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Personalized Governance Strategy for Patient Data in a Digital One Health Surveillance System

Procedia Computer Science 2025年 254卷 20-29页

作者： Edoghogho Olaye Imonikosaye Omafovbe Williams O. Aigbe Daniel Obuh Department of Computer Engineering Faculty of Engineering University of Benin Edo State Nigeria Fraunhofer-Institute for Electronic Nano Systems (ENAS) Chemnitz Germany.

The integration of personal devices in health surveillance has introduced significant patient data risks. In this research, we developed a Patients’ Personal Data Sovereignty System (PPDSS) to intelligently mask the patient's personal and sensitive data that should not be part of the data for analysis, ensuring these sensitive records do not find its way into machine learning models or to the Local Storage of the Capture Device. The approach presented in this paper is to ensure a high level of privacy and confidentiality for patients’ private health information (such as name, address, age and phone number) from the process of data collection, transmission, and storage to data analysis. The PPDSS is an android application built using new Flutter-based Cross Platform Technology which allows us to target other devices in future with same code base is designed to handle the data masking and elimination of Personal Identifiable Patients Data captured using the device camera before sending to the Machine Learning Models as texts. Records were captured from paper records using the camera on a smartphone installed with PPDSS. The image captured by PPDSS was obfuscated and then converted to text using AI-powered optical character recognition (OCR). The result is a personalized governance strategy of patient data, which ensures personal data privacy, confidentiality, and ethical use while maximizing the benefits of data-driven insights. The paper contributes to data governance by proposing a way of solving the problems at the point of data collection, rather than after the data have been collected.

关键词： digital sovereignty data privacy machine learning data obfuscation digital health surveillance healthcare records

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Challenges and Research Needs in the Offer Phase of Special Purpose Machinery and Plant engineering - A Qualitative Study with Industry Experts

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IEEE engineering Management Review 2024年

作者： Wilke, D. Mansheim, J. Dumitrescu, R. Fraunhofer Institute for Mechatronic Systems Design IEM in Paderborn Germany Advanced Systems Engineering at the Heinz Nixdorf Institute University of Paderborn and is director of strategy Fraunhofer Institute for Mechatronic Systems Design IEM in Paderborn Germany

Customization is an important competitive factor that enables companies to serve their customers better and succeed in an increasingly demanding and diverse market world. Instead of producing mass products for a broad target group, products or services are designed and produced to meet customers' individual needs and preferences. However, this also confronts companies with the challenge of meeting the diversity of customer needs. The special purpose machinery and plant engineering industry, a major European and especially German industry, is the industry considered in this article. To analyze the challenges in more detail, the authors conduct an interview study with experts from this industry. The authors examine the challenges faced by the experts during the offer phase, when the first design of a customized machine is created, and the phase that determines the company's success. Relevant literature is discussed and gaps in research are identified. © 1973-2011 IEEE.

关键词： Competition

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Information Theoretic Heuristics to Find the Minimal SOP Expression Considering Don’t Care Using Binary Decision Diagrams 3rd

Information Theoretic Heuristics to Find the Minimal SOP Exp...

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3rd EAI International Conference on Cognitive Computing and Cyber Physical systems, IC4S 2022

作者： Padmavathy, N. Jhansi, K.S. Ormila, K. Valarmathi, B. Department of Electronics and Communication Engineering Vishnu Institute of Technology Bhimavaram08544 India Department of Electrical and Electronics Engineering AMK Technological Polytechnic College Chennai India Department of Software and Systems Engineering School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9783031289743

Logic minimization plays a significant part in decreasing the complexity of the circuit since the number of Gates will be diminished… Till today, the conventional or traditional approaches like Boolean laws;Karnaugh map;Quine-Mccluskey are in existence for Boolean expression simplification. The above approaches have several drawbacks;to name a few – logical synthesis complexity, multiple solutions using k-maps, the number of cells increases exponentially with number of variables in a Boolean function and more computational time is needed while solving using Quine Mccluskey. Moreover, the implementation of k-map and Quine-Mccluskey method is difficult in logic synthesis of chip design. The solution to all above drawbacks is the use of Binary Decision Diagrams which is faster and its applicability to large circuits is possible. The main purpose of this work is to reduce the Boolean expressions considering DC function and also to calculate the entropy of the simplified Boolean expression using binary decision diagrams. © 2023, ICST institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Binary decision diagrams

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Inductive Sintering Module for Improved Multi-Die Attach in the Field of Power Packaging 12

Inductive Sintering Module for Improved Multi-Die Attach in ...

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12th IEEE CPMT Symposium Japan, ICSJ 2023

作者： Hofmann, Christian Rochala, Patrick Kroll, Martin Panhale, Sushant Baum, Mario Kuhn, Harald Oi, Kiyoshi Sanada, Masaki Murayama, Kei Fraunhofer Institute for Electronic Nano Systems Enas Department System Packaging Chemnitz Germany Chemnitz University of Technology Professorship for Forming and Joining Chemnitz Germany Fraunhofer Institute for Electronic Nano Systems Enas Department Systems Engineering Chemnitz Germany Fraunhofer Institute for Electronic Nano Systems Enas Chemnitz Germany Shinko Electric Industries CO. LTD. Process Development Dept. Nagano Japan

This work presents a heating technology for multi-die attach based on inductive silver sintering. Thus, the heat for the sintering reaction is confined locally to the bond region, avoiding heating of the entire power ... 详细信息

ISBN: (纸本)9798350325157

关键词： Sintering

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Intrusion Detection Mechanism Using Deep Learning 5

Intrusion Detection Mechanism Using Deep Learning

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5th International Conference on IoT Based Control Networks and Intelligent systems, ICICNIS 2024

作者： Ananthi, P. Nirmaladevi, K. Naveen Kumar, S. Kongu Engineering College Department of Computer Technology-PG Tamilnadu Erode638060 India Madanapalle Institute of Technology & Science Department of Computer Applications India M.Sc Software Systems Kongu Engineering College Erode India

ISBN: (纸本)9798331518097

In today's connected and data-driven world, networks and digital systems need to be protected from malicious attacks. The effectiveness of conventional Intrusion Detection systems (IDS) in recognizing and impeding novel threats and intricate methodologies has its limits. This study introduces a new method for improving network security by using deep learning techniques in the design and development of an intrusion detection system (IDS). The suggested Intrusion Detection System uses deep learning techniques, specifically Long Short Term Memory (LSTM) and Convolutional Neural Networks (CNNs), to identify and react to known and new security threats. Data can be taught to be encoded into a lower-dimensional representation and then decoded back into its original form using autoencoders (AEs). Autoencoders are a cutting-edge intrusion detection system that can lower false positives and raise overall detection accuracy because of their capacity to automatically learn from and adjust to the ever-changing threat landscape. They enhance computer networks capacity to spot irregularities and possible security threats. With the deep learning model obtaining a high accuracy of 99% in real-world threat detection, the results show that deep learning is effective in developing intrusion detection systems. © 2024 IEEE.

关键词： Network intrusion

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Gloss: Guiding Large Language Models to Answer Questions from System Logs 31

Gloss: Guiding Large Language Models to Answer Questions fro...

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31st IEEE International Conference on software Analysis, Evolution and Reengineering, SANER 2024

作者： Huang, Shaohan Liu, Yi Qi, Jiaxing Shang, Jing Xiao, Zhiwen Fung, Carol Wu, Zhihui Yang, Hailong Luan, Zhongzhi Qian, Depei Sino-German Joint Software Institute Beihang University Beijing China China Mobile Information Technology Center Beijing China Concordia Institute for Information Systems Engineering Concordia University Quebec Canada

ISBN: (纸本)9798350330663

System logs contain valuable information and they have emerged as one of the most crucial data sources for system monitoring aimed at enhancing service quality. IT support teams and system administrators are in dire need of an intelligent log-based QA system to help them quickly identify, diagnose, and resolve issues. In this paper, we propose a novel method for constructing log-based question-answering (QA) data using large language models, addressing challenges associated with limited dataset size and diversity in existing log-based QA systems. Our pipeline consists of three steps: generating questions, answering log questions, and refining question-answer pairs. The purpose of the generating questions is to create a diverse set of log-related queries that cover a wide range of potential issues. The second step, answering log questions, aims to extract relevant information from the logs to address the generated questions. This step ensures accurate and context-aware responses. Refining question-answer pairs is intended to improve the overall quality and consistency of the generated log-based QA data. We present a case study using ChatGPT to generate a new dataset, LogQuAD, containing over 28,000 question-answer pairs derived from more than 31,000 raw logs, representing a significant increase compared to existing datasets like LogQA. In our experimental setting, we sample half of the data as the training set and use memory-effect fine-tuning to fine-tune the model, named Gloss. Experimental results show that our method can generate high-quality log-based QA data, leading to improved performance of log-based QA models. Notably, our fine-tuned 7B model outperforms the LLaMA-65B model. This approach can potentially save valuable time for IT support teams and system administrators, enabling proactive problem resolution and optimal system performance. © 2024 IEEE.

关键词： Large datasets

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Categorizing methods for integrating machine learning with executable specifications

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Science China(Information Sciences) 2024年第1期67卷 5-19页

作者： David HAREL Raz YERUSHALMI Assaf MARRON Achiya ELYASAF Department of Computer Science and Applied Mathematics Weizmann Institute of Science Department of Software and Information Systems Engineering Ben-Gurion University of the Negev

Deep learning(DL),which includes deep reinforcement learning(DRL),holds great promise for carrying out real-world tasks that human minds seem to cope with quite *** promise is already delivering extremely impressive results in a variety of ***,while DL-enabled systems achieve excellent performance,they are far from *** has been demonstrated,in several domains,that DL systems can err when they encounter cases they had not hitherto ***,the opacity of the produced agents makes it difficult to explain their behavior and ensure that they adhere to various requirements posed by human *** the other end of the software development spectrum of methods,behavioral programming(BP) facilitates orderly system development using self-standing executable modules aligned with how humans intuitively describe desired system *** this paper,we elaborate on different approaches for combining DRL with BP and,more generally,machine learning(ML) with executable specifications(ES).We begin by defining a framework for studying the various approaches,which can also be used to study new emerging approaches not covered *** then briefly review state-of-the-art approaches to integrating ML with ES,continue with a focus on DRL,and then present the merits of integrating ML with *** conclude with guidelines on how this categorization can be used in decision making in system development,and outline future research challenges.

关键词： machine learning artificial intelligence grey box learning domain knowledge rules behavioral programming deep reinforcement learning survey

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Enhanced Student Stress Prediction in Higher Education Using a TriBranch CNN Framework 3

Enhanced Student Stress Prediction in Higher Education Using...

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3rd International Conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Kunwar, Fateh Bahadur Srivastava, Satyajee Dhivya, R. Gulati, Monika Amudha, K. Raj, D. Antony Arul Geetanjali Institute of Technical Studies Department of Computer Science and Engineering Udaipur India School of Engineering and Technology Manav Rachna International Institute of Research and Studies Department of Computer Science and Engineering Faridabad India M.Kumarasamy College of Engineering Department of Information Technology Karur India Mullana Ambala India Kongunadu College of Engineering and Technology Department of Electronics and Communication Engineering Trichy India Psg College of Arts & Science Department of Software Systems Coimbatore India

ISBN: (纸本)9798331508456

Students' stress levels have a significant impact on their academic performance in higher education, thus it's important to identify signs of stress early on and intervene to improve academic performance. Machine learning has evolved into a powerful tool for stress prediction, allowing for proactive support of at-risk youngsters. In order to better anticipate college students' stress levels, this study presents an enhanced TriBranch CNN framework. In order to provide the best possible performance, the technique includes data preparation, feature extraction, and model training. The dimensionality and redundancy are reduced using Principal Component Analysis (PCA). After being trained and tested against other models, the Fully Conditional Temporal Bayesian Network (Fully CTBN) outperformed them all with an accuracy of 93.29%, beating both traditional CNN and FCN approaches. These findings demonstrate that the Fully CTBN approach is useful for identifying students who may be struggling academically and providing schools with the information they need to implement data-driven interventions that boost student well-being and performance. © 2025 IEEE.

关键词： Students

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Autonomous sortie scheduling for carrier aircraft fleet under towing mode

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Defence Technology(防务技术) 2025年第1期43卷 1-12页

作者： Zhilong Deng Xuanbo Liu Yuqi Dou Xichao Su Haixu Li Lei Wang Xinwei Wang Department of Engineering Mechanics State Key Laboratory of Structural AnalysisOptimization and CAE Software for Industrial EquipmentDalian University of TechnologyDalian116024China School of Mathematical Sciences Dalian University of TechnologyDalian 116024China Naval Aviation University Yantai 264001China China State Shipbuilding Corporation Systems Engineering Research Institute Beijing 100094China Department of Automation School of Information Science and TechnologyTsinghua UniversityBeijing 100084China

Safe and efficient sortie scheduling on the confined flight deck is crucial for maintaining high combat effectiveness of the aircraft *** primary difficulty exactly lies in the spatiotemporal coordination,i.e.,allocation of limited supporting resources and collision-avoidance between heterogeneous dispatch *** this paper,the problem is investigated in the perspective of hybrid flow-shop scheduling problem by synthesizing the precedence,space and resource ***,eight processing procedures are abstracted,where tractors,preparing spots,catapults,and launching are virtualized as *** analyzing the constraints in sortie scheduling,a mixed-integer planning model is *** particular,the constraint on preparing spot occupancy is improved to further enhance the sortie *** basic trajectory library for each dispatch entity is generated and a delayed strategy is integrated to address the collision-avoidance *** efficiently solve the formulated HFSP,which is essentially a combinatorial problem with tightly coupled constraints,a chaos-initialized genetic algorithm is *** solution framework is validated by the simulation environment referring to the Fort-class carrier,exhibiting higher sortie efficiency when compared to existing *** animation of the simulation results is available at ***/video/BV14t421A7Tt/.The study presents a promising supporting technique for autonomous flight deck operation in the foreseeable future,and can be easily extended to other supporting scenarios,e.g.,ammunition delivery and aircraft maintenance.

关键词： Carrier aircraft Autonomous sortie scheduling Resource allocation Collision-avoidance Hybrid flow-shop scheduling problem

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Security Aspects of Container Orchestration in Kubernetes Environments 24

Security Aspects of Container Orchestration in Kubernetes En...

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24th International Symposium INFOTEH-JAHORINA, INFOTEH 2025

作者： Stanišić, Sava Vesković, Milan Ristić, Olga Dordević, Borislav Faculty of Technical Sciences Department of Computer and Software Engineering Čačak Serbia Mihajlo Pupin Institute Imp Computer Systems Belgrade Serbia

ISBN: (纸本)9798331515799

Security is one of the key challenges in container orchestration, especially in complex environments. This paper explores the security aspects of implementing containerized applications using Docker within a Kubernetes cluster. The first part of the paper describes Docker, Kubernetes, and various ways of applying them within DevOps methodology. It then presents potential vulnerabilities during the implementation of these technologies, as well as vulnerabilities specific to Docker and Kubernetes. Subsequently, some solutions for securing a Kubernetes environment are described. © 2025 IEEE.

关键词： Docker Kubernetes security

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