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IEEE/CAA Journal of Automatica Sinica 2020年第4期7卷 1134-1153页

作者： Jiahai Wang Yuyan Sun Zizhen Zhang Shangce Gao the Department of Computer Science and also with the Key Laboratory of Machine Intelligence and Advanced ComputingMinistry of Educationand also with the Guangdong Key Laboratory of Big Data Analysis and ProcessingSun Yat-sen UniversityGuangzhou 510275China the Department of Computer Science Sun Yat-sen UniversityGuangzhou 510275China the Faculty of Engineering University of ToyamaToyama-shi 930-8555Japan

The multitrip pickup and delivery problem with time windows and manpower planning(MTPDPTW-MP)determines a set of ambulance routes and finds staff assignment for a hospital. It involves different stakeholders with diverse interests and objectives. This study firstly introduces a multiobjective MTPDPTW-MP(MO-MTPDPTWMP) with three objectives to better describe the real-world scenario. A multiobjective iterated local search algorithm with adaptive neighborhood selection(MOILS-ANS) is proposed to solve the problem. MOILS-ANS can generate a diverse set of alternative solutions for decision makers to meet their requirements. To better explore the search space, problem-specific neighborhood structures and an adaptive neighborhood selection strategy are carefully designed in MOILS-ANS. Experimental results show that the proposed MOILS-ANS significantly outperforms the other two multiobjective algorithms. Besides, the nature of objective functions and the properties of the problem are analyzed. Finally, the proposed MOILS-ANS is compared with the previous single-objective algorithm and the benefits of multiobjective optimization are discussed.

关键词： Adaptive neighborhood selection manpower planning multiobjective optimization multitrip pickup and delivery problem with time windows

Hierarchical Reverse Games for the Resource Ecosystem in Cloud-Edge computing

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IEEE Transactions on Consumer Electronics 2024年

作者： Pei, Manlin Wang, Chenyu Wang, Xia Zheng, Zhigao Huang, Jianhui Wang, Shengling Guo, Yufei School of Computer and Information Technology Beijing Jiaotong University Beijing China Department of Computer Science Georgia State University United States Beijing University of Technology School of Statistics and Data Science Faculty of Science Beijing China Institute of Artificial Intelligence School of Computer Science National Engineering Research Center for Multimedia Software Hubei Key Laboratory of Multimedia and Network Communication Engineering Hubei Luojia Laboratory Wuhan University Wuhan China Chinese Academy of Sciences Institute of Computing Technology Beijing China Beijing Normal University School of Artificial Intelligence Beijing China George Washington University Department of Statistics United States

Edge computing has emerged as a killer technology for a hyper-connected world due to its distributed architecture and customer-proximity property. Combined edge nodes with the cloud data center, a cloud-edge computing paradigm is formed, whose resource ecosystem calls for competitive pricing and optimal resource allocation. However, it is not feasible to realize global information-based optimization since some information is kept private to their owners. To solve this challenge, this paper develops the user-independent hierarchical reverse game and the user-assisted hierarchical reverse game to depict the relationship among the resource provider, the resource tenant, and the resource user. The aim is to seek the optimal strategies for players without revealing any private information. The common trait of both games is to force the players to report the optimal strategies based on their actual private information. This salient trait can produce privacy-preservation optimization since there is no requirement for the players to release their individual information directly, and the optimal strategies of other players can be deduced naturally. The difference between the user-independent hierarchical reverse game and the user-assisted one is that the game rules are stipulated entirely by the provider and the tenants (edge nodes) in the former, while some game rules can be customized according to the advice of users in the latter. This leads the user-independent hierarchical reverse game is more concise and easy to guarantee the incentive compatibility constraint, while the user-assisted hierarchical reverse game can attract more users since the game rules can adapt to their economic status, requirements, and preferences. © 1975-2011 IEEE.

关键词： Cloud platforms

Integrating local energetics into Maxwell-Calladine constraint counting to design mechanical metamaterials

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arXiv 2022年

作者： Rocks, Jason W. Mehta, Pankaj Department of Physics Boston University BostonMA02215 United States Biological Design Center Boston University BostonMA02215 United States Faculty of Computing and Data Science Boston University BostonMA02215 United States

The Maxwell-Calladine index theorem plays a central role in our current understanding of the mechanical rigidity of discrete materials. By considering the geometric constraints each material component imposes on a set of underlying degrees of freedom, the theorem relates the emergence of rigidity to constraint counting arguments. However, the Maxwell-Calladine paradigm is significantly limited - its exclusive reliance on the geometric relationships between constraints and degrees of freedom completely neglects the actual energetic costs of deforming individual components. To address this limitation, we derive a generalization of the Maxwell-Calladine index theorem based on susceptibilities that naturally incorporate local energetic properties such as stiffness and prestress. Using this extended framework, we investigate how local energetics modify the classical constraint counting picture to capture the relationship between deformations and external forces. We then combine this formalism with group representation theory to design mechanical metamaterials where differences in symmetry between local energy costs and structural geometry are exploited to control responses to external forces. © 2022, CC BY.

关键词： Metamaterials

Federated Learning in STAR-RIS Aided SAGINs

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IEEE Communications Standards Magazine

IEEE Communications Standards Magazine 2025年

作者： Wu, Min Guo, Kefeng Dev, Kapal Hamadi, Hussam Al Singh, Keshav Khowaja, Sunder Ali Li, Xingwang Space Engineering University School of Space Information Beijing101407 China Munster Technological University CONNECT Centre Department of Computer Science Ireland College of Engineering and IT University of Dubai United Arab Emirates National Sun Yat-sen University Institute of Communications Engineering Kaohsiung Taiwan Technological University Dublin School of Computing Faculty of Computing Digital and Data Ireland Henan Polytechnic University School of Physics and Electronic Information Engineering Jiaozuo454000 China Jiaozuo Key Laboratory of Crow-Sensing Network Jiaozuo454003 China

Satellite-aerial-ground integrated network (SAGIN) is considered to be an advanced architecture that offers seamless coverage, flexible access, and efficient connectivity. However, several challenges emerge during the construction of SAGINs, which include complex propagation environments and limited spectrum resources. The aforementioned challenges can be addressed in a cost effective manner by integrating simultaneous transmission and reflecting reconfigurable intelligent surfaces (STAR-RISs). The STAR-RISs are capable of reflecting and transmitting the incident signals passively in order to serve users on both sides of the panel. Given the self-organizing nature of STAR-RIS-aided SAGINs, meticulous resource allocation and transmission strategy adaptation are imperative to accommodate changes in multiple communication node environments. Recently, federated learning has been envisioned as a pioneering distributed machine learning (ML) approach for managing the multi-objective optimization problem in STAR-RIS-aided SAGINs. This approach strives to balance the trade-offs between wireless communication performance and model efficiency. In this work, we present the advantages and potential directions for employing federated learning in STAR-RIS-aided SAGINs through multi-objectives optimization. Furthermore, to illustrate the practical application of federated learning in STAR-RIS-aided SAGINs, we present two case studies utilizing federated deep reinforcement learning, based on the non-orthogonal multiple access (NOMA) scheme. © 2017 IEEE.

关键词： Deep reinforcement learning

Thermodynamic origins of topological protection in nonequilibrium stochastic systems

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arXiv 2022年

作者： Mehta, Pankaj Rocks, Jason W. Department of Physics Boston University BostonMA02215 United States Biological Design Center Boston University BostonMA02215 United States Faculty of Computing and Data Science Boston University BostonMA02215 United States

Topological protection has emerged as an organizing principle for understanding and engineering robust collective behavior in electronic and material systems. Recent work suggests that topology may also play a role in organizing stochastic processes relevant to biology and self-assembly. Here, we show that topological protection in chemical networks can be understood entirely in terms of nonequilibrium thermodynamics. We illustrate these ideas using simple examples inspired by the literature. © 2022, CC BY.

关键词： Stochastic systems

Leveraging mobile phone data for migration flows

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arXiv 2021年

作者： Luca, Massimiliano Barlacchi, Gianni Lepri, Bruno Oliver, Nuria Faculty of Computer Science Free University of Bolzano Mobile and Social Computing Lab Bruno Kessler Foundation Amazon Alexa Mobile and Social Computing Lab Bruno Kessler Foundation Data-Pop Alliance ELLIS Unit Alicante Foundation Data-Pop Alliance

Statistics on migration flows are often derived from census data, which suffer from intrinsic limitations, including costs and infrequent sampling. When censuses are used, there is typically a time gap - up to a few years - between the data collection process and the computation and publication of relevant statistics. This gap is a significant drawback for the analysis of a phenomenon that is continuously and rapidly changing. Alternative data sources, such as surveys and field observations, also suffer from reliability, costs, and scale limitations. The ubiquity of mobile phones enables an accurate and efficient collection of up-to-date data related to migration. Indeed, passively collected data by the mobile network infrastructure via aggregated, pseudonymized Call Detail Records (CDRs) is of great value to understand human migrations. Through the analysis of mobile phone data, we can shed light on the mobility patterns of migrants, detect spontaneous settlements and understand the daily habits, levels of integration, and human connections of such vulnerable social groups. This Chapter discusses the importance of leveraging mobile phone data as an alternative data source to gather precious and previously unavailable insights on various aspects of migration. Also, we highlight pending challenges that would need to be addressed before we can effectively benefit from the availability of mobile phone data to help make better decisions that would ultimately improve millions of people's lives. Copyright © 2021, The Authors. All rights reserved.

关键词： Population statistics

CellWalker2: Multi-omic discovery using hierarchical cell type relationships

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Cell Genomics

Cell Genomics 2025年 100886页

作者： Hu, Zhirui Przytycki, Pawel F. Pollard, Katherine S. Gladstone Institute of Data Science & Biotechnology 1650 Owens Street San Francisco 94158 CA United States Faculty of Computing & Data Sciences Boston University 665 Commonwealth Avenue Boston 02215 MA United States Department of Epidemiology & Biostatistics University of California San Francisco 1650 Owens Street San Francisco 94158 CA United States Chan Zuckerberg Biohub SF 499 Illinois Street San Francisco 94158 CA United States

Tissues are composed of cells with a wide range of similarities to each other, yet existing methods for single-cell genomics treat cell types as discrete labels. To address this gap, we developed CellWalker2, a graph diffusion-based model for the annotation and mapping of multi-modal data. With our open-source software package, hierarchically related cell types can be probabilistically matched across contexts and used to annotate cells, genomic regions, or gene sets. Additional features include estimating statistical significance and enabling gene expression and chromatin accessibility to be jointly modeled. Through simulation studies, we show that CellWalker2 performs better than existing methods in cell-type annotation and mapping. We then use multi-omics data from the brain and immune system to demonstrate CellWalker2’s ability to assign high-resolution cell-type labels to regulatory elements and TFs and to quantify both conserved and divergent cell-type relationships between species. © 2025 The Author(s)

关键词： cell type comparative genomics gene regulation graph hierarchical multi-omics single cell transcription factors

Two-stream Multi-level Dynamic Point Transformer for Two-person Interaction Recognition

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arXiv 2023年

作者： Liu, Yao Cui, Gangfeng Luo, Jiahui Chang, Xiaojun Yao, Lina School of Computer Science and Engineering University of New South Wales SydneyNSW2052 Australia School of Computing and Information Systems University of Melbourne MelbourneVIC3010 Australia Faculty of Engineering and Information Technology University of Technology Sydney SydneyNSW2007 Australia Data 61 CSIRO School of Computer Science and Engineering University of New South Wales SydneyNSW2015 Australia

As a fundamental aspect of human life, two-person interactions contain meaningful information about people's activities, relationships, and social settings. Human action recognition serves as the foundation for many smart applications, with a strong focus on personal privacy. However, recognizing two-person interactions poses more challenges due to increased body occlusion and overlap compared to single-person actions. In this paper, we propose a point cloud-based network named Two-stream Multi-level Dynamic Point Transformer for two-person interaction recognition. Our model addresses the challenge of recognizing two-person interactions by incorporating local-region spatial information, appearance information, and motion information. To achieve this, we introduce a designed frame selection method named Interval Frame Sampling (IFS), which efficiently samples frames from videos, capturing more discriminative information in a relatively short processing time. Subsequently, a frame features learning module and a two-stream multi-level feature aggregation module extract global and partial features from the sampled frames, effectively representing the local-region spatial information, appearance information, and motion information related to the interactions. Finally, we apply a transformer to perform self-attention on the learned features for the final classification. Extensive experiments are conducted on two large-scale datasets, the interaction subsets of NTU RGB+D 60 and NTU RGB+D 120. The results show that our network outperforms state-of-the-art approaches in most standard evaluation settings. Copyright © 2023, The Authors. All rights reserved.

关键词： Large datasets

ZETA: ZEro-Trust Attack Framework with Split Learning for Autonomous Vehicles in 6G Networks

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ZETA: ZEro-Trust Attack Framework with Split Learning for Au...

IEEE Conference on Wireless Communications and Networking

作者： Sunder Ali Khowaja Parus Khuwaja Kapal Dev Keshav Singh Lewis Nkenyereye Dan Kilper Faculty of Computing Digital and Data Technological University Dublin Ireland Institute of Business Administration University of Sindh Pakistan Department of Computer Science CONNECT Centre Munster Technological University Ireland Institute of Communications Engineering (ICE) National Sun Yat-sen University Taiwan Department of Computer and Information Security Sejong University South Korea CONNECT Centre School of Engineering Trinity College Dublin Ireland

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

In past, due to data and model security concerns, modern communication systems mainly focus on the use of edge computing devices for enabling immersive applications and services. Federated learning is one of the preferred solutions but it stresses the computation capability of the edge devices for immersive applications. Much research is now focusing on split learning as an alternative due to its ability of performing joint training with limited computing resources. However, split learning is also vulnerable to data reconstruction, feature space hijacking, and model inversion attacks, which are quite common concerning immersive applications such as Metaverse. In this regard, we propose a ZEro-Trust Attack (ZETA) framework for data reconstruction and model inversion attacks for autonomous vehicles opting for split learning strategies. We propose the joint training of client, server, and shadow models for both the reconstruction and main task to fool existing methods. Our experimental results demonstrate that the proposed method is capable of reconstructing client's data with an error of 0.0032. This study is proposed as a basis to design more sophisticated defense mechanisms for autonomous vehicles to protect user services in 5G/6G networks.

关键词： Training Performance evaluation Sensitivity analysis Computational modeling data models Zero Trust Servers

The role of learners’ competencies in artificial intelligence education

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Computers and Education: Artificial Intelligence 2022年 3卷

作者： Sanusi, Ismaila Temitayo Olaleye, Sunday Adewale Agbo, Friday Joseph Chiu, Thomas K.F. School of Computing University of Eastern Finland P.O.Box 111 Joensuu80101 Finland School of Business JAMK University of Applied Sciences Jyväskylä Finland School of Computing and Data Science Willamette University SalemOR United States School of Computing University of Eastern Finland P.O. Box 111 FIN Joensuu80101 Finland Department of Curriculum and Instruction Faculty of Education The Chinese University of Hong Kong Shatin Hong Kong

As society deals with the advances and disruptions owing to artificial intelligence, children must understand how it works. Especially that children grow up with these technologies will help them develop into informed citizens and better understand the world around them. While artificial intelligence education has been considered relevant, there is a growing global trend to teach artificial intelligence across K-12 levels. This development has necessitated designing and implementing artificial intelligence curriculum and related resources in schools. Notwithstanding that the developed curriculum may be adopted in another context, unique needs exist that suggest contextual and cultural values be considered. Besides, the current curriculum and resources designed to promote artificial intelligence education literacy are eastern and western-centric, which indicates a clear gap in artificial intelligence education in Africa. Therefore, this study examines the competencies required to be artificial intelligence literate, utilizing Nigerian secondary school students’ data considering gender variation and school ownership type. A total of 605 students provided valuable responses for the analysis done with WarpLS software. We performed structural equation modelling to understand the relationship among the adopted variables utilized in the study. This study reveals the importance of teamwork and the significance of human-tool collaboration in artificial intelligence literacy through course content. This finding emphasizes the significance of teamwork among students to keep up with the pace of emerging technologies. The multigroup analysis also reveals no significant differences across gender and school type. We conclude the study with the implication of the findings and proposed future research agenda. © 2022 The Authors

关键词： Artificial intelligence