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Digital transformation is now reaching into topics like End-of-life Care, Funeral Culture, and Coping with Grief. Those developments are inevitably accompanied by the growing challenge to design IT systems that are appropriate and helpful for the stakeholders involved. Our aim in this paper is to further introduce the rather new combined research field of Socioinformatics and Thanatology (the scientific study of death and dying) and to present it with the first results on which requirements to consider for the design of digital tools within ‘Thanatopractice’. By using Participatory Design and the Sustainability Awareness Framework (SusAF) in the context of three workshops on socio-technical systems (Online Pastoral Care, Virtual Graveyards, and AI Memory Avatars), we want to sensitize software practitioners to the multidimensional impacts of their products and services in a field, which the participants in the workshops often described as “highly sensitive”.
XAI for Semantic Dependency : How to understand the impact of higher-level concepts on AI results
(2023)
For many practitioners, considering sustainability during a software development project is a challenge. The Sustainability Awareness Framework (SusAF) is a tool for thinking through short, medium-and long-term impacts of socio-technical systems on its surrounding environment. While SusAF has been used by several companies, is not widely adopted in industry yet. In this Vision Paper, we discuss the options for extending the reach of SusAF and what it would take to evolve SusAF into a (de-facto) standard
Data scientists, researchers and engineers want to understand, whether machine learning models for object detection work accurate and precise. Networks like Yolo use bounding boxes as a result to localize the object in the image.
The principal aim of this paper is to address the problem of a lack of an effective metric for evaluating the results of bounding box regression in object detection networks when boxes do not overlap or lie completely within each other.
The standard known metrics, like IoU, lack of differentiating results, which do not overlap but differ in the distance between predicted bounding box and label.
To solve this challenge, we propose a new metric called UIoU (Unified Intersection over Union) that combines the best properties of existing metrics (IoU, GIoU and DIoU) and extends them with a similarity factor. By assigning weight to each component of the metric, it allows for a clear differentiation between the three possible cases of box positions (not overlapping, overlapping, boxes inside each other).
The result of this paper is a new metric that outperforms the existing metrics such as IoU, GIoU and DIoU by providing a more understandable measure of the performance of object detection models. This provides researchers and users in the field of explainable AI with a metric that allows the evaluation and comparison of prediction and label bounding boxes in an understandable way.
As industrial networks continue to expand and connect more devices and users, they face growing security challenges such as unauthorized access and data breaches. This paper delves into the crucial role of security and trust in industrial networks and how trust management systems (TMS) can mitigate malicious access to these networks.
The TMS presented in this paper leverages distributed ledger technology (blockchain) to evaluate the trustworthiness of blockchain nodes, including devices and users, and make access decisions accordingly. While this approach is applicable to blockchain, it can also be extended to other areas. This approach can help prevent malicious actors from penetrating industrial networks and causing harm. The paper also presents the results of a simulation to demonstrate the behavior of the TMS and provide insights into its effectiveness.
Transcultural Student Research on SDGs - A Higher Education Project for Sustainable Development
(2023)
In this paper, we present a study on the utilization of smart medical wearables and the user manuals of such devices. A total of 342 individuals provided input for 18 questions that address user behavior in the investigated context and the connections between various assessments and preferences. The presented work clusters individuals based on their professional relation to user manuals and analyzes the obtained results separately for these groups.
Year after year, software engineers celebrate new achievements in the field of AI. At the same time, the question about the impacts of AI on society remains insufficiently answered in terms of a comprehensive technology assessment. This article aims to provide software practitioners with a theoretically grounded and practically tested approach that enables an initial understanding of the potential multidimensional impacts. Subsequently, the results form the basis for discussions on AI software requirements. The approach is based on the Sustainability Awareness Framework (SusAF) and Participatory Design. We conducted three workshops on different AI topics: 1. Autonomous Driving, 2. Music Composition, and 3. Memory Avatars. Based on the results of the workshops we conclude that a two-level approach should be adopted: First, a broad one that includes a diverse selection of stakeholders and overall impact analysis. Then, in a second step, specific approaches narrowing down the stakeholders and focusing on one or few impact areas.
The charge response of a force applied to piezoelectric stack actuators was characterized in the range of 0 N – 20 N for application in piezoelectric self-sensing. Results show linear behavior between ap-plied force and collected charge for both actuators tested in this study. One actuator exhibits a 3.55 times higher sensitivity slope than the other related to its larger capacitance. An error analysis reveals a reduction of relative error in charge measurement with rising forces applied to the actuators.