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1st ACM Virtual Global computing Education Conference V. 2, SIGCSE Virtual 2024

作者： Folajimi, Yetunde School of Computing and Data Science Wentworth Institute of Technology BostonMA United States

ISBN: (纸本)9798400706042

This study evaluates the effectiveness of four leading large language models (LLMs), GPT-3, GPT-4, GPT-4o, and BERT, in generating quiz questions for Java and Python programming courses. We aim to recognize how LLMs can effectively produce educationally valuable questions that meet specific pedagogical criteria, including technical precision, relevance to course objectives, linguistic clarity, and pedagogical appropriateness. Each model was prompted to generate 200 Java and 200 Python quiz questions, totaling 1600 unique questions. These questions are currently being evaluated based on both quantitative and qualitative assessments by a team of computer science educators. Preliminary findings suggest that GPT-4 outperforms BERT in terms of technical precision. Further analysis is ongoing to assess the performance of the models in generating contextually appropriate and educationally useful questions, offering insights into their potential integration into computer science curricula. This work seeks to contribute to the broader discourse on the utility of LLMs in educational settings, specifically within the scope of automated content creation to enhance teaching and assessment methodologies in computer science education. © 2024 Copyright is held by the owner/author(s).

关键词： Java programming language

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Minimal Context-Switching data Race Detection with dataflow Tracking

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Journal of Computer science & Technology 2024年第1期39卷 211-226页

作者：郑龙李洋辛杰刘海峰郑然廖小飞金海 National Engineering Research Center for Big Data Technology and System School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Services Computing Technology and System Laboratory School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Cluster and Grid Computing Laboratory School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China

data race is one of the most important concurrent anomalies in multi-threaded *** con-straint-based techniques are leveraged into race detection,which is able to find all the races that can be found by any oth-er sound race ***,this constraint-based approach has serious limitations on helping programmers analyze and understand data ***,it may report a large number of false positives due to the unrecognized dataflow propa-gation of the ***,it recommends a wide range of thread context switches to schedule the reported race(in-cluding the false one)whenever this race is exposed during the constraint-solving *** ad hoc recommendation imposes too many context switches,which complicates the data race *** address these two limitations in the state-of-the-art constraint-based race detection,this paper proposes DFTracker,an improved constraint-based race detec-tor to recommend each data race with minimal thread context ***,we reduce the false positives by ana-lyzing and tracking the dataflow in the *** this means,DFTracker thus reduces the unnecessary analysis of false race *** further propose a novel algorithm to recommend an effective race schedule with minimal thread con-text switches for each data *** experimental results on the real applications demonstrate that 1)without removing any true data race,DFTracker effectively prunes false positives by 68%in comparison with the state-of-the-art constraint-based race detector;2)DFTracker recommends as low as 2.6-8.3(4.7 on average)thread context switches per data race in the real world,which is 81.6%fewer context switches per data race than the state-of-the-art constraint based race ***,DFTracker can be used as an effective tool to understand the data race for programmers.

关键词： data race satisfiability modulo theory multi-threaded program dynamic detection

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Deep Neural Networks Based Feature Extraction with CBIR: An Exhaustive Analysis 1

Deep Neural Networks Based Feature Extraction with CBIR: An ...

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1st International Conference on Trends in Engineering Systems and Technologies, ICTEST 2024

作者： Praveena, S. Veeramakali, T. Srm Institute of Science and Technology Department of Data Science and Business Systems School of Computing Tamilnadu Kattankulathur India

ISBN: (数字)9798350307795

ISBN: (纸本)9798350307795

The need for having sufficient space to store and retrieve data has grown in importance in today's fast-paced worlde. Hence, end-users struggle to access particular facts, leading to difficulties in obtaining reliable statistics. The text-based image retrieval (TBIR) system is being enhanced by researchers to address these issues. Based on the user's query or annotation, TBIR finds relevant facts. This is the best method for users to get all the important data. The database unfortunately produces more unwanted data due to the complexity and length of the operation. A technology known as content-based image retrieval (CBIR), offers a way around this problem. The user's query or input images are used to determine the relevant output images. Machine learning and deep learning algorithms are highly suited for CBIR- based image classification and prediction. The notion of machine learning as a potential remedy for the semantic gap problem has been extensively studied. Given the recent successes of deep learning, there is evidence to suggest that we are capable of bridging the semantic gap in CBIR. The various techniques for feature extraction and similarity measurement are thoroughly reviewed in this paper. The benefits and drawbacks of deep learning networks and CBIR-based machine learning techniques are further investigated through comparison analysis portion. Consequently, detailed explanations of techniques utilizing deep learning for identifying skin disorders are provided. Following to that, a variety of CBIR approaches and the Corel 5K dataset are used to assess the various performance measures. © 2024 IEEE.

关键词： Feature extraction

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A Comprehensive Analysis of Image Processing Methods for Agricultural Product Quality Control

A Comprehensive Analysis of Image Processing Methods for Agr...

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2024 International Conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Nandhini, E. Vadivu, G. School of Computing Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur Chennai India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543617

In the post-harvest stages of agricultural products, labor shortages and poor-quality control lead to significant market losses. The automated industries for agricultural products that use machine learning are evolving, with a recent focus on deep learning (DL). Computer vision (CV) serves as an essential part in the automation method for fruits and vegetables, where image processing (IP) ensures proper product categorization, addressing the issue of product loss. This paper focuses on IP techniques in sequential procedures, which can improve efficiency and precision in agricultural product sorting and grading. Deep neural networks and algorithms are effective because they achieve high classification accuracy with a low loss percentage. © 2024 IEEE.

关键词： Industries Accuracy Reviews Image processing Scalability Quality control Loss measurement IP networks Reliability Sorting

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Predicting Employee Churn- data Driven Approach for Retention Management

Predicting Employee Churn- Data Driven Approach for Retentio...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Jayaraj, R. Mehrotra, Safal Kumar, Harshit School of Computing Srm Institute of Science and Technology Dept. of Data Science and Business Systems Chennai India

ISBN: (纸本)9798331510022

Employee churn is a critical challenge faced by organizations across industries, leading to disruptions in productivity and increased costs associated with recruitment and training. In this study, we propose an effective solution leveraging Random Forests to predict employee churn accurately and efficiently. Our approach addresses the inherent complexities of employee turnover prediction while considering resource constraints commonly encountered in practical applications. The proposed model incorporates feature engineering techniques and ensemble learning principles to harness the predictive power of Random Forests. Through a comprehensive evaluation on real-world employee datasets, we demonstrate the superior performance of our model in comparison to existing state-of-the-art methods. Our analysis encompasses various evaluation metrics, including accuracy, precision, recall, and F1-score, showcasing the robustness and reliability of our model across diverse organizational contexts. © 2024 IEEE.

关键词： Contrastive Learning

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Influence of different form of multimedia on motivation and topic perception in augmented reality learning experiences

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Multimedia Tools and Applications 2025年 1-28页

作者： Yap, Shook Chin Rahmat, Rahmita Wirza Ali, Siti Khadijah School of Computing and Data Science Xiamen University Malaysia Selangor Sepang Malaysia Faculty of Computer Science and Information Technology Universiti Putra Malaysia Selangor Serdang Malaysia

It has been widely proven that Augmented Reality (AR) brings numerous benefits in learning experiences, including enhancing learning outcomes and motivation. However, not many studies investigate how different forms of multimedia help in motivating students in AR learning experiences. Instead of examining whether AR can be used in education to motivate students, our research focused on investigating how and when AR learning experiences influence motivation when integrated with multimedia learning. A between-subjects 2 × 3 factorial design was employed in this study. The first factor was the type of AR (static-AR or animated-AR), and the second factor was the modality of the description (concurrent narration-text, text only, or narration only). Six experimental conditions were designed, each equipped with different versions of AR learning applications with various combinations of multimedia formats. One hundred and forty-four computer science and information technology undergraduate students (70 males and 74 females) were randomly assigned to one of the six experimental conditions. data collection was conducted with questionnaires, and data were analyzed using univariate analysis of variance (ANOVA). Results show that attention and satisfaction subscales of the ARCS motivational model can be enhanced with 3D animation implemented in AR. Descriptions delivered with concurrent narration and written text in AR are effective in enhancing motivation as well. Besides, participants’ perceived understanding and interest in learning the topic increased after AR learning experiences. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Contrastive Learning

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Convolutional Neural Network-Based Multi-Fruit Classification and Quality Grading with a Gradio Interface

Convolutional Neural Network-Based Multi-Fruit Classificatio...

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2024 International Conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Nandhini, E. Vadivu, G. School of Computing Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur Chennai India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543617

Agricultural products are experiencing significant losses, especially after the harvesting phase, which is the period from fruit storage to the product reaching the consumer's hands. A lack of human resources, time consumption, product handling, and human errors during the post-harvest stage of the fruit grading and sorting process result in huge losses. Deep learning (DL) offers an automated process for classifying and identifying fruits. Developing an architecture for a VGG16 convolutional neural network (CNN) within the Gradio user interface, utilizing five distinct fruit types as the model's dataset, such as apple, banana, guava, orange, apple, and pomegranate. For five different sets of fresh and defect fruits, the proposed model got 91.1% and 89.1% of test accuracy for multi-fruit clas- sification and fruit status prediction, respectively. This machine- based automated model has the potential to reduce losses and enhance fruit grading and sorting efficiency, thereby significantly impacting sustainable and productive agricultural industries through increased productivity. © 2024 IEEE.

关键词： Convolutional neural networks

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UCat: heterogeneous memory management for unikernels

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Frontiers of Computer science 2023年第1期17卷 51-61页

作者： Chong TIAN Haikun LIU Xiaofei LIAO Hai JIN National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab/Cluster and Grid Computing LabSchool of Computing Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China

Unikernels provide an efficient and lightweight way to deploy cloud computing services in application-specialized and single-address-space virtual machines (VMs). They can efficiently deploy hundreds of unikernel-based VMs in a single physical server. In such a cloud computing platform, main memory is the primary bottleneck resource for high-density application deployment. Recently, non-volatile memory (NVM) technologies has become increasingly popular in cloud data centers because they can offer extremely large memory capacity at a low expense. However, there still remain many challenges to utilize NVMs for unikernel-based VMs, such as the difficulty of heterogeneous memory allocation and high performance overhead of address *** this paper, we present UCat, a heterogeneous memory management mechanism that support multi-grained memory allocation for unikernels. We propose front-end/back-end cooperative address space mapping to expose the host memory heterogeneity to unikernels. UCat exploits large pages to reduce the cost of two-layer address translation in virtualization environments, and leverages slab allocation to reduce memory waste due to internal memory fragmentation. We implement UCat based on a popular unikernel--OSv and conduct extensive experiments to evaluate its efficiency. Experimental results show that UCat can reduce the memory consumption of unikernels by 50% and TLB miss rate by 41%, and improve the throughput of real-world benchmarks such as memslap and YCSB by up to 18.5% and 14.8%, respectively.

关键词： unikernel virtualization non-volatile memory heterogeneous memory large page slab allocation

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Fuzzy knowledge based intelligent decision support system for ground based air defence

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Journal of Ambient Intelligence and Humanized computing 2024年第4期15卷 2317-2340页

作者： Ahmad, Adnan Amjad, Rawan Basharat, Amna Farhan, Asma Ahmad Abbas, Ali Ezad Department of Computer Science School of Computing National University of Computer and Emerging Sciences Islamabad Pakistan Department of Data Science School of Computing National University of Computer and Emerging Sciences Lahore Pakistan Islamabad Pakistan

This research proposes an Intelligent Decision Support System for Ground-Based Air Defense (GBAD) environments, which consist of Defended Assets (DA) on the ground that require protection from enemy aerial threats. A Fire Control Officer is responsible for assessing threats and assigning the most appropriate weapon to neutralize them. However, the decision-making process can be prone to errors, risking resource wastage and endangering DA protection. To address this problem, this research proposes a hybrid approach that combines a knowledge-driven fuzzy inference system with machine learning models to optimize resource allocation while incorporating expert knowledge in the decision-making process. Since sensory data obtained from multiple radars may be incomplete or incorrect, a fuzzy knowledge graph-based system is used for data fusion and providing it to the connected modules. Feature selection is optimized by including the most important parameters, such as the vitality of defended assets and threat score, in the threat evaluation. The results from these subsystems are visualized using a Geographical Information System, allowing for real-time mapping of the GBAD environment and displaying the results in a user-friendly web interface. The proposed system has undergone rigorous testing and evaluation, resulting in an efficient and accurate weapon assignment model with a low RMSE value of 0.037. Overall, this Intelligent Decision Support System provides an effective solution for optimizing decision-making processes in GBAD environments and can significantly improve DA protection. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Graphic methods

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Real-time Detection and Monitoring of Contagious Diseases using Wearable Sensors and Lightweight Model in Edge Networks

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IEEE Sensors Journal 2025年第11期25卷 20743-20750页

作者： Doulani, Khushbu Adhikari, Mainak Guru Nanak Institutions Hyderabad501506 India Indian Institute of Science Education and Research School of Data Science Thiruvananthapuram695551 India

Infectious/Contagious diseases remain a significant global health challenge, necessitating accurate identification to mitigate their spread. The widespread adoption of wearable healthcare devices capable of continuously monitoring physiological parameters, presents a unique opportunity for enhancing disease detection strategies. In this paper, we develop a new data Fusion-enabled Explainable Artificial Intelligence-assisted Light-GBM (FuXAI) model in edge networks to predict contagious disease in the early stage. The overall contributions of the proposed FuXAI model are three-fold. Firstly, we create a comprehensive health profile for each individual by employing data fusion techniques and integrating health parameters received from multiple sources. Secondly, we integrate a new lightweight Machine Learning (ML) model, the Light Gradient Boosting Model (LightGBM), to predict the disease at resource-constraint edge devices over the fused data. Finally, we leverage the power of Explainable Artificial Intelligence to develop interpretable algorithms, integrating with the LightGBM that can identify subtle patterns and correlations within the fused data, potentially revealing early warning signs of infectious diseases and creating trust in medical professionals during decision-making. Extensive simulation results of the proposed model over the standard ML models using benchmark datasets demonstrate the effectiveness of the model. © 2001-2012 IEEE.

关键词： Electronic health record

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