Reviewerly/exHarmony huggingface dataset:
Studying How Digital Luthiers Choose Their Tools

Digital lutherie is a sub-domain of digital craft focused on creating digital musical instruments: high-performance devices for musical expression. It represents a nuanced and challenging area of human-computer interaction that is well established and mature, offering the opportunity to observe designers’ work on highly demanding human-computer interfaces. This paper explores how and why digital luthiers choose their tools and how these tools relate to the challenges they face. Findings from 27 standardised open-ended interviews with prominent digital luthiers from commercial, research, independent and artistic backgrounds are analysed through reflexive thematic analysis. Our discussion explores their perspectives, finding that a process of pragmatic rationalisation and environmental influences play a significant role in tool selection. We also present how challenges faced by digital luthiers relate to social creativity and meta-design. These findings build upon the existing literature that examines the designer-tool relationship.
A graph signal processing‐based multiple model <scp>Kalman</scp> filter (<scp>GSP‐MMKF</scp>) tool for predictive analytics: An air separation unit process application

Abstract The industrial Air Separations Unit (ASU) is a complicated and tightly operated process. The use of dynamic process analytics is also a key element of safe and economic operation of these processes, with increasing focus on predictive analytics to take preemptive actions. With the availability of real‐time data from hundreds of sensors, the data analysis process should also consider the topology of the data, as seen in sensor networks. In this paper, a novel tool is presented that considers the complex connectivity patterns in the sensor network and uses local adaptive disturbance estimations to predict global network‐scale trends. The paper introduces the emerging field of Graph Signal Processing (GSP) and presents a rigorous derivation of the tool starting from the extraction of the sensor‐network (in a graph theoretical sense) from the data. This network, which is in the form of a matrix, is then used to derive a Kalman‐filter type of state‐space model driven by input disturbances. Multiple disturbance models (e.g., step, ramp, periodic) are included to allow the model to have different kinds of disturbance propagation. Each graph node (representing the sensors used) dynamically adapts to the most recent detected disturbance individually. These estimated disturbances are propagated to the global network using the graph. Modifications to ensure stability are also discussed. The fidelity of the tool is tested on certain downtime events and the paper concludes by discussing the advantages of the method and planned future improvements.
Does Dynamically Drawn Text Improve Learning? Investigating the Effect of Text Presentation Styles in Video Learning

Dynamically drawn content (e.g., handwritten text) in learning videos is believed to improve users’ engagement and learning over static powerpoint-based ones. However, evidence from existing literature is inconclusive. With the emergence of Optical Head-Mounted Displays (OHMDs), recent work has shown that video learning can be adapted for on-the-go scenarios. To better understand the role of dynamic drawing, we decoupled dynamically drawn text into two factors (font style and motion of appearance) and studied their impact on learning performance under two usage scenarios (while seated with desktop and walking with OHMD). We found that although letter-traced text was more engaging for some users, most preferred learning with typeface text that displayed the entire word at once and achieved better recall (46.7% higher), regardless of the usage scenarios. Insights learned from the studies can better inform designers on how to present text in videos for ubiquitous access.
Shape-Consistent Generative Adversarial Networks for Multi-Modal Medical Segmentation Maps

Image translation across domains for unpaired datasets has gained interest and great improvement lately. In medical imaging, there are multiple imaging modalities, with very different characteristics. Our goal is to use cross-modality adaptation between CT and MRI whole cardiac scans for semantic segmentation. We present a segmentation network using synthesised cardiac volumes for extremely limited datasets. Our solution is based on a 3D cross-modality generative adversarial network to share information between modalities and generate synthesized data using unpaired datasets. Our network utilizes semantic segmentation to improve generator shape consistency, thus creating more realistic synthesised volumes to be used when re-training the segmentation network. We show that improved segmentation can be achieved on small datasets when using spatial augmentations to improve a generative adversarial network. These augmentations improve the generator capabilities, thus enhancing the performance of the Segmentor. Using only 16 CT and 16 MRI cardiovascular volumes, improved results are shown over other segmentation methods while using the suggested architecture. Our code is publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Investigating Culturally Responsive Design for Menstrual Tracking and Sharing Practices Among Individuals with Minimal Sexual Education

Human-Computer Interaction (HCI) research on menstrual tracking has emphasized the need for more inclusive design of mechanisms for tracking and sharing information on menstruation. We investigate menstrual tracking and data-sharing attitudes and practices in educated, young (20-30 years old) menstruating individuals based in the United States, with self-identified minimal menstrual education backgrounds. Using interviews (N=18), a survey (N=62), and participatory design (N=7), we find that existing mechanisms for tracking and sharing data on menstruation are not adequately responsive to the needs of those who seek relevant menstrual education, are not in the sexual majority, and/or wish to customize what menstrual data they share and with whom. Our analysis highlights a design gap for participants with minimal sexual education backgrounds who wish to better understand their cycles. We also contribute a deepened understanding of structural health inequities that impact menstrual tracking and sharing practices, making recommendations for technology-mediated menstrual care.
Designing for Inaccessible Emergency Medical Service Contexts: Development and Evaluation of the Contextual Secondary Video Toolkit

Designing technology for emergency medical services (EMS) can be difficult, for example, due to limited access to domain experts. To support designers who aim to engage in a participatory design process in EMS environments, we created and evaluated the Contextual Secondary Video Toolkit (CSVT). This method uses secondary video material and design cards that allow domain experts to identify and prioritise challenges in their work environment and generate design ideas that address them. We illustrate the effects of the CSVT on design processes by analysing four workshops during which aeromedical EMS staff explored the potential of augmented reality to support their work. Our results indicate that the CSVT can support reflection about work practices, aid the generation of design ideas, and facilitate genuine participation. Furthermore, our data indicates that the use of secondary video in design projects is appropriate and even has certain advantages compared to primary field video.
From Chairs To Brains: Customizing Optical Flow For Surgical Activity Localization

Recent approaches for surgical activity localization rely on motion features derived from the optical flow (OF). However, although they consider state-of-the-art CNNs when computing the OF, they typically resort to pre-trained implementations which are domain-unaware. We address this problem in two ways: (i) Using the pre-trained OF-CNN of recent localization approach, we analyze the impact of video properties such as reflections, motion and blur on the quality of the OF from neurosurgical data. (ii) Based on this analysis, we design a specifically tailored synthetic training dataset which allows us to customize the pre-trained OF-CNN for surgical activity localization. Our evaluation clearly shows the benefit of this customization approach. It not only leads to an improved accuracy of the OF itself but, even more importantly, also to an improved performance for the actual localization task.
Deep Hierarchical Multiple Instance Learning for Whole Slide Image Classification

Whole slide scanning is a powerful tool in clinical diagnosis and pathological research. However, it’s time-consuming to acquire localized annotations in whole slide images (WSIs). Recently, deep multiple instance learning (MIL) approaches were proposed to classify WSIs with only global annotations. Two main challenges, interpretability and utilizing multiple-scale information, remain to be solved in these approaches. In this study, we proposed a deep hierarchical multiple in-stance learning model to tackle these challenges. We introduced max-max ranking loss to better leverage the standard MIL assumption for better interpretability. A hierarchical architecture was designed to reduce computational costs and to utilize multiple-scale information. Our model was evaluated in a large WSI dataset CAMELYON16 with accuracy and AUC as metrics. Experimental results showed that our model achieved the best performance.
How and why we run

Running offers a convenient and affordable method of keeping fit and maintaining good cardiovascular health. In this paper, we describe the experiences of a group of runners whose practices are rarely profiled: individuals who identify as legally-blind, some of whom run competitively, while others run for purposes of leisure. We specifically focus on planning strategies undertaken, experiences running both with and without sighted guides, the impact of situational and environmental factors, and the ways in which technology is utilized. The study has revealed a set of insights which if capitalized upon could offer considerable promise to support independent running. These include examining ways to reduce levels of uncertainty faced by runners, supporting autonomy, providing greater levels of environmental awareness, and aiding socialization when running. Findings from the work offer promise to interface designers to improve inclusiveness when developing technologies to support runners.
Actor-Critic for Multi-Agent Reinforcement Learning with Self-Attention

The rapid development of deep reinforcement learning makes it widely used in multi-agent environments to solve the multi-agent cooperation problem. However, due to the instability of multi-agent environments, the performance is insufficient when using deep reinforcement learning algorithms to train each agent independently. In this work, we use the framework of centralized training with decentralized execution to extend the maximum entropy deep reinforcement learning algorithm Soft Actor-Critic (SAC) and proposes the multi-agent deep reinforcement learning algorithm MASAC based on the maximum entropy framework. Proposed model treats all the agents as part of the environment, it can effectively solve the problem of poor convergence of algorithms due to environmental instability. At the same time, we have noticed the shortcoming of centralized training, using all the information of the agents as input of critics, and it is easy to lose the information related to the current agent. Inspired by the application of self-attention mechanism in machine translation, we use the self-attention mechanism to improve the critic and propose the ATT-MASAC algorithm. Each agent can discover their relationship with other agents through encoder operation and attention calculation as part of the critic networks. Compared with the recent multi-agent deep reinforcement learning algorithms, ATT-MASAC has better convergence effect. Also, it has better stability when the number of agents in the environment increases.
Coupling Deep Deformable Registration with Contextual Refinement for Semi-Supervised Medical Image Segmentation

Image segmentation is an important prerequisite of computer-aided diagnosis which has been applied in a wide range of clinical applications. Current learning-based methods mostly rely on sufficient annotated datasets which is expensive and time-consuming. In this study, we develop a semi-supervised learning paradigm integrating contextual refinement into deformable registration-based segmentation processes. By introducing deformable atlas prior, our method is capable to segment the image with no well-defined relation between regions and pixels intensities. A contextual refinement segmentation network is appended to further constrain unreasonable results. Inheriting from the merits of both prior knowledge and deep representation, our approach achieves a more satisfying performance than the state-of-the-art methods qualitatively and quantitatively on multiple medical image datasets.
Comparative Analysis of Prediction on Solar Radiation in Energy Generation System using Random Forest and Decision Tree

The solar radiation estimation is very important for developing and design of solar energy production system in generation of non-renewable energy. But the data set of Global solar radiation is not easily obtainable in all places of India due to some technical issues and cost in measurement technologies. Consequently it is important to forecasting the solar radiation prediction using some techniques by input parameters namely Time, Radiation, Temperature, Pressure, Humidity, Wind Direction, Speed, Time Sun rise and Time sun set. In this paper the author focused on analyzing the solar radiation prediction using Random Forest technique. This analysis gives more clear knowledge in prediction performance using machine learning algorithms.
Designing Interfaces to Display Sensor Data: A Case Study in the Human-Building Interaction Field Targeting a University Community

The increase of smart buildings with Building Information Modeling (BIM) and Building Management Systems (BMS) has created a large amount of data, including those coming from sensors. These data are intended for monitoring the building conditions by authorized personnel, not being available to all building occupants. In this paper, we evaluate, from a qualitative point of view, if a user interface designed for a specific community can increase occupants' context-awareness about environmental issues within a building, supporting them to make more informed decisions that best suit their needs. We designed a user interface addressed to the student community of a smart campus, adopting an Iterative Design Cycle methodology, and engaged 48 students by means of structured interviews with the aim of collecting their feedback and conducting a qualitative analysis. The results obtained show the interest of this community in having access to information about the environmental data within smart campus buildings. For example, students were more interested in data about temperature and brightness, rather than humidity. As a further result of this study, we have extrapolated a series of design recommendations to support the creation of map-based user interfaces that we found to be effective in such contexts.
Outside Where? A Survey of Climates and Built Environments in Studies of HCI outdoors

We found significant gaps in the climates and built environments used as settings for studies of HCI outdoors. The experience of using a computer outdoors varies widely depending on location-specific factors such as weather and the availability of electricity. We surveyed 699 papers from CHI venues and found 101 studies involving a person and a computer interacting outdoors for which we could determine the study location. We categorized each study location by climate using the Köppen-Geiger scheme and by built environment using the Recreation Opportunity Spectrum. 91 of 101 studies took place in temperate or continental climates and 82 took place in urban settings. Emerging understanding of the ongoing impacts of climate change increases the importance of investigating HCI outdoors in a wider range of weather conditions. While some primitive natural settings have been preserved against development at great cost, we found no studies of HCI outdoors in those settings.
Rating Ease of Readability using Transformers

Understanding and rating text complexity accurately can have a considerable impact on learning and education. In the past few decades, educators used traditional readability formulas to match texts with the readability level of students. This tends to oversimplify the different dimensions of text difficulty. Presently, transformer-based-language models have brought field of Natural Language Processing to a new era by understanding the text in a better way and achieving great success on many tasks. In this study, we assess the effectiveness of different pre-trained transformers on rating ease of readability. We propose a model built on top of the pre-trained Roberta transformer with weighted pooling, which uses multiple hidden states information effectively to do this task more accurately. Our experiments are done on a Dataset of English excerpts annotated by language experts which is extracted from Kaggle. On this Dataset, Our proposed model achieved 71% improvement over the traditional Flesch formula and a significant boost over other Transformer models and Long Short Term Memory(LSTM).
'She's Just My Life': Digital Design to Support Women's Self-Other Care in Relationships with their Mothers

Recent HCI research has suggested a move from individualistic models of digital care and wellbeing to considering the family unit as a locus of support in this area; however, little work has examined the complex, granular everyday experience of such relationships, and the role of gender, class, and care is underexplored. This study focuses on women's familial relationships through interviews with 6 Irish women about their relationships with their mothers, as well as ways in which they maintain the care of themselves and others within these relationships. Our thematic analysis of this data generated four themes: self-other care, leaky boundaries, changes over the lifecourse, and space and conflict – from which we ideated a series of design concepts, six of which are presented here with critiques from our participants. From this exploratory work, we delineate four directions for future HCI research into women's close relationships.
Web Application based Event Organisation Portal using MEAN Stack

With the tremendous use of the internet nowadays makes web development more demanding and also makes challenging at the same time for developers. These challenges have been overcome by the latest technologies. One of these technologies is the MEAN stack. This paper examines the usage of MEAN stack components (MongoDB, Express Js, Angular and NodeJs) and develops a web application. Prominently this have presented end-to-end use of MEAN stack components in a web application that is from implementation to its working. MEAN stack with its efficient features provides fast, scalable and notable results for developing systematic mobile and web application. Using MEAN stack, portal “Event Organisation” is created as an institute level portal to maintain connection between students and institute.
Supporting the Design of Smart Speakers to Foster a Sense of Ownership in Asian Indian Families

Smart speakers are increasingly being adopted by families in the U.S. In many cases, a smart speaker is shared by family members, which might make the sense of ownership uncertain. Through a diary and interview-based study with 20 Asian Indian parents and teenagers living in the U.S., this study through thematic analysis highlights various aspects of smart speakers that support or hinder the fulfilment of the psychological needs – self-efficacy, self-identity, territoriality, autonomy, and accountability and responsibility – that foster a sense of ownership. The paper also discusses the experiences of a cultural group that is not well represented in prior HCI work, and thus, it will add useful nuances and knowledge about inclusivity to the study of smart-speaker technology in HCI. Finally, it contributes six actionable design guidelines, including guidelines that relate to maintaining conversational context across different interactions and fostering a sense of ownership among users who share smart speakers, through the use of territorial markers.
Aware: Intuitive Device Activation Using Prosody for Natural Voice Interactions

Voice interactive devices often use keyword spotting for device activation. However, this approach suffers from misrecognition of keywords and can respond to keywords not intended for calling the device (e.g., ”You can ask Alexa about it.”), causing accidental device activations. We propose a method that leverages prosodic features to differentiate calling/not-calling voices (F1 score: 0.869), allowing devices to respond only when called upon to avoid misactivation. As a proof of concept, we built a prototype smart speaker called Aware that allows users to control the device activation by speaking the keyword in specific prosody patterns. These patterns are chosen to represent people’s natural calling/not-calling voices, which are uncovered in a study to collect such voices and investigate their prosodic difference. A user study comparing Aware with Amazon Echo shows Aware can activate more correctly (F1 score 0.93 vs. 0.56) and is easy to learn and use.
Vocabulary Taught Via Mobile Application Gamification: Receptive, Productive and Long-Term Usability of Words Taught Using Quizlet and Quizlet Live

Much of the research into Quizlet to date looks at Quizlet only in a holistic or general way to determine its impact on vocabulary acquisition. This paper addresses the need for a more detailed look into exactly what impact Quizlet has on vocabulary learning. It looks at three things specificall:; the effect of vocabulary deployed via Quizlet on: 1) receptive vocabulary retention, 2) productive vocabulary use, and 3) the long-term retention and ability to use vocabulary learned via Quizlet. The study investigates the implementation of Quizlet gamified features within a basic-level English second language course. What was found is that Quizlet has a significant impact on receptive vocabulary knowledge, but a much lesser impact on productive vocabulary and long-term vocabulary retention.
One Week in the Future: Previs Design Futuring for HCI Research

We explore the use of cinematic "pre-visualization" (previs) techniques as a rapid ideation and design futuring method for human computer interaction (HCI) research. Previs approaches, which are widely used in animation and film production, use digital design tools to create medium-fidelity videos that capture richer interaction, motion, and context than sketches or static illustrations. When used as a design futuring method, previs can facilitate rapid, iterative discussions that reveal tensions, challenges, and opportunities for new research. We performed eight one-week design futuring sprints, in which individual HCI researchers collaborated with a lead designer to produce concept sketches, storyboards, and videos that examined future applications of their research. From these experiences, we identify recurring themes and challenges and present a One Week Futuring Workbook that other researchers can use to guide their own futuring sprints. We also highlight how variations of our approach could support other speculative design practices.