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Implicit Self-Adjusting computation for Purely Functional Programs

ACM SIGPLAN NOTICES 2011年第9期46卷 129-141页

作者： Chen, Yan Dunfield, Joshua Hammer, Matthew A. Acar, Umut A. Max Planck Institute for Software Systems Germany

computational problems that involve dynamic data, such as physics simulations and program development environments, have been an important subject of study in programming languages. Building on this work, recent advances in self-adjusting computation have developed techniques that enable programs to respond automatically and efficiently to dynamic changes in their inputs. Self-adjusting programs have been shown to be efficient for a reasonably broad range of problems but the approach still requires an explicit programming style, where the programmer must use specific monadic types and primitives to identify, create and operate on data that can change over time. We describe techniques for automatically translating purely functional programs into self-adjusting programs. In this implicit approach, the programmer need only annotate the (top-level) input types of the programs to be translated. Type inference finds all other types, and a type-directed translation rewrites the source program into an explicitly self-adjusting target program. the type system is related to information-flow type systems and enjoys decidable type inference via constraint solving. We prove that the translation outputs well-typed self-adjusting programs and preserves the source program's input-output behavior, guaranteeing that translated programs respond correctly to all changes to their data. Using a cost semantics, we also prove that the translation preserves the asymptotic complexity of the source program.

关键词： algorithms Languages Performance

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Evolutionary-Based Ensemble Under-Sampling for Imbalanced Data

Evolutionary-Based Ensemble Under-Sampling for Imbalanced Da...

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international conference on Wavelet Active Media Technology and Information Processing (ICWAMTIP)

作者： YONGQING ZHANG RONGZHAO LU JI HUANG DONGRUI GAO School of Computer Science Chengdu University of Information Technology Chengdu 610225

ISBN: (数字)9781728142425

ISBN: (纸本)9781728142432

Class imbalance is a prevalent problem in the real world, which mainly refers to the uneven distribution of samples of each class, and it will lead to a serious decline in the learning performance. the classification results will seriously bias to the majority class and ignore the minority class. However, the accuracy of the minority class is usually the focus of attention. therefore, in this paper, an evolutionary-based ensemble under-sampling (EEU) algorithm is proposed to solve this problem. Specifically, evolutionary algorithm is used to under sample the data and multiple base classifiers are trained by ensemble learning. the advantage of this algorithm lies in that it can improve the accuracy of minority class. Comparison experiments are performed on five UCI datasets, and the results demonstrate that EEU outperforms other sampling methods.

关键词： Classification algorithms Sampling methods Evolutionary computation Machine learning Forestry Information technology Machine learning algorithms

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Privacy-Preserving Support Vector Machines with Flexible Deployment and Error Correction 1

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16th international conference on Information Security Practice and Experience (ISPEC)

作者： Huang, Weican Ding, Ning Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn 800 Dongchuan RD Shanghai 200240 Peoples R China

ISBN: (数字)9783030932060

ISBN: (纸本)9783030932053;9783030932060

Support vector machines (SVMs) are one of the most commonly used models for classification problems in machine learning. Nowadays there is an important scenario that many different parties jointly perform SVM training by integrating their individual data, while at the same time it is required that privacy of data can be preserved. At present there are three main routes to achieving privacy-preserving SVM. First, all parties jointly generate kernel matrices privately and then use them for remaining training (e.g. Yu et al. 2006). Second, based on the first route, an additional randomization is adopted to randomize kernel matrices in order to (heuristically) hide information exposed by kernel matrices (e.g. Mangasarian et al. 2008). third, also the securest one, all parties run MPC protocols for computing whole optimization algorithms privately (not merely the generation of kernel matrices as the first two routes do) (e.g. Liu et al. 2018 and Wang et al. 2020). In this paper we propose a new efficient privacy-preserving SVM protocol in the third route that privately realizes the gradient descent method to optimize SVM and its security is proven in the semi-honest model. Our protocol admits the following advantages. - the protocol is of flexible deployment. It supports the deployment of arbitrarily multiple servers and multiple clients. - the protocol can tolerate dropping-out of some servers. - the protocol admits the ability of malicious-error-message correction (which is actually beyond the semi-honest security). If a small number of messages are corrupted, it can still recover correct messages as desired. We remark that none of the above advantages can be obtained by some known work. Moreover, when compared to the privacy-preserving SVM by Liu et al. 2018 and Wang et al. 2020, our protocol achieves higher efficiency. We implement our protocol in Python and the experiments verify its efficiency.

关键词： Privacy-preserving SVM Multi-party computation Shamir's secret sharing

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Joint Service Caching and computation Offloading to Maximize System Profits in Mobile Edge-Cloud Computing

Joint Service Caching and Computation Offloading to Maximize...

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international conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Qingyang Fan Junyu Lin Guangsheng Feng Zihan Gao Huiqiang Wang Yafei Li College of Computer Science and Technology Harbin Engineering University Harbin China Institute of Information Engineering Chinese Academy of Sciences Beijing China Peng Cheng Laboratory Shenzhen Guangdong China

ISBN: (数字)9781728199160

ISBN: (纸本)9781728192369

Considering the advantages of mobile edge computing (MEC), such as low latency, high bandwidth, etc., more and more mobile services are cached to mobile edge servers. However, due to limited computing resources and storage capacity of mobile edge servers, it is hard to guarantee that all services are cached and all computation offloading requests are satisfied. In this paper, we jointly optimize service caching and computation offloading to maximize system profits in mobile edge-cloud computing (MECC). the problem is formalized as a nonconvex optimization problem with discrete variables. We propose a Dynamic Joint computation Offloading and Service Caching algorithm (DJOSC) to solve the problem. Specifically, a regularization technique and Lyapunov optimization theory are used to transform the problem into two subproblems, which are solved by convex optimization techniques. Numerical evaluations show that the maximum system profits can be achieved under different computing resources, storage capacities and bandwidth capacities.

关键词： Heuristic algorithms Bandwidth Transforms Convex functions Sensors Servers Optimization

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Real-time Task Offloading for Data and computation Intensive Services in Vehicular Fog Computing Environments

Real-time Task Offloading for Data and Computation Intensive...

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international conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Chunhui Liu Kai Liu Xincao Xu Hualing Ren Feiyu Jin Songtao Guo College of Computer Science Chongqing University Chongqing China

ISBN: (数字)9781728199160

ISBN: (纸本)9781728192369

Recent advances in wireless communication, sensing, and computing technologies have paved the way for the development of a new era of Internet of Vehicles (IoV). Nevertheless, it is challenging to process data and computation intensive tasks with strict time constraints due to heterogeneous communication, storage, and computation capacities of IoV network nodes, spotty wireless connections in vehicles and infrastructures, unevenly distributed workload, and high vehicles mobility. In this paper, we propose a two-layer vehicular fog computing (VFC) architecture to explore the synergistic effect of the cloud, the fog nodes, and the terminals on processing data and computation intensive IoV tasks. then, we formulate the real-time task offloading model, aiming at maximizing the task service ratio. Further, considering the dynamic requirements and resource constraints, we propose a real-time task offloading algorithm to adaptively categorize all tasks into four types, and then cooperatively offload them. Finally, we build the simulation model and give a comprehensive performance evaluation, which validates the performance of the proposed method.

关键词： Wireless communication Adaptation models computational modeling Heuristic algorithms Real-time systems Sensors Task analysis

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Dynamic Spectrum Allocation Enabled Multi-user Latency Minimization in Mobile Edge Computing

Dynamic Spectrum Allocation Enabled Multi-user Latency Minim...

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international conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Yang Li Yuan Wu Weijia Jia State Key Laboratory of Internet of Things for Smart City University of Macau Taipa Macau China University of Macau Taipa Macau China BNU-HKBU United International College BNU-UIC Institute of Artificial Intelligence and Future Networks Beijing Normal University (BNU Zhuhai) Zhuhai Guangdong China

ISBN: (数字)9781728199160

ISBN: (纸本)9781728192369

Mobile edge computing (MEC) has been envisioned as an efficient solution to provide computation-intensive yet latency-sensitive services for terminal devices. In this paper, we investigate multi-user computation off loading in MEC and propose a joint optimization of off loading decisions, bandwidth and computation-resource allocations, with the objective of minimizing the total latency for completing all users' tasks. Due to the non-convexity of the formulated joint optimization problem, we identify its layer structure and decompose it into two problems, i.e·, a sub-problem and a top-problem. For the sub-problem, we propose a bisection-search based algorithm to efficiently find the optimal off loading solutions under a given feasible top-problem solution. then, we use a linear-search based algorithm to obtain the optimal solution of the top-problem. Numerical results are provided to validate our proposed algorithm for minimizing the total latency in MEC-based multi-user computation off loading. We also demonstrate the advantage of our proposed algorithm in comparison with the conventional multi-user computation off loading schemes.

关键词： Heuristic algorithms Bandwidth Resource management Servers Task analysis Optimization Edge computing

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Distributed Nash Equilibrium computation for Mixed-order Multi-player Games

Distributed Nash Equilibrium Computation for Mixed-order Mul...

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international conference on Control and Automation (ICCA)

作者： Jizhao Yin Maojiao Ye School of Automation Nanjing University of Science and Technology P.R. China

ISBN: (数字)9781728190938

ISBN: (纸本)9781728190945

Noticing that agents with different dynamics may work together, this paper considers Nash equilibrium computation for a class of games in which first-order integrator-type players and second-order integrator-type players interact in a distributed network. To deal with this situation, we firstly exploit a centralized method for full information games. In the considered scenario, the players can employ its own gradient information, though it may rely on all players' actions. Based on the proposed centralized algorithm, we further develop a distributed counterpart. Different from the centralized one, the players are assumed to have limited access into the other players' actions. Appropriate Lyapunov functions are constructed to investigate the effectiveness of the proposed methods analytically. It is shown that the proposed method would drive the players' actions to the Nash equilibrium exponentially. Lastly, the theoretical results are numerically verified by simulation examples.

关键词： Games Nash equilibrium Multi-agent systems Vehicle dynamics Distributed algorithms Writing Symmetric matrices

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Highlights of Practical Applications of Agents, Multi-Agent Systems, and Complexity: the PAAMS Collection 1st ed. 2018

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丛书名： Communications in Computer and Information Science

2018年

作者： Javier Bajo Juan M. Corchado Elena María Navarro Martínez Eneko Osaba Icedo Philippe Mathieu Patrycja Hoffa-Dąbrowska Elena del Val Sylvain Giroux Antonio J.M. Castro Nayat Sánchez-Pi Vicente Julián Ricardo Azambuja Silveira Alberto Fernández Rainer Unland Rubén Fuentes-Fernández

ISBN: (数字)9783319947792

ISBN: (纸本)9783319947785

this book constitutes the refereed proceedings of the 11 workshops co-located with the 16th international conference on Practical Applications of Agents and Multi-Agent Systems, PAAMS 2018, held in Toledo, Spain, in June 2018. the 47 full papers presented were carefully reviewed and selected from 72 submissions. the volume presents the papers that have been accepted for the following workshops: Workshop on Agents and Multi-agent Systems for AAL and e-HEALth; Workshop on Agent based Applications for Air Transport; Workshop on Agent-based Artificial Markets computational Economics; Workshop on Agent-Based Solutions for Manufacturing and Supply Chain; Workshop on MAS for Complex Networks and Social computation; Workshop on Intelligent Systems and Context Information Fusion; Workshop on Multi-agent based Applications for Energy Markets, Smart Grids and Sustainable Energy Systems; Workshop on Multiagent System based Learning Environments; Workshop on Smart Cities and Intelligent Agents; Workshop on Swarm Intelligence and Swarm Robotics; Workshop on Multi-Agent Systems and Simulation.

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Joint computation Offloading and Multi-User Scheduling for Max-Latency Minimization in MEC-Aided Cell-Free Networks

Joint Computation Offloading and Multi-User Scheduling for M...

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international conference on Wireless Communications and Signal Processing (WCSP)

作者： Yanzan Sun Xueyang Jiang Guangjin Pan Shunqing Zhang Shugong Xu School of Communication and Information Engineering Shanghai University Shanghai China

ISBN: (数字)9798350390643

ISBN: (纸本)9798350390650

Combining cell-free networks and mobile edge computing (MEC) is expected to meet the low latency and multi-user access requirements of the Industrial Internet of things (IIoT). Firstly, this paper considers MEC-aided cell-free networks in multi-user access scenarios, our goal is minimizing the maximum user latency. Secondly, in order to mitigate the impact of both multi-user interference and pilot contamination on system performance, we divide the frequency band into multiple sub-channels and dynamically schedule users on sub-channels in the uplink. Subsequently, we jointly optimize computation offloading, multi-scheduling and computational resources allocation, resulting in a complex non-convex optimization problem. Finally, To address this problem, we introduce a Bisection Search Feasibility (BSF)-Proximal Policy Optimization (PPO) algorithm, PPO algorithm is utilized to determine user scheduling strategy, and then the remaining non-convex problem is converted into a convex opti-mization problem using BSF algorithm. the simulation results demonstrate that the suggested algorithm achieves rapid convergence and surpasses the performance of baseline approaches.

关键词： Wireless communication Job shop scheduling Processor scheduling Simulation Heuristic algorithms Signal processing algorithms Resource management Optimization Industrial Internet of things Convergence

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Variable-length Evolutionary Optimization for Multi-objectiveWorker Transfer Scheduling in Complex Product Assembly Line

Variable-length Evolutionary Optimization for Multi-objectiv...

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international conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC

作者： Shudao Cai Qiucheng Ye School of Mechanical and Electrical Engineering Chengdu University of Technology Chengdu China School of Mechatronic Engineering and Automation Shanghai University Shanghai China

ISBN: (数字)9798331540241

ISBN: (纸本)9798331540258

Scheduling in complex product assembly lines is an actively studied topic. this study focuses on the redistribution planning of workers in complex product assembly workstations, known as worker transfer scheduling (WTS). Worker transfer allows the number of workers allocated to each workstation to change, thus affecting productivity. However, due to its variable-length solution space, WTS cannot be solved well by traditional evolutionary algorithms (EAs). To address these issues, we propose a non-dominated sorting EA for variable-length encoding (NSEA-VE). Applying modified evolutionary operators, NSEA-VE can explore inter-line relationships of variable-length encodings. the mechanism of the algorithm ensures the exchange of information for solution spaces. Compared with other state-of-the-art algorithms, experimental results show that the NSEA-VE performs better in terms of commonly used evaluation metrics.

关键词： Productivity Measurement Human-machine systems Evolutionary computation Encoding Workstations Planning Assembly Optimization Sorting

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