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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Coletta, Andrea Giorgi, Flavio Maselli, Gaia Prata, Matteo Silvestri, Domenicomichele Ashdown, Jonathan Restuccia, Francesco J.P. Morgan Ai Research New York United States Sapienza University of Rome Department of Computer Science Italy Air Force Research Laboratory United States Northeastern University Institute for the Wireless Internet of Things United States

ISBN: (纸本)9798350334142

To perform advanced surveillance, Unmanned Aerial Vehicles (UAVs) require the execution of edge-assisted computer vision (CV) tasks. In multi-hop UAV networks, the successful transmission of these tasks to the edge is severely challenged due to severe bandwidth constraints. For this reason, we propose a novel A2-UAV framework to optimize the number of correctly executed tasks at the edge. In stark contrast with existing art, we take an application-aware approach and formulate a novel Application-Aware Task Planning Problem (A2-TPP) that takes into account (i) the relationship between deep neural network (DNN) accuracy and image compression for the classes of interest based on the available dataset, (ii) the target positions, (iii) the current energy/position of the UAVs to optimize routing, data pre-processing and target assignment for each UAV. We demonstrate A2-TPP is NP-Hard and propose a polynomial-time algorithm to solve it efficiently. We extensively evaluate A2-UAV through real-world experiments with a testbed composed by four DJI Mavic Air 2 UAVs. We consider state-of-the-art image classification tasks with four different DNN models (i.e., DenseNet, ResNet152, ResNet50 and MobileNet-V2) and object detection tasks using YoloV4 trained on the ImageNet dataset. Results show that A2-UAV attains on average around 38% more accomplished tasks than the state of the art, with 400% more accomplished tasks when the number of targets increase significantly. To allow full reproducibility, we pledge to share datasets and code with the research community. © 2023 IEEE.

关键词： Deep neural networks

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Intrusion Detection Systems for Industrial Internet of Things: A Survey

Intrusion Detection Systems for Industrial Internet of Thing...

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2021 International Conference on Theoretical and Applicative Aspects of computer science, ICTAACS 2021

作者： Hamouda, Djallel Ferrag, Mohamed Amine Benhamida, Nadjette Seridi, Hamid Labstic Laboratory Department of Computer Science Guelma University B.P. 401 24000 Algeria

ISBN: (纸本)9781665496551

Industrial Internet of Things (IIoT) applies Internet of Things (IoT) technology in industrial systems, to optimize business processes efficiency, service quality, and reliability. However, with a large of isolated IoT networks deployed in various industries, many vulnerabilities have been exposed to security incidents and posed threats to IIoT security. An intrusion detection system (IDS) is a security monitoring mechanism that promotes cyber security solutions for information systems. The system's role is to detect abnormal activities of intruders and enable preventive measures to avoid risks. However, applying a traditional IDS-based solution to IIoT is challenging due to its particular characteristics such as resource-constrained, data privacy, and heterogeneity. Researchers are using the new emerging technologies such as Fog/Edge computing, Machine Learning (ML), Deep Learning (DL) to deploy an effective and adaptive IDS for various IIoT operating environments. This study focus is on the development of IDS in particular industrial environments. To this end, we provide a systemic review that addresses IDS deployment strategies, detection approaches, and methodologies and data sources used for evaluation. We also present some suggestions and challenges to be considered when designing IDS-based security for Industrial IoT as future research. © 2021 IEEE.

关键词： Internet of things

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P-13.6: Characterization and Performance Evaluation of Photo-Aligned Azo-Dye Thin-Film Polarizers

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SID Symposium Digest of Technical Papers 2024年第S1期55卷 1383-1386页

作者： Yue-Chu Cheng Man-Chun Tseng Yeuk-Lung Ho Olena Vashchenko Valerii Vashchenko Shu-Tuen Tang Sze-Yan Yeung Hoi-Sing Kwok State Key Laboratory of Advanced Displays and Optoelectronics Technologies Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong S.A.R China

This study presents the characterization and performance evaluation of photo-aligned azo-dye thin-film polarizers, highlighting their ultra-thin profiles and superior optical functionalities. Utilizing cost-effective solution-processing methods, these high-efficiency polarizers were developed, with the research pinpointing optimal experimental conditions for peak performance.

关键词： Thin-Film Polarizer Photoalignment Display

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A Conversational Human-computer Interface for Smart Energy System Simulation Environments

A Conversational Human-Computer Interface for Smart Energy S...

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2023 Winter Simulation Conference, WSC 2023

作者： Dengler, Gabriel Bazan, Peter German, Reinhard Lalbakhsh, Pooia Liebmann, Ariel Friedrich-Alexander-Universität Erlangen-Nürnberg Laboratory of Computer Networks and Communication Systems Martensstr. 3 BY Erlangen91058 Germany Monash University Faculty of Information Technology Department of Data Science and Ai ClaytonVIC3168 Australia

ISBN: (纸本)9798350369663

This paper introduces a conversational framework that enhances the usability of smart energy system simulations. This study is centered around OpenAI's Generative Pre-trained Transformer (GPT), a fine-tuned conversational model that allows users to communicate with the system in a natural way. Therefore, users can describe their simulation scenarios in plain language and GPT seamlessly translates these descriptions into Python scripts, used as inputs to the simulation environment, in our case, AnyLogic Simulation Software. Our framework is based on the i7-AnyEnergy core framework to compute distribution flows and relevant statistics. The proposed human-machine interface facilitates and accelerates simulation modeling, as demonstrated through the two scenarios we have provided in this paper. Overall, our conversational framework has the potential to significantly improve the user experience of smart energy system simulation environments. By simplifying the interaction between users and complex simulation models, we enable users to obtain valuable insights rapidly and more easily. © 2023 IEEE.

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Ensuring Minority Group Rights in Social IoT with Fairness-aware Federated Graph Node Classification 21

Ensuring Minority Group Rights in Social IoT with Fairness-a...

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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on Big Data and Cloud Computing, 16th IEEE International Conference on Social Computing and Networking and 13th International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Mao, Qinghua Lin, Xi Li, Gaolei Chen, Lixing Liu, Yuchen Qi, Yuxin Li, Jianhua Shanghai Jiao Tong University School of Electronic Information and Electrical Engineering Shanghai China Shanghai Key Laboratory of Integrated Administration Technologies for Information Security Shanghai China North Carolina State University Department of Computer Science NC United States

ISBN: (纸本)9798350329223

Graph neural networks (GNNs) are a powerful tool to predict the categories of IoT nodes for providing diverse high-quality services in the social Internet of Things (SIoT). Intuitively, SIoT could be divided into several communities based on similar interests and mutual trust, whereas interaction between cross-community nodes may intrigue data privacy. With the popularity of federated learning (FL), a global GNN model can be collaboratively trained by exploiting knowledge from multiple parties in a privacy-preserving way. However, since node attributes across different SIoT communities are usually multi-source and heterogeneous, the performance of an optimum global model may exhibit potential bias against minority social groups, i.e, unfairness, thereby undermining the efficiency of node classification and social behavior analysis. To address this problem, we propose PFedNC, a proportional fairness-aware federated learning framework for node classification in SIoT system. Specifically, we incorporate proportional fairness and related Nash bargaining solution into FL optimization objective, which imposes more fairness while maintaining a similar average performance. Moreover, we design a class-balanced re-weighting loss function to mitigate imbalanced class distribution caused by non-IID node distribution. Extensive experiments on two node-level SIoT simulation datasets demonstrate that our PFedNC framework effectively encourages performance fairness among social groups and achieves a favorable tradeoff between fairness and efficiency. © 2023 IEEE.

关键词： Internet of things

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Fight Fire with Fire: How Much Can We Trust ChatGPT on Source Code-Related Tasks?

arXiv

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arXiv 2024年

作者： Yu, Xiao Liu, Lei Hu, Xing Keung, Jacky Wai Liu, Jin Xia, Xin State Key Laboratory of Blockchain and Data Security Zhejiang University Zhejiang Hangzhou China Faculty of Electronic and Information Engineering Xi’an Jiaotong University Shanxi Xi’an China Department of Computer Science City University of Hong Kong Hong Kong School of Computer Science Wuhan University Wuhan China College of Computer Science and Technology Zhejiang University Hangzhou China

With the increasing utilization of large language models such as ChatGPT during software development, it has become crucial to verify the quality of code content it generates. Recent studies proposed utilizing ChatGPT as both a developer and tester for multi-agent collaborative software development. The multi-agent collaboration empowers ChatGPT to produce test reports for its generated code, enabling it to self-verify the code content and fix bugs based on these reports. However, these studies did not assess the effectiveness of the generated test reports in validating the code. Therefore, we conduct a comprehensive empirical investigation to evaluate ChatGPT’s self-verification capability in code generation, code completion, and program repair. We request ChatGPT to (1) generate correct code and then self-verify its correctness;(2) complete code without vulnerabilities and then self-verify for the presence of vulnerabilities;and (3) repair buggy code and then self-verify whether the bugs are resolved. Our findings on two code generation datasets, one code completion dataset, and two program repair datasets reveal the following observations: (1) ChatGPT often erroneously predicts its generated incorrect code as correct, its vulnerable completed code as non-vulnerable, and its failed program repairs as successful during its self-verification. (2) The self-contradictory hallucinations in ChatGPT’s behavior arise: (a) ChatGPT initially generates code that it believes to be correct but later predicts it to be incorrect;(b) ChatGPT initially generates code completions that it deems secure but later predicts them to be vulnerable;(c) ChatGPT initially outputs code that it considers successfully repaired but later predicts it to be buggy during its self-verification. (3) The self-verification capability of ChatGPT can be enhanced by asking the guiding question, which queries whether ChatGPT agrees with assertions about incorrectly generated or repaired code and vulnerabil

关键词： Software design

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Anesthetic drug discovery with computer-aided drug design and machine learning

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Anesthesiology and Perioperative science 2024年第1期2卷 1-25页

作者： Liu, Xianggen Xue, Zhe Luo, Mingmin Ke, Bowen Lv, Jiancheng College of Computer Science Sichuan University Chengdu China Laboratory of Anesthesia and Critical Care Medicine Department of Anesthesiology Translational Neuroscience Center West China Hospital Sichuan University Chengdu China

computer-aided drug design (CADD) has emerged as a highly effective and indispensable tool for streamlining the drug discovery process, leading to significant reductions in cost and time. The integration of CADD with machine learning (ML) and deep learning (DL) technologies further enhances its potential and promises novel advancements in the field. In this article, we provide a review of the computational methods employed in the development of novel anesthetics, outlining their respective advantages and limitations. These techniques have demonstrated their utility across various stages of drug discovery, encompassing the exploration of target-ligand interactions, identification and validation of new binding sites, de novo drug design, evaluation and optimization of absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties in lead compounds, as well as prediction of adverse effects. Through an in-depth exploration of computational approaches and their applications, this article aims to help relevant researchers develop safer and more effective anesthetic drugs.

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Satellite-UAV Networks for 6G Control: A Sensing-Communication-Computing-Control Closed Loop Perspective

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IEEE Network 2025年

作者： Lei, Chengleyang Fang, Xinran Feng, Wei Chen, Yunfei Xiao, Ming Ge, Ning Mao, Shiwen The Department of Electronic Engineering State Key Laboratory of Space Network and Communications Tsinghua University Beijing100084 China The Department of Engineering University of Durham DurhamDH1 3LE United Kingdom The Department of Information Science and Engineering School of Electrical Engineering and Computer Science Royal Institute of Technology Sweden The Department of Electrical and Computer Engineering Auburn University AuburnAL36849 United States

The future sixth-generation (6G) networks are envisioned to support machines and robots besides human beings. In order to efficiently support machines and robots working in remote and post-disaster areas, satellites and unmanned aerial vehicles (UAVs) can be utilized. In this article, we investigate how satellite-UAV networks can serve robots for their control tasks. We introduce the typical use cases of the satellite-UAV networks for control and outline the relevant challenges. The control process usually involves sensing, communication, computing, and control components, which forms a sensing-communication-computing-control (SC3) closed loop. In analogy with the reflex arc structure of humans, we reveal the indivisibility of the SC3 closed loops and propose a closed-loop-oriented system design framework. Within this framework, we take the closed-loop control performance as the design target, and highlight the holistic consideration of sensing, communication, computing, and control components in the SC3 closed loop. Furthermore, we discuss the extended SC3 closed loop with multiple sensors and robots to decompose complex control systems. A case study is provided to show the performance gain of the proposed closed-loop-oriented system design. Finally, we outline open issues in designing more efficient satellite-UAV networks for control. ©1986-2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Rapid,artifact-reduced,image reconstruction for super-resolution structured illumination microscopy

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The Innovation 2023年第3期4卷 63-69页

作者： Zhaojun Wang Tianyu Zhao Yanan Cai Jingxiang Zhang Huiwen Hao Yansheng Liang Shaowei Wang Yujie Sun Tongsheng Chen Piero R.Bianco Kwangsung Oh Ming Lei MOE Key Laboratory for Nonequlbrum Synthesis and Modulation of Condensed Matter Scholl of PhysicsXian Jiaotong UniversityXran 710049China Collge of Science Northwest A&F UniversityYangling 712100China a State Key Laboratory of Membrane Biology&Biomedical Plioneer Innovatin Center(BIOPIC)&School of Life Sciences Peking UniversityBeijing 100871China MOE Key Laboratory of Laser Lfe Science&Guangdong Provincial Key Laboratary of Laser Lfe Science Callege of BiophotonicsSouth ChinaNormal UniversityGuangzhou510631China Department of Pharmaceutical Sciences College of PharmacyUniversity of Nebraska Medical Center0mahaNE 68198-6025USA Department af Computer Science Callege af Infomation Science&TechnologyUniversity of Nebraska 0mahaOmahaNE 68182USA

Super-resolution structured ilumination microscopy(SR-SIM)is finding increasing application in biomedical research due to its superior ability to visualize subcellular dynamics in living ***,during image reconstruction artifacts can be introduced and when coupled with time-consuming postprocessing procedures,limits this technique from becoming a routine imaging tool for *** address these issues,an accelerated,artifact-reduced reconstruction algorithm termed joint space frequency reconstruction-based artifact reduction algorithm(JSFR-AR-SIM)was developed by integrating a high-speed reconstruc tion framework with a high-fidelity optimization approach designed to suppress the sidelobe ***,JSFR-AR-SIM produces high-quality,super-resolution images with minimal artifacts,and the reconstruction speed is *** anticipate this algorithm to facilitate SR-SIM becoming a routine tool in biomedical laboratories.

关键词： image resolution routine

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Elucidation of the neutralizing antibody evasion of emergent SARS-CoV-2 Omicron sub-lineages using structural analysis

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science China(Life sciences) 2023年第12期66卷 2935-2938页

作者： Cong Sun Yuan-Tao Liu Yin-Feng Kang Chu Xie Shu-Xin Li Yu-Tong Lu Mu-Sheng Zeng State Key Laboratory of Oncology in South China Collaborative Innovation Center for Cancer MedicineGuangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and TherapyDepartment of Experimental ResearchSun Yat-sen University Cancer CenterSun Yat-sen UniversityGuangzhou510060China National Supercomputer Center in Guangzhou School of Computer Science and EngineeringSun Yat-sen UniversityGuangzhou 510006China

Dear Editor,The continued evolution of the SARS-CoV-2 Omicron variant has contributed to the emergence of various novel sublineages that can evade the neutralizing activities of antibodies and vaccine sera,including B... 详细信息

关键词： neutral has continued

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