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Applying Competition-Based Learning to Stimulate Students' Practical and Competitive AI Ability in a Machine Learning Curriculum

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IEEE Transactions on Education 2024年第2期67卷 256-265页

作者： Chang, Hui-Tzu Lin, Chia-Yu National Yang Ming Chiao Tung University Center for Institutional Research and Data Analytics Hsinchu30010 Taiwan National Central University Department of Computer Science and Information Engineering Taoyuan City320317 Taiwan

Contribution: This study incorporates competition-based learning (CBL) into machine learning courses. By engaging students in innovative problem-solving challenges within information competitions, revealing that students' participation in online problem-solving competitions can improve their information technology, and showcase competitions can enhance their competition ability. Background: The CBL model seamlessly integrates project-based learning and competition, placing a strong emphasis on both collective learning and outcomes. This approach cultivates motivation among team members, driving them to enhance their learning and translate knowledge into practical experience. research Questions: The objective is to examine the disparities in the development of theoretical knowledge, information technology, AI practical ability, and competition ability among students participating in online problem-solving competitions and showcase competitions, and discusses the potential moderating effect of competition type on the relationships between variables in the hypothetical model. Methodology: The study involved 74 students enrolled in machine learning course at a university. The students were given theoretical knowledge and information technology pretests and posttests in the 2nd and 17th weeks, respectively. In the 18th week, the students presented their projects using slideshows and were graded by judges while also submitting their final competition proposal and slides. Findings: Students in online problem-solving competitions can enhance their information technology, while those participating in showcase competitions can improve their competitive ability. Moreover, the competition type was found to moderate the relationships among theoretical knowledge, information technology, and AI model accuracy. The findings suggest that incorporating CBL into machine learning courses effectively cultivates students' AI practical and competitive abilities. © 1963-2012 IEEE.

关键词： Competition

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Deep Learning for Violence Detection in Surveillance: The Role of Transfer Learning and Pre-Trained Models 24

Deep Learning for Violence Detection in Surveillance: The Ro...

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24th International Arab Conference on Information Technology, ACIT 2023

作者： Ilyas, Anum Obaid, Surayya Bawany, Narmeen Zakaria Center for Computing Research Jinah University for Women Department of Computer Science and Software Engineering Karachi Pakistan

ISBN: (纸本)9798350384307

In recent years, the use of CCTV footage for proactive crime prevention has surged, particularly in public places like airports, train stations, and malls. However, the efficacy of these surveillance systems becomes questionable for their substantial reliance on human resources which may lead to erroneous or delayed responses. This research takes a comprehensive approach for violence detection which was previously limited to binary classification, by categorizing violence into four distinct classes: abuse, arson, assault, and fight. It employs transfer learning combined with computer vision techniques for violence detection and classification in video footages. The study compares the performance of four pre-trained neural networks, namely DenseNet121, VGG16, MobileNet, and Xception. The dataset created for this research is compiled from three datasets available on Kaggle. The results reveal that the Xception model performed comparatively better achieving the highest AUC score of 98.39%, while the VGG16 model attained the lowest AUC score of 96%. In addition to the AUC score, precision, recall, and fl-score are employed as performance metrics. Transfer learning with convolutional neural networks (CNN) significantly reduced computational requirements and time. Automating the detection and categorization of violent behavior through the employed approach has the potential to reduce the risk of fatalities and injuries in public areas. It also improves the speed and accuracy of threat detection, enabling swift preventive actions by authorities. © 2023 IEEE.

关键词： computer vision Deep Learning Pre-trained Models Transfer Learning Violence Categorization

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A Blockchain based Framework for Verifying Drug Authenticity 24

A Blockchain based Framework for Verifying Drug Authenticity

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24th International Arab Conference on Information Technology, ACIT 2023

作者： Hamid, Soomaiya Bawany, Narmeen Zakaria Fatima, Kiran Jinnah University for Women Center for Computing Research Department of Computer Science and Software Engineering Karachi Pakistan

ISBN: (纸本)9798350384307

Counterfeit drugs are fake medicines that are potentially harmful for health. Safeguarding the integrity of pharmaceutical distribution plays a crucial role in preventing the circulation of counterfeit drugs. Traditional drug supply chain systems have no effective mechanism to ensure the traceability and integrity of the drug supply chain process. Various ICT-based solutions have been adopted previously and numerous regulations were passed by drug regulatory authorities to ensure interoperable and traceable drug distribution process. However, these solutions could not guarantee a tamper-proof history of supply chain process. Blockchain technology has gained traction in many diverse fields including real-estate, identity management, digital voting, healthcare, etc. Therefore, we have proposed a decentralized framework to insure the authenticity and traceability of the drugs. Moreover, this study presents an overview of how blockchain technology can be employed to counter the fake medicines circulation by embedding the traceability in drug supply chain process. Comparison of various blockchain-based solutions and open research challenges in this regard are also presented. © 2023 IEEE.

关键词： Blockchain Technology Counterfeit Drugs decentralized framework Drug Supply Chain Traceability of Drugs Traditional Pharmaceutical Supply Chain

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RAUIE: A Relation-Augmented Document-Level Event Extraction Model Based on UIE 5

RAUIE: A Relation-Augmented Document-Level Event Extraction ...

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5th International Seminar on Artificial Intelligence, Networking and Information Technology, AINIT 2024

作者： Liang, Yiyuan Jia, Hong Wang, Licai Huang, Yangchen Yang, Zhe Big Data Research and Development Center North China Institute of Computing Technology Beijing China Tsinghua University Department of Computer Science and Technology Beijing China The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong

ISBN: (纸本)9798350385557

In document-level event extraction tasks, multiple event types often coexist within a document (the multi-event problem), and arguments may play different roles across different events (the argument intertwining problem). Existing methods often suffer from significantly reduced event extraction accuracy, mainly when focusing on scenarios with scarce annotated data and imbalanced event-type distributions. This paper emphasizes enhancing the correlation between event triggers and arguments to address these challenges. Therefore, we propose the Relation-Augmented UIE Model (RADIE), which introduces entity relationships into prompts using prompt-based learning and designs a relation-augmentation algorithm to improve the accuracy and completeness of event extraction. Experimental results demonstrate that this approach significantly outperforms baseline methods, mainly showcasing excellent performance on datasets with long-tail distributions. © 2024 IEEE.

关键词： document-level event extraction relation augment Universal Information Extraction

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Optimizing Length of Stay Prediction After Intubation: An Advanced Machine Learning Model with Real-time Vital Sign Integration 7

Optimizing Length of Stay Prediction After Intubation: An Ad...

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7th International Conference on Image Information Processing, ICIIP 2023

作者： Sundas, Amit Singh, Gurpreet Badotra, Sumit Verma, Amit Kaur, Balwinder Lovely Professional University Department of Computer Science And Engineering Punjab Phagwara India Bennett University School of Computer Engineering And Technology Greater Noida India University Center For Research And Development Department of Computer Science And Engineering Chandigarh India

ISBN: (纸本)9798350371406

In the critical care unit, bedside monitors track patients' vital signs to help physicians make choices. With increased storage and analysis capability, huge datasets may be processed faster. Lately, physiological data have showed promise for identifying specific consequences and occurrences. At the critical care unit. Our method was aimed to Live bedside vital sign data may be automatically retrieved. Devices that can predict patient mechanical ventilation for more than 4 days in a pediatric critical care unit. 284 ICU patients had their vital signs and clinical history documented continuously from admission. 24 hours of artificial breathing in Boston Children's Hospital's pediatric medical-surgical critical care unit. They trained many machine learning models using subsets. Assess the likelihood of each person staying long. We examined our systems' capacity to anticipate outcomes for unknown persons. Adding vital sign data increased model performance to 90% (area under the curve) from >83% when using just vital sign data. static participant clinical data in electronic health records. For research group representative sample reliability, we employed this approach on 300 individually trained tests employing a variety of sets for training and hold-out validation. Other deep learning methods failed to forecast like ours. Gaining equivalent work outcomes prediction skills. We may be able to help create scalable ICU-based prediction systems that leverage real-time patient monitor data. © 2023 IEEE.

关键词： Decision support systems

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The Path-Label Reconciliation (PLR) Dissimilarity Measure for Gene Trees 24

The Path-Label Reconciliation (PLR) Dissimilarity Measure fo...

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24th International Workshop on Algorithms in Bioinformatics, WABI 2024

作者： López Sánchez, Alitzel Ramírez-Rafael, José Antonio Flores-Lamas, Alejandro Hernández-Rosales, Maribel Lafond, Manuel Computer Science Department Université de Sherbrooke Canada Center for Research and Advanced Studies of the National Polytechnic Institute Gto. Irapuato Mexico Department of Computer Science Interdisciplinary Center for Bioinformatics University of Leipzig Germany Max Planck Institute for Mathematics in the Sciences Leipzig Germany

ISBN: (纸本)9783959773409

In this study, we investigate the problem of comparing gene trees reconciled with the same species tree using a novel semi-metric, called the Path-Label Reconciliation (PLR) dissimilarity measure. This approach not only quantifies differences in the topology of reconciled gene trees, but also considers discrepancies in predicted ancestral gene-species maps and speciation/duplication events, offering a refinement of existing metrics such as Robinson-Foulds (RF) and their labeled extensions LRF and ELRF. A tunable parameter α also allows users to adjust the balance between its species map and event labeling components. We show that PLR can be computed in linear time and that it is a semi-metric. We also discuss the diameters of reconciled gene tree measures, which are important in practice for normalization, and provide initial bounds on PLR, LRF, and ELRF. To validate PLR, we simulate reconciliations and perform comparisons with LRF and ELRF. The results show that PLR provides a more evenly distributed range of distances, making it less susceptible to overestimating differences in the presence of small topological changes, while at the same time being computationally efficient. Our findings suggest that the theoretical diameter is rarely reached in practice. The PLR measure advances phylogenetic reconciliation by combining theoretical rigor with practical applicability. Future research will refine its mathematical properties, explore its performance on different tree types, and integrate it with existing bioinformatics tools for large-scale evolutionary analyses. The open source code is available at: https://***/project/parle/. © Alitzel López Sánchez, José Antonio Ramírez-Rafael, Alejandro Flores-Lamas, Maribel Hernández-Rosales, and Manuel Lafond1

关键词： evolutionary scenarios gene trees Reconciliation species trees

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Adaptive VQVAE: a learning-based image compression framework with vector quantization 23

Adaptive VQVAE: a learning-based image compression framework...

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11th International Conference on computer and Communications Management, ICCCM 2023

作者： Chen, Ke-Hong Lin, Yih-Chuan Department of Computer Science and Information Engineering National Formosa University Taiwan Smart Machinery and Intelligent Manufacturing Research Center Department of Computer Science and Information Engineering National Formosa University Taiwan

ISBN: (纸本)9798400707735

With the promising effectiveness of learned image compression, a substantial amount of research has been devoted to this field, following the publication of Ballé's paper. However, there are still some issues to be concerned before it becomes truly practical tools. The JPEG AI standard proposed by the JPEG team advocates that all machine vision tasks should be uniformly expressed under the same specifications of learned image format. While the JPEG AI standard is still under development, vector quantization (VQ) which is an important signal compression technique presents advantages in this context. This paper focuses on the research of vector quantization for image compression, and proposes an improved method called Adaptive VQVAE (Vector-Quantized Variational Autoencoder) to compactly represent the latent space of convolutional neural network. By transmitting an additional 1% of auxiliary information, the original performance of VQ is enhanced by approximately 1 dB PSNR for encoding the information from the latent space in an autoencoder architecture. In addition to the rate-distortion performance, the performance of vector quantized feature maps is also evaluated in terms of accuracy of computer vision tasks with the compressed format. The implementation results of Adaptive VQVAE have been uploaded to GitHub and are available for research purposes and comparison. © 2023 ACM.

关键词： Vector quantization

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Log Preservation in Custody Dual Blockchain With Energy Regime and Obfuscation Shuffle

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IEEE Transactions on Network science and Engineering 2024年第4期11卷 3495-3511页

作者： Chew, Chit-Jie Lee, Wei-Bin Chen, Tzu-Hao Lin, Iuon-Chang Lee, Jung-San Feng Chia University Department of Information Engineering and Computer Science Taichung407 Taiwan Hon Hai Research Institute Information Security Research Center Taipei236 Taiwan National Chung Hsing University Department of Management Information Systems Taichung402 Taiwan

The rise of cybercrime has led to an explosion of threats in recent years, resulting in the increasingly important of digital forensics. To achieve successful investigation and prosecution, the log preservation is a critical aspect of incident response. The blockchain technology is considered as one of the powerful log storage mechanism, which bringing data transparency and non-tempering property aside from taking off the hypothesis of a trusted third party. Nevertheless, the sensitivity of the data makes it vulnerable to malicious attacks, and the adoption of blockchain has significantly burdened participants regarding consensus time and storage. To address these challenges, we aim to develop a custody dual blockchain (CDB) to secure the confidentiality and integrity of log data. The design of reputation consensus attains that only attractive participants need to offer computation and storage for efficiency enhancing, while the energy regime constructs a positive feedback loop that invigorates users to contribute to CDB. Moreover, the obfuscation shuffle effectively obscures the explicit log plaintext to prevent illegal access. Thus, the confidential data on the CDB nodes can even be used as evidence of digital forensics. Simulation results have demonstrated that the ledger overhead can be significantly reduced, and CDB could pass the robust examination of two formal proof tools: Proverif (Blanchet et al. 2021) and AVISPA. © 2013 IEEE.

关键词： Blockchain

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Unleashing the Power of Multi-Agent Reinforcement Learning for Algorithmic Trading in the Digital Financial Frontier and Enterprise Information Systems

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computers, Materials & Continua 2024年第8期80卷 3123-3138页

作者： Saket Sarin Sunil K.Singh Sudhakar Kumar Shivam Goyal Brij Bhooshan Gupta Wadee Alhalabi Varsha Arya Chandigarh College of Engineering and Technology Chandigarh160019India Department of Computer Science and Information Engineering Asia UniversityTaichung413Taiwan Symbiosis Centre for Information Technology(SCIT) Symbiosis International UniversityPune411057India Center for Interdisciplinary Research University of Petroleum and Energy Studies(UPES)Dehradun248007India Department of Computer Science Immersive Virtual Reality Research GroupKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Business Administration Asia UniversityTaichung413Taiwan Department of Electrical and Computer Engineering Lebanese American UniversityBeirut11022801Lebanon UCRD Chandigarh UniversityChandigarh140413India

In the rapidly evolving landscape of today’s digital economy,Financial Technology(Fintech)emerges as a trans-formative force,propelled by the dynamic synergy between Artificial Intelligence(AI)and Algorithmic *** in-depth investigation delves into the intricacies of merging Multi-Agent Reinforcement Learning(MARL)and Explainable AI(XAI)within Fintech,aiming to refine Algorithmic Trading *** meticulous examination,we uncover the nuanced interactions of AI-driven agents as they collaborate and compete within the financial realm,employing sophisticated deep learning techniques to enhance the clarity and adaptability of trading *** AI-infused Fintech platforms harness collective intelligence to unearth trends,mitigate risks,and provide tailored financial guidance,fostering benefits for individuals and enterprises navigating the digital *** research holds the potential to revolutionize finance,opening doors to fresh avenues for investment and asset management in the digital ***,our statistical evaluation yields encouraging results,with metrics such as Accuracy=0.85,Precision=0.88,and F1 Score=0.86,reaffirming the efficacy of our approach within Fintech and emphasizing its reliability and innovative prowess.

关键词： Neurodynamic Fintech multi-agent reinforcement learning algorithmic trading digital financial frontier

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DIVIDE AND NOT FORGET: ENSEMBLE OF SELECTIVELY TRAINED EXPERTS IN CONTINUAL LEARNING 12

DIVIDE AND NOT FORGET: ENSEMBLE OF SELECTIVELY TRAINED EXPER...

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12th International Conference on Learning Representations, ICLR 2024

作者： Rypeść, Grzegorz Cygert, Sebastian Khan, Valeriya Trzciński, Tomasz Zieliński, Bartosz Twardowski, Bartlomiej IDEAS-NCBR Poland Warsaw University of Technology Poland Gdańsk University of Technology Poland Tooploox Poland Jagiellonian University Poland Computer Vision Center Barcelona Spain Department of Computer Science Universitat Autònoma de Barcelona Spain

Class-incremental learning is becoming more popular as it helps models widen their applicability while not forgetting what they already know. A trend in this area is to use a mixture-of-expert technique, where different models work together to solve the task. However, the experts are usually trained all at once using whole task data, which makes them all prone to forgetting and increasing computational burden. To address this limitation, we introduce a novel approach named SEED. SEED selects only one, the most optimal expert for a considered task, and uses data from this task to fine-tune only this expert. For this purpose, each expert represents each class with a Gaussian distribution, and the optimal expert is selected based on the similarity of those distributions. Consequently, SEED increases diversity and heterogeneity within the experts while maintaining the high stability of this ensemble method. The extensive experiments demonstrate that SEED achieves state-of-the-art performance in exemplar-free settings across various scenarios, showing the potential of expert diversification through data in continual learning. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

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