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16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Mathur, Abhinav Shikha Sethia, Divyashikha Delhi Technological University Department of Computer Science and Engineering New Delhi India Delhi Technological University Department of Software Engineering New Delhi India

ISBN: (纸本)9798350383119

Stress is a significant concern in the work-place, as it contributes to 80% of workplace injuries, and medical professionals are particularly susceptible, especially during emergencies such as the Covid-19 outbreak. Nurses play a crucial role in providing care in hospitals. Stress prediction for nurses in a hospital environment is essential to work optimally without suffering from the chronic effects of stress. This paper aims to investigate the application of body sensors and machine learning algorithms in monitoring physiological indicators to detect stress levels in nurses. It uses a multimodal sensor dataset of Blood Volume Pulse (BVP), Electrodermal Activity (EDA), and Inter-Beat Interval (IBI) signals from 15 nurses. The study trains and evaluates several classification models separately for each physiological signal and the multimodal data. Performance evaluation reveals that the decision tree outperforms other models with a weighted F1 score of 0.99 for both 2-level and 3-level multimodal stress classification. Furthermore, the IBI-derived Heart Rate Variability (HRV) features is more promising than other physiological signals, with a weighted F1 score of 0.84 and 0.79 for 2-level and 3-level stress classification, respectively. The findings show that multimodal data can help reliably detect stress for nurses and ensure stress-free working in hospital environments to reduce human errors. © 2024 IEEE.

关键词： Body sensors Heart Rate Variability Machine Learning Multimodal Stress Detection

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A Decision Support System for Enhancing Transportation Services Using Aspect-Based Sentiment Analysis

A Decision Support System for Enhancing Transportation Servi...

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Benaddi, Lamya Ouaddi, Charaf Naimi, Lahbib Souha, Adnane Jakimi, Abdeslam Ouchao, Brahim Moulay Ismail University Software Engineering and Information Systems Engineering Team Faculty of Science and Technology Errachidia Department of Computer Sciences Morocco

ISBN: (纸本)9798350369106

This paper proposes an innovative decision support system based on sentiment analysis, specifically designed for the transportation sector. The system employs an aspect-based sentiment analysis approach, which accurately identifies and classifies customer opinions on key transportation service aspects such as comfort, driver behavior, punctuality, and vehicle condition. By integrating the BERT model, known for its advanced natural language processing capabilities, our system provides an in-depth and reliable analysis of customer sentiments. This analysis enables managers and decision-makers to better understand the strengths and weaknesses of the provided transportation services and to precisely target areas that require improvement. By offering actionable insights, our approach aims to enhance the user experience, improve service quality, and increase overall customer satisfaction. © 2024 IEEE.

关键词： Customer satisfaction

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EdgeMob: A Context-Aware Dynamic Service Migration for Wearable IoT in Healthcare 1

EdgeMob: A Context-Aware Dynamic Service Migration for Weara...

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1st International Conference on Computing, Internet of Things and Microwave Systems, ICCIMS 2024

作者： Raza, Imran Hussain, Syed Asad Latif, Sana COMSATS University Islamabad Lahore Department of Computer Science Lahore Pakistan Department of Software Engineering Lahore Pakistan

ISBN: (纸本)9798350351736

Edge Computing (EC) is a distributed network architecture offering computation and storage resources at the network edge, addressing cloud computing limitations by optimizing latency and enhancing data safety and privacy for real-time services. It also supports intelligent solutions for IoT-based smart healthcare systems through Deep Learning (DL). DL aids in resource allocation optimization and disease prediction. However, the surge in wearable device usage introduces challenges in user mobility and migration, leading to increased latency and service inconsistencies. To tackle this, we propose EdgeMob, an intelligent and context-aware scheme deployed as a federation of edge servers. EdgeMob utilizes Q-learning-based reinforcement learning for personalized service migration decisions based on user mobility and Quality of Service (QoS). We evaluate our system against existing solutions, considering parameters such as user-perceived latency, migration cost, and network bandwidth utilization. © 2024 IEEE.

关键词： Electronic health record

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Exploring, Analyzing, and Benchmarking the Job Satisfaction Levels of Developers 8th

Exploring, Analyzing, and Benchmarking the Job Satisfaction ...

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8th International Conference on Information and Communication Technology for Intelligent Systems, ICTIS 2024

作者： Hussain, Shahid Wang, Wen-Li Khan, Saif Ur Rahman Rahim, Muhammad Abdul Basit Ur Ibrahim, Naseem Zhao, Jie Department of Computer Science and Software Engineering Penn State University Erie United States Department of Software Engineering Shifa Tameer-e-Millat University Islamabad Pakistan Department of Computer Science California State University Long Beach United States

ISBN: (纸本)9789819757985

The long-term participation of trained and competent employees is required to improve the morale, productivity, safety, value, and autonomy of an organization. In small and medium-sized software industries, the long-term involvement of developers will not only boost the efficiency of software development but also help train their team members. Although software industries have managed to stimulate developers’ productivity and reduce their turnover with a better compensation plan, the incentive through money has caused contradictory arguments. Hence, there is a need to employ a more systematic way to explore, analyze, and calibrate the developer’s needs, so that the managerial roles can make informed decisions and incorporate influential motivators to increase a developer’s job satisfaction level. In this respect, we are conducting an empirical study using the basics of Maslow's theory, a motivational theory in psychology, to (1) explore the developer’s motivation level in software industries, (2) benchmark the developer’s needs for job satisfaction at each level, and (3) analyze the job-hunting factors for developers’ turnover. We conducted a questionnaire-based study to gather feedback from 128 developers in 10 software industries with four types of analyses being conducted. The results indicate that satisfaction levels can vary across software industries, but the management can anticipate developers’ high-level needs from their low-level needs to determine influencing and motivating factors for job satisfaction improvements. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Job satisfaction

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A Trust-Based Hierarchical Consensus Mechanism for Consortium Blockchain in Smart Grid

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Tsinghua Science and Technology 2023年第1期28卷 69-81页

作者： Xingguo Jiang Aidong Sun Yan Sun Hong Luo Mohsen Guizani School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and TelecommunicationsBeijing 100876China Institute of Food safety and Nutrition Jiangsu Academy of Agricultural SciencesNanjing 210000China Computer Science and Engineering Department Qatar UniversityDoha 2713Qatar

As the smart grid develops rapidly,abundant connected devices offer various trading *** raises higher requirements for secure and effective data *** centralized data management does not meet the above ***,smart grid with conventional consortium blockchain can solve the above ***,in the face of a large number of nodes,existing consensus algorithms often perform poorly in terms of efficiency and *** this paper,we propose a trust-based hierarchical consensus mechanism(THCM)to solve this ***,we design a hierarchical mechanism to improve the efficiency and ***,intra-layer nodes use an improved Raft consensus algorithm and inter-layer nodes use the Byzantine Fault Tolerance ***,we propose a trust evaluation method to improve the election process of ***,we implement a prototype system to evaluate the performance of *** results demonstrate that the consensus efficiency is improved by 19.8%,the throughput is improved by 12.34%,and the storage is reduced by 37.9%.

关键词： consortium blockchain consensus algorithm trust evaluation method smart grid Internet of Things(IoT)

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Classification Of Hookah Images With Deep Learning 8

Classification Of Hookah Images With Deep Learning

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8th International Artificial Intelligence and Data Processing Symposium, IDAP 2024

作者： Arslan, Mustafa Tahir Ozkaynak, Emrah Karabuk University Institute of Postgraduate Education Department of Computer Engineering Karabük Turkey Karabuk University Software Engineering Department Karabük Turkey

ISBN: (纸本)9798331531492

With the proliferation of social media usage, there has been a notable increase in posts related to hookah consumption, driven by its visually appealing presentation and distinctive consumption method. Such posts, especially in the context of the decreasing age of social media users, have the potential to exert an enticing influence. The potential impact of social media content on tobacco product consumption underscores the importance of identifying whether these posts contain tobacco-related imagery. Deep learning techniques have become increasingly prevalent in the detection of tobacco products within images and videos. However, existing studies have predominantly focused on cigarette detection due to its relatively higher consumption rates, leaving the area of hookah detection underexplored. In this study, the detection of hookah images in social media content was undertaken using the YOLO v5 algorithm, a version of convolutional neural networks, for the classification of hookah as an object in digital images. To achieve this, a dataset comprising 7200 hookah images of varying shapes, colors, and sizes was compiled from social media platforms and various e-commerce websites. The dataset was trained using the YOLO v5 xl model, and the experimental results indicated a detection accuracy of 98.88%. The findings of this experimental study indicate that the developed application could be employed to generate alerts or obscure hookah images in social media content. Consequently, this approach could contribute to raising awareness regarding the risks associated with hookah use and mitigating its potentially alluring effects. © 2024 IEEE.

关键词： Convolutional neural networks

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AI-Enhanced AUTOSAR Configuration: Efficient Methods for Dataset Generation and Automated Code Production 8

AI-Enhanced AUTOSAR Configuration: Efficient Methods for Dat...

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8th IEEE International Forum on Research and Technologies for Society and Industry Innovation, RTSI 2024

作者： El-Gnainy, Nada Shanouda, Mira Essam, Ahmed Ibrahim, Anas Abdallah William, John Elsharkawy, Mariam Moustafa, Passant Ahmed Al Ansary, Mohamed Mahmoud, Hossam Moro, Ahmed Salama, Cherif Siemens Digital Industries Software Cairo Egypt The American University in Cairo Department of Computer Science and Engineering Egypt Ain Shams University Department of Computer and Systems Engineering Cairo Egypt

ISBN: (纸本)9798350362138

This research presents a novel approach to stream-line the configuration of AUTOSAR (Automotive Open System Architecture) modules using Artificial Intelligence (AI)-based tools. Traditional methods of generating AUTOSAR-compliant ARXML files are time-consuming and require meticulous man-ual input, posing significant challenges for developers. In this study, an AI-driven methodology is introduced to address these challenges. By mapping English descriptions to YAML through a Python script and subsequently converting YAML to Autosar XML (ARXML), this approach conserves tokens and significantly accelerates the AI training process. The AI model, trained on a dataset of 12,000 samples and tested on 3,000 unseen inputs, achieved a 100% accuracy rate in converting descriptions. Additionally, a comprehensive configurator tool and a code generator were developed to facilitate seamless integration and deployment of the generated configurations. The proposed solution not only reduces manual effort but also increases reliability in AUTOSAR configuration, offering a practical tool for developers in the automotive software industry. © 2024 IEEE.

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IoT-Assets Taxonomy, Threats Assessment and Potential Solutions 6

IoT-Assets Taxonomy, Threats Assessment and Potential Soluti...

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6th Global Conference on Wireless and Optical Technologies, GCWOT 2023

作者： Zardari, Shehnila Nisar, Nida Fatima, Zainab Dhirani, Lubna Luxmi NED University of Engineering & Technology Department of Software Engineering Karachi Pakistan University of Limerick Department of Electronic and Computer Engineering Limerick Ireland

ISBN: (纸本)9798350333718

Internet of Things (IoT) is a system of interconnected devices and networks that provides autonomous functioning capability. Increasing expansion rate of IoT system in diverse set of domains has resulted in inclination of associated risks as well. This research paper presents classification of assets which are part of IoT system, identify platform-independent threats, and consolidate potential solutions to secure the IoT system from threats. Various researchers have conducted studies which are focused on specific industry/domain. Therefore, there is an inevitable necessity to present generalized assessment of IoT system and associated threats, irrespective of the industry or domain. In addition, this research presents prioritization, in terms of criticality, of IoT assets and threats that will enable stakeholders to identify the items that are crucial and items that can be ignored considering the low priority. This would enable researchers and stakeholders of IoT system (such as policy makers, end-users, manufacturers, and security experts *** have a deeper understanding of the implications and have an insight on solutions/recommendations in a broad-spectrum. Survey findings showed that DDoS (distributed denial of service), privacy attack, and information modification are the top three threats in terms of criticality ranking. A generalized (industry-independent) set of recommendations have been consolidated based on literature analysis and survey findings in three categories, which are related to policies, organizational measures, and technical measures. © 2023 IEEE.

关键词： Internet of things

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Generative Adversarial Networks (GANs) for Image Synthesis and Augmentation 4

Generative Adversarial Networks (GANs) for Image Synthesis a...

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4th IEEE International Conference on Electronic Technology, Communication and Information, ICETCI 2024

作者： Wen, Yuxin Mei, Zhenhao Qi, Chuyue Xinjiang University Department of Software Engineering Urumqi China Hefei University of Technology School of Computer Science and Information Engineering Hefei China

ISBN: (纸本)9798350361643

In the realm of medical imaging, a scarcity of reliable, sizable datasets for training supervised deep learning models persists. One solution involves leveraging Generative Adversarial Networks (GANs) to fabricate synthetic deepfake images, bridging this gap and enriching available data. Deepfake technology seamlessly transfers crucial features from source to target images or videos, mimicking the source with remarkable fidelity. Over the past decade, propelled by cutting-edge deep learning techniques and vast datasets, medical image processing has made monumental strides. Supervised deep learning models now achieve superhuman performance across diverse medical imaging applications. Synthetic deepfake images, produced through techniques like GANs, serve various purposes, including dataset balancing, modal translation, and expansion. To efficiently augment training data for deep learning, a hierarchical GAN (HGAN) framework is proposed, focusing on generating high-quality knee images. HGAN integrates spectral normalization in discrimination and pixel normalization in generation, enhancing training stability. Evaluation metrics such as AM Score and Mode Score are employed to benchmark HGAN against alternatives like PGGAN, reflecting its efficacy in advancing medical imaging research and application. © 2024 IEEE.

关键词： Medical imaging

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Profit Maximization of Delay-Sensitive, Differential Accuracy Inference Services in Mobile Edge Computing

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IEEE Transactions on Mobile Computing 2025年第07期24卷 6209-6224页

作者： Zhang, Yuncan Liang, Weifa Xu, Zichuan Jia, Xiaohua Yang, Yuanyuan City University of Hong Kong Department of Computer Science Hong Kong Hong Kong Dalian University of Technology School of Software Dalian China Stony Brook University Department of Electrical and Computer Engineering Stony BrookNY United States

The integration of Artificial Itelligence (AI) and edge computing has sparked significant interest in edge inference services. In this paper, we consider delay-sensitive, differential accuracy inference services in a Mobile Edge Computing (MEC) network while meeting user stringent delay and accuracy requirements. We formulate two novel profit maximization problems under static and dynamic settings of service request arrivals, with the aim of maximizing the accumulative profit of admitted requests. We assign differential accuracy service requests to the corresponding resolution instances of their requested service models, assuming that each resolution instance can serve up to L≥ 1 the same type of service requests. Since the profit maximization problem is NP-hard, we first formulate an Integer Linear program (ILP) solution if the problem size is small or medium;otherwise, we devise a constant randomized algorithm with high probability. Then, we consider dynamic service request admissions without the knowledge of future request arrivals for a given finite time horizon, for which we develop a simple yet effective prediction mechanism to accurately predict the number of different resolution instances of each model needed, and pre-deploy the predicted number of resolution instances into cloudlets to reduce instantiating delays. We then devise an online algorithm with a provable competitive ratio for the dynamic profit maximization problem by leveraging the primal-dual dynamic updating technique. Finally, we evaluate the performance of the proposed algorithms by simulations. The simulation results demonstrate that the proposed algorithms are promising. © 2002-2012 IEEE.

关键词： Mobile edge computing

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