Extending the Participant’s Voice to Guide Artificial Intelligence Transformation Using Futures Methodology and Layered User Story Analysis

2021 ◽  
pp. 194675672110303
Author(s):  
Elissa Farrow
Keyword(s):  
Artificial Intelligence ◽  
User Requirements ◽  
Participatory Process ◽  
Agile Method ◽  
Analysis Process ◽  
It Impacts ◽  
User Stories ◽  
User Story ◽  
Causal Layered Analysis ◽  
Learning And Adaptation

Artificial intelligence (AI) is a vital driver of the next wave of automatisation of Industry 4.0. It impacts product-based and service-based organisations and is becoming an investment stream in organisational transformation strategies. The transformation teams that deploy AI use agile incremental methodologies that ideally match the learning and adaptation requirements for the machine, as well as the human user who is the source of data and requires a service response. This research outlines a layered analysis process of 65 user stories (a common agile method of obtaining user requirements) generated via a participatory process, involving 110 participants in three workshop settings, in what they determined AI would not do. The results outline the workshop approach undertaken to generate user stories and the analysis of user stories via persona and futures methodology causal layered analysis (Inayatullah S. 1998. Causal layered analysis. Futures 30(8): 815-829). The final component of the analysis generated a futures focussed set of guiding principles that can be used as a lens to broaden the transformation teams perspective in AI deployment. Concepts also consider the importance of futures literacy as a key competency of AI creation teams.

scholarly journals On the question of a method for researching user requirements for software

2021 ◽  
pp. 73-79
Author(s):  
Юлия Юрьевна Липко ◽  
Джульетта Абугалиевна Крымшокалова ◽  
Залина Асланбековна Шогенова ◽  
Джабар Аскерович Лигидов
Keyword(s):  
Software Development ◽  
Automated Analysis ◽  
The Other ◽  
User Requirements ◽  
Systematic Analysis ◽  
Other Hand ◽  
User Stories ◽  
User Story ◽  
Basic Concepts ◽  
Generative Approach

Методы User Story все чаще используются в качестве основы артефактов проектирования требований при разработке программного обеспечения. На практике доказано, что метод User Story является более эффективным для описания основных целей системы. Но непрерывное управление работой программного обеспечения может быть особенно трудоемким и подверженным ошибкам, особенно при оценке качества или объема пользовательских историй и наблюдении за общей картиной системы. С другой стороны, эти модели были признаны эффективными инструментами коммуникации и анализа цели. В рамках данной работы рассмотрены и проанализированы методы выявления и представления требований к разработке программного обеспечения. В статье предлагается генеративный подход для создания диаграмм надежности на основе автоматизированного анализа пользовательских историй. Истории преобразуются в диаграммы, что позволяет разработчикам требований и пользователям проверять основные концепции и функциональные этапы, лежащие в основе историй, и обнаруживать искаженные или избыточные истории. Такие модели также открывают двери для автоматизированного систематического анализа. The User Story methods (user stories) increasingly are used as the basis of requirements design artifacts in software development. In practice, it is proved that the User Story method is more effective for describing the main goals of the system. But continuous management of software operation can be particularly time-consuming and error-prone, especially when evaluating the quality or volume of user stories and observing the overall picture of the system. On the other hand, these models were recognized as effective tools for communication and goal analysis. Within the framework of this work, methods for identifying and presenting requirements for software development are considered and analyzed. In the article, we propose a generative approach for creating reliability diagrams based on automated analysis of user stories. Stories are converted into diagrams, which allow requirements developers and users to check the basic concepts and functional stages underlying the stories, and detect distorted or redundant stories. Such models also open the door for automated systematic analysis.

Assembly FMEA: A Simplified Method for Identifying Assembly Errors

2003 ◽  
Author(s):  
Steven Kmenta ◽  
Brent Cheldelin ◽  
Kosuke Ishii
Keyword(s):  
Artificial Intelligence ◽  
Failure Modes ◽  
Electronic Circuits ◽  
Manual Assembly ◽  
Manufacturing Defects ◽  
Novel Technique ◽  
Analysis Process ◽  
Simplified Method ◽  
Process Failure ◽  
Assembly Defect

Manual assembly errors are a significant source of manufacturing defects. Therefore, an efficient method is needed to identify and alleviate potential assembly defects. Process Failure Modes and Effects Analysis (Process FMEA) is one technique used to anticipate, evaluate, and resolve potential manufacturing and assembly issues. However, performing FMEA is widely considered to be tedious and time-consuming, and not always worth the effort. In response, many researchers have attempted to automate FMEA using Artificial Intelligence (AI) to make it less arduous. Unfortunately, automated techniques are limited to systems with predictable behaviors (e.g., electronic circuits) and are rarely used on unpredictable processes such as manual assembly. “Assembly FMEA” is a novel technique developed specifically to identify manual assembly errors. Assembly defect levels are related to assembly complexity, which can be estimated using “Design for Assembly” (DFA) time penalties. Hence, Assembly FMEA uses a series of DFA-related questions to elicit potential assembly defects. The questions help to focus, standardize, and expedite the FMEA process. Assembly FMEA quickly identifies a large number of assembly errors with significantly less effort than conventional FMEA. This paper describes the Assembly FMEA procedure and illustrates its use on a conceptual design and on an existing product.

scholarly journals The Phenomenon of Competitive Human Resources and How It Impacts Business Growth

2021 ◽  
Author(s):  
Arvin Winatha
Keyword(s):  
Artificial Intelligence ◽  
Information Technology ◽  
Human Resources ◽  
Industrial Revolution ◽  
Business Organizations ◽  
Business Growth ◽  
Global Era ◽  
It Impacts ◽  
The World ◽  
Fourth Wave

Increasingly tight business competition map of industry has been the main focus for everyone in the world, especially in the industry we call it as the Industry era 4.0 . The awareness of this competiton has made many business organizations in the world, including Indonesia busy preparing themselves, particularly those related to the development of human resources, to be ready to compete in this global era. The Fourth wave of industrial revolution is marked by the use of information technology, artificial intelligence, and automatic engines or vehicles that have been going on since years before.

Artificial intelligence: a new educational challenge

2021 ◽  
Vol 605 (10) ◽  
pp. 28-40
Author(s):  
Sergo Kuruliszwili
Keyword(s):  
Artificial Intelligence ◽  
Educational System ◽  
Rapid Development ◽  
Educational Context ◽  
Educational Content ◽  
It Impacts ◽  
Educational Challenge

Rapid development of Artificial Intelligence is influencing most of the human’s domains. It impacts our reality in quantitative and qualitative way. This situation is challenging, also for the educational system – in many aspects. Analysis of this situation in the educational context is important and urgent matter. In the article author is attempting to explain and to structure the problem, pointing out, both chances and the threats, of the phenomenon, focusing on the area of educational content the measures.

A new approach for association rules mining using computational and artificial intelligence

2020 ◽  
Vol 39 (5) ◽  
pp. 7233-7246
Author(s):  
Fahed Yoseph ◽  
Markku Heikkilä
Keyword(s):  
Artificial Intelligence ◽  
Association Rules ◽  
Market Basket Analysis ◽  
Holistic View ◽  
Association Rules Mining ◽  
Point Of Sale ◽  
Market Basket ◽  
Market Intelligence ◽  
Product Assortment ◽  
Analysis Process

Market Intelligence is knowledge extracted from numerous data sources, both internal and external, to provide a holistic view of the market and to support decision-making. Association Rules Mining provides powerful data mining techniques for identifying associations and co-occurrences in large databases. Market Basket Analysis (MBA) uses ARM to gain insights from heterogeneous consumer shopping patterns and examines the effects of marketing initiatives. As Artificial Intelligence (AI) more and more finds its way to marketing, it entails fundamental changes in the skills-set required by marketers. For MBA, AI provides important ways to improve both the outcomes of the market basket analysis and the performance of the analysis process. In this study we demonstrate the effects of AI on MBA by our proposed new MBA model where results of computational intelligence are used in data preprocessing, in market segmentation and in finding market trends. We show with point-of-sale (POS) data of a small, local retailer that our proposed “Åbo algorithm” MBA model increases mining performance/intelligence and extract important marketing insights to assess both demand dynamics and product popularity trends. Additionally, the results show how, as related to the 80/20 percent rule, 78% of revenue is derived 16% of the product assortment.

scholarly journals Malaysian Medical Device Regulation for Artificial Intelligence in Healthcare: Have all the pieces fallen into position?

2021 ◽  
Vol 6 (16) ◽  
pp. 137-144
Author(s):  
Nurus Sakinatul Fikriah Mohd Shith Putera ◽  
Sarah Munirah Abdullah ◽  
Noraiza Abdul Rahman ◽  
Rafizah Abu Hassan ◽  
Hartini Saripan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Medical Device ◽  
Medical Devices ◽  
Publishing House ◽  
Legal Reasoning ◽  
Healthcare Applications ◽  
Artificial Intelligence And Law ◽  
Learning And Adaptation ◽  
Research Findings ◽  
Medical Device Regulation

Artificial Intelligence (AI) ability of self-learning and adaptation has challenged the medical device regulation in overseeing the safety and effectiveness of medical devices. Thus, this research aims to evaluate the adequacy of the pre-market requirements under the Medical Device Act 2012 in governing AI modification. Employing the doctrinal research methodology, systematic means of legal reasoning pertinent to AI for healthcare applications are produced. An effective medical device regulation is pivotal to foster trustworthiness in the governance and adoption of AI. However, the research findings indicate the deficiency of the current conformity assessment for medical devices in addressing AI modifications. Keywords: Artificial Intelligence and Law, Artificial Intelligence and Medical Device Regulation, Malaysian Medical Device Regulation eISSN: 2398-4287© 2021. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BYNC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians/Africans/Arabians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia. DOI: https://doi.org/10.21834/ebpj.v6i16.2635

scholarly journals Requirement Analysis for the DiSSCo Research Infrastructure

2019 ◽  
Vol 3 ◽  
Author(s):  
Niels Raes ◽  
Emily van Egmond ◽  
Wouter Addink ◽  
Alex Hardisty
Keyword(s):  
Climate Change ◽  
Technical Support ◽  
Research Infrastructure ◽  
Digital Data ◽  
Flow Diagram ◽  
Collection Management ◽  
Requirement Analysis ◽  
User Stories ◽  
User Story ◽  
The Impact

DiSSCo – the Distributed System of Scientific Collections – will mobilise, unify and deliver bio- and geo-diversity information at the scale, form and precision required by scientific communities, and thereby transform a fragmented landscape into a coherent and responsive research infrastructure. At present DiSSCo has 115 partners from 21 countries across Europe. The DiSSCo research infrastructure will enable critical new insights from integrated digital data to address some of the world's greatest challenges, such as biodiversity loss, food security and impacts of climate change. A requirement analysis for DiSSCo was conducted to ensure that all of its envisioned future uses are accommodated through a large survey using epic user stories. An epic user story has the following format: As [e.g. scientist] I want to [e.g. map the distribution of a species through time] so that I [e.g. analyse the impact of climate change] for this I need [e.g. all georeferenced specimens records through time] Several consultation rounds within the ICEDIG community resulted in 78 unique user stories that were assigned to one, or more, out of seven recognized stakeholder categories: Research, Collection management, Technical support, Policy, Education, Industry, and External. Research, Collection management, Technical support, Policy, Education, Industry, and External. Each user story was assessed for the level of collection detail it required; four levels of detail were recognised: Collection, Taxonomic, Storage unit, and Specimen level. Furthermore, it was assessed whether the future envisioned use of digitised natural history collections were possible without the DiSSCo research infrastructure. Subsequently 1243 identified stakeholders were invited to review the DiSSCo user stories through a Survey Monkey questionnaire. Additionally, an invitation for review was posted in several Facebook groups and announced on Twitter. A total of 379 stakeholders responded to the invitation, which led to 85 additional user stories for the envisioned use of the DiSSCo research infrastructure. In order to assess which component of the DiSSCo data flow diagram should facilitate the described user story, all user stories were mapped to the five phases of the DiSSCo Data Management Cycle (DMC), including data: acquisition, curation, publishing, processing, and use. acquisition, curation, publishing, processing, and use. At present, the user stories are being analysed and the results will be presented in this symposium.

scholarly journals Expert System Diagnosis of Television Damage with Depth First Search Method Using Vb.Net Programming Language

Tech-E ◽  
2018 ◽  
Vol 1 (2) ◽  
pp. 50
Author(s):  
Dicky Surya Dwi Putra
Keyword(s):  
Artificial Intelligence ◽  
Expert System ◽  
Expert Systems ◽  
Knowledge Base ◽  
Programming Language ◽  
System Analysis ◽  
Search Method ◽  
Depth First Search ◽  
Analysis Process ◽  
System Diagnosis

The expert system is part of an artificial intelligence consisting of the knowledge and experience of an expert who is included in the knowledge base. The expert system can help someone who is still lay in solving the problem. Television is a medium of communication that receives broadcasts of moving pictures and sounds. One example of an expert system created is an expert system to diagnose television damage using the Depth First Search method using the VB.NET programming language. With the application of expert systems in diagnosing television damage, expected in the analysis process becomes faster. Expert system analysis on the damage of television can be known directly to assist in knowing what damage experienced by television and what steps to improve television. In this case, the author has consulted with an expert in the field of television damage to build a knowledge base 

Requirements Specification using User Stories

2011 ◽  
pp. 71-89 ◽  
Author(s):  
V. Monochristou ◽  
M. Vlachopoulou
Keyword(s):  
Software Development ◽  
Interaction Design ◽  
System Development ◽  
Requirements Specification ◽  
Software Requirements ◽  
User Requirements ◽  
Agile Methodologies ◽  
User Stories ◽  
Software Requirements Specification ◽  
Waterfall Model

Collecting and analyzing user requirements is undoubtedly a really complicated and often problematic process in software development projects. There are several approaches, which suggest ways of managing user’s requirements; some of the most well-known are IEEE 830 software requirements specification (SRS), use cases, interaction design scenarios, etc. Many software experts believe the real user requirements emerge during the development phase. By constantly viewing functional sub-systems of the whole system and participating, in fact, in all phases of system development, customers/users can revise their requirements by adding, deleting, or modifying them. However, in order for this to become possible, it is important to adopt a totally different approach than the traditional one (waterfall model approach), concerning not only the management of user’s requirements, but also the entire software development process in general. Agile methodologies represent this different approach since the iterative and incremental way of development they propose includes user requirements revision mechanisms and user active participation throughout the development of the system. The most famous approach concerning requirements specification among the supporters of the agile methodologies is probably user stories. User stories and their main characteristics are thoroughly demonstrated in this chapter. After reading this chapter, the authors hope that the reader may have gained all the basic understanding regarding the use of user stories.

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