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The Journal of Machine Learning Research 2023年第1期24卷 8447-8521页

作者： Wenhao Li Bo Jin Xiangfeng Wang Junchi Yan Hongyuan Zha School of Data Science The Chinese University of Hong Kong Shenzhen Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China School of Software Engineering Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China School of Computer Science and Technology Key Laboratory of Mathematics and Engineering Applications Ministry of Education East China Normal University Shanghai China Department of Computer Science and Engineering Key Laboratory of Artificial Intelligence Ministry of Education Shanghai Jiao Tong University Shanghai China

Traditional centralized multi-agent reinforcement learning (MARL) algorithms are sometimes unpractical in complicated applications due to non-interactivity between agents, the curse of dimensionality, and computation complexity. Hence, several decentralized MARL algorithms are motivated. However, existing decentralized methods only handle the fully cooperative setting where massive information needs to be transmitted in training. The block coordinate gradient descent scheme they used for successive independent actor and critic steps can simplify the calculation, but it causes serious bias. This paper proposes a exible fully decentralized actor-critic MARL framework, which can combine most of the actor-critic methods and handle large-scale general cooperative multi-agent settings. A primal-dual hybrid gradient descent type algorithm framework is designed to learn individual agents separately for decentralization. From the perspective of each agent, policy improvement and value evaluation are jointly optimized, which can stabilize multi-agent policy learning. Furthermore, the proposed framework can achieve scalability and stability for the large-scale environment. This framework also reduces information transmission by the parameter sharing mechanism and novel modeling-other-agents methods based on theory-of-mind and online supervised learning. Sufficient experiments in cooperative Multi-agent Particle Environment and StarCraft II show that the proposed decentralized MARL instantiation algorithms perform competitively against conventional centralized and decentralized methods.

关键词： cooperative MARL decentralized actor-critic primal-dual method

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Towards Reliable Participation in UAV-Enabled Federated Edge Learning on Non-IID Data

arXiv

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arXiv 2023年

作者： Cheriguene, Youssra Jaafar, Wael Yanikomeroglu, Halim Kerrache, Chaker Abdelaziz Laboratoire d'Informatique et de Mathématiques Université Amar Telidji de Laghouat Laghouat Algeria Department of Software and Information Technology Engineering École de Technologie Supérieure MontrealQC Canada Lab Department of Systems and Computer Engineering Carleton University OttawaON Canada

Federated Learning (FL) is a decentralized machine learning (ML) technique that allows a number of participants to train an ML model collaboratively without having to share their private local datasets with others. When participants are unmanned aerial vehicles (UAVs), UAV-enabled FL would experience heterogeneity due to the majorly skewed (non-independent and identically distributed -IID) collected data. In addition, UAVs may demonstrate unintentional misbehavior in which the latter may fail to send updates to the FL server due, for instance, to UAVs' disconnectivity from the FL system caused by high mobility, unavailability, or battery depletion. Such challenges may significantly affect the convergence of the FL model. A recent way to tackle these challenges is client selection, based on customized criteria that consider UAV computing power and energy consumption. However, most existing client selection schemes neglected the participants' reliability. Indeed, FL can be targeted by poisoning attacks, in which malicious UAVs upload poisonous local models to the FL server, by either providing targeted false predictions for specifically chosen inputs or by compromising the global model's accuracy through tampering with the local model. Hence, we propose in this paper a novel client selection scheme that enhances convergence by prioritizing fast UAVs with high-reliability scores, while eliminating malicious UAVs from training. Through experiments, we assess the effectiveness of our scheme in resisting different attack scenarios, in terms of convergence and achieved model accuracy. Finally, we demonstrate the performance superiority of the proposed approach compared to baseline methods. © 2023, CC BY.

关键词： Edge computing

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Federated Quality Profiling: A quality evaluation of patient monitoring at the Edge

Federated Quality Profiling: A quality evaluation of patient...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Alramzana Nujum Navaz Mohamed Adel Serhani Hadeel T. El Kassabi Department of Information Systems and Security College of Information Technology United Arab Emirates University Al Ain United Arab Emirates University Department of Computer Science and Software Engineering Gina Cody School of Engineering and Computer Science Concordia University Montreal Canada

Continuous monitoring of patients involves collecting and analyzing sensory data from a multitude of sources. To overcome communication overhead, ensure data privacy and security, reduce data loss, and maintain efficient resource usage the processing and analytics are moved close to where the data is located (e.g., the Edge). Data quality (DQ) can be degraded because of imprecise or malfunctioning sensors, dynamic changes in the environment, transmission failures, or delays. Therefore, can mislead clinical judgments and cause incorrect actions, if not managed properly. In this paper, inspired by Federated Learning (FL), we propose a novel approach using Federated Data Quality (FDQ) Profiling to assess DQ at the edge considering a global quality profile aggregated based on local profiles. We conducted experiments to evaluate the effect of FDQ profiling on DQ improvement considering the assessment of outlier detection and unbalanced data. The results demonstrated that the improved DQ has a positive impact on the accuracy of four conventional machine learning models.

关键词： Wireless communication Patient monitoring Data privacy Image edge detection Data integrity Machine learning Sensors

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Are automated static analysis tools worth it? An investigation into relative warning density and external software quality

arXiv

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arXiv 2021年

作者： Trautsch, Alexander Herbold, Steffen Grabowski, Jens Institute of Computer Science University of Goettingen Germany Institute of Software and Systems Engineering TU Clausthal Germany

Automated Static Analysis Tools (ASATs) are part of software development best practices. ASATs are able to warn developers about potential problems in the code. On the one hand, ASATs are based on best practices so there should be a noticeable effect on software quality. On the other hand, ASATs suffer from false positive warnings, which developers have to inspect and then ignore or mark as invalid. In this article, we ask the question if ASATs have a measurable impact on external software quality, using the example of PMD for Java. We investigate the relationship between ASAT warnings emitted by PMD on defects per change and per file. Our case study includes data for the history of each file as well as the differences between changed files and the project in which they are contained. We investigate whether files that induce a defect have more static analysis warnings than the rest of the project. Moreover, we investigate the impact of two different sets of ASAT rules. We find that, bug inducing files contain less static analysis warnings than other files of the project at that point in time. However, this can be explained by the overall decreasing warning density. When compared with all other changes, we find a statistically significant difference in one metric for all rules and two metrics for a subset of rules. However, the effect size is negligible in all cases, showing that the actual difference in warning density between bug inducing changes and other changes is small at best. Copyright © 2021, The Authors. All rights reserved.

关键词： Static analysis

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Scope-bounded reachability in valence systems

arXiv

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arXiv 2021年

作者： Shetty, Aneesh K. Krishna, S. Zetzsche, Georg Department of Computer Science & Engineering IIT Bombay Max Planck Institute for Software Systems Kaiserslautern Germany

Multi-pushdown systems are a standard model for concurrent recursive programs, but they have an undecidable reachability problem. Therefore, there have been several proposals to underapproximate their sets of runs so that reachability in this underapproximation becomes decidable. One such underapproximation that covers a relatively high portion of runs is scope boundedness. In such a run, after each push to stack i, the corresponding pop operation must come within a bounded number of visits to stack i. In this work, we generalize this approach to a large class of infinite-state systems. For this, we consider the model of valence systems, which consist of a finite-state control and an infinite-state storage mechanism that is specified by a finite undirected graph. This framework captures pushdowns, vector addition systems, integer vector addition systems, and combinations thereof. For this framework, we propose a notion of scope boundedness that coincides with the classical notion when the storage mechanism happens to be a multi-pushdown. We show that with this notion, reachability can be decided in PSPACE for every storage mechanism in the framework. Moreover, we describe the full complexity landscape of this problem across all storage mechanisms, both in the case of (i) the scope bound being given as input and (ii) for fixed scope bounds. Finally, we provide an almost complete description of the complexity landscape if even a description of the storage mechanism is part of the input. © 2021, CC BY.

关键词： Vectors

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Leveraging AI and NLP in Chatbot Development: An Experimental Study

Leveraging AI and NLP in Chatbot Development: An Experimenta...

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Frontiers of Information Technology (FIT)

作者： Abdul Wahab Paracha Usama Arshad Raja Hashim Ali Zain Ul Abideen Muhammad Huzaifa Shah Talha Ali Khan Ali Zeeshan Ijaz Nisar Ali Abu Bakar Siddique Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Leioa Basque Country Spain Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany Faculty of Electronic Systems Engineering University of Regina Regina Canada

In the current era of chatbots, this research delves into the advancements in AI chatbots, drawing on artificial intelligence (AI) and natural language processing (NLP) techniques to mimic human-like conversations. A particular focus is given to the potential of chatbots in facilitating multitasking dialogues, offering emotional support, and addressing complex subject matter, all the while respecting user privacy and trust. The implemented chatbot model is trained on a neural network, using Keras and TensorFlow libraries. This model’s performance indicates a considerable dependence on the dataset’s size, with larger datasets leading to better outcomes by providing more extensive language usage and context examples. Additionally, we also analyze the effect of varying architectures and hyperparameters on chatbot performance. The significance of localizing chatbots to adapt to different languages and cultures is also highlighted. While promising, the study identifies areas of improvement, suggesting future research directions in enhancing language capture techniques, expanding training datasets, and integrating emotional intelligence within chatbot systems.

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A New Atmospheric Dataset for GIIRS Sampled from ERA5 Using Shannon Entropy Method

A New Atmospheric Dataset for GIIRS Sampled from ERA5 Using ...

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2020 International Conference on Innovative Solutions in Hydropower and Environmental and Civil engineering

作者： Zhang, Qi Yu, Yi Zhang, Weimin College of Meteorology and Oceanography National University of Defense Technology Changsha410073 China College of Computer Science National University of Defense Technology Changsha410073 China Key Laboratory of Software Engineering for Complex Systems Changsha410073 China

Geostationary Interferometric Infrared Sounder (GIIRS) on board the new Chinese geostationary meteorological satellite Fengyun-4A is aimed to obtain three-dimensional atmospheric information regularly at the regional scale. Since the existing atmospheric datasets are global covered and they are not representative of the GIIRS detection area, a diverse and local representative atmospheric dataset is required for GIIRS in training transmittance parameterization scheme of fast radiative transfer model and retrieving atmospheric profiles. The new atmospheric dataset for GIIRS application in this paper is sampled from the European Centre for Medium-Range Weather Forecasts (ECMWF) fifth-generation reanalysis (ERA5) 2019 dataset using Shannon entropy sampling method. The new dataset demonstrates the feature of uniform distribution within multiple atmospheric variables' (i.e. atmospheric temperature, specific humidity, 2 meter temperature) variation ranges and GIIRS's spatial coverage. To address the issue derived from the climate difference and the varied vertical pressure structure of regions with great differences in topographic height, the study area is divided into six categories regarding of topographic height categories where atmospheric dataset are sampled separately. Seasonal variation is also taken into account in the process of sampling. Compared with the initial randomly sampled dataset, the atmospheric and 2 meter temperature in the new dataset demonstrate a higher level of uniformity within their variation ranges. The statistical results indicate that the differences between samples in the new dataset are enlarged, meanwhile, the new dataset well retains the statistical characteristics of the original ERA5 dataset. The new dataset has a potential in the study of the utility of GIIRS data in the numerical weather forecast. © Published under licence by IOP Publishing Ltd.

关键词： Weather forecasting

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VS-Net: A Voxel Encoding and Sparse Convolution Embedded Network for LiDAR 3D Object Detection

VS-Net: A Voxel Encoding and Sparse Convolution Embedded Net...

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2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： Liu, Meng Niu, Jianwei Liu, Yu The China North Artificial Intelligence and Innovation Research Institute Beijing100072 China The China North Vehicle Research Institute Beijing100072 China The State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing100191 China The State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China The Hangzhou Innovation Institute Beihang University Hangzhou310051 China The Zhengzhou University Research Institute of Industrial Technology School of Information Engineering Zhengzhou University Zhengzhou450001 China

ISBN: (纸本)9781665481090

Accurate and efficient 3D object detection in LiDAR point cloud is critical for autonomous driving. In this paper, we provide a solution for LiDAR-only detection which employs voxel features and a sparse convolution network. Specifically, we utilized voxel centers to encode point clouds' voxel features. Then, combining the sparse convolution method, we leveraged residual learning to design the backbone network. The detector output predicted results for multi-category and multi-object detection. Our method was evaluated on three popular and challenging datasets (KITTI, nuScence and Waymo). Experimental results demonstrated that the accuracy and speed (27.8 FPS) of our model could effectively support object detection for autonomous driving in various scenarios. © 2022 IEEE.

关键词： Object detection

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Transformer-based Compound Correlation Miner for Smart Home Anomaly Detection

Transformer-based Compound Correlation Miner for Smart Home ...

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IEEE International Conference on Cloud Networking (CLOUDNET)

作者： Andrew D’Angelo Chenglong Fu Xiaojiang Du Paul Ratazzi Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken NJ USA Department of Software and Information Systems University of North Carolina at Charlotte Charlotte NC USA Information Warfare Division Air Force Research Laboratory Rome NY USA

IoT-enabled smart homes can be double-edged swords. On one side, the convenience and efficiency brought about by smart device integrations can improve the standard of living dramatically. However, on the other side, the prevalent interconnectivity among home devices can drastically increase the potential risk of attack. Therefore, in order to reduce the possibility of a successful attack we propose a complex correlation-based anomaly detection system powered by intricate two-to-one correlations. Through the use of a state-of-the-art transformer model, we present a novel correlation mining mechanism that leverages the power of attention weights to develop an understanding of the underlying correlations that exist between IoT events in a smart home environment. Using this knowledge, we use a special validation algorithm to verify 52 two-to-one correlations in our system. Furthermore, we simulate four distinct attack scenarios and attain an average detection accuracy and recall of 96.59% and 97.38% respectively. Our results indicate that our method is effective at identifying a range of IoT attacks and successfully demonstrates the capabilities of IoT correlations.

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Dense Network-Based Spectral-Polarization Image Fusion: Multispectral Data Enhancement via Encoder-Decoder Approach 6

Dense Network-Based Spectral-Polarization Image Fusion: Mult...

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6th International Conference on Information Technologies and Electrical engineering, ICITEE 2023

作者： Karim, Shahid Tong, Geng Li, Jinyang Yu, Yiting Ibrar, Muhammad Mehmood, Faisal Research & Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen518057 China College of Mechanical Engineering Northwestern Polytechnical University Xi'an710072 China Key Laboratory of Micro/Nano Systems for Aerospace Ministry of Education Northwestern Polytechnical University Xi'an710072 China Shaanxi Province Key Laboratory of Micro and Nano Electro-Mechanical Systems Northwestern Polytechnical University Xi'an701172 China Research Center of Smart Sensing Chips Ningbo Institute of Northwestern Polytechnical University Ningbo315103 China Software College Shenyang Normal University Shenyang China School of Electronic and Computer Engineering Shenzhen Graduate School Peking University Shenzhen518055 China

ISBN: (纸本)9798400708299

Images are obtained by perceiving the sunlight reflected by objects or scenes, and due to limited solar irradiance, spatial resolution certainly decreases. In contrast, multispectral sensors hold the spatial information of ground objects even though the obtained images only have a few bands, which means that the obtained images have high spatial but insufficient spectral resolution. To overcome these limitations, we have developed a spectral and polarization image fusion technique that improves several perspectives of image fusion, such as intensity, edges, textures, and substantial information. Our proposed method utilizes an encoder-decoder approach, which contains dense and convolutional blocks to simulate the source images. The polarization images have been used to develop DoLP images that help to improve fused images' quality. We can perceive the visual quality of fused images from the experimental results. © 2023 ACM.

关键词： Image fusion

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