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Turning Large Language Models into AI Assistants for Startups Using Prompt Patterns

Turning Large Language Models into AI Assistants for Startup...

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24th International Conference on Agile Software Development (XP)

作者： Wang, Xiaofeng Attal, Mohammad Idris Rafiq, Usman Hubner-Benz, Sylvia Free Univ Bozen Bolzano I-39100 Bolzano Italy

ISBN: (纸本)9783031485497;9783031485503

Most startups operate with limited resources and experience. AI technologies enable them to accomplish many tasks under these constraints. The recent advance of large language models (LLMs) offers new opportunities to support startup endeavors. Given the nascent nature of LLMs, how they could be utilized to support startups is yet to be investigated. Since prompt engineering is believed to be at the core of the effective use of LLMs, we aim to understand how to apply prompt engineering to turn LLMs into AI assistants for startups. As the first step, we investigated the application of a set of prompt patterns to ChatGPT, arguably the most widely known LLM currently. The preliminary results show that some patterns are more suitable for brainstorming which is a typical activity conducted by early-stage startups. Prompt-tuned questions may lead to more specific and more detailed responses, but it is not guaranteed. Meantime, human factors play an important role in the effective application of prompt patterns. Large-size and systematic studies are needed to apply the right patterns to different questions, taking into account the differences among startups in terms of their startup knowledge, domain knowledge, and their attitudes and behaviors towards LLMs.

关键词： Startups AI Assistant Large Language Models Prompt Engineering Prompt Pattern

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A New Data-Driven Modelling Framework for Moisture Content Prediction in Continuous Pharmaceutical Tablet Manufacturing 11th

A New Data-Driven Modelling Framework for Moisture Content P...

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11th International Conference on Industrial Engineering and Applications - Europe (ICIEA - Europe)

作者： Deebes, Motaz Mahfouf, Mahdi Omar, Chalak Univ Sheffield Dept Automat & Control Syst Engn Sheffield England Univ Sheffield Dept Multidisciplinary Engn Educ Sheffield England

ISBN: (纸本)9783031581120;9783031581137

Continuous manufacturing of pharmaceutical tablets integrates multiple unit operations such as twin screw granulation and fluidized bed drying to transform powder into final dosage form such as tablets. However, complex process interactions can lead to variability in critical quality attributes including moisture content of the produced granules. This study presents an innovative multi-stage modelling framework to predict granule moisture content based on the twin screw granulator and the fluidized bed dryer process parameters. Machine learning techniques, including gradient boosting regression, and support vector regression were utilised to enhance predictive performance in ensemble method. Using data from a pilot-scale integrated continuous line, the stacking ensemble model achieved excellent accuracy with (R-2) of 91% for moisture content prediction. The Machine learning modelling framework demonstrates strong potential for advancing process knowledge, and optimization in continuous manufacturing of pharmaceutical tablets based on wet granultion.

关键词： Continuous Manufacturing Twin Screw Granulation Fluidized Bed Drying Machine Learning Pilot Plant Scale Predictive Modelling

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Enhancing Research Clarity: Ontology-Based Modeling of Argumentation in RPML 1

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36th International Conference on Advanced information Systems Engineering (CAiSE)

作者： Hinkelmann, Knut Afonina, Valeriia Montecchiari, Devid FHNW Univ Appl Sci & Arts Northwestern Switzerlan Olten Switzerland UNICAM Univ Camerino Sch Sci & Technol Camerino Italy Univ Pretoria Dept Informat Pretoria South Africa

ISBN: (数字)9783031610035

ISBN: (纸本)9783031610028;9783031610035

Navigating the research process, from problem identification to argumentation construction, challenges novice researchers. This study introduces RPML (Research Problem Modeling Language), a meta-model and ontology designed to address these challenges by visually representing key aspects of research argumentation. RPML enhances clarity and coherence in research discourse by providing researchers with a visual representation of argumentation for a research problem. RPML is represented as a specialization of the OMG business Motivation Model and Toulmin's argumentation model approach. This enables researchers to gain a comprehensive overview of their research projects, identify research problems, build robust argumentation, and select suitable research strategies.

关键词： Motivation Modeling Argumentation Modeling Research Design Ontology-Based Modeling Problem Identification Ontology Metamodel Ontology-Based Metamodeling

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Ontology Engineering of an IoT System for Monitoring Hypertension 25th

Ontology Engineering of an IoT System for Monitoring Hyperte...

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25th International Conference on Enterprise information Systems (ICEIS)

作者： de Souza, Pedro Lopes de Souza, Wanderley Lopes Pires, Luis Ferreira Moreira, Joao Luiz Rebelo Ciferri, Ricardo Rodrigues Univ Twente Fac Elect Engn Math & Comp Sci Semant Cybersecur & Serv Grp POB 217 Enschede Netherlands Univ Fed Sao Carlos Comp Deparment Grad Program Comp Sci Ubiquitous Comp Grp POB 676 Sao Carlos Brazil

ISBN: (纸本)9783031647543;9783031647550

Internet of Things (IoT), Ubiquitous Computing, and Cloud Computing technologies can be used to build telemedicine systems to support the Pervasive Healthcare's holistic vision of providing healthcare anywhere, anytime, to anyone. According to the World Health Organization (WHO), Non-communicable diseases (NCDs) are one of the greatest health challenges since they cause about 70% of deaths worldwide. For some NCDs, such as hypertension, diabetes, asthma and obesity, ICT technologies allow continuous follow-up of patients, enabling them and their health professionals to exercise more active control of these diseases. In this context, we developed the Ontology-Driven IoT System for Monitoring Hypertension (ODIoT-SMH), which employs the aforementioned technologies for monitoring hypertensive patients and was designed following a three-layer architecture: the Sensor Layer contains the Sensor Platform built in a wearable IoT device, which captures the patient's vital signals blood pressure, heart rate and body temperature;the Fog Layer contains the Mobile App application that runs on the patient's mobile device, where the clinical data are aggregated and modelled to be presented to the patient;and the Cloud Layer contains the API Server and Web App applications, where the clinical data are processed, managed, and archived to be viewed and analysed by health professionals. To deal with semantic interoperability issues that arise when many different devices and system components must interoperate, this system encompasses an ontology-based semantic model that was designed, implemented and deployed at the Cloud Layer. This paper focuses on the development of the ODIoT-SMH ontology and shows its applicability and benefits.

关键词： IoT Ontology engineering Healthcare

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Survey of AI Tool Usage in Programming Course: Early Observations

Survey of AI Tool Usage in Programming Course: Early Observa...

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24th International Conference on Agile Software Development (XP)

作者： Saari, Mika Rantanen, Petri Nurminen, Mikko Kilamo, Terhi Systa, Kari Abrahamsson, Pekka Tampere Univ Tampere Finland

ISBN: (纸本)9783031485497;9783031485503

This study explores the role of artificial intelligence (AI) in university teaching, with a focus on the teaching of programming. Despite the growing use of AI in education, both students and teachers often struggle to understand its role and implications. To address this gap, we conducted a survey of a wide group of students (n = 200) to assess their experiences and perspectives on the use of AI in programming education. The survey results suggest that AI is becoming increasingly involved in university teaching, particularly in the teaching of programming. However, students require guidance from teachers in order to use AI tools effectively in their studies. The study concludes that the goal of teaching programming should be to guide students in the responsible use of AI. Overall, the study highlights the need for greater awareness and understanding of AI in university teaching and the fact that teachers have an important role to play in providing guidance to students on the responsible use of AI tools.

关键词： AI Education Programming

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Grouping Local Process Models 5th

Grouping Local Process Models

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5th International Conference on Process Mining (ICPM)

作者： Peeva, Viki van der Aalst, Wil M. P. Rhein Westfal TH Aachen Chair Proc & Data Sci PADS Aachen Germany

ISBN: (纸本)9783031561061;9783031561078

In recent years, process mining emerged as a proven technology to analyze and improve operational processes. An expanding range of organizations using process mining in their daily operation brings a broader spectrum of processes to be analyzed. Some of these processes are highly unstructured, making it difficult for traditional process discovery approaches to discover a start-to-end model describing the entire process. Therefore, the subdiscipline of Local Process Model (LPM) discovery tries to build a set of LPMs, i.e., smaller models that explain sub-behaviors of the process. However, like other pattern mining approaches, LPM discovery algorithms also face the problems of model explosion and model repetition, i.e., the algorithms may create hundreds if not thousands of models, and subsets of them are close in structure or behavior. This work proposes a three-step pipeline for grouping similar LPMs using various process model similarity measures. We demonstrate the usefulness of grouping through a real-life case study, and analyze the impact of different measures, the gravity of repetition in the discovered LPMs, and how it improves after grouping on multiple real event logs.

关键词： Local process models Model grouping Model clustering Model similarity Process model comparison

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A Conceptual Model of Digital Immune System to Increase the Resilience of Technology Ecosystems 1

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18th International Conference on Research Challenges in information Sciences (RCIS)

作者： Krauze, Beate Grabis, Janis Riga Tech Univ 6A Kipsalas St LV-1048 Riga Latvia

ISBN: (数字)9783031594656

ISBN: (纸本)9783031594649;9783031594656

In the light of technological advancements, disruptions and regulatory changes, various guidelines and standards emphasize the need for technology resilience. However, they often lack explicit evaluation methods, leaving organizations to determine their own implementation and assessment approaches. This absence of specific guidance amplifies the challenges organizations face in ensuring business continuity when critical systems fail. To address this, a "digital immune system" is proposed - a holistic approach to safeguard digital assets and mitigate IT-related risks. Digital immune system integrates processes, analytics and technologies to strengthen IT architecture, business operations and incorporates assessments to evaluate technology ecosystem resilience. Despite the acknowledged need for technology resilience, existing frameworks fall short in providing practical evaluation methods for the digital immune system. This paper confronts this challenge by focusing on the interconnected components of technologies, data and processes, considering emerging threats and compliance requirements. The goal of the research is to design an assessment framework for the digital immune system and establish a Digital Immune System Maturity Model. The model offers a structured path for organizations to measure and improve their IT risk assessment, resilience and business continuity plans.

关键词： Digital Immune System Resilience business Continuity Planning IT Risk Management

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ML-Enabled Systems Model Deployment and Monitoring: Status Quo and Problems 16th

ML-Enabled Systems Model Deployment and Monitoring: Status Q...

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16th Software Quality Days Conference (SWQD)

作者： Zimelewicz, Eduardo Kalinowski, Marcos Mendez, Daniel Giray, Gorkem Alves, Antonio Pedro Santos Lavesson, Niklas Azevedo, Kelly Villamizar, Hugo Escovedo, Tatiana Lopes, Helio Biffl, Stefan Musil, Juergen Felderer, Michael Wagner, Stefan Baldassarre, Teresa Gorschek, Tony Pontif Catholic Univ Rio De Janeiro PUC Rio Rio De Janeiro Brazil Blekinge Inst Technol BTH Karlskrona Sweden Vienna Univ Technol TU Wien Vienna Austria German Aerosp Ctr DLR Cologne Germany Univ Cologne Cologne Germany Tech Univ Munich Munich Germany Univ Bari Bari Italy Fortiss GmbH Munich Germany

ISBN: (纸本)9783031562808;9783031562815

[Context] Systems that incorporate Machine Learning (ML) models, often referred to as ML-enabled systems, have become commonplace. However, empirical evidence on how ML-enabled systems are engineered in practice is still limited;this is especially true for activities surrounding ML model dissemination. [Goal] We investigate contemporary industrial practices and problems related to ML model dissemination, focusing on the model deployment and the monitoring ML life cycle phases. [Method] We conducted an international survey to gather practitioner insights on how ML-enabled systems are engineered. We gathered a total of 188 complete responses from 25 countries. We analyze the status quo and problems reported for the model deployment and monitoring phases. We analyzed contemporary practices using bootstrapping with confidence intervals and conducted qualitative analyses on the reported problems applying open and axial coding procedures. [Results] Practitioners perceive the model deployment and monitoring phases as relevant and difficult. With respect to model deployment, models are typically deployed as separate services, with limited adoption of MLOps principles. Reported problems include difficulties in designing the architecture of the infrastructure for production deployment and legacy application integration. Concerning model monitoring, many models in production are not monitored. The main monitored aspects are inputs, outputs, and decisions. Reported problems involve the absence of monitoring practices, the need to create custom monitoring tools, and the selection of suitable metrics. [Conclusion] Our results help provide a better understanding of the adopted practices and problems in practice and support guiding ML deployment and monitoring research in a problem-driven manner.

关键词： Machine Learning Deployment Monitoring

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Analysis of Safety and Cybersecurity Management Systems Interaction in Decision-Making Including Correlation for Automated Driving Systems 25th

Analysis of Safety and Cybersecurity Management Systems Inte...

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25th International Conference on Enterprise information Systems (ICEIS)

作者： Wagner, Florence Khatun, Marzana Jung, Rolf Glass, Michael Inst Driver Assistance & Connected Mobil D-87737 Benningen Germany Kempten Univ Appl Sci D-87435 Kempten Germany Ulm Univ D-89081 Ulm Germany

ISBN: (纸本)9783031647475;9783031647482

Safety Management Systems (SMS) and Cybersecurity Management Systems (CSMS) need to be investigated in a systematic manner to support the development and advances for Automated Driving Systems (ADS). Self-driving vehicles are accompanied by new and unknown risks which must be considered and managed by means of new and/or adapted management processes to ensure safe ADS that include functional safety and cybersecurity. Functional safety risks can influence cybersecurity and vice-versa. This leads to the statement that interactions between two management systems need to be implemented in organizations. In order to make this interaction process understandable and structured, this paper is identifying the relevant management factors for interaction of SMS and CSMS and analyzes their degree of influence. The main contributions of this paper are, (i) identification of the correlation between management factor interactions bases on the previously performed survey paper and additionally (ii) indication of cluster and pattern of management factors to support the organizational implementation and understanding of the interaction.

关键词： SMS CSMS ADS Spearman correlation

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Clinical Event Knowledge Graphs: Enriching Healthcare Event Data with Entities and Clinical Concepts - Research Paper 5th

Clinical Event Knowledge Graphs: Enriching Healthcare Event ...

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5th International Conference on Process Mining (ICPM)

作者： Aali, Milad Naeimaei Mannhardt, Felix Toussaint, Pieter Jelle Norwegian Univ Sci & Technol Trondheim Norway Eindhoven Univ Technol Eindhoven Netherlands

ISBN: (纸本)9783031561061;9783031561078

Clinical processes include admission, discharge, medication administration, diagnostic testing, and others. Process mining promises to provide insights for improving such processes. An issue in analyzing clinical processes is that recorded events concerning the treatment of patients are often not only related to the patients themselves, but also the event and activities terminology need to be interpretable globally in different health organizations. Specifically, in the case of multimorbidity, it is expected that clinical events recorded for a patient relate to multiple disorders and are linked to many different clinical concepts from various medical specialties. This hampers the application of process mining as extracting a single-entity event log gives an incomplete view of the patient trajectory, and relating events and activities to clinical terminology understood by medical professionals is complex. We propose to address these issues by building a clinical event knowledge graph that combines multi-entity event data from clinical systems with clearly defined clinical terms from coding systems such as ICD10-cm and a systematically organized collection of medical terms such as SNOMED CT. Our contribution is a framework that facilitates building such a clinical event knowledge graph. We evaluate the proposed framework by showing the feasibility of applying it to the MIMIC-IV dataset. We validated a set of process-related questions on multi-morbid patients with clinical experts. By leveraging the graph, these questions can be answered at the abstraction level of clinical terms. This may facilitate the involvement of medical professionals in the analysis, leading to the enhanced management of healthcare processes.

关键词： Healthcare Process Mining multi-entity event data clinical concepts

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