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TERMS: Task management policies to achieve high performance for mixed workloads using surplus resources

Journal of Parallel and Distributed Computing 2022年 170卷 74-85页

作者： Yu, Jinyu Tong, Wei Lv, Pengze Feng, Dan Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology School of Computer Science and Technology Huazhong University of Science and Technology Ministry of Education of China Hubei Wuhan China

Resource contentions and performance interferences can lead to workload performance degradation in mixed-workload deployment clusters. Previous work guarantees the resource requirements of latency-sensitive tasks and reduces performance losses to batch jobs by reclaiming surplus resources from over-provisioned tasks. While the fragmentation of resources leads to a mismatch between provisioned resources and task requirements, resulting in high operation overheads and losses of task fairness. This paper proposes TERMS, the task management policies based on task relevance, resource distribution, and task fairness to achieve efficient and low-cost task management. TERMS mainly includes three types of management policies. The task scheduling policy can schedule new tasks according to task relevance. Task selection strategies select tasks for resource provisioning and task resumption based on resource requirements and task fairness. If necessary, the node selection strategy can be used to choose befitting target nodes based on task relevance and node resource information for task migration when eliminating straggler tasks. Evaluation results show that TERMS can further improve the performance of latency-sensitive services and batch jobs, reduce management overheads, and avoid operation failures. © 2022 Elsevier Inc.

关键词： Scheduling

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Multi-Modal Brain Graph Learning of Shared-Specific Features for Schizophrenia Diagnosis

Multi-Modal Brain Graph Learning of Shared-Specific Features...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Huang, Yin Chen, Geng Wen, Xuyun Shen, Dinggang Northwestern Polytechnical University School of Computer Science and Engineering Xi'an China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing China ShanghaiTech University School of Biomedical Engineering State Key Laboratory of Advanced Medical Materials and Devices Shanghai China Shanghai United Imaging Intelligence Co. Ltd. Shanghai China Shanghai Clinical Research and Trial Center Shanghai China

ISBN: (纸本)9798350386226

Recent years have witnessed promising progress in schizophrenia diagnosis with multi-modal brain networks. However, existing works usually focus on either learning the specific characteristics of each modality or exploiting the shared information among modalities. Few methods explicitly explore the shared information as well as preserve the modality-specific characteristics. In this paper, we propose a shared-specific graph learning (S2GL) framework for schizophrenia diagnosis, which benefits disease diagnostic performance by exploiting shared information and characteristics unique to each modality. Specifically, two modality-specific graph learning modules are adopted to learn multi-level specific representations of the structural brain graph and functional brain graph, respectively. Meanwhile, a structural-functional shared graph learning module is designed to mine the shared features between modalities by exploiting their correlations, providing cross-enhanced representations for each modality. These representations can not only provide beneficial information for mining high-order modality-specific features but also be fused to generate shared representations for disease diagnosis. Next, we develop a shared-specific graph feature aggregation module to integrate the multi-level specific and shared representations, which can provide comprehensive multi-modal information to boost disease diagnosis. Finally, we formulate a unified end-to-end framework for schizophrenia diagnosis. Experiments on an open schizophrenia dataset demonstrate that our S2GL is effective and superior to cutting-edge methods. © 2024 IEEE.

关键词： Contrastive Learning

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Object Topological Character Acquisition by Inductive Learning

arXiv

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arXiv 2023年

作者： Hui, Wei Yu, Liping Wei, Yiran Laboratory of Algorithm for Cognitive Model School of Computer Science Shanghai Key Laboratory of Data Science Fudan University China Shanghai Pinghe School China

Understanding the shape and structure of objects is undoubtedly extremely important for object recognition, but the most common pattern recognition method currently used is machine learning, which often requires a large number of training data. The problem is that this kind of object-oriented learning lacks a priori knowledge. The amount of training data and the complexity of computations are very large, and it is hard to extract explicit knowledge after learning. This is typically called ‘knowing how without knowing why’. We adopted a method of inductive learning, hoping to derive conceptual knowledge of the shape of an object and its formal representation based on a small number of positive examples. It is clear that implementing object recognition is not based on simple physical features such as colors, edges, textures, etc., but on their common geometry, such as topologies, which are stable, persistent, and essential to recognition. In this paper, a formal representation of topological structure based on object’s skeleton (RTS) was proposed and the induction process of’seeking common ground’ is realized. This research helps promote the method of object recognition from empiricism to rationalism © 2023, CC BY-NC-ND.

关键词： Object recognition

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Efficient Persistent Fault Analysis with Small Number of Chosen Plaintexts

IACR Transactions on Cryptographic Hardware and Embedded Sys...

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IACR Transactions on Cryptographic Hardware and Embedded Systems 2023年第2期2023卷 519-542页

作者： Zhang, Fan Huang, Run Feng, Tianxiang Gong, Xue Tao, Yulong Ren, Kui Zhao, Xinjie Guo, Shize School of Cyber Science and Technology College of Computer Science and Technology Zhejiang University Hangzhou310027 China Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies Hangzhou310027 China Key Laboratory of Blockchain and Cyberspace Governance of Zhejiang Province Hangzhou310027 China Henan Province Key Laboratory of Cyberspace Situation Awareness Zhengzhou450001 China Shanghai Institute of Satellite Engineering Shanghai201109 China

In 2018, Zhang et al. introduced the Persistent Fault Analysis (PFA) for the first time, which uses statistical features of ciphertexts caused by faulty Sbox to recover the key of block ciphers. However, for most of the variants of PFA, the prior knowledge of the fault (location and value) is required, where the corresponding analysis will get more difficult under the scenario of multiple faults. To bypass such perquisite and improve the analysis efficiency for multiple faults, we propose Chosen-Plaintext based Persistent Fault Analysis (CPPFA). CPPFA introduces chosen-plaintext to facilitate PFA and can reduce the key search space of AES-128 to extremely small. Our proposal requires 256 ciphertexts, while previous state-of-the-art work still requires 1509 and 1448 ciphertexts under 8 and 16 faults, respectively, at the only cost of requiring 256 chosen plaintexts. In particular, CPPFA can be applied to the multiple faults scenarios where all fault locations, values and quantity are unknown, and the worst time complexity of CPPFA is O(28+nf) for AES-128, where nf represents the number of faults. The experimental results show that when nf > 4, 256 pairs of plaintext-ciphertext can recover the master key of AES-128. As for LED-64, only 16 pairs of plaintext-ciphertext reduce the remaining key search space to 210. © 2023, Ruhr-University of Bochum. All rights reserved.

关键词： Side channel attack

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Boosting MLPs on Graphs via Distillation in Resource-Constrained Environment

Boosting MLPs on Graphs via Distillation in Resource-Constra...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Yang, Zhihui Qu, Zhihao Jia, Ninghui Hu, Shihong Ye, Baoliu Zeng, Deze Hohai University Key Laboratory of Water Big Data Technology of Ministry of Water Resources Nanjing China Nanjing University National Key Laboratory for Novel Software Technology Nanjing China School of Computer Science China University of Geosciences Nanjing China

ISBN: (纸本)9781665410205

Graph Neural Networks (GNNs) have emerged as a powerful technique across various applications, due to their effective message-passing mechanism. However, their deployment is constrained by limited computational resources, energy concerns, and low-latency processing requirements. While existing works employ logit-based knowledge distillation from GNNs to guide Multilayer Perceptrons (MLPs) training, these methods may lead to reduced accuracy and compromised robustness. These drawbacks arise from two primary factors: the insufficient exploitation of the rich information embedded within the graph structures and the inherent susceptibility of MLPs to noisy data. To tackle these issues, we propose a Mixed Multi-order Knowledge Distillation (MMKD) method, which combines the GNN's logits with hidden layer information through the multi-order distillation to improve the accuracy of the MLP. Moreover, we employ both raw data and perturbed data as input, enhancing the density of knowledge extraction as well as the MLPs' generalization. Extensive experiments across seven benchmark datasets verify the superior performance of our approach in terms of effectiveness and robustness. In comparison with the baseline, our approach achieves an accuracy improvement of up to 8.68% in typical GNN tasks. © 2024 IEEE.

关键词： Graph neural networks

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Nearly Optimal Regret for Decentralized Online Convex Optimization 37

Nearly Optimal Regret for Decentralized Online Convex Optimi...

引用

37th Annual Conference on Learning Theory, COLT 2024

作者： Wan, Yuanyu Wei, Tong Song, Mingli Zhang, Lijun The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China School of Software Technology Zhejiang University Ningbo China Institute of Blockchain and Data Security Hangzhou China School of Computer Science and Engineering Southeast University Nanjing China National Key Laboratory for Novel Software Technology Nanjing University Nanjing China

We investigate decentralized online convex optimization (D-OCO), in which a set of local learners are required to minimize a sequence of global loss functions using_only local computations and communications. Previous studies have established O(n5/4ρ-1/2 √T) and O(n3/2ρ-1 log T) regret bounds for convex and strongly convex functions respectively, where n is the number of local learners, ρ -1/4 √T) and Õ(nρ-1/2 log T). The primary technique is to design an online accelerated gossip strategy that enjoys a faster average consensus among local learners. Furthermore, by carefully exploiting the spectral properties of a specific network topology, we enhance the lower bounds for convex and strongly convex functions to Ω(nρ-14 √T) and Ω(nρ-1/2), respectively. These lower bounds suggest that our algorithms are nearly optimal in terms of T, n, and ρ. © 2024 Y. Wan, T. Wei, M. Song & L. Zhang.

关键词： Optimization algorithms

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Designing a critical success factors model for Chinese self-run e-business from resource orchestration theory

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Journal of Management Analytics 2025年第1期12卷 174-199页

作者： Bingfeng Bai Yuan Yin Junjun Gao School of Business Jinling Institute of TechnologyNanjingPeople’s Republic of China School of Computer Science Nanjing University of Posts and TelecommunicationsNanjingPeople’s Republic of China Huishang Futures HefeiPeople’s Republic of China College of International Business Shanghai International Studies UniversityShanghaiPeople’s Republic of China Key Laboratory of Brain-Machine Intelligence for Information Behavior(Ministry of Education and Shanghai) Shanghai International Studies UniversityShanghaiPeople’s Republic of China School of Management Shanghai UniversityShanghaiPeople’s Republic of China

E-business develops rapidly and attracts a large number of merchants and consumers in the past 20 ***,the debate over e-business mode and its critical success factors(CSFs)is a research hotspot in the industry and the *** this study,we propose the CSFs framework of self-run e-business by combining systematic literature review,resource orchestration theory,and empirical firm *** quantitative method of content analysis is targeted at the interview data received from 90 managers of *** by software NVivo *** construct a self-run e-business CSFs framework and find out that the CSFs of self-run e-business are products,organization,and supply chain,*** addition,this study compares different characteristics and their impact on performance in platform-type and self-run *** managers can derive a better understanding and measurement of e-business activities.

关键词： self-run e-business firm operations critical success factors resource orchestration theory content analysis

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DetectBench: Can Large Language Model Detect and Piece Together Implicit Evidence?

arXiv

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arXiv 2024年

作者： Gu, Zhouhong Zhang, Lin Zhu, Xiaoxuan Chen, Jiangjie Huang, Wenhao Zhang, Yikai Wang, Shusen Ye, Zheyu Gao, Yan Feng, Hongwei Xiao, Yanghua Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China Meta United States Xiaohongshu Inc. China

Detecting evidence within the context is a key step in the process of reasoning task. Evaluating and enhancing the capabilities of LLMs in evidence detection will strengthen context-based reasoning performance. This paper proposes a benchmark called DetectBench for verifying the ability to detect and piece together implicit evidence within a long context. DetectBench contains 3,928 multiple-choice questions, with an average of 994 tokens per question. Each question contains an average of 4.55 pieces of implicit evidence, and solving the problem typically requires 7.62 logical jumps to find the correct answer. To enhance the performance of LLMs in evidence detection, this paper proposes Detective Reasoning Prompt and Finetune. Experiments demonstrate that the existing LLMs’ abilities to detect evidence in long contexts are far inferior to humans. However, the Detective Reasoning Prompt effectively enhances the capability of powerful LLMs in evidence detection, while the Finetuning method shows significant effects in enhancing the performance of weaker LLMs. Moreover, when the abilities of LLMs in evidence detection are improved, their final reasoning performance is also enhanced accordingly. The benchmark is available in https://***/MikeGu721/DetectBench. © 2024, CC BY.

关键词： Case based reasoning

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FOLT: Fast Multiple Object Tracking from UAV-captured Videos Based on Optical Flow

arXiv

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arXiv 2023年

作者： Yao, Mufeng Wang, Jiaqi Peng, Jinlong Chi, Mingmin Liu, Chao School of Computer Science Shanghai Key Laboratory of Data Science Fudan University China Tencent Youtu Lab School of Computer Science Shanghai Key Laboratory of Data Science Fudan University Zhengzhou Zhongke Institute of Integrated Circuit and System Application China

Multiple object tracking (MOT) has been successfully investigated in computer vision. However, MOT for the videos captured by unmanned aerial vehicles (UAV) is still challenging due to small object size, blurred object appearance, and very large and/or irregular motion in both ground objects and UAV platforms. In this paper, we propose FOLT to mitigate these problems and reach fast and accurate MOT in UAV view. Aiming at speed-accuracy trade-off, FOLT adopts a modern detector and light-weight optical flow extractor to extract object detection features and motion features at a minimum cost. Given the extracted flow, the flow-guided feature augmentation is designed to augment the object detection feature based on its optical flow, which improves the detection of small objects. Then the flow-guided motion prediction is also proposed to predict the object's position in the next frame, which improves the tracking performance of objects with very large displacements between adjacent frames. Finally, the tracker matches the detected objects and predicted objects using a spatially matching scheme to generate tracks for every object. Experiments on Visdrone and UAVDT datasets show that our proposed model can successfully track small objects with large and irregular motion and outperform existing state-of-the-art methods in UAV-MOT tasks. Copyright © 2023, The Authors. All rights reserved.

关键词： Optical flows

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ANIMC: A Soft Approach for Autoweighted Noisy and Incomplete Multiview Clustering

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2022年第2期3卷 192-206页

作者： Fang, Xiang Hu, Yuchong Zhou, Pan Wu, Dapeng Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering The School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Information Storage System Ministry of Education of China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States

Multiview clustering has wide real-world applications because it can process data from multiple sources. However, these data often contain missing instances and noises, which are ignored by most multiview clustering methods. Missing instances may make these methods difficult to use directly, and noises will lead to unreliable clustering results. In this article, we propose a novel autoweighted noisy and incomplete multiview clustering (ANIMC) approach via a soft autoweighted strategy and a doubly soft regular regression model. First, by designing adaptive semiregularized nonnegative matrix factorization, the soft autoweighted strategy assigns a proper weight to each view and adds a soft boundary to balance the influence of noises and incompleteness. Second, by proposing θ-norm, the doubly soft regularized regression model adjusts the sparsity of our model by choosing different θ. Compared with previous methods, ANIMC has three unique advantages: 1) it is a soft algorithm to adjust our approach in different scenarios, thereby improving its generalization ability;2) it automatically learns a proper weight for each view, thereby reducing the influence of noises;3) it performs doubly soft regularized regression that aligns the same instances in different views, thereby decreasing the impact of missing instances. Extensive experimental results show its superior advantages over other state-of-the-art works. © 2020 IEEE.

关键词： Clustering algorithms

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