Need-driven decision-making and prototyping for DLT: Framework and
web-based tool
- URL: http://arxiv.org/abs/2307.09188v1
- Date: Tue, 18 Jul 2023 12:19:47 GMT
- Title: Need-driven decision-making and prototyping for DLT: Framework and
web-based tool
- Authors: Tomas Bueno Mom\v{c}ilovi\'c, Matthias Buchinger, Dian Balta
- Abstract summary: Multiple groups attempted to disentangle the technology from the associated hype and controversy.
We develop a holistic analytical framework and open-source web tool for making evidence-based decisions.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In its 14 years, distributed ledger technology has attracted increasing
attention, investments, enthusiasm, and user base. However, ongoing doubts
about its usefulness and recent losses of trust in prominent cryptocurrencies
have fueled deeply skeptical assessments. Multiple groups attempted to
disentangle the technology from the associated hype and controversy by building
workflows for rapid prototyping and informed decision-making, but their mostly
isolated work leaves users only with fewer unclarities. To bridge the gaps
between these contributions, we develop a holistic analytical framework and
open-source web tool for making evidence-based decisions. Consisting of three
stages - evaluation, elicitation, and design - the framework relies on input
from the users' domain knowledge, maps their choices, and provides an output of
needed technology bundles. We apply it to an example clinical use case to
clarify the directions of our contribution charts for prototyping, hopefully
driving the conversation towards ways to enhance further tools and approaches.
Related papers
- A Survey on Personalized Content Synthesis with Diffusion Models [57.01364199734464]
PCS aims to customize the subject of interest to specific user-defined prompts.
Over the past two years, more than 150 methods have been proposed.
This paper offers a comprehensive survey of PCS, with a particular focus on the diffusion models.
arXiv Detail & Related papers (2024-05-09T04:36:04Z) - The AffectToolbox: Affect Analysis for Everyone [10.526991118781913]
AffectToolbox is a novel software system that aims to support researchers in developing affect-sensitive studies and prototypes.
The proposed system addresses the challenges posed by existing frameworks, which often require profound programming knowledge and cater primarily to power-users or skilled developers.
The architecture encompasses a variety of models for emotion recognition on multiple affective channels and modalities, as well as an elaborate fusion system to merge multi-modal assessments into a unified result.
arXiv Detail & Related papers (2024-02-23T08:55:47Z) - Charting a Path to Efficient Onboarding: The Role of Software
Visualization [49.1574468325115]
The present study aims to explore the familiarity of managers, leaders, and developers with software visualization tools.
This approach incorporated quantitative and qualitative analyses of data collected from practitioners using questionnaires and semi-structured interviews.
arXiv Detail & Related papers (2024-01-17T21:30:45Z) - Designing Explainable Predictive Machine Learning Artifacts: Methodology
and Practical Demonstration [0.0]
Decision-makers from companies across various industries are still largely reluctant to employ applications based on modern machine learning algorithms.
We ascribe this issue to the widely held view on advanced machine learning algorithms as "black boxes"
We develop a methodology which unifies methodological knowledge from design science research and predictive analytics with state-of-the-art approaches to explainable artificial intelligence.
arXiv Detail & Related papers (2023-06-20T15:11:26Z) - Human-centered trust framework: An HCI perspective [1.6344851071810074]
The rationale of this work is based on the current user trust discourse of Artificial Intelligence (AI)
We propose a framework to guide non-experts to unlock the full potential of user trust in AI design.
arXiv Detail & Related papers (2023-05-05T06:15:32Z) - Let's Go to the Alien Zoo: Introducing an Experimental Framework to
Study Usability of Counterfactual Explanations for Machine Learning [6.883906273999368]
Counterfactual explanations (CFEs) have gained traction as a psychologically grounded approach to generate post-hoc explanations.
We introduce the Alien Zoo, an engaging, web-based and game-inspired experimental framework.
As a proof of concept, we demonstrate the practical efficacy and feasibility of this approach in a user study.
arXiv Detail & Related papers (2022-05-06T17:57:05Z) - Distributed intelligence on the Edge-to-Cloud Continuum: A systematic
literature review [62.997667081978825]
This review aims at providing a comprehensive vision of the main state-of-the-art libraries and frameworks for machine learning and data analytics available today.
The main simulation, emulation, deployment systems, and testbeds for experimental research on the Edge-to-Cloud Continuum available today are also surveyed.
arXiv Detail & Related papers (2022-04-29T08:06:05Z) - Personalized multi-faceted trust modeling to determine trust links in
social media and its potential for misinformation management [61.88858330222619]
We present an approach for predicting trust links between peers in social media.
We propose a data-driven multi-faceted trust modeling which incorporates many distinct features for a comprehensive analysis.
Illustrated in a trust-aware item recommendation task, we evaluate the proposed framework in the context of a large Yelp dataset.
arXiv Detail & Related papers (2021-11-11T19:40:51Z) - AI Explainability 360: Impact and Design [120.95633114160688]
In 2019, we created AI Explainability 360 (Arya et al. 2020), an open source software toolkit featuring ten diverse and state-of-the-art explainability methods.
This paper examines the impact of the toolkit with several case studies, statistics, and community feedback.
The paper also describes the flexible design of the toolkit, examples of its use, and the significant educational material and documentation available to its users.
arXiv Detail & Related papers (2021-09-24T19:17:09Z) - HyMap: eliciting hypotheses in early-stage software startups using
cognitive mapping [10.60958748634425]
We aim to develop a technique to identify hypotheses for early-stage software startups.
We developed the HyMap, a hypotheses elicitation technique based on cognitive mapping.
arXiv Detail & Related papers (2021-02-18T17:29:47Z) - Incentive Mechanism Design for Resource Sharing in Collaborative Edge
Learning [106.51930957941433]
In 5G and Beyond networks, Artificial Intelligence applications are expected to be increasingly ubiquitous.
This necessitates a paradigm shift from the current cloud-centric model training approach to the Edge Computing based collaborative learning scheme known as edge learning.
arXiv Detail & Related papers (2020-05-31T12:45:06Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.