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Evolving Safety Protocols: Deep Learning-Enabled Detection of Personal Protective Equipment 1

4th International Conference on computer Vision, High-Performance Computing, Smart Devices, and Networks, CHSN 2023

作者： Alahmid, Mustafa Bhimani, Kishankumar Saradva, Khushbu Ghildiyal, Sushil Saeedi, Sayed Aryan Ali, Saraa Yan, Han Filippov, Evgeny Faculty of Computer Science and Technology Saint Petersburg Electrotechnical University "LETI" Saint Petersburg Russia Laboratory on AI for Computational Biology Faculty of Computer Science HSE University Moscow Russia Department of Computer Science and Engineering Indian Institute of Technology Ropar Punjab India Laboratory of Methods for Big Data Analysis Faculty of Computer Science HSE University Moscow Russia Deggendorf Institute of Technology Bavaria Germany

ISBN: (数字)9789819777945

ISBN: (纸本)9789819777938

To give shift in safety protocols, we have employed advanced deep learning algorithms and frameworks (Shrestha and Mahmood in IEEE Access 7:53,040–53,065, 2019 [25]) to construct an innovative AI model. The designed model detects the usage of personal protective equipment (PPE) (Personal protective equipment. Geneva: World Health Organization, 2020 [18]) by workers in high-risk industries such as construction and manufacturing. We have used Google’s TensorFlow object detection API (Sai and Sasikala in Object detection and count of objects in image using tensor flow object detection API, pp 542–546, 2019 [22]) to modify and train a model for dual purposes: PPE detection and face recognition. The state-of-the-art of this research is to substantially enhance safety compliance by addressing the prevalent issue of PPE non-compliance. To emphasis this, we have developed a pioneering software prototype that synergizes PPE detection with a face recognition-based clock-in system. This prototype demonstrates impressive object detection metrics with a mean average precision (mAP) of 0.9 for vests and 0.85 for helmets. Moreover, it exhibited efficient face recognition with a successful threshold range of 17–20%. The implementation of AI in our system promises significant enhancements to worker safety, while concurrently reducing the financial burden associated with big hazards and accidents. Beyond the development and performance of the system, this paper provides a thorough exploration of the encountered challenges, potential real-world applications (particularly in employee monitoring and clock-in systems), and the future implications of this study on research and practical applications in the field of AI-integrated safety compliance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Software prototyping

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ControlVideo: conditional control for one-shot text-driven video editing and beyond

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science China(Information sciences) 2025年第3期68卷 150-162页

作者： Min ZHAO Rongzhen WANG Fan BAO Chongxuan LI Jun ZHU Department of Computer Science and Technology Institute for AI Tsinghua-Bosch Joint ML CenterTsinghua Laboratory of Brain and Intelligence Lab Tsinghua University ShengShu Technology Gaoling School of Artificial Intelligence Renmin University of China Beijing Key Laboratory of Big Data Management and Analysis Methods Pazhou Laboratory (Huangpu)

This paper presents ControlVideo for text-driven video editing — generating a video that aligns with a given text while preserving the structure of the source video. Building on a pre-trained text-to-image diffusion model, ControlVideo enhances the fidelity and temporal consistency by incorporating additional conditions(such as edge maps), and fine-tuning the key-frame and temporal attention on the source video-text pair via an in-depth exploration of the design space. Extensive experimental results demonstrate that ControlVideo outperforms various competitive baselines by delivering videos that exhibit high fidelity w.r.t. the source content, and temporal consistency, all while aligning with the text. By incorporating low-rank adaptation layers into the model before training, ControlVideo is further empowered to generate videos that align seamlessly with reference images. More importantly, ControlVideo can be readily extended to the more challenging task of long video editing(e.g., with hundreds of frames), where maintaining long-range temporal consistency is crucial. To achieve this, we propose to construct a fused ControlVideo by applying basic ControlVideo to overlapping short video segments and key frame videos and then merging them by pre-defined weight functions. Empirical results validate its capability to create videos across 140 frames, which is approximately 5.83 to 17.5 times more than what previous studies achieved. The code is available at https://***/thu-ml/controlvideo.

关键词： diffusion models controllable generation text-driven editing video editing long video editing

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A Large-Scale Group Decision-Making Approach Employing Large Language Models to Detect Assertive Groups 23

A Large-Scale Group Decision-Making Approach Employing Large...

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23rd International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques, SoMeT 2024

作者： Carbó-García, Santiago Trillo, José Ramón Tapia, Juan Miguel Pérez, Ignacio Javier Herrera-Viedma, Enrique Cabrerizo, Francisco Javier Andalusian Research Institute in Data Science and Computational Intelligence Department of Computer Science and Artificial Intelligence University of Granada Granada Spain Department of Quantitative Methods for Business and Economics University of Granada Granada Spain

ISBN: (纸本)9781643685380

Large-Scale Group Decision-Making, propelled by the advent of Large Language Models and the imperative for assertiveness in decision-making processes, emerges as a pivotal area of research. This paper navigates through the landscape of Large-Scale Group Decision-Making, delineating its significance across diverse domains, including social networks and e-democracy. Amidst its nascent status, Large-Scale Group Decision-Making encounters formidable challenges, particularly in information management and fostering consensus among a multitude of experts. This paper aims to illuminate the goals and hurdles facing Large-Scale Group Decision-Making approaches through an exhaustive review of contemporary literature and methodologies. By confronting these challenges head-on, Large-Scale Group Decision-Making holds the potential to redefine decision-making paradigms, bolster assertiveness, and elevate collective problem-solving capabilities in an increasingly interconnected world. © 2024 IOS Press. All rights reserved.

关键词： Information management

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Verification of Genuineness of Educational Internet Resources Using Machine Learning methods 4

Verification of Genuineness of Educational Internet Resource...

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4th International Conference on Technology Enhanced Learning in Higher Education, TELE 2024

作者： Privalov, Aleksandr N. Smirnov, Vadim A. Institute of Advanced Information Technologies Tula State Lev Tolstoy Pedagogical University Tula Russia Ivanovo State University Department of Mathematics Computer Science and Teaching Methods Shuya Russia

ISBN: (纸本)9798350353532

The analysis of various sources has been carried out, confirming the demand for protective solutions against information threats from fake websites of organizations. The relevance of the research topic is confirmed by the presence of addresses similar in spelling to popular educational Internet resources in the list of domain names of the RU zone registered over the past year. When developing security solutions, it is proposed to use a site verifying the genuineness method based on the use of machine learning algorithms. A review of research by various authors has been conducted in order to determine the most frequently used features for verifying the genuineness of Internet resources in the machine learning models. The authors propose their own verifying the genuineness features that can be used in a situation where data about an Internet resource is extracted from the domain name registry. The proposed features differ from the known ones in that they contain signs of micro-markup on the site and storage of site settings files for search engines on servers. The developed features were tested using a naive Bayesian classifier as a machine learning algorithm. The proposed features can be used in the implementation of a software tool to counteraction threats from fake educational sites. © 2024 IEEE.

关键词： Adversarial machine learning

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Comparative Analysis of COVID-19 Detection methods Based on Neural Network

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computers, Materials & Continua 2023年第7期76卷 1127-1150页

作者： Inès Hilali-Jaghdam Azhari A Elhag Anis Ben Ishak Bushra M.Elamin Elnaim Omer Eltag Mohammed Elhag Feda Muhammed Abuhaimed S.Abdel-Khalek Computer Sciences and Information Technology Programs Applied CollegePrincess Nourah bint Abdulrahman UniversityRiyadhSaudi Arabia Department of Mathematics and Statistics College of ScienceTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Quantitative Methods Higher Institute of ManagementUniversity of Tunis1073Tunisia Department of Computer Science College of Science and Humanities in Al-SulailPrince Sattam Bin Abdulaziz UniversityAl KharjSaudi Arabia Department of Computer Science Faculty of Science and ArtsKing Khalid UniversityAbha61421Saudi Arabia Department of Mathematics Faculty of ScienceSohag UniversitySohagEgypt

In 2019,the novel coronavirus disease 2019(COVID-19)ravaged the *** of July 2021,there are about 192 million infected people worldwide and 4.1365 million *** present,the new coronavirus is still spreading and circulating in many places around the world,especially since the emergence of Delta variant strains has increased the risk of the COVID-19 pandemic *** symptoms of COVID-19 are diverse,and most patients have mild symptoms,with fever,dry cough,and fatigue as the main manifestations,and about 15.7%to 32.0%of patients will develop severe *** are screened in hospitals or primary care clinics as the initial step in the therapy for *** transcription-polymerase chain reaction(PCR)tests are still the primary method for making the final diagnosis,in hospitals today,the election protocol is based on medical imaging because it is quick and easy to use,which enables doctors to diagnose illnesses and their effects more *** to this approach,individuals who are thought to have COVID-19 first undergo an X-ray session and then,if further information is required,a CT-scan *** methodology has led to a significant increase in the use of computed tomography scans(CT scans)and X-ray pictures in the clinic as substitute diagnostic methods for identifying *** provide a significant collection of various datasets and methods used to diagnose COVID-19,this paper provides a comparative study of various state-of-the-art *** impact of medical imaging techniques on COVID-19 is also discussed.

关键词： Neural networks frameworks intelligent systems COVID-19 CT

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Heuristic Algorithm for Solving Travelling Salesman Problem: Adding the Endpoints of the Longest Edges 5

Heuristic Algorithm for Solving Travelling Salesman Problem:...

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5th International Conference on Problems of Cybernetics and Informatics, PCI 2023

作者： Nuriyeva, Fidan Dokuz Eylul University Department of Computer Science Izmir Turkey Institute of Control Systems Laboratory of Recognition Identification and Methods of Optimal Solutions Baku Azerbaijan

ISBN: (纸本)9798350319064

This study proposes, a new heuristic algorithm based on insertion heuristic to solve the symmetric Travelling Salesman Problem. In the proposed algorithm, firstly the two longest edges are selected and the starting tour passing through their endpoints is constructed, then the endpoints of the next longest edges are added to this tour and the solution passing through all the vertices is found. This problem is known to be NP-Hard. The proposed method was successfully tested on different-sized problems. The experimental results show that the proposed algorithm outperforms many existing methods. © 2023 IEEE.

关键词： Traveling salesman problem

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Autoencoder Neural Networks for Anomaly Detection in Wind Turbines 19th

Autoencoder Neural Networks for Anomaly Detection in Wind Tu...

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19th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2024

作者： Coronel, D. Guevara, C. Santos, M. Computer Science Faculty Complutense University of Madrid Madrid28040 Spain Quantitative Methods Department CUNEF University Madrid28040 Spain Institute of Knowledge Technology Complutense University of Madrid Madrid28040 Spain

ISBN: (纸本)9783031750090

Wind energy has emerged as a crucial solution in the search for sustainable and environmentally friendly energy sources. As global energy demand grows rapidly and the need to reduce greenhouse gas emissions increases, wind turbines have become a key technology for the transition to a cleaner and more sustainable energy future. This paper focuses on the application of autoencoder neural networks for the accurate and efficient detection of anomalies in wind turbines. The main objective is to significantly improve monitoring and predictive maintenance of wind turbines and thus enhance wind energy availability. The dataset used includes records of wind turbines with both, healthy and faulty conditions. Healthy records serve as a baseline to illustrate the expected amplitude of the turbine vibrations under optimal operation conditions. In contrast, failure records arising from blade surface erosion, blade unbalanced, and blade twist are used to evaluate the failure detection model. The proposed algorithm demonstrates a precision of 95.42% for blade unbalanced failures, 94.63% for blade surface erosion, and 87.54% for blade twist faults. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Windmill

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Enhancing Password Security: Analyzing Password Management Practices Among IT Students 7

Enhancing Password Security: Analyzing Password Management P...

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7th IEEE International Conference and Workshop Obuda on Electrical and Power Engineering, CANDO-EPE 2024

作者： Kollar, Anita Mihalovicsne Katona, Jozsef Institute of Computer Engineering University of Dunaujvaros Department of Software Development and Application Dunaujvaros Hungary John Von Neumann University GAMF Faculty of Engineering and Computer Science Kecskemet Hungary Budapest Business University Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest Hungary

ISBN: (纸本)9798331531119

Today, with the digital world in its full swing, strong password management is key to protecting sensitive information from both internal and external threats. The current research explores the passwords managing behavior of the IT students at the University of Dunaujvaros. A survey made in May 2024 questioned 68 students, 59 males and 9 females, about behaviors such as using passwords with complexity, not repeating them for multiple accounts or machines, changing them regularly, and which applications they use for password management. The findings have depicted that, as a whole, students appear to be moderately sensitive about the password security instructions. Nonetheless, most continue to indulge in unsafe practices, including easily remembered or weak passwords and infrequent password changes. Furthermore, approximately half of the responders use password management applications, indicating room for significant improvement in the future. The results suggest that intervention and special education measures should be taken to enhance the practice of password security among future IT experts. This research has good potential to contribute toward strategies designed for people behaving safely in password management, and hence promote a secure digital environment. © 2024 IEEE.

关键词： Students

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Transformative Applications and Key Challenges of Generative AI 7

Transformative Applications and Key Challenges of Generative...

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7th IEEE International Conference and Workshop Obuda on Electrical and Power Engineering, CANDO-EPE 2024

作者： Kovari, Attila Katona, Jozsef Eszterházy Károly Catholic University Institute of Digital Technology Faculty of Computer Science Eger Hungary University of Dunaújváros Department of Institute of Engineering Dunaujvaros Hungary John Von Neumann University GAMF Faculty of Engineering and Computer Science Kecskemét Hungary Institute of Computer Engineering University of Dunaujvaros Department of Software Development and Application Dunaujvaros Hungary Budapest Business University Faculty of Finance and Accountancy Department of Applied Quantitative Methods Budapest Hungary

ISBN: (纸本)9798331531119

The paper discusses the integration and use of generative artificial intelligence technologies in education. Generative AI, such as OpenAI, in particular the GPT model, has fundamentally changed the way humans interact with machines. Advanced technologies in the field of generative AI in education are mostly used to personalize learning experiences by dynamically customizing learning materials according to the needs of learners, thus enabling the improvement of outcomes. This article explores the use of generative AI tools with the aim of assessing how useful students perceive these tools to be. The findings show mixed receptivity from users, highlighting the critical balance in using innovative AI capabilities through AI. © 2024 IEEE.

关键词： Generative adversarial networks

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Hyper Elliptic Curve Based Certificateless Signcryption Scheme for Secure IIoT Communications

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computers, Materials & Continua 2022年第5期71卷 2515-2532页

作者： Usman Ali Mohd Yamani Idna Idris Jaroslav Frnda Mohamad Nizam Bin Ayub Roobaea Alroobaea Fahad Almansour Nura Modi Shagari Insaf Ullah Ihsan Ali Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversity of MalayaKuala Lumpur50603Malaysia Department of Computer Science University of SwatSaidu Sharif19130Pakistan Center for Research in Mobile Cloud Computing University of MalayaKuala Lumpur50603Malaysia Department of Quantitative Methods and Economic Informatics Faculty of Operation and Economics of Transport and CommunicationsUniversity of Zilina01026 ZilinaSlovakia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Science College of Sciences and Arts in RassQassim UniversityBuraydah51452Saudi Arabia Department of Computer Science Hamdard Institute of Engineering and TechnologyIslamabad44000Pakistan

Industrial internet of things (IIoT) is the usage of internet of things(IoT) devices and applications for the purpose of sensing, processing andcommunicating real-time events in the industrial system to reduce the unnecessary operational cost and enhance manufacturing and other industrial-relatedprocesses to attain more profits. However, such IoT based smart industriesneed internet connectivity and interoperability which makes them susceptibleto numerous cyber-attacks due to the scarcity of computational resourcesof IoT devices and communication over insecure wireless channels. Therefore, this necessitates the design of an efficient security mechanism for IIoTenvironment. In this paper, we propose a hyperelliptic curve cryptography(HECC) based IIoT Certificateless Signcryption (IIoT-CS) scheme, with theaim of improving security while lowering computational and communicationoverhead in IIoT environment. HECC with 80-bit smaller key and parameterssizes offers similar security as elliptic curve cryptography (ECC) with 160-bitlong key and parameters sizes. We assessed the IIoT-CS scheme security byapplying formal and informal security evaluation techniques. We used Realor Random (RoR) model and the widely used automated validation of internet security protocols and applications (AVISPA) simulation tool for formalsecurity analysis and proved that the IIoT-CS scheme provides resistance tovarious attacks. Our proposed IIoT-CS scheme is relatively less expensivecompared to the current state-of-the-art in terms of computational cost andcommunication overhead. Furthermore, the IIoT-CS scheme is 31.25% and 51.31% more efficient in computational cost and communication overhead,respectively, compared to the most recent protocol.

关键词： IoT security authentication protocols hyperelliptic curve cryptography certificateless public key cryptography

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