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arXiv 2022年

作者： Zhao, Xusheng Wu, Jia Peng, Hao Beheshti, Amin Monaghan, Jessica J.M. McAlpine, David Hernandez-Perez, Heivet Dras, Mark Dai, Qiong Li, Yangyang Yu, Philip S. He, Lifang Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China School of Computing Macquarie University Sydney Australia Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China National Acoustic Laboratories Sydney Australia Department of Linguistics The Australian Hearing Hub Macquarie University Sydney Australia CAEIT Beijing China Department of Computer Science University of Illinois ChicagoIL United States Computer Science & Engineering Lehigh University PA United States

Modern neuroimaging techniques enable us to construct human brains as brain networks or connectomes. Capturing brain networks' structural information and hierarchical patterns is essential for understanding brain functions and disease states. Recently, the promising network representation learning capability of graph neural networks (GNNs) has prompted related methods for brain network analysis to be proposed. Specifically, these methods apply feature aggregation and global pooling to convert brain network instances into vector representations encoding brain structure induction for downstream brain network analysis tasks. However, existing GNN-based methods often neglect that brain networks of different subjects may require various aggregation iterations and use GNN with a fixed number of layers to learn all brain networks. Therefore, how to fully release the potential of GNNs to promote brain network analysis is still non-trivial. In our work, a novel brain network representation framework, BN-GNN, is proposed to solve this difficulty, which searches for the optimal GNN architecture for each brain network. Concretely, BN-GNN employs deep reinforcement learning (DRL) to automatically predict the optimal number of feature propagations (reflected in the number of GNN layers) required for a given brain network. Furthermore, BN-GNN improves the upper bound of traditional GNNs' performance in eight brain network disease analysis tasks. © 2022, CC BY.

关键词： Reinforcement learning

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Trusted AI in Multi-agent Systems: An Overview of Privacy and Security for Distributed Learning

arXiv

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arXiv 2022年

作者： Ma, Chuan Li, Jun Wei, Kang Liu, Bo Ding, Ming Yuan, Long Han, Zhu Poor, H. Vincent Zhejiang Laboratory Hangzhou311121 China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China The School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210096 China The Department of Computing Hong Kong Polytechnic University 999077 Hong Kong The University of Technology SydneyNSW2007 Australia Data61 CSIRO NSW2015 Australia The School of Computer Science Nanjing University of Science and Technology Nanjing210096 China The Department of Electrical and Computer Engineering The University of Houston HoustonTX77004 United States Department of Computer Science and Engineering Kyung Hee University Seoul Korea Republic of The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

Motivated by the advancing computational capacity of distributed end-user equipment (UE), as well as the increasing concerns about sharing private data, there has been considerable recent interest in machine learning (ML) and artificial intelligence (AI) that can be processed on distributed UEs. Specifically, in this paradigm, parts of a ML process are outsourced to multiple distributed UEs. Then the processed information is aggregated on a certain level at a central server, which turns a centralized ML process into a distributed one, and brings about significant benefits. However, this new distributed ML paradigm raises new risks of privacy and security issues. In this paper, we provide a survey on the emerging security and privacy risks of distributed ML from a unique perspective of information exchange levels, which are defined according to the key steps of a ML process, i.e.: i) the level of pre-processed data, ii) the level of learning models, iii) the level of extracted knowledge and, iv) the level of intermediate results. We explore and analyze the potential of threats for each information exchange level based on an overview of the current state-of-the-art attack mechanisms, and then discuss the possible defense methods against such threats. Finally, we complete the survey by providing an outlook on the challenges and possible directions for future research in this critical area. Copyright © 2022, The Authors. All rights reserved.

关键词： Multi agent systems

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Convex Optimization for Nonequilibrium Steady States on a Hybrid Quantum Processor

引用

Physical Review Letters 2023年第24期130卷 240601-240601页

作者： Jonathan Wei Zhong Lau Kian Hwee Lim Kishor Bharti Leong-Chuan Kwek Sai Vinjanampathy Centre for Quantum Technologies National University of Singapore 3 Science Drive 2 Singapore 117543 Joint Center for Quantum Information and Computer Science and Joint Quantum Institute NIST/University of Maryland College Park Maryland 20742 USA Institute of High Performance Computing (IHPC) Agency for Science Technology and Research (A*STAR) 1 Fusionopolis Way No. 16-16 Connexis Singapore 138632 Republic of Singapore MajuLab CNRS-UNS-NUS-NTU International Joint Research Unit Singapore UMI 3654 Singapore National Institute of Education Nanyang Technological University 1 Nanyang Walk Singapore 637616 Singapore School of Electrical and Electronic Engineering Block S2.1 50 Nanyang Avenue Singapore 639798 Singapore Department of Physics Indian Institute of Technology-Bombay Powai Mumbai 400076 India Centre of Excellence in Quantum Information Computation Science and Technology Indian Institute of Technology Bombay Powai Mumbai 400076 India

Finding the transient and steady state properties of open quantum systems is a central problem in various fields of quantum technologies. Here, we present a quantum-assisted algorithm to determine the steady states of open system dynamics. By reformulating the problem of finding the fixed point of Lindblad dynamics as a feasibility semidefinite program, we bypass several well-known issues with variational quantum approaches to solving for steady states. We demonstrate that our hybrid approach allows us to estimate the steady states of higher dimensional open quantum systems and discuss how our method can find multiple steady states for systems with symmetries.

关键词： Learning theory Machine learning Open quantum systems & decoherence Quantum algorithms Nonequilibrium systems

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Software Architecture for Quantum computing Systems - A Systematic Review

arXiv

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arXiv 2022年

作者： Khan, Arif Ali Ahmad, Aakash Waseem, Muhammad Liang, Peng Fahmideh, Mahdi Mikkonen, Tommi Abrahamsson, Pekka M3S Empirical Software Engineering Research Unit University of Oulu Oulu90014 Finland School of Computing and Communications Lancaster University Leipzig Germany School of Computer Science Wuhan University Wuhan China School of Business University of Southern Queensland QLD Australia Faculty of Information Technology University of Jyväskylä JyvaskylaFI-40014 Finland

Quantum computing systems rely on the principles of quantum mechanics to perform a multitude of computationally challenging tasks more efficiently than their classical counterparts. The architecture of software-intensive systems can empower architects who can leverage architecture-centric processes, practices, description languages to model, develop, and evolve quantum computing software (quantum software for short) at higher abstraction levels. We conducted a Systematic Literature Review (SLR) to investigate (i) architectural process, (ii) modeling notations, (iii) architecture design patterns, (iv) tool support, and (iv) challenging factors for quantum software architecture. Results of the SLR indicate that quantum software represents a new genre of software-intensive systems;however, existing processes and notations can be tailored to derive the architecting activities and develop modeling languages for quantum software. Quantum bits (Qubits) mapped to Quantum gates (Qugates) can be represented as architectural components and connectors that implement quantum software. Tool-chains can incorporate reusable knowledge and human roles (e.g., quantum domain engineers, quantum code developers) to automate and customize the architectural process. Results of this SLR can facilitate researchers and practitioners to develop new hypotheses to be tested, derive reference architectures, and leverage architecture-centric principles and practices to engineer emerging and next generations of quantum software. © 2022, CC BY.

关键词： Modeling languages

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Influence evaluation of academic papers via citation characteristics analysis 5th

Influence evaluation of academic papers via citation charact...

引用

5th International Conference on Technology in Education, ICTE 2020

作者： Wu, Qingyuan Hu, Jiahua Rao, Yanghui Wang, Fu Lee Xie, Haoran School of Internet Finance and Information Engineering Guangdong University of Finance Guangzhou China School of Management Beijing Normal University Zhuhai China School of Data and Computer Science Sun Yat-Sen University Guangzhou China School of Science and Technology The Open University of Hong Kong Hong Kong Department of Computing and Decision Sciences Lingnan University Hong Kong

ISBN: (纸本)9789813345935

Citation evaluation has become one of the important indexes to evaluate the paper influence. In the academic area, paper influence is quite important to evaluate the author’s academic level, in addition to help researchers to identify important academic literatures. The traditional citation evaluation methods are mainly developed based on the citation frequency. Nevertheless, other citation characteristics, such as the nature, depth, and distribution are also valuable. Citation nature includes positive, neutral, and negative citations. The depth can be divided into deep, medium, and shallow citations, which could reveal the correlation and the degree of paper reference between citing and cited papers. The citation distribution contains three types: forepart, middle, and tail citations. The data sample of the empirical study is collected from highly cited papers in Chinese and English core journals. Firstly, by using content analysis methods, we analyze the citation evaluation from three dimensions of Chinese papers. Secondly, we compare the similarities and differences between Chinese and English papers. Thirdly, we show a series of results to verify the accuracy and effectiveness of our method. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Citation depth Citation distribution Citation evaluation Citation nature Content analysis

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Generalization of multivariable Laplace transform based on Tsallis q-exponential and its inverse using Post-Widder’s method

arXiv

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arXiv 2022年

作者： Samu Shahabuddin, Naina Mohammed Karthikeyan, Jeevanandham Ahmed Abduljaffer, Basherrudin Mahmud Ali, Manirul Radhakrishnan, Chandrashekar Department of Physics Government Arts College Tiruppur District Tamilnadu Udumalpet642126 India School of Physics Madurai Kamaraj University Madurai625021 India Centre for Quantum Science and Technology Chennai Institute of Technology Chennai600069 India Department of Computer Science Data Science and Engineering NYU Shanghai 567 Yangsi West Road Pudong New District Shanghai200124 China Centre for Quantum Information Communication and Computing Indian Institute of Technology Madras Chennai600036 India

A generalization of the multivariate Laplace transform based on the Tsallis q-exponential is given in the present work for a new type of kernel. We also define the inverse transform for this generalized multivariate transform based on the complex integration method. We prove identities corresponding to the Laplace transform and inverse transform like the q-convolution theorem, the action of generalized derivative and generalized integration on the Laplace transform. We then derive a q-generalization of the inverse Laplace transform based on the Post-Widder’s method which bypasses the necessity for a complex contour integration. This extension of the Post-Widder’s method has been carried out for the multivariate case as well. We demonstrate the usefulness of this in computing the Laplace and inverse Laplace transform of some elementary functions. The inverse Laplace transform has been calculated using both the complex integration method as well as the Post-Widder’s methods and the results obtained through these two methods agree with each other. © 2022, CC BY.

关键词： Integration

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THE ENERGY PREDICTION SMART-METER dataSET: ANALYSIS OF PREVIOUS COMPETITIONS AND BEYOND

arXiv

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arXiv 2023年

作者： Pekaslan, Direnc Alonso-Moral, Jose Maria Bandara, Kasun Bergmeir, Christoph Bernabé-Moreno, Juan Eigenmann, Robert Einecke, Nils Ergen, Selvi Godahewa, Rakshitha Hewamalage, Hansika Lago, Jesus Limmer, Steffen Rebhan, Sven Rabinovich, Boris Rajapasksha, Dilini Song, Heda Wagner, Christian Wu, Wenlong Magdalena, Luis Triguero, Isaac School of Computer Science University of Nottingham United Kingdom Centro Singular de Investigación en Tecnoloxías Intelixentes Universidade de Santiago de Compostela Spain School of Computing and Information Systems University of Melbourne Australia Department of Computer Science and Artificial Intelligence University of Granada Spain IBM Research Europe Dublin Ireland E.ON Digital Technology GmbH Munich Germany Honda Research Institute Europe GmbH Offenbach Germany National Grid ESO United Kingdom Coles Group Hawthorn EastVIC Australia Amazon United States Amdocs Israel University of Missouri Colombia CIS VP TA Universidad Politécnica de Madrid Spain Department of Data Science and Artificial Intelligence Monash University Australia

This paper presents the real-world smart-meter dataset and offers an analysis of solutions derived from the Energy Prediction Technical Challenges, focusing primarily on two key competitions: the IEEE Computational Intelligence Society (IEEE-CIS) Technical Challenge on Energy Prediction from Smart Meter data in 2020 (named EP) and its follow-up challenge at the IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) in 2021 (named as XEP). These competitions focus on accurate energy consumption forecasting and the importance of interpretability in understanding the underlying factors. The challenge aims to predict monthly and yearly estimated consumption for households, addressing the accurate billing problem with limited historical smart meter data. The dataset comprises 3,248 smart meters, with varying data availability ranging from a minimum of one month to a year. This paper delves into the challenges, solutions and analysing issues related to the provided real-world smart meter data, developing accurate predictions at the household level, and introducing evaluation criteria for assessing interpretability. Additionally, this paper discusses aspects beyond the competitions: opportunities for energy disaggregation and pattern detection applications at the household level, significance of communicating energy-driven factors for optimised billing, and emphasising the importance of responsible AI and data privacy considerations. These aspects provide insights into the broader implications and potential advancements in energy consumption prediction. Overall, these competitions provide a dataset for residential energy research and serve as a catalyst for exploring accurate forecasting, enhancing interpretability, and driving progress towards the discussion of various aspects such as energy disaggregation, demand response programs or behavioural interventions. © 2023, CC BY-NC-ND.

关键词： Smart meters

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Cooperative Sensing and Heterogeneous information Fusion in VCPS: A Multi-agent Deep Reinforcement Learning Approach

arXiv

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arXiv 2022年

作者： Xu, Xincao Liu, Kai Dai, Penglin Xie, Ruitao Cao, Jingjing Luo, Jiangtao The College of Computer Science Chongqing University Chongqing400040 China The School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China The National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu611756 China The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The School of Transportation and Logistics Engineering Wuhan University of Technology Hubei430063 China The Electronic Information and Networking Research Institute Chongqing University of Posts and Telecommunications Chongqing400065 China

Cooperative sensing and heterogeneous information fusion are critical to realize vehicular cyber-physical systems (VCPSs). This paper makes the first attempt to quantitatively measure the quality of VCPS by designing a new metric called Age of View (AoV). Specifically, we first present the system architecture where heterogeneous information can be cooperatively sensed and uploaded via vehicle-to-infrastructure (V2I) communications in vehicular edge computing (VEC). Logical views are constructed by fusing the heterogeneous information at edge nodes. Further, we formulate the problem by deriving a cooperative sensing model based on the multi-class M/G/1 priority queue, and defining the AoV by modeling the timeliness, completeness and consistency of the logical views. On this basis, a multi-agent deep reinforcement learning solution is proposed. In particular, the system state includes vehicle sensed information, edge cached information and view requirements. The vehicle action space consists of the sensing frequencies and uploading priorities of information. A difference-reward-based credit assignment is designed to divide the system reward, which is defined as the VCPS quality, into the difference reward for vehicles. Edge node allocates V2I bandwidth to vehicles based on predicted vehicle trajectories and view requirements. Finally, we build the simulation model and give a comprehensive performance evaluation, which conclusively demonstrates the superiority of the proposed solution. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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Learning with Group Noise

arXiv

引用

arXiv 2021年

作者： Wang, Qizhou Yao, Jiangchao Gong, Chen Liu, Tongliang Gong, Mingming Yang, Hongxia Han, Bo Department of Computer Science Hong Kong Baptist University Hong Kong Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of MoE School of Computer Science and Engineering Nanjing University of Science and Technology China Data Analytics and Intelligence Lab Alibaba Group China Department of Computing Hong Kong Polytechnic University Hong Kong Trustworthy Machine Learning Lab School of Computer Science University of Sydney Australia School of Mathematics and Statistics University of Melbourne Australia

Machine learning in the context of noise is a challenging but practical setting to plenty of real-world applications. Most of the previous approaches in this area focus on the pairwise relation (casual or correlational relationship) with noise, such as learning with noisy labels. However, the group noise, which is parasitic on the coarse-grained accurate relation with the fine-grained uncertainty, is also universal and has not been well investigated. The challenge under this setting is how to discover true pairwise connections concealed by the group relation with its fine-grained noise. To overcome this issue, we propose a novel Max-Matching method for learning with group noise. Specifically, it utilizes a matching mechanism to evaluate the relation confidence of each object (cf. Figure 1) w.r.t. the target, meanwhile considering the Non-IID characteristics among objects in the group. Only the most confident object is considered to learn the model, so that the fine-grained noise is mostly dropped. The performance on a range of real-world datasets in the area of several learning paradigms demonstrates the effectiveness of Max-Matching. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

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Time-Aware Missing Traffic Flow Prediction for Sensors with Privacy-Preservation

Time-Aware Missing Traffic Flow Prediction for Sensors with ...

引用

作者： Lianyong Qi Fan Wang Xiaolong Xu Wanchun Dou Xuyun Zhang Mohammad R.Khosravi Xiaokang Zhou School of Computer Science Qufu Normal University School of Computer and Software Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science and Technology State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Department of Computing Macquarie University Department of Computer Engineering Persian Gulf University Department of Electrical and Electronic Engineering Shiraz University of Technology Faculty of Data Science Shiga University

With the continuous development of Io T, a number of sensors establish on the roadside to monitor traffic conditions in real time. The continuously traffic data generated by these sensors makes traffic management feasible. However, loss of data may occur due to inevitable sensor failure, impeding traffic managers to understand traffic dynamics clearly. In this situation, it is becoming a necessity to predict missing traffic flow accurately for effective traffic management. Furthermore, the traffic sensor data are often distributed and stored by different agencies,which inhibits the multi-party sensor data sharing significantly due to privacy concerns. Therefore, it has become a major obstacle to balance the tradeoff between data sharing and vehicle privacy. In light of these challenges, we propose a privacyaware and accurate missing traffic flow prediction approach based on time-aware Locality-Sensitive Hashing technique. At last, we deploy a set of experiments based on a real traffic dataset. Experimental reports demonstrate the feasibility of our proposal in terms of traffic flow prediction accuracy and efficiency while guaranteeing sensor data privacy.

关键词： Traffic flow prediction Multi-party sensors Privacy Locality-Sensitive Hashing

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