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2024 International Conference on Frontiers of Information Technology, FIT 2024

作者： Rafi, Saima Akbar, Muhammad Azeem Khan, Arif Ali School of Computing Engineering & the Built Environment Edinburgh Napier University EH10 5DT United Kingdom Software Engineering Department Lappeenranta-Lahti University of Technology Lappeenranta15210 Finland M3S Empirical Software Engineering Research Unit University of Oulu Oulu90570 Finland

ISBN: (纸本)9798331510503

Ensuring product security in software development is crucial, encompassing hardware, firmware, and holistic protection measures. With rising cyber threats, organizations face challenges in safeguarding software products, often viewing security as a bottleneck. To address these issues, we conducted an empirical study with 22 practitioners across various industries, exploring challenges and practices in product security. Our findings highlight both the applicability of security practices and the new challenges in their implementation. This research contributes valuable insights, enabling organizations to proactively address security concerns and develop robust strategies for protecting their software products. © 2024 IEEE.

关键词： Challenges empirical study Practices Product security

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Novel Approach for Enhancing Mental Well-Being Through Machine Learning Techniques 5

Novel Approach for Enhancing Mental Well-Being Through Machi...

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5th International Conference on Advancements in computing, ICAC 2023

作者： Roshani, O.V.D.E. Ayuwardhana, H.M.K.J.J. Rodrigo, P.H.M.S. Hewageegana, R.U. Fernando, Harinda De Silva, Dilshan I. Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

Frequently, individuals undergo specific episodes of mental health challenges throughout their lifetime. But the COVID pandemic has triggered a surge in mental health disorders arising from isolation, monotonous routines, demanding workloads, financial disparities, and disruptions to daily schedules. Furthermore, the global pandemic has induced constant anxiety and stress. Beyond the pandemic, the competition and intense pressure of the modern world impact mental health. Access to advanced mental health solutions and the necessary familiarity remain limited for most of the population. Given the integration of technology into daily life, diverse remedies, including mobile and web applications, have emerged to tackle the escalating challenge of mental health disorders. This study proposes an accessible and cost-effective approach that employs machine learning to detect stress levels and discern user emotions from journal entries and facial expressions while integrating self-journaling, video recommendations, and visual content generation to stimulate positive emotions and relieve stress. © 2023 IEEE.

关键词： Cost effectiveness

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Research on performance optimization of virtual data space across WAN

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Frontiers of Computer Science 2024年第6期18卷 167-187页

作者： Jiantong HUO Zhisheng HUO Limin XIAO Zhenxue HE State Key Laboratory of Software Development Environment Beihang UniversityBeijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China High Performance Computing Center Beihang UniversityBeijing 100191China Hebei Key Laboratory of Agricultural Big Data Hebei Agricultural UniversityBaoding 071001China

For the high-performance computing in a WAN environment,the geographical locations of national supercomputing centers are scattered and the network topology is complex,so it is difficult to form a unified view of *** aggregate the widely dispersed storage resources of national supercomputing centers in China,we have previously proposed a global virtual data space named GVDS in the project of“High Performance computing Virtual Data Space”,a part of the National Key Research and Development Program of *** GVDS enables large-scale applications of the high-performance computing to run efficiently across ***,the applications running on the GVDS are often data-intensive,requiring large amounts of data from multiple supercomputing centers across *** this regard,the GVDS suffers from performance bottlenecks in data migration and access across *** solve the above-mentioned problem,this paper proposes a performance optimization framework of GVDS including the multitask-oriented data migration method and the request access-aware IO proxy resource allocation *** a WAN environment,the framework proposed in this paper can make an efficient migration decision based on the amount of migrated data and the number of multiple data sources,guaranteeing lower average migration latency when multiple data migration tasks are running in *** addition,it can ensure that the thread resource of the IO proxy node is fairly allocated among different types of requests(the IO proxy is a module of GVDS),so as to improve the application’s performance across *** experimental results show that the framework can effectively reduce the average data access delay of GVDS while improving the performance of the application greatly.

关键词： storage aggregation across WANs large-scale applications GVDS data migration allocation of IO proxy resource

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Improved Double Deep Q Network-Based Task Scheduling Algorithm in Edge computing for Makespan Optimization

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Tsinghua Science and Technology 2024年第3期29卷 806-817页

作者： Lei Zeng Qi Liu Shigen Shen Xiaodong Liu School of Computer Science Nanjing University of Information Science and TechnologyNanjing 210044China School of Software Nanjing University of Information Science and TechnologyNanjing 210044China School of Information Engineering Huzhou UniversityHuzhou 313000China School of Computing Edinburgh Napier UniversityEdinburghEH105DTUK

Edge computing nodes undertake an increasing number of tasks with the rise of business ***,how to efficiently allocate large-scale and dynamic workloads to edge computing resources has become a critical *** study proposes an edge task scheduling approach based on an improved Double Deep Q Network(DQN),which is adopted to separate the calculations of target Q values and the selection of the action in two networks.A new reward function is designed,and a control unit is added to the experience replay unit of the *** management of experience data are also modified to fully utilize its value and improve learning *** learning agents usually learn from an ignorant state,which is *** such,this study proposes a novel particle swarm optimization algorithm with an improved fitness function,which can generate optimal solutions for task *** optimized solutions are provided for the agent to pre-train network parameters to obtain a better cognition *** proposed algorithm is compared with six other methods in simulation *** show that the proposed algorithm outperforms other benchmark methods regarding makespan.

关键词： edge computing task scheduling reinforcement learning makespan Double Deep Q Network(DQN)

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Adapting Pedagogical Frameworks to Online Learning: Simulating the Tutor Role with LLMs and the QUILT Model 6

Adapting Pedagogical Frameworks to Online Learning: Simulati...

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6th International Conference on Advancements in computing, ICAC 2024

作者： Madhubhashana, Sandaru Ranhinda, Vibhashana Jayakody, Dineth Shavinda, Lakmuthu Nawarathne, Malithi Haddela, Prasanna Sri Lanka Institute of Information Technology Dept. of Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Malabe Sri Lanka

ISBN: (纸本)9798331517878

This paper presents SoloScholar, a novel asynchronous online learning platform designed to deliver personalized education to large undergraduate student groups by integrating established pedagogical frameworks with Large Language Models (LLMs). Leveraging Bloom's Revised Taxonomy, learners are categorized into beginner, intermediate, and advanced levels to personalize the educational content. The platform employs the Questioning and Understanding to Improve Learning and Thinking (QUILT) model to structure tutorial sessions and simulate the teaching role of a tutor at a Higher Education Institute (HEI). To generate contextually relevant and cognitively appropriate questions, carefully crafted prompts are fed into LLMs, synthesizing information from reliable knowledge sources. The efficacy of SoloScholar was evaluated through a dual assessment approach: a technical evaluation using the Retrieval Augmented Generation Assessment (RAGAS) framework and a human evaluation involving academic experts. The technical evaluation demonstrated high scores in answer relevancy and context utilization across all learning levels. Similarly, quantitative responses from academic experts indicated strong satisfaction with the quality and appropriateness of the generated content. The findings underscore the potential of integrating LLMs with pedagogical models to enhance personalization in online education. SoloScholar effectively addresses challenges associated with large-group teaching by providing adaptive learning experiences that cater to diverse cognitive needs. © 2024 IEEE.

关键词： Contrastive Learning

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An Innovative Study on the Detection of Foreign Objects in Electric Wires 5

An Innovative Study on the Detection of Foreign Objects in E...

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

作者： Hu, Fangyu Sun, Haoyu Zhang, Junhao Li, Yuhui School of Software Harbin Institute of Technology Harbin China Harbin Institute of Technology Faculty of Computing Harbin China School of Automotive Engineering Harbin Institute of Technology Harbin China

ISBN: (纸本)9798350385557

With the development of modern society, the demand for electricity consumption is also gradually increasing, timely power inspection, the removal of foreign bodies at the power line is very important. Aiming at the problems that manual inspection consumes a lot of human resources, time and low efficiency, and aiming at the problems of poor performance and low accuracy of target detection model, this paper proposes an automatic power line foreign body detection method based on YOLOv5-6.0 target detection model. In order to extract features better, the C3 module in the model is changed to CBAMC3 module which introduces CBAM attention mechanism. Meanwhile, the performance of the YOLOv5-6.0 model before and after the improvement in training and foreign body detection was compared in this paper to prove the effectiveness of the improvement. © 2024 IEEE.

关键词： Object detection

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Image Tampering Detection With Frequency-Aware Attention and Multiview Fusion

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第3期6卷 614-625页

作者： Xu, Xu Chen, Junxin Lv, Wenrui Wang, Wei Zhang, Yushu Dalian University of Technology School of Software Dalian116621 China Northeastern University School of Computer Science and Engineering Shenyang110004 China Shenzhen MSU-BIT University Guangdong-Hong Kong-Macao Joint Laboratory for Emotion Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen518172 China Jiangxi University of Finance and Economics School of Computing and Artificial Intelligence Nanchang330013 China

Manipulated images are flooding our daily lives, which poses a threat to social security. Recently, many studies have focused on image tampering detection. However, they have poor performance on independent validation due to differences in image scenes and tampering methods. The key question is how to design a network that is able to adaptively enhance the tampering information and suppress the generalization features during training. To this end, we propose a dual-branch network with a frequency adaptation paradigm and a feature fusion module for robust tampering image detection. First, this paradigm is designed to adaptively highlight tampering features through frequency conversion and learnable weight. Second, a feature fusion module is developed to filter redundant features and dynamically fuse two-branch features. Experiments on eight typical datasets demonstrate that our model has advantages over state-of-the-art algorithms, and our paradigm can well empower semantic segmentation networks for tampering detection. © 2024 IEEE.

关键词： Semantic Segmentation

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Unmasking Fake News with Emotional Cues: A Comparative Study of Sentiment Reasoning Models 4

Unmasking Fake News with Emotional Cues: A Comparative Study...

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4th International Conference on Advanced Research in computing, ICARC 2024

作者： Abishethvarman, Vadivel Banujan, Kuhaneswaran Kumara, B.T.G.S. Ravikumar, Nirubikaa Sabaragamuwa University of Sri Lanka Department of Computing and Information Systems Belihuloya Sri Lanka Australia Sabaragamuwa University of Sri Lanka Department of Software Engineering Belihuloya Sri Lanka

ISBN: (纸本)9798350384864

Discerning truth from deception requires innovative tools in an era besieged by misinformation. Fake news is a form of misleading information delivered as reliable news. It is mainly made up of intentionally manipulating public opinion, decision bias, and exploiting general credulity. Various supervised learning-based fake news detection and classifications only focus on the semantic values due to their subjectivity in the current context. Sentiment analysis is also a common interpretation mechanism of information mining. This research explores the role of sentiment analysis in the context of fake news detection. Sentiment analysis, a facet of natural language processing, delves into the nuanced sentiments within the text, unveiling hidden narratives and differentiating between authentic reporting and carefully crafted deception. The study employs various sentiment reasoning models, including VADER, TextBlob, and Roberta, focusing on comparative analysis. The VADER model, complemented by a Bag of Words approach, emerges as the most effective, outperforming, compared to TextBlob and RoBERTa. Especially BERT sentiment reasoner model underperformed with uneven distribution in this fake news domain. The best VADER sentiments are taken for the transitions of sequential neural network architectures such as Simple Neural Network (SNN), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) featuring Long Short-Term Memory (LSTM). The LSTM model captures intricate patterns, dependencies, and contextual information. Evaluation metrics, encompassing accuracy, precision, recall, and F1 score, provide a comprehensive understanding of models' performance. The RNN LSTM performed well compared to the other sequential models. It primely emphasised the multifaceted landscape of sentiment analysis, presenting a robust strategy for combating the proliferation of fake news in an information-abundant age. Combining Sentiment Reasoning in VADER and RNN LSTM presents a rob

关键词： Sentiment analysis

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A Study of DM-TPACK Evaluation Methods for Middle School Mathematics Teachers 24

A Study of DM-TPACK Evaluation Methods for Middle School Mat...

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8th International Conference on Education and Multimedia Technology, ICEMT 2024

作者： Zhang, En Chen, Ruxian Chen, Sailan Chen, Yuanhua Wang, Ying Yin, Ruiyao Institute of Computing Science and Technology Guangzhou University Guangzhou China Institute of Mathematics and Information Science Guangzhou University Guangzhou China Software Engineering Institute of Guangzhou Guangzhou China

ISBN: (纸本)9798400717611

The theoretical framework of Technological Pedagogical Content Knowledge (TPACK) provides a foundation for the integration of information technology and disciplines. Dynamic mathematical software, as the most widely used mathematics discipline software by mathematics teachers, can effectively improve classroom efficiency and improve students' mathematical thinking. This is exemplified by China's first leading dynamic mathematics discipline teaching tool in the mobile Internet environment: NetPad. In light of the ongoing evolution of information technology, junior high school mathematics teachers must consistently enhance their capacity to leverage dynamic mathematics technology for instructional purposes, in order to align with the evolving demands of teacher development in the contemporary era. Therefore, this paper analyzes the research status of TPACK measurement methods, based on the relevant theoretical studies of TPACK, takes the research of core component TPACK as the entry point, integrates the dynamic mathematical technology, divides the dimensions of junior high school mathematics teachers' Dynamic Mathematics Technological Pedagogical Content Knowledge (DM-TPACK) level, refers to the junior high school mathematics teachers' TPACK scale by Yuanmei Duan, constructs a DM-TPACK evaluation scale and uses Think Aloud Method and Delphi Method to revise and improve, forming the final DM-TPACK questionnaire for junior high school mathematics teachers. Finally, the questionnaire was subjected to a reliability test, and the results showed that the evaluation instrument had good internal consistency and reliability. This study provides a basis for measuring the TPACK level of junior high school mathematics teachers' integration of dynamic mathematical techniques, and provides a reference for the development of a TPACK assessment tool for teachers. © 2024 Copyright held by the owner/author(s).

关键词： Teaching

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Data-Driven Analysis of Climate Change Impacts on Public Health: A Systematic Review and Future Directions

Data-Driven Analysis of Climate Change Impacts on Public Hea...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Yarmohammadtoosky, Sahar Xie, Ying Kennesaw State University School of Data Science and Analytics MariettaGA United States Kennesaw State University College of Computing and Software Engineering MariettaGA United States

ISBN: (纸本)9798350362480

Climate change profoundly impacts human health, ecosystems, and food and water security. This review provides a comprehensive analysis of climate change's effects on human health using data science methods, including statistical and machine learning models, to assess current knowledge. It identifies critical gaps in data collection and methodological limitations across different regions and climates. Key recommendations include improving data resolution and duration to enhance model accuracy, as well as fostering collaboration between data scientists, public health experts, and climate specialists. The review underscores the need for innovative, data-driven models to better predict and address the health impacts of climate change. © 2024 IEEE.

关键词： Electronic health record

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