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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Qiao, Qi Shi, Jun Jiang, Zhiguo Wang, Wei Wu, Haibo Zheng, Yushan Hefei University of Technology School of Computer Science and Information Engineering Hefei230601 China Hefei University of Technology School of Software Hefei230601 China Beihang University Image Processing Center School of Astronautics Beijing102206 China University of Science and Technology of China Division of Life Sciences and Medicine Department of Pathology First Affiliated Hospital of Ustc Hefei230036 China University of Science and Technology of China Intelligent Pathology Institute Division of Life Sciences and Medicine Hefei230036 China Beihang University School of Engineering Medicine Beijing Advanced Innovation Center for Biomedical Engineering Beijing100191 China

ISBN: (纸本)9798350386226

Traditional PCR/NGS-based multigene panel testing is time-consuming and costly. Predicting EGFR mutations directly from H&E stained whole slide images (WSIs) can alleviate these limitations. Furthermore, histopathological reports contain valuable textual information that correlates with tissue areas in WSIs. However, recent research mainly analyses EGFR mutation status only from a single modality, ignoring rich information contained in reports. In this paper, we propose a report-guided cross-modal representation learning method for predicting EGFR mutations by WSIs. Specifically, we reconstruct report-level embeddings through exploring intrinsic relationships between diagnostic words in histopathological reports and tissue areas in WSIs. Finally, reconstructed histopathological report embedding and aggregated WSI embedding are fused for final prediction. More importantly, molecular testing report is also introduced as prior supervision information at the training stage to guarantee semantic consistency of fused feature and molecular report embedding. We evaluate our method on the TCGA-EGFR public benchmark dataset and an in-house clinical dataset (USTC-EGFR). Experimental results demonstrate that our method outperforms existing approaches in EGFR mutation prediction, highlighting the benefits of cross-modal learning in enhancing feature representational ability. The code is available at https://***/HFUT-miaLab/RCRL. © 2024 IEEE.

关键词： Supervised learning

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SolSearch: An LLM-Driven Framework for Efficient SAT-Solving Code Generation

SolSearch: An LLM-Driven Framework for Efficient SAT-Solving...

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IEEE/ACM International Conference on software engineering: New Ideas and Emerging Technologies Results Track (ICSE-NIER)

作者： Junjie Sheng Yanqiu Lin Jiehao Wu Yanhong Huang Jianqi Shi Min Zhang Xiangfeng Wang School of Computer Science and Technology East China Normal University Shanghai China Software Engineering Institute East China Normal University Shanghai China School of Computer Science and Technology Shanghai Formal-Tech Information Technology Co. Lt East China Normal University Shanghai China

ISBN: (数字)9798331537111

ISBN: (纸本)9798331537128

The Satisfiability (SAT) problem is a core challenge with significant applications in software engineering, including automated testing, configuration management, and program verification. This paper presents SolSearch, a novel framework that harnesses large language models (LLMs) to discover and optimize SAT-solving strategies automatically. Leveraging a curriculum-based, trial-and-error process, SolSearch enables the LLM to iteratively modify and generate SAT solver code, thereby improving solving efficiency and performance. This automated SAT-solving paradigm has the advantage of being plug-and-play, allowing integration with any SAT solver and accelerating the development or design process of new SAT solvers (new methods). Our preliminary experimental results are encouraging by demonstrating that the LLM-powered paradigm improves state-of-the-art SAT solvers on general SAT benchmarks and significantly enhances the performance of the widely used Z3 solver (11% on PAR-2 score). These results highlight the potential for using LLM-driven methods to advance solver adaptability and effectiveness in real-world software engineering challenges. Future research directions are discussed to further refine and validate this approach, offering a promising avenue for integrating AI with traditional software engineering tasks.

关键词： Codes Large language models Configuration management Benchmark testing software engineering

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A Comprehensive Approach to Identify Stroke based on Real-time Medical Data

A Comprehensive Approach to Identify Stroke based on Real-ti...

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Cognitive Robotics and Intelligent Systems (ICC - ROBINS), International Conference on

作者： M. Maragatharajan Shanmugapriya S M. B. Sudhan Deeptha R. Sankar Muthu Paramasivam School of Computing Science and Engineering VIT Bhopal University Kothrikalan Madhya Pradesh India Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Srivilliputhur Tamilnadu India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai Tamilnadu Department of Information Technology SRM Institute of Science and Technology Ramapuram Chennai Tamilnadu India Distinguished Software Engineer Palo Alto Networks Inc Santa Clara California USA

ISBN: (数字)9798350372748

ISBN: (纸本)9798350372755

Strokes or cardiovascular disorders continue to be a major global health concern. Proactive intervention and timely detection greatly reduce the morbidity and death related to strokes. The goal of this research is to build a prediction model using machine learning techniques to assess a person’s risk of stroke based on a variety of sociodemographic and health characteristics. The dataset used in this study consists of a large collection of medical records. It contains important details including age, gender, BMI, smoking habits, heart disease, hypertension, and the type of residential property. Using this information, a Decision Tree Classifier is trained to predict the likelihood of strokes, enabling early detection and customized treatment plans. A Decision Tree Classifier with a predetermined maximum depth is used in the proposed methodology’s rigorous data preprocessing, which includes handling missing values, encoding categorical features, and developing models. Both the training and testing datasets are used to evaluate the model’s performance using evaluation measures including accuracy, precision, and recall. The model’s insights provide important characteristics that influence the prediction of stroke risk, assisting medical professionals in focused risk assessment and preventive intervention. The proposed method has the potential to improve healthcare practices by facilitating prompt interventions and personalized treatment for stroke risk individuals. The proposed model has resulted in 86.78% accuracy for this dataset by using the traditional machine learning algorithms. This work highlights the value of predictive modelling in the healthcare industry by demonstrating how it may improve early detection techniques and lessen the impact of strokes on public health.

关键词： Training Machine learning algorithms Machine learning Predictive models Stroke (medical condition) Real-time systems Risk management

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Effectiveness of Using Deep Learning for Blister Blight Identification in Sri Lankan Tea

Effectiveness of Using Deep Learning for Blister Blight Iden...

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International Research Conference on Smart Computing and Systems engineering (SCSE)

作者： G.H.A.U. Hewawitharana U.M.L.A. Nawarathne A.S.F. Hassan Lochana M. Wijerathna G.D. Sinniah Samitha P. Vidhanaarachchi Jagath Wickramarathne Janaka L. Wijekoon Department of Information Technology Sri Lanka Institute of Information Technology Malabe Sri Lanka Plant Pathology Division Tea Research Institute of Sri Lanka Talawakelle Sri Lanka Department of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka Systems Engineering Department Military Technological College Muscat Oman

Ceylon tea industry faces a major challenge in the form of pathogen-induced crop loss, with Blister Blight (BB) caused by Exobasidium vexans posing the greatest threat, leading to harvest losses of over 30%. This fungus attacks the tender tea shoots, resulting in a direct negative impact on the tea harvest. This paper presents a system to identify the suspicious tea leaves and BB disease at its early stages along with an assessment of severity, offering a potential solution to this critical issue. By utilizing real-time object detection, the system filters out non-tea leaves from the captured initial image of a segment of a tea plant. The identified tea leaves are then subjected to BB identification and severity assessment based on differing visual symptoms of the BB stages. This approach enables the system to accurately identify BB in the initial stage and severity stage, allowing for timely and targeted intervention to minimize crop losses. The YOLOv8 model has been able to correctly identify 98% of the objects it has detected as relevant (precision), and it has been able to correctly identify 96% of all the relevant objects present in the scene (recall). The Residual Network 50 (Resnet50) convolutional neural network (CNN) model was selected as the final model, achieving an accuracy of 89.90% during the training phase and an accuracy of 88.26% during the testing phase.

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A Trust-Based Personalized Differential Privacy Guarantees for Online Social Networks

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IEEE Transactions on Network and Service Management 2025年

作者： Chen, Jiajun Hu, Chunqiang Sheng, Weihong Li, Ruinian Zhao, Ruifeng Yu, Jiguo Chongqing University College of Computer Science Chongqing401331 China Chongqing University School of Big Data and Software Engineering Chongqing401331 China Bowling Green State University Computer Science Department United States Electric Power Dispatching and Control Center of Guangdong Power Grid Co. Ltd Guangzhou China University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu611731 China Qilu University of Technology Big Data Institute Jinan250353 China

Online social networks have emerged as a significant data source, but the extensive collection and utilization of personal information have given rise to profound concerns regarding privacy. From a legislative and policy perspective, and in alignment with the concept of privacy as control, users have the right to control their personal privacy information. However, users often encounter challenges in terms of understanding and effectively managing their privacy settings to align with their specific privacy requirements. To address this issue, in this paper, we incorporate the concept of trust and propose a trust-based personalized differential privacy model for online social networks, denoted as TPDP, which relies on a trusted central server to facilitate its operation. Specifically, when a user requests access to another user's personal information, the TPDP mechanism provides a privacy response, where the privacy level is determined based on the direct and indirect trust values among users, calculated automatically by the trusted central server. Furthermore, the proposed TPDP model offers user-to-user personalized differential privacy protection from the perspectives of network structures, trust-related factors, and trust propagation patterns. Finally, we validate the model's feasibility and assess the privacy-utility trade-off, as well as its robustness against attacks, through theoretical analysis and performance evaluation. © 2004-2012 IEEE.

关键词： Differential privacy

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Binary Equilibrium Optimizer Algorithm

Journal of Network Intelligence

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Journal of Network Intelligence 2022年第1期7卷 45-58页

作者： Hu, Pei Chu, Shu-Chuan Snášel, Václav Pan, Jeng-Shyang College of Computer Science and Engineering Shandong University of Science and Technology No.579 Qianwan’gang Road Shandong Qingdao266590 China School of Computer and Software Nanyang Institute of Technology No.80 Changjiang Road Henan Nanyang473004 China College of Science and Engineering Flinders University Sturt Rd Bedford ParkSA5042 Australia Department of Computer Science VŠB—Technical University of Ostrava 17. listopadu 2172/15 Poruba Ostrava708 00 Czech Republic Department of Information Management Chaoyang University of Technology No.168 Jifeng E. Rd. Wufeng District Taichung413310 Taiwan

Equilibrium optimizer (EO) is a new proposed meta-heuristic algorithm by utilizing the mass balance model of the control volume. In order to solve the binary appli-cations, this paper proposes a binary version of equilibrium optimizer (BEO). BEO takes advantage of the structure of EO, only modifying the equations of equilibrium concentra-tion and position updating. Through the benchmark functions and Wilcoxon’s rank sum test, it compares BEO with binary bat algorithm, binary differential evolution, binary grey wolf optimizer, binary particle swarm optimization, and a binary hybrid algorithm of particle swarm optimization and gravitational search algorithm. BEO shows excep-tional performance in the solving quality, time complexity and convergence. Simulation results prove that BEO acquires the least classification errors while having a small number of features in the KNN. © 2022, Taiwan Ubiquitous information CO LTD. All rights reserved.

关键词： Particle swarm optimization (PSO)

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A Review of Quantum Cybersecurity: Threats, Risks and Opportunities 1

A Review of Quantum Cybersecurity: Threats, Risks and Opport...

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1st International Conference on AI in Cybersecurity, ICAIC 2022

作者： Faruk, Md Jobair Hossain Tahora, Sharaban Tasnim, Masrura Shahriar, Hossain Sakib, Nazmus Kennesaw State University Department of Software Engineering and Game Development United States Kennesaw State University Department of Computer Science United States Kennesaw State University Institute for Cybersecurity Workforce Development United States Kennesaw State University Department of Information Technology United States University at Buffalo The State University of New York Department of Computer Science and Engineering United States

ISBN: (数字)9781665400435

ISBN: (纸本)9781665400435

The promise of quantum computing is not speeding up conventional computing rather delivering an exponential advantage for certain classes of problems, with profound implications for cybersecurity for instance. With the advent and development of quantum computers, cyberspace security can surely become the most critical problem for the Internet in near future. On contrary, prosaic quantum technology can be promising to transform cybersecurity. This research aims to synthesize basic and fundamental studies concerning quantum cybersecurity that can be emerged both as a threat and solution to critical cybersecurity issues based on a systematic study. We provide a comprehensive, illustrative description of the current state-of-the-art quantum computing and cybersecurity and present the proposed approaches to date. Findings in quantum computing cybersecurity suggest that quantum computing can be adopted for the betterment of cybersecurity threats while it poses the most unexpected threats to cybersecurity. The focus and depth of this systematic survey not only provide quantum and cybersecurity practitioners and researchers with a consolidated body of knowledge about current trends in this area but also underpins a starting point for further research in this field. © 2022 IEEE.

关键词： Cybersecurity

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Face Skin Disease Detection and Community based Doctor Recommendation System

Face Skin Disease Detection and Community based Doctor Recom...

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Advancements in Computing (ICAC), International Conference on

作者： M.A.A. Udara D.G. Wimalki Dilshani M.S.W. Mahalekam V.Y. Wickramaarachchi Jenny Krishara Dinuka Wijendra Department of Computer Science & Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398106

In our country, skin diseases are more common than other diseases because of the climate. Skin diseases are occurring almost on all groups of ages among people. It is one of the most common types of diseases where some can be painful, and some can cause fatal to human life. The delay of the disease detection, difficulties of identify the infected area, Ignorance of the spread of the disease and treatments may threat to the patient’s life. Most of the time this process is performed manually which can lead to human errors and takes days for providing the results. This paper reports a smart solution that assists the patients by detecting the disease, identify the current infected area of the disease, recommend best doctors, provide community-based prevention guidelines, and predict the future risk. Also due to this economic crisis, we suggest that it’s much easier if the patient can do these skin check-ups systematically to continuously monitor and detect skin disease to get proper medical attention. As treatment procedures can be different from each doctor and impact will be different, we are working on community-based platform where we can get patients’ reviews about doctors and preventive guidelines. Depending on the performance evaluations, the results obtained from the proposed method for disease identifications are in the range of 90% - 95% of accuracy.

关键词： Performance evaluation Economics Medical services Skin Recommender systems Monitoring Faces

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Adaptivo: A Personalized Adaptive E-Learning System based on Learning Styles and Prior Knowledge

Adaptivo: A Personalized Adaptive E-Learning System based on...

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International Conference on Informatics and Computing (ICIC)

作者： M.A.M Rishard S.L Jayasekara E.M.P.U Ekanayake K.M.J.S Wickramathilake Shyam Reyal Kalpani Manathunga Jagath Wickramarathne Department of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350345728

The rapid advancement of technology and the internet has resulted in an increase in the number of learners seeking e-learning. Though E-Learning is widely used most e-learning systems provide the same set of learning resources and learning paths to each student, regardless of their personal preferences. In recent years there has been increasing attention towards the characteristics of learners such as the learning styles and the knowledge level of the learner. This research paper proposes a personalized adaptive E-learning system called “Adaptivo” that provides a personalized learning experience to the learners based on their learning style and knowledge level. To make the learning process more efficient and engaging, Adaptivo takes into account the specific differences between learners in terms of time, online interactions and learning duration. It then builds a personalized learning path depending on each learner's learning style and knowledge level. The main aim of this study is to investigate the impact of the proposed adaptive learning approach on learners. The results show that the students appreciate the approach, are highly satisfied, and performed better when content is personalized according to their learning style and prior knowledge.

关键词： Adaptive learning Electronic learning Adaptive systems Informatics

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Convergence of 5G with Internet of Things for Enhanced Privacy

Convergence of 5G with Internet of Things for Enhanced Priva...

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Future of information and Communication Conference, FICC 2021

作者： Batool, Amreen Sun, Baoshan Saleem, Ali Ali, Jawad School of Computer Science and Software Engineering Tiangong University Tianjin China Department of Computer Science COMSATS University Islamabad Lahore Pakistan Malaysian Institute of Information Technology Universiti Kuala Lumpur Kuala Lumpur Malaysia

ISBN: (纸本)9783030730994

In this paper, we address the issue of privacy in 5th generation (5G) driven Internet of Things (IoT) and related technologies while presenting a comparison with previous technologies for communication and unaddressed issues in 5G. Initially, an overview of 5G driven IoT is presented with details about both technologies and eventually leading to problems that 5th generation will face. Details about 5G are also presented while comparing them with previous technologies. The architecture of 5G is presented hence explaining layers of 5G and technologies like SDN, NFV and cloud computing that compose these layers. The architecture for 5g based IoT is also presented for providing visual understanding as well as explained based on how this addresses the issues present in 4G. Privacy is highlighted in 5G driven IoT while providing details about how SDN, NFV and cloud computing helps in elimination of this issue. The issues presented will be compared with 4G based IoT and solutions are provided about mitigation of these issues particularly bandwidth and security. Moreover, techniques used by 4G and 5G technologies for handling the issues of privacy in IoT are presented in a nutshell as a table. Paper also presents a detailed overview of technologies making 5G possible meanwhile giving an explanation about how these technologies resolve privacy issues in 5G. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Internet of things

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