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An Online Computation Offloading Mechanism for Mobile Edge Computing in Ultra-Dense Small Cell Networks

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An Online Computation Offloading Mechanism for Mobile Edge C...

Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Junyi He Di Zhang Yuezhi Zhou Yaoxue Zhang Department of Computer Science and Technology Tsinghua University China Key Laboratory of Pervasive Computing Ministry of Education China School of Software Engineering Beijing Jiaotong University China

ISBN: (数字)9781728140346

ISBN: (纸本)9781728140353

Combining the ultra-dense deployment of small-cell base stations (SBSs) and mobile edge computing (MEC) will largely enhance network capacity and provide low-latency computing services for various resource-hungry mobile applications in the future 5G networks. However, performing effective computation offloading will face many new challenges, due to the online nature resulted from the dynamic arrival of tasks and the limited computational resources provided by self-interested SBS owners. In this paper, we develop a novel online computation offloading mechanism, in order to optimize social welfare (system performance) under the task's dynamic arrival and constrained resources capacity. Our mechanism runs in the online fashion without prior knowledge of the future system dynamics and can achieve close-optimal performance compared to the optimal solutions with the complete information. Theoretical analysis shows that the proposed online mechanism possesses several desired properties, including individual rationality, truthfulness, computational efficiency, and provable competitive ratio. Moreover, we carry out extensive simulations to demonstrate the mechanism efficiency.

关键词： Task analysis Servers Resource management Edge computing Mobile handsets Cost accounting Base stations

Relational proofs for quantum programs

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arXiv 2019年

作者： Barthe, Gilles Hsu, Justin Ying, Mingsheng Yu, Nengkun Zhou, Li Max Planck Institute for Security and Privacy Germany IMDEA Software Institute Spain Department of Computer Sciences University of Wisconsin-Madison United States Centre for Quantum Software and Information University of Technology Sydney Australia State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China Tsinghua University China Centre for Quantum Software and Information University of Technology Sydney Australia Max Planck Institute for Security and Privacy Germany Department of Computer Science and Technology Tsinghua University China

Relational verification of quantum programs has many potential applications in quantum and post-quantum security and other domains. We propose a relational program logic for quantum programs. The interpretation of our logic is based on a quantum analogue of probabilistic couplings. We use our logic to verify non-trivial relational properties of quantum programs, including uniformity for samples generated by the quantum Bernoulli factory, reliability of quantum teleportation against noise (bit and phase flip), security of quantum one-time pad and equivalence of quantum walks. Copyright © 2019, The Authors. All rights reserved.

关键词： Application programs

MALP: A More Effective Meta-Paths Based Link Prediction Method in Partially Aligned Heterogeneous Social Networks

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MALP: A More Effective Meta-Paths Based Link Prediction Meth...

International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Kai Zhu Meng Cao Heng-yang Lu National Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Computer Science and Technology Nanjing University Nanjing China School of Internet of Things Engineering Jiangnan University Wuxi China

In general, online social networks include different types of nodes and edges, which means that online social networks are a type of heterogeneous information network. Link prediction is a very important research problem in heterogeneous social networks. The solution to this problem is generally to predict the possibility of a link between two nodes by extracting the characteristics of the nodes in the network. However, the information provided by a single network may not be sufficient, so useful information can be passed from other networks to assist in link prediction in the target network. This is called a partially aligned heterogeneous social network link prediction problem. In this paper, a method, called Meta-path and AUC optimization based Link Predictor(MALP), is proposed to predict the social links in the partially aligned social networks at the same time with a semi-supervised AUC optimization technology. Experimental results on real social network data show that our approach exhibits better predictive performance than other state-of-the-art methods.

关键词：

Heterogeneity-aware gradient coding for straggler tolerance

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arXiv 2019年

作者： Wang, Haozhao Guo, Song Tang, Bin Li, Ruixuan Li, Chengjie School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Department of Computing Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong National Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China

Gradient descent algorithms are widely used in machine learning. In order to deal with huge volume of data, we consider the implementation of gradient descent algorithms in a distributed computing setting where multiple workers compute the gradient over some partial data and the master node aggregates their results to obtain the gradient over the whole data. However, its performance can be severely affected by straggler workers. Recently, some coding-based approaches are introduced to mitigate the straggler problem, but they are efficient only when the workers are homogeneous, i.e., having the same computation capabilities. In this paper, we consider that the workers are heterogenous which are common in modern distributed systems. We propose a novel heterogeneity-aware gradient coding scheme which can not only tolerate a predetermined number of stragglers but also fully utilize the computation capabilities of heterogenous workers. We show that this scheme is optimal when the computation capabilities of workers are estimated accurately. A variant of this scheme is further proposed to improve the performance when the estimations of the computation capabilities are not so accurate. We conduct our schemes for gradient descent based image classification on QingCloud clusters. Evaluation results show that our schemes can reduce the whole computation time by up to 3× compared with a state-of-the-art coding scheme. Copyright © 2019, The Authors. All rights reserved.

关键词： Gradient methods

Multi-task clustering with model relation learning 27

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Multi-task clustering with model relation learning

27th International Joint Conference on Artificial Intelligence, IJCAI 2018

作者： Zhang, Xiaotong Zhang, Xianchao Liu, Han School of Software Dalian University of Technology China Key Laboratory for Ubiquitous Network Service Software of Liaoning Province United Kingdom Department of Computer Science University of Rochester United States

ISBN: (纸本)9780999241127

Multi-task clustering improves the clustering performance of each task by transferring knowledge among the related tasks. An important aspect of multi-task clustering is to assess the task relatedness. However, to our knowledge, only two previous works have assessed the task relatedness, but they both have limitations. In this paper, we propose a multi-task clustering with model relation learning (MTCMRL) method, which automatically learns the model parameter relatedness between each pair of tasks. The objective function of MTCMRL consists of two parts: (1) within-task clustering: clustering each task by introducing linear regression model into symmetric nonnegative matrix factorization;(2) cross-task relatedness learning: updating the parameter of the linear regression model in each task by learning the model parameter relatedness between the clusters in each pair of tasks. We present an effective alternating algorithm to solve the non-convex optimization problem. Experimental results show the superiority of the proposed method over traditional single-task clustering methods and existing multitask clustering methods. © 2018 International Joint Conferences on Artificial Intelligence. All right reserved.

关键词： Matrix algebra

A novel multiple instance learning framework for COVID-19 severity assessment via data augmentation and self-supervised learning

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arXiv 2021年

作者： Li, Zekun Zhao, Wei Shi, Feng Qi, Lei Xie, Xingzhi Wei, Ying Ding, Zhongxiang Gao, Yang Wu, Shangjie Liu, Jun Shi, Yinghuan Shen, Dinggang State Key Laboratory for Novel Software Technology Nanjing University China National Institute of Healthcare Data Science Nanjing University Nanjing210046 China School of Computer Science and Artificial Intelligence Southeast University Nanjing210018 China Department of Research and Development Shanghai United Imaging Intelligence Co. Ltd. Shanghai201807 China School of Biomedical Engineering ShanghaiTech University Shanghai China Department of Artificial Intelligence Korea University Seoul02841 Korea Republic of Department of Radiology Second Xiangya Hospital Central South University Hunan410011 China Department of Radiology Quality Control Center Changsha410011 China Department of Radiology Hangzhou First People's Hospital Zhejiang University School of Medicine Hangzhou Zhejiang China Department of Pulmonary and Critical Care Medicine Second Xiangya Hospital Central South University Changsha410011 China

How to fast and accurately assess the severity level of COVID-19 is an essential problem, when millions of people are suffering from the pandemic around the world. Currently, the chest CT is regarded as a popular and informative imaging tool for COVID-19 diagnosis. However, we observe that there are two issues - weak annotation and insufficient data that may obstruct automatic COVID-19 severity assessment with CT images. To address these challenges, we propose a novel three-component method, i.e., 1) a deep multiple instance learning component with instance-level attention to jointly classify the bag and also weigh the instances, 2) a bag-level data augmentation component to generate virtual bags by reorganizing high confidential instances, and 3) a self-supervised pretext component to aid the learning process. We have systematically evaluated our method on the CT images of 229 COVID-19 cases, including 50 severe and 179 non-severe cases. Our method could obtain an average accuracy of 95.8%, with 93.6% sensitivity and 96.4% specificity, which outperformed previous works. Copyright © 2021, The Authors. All rights reserved.

关键词： computerized tomography

Phishing Detection with Image Retrieval Based on Improved Texton Correlation Descriptor

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computers, Materials & Continua 2018年第12期57卷 533-547页

作者： Guoyuan Lin Bowen Liu Pengcheng Xiao Min Lei Wei Bi School of Computer Science and Technology China University of Mining&TechnologyXuzhou221116China State Key Laboratory for Novel Software Technology hosted at Nanjing University Nanjing210023China Department of Mathematics University of EvansvilleIndiana47722USA Information Security Center Beijing University of Posts and TelecommunicationsBeijing100876China SeeleTech Corporation San Francisco94107USA Zabatech Corporation Beijing100088China

Anti-detection is becoming as an emerging challenge for *** paper solves the threats of anti-detection from the threshold setting *** webpages are considered to complicate threshold setting condition when the threshold is *** to the common visual behavior which is easily attracted by the salient region of webpages,image retrieval methods based on texton correlation descriptor(TCD)are improved to obtain enough webpages which have similarity in the salient region for the images of *** are two steps for improving TCD which has advantage of recognizing the salient region of images:(1)This paper proposed Weighted Euclidean Distance based on neighborhood location(NLW-Euclidean distance)and double cross windows,and combine them to solve the problems in TCD;(2)Space structure is introduced to map the image set to Euclid space so that similarity relation among images can be used to complicate threshold setting *** results show that the proposed method can improve the effectiveness of anti-phishing and make the system more stable,and significantly reduce the possibilities of being hacked to be used as mining systems for blockchain.

关键词： Anti-phishing blockchain texton correlation descriptor weighted euclidean distance image retrieval

Foundation models and intelligent decision-making: Progress, challenges, and perspectives

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Innovation 2025年第6期6卷 100948页

作者： Huang, Jincai Xu, Yongjun Wang, Qi Wang, Qi [Cheems] Liang, Xingxing Wang, Fei Zhang, Zhao Wei, Wei Zhang, Boxuan Huang, Libo Chang, Jingru Ma, Liantao Ma, Ting Liang, Yuxuan Zhang, Jie Guo, Jian Jiang, Xuhui Fan, Xinxin An, Zhulin Li, Tingting Li, Xuefei Shao, Zezhi Qian, Tangwen Sun, Tao Diao, Boyu Yang, Chuanguang Yu, Chenqing Wu, Yiqing Li, Mengxian Zhang, Haifeng Zeng, Yongcheng Zhang, Zhicheng Zhu, Zhengqiu Lv, Yiqin Li, Aming Chen, Xu An, Bo Xiao, Wei Bai, Chenguang Mao, Yuxing Yin, Zhigang Gui, Sheng Su, Wentao Zhu, Yinghao Gao, Junyi He, Xinyu Li, Yizhou Jin, Guangyin Ao, Xiang Zhai, Xuehao Tan, Haoran Yun, Lijun Shi, Hongquan Li, Jun Fan, Changjun Huang, Kuihua Harrison, Ewen Leung, Victor C.M. Qiu, Sihang Dong, Yanjie Zheng, Xiaolong Wang, Gang Zheng, Yu Wang, Yuanzhuo Guo, Jiafeng Wang, Lizhe Cheng, Xueqi Wang, Yaonan Yang, Shanlin Fu, Mengyin Fei, Aiguo Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Department of Automation Tsinghua University Beijing100084 China Huazhong University of Science and Technology Wuhan430074 China School of Automation Beijing Institute of Technology Beijing100081 China School of Information Science and Engineering Dalian Polytechnic University Dalian116034 China National Engineering Research Center for Software Engineering Peking University Beijing100871 China Department of Oral Implantology Peking University School and Hospital of Stomatology Beijing100081 China Guangzhou511453 China College of Information and Electrical Engineering China Agricultural University Beijing100083 China IDEA Research International Digital Economy Academy Shenzhen518057 China Institute of Automation Chinese Academy of Sciences Beijing100190 China The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China College of Science National University of Defense Technology Changsha410073 China Center for Systems and Control College of Engineering Peking University Beijing100871 China Gaoling School of Artificial Intelligence Renmin University of China Beijing100872 China College of Systems Engineering National University of Defense Technology Changsha410073 China Laboratory for Big Data and Decision National University of Defense Technology Changsha410073 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore School of Electrical Engineering Chongqing University Chongqing400044 China Department of Mathematics The University of Hong Kong Hong Kong SAR999077 China School of Food Science and Technology Dalian Polytechnic University Dalian116034 China Centre for Medical Informatics University of Edinburgh EdinburghEH16 4UX United Kingdom Department of Stomatology Peking Union Medical College Hospital Chinese Academ

Intelligent decision-making (IDM) is a cornerstone of artificial intelligence (AI) designed to automate or augment decision processes. Modern IDM paradigms integrate advanced frameworks to enable intelligent agents to make effective and adaptive choices and decompose complex tasks into manageable steps, such as AI agents and high-level reinforcement learning. Recent advances in multimodal foundation-based approaches unify diverse input modalities—such as vision, language, and sensory data—into a cohesive decision-making process. Foundation models (FMs) have become pivotal in science and industry, transforming decision-making and research capabilities. Their large-scale, multimodal data-processing abilities foster adaptability and interdisciplinary breakthroughs across fields such as healthcare, life sciences, and education. This survey examines IDM's evolution, advanced paradigms with FMs and their transformative impact on decision-making across diverse scientific and industrial domains, highlighting the challenges and opportunities in building efficient, adaptive, and ethical decision systems. © 2025 The Authors

关键词： Reinforcement learning

Stochastic optimization for non-convex inf-projection problems

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arXiv 2019年

作者： Yan, Yan Xu, Yi Zhang, Lijun Wang, Xiaoyu Yang, Tianbao Department of Computer Science University of Iowa Iowa CityIA52242 United States National Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China Intellifusion

In this paper, we study a family of non-convex and possibly non-smooth inf-projection minimization problems, where the target objective function is equal to minimization of a joint function over another variable. This problem include difference of convex (DC) functions and a family of bi-convex functions as special cases. We develop stochastic algorithms and establish their first-order convergence for finding a (nearly) stationary solution of the target non-convex function under different conditions of the component functions. To the best of our knowledge, this is the first work that comprehensively studies stochastic optimization of non-convex inf-projection minimization problems with provable convergence guarantee. Our algorithms enable efficient stochastic optimization of a family of non-decomposable DC functions and a family of bi-convex functions. To demonstrate the power of the proposed algorithms we consider an important application in variance-based regularization, and experiments verify the effectiveness of our inf-projection based formulation and the proposed stochastic algorithm in comparison with previous stochastic algorithms based on the min-max formulation for achieving the same effect. Copyright © 2019, The Authors. All rights reserved.

关键词： Functions