arXiv subjects

Josh Andres

Publications and source records attributed to Josh Andres.

7 recordsLinked to original sources

arXiv2024

Tonal Cognition in Sonification: Exploring the Needs of Practitioners in Sonic Interaction Design

Research into tonal music examines the structural relationships among sounds and how they align with our auditory perception. The exploration of integrating tonal cognition into sonic interaction design, particularly for practitioners lacking extensive musical knowledge, and developing an accessible software tool, remains limited. We report on a study of designers to understand the sound creation practices of industry experts and explore how infusing tonal music principles into a sound design tool can better support their craft and enhance the sonic experiences they create. Our study collected qualitative data through semi-structured individual and focus group interviews with six participants. We developed a low-fidelity prototype sound design tool that involves practical methods of functional harmony and interaction design discussed in focus groups. We identified four themes through reflexive thematic analysis: decision-making, domain knowledge and terminology, collaboration, and contexts in sound creation. Finally, we discussed design considerations for an accessible sonic interaction design tool that aligns auditory experience more closely with tonal cognition.

cs.HC↗

arXiv2024

On the Design and Study of an Installation for Office Workers to Amplify Temporal Diversity and Connection to Nature

We present the design and user study of an installation for office workers, enabling moments of temporal diversity and connection to nature. The installation is a form of creative computing experience that departs from the traditional focus on office technologies for productivity. Drawing on neuroscience insights and the slowing effect of nature sounds on time perception, we created an immersive, slow interaction, generative AI installation that composes an audiovisual space - serving as a perceptual portal into temporal realms beyond the linear rhythm of the office. Our study investigates the lived experiences of 18 office workers, gathered via explicitation interviews, observational notes, and video recordings, analysed through an inductive thematic analysis. Key findings highlight the ephemeral qualities in creative computing experiences using generative AI, its potential to foster contemplative practices, amplify ecological temporalities, and reshape office workers' engagement with their environment. Our design and user study offer research and practical implications for utilising creative computing to enrich office experiences.

cs.HC↗

arXiv2024

Understanding and Shaping Human-Technology Assemblages in the Age of Generative AI

Generative AI capabilities are rapidly transforming how we perceive, interact with, and relate to machines. This one-day workshop invites HCI researchers, designers, and practitioners to imaginatively inhabit and explore the possible futures that might emerge from humans combining generative AI capabilities into everyday technologies at massive scale. Workshop participants will craft stories, visualisations, and prototypes through scenario-based design to investigate these possible futures, resulting in the production of an open-annotated scenario library and a journal or interactions article to disseminate the findings. We aim to gather the DIS community knowledge to explore, understand and shape the relations this new interaction paradigm is forging between humans, their technologies and the environment in safe, sustainable, enriching, and responsible ways.

cs.HC↗

arXiv2023

Fused Spectatorship: Designing Bodily Experiences Where Spectators Become Players

Spectating digital games can be exciting. However, due to its vicarious nature, spectators often wish to engage in the gameplay beyond just watching and cheering. To blur the boundaries between spectators and players, we propose a novel approach called "Fused Spectatorship", where spectators watch their hands play games by loaning bodily control to a computational Electrical Muscle Stimulation (EMS) system. To showcase this concept, we designed three games where spectators loan control over both their hands to the EMS system and watch them play these competitive and collaborative games. A study with 12 participants suggested that participants could not distinguish if they were watching their hands play, or if they were playing the games themselves. We used our results to articulate four spectator experience themes and four fused spectator types, the behaviours they elicited and offer one design consideration to support each of these behaviours. We also discuss the ethical design considerations of our approach to help game designers create future fused spectatorship experiences.

cs.HC↗

arXiv2021

AutoDS: Towards Human-Centered Automation of Data Science

Data science (DS) projects often follow a lifecycle that consists of laborious tasks for data scientists and domain experts (e.g., data exploration, model training, etc.). Only till recently, machine learning(ML) researchers have developed promising automation techniques to aid data workers in these tasks. This paper introduces AutoDS, an automated machine learning (AutoML) system that aims to leverage the latest ML automation techniques to support data science projects. Data workers only need to upload their dataset, then the system can automatically suggest ML configurations, preprocess data, select algorithm, and train the model. These suggestions are presented to the user via a web-based graphical user interface and a notebook-based programming user interface. We studied AutoDS with 30 professional data scientists, where one group used AutoDS, and the other did not, to complete a data science project. As expected, AutoDS improves productivity; Yet surprisingly, we find that the models produced by the AutoDS group have higher quality and less errors, but lower human confidence scores. We reflect on the findings by presenting design implications for incorporating automation techniques into human work in the data science lifecycle.

cs.HC↗

arXiv2019

AutoAIViz: Opening the Blackbox of Automated Artificial Intelligence with Conditional Parallel Coordinates

Artificial Intelligence (AI) can now automate the algorithm selection, feature engineering, and hyperparameter tuning steps in a machine learning workflow. Commonly known as AutoML or AutoAI, these technologies aim to relieve data scientists from the tedious manual work. However, today's AutoAI systems often present only limited to no information about the process of how they select and generate model results. Thus, users often do not understand the process, neither do they trust the outputs. In this short paper, we provide a first user evaluation by 10 data scientists of an experimental system, AutoAIViz, that aims to visualize AutoAI's model generation process. We find that the proposed system helps users to complete the data science tasks, and increases their understanding, toward the goal of increasing trust in the AutoAI system.

cs.LG↗

arXiv2019

Towards a Predictive Patent Analytics and Evaluation Platform

The importance of patents is well recognised across many regions of the world. Many patent mining systems have been proposed, but with limited predictive capabilities. In this demo, we showcase how predictive algorithms leveraging the state-of-the-art machine learning and deep learning techniques can be used to improve understanding of patents for inventors, patent evaluators, and business analysts alike. Our demo video is available at http://ibm.biz/ecml2019-demo-patent-analytics

cs.DL↗