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International Conference on computer Supported Cooperative Work in Design

作者： Jiawei Jiang Xiang Tian Yubing Han Chunyu Hu Bin Feng Jiguo Yu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China School of Information Science and Technology Taishan University Taian China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Big Data Institute Qilu University of Technology Jinan China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

Owing to the easy deployment and mobile flexibility, Unmanned Aerial Vehicle (UAV) assisted Mobile Edge Computing (MEC) has been deemed as one potential technology for handling the computation-intensive tasks at terminal devices (TDs). In this work, a MEC architecture assisted by UAVs is designed which achieves efficient offloading, computing, and downloading for tasks from multiple TDs via aerial to aerial collaboration of two UAVs. In this architecture, the task offloading process contains two parts, i.e., the offloading from TDs to a mobile UAV which flies around TDs, and the offloading from the mobile UAV to a hovering UAV which hovers in the air. The computing tasks from TDs will be divided into three parts allocated to the TDs themselves, and both two UAVs. Upon completion of computation, the computation results are downloaded to the TDs. The optimization objective is to seek for an optimal task division strategy to attain the weighted total energy consumption minimization for all devices. Since the formulated optimization problem is not convex, we develop a two-step iteration algorithm which jointly optimizes computing frequency, task allocation volume, as well as UAV's trajectory based on the method of block coordinate descent. Simulation results confirm the effectiveness and performance advantages of the designed algorithm.

关键词： Energy consumption Simulation Collaboration computer architecture Autonomous aerial vehicles Minimization Resource management Jamming Trajectory optimization Radio spectrum management

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Integrating Design Thinking into Software Process Improvement 4

Integrating Design Thinking into Software Process Improvemen...

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Khalid, Anum Hashmi, Amber Sarwar Kiani, Azaz Ahmed Muzammal, Syeda Mariam Jhanjhi, N.Z. Bibi, Ruqia Kumar, Sandeep Foundation University Department of Software Engineering Islamabad Pakistan Rawalpindi Women University Department of Software Engineering Rawalpindi Pakistan National University of Modern Languages Rawalpindi Pakistan School of Computer Science Taylor's University Faculty of Innovation and Technology Subang Jaya Malaysia University Institute of Information Technology Pir Mehr Ali Shah Arid Agriculture University Rawalpindi Pakistan Lovely Professional University Division of Research and Development Phagwara144411 India KKR and KSR institute of technology and sciences Andhra Pradesh Guntur India

ISBN: (纸本)9798350353266

The need for software improvement is growing widely due to the demand for technology development. The issues that arise by expanding the software processes in the workplace are expense, lack of understanding, time constrained, and lack user experience. This research paper focuses on proposing a framework for integrating design thinking into software process improvement. The secret to project success may lie in fusing methodological and reusable approaches with human-centric techniques. A mixed-methods research strategy is proposed to compile thorough information and reach conclusions. In addition, it incorporates components of both qualitative and quantitative research (secondary research and descriptive analysis). The assumption is that the existing model significantly affects the user satisfaction variable. This indicates a statistically significant association between the individual's experience (number of years) and design thinking integration into the software process improvement. The variables in the proposed model and the dependent variable user satisfaction accounts for 6.1 © 2024 IEEE.

关键词： Software design

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Automatic Fish Species Identification Using Convolutional Neural Networks (CNN)

Automatic Fish Species Identification Using Convolutional Ne...

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Sustainable Expert Systems (ICSES), International Conference on

作者： I. Mettildha Mary T. Ragunthar M. Priyadharsini H Shyam Krishnaa M K Sujit Department of Computer Science and Engineering(Cyber Security) Dr.N.G.P Institute of Technology Coimbatore Tamil Nadu India Department of Computing Technologies SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Information Security School of Computer Science and Engineering (SCOPE) Vellore Institute Technology Vellore Tamil Nadu India Siemens Healthineers Bangalore Atheniac Bangalore

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

Accurate identification of fish species is critical for applications in fisheries management., biodiversity monitoring., and conservation efforts. Conventional manual identification techniques take a lot of time and are prone to mistakes., particularly when working with big datasets and species that have similar physical characteristics. Automating tasks related to species identification has shown promise in recent breakthroughs in machine learning., especially in deep learning techniques like Convolutional Neural Networks (CNNs) and transfer learning. However., these systems face several challenges., including high variability in fish appearances., limited availability of labelled datasets., and the computational overhead required for real-time classification. This research study presents a fish species classification system using a transfer learning approach based on MobileNetV2. MobileNetV2., known for its efficiency and lightweight architecture., is fine-tuned on a custom fish dataset to enhance species identification accuracy while minimizing computational requirements. To increase the model's resilience and ability to generalise to new species., data augmentation approaches are used. The system is designed to deliver high classification accuracy while maintaining computational efficiency., making it suitable for deployment in real-world applications with limited resources. Experimental results demonstrate that the proposed system achieves significant improvements in accuracy compared to traditional methods., while also addressing the challenges of dataset variability and overfitting.

关键词： Accuracy Transfer learning Fish Real-time systems Computational efficiency Convolutional neural networks Object recognition Monitoring Resilience Overfitting

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Dynamic 3D UAV Placement Optimization: Improved Bonobo Optimizer for Enhanced Coverage and Communication 35

Dynamic 3D UAV Placement Optimization: Improved Bonobo Optim...

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35th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024

作者： Yahia, Selma Taleb, Sylia Mekhmoukh Loscri, Valeria Saadi, Amylia Ait Ho, Tu Dac Vo, Van Nhan Eldeeb, Hossien Muhaidat, Sami Inria Lille-Nord Europe Lille 59000 France University of m'Hamed Bougara List Laboratory Boumerdes35000 Algeria Esme Engineering School Ivry-sur-Seine94200 France Norwegian University of Science and Technology Department of Information Security and Communication Technology Norway The Arctic University of Norway Department of Electrical Engineering Norway Duy Tan University Faculty of Information Technology Viet Nam Duy Tan University The Institute of Research and Development Viet Nam Khalifa University 6G Research Center Computer and Communication Engineering Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350362244

Unmanned aerial vehicles (UAVs) offer a promising solution for enhancing network coverage, reliability, and data speed in future wireless network generations. However, deploying UAVs as aerial base stations requires careful consideration of crucial design factors, including three-dimensional (3D) placement and performance optimization tailored to specific applications. In this paper, the 3D placement of multiple UAVs, acting as aerial base stations, is investigated in a dynamic user scenario. First, a closed-form expression for the coverage probability is derived. Then, to maximize the network coverage and sum rate while ensuring reliable and energy efficient system, a joint multi-objective optimization problem is formulated considering the real-time user movements. To solve the problem, an improved Chaos-based Bonobo Optimizer (CBO) scheme is proposed which combines chaotic maps with the Bonobo Optimizer (BO) algorithm. The obtained results demonstrate the superior performance of the proposed approach compared with different benchmark algorithms. The results reveal that the proposed CBO algorithm offers a minimum of 1 5 % and 9 0 ~ M b i t / s ~ i m p r o v e m e n t s ~ in coverage and sum rate, respectively. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Ethical Considerations in Explainable AI: Balancing Transparency and User Privacy in English Language-based Virtual Assistants 5

Ethical Considerations in Explainable AI: Balancing Transpar...

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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Alijoyo, Franciskus Antonius Sandya Sneha Sri, S. Alapati, Purnachandra Rao Yuldashev, Dilyorjon Ponni Valavan, M. Center for Risk Management and Sustainability Indonesia School of Business and Information Technology Stmik Likmi Bandung Indonesia Krishna University Andhra Pradesh India Prasad v Potluri Siddhartha Institute of Technology Andhra Pradesh Vijayawada Kanuru India Turin Polytechnic University in Tashkent Automatic Control and Computer Engineering Department Tashkent Uzbekistan K.Ramakrishnan College of Engineering Trichy Department of Artificial Intelligence and Data Science Tamil Nadu India

ISBN: (纸本)9798350385649

English Language-Based Virtual Assistants (ELB-VAs) are AI-powered systems designed to comprehend and respond to user queries in the English language, exemplified by virtual assistants like Siri or Alexa. The need for balancing transparency and user privacy in ELB-VAs is paramount due to their pervasive integration into daily life. Ensuring transparency imbues user trust, while safeguarding privacy addresses ethical concerns associated with personal data. Existing methods involve clear privacy policies, user-controlled data sharing settings, and encryption. However, drawbacks include user confusion and potential biases. To address these limitations, this study proposes a novel approach. Methodologically, it integrates pre-processing techniques such as lowercasing and tokenization, coupled with a Natural Language Understanding model. This model undergoes intent and entity recognition training, enhancing accuracy, and incorporates privacy-aware response generation, ensuring informative yet privacy-conscious interactions. The implementation of the study's results is carried out using Python tools, showcasing improved metrics and response times. This approach contributes to a more transparent and privacy-respecting user experience, aligning with evolving ethical norms and setting the stage for advancements in ELB-VA technology. This comprehensive exploration bridges existing gaps, emphasizing the ethical imperative of user-centric and privacy-aware AI interactions in ELB- VAs. The proposed NLU model exhibits a substantial increase in accuracy compared to other methods, with an impressive accuracy value of 99.1%• On average, it outperforms the Random Forest and Decision Tree models by 15.7 percentage points, highlighting its superior predictive capabilities in the evaluated task. This comprehensive exploration aligns with evolving ethical norms and establishes a foundation for future advancements in ELB-VA technology. © 2024 IEEE.

关键词： Transparency

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A new framework to classify the cancerous and non-cancerous pap smear images using filtering techniques to improve accuracy 1

A new framework to classify the cancerous and non-cancerous ...

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1st International Conference on Artificial Intelligence Trends and Pattern Recognition, ICAITPR 2022

作者： Saravanan, M.S. Cruz Antony, J. Pavan Kumar, V. Veeramanickam, M.R.M. Upadhyaya, Makarand Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Artificial Intelligence Chennai India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India Department of Information Technology Andhra Pradesh Bhimavaram India Chitkara University Punjab India University of Bahrain College of Business Administration Department of Management & Marketing Bahrain

ISBN: (数字)9781665425216

ISBN: (纸本)9781665425216

Cervical cancer is not new to the research at the same time it has more impact on society to motivate to find better solutions to predict at the earlier stage to avoid the severity of the patient. Cervical cancer has various stages of severity and it will be analyzed using various diagnosis methods. Therefore to identify the severity, this research study will use machine learning approaches to analyze the medical diagnosis inputs. In this study, the cancerous pap smear images are given as input on machine learning algorithms and predicted the severity of decease for better treatment. Therefore this research paper proposes a new framework to classify the cancerous and non-cancerous pap smear images using filtering techniques to improve the better accuracy of the existing research studies. © 2022 IEEE.

关键词： Diagnosis

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Intracardiac Electrogram Signals Key Feature Points Recognition Using Dense Convolutional Network

Intracardiac Electrogram Signals Key Feature Points Recognit...

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Pattern Recognition and Machine Learning (PRML), IEEE International Conference on

作者： Jiang Yihang Gu Kaihao Wu Xiaomei Department of Biomedical Engineering School of information Science and Technology Fudan University Shanghai China Department of Biomedical Engineering Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention (MICCAI) of Shanghai School of information Science and Technology Fudan University Academy for Engineering and Technology Shanghai Engineering Research Center of Assistive Devices Shanghai China Yiwu Research Institute of Fudan University Zhejiang China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

During radiofrequency ablation procedures for atrial fibrillation patients, physicians can assist in locating ablation targets by analyzing the conduction pathways and low-voltage areas of the intracardiac electrogram signals. The feature points of intracardiac electrogram signals serve as criteria for assessing excitation patterns and low-voltage areas. Therefore, extracting feature points from intracardiac signals is of significant value. To identify key points in intracardiac electrogram signals through deep learning, this experiment collected intracardiac electrogram signals from 21 clinical patients, which were then filtered using wavelet transformation. Subsequently, each excitation was treated as a signal group, indicating the key feature points within. Finally, a total of 36,183 groups of intracardiac electrogram signals annotated with key points were obtained. The dataset consisted of data from 17 patients allocated for training, data from 2 patients reserved for testing, and data from the remaining 2 patients designated for validation purposes. Training was conducted using a DenseNet network incorporating both convolutional attention and feature pyramid modules. The final network successfully identifies the feature points of intracardiac electrogram signals. It outputs six feature points, including the starting point, first rising point, first falling point, peak point, valley point, and termination point, with average absolute errors of only 0.34, 0.27, 0.31, 0.01, 0.27, and 0.42 ms, respectively. The final detection results are highly satisfactory, indicating the potential utility of the findings in assisting the analysis of intracardiac electrogram signals. The research outcomes presented in this paper lay a foundation for the analysis of intracardiac electrogram signals.

关键词： Training Wavelet transforms Radio frequency Low voltage Training data Medical services Myocardium Feature extraction Pattern recognition Testing

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Research on Remote Sensing Image Matching Based on Improved SuperPoint

Research on Remote Sensing Image Matching Based on Improved ...

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Artificial Intelligence Innovation (ICAII), International Conference on

作者： Zhuo Wang Hongli Li Yang Liu Kaimin Sun Xuhui Chen Yuntao Wu School of Computer Science Engineering Wuhan Institute of Technology Wuhan China School of Computer Science and Engineering Wuhan Institute of Technology Wuhan China ChangJiang Waterway Bureau Survey Center Wuhan China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan China

Image matching is a fundamental technology for data processing and analysis in remote sensing robots. To address the limitations of deep learning models in image matching, particularly the poor matching performance and limited number of matching points in remote sensing images, this paper presents an enhanced SuperPoint model based on deep learning. It incorporates a sub-model called DC-SuperPoint for feature point extraction and description. Within the feature extraction network, the CBAM (Convolutional Block Attention Module) attention mechanism is introduced to enhance the salience of point features in the graph. Additionally, the network extraction branch for feature points and descriptors is replaced with deformable convolution DCNV2 (Deformable Convolutional Networks v2), which is specifically designed to learn different types of features in various scenarios. Comparative experiments are conducted on two different datasets to evaluate the algorithm. The experimental results demonstrate that the proposed improved algorithm achieves a matching accuracy of 49.2% for the images, surpassing the original algorithm by 30% in terms of the number of matching points. Moreover, it exhibits better adaptability to different orientations compared to the original model. The accuracy is significantly improved to a certain extent, and the overall performance surpasses that of the original algorithm. These findings indicate that the proposed method effectively enhances matching efficiency and accuracy in scenarios with varying levels of complexity.

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Sign Language Translation using Frame and Event Stream: Benchmark Dataset and Algorithms

arXiv

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arXiv 2025年

作者： Wang, Xiao Li, Yuehang Wang, Fuling Jiang, Bo Wang, Yaowei Tian, Yonghong Tang, Jin Luo, Bin School of Computer Science and Technology Anhui University Hefei China Harbin Institute of Technology Shenzhen China Peng Cheng Laboratory Shenzhen China National Key Laboratory for Multimedia Information Processing Peking University China School of Electronic and Computer Engineering Shenzhen Graduate School Peking University China

Accurate sign language understanding serves as a crucial communication channel for individuals with disabilities. Current sign language translation algorithms predominantly rely on RGB frames, which may be limited by fixed frame rates, variable lighting conditions, and motion blur caused by rapid hand movements. Inspired by the recent successful application of event cameras in other fields, we propose to leverage event streams to assist RGB cameras in capturing gesture data, addressing the various challenges mentioned above. Specifically, we first collect a large-scale RGB-Event sign language translation dataset using the DVS346 camera, termed VECSL, which contains 15,676 RGB-Event samples, 15,191 glosses, and covers 2,568 Chinese characters. These samples were gathered across a diverse range of indoor and outdoor environments, capturing multiple viewing angles, varying light intensities, and different camera motions. Due to the absence of benchmark algorithms for comparison in this new task, we retrained and evaluated multiple state-of-the-art SLT algorithms, and believe that this benchmark can effectively support subsequent related research. Additionally, we propose a novel RGB-Event sign language translation framework (i.e., M2-SLT) that incorporates fine-grained micro-sign and coarse-grained macro-sign retrieval, achieving state-of-the-art results on the proposed dataset. Both the source code and dataset will be released on https://***/Event-AHU/OpenESL. Copyright © 2025, The Authors. All rights reserved.

关键词： Benchmarking

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Delay Tolerant and Energy Reduced Task Allocation in Internet of Things with Cloud Systems 1

Delay Tolerant and Energy Reduced Task Allocation in Interne...

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1st IEEE International Interdisciplinary Humanitarian Conference for Sustainability, IIHC 2022

作者： Raghunath Patil, Dipak Borkar, Bharat Markad, Ashok Kadlag, Sunil Kumbhkar, Makhan Jamal, Ahmad Amrutvahini College of Engineering Dept. of Computer Engineering Pune Sangamner India Amrutvahini College of Engineering Dept. of Information Technology Pune Sangamner India Amrutvahini College of Engineering Department of Electrical Engineering Pune Sangamner India Christian Eminent College Indore & Visiting Faculty School of Data Science and Forecasting Devi Ahilya University Department of Computer Science & Electronics Indore India Tulas Institute Department of Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9781665456876

Through the utilization of the Internet, cloud computing supplies storage and computation resources to deliver services for many sectors. The speed of such systems suffers, though, since delay-sensitive systems, such as smart city and health applications, increasingly need for processing over massive volumes of data transmitted to centralised datacenters. When compared to cloud services, the paradigms of fog as well as edge computing provide innovative solutions by affecting the financial closer to the customer and by having low energy consumption and latency effectiveness. To optimise expense and resource efficiency, enhance QoS, and increase security and privacy, it is crucial to discover the best locations for services and assets inside the 3 IoT. The bipartite graph task scheduling method we provide in this research enhances cost effectiveness in real world applications with strict deadlines. The proposed method's performance in terms of delay, network congestion, as well as cost is deployed and tested to use the iFogSim simulator, an expanded version of CloudSim. The evaluation results demonstrate that the suggested algorithm outperforms Round-Robin and Minimum Response Time methods in terms of both cost as well as throughput. © 2022 IEEE.

关键词： Internet of things

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