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Psychological Mechanism of Civil Aviation Staff’s Unsafe Behavior 24th

Psychological Mechanism of Civil Aviation Staff’s Unsafe Be...

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24th international conference on Human-computer Interaction, HCI international, HCII 2022

作者： Zhang, Chunyang Zhang, Yuan Aviation Safety Institute China Academy of Civil Aviation Science and Technology Beijing100028 China

ISBN: (纸本)9783031063879

In the aviation field, the concept of systems management has been widely recognized, and the human factor analysis of aviation accident/unsafe behavior has been more focused on finding system vulnerabilities. However, the analysis of unsafe behavior from the perspective of individual psychology is relatively low. In order to promote the safety of civil aviation and to reduce unsafe behaviors such as errors and violations caused by civil aviation personnel, the current study concluded the psychological factors associated with the unsafe behavior of civil aviation practitioners. the study was based on the core principles of safety psychology and the human factors analysis and classification system (HFACS). the psychological factors influencing the unsafe behaviors of civil aviation employees included risk perception, safe attention, memory, negative emotions, job burnout, self-control, safety attitude, safety motivation, and personality traits. Combined with the social cognition theoretical framework and the psychological factors associated with the unsafe behavior of civil aviation personnel, a model was formed. the model provides theoretical and practical reference during the analysis of psychological factors such as errors and violations caused by civil aviation personnel. © 2022, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Factor analysis

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Ballot Tabulation Using Deep Learning

Ballot Tabulation Using Deep Learning

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IEEE international conference on Information Reuse and Integration (IRI)

作者： Fei Zhao Chengcui Zhang Nitesh Saxena Dan Wallach Akm Shahariar Azad Rabby Dept. of Computer Science The University of Alabama at Birmingham Dept. of Computer Science & Engineering Texas A&M University Dept. of Computer Science Rice University

Currently deployed election systems that scan and process hand-marked ballots are not sophisticated enough to handle marks insufficiently filled in (e.g., partially filled-in), improper marks (e.g., using check marks or crosses instead of filling in bubbles), or marks outside of bubbles, other than setting a threshold to detect whether the pixels inside bubbles are dark and dense enough to be counted as a vote. the current works along this line are still largely limited by their degree of automation and require substantial manpower for annotation and adjudication. In this study, we propose a highly automated deep learning (DL) mark segmentation model-based ballot tabulation assistant able to accurately identify legitimate ballot marks. For comparison purposes, a highly customized traditional computer vision (T-CV) mark segmentation-based method has also been developed to compare with the DL-based tabulator, with a detailed discussion included. Our experiments conducted on two real election datasets achieved the highest accuracy of 99.984% on ballot tabulation. In order to further enhance our DL model’s capability of detecting the marks that are underrepresented in training datasets, e.g., insufficiently or improperly filled marks, we propose a Siamese network architecture that enables our DL model to exploit the contrasting features between a hand-marked ballot image and its corresponding blank template image to detect marks. Without the need for extra data collection, by incorporating this novel network architecture, our DL model-based tabulation method not only achieved a higher accuracy score but also substantially reduced the overall false negative rate.

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Increasing the Robustness of Fault Detection for Induction Motors based on Neural Networks and the Winding Function Method

Increasing the Robustness of Fault Detection for Induction M...

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international conference on the Computation of Electromagnetic Fields (COMPUMAG)

作者： Moritz Benninger Marcus Liebschner Christian Kreischer Faculty of Electronics and Computer Science University of Applied Sciences Aalen Aalen Germany Chair for Electrical Machines and Drive Systems Helmut Schmidt University Hamburg Germany

this paper presents how a feed forward neural network can be trained on simulated data with high robustness for practical fault detection in induction motors. the basic methodology for the fault detection is a combination of model- and machine-learning-based approaches. the applied framework consists of a feed forward neural network and a multiple coupled circuit model based on the winding function method. the dataset of stator currents in healthy and faulty states simulated by the model allows a neural network-based classification of different faults without the need to measure currents under real fault conditions. However, this approach suffers from the difficulty of transferring the extracted fault characteristics from the simulated data to the measured stator currents. therefore, the effect of ensemble learning on increasing the robustness of fault detection is investigated in detail. In addition, an analysis of the influence of the hyperparameters of the neural network on the transferability of the extracted fault characteristics from the simulated stator currents is carried out. As a result, it is found that both techniques increase the robustness of the methodology for fault detection.

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Fine-Tuning Pre-Trained Models for Automated Analysis of Ophthalmic Imaging in Diagnosing Eye Diseases

Fine-Tuning Pre-Trained Models for Automated Analysis of Oph...

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Information Technology (ACIT)

作者： Rabia Emhamed Al Mamlook Ahmad Nasayreh Hasan Gharaibeh Mohammad Aljaidi Qais Al-Na'amneh Dalia Alzu'bi Hanin Bzizi Abdelrahman Abdeltawab Dep. of Business Administration Trine University Indiana USA Department of Industrial Engineering University Zawia Tripoli Libia Dep. Computer Science Yarmouk University Irbid Jordan Dep. of Computer Science Zarqa University Zarqa Jordan Dep. of Cyber Security Applied Science Private University Amman Jordan Dep. Information and Systems Engineering Concordia University Montreal Canada Dep of Biomedical Science Western Michigan University Kalamazoo MI USA Faculty of pharmacy Bani Sweif university Bani Suwayf Egypt

this paper searches into the convergence of such ad-vanced techniques with architectures like DenseNet201, VGG16, InceptionResNetV2, and NasNetMobile. Our focus centers on harnessing deep learning capabilities for the precise detection of eye ailments. Preliminary findings spotlight a notable uplift in diagnostic precision. Among the tested algorithms, the Nas-NetMobile architecture emerged superior, boasting an impressive accuracy rate of 92% on the chosen dataset. through this work, we extend a significant advancement in the realm of automated eye disease diagnosis, potentially equipping medical professionals with sharper, swifter diagnostic tools. the outcomes presented herein could play a seminal role in sculpting more sophisticated and precise diagnostic approaches within ophthalmology.

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Meta-RFF: Few-Shot Open-Set Incremental Learning for RF Fingerprint Recognition via Multi-phase Meta Task Adaptation 18th

Meta-RFF: Few-Shot Open-Set Incremental Learning for RF Fin...

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18th international conference on Wireless Artificial Intelligent Computing systems and Applications, WASA 2024

作者： Li, Taotao Wen, Zhenyu Jiang, Yuting Wang, Jian Su, Jie Hong, Zhen Yu, Li He, Shibo The Institute of Cyberspace Security and the College of Information Engineering Zhejiang University of Technology Hangzhou China University of Science and Technology of China Hefei China College of Control Science and Technology Zhejiang University Hangzhou China

ISBN: (纸本)9783031714634

RF fingerprinting (RFF) is a non-encrypted authentication technique that provides an additional layer of security for wireless devices, which has a promising application. However, existing RFF recognition techniques that rely on deep learning (DL) are usually available with limited equipment. In actual application scenarios, new wireless devices are constantly appearing, such as unknown drones that appear suddenly in the sky. In such cases, the RF monitoring system should equip the ability to discover the unknown device (i.e., open-set recognition (OSR)) and use captured few samples of new devices incrementally updating knowledge of the system. this requirement brings two challenges: 1) incremental updates from few-shot samples are prone to lead to catastrophic forgetting and over-fitting problems;2) constructing a reliable OSR mechanism for new devices with few-shot samples is difficult. To tackle this challenge, for the first time, we propose a novel few-shot open-set incremental learning (FSOSIL) framework via meta-learning for RFF recognition (Meta-RFF ). the core idea of Meta-RFF is to simulate few-shot RF signal incremental learning by constructing many pseudo-FSOSIL tasks. In particular, to strengthen the OSR capability, we further design RF feature augmentation and open space learning modules. the algorithm is validated on the large-scale aircraft recognition dataset (namely ADS-B), which shows that the close-set accuracy and open-set AUROC of the new class improve the performance by about 10–20% compared to other algorithms with 1-shot. And in 10 increments, our algorithm possesses a lower performance decay rate (about 3%). © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Internal Hidden Processing Model-Based for SELECT Command in Structured Query Language

Internal Hidden Processing Model-Based for SELECT Command in...

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Information Technology (ACIT)

作者： Hasan Idhaim Mohammad Aljaidi Ghassan Samara Mais Haj Qasem Ayoub Alsarhan Muhammad Khalid Department of Computer Information Systems Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa Jordan Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan Department of Information Technology Faculty of Prince AI-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa Jordan Department of Computer Sciences and Technology University of Hull Kingston upon Hull UK

the most used command in Structured Query Language (SQL) for retrieving data is select. In order to secure the retrieved data, this paper uses the hidden processing scheme (HPS).HPS breaks down the execution of user query into five phases to protect data. this data is identical to the select statement's physical execution. the model is based on the axiom that better results can be obtained by having a better understanding of how things work. the proposed scheme divides the query into five steps and improves the accuracy of retrieved data significantly, this will aid in the enhancing security, comprehension, and outcome anticipation needed to improve the SQL development research field. this work presents a theoretical SQL's statements modeling to predict the query output result that is not revealed in detail in the physical execution. the work is a uniquely researched paper in the field of database.

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Navigating the Privacy and Cybersecurity Risks of Smart Homes

Navigating the Privacy and Cybersecurity Risks of Smart Home...

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Information Technology (ACIT)

作者： Fatimah Abdulrahman Alghamdi Abdulrahman Abdullah Alghamdi dept. Computer Science and Engineering College of Computing and Artificial Intelligence Jeddah University Jeddah Saudi Arabia dept. Computer Science College of Computing and Information Technology Shaqra University Shaqra Saudi Arabia

the increasing popularity of smart home devices has led to a growing concern over cyberthreats to these devices. this study aims to investigate the perceived cyberthreats to smart home devices and identify ways to protect privacy and countermeasure against these threats. Five experts in the field of smart home technology were selected for this study. Semi-structured interviews were conducted with the selected experts over the telephone. the transcripts of the interviews were analyzed using thematic analysis. the main findings of the study indicate that the most significant cyberthreats to smart home devices are: Voice recordings (35.5 % ), Camera and microphone access (30.1 %), Location tracking (24.9 % ). Personal information (20.1%), Data breaches (15.1 %), third-party access (7.4 %), Privacy invasion (7.4%), Lack of control over personal information (5.0 % ), Cyber-attacks (4.8%), Security vulnerabilities in smart devices (4.8 % ) were also identified as potential threats. the study suggests that protecting personal data and ensuring device security are key considerations for safeguarding smart home devices and that it is important to take the necessary precautions to protect smart home devices from cyber threats, and to be aware of the potential risks that these devices pose to personal privacy and security.

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Design of a Smart and Green Industry 4.0 Architecture

Design of a Smart and Green Industry 4.0 Architecture

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Information Technology (ACIT)

作者： Khaled Hamdan Okba Kazar Dept. of Computer Science and Software Engineering College of IT UAEU UAE Dept. of Information Systems & Security College of IT UAEU UAE

the industrial sector has entered a phase of profound change which sees digital technologies being integrated into the heart of industrial processes. this fourth industrial era gives birth to a new generation of factory called “Digital Factory”, “Industry 4.0”. this industrial change is the result of the integration of new digital technologies into manufacturing processes and the optimization of energy consumption. the commitment to the environment is also a key and success factor of Industry 4.0 and green in particular the reduction of carbon emissions and pollution. this is how smart green industry try to take in charge the problem of energy consumption and to reduce the Co2 pollution. the goal of this paper is to present the new model of smart green industry using distributed artificial intelligence based on multi-agent system (MAS). the motivation to use the intelligent approach based on MAS is based on the similarity between the fundamental concepts of the cyber-physical system and those contained in the architecture and operation of MAS such as communication, coordination, decision and cooperation.

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Heartbeat Abnormality Detection in Phonocardiogram Signals using Wavelet Time Scattering and Optimized KNN Classification

Heartbeat Abnormality Detection in Phonocardiogram Signals u...

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Information Technology (ACIT)

作者： Shamik Tiwari Piyush Maheshwari School of Computer Science University of Petroleum and Energy Studies Dehradun India The British University in Dubai (BUiD) Dubai United Arab Emirates

Heart auscultation continues to play an essential role in heart health diagnosis. However, many places worldwide have a shortage of suitably qualified medical practitioner's adept at this ability. this highlights the critical need to develop accurate automated systems for evaluating Phonocardiogram (PCG) data. PCGs are acoustic recordings that capture the noises made by the heart during its systolic and diastolic cycles. To solve this issue, we suggest using Wavelet Time Scattering with an optimized XGBoost classifier and K-Nearest Neighbors (KNN) classifier to detect irregular heartbeats in PCG signals. the results are promising, as the optimized KNN classifier obtains an impressive accuracy rate of 92.5% when combined with five-fold cross-validation, which is better than XGBoost classifier, which gains 87.93%. this demonstrates the efficacy of the optimized KNN in improving the automated interpretation of PCG data and assisting in the early diagnosis of heart-related problems.

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Integrated Device for controllable Droplet Generation and Detection on Open Array Chip 16th

Integrated Device for Controllable Droplet Generation and De...

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16th international conference on Intelligent Robotics and Applications, ICIRA 2023

作者： Zhou, Zijian Wang, Jie Li, Yuxin Zhou, Jia Du, Lin School of Mechanical Engineering University of Shanghai for Science and Technology Shanghai200433 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200433 China State Key Laboratory of ASIC and System School of Microelectronics Fudan University Shanghai200433 China

ISBN: (纸本)9789819965007

the open array chip is an incredibly valuable tool for analyzing single molecule levels. Its efficiency, speed, compatibility, and precision make it an essential part of this process. However, efficient sample addition has become a significant challenge in the development of open array chips due to the coupling of the solid-liquid interface and the need for high-precision control. To address this challenge, we have developed an integrated adding sample device using technology of generates and detects controllable droplets array based on an open array chip with a biomimetic structure. this device combines a microfluidic chip, image detection, and smear speed regulation. We have studied the factors that affect the efficiency of sample loading, such as the number, speed and fluctuation of smears, and microwell size. the experiments have shown that the open array chip with the microwell’s diameter of 350 µm can generate arrays of ~470/cm2 ~8 nL droplets, significantly reducing controlled speed fluctuation errors by7.24% and volume fluctuation errors by 24% compared to traditional manual ways. the device has also displayed excellent performance on chips with microwell diameters of 120, 350, 700, and 1300 µm, respectively, with a notable first-time success rate of up to 91% on chips with microwell diameters of 120 µm. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Microfluidic chips

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