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Mobile Health from Developers’ Perspective

SN Computer Science 2024年第1期5卷 132页

作者： Oliveira, Pedro Almir M. Junior, Evilasio Costa Andrade, Rossana M. C. Santos, Ismayle S. Neto, Pedro A. Santos Group of Computer Networks Software Engineering and Systems (GREat) Federal University of Ceará Ceará Brazil Federal Institute of Maranhão (IFMA) Maranhão Brazil Laboratory of Software Optimization and Testing (LOST) Federal University of Piauí Piauí Brazil

This paper extends an initial investigation of eHealth from the developers’ perspective. In this extension, our focus is on mobile health data. Despite the significant potential of this development area, few studies try to understand the challenges faced by these professionals. This perspective is relevant to identify the most used technologies and future perspectives for research investigation. Using a KDD-based process, this work analyzed eHealth and mHealth discussions from Stack Overflow (SO) to comprehend this developers’ community. We got and processed 6082 eHealth and 1832 mHealth questions. The most discussed topics include manipulating medical images, electronic health records with the HL7 standard, and frameworks to support mobile health (mHealth) development. Concerning the challenges faced by these developers, there is a lack of understanding of the DICOM and HL7 standards, the absence of data repositories for testing, and the monitoring of health data in the background using mobile and wearable devices. Our results also indicate that discussions have grown mainly on mHealth, primarily due to monitoring health data through wearables and about how to optimize resource consumption during health-monitoring. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： eHealth mHealth Mining software repositories Stack overflow

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Towards Specification Completion for systems with Emergent Behavior based on DevOps

Towards Specification Completion for Systems with Emergent B...

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2022 International Conference on Computational Science and Computational Intelligence, CSCI 2022

作者： Ailane, Mohamed Toufik Aniculaesei, Adina Knieke, Christoph Rausch, Andreas Sholichin, Fauzi Clausthal University of Technology Institute for Software and Systems Engineering Clausthal-Zellerfeld Germany

ISBN: (纸本)9798350320282

software systems may experience multiple emergent behaviors during their operation time. These emergent system behaviors occur when system engineers develop their system under the closed-world assumption, but this assumption is not met during its operation. This means that system engineers work on the basis that they have complete knowledge of the system and its environment during its design, when the system specification that has been created is actually incomplete. In this paper, an observation of an emergent behavior is considered to be a solid proof that the system model specification is still incomplete. A conceptual framework is proposed to harness the emergent behavior and complete the system specification that is provided during the its design. The framework consists of two parts, system development and system operations. It is built on a model-driven approach in order to provide a clear definition of the emergent behavior and a concrete development scheme. The framework exploits the DevOps paradigm as a successful paradigm to achieve the ultimate goal of developing complete system models through the continuous specification completion based on the observed emergent behavior. The goal of this framework is to help develop high-quality and reliable emergent systems based on the specification derived from the emergent behavior that occurred at run time. © 2022 IEEE.

关键词： Specifications

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Using Gamification to Increase Healthcare Professionals’ Cybersecurity Awareness

Using Gamification to Increase Healthcare Professionals’ Cy...

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International Conference on Smart Applications and Sustainability in the Artificial Intelligence of Things, SAS-AIoT 2024

作者： Salama, Ramiz Alturjman, Sinem Altrjman, Chadi Al-Turjman, Fadi Department of Computer Engineering AI and Robotics Institute Research Center for AI and IoT Near East University Nicosia Cyprus Artificial Intelligence Software Information Systems Engineering Departments AI and Robotics Institute Near East University Nicosia Cyprus

ISBN: (纸本)9783031782756

Cybersecurity knowledge is essential in today’s increasingly digital healthcare environment to protect sensitive patient data and guarantee the integrity of healthcare systems. But conventional cybersecurity training approaches frequently fail to hold healthcare professionals’ attention, which results in inadequate application and retention of the material. In order to increase cybersecurity awareness among healthcare professionals, this study investigates the use of gamification—a technological technique that incorporates game design aspects into non-gaming contexts—as an innovative method. A mixed-methods research methodology is used in the study to assess the effectiveness of a cybersecurity training program that is gamified. To encourage user involvement and motivation, the program incorporates aspects like leaderboards, interactive scenarios, badges, and points. The study included a total of 100 healthcare professionals from various departments, offering a thorough understanding of the program’s effects across a range of positions and experience levels. Pre- and post-training surveys were used to gather quantitative data on participant changes in knowledge, attitudes, and behaviors pertaining to cybersecurity procedures. Furthermore, semi-structured interviews yielded qualitative insights that further elucidated the participants’ perspectives and experiences with the gamified training method. Comparing gamification to conventional training approaches, the results show that gamification dramatically improves engagement and knowledge retention. The willingness of the participants to acquire and use cybersecurity techniques in their daily lives has reportedly increased. Additionally, the gamified components strengthened the training objectives by fostering a competitive yet cooperative learning environment. The potential of gamification as a game-changing instrument for cybersecurity education in healthcare settings is highlighted by this study. Healthcare busines

关键词： Cybersecurity

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A flexible operation-based infrastructure for collaborative model-driven engineering

A flexible operation-based infrastructure for collaborative ...

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Modellierung 2024

作者： Herac, Edvin Marchezan, Luciano Assunção, Wesley Haas, Rainer Egyed, Alexander Johannes Kepler University Linz Institute of Software Systems Engineering Altenberger Straße 69 Linz4040 Austria Linz Center of Mechatronics GmbH Austria

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Applied Machine Learning Methods with Long-Short Term Memory Based Recurrent Neural Networks for Multivariate Temperature Prediction

arXiv

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arXiv 2025年

作者： Lukić, Bojan Clausthal University of Technology Institute for Software and Systems Engineering Germany

This paper gives an overview on how to develop a dense and deep neural network for making a time series prediction. First, the history and cornerstones in Artificial Intelligence and Machine Learning will be presented. After a short introduction to the theory of Artificial Intelligence and Machine Learning, the paper will go deeper into the techniques for conducting a time series prediction with different models of neural networks. For this project, Python’s development environment Jupyter, extended with the TensorFlow package and deep-learning application Keras is used. The system setup and project framework are explained in more detail before discussing the time series prediction. The main part shows an applied example of time series prediction with weather data. For this work, a deep recurrent neural network with Long Short-Term Memory cells is used to conduct the time series prediction. The results and evaluation of the work show that a weather prediction with deep neural networks can be successful for a short time period. However, there are some drawbacks and limitations with time series prediction, which will be discussed towards the end of the paper. © 2025, CC BY-SA.

关键词： Prediction models

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Diagnosis and remaining useful life prediction of end-of-life battery systems for second-life applications 17

Diagnosis and remaining useful life prediction of end-of-lif...

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17th CIRP Conference on Intelligent Computation in Manufacturing engineering, CIRP ICME 2023

作者： Wanner, Johannes Singh, Soumya Nagel, Tobias Patel, Arpit Baazouzi, Sabri Grimm, Julian Birke, Kai Peter Fraunhofer Institute for Manufacturing Engineering and Automation IPA Nobelstrasse 12 Stuttgart70569 Germany Electrical Energy Storage Systems Institute for Photovoltaics University of Stuttgart Pfaffenwaldring 47 Stuttgart70569 Germany

The realization of a circular economy for lithium-ion batteries demands technological advancements capable of performing diagnosis on end-of-life batteries. This will ensure improvement of the life cycle performance of battery systems, considering the most diverse value chains. A key challenge is considering the variety of battery conditions at their end-of-life and the multiple reuse options in second-life applications. This paper presents the different approaches of software-based State of Health (SOH) determination and remaining useful life (RUL) prediction on battery cells. This includes the use and performance comparison of classical regression methods, machine learning regression methods and physical battery models in determining the SOH and degradation process of battery cells. In addition, we consider the resulting RUL conditions of typical second-life applications of electric vehicles (EVs), forklifts, grid stabilisation storage systems, and home storage systems. These results enable the selection and use of electrical and software characterisation methods for efficient battery management diagnostics and end-of-life characterisation. © 2024 Elsevier B.V.. All rights reserved.

关键词： BMS Diagnosis Degradation modelling Li-Ion-Battery Remaining Useful Life Second-life Application

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In silico prediction methods of self-interacting proteins:an empirical and academic survey

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Frontiers of Computer Science 2023年第3期17卷 199-216页

作者： Zhanheng CHEN Zhuhong YOU Qinhu ZHANG Zhenhao GUO Siguo WANG Yanbin WANG College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060China School of Computer Science Northwestern Polytechnical UniversityXi’an 710129China Institute of Machine Learning and Systems Biology School of Electronics and Information EngineeringTongji UniversityShanghai 201804China School of Cyber Science and Technology Zhejiang UniversityHangzhou 310058China

In silico prediction of self-interacting proteins(SIPs)has become an important part of *** is an urgent need to develop effective and reliable prediction methods to overcome the disadvantage of high cost and labor intensive in traditional biological wet-lab *** goal of our survey is to sum up a comprehensive overview of the recent literature with the computational SIPs prediction,to provide important references for actual work in the *** this review,we first describe the data required for the task of DTIs ***,some interesting feature extraction methods and computational models are presented on this topic in a timely ***,an empirical comparison is performed to demonstrate the prediction performance of some classifiers under different feature extraction and encoding ***,we conclude and highlight potential methods for further enhancement of SIPs prediction performance as well as related research directions.

关键词： proteomics self-interacting proteins feature extraction prediction model

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Large Language Model-Based Optimization for System-Level Test Program Generation 37

Large Language Model-Based Optimization for System-Level Tes...

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37th IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology systems, DFT 2024

作者： Schwachhofer, Denis Domanski, Peter Becker, Steffen Wagner, Stefan Sauer, Matthias Pfluger, Dirk Polian, Ilia Institute of Computer Engineering and Computer Architecture University of Stuttgart Stuttgart Germany Institute of Software Engineering University of Stuttgart Stuttgart Germany Institute for Parallel and Distributed Systems University of Stuttgart Stuttgart Germany Technical University of Munich Heilbronn Germany Advantest Europe Boeblingen Germany

ISBN: (纸本)9798350366884

System-Level Test (SLT) is essential for testing integrated circuits, focusing on functional and non-functional properties of the Device under Test (DUT). Traditionally, test engineers manually create tests with commercial software to simulate the DUT's end-user environment. This process is both time-consuming and offers limited control over non-functional properties. This paper proposes Large Language Models (LLMs) enhanced by Structural Chain of Thought (SCoT) prompting, a temperature schedule, and a pool of previously generated snippets to generate high-quality code snippets for SLT. We repeatedly query the LLM for a better snippet using previously generated snippets as examples, thus creating an iterative optimization loop. This approach can automatically generate snippets for SLT that target specific non-functional properties, reducing time and effort. Our findings show that this approach improves the quality of the generated snippets compared to unstructured prompts containing only a task description. © 2024 IEEE.

关键词： Functional Test Large Language Models Optimization System-Level Test Test Generation

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SHEDDING LIGHT ON TASK DECOMPOSITION IN PROGRAM SYNTHESIS: THE DRIVING FORCE OF THE SYNTHESIZER MODEL

arXiv

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arXiv 2025年

作者： Zenkner, Janis Sesterhenn, Tobias Bartelt, Christian Machine Learning and Cognitive Software Institute for Software and Systems Engineering TU Clausthal Germany

Task decomposition is a fundamental mechanism in program synthesis, enabling complex problems to be broken down into manageable subtasks. ExeDec, a state-of-the-art program synthesis framework, employs this approach by combining a Subgoal Model for decomposition and a Synthesizer Model for program generation to facilitate compositional generalization. In this work, we develop REGISM, an adaptation of ExeDec that removes decomposition guidance and relies solely on iterative execution-driven synthesis. By comparing these two exemplary approaches—ExeDec, which leverages task decomposition, and REGISM, which does not—we investigate the interplay between task decomposition and program generation. Our findings indicate that ExeDec exhibits significant advantages in length generalization and concept composition tasks, likely due to its explicit decomposition strategies. At the same time, REGISM frequently matches or surpasses ExeDec’s performance across various scenarios, with its solutions often aligning more closely with ground truth decompositions. These observations highlight the importance of repeated execution-guided synthesis in driving task-solving performance, even within frameworks that incorporate explicit decomposition strategies. Our analysis suggests that task decomposition approaches like ExeDec hold significant potential for advancing program synthesis, though further work is needed to clarify when and why these strategies are most effective. © 2025, CC BY.

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Improving Fake News Detection with a Mixture of Experts: Leveraging Sentiment Analysis and Sarcasm Detection as Core Expert Models 6

Improving Fake News Detection with a Mixture of Experts: Lev...

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6th International Conference on Advancements in Computing, ICAC 2024

作者： Gunasekara, Tharindu Thihan, Disira Kodithuwakku, Sansika Dissanayake, Dinuka Fernando, Harinda Vidhanaarachchi, Samitha Sindhujan, Archchana Sri Lanka Institute of Information Technology Malabe Dept. of Software Engineering Sri Lanka Sri Lanka Institute of Information Technology Malabe Dept. of Computer Computer Systems Engineering Sri Lanka University of Surrey Dept. of Computer Science and Electronic Engineering United Kingdom

ISBN: (纸本)9798331517878

Fake news continues to proliferate, posing an increasing threat to public discourse. The paper proposes a framework of a Mixture of Experts, Sentiment Analysis, and Sarcasm Detection experts for improved fake news detection. This approach captures the emotional cues in the text through a Sentiment Analysis expert, which is based on bidirectional encoder representations from Transformers (BERT) models with sentiment vectors generated using SentiWordNet and Integrated Gradients. It combines a sarcasm detection expert based on BERT, recognizing sarcasm and its type to help classify fake news. By fusing these experts through a Mixture of Experts gateway, subtle linguistic cues often found in fake news are more effectively analyzed, leading to improved accuracy in detecting misinformation. Experimental results are presented as 96% for the Sarcasm expert with the BERT base model and 83% for the Sentiment Analysis expert with the distilled version of the BERT (DistilBERT) base model, proving the effectiveness of the proposed approach in beating traditional methods. © 2024 IEEE.

关键词： fake news detection gateway mixture of experts sarcasm detection sentiment analysis

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