emergence of large language models (LLMs) has increasingly drawn attention to the use of LLMs for human-like planning. Existing work on LLM-based planning either focuses on leveraging the inherent language generation capabilities of LLMs to produce free-style plans or employs reinforcement learning approaches to learn decision-making for a limited set of actions within restricted environments. However, both approaches exhibit significant discrepancies between the open and executable requirements in real-world planning. In this paper, we propose a new planning task-open grounded planning. The primary objective of open grounded planning is to ask the model to generate an executable plan based on a variable action set, thereby ensuring the executability of the produced plan. To this end, we establish a benchmark for open grounded planning spanning a wide range of domains. Then we test current state-of-the-art LLMs along with five planning approaches, revealing that existing LLMs and methods still struggle to address the challenges posed by grounded planning in open domains. The outcomes of this paper define and establish a foundational dataset for open grounded planning, and shed light on the potential challenges and future directions of LLM-based planning. Our code and datasets are at https://***/Shiguang-Guo/Open-Grounded-Planning. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Machine learning automation

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National science Review 2024年第8期11卷 7-8页

作者： Zongben Xu Zhi-Hua Zhou Wenwu Zhu School of Mathematics and Statistics Xi'an Jiaotong University National Key Laboratory for Novel Software Technology Nanjing University Department of Computer Science and Technology Tsinghua University

With the exponential growth of big data and advancements in large-scale foundation model techniques, the field of machine learning has embarked on an unprecedented golden era. This period is characterized by significant innovations across various aspects of machine learning, including data exploitation, network architecture development, loss function settings and algorithmic innovation.

关键词： automation learning machine

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