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IEEE Conference on Global Communications (GLOBECOM)

作者： Meng Tian Zhicheng Liu Chao Qiu Xiaofei Wang Dusit Niyato Victor C. M. Leung College of Intelligence and Computing Tianjin University Tianjin China College of Computing and Data Science Nanyang Technological University Singapore College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Recent advances in the diffusion model mark a significant leap in AI-generated image technology while extending its application to the Internet of Things (IoT). However, deploying these models on resource-constrained edge devices presents considerable challenges, primarily due to their high computational energy demands and stringent quality requirements. In response to these challenges, we introduce a collaborative inference system tailored for green edge networks. First, we propose a multi-exit U-ViT model that achieves the balance between inference quality and processing speed by allowing adaptive selection of exit points during diffusion for efficient processing. Next, we develop a novel generative AI-assisted reinforcement learning algorithm that controls the exit selections and offloading decisions of the device to achieve maximum global gain. Furthermore, we design a novel policy network incorporating an attention-based state-embedded policy network to enhance the algorithm’s ability to perceive and make decisions about the state of the environment. Experimental results demonstrate that our system achieves energy-efficient inference while ensuring the quality of the generated content.

关键词： Energy consumption Adaptation models Generative AI Computational modeling Image edge detection Green products Collaboration Inference algorithms Energy efficiency Internet of Things

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Encryption Algorithm for Securing Non-Disclosure Agreements in Outsourcing Offshore software Maintenance

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computers, Materials & Continua 2022年第11期73卷 3827-3845页

作者： Atif Ikram Masita Abdul Jalil Amir Bin Ngah Nadeem Iqbal Nazri Kama Azri Azmi Ahmad Salman Khan Yasir Mahmood Assad Alzayed Faculty of Ocean Engineering Technology and Informatics University Malaysia TerengganuKuala TerengganuMalaysia Department of Computer Science&Information Technology Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Department of Software Engineering Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Advanced Informatics Department Razak Faculty of Technology and InformaticsUniversiti Teknologi Malaysia54100Kuala LumpurMalaysia Computer Science and Information Systems Department College of Business Studies Public Authority for Applied Education and Training(PAAET)AdailiyaKuwait

Properly created and securely communicated,non-disclosure agreement(NDA)can resolve most of the common disputes related to outsourcing of offshore software maintenance(OSMO).Occasionally,these NDAs are in the form of *** the work is done offshore,these agreements or images must be shared through the Internet or stored over the *** breach of privacy,on the other hand,is a potential threat for the image owners as both the Internet and cloud servers are not void of *** article proposes a novel algorithm for securing the NDAs in the form of *** an agreement is signed between the two parties,it will be encrypted before sending to the cloud server or travelling through the public network,the *** the image is input to the algorithm,its pixels would be scrambled through the set of randomly generated rectangles for an arbitrary amount of *** confusion effects have been realized through an XOR operation between the confused image,and chaotic ***,5D multi-wing hyperchaotic system has been employed to spawn the chaotic vectors due to good properties of chaoticity it *** machine experimentation and the security analysis through a comprehensive set of validation metric vividly demonstrate the robustness,defiance to the multifarious threats and the prospects for some real-world application of the proposed encryption algorithm for the NDA images.

关键词： Non-disclosure agreement encryption decryption secret key chaoticmap confusion diffusion

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Development of Area-Based Explosive Trace Detection Using Deep Transfer Learning

Development of Area-Based Explosive Trace Detection Using De...

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Innovative engineering sciences and Technological Research (ICIESTR), International Conference on

作者： Monday F. Ohemu Ambrose A. Azeta Ibrahim A. Adeyanju Chukwuemeka C. Obasi Department of Electrical/Electronics Engineering Airforce Institute of Technology Kaduna Nigeria Department of Software Engineering Namibia University of Science and Technology Namibia Department of Computer Engineering Federal University Oye-Ekiti Nigeria Department of Computer Engineering Edo State University Uzairue

ISBN: (数字)9798350348637

ISBN: (纸本)9798350348644

Terrorism poses a significant global threat, necessitating comprehensive security measures to protect people and critical infrastructure. With the increasing frequency of explosive-based attacks, there is a pressing need for advanced detection systems to mitigate potential devastation. This paper presents the development of an area-based explosive trace detection system using deep transfer learning. Leveraging deep learning technology trained on a substantial dataset collected from sensor networks, the proposed model, named Deep Transfer Learning for Explosive Trace Detection (DTLETD), demonstrates remarkable capabilities in classifying explosive gases based on concentrations of Carbon (C), Hydrogen (H), Oxygen (O), and Nitrogen (N). The methodology involves converting the dataset into 2D data using a serial data to image generator, followed by the adaptation of a pre-trained model for transfer learning. The DTLETD model outperformed conventional Convolutional Neural Network (CNN) models, achieving a training time reduction of about 92 seconds compared to 1287 seconds for the CNN model. Additionally, the transfer learning model exhibits faster convergence with nearly zero losses during training and validation, yielding an impressive accuracy of 99.7% and an average AUC value of approximately 0.89. This research significantly advances the field of explosive trace detection by integrating machine learning-based approaches with deep transfer learning techniques, thereby enhancing security protocols and mitigating potential threats.

关键词： Training Adaptation models Accuracy Protocols Terrorism Transfer learning Pressing Explosives Data models Convolutional neural networks

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SmartEdge: Smart Healthcare End-to-End Integrated Edge and Cloud Computing System for Diabetes Prediction Enabled by Ensemble Machine Learning 15

SmartEdge: Smart Healthcare End-to-End Integrated Edge and C...

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15th IEEE International Conference on Cloud Computing Technology and science, CloudCom 2024

作者： Hennebelle, Alain Dieng, Qifan Ismail, Leila Buyya, Rajkumar Independent Researcher in Information Technology Systems and Artificial Intelligence Applications Abu Dhabi United Arab Emirates Lab School of Computing and Information Systems University of Melbourne Melbourne Australia Lab Department of Computer Science and Software Engineering Al-Ain Abu Dhabi United Arab Emirates

ISBN: (纸本)9798331507589

The Internet of Things (IoT) revolutionizes smart city domains such as healthcare, transportation, industry, and education. The Internet of Medical Things (IoMT) is gaining prominence, particularly in smart hospitals and Remote Patient Monitoring (RPM). The vast volume of data generated by IoMT devices should be analyzed in real-time for health surveillance, prognosis, and prediction of diseases. Current approaches relying on Cloud computing to provide the necessary computing and storage capabilities do not scale for these latency-sensitive applications. Edge computing emerges as a solution by bringing cloud services closer to IoMT devices. This paper introduces SmartEdge, an AI-powered smart healthcare end-to-end integrated edge and cloud computing system for diabetes prediction. This work addresses latency concerns and demonstrates the efficacy of edge resources in healthcare applications within an end-to-end system. The system leverages various risk factors for diabetes prediction. We propose an Edge and Cloud-enabled framework to deploy the proposed diabetes prediction models on various configurations using edge nodes and main cloud servers. Performance metrics are evaluated using, latency, accuracy, and response time. By using ensemble machine learning voting algorithms we can improve the prediction accuracy by 5% versus a single model prediction. © 2024 IEEE.

关键词： Electronic health record

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Learning Two-factor Representation for Magnetic Resonance Image Super-resolution

Learning Two-factor Representation for Magnetic Resonance Im...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Weifeng Wei Heng Chen Pengxiang Su School of Information Engineering Nanchang University Nanchang China School of Mathematics and Computer Science Nanchang University Nanchang China School of Software Nanchang University Nanchang China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Magnetic Resonance Imaging (MRI) requires a trade-off between resolution, signal-to-noise ratio, and scan time, making high-resolution (HR) acquisition challenging. Therefore, super-resolution for MR image is a feasible solution. However, most existing methods face challenges in accurately learning a continuous volumetric representation from low-resolution image or require HR image for supervision. To solve these challenges, we propose a novel method for MR image super-resolution based on two-factor representation. Specifically, we factorize intensity signals into a linear combination of learnable basis and coefficient factors, enabling efficient continuous volumetric representation from low-resolution MR image. Besides, we introduce a coordinate-based encoding to capture structural relationships between sparse voxels, facilitating smooth completion in unobserved regions. Experiments on BraTS 2019 and MSSEG 2016 datasets demonstrate that our method achieves state-of-the-art performance, providing superior visual fidelity and robustness, particularly in large up-sampling scale MR image super-resolution.

关键词： Visualization Image coding Magnetic resonance imaging Superresolution Magnetic resonance Encoding Robustness Speech processing Signal resolution Signal to noise ratio

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The Classification of Multiple-domain Samples based on Multiple Deep Learning Models 22

The Classification of Multiple-domain Samples based on Multi...

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2nd International Conference on Control and Intelligent Robotics, ICCIR 2022

作者： Xu, Junting Yang, Ping Jin, Guanghao Song, Qingzeng School of Computer Science and Technology Tian Gong University Tianjin China School of Software Tian Gong University Tianjin China School of Telecommunication Engineering Beijing Polytechnic Beijing China

ISBN: (纸本)9781450397179

This paper proposed a classification method that can classify the samples of multiple domains based on the cooperation of multiple models. On a sample, our method firstly predicts the domain that may contains this sample based on the output of trained models. Then, our method uses the models that are trained on this domain to classify the label of this sample. Our method can reduce the difficulty of training high performance model while can ensure high accuracy on the samples of multiple domains. Experiments show that our method achieved higher accuracy than the existing methods. © 2022 ACM.

关键词： Deep learning

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Transformers in Small Object Detection: A Benchmark and Survey of State-of-the-Art

arXiv

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arXiv 2023年

作者： Rekavandi, Aref Miri Rashidi, Shima Boussaid, Farid Hoefs, Stephen Akbas, Emre Bennamoun, Mohammed Department of Computer Science and Software Engineering The University of Western Australia Australia Department of Electrical Electronics and Computer Engineering The University of Western Australia Australia Defence Science and Technology Group Australia Department of Computer Engineering Middle East Technical University Turkey

Transformers have rapidly gained popularity in computer vision, especially in the field of object recognition and detection. Upon examining the outcomes of state-of-the-art object detection methods, we noticed that transformers consistently outperformed well-established CNN-based detectors in almost every video or image dataset. While transformer-based approaches remain at the forefront of small object detection (SOD) techniques, this paper aims to explore the performance benefits offered by such extensive networks and identify potential reasons for their SOD superiority. Small objects have been identified as one of the most challenging object types in detection frameworks due to their low visibility. We aim to investigate potential strategies that could enhance transformers’ performance in SOD. This survey presents a taxonomy of over 60 research studies on developed transformers for the task of SOD, spanning the years 2020 to 2023. These studies encompass a variety of detection applications, including small object detection in generic images, aerial images, medical images, active millimeter images, underwater images, and videos. We also compile and present a list of 12 large-scale datasets suitable for SOD that were overlooked in previous studies and compare the performance of the reviewed studies using popular metrics such as mean Average Precision (mAP), Frames Per Second (FPS), number of parameters, and more. Researchers can keep track of newer studies on our web page, which is available at: https://***/arekavandi/Transformer-SOD. © 2023, CC BY-NC-SA.

关键词： Object recognition

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Intelligent Question Answering System Based on Knowledge Graph 23

Intelligent Question Answering System Based on Knowledge Gra...

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23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Feng, Xijin Liu, Qi Liu, Xiaodong School of Computer and Software Nanjing University of Information Science and Technology Engineering Research Center of Digital Forensics Nanjing China School of Computing Edinburgh Napier University Edinburgh United Kingdom

ISBN: (纸本)9781665494571

In order to build a smart city and pursue more efficient city management, various industries have introduced intelligent question answering into process management. The intelligent question answering system based on the knowledge graph is dedicated to feeding back the results of natural language questions to users, which greatly simplifies the urban management process. From the four methods of template-based, semantic parsing, sorting and retrieval, and deep learning, the intelligent question answer system based on the knowledge graph is summarized, and combined with the current application status, it summarizes and introduces the question answering system based on the knowledge graph that helps various industries in the construction of smart cities. It helps to provide new ideas for more researchers who are committed to urban management and knowledge graph question answering. © 2021 IEEE.

关键词： Industries Deep learning Smart cities Semantics Natural languages Sorting

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FT-AED: Benchmark Dataset for Early Freeway Traffic Anomalous Event Detection

arXiv

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arXiv 2024年

作者： Coursey, Austin Ji, Junyi Quinones-Grueiro, Marcos Barbour, William Zhang, Yuhang Derr, Tyler Biswas, Gautam Work, Daniel B. Institute for Software Integrated Systems United States Department of Computer Science Department of Civil and Environmental Engineering Vanderbilt University United States

Early and accurate detection of anomalous events on the freeway, such as accidents, can improve emergency response and clearance. However, existing delays and errors in event identification and reporting make it a difficult problem to solve. Current large-scale freeway traffic datasets are not designed for anomaly detection and ignore these challenges. In this paper, we introduce the first large-scale lane-level freeway traffic dataset for anomaly detection. Our dataset consists of a month of weekday radar detection sensor data collected in 4 lanes along an 18-mile stretch of Interstate 24 heading toward Nashville, TN, comprising over 3.7 million sensor measurements. We also collect official crash reports from the Nashville Traffic Management Center and manually label all other potential anomalies in the dataset. To show the potential for our dataset to be used in future machine learning and traffic research, we benchmark numerous deep learning anomaly detection models on our dataset. We find that unsupervised graph neural network autoencoders are a promising solution for this problem and that ignoring spatial relationships leads to decreased performance. We demonstrate that our methods can reduce reporting delays by over 10 minutes on average while detecting 75% of crashes. Our dataset and all preprocessing code needed to get started are publicly released at https://***/ft-aed/to facilitate future research. Copyright © 2024, The Authors. All rights reserved.

关键词： Anomaly detection

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Electrocoagulation-based AZO DYE (P4R) Removal Rate Prediction Model using Deep Learning

Electrocoagulation-based AZO DYE (P4R) Removal Rate Predicti...

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2023 International Symposium on Green Technologies and Applications, ISGTA 2023

作者： Akoulih, Meryem Tigani, Smail Byoud, Fouzia Rharib, Meryem El Saadane, Rachid Pierre, Samuel Chehri, Abdellah Ghachtouli, Sanae El IME Lab Faculty of Science Hassan 2 University Casablanca20250 Morocco Research and Development Unit Accsellium LLC Fez Morocco Electrical Engineering Department SIRC-LaGeS Hassania School of Public Labors Casablanca20250 Morocco Department of Computer and Software Engineering Polytechnique Montréal MontréalQC Canada Department of Mathematics and Computer Science Royal Military College of Canada KingstonON11 K7K 7B4 Canada

This article presents a study on the effectiveness of electrocoagulation (EC) for the removal of azo dyes from wastewater. The analysis was performed using a combination of statistical methods, including density estimation, correlation analysis, and deep learning for electrocoagulation performance prediction. The results showed that electrocoagulation was able to effectively remove azo dyes from the wastewater, considering the energy consumption and the mass of flocs being important factors in the process. Deep Learning (DL) is used to build our predictive model using the datasets collected during the experimentation stage. Overall, the findings suggest that electrocoagulation is a promising technique for the treatment of wastewater containing azo dyes, and that the use of statistical and machine learning methods can aid in the optimization of the process. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Azo dyes

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