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Cost-Optimized Task Offloading for Dependent Applications in Collaborative Edge and Cloud Computing

IEEE Internet of Things Journal 2025年第9期12卷 12975-12988页

作者： Yuan, Haitao Hu, Qinglong Wang, Shen Bi, Jing Buyya, Rajkumar Lu, Jinhu Yang, Jinhong Zhang, Jia Zhou, MengChu Beihang University School of Automation Science and Electrical Engineering Beijing100191 China City University of Hong Kong Department of Computer Science Kowloon Tong Hong Kong Beijing University of Technology School of Software Engineering Faculty of Information Technology Beijing100124 China Laboratory School of Computing and Information Systems MelbourneVIC3010 Australia CSSC Systems Engineering Research Institute Beijing100036 China Southern Methodist University Lyle School of Engineering Department of Computer Science DallasTX75205 United States New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States

A collaborative system that includes mobile devices (MDs), edge nodes (ENs), and the cloud is needed where ENs at the network edge can run offloaded tasks of MDs with limited resources and energy for timely processing for latency-sensitive applications. Unlike existing studies, we formulate a total cost minimization problem for the system for applications, which can be divided into several interdependent subtasks. Each subtask can be executed in MDs, ENs, and the cloud. This work formulates a mixed-integer nonlinear program to minimize the total system cost. To address it, a novel meta-heuristic optimization algorithm called Genetic Simulated-annealing-based Particle swarm optimization with Auto-Encoder (GSPAE) is proposed, which innovatively combines feature extraction of deep learning and global search of meta-heuristic optimization. Genetic operations provide diverse solutions, the Metropolis acceptance of annealing offers a robust global search, and autoencoders (AEs) extract distribution characteristics of particles toward high-quality regions for fast convergence. Thus, GSPAE optimizes the associations between ENs and MDs and the scheduling of subtasks among MDs, ENs, and the cloud. Experiments with large-scale Google cluster datasets show that compared to state-of-the-art benchmark methods, GSPAE reduces the total cost by at least 17% while strictly meeting limits of application latency, available energy, computing, and communication resources of ENs and MDs. © 2014 IEEE.

关键词： Mobile edge computing

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An Intelligent Optimization Technique Of Automatic Speech Recognition For Smart Homes 13

An Intelligent Optimization Technique Of Automatic Speech Re...

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13th International Conference on Research in ComputIng at Feminine, RIF 2024

作者： Aly, Rabab Hamed M. Hussein, Aziza I. The Higher Institute for Management and Information Technology Minya Egypt Electrical and Computer Engineering Department College of Engineering Effat University Jeddah Saudi Arabia Computer Science Department Faculty of Computer Science Nahda University Beni Suef Egypt

The creation of new approaches to the design and configuration of smart buildings relies heavily on AI tools and Machine Learning (ML) algorithms, particularly optimization techniques. The widespread use of electronic devices has sparked a strong desire to incorporate the Internet of Things (IoT) into houses, leading to the development of smart homes. As networked gadgets proliferate rapidly, this phenomenon is characterized by rapid proliferation. In smart buildings, smart cities, smart grids, and smart homes, interconnected electronic devices are becoming more popular. The objective of this paper is to enhance the functionality of home automation systems through the performance of speech recognition using the Bat-Salp Swarm Optimization (BSSO). This paper investigates the notion of (BSSO), a data analysis methodology that facilitates the automated construction of analytical models. The implementation of BSSO provides an enhancement to the feature selection process in speech recognition, providing an approximation solution that improves the accuracy of system decisions. The use of the BSSO technique improves the precision of the voice recognition system and also incorporates an Artificial Neural Network (ANN) for the classification part. The findings substantiated the efficacy of the employed methodology. © 2024 Copyright for this paper by its authors.

关键词： Bat Optimization Algorithm Salp Optimization Algorithm Smart homes Speech recognition

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AFuNet: an attention-based fusion network to classify texts in a resource-constrained language

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Neural Computing and Applications 2025年第9期37卷 6725-6748页

作者： Hossain, Md. Rajib Hoque, Mohammed Moshiul Dewan, M. Ali Akber Hoque, Enamul Siddique, Nazmul Department of Computer Science & Engineering Chittagong University of Engineering & Technology Chittagong4349 Bangladesh School of Computing and Information Systems Faculty of Science and Technology Athabasca University AthabascaABT9S 3A3 Canada School of Information Technology York University TorontoONM3J 1P3 Canada School of Computing Engineering and Intelligent Systems Ulster University Coleraine United Kingdom

In the era of widespread Internet use and extensive social media interaction, the digital realm is accumulating vast amounts of unstructured text data. This unstructured data often contain undesirable information, necessitating time-consuming manual classification efforts. An intelligent text classification system capable of automatically categorizing digitized texts based on semantic meaning is crucial. However, this task is particularly challenging for low-resource languages like Bengali due to a shortage of annotated corpora, issues with out-of-vocabulary words, lack of domain-specific hyperparameter tuning, limited ability to extract generalized text features, and class imbalances within the corpus. AFuNet: an attention-based fusion network to classify texts in a resource-constrained language. AFuNet undergoes a comprehensive four-phase experimental process, including baseline model evaluation and hyperparameter tuning, late fusion and model selection, attention-based early fusion and model identification, and an ablation study with impact analysis. Fine-tuned based on five Bengali text classification corpora, AFuNet achieves impressive accuracies: 96.60 ± 0.2 (BTCC11), 85.37 ± 0.2 (OSBC), 97.35 ± 0.2 (BARD), 93.74 ± 0.2 (IndicNLP), and 96.51 ± 0.2 (ProthomAlo). In comparison with previous state-of-the-art models on these corpora, AFuNet demonstrates significant accuracy improvements ranging from 0.54% to 4.49%, showcasing its effectiveness in advancing text classification capabilities for the Bengali language. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Semantics

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Boundary Extraction for Unseen Entities Prediction Using Bidirectional Long Short-Term Memory 17th

Boundary Extraction for Unseen Entities Prediction Using Bid...

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17th International Conference on Multi-disciplinary Trends in Artificial Intelligence, MIWAI 2024

作者： Kuntichod, Watcharet Lee, Wilaiporn Prayote, Akara Department of Computer and Information Science King Mongkut’s University of Technology North Bangkok Bangkok10800 Thailand Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangkok10800 Thailand

ISBN: (纸本)9789819606917

Named Entities Recognition (NER), which is a task to identify and classify named entities within text, has gained significant popularity in recent years. This task often requires pre-labeled data and large datasets to achieve high accuracy. A key challenge in NER is extracting unseen entities that have not been previously labeled or are new terms, posing difficulties in adapting to rapidly changing data and the emergence of new vocabularies. In this article, we propose a model utilizing Bidirectional Long Short-Term Memory (Bi-LSTM) for boundary extraction. The model aims to enhance the detection and labeling of unseen entities by leveraging Bi-LSTM's strengths in capturing contextual information and accurately identifying entities boundaries. The model demonstrates high potential in identifying unseen names without pre-labeling. Our study demonstrates the model's showcasing its effectiveness in discovering unlabeled words and unseen entities in Thai language datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Labeled data

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Eddy Detection of Lakes in the Qinghai–Tibet Plateau Based on Optical Remote Sensing and Deep Learning

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 11996-12007页

作者： Bai, Bingxin Mu, Lixia Yang, Jie Huang, Baoxiang Ma, Chunyong Chen, Ge Ocean University of China Faculty of Information Science and Engineering Qingdao266100 China Laoshan Laboratory Qingdao266237 China Ocean University of China College of Marine Technology Qingdao266100 China Qingdao University College of Computer Science and Technology Qingdao266071 China

Lake eddies are dynamic phenomena prevalent in large lake systems, playing a critical role in affecting lake physics, nutrient transport, and ecological balance. Efficient and precise detection of these features is essential for advancing scientific understanding of lake dynamics and for improving environment management strategies. Despite their importance, the study of lake eddies remains under-explored, and current detection methods are insufficient. This study addresses this gap by proposing a lake eddy identification framework based on the YOLOv7 deep learning model, utilizing Landsat 8/9 satellite imagery acquired from April 2013 to December 2023. Qinghai Lake, Selin Co, and Nam Co on the Qinghai–Tibet Plateau were selected as the study areas. The method involves the development of an image enhancement process to optimize the visibility of lake eddies in remote sensing imagery, followed by the implementation of the YOLOv7 model for detection. The framework identified 912 cyclonic eddies and 102 anticyclonic eddies on the three aforementioned lakes, providing detailed information on their spatial distribution and morphology. This approach demonstrates significant potential for advancing the study of lake hydrodynamics and energy transport substances in aquatic ecosystems. © 2008-2012 IEEE.

关键词： Aquatic ecosystems

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Image Contrast Enhancement using Fuzzy Logic with Metaheuristics 24

Image Contrast Enhancement using Fuzzy Logic with Metaheuris...

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11th International Conference on Networking, Systems and Security, NSysS 2024

作者： Kabir, Mohimenul Mobin, Jaiaid Hassanat, Ahmad Sohel Rahman, M. Department of Computer Science Bangladesh University of Engineering and Technology Dhaka Bangladesh Rochester Institute of Technology Rochester United States Faculty of Information Technology Mutah University Karak Mutah Jordan Bangladesh University of Engineering and Technology Dhaka Bangladesh

ISBN: (纸本)9798400711589

In this work, we present a method to enhance image contrast using a fuzzy logic-based transformation function. Our goal is to develop an image-specific transformation by tuning the fuzzy system. We employ Hill Climbing and Genetic Algorithms in various configurations to evolve the fuzzy system, conducting multiple experiments. Different variants of our method are tested on several images, and the two variants with the highest fitness scores are selected. Additionally, we compare against existing contrast enhancement technique and conduct a survey to evaluate the visual improvements made by these two variants. The survey results indicate that one method effectively enhances image contrast visually. © 2024 Copyright held by the owner/author(s).

关键词： computer circuits

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Deep learning-based method for detection of copy-move forgery in videos

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Neural Computing and Applications 2025年第14期37卷 8451-8464页

作者： Elbarougy, Reda Abdelfatah, Osama Behery, G.M. El-Badry, Noha M. Department of Artificial Intelligence and Data Science College of Computer Science and Engineering University of Ha’il Ha’il81481 Saudi Arabia Department of Information Technology Faculty of Computer and Artificial Intelligence Damietta University New Damietta Kafr Saad Egypt Department of Computer Science Faculty of Computer and Artificial Intelligence Damietta University New Damietta Kafr Saad Egypt Department of Mathematics Faculty of Science Damietta University New Damietta Kafr Saad Egypt

Video forgery is one of the most serious problems affecting the credibility and reliability of video content. Therefore, detecting video forgery presents a major challenge for researchers due to the diversity of forgery types, the modernity of the programs used in forgery operations, and the abundance of information and content present in videos. The seriousness of this issue arises from the widespread use of videos in vital fields that require very high accuracy with no room for doubt or error, such as courtrooms, journalism, and others. Copy-move forgery is one of the most common and dangerous types of video forgery because of the difficulty in identifying it with the naked eye and the great diversity of forgery techniques involved in this particular type. One of the biggest challenges researchers have faced in the past is the complexity of the steps required to detect forgery in videos, leading to significant computational complexity and time consumption. The proposed method also aims to achieve better results than previous methods while reducing computational operations. Ultimately, forgery is detected with great efficiency. Compared to previous methods used for detecting copy-move forgery in the Rewind dataset, the proposed method achieves the highest F1, reaching 0.86, with a significant difference of 0.13 compared to the best result of the previous methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Deep learning

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DiRLoc: Disentanglement Representation Learning for Robust Image Forgery Localization

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IEEE Transactions on Dependable and Secure Computing 2025年第3期22卷 2841-2854页

作者： Sheng, Ziqi Qu, Zuomin Lu, Wei Cao, Xiaochun Huang, Jiwu Sun Yat-sen University School of Computer Science and Engineering MoE Key Laboratory of Information Technology Guangdong Province Key Laboratory of Information Security Technology Guangzhou510006 China School of Cyber Science and Technology Sun Yat-sen University Shenzhen Campus Shenzhen518107 China Shenzhen MSU-BIT University Guangdong Laboratory of Machine Perception and Intelligent Computing Faculty of Engineering Shenzhen518116 China

Deep Learning image forgery localization methods have achieved remarkable results but cannot maintain comparable performance when the forgery images are JPEG compressed, a format that is widely used in daily information transmission. The robustness against JPEG compression has become a bottleneck to the practical application of image forgery localization. To address this issue, a robust image forgery localization framework is proposed against the performance degradation caused by JPEG compression. Specifically, a cutting-edge progressive disentanglement strategy is proposed that incorporates coarse-grained image disentanglement to mitigate the detrimental effects of general JPEG compression, while harnessing the ability of fine-grained element disentanglement to separate multi-scale artifacts, thereby minimizing interference from content information. Moreover, the decision strategy is carefully designed to reinforce subtle signals from tampered areas, including artifacts fusion block reasoning multi-scale artifacts and dual attention block that learn more about forgery-related features. Extensive visualizations and experiments demonstrate that our method can achieve competitive performance in general JPEG-resistant image forgery localization, especially in the performance of generalization experiments. © 2004-2012 IEEE.

关键词： Image compression

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Machine Learning-Based Prediction of Corrosion Inhibition Efficiency of Expired Pharmaceuticals: Model Development and Application

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Journal of Bio- and Tribo-Corrosion 2025年第1期11卷 1-12页

作者： Putra, Dzaki Asari Surya Ardyansyah, Nibras Bahy Putranto, Nicholaus Verdhy Trisnapradika, Gustina Alfa Akrom, Muhamad Rustad, Supriadi Dipojono, Hermawan Kresno Maezono, Ryo Faculty of Computer Science Study Program in Informatics Engineering Universitas Dian Nuswantoro Semarang50131 Indonesia Research Center for Materials Informatics Faculty of Computer Science Universitas Dian Nuswantoro Semarang50131 Indonesia Quantum and Nano Technologies Research Group Faculty of Industrial Technology Institut Teknologi Bandung Bandung40132 Indonesia School of Information Science Japan Advanced Institute of Science and Technology Ishikawa923-1292 Japan

Corrosion poses a significant challenge in industries due to material degradation and high maintenance costs, making effective inhibitors essential. Recent studies suggest expired pharmaceuticals as alternative corrosion inhibitors, but identifying the most efficient compounds can be time-consuming. This study applies machine learning models, particularly Gradient Boosting Regressor (GBR), to predict corrosion inhibition efficiency (CIE). The developed model is integrated into a web-based application, enabling real-time, accurate CIE predictions. Results show GBR as the more precise model for predicting CIE, with low error rates. The streamlit-based web application offers a user-friendly platform, facilitating quick identification of suitable inhibitors. It is flexible, supporting analysis beyond corrosion inhibitors, and significantly enhances the efficiency of the evaluation process. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Reliable Provisioning of Low-Latency and High-Bandwidth Extended Reality Live Streams

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IEEE Journal on Selected Areas in Communications 2025年第5期43卷 1755-1766页

作者： Le, Giap Hoang, Vinh Truong Ferdousi, Sifat Marotta, Andrea Xu, Sugang Hirota, Yusuke Awaji, Yoshinari Tornatore, Massimo Mukherjee, Biswanath UC Davis Department of Electrical and Computer Engineering DavisCA95616 United States Ho Chi Minh City Open University Faculty of Information Technology Ho Chi Minh City700000 Viet Nam University of L’Aquila Department of Information Engineering Computer Science and Mathematics L’Aquila67100 Italy NICT Tokyo184-8795 Japan Politecnico di Milano Department of Electronics Information and Bioengineering Milan20133 Italy

The networking industry is offering new services leveraging recent technological advances in connectivity, storage, and computing such as mobile communications and edge computing. In this regard, extended reality, a term encompassing virtual reality, augmented reality, and mixed reality, can provide unprecedented user experience and pioneering service opportunities such as: live concerts, sports, and other events;interactive gaming and entertainment;immersive education, training, and demos. These services require high-bandwidth, low-latency, and reliable connections, and are supported by next-generation ultra-reliable and low-latency communications in the vision of 6G mobile communication systems. In this work, we devise a novel scheme, called backup from different data centers with multicast and adaptive bandwidth provisioning, to admit reliable, low-latency, and high-bandwidth extended reality live streams in next-generation networks. We consider network services where contents are non-cacheable and investigate how backup services can be offered by different data centers with multicast and adaptive bandwidth provisioning. Our proposed service-provisioning scheme provides protection not only against link failures in the physical network but also against computing and storage failures in data centers. We develop scalable algorithms for the service-provisioning scheme and evaluate their performance on various complex network instances in a dynamic environment. Numerical results show that, compared to conventional service-provisioning schemes such as those seeking backup services from the same data center, our proposed service-provisioning scheme efficiently utilizes network resources, ensures higher reliability, and guarantees low latency;hence, it is highly suitable for extended reality live streams. © 1983-2012 IEEE.

关键词： Multicasting

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