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International Conference on Internet of Things and Applications (IoT)

作者： S. Mohammadali Zanjani Hossein Shahinzadeh Seyed Mohamad Kargar Majid Moazzami Farshad Ebrahimi Mohammadreza Hemmati Smart Microgrid Research Center Najafabad Branch Islamic Azad University Najafabad Iran Department of Electrical Engineering Najafabad Branch Islamic Azad University Najafabad Iran Department of Electrical Engineering Amirkabir University of Technology Tehran Iran Computer Science Research Centre Royal College of Art London United Kingdom Department of Electrical and Computer Engineering University of Houston Houston TX USA Faculty of Computer Engineering Najafabad Branch Islamic Azad University Najafabad Iran

The Internet of Things (IoT) is a highly diverse system of services and technologies. However, the lack of a suitable processing platform can result in inappropriate outputs when transferring the large volume of heterogeneous data generated by devices to users. Ensuring the reliability of processed data is fundamental for maintaining integrity and reliability in IoT systems. As the number of connected devices increases, so does the risk of cyber-attacks. Hackers exploit vulnerabilities through methods like denial-of-service attacks, malicious emails, viruses, and Trojans, compromising IoT devices for their advantage. Recent studies indicate that 70% of IoT devices are vulnerable to attacks. Enhancing IoT security involves methods such as data encryption, security updates, security protocols, access control, and secure networks. Educating and raising awareness among users also plays a crucial role. Addressing these aspects and employing appropriate security measures are key to improving IoT security. This article reviews IoT challenges, security improvement methods, and future directions. Attention to data encryption, security updates, security protocols, access control, and secure networks is essential for enhancing IoT security.

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On the Effectiveness of Function-Level Vulnerability Detectors for Inter-Procedural Vulnerabilities

On the Effectiveness of Function-Level Vulnerability Detecto...

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International Conference on Software Engineering (ICSE)

作者： Zhen Li Ning Wang Deqing Zou Yating Li Ruqian Zhang Shouhuai Xu Chao Zhang Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Jin YinHu Laboratory Wuhan China Department of Computer Science University of Colorado Colorado Springs Colorado Springs Colorado USA Institute for Network Sciences and Cyberspace Tsinghua University Beijing China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

Software vulnerabilities are a major cyber threat and it is important to detect them. One important approach to detecting vulnerabilities is to use deep learning while treating a program function as a whole, known as function-level vulnerability detectors. However, the limitation of this approach is not understood. In this paper, we investigate its limitation in detecting one class of vulnerabilities known as inter-procedural vulnerabilities, where the to-be-patched statements and the vulnerability-triggering statements belong to different functions. For this purpose, we create the first Inter -Procedural Vulnerability Dataset (InterPVD) based on C/C++ open-source software, and we propose a tool dubbed VulTrigger for identifying vulnerability-triggering statements across functions. Experimental results show that VulTrigger can effectively identify vulnerability-triggering statements and inter-procedural vulnerabilities. Our findings include: (i) inter-procedural vulnerabilities are prevalent with an average of 2.8 inter-procedural layers; and (ii) function-level vulner-ability detectors are much less effective in detecting to-be-patched functions of inter-procedural vulnerabilities than detecting their counterparts of intra-procedural vulnerabilities.

关键词： Deep learning Detectors Open source software Software engineering

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Understanding Dynamic Associations: Gait Recognition via Cross-view Spatiotemporal Aggregation Network

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IEEE Transactions on Circuits and Systems for Video Technology 2022年 1-1页

作者： Chen, Jianyu Wang, Zhongyuan Zheng, Caixia Zeng, Kangli Zou, Qin Xiong, Zixiang School of Computer Science National Engineering Research Center for Multimedia Software Wuhan University Wuhan China Key Laboratory of Applied Statistics of MOE Northeast Normal University Changchun China School of Computer Science Wuhan University Wuhan China Department of Electrical and Computer Engineering Texas A&M University College Station TX USA

Gait recognition is a technique to recognize subjects under long-distance conditions. Existing gait recognition methods generally rely on extracting different motion representations of body parts to capture the gait information. However, the differences in appearance silhouettes between subjects are not discriminative enough to identify the distinct gaits. Therefore, exploring the representations of unique motion differences of subjects from the temporal domain is an effective way to boost gait recognition performance. In this paper, we propose a cross-view spatiotemporal aggregation network (CSTAN) to learn the most discriminative motion representations of subjects from multi-scale features. Particularly, CSTAN uses temporal contextual feature aggregation (TCFA) block to interact multi-scale features, increasing the diversity of temporal representations in feature sequences and producing suitable motion representations. Besides, a potential spatial feature assessment (PSFA) block is proposed to address the problem of missing silhouettes due to occlusion. Considering the multi-view context, we propose a cross-view prediction learning (CVPL) block to mitigate the impact of the view itself. By aggregating features in the spatiotemporal and view domains, CSTAN can learn a more appropriate representation of human motion differences. Extensive experiments on two public gait recognition datasets demonstrate the state-of-the-art performance of our proposed CSTAN. It is worth mentioning that the performance of CSTAN achieves a breakthrough in some complex environments, e.g., the subjects wearing different coats or carrying a bag. IEEE

关键词： Data mining

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Applications in Home Improvement Retailer, Koctas

Applications in Home Improvement Retailer, Koctas

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Business Analytics for Technology and Security (ICBATS), International Conference on

作者： Nahia Mourad Hammad Raza Muhamamd Amjad Muhammed Ubaid Ullah Nouh Sabri Elmitwally College of Engineering & IT British University in Dubai Dubai United Arab Emirates Department of Computer Science National College of Business Administration and Economics Lahore Pakistan School of Computer Science NCBA&E Lahore Pakistan Applied Science Research Center Applied Science Private University Amman Jordan Minhaj University Lahore Paksitan School of Computing and Digital Technology Birmingham City University Birmingham B4 7XG UK Department of Computer Science Faculty of Computers and Artifcial Intelligence Cairo University Giza

It sounds like Koçtaş is a leader in the home improvement sector in Turkey, and they are focused on providing the best service and customer experience possible. They are also actively working to accelerate their digital investments and use their vast amount of customer data to innovate in the industry. One way they are using this data is by collecting and analyzing video camera images using AI. This allows them to detect humans and identify which products and shelves are most viewed in their stores. This information can then be used to optimize store layout and product placement for a better customer experience. Another way Koçtaş is innovating is through the implementation of kiosks that use Natural Language Processing (NLP) to interact with customers. These kiosks can understand and respond to questions asked by customers using AI, providing a more personalized and human-like ***, Koçtaş is using Dynamic Creative Optimization to create personalized advertisements for their customers. This method allows them to optimize the content and format of their ads based on the individual preferences and behavior of their customers, leading to more effective ***, Koçtaş is using technology and data to drive innovation and provide a better customer experience in the home improvement industry.

关键词： Industries Technological innovation Layout Cameras Natural language processing Security Artificial intelligence

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Active vision: A promising technology for achieving zero-defect manufacturing

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Procedia computer science 2024年 232卷 2821-2830页

作者： Konstantinos A. Tsintotas Ioannis Kansizoglou Fotios K. Konstantinidis Spyridon G. Mouroutsos Georgios Ch. Syrakoulis Foivos Psarommatis Yiannis Aloimonos Antonios Gasteratos Department of Production and Management Engineering Democritus University of Thrace 12 Vas. Sophias Xanthi GR-671 32 Greece Department of Electrical and Computer Engineering Democritus University of Thrace Kimmeria Xanthi GR-671 00 Greece Department of Informatics SIRIUS University of Oslo Gaustadalleen 23B 0373 Oslo Noway Research Center on Production Engineering and Management (CIGIP) Universitat Polite`cnica de València Spain Department of Computer Science University of Maryland College Part MD-207 42 United States

Quality constitutes the cutting-edge of contemporary industry, much more sharpened with the emergence of Industry 4.0. Yet, the assurance of delivering high-quality products remains a challenging problem that requires cautious controls and timely actions through the entire phases of the production cycle. To that end, zero-defect manufacturing (ZDM) has given prominence to a comprehensive methodology that ensures quality control and defects eradication at every phase of the procedure, prioritizing the continuous supervision of each distinctive component to satisfy the corresponding quality assurance standards. Continuous supervision and detection of possible flaws are subjective to exploiting high-tech sensors for high-precision monitoring and assessment. Considering the above, adopting active vision technology can further enhance the promising capabilities of ZDM. The paper highlights the active vision's benefits to the ZDM strategy and introduces a practical framework for applying such technology through the different stages. Innovations and challenges that have to be taken into consideration are also discussed.

关键词： Active vision Zero Defect Manufacturing Industry 4.0 Machine vision Manufacturing

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SweepEvGS: Event-Based 3D Gaussian Splatting for Macro and Micro Radiance Field Rendering from a Single Sweep

arXiv

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arXiv 2024年

作者： Wu, Jingqian Zhu, Shuo Wang, Chutian Shi, Boxin Lam, Edmund Y. Department of Electrical and Electronic Engineering The University of Hong Kong Pokfulam Hong Kong National Key Laboratory for Multimedia Information Processing and National Engineering Research Center of Visual Technology School of Computer Science Peking University Beijing100871 China

—Recent advancements in 3D Gaussian Splatting (3D-GS) have demonstrated the potential of using 3D Gaussian primitives for high-speed, high-fidelity, and cost-efficient novel view synthesis from continuously calibrated input views. However, conventional methods require high-frame-rate dense and high-quality sharp images, which are time-consuming and inefficient to capture, especially in dynamic environments. Event cameras, with their high temporal resolution and ability to capture asynchronous brightness changes, offer a promising alternative for more reliable scene reconstruction without motion blur. In this paper, we propose SweepEvGS, a novel hardware-integrated method that leverages event cameras for robust and accurate novel view synthesis across various imaging settings from a single sweep. SweepEvGS utilizes the initial static frame with dense event streams captured during a single camera sweep to effectively reconstruct detailed scene views. We also introduce different real-world hardware imaging systems for real-world data collection and evaluation for future research. We validate the robustness and efficiency of SweepEvGS through experiments in three different imaging settings: synthetic objects, real-world macro-level, and real-world micro-level view synthesis. Our results demonstrate that SweepEvGS surpasses existing methods in visual rendering quality, rendering speed, and computational efficiency, highlighting its potential for dynamic practical applications. Copyright © 2024, The Authors. All rights reserved.

关键词： Rendering (computer graphics)

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FSLNet: Leveraging Skeleton Cue Guided FBIC for Bionic Intelligent Surveillance Robot via Transformer

FSLNet: Leveraging Skeleton Cue Guided FBIC for Bionic Intel...

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IEEE International Conference on Robotics and Biomimetics

作者： Hai Liu Qiang Chen Zhibing Liu Yongjian Deng Zhaoli Zhang You-Fu Li National Engineering Research Center for E-Learning Central China Normal University Wuhan China College of Computer Science Beijing University of Technology Beijing China Department of Mechanical Engineering City University of Hong Kong Kowloon Hong Kong

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Fine-grained bird image classification (FBIC) plays a critical role in robotic vision tracking and monitoring of endangered bird species. Traditional surveillance robots often face major challenges when operating in complex environments, especially under extreme conditions involving occlusions and complex backgrounds. To overcome these limitations, we developed a novel bionic intelligent surveillance robot that incorporates FSLNet - a fine-grained skeletal learning network that integrates skeletal tokens into a vision Transformer (ViT) framework. Unlike traditional CNN-based models, FSLNet leverages skeletal tokens to capture key invariant features, improving the ability to distinguish between visually similar bird species under challenging circumstances. The architecture of our model includes visual token construction, skeleton token construction, and a transformer-based backbone for thorough feature learning. Extensive experiments on CUB-200-2011 and NABirds datasets demonstrate that our method achieves superior performance compared to state-of-the-art approaches.

关键词： Visualization Accuracy Biological system modeling Surveillance Birds Transformers Feature extraction Skeleton Robots Image classification

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Identification of Clinical Features Associated with Mortality in COVID-19 Patients

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Operations research Forum 2023年第1期4卷 1-20页

作者： Eskandarian, Rahimeh Alizadehsani, Roohallah Behjati, Mohaddeseh Zahmatkesh, Mehrdad Sani, Zahra Alizadeh Haddadi, Azadeh Kakhi, Kourosh Roshanzamir, Mohamad Shoeibi, Afshin Hussain, Sadiq Khozeimeh, Fahime Darbandy, Mohammad Tayarani Joloudari, Javad Hassannataj Lashgari, Reza Khosravi, Abbas Nahavandi, Saeid Islam, Sheikh Mohammed Shariful Internal Medicine Research Center Semnan University of Medical Sciences Semnan Iran Institute for Intelligent Systems Research and Innovation (IISRI) Deakin University Victoria Australia Rajaei Cardiovascular Medical and Research Center Iran University of Medical Sciences Tehran Iran Omid Hospital Iran University of Medical Sciences Tehran Iran Department of Biology Faculty of Basic Sciences Shahrekord Branch Islamic Azad University Shahrekord Iran Department of Computer Engineering Faculty of Engineering Fasa University Fasa 74617-81189 Iran Data Science and Computational Intelligence Institute University of Granada Granada Spain System Administrator Dibrugarh University Assam 786004 India School of Architecture Islamic Azad University Taft Taft *** Iran Department of Computer Engineering Faculty of Engineering University of Birjand Birjand Iran Department of Computer Engineering Amol Institute of Higher Education Amol Iran Institute of Medical Science and Technology Shahid Beheshti University Tehran Iran School of Engineering and Applied Sciences Harvard Paulson Harvard University Allston 02134 MA United States Institute for Physical Activity and Nutrition School of Exercise and Nutrition Sciences Deakin University Geelong 3220 VIC Australia Cardiovascular Division The George Institute for Global Health Newtown Australia Sydney Medical School University of Sydney Camperdown Australia

Understanding clinical features and risk factors associated with COVID-19 mortality is needed to early identify critically ill patients, initiate treatments and prevent mortality. A retrospective study on COVID-19 patients referred to a tertiary hospital in Iran between March and November 2020 was conducted. COVID-19-related mortality and its association with clinical features including headache, chest pain, symptoms on computerized tomography (CT), hospitalization, time to infection, history of neurological disorders, having a single or multiple risk factors, fever, myalgia, dizziness, seizure, abdominal pain, nausea, vomiting, diarrhoea and anorexia were investigated. Based on the investigation outcome, decision tree and dimension reduction algorithms were used to identify the aforementioned risk factors. Of the 3008 patients (mean age 59.3 ± 18.7 years, 44% women) with COVID-19, 373 died. There was a significant association between COVID-19 mortality and old age, headache, chest pain, low respiratory rate, oxygen saturation < 93%, need for a mechanical ventilator, having symptoms on CT, hospitalization, time to infection, neurological disorders, cardiovascular diseases and having a risk factor or multiple risk factors. In contrast, there was no significant association between mortality and gender, fever, myalgia, dizziness, seizure, abdominal pain, nausea, vomiting, diarrhoea and anorexia. Our results might help identify early symptoms related to COVID-19 and better manage patients according to the extracted decision tree. The proposed ML models identified a number of clinical features and risk factors associated with mortality in COVID-19 patients. These models if implemented in a clinical setting might help to early identify patients needing medical attention and care. However, more studies are needed to confirm these findings. © 2023, The Author(s).

关键词： COVID‐19 Machine learning Mortality Risk factors Symptoms

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HRPE-Enhanced AI-Based 5G Indoor Localization in Presence of Specular and Dense Multipaths

HRPE-Enhanced AI-Based 5G Indoor Localization in Presence of...

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European Conference on Antennas and Propagation, EuCAP

作者： Yuchen Shi Xiaoxiao Yang Yuying Sun José Rodríguez-Piñeiro Xueming Hong Tomás Domínguez-Bolaño Xuefeng Yin College of Electronics and Information Engineering Tongji University Shanghai China School of Elec. Science and Engineering Xiamen University China School of Informatics Xiamen University China CITIC Research Center & Department of Computer Engineering University of A Coruña Spain

Fifth generation (5G) communication systems are expected to provide an ubiquitous solution for indoor positioning, and deep neural networks (DNNs) have been recently proposed for this purpose. However, DNN-based positioning solutions are in general very dependent on the training data. In this paper, an architecture for positioning based on wireless signals is proposed, namely the SAGE-Enhanced CEAP (SE-CEAP). The architecture considers firstly an enhancement of the acquired Channel State Information (CSI) data by means of a high-resolution parameter estimation (HRPE) method, namely the space-alternating generalized-expectation maximization (SAGE) algorithm, and secondly a specifically designed DNN for localization, namely the CNN-Enblock AI Positioning (CEAP). The provided results, considering a realistic 5G New Radio (NR) deployment in an indoor scenario, show that the proposed architecture outperforms other classical DNN-based localization approaches, not only in localization accuracy, but also in generalizability of the results, which is a common drawback of DNN-based solutions.

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Threshold voltage modulation in monolayer MoS_(2) field-effect transistors via selective gallium ion beam irradiation

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science China Materials 2022年第3期65卷 741-747页

作者： Baoshan Tang Yunshan Zhao Changjie Zhou Mingkun Zhang Huili Zhu Yida Li Jin Feng Leong Hao Shuai Hao Gong Weifeng Yang Department of Microelectronics and Integrated Circuit School of Electronic Science and EngineeringXiamen UniversityXiamen 361005China Department of Electrical and Computer Engineering National University of SingaporeSingapore 117576Singapore NNU-SULI Thermal Energy Research Center(NSTER)&Center for Quantum Transport and Thermal Energy Science(CQTES) School of Physics and TechnologyNanjing Normal UniversityNanjing 210023China Department of Physics School of ScienceJimei UniversityXiamen 361021China Engineering Research Center of Integrated Circuits for Next-Generation Communications Ministry of EducationSouthern University of Science and TechnologyShenzhen 518055China Department of Materials Science and Engineering National University of SingaporeSingapore 117575Singapore

Electronic regulation of two-dimensional(2 D)transition metal dichalcogenides(TMDCs)is a crucial step towards next-generation optoelectronics and ***,we demonstrate controllable and selective-area defect engineering in 2D molybdenum disulfide(MoS_(2))using a focused ion beam with a low-energy gallium ion(Ga^(+))*** find that the surface defects of MoS_(2)can be tuned by the precise control of ion energy and ***,the fieldeffect transistors based on the monolayer MoS_(2)show a significant threshold voltage modulation over 70 V after Ga+***-principles calculations reveal that the Ga impurities in the monolayer MoS_(2)introduce a defect state near the Fermi level,leading to a shallow acceptor level of 0.25 eV above the valence band *** defect engineering strategy enables direct writing of complex pattern at the atomic length scale in a controlled and facile manner,tailoring the electronic properties of 2D TMDCs for novel devices.

关键词： two-dimensional transition metal dichalcogenides field-effect transistors defect engineering Ga ion irradiation

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