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IEEE International Conference on Communications Workshops, ICC

作者： Daniel Manu Youzuo Lin Jingjing Yao Zhirun Li Xiang Sun Department of Electrical and Computer Engineering University of New Mexico Albuquerque NM USA School of Data Science and Society University of North Carolina at Chapel Hill Chapel Hill NC USA Los Alamos National Laboratory Los Alamos NM USA Department of Computer Science Texas Tech University Lubbock TX USA

ISBN: (数字)9798350304053

ISBN: (纸本)9798350304060

Seismic data/images are critical to understand the structure of subsurfaces. However, for accurate structural analysis, seismic images need to be converted into velocity images that can recognize the depth and thickness of subsurface layers, and this conversion is usually achieved by using seismic full-waveform inversion (FWI). Various Deep neural networks (DNNs) have recently been proposed to replace FWI, and well-trained DNNs typically have lower computational costs but generate similar velocity images as compared to FWI. Yet, training a DNN model is nontrivial, which requires transmitting seismic data that are sensed by seismic receivers in the field to a centralized data center, leading to data privacy and security issues. Seismic field tests are normally conducted in rural areas, where data centers and Internet infrastructures are not available. Hence, it is impossible to train a DNN and achieve seismic inversion in realtime. In this paper, we propose the Asynchronous Federated Learning for Seismic Inversion (AsyncFedInv) framework, which applies multiple lIT devices in terms of edge computing boards to collaboratively train a compact UNet model in realtime based on a novel asynchronous federated learning, where 1) a staleness function is applied to mitigate model staleness, and 2) clients that generate similar local models would suspend its training, thus reducing the communication costs and energy consumption. Simulation results demonstrate that AsyncFedInv achieves a similar convergence rate but lower training loss and better testing performance as compared to a baseline algorithm FedAvg.

关键词： Training data centers Costs Federated learning Computational modeling Simulation Artificial neural networks

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Integrating Artificial Intelligence and Multiple Intelligences for Advanced Educational Models

Integrating Artificial Intelligence and Multiple Intelligenc...

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Electrical Electronics and Computing Technologies (ICEECT), International Conference on

作者： Suresh Palarimath Pyingkodi Maran R. Venkateswaran Wilfred Blessing N.R K. V Shiny S. Renuga College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Sultanate of Oman Department of Computer Applications Kongu Engineering College Erode India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Sultanate of Oman Department of Computer Science and Engineering Bharath Institute of Higher Education and Research Chennai India Department of Artificial Intelligence and Data Science Panimalar Engineering College Chennai India

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

The aim of educational innovation is to foster students' creative and problem-solving skills via the integration of several disciplines, including science, technology, engineering, art, and mathematics. Efficiently identifying and fostering the various abilities of pupils continues to be a crucial obstacle. This research presents a sophisticated educational approach that combines the notion of multiple intelligences with artificial intelligence (AI) technology to tackle this problem. This concept improves the teaching environment by including intelligent auxiliary services for instructors and students via the use of smart speech and picture interaction. Artificial intelligence (AI) integrated into the multiple intelligence’s framework enables the ability to observe, analyze, and implement personalized teaching tactics in real-time using machine learning. The suggested AI-assisted education paradigm aims to enhance teacher-student interactions and facilitate personalized instruction. The study provides evidence that this technique successfully facilitates personalized and captivating learning experiences, promoting students' diverse talents and augmenting their creativity and problem-solving capabilities. This approach seeks to fundamentally transform conventional educational techniques by addressing the disparity between existing educational practices and the future requirements of the workforce.

关键词： Technological innovation Education Machine learning Transforms Real-time systems Problem-solving Artificial intelligence Pupils Creativity STEM

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Improving Fast Adversarial Training Paradigm: An Example Taxonomy Perspective

arXiv

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arXiv 2024年

作者： Gui, Jie Jiang, Chengze Dong, Minjing Tong, Kun Shi, Xinli Tang, Yuan Yan Tao, Dacheng The School of Cyber Science and Engineering Southeast University Nanjing210000 China Purple Mountain Laboratories Nanjing210000 China The Department of Computer Science City University of Hong Kong Hong Kong The Department of Computer and Information Science University of Macau 999078 China The College of Computing & Data Science Nanyang Technological University #32 Block N4 #02a-014 50 Nanyang Avenue Singapore639798 Singapore

While adversarial training is an effective defense method against adversarial attacks, it notably increases the training cost. To this end, fast adversarial training (FAT) is presented for efficient training and has become a hot research topic. However, FAT suffers from catastrophic overfitting, which leads to a performance drop compared with multi-step adversarial training. However, the cause of catastrophic overfitting remains unclear and lacks exploration. In this paper, we present an example taxonomy in FAT, which identifies that catastrophic overfitting is caused by the imbalance between the inner and outer optimization in FAT. Furthermore, we investigated the impact of varying degrees of training loss, revealing a correlation between training loss and catastrophic overfitting. Based on these observations, we redesign the loss function in FAT with the proposed dynamic label relaxation to concentrate the loss range and reduce the impact of misclassified examples. Meanwhile, we introduce batch momentum initialization to enhance the diversity to prevent catastrophic overfitting in an efficient manner. Furthermore, we also propose Catastrophic Overfitting aware Loss Adaptation (COLA), which employs a separate training strategy for examples based on their loss degree. Our proposed method, named example taxonomy aware FAT (ETA), establishes an improved paradigm for FAT. Experiment results demonstrate our ETA achieves state-of-the-art performance. Comprehensive experiments on four standard datasets demonstrate the competitiveness of our proposed method. Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

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IoT-Enhanced Innovations in Drug Delivery Systems for Targeted Therapy in Autoimmune Disorders

IoT-Enhanced Innovations in Drug Delivery Systems for Target...

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International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： V Bhuvaneshwari R Sadaieswaran P Malathi N Legapriyadharshini T Thirumalaikumari S R Raja Department of Computer Science and Engineering Acharya College of Engineering Achariyapuram Puducherry India Department of Computer Applications Tagore College of arts and science Chennai Tamil Nadu India Department of Data Analytics Saveetha College of Liberal Arts and Sciences SIMATS Deemed to be University Chennai Tamil Nadu India Department of Computer Application Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Computer Science Saveetha College of Liberal Arts and Sciences SIMATS Deemed to be University Chennai Tamil Nadu India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

The growing number of autoimmune diseases necessitates the needs novel drug delivery system alternatives to improve targeted therapy. The study deals with the immediate requirement for improvements by investigating the limitations of existing systems, highlighting their limitations in precision, patient adherence, and real-time monitoring. The proposed system combines Internet of Things (IoT) technology into medicine distribution, providing an extensive structure that makes use of smart devices, sensors, and networking solutions. The integration attempts to improve patient participation and treatment efficacy by ensuring tailored regimens. The study demonstrates promising discoveries in optimizing therapeutic interventions, cost-effectiveness, and data-driven insights through an in-depth evaluation of case studies and successful implementations. The proposed IoT-enhanced drug delivery system not only addresses the limitations of existing systems but also represents an evolution for healthcare professionals and pharmaceutical businesses. Patient privacy and data security are essential for ethical and regulatory reasons. The study's results and analysis highlight the proposed system's potential for overcoming limitations and enhancing targeted therapy for autoimmune disorders, setting the groundwork for a future in which IoT seamlessly interacts with drug delivery systems to transform patient care.

关键词： data privacy data security Transforms Drug delivery Sensor systems Real-time systems Autoimmune diseases Internet of Things Smart devices Intelligent sensors

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Smart Retail Store Surveillance and Security with Cloud-Powered Video Analytics and Transfer Learning Algorithms

Smart Retail Store Surveillance and Security with Cloud-Powe...

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Intelligent Cyber Physical Systems and Internet of Things (ICoICI), International Conference on

作者： P. Sivalakshmi U. Kavitha R. Usha Ojasvi Pattanaik S.P. Maniraj C. Srinivasan Department of Electronics and Communication Engineering R.M.K. College of Engineering and Technology Chennai Tamil Nadu India School of Commerce Veltech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Mathematics S. A. Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Vignan Institute of Management and Technology for Women Ghatkesar JNTU Hyderabad India Department of Data Science and Business Systems Faculty of Engineering & Technology SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

Retail security and surveillance systems have transformed into data-driven smart solutions thanks to the fast development of technology. This paper presents a new method for improving retail shop security and surveillance by combining transfer learning (TL) algorithms with cloud-powered video analytics. It is now possible to remotely interpret, analyze, and store massive amounts of video data produced by security cameras using cloud computing capabilities. In addition, TL methods are used to customize pre-trained deep learning models for retail settings, allowing for precise event and anomaly identification. This method drastically reduces the computing load on local devices and makes surveillance systems more accurate and responsive. One of the main advantages of the proposed system is that it can monitor the store premises in real time. It can also automatically identify suspicious actions like stealing or damage and inform staff or security about possible risks. By using cloud infrastructure, the system can also adapt to changing security needs and expand dynamically to handle increasing data volumes. Retailers may find useful information for refining shop layouts, product placements, and marketing tactics by integrating cloud-powered video analytics, which allows advanced functions like crowd analysis, heat mapping, and customer behavior analysis. A thorough plan for smart retail store security and surveillance, using cloud computing, video analytics, and TL algorithms to build a strong and effective system for protecting digital retail spaces.

关键词： Cloud computing Machine learning algorithms Accuracy Surveillance Visual analytics Scalability Transfer learning Real-time systems Safety Security

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Early Stage Design of a mHealth Intervention for Managing Gestational Diabetes Mellitus in Bangladeshi Women 25th

Early Stage Design of a mHealth Intervention for Managing...

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25th International Conference on Human-computer Interaction, HCII 2023

作者： Islam, Ashraful Ahmed, Eshtiak Islam, Md Rakibul EMPATHY Lab Center for Computational and Data Sciences Independent University Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering Independent University Bangladesh Dhaka Bangladesh Faculty of Information Technology and Communication Sciences Tampere University Tampere Finland Computer Science Department University of Wisconsin - Eau Claire Eau ClaireWI United States

ISBN: (纸本)9783031492112

Gestational Diabetes Mellitus (GDM) is a rising concern worldwide, particularly in low-resource countries such as Bangladesh, where access to healthcare facilities is limited and awareness of GDM management is inadequate. This work proposes an early-stage design of user interfaces (UIs) for a mobile health (mHealth) intervention using the Bengali language that allows users to input daily measures of blood glucose, weight, and other GDM-related health data. The intervention is designed to provide users with personalized feedback based on their input and the necessary coaching for managing GDM. It also reminds users to take medication if prescribed by their doctors. The app has six key features: blood glucose tracking, food logging, medication reminders, activity tracking, educational resources, and personalized recommendations. Overall, this work provides a valuable contribution to the field of mHealth-based maternal and child health in Bangladesh. However, further research is necessary to evaluate the effectiveness and feasibility of the intervention in real-world scenarios. Such evaluations will provide valuable insights into its potential impact and help refine the design to better serve the needs of GDM patients in Bangladesh. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： mHealth

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MassSpecGym: a benchmark for the discovery and identification of molecules 24

MassSpecGym: a benchmark for the discovery and identificatio...

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

作者： Roman Bushuiev Anton Bushuiev Niek F. de Jonge Adamo Young Fleming Kretschmer Raman Samusevich Janne Heirman Fei Wang Luke Zhang Kai Dührkop Marcus Ludwig Nils A. Haupt Apurva Kalia Corinna Brungs Robin Schmid Russell Greiner Bo Wang David S. Wishart Li-Ping Liu Juho Rousu Wout Bittremieux Hannes Rost Tytus D. Mak Soha Hassoun Florian Huber Justin J.J. van der Hooft Michael A. Stravs Sebastian Böcker Josef Sivic Tomáš Pluskal Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences and Czech Institute of Informatics Robotics and Cybernetics Czech Technical University Czech Institute of Informatics Robotics and Cybernetics Czech Technical University Bioinformatics Group Wageningen University & Research Department of Computer Science University of Toronto Chair for Bioinformatics Institute for Computer Science Friedrich Schiller University Jena Department of Computer Science University of Antwerp Department of computing science University of Alberta and Alberta Machine Intelligence Institute Department of Molecular Genetics University of Toronto Bright Giant GmbH Department of Computer Science Tufts University Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences Department of computing science and Department of Biological Sciences University of Alberta Department of Computer Science Aalto University Mass Spectrometry Data Center National Institute of Standards and Technology Department of Computer Science and Department of Chemical and Biological Engineering Tufts University Centre for Digitalisation and Digitality University of Applied Sciences Düsseldorf Bioinformatics Group Wageningen University & Research and Department of Biochemistry University of Johannesburg Eawag: Swiss Federal Institute of Aquatic Science and Technology

ISBN: (纸本)9798331314385

The discovery and identification of molecules in biological and environmental samples is crucial for advancing biomedical and chemical sciences. Tandem mass spectrometry (MS/MS) is the leading technique for high-throughput elucidation of molecular structures. However, decoding a molecular structure from its mass spectrum is exceptionally challenging, even when performed by human experts. As a result, the vast majority of acquired MS/MS spectra remain uninterpreted, thereby limiting our understanding of the underlying (bio)chemical processes. Despite decades of progress in machine learning applications for predicting molecular structures from MS/MS spectra, the development of new methods is severely hindered by the lack of standard datasets and evaluation protocols. To address this problem, we propose MassSpecGym - the first comprehensive benchmark for the discovery and identification of molecules from MS/MS data. Our benchmark comprises the largest publicly available collection of high-quality labeled MS/MS spectra and defines three MS/MS annotation challenges: de novo molecular structure generation, molecule retrieval, and spectrum simulation. It includes new evaluation metrics and a generalization-demanding data split, therefore standardizing the MS/MS annotation tasks and rendering the problem accessible to the broad machine learning community. MassSpecGym is publicly available at https://***/pluskal-lab/MassSpecGym.

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Multiple-Input Variational Auto-Encoder for Anomaly Detection in Heterogeneous data

arXiv

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arXiv 2025年

作者： Dinh, Phai Vu Nguyen, Diep N. Hoang, Dinh Thai Nguyen, Quang Uy Dutkiewicz, Eryk School of Electrical and Data Engineering University of Technology Sydney SydneyNSW2007 Australia Computer Science Department Institute of Information and Communication Technology Le Quy Don Technical University Hanoi Viet Nam

Outlier/anomaly detection plays a pivotal role in AI applications, e.g., in classification, and intrusion/threat detection in cybersecurity. However, most existing methods face challenges of heterogeneity amongst feature subsets posed by non-independent and identically distributed (non-IID) data. To address this, we propose a novel neural network model called Multiple-Input Auto-Encoder for Anomaly Detection (MIAEAD). MIAEAD assigns an anomaly score to each feature subset of a data sample to indicate its likelihood of being an anomaly. This is done by using the reconstruction error of its sub-encoder as the anomaly score. All sub-encoders are then simultaneously trained using unsupervised learning to determine the anomaly scores of feature subsets. The final Area Under the ROC Curve (AUC) of MIAEAD is calculated for each sub-dataset, and the maximum AUC obtained among the sub-datasets is selected. To leverage the modelling of the distribution of normal data to identify anomalies of the generative models, we then develop a novel neural network architecture/model called Multiple-Input Variational Auto-Encoder (MIVAE). MIVAE can process feature subsets through its sub-encoders before learning the distribution of normal data in the latent space. This allows MIVAE to identify anomalies that significantly deviate from the learned distribution. We theoretically prove that the difference in the average anomaly score between normal samples and anomalies obtained by the proposed MIVAE is greater than that of the Variational Auto-Encoder (VAEAD), resulting in a higher AUC for MIVAE. Extensive experiments on eight real-world anomaly datasets from different domains, e.g., health, finance, cybersecurity, and satellite imaging, demonstrate the superior performance of MIAEAD and MIVAE over conventional methods and the state-of-the-art unsupervised models, by up to 6% in terms of AUC score. Furthermore, experimental results show that the AUC obtained by MIAEAD and MIVAE is mostly no

关键词： Cybersecurity

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6G and AI: Viewpoints, Processes, and Technologies

6G and AI: Viewpoints, Processes, and Technologies

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

作者： S.A. Shoyusupov B. Lanitha Shanky Goyal Vishal Goyal Saif O. Husain Praveen Sharma Department of Physics and Chemistry Tashkent Institute of Irrigation and Agricultural Mechanization Engineers National Research University Tashkent Uzbekistan Department of Artificial Intelligence and Data Science Karpagam Academy of Higher Education Coimbatore Department of Computer Science Engineering Chandigarh Engineering College Chandigarh Group of Colleges Jhanjeri Mohali Punjab India Department of Electronics and Communication Engineering GLA University Mathura India Department Of Computers Techniques Engineering College Of Technical Engineering The Islamic University Najaf Iraq Department of Computer Science & Engineering IES Institute of Technology and Management IES University Bhopal M.P. India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

The rapid improvement of remote systems is being driven by the growing use of fake intelligence. The shift from "interdependent items" to "associated insights" is expected to be fundamentally altered by sixth-generation (6G) distant innovation. However, because of their vast analytics foundations and deep neural networks, state-of-the-art AI frameworks entirely rely on computer and interactions. As a result, excessive inactivity, excessive energy use, organizational obstruction, and protection leakage occur throughout the planning and deduction phases. Edge AI might be a game-changer for 6G, improving the practicality, efficacy, security, and validity of 6G systems in general, with its steady integration of sensing, communication, processing, and intelligence. It does this by coordinating modelling with duction and teaching skills at the organise edge. In this research, we will outline our methodology for robust and flexible uncontrolled neural network models and coordinated Bluetooth connections in edge AI contexts. A comprehensive end-to-end frame plan for supporting edge AI, service-driven use efficiency tactics, and contemporary concepts in remote organize building will all be safeguarded. In addition, uniformity, phases for software and hardware components, state-of-the-art AI applications, and real-world examples are presented to support industrialization and subsequently deployment.

关键词： 6G mobile communication Technological innovation Edge AI Software Telecommunication computing Sensors Resource management Reliability Artificial intelligence Protection

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Large Models for Aerial Edges: An Edge-Cloud Model Evolution and Communication Paradigm

arXiv

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arXiv 2024年

作者： Zhang, Shuhang Liu, Qingyu Chen, Ke Di, Boya Zhang, Hongliang Yang, Wenhan Niyato, Dusit Han, Zhu Poor, H. Vincent The Pengcheng Laboratory Shenzhen518055 China The School of Electronic and Computer Engineering Peking University Shenzhen518055 China The Department of Electronics Peking University Beijing100871 China The College of Computing and Data Science Nanyang Technological University 639798 Singapore The Department of Electrical and Computer Engineering The University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of The Department of Electrical Engineering Princeton University PrincetonNJ08544 United States

The future sixth-generation (6G) of wireless networks is expected to surpass its predecessors by offering ubiquitous coverage through integrated air-ground facility deployments in both communication and computing domains. In this network, aerial facilities, such as unmanned aerial vehicles (UAVs), conduct artificial intelligence (AI) computations based on multi-modal data to support diverse applications including surveillance and environment construction. However, these multi-domain inference and content generation tasks require large AI models, demanding powerful computing capabilities and finely tuned inference models trained on rich datasets, thus posing significant challenges for UAVs. To tackle this problem, we propose an integrated air-ground edge-cloud model evolution framework, where UAVs serve as edge nodes for data collection and small model computation. Through wireless channels, UAVs collaborate with ground cloud servers, providing large model computation and model updating for edge UAVs. With limited wireless communication bandwidth, the proposed framework faces the challenge of information exchange scheduling between the edge UAVs and the cloud server. To tackle this, we present joint task allocation, transmission resource allocation, transmission data quantization design, and edge model update design to enhance the inference accuracy of the integrated air-ground edge-cloud model evolution framework by mean average precision (mAP) maximization. A closed-form lower bound on the mAP of the proposed framework is derived based on the mAP of the edge model and mAP of the cloud model, and the solution to the mAP maximization problem is optimized accordingly. Simulations, based on results from vision-based classification experiments, consistently demonstrate that the mAP of the proposed integrated air-ground edge-cloud model evolution framework outperforms both a centralized cloud model framework and a distributed edge model framework across various communica

关键词： Bandwidth

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