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6th International conference on communication and computational technologies, ICCCT 2024

作者： Jain, Samyak Pal, Aarush Andrew, J. Maheshwari, Saatvik Department of Computer Science and Engineering and Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India

ISBN: (纸本)9789819774258

This study addresses gastrointestinal cancer radiation therapy challenges by implementing advanced deep learning techniques. We focus on automating manual segmentation tasks during treatment planning to enhance efficiency and patient tolerance. Our methodology uses a diverse 3D MRI scan dataset to employ image analysis, Convolutional Neural Networks (CNNs), and Image Segmentation. The primary architectural choices involve the U-Net and InceptionResNetv2 models, contributing to developing a predictive segmentation model. This substantial result underscores the effectiveness of our approach in achieving accurate segmentations in the clinical setting. The overarching objective of our project is to automate the segmentation of the stomach and intestines in MRI scans, thereby streamlining radiation therapy planning for improved efficiency and patient care. In our evaluations, the implemented model demonstrates a commendable dice coefficient of 0.7, as indicated by metrics such as dice loss. Anticipated outcomes encompass reduced treatment times, enhanced patient comfort, and optimised tumour radiation doses while minimising healthy tissue exposure. The successful implementation of this advanced model holds the potential to revolutionise current radiation therapy practices, significantly elevating cancer treatment outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Machine Learning Approaches for Stroke Detection and SMOTE for Imbalanced Data 8th

Machine Learning Approaches for Stroke Detection and SMOTE f...

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8th International Conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2023

作者： Ruchitha, H.K. Sharma, B. S. Sanjana Sneha Rao, Trupthi Kodipalli, Ashwini Devamane, Shridhar B. Department of Artificial Intelligence and Data Science Global Academy of Technology Karnataka Bangalore India Department of Computer Science and Engineering Global Academy of Technology Karnataka Bangalore India

ISBN: (纸本)9789819976324

The majority of strokes will be caused by an unanticipated blockage of pathways by the heart and brain. To provide analytical data backing for timely, patient stroke prevention and detection, by creating a highly accurate and well-risk prediction model based on stroke risk factors using machine learning models (ML). We used numerous ML algorithms to analyse influencing factors to perform early detection, timely diagnosis, and timely treatment. In this paper, we have applied Decision Tree Classifier (DTC), K-Nearest Neighbours (KNN), and Logistic Regression (LR) for the analysis. The results were misleading because the original dataset had a data imbalance. To tackle this issue, the synthetic minority oversampling technique (SMOTE) was used in a sampling technique. There is also the use of k-fold cross-validation to extract more information about the performance of the algorithm and additionally comparing the accuracy of the three models. Therefore, this model can be used to support the decision-making of health care professionals. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Logistic regression

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AdaPIP: Adaptive picture-in-picture guidance for 360° film watching

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Computational Visual Media 2024年第3期10卷 487-503页

作者： Yi-Xiao Li Guan Luo Yi-Ke Xu Yu He Fang-Lue Zhang Song-Hai Zhang Department of Computer Science and Technology Tsinghua UniversityBeijingChinaand Beijing National Research Center for Information Science and Technology(BNRist)Tsinghua UniversityBeijingChina Key Laboratory of Space Utilization Technology and Engineering Center for Space UtilizationChinese Academy of SciencesBeijingChina Victoria University of Wellington WellingtonNew Zealand

360°videos enable viewers to watch freely from different directions but inevitably prevent them from perceiving all the helpful *** mitigate this problem,picture-in-picture(PIP)guidance was proposed using preview windows to show regions of interest(ROIs)outside the current view *** identify several drawbacks of this representation and propose a new method for 360° film watching called *** enhances traditional PIP by adaptively arranging preview windows with changeable view ranges and *** addition,AdaPIP incorporates the advantage of arrow-based guidance by presenting circular windows with arrows attached to them to help users locate the corresponding ROIs more *** also adapted AdaPIP and Outside-In to HMD-based immersive virtual reality environments to demonstrate the usability of PIP-guided approaches beyond 2D *** user experiments on 2D screens,as well as in VR environments,indicate that AdaPIP is superior to alternative methods in terms of visual experiences while maintaining a comparable degree of immersion.

关键词： 360°videos picture-in-picture(PIP) virtual reality(VR) visual guidance

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Light People:*** Dai,*** Lo,and *** Su-innovators in silicon photonics

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Light(science & Applications) 2024年第12期13卷 2880-2885页

作者： Yating Wan Chenzi Guo Electrical and Computer Engineering the ComputerElectrical and Mathematical Sciences and Engineering DivisionKing Abdullah University of Science and Technology23955-6900 ThuwalSaudi Arabia Changchun Institute of Optics Fine Mechanics and PhysicsChinese Academy of SciencesChangchunChina

In this edition of Light People,we are excited to feature *** Dai(Zhejiang University),*** Su(Shanghai Jiao Tong University),and *** Lo(Advanced Micro Foundry Pte Ltd,Singapore),three prominent researchers shaping the future of silicon *** collaborative work addresses critical issues in silicon photonics,including reducing propagation losses,enlarging the functionalities and enhancing building blocks,integrating efficient laser sources,expanding applications,and pushing the boundaries of optical and electronic *** this interview,we delve into their academic journeys,challenges,and future visions,offering insights into the ongoing evolution of silicon photonics and its potential to transform *** a deeper exploration of their experiences and advice,the full interview is available in the Supplementary material.

关键词： interview offering photonics

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Light People:*** May Lau,newly elected US NAE member in Hong Kong,talks about future of photonics and women in science

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Light(science & Applications) 2024年第4期13卷 602-608页

Editorial Photonics technology remains a driving force in today’s scientific landscape,marked by continuous innovation and crossdisciplinary *** an enlighting conversation with Light:science&Applications,*** May Lau,a pioneer in photonics research,shares her deep insights on the evolution of technologies of LEDs,lasers,challenges of hetero-epitaxy,and the future of micro-LEDs and quantum dot *** honored as a member of the US National academy of engineering(NAE)for her significant contributions to photonics and electronics using III-V semiconductors on silicon,*** stands out as the sole Hong Kong scholar inducted into the NAE this year,joining 114 new and 21 international *** this exclusive Light People interview,*** shares her journey as a pioneering woman in engineering,her commitment to mentorship and academia,and her perspective on advancing female representation in *** summary provided is distilled from ***’s thoughtful responses during the *** a deeper exploration of ***’s experiences and advice,the full interview is available in the Supplementary material.

关键词： interview lighting driving

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Explainable Recommendation System Based on Aspect-Based Sentiment Analysis 21

Explainable Recommendation System Based on Aspect-Based Sent...

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21st International computer Conference on Wavelet Active Media technology and Information Processing, ICCWAMTIP 2024

作者： Xiaolong, Zhou Shijiao, Han Zhenze, Li School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China China Academy of Aerospace Science and Innovation Beijing China

ISBN: (纸本)9798331519254

At information age, the rapid increase of information caused Information Overload problem, which made recommendation system (RS) come into being and push forward the quick development. Nowadays RS has been widely used in human society. Meanwhile, RS also faces challenges like data sparsity and poor explainability. An effective way to address data sparsity challenge is to exploit content information (like review data) for modeling users and items. By further mining content information, deep relationship between user-item interactions can be uncovered and therefore the explanation for recommendation results can be provided. In order to fully mine the information in review data and make precise recommendation with corresponding explanation, a recommendation model for rating prediction task is proposed in this paper. It exploits aspect-based sentiment analysis related theory to mine review features, incorporates the modeling of user preference and item properties with fine-grained sentiment information, which solves problems that previous model has poor context feature extraction ability and the sentiment analysis results are lack of reliability. Extensive experiments show that this model outperforms baseline models in rating prediction task and can provide explanations for the rating prediction results in view of sentiment. © 2024 IEEE.

关键词： Prediction models

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Overcoming Spatial Constraints in VR:A Survey of Redirected Walking Techniques

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Journal of computer science & technology 2024年第4期39卷 841-870页

作者： Jia-Hong Liu Yang-Fu Ren Qi Wen Gan Kui Huang Fiona Xiao Yu Chen Er-Xia Luo Khang Yeu Tang Yue-Yao Fu Cheng-Wei Fan Sen-Zhe Xu Song-Hai Zhang Academy of Arts and Design Tsinghua UniversityBeijing 100084China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China College of Computer Technology and Application Qinghai UniversityXining 810016China Zhili College Tsinghua UniversityBeijing 100084China School of Computer Beijing Information Science and Technology UniversityBeijing 100192China Department of Physics Tsinghua UniversityBeijing 100084China School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China

As the virtual reality(VR)technology strives to provide immersive and natural user experiences,the challenge of aligning vast virtual environments with limited physical spaces remains *** survey comprehensively explores the advancements in redirected walking(RDW)techniques aimed at overcoming spatial constraints in *** addresses this by subtly manipulating users’physical movements to allow for seamless navigation within constrained *** survey delves into gain perception mechanisms,detailing how slight discrepancies between virtual and real-world movements can be utilized without user awareness,thus extending the effective navigable *** RDW control algorithms for gain-based RDW are analyzed,highlighting their implementation and effectiveness in maintaining immersion and minimizing perceptual ***,novel methods extending beyond traditional gain-based techniques are discussed,showcasing innovative approaches that further refine VR *** practical implications of RDW in enhancing safety and reducing physical collisions in VR environments are underscored,alongside its potential to improve user experience by aligning virtual exploration more closely with natural human behavior *** a thorough review of existing literature and recent advancements,this survey provides a systematic understanding for researchers,developers,and industry *** underscores the importance of RDW in the future of VR,emphasizing RDW's role in making VR more accessible and practical across various applications,from education and training to therapy and *** paper concludes with a forward-looking perspective on the continued evolution and potential of RDW in revolutionizing virtual reality experiences.

关键词： virtual reality(VR) redirected walking(RDW) redirection gain perception

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Remote Sensing Classification of River Using Machine Learning Methods 12

Remote Sensing Classification of River Using Machine Learnin...

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12th Academic Conference of Geology Resource Management and Sustainable Development, GRMSD 2024

作者： Cui, Hanwen Li, Cheng Tian, Xiaolin School of Computer Science Zhuhai College of Science and Technology Guangdong Zhuhai519041 China Academy of Interdisciplinary Studies Hong Kong University of Science and Technology 999077 Hong Kong School of Computer Science and Engineering Faculty of Innovation Engineering Macau University of Science and Technology 999078 China

ISBN: (纸本)9798331314545

In the realm of global sustainable development, effective management of water resources and the protection of ecological environments have increasingly emerged as focal points for both governmental and international organizations. Rivers, as critical conduits of water resources, fulfill various essential functions, including transportation, irrigation, aquaculture, tourism, and climate regulation, in addition to their provision of water for human consumption and agricultural purposes. This study predominantly explores the application of remote sensing technology and machine learning methodologies to classify rivers. The research is situated in Zhuhai City, characterized by a vertically distributed river network. It employs eXtreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM), both based on gradient boosting decision trees (GBDT). The data source for this investigation consists of high spatial resolution imagery from Gaofen-2 (GF-2) remote sensing satellites. The results indicate that both models achieve an overall accuracy (OA) exceeding 96.50%, with Kappa coefficients surpassing 0.92. Notably, the LightGBM model demonstrates superior performance over XGBoost in terms of both classification accuracy and operational efficiency. The utilization of remote sensing images in conjunction with machine learning techniques for the classification and monitoring of rivers offers a robust scientific framework for enhancing water resource management. Furthermore, these methodologies can assist in the formulation of effective strategies for the sustainable utilization and protection of water resources. © 2024 Proceedings of the 12th Academic Conference of Geology Resource Management and Sustainable Development, GRMSD 2024. All rights reserved.

关键词： Rivers

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AAST-NLP at ArAIEval Shared Task: Tackling Persuasion Technique and Disinformation Detection using Pre-Trained Language Models On Imbalanced Datasets 1

AAST-NLP at ArAIEval Shared Task: Tackling Persuasion Techni...

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1st Arabic Natural Language Processing Conference, ArabicNLP 2023

作者： El-Sayed, Ahmed Nasr, Omar El-Madany, Nour El-Din Department of Computer Engineering Arab Academy for Science and Technology Egypt Department of Electronics And Communication Arab Academy for Science and Technology Egypt

ISBN: (纸本)9781959429272

This paper presents the pipeline developed by the AAST-NLP team to address both the persuasion technique detection and disinformation detection shared tasks. The proposed system for all the tasks’ sub-tasks consisted of preprocessing the data and finetuning AraBERT on the given datasets, in addition to several procedures performed for each subtask to adapt to the problems faced in it. The previously described system was used in addition to Dice loss as the loss function for sub-task 1A, which consisted of a binary classification problem. In that sub-task, the system came in eleventh place. We trained AraBERT for task 1B, which was a multilabel problem with 24 distinct labels, using binary cross-entropy to train a classifier for each label. On that sub-task, the system came in third place. We utilised AraBERT with Dice loss on both subtasks 2A and 2B, ranking second and third among the proposed models for the respective subtasks. © 2023 Association for Computational Linguistics.

关键词： Natural language processing systems

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Introduction to the Special Issue on Formal Methods and Models for System Design

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ACM Transactions on Embedded Computing Systems 2025年第3期24卷 1-3页

作者： Brandt, Jens Saha, Indranil Zhang, Lijun Hochschule Niederrhein Krefeld Germany Department of Computer Science and Engineering Indian Institute of Technology Kanpur Kanpur India State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China

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