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Unsupervised machine learning approach for tailoring educational content to individual student weaknesses

High-Confidence computing 2024年第4期4卷 91-104页

作者： Shabab Intishar Rahman Shadman Ahmed Tasnim Akter Fariha Ammar Mohammad Muhammad Nayeem Mubasshirul Haque Sriram Chellappan Jannatun Noor Department of Computer Science and Engineering BRAC UniversityDhaka 1212Bangladesh Department of Computer Science and Engineering University of South FloridaFL 33620USA Computing for Sustainability and Social Good(C2SG)Research Group Department of Computer Science and EngineeringSchool of Data and SciencesBRAC UniversityDhaka 1212Bangladesh

By analyzing data gathered through Online Learning(OL)systems,data mining can be used to unearth hidden relationships between topics and trends in student ***,in this paper,we show how data mining techniques such as clustering and association rule algorithms can be used on historical data to develop a unique recommendation system *** our implementation,we utilize historical data to generate association rules specifically for student test marks below a threshold of 60%.By focusing on marks below this threshold,we aim to identify and establish associations based on the patterns of weakness observed in the past ***,we leverage K-means clustering to provide instructors with visual representations of the generated *** strategy aids instructors in better comprehending the information and associations produced by the *** clustering helps visualize and organize the data in a way that makes it easier for instructors to analyze and gain insights,enabling them to support the verification of the relationship between *** can be a useful tool to deliver better feedback to students as well as provide better insights to instructors when developing their *** paper further shows a prototype implementation of the above-mentioned concepts to gain opinions and insights about the usability and viability of the proposed system.

关键词： Recommendation system Clustering F-P growth Apriori Associative pattern E-learning sphere Online learning

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Showcasing Native Wildflowers in Virtual Reality

Showcasing Native Wildflowers in Virtual Reality

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2022 International Conference on Computational science and Computational Intelligence, CSCI 2022

作者： Wagner, Andy Deligiannidis, Leonidas School of Computing and Data Science Wentworth Institute of Technology BostonMA02115 United States

ISBN: (纸本)9798350320282

The goal of this project is to develop a virtual reality program on the Oculus Quest 2 to showcase and educate users on native wildflowers that can be found in North America, specifically in the United States. We have created a virtual environment with framed images of some of America's native flowers and, using interactive menus, we provide educational information on each plant. In addition to the 2D images, a 360-image viewer was implemented to show some of the native habitats of these plants to create a more immersive learning experience. Using the Oculus Quest's experimental hand tracking feature, users can switch between using the Oculus controller and using their actual hands as the controller, allowing the users to choose how they interact with the virtual environment. The United States is home to many beautiful native flowers and by showcasing them in virtual reality anyone can experience these flowers and learn about them. © 2022 IEEE.

关键词： Virtual reality

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Diabetic Retinopathy Detection

Diabetic Retinopathy Detection

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data Engineering and Communication Systems (ICDECS), International Conference on

作者： S Sharanya Arnav Vinod Rai Sai Vedanth Department of Data Science and Business Systems School of computing SRM Institute of Science and Technology

In this pivotal study, we delve into the imperative realm of Diabetic Retinopathy (DR), a sight-threatening eye disease, introducing a nuanced and comprehensive approach to its detection through cutting-edge deep learning techniques. Our focus centers on the meticulous comparison of four distinguished convolutional neural network (CNN) architectures: CNN, VGG16, ResNet50, and EfficientNetB5. The dataset comprises five meticulously curated image categories, delineating various DR stages. The discerning outcomes illuminate the consistent superiority of the CNN and VGG16 algorithms, showcasing a commendable edge in accuracy. To amplify DR detection precision, we introduce an innovative hybrid model amalgamating the robust attributes of both CNN and VGG16. This hybrid model triumphs with an extraordinary accuracy rate of 90.5%, surpassing individual performances of the four algorithms. In summation, our groundbreaking study unequivocally establishes the efficacy of a hybrid CNN-VGG16 model for Diabetic Retinopathy detection, underscoring its substantial superiority in accuracy when compared to each of the individual algorithms, advocating for the strategic integration of diverse deep learning models to fortify the accuracy and dependability of medical image analysis. The repercussions of our findings promise to revolutionize the landscape of early detection and holistic management of DR, fulfilling an unmet healthcare need.

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Beyond Labels and Topics: Discovering Causal Relationships in Neural Topic Modeling 24

Beyond Labels and Topics: Discovering Causal Relationships i...

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33rd ACM Web Conference, WWW 2024

作者： Tang, Yi-Kun Huang, Heyan Shi, Xuewen Mao, Xian-Ling School of Computer Science and Technology Beijing Institute of Technology Beijing China School of Data Science and Artificial Intelligence Dongbei University of Finance and Economics Liaoning Dalian China

ISBN: (纸本)9798400701719

Topic models that can take advantage of labels are broadly used in identifying interpretable topics from textual data. However, existing topic models tend to merely view labels as names of topic clusters or as categories of texts, thereby neglecting the potential causal relationships between supervised information and latent topics, as well as within these elements themselves. In this paper, we focus on uncovering possible causal relationships both between and within the supervised information and latent topics to better understand the mechanisms behind the emergence of the topics and the labels. To this end, we propose Causal Relationship-Aware Neural Topic Model (CRNTM), a novel neural topic model that can automatically uncover interpretable causal relationships between and within supervised information and latent topics, while concurrently discovering high-quality topics. In CRNTM, both supervised information and latent topics are treated as nodes, with the causal relationships represented as directed edges in a Directed Acyclic Graph (DAG). A Structural Causal Model (SCM) is employed to model the DAG. Experiments are conducted on three public corpora with different types of labels. Experimental results show that the discovered causal relationships are both reliable and interpretable, and the learned topics are of high quality comparing with eight start-of-the-art topic model baselines. © 2024 ACM.

关键词： Directed graphs

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Fill in the Gap: Two-Stage Prediction Modeling Framework for Understanding Human Biosensor data 15

Fill in the Gap: Two-Stage Prediction Modeling Framework for...

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15th International Conference on Information and Communication Technology Convergence, ICTC 2024

作者： Ro, Juneyoung Yoon, Yoonjin Graduate School of Data Science Korea Advanced Institute of Science and Technology Daejeon Korea Republic of Korea Advanced Institute of Science and Technology Department of Civil and Environmental Engineering Daejeon Korea Republic of

ISBN: (纸本)9798350364637

Human behaviors can be understood from various forms of information such as sleep, emotions or health conditions. With the aid of advanced technology, sensors can capture the conditions of humans and provide valuable insights. In this work, we used imputation strategies to overcome the challenge of incomplete data records and feature misalignment. A two-stage modeling of a Random Forest classifier was done to make final predictions on the test set. This relatively simple yet heavily data-centric approach leverages the strengths of multi-output classification and sequential learning by enhancing data integrity and feature consistency. © 2024 IEEE.

关键词： Prediction models

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An Empirical Study on Cross-lingual Vocabulary Adaptation for Efficient Language Model Inference

An Empirical Study on Cross-lingual Vocabulary Adaptation fo...

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2024 Findings of the Association for Computational Linguistics, EMNLP 2024

作者： Yamaguchi, Atsuki Villavicencio, Aline Aletras, Nikolaos School of Computer Science University of Sheffield United Kingdom Department of Computer Science Institute of Data Science and Artificial Intelligence University of Exeter United Kingdom The Alan Turing Institute United Kingdom

ISBN: (纸本)9798891761681

The development of state-of-the-art generative large language models (LLMs) disproportionately relies on English-centric tokenizers, vocabulary and pre-training data. Despite the fact that some LLMs have multilingual capabilities, recent studies have shown that their inference efficiency deteriorates when generating text in languages other than English. This results in increased inference time and costs. Cross-lingual vocabulary adaptation (CVA) methods have been proposed for adapting models to a target language aiming to improve downstream performance. However, the effectiveness of these methods on increasing inference efficiency of generative LLMs has yet to be explored. In this paper, we perform an empirical study of five CVA methods on four generative LLMs (including monolingual and multilingual models) across four typologically-diverse languages and four natural language understanding tasks. We find that CVA substantially contributes to LLM inference speedups of up to 271.5%. We also show that adapting LLMs that have been pre-trained on more balanced multilingual data results in downstream performance comparable to the original models. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Application of improved virtual sample and sparse representation in face recognition

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1391-1402页

作者： Yongjun Zhang Zewei Wang Xuexue Zhang Zhongwei Cui Bob Zhang Jinrong Cui Lamin LJanneh College of Computer Science and Technology Guizhou UniversityGuiyangChina Big Data Science and Intelligent Engineering Research Institute Guizhou Education UniversityGuiyangChina School of Mathematics and Big Data Guizhou Education UniversityGuiyangChina Department of Computer and Information Science University of MacaoAvenida da UniversidadeTaipaMacaoChina School of Mathematics and Information South China Agricultural UniversityGuangzhouChina

Sparse representation plays an important role in the research of face *** a deformable sample classification task,face recognition is often used to test the performance of classification *** face recognition,differences in expression,angle,posture,and lighting conditions have become key factors that affect recognition ***,there may be significant differences between different image samples of the same face,which makes image classification very ***,how to build a robust virtual image representation becomes a vital *** solve the above problems,this paper proposes a novel image classification ***,to better retain the global features and contour information of the original sample,the algorithm uses an improved non‐linear image representation method to highlight the low‐intensity and high‐intensity pixels of the original training sample,thus generating a virtual ***,by the principle of sparse representation,the linear expression coefficients of the original sample and the virtual sample can be calculated,*** obtaining these two types of coefficients,calculate the distances between the original sample and the test sample and the distance between the virtual sample and the test *** two distances are converted into distance ***,a simple and effective weight fusion scheme is adopted to fuse the classification scores of the original image and the virtual *** fused score will determine the final classification *** experimental results show that the proposed method outperforms other typical sparse representation classification methods.

关键词： representation sample image

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Mining Evidence about Your Symptoms: Mitigating Availability Bias in Online Self-Diagnosis 25

Mining Evidence about Your Symptoms: Mitigating Availability...

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2025 CHI Conference on Human Factors in computing Systems, CHI 2025

作者： Zhang, Junti Zhu, Zicheng Li, Jingshu Lee, Yi-Chieh Institute of Data Science National University of Singapore Singapore Singapore School of Computing National University of Singapore Singapore Singapore Computer Science School of Computing National University of Singapore Singapore Singapore Computer Science National University of Singapore Singapore Singapore

ISBN: (纸本)9798400713941

People frequently exposed to health information on social media tend to overestimate their symptoms during online self-diagnosis due to availability bias. This may lead to incorrect self-medication and place additional burdens on healthcare providers to correct patients' misconceptions. In this work, we conducted two mixed-method studies to identify design goals for mitigating availability bias in online self-diagnosis. We investigated factors that distort self-assessment of symptoms after exposure to social media. We found that availability bias is pronounced when social media content resonated with individuals, making them disregard their own evidences. To address this, we developed and evaluated three chatbot-based symptom checkers designed to foster evidence-based self-reflection for bias mitigation given their potential to encourage thoughtful responses. Results showed that chatbot-based symptom checkers with cognitive intervention strategies mitigated the impact of availability bias in online self-diagnosis. © 2025 Copyright held by the owner/author(s).

关键词： Availability bias Cognitive intervention Conversational Agents Self-diagnosis Social media

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OOP: Object-Oriented Programming Evaluation Benchmark for Large Language Models 62

OOP: Object-Oriented Programming Evaluation Benchmark for La...

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Findings of the 62nd Annual Meeting of the Association for Computational Linguistics, ACL 2024

作者： Wang, Shuai Ding, Liang Shen, Li Luo, Yong Du, Bo Tao, Dacheng Institute of Artificial Intelligence School of Computer Science Wuhan University China Hubei Luojia Laboratory Wuhan China The University of Sydney Australia School of Cyber Science and Teachnology Sun Yat-sen University China College of Computing & Data Science Nanyang Technology University Singapore

ISBN: (纸本)9798891760998

Advancing automated programming necessitates robust and comprehensive code generation benchmarks, yet current evaluation frameworks largely neglect object-oriented programming (OOP) in favour of functional programming (FP), e.g., HumanEval and MBPP. To address this, (1) our study introduces a pioneering OOP-focused benchmark, featuring 431 Python programs that encompass essential OOP concepts and features like classes and encapsulation methods. (2) We propose a novel evaluation metric, pass@o, tailored for OOP, enhancing traditional pass@k metric. (3) Our evaluation of 23 leading large language models (LLMs), including both general and code-specialized models, reveals three key insights: 1) pass@o offers a more relevant and comprehensive assessment for OOP code generation;2) Despite excelling in FP, code-specialized LLMs like WizardCoder lag in OOP compared to models like ChatGPT;3) The poor performance of all advanced LLMs on our OOP benchmark highlights a critical need for improvements in this field. Our benchmark and scripts are publicly released at: https://***/alphadl/OOP-eval. © 2024 Association for Computational Linguistics.

关键词： Object oriented programming

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Enhancing Quantitative Reasoning Skills of Large Language Models through Dimension Perception 40

Enhancing Quantitative Reasoning Skills of Large Language Mo...

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40th IEEE International Conference on data Engineering, ICDE 2024

作者： Huang, Yuncheng He, Qianyu Liang, Jiaqing Jiang, Sihang Xiao, Yanghua Chen, Yunwen School of Computer Science Shanghai Key Laboratory of Data Science Fudan University China School of Data Science Fudan University China DataGrand Co. LTD. Research Group of Computational and AI Communication Institute for Global Communications and Integrated Media Fudan University China

ISBN: (纸本)9798350317152

Quantities are distinct and critical components of texts that characterize the magnitude properties of entities, providing a precise perspective for the understanding of natural language, especially for reasoning tasks. In recent years, there has been a flurry of research on reasoning tasks based on large language models (LLMs), most of which solely focus on numerical values, neglecting the dimensional concept of quantities with units despite its importance. We argue that the concept of dimension is essential for precisely understanding quantities and of great significance for LLMs to perform quantitative reasoning. However, the lack of dimension knowledge and quantity-related benchmarks has resulted in low performance of LLMs. Hence, we present a framework to enhance the quantitative reasoning ability of language models based on dimension perception. We first construct a dimensional unit knowledge base (DimUnitKB) to address the knowledge gap in this area. We propose a benchmark DimEval consisting of seven tasks of three categories to probe and enhance the dimension perception skills of LLMs. To evaluate the effectiveness of our methods, we propose a quantitative reasoning task and conduct experiments. The experimental results show that our dimension perception method dramatically improves accuracy (43.55%→50.67%) on quantitative reasoning tasks compared to GPT-4. © 2024 IEEE.

关键词： Machine learning

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