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IEEE Transactions on Green Communications and Networking 2025年

作者： Takenaka, Katsuyuki Sakai, Kazuya Chiang, Ivan Brian Sun, Min-Te Ku, Wei-Shinn Tokyo Metropolitan University Department of Electrical Engineering and Computer Science 6-6 Asahigaoka Hino Tokyo191-0065 Japan National Central University Department of Computer Science and Information Engineering Taoyuan320 Taiwan Auburn Universiry Department of Computer Science and Software Engineering AuburnAL36849 United States

Mobile social network (MSN) technologies are producing viable solutions to many critical scenarios, including disaster recovery, mobile crowdsourcing, mobile file caching, mobile agent systems, and so on. MSN performance as well as practices can be further improved by drones, which are small battery-powered unmanned aerial vehicles. However, to the best of our knowledge, there is no routing protocol designed for drone-augmented MSNs. Therefore, in this paper, we first introduce a new routing paradigm, called drone-augmented opportunistic routing for landmark-based MSNs. By incorporating the random mobility of mobile nodes as well as the deterministic mobility of drones, the opportunistic path model is defined. The key idea of our path model is the reduction to anycast-like routing to multiple destinations with different deadlines, in which the high mobility of drones is exploited. Then, we propose the drone-augmented opportunistic routing (DAOR) protocol, which forwards messages along the path with the smallest weight based on the greedy strategy. The simulation results demonstrate that the proposed DAOR achieves its design goals. © 2025 IEEE.

关键词： Drones

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A Robust Methodology for Fruit Quality Prediction and Estimation Using Intelligent Learning Based Image Processing Logic

A Robust Methodology for Fruit Quality Prediction and Estima...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Malathi, K. Babu, Vunnava Dinesh Ramkumar, M. Ayyadurai, M. Vivek, K. Aljawawdeh, Hamzeh J. Saveetha University Department of Cse Chennai India Rv Institute of Technology Department of Cse Guntur India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Department of software engineering Faculty of information technology Zarqa University Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

Fruit quality prediction plays a vital role in agricultural and food industries, ensuring that only high-quality produce reaches the consumer. Traditional methods of assessing fruit quality rely on manual inspection, which is both time-consuming and prone to human error. This paper presents a robust methodology for fruit quality prediction and estimation, utilizing a hybrid deep learning model that integrates DenseN et for feature extraction with LightGBM for efficient classification. The proposed model is designed to automate the process of fruit quality assessment by analyzing key attributes such as color, texture, and shape from high-resolution fruit images. The Fruit360 dataset was used for training and testing, with images classified into categories such as fresh, ripe, overripe, and damaged. A comprehensive comparison with nine existing models, including SVM, Random Forest, and XGBoost, was conducted using performance metrics such as accuracy, precision, recall, and Fl-score. The proposed model achieved an accuracy of 96.78%, significantly outperforming all other models. Furthermore, the model's precision and recall reached 95.33% and 95.67%, respectively, demonstrating its reliability in distinguishing high-quality fruits from low-quality ones. © 2024 IEEE.

关键词： Fruits

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Protein-Protein Interaction Sites Prediction Using Graph Convolutional Networks

Protein-Protein Interaction Sites Prediction Using Graph Con...

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International Conference on computer and Applications (ICCA)

作者： Noor Jamal Alkhateeb Mamoun Awad Department of Computer Science and Software Engineering United Arab Emriates University Alain UAE

ISBN: (数字)9798350367560

ISBN: (纸本)9798350367577

Protein-protein interactions (PPI) are essential in keeping the cells functioning properly. Identifying PPI binding sites is a fundamental problem in system Biology, and it contributes to a better understanding of lower organisms such as viruses, a multitude-drug design and vaccines. The experimental methods for PPI binding sites identification are slow and expensive. Therefore, great research efforts have been attempted to improve the performance of computational methods. In this paper, we use a deep learning model based on Graph Convolutional Networks (GCN) to predict putative interaction sites on the surface of an isolated protein. We extracted features from both protein sequence and structure to enhance the accuracy of PPI binding sites predictions. Our model achieved higher accuracy compared to other models.

关键词： Proteins Deep learning Accuracy Graph convolutional networks Computational modeling Systems biology Predictive models Feature extraction Protein sequence Vaccines

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Deepfake Detection Using EfficientNet and XceptionNet 11

Deepfake Detection Using EfficientNet and XceptionNet

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11th IEEE International Conference on Intelligent Computing and Information Systems, ICICIS 2023

作者： Yasser, Basma Hani, Jumana El-Gayar, Salma Amgad, Omar Ahmed, Nourhan Ebied, Hala M. Amr, Habiba Salah, Mohamed Ain Shams University Software Engineering Faculty of Computer and Information Sciences Cairo Egypt Ain Shams University Faculty of Computer and Information Sciences Computer Science Department Cairo Egypt Ain Shams University Faculty of Computer and Information Sciences Scientific Computing Department Cairo Egypt

ISBN: (纸本)9798350322101

The increasing prevalence of manipulated media, particularly deepfake videos, poses significant challenges in distinguishing real from fake content. This paper addresses the issue of detecting deepfake videos using advanced CNN architectures such as EfficientNet-B4 and XceptionNet. The FF++ and Celeb-DF (v2) datasets are used to compare real and fake videos. The methodology involves preprocessing the Celeb- DF dataset by extracting frames and isolating faces, training the models, and evaluating their performance using log loss and Area Under the Curve (AUC) metrics. The study shows that both models are effective in accurately classifying real and fake videos and highlights the importance of continuously updating deepfake detection algorithms in response to evolving deepfake generation techniques. © 2023 IEEE.

关键词： Fake detection

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Image Reconstruction Improvement of Variable Coded Aperture using Deep Learning Method for Gamma and Lensless Imaging Applications

Image Reconstruction Improvement of Variable Coded Aperture ...

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2023 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2023

作者： Schwarz, Ariel Shemer, Amir Danan, Eliezer Cohen, Noa E. Danan, Yossef Department of Electrical and Electronics Engineering Azrieli College of Engineering Jerusalem9103501 Israel Faculty of Engineering Bar-Ilan University Ramat-Gan5290002 Israel School of Software Engineering and Computer Science Azrieli College of Engineering Jerusalem9103501 Israel

ISBN: (纸本)9798350345995

In gamma ray imaging for nuclear medicine, coded aperture is used to improve sensitivity. one of the main reconstructing methods is inverse filtering (deconvolution), where the recorded image is cross-correlated with periodic inverse filter of coded array. The reconstruction is free of coding noise for arbitrary array. However, amplification of quantum noise affect the reconstructed image. Although it is improved by Wiener filtering, the major problem is small terms in the spectral distribution of coded masks. Statistically and experimentally pinhole arrays have at least one term which is zero, resulting unacceptably noisy reconstruction. In our previous research we presented new approach of variable coded aperture (VCA) design for far and near field imaging applications [1-4]. The imaging system is based on time multiplexing method using variable multi pinhole array. The unique variable design enables to overcome the spatial frequencies cutoff and small terms in the Fourier transform exists in static multi pinhole array. The overall pinholes positions are designed to avoid spatial frequencies loss. However, traces of duplications can still be detected in reconstruction using Wiener filtering. Furthermore, since coded aperture blocks most of the photons that enter the detector, the dynamic range of the image is limited, thus leading to low contrast image and inadequate colors gamut. © 2023 IEEE.

关键词： Quantum noise

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A Framework for Facilitating Reproducible News Sentiment Impact Analysis 2022

A Framework for Facilitating Reproducible News Sentiment Imp...

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5th International Conference on software engineering and Information Management, ICSIM 2022

作者： Chen, Weisi Al-Qudah, Islam Rabhi, Fethi School of Software Engineering Xiamen University of Technology China Department of Computer Science University of Sharjah United Arab Emirates School of Computer Science and Engineering University of New South Wales Australia

ISBN: (纸本)9781450395519

The proliferation of outlets for news media in recent decades has contributed to faster issuance of news data. News analysis has been one of the key activities conducted by researchers in a broad variety of research disciplines. In general, the analysis process used in these studies includes interpreting the content of the news items, and then discovering their impact in a specific area. In this paper, we delve into the field of news analysis applied to the financial domain and explore news sentiment impact analysis in the context of financial markets. Existing studies lack systematic methods to assimilate financial context and evaluate the impact of a given news dataset on relevant entities financial market performance. We introduce an improved version of the framework called News Sentiment Impact Analysis (NSIA) that encompasses models, supporting software architecture and processes for defining various financial contexts and conducting news sentiment impact analysis. The framework is then evaluated using a prototype implementation and a case study that investigates the impact of extremely negative news on the stock price of the related entities. The results demonstrate the functionality, usability and reproducibility of the framework, and its capability to bridge the gap between generating news sentiment and evaluating its impact in selected financial contexts. © 2022 ACM.

关键词： software architecture

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computer-Vision-Based Non-Contact Paste Concentration Measurement*

Computer-Vision-Based Non-Contact Paste Concentration Measur...

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2024 International Conference on Automation in Manufacturing, Transportation and Logistics, ICaMaL 2024

作者： Liang, Tailin Yuan, Zhaolin School of Computer Science and Engineering Beihang University State Key Laboratory of Software Development Environment Beijing China Research Institute of Advanced Manufacturing Hong Kong Polytechnic University Department of Industrial and Systems Engineering Hong Kong Hong Kong

ISBN: (纸本)9798350378658

For monitoring the paste concentration, existing techniques, such as ultrasonic concentration meters and neutron meters, suffer from radiation hazards and low precision in high concentrations. This paper proposes a novel non-contact concentration measurement method based on deep learning. With the dataset collected by preparing the paste with different concentrations and taking photographs from the paste surface, a convolutional neural network is trained to extract the features from images and predict the concentration. The experiments indicate that the measurement accuracy is close to 88.79 % for the manual stirring paste dataset and 91.42 % for the automatic stirring paste dataset, which are sufficient in industrial applications. As a substitution of traditional concentration measurement instruments, the proposed vision-based concentration measurement method is non-contact and its accuracy is sufficient in industrial applications. © 2024 IEEE.

关键词： Radiation hazards

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Towards Collaborative Simultaneous Localization and Mapping: A Survey of the Current Research Landscape

Field Robotics

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Field Robotics 2025年 2卷 971-1000页

作者： Pierre-Yves Lajoie Benjamin Ramtoula Fang Wu Giovanni Beltrame Department of Computer and Software Engineering Polytechnique Montréal Canada Oxford Robotics Institute Department of Engineering Science University of Oxford United Kingdom Desay SV Automotive Singapore

Motivated by the tremendous progress, we witnessed in recent years, this paper presents a survey of the scientific literature on the topic of Collaborative Simultaneous Localization and Mapping (C-SLAM), also known as multi-robot SLAM. With fleets of self-driving cars on the horizon and the rise of multi-robot systems in industrial applications, we believe that Collaborative SLAM will soon become a cornerstone of future robotic applications. In this survey, we introduce the basic concepts of C-SLAM and present a thorough literature review. We also outline the major challenges and limitations of C-SLAM in terms of robustness, communication, and resource management. We conclude by exploring the area's current trends and promising research avenues.

关键词： Robots Simultaneous localization and mapping Robot kinematics Robot sensing systems Collaboration Surveys Multi-robot systems Sensors Market research Trajectory

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The Influence of Eye Gaze Interaction Technique Expertise and the Guided Evaluation Method on Text Entry Performance Evaluations

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Proceedings of the ACM on Human-computer Interaction 2025年第3期9卷 1-19页

作者： Mutasim, Aunnoy K Batmaz, Anil Ufuk Hudhud Mughrabi, Moaaz Stuerzlinger, Wolfgang Simon Fraser University Vancouver Canada Computer Science and Software Engineering Department Concordia University Montreal Canada Mechatronics Engineering Department Kadir Has University Istanbul Turkey

Any investigation of learning unfamiliar text entry systems is affected by the need to train participants on multiple new components simultaneously, such as novel interaction techniques and layouts. The Guided Evaluation Method (GEM) addresses this challenge by bypassing the need to learn layout-specific skills for text entry. However, a gap remains as the GEM's performance has not been assessed in situations where users are unfamiliar with the interaction technique involved, here eye-gaze-based dwell. To address this, we trained participants on only the eye-gaze-based interaction technique over eight days with QWERTY and then evaluated their performance on the OPTI layout with the GEM. Results showed that the unfamiliar OPTI layout outperformed QWERTY, with QWERTY's speed aligning with previous findings, suggesting that interaction technique expertise significantly impacts performance outcomes. Importantly, we also identified that for scenarios where the familiarity with the involved interaction technique(s) is the same, the GEM analyzes the performance of keyboard layouts effectively and quickly identifies the best option. © 2025 ACM.

关键词： OPTI QWERTY dwell eye tracking soft keyboards text entry virtual reality

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TBSR: Tile-Based 360° Video Streaming with Super-Resolution on Commodity Mobile Devices

TBSR: Tile-Based 360° Video Streaming with Super-Resolution...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Lei Zhang Haobin Zhou Haiyang Wang Laizhong Cui College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Computer Science University of Minnesota Duluth USA

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Streaming 360° videos demands excessive bandwidth. Tile-based streaming and super-resolution are two widely studied approaches to alleviate bandwidth shortage and enhance user experience in such real-time video streaming systems. The former prioritizes the transmission of a fraction of the 360° video according to the user viewport, while the latter enhances the streamed video in higher resolutions through computations. However, these two approaches bring substantial complexity and computation overhead and thus suffer from resource bottlenecks due to the constrained mobile hardware. This paper proposes TBSR, a practical mobile 360° video streaming system that incorporates in-time super-resolution with tile-based streaming on commodity mobile devices. We present the designs of three key mechanisms, including a rate adaptation method with macro tile grouping to reduce decoding computations, a decoding and SR scheduler for different types of tasks to achieve the best cost efficiency, and the workload adjustment method to control the amount of tasks given the available capabilities. We further implement the TBSR prototype. Our performance evaluation shows that TBSR outperforms the existing methods, improving QoE quality by up to 32% and bandwidth savings by 26%.

关键词： Costs Superresolution Prototypes Bandwidth Streaming media Mobile handsets Decoding

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