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26th International Workshop on Mobile Computing Systems and Applications, HOTMOBILE 2025

作者： Huang, Runxi Ouyang, Xiaomin Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

ISBN: (纸本)9798400714030

Achieving real-time multimodal inference on devices is crucial for time-critical and privacy-sensitive applications. Existing methods primarily focus on optimizing the latency of model inference while neglecting the data collection process. We introduce MMEdge, an on-device, end-to-end multimodal inference framework. MMEdge employs a cross-modality speculative sensing and encoding approach to enhance inference efficiency and robustness at runtime. © 2025 Copyright held by the owner/author(s).

关键词： Edge computing

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A Flow-Based Performance Evaluation on RYU SDN Controller

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Journal of The Institution of Engineers (India): Series B 2024年第2期105卷 203-215页

作者： Ram, Anil Dutta, Manash Pratim Chakraborty, Swarnendu Kumar Department of Computer Science and Engineering NIT Jote Arunachal Pradesh Jote Itanagar791113 India Department of Computer Science and Information Technology Cotton University Pan Bazaar Assam Guwahati781001 India

Software-defined networking (SDN) is software-based networking technology which overcomes the complication tackled by traditional networking structures. The knowledge behind the SDN gives more flexibility to handle network as compared to traditional network since SDN detached the control plane (CP) from the data plane. As the SDN is a software-based technology, evaluation of performance is needed. The heart of SDN is SDN controller which is located in CP. SDN controller is accountable for the monitoring, decision making and investigation of factual time data traffic. Due to substantial rise of distributed real-time applications, there is a prerequisite for an efficient controller in networking sectors, data centers, academic and examination. As a result, it is critical to look at the presentation of a free-source specified controller in order to offer effective traffic routing, which will lead to better resource usage and improved network performance matrices. For network traffic analysis, the study proposes an execution of SDN structural design utilizing an RYU SDN controller. The present spadework assesses the performance of a bespoke network topology based on SDN architecture for device-to-device performance characteristics such as round trip time, bandwidth, and throughput. In the present default network topology for SDN, the simulation results show that our approach outperforms its competitors. © The Institution of Engineers (India) 2024.

关键词： Controllers

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A lightweight approach for intrusion detection in WSNs based on DCGAN

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International Journal of Information technology (Singapore) 2025年第2期17卷 951-957页

作者： Devi, Manu Nandal, Priyanka Sehrawat, Harkesh Department of Computer Science and Engineering University Institute of Engineering and Technology Maharshi Dayanand University Haryana India Department of Computer Science and Engineering Maharaja Surajmal Institute of Technology New Delhi India

Ensuring strong security procedures is crucial in the rapidly advancing realm of wireless sensor networks (WSNs) in order to protect sensitive data and preserve network integrity. The resource limitations and unpredictable environments that characterize WSNs frequently provide challenges for traditional intrusion detection systems (IDS). To overcome these issues, this research introduces a unique lightweight intrusion detection system that utilizes Deep Convolutional Generative Adversarial Networks (DCGAN). The proposed system employs wireless sensor network blackhole flooding selective forwarding (WSN-BFSF) dataset for training and evaluation. The suggested approach improves the IDS’s detection capabilities in addition to reducing computing overhead. The findings show that the lightweight DCGAN-based IDS provides an improved defense against new security threats, making it a good fit for real-time deployment in WSNs. Here, the performance metrics like receiver operating characteristic (ROC) is evaluated to check the efficiency of the model. The proposed model is assessed with the existing studies based on metrics such as accuracy, precision, recall and f1-score. The results show that the proposed approach attains the highest accuracy (94%) thus outperforming the existing methods and studies. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Deep Learning (DL) Generative Adversarial Networks (GAN’s) Intrusion Detection System (IDS) Lightweight DCGAN Wireless Sensor Networks (WSN’s)

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Real-Time Sketching of Harshly Lit Driving Environment Perception by Neuromorphic Sensing

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-15页

作者： Han, Yuqi Suo, Jinli Zhang, Bo Zhao, Jiachen Cheng, Yuxiao Dai, Qionghai School of Computer Science and Technology Dalian University of Technology Liaoning Province China Department of Automation Tsinghua University Beijing China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China

Visual sensors are indispensable for automatic vehicles, to achieve comprehensive environmental perception for navigation, but their deteriorated performance in harsh illuminations largely sets back the practical use of autonomous driving technologies. A promising solution is to use a bio-inspired event sensor that asynchronously records the intensity changes with high sensitivity, fast response, and large dynamic range, which assists situational awareness of moving vehicles in harshly lit scenarios. However, the sensing of event sensors comes with heavy noise and sparse signals, due to either severe photon starvation or limited acquisition bandwidth. In this paper, we propose an approach for real-time sketching of the harshly lit driving environment (RIDE), to outline the driving surroundings from noisy sporadic measurements. We address confronted challenges as follows: (i) map the raw event signals into a low dimensional space and cluster the features to depict the spatial-temporal correlation within raw events;(ii) design a general inference network to construct continuous motion fields of the scene from the encoded features of noisy sporadic raw measurements;(iii) construct the pseudo-ground-truth via the unsupervised motion compensation as the label of the above network learning, achieving real-time inference. Our approach is experimentally validated on real traffic data and displays high-fidelity perception capability for extremely dark scenes and scenarios with high dynamic range. Also, we investigate RIDE's effectiveness in the downstream task—detection of traffic participants. In a nutshell, the proposed RIDE provides high-fidelity sensing of harshly lit environments and lays the foundation for the all-day visual navigation of autonomous vehicles. IEEE

关键词： computer vision

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Resource-aware routing in opportunistic networks: existing protocols and open research issues

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International Journal of Cloud Computing 2023年第2-4期12卷 367-398页

作者： Rani Malik, Amita Department of Computer Science and Engineering Deenbandhu Chhotu Ram University of Science and Technology Murthal Sonipat131039 India

Opportunistic networks provide an effective communication in challenging scenarios such as interplanetary communication, post-disaster situation, etc. As the nodes have limited resources, thus designing an efficient routing protocol in opportunistic networks always remains a challenging task. Several review papers presenting features and applications of opportunistic networks, mobility models, challenges, taxonomies of routing, and incentive schemes exist, but a comprehensive review on resource-aware routing appears to be missing. Therefore, we propose a novel taxonomy for categorising existing resource-aware routing protocols proposed in this field. These are mainly categorised into energy-aware, buffer aware, bandwidth aware, and hybrid routing that considers the energy of nodes, buffer space, bandwidth, and multiple parameters respectively to make forwarding or routing decision. Further, we discuss various existing routing protocols with their strengths and weaknesses, leading to open research issues. This survey aims to help future researchers devise a novel routing protocol for opportunistic networks. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Routing protocols

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Enhancing waste sorting and recycling efficiency: robust deep learning-based approach for classification and detection

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Neural Computing and Applications 2025年第6期37卷 4567-4583页

作者： Sayem, Faizul Rakib Islam, Md. Sakib Bin Naznine, Mansura Nashbat, Mohammad Hasan-Zia, Mazhar Kunju, Ali K Ansaruddin Khandakar, Amith Ashraf, Azad Majid, Molla Ehsanul Kashem, Saad Bin Abul Chowdhury, Muhammad E. H. Department of Electrical and Electronic Engineering University of Dhaka Dhaka Bangladesh Department of Biomedical Engineering Military Institute of Science and Technology Dhaka Bangladesh Department of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi6204 Bangladesh Chemical Engineering Department University of Doha for Science and Technology Doha Qatar Department of Electrical Engineering Qatar University Doha2713 Qatar Computer Applications Department Academic Bridge Program Qatar Foundation Doha Qatar Department of Computing Science AFG College With the University of Aberdeen Doha Qatar

Given the severity of waste pollution as a major environmental concern, intelligent and sustainable waste management is becoming increasingly crucial in both developed and developing countries. The material composition and volume of urban solid waste are key considerations in processing, managing, and utilizing city waste. Deep learning technologies have emerged as viable solutions to address waste management issues by reducing labor costs and automating complex tasks. However, the limited number of trash image categories and the inadequacy of existing datasets have constrained the proper evaluation of machine learning model performance across a large number of waste classes. In this paper, we present robust waste image classification and object detection studies using deep learning models, utilizing 28 distinct recyclable categories of waste images comprising a total of 10,406 images. For the waste classification task, we proposed a novel dual-stream network that outperformed several state-of-the-art models, achieving an overall classification accuracy of 83.11%. Additionally, we introduced the GELAN-E (generalized efficient layer aggregation network) model for waste object detection tasks, obtaining a mean average precision (mAP50) of 63%, surpassing other state-of-the-art detection models. These advancements demonstrate significant progress in the field of intelligent waste management, paving the way for more efficient and effective solutions. © The Author(s) 2024.

关键词： Deep learning for waste management Intelligent waste management Object detection in waste management Sustainable waste processing Urban solid waste Waste image classification

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Optimizing XGBoost for Enhanced Electrical Theft Detection: A Fusion of Particle Swarm and Jaya Optimization Techniques 4

Optimizing XGBoost for Enhanced Electrical Theft Detection: ...

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4th IEEE International Conference on Sustainable Energy and Future Electric Transportation, SEFET 2024

作者： Prusty, Sashikanta Nayak, Debasish Swapnesh Kumar Moharana, Meena Prusty, Sushree Gayatri Priyadarsini Sahoo, Manmohan Rautaray, Jyotirmayee Department of Computer Science & Engineering Bhubaneswar India Centurion University of Technology & Management Department of Computer Science & Engineering Bhubaneswar India Department of Computer Science & Engineering Bhubaneswar India Odisha University of Technology & Research Department of Computer Science & Engineering Bhubaneswar India

ISBN: (纸本)9798350383997

The most serious and hazardous for an electrical provider these days are non-technical losses caused due to electricity theft. The economy as a whole is impacted by fraudulent electricity usage, which lowers supply quality, raises generator load, and drives up costs for legal customers. To avoid these issues, this work proposes a smart electrical theft detection system using the XGBoost model combined with two optimization techniques Particle Swarm Optimization (PSO) and Jaya Optimization (JO). Furthermore, to handle the missing instances in the State Grid Corporation of China (SGCC) imbalanced dataset, we implemented a novel LORAS (Local Outlier-Robust Adaptive Synthetic) technique that balances the target class by making fake data points near the edges of different classes. Finally, the results show that the optimized XGBoost-PSO technique significantly classifies into two distinct categories (i.e. theft as '1' or no theft as '0') with having higher F1 Score (PSO Optimized): 0.9552 and AUC (PSO Optimized): 0.9667. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Leveraging EfficientNetB3 in a Deep Learning Framework for High-Accuracy MRI Tumor Classification

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computers, Materials & Continua 2024年第10期81卷 867-883页

作者： Mahesh Thyluru Ramakrishna Kuppusamy Pothanaicker Padma Selvaraj Surbhi Bhatia Khan Vinoth Kumar Venkatesan Saeed Alzahrani Mohammad Alojail Department of Computer Science&Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bengaluru562112India School of Computer Science and Engineering VIT-AP UniversityAmaravati522241India Department of Computer Science&Technology Madanapalle Institute of Technology&ScienceMadanapalle517325India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK School of Computer Science Engineering&Information Systems(SCORE) Vellore Institute of Technology(VIT)Vellore632014India Management Information System Department College of Business AdministrationKing Saud UniversityRiyadh11531Saudi Arabia Adjunct Research Faculty Centre for Research Impact&OutcomeChitkara UniversityRajpuraPunjab140401India

Brain tumor is a global issue due to which several people suffer,and its early diagnosis can help in the treatment in a more efficient *** different types of brain tumors,including gliomas,meningiomas,pituitary tumors,as well as confirming the absence of tumors,poses a significant challenge using MRI *** approaches predominantly rely on traditional machine learning and basic deep learning methods for image *** methods often rely on manual feature extraction and basic convolutional neural networks(CNNs).The limitations include inadequate accuracy,poor generalization of new data,and limited ability to manage the high variability in MRI *** the EfficientNetB3 architecture,this study presents a groundbreaking approach in the computational engineering domain,enhancing MRI-based brain tumor *** approach highlights a major advancement in employing sophisticated machine learning techniques within computer science and Engineering,showcasing a highly accurate framework with significant potential for healthcare *** model achieves an outstanding 99%accuracy,exhibiting balanced precision,recall,and F1-scores across all tumor types,as detailed in the classification *** successful implementation demonstrates the model’s potential as an essential tool for diagnosing and classifying brain tumors,marking a notable improvement over current *** integration of such advanced computational techniques in medical diagnostics can significantly enhance accuracy and efficiency,paving the way for wider *** research highlights the revolutionary impact of deep learning technologies in improving diagnostic processes and patient outcomes in neuro-oncology.

关键词： Deep learning MRI brain tumor cassification EfficientNetB3 computational engineering healthcare technology artificial intelligence in medical imaging tumor segmentation neuro-oncology

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Imaging and structure analysis of ferroelectric domains,domain walls,and vortices by scanning electron diffraction

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npj Computational Materials 2024年第1期10卷 2154-2161页

作者： Ursula Ludacka Jiali He Shuyu Qin Manuel Zahn Emil Frang Christiansen Kasper A.Hunnestad Xinqiao Zhang Zewu Yan Edith Bourret István Kézsmárki Antonius T.Jvan Helvoort Joshua Agar Dennis Meier Department of Materials Science and Engineering NTNU Norwegian University of Science and TechnologyTrondheimNorway Department of Computer Science and Engineering Lehigh UniversityBethlehemPAUSA Department of Mechanical Engineering and Mechanics Drexel UniversityPhiladelphiaPAUSA Experimental Physics V University of AugsburgAugsburgGermany Department of Physics NTNU Norwegian University of Science and TechnologyTrondheimNorway Department of Physics ETH ZurichZürichSwitzerland Materials Sciences Division Lawrence Berkeley National LaboratoryBerkeleyCAUSA

Direct electron detectors in scanning transmission electron microscopy give unprecedented possibilities for structure analysis at the *** electronic and quantum materials,this new capability gives access to,for example,emergent chiral structures and symmetry-breaking distortions that underpin functional *** nanoscale structural features with statistical significance,however,is complicated by the subtleties of dynamic diffraction and coexisting contrast mechanisms,which often results in a low signal-to-noise ratio and the superposition of multiple signals that are challenging to deconvolute.

关键词： properties structure ferroelectric

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Developing Lexicons for Enhanced Sentiment Analysis in Software Engineering:An Innovative Multilingual Approach for Social Media Reviews

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computers, Materials & Continua 2024年第5期79卷 2771-2793页

作者： Zohaib Ahmad Khan Yuanqing Xia Ahmed Khan Muhammad Sadiq Mahmood Alam Fuad AAwwad Emad A.A.Ismail School of Automation Beijing Institute of TechnologyBeijing100081China Department of Computer Science and Technology University of Science and Technology BannuKPKBannu28100Pakistan School of Computer Science and Engineering Central South UniversityChangsha410083China Department of Quantitative Analysis College of Business AdministrationKing Saud UniversityP.O.Box 71115Riyadh11587Saudi Arabia

Sentiment analysis is becoming increasingly important in today’s digital age, with social media being a significantsource of user-generated content. The development of sentiment lexicons that can support languages other thanEnglish is a challenging task, especially for analyzing sentiment analysis in social media reviews. Most existingsentiment analysis systems focus on English, leaving a significant research gap in other languages due to limitedresources and tools. This research aims to address this gap by building a sentiment lexicon for local languages,which is then used with a machine learning algorithm for efficient sentiment analysis. In the first step, a lexiconis developed that includes five languages: Urdu, Roman Urdu, Pashto, Roman Pashto, and English. The sentimentscores from SentiWordNet are associated with each word in the lexicon to produce an effective sentiment score. Inthe second step, a naive Bayesian algorithm is applied to the developed lexicon for efficient sentiment analysis ofRoman Pashto. Both the sentiment lexicon and sentiment analysis steps were evaluated using information retrievalmetrics, with an accuracy score of 0.89 for the sentiment lexicon and 0.83 for the sentiment analysis. The resultsshowcase the potential for improving software engineering tasks related to user feedback analysis and productdevelopment.

关键词： Emotional assessment regional dialects SentiWordNet naive bayesian technique lexicons software engineering user feedback

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