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A Paradigm Shift to Causal Model-Driven Decision-Making With Generative AI 13th

A Paradigm Shift to Causal Model-Driven Decision-Making Wit...

13th International conference on AI and Multimodal Services, AIMS 2024, Held as Part of the Services conference Federation, SCF 2024

作者： He, Sheng Ning, Yishuang Zhang, Liang-Jie Lei, Kai National Engineering Research Center for Supporting Software of Enterprise Internet Services Shenzhen518057 China Kingdee Research Kingdee International Software Group Co. Ltd. Shenzhen518057 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Peking University Shenzhen518055 China

ISBN: (纸本)9783031776809

In recent years, the rise of big data has popularized data-driven decision-making. However, the interpretability shortcomings of artificial intelligence (AI) models limit their reliability for critical decisions. This paper proposes a paradigm shift from conventional data-driven to causal model-driven decision-making, leveraging advancements in causal inference and large language models (LLMs). By applying the capabilities of generative AI, this paradigm shift enables the fusion of extensive domain knowledge, facilitating the development of causal models that capture the complexity of real-world systems and problems. The model-driven approach provides a better understanding of the causal mechanisms, relationships, and dynamics compared to correlational data-driven methods. Moreover, we introduce the concept of a composable business model based on modular causal components. We present a methodology for constructing robust causal model-driven decision frameworks, emphasizing the comprehensive utilization of generative AI to incorporate domain knowledge with causal inferences. Through an in-depth analysis of case studies across multiple domains, this model-driven approach empirically showcases its potential to improve decision quality, optimize resource allocation, and enhance process efficiency. Additionally, we critically discuss future research directions and challenges in the evolutionary trajectory of causal model-driven decision-making. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Generative adversarial networks

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Research on Intelligent Generation of Spaceborne software Code based on knowledge Graph

Research on Intelligent Generation of Spaceborne Software Co...

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2025 2nd International conference on Algorithms, software engineering and Network Security (ASENS)

作者： Qian Wu Xiaofeng Li Xiaobing Xie Tianyi Qi Ruoxi Xu Beijing Institute of Control Engineering Beijing China Beijing Institute of Control and Electronic Technology Beijing China

ISBN: (数字)9798331520298

ISBN: (纸本)9798331520304

Parameter configuration of spacecraft software involves the knowledge of the overall technology and scheme as well as the cumbersome operation steps. Traditional manual binding value transfer and code writing cannot effectively support the increasing scale of spaceborne software and satisfy the development cycle requirements. A new method is explored to generate the spaceborne software code intelligently. Firstly, the intensive knowledge of the parameter initialization code was analyzed , which was extracted into declarative knowledge and relational knowledge. The former was formally expressed, and the latter was linked to generate a solidified knowledge base. A prototype tool system was developed that could intelligently generate the parameter initialization code for the spacecraft software. Finally, one satellite control software was taken as an example to verify the effectiveness of the proposed method. The results showed that the intelligent code generation method based on knowledge graph could effectively simplify the process, reduce the error rate, and better meet the requirements of high quality and fast delivery for the spaceborne software.

关键词： Space vehicles Codes Satellites Error analysis Prototypes XML knowledge graphs Manuals Writing software

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2nd International conference on Data Science and Network engineering, ICDSNE 2024

2nd International Conference on Data Science and Network Eng...

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2nd International conference on Data Science and Network engineering, ICDSNE 2024

ISBN: (纸本)9789819783359

The proceedings contain 29 papers. The special focus in this conference is on Data Science and Network engineering. The topics include: Explainable AI Insights into a Time Series Weather Prediction Model Using Stacked LSTM;patient-Specific and Patient-Independent Seizure Prediction Using Ensemble Learning Technique;an Intuitive and Modular Framework for Enhanced Human-Machine Synergy;Wavelet-Transformed K-NN Pipeline for EEG-based Eye Blink Classification with Time Wrapping;enabling Cursor Control Through Eye Movement Using Hidden Markov Model;Comparative Study: Word2Vec Versus TF-IDF in software Defect Predictions;an Improved Deep Learning Framework based on Multi-Scale Convolutional Architecture for Road Crack Detection;knowledge Graph Relation Learning Using GAN-BERT;sightAssist: A Multi-facility Machine Learning Approach for Empowering the Visually Impaired;Predicting COVID-19 Cases in India Using ARIMA, Prophet, LSTM and Data Analysis Using Power BI;Classification of Muti-Labeled Retinal Diseases in Retinal Fundus Images Using CNN Model ResNet18;LSTM-based Portfolio Optimization with Gerber Covariance Estimator for Increased Robustness;handwritten Character Recognition from Small Grayscale Images Using Pre-trained Models;Forecasting Heart Disease Using Deep BI-DI Neural Networks;emo-Tune: Harnessing Emotion-based Music for Patient Wellness;Making Data Secure in Detecting ADHD with Supervised Learning;an Analysis of Automatic Question Generation Research Progress and Challenges;a Federated Learning Approach Towards a Privacy-Preserving Technique for Brain Tumor Classification;low Cost Emergency Communication System for Disaster Affected Areas;a Slot-Integrated based Partial Ground and Tapered Patch Antenna for Satellite Communications;a Review on Wireless Power Transfer Systems;application of Big Data to Traffic Generated in Mechanisms Containment on Optical Burst Switching Distributed Networks;a Real-Time Arm-Worn Sensor-based Human Fall Alert Notification Mode

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Improving Machine Learning Algorithm’s Accuracy for Detecting Code Smell Using Data Balancing and Parameter Optimization 5th

Improving Machine Learning Algorithm’s Accuracy for Detecti...

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5th International conference on Computing, Communications, and Cyber Security, IC4S 2023

作者： Nandini, Abhishilpa Singh, Randeep Rathee, Amit Department of Computer Science and Engineering IEC University Solan India Department of Computer Science GC Barota Haryana Sonipat India

ISBN: (纸本)9789819773701

Detecting code smells is vital for improving software quality, improving its maintainability, and reducing software failures and faults. Code smell detection using machine learning-based methods has become more prevalent in recent years, as they easily overcome the limitations of heuristic-based methods that are based mostly on rules that must be manually created. Data imbalance and parameter optimization are found to be directly affecting performance of underlying machine learning models. However, to the best of our knowledge about literature of the field, little experimental evaluation is carried out in the direction of impact of data balancing and parameter tuning on machine learning models. Therefore, this paper considers it important to propose a new data balancing and parameter optimization technique. Further, the proposed approach is experimentally validated by considering standard code smell datasets. The empirical evaluation considers four code smells taken from two categories (two at method level and rest of the two are considered at class level) and studied four machine learning models, viz., Decision Tree, Random Forest, kNN, and SVM. The obtained experimental results justify the suitability of proposed data balancing and parameter optimization techniques in improving performance of machine learning models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Foundations of Practice: An Ethnographic Exploration of software Documentation and its Cultural Implications for an Agile Startup

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Ethnographic Praxis in Industry conference Proceedings 2025年第1期2024卷 378-396页

作者： NICOLE TIETZ-SOKOLSKAYA SUZANNE WALSH Principal Engineer Remesh Research Consultant Remesh

This case study offers an in-depth analysis of the foundational role of documentation for software quality, efficiency, and organizational success. It includes an inside look how a start-up overcame inefficiencies and scaling challenges by implementing a sociotechnical documentation practice, and 10 recommendations for implementation or assessment based the case study data and a wider analysis of research and expertise. Time pressures of agile workflows that prioritize writing code, coupled with rapidly scaling software and startups, create an environment where documentation often takes a back seat. But its value to organizations and engineering teams as a source of knowledge and a practice of knowledge sharing becomes clear when critical inefficiencies and even chaos to prompt an overhaul of norms and behaviors. The experience at Remesh demonstrates the value of an ethnographic approach to gain a holistic understanding of engineering and work practices and to implement positive change.

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KGHeartBeat: An Open Source Tool for Periodically Evaluating the Quality of knowledge Graphs 23rd

KGHeartBeat: An Open Source Tool for Periodically Evaluati...

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23rd International Semantic Web conference, ISWC 2024

作者： Pellegrino, Maria Angela Rula, Anisa Tuozzo, Gabriele Dipartimento di Informatica Unviersità degli Studi di Salerno SA Fisciano Italy Department of Information Engineering University of Brescia Brescia Italy

ISBN: (纸本)9783031778469

knowledge Graphs are an extraordinary source of data due to their vastness, the topics heterogeneity and the presence of sources curated by companies, research groups, volunteers, and dedicated communities. Identifying high-quality knowledge Graphs requires supporting developers and end-users in comparing and assessing data quality of publicly available knowledge Graphs. However, no fully working and maintained knowledge Graph quality assessment tool was found during the review of related research. This article fully describes KGHeartBeat, a community shared open-source knowledge graph quality assessment tool designed to periodically perform quality analysis on a wide range of freely available knowledge graphs registered on the LOD Cloud and DataHub. Users can either visually explore the quality assessment report and compare knowledge graphs via a freely available web-based interface or download data analysis results for further analysis. Moreover, KGHeartBeat is also released as APIs so developers can easily integrate them into any quality management tool. As a proof of concept, we discuss different use cases to show KGHeartBeat in practice, demonstrating how it can be used to compare multiple knowledge Graphs, assess quality dimensions over time, and report performance analysis in terms of execution time. Resource type Community Shared software Framework License MIT Web-app http://***:12280/kgheartbeat Permanent URL https://***/records/10990547 Pypi package https://***/project/kgheartbeat © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： knowledge graph

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Conversational Symptom Checker Chatbot for Disease Prediction and Suggesting Nearby Hospitals using Machine Learning and Location Services 3

Conversational Symptom Checker Chatbot for Disease Predictio...

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3rd International conference on Electronics and Renewable Systems, ICEARS 2025

作者： Burle, Rushikesh Gundewar, Swapnil Gaurkhede, Sanskruti Fulkar, Bhushan Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Mahartashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Mahartashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Mahartashtra Wardha442001 India

ISBN: (纸本)9798331509675

A chatbot, sometimes known as a conversational agent, is software that uses natural language to interact with people. Travelling to and from a hospital for treatment takes a lot of time for people with hectic schedules, which makes many of them disregard their health issues. People frequently postpone going to the hospital for small problems that might develop into serious illnesses. For these people, interacting with chatbots to get helpful medical information is a cost-effective, time-efficient, and convenient option. This paper suggests a framework for a machine learning-based chatbot that combines natural language, machine learning, and statistical analysis. gaining knowledge of the technologies in a Spark cluster to help create an intelligent chatbot. In this work, we conducted a thorough analysis of current literature, looking at a large number of chatbot-related papers over the last five years. Next, we introduced many related tasks to our chatbot, which uses user symptoms to predict diseases and, depending on the user's location, recommends hospitals in the area. Users will be able to easily engage with the chatbot using the system's online interface. © 2025 IEEE.

关键词： Chatbots

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Minimizing Inconsistency in Pairwise Comparison Matrices Using Genetic Algorithm 59

Minimizing Inconsistency in Pairwise Comparison Matrices Usi...

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59th Annual conference on Information Sciences and Systems, CISS 2025

作者： Sayadi, Atiyeh Shajari, Baran Janicki, Ryszard McMaster University Department of Computing and Software Hamilton Canada

ISBN: (纸本)9798331513269

This paper introduces a novel approach to reduce inconsistencies in pairwise comparison matrices using a genetic algorithm inspired by the process of natural selection. The method applies a distance-based inconsistency index as the fitness function within an evolutionary process. Through experiments, we show that our genetic algorithm greatly reduces inconsistencies over generations. Smaller matrices quickly become consistent in just a few generations, showing the method's effectiveness and efficiency for different scenarios and use cases. Larger matrices, however, require more generations to reach the acceptable consistency level. The algorithm works well for different matrix sizes, adapting effectively to various challenging situations. © 2025 IEEE.

关键词： Genetic algorithms

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Axiomatic software Architecture

Axiomatic Software Architecture

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IEEE International conference on software Architecture Workshops (ICSAW)

作者： Aydin Homay Mario de Sousa Chair of Industrial Communications Technische Universität Dresden Dresden Germany Faculty of Engineering University of Porto Porto Portugal

ISBN: (数字)9798331533366

ISBN: (纸本)9798331533373

In his March 2009 resume, David L. Parnas noted that the phrase “software engineering” was coined to urge software designers to adopt the knowledge and discipline of engineers in other domains, allowing them to rely on well-developed problem-solving skills, analytical methods, mathematics, and scientific knowledge instead of relying exclusively on intuition and natural ability. We attempt to work towards that goal by reintroducing axiomatic design into software engineering, addressing major concerns and critiques highlighted by previous research in the field. The axiomatic design offers a scientific approach to improve design activities based on rational and logical thought processes and tools. The ultimate goal of axiomatic design is to encourage solution-neutral thinking to identify problems and requirements, fostering human designer's creativity, reducing random search process, and minimizing random trial-and-error to determine the best design among those proposed.

关键词： knowledge engineering Design engineering software design software architecture Computer architecture software systems Search problems Mathematics Problem-solving Creativity

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MDE in the Era of Generative AI 17th

MDE in the Era of Generative AI

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17th International conference on Verification and Evaluation of Computer and Communication Systems, VECoS 2024

作者： Alaoui Mdaghri, Ahmed Ouederni, Meriem Chaari, Lotfi University of Nantes Nantes France Toulouse INP University of Toulouse IRIT Toulouse France

ISBN: (纸本)9783031853555

Domain-Specific Languages (DSLs) play a vital role in software development, enabling the concise expression of domain-specific concepts and requirements. In this study, we propose a novel approach leveraging Large Language Models (LLMs) to assist the DSLs modelling starting from natural language description. Our solution is a proof of concept where Model Driven engineering (MDE) is revisited taking advantage from the power of generative AI. Starting from human friendly description and domain modelling language document type, LLM-based system extracts relevant domain knowledge and builds the corresponding DSL model. Such a model is then validated through an iterative process. We applied our proposal to several case studies from different application domains including software engineering, healthcare, and finance. Furthermore, we consider a wide range of existing LLMs usually adapted for code generation. We also study the effectiveness of our solution through multi-criteria experimental evaluation. Lastly, the results demonstrate the feasibility and efficiency of our LLM driven MDE for DSL development, and then advancing domain-specific modelling practices. By doing so, we would enable the developer to save time and effort for further tasks such as functional properties’ verification. A demo as well as a web application for our developed solution are available online via the following link https://***/demo-ecore-gen/. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： software design

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