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arXiv 2023年

作者： Guella, Jean Carlo Jäger, Janin Unicamp Institute of Mathematics Statistics and Scientific Computing Campinas Brazil KU Eichstätt-Ingolstadt Mathematisches Institute for machine learning and data science Ingolstadt Germany

The paper studies strictly positive definite kernels on compact Riemannian manifolds. We state new conditions to ensure strict positive definiteness for general kernels and kernels with certain convolutional structure. We also state conditions for such kernels on product manifolds. As an example conditions for products of two-point homogeneous spaces are *** Codes 33B10, 33C20, 42A16, 46E22, 65D05 Copyright © 2023, The Authors. All rights reserved.

关键词： Geometry

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Hyperbolic Geometric Latent Diffusion Model for Graph Generation 41

Hyperbolic Geometric Latent Diffusion Model for Graph Genera...

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41st International Conference on Machine Learning, ICML 2024

作者： Fu, Xingcheng Gao, Yisen Wei, Yuecen Sun, Qingyun Peng, Hao Li, Jianxin Li, Xianxian Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China Institute of Artificial Intelligence Beihang University Beijing China School of Software Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing School of Computer Science and Engineering Beihang University Beijing China

Diffusion models have made significant contributions to computer vision, sparking a growing interest in the community recently regarding the application of them to graph generation. Existing discrete graph diffusion models exhibit heightened computational complexity and diminished training efficiency. A preferable and natural way is to directly diffuse the graph within the latent space. However, due to the non-Euclidean structure of graphs is not isotropic in the latent space, the existing latent diffusion models effectively make it difficult to capture and preserve the topological information of graphs. To address the above challenges, we propose a novel geometrically latent diffusion framework HypDiff. Specifically, we first establish a geometrically latent space with interpretability measures based on hyperbolic geometry, to define anisotropic latent diffusion processes for graphs. Then, we propose a geometrically latent diffusion process that is constrained by both radial and angular geometric properties, thereby ensuring the preservation of the original topological properties in the generative graphs. Extensive experimental results demonstrate the superior effectiveness of HypDiff for graph generation with various topologies. Copyright 2024 by the author(s)

关键词： Graph theory

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Enhancement of IoT based Flood Detection and Prevention using Arduino UNO with WiFi Module 9

Enhancement of IoT based Flood Detection and Prevention usin...

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9th International Conference on Advanced computing and Communication Systems, ICACCS 2023

作者： Charaan, R. M. Dilip Shobana, J. Krishnamoorthy, P. Princy, B. Anni Abinaya, R.J. Murugesan, K. Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu India SRM Institute of Science and Technology School of Computing Department of Data Science and Business Systems Tamil Nadu Kattankulathur India Siddharth Institute of Engineering & Technology Department of Computer Science and Engineering Tamil Nadu India Panimalar Engineering College Department of Computer and Communication Engineering Tamil Nadu India Karpagam Academy of Higher Education Department of Electronics and Communication Engineering Tamil Nadu India Loyola Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu India

ISBN: (纸本)9798350397376

The overflow of water from a lake or river usually causes flooding. Sometimes, a dam breach might result in the unexpected release of vast quantities of water. Some of the water seeps into the ground, flooding the region. In a station, rivers are involving the riverbanks. Along with a lack of goods and houses, businesses, and offices, street infrastructure floods contain bacteria, sewage from waste sites, and chemical spills, which later cause a number of diseases. The rate of change in river stage in real time, which can assist signal the gravity and immediacy of this hazard, is a crucial piece of information for flood predictions. Understanding the type of storm that produced the moisture, including its duration, strength, and actual extent, is important for determining the possible severity of the flood. In this system, an Arduino Uno is connected to four separate sensors: a humidity sensor, a flow sensor, a float sensor, and an ultrasonic sensor. The float sensor detects when the water is full. With the aid of IOT, these sensor combinations are utilised to predict floods, alert the appropriate authorities, and sound an immediate alarm in adjacent communities to rapidly relay information about potential floods. These sensors provide data via the IOT's WiFi module. When flooding conditions are detected, the system warns the nearby villages and places and estimates how long it would take for help to reach at a particular location. The technology also determines when it might be considered a flood and gives them a window of time to leave in case it does. © 2023 IEEE.

关键词： Floods

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Classifier Guidance Enhances Diffusion-based Adversarial Purification by Preserving Predictive Information

arXiv

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arXiv 2024年

作者： Zhang, Mingkun Li, Jianing Chen, Wei Guo, Jiafeng Cheng, Xueqi CAS Key Laboratory of AI Safety Institute of Computing Technology Chinese Academy of Sciences Beijing China Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

Adversarial purification is one of the promising approaches to defend neural networks against adversarial attacks. Recently, methods utilizing diffusion probabilistic models have achieved great success for adversarial purification in image classification tasks. However, such methods fall into the dilemma of balancing the needs for noise removal and information preservation. This paper points out that existing adversarial purification methods based on diffusion models gradually lose sample information during the core denoising process, causing occasional label shift in subsequent classification tasks. As a remedy, we suggest to suppress such information loss by introducing guidance from the classifier confidence. Specifically, we propose Classifier-cOnfidence gUided Purification (COUP) algorithm, which purifies adversarial examples while keeping away from the classifier decision boundary. Experimental results show that COUP can achieve better adversarial robustness under strong attack methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

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SHELLEY: Exploring Learning-Based Network Alignment on Biological data

SHELLEY: Exploring Learning-Based Network Alignment on Biolo...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： De Luca, Riccardo Petti, Manuela Guzzi, Pietro H. Tieri, Paolo Sapienza University of Rome Program in Data Science Rome Italy Sapienza University of Rome Diag Dept. Comp. Contr. Manag. Eng. Rome Italy Magna Graecia University Dept. Umg Catanzaro Italy Cnr National Research Council Iac Institute for Applied Computing Rome Italy

ISBN: (纸本)9798350386226

Global network alignment is the computational problem of determining the similarity between nodes of different networks to establish a one-to-one correspondence between them. It has important applications in the biological field, particularly for discovering similar roles between the elements of different systems or for transferring knowledge from a well-studied system to another. In this paper, we present SHELLEY, a tool that facilitates the development, testing, and combination of learning-based network alignment algorithms by providing a set of modules that allow for the recreation and combination of both representation learning methods (RLMs) and deep matching methods (DMMs). We then present a case study in which we apply this tool to a protein-protein interaction network (PPI), demonstrating how the representation phase of RLMs is crucial for model robustness against *** code of SHELLEY is available at: https://***/rickydeluca/shelley © 2024 IEEE.

关键词： Contrastive Learning

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Wireless Power Transfer Technologies: State-Of-The-Art and Future Prospects

Wireless Power Transfer Technologies: State-Of-The-Art and F...

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Communication, computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： Vivek Justus S.Viveka M.Sumithra Purushothaman V V.Samuthira Pandi Desidi Narsimha Reddy Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India Department of Electronics and Communication Engineering Chennai Institute of Technology Chennai Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai Tamil Nadu India Data Consultant (Data Governance Data Analytics: Enterprise Performance Management AI&ML) Soniks Consulting LLC Texas USA

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

Wireless Power Transfer (WPT) technologies represent a transformative approach to energy transmission, offering advantages over traditional wired systems such as convenience, flexibility, and reduced maintenance. This paper presents a comprehensive review of current advancements and future prospects in WPT. The review encompasses three primary technologies: electromagnetic induction, magnetic resonance, and microwave transmission, each offering unique benefits and challenges. Recent developments have significantly enhanced WPT efficiency, extended transmission distances, and enabled integration with emerging technologies like Internet of Things (IoT) devices and electric vehicle (EV) charging infrastructure. Looking forward, the paper identifies several key research directions and challenges. These include improving efficiency through advanced materials and metamaterials, standardization efforts to promote interoperability and safety, and exploration of new applications such as wireless charging in smart cities and space-based applications. The potential impact of WPT on reducing reliance on fossil fuels and enabling sustainable energy practices underscores its importance in future energy landscapes. By synthesizing current knowledge and highlighting future opportunities, this study aims to guide researchers, engineers, and policymakers in accelerating the development and adoption of WPT technologies, paving the way for a moreefficient and interconnected energy ecosystem.

关键词： Costs Reviews Smart cities Magnetic resonance Wireless power transfer Standardization Telecommunication computing Smart grids Internet of Things Tuning

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MG-Net: learn to customize QAOA with circuit depth awareness 24

MG-Net: learn to customize QAOA with circuit depth awareness

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Yang Qian Xinbiao Wang Yuxuan Du Yong Luo Dacheng Tao School of Computer Science Faculty of Engineering University of Sydney New South Wales Australia Institute of Artificial Intelligence School of Computer Science Wuhan University Wuhan China College of Computing and Data Science Nanyang Technological University Singapore Singapore

ISBN: (纸本)9798331314385

Quantum Approximate Optimization Algorithm (QAOA) and its variants exhibit immense potential in tackling combinatorial optimization challenges. However, their practical realization confronts a dilemma: the requisite circuit depth for satisfactory performance is problem-specific and often exceeds the maximum capability of current quantum devices. To address this dilemma, here we first analyze the convergence behavior of QAOA, uncovering the origins of this dilemma and elucidating the intricate relationship between the employed mixer Hamiltonian, the specific problem at hand, and the permissible maximum circuit depth. Harnessing this understanding, we introduce the Mixer Generator Network (MG-Net), a unified deep learning framework adept at dynamically formulating optimal mixer Hamiltonians tailored to distinct tasks and circuit depths. Systematic simulations, encompassing Ising models and weighted Max-Cut instances with up to 64 qubits, substantiate our theoretical findings, highlighting MG-Net's superior performance in terms of both approximation ratio and efficiency.

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Transformative effects of ChatGPT on modern education: Emerging Era of AI Chatbots

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Internet of Things and Cyber-Physical Systems 2024年第1期4卷 19-23页

作者： Gill, Sukhpal Singh Xu, Minxian Patros, Panos Wu, Huaming Kaur, Rupinder Kaur, Kamalpreet Fuller, Stephanie Singh, Manmeet Arora, Priyansh Parlikad, Ajith Kumar Stankovski, Vlado Abraham, Ajith Ghosh, Soumya K. Lutfiyya, Hanan Kanhere, Salil S. Bahsoon, Rami Rana, Omer Dustdar, Schahram Sakellariou, Rizos Uhlig, Steve Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Raygun Performance Monitoring Wellington New Zealand Center for Applied Mathematics Tianjin University Tianjin China Department of Science Kings Education London United Kingdom Cymax Group Technologies BC Canada QM Academy Queen Mary University of London London United Kingdom Jackson School of Geosciences University of Texas at Austin AustinTX United States Centre for Climate Change Research Indian Institute of Tropical Meteorology Pune India Microsoft Hyderabad India Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Machine Intelligence Research Labs AuburnWA United States Faculty of Computing and Data Science FLAME University Maharashtra Pune India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Department of Computer Science University of Western Ontario London Canada Sydney Australia School of Computer Science University of Birmingham United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom Laboratory School of Computing and Information Systems The University of Melbourne Australia

ChatGPT, an AI-based chatbot, offers coherent and useful replies based on analysis of large volumes of data. In this article, leading academics, scientists, distinguish researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research discusses ChatGPT capabilities and its use in the education sector, identifies potential concerns and challenges. Our preliminary evaluation shows that ChatGPT perform differently in different subject areas including finance, coding, maths, and general public queries. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions, transforming education through smartphones and IoT gadgets, and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported "hallucinations" within GenerativeAI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial. © 2023 The Authors

关键词： Machine learning

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A systematic review of machine learning approaches for detecting deceptive activities on social media: methods, challenges, and biases

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International Journal of data science and Analytics 2025年 1-26页

作者： Liu, Yunchong Shen, Xiaorui Zhang, Yeyubei Wang, Zhongyan Tian, Yexin Dai, Jianglai Cao, Yuchen School of Engineering and Applied Science University of Pennsylvania Philadelphia USA Khoury College of Computer Science Northeastern University Seattle USA Center for Data Science New York University New York USA College of Computing Georgia Institute of Technology Bellevue USA Department of EECS University of California Berkeley Berkeley USA

The growing prevalence of misinformation, spam, and fake accounts on social media platforms such as Twitter, Facebook, and Instagram presents a serious threat to public discourse and safety. To address this, machine learning (ML) and deep learning (DL) models have been widely applied for detecting deceptive content. This systematic review analyzes 36 peer-reviewed studies published between January 2010 and July 2024 that apply ML/DL techniques to combat deception across major platforms. Among these, random forest and support vector machines were the most frequently used ML models (17 and 16 studies, respectively), while deep learning models such as artificial neural networks (8 studies) and LSTM (2 studies) also showed promising results. Performance metrics, including F1 scores and AUROC, were extracted when available, with several studies reporting F1 scores exceeding 90% for specific tasks like fake account detection. Despite these advances, 86% of studies inadequately addressed class imbalance, and only 39% consistently tuned hyperparameters. Furthermore, over 70% did not mention strategies for handling linguistic challenges such as negations. This review identifies key methodological limitations—including sample representativeness, inconsistent preprocessing, and reliance on accuracy in imbalanced settings—and offers recommendations to enhance generalizability, bias mitigation, and real-world applicability of ML-based misinformation detection.

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Continuous Tweet Stream Analysis using Apache Spark Streaming API & Kafka 15

Continuous Tweet Stream Analysis using Apache Spark Streamin...

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15th International Conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024

作者： Nisha, A.G. Shivaprasad, N. Nischitha, C.B. Shashikala, B.M. Sujithra, M. Chaba, Yogesh Electronics & Communications Engineering JSS Science & Technology University Mysuru India Dept.of Electronics & Communications Engineering Sri Jayachamarajendra College of Engineering JSS Science & Technology University Mysuru India Dept.of Computer Science St.Joseph's First Grade College Mysore India Dept.of Computer Applications Sri Jayachamarajendra College of Engineering JSS Science & Technology University Mysuru India Department of Computing-Data Science Coimbatore Institute of Technology Coimbatore India Department of Computer Science and Engineering Guru Jambeshwar University of Science and Technology Haryana Hisar India

ISBN: (纸本)9798331300579

Social media has become a trend nowadays people are spending more time in social media platform like Facebook, Twitter, Instagram to share their opinions, creating new interests, ideas and generate *** provide a major platform for collecting information which helps us to analyze them for our *** such microblogging social network is twitter which allows information collaboration and quick information sharing that provides relief from rising *** has several millions of people tweeting and expressing their perspectives on a due to its distinctively brief and straightforward expressing *** enormous volume of raw data from tweets can be utilised for analysis and processing based on industrial or business *** this technical paper, the application of tweet sentiment analysis by extracting a vast number of tweets using continuous tweet stream provided by Spark and Kafka has been *** system fetches live tweets from Twitter based on the search criteria, process the tweets, by using text mining techniques and classify them based on different labels. © Grenze Scientific Society, 2024.

关键词： Tweets

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