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Spatiotemporal Representation Learning on Event Stream 7

Spatiotemporal Representation Learning on Event Stream

引用

7th International Symposium on Autonomous Systems, ISAS 2024

作者： Yang, Beibei Li, Weiling Jiang, Guangyu Liu, Zhigang Zhong, Yurong Fang, Yan Dongguan University of Technology School of Computer Science and Technology Dongguan China Kennesaw State University Department of Electrical and Computer Engineering MariettaGA United States

ISBN: (纸本)9798350363173

The spatiotemporal correlation of events obtained by event camera contains the operational laws of moving targets. For deeper understanding events, an effective spatiotemporal representation learning-based model is desired. Note that events are dense in time and sparse in space, how to capture the global correlations of all events in the event stream simultaneously while well addressing the sparse input is a challenging task. To fulfil this task, an Elastic Net-incorporated tensor network (ENTN) model is proposed in this work with twofold ideas: a) utilizing a fully-connected 3rd-order tensor network for tensor decomposition to implement spatiotemporal representation learning on event stream, b) incorporating Elastic-net regularization to perform better feature selection. Experimental results indicate that ENTN can represent the spatiotemporal correlation of events in an event stream with high quality. © 2024 IEEE.

关键词： Tensors

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Can Deep Learning Outperform Traditional Methods in Upscaling IR Images for Solar Irradiance Estimation? 26

Can Deep Learning Outperform Traditional Methods in Upscalin...

引用

26th Irish Machine Vision and Image Processing Conference, IMVIP 2024

作者： Hossain, Md Yearat Nijhum, Ifran Rahman Rakib, Md Mahbub Hasan Dev, Soumyabrata The ADAPT SFI Research Centre Ireland School of Computer Science University College Dublin Ireland Department of Electrical and Computer Engineering North South University Bangladesh

ISBN: (纸本)9781837242672

Infrared imaging provides crucial information in the infrared spectrum, invisible to standard visible-light imaging systems. However, high-resolution infrared imaging is challenging and expensive, limiting its applications. Deep learning, particularly Convolutional Neural Networks (CNNs), has significantly advanced image processing but relies on high-resolution images for optimal performance. To address the difficulty of acquiring high-resolution infrared images, image upscaling techniques, including traditional interpolation and modern deep learning methods, can be used. This study compares these methods using the GIRASOL dataset for solar irradiance estimation. We evaluated bicubic, bilinear, and nearest neighbor interpolation against deep learning image upscaling models SwinIR and RealESRGAN. Despite their success in upscaling visible spectrum images, these models did not effectively enhance low-resolution infrared images, resulting in less accurate CNN predictions for solar irradiance compared to traditional methods. © This is an open access article published by the IET under the Creative Commons Attribution License (http://***/licenses/by/3.0/)

关键词： Convolutional neural networks

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Deep Learning Based Corona Discharge Severity Classification for High Voltage Equipment 50

Deep Learning Based Corona Discharge Severity Classification...

引用

50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Messaoudi, Maher Kameli, Sayed Mohammad Refaat, Shady S. Abu-Rub, Haitham Trabelsi, Mohamed Department of Electrical and Computer Engineering Texas A&M University at Qatar Doha Qatar School of Physics Engineering and Computer Science University of Hertfordshire Hatfield United Kingdom Electronics and Communications Engineering Department Kuwait College of Science and Technology Kuwait

ISBN: (纸本)9781665464543

Discharges, such as partial discharges (PDs) and corona discharges (CDs) are the most common faults that occur in insulation materials used in high voltage (HV) equipment. A high repetition rate of discharge activity indicates the severity of the defects that shorten the lifetime of electrical equipment, leading to insulation failure. To solve this, this paper proposes an efficient classification technique for corona discharge defect intensity using features obtained from statistical parameters such as the ignition voltage of CDs. The Recurrent Neural Network (RNN) is proposed to identify the intensity of corona discharges. A comprehensive experimental evaluation is conducted, to demonstrate the capabilities of the proposed solution. The exceptional predictive abilities of the long short-term memory (LSTM) method are the primary benefit of the proposed approach presented, with a potential for enhancing the performance of CD detection systems. The obtained results demonstrate the accuracy of the proposed model, indicating its potential for deployment in practical applications. The innovative approaches utilized in this paper will help engineers and operators quickly determine the severity (sharpness and curvature) of the protrusions or surface defects that cause CDs, solely based on measurements of the ignition voltage. © 2024 IEEE.

关键词： Long short-term memory

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Deep Learning-Based Classification of Conference Paper Reviews: Accept or Reject? 23th

Deep Learning-Based Classification of Conference Paper Revie...

引用

23rd International Conference on Intelligent Systems Design and Applications, ISDA 2023

作者： Prama, Tabia Tanzin Biswas, Al Amin Anwar, Md Musfique Department of Computer Science and Engineering Jahangirnagar University Dhaka Bangladesh Department of Computer Science and Engineering Bangabandhu Sheikh Mujibur Rahman University Kishoreganj Bangladesh

ISBN: (纸本)9783031647758

Peer review plays a crucial role in ensuring that papers published in conferences are as true, valid, and accurate as possible. As part of the publication process, the scholarly work of an academic author undergoes review by domain experts, reflecting the commitment to uphold integrity. With the growing volume of scholarly submissions at the conference, the classification of the paper reviews through an automated classification system has become a pressing requirement. The primary aim of this research is to develop an automated classification system for conference paper reviews into two categories: accept and reject. However, the objective of this paper is twofold: developing an automated classification system and explaining the decision-making process through explainable artificial intelligence (XAI). First, to develop an automated classification system, we present a deep learning-based framework. Next, to explain the results produced by deep learning (DL) models, Local Interpretable Model-Agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), and Anchor Explainer have been applied. Among the applied DL techniques, the Bi-GRU-LSTM-CNN model attained the highest accuracy of 95.33% which indicates that the proposed methodology is able to replicate the human decision-making process in an effective manner. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Decision making

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Temporal Dynamics of Consumer Engagement in E-Commerce

Temporal Dynamics of Consumer Engagement in E-Commerce

引用

2025 International Conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Thiyagarajan, Gomathi Swathi, Y. Cmr Institute of Technology Department of Computer Applications Bengaluru India Gopalan College of Engineering and Management Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331538538

This paper explores into an indepth analysis of consumer behavior on an e-commerce platform, extending the study period to include November 2019 through February 2020. Looking at user interactions that come under the categories of views, carts, purchases, and removals from cart along with process discovery visualizations will help identify complex patterns of engagement, measure the impact of interface changes and quantify marketing intervention's successes. This research uses sophisticated data analytics through process mining to provide recommendations for improving the e-commerce journey through increased user satisfaction and better conversion rates. © 2025 IEEE.

关键词： Marketplaces

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Generalization of Relay Node Deployment Problem for Wireless Sensor Network on jMetal 15th

Generalization of Relay Node Deployment Problem for Wirele...

引用

15th International Conference on Genetic and Evolutionary Computing, ICGEC 2023

作者： Kong, Lingping Gajdos, Petr Pan, Jeng-Shyang Snášel, Václav Faculty of Electrical Engineering and Computer Science VŠB-Technical University of Ostrava Ostrava Czech Republic College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China

ISBN: (纸本)9789819700677

In Wireless Sensor Networks (WSNs), the relay nodes deployment problem is essential, as the relay nodes serve as cluster heads aggregating the data packages from sensors. Further, the relay nodes transmit collected packets to the Sink node (central processing point) via wired or wireless paths, thus forming a connected network. Various sensor networks tools, such as NetTopo, PiccSIM integrated with NS2, and Atarraya, improve the performance of simulation and protocols in WSNs. Those tools are not easy to start with for researchers whose research field is uncorrelated to the sensor network. Moreover, the studies based on heuristic algorithms on this problem focus more on a two-dimensional plane or an entire three-dimensional space with homogeneous nodes and omnidirectional sensors. Few pieces of research concentrate on relay nodes on three-dimensional terrain contour deployment, which simulates more natural circumstances to a real-life situation. Besides, due to the diverse optimization objectives, arrangement strategies, and sensor node requirements for distinct networks, conducting experiments to compare with existing deployed methods become more challenging. A generalization tool integrating the problem, network environment, and optimization objectives into a framework is essential, where a user can plug in blocks easily with framework interfaces, which makes the comparison more convenient. This design concentrates on users unfamiliar with network settings/models, such as NS2, adopting heuristic algorithms to solve WSN-related problems. In this study, we fill this gap by implementing a generalization framework based on the jMetal toolbox, which makes the WSN problem a benchmark, and benefits its solving by metaheuristic algorithms. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Heuristic algorithms

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Univariate Time Series Data Correction Method using LSTM Autoencoder with Temporal Distance 6

Univariate Time Series Data Correction Method using LSTM Aut...

引用

6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024

作者： Yun, Sohyeon Kim, Han-Joon University of Seoul Department of Electrical and Computer Engineering Seoul Korea Republic of

ISBN: (纸本)9798350344349

High-quality data is essential to increase the reliability of machine learning-based prediction models. For time series data, anomalies significantly reduce the accuracy of prediction models. In this paper, we propose a novel time series data correction method that converts abnormal values of univariate time series data into normal ones. For anomaly detection and correction, we utilize the LSTM Autoencoder model, where we propose a new weight function that considers temporal distance. Through experiments using the open NAB data, we show that our proposed method is superior to the recent conventional methods. © 2024 IEEE.

关键词： Anomaly detection

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An Internet of Things Testbed for Education and Community Research

An Internet of Things Testbed for Education and Community Re...

引用

2023 IEEE International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2023

作者： Swaim, John Workman, Chad Di, Jia Du, Xiaojiang University of Arkansas Computer Science and Computer Engineering Department FayettevilleAR United States University of Arkansas Electrical Engineering Department FayettevilleAR United States Stevens Institute of Technology Electrical and Computer Engineering Department HobokenNJ United States

ISBN: (纸本)9798350322347

The testbed presented in this study supplies various devices to emulate a smart home. The paper highlights how devices can be connected and programmed to perform functions using an application programming interface. Remote-controlled robots in the testbed enable a user to manipulate, monitor, and configure home-based Internet-of-Things (IoT) technologies. The paper describes the equipment used in the testbed, including a wireless security camera, a smart lock, a climate sensor, and two types of robots. Security measures implemented in the testbed are also discussed. Several application scenarios are presented and analyzed on how they were accomplished to demonstrate the functionalities. The smart home testbed is a useful resource for education and development, as it allows for sufficient performance using a single control point. © 2023 IEEE.

关键词： Testbeds

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Secure and Efficient Deduplication for Multimedia Sharing in Privacy-Sensitive Cloud Ecosystems 3

Secure and Efficient Deduplication for Multimedia Sharing in...

引用

3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Suresh, A. Murali, Erasappa Anjali Sri, V. Sumana, E. Sathwik, Gopaluni Venkata Sai Devendra Reddy, P. Department of Computer Science and Engineering Siddharth Institute of Engineering & Technology Puttur India

ISBN: (纸本)9798331527549

Current cloud-based systems face the challenge of managing storage and eliminating redundant data associated with the exponentially growing multimedia content, where numerous semantically similar but nonidentical files are included. In this paper we introduce a novel secure deduplication framework for encrypted cloud storage. This proposed system employs similarity-preserving hashing, fuzzy PoW protocol, and attribute-based encryption to perform efficient deduplication without compromising security and providing ownership verification. Security and deduplication rate scalability are improved over brute-force and ownership-faking attacks by dynamically varying thresholds based on file characteristics. Experimental evaluation shows that a 48% average deduplication rate and resistance to common attack vectors make it suitable for real-world cloud storage scenarios. The framework fills critical gaps in existing approaches and provides an innovative solution for dealing with similarity-aware data deduplication in privacy-sensitive environments. © 2025 IEEE.

关键词： Cloud storage

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Removing Speckle Noise from Synthetic-Aperture Radar Images Using Artificial Intelligence Techniques 11

Removing Speckle Noise from Synthetic-Aperture Radar Images ...

引用

11th International Symposium on Telecommunication, IST 2024

作者： Moein, Fateme Taban, Mohammad Reza Isfahan University of Technology Department of Electrical and Computer Engineering Isfahan Iran

ISBN: (纸本)9798350356250

In recent times, Synthetic-Aperture Radar (SAR) images have gained significant importance due to their extensive use in both military and civilian applications. However, these images are often compromised by speckle noise, which severely hampers their interpretability and utility across various domains. This paper explores a range of image denoising techniques, with a particular focus on deep learning-based approaches, while also considering traditional methods. Given the critical nature of the subject, real SAR images have been rigorously analyzed. The study emphasizes that speckle noise in real SAR images is far more detrimental than in simulated scenarios, revealing the limitations of artificial intelligence algorithms when used in isolation for denoising tasks. To address this challenge, the proposed algorithm in this paper first preprocesses the real data using classical methods before applying a neural network for denoising. Both quantitative metrics and visual assessments demonstrate that this hybrid approach not only outperforms standard denoising methods but also preserves critical information in SAR images more effectively. © 2024 IEEE.

关键词： Radar imaging

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