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29th IEEE Asia Pacific Conference on Communications, APCC 2024

作者： Alkofahi, Hamza Altarawneh, Amani Jordan University of Science and Technology Department of Software Engineering Irbid Jordan Tennessee Tech University Department of Computer Science CookevilleTN United States

ISBN: (纸本)9798331517816

Remote keyless entry (RKE) systems have become a standard feature in modern vehicles, offering the convenience of wireless control. However, this convenience is accompanied by significant security vulnerabilities, particularly susceptibility to jamming attacks. These attacks disrupt communication between the key fob and the vehicle, potentially leading to unauthorized access and vehicle theft. This study investigates the effectiveness of adaptive power control as a countermeasure to jamming attacks on RKE systems and examines the influence of jammer placement on data transmission success rates. We conducted experiments using a custom-built jammer, transmitter, and receiver to simulate real-world scenarios. Our findings demonstrate that jammer proximity significantly affects transmission success, and while increasing key fob power can initially mitigate jamming, its effectiveness diminishes beyond a certain threshold. © 2024 IEEE.

关键词： Jamming

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Smart Cities Paradigm with AI-Enabled Effective Requirements engineering 18

Smart Cities Paradigm with AI-Enabled Effective Requirements...

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18th IEEE International Conference on Emerging Technologies, ICET 2023

作者： Bashir, Asima Latif, Niha Khan, Arisha Masood Sohail, Muhammad Mcs National University of Science and Technology Department of Computer Software Engineering Islamabad Pakistan

ISBN: (纸本)9798350328172

Effective requirements gathering and management tools are key factors for sustainable smart cities. These tools enable stakeholders including government officials, private sector partners, and citizens to identify and prioritize requirements, manage changes, and make sure that the product is as per their expectations. In the context of smart cities, these tools must be able to capture the requirements for a wide range of systems, including transport, energy sector, citizens safety, and environment maintenance. Overall, effective requirements gathering and management tools are critical to the success of smart cities, enabling them to be more efficient, sustainable and responsive. With the advent of new technologies, there is a growing need for advanced tools to support the RE process for smart cities. This paper investigates different artificial in-telligence (AI) based requirements gathering and management tools. Amongst those techniques dependent on natural language processing (NLP) and machine learning (ML) in recommendation systems for requirements gathering and ReQtest, ReqSuite, Jama Connect, Visure, Xabrio, and IBM DOORS as management tools have been proposed. Their summarized performance revealed different scores across parameters highlighting their impact as effective tools for smart city paradigm. The study concludes that AI-based techniques and tools can be employed in smart cities, with selection based on project requirements and constraints. © 2023 IEEE.

关键词： Recurrent neural networks

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K Semantics for Security Policy Enforcement on Android Applications with Practical Cases 2nd

K Semantics for Security Policy Enforcement on Android Appli...

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2nd EAI International Conference on Computational Intelligence and Communications, CICom 2021

作者： Ziadia, Marwa Mejri, Mohamed Fattahi, Jaouhar Department of Computer Science and Software Engineering Laval University QuébecQC Canada

ISBN: (纸本)9783031195228

As much as they have positioned Android among the most widely used operating systems, Android applications have helped malware creators to break in and infect its devices. A long list of threats caused by downloaded applications targets the integrity of the system and the privacy of the users. To protect Android users from such applications, we propose a formal environment for security policy enforcement. The main idea is to establish a synergy between the aspect-oriented paradigm and formal methods such as the program rewriting technique. The purpose of our approach is to rewrite the application program by adding control tests at certain carefully selected points to ensure that the security policy is respected. The new version of the program preserves all the good behaviors of the original version that comply with the security policy and acts against the bad ones. The particularity of this approach is that all the steps for security policy enforcement are performed automatically using the K-framework. This semantic engineering framework handles the whole process, from the formal specification of the policy and the program to the enforcement technique. Two practical cases are fully discussed in this paper. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Semantics

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MedNetV3: A Deep Learning Model for MRI Brain Tumor Detection with MobileNetV3 and Advanced Data Augmentation 21

MedNetV3: A Deep Learning Model for MRI Brain Tumor Detectio...

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21st International computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Tariq, Hussain Ahmed Fengli, Zhang Salim Shakib, Muhamud Md Javed, Iram School of Information and Software Engineering University of Electronic Science and Technology of China China University of Agriculture Department of Computer Science Faisalabad Pakistan

ISBN: (纸本)9798331519254

In this paper, we present a novel deep learning model medical network (MedNetV3) developed for brain tumor detection. It incorporates advanced data augmentation techniques based on the MobileNetV3 architecture. MedNetV3 is developed for higher classification accuracy at the cost of slightly lower computational efficiency and is therefore suitable for clinical use. The proposed model overcomes the class imbalance problem and can make the model robust to the variations of MRI scans included in the extensive data augmentation methods, such as random rotations, flips. The proposed model trained and evaluated using a publicly available MRI dataset. It achieved a high Area Under the Curve (AUC) value of 0.99 for both the Precision-Recall and Receiver Operating Characteristic (ROC) curves. The results show that MedNetV3 is effective in correctly distinguishing brain tumors and therefore has the potential to be used in medical image classification. © 2024 IEEE.

关键词： Deep learning

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From Faces to Traffic Lights: A Multi-Scale Approach for Emotional State Representation 25

From Faces to Traffic Lights: A Multi-Scale Approach for Emo...

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25th IEEE International Conferences on High Performance Computing and Communications, 9th International Conference on Data science and Systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Nguyen, Minh Yan, Wei Qi Auckland University of Technology Department Computer Science and Software Engineering Auckland New Zealand

ISBN: (纸本)9798350330014

This paper presents an innovative Smart Traffic Light System designed to assess Emotional Expression in enclosed settings such as corporate offices and educational facilities. The system combines artificial intelligence, super-resolution algorithms, and facial recognition to provide both real-time and longitudinal insights into the collective emotional atmosphere. Utilizing a three-tier light indicator, the system visualizes emotional trends in the short-term (immediate), medium-term (daily or weekly), and long-term (semi-annual or annual) contexts, employing a color-coded scheme to denote positive or negative emotional states. This augments situational awareness and contributes to the emotional well-being of individuals in these settings. The underlying technology leverages closed-circuit television (CCTV) footage and introduces a state-of-the-art temporal expression recognition mechanism to capture a broad spectrum of human emotions. A notable innovation is the incorporation of facial super-resolution techniques to enhance the quality of low-resolution facial data, thereby boosting the system's overall accuracy and robustness. This enhancement is accomplished while maintaining adherence to ethical standards for data collection and usage. This system has implications that reach far beyond simple emotional recognition. It serves as a cornerstone for the development of emotionally intelligent smart cities. Providing both immediate feedback and historical data on community emotional states, it enables more informed decision-making, streamlined resource allocation, and ultimately, an improvement in the overall quality of life. This research contributes to the evolving discussion on how smart cities can foster communities that are not just more efficient, but also more emotionally resilient. © 2023 IEEE.

关键词： Smart city

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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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A Review of Lightweight Security and Privacy for Resource-Constrained IoT Devices

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computers, Materials & Continua 2024年第1期78卷 31-63页

作者： Sunil Kumar Dilip Kumar Ramraj Dangi Gaurav Choudhary Nicola Dragoni Ilsun You Department of Computer Science and Engineering National Institute of Technology JamshedpurJamshedpurIndia School of Computing Science and Engineering VIT University BhopalBhopal466114India Center for Industrial Software The Maersk Mc-Kinney Moller InstituteUniversity of Southern DenmarkSonderborgOdenseDenmark Department of Applied Mathematics and Computer Science Technical University of DenmarkLyngby2800Denmark Department of Information Security Cryptologyand MathematicsKookmin UniversitySeoulSouth Korea

The widespread and growing interest in the Internet of Things(IoT)may be attributed to its usefulness in many different *** settings are probed for data,which is then transferred via linked *** are several hurdles to overcome when putting IoT into practice,from managing server infrastructure to coordinating the use of tiny *** it comes to deploying IoT,everyone agrees that security is the biggest *** is due to the fact that a large number of IoT devices exist in the physicalworld and thatmany of themhave constrained resources such as electricity,memory,processing power,and square *** research intends to analyse resource-constrained IoT devices,including RFID tags,sensors,and smart cards,and the issues involved with protecting them in such restricted *** lightweight cryptography,the information sent between these gadgets may be *** order to provide a holistic picture,this research evaluates and contrasts well-known algorithms based on their implementation cost,hardware/software efficiency,and attack resistance *** also emphasised how essential lightweight encryption is for striking a good cost-to-performance-to-security ratio.

关键词： IoT a sensor device lightweight cryptography block cipher smart card security and privacy

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Body Sensor-Based Multimodal Nurse Stress Detection Using Machine Learning 16

Body Sensor-Based Multimodal Nurse Stress Detection Using Ma...

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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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Increasing e-Health systems security and availability by using noSQL databases 23

Increasing e-Health systems security and availability by usi...

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23rd RoEduNet Conference: Networking in Education and Research, RoEduNet 2024

作者： Contasel, Cristian Palacean, Alexandru-Viorel Turcanu, Dinu Stoica, Victor-Valentin National University of Science and Technology POLITEHNICA Bucharest Department of Computer Science and Engineering Bucharest Romania Technical University of Moldova Department of Software Engineering and Automatics Chişinǎu Moldova

ISBN: (纸本)9798331540388

As e-Health systems become more widely used starting with COVID-19 pandemic, the amount of data they collect increases significantly. The volume, diversity, and unpredictability of patient data necessitate distinct storage types for long-Term preservation. As a result, one of the primary problems with e-Health systems is making patient data accessible and combined into different styles so that AI algorithms can analyze it. This paper presents an improved persistence system that utilizes NoSQL databases such as Couchbase. The aim of this system is to improve overall system performance while reducing security risks associated with data availability, confidentiality, and integrity. © 2024 IEEE.

关键词： Electronic health record

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CMCF-Net: An End-to-End Context Multiscale Cross-Fusion Network for Robust Copy-Move Forgery Detection

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IEEE Transactions on Multimedia 2024年 26卷 6090-6101页

作者： Xiong, Lizhi Xu, Jianhua Yang, Ching-Nung Zhang, Xinpeng Nanjing University of Information Science and Technology School of Computer Science Nanjing210044 China Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology Engineering Research Center of Digital Forensics Ministry of Education Nanjing210044 China Nanjing University of Information Science and Technology Software Engineering Nanjing210044 China National Dong Hwa University Department of Computer Science and Information Engineering Hualien974301 Taiwan Fudan University School of Computer Science Shanghai200433 China

Image copy-move forgery detection (CMFD) has become a challenging problem due to increasingly powerful editing software that makes forged images increasingly realistic. Existing algorithms that directly connect multiple scales of features in the encoder part may not effectively aggregate contextual information, resulting in poor performance. In this paper, an end-to-end context multiscale cross-fusion network (CMCF-Net) is proposed to detect image copy-move forgery. The proposed network consists of a multiscale feature extraction fusion (MSF) module and a multi-information fusion decoding (MFD) module. Multiscale information is efficiently extracted and fused in the MSF module utilizing stacked-scale feature fusion, which improves the network's forgery localization ability on objects of different scales. The MFD module employs contextual information combination and weighted fusion of multiscale information to guide the network in obtaining relevant clues from correlated information at multiple different scales. Experimental results and analysis have demonstrated that the proposed CMCF-Net achieves the best localization results with higher robustness. © 1999-2012 IEEE.

关键词： Feature extraction

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