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How CanWe Develop Explainable Systems? Insights from a Literature Review and an Interview Study 16

How CanWe Develop Explainable Systems? Insights from a Liter...

引用

IEEE/ACM Joint 16th International conference on software and System Processes / 17th ACM/IEEE International conference on Global software engineering (ICSSP/ICGSE)

作者： Chazette, Larissa Kluender, Jil Balci, Merve Schneider, Kurt Leibniz Univ Hannover Software Engn Grp Hannover Germany

ISBN: (纸本)9781450396745

Quality aspects such as ethics, fairness, and transparency have been proven to be essential for trustworthy software systems. Explainability has been identified not only as a means to achieve all these three aspects in systems, but also as a way to foster users' sentiments of trust. Despite this, research has only marginally focused on the activities and practices to develop explainable systems. To close this gap, we recommend six core activities and associated practices for the development of explainable systems based on the results of a literature review and an interview study. First, we identified and summarized activities and corresponding practices in the literature. To complement these findings, we conducted interviews with 19 industry professionals who provided recommendations for the development process of explainable systems and reviewed the activities and practices based on their expertise and knowledge. We compared and combined the findings of the interviews and the literature review to recommend the activities and assess their applicability in industry. Our findings demonstrate that the activities and practices are not only feasible, but can also be integrated in different development processes.

关键词： explainability explainable systems software process interview study

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Deep Padding and Alignment Strategies for Irregular Multivariate Clinical Time Series 28th

Deep Padding and Alignment Strategies for Irregular Multivar...

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28th International conference on knowledge based and Intelligent information and engineering Systems, KES 2024

作者： Bignoumba, Nzamba Yahia, Sadok Ben Mellouli, Nedra Department of Software Science Tallinn University of Technology Akadeemia tee 15a Tallinn12618 Estonia ESILV Léonrd de Vinci Group Paris la Défense France Centre for Industrial Software University of Southern Denmark Alsion 2 Sønderborg6400- Denmark LIASD Université Paris 8 Paris France

To improve the accuracy of an RNN when processing sparse and irregular multivariate clinical time series, we introduce two stacked deep learning models built on top of it, namely Padd-GRU and Alignment-driven Neural Network (ALNN). The Padd-GRU performs data-driven padding and imputation to obtain equal-length univariate and fill-in missing values, respectively. Then, the ALNN component transforms the resulting padded irregular multivariate clinical time series into a pseudo-aligned (or pseudo-regular) latent multivariate time series. We use the MIMIC-3 and PhysioNet databases to evaluate and compare our model to the state-of-the-art models on the mortality prediction task. © 2024 The Authors.

关键词： Time series

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MDE in the Era of Generative AI 17th

MDE in the Era of Generative AI

引用

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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Voice Pathology Detection System Using Machine Learning based on Internet of Things 15

Voice Pathology Detection System Using Machine Learning Base...

引用

15th International conference on software, knowledge, Information Management and Applications, SKIMA 2023

作者： Hakim Bin Sham, Akmal Mu'azzah Abdul Latiff, Nurul Taha Al-Dhief, Fahad Fariesya Suhaila Md Sazihan, Nurul Rahman, Sanzida Aishah Muhammad, Nor Universiti Teknologi Malaysia Faculty of Electrical Engineering Johor Malaysia

ISBN: (纸本)9798350316551

Due to the rising incidence of voice abnormalities in the general population, voice pathology detection is an area of study that is fast expanding and receiving a lot of interest. The current method of diagnosing voice pathology is time-consuming and relies on expert practitioners' subjective evaluations. As a result, an automatic and effective method of voice pathology identification is becoming more and more necessary. By creating an Internet of Things (IoT) based speech pathology detection system based on Mel-Frequency Cepstral Coefficients (MFCC) and Support Vector Machines (SVM) algorithms, this research intends to fill this demand. In this study, the voice signals are taken from the German Saarbrucken Voice Database (SVD). The vowel /a/ was selected for this work due to its superior outcomes. This method presents a quick and easy-to-use web-based system approach that can be used by both healthcare professionals and the general public. In addition, this paper has revealed the bright future of IoT in the field of voice pathology identification. © 2023 IEEE.

关键词： Support vector machines

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Global Decision Making Support for Complex System Development 32

Global Decision Making Support for Complex System Developmen...

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32nd IEEE International Requirements engineering conference (RE)

作者： Burgueno, Lola Foures, Damien Combemale, Benoit Kienzle, Jorg Mussbacher, Gunter Univ Malaga ITIS Software Malaga Spain Airbus Grp DDMS Toulouse France Univ Rennes ESIR Rennes France Univ Rennes IRISA Rennes France McGill Univ Sch Comp Sci Montreal PQ Canada McGill Univ Dept Elect & Comp Engn Montreal PQ Canada INRIA Rennes France

ISBN: (纸本)9798350395129;9798350395112

To succeed with the development of modern and complex systems (e.g., aircrafts or production systems), organizations must have the agility to adapt faster to constantly evolving requirements in order to deliver more reliable and optimized solutions that can be adapted to the needs and environments of their stakeholders including users, customers, suppliers, and partners. However, stakeholders do not have sufficiently explicit and systematic support for global decision making, considering the vast decision space and complex inter-relationships. This decision space is characterized by increasing yet inadequately represented variability and the uncertainty of the impact of decisions on stakeholders and the solution space. This leads to an ad-hoc decision making process that is slow, error-prone, and often favors local knowledge over global, organization-wide objectives. As a result, one team's design decisions may impose too restrictive requirements on another team. In this paper, we evaluate our understanding of global decision making in the context of complex system development based on a conceptual model which explicitly represents and manages decision spaces including variability and impacts. We have conducted our evaluation by means of an exploratory case study where we interviewed domain experts with an average of 20 years of experience in complex system industries and report the key findings and remaining challenges. In the future, we aim at providing explicit and systematic tool-supported approaches for global decision making support for complex systems.

关键词： Global Decision Making Multi-Stakeholder Variability Impact Requirements Design

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Few-shot Semantic Segmentation via Perceptual Attention and Spatial Control 24

Few-shot Semantic Segmentation via Perceptual Attention and ...

引用

32nd ACM International conference on Multimedia, MM 2024

作者： Shi, Guangchen Zhu, Wei Wu, Yirui Zhao, Danhuai Zheng, Kang Lu, Tong National Key Lab for Novel Software Technology Nanjing University Nanjing China China Mobile Zijin Innovation Insititute Nanjing China College of Computer Science and Software Engineering Hohai University Nanjing China

ISBN: (纸本)9798400706868

Few-shot semantic segmentation (FSS) aims to locate pixels of unseen classes with clues from a few labeled samples. Recently, thanks to profound prior knowledge, diffusion models have been expanded to achieve FSS tasks. However, due to probabilistic noising and denoising processes, it is difficult for them to maintain spatial relationships between inputs and outputs, leading to inaccurate segmentation masks. To address this issue, we propose a Diffusion-based Segmentation network (DiffSeg), which decouples probabilistic denoising and segmentation processes. Specifically, DiffSeg leverages attention maps extracted from a pretrained diffusion model as support-query interaction information to guide segmentation, which mitigates the impact of probabilistic processes while benefiting from rich prior knowledge of diffusion models. In the segmentation stage, we present a Perceptual Attention Module (PAM), where two cross-attention mechanisms capture semantic information of support-query interaction and spatial information produced by the pretrained diffusion model. Furthermore, a self-attention mechanism within PAM ensures a balanced dependence for segmentation, thus preventing inconsistencies between the aforementioned semantic and spatial information. Additionally, considering the uncertainty inherent in the generation process of diffusion models, we equip DiffSeg with a Spatial Control Module (SCM), which models spatial structural information of query images to control boundaries of attention maps, thus aligning the spatial location between knowledge representation and query images. Experiments on PASCAL-5i and COCO datasets show that DiffSeg achieves new state-of-the-art performance with remarkable advantages. © 2024 ACM.

关键词： Semantic Segmentation

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International conference on Artificial Intelligence and Industrial Design, AIID 2022

International Conference on Artificial Intelligence and Indu...

引用

2022 International conference on Artificial Intelligence and Industrial Design, AIID 2022

ISBN: (纸本)9781510663893

The proceedings contain 47 papers. The topics discussed include: prediction of steering wheel angle at night based on CNN;research on fiber material related experience innovation in the perspective of smart textile installation design;design of game software for children with autism spectrum disorder based on augmented reality;research on interaction design of permission application platform based on scene theory;the design of multi-function inflatable blanket for emergency rescue on the basis of AHP/QFD;intelligent extrusion die design based on knowledge graph;a grief farewell scene design for children based on virtual reality;research on immersive experience of college cultural image under digital media design;simulation of gas filling in GM tube based on Garfield++ software;and research on human-computer interaction framework model design of smart home terminal from the perspective of information ecology.

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Speech-to-text Transfiguration in Language Numerals for Perpetual Deaf Patients 2

Speech-to-text Transfiguration in Language Numerals for Perp...

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2nd International conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2023

作者： Pradeep Kumar, B.P. Venkatesan, C. Suhas, A.R. Balamurugan, D. Sathiyapriya, V. HKBK College of Engineering Department of Electronics and Communication Engineering Bangalore India Sona College of Technology Department of Computer Science and Engineering Salem India Knowledge Institute of Technology Department of Computer Science and Engineering Salem India

ISBN: (纸本)9798350397635

This paper primarily designates the isolated words transfiguration in the Kannada language based on a few techniques like Mel Frequency Cepstral Coefficients (MFCCs), Euclidean distance measure, Wavelet Transforms and Vector Quantization (VQ) technique. The contiguous match to the test speech exclamation is found by comparing the Euclidean distance value of the MFCC coefficients with that of the utterances present in the training set. The matched sampled word is detected by the display of its text equivalent message trained during the test phase. MATLAB R2019b software is employed to build the framework. © 2023 IEEE.

关键词： Wavelet transforms

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knowledge Graph based Medical Chatbot building 4

Knowledge Graph Based Medical Chatbot building

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4th IEEE Global conference for Advancement in Technology, GCAT 2023

作者： Bhuvanesh Shathyan, R. Farida Begam, M. Jashwanth, K. Jayaprakash, Anirudh Department of Information Science and Engineering CMR Institute of Technology Bengaluru India

ISBN: (纸本)9798350305258

To have a good and unproblematic life without any health risks, it is very important to get medical advice on any health-related problems. However, getting medical advice incurs costs. Chatbots are AI/ML based software which may be trained with a lot of inquiries and responses and match users' inquiries against a large repository of evidence-based medical data to provide simple answers. To reduce the healthcare costs and improve accessibility of medical knowledge a medical chatbot can be built using ML and NLP techniques. In the existing system of such chatbots several databases are connected together using join statements making it more complicated to access the data. The medical knowledge is vast and varied hence giving many disadvantages to use fixed schema. In this paper we have proposed a knowledge graph based method of chatbot creation for the healthcare field. When the patient enters the symptoms, they are suffering from, then chatbot evaluates and based on the evaluation recognizes the disease. The basic idea of our work is to build a chatbot which can evaluate the symptoms and using this evaluation, rank the possible disease which the patient could be suffering from. The chatbot gets its knowledge from a knowledge graph built on an extensive TigerGraph database. The data from the TigerGraph database can be accessed using different analysis queries. The chatbot will be considered profitable only when it can diagnose all kinds of disease and provides the necessary advice measures to be taken. The knowledge graph used here can be improvised by including results from lab tests to arrive at a better diagnosis and increase the precautions by including the medicines to be taken. The chatbot can only be as smart as the knowledge graph database and hence the evaluations made must be checked with the medical professional. The chatbot has good accuracy in predicting the disease the user is suffering from. © 2023 IEEE.

关键词： knowledge Graph Random Forest Classifier Rasa TigerGraph

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An Integrated software engineering Curriculum Through Project-based Learning (PBL) 129

An Integrated Software Engineering Curriculum Through Projec...

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129th ASEE Annual conference and Exposition: Excellence Through Diversity, ASEE 2022

作者： Afshar, Yalda Moshirpour, Mohammad Marasco, Emily Ann Kawash, Jalal Behjat, Laleh Moussavi, Mahmood Department of Electrical and Software Engineering University of Calgary Canada Department of Computer Science University of Calgary Canada

software development life cycle is an integrated process which involves many different tasks including requirements elicitation and analysis, design and development, testing, and deployment. As a result, software engineering students do not only need an in-depth knowledge of each of these areas, they also need to have a practical understanding about how the topics integrate together. However, technical courses that teach concepts such as object-oriented programming, architecture, testing and databases are traditionally delivered as separate courses despite interconnections and overlap. Although the current approach has the benefit of encouraging proficiency in a specific subject, students often lack the knowledge of how these topics may integrate together. In this paper, we propose a pedagogical approach to integrate final projects of two separate courses in a course-based masters of software engineering program. In this approach, students work on a joint project for software design and architecture as well as a database design course using a model-first approach. Students implement the object-oriented programming phase in the first course, then they continue implementing a corresponding database in the second course. The study data is collected from an end-of-term survey and final projects' artifacts. Through analyzing the data, we evaluate the students' challenges as well as perception using this approach. The results from our experiment show students' satisfaction of the overall learning from the joint project. © American Society for engineering Education, 2022.

关键词： Students

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