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A reinforcement learning framework based on regret minimization for approximating best response in fictitious self-play 24

A reinforcement learning framework based on regret minimizat...

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24th IEEE international conference on High Performance Computing and Communications, 8th IEEE international conference on Data science and systems, 20th IEEE international conference on Smart City and 8th IEEE international conference on Dependability in Sensor, Cloud and Big Data systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Xu, Yanran He, Kangxin Hu, Shu Li, Hui College of Computer Science Sichuan University Chengdu China Sichuan University National Key Laboratory of Fundamental Science on Synthetic Vision Chengdu China

ISBN: (纸本)9798350319934

Finding Nash equilibrium in imperfect information game is of significance because many real-world games can be described as games with partially observed players. Neu-ral Fictitious Self-Play (NFSP) combines Fictitious Self-Play (FSP), a popular game-theoretic model, with machine learning to approximate Nash equilibrium without domain knowledge. In two-player zero-sum game, when both partially informed agents update their strategies simultaneously, for each agent, the environment is unstable and partially observable. For one thing, the instability of environment brings error in approximate best response generated by reinforcement learning algorithm. For another, it is necessary to explore more about the opponent's strategy. To solve the problems mentioned above, we propose a new reinforcement learning framework named Expected Average Regret Minimization (EARM), which can approximate better best response by estimating expected average regret and different regret minimizer. In this framework, we propose a new regret minimizer called Temperature Regret Matching (TRM) to control the exploration degree flexibly. then we propose a novel algorithm EARM-FSP by combining EARM with FSP, which solves the slow convergence problem in NFSP. EARM-FSP converges faster and reaches a better local optima in imperfect information games on OpenSpiel, e.g., Kuhn Poker, Leduc Poker and Liar's Dice. © 2022 IEEE.

关键词： Game theory

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Semi-supervised Graph Convolutional Neural Network Based Classification for Auto Parts Inventory Management 24

Semi-supervised Graph Convolutional Neural Network Based Cla...

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作者： Weng, Fangpeng Guo, Bing Suo, Xinhua Wu, Xin Hu, Yuchuan Zheng, Qin Sichuan University Computer Science Department Chengdu China

ISBN: (纸本)9798350319934

Auto parts inventory management is an important link of the automobiles multi-value chain, which has a certain impact on the upstream procurement, downstream production and other links. However, there are many kinds of auto parts, which will lead to improper allocation of funds and uneven allocation of resource when managed according to a unified standard. therefore, they need to be managed by categories. At present, the industry mainly adopts the ABC methods of stores control for classification. But this method has some shortcomings, such as single classification standard and no consideration of the correlation between materials, etc. In order to solve these problems, we introduce a semi-supervised graph convolutional neural network based classification method, which models the graph of automobile multi-value chain data according to the strong correlation between auto parts, and reclassifies them. through the experiment in Geely's multi-value chain data set, we achieve an 91.7% accuracy rate and obtain a better classification results, which provided new ideas for inventory management classification. © 2022 IEEE.

关键词： Classification (of information)

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Improving the Inference of Sibling Autonomous systems 24th

Improving the Inference of Sibling Autonomous Systems

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24th Annual international conference on Passive and Active Measurement (PAM)

作者： Chen, Zhiyi Bischof, Zachary S. Testart, Cecilia Dainotti, Alberto Georgia Inst Technol Atlanta GA 30332 USA

ISBN: (纸本)9783031284854;9783031284861

Correctly mapping Autonomous systems (ASes) to their owner organizations is critical for connecting AS-level and organization-level research. Unfortunately, constructing an accurate dataset of ASto-organization mappings is difficult due to a lack of ground truth information. CAIDA AS-to-organization (CA2O), the current state-of-the-art dataset, relies heavily on Whois databases maintained by Regional Internet Registries (RIRs) to infer the AS-to-organization mappings. However, inaccuracies in Whois data can dramatically impact the accuracy of CA2O, particularly for inferences involving ASes owned by the same organization (referred to as sibling ASes). In this work, we leverage PeeringDB (PDB) as an additional data source to detect potential errors of sibling relations in CA2O. By conducting a meticulous semi-manual investigation, we discover two pitfalls of using Whois data that result in incorrect inferences in CA2O. We then systematically analyze how these pitfalls influence CA2O. We also build an improved dataset on sibling relations, which corrects the mappings of 12.5% of CA2O organizations with sibling ASes (1,028 CA2O organizations, associated with 3,772 ASNs). To make this process reproducible and scalable, we design an automated approach to recreate our manually-built dataset with high fidelity. the approach is able to automatically improve inferences of sibling ASes for each new version of CA2O.

关键词： AS-to-organization mapping Sibling ASes Whois databases

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Smart Health Records Sharing Scheme based on Partially Policy-Hidden CP-ABE with Leakage Resilience 24

Smart Health Records Sharing Scheme based on Partially Polic...

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作者： Acheampong, Edward Mensah Zhou, Shijie Liao, Yongjian Antwi-Boasiako, Emmanuel Obiri, Isaac Amankona School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Science University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350319934

With the rapid innovation of cloud computing technologies, which has enhanced the application of the Internet of things (IoT), smart health (s-health) is expected to enhance the quality of the healthcare system. However, s-health records (SHRs) outsourcing, storage, and sharing via a cloud server must be protected and users attribute privacy issues from the public domain. Ciphertext policy attribute-based encryption (CP-ABE) is the cryptographic primitive which is promising to provide fine-grained access control in the cloud environment. However, the direct application of traditional CP-ABE has brought a lot of security issues like attributes' privacy violations and vulnerability in the future by potential powerful attackers like side-channel and cold-bot attacks. To solve these problems, a lot of CP-ABE schemes have been proposed but none of them concurrently support partially policy-hidden and leakage resilience. Hence, we propose a new Smart Health Records Sharing Scheme that will be based on Partially Policy-Hidden CP-ABE with Leakage Resilience which is resilient to bound leakage from each of many secret keys per user, as well as many master keys, and ensure attribute privacy. Our scheme hides attribute values of users in both secret key and ciphertext which contain sensitive information in the cloud environment and are fully secure in the standard model under the static assumptions. © 2022 IEEE.

关键词： Access control

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Algorithm for Solving the Navigation Problem for Ultra-Wideband Local Navigation System Based on the Extended Kalman Filter for Time of Arrival Architecture 24

Algorithm for Solving the Navigation Problem for Ultra-Wideb...

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24th IEEE international conference of Young Professionals in Electron Devices and Materials, EDM 2023

作者： Chugunov, Alexander A. Kulikov, Roman S. Malyshev, Alexander P. Petukhov, Nikita I. Chernyh, Sergey V. Orobchenko, Stepan V. National Research University Mpei Dept. of Radio Systems Moscow Russia

ISBN: (纸本)9798350336870

At the moment, for positioning objects in local navigation systems, the difference-range method of positioning is widely used. this approach does not require synchronization of time scales on the user and system sides, allows a larger number of simultaneously tracked objects, but has a lower accuracy than the ranging method. this paper presents a new approach to solving a navigation problem for ultra-wideband local navigation systems built on a pseudorange architecture. the algorithm is based on the use of an extended Kalman filter, which makes it possible to determine the location of an object with high accuracy. A feature of the algorithm is the ability to estimate the discrepancy between the time scales of the user and the system, followed by taking into account the obtained value to correct the calculated ranges. this approach will provide a number of advantages: work on a smaller number of control points, increase in accuracy, which allows you to maintain an acceptable quality of navigation when leaving the working area. the performance and quality of the proposed algorithm were tested using computer simulations. © 2023 IEEE.

关键词： Extended Kalman filters

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24th international conference on Intelligent Data Engineering and Automated Learning, IDEAL 2023

24th International Conference on Intelligent Data Engineerin...

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24th international conference on Intelligent Data Engineering and Automated Learning, IDEAL 2023

ISBN: (纸本)9783031482311

the proceedings contain 49 papers. the special focus in this conference is on Intelligent Data Engineering and Automated Learning. the topics include: GPU-Based Acceleration of the Rao Optimization Algorithms: Application to the Solution of Large systems of Nonlinear Equations;direct Determination of Operational Value-at-Risk Using Descriptive Statistics;using Deep Learning Models to Predict the Electrical Conductivity of the Influent in a Wastewater Treatment Plant;unsupervised Defect Detection for Infrastructure Inspection;Generating Adversarial Examples Using LAD;emotion Extraction from Likert-Scale Questionnaires: – An Additional Dimension to Psychology Instruments –;recent Applications of Pre-aggregation Functions;a Probabilistic Approach: Querying Web Resources in the Presence of Uncertainty;domain Adaptation in Transformer Models: Question Answering of Dutch Government Policies;complexity-Driven Sampling for Bagging;sustainable On-Street Parking Mapping with Deep Learning and Airborne Imagery;hebbian Learning-Guided Random Walks for Enhanced Community Detection in Correlation-Based Brain Networks;extracting Automatically a Domain Ontology from the "Book of Properties" of the Archbishop’s Table of Braga;language Models for Automatic Distribution of Review Notes in Movie Production;extracting Knowledge from Incompletely Known Models;threshold-Based Classification to Enhance Confidence in Open Set of Legal Texts;comparing Ranking Learning Algorithms for Information Retrieval systems;analyzing the Influence of Market Event Correction for Forecasting Stock Prices Using Recurrent Neural Networks;measuring the Relationship Between the Use of Typical Manosphere Discourse and the Engagement of a User with the Pick-Up Artist Community;uniform Design of Experiments for Equality Constraints;a Pseudo-Label Guided Hybrid Approach for Unsupervised Domain Adaptation;globular Cluster Detection in M33 Using Multiple Views Representation Learning;segmentation of Brachial Plexus

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Proceedings of the 24th Annual conference Towards Autonomous Robotic systems, TAROS 2023

Proceedings of the 24th Annual Conference Towards Autonomous...

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Proceedings of the 24th Annual conference Towards Autonomous Robotic systems, TAROS 2023

ISBN: (纸本)9783031433597

the proceedings contain 40 papers. the special focus in this conference is on Towards Autonomous Robotic systems. the topics include: Developing an Integrated Runtime Verification for Safety and Security of Industrial Robot Inspection System;sonification of Ionising Radiation Data for Robot Operators;automating a Telepresence Robot for Human Detection, Tracking, and Following;towards Multimodal Sensing and Interaction for Assistive Autonomous Robots;CPG-Based Locomotion control of a Quadruped Robot with an Active Spine;low-Resolution Sensing for Sim-to-Real Complex Terrain Robots;towards Wait-and-Catch Routine of a Dynamic Swinging Object Using a Prototype Robotic Arm Manipulator;simultaneous Base and Arm Trajectories for Multi-target Mobile Agri-Robot;Design and Kinematic Analysis of a 3D-Printed 3DOF Robotic Manipulandum;rapid Development and Performance Evaluation of a Potato Planting Robot;sim-to-Real Deep Reinforcement Learning with Manipulators for Pick-and-Place;fast 3D Semantic Segmentation Using a Self Attention Network and Random Sampling;an Assessment of Self-supervised Learning for Data Efficient Potato Instance Segmentation;Automated 3D Mapping, Localization and Pavement Inspection with Low Cost RGB-D Cameras and IMUs;optimized Custom Dataset for Efficient Detection of Underwater Trash;a Geometric Algebra Solution to the 3D Registration Problem;active Anomaly Detection for Autonomous Robots: A Benchmark;hardware Validation of Adaptive Fault Diagnosis in Swarm Robots;mobile Robots for Collaborative Manipulation over Uneven Ground Using Decentralised Impedance control;multi-agent Collaborative Target Search Based on Curiosity Intrinsic Motivation;an Automated Precision Spraying Evaluation System;simulation of Collective Bernoulli-Ball System for Characterizing Dynamic Self-stability;casting vs Injection Moulding: A Comparison Study for In-Lab Low-Cost Soft Robot Fabrication;estimation of Soft Body Deformation by Using Light;reduced-Order Modeling of a Sof

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Inference-Based Deep Reinforcement Learning for Physics-Based Character control 24

Inference-Based Deep Reinforcement Learning for Physics-Base...

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作者： Chen, Yuxi Huang, Chenghao Yuan, Weimin Wu, Yutong Zhang, Ziyi Tang, Hao Zhang, Yanru College of Computer Science and Engineering University of Electronic Science and Technology of China China University of Nottingham Ningbo China China Chengdu DiShiWuXian Technology Co. Ltd. China Shenzhen Institute of Advanced Study University of Electronic Science and Technology of China China

ISBN: (纸本)9798350319934

Character motion synthesis and control has shown great significance in the field of character animation. Synthesizing more human-like behaviors and motion trajectories is generally acknowledged to be a significant challenge. Although studies have been proposed to cope with this challenge, we find that they fail to perform well on generating natural trajectories when the agent is required to change current motion orientation to another to reach a target point. Meanwhile, due to the intrinsic properties of the training process of deep reinforcement learning (DRL), such as bad reproducibility, low stability, and high costs on resources and training time, the performances are generally disappointing. To this end, in this paper we introduce a new concept, Redirection Factor, to revise the turning angle and guide the agent to move in a proper orientation to the target point. Different from previous works where the agent followed a policy trained by DRL reward function implicitly to reach the target point, our method decouples the calculation procedure during turning from the whole DRL network and define it as inference process, to explicitly calculate the concrete trajectories with a mathematical approach. Intuitively, such way of producing trajectories is more in line with the behaviors of human beings, so that motion behaviors can be generated with higher fidelity and naturalness. Furthermore, based on the DRL reward function in previous works, we take the turning angle into consideration, thus leading to more effective task-related rewards. Besides, in training process, we establish a new way to represent the relationship between the rotation of joints and their displacement. Based on such representation framework, we just keep essential skeletal features alive to avoid redundancy, which significantly relieves the complexity of the whole networks. Experiments show that our method outperforms previous works on various usual and unusual motion control tasks, and at the s

关键词： Reinforcement learning

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ESPRESSO: A Framework for Empowering Search on Decentralized Web 24th

ESPRESSO: A Framework for Empowering Search on Decentraliz...

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24th international conference on Web Information systems Engineering, WISE 2023

作者： Ragab, Mohamed Savateev, Yury Moosaei, Reza Tiropanis, thanassis Poulovassilis, Alexandra Chapman, Adriane Roussos, George School of Electronics and Computer Science University of Southampton Southampton United Kingdom School of Computing and Mathematical Sciences Birkbeck University of London London United Kingdom

ISBN: (纸本)9789819972531

the centralization of the Web has led to significant risks to privacy, security, and user autonomy, prompting the need for decentralization. Solid is a set of standards, protocols, and technologies that seeks to enable Web re-decentralization based on the existing W3C recommendations. In Solid, users store their data in personal online data stores (pods) with full control and sovereignty over which individuals and applications get access to them. However, the current state of the Web and Web-based applications rely heavily on search functionality using centralized indices. this poses significant challenges when it comes to searching or querying large-scale data stored in decentralized user-controlled pods, where different individuals and applications have varied access to data. To address this gap, we propose the ESPRESSO framework, which aims to enable individuals or applications to search Solid pods at a large scale while pod owners maintain control over access to their data. ESPRESSO considers access rights and caching needs while facilitating the performance of distributed queries. Our framework offers a vision for empowering search utilities in the decentralized Web, utilizing the Solid framework, and opens up new research directions for future decentralized search applications. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Linked data

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Parallel-Friendly and Work-Efficient Single Source Shortest Path Algorithm on Single-Node System 24

Parallel-Friendly and Work-Efficient Single Source Shortest ...

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作者： Zhang, Jie Zhang, Yuan Sun, Yiming Cao, Huawei An, Xuejun Ye, Xiaochun Institute of Computing Technology Cas State Key Lab of Processors Beijing China School of Computer and Control Engineering Ucas Beijing China Nanjing Institute of Information Superbahn Cas Nanjing China

ISBN: (纸本)9798350319934

the Single-Source Shortest Path (SSSP) algorithm is one of the most important kernels used by a variety of high-level graph processing applications. Although having been extensively studied for single-node scenarios, SSSP algorithm still faces difficulties when being deployed on scale-free graph, which brings about redundant edge traversals, expensive atomic operations and random memory access. In this paper, three parallel-friendly and work-efficient opti-mizations are introduced to improve the performance of parallel SSSP algorithm with shared memory on the single-node system. First, a label-aware pruning technique is proposed to reduce the redundant edge traversals. then, atomic operations caused by race conditions are eliminated by using destination-driven sub graph partitioning. Finally, we propose a double-ordered bucket strategy to further improve the efficiency of SSSP. the experiment demonstrates that the average speedup reaches to 27.58x compared with original implement for R-MAT graphs. Meanwhile, our method achieves up to 2.12x for R-MAT graphs and 4.45 x for real-world graphs on average compared to previous state-of-the-art parallel implementation. © 2022 IEEE.

关键词： Memory architecture

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