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International Conference on Intelligent Transportation

作者： Kamal Acharya Alvaro Velasquez Yongxin Liu Dahai Liu Liang Sun Houbing Herbert Song Department of Information Systems Security and Optimization for Networked Globe Laboratory (SONG Lab) University of Maryland Baltimore County Baltimore MD USA Department of Computer Science University of Colorado Boulder CO USA Mathematics Department and College of Aviation Embry-Riddle Aeronautical University Daytona Beach FL USA Department of Mechanical Engineering Baylor University Waco TX USA

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Weather disaster related emergency operations pose a great challenge to air mobility in both aircraft and airport operations, especially when the impact is gradually approaching. We propose an optimized framework for adjusting airport operational schedules for such pre-disaster scenarios. We first, aggregate operational data from multiple airports and then determine the optimal count of evacuation flights to maximize the impacted airport's outgoing capacity without impeding regular air traffic. We then propose a novel Neural Network (NN) accelerated Genetic Algorithm (GA) for evacu-ation planning. Our experiments show that integration yielded comparable results but with smaller computational overhead. We find that the utilization of a NN enhances the efficiency of a GA, facilitating more rapid convergence even when operating with a reduced population size. This effectiveness persists even when the model is trained on data from airports different from those under test. Data and code available at github 1 1 Github: https://***/lotussavy/ITSC-2024.

关键词： Schedules Atmospheric modeling Computational modeling Disasters Artificial neural networks Airports Intelligent transportation systems Genetic algorithms Convergence Meteorology

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BG-GAN: Generative AI Enable Representing Brain Structure-Function Connections for Alzheimer's Disease

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IEEE Transactions on Consumer Electronics 2025年

作者： Zhou, Tong Ding, Chen Jing, Changhong Liu, Feng Hung, Kevin Pham, Hieu Mahmud, Mufti Lyu, Zhihan Qiao, Sibo Wang, Shuqiang Tsang, Kim-Fung Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology Shenzhen518055 China University of Chinese Academy of Sciences Beijing100864 China Stevens Institute of Technology Department of Systems and Enterprises United States Hong Kong Metropolitan University School of Science and Technology Hong Kong Hong Kong VinUniversity College of Engineering and Computer Science VinUni-Illinois Smart Health Center Viet Nam King Fahd University of Petroleum and Minerals Interdisciplinary Research Center for Biosystems and Machines Department of Information and Computer Science SDAIA-KFUPM Joint Research Center for AI Dhahran Saudi Arabia Uppsala University Department of Game Design Faculty of Arts Sweden Tiangong University School of Software Tianjin300387 China

The relationship between brain structure and function is critical for revealing the pathogenesis of brain disorders, including Alzheimer's disease (AD). However, mapping brain structure to function connections is a very challenging task. In this work, a bidirectional graph generative adversarial network (BG-GAN) is proposed to represent brain structure-function connections. Specifically, by designing a module incorporating inner graph convolution network (InnerGCN), the generators of BG-GAN can employ features of direct and indirect brain regions to learn the mapping function between the structural domain and the functional domain. Besides, a new module named Balancer is designed to counterpoise the optimization between generators and discriminators. By introducing the Balancer into BG-GAN, both the structural generator and functional generator can not only alleviate the issue of mode collapse but also learn complementarity of structural and functional features. Experimental results using Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset show that both generated structure and function connections can improve the identification accuracy of AD. The experimental findings suggest that the relationship between brain structure and function is not a complete one-to-one correspondence. They also suggest that brain structure is the basis of brain function, and the strong structural connections are majorly accompanied by strong functional connections. © 2025 IEEE.

关键词： Generative adversarial networks

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Disentangled Ontology Embedding for Zero-shot Learning

arXiv

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arXiv 2022年

作者： Geng, Yuxia Xu, Yajing Huang, Yufeng Chen, Jiaoyan Chen, Zhuo Xiong, Feiyu Zhang, Wen Pan, Jeff Z. Chen, Huajun College of Computer Science and Technology Zhejiang University Hangzhou China School of Software Technology Zhejiang University Ningbo China Department of Computer Science University of Oxford Oxford United Kingdom Alibaba Group Hangzhou China School of Informatics The University of Edinburgh Edinburgh United Kingdom College of Computer Science and Technology Zhejiang University ZJU-Hangzhou Global Scientific and Technological Innovation Center Alibaba-Zhejiang University Joint Institute of Frontier Technologies China

Knowledge Graph (KG) and its variant of ontology have been widely used for knowledge representation, and have shown to be quite effective in augmenting Zero-shot Learning (ZSL). However, existing ZSL methods that utilize KGs all neglect the intrinsic complexity of inter-class relationships represented in KGs. One typical feature is that a class is often related to other classes in different semantic aspects. In this paper, we focus on ontologies for augmenting ZSL, and propose to learn disentangled ontology embeddings guided by ontology properties to capture and utilize more fine-grained class relationships in different aspects. We also contribute a new ZSL framework named DOZSL, which contains two new ZSL solutions based on generative models and graph propagation models, respectively, for effectively utilizing the disentangled ontology embeddings. Extensive evaluations have been conducted on five benchmarks across zero-shot image classification (ZS-IMGC) and zero-shot KG completion (ZS-KGC). DOZSL often achieves better performance than the state-of-the-art, and its components have been verified by ablation studies and case studies. Our codes and datasets are available at https://***/zjukg/DOZSL. Copyright © 2022, The Authors. All rights reserved.

关键词： Zero-shot learning

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Mapping Tokenomics Arrangements to Expand the Digital Nomad Ecosystem

Mapping Tokenomics Arrangements to Expand the Digital Nomad ...

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International Conference on computer Supported Cooperative Work in Design

作者： Marcos Antonio de Almeida António Correia Jano Moreira de Souza Daniel Schneider Systems Engineering and Computer Science Program (PESC/COPPE/UFRJ) Rio de Janeiro Brazil INESC TEC and University of Trás-os-Montes e Alto Douro Vila Real Portugal College of Information Science & Technology University of Nebraska at Omaha Omaha NE USA Postgraduate Program in Informatics PPGI/UFRJ Rio de Janeiro Brazil Tércio Pacitti Institute of Computer Applications and Research NCE/UFRJ Rio de Janeiro Brazil

The Massification of remote work, in response to the COVID-19 pandemic, has been causing significant changes in productive and working arrangements, both for individuals, organizations, and society. At the level of personal life philosophy, for example, this transformation can be evidenced in the dissemination of digital nomadism values such as work/leisure balance among corporate workers. On the other hand, at the level of gig/crowd work platforms, the emergence of tensions may indicate the exhaustion of sociotechnical design models adopted by big techs. We show how evidence collected from digital nomads in empirical ethnography studies can inform HCI/CSCWD researchers of design-oriented strands to explore emerging opportunities in new creative digital crypto-economic ecosystems. Finally, we present a proposed research agenda to explore DNs’ activities in the crypto-economic ecosystem.

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Advanced Discrete Tracking Control for Nonlinear systems: Development and Application

Advanced Discrete Tracking Control for Nonlinear Systems: De...

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European Control Conference (ECC)

作者： Samia Charfediine Sondoss Ben Aoun Faiçal Hamidi Catalin Dimon Research Unit of Photovoltaic Wind and Geothermal Systems University of Gabès Tunisia Department of Computer Engineering College of Computer Science and Engineering University of Ha’il Hail Saudi Laboratory “Modélisation Analyse et Commande des Systèmes” University of Gabes Gabes Tunisia Faculty of Automatics and Computers University Politehnica of Bucharest Bucharest Romania

An innovative fuzzy discrete control technique is presented in this paper. An analytical discrete tracking control strategy offering high dynamic efficiency is developed using the gain scheduling methodology combined with the feedback linearization theory. For the stability analysis study, an enlarged basin of attraction around operating points is estimated using a non-Lyapunov method. To ensure a high trajectory tracking accuracy with specified dynamics, a numerical approach is implemented with the use of fuzzy logic reasoning. While optimizing the running time of the designed algorithm, the control strategy is shown to be easy to implement. As a result, the trajectory tracking objective is achieved with satisfactory dynamic characteristics. Through numerical simulation analysis, the efficiency of the designed strategy is established.

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A Key Transport Protocol for Advance Metering Infrastructure (AMI) Based on Public Key Cryptography 1

A Key Transport Protocol for Advance Metering Infrastructure...

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1st Annual International Conference on Cyber Warfare and Security, ICCWS 2020

作者： Naseer, Hira Mumtaz Bhutta, Muhammad Nasir Alojail, Mohammed Ali Institute of Southern Punjab Department of Computer Science Multan Pakistan College of Computer Science and Information Technology King Faisal University Dept.Information Systems Al Ahsa Saudi Arabia

ISBN: (纸本)9781728168401

Advance metering infrastructure (AMI) is an important element of the Smart Grid as it is expectable to simplify the transport of meter reading and control information from meters to the utility provider. Smart meters are generally located in insecure area of house and can potentially be tampered which can lead towards exposure of consumer usage. Security is an important factor to achieve secure communication and providing protection from malicious attacks in these Advanced Metering Infrastructure (AMI) based smart grids in unfavorable situations. The management of keys especially key distribution in AMI is an open challenge. The proposed protocol consists of four phases: Initialization, Registration, Mutual authentication and Key exchange to establish a session key by the Head-End System (HES) and the Smart Meter (SM) after authenticating each other. The paper also contains discussion on the security of the proposed protocol and simulation evaluation for selected communication parameters. © 2020 IEEE.

关键词： Smart power grids

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3D Beamforming Based on Deep Learning for Secure Communication in 5G and beyond Wireless Networks

3D Beamforming Based on Deep Learning for Secure Communicati...

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2021 IEEE Globecom Workshops, GC Wkshps 2021

作者： Yang, Helin Lam, Kwok-Yan Nie, Jiangtian Zhao, Jun Garg, Sahil Xiao, Liang Xiong, Zehui Guizani, Mohsen Nanyang Technological University Strategic Centre for Research in Privacy-Preserving Technologies and Systems Singapore Nanyang Technological University School of Computer Science and Engineering Singapore Ecole de Technologie Superieure Universite du Quebec Electrical Engineering Department Montreal Canada Xiamen University School of Informatics Xiamen China Singapore University of Technology and Design Pillar of Information System Technology and Design Singapore Qatar University Department of Computer Science and Engineering Doha Qatar

ISBN: (纸本)9781665423908

Three-dimensional (3D) beamforming is a potential technique to enhance communication security of new generation networks such as 5G and beyond. However, it is difficult to achieve optimal beamforming due to the challenges of nonconvex optimization problem and imperfect channel state information (CSI). To tackle this problem, this paper proposes a novel deep learning-based 3D beamforming scheme, where a deep neural network (DNN) is trained to optimize the beamforming design for wireless signals in order to guard against eavesdropper under the imperfect CSI. With our approach, the system is capable of training the DNN model offline, and the trained model can then be adopted to instantaneously select the 3D secure beamforming matrix for achieving the maximum secrecy rate of the system, which is measured by the signal received by eavesdroppers outside the path of the beam. Simulation results demonstrate that the proposed solution outperforms the classical deep learning algorithm and 2D beamforming solution in terms of the secrecy rate and robust performance. © 2021 IEEE.

关键词： Beamforming

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Bounding the Escape Time of a Linear Dynamical System over a Compact Semialgebraic Set

arXiv

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arXiv 2022年

作者： D'Costa, Julian Lefaucheux, Engel Neumann, Eike Ouaknine, Joël Worrell, James Department of Computer Science University of Oxford United Kingdom Max Planck Institute for Software Systems Saarland Informatics Campus Germany Université de Lorraine Inria Loria Nancy France Swansea University Swansea United Kingdom Keble College Oxford United Kingdom

We study the Escape Problem for discrete-time linear dynamical systems over compact semialgebraic sets. We establish a uniform upper bound on the number of iterations it takes for every orbit of a rational matrix to escape a compact semialgebraic set defined over rational data. Our bound is doubly exponential in the ambient dimension, singly exponential in the degrees of the polynomials used to define the semialgebraic set, and singly exponential in the bitsize of the coefficients of these polynomials and the bitsize of the matrix entries. We show that our bound is tight by providing a matching lower bound. Copyright © 2022, The Authors. All rights reserved.

关键词： Matrix algebra

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GSLB: The Graph Structure Learning Benchmark

arXiv

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arXiv 2023年

作者： Li, Zhixun Wang, Liang Sun, Xin Luo, Yifan Zhu, Yanqiao Chen, Dingshuo Luo, Yingtao Zhou, Xiangxin Liu, Qiang Wu, Shu Yu, Jeffrey Xu Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong Center for Research on Intelligent Perception Computing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China Department of Automation University of Science and Technology of China China School of Cyberspace Security Beijing University of Posts and Telecommunications China Department of Computer Science University of California Los Angeles United States Heinz College of Information Systems and Public Policy Machine Learning Department School of Computer Science Carnegie Mellon University United States

Graph Structure Learning (GSL) has recently garnered considerable attention due to its ability to optimize both the parameters of Graph Neural Networks (GNNs) and the computation graph structure simultaneously. Despite the proliferation of GSL methods developed in recent years, there is no standard experimental setting or fair comparison for performance evaluation, which creates a great obstacle to understanding the progress in this field. To fill this gap, we systematically analyze the performance of GSL in different scenarios and develop a comprehensive Graph Structure Learning Benchmark (GSLB) curated from 20 diverse graph datasets and 16 distinct GSL algorithms. Specifically, GSLB systematically investigates the characteristics of GSL in terms of three dimensions: effectiveness, robustness, and complexity. We comprehensively evaluate state-of-the-art GSL algorithms in node- and graph-level tasks, and analyze their performance in robust learning and model complexity. Further, to facilitate reproducible research, we have developed an easy-to-use library for training, evaluating, and visualizing different GSL methods. Empirical results of our extensive experiments demonstrate the ability of GSL and reveal its potential benefits on various downstream tasks, offering insights and opportunities for future research. The code of GSLB is available at: https://***/GSL-Benchmark/GSLB. © 2023, CC BY.

关键词： Benchmarking

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Streamlining River Flood Prevention with an Integrated AIoT Framework

Streamlining River Flood Prevention with an Integrated AIoT ...

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ACS/IEEE International Conference on computer systems and Applications

作者： Zakaria Boulouard Mariya Ouaissa Mariyam Ouaissa Moez Krichen Mutiq Almutiq Mohammad Algarni LIM Hassan II University Casablanca Morocco Computer Systems Engineering Laboratory Cadi Ayyad University Marrakech Morocco Laboratory of Information Technologies Chouaib Doukkali University El Jadida Morocco Department of Information Technology Al-Baha University Al-Baha Saudi Arabia Department of Management Information Systems and Production Management College of Business and Economics Qassim University Buraidah Saudi Arabia Computer Science Department Al-Baha University Al-Baha Saudi Arabia

River floods stand among the natural disasters with far-reaching impacts, affecting human lives, the economy, infrastructure, agriculture, and more. Substantial investments by organizations are directed toward innovative strategies for flood prevention. The concept of Artificial Intelligence of Things (AIoT), a fusion of Artificial Intelligence and Internet of Things technologies, has showcased its prowess across various domains. In this paper, we introduce an AIoT framework where river flood sensors, located in every region, transmit their data using LoRaWAN technology to local broadcast centers in proximity. These broadcast centers subsequently forward the data via 4G/5G networks to a centralized cloud server. The server employs efficient AI algorithms to analyze the data and predict river conditions nationwide, contributing to proactive flood prevention. This approach has demonstrated effectiveness on multiple fronts. LoRaWAN-based communication between sensor nodes and broadcast centers offers reduced energy consumption and expanded coverage, while AI-driven data analysis enhances the accuracy of river flood predictions.

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