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automated Scoring of Logical Consistency of Japanese Essays 24th

Automated Scoring of Logical Consistency of Japanese Essays

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24th international conference on Artificial Intelligence in Education (AIED)

作者： Nakamoto, Sayaka Shimada, Kazutaka Kyushu Inst Technol Iizuka Japan

ISBN: (纸本)9783031363351;9783031363368

automated essay scoring (AES) is to estimate the scores of essays automatically. Two types of AES models are commonly used: handcrafted feature-based and neural-based models. In this paper, we introduce AES systems based on the two types for evaluating the logical consistency of Japanese essays. In addition, to enhance the performance of models, we integrate the neural-based model with the handcrafted features: a hybrid AES system. In the experiment, we show the effectiveness of our hybrid AES system. Besides, most of our AES models obtained higher QWK scores than human evaluators.

关键词： automated Essay Scoring Machine learning Education Technology

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A Multi-view learning Approach for the Autonomic Management of Big Services 22nd

A Multi-view Learning Approach for the Autonomic Management ...

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22nd international conference on Web Information Systems engineering (WISE)

作者： Ghedass, Fedia Ben Charrada, Faouzi Tunis Elmanar Univ LIMTIC Fac Sci Tunis Tunisia

ISBN: (纸本)9783030915605;9783030915599

Big services have recently emerged as a solution to process, encapsulate and offer huge volumes of data as a service. However, its management operations are beyond the ability of human administrators, due to several challenges including big services' large scale nature and complexity, the heterogeneity of its components, the dynamicity and uncertainty of its hosting cloud environments. To cope with these challenges, we endow big services with self-* capabilities and we propose an autonomic computing architecture for big services. We also take advantage of two recent technologies called knowledge graphs and multi-view learning, to represent the managed big service's information (service descriptions, services' and data sources' quality levels, management policies) as a heterogeneous information network. Finally, a decision mechanism to select and trigger the appropriate management policies is defined and validated through a set of experiments.

关键词： Big service management Autonomic computing Knowledge graph Multi-view learning Graph neural network

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Collaborative Federated learning Cloud Based System 25th

Collaborative Federated Learning Cloud Based System

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25th international conference on Internet Computing and IoT, ICOMP 2024, and 22nd international conference on Embedded Systems, Cyber-physical Systems, and Applications, ESCS 2024, held as part of the World Congress in Computer Science, Computer engineering and Applied Computing, CSCE 2024

作者： Saha, Partha Pratim Sehgal, Naresh K. Faezipour, Miad Wipro Technologies Limited Maharashtra Pune India Deeply Human AI Inc. Santa ClaraCA United States School of Engineering Technology Electrical and Computer Engineering Technology Purdue University West LafayetteIN United States

ISBN: (纸本)9783031859229

Machine learning is deployed in various clinical and healthcare informatics applications, with centralized and decentralized learning schemes, each offering performance and security advantages and disadvantages. In centralized machine learning, all the data travels to a central location where the machine learning training code runs on it. A central server potentially represents a single point of failure - which is one of the issues of centralized learning. Another issue is the need for all participants to trust the central authority with their datasets. In a decentralized machine learning solution, data stays at the participating local sites, while the machine learning training code travels to each site. It needs parties to run a common binary code on each of their datasets and trust the incoming program, thus avoiding a single point of failure, but potentially creating a security hazard with malicious code. This can be addressed by using a mutually agreed upon signed binary code. Another issue is the training run time in decentralized learning due to multiple hops between different dataset locations. System designers often face tradeoffs between the higher performance of centralized machine learning vs. the better security of decentralized machine learning. In this work, we propose a novel Collaborative Federated learning (CFL) solution that combines the advantages of centralized and decentralized machine learning schemes, without compromising security. We executed our simulation using synthetic data for 30 iterations to observe the behavior of code and dataset sizes with various incremental data sharing options. Interestingly, we observed that when more data is shared centrally, data security issues become more pervasive, however, machine learning training performance improves proportionally. The challenge is to balance between performance and security considerations by partitioning appropriate amount of data to be shared centrally. © The Author(s), under exclusiv

关键词： Federated learning

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intelligent Traffic Management System: An Advanced Solution for Helmet Compliance, Traffic Signal Violation Detection, Number Plate Identification, Cell Phone Usage Tracking, and Proximity-Based Police Station Alerts

Intelligent Traffic Management System: An Advanced Solution ...

引用

Computer, Communication and Control (IC4), international conference on

作者： S. Kayalvizhi J Jayanth V Karthikeyan R Nithish Kumar Computer Science and Engineering Easwari engineering college (Anna University) Chennai India

intelligent Traffic Management System (ITMS) is a sophisticated solution designed to address a variety of urban traffic management challenges. This comprehensive system employs cutting-edge technologies to ensure helmet compliance, detect traffic signal violations, identify license plates, track cell phone usage, and provide police stations with proximity-based alerts. The system can accurately identify helmetless individuals by implementing computer vision algorithms. ITMS uses computer vision and machine learning techniques to detect traffic signal violations in addition to monitoring helmet compliance. The system can identify and record instances of red light running by analyzing live video feeds from traffic cameras, allowing law enforcement to issue appropriate penalties. Furthermore, ITMS includes a number plate identification feature that extracts license plate information using optical character recognition. This enables efficient and automated vehicle movement monitoring, allowing for effective traffic surveillance and the identification of vehicles involved in criminal activities. Another important feature of ITMS is its ability to monitor cell phone usage while driving. The system can detect drivers using cell phones and generate alerts to deter distracted driving by leveraging mobile network data and smart devices. Finally, ITMS sends proximity-based alerts to police stations, allowing for faster response times and more efficient deployment of law enforcement personnel in the event of an emergency or traffic incident. In summary, ITMS is a sophisticated and all-encompassing solution that combines advanced technologies to ensure helmet compliance, detect traffic signal violations, identify license plates, track cell phone usage, and provide proximity-based police station alerts. ITMS aims to improve road safety and streamline traffic management by combining these features into a single system for a better commuting experience.

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An Examination of the Function of Artificial-Intelligence in the Diagnosis of Autism-Spectrum-Disorder

An Examination of the Function of Artificial-Intelligence in...

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Self Sustainable Artificial Intelligence Systems (ICSSAS), international conference on

作者： Raghav Garg Chandni Manral Tripuresh Joshi Mamta Bisht K. S. Bharath Pushpendra Singh Kharayat Deptt. of Computer Science & Engg. Tula’s Institute Dehradun U.K India College of Nursing Graphic Era Hill University Bhimtal Campus India Deptt. of Electronics & Comm. Engg. Tula’s Institute Dehradun U.K India School of Applied and Life Sciences Uttaranchal University Dehradun India Chettinad School of Pharmaceutical Sc. Chettinad Academy of Research & Education Chettinad Hospital & Research Institute Kelambakkam India Deptt. of Electrical Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798350368413

ISBN: (纸本)9798350368420

Autism Spectrum Disorder (ASD) is a brain disease that makes it hard to do things over and over, connect with others, and talk. Early identification is very important, even if biology is the main reason. ML seems like a hopeful way to identify the condition more quickly and cheaply. This study uses different machine learning methods to find important ASD traits in order to make the testing process better and more automated. Due to the fast development of artificial intelligence, it is now possible to use smart methods to do early, large-scale, and pointless screening and detection of autism. In the future, researchers should work on creating an intelligent medical screening and detection system for autism patients, as well as screening tools and a patient recognition model that is smart and uses multiple types of data.

关键词： Neuroimaging Autism Accuracy Soft sensors Refining Machine learning Prediction algorithms data models Reliability Testing

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A Robust Convolutional Neural Network for Electrical Impedance Tomography Image Reconstruction

A Robust Convolutional Neural Network for Electrical Impedan...

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IEEE SSD international Multi-conference on Systems, Signals and Devices

作者： Mariem Hafsa Kailun Lin Najoua Essoukri Ben Amara Olfa Kanoun Ecole Nationale d'Ingénieurs de Sousse LATIS-Laboratory of Advanced Technology and Intelligent Systems Université de Sousse Sousse Tunisie Professorship of Measurement and Sensor Technology Technische Universität Chemnitz Chemnitz Germany Department of Mechanical Engineering Technische Universität Chemnitz Chemnitz Germany

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Electrical Impedance Tomography (EIT) is a widely used non-invasive imaging method in a variety of application fields. Most EIT applications require an accurate and high-quality reconstructed inner conductivity distribution image. Several solutions have been developed for reliable EIT image reconstruction, but they still suffer from limitations such as low spatial resolution and inconsistency in real measurement scenarios. In this paper, we propose using physically based, extensive simulation data to train a Convolutional Neural Network (CNN) model with 5-fold cross-validation. We generate a set of 10.000 data samples, which builds a comprehensive basis for its training and ensures reliability and generalization capability. The quantitative evaluation demonstrates good reconstruction performance, achieving a Mean Squared Error (MSE) of 0.0151 and an Image Correlation Coefficient (ICC) of 0.97 in the best-performing fold for the simulated data. The method's effectiveness is particularly validated through experimental measurements, where it significantly outperforms traditional Gauss-Newton reconstruction in terms of image sharpness and boundary definition. The algorithm is robust and easily applicable for EIT imaging.

关键词： Training Image quality Electrical impedance tomography Voltage measurement Impedance measurement Imaging Robustness Convolutional neural networks Spatial resolution Image reconstruction

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ChatGPT in the Malaysian Classroom: Assessing Student Acceptance and Effectiveness Through UTAUT2 and IS Success Models

ChatGPT in the Malaysian Classroom: Assessing Student Accept...

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IEEE SSD international Multi-conference on Systems, Signals and Devices

作者： Hala Najwan Sabeh Daisy Mui Hung Kee Rand Basil Mohammed Safa Anmar Albarwary Savriddin Khalilov Information Technology Department Tishk International University Erbil Iraq School of Management Universiti Sains Malaysia Penang Malaysia Mechatronics Engineering Department Tishk International University Erbil Iraq

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

ChatGPT, an advanced artificial intelligence (AI) tool, is gaining prominence in higher education for its ability to provide timely academic support to students, enhancing their learning experiences. By generating original content based on user prompts, ChatGPT has the potential to transform academic assistance. This study investigates the key factors influencing user satisfaction (effectiveness) and behavioral intention (acceptance) toward ChatGPT. We propose an integrated research model that combines the “Unified Theory of Acceptance and Use of Technology 2 (UTAUT2)” and the “Information System Success (ISS) model”, incorporating personal innovativeness as an additional factor. Using PLS-SEM, data from 202 students at a leading Malaysian research university were analyzed. Our findings reveal that social influence is the most significant predictor of behavioral intention, while system quality is the dominant driver of user satisfaction. Furthermore, satisfaction was found to significantly enhance students' behavioral intention to continue using ChatGPT. This highlights the role of user satisfaction in promoting acceptance of AI-driven educational tools. Our results provide recommendations for future research on the acceptance of AI tools such as ChatGPT in university education, suggesting the need for further exploration from both educators' and students' perspectives.

关键词： Education Transforms learning (artificial intelligence) Chatbots Information systems

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A Batch Authentication Protocol Based on Small Exponent Test for Internet of Vehicles 19

A Batch Authentication Protocol Based on Small Exponent Test...

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2022 IEEE SmartWorld, 19th IEEE international conference on Ubiquitous Intelligence and Computing, 2022 IEEE international conference on Autonomous and Trusted Vehicles conference, 22nd IEEE international conference on Scalable Computing and Communications, 2022 IEEE international conference on Digital Twin, 8th IEEE international conference on Privacy Computing and 2022 IEEE international conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Zhang, Ruirui Xu, Zisang Xu, Jianbo Computer and Communication Engineer Institute Changsha University of Science and Technology Changsha China Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan China

ISBN: (纸本)9798350346558

intelligent Transportation System (ITS) is a road transportation system that uses advanced technologies such as the Internet of Things (IoT), big data, and artificial intelligence (AI) to realize dynamic real-time information interaction between people, vehicles and roads. As an indispensable part of the ITS, the Internet of Vehicles (IoV) has received extensive attention from many researchers. Since vehicles entering the range of RSU communication need to be authenticated. Therefore, in a high vehicle traffic flow environment, it is easily to form a computation or communication bottleneck at the RSU. To solve the above problems, some researchers have proposed many batch authentication protocols. However, on the one hand, Some proposed batch authentication protocols use computationally expensive bilinear pairing operations. On the other hand, some proposed protocols do not consider the existence of semi-trusted vehicle nodes. Therefore, this paper proposes a batch authentication protocol based on the small exponent test for IoV. In the protocol, the RSU will not send any authentication secret parameters to the proxy vehicle, thus eliminating the security risk of semi-trusted nodes leaking secret parameters. Meanwhile, the proposed protocol is based on the small exponent test method, which reduces the computation cost while ensuring security. Finally, the proposed protocol is proved to be secure by mathematical derivation, and the performance analysis results also show that the proposed protocol has lower communication cost. © 2022 IEEE.

关键词： Vehicles

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From data to Decisions: Enhancing Loan Applications with Process Mining Techniques

From Data to Decisions: Enhancing Loan Applications with Pro...

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international conference on ICT and Knowledge engineering

作者： Sonram Mongkolrob Sarayut Intarasema Wichian Premchaiswadi Graduate School of Information Technology Siam University Bangkok Thailand Department of Computer Engineering Siam University Bangkok Thailand

ISBN: (数字)9798350391435

ISBN: (纸本)9798350391442

This research investigates the loan application process using Process Mining techniques combined with Generative AI to identify inefficiencies and propose improvements. data from the BPI Challenge 2017 was analyzed using Process Mining tools, particularly the Fuzzy Miner algorithm, to discover and assess workflow patterns and bottlenecks in the loan application process. Key activities were examined, including W _ Validate Application, W _Call after offers, and W _Handle leads, which exhibited excessive processing times. The analysis revealed significant delays, particularly in the W _ Validate Application step, which took an unprecedented 103.3 years. Redundant activities were also identified, negatively impacting the overall efficiency of the process. The integration of Generative AI facilitated accurate data analysis, but the need for expert interpretation remains critical for actionable insights. To enhance process efficiency, implementing Robotic Process Automation (RPA) and Generative AI in areas with identified delays is recommended. Improving interdepartmental communication and coordination is essential for a streamlined workflow. Additionally, utilizing AI systems for risk detection in complex steps, such as W _Assess Potential Fraud, can significantly improve processing speed and accuracy. This study contributes to the field of process improvement by highlighting the effectiveness of combining Process Mining with Generative AI., offering a systematic approach to identify and address bottlenecks in business processes. The findings provide valuable insights for organizations aiming to enhance operational efficiency.

关键词： Process mining intelligent automation Accuracy Systematics data analysis Generative AI Redundancy Organizations Delays Fraud

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Efficacy of Local Cache for Performance Improvement of Reliable data Transmission in Aggregated Robot Processing Architecture

Efficacy of Local Cache for Performance Improvement of Relia...

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international conference on Control, Automation and Systems ( ICCAS)

作者： Abdul Jalil Jun Kobayashi Department of Computer Science and Systems Engineering Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Japan Department of Intelligent and Control Systems Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Japan

This study aims to improve the performance of message data transmission in Aggregated Robot Processing (ARP) architecture where the RELIABLE option of the RELIABILITY QoS (Quality of Service) policy is applied to a ROS 2 (Robot Operating System 2) node communication. Basically, the RELIABLE option guarantees that a publisher properly send all message data to a subscriber. However, the publisher could fail to transmit some message data to the subscriber even in the RELIABLE QoS option unless the buffer size of the publisher is enough. We introduce local cache to a sensing component to alleviate the issue in the case that the sensors output the same value in a row. The experimental results showed that the local cache improved the latency and reduced the message loss in node communication.

关键词： Performance evaluation Operating systems Process control Quality of service Robot sensing systems Control systems Sensors

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