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20th Principle and Practice of Data and Knowledge Acquisition Workshop, PKAW 2024, held in conjunction with the 21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Hori, Tatsuya Yamauchi, Koichiro Graduate school of Engineering Department of Computer Science Chubu University Kasugai Japan

ISBN: (纸本)9789819600250

Short answer scoring (SAS) that automatically scores learner’ answers based on given rubric. Because SAS uses different rubrics for different prompts, it is necessary to generate training data for each prompt, which is time-consuming and labor-intensive. We propose a human-in-the-loop framework that achieves SAS with a minimal amount of training data through active data creation and efficient training. We fine-tuned the Sentence BERT model with a small dataset by using SetFit (Lewis Tunstall, et al. (2022).) and then apply active learning (AL) to an unlabeled data pool. The grader scores the data selected by AL, creating new training data that are then used to further fine-tune the models. For the experiments, we used six prompts from a Japanese SAS dataset. Our experimental results demonstrate that the proposed method outperforms previous studies in most cases. We also examined the potential challenges when applying this framework in practical scenarios. This framework significantly reduces the graders’ time and effort by enabling automatic scoring with minimal training data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Active learning

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Adversarial Robustness in DeepFake Detection: Enhancing Model Resilience with Defensive Strategies 4

Adversarial Robustness in DeepFake Detection: Enhancing Mode...

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4th International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2024

作者： Pallavi, N. Pallavi, T.P. Bylaiah, Sushma Goutam, R. M S Ramaiah Institute of Technology Department of Computer Science and Engineering Karnataka India Karnataka India Atria Institute of Technology Department of Computer Science and Engineering Karnataka India

ISBN: (纸本)9798331506490

Detection of DeepFakes presents formidable challenges primarily due to adversarial attacks that can dramatically reduce model accuracy. Current detection models are reasonably effective in real scenarios;however, these detectors are vulnerable to adversarial attacks, resulting in misclassifications. The current study examines defensive approaches to strengthen the robustness of DeepFake detection approaches when facing adversarial attacks. Explored several alternatives, including adversarial training, input preprocessing, and model ensembles against a manipulated media dataset. The results demonstrate that the model has predicted image appropriately at average of 86.34% but after the adversarial attacks the prediction of the image was 99.8% inappropriate. although adversarial attacks greatly lower the effectiveness of a model, it is possible to improve the robustness of a model through defense methods, particularly adversarial training in combination with an ensemble method which predicted the image appropriately at an average of 52.06%. However, despite the improvement, the defense methods still fail to protect all adversarial examples. The results illustrate the importance of developing new methods of defense against DeepFake detection in light of future adjustments and variants of an attack. © 2024 IEEE.

关键词： adversarial attack attacks deepfake deeplearning detection system

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Analyzing Personality Insights Through Machine Learning 9

Analyzing Personality Insights Through Machine Learning

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9th IEEE International Conference on Smart Structures and Systems, ICSSS 2023

作者： Mohan Krishna, Gaddam Venkata Vijay Anand, M. Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350384208

"Individuality"refers to distinct collaboration of characteristics that characterize a person's ideas, feelings, and actions. In a time when options and chances abound, many people place a high value on their pursuit of self-actualization and personal growth. One of the most important aspects of the human experience is still coming to terms with one's true self, acknowledging one's talents, and working on areas that require improvement. Although this journey has historically been driven by reflection and self-awareness, the quick development of technology has brought a novel strategy: the use of logistic regression to enable personality prediction, which empowers people in their quest for self-realization. Self-discovery has on a new dimension in the digital age. It entails utilizing machine learning and data science to decode the complexities of individual traits and provide avenues for development and fulfillment. This research presents an innovative approach to self-initiated forecasting of personality using the logistic regression technique and the Myers-Briggs Type Indicator (MBTI). The MBTI offers a methodical framework that enables people to investigate and comprehend their personality qualities. Our goal is to unleash the power of technology to help people on their journey toward self-realization by integrating logistic regression into this framework. The outcomes demonstrate how well the logistic regression algorithm works to provide insightful information about people's personalities, empowering them to make wise decisions that will lead to self-realization and personal development. © 2023 IEEE.

关键词： Logistic regression

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Multimodal Integration, Fine Tuning of Large Language Model for Autism Support 5

Multimodal Integration, Fine Tuning of Large Language Model ...

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5th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2024

作者： Pai, Krishna Jagwani, Vidhita Pandita, Shivalik Kalbande, Dhananjay Sardar Patel Institute of Technology Department of Computer Engineering Mumbai India Sardar Patel Institute of Technology Department of Computer Science and Engineering Mumbai India

ISBN: (纸本)9798350395235

This research project centres on creating an inclusive environment for the specially-abled. Created an app that integrates real-time object detection and speech-to-text capabilities which provides a multi-modal user experience with the pre-trained Model. Incorporated parameter-efficient fine-tuning using Low-Rank Adaptation (LoRA) to emulate the behaviour of an autism *** second module of the research employs YOLOv5 and machine learning to analyze therapy videos to get interaction timestamps and statistical insights. It automates the identification of interactions and serves as a valuable aid to guardians and *** paper details the methodology for real-time object detection using YOLO, SSD Mobilenet and enhances understanding in the context of adaptation of Language Models for a particular use case. Furthermore, dives deeper into the intricacies of interaction monitoring using bounding boxes in YOLO. By pushing the boundaries of Large Language Model capabilities, this research not only refines language models but also lays the foundation for their application in dynamic contexts of the real world, exemplifying their revolutionary potential. © 2024 IEEE.

关键词： Image recognition

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Machine Vision Technique with Image Segmentation in Digital Diagnostic System for Lung Cancer Detection

Machine Vision Technique with Image Segmentation in Digital ...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Rawat, Devyani Sharma, Sachin Bhadula, Shuchi Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798331508685

Within the domain of medical diagnostics, the integration of machine vision strategies holds guarantee for upgrading precision and effectiveness in disease discovery. Lung cancer remains a critical worldwide wellbeing concern, requiring progressed imaging advances for early and precise conclusion. This paper presents an advanced symptomatic framework enabled by machine vision, especially centring on image segmentation techniques. Through a comprehensive survey of existing writing, we investigate the scene of image segmentation procedures connected to lung cancer location. Hence, we propose a novel system leveraging state-of-the-art machine vision calculations to segment lung images successfully. Our framework points to move forward upon current symptomatic hones by robotizing the division prepare, subsequently encouraging more precise and opportune distinguishing proof of cancerous districts. We examine the potential suggestions of this approach in clinical settings, including its capacity to help radiologists in elucidation and decision-making. Besides, we highlight avenues for future investigate and advancement to refine and optimize the proposed framework for broader clinical selection. © 2025 IEEE.

关键词： Lung cancer

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Incentive mechanism design via smart contract in blockchainbased edge-assisted crowdsensing

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Frontiers of computer science 2025年第3期19卷 65-85页

作者： Chenhao YING Haiming JIN Jie LI Xueming SI Yuan LUO Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China Shanghai Jiao Tong University(Wuxi)Blockchain Advanced Research Center Wuxi 214101China

Edge-assisted mobile crowdsensing(EMCS)has gained significant attention as a data collection ***,existing incentive mechanisms in EMCS systems rely on centralized platforms,making them impractical for the decentralized nature of EMCS *** address this limitation,we propose CHASER,an incentive mechanism designed for blockchain-based EMCS(BEMCS)*** fact,CHASER can attract more participants by satisfying the incentive requirements of budget balance,double-side truthfulness,double-side individual rationality and also high social ***,the proposed BEMCS system with CHASER in smart contracts guarantees the data confidentiality by utilizing an asymmetric encryption scheme,and the anonymity of participants by applying the zero-knowledge succinct non-interactive argument of knowledge(zk-SNARK).This also restrains the malicious behaviors of ***,most simulations show that the social welfare of CHASER is increased by approximately when compared with the state-of-the-art ***,CHASER achieves a competitive ratio of approximately 0.8 and high task completion rate of over 0.8 in large-scale *** findings highlight the robustness and desirable performance of CHASER as an incentive mechanism within the BEMCS system.

关键词： mobile crowdsensing edge computing blockchain smart contract incentive mechanism

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Artificial Intelligence and its Effectiveness in Modern Teaching

Artificial Intelligence and its Effectiveness in Modern Teac...

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2023 International Conference on Data science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Alomran, Hanadi Mohammad Alhazmi, Omar Hussain Taibah University College of Computer Science and Engineering Department of Computer Science Madinah Saudi Arabia

ISBN: (纸本)9798350348910

Artificial intelligence (AI) is not a new term;its beginnings go back to the beginnings of the fifth decade in the year 1955. Since the emergence of artificial intelligence, it has been in the process of continuous development and spread of its technologies, until it recently reached the most prominent application, which is the ChatGPT program. AI technology is one of the most important modern technologies that contribute to the development of many fields, raise the level of quality, improve productivity, and increase efficiency. Education is one of the most important areas affected by modern technologies, which moved education from the ordinary level to the level of creativity and innovation. The idea of this research comes from several reasons: 1- The rapid transfer of education to a larger world in a short period, 2- The many positives that artificial intelligence had and acquired;3- The need for the teacher to develop himself in this field and keep pace with the modern era, 4- The absence of motivation among many students, and the infiltration of boredom and lethargy among their ranks. 5- The need for the courses taught by the student to adapt technology in this era, and the inclusion of artificial intelligence completes that. 6- Enriching the research library with a study related to the important topic needed by the teacher and the student. 7- The limited thinking of some students and the lack of scientific communication and its stopping at a certain point. © 2023 IEEE.

关键词： Artificial intelligence

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Applying Transformer Models for Automatic Build Errors Classification of Java-Based Open Source Projects 24

Applying Transformer Models for Automatic Build Errors Class...

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46th International Conference on Software engineering: Companion, ICSE-Companion 2024

作者： Lee, Jonathan Li, Mason Hsu, Kuo-Hsun Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Department of Computer Science National Taichung University of Education Taichung Taiwan

ISBN: (纸本)9798400705021

In open-source development, encountering build failures is a common challenge. Addressing these issues requires analyzing the causes of errors and developing solutions for fixing them. In this work, we fine-tuned Google's BERT, a well-known language model excellent in transfer learning, to address build issues in Gradle Java projects. Our strategy utilizes this model to classify error logs and identify fixing solutions. This approach extends our previous work, Gradle ACFix, an automated build error fixing system, to explore the potential of using machine learning to classify error types and identify appropriate fixing strategies for software projects. We gathered a dataset of 11,483 open-source Gradle Java projects from GitHub for this research. The model's evaluation on the error logs of these projects demonstrated a high accuracy rate exceeding 98%. © 2024 IEEE computer Society. All rights reserved.

关键词： Errors

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Fuzzy validation of Taguchi-based convolutional fuzzy neural classifier for lung cancer imaging

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Multimedia Tools and Applications 2024年第41期83卷 89415-89437页

作者： Chang, Tsang-Chuan Lin, Cheng-Jian Yang, Tang-Yun Department of Intelligent Production Engineering National Taichung University of Science and Technology Taichung404336 Taiwan Department of Computer Science and Information Engineering National Chin-Yi University of Technology Taichung411030 Taiwan

While deep learning technology is widely used in the field of image classification and recognition, parameter setting for convolutional neural networks is complex, and a high number of parameters make the technology difficult to apply in practice. Therefore, this study proposes a Taguchi-based convolutional fuzzy neural classifier (T-CFNC) to classify computed tomography (CT) images. Two layers of convolution and pooling are used to extract features of the input images, and a fuzzy neural network is used to replace fully-connected neural networks to reduce the number of model parameters. To reduce cost and time, the Taguchi experimental design method determines the optimal combination of model parameters with the minimal number of experiments. The SPIE-AAPM lung CT challenge dataset was used to validate the proposed T-CFNC model. Experimental results indicate accuracy of 99.95%, a true positive rate of 99.97%, and a true negative rate of 99.94%. While the confusion matrix is commonly applied to evaluate model performance, its accuracy varies with the quality of model training, and it is easily affected by extreme values. Single and average values also incur the possibility of misjudgment. We therefore further propose fuzzy validation for performance evaluation. Results confirm the superiority of the proposed T-CFNC model in terms of lung cancer image classification over the traditional CFNC model. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Lung cancer

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Independent Channel Sensing and Allocation Using A Genetic Model in Modern Communication Networks 2

Independent Channel Sensing and Allocation Using A Genetic M...

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2nd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Chandramma Narasimhayya, B.E. Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350347456

In modern communication networks, channel sensing and allocation is a crucial part of achieving reliable and efficient network performance. A common approach to this problem is to use a genetic algorithm (GA) to optimize a given objective function. This method can be used to efficiently allocate communication channels among a group of users, and has been widely used in various communication networks. A genetic algorithm is based on the principles of evolutionary biology, and is an optimization technique that uses a population of solutions that evolve to a better solution over time. The algorithm begins by generating a population of solutions, which can be represented by an array of parameters. These parameters are then evaluated using a given objective function, which measures the fitness of each solution. The best solutions are then selected to form the next generation, while the less fit solutions are eliminated. This process is repeated until a given number of generations has been reached, or until the best solution has been found. © 2023 IEEE.

关键词： Genetic algorithms

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