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Proceedings of the 33rd USENIX Conference on Security Symposium

作者： Shidong Pan Zhen Tao Thong Hoang Dawen Zhang Tianshi Li Zhenchang Xing Xiwei Xu Mark Staples Thierry Rakotoarivelo David Lo School of Computing Australian National University and Software and Computational Systems Research Program CSIRO's Data61 Software and Computational Systems Research Program CSIRO's Data61 Khoury College of Computer Sciences Northeastern University School of Computing and Information Systems Singapore Management University

ISBN: (纸本)9781939133441

Privacy policies have emerged as the predominant approach to conveying privacy notices to mobile application users. In an effort to enhance both readability and user engagement, the concept of contextual privacy policies (CPPs) has been proposed by researchers. The aim of CPPs is to fragment privacy policies into concise snippets, displaying them only within the corresponding contexts within the application's graphical user interfaces (GUIs). In this paper, we first formulate CPP in mobile application scenario, and then present a novel multimodal framework, named SEEPRIVACY, specifically designed to automatically generate CPPs for mobile applications. This method uniquely integrates vision-based GUI understanding with privacy policy analysis, achieving 0.88 precision and 0.90 recall to detect contexts, as well as 0.98 precision and 0.96 recall in extracting corresponding policy segments. A human evaluation shows that 77% of the extracted privacy policy segments were perceived as well-aligned with the detected contexts. These findings suggest that SEEPRIVACY could serve as a significant tool for bolstering user interaction with, and understanding of, privacy policies. Furthermore, our solution has the potential to make privacy notices more accessible and inclusive, thus appealing to a broader demographic. A demonstration of our work can be accessed at https://***/SeePrivacy/

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Mapping the Scientific Landscape of Integrated Scheduling Towards Industry 5.0 with A Bibliometric Analysis 6

Mapping the Scientific Landscape of Integrated Scheduling To...

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6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Putri Ardyanti, Anak Agung Ayu Xie, Zhiqiang Karnyoto, Andrea Stevens Pardamean, Bens Estiyanti, Ni Made School of Computer Science and Technology Harbin University of Science and Technology Harbin150000 China Faculty of Information Technology and Design Primakara University Bali 80226 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia School of Management and Business University of Debrecen Debrecen Hungary

The industry is rapidly transitioning from the 4.0 era to the 5.0 era, prompting renewed interest among scholars in scheduling problems. They allow operations to process and assemble various components simultaneously. This bibliometric analysis provides a comprehensive understanding of the diverse research perspectives on the potential challenges of integrated scheduling within the framework. We identified 357 Scopus articles and analysed co-occurrence keyword (CNK) by VOSViewer tools and Global Citation Score (GCS) by Scopus tools. The research data obtained from 1993 to 2023 mainly in the subject areas of Engineering, Computer Science, Maths, and others. CNK identified six cluster keywords, and nine most cited articles based on normalised GCS. These results contribute to our understanding of the development of scheduling algorithms, shifting from an emphasis on manufacturing transfers to contemporary demand-centric research directions. Furthermore, this research provides a valuable perspective for policymakers, plant personnel, and manufacturing managers to make informed decisions. © 2025 The Authors. Published by Elsevier B.V.

关键词： Job shop scheduling

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Enhanced Performance and data Privacy in Lung Nodule Classification via Federated Deep Learning Approach 24

Enhanced Performance and Data Privacy in Lung Nodule Classif...

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2024 7th International Conference on Healthcare Service management, ICHSM 2024

作者： Nguyen, Duc-Khanh Li, Ai-Hsien Adam Lai, Yen-Jun Chiu, Yen-Ling Phan, Dinh-Van Chien, Ting-Ying Chan, Chien-Lung Department of Information Management Yuan Ze University Taoyuan Taiwan Department of Statistics and Informatics University of Economics The University of Danang Danang Viet Nam Division of Cardiology Far Eastern Memorial Hospital New Taipei Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Radiology Far Eastern Memorial Hospital New Taipei Taiwan Graduate Institute of Medicine and Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Medical Research Far Eastern Memorial Hospital New Taipei Taiwan Department of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan ZDT Group- YZU Joint Research and Development Center for Big Data Taoyuan Taiwan Yuan Ze University Taoyuan Taiwan

ISBN: (纸本)9798400710162

This study explores the feasibility of deep learning for classifying nodule neoplasms, analyzing their performance on two openly available datasets, LUNGx SPIE, and LIDC-IDRI. These datasets offer valuable diversity in image quantity, quality, and malignancy annotation methodologies, posing a real-world challenge for effective model development. To handle the disparities in these diverse datasets, we aim to develop a robust classification model trained using federated learning. This decentralized approach allows model training across datasets without sharing patient data, preserving privacy and overcoming data fragmentation. Our experiments demonstrate that federated learning enables the development of a robust and superior deep learning model compared to individual models trained on limited data. This combination of deep learning, and federated learning holds immense promise for nodule neoplasm classification and can significantly improve patient care and outcomes. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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Mapping the Scientific Landscape of Integrated Scheduling Towards Industry 5.0 with A Bibliometric Analysis

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Procedia Computer Science 2025年 253卷 84-93页

作者： Anak Agung Ayu Putri Ardyanti Zhiqiang Xie Andrea Stevens Karnyoto Bens Pardamean Ni Made Estiyanti School of Computer Science and Technology Harbin University of Science and Technology Harbin 150000 China Faculty of Information Technology and Design Primakara University Bali 80226 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Doctoral School of Management and Business University of Debrecen Debrecen Hungary

关键词： integrated scheduling assembly job shop scheduling heuristic algorithm genetic algorithm bibliometric analysis

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Enhancing Clinical Accuracy in Middle Ear Disease Diagnosis with a Cloud-Based AI System Integrating CNNs and LLMs 6th

Enhancing Clinical Accuracy in Middle Ear Disease Diagnosis ...

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6th International Conference on Biomedical and Health Informatics, ICBHI 2024

作者： Chu, Yuan-Chia Lin, Kuan-Hsun Chen, Yen-Chi Hsu, Chien-Yeh Kuo, Chen-Tsung Cheng, Yen-Fu Liao, Wen-Huei Department of Information Management Taipei Veterans General Hospital Taipei112 Taiwan Big Data Canter Taipei Veterans General Hospital Taipei112 Taiwan Department of Information Management National Taipei University of Nursing and Health Sciences Taipei11219 Taiwan Kaohsiung820 Taiwan Institute of Brain Science National Yang Ming Chiao Tung University 3F Shouren Building No. 155 Sec. 2 Linong Street Beitou District Taipei112 Taiwan Master Program in Global Health and Health Security College of Public Health Taipei Medical University Taipei11042 Taiwan Department of Otolaryngology-Head and Neck Surgery Taipei Veterans General Hospital No. 201 Sec. 2 Shipai Road Beitou District Taipei112 Taiwan Faculty of Medicine National Yang Ming Chiao Tung University Taipei112 Taiwan Department of Medical Research Taipei Veterans General Hospital Taipei112 Taiwan

ISBN: (纸本)9783031863226

Middle ear diseases, such as otitis media and middle ear effusion, pose significant diagnostic challenges, especially in primary care settings, where misdiagnoses or delayed treatments can lead to severe complications and unnecessary antibiotic use. To address these issues, we developed a cloud-based AI system that integrates convolutional neural networks (CNNs) and large language models (LLMs) to assist clinicians in diagnosing middle ear diseases with high accuracy. This study involved a retrospective analysis of 2,820 de- identified otoendoscopic images collected from Taipei Veterans General Hospital between January 2011 and December 2019. The CNN models, including InceptionV3, were optimized through transfer learning and validated against independent test datasets. The AI system, deployed on cloud servers, enables real-time analysis via a smartphone application, with the LLM providing contextual medical advice based on CNN outputs. The system achieved a diagnostic accuracy of 97.6%, comparable to otolaryngology specialists (98.2%) and significantly higher than general practitioners (84.3%), with a precision of 96.8%, recall of 95.4%, and F1-score of 96.1%. The area under the ROC curve (AUC) was 0.98, indicating excellent model performance. Additionally, the integration of the LLM improved clinical utility by offering relevant medical advice, which was highly rated in usability tests. This cloud- based AI system demonstrates high diagnostic accuracy and practical utility, particularly in resource-limited settings, making it a valuable tool for enhancing patient safety and clinical outcomes. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Knowledge and information management: Is it possible to do interesting and important research, get funded, be useful and appreciated? 02

Knowledge and information management: Is it possible to do i...

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Proceedings of the Eleventh International Conference on information and Knowledge management (CIKM 2002)

作者： Zemankova, Maria Information and Data Management Program National Science Foundation

ISBN: (纸本)9781581134926

The survey of the CIKM Call for Papers for the period 1998-2002 demonstrates that the CIKM organizers very accurately "identify challenging problems facing the development of future knowledge and information systems [in] applied and theoretical research" and also play an important role fostering "bridging traditionally separated areas such as databases and information retrieval, or those that apply techniques from one area to another". The presented CIKM papers also indicate that researchers work on interesting problems. This paper discusses some additional research topics for future consideration by the CIKM community.

关键词： information management

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The design and implementation of a corporate householding knowledge processor to improve data quality

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JOURNAL OF management information SYSTEMS 2003年第3期20卷 41-69页

作者： Madnick, S Wang, R Xian, X John Norris Maguire Professor of Information Technology at the Sloan School of Management MIT Professor of Engineering Systems School of Engineering MIT Co-Director for MIT Total Data Quality Management Program Director MIT Information Quality Program Oracle Corporation

Advances in corporate householding are needed to address certain categories of data quality problems caused by data misinterpretation. In this paper, we first summarize some of these data quality problems and our more recent results from studying corporate householding applications and knowledge exploration. Then we outline a technical approach to a corporate householding knowledge processor (CHKP) to solve a particularly important type of corporate householding problem-entity aggregation. We illustrate the operation of the CHKP by using a motivational example in account consolidation. Our CHKP design and implementation uses and expands on the COntext INterchange (COIN) technology to manage and process corporate householding knowledge.

关键词： context mediation corporate household corporate householding database interoperability data quality enterprise knowledge management

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Implementation Challenge of Artificial Intelligence in Human Resource management for Employee Performance Monitoring Indonesia Companies Perspective 11

Implementation Challenge of Artificial Intelligence in Human...

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11th International Conference on ICT for Smart Society, ICISS 2024

作者： Wirawan, Edi Yusuf Gusan Putra, Merios Nugroho, Budi Program of Management Brawijaya University Jakarta Campus Indonesia Program of Information Systen Diponegoro University Semarang Indonesia Program of Information System Stmik Tazkia Bogor Indonesia Res. Center for Data and Information Science National Research and Innovation Agency Bandung Indonesia

ISBN: (纸本)9798350389654

The main problem solved is to Improve Employee Performance Monitoring by Integrating Artificial Intelligence (AI) in Human Resource management for Indonesian Companies. The paper suggests using AI technologies to analyze and process employee performance data. The overarching goal of this new approach is to identify patterns and red flags at the individual and team levels that can be used to inform stronger performance management strategies such as guided coaching, pointed feedback, or calibrated evaluations. This study is written as a schematic literature review, using sourced research articles from Indonesia and other countries to obtain the effects of AI on employee performance. This exercise showed how AI works in performance patterns and assists HR strategies. The findings AI has an impact on recognizing patterns in empirical data of how employees perform, helps to tailor training/development programs, and improves talent management and succession planning. Those results signify that AI has the potential to be a game-changer for HR practices in Indonesia. AI in HRM using employee performance monitoring seems an encouraging alternative for Indonesia. Through data-driven decision-making and automated HR routine tasks with many challenges and should prepare well to implement. © 2024 IEEE.

关键词： Resource allocation

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Disease data Modeling by Using Machine Learning Approach 11

Disease Data Modeling by Using Machine Learning Approach

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11th International Conference on information Technology, Computer and Electrical Engineering, ICITACEE 2024

作者： Wang, Gunawan Wardhana, Rakha Gusti Sibuea, Farida Gui, Anderes Bina Nusantara University Binus Graduate Program-Master of Information Systems Management Information Systems Management Department Jakarta11480 Indonesia Data Analytics Department Jakarta Indonesia School of Information Systems Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798350389289

Since Covid outbreak in 2020, Indonesian Ministry of Health (MoH) has intensively embraced digital transformation initiatives in healthcare management and decision-making process. The latest healthcare digital transformation roadmap 2024, has emphasized the use of AI technology in data analytics and support policy making. The article examines the use of common Machine Learning (AI) algorithms, such as: Logistic Regression, Random Forest, Support Vector Machine (SVM), Decision Tree, and XGBoost. The article applies these algorithms to analyse the common diseases faced by Indonesian such as Measles, Malaria, Leprosy, and Diphtheria. Those diseases are classical diseases, however, still infecting many people in large areas. The article takes a case study of national health profile within period 2017-2022, to study the data model of those diseases. The outcome of the article is expected to be used for further policy making. © 2024 IEEE.

关键词： Adversarial machine learning

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Finding customer behavior insights for content creation in material and product sourcing using specialized topic analysis 22

Finding customer behavior insights for content creation in m...

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22nd International Conference on Electronic Business, ICEB 2022

作者： Chotisarn, Noptanit Siriphongwatana, Phanuphong Thanisuwiphat, Phattranit Gulyanon, Sarun Nadee, Winai Department of Management Information Systems Thammasat Business School Thammasat University Bangkok Thailand Data Science and Innovation Program College of Interdisciplinary Studies Thammasat University Pathum Thani Thailand

In content creation, customer behavior insights are very important as they help creators find and create the content that drives sales. To comprehend customer needs, content creators need not just generalized information but also specific information, which can be different across markets and cultures. This information also needs some standards so it can be analyzed systematically. This paper aims to obtain customer insight into web content. Inside the web content, one possible source of this information is the tags based on customer feedback and the related entities. In this case, the product review data were collected and analyzed. However, manually analyzing feedback is a time-consuming activity. In this work, we formulated the topic analysis problem specialized for material and product sourcing, which could benefit product analysis and development. Technically, we also compared different text processing and classification methods, which set the benchmarks for reviewing the model performance in the future. © 2022 International Consortium for Electronic Business. All rights reserved.

关键词： Sales

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