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10th IEEE Smart World Congress, SWC 2024

作者： You, Feiran Du, Hongyang Kang, Jiawen Ni, Wei Niyato, Dusit Jamalipour, Abbas The University of Sydney School of Electrical and Computer Engineering SydneyNSW2006 Australia SydneyNSW2122 Australia University of Hong Kong Department of Electrical and Electronic Engineering Pok Fu Lam Hong Kong Guangdong University of Technology School of Automation China Nanyang Technological University College of Computing and Data Science Singapore

ISBN: (纸本)9798331520861

The rapid growth of Artificial Intelligence-Generated Content (AIGC) services has led to increased mobile user participation in related computations and interactions. This development has enabled AI-generated characters to interact with Virtual Reality (VR) users in real time, making the VR experience more interactive and personalized. In this paper, we consider an MEC system where VR users engage in AIGC services, focusing on the Generative Diffusion Model (GDM)based image generation tasks. Specifically, VR users initiate requests for computing resources, while computation offloading distributes the processing load across the MEC system. To manage AIGC edge computation offloading and cloudlet-VR user connections jointly, a data Center Operator (DCO) employs a centralized Proximal Policy Optimization (PPO) algorithm. To protect VR users' privacy while preserving PPO functionality, we employ the Generative Diffusion Model (GDM), specifically the Denoising Diffusion Implicit Model (DDIM), which first introduces noise to the PPO state, then conducts a denoising process to recover the state information. We further employ Inverse Reinforcement Learning (IRL) to infer rewards for the recovered states, using expert demonstrations trained by the PPO. The similarity between PPO-generated rewards and IRL-inferred rewards is then computed. Simulation results demonstrate that our proposed approach successfully achieves computation offloading while protecting VR users' privacy within the PPO centralized management framework. © 2024 IEEE.

关键词： Virtual environments

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On the Element-Wise Representation and Reasoning in Zero-Shot Image Recognition: A Systematic Survey

arXiv

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arXiv 2024年

作者： Guo, Jingcai Rao, Zhijie Chen, Zhi Guo, Song Zhou, Jingren Tao, Dacheng Department of Computing The Hong Kong Polytechnic University Hong Kong School of Electrical Engineering and Computer Science The University of Queensland BrisbaneQLD4072 Australia Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong Alibaba Group Hangzhou311121 China College of Computing and Data Science Nanyang Technological University Singapore

Zero-shot image recognition (ZSIR) aims to recognize and reason in unseen domains by learning generalized knowledge from limited data in the seen domain. The gist of ZSIR is constructing a well-aligned mapping between the input visual space and the target semantic space, which is a bottom-up paradigm inspired by the process by which humans observe the world. In recent years, ZSIR has witnessed significant progress on a broad spectrum, from theory to algorithm design, as well as widespread applications. However, to the best of our knowledge, there remains a lack of a systematic review of ZSIR from an element-wise perspective, i.e., learning fine-grained elements of data and their inferential associations. To fill the gap, this paper thoroughly investigates recent advances in element-wise ZSIR and provides a sound basis for its future development. Concretely, we first integrate three basic ZSIR tasks, i.e., object recognition, compositional recognition, and foundation model-based open-world recognition, into a unified element-wise paradigm and provide a detailed taxonomy and analysis of the main approaches. Next, we summarize the benchmarks, covering technical implementations, standardized datasets, and some more details as a library [link]. Last, we sketch out related applications, discuss vital challenges, and suggest potential future directions. Copyright © 2024, The Authors. All rights reserved.

关键词： Zero-shot learning

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Blockchain and Machine Learning Integration for Safe data Sharing in Finance and HR

Blockchain and Machine Learning Integration for Safe Data Sh...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Sivakumar, T.B. Prathiba, E. Sharma, Shweta Prasad, Rashmi Saket Ramaratnam, M.S. Krishnamoorthy, V. School of Computing Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Cse Chennai India Mailam Engineering College Department of Artificial Intelligence and Data Science Mailam India Shambhunath Institute of Engineering & Technology Department of Business Administration Prayagraj India D Y Patil Pgdm Institute Akurdi Pune India Sri Chandrasekharendra Saraswathi Viswa Mahavidyalaya Department of Management Studies Kanchipuram India Kongu Engineering College Department of Management Studies Perundurai Erode India

ISBN: (纸本)9798350360660

Modern finance and HR businesses have challenges in exchanging data due to centralized systems that are subject to hacking and lack transparency. The study proposes employing blockchain technology and machine intelligence (ML) to address these issues holistically. The proposed system enhances integrity and resilience to assaults by utilizing a decentralized ledger for secure data exchange. ML algorithms enable predictive analytics, risk management, and personalized insights. The proposed system outperforms the existing system in terms of accuracy, security, and efficiency. The accuracy of fraud detection increases from 0.75 to 0.90, as does the estimate of staff turnover from 0.80 to 0.95. The proposed system achieves an overall accuracy of 95%, surpassing the existing system's 85%. Blockchain and ML integration can alter data management methods in finance and HR industries, with improved security measures resulting in a risk score of 2 compared to 7 in the existing system. © 2024 IEEE.

关键词： Decentralized finance

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Reveal training performance mystery between Tensor Flow and PyTorch in the single GPU environment

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science China(Information sciences) 2022年第1期65卷 147-163页

作者： Hulin DAI Xuan PENG Xuanhua SHI Ligang HE Qian XIONG Hai JIN National Engineering Research Center for Big Data Technology and System Service Computing Technology and System LabSchool of Computer Science and Technology Huazhong University of Science and Technology Department of Computer Science University of Warwick

Deep learning has gained tremendous success in various fields while training deep neural networks(DNNs) is very compute-intensive, which results in numerous deep learning frameworks that aim to offer better usability and higher performance to deep learning practitioners. Tensor Flow and Py Torch are the two most popular frameworks. Tensor Flow is more promising within the industry context, while Py Torch is more appealing in academia. However, these two frameworks differ much owing to the opposite design philosophy:static vs dynamic computation graph. Tensor Flow is regarded as being more performance-friendly as it has more opportunities to perform optimizations with the full view of the computation graph. However, there are also claims that Py Torch is faster than Tensor Flow sometimes, which confuses the end-users on the choice between them. In this paper, we carry out the analytical and experimental analysis to unravel the mystery of comparison in training speed on single-GPU between Tensor Flow and Py Torch. To ensure that our investigation is as comprehensive as possible, we carefully select seven popular neural networks, which cover computer vision, speech recognition, and natural language processing(NLP). The contributions of this work are two-fold. First, we conduct the detailed benchmarking experiments on Tensor Flow and Py Torch and analyze the reasons for their performance difference. This work provides the guidance for the end-users to choose between these two frameworks. Second, we identify some key factors that affect the performance,which can direct the end-users to write their models more efficiently.

关键词： deep learning performance comparison TensorFlow PyTorch

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ℓ1,2-Norm and CUR Decomposition based Sparse Online Active Learning for data Streams with Streaming Features

ℓ1,2-Norm and CUR Decomposition based Sparse Online Active ...

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IEEE International Conference on Big data

作者： Zhong Chen Yi He Di Wu Liudong Zuo Keren Li Wenbin Zhang Zhiqiang Deng School of Computing Southern Illinois University School of Data Science William & Mary College of Computer and Information Science Southwest University Computer Science Department California State University Dominguez Hills Department of Mathematics University of Alabama at Birmingham School of Computing and Information Sciences Florida International University Department of Civil and Environmental Engineering Louisiana State University

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Aiming at learning from a sequence of data instances over time, online learning has attracted increasing attention in the big data era. As two important variants, sparse online learning has been extensively explored by facilitating sparse constraints for online models such as truncated gradient, ℓ 1 -norm regularization, ℓ 1 -ball projection, and regularized dual averaging; while online active learning aims to build an online prediction model with a limited number of labeled instances, deploying the so called query strategies to select informative instances over time. However, most existing studies consider sparse online learning or online active learning with fixed feature spaces, whereby in real practice the features may be dynamically evolved over time. To the end, we propose a novel unified one-pass online learning framework named OASF for simultaneously online active learning and sparse online learning tailored for data streams described by open feature spaces, where new features can emerge constantly, and old features may be vanished over various time spans. Specifically, we technically develop an effective online CUR matrix decomposition based on the ℓ 1,2 mixed norm constraint for simultaneously selecting important up-to-date samples in a sliding window and facilitating stable and meaningful features in open feature spaces over time. If the loss function is simultaneously Lipschitz and convex, a sub-linear regret bound of our proposed algorithm is guaranteed with. Extensive experiments that are conducted with multiple streaming datasets have demonstrated the effectiveness of the proposed OASF compared with state-of-the-art online active learning and sparse online learning methods.

关键词： Sensitivity analysis Error analysis Active learning Noise Predictive models Big data Stability analysis Matrix decomposition Sparse matrices Streams

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EFFICIENT NONLINEAR MULTISCALE SPECTRAL GFEM APPLIED TO COMPOSITE AEROSPACE STRUCTURES 23

EFFICIENT NONLINEAR MULTISCALE SPECTRAL GFEM APPLIED TO COMP...

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23rd International Conference on Composite Materials, ICCM 2023

作者： Bénézech, J. Seelinger, L. Reinarz, A. Dodwell, T.J. Butler, R. Scheichl, R. Material & Structure Research Centre University of Bath Bath United Kingdom Interdisciplinary Center for Scientific Computing Institute for Applied Mathematics Heidelberg University Heidelberg Germany Department of Computer Science Durham University Durham United Kingdom Institute of Data Science and AI University of Exeter Exeter United Kingdom DigiLab Exeter United Kingdom

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Artificial Intelligence for the Classification of Neuromuscular Diseases Using Dominant MUAP 8

Artificial Intelligence for the Classification of Neuromuscu...

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8th International Conference on Smart Structures and Systems, ICSSS 2022

作者： Maheshwary, Priti Vinu, W. Velvadivu, P. Shukla, Surendra Kumar Srivastava, Prabhat Kumar Pareek, Prakash Centre for Internet of Things and Advance Computing Rabindranath Tagore University Bhopal India Annamalai University Department of Physical Education Tamilnadu Chidambaram India Coimbatore Institute of Technology Department of Computing-Data Science Tamilnadu Coimbatore India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Quantum University Department of Computer Science and Engineering Uttrakhand Roorkee India Vishnu Institute of Technology Department of Electronics and Communication Engineering Andhra Pradesh Bhimavaram India

ISBN: (纸本)9781665497619

Motor-unit-action-potentials in an electromyographic isolated signals shape & sounds are valuable diagnosis information for neuro-muscular disorders-treatment & management. An expert can analyse these parameters qualitatively or quantitative by pattern recognition techniques. Because of benefits of the quantitatively method of EMG, producing robust automated MUAP types is investigated, & many systems is produced for this purpose, but correctness of the previous methods isn't peak enough to use in clinical settings. The developed system uses an EMG signal decomposition mechanism to extract both time & frequency domain for M-U-A-P's retrieved from E-M-G. Un-similar type of algorithms was studied, including 1 & many types with many sub-sets characteristics. The multi-classifier methods proposed here performed well in experiments by real set EMG. The multi-classifier, which aggregates base classifiers using multiple feature sets & both trainable & non-trainable fusion techniques, performed the best of the methods studied, with an average precision is 98%. © 2022 IEEE.

关键词： Support vector machines Time-frequency analysis Shape Neuromuscular Morphology Electromyography Pattern recognition

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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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Residual VGGNet for Unveiling Optimal Process Parameters of Oil Rig Welding Through Mathematical Modeling and Optimization 15

Residual VGGNet for Unveiling Optimal Process Parameters of ...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Dhas, John T. Mesia Saikia, Utpal Arun, K.K. Josphine, C.Vimala Kingslin, M.Theodore Padmavathi, N. School of Computing Department of Computer Science and Engineering Tamilnadu Chennai India Silapathar College Department of Mathematics Dhemaji Assam Silapathar India Kumaraguru College of Technology Department of Mechanical Engineering Tamilnadu Coimbatore India Artificial Intelligence and Data Science R.M.K Engineering College Kavaraipettai India R.M.K College of Engineering & Technology Department of Electronics & Communication Engineering Tamilnadu Puduvoyal India R.M.D. Engineering College Department of Electronics and Instrumentation Engineering Tamilnadu India

ISBN: (纸本)9798350370249

The optimization of welding techniques for oil rig components is crucial to upholding the structural integrity and safety of offshore drilling activities. In this study, we utilize a residual VGGNet architecture, which is a modified version of the well-known VGGNet, for the purpose of extracting crucial information from images of welding processes. The methodology employed in this study involves the collection of a complete dataset comprising welding process images obtained from diverse oil rig welding activities. The provided images are accompanied by annotations that provide the associated welding parameters, including current, voltage, and welding speed. The residual VGGNet is subsequently trained to acquire knowledge about the complex associations between picture features and welding parameters. In order to develop a reliable mathematical model, regression techniques are utilized to precisely establish the relationship between the extracted features and welding parameters. The performance of the model is evaluated using cross-validation and compared to traditional regression models to emphasize its greater ability to capture intricate patterns within the data. © 2024 IEEE.

关键词： mathematical modeling oil rig welding optimization Residual VGGNet welding parameters

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Network Intrusion Detection System using Deep Learning Paradigm

Network Intrusion Detection System using Deep Learning Parad...

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IEEE International Conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： Olamatanmi Josephine Mebawondu Iyin Adenuga Adebayọ Olusọla Adetunmbi Jacob Olorunshogo Mebawondu Department of Computing Afe Babalola University Ado-Ekiti Ado-Ekiti Nigeria Department of Computer Science Federal University of Technology Akurẹ Nigeria Department of Data Science Federal University of Technology Akurẹ Nigeria Department of Computer Science Federal Polytechnic Nasarawa Nasarawa Nigeria

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

Network Intrusion Detection Systems (NIDS) are essential for mitigating the pervasive threats posed by hackers in computer networks. With the rise in network traffic, applying deep learning techniques to enhance IDS performance has become crucial. Deep learning (DL), a subset of machine learning, involves multiple layers of information processing for learning. This paper compares the effectiveness of Deep Belief Networks (DBN), Convolutional Neural Networks (CNN), and Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN) in developing a high-performance IDS. The deep learning techniques were validated using standard intrusion datasets that include novel attack patterns. The experimental results demonstrated accuracies of 80.61% for LSTM-RNN, 55% for DBN, and 81.07% for CNN, indicating that CNN outperformed the other techniques.

关键词： Deep learning Humanities Recurrent neural networks Network intrusion detection Telecommunication traffic Information processing Information and communication technology Convolutional neural networks Standards Long short term memory

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