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Proceedings of the 41st International Conference on Machine Learning

作者： Bin Zhang Hangyu Mao Lijuan Li Zhiwei Xu Dapeng Li Rui Zhao Guoliang Fan Institute of Automation Chinese Academy of Sciences and School of Artificial Intelligence University of Chinese Academy of Sciences SenseTime Research School of Computer Science and Technology Shandong University

Asynchronous action coordination presents a pervasive challenge in Multi-Agent Systems (MAS), which can be represented as a Stackelberg game (SG). However, the scalability of existing Multi-Agent Reinforcement Learning (MARL) methods based on SG is severely restricted by network architectures or environmental settings. To address this issue, we propose the Stackelberg Decision Transformer (STEER). It efficiently manages decision-making processes by incorporating the hierarchical decision structure of SG, the modeling capability of autoregressive sequence models, and the exploratory learning methodology of MARL. Our approach exhibits broad applicability across diverse task types and environmental configurations in MAS. Experimental results demonstrate both the convergence of our method towards Stackelberg equilibrium strategies and its superiority over strong baselines in complex scenarios.

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An incremental software architecture recovery technique driven by code changes

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Frontiers of Information Technology & Electronic Engineering 2022年第5期23卷 664-677页

作者： Li WANG Xianglong KONG Jiahui WANG Bixin LI School of Computer Science and Engineering Southeast UniversityNanjing 210096China Jiangsu Automation Research Institute Lianyungang 222061China Huawei Digital Technology Lab Suzhou 215125China

It is difficult to keep software architecture up to date with code changes during software *** is caused by the limitations of standard development specifications and human power resources,which may impact software *** solve this problem,we propose an incremental software architecture recovery(ISAR)*** technique obtains dependency information from changed code blocks and identifies different strength-level ***,we use double classifiers to recover the architecture based on the method of mapping code-level changes to architecture-level *** is evaluated on 10 open-source projects,and the results show that it performs more effectively and efficiently than the compared *** also find that the impact of low-quality architectural documentation on effectiveness remains stable during software evolution.

关键词： Architecture recovery Software evolution Code change

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Your Camera Improves Your Point Cloud Compression

Your Camera Improves Your Point Cloud Compression

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yuhuan Lin Tongda Xu Ziyu Zhu Yanghao Li Zhe Wang Yan Wang Institute for AI Industry Research (AIR) Tsinghua University Department of Automation Tsinghua University Department of Computer Science Tsinghua University

LiDAR point cloud compression is important for autonomous driving as it consumes a lot of storage and bandwidth. Although the fusion of camera and LiDAR for vision perception has been well studied, it remains unexplored that how we can improve the compression of LiDAR point cloud data using cross-modal information from cameras. In this paper’ we propose a multi-modality compression framework for LiDAR point cloud by exploiting the depth information predicted from its paired image. To the best of our knowledge’ our model is the first multi-modality compression framework for point cloud. Specifically’ we first represent point cloud based on octrees to reduce spatial redundancy. Then’ we propose a cross-modal fusion structure to improve the compression of these octrees’ with depth distribution extracted from the camera pixels and acts as side information. Compared to previous state-of-the-art (SOTA) method, our approach obtains up to 8.10% compression rate gain for LiDAR point cloud compression.

关键词： Point cloud compression Laser radar Image coding Redundancy Octrees Bandwidth Signal processing

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Moving Object Detection Based on Weighted Kernel Norm and Saliency Constrained RPCA

Moving Object Detection Based on Weighted Kernel Norm and Sa...

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2022 Workshop on Pattern Recognition and Data Mining, WPRDM 2022

作者： Zhao, Junhao Jiang, Zheng Liu, Bin Zhang, Ling Engineering Research Center of Metallurgical Automation and Measurement Technology Ministry of Education Wuhan University of Science and Technology Hubei Wuhan430081 China Institute of Information Science and Engineering Wuhan University of Science and Technology Hubei Wuhan430081 China Institute of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan430081 China

Focusing on the issue that the accuracy of object detection is reduced when the foreground target moves slowly and there is interference in the background, a moving object detection method based on weighted kernel norm and saliency constraint RPCA is proposed. In the new method, the weighted kernel norm is used to restore a relatively clean background, which is helpful to separate the slow moving target from the background. The l 1 norm is used to constrain the sparsity of moving objects, and the Frobenius norm is used to detect noise, and a saliency constraint is introduced to detect slow-moving objects. Experiments show that this method can effectively deal with the problems of slow foreground motion and background interference. Compared with the suboptimal algorithm, the average measured value F of the proposed algorithm is improved by 15%. © Published under licence by IOP Publishing Ltd.

关键词： Object detection

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Risk estimation in a Markov cost process: lower and upper bounds 24

Risk estimation in a Markov cost process: lower and upper bo...

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Proceedings of the 41st International Conference on Machine Learning

作者： Gugan Thoppe Prashanth L. A. Sanjay P. Bhat Dept. of Computer Science and Automation Indian Institute of Science (IISc) Bengaluru India and Robert Bosch Centre for Data Science and Artificial Intelligence IIT Madras Chennai India Dept. of Computer Science and Engineering Indian Institute of Technology Madras Chennai India TCS Research Hyderabad India

We tackle the problem of estimating risk measures of the infinite-horizon discounted cost of a Markov cost process. The risk measures we study include variance, Value-at-Risk (VaR), and Conditional Value-at-Risk (CVaR). First, we show that estimating any of these risk measures with ε-accuracy, either in expected or high-probability sense, requires at least Ω(1/ε2) samples. Then, using a truncation scheme, we derive an upper bound for the CVaR and variance estimation. This bound matches our lower bound up to logarithmic factors. Finally, we discuss an extension of our estimation scheme that covers more general risk measures satisfying a certain continuity criterion, such as spectral risk measures and utility-based shortfall risk. To the best of our knowledge, our work is the first to provide lower and upper bounds for estimating any risk measure beyond the mean within a Markovian setting. Our lower bounds also extend to the infinite-horizon discounted costs' mean. Even in that case, our lower bound of Ω(1/ε2) improves upon the existing Ω(1/ε) bound (Metelli et al., 2023).

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Sentiment Analysis on Drug Reviews Using Diverse Classification Techniques 3

Sentiment Analysis on Drug Reviews Using Diverse Classificat...

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3rd International Conference on Communication, Computing and Industry 4.0, C2I4 2022

作者： Duraisamy, Premkumar Natarajan, Yuvaraj Preethaa, K.R.Sri Mouthami, K. KPR Institute of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Research Professor Intelligent Construction Automation Center Kyungpook National University Deagu Korea Republic of

ISBN: (纸本)9781665474979

Free text reviews are abundantly distributed over the internet among the standard population of the world. Although online doctors were consulted during the pandemic, many drugs are taken without prescription by ordinary people. Reviews are distributed about the medicines through online feeds, posts and blogs, which are merely helpful in identifying the side effects and their adverse reactions in combination with another drug or standalone adverse reactions. Spelling mistakes, jargon and short names are normalized throughout the medical terminologies using Natural language processing (NLP) and RxNorm from the reviews extracted from these sources. The concept of Bidirectional Encoder Representations from Transformers (BERT) is used to mine the association rules for the drug interactions when used in conjugation with another drug, and the idea of Adaptive Fuzzy logic neural networks is used to validate the results of association rules derived from the BERT. Inconsistencies identified by the regulations generated were rejected efficiently by the Support vector machines (SVM), Radial Basis Function (RBF)and the system shows 60.11 percentage of accuracy with 10 cross-fold validation, whereas the results are equivalent to the accuracy obtained using SVM with RBF kernel. The data set is reduced from its dimension using Linear discriminant analysis (LDA) by applying the mining process and BERT on two drugs, namely 'Azithromycin' in conjugation with Buprenorphine has Type 'A' side effects, and Prozac in conjugation with Metronidazole shows different side effects. Deplin and Zoloft give very positive symptoms and are independent of dosage interactions. © 2022 IEEE.

关键词： Bidirectional Encoder Representations from Transformers (BERT) Linear discriminant analysis (LDA) Radial Basis Function (RBF) Support vector machines (SVM)

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Symmetric block-cyclic distribution: fewer communications leads to faster dense cholesky factorization 22

Symmetric block-cyclic distribution: fewer communications le...

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Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis

作者： Olivier Beaumont Philippe Duchon Lionel Eyraud-Dubois Julien Langou Mathieu Vérité French Institute for Research in Computer Science and Automation (INRIA) Bordeaux France Laboratoire Bordelais de Recherche en Informatique (LaBRI) Bordeaux France University of Colorado Denver

We consider the distributed Cholesky factorization on homogeneous nodes. Inspired by recent progress on asymptotic lower bounds on the total communication volume required to perform Cholesky factorization, we present an original data distribution, Symmetric Block Cyclic (SBC), designed to take advantage of the symmetry of the matrix. We prove that SBC reduces the overall communication volume between nodes by a factor of square root of 2 compared to the standard 2D block-cyclic distribution. SBC can easily be implemented within the paradigm of task-based runtime systems. Experiments using the Chameleon library over the StarPU runtime system demonstrate that the SBC distribution reduces the communication volume as expected, and also achieves better performance and scalability than the classical 2D block-cyclic allocation scheme in all configurations. We also propose a 2.5D variant of SBC and prove that it further improves the communication and performance benefits.

关键词： load balancing and scheduling algorithms algorithms for numerical methods and algebraic systems

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Graphene-based dual-function acoustic transducers for machine learning-assisted human-robot interfaces

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InfoMat 2023年第2期5卷 66-79页

作者： Hao Sun Xin Gao Liang-Yan Guo Lu-Qi Tao Zi Hao Guo Yangshi Shao Tianrui Cui Yi Yang Xiong Pu Tian-Ling Ren Institute of Microelectronics and Beijing National Research Center for Information Science and Technology(BNRist) Tsinghua UniversityBeijingPeople's Republic of China CAS Center for Excellence in Nanoscience Beijing Key Laboratory of Micro-nano Energy and SensorBeijing Institute of Nanoenergy and NanosystemsChinese Academy of SciencesBeijingPeople's Republic of China State Key Laboratory of Power Transmission Equipment and System Security and New Technology School of Electrical EngineeringChongqing UniversityChongqingPeople's Republic of China School of Computer Science&Technology Beijing Institute of TechnologyBeijingPeople's Republic of China School of Automation and Electrical Engineering University of Science and Technology BeijingBeijingPeople's Republic of China

Human–robot interface(HRI)electronics are critical for realizing robotic ***,we report graphene-based dual-function acoustic transducers for machine learning-assisted human–robot interfaces(GHRI).The GHRI functions both an artificial ear through the triboelectric acoustic sensing mechanism and an artificial mouth through the thermoacoustic sound emission *** success of the integrated device is also attributed to the multifunctional laser-induced graphene,as either triboelectric materials,electrodes,or thermoacoustic *** systematically optimizing the structure parameters,the GHRI achieves high sensitivity(4500 mV Pa^(–1))and operating durability(1000000 cycles and 60 days),capable of recognizing speech identities,emotions,content,and other information in the human *** the assistance of machine learning,30 speech categories are trained by a convolutional neural network,and the accuracy reaches 99.66%and 96.63%in training datasets and test ***,GHRI is used for artificial intelligence communication based on recognized speech *** work shows broad prospects for the development of robotic intelligence.

关键词： human-robot interface laser-induced graphene machine learning thermoacoustic triboelectric nanogenerator

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Security Assessment Algorithms for Transmission and Services in SDN-Based Mobile Networks

Security Assessment Algorithms for Transmission and Services...

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International Conference on computer and Communications (ICCC)

作者： Wei Zhu Chunhui Bao Tianxiang Chen Yi Zhuang Embedded Computer System Architecture Jiangsu Automation Research Institute Lianyungang China Faculty of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China

In response to the challenge of comprehensively security assessment and analysis in existing mobile networks from both upper-layer services and network transmission levels, A SDN-based security assessment model for transmission and service security in mobile networks (SAMTS) is proposed. This model introduces security assessment algorithms, in which static configuration information and dynamic abnormal information are considered comprehensively to judge the trend and the risk degree of attack when evaluating the security of network nodes. Meanwhile, the node importance of service orientation and transmission is integrated for security evaluation. The effectiveness of the proposed method is verified by simulation and comparison experiments, demonstrating its higher evaluation accuracy compared with similar algorithms.

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Analyzing Risk Level from RoI and VoI for Robust Data Privacy using Fuzzy Inference System

Analyzing Risk Level from RoI and VoI for Robust Data Privac...

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International Conference on Robotics,Electrical and Signal Processing Techniques (ICREST)

作者： Rabab Khan Rongon M. Akhtaruzzaman Krishna Das Shekh Tanjil Sharif Department of Computer Science and Engineering Military Institute of Science and Technology Dhaka Bangladesh Quantum Robotics and Automation Research Group (QRARG) Dhaka Bangladesh International University of Business Agriculture and Technology (IUBAT) Dhaka Bangladesh

ISBN: (数字)9798331530952

ISBN: (纸本)9798331530969

In the modern digital environment, securing data privacy has become increasingly critical as organizations confront rising threats from cyber-attacks and data breaches. To safeguard sensitive information, businesses must invest in robust data protection measures, yet balancing these investments with their potential benefits remains challenging. This research addresses this issue by proposing a novel framework that integrates Return on Investment (RoI) and Value of Information (VoI) within a Fuzzy Inference System (FIS) to assess data privacy risks effectively. RoI measures the financial return relative to the cost of data protection investments, indicating the economic value derived from such measures. VoI, on the other hand, evaluates the worth of the information protected by these investments, reflecting its impact on decision-making and operational efficiency. By employing a FIS, which accommodates uncertainties and imprecise data, this framework provides a nuanced approach to risk assessment, enabling organizations to optimize their data privacy strategies. The integration of RoI and VoI within the FIS allows for a comprehensive evaluation of risk levels, facilitating more informed decision-making and improved risk management. This research aims to enhance data privacy practices by offering a methodical and adaptable tool for evaluating and balancing investments in data protection.

关键词： Fuzzy logic Analytical models Uncertainty Decision making Data protection Signal processing algorithms Organizations Mathematical models Risk management Investment

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