Reviewerly/exHarmony huggingface dataset:
Putting the Waz on Social Media: Infrastructuring Online Islamic Counterpublic through Digital Sermons in Bangladesh

While the presence of religious content is rapidly increasing over digital media, the HCI literature on digital media production has remained mostly limited by its focus on secular contents and analyses. Hence, the production, politics, and impact of such religious videos from the Global South have remained understudied in HCI. In this paper, we shed light on this topic through our nine-month-long ethnographic study on the production, sharing, and consumption of Islamic sermon videos (locally known as Waz) in Bangladesh. We report how faith, informal learning, local collaboration, creativity, and care play crucial roles in creating Islamic sermon videos and their proliferation online. We discuss how the sermon videos create a religious counterpublic in Bangladesh. We further discuss how such faith-based media production makes important lessons pertinent to the national grassroots politics in the Global South, politics of social media platforms, and HCI4D scholarship.
Consumption experiences in the research process

Data collection is often a laborious enterprise that forms part of the wider craft skill of doing research. In this essay, I try to understand whether parts of research processes in Human-Centred Computing (HCC) have been commodified, with a particular focus on data collection. If data collection has been commodified, do researchers act as producers or consumers in the process? And if researchers are consumers, has data collection become a consumption experience? If so, what are the implications of this? I explore these questions by considering the status of craft and consumption in the research process and by developing examples of consumption experiences. I note the benefits of commodity research artefacts, while highlighting the potentially deleterious effects consumption experiences could have on our ability to generate insights into the relations between people and technology. I finish the paper by relating consumption experiences to contemporary issues in HCC and lay out a programme of empirical work that would help answer some of the questions this paper raises.
Causal relevance of conditionals: semantics or pragmatics?

Abstract In this paper we argue that the antecedent of a (non-analytic) conditional is causally relevant to the consequent, … at least if standard background conditions hold. Natural counterexamples to the causal relevance analysis are argued to be cases where the standardly assumed background condition(s) do not hold.
The Challenge of (Non-)Disclosure: Exploring the Lived Experience of Ethiopian Adolescents with HIV and Their Attitudes Toward Technology

Adolescents in sub-Saharan Africa are at the epicenter of the global HIV epidemic. Yet, technology to support HIV management overwhelmingly focuses on adult medication adherence, neglecting the complex lives of adolescents in low-income regions. We present findings from interviews and focus groups that included twelve HIV-positive adolescents in Ethiopia, and eleven adults from their care circles. We leverage the Integrated Behavioral Model to examine the lived experience of HIV and the space for technology. Additionally, we present an inductive thematic analysis, which highlights non-disclosure as a central theme, i.e., adolescents remaining unaware of their HIV status. Drawing from these findings, we discuss how to account for (the lack of) disclosure in the design of technology to support HIV management, and reflect on whether technology could (and should) support the process. We further highlight the risks that researchers and designers need to be aware of when designing HIV management technology for this audience.
VisGuide: User-Oriented Recommendations for Data Event Extraction

Data exploration systems have become popular tools with which data analysts and others can explore raw data and organize their observations. However, users of such systems who are unfamiliar with their datasets face several challenges when trying to extract data events of interest to them. Those challenges include progressively discovering informative charts, organizing them into a logical order to depict a meaningful fact, and arranging one or more facts to illustrate a data event. To alleviate them, we propose VisGuide—a data exploration system that generates personalized recommendations to aid users' discovery of data events in breadth and depth by incrementally learning their data exploration preferences and recommending meaningful charts tailored to them. As well as user preferences, VisGuide's recommendations simultaneously consider sequence organization and chart presentation. We conducted two user studies to evaluate 1) the usability of VisGuide and 2) user satisfaction with its recommendation system. The results of those studies indicate that VisGuide can effectively help users create coherent and user-oriented visualization trees that represent meaningful data events.
Mapping an Observation-Based Global Solar Irradiance Climatology across the Conterminous United States

Abstract The exponential growth in solar radiation measuring stations across the conterminous United States permits the generation of gridded solar irradiance data that capture the spatiotemporal variability of solar irradiance far more accurately than was previously possible from ground-based observations. Taking advantage of these observations, we generated a 30-yr climatology (1991–2020) of mean monthly global irradiance at a resolution of 30 arc s (∼800 m) on both a horizontal surface and a sloped ground surface. This paper describes the methods used to generate the gridded data, which include extensive quality control of station data, spatial interpolation of effective cloud transmittance using the “PRISM” method, and simulation of the effects of elevation, shading, and reflection from nearby terrain on solar irradiance. A comparison of the new dataset with several other solar radiation products reveals some spatial features in solar radiation that are either lacking or underresolved in some or all of the other datasets. Examples of these features include strong gradients near foggy coastlines and along mountain ranges where there is persistent orographically driven cloud formation. The workflow developed to create the long-term means will be used as a template for generating time series of monthly and daily solar radiation grids up to the present.
The Electronicists

This paper offers a theoretical and methodological framework of 'techno-aesthetic encounters' that supports nonlinear (situated, materially-driven, and multi-sensory) and art-based modes of inquiry in HCI and the broader STEM fields. We first investigate recent literatures in HCI and science and technology studies (STS) that explore nonlinear modes of practice and creativity in processes of technology design, and argue that better recognition of these dynamics may open space for art-based and nonlinear leaners and makers to more actively engage in HCI research and design. To meet this need, we study three renowned art-and-engineering practitioners (Klüver, Paik, Moog) and our own experimental project titled 'The Electronicists' in which participants from different disciplines collaborated to produce three hybrid works. Based on this work, we propose a framework of 'techno-aesthetic encounters' that pursues event-based creativity through the mediation of engineering, art, and humanistic engagements. We suggest trust-based experiments, error-engaged studio, and art-based ethnography as promising methodological tenets of this approach.
What are you Grateful for? - Enhancing Gratitude Routines by Using Speech Assistants

This paper presents an extension for Amazon's Alexa, which provides a gratitude journal, and investigates its effectiveness compared to a regular paper-based version. Decades of research demonstrate that expressing gratitude has various psychological and physical benefits. At the same time, gratitude routines run the risk of being a hassle activity, which diminishes the positive outcome. Speech assistants might help to integrate gratitude routines more easily in an intuitive way using voice input. The results of our 8-day field study with two experimental groups (Alexa group vs. Paper group, N = 8) show that users see the benefits, that Alexa was effective in reducing participants' stress and that both groups express their gratitude differently. The positive effect of Alexa was restricted by a security setting (limiting user input to eight seconds) imposed by Amazon, which has now been repealed. The findings give practical and theoretical implications of how verbal gratitude expression affects participants' well-being.
Construction of a Linked Data Set of COVID-19 Knowledge Graphs: Development and Applications

With the continuous spread of COVID-19, information about the worldwide pandemic is exploding. Therefore, it is necessary and significant to organize such a large amount of information. As the key branch of artificial intelligence, a knowledge graph (KG) is helpful to structure, reason, and understand data.To improve the utilization value of the information and effectively aid researchers to combat COVID-19, we have constructed and successively released a unified linked data set named OpenKG-COVID19, which is one of the largest existing KGs related to COVID-19. OpenKG-COVID19 includes 10 interlinked COVID-19 subgraphs covering the topics of encyclopedia, concept, medical, research, event, health, epidemiology, goods, prevention, and character.In this paper, we introduce the key techniques exploited in building COVID-19 KGs in a top-down manner. First, the schema of the modeling process for each KG in OpenKG-COVID19 is described. Second, we propose different methods for extracting knowledge from open government sites, professional texts, public domain-specific sources, and public encyclopedia sites. The curated 10 COVID-19 KGs are further linked together at both the schema and data levels. In addition, we present the naming convention for OpenKG-COVID19.OpenKG-COVID19 has more than 2572 concepts, 329,600 entities, 513 properties, and 2,687,329 facts, and the data set will be updated continuously. Each COVID-19 KG was evaluated, and the average precision was found to be above 93%. We have developed search and browse interfaces and a SPARQL endpoint to improve user access. Possible intelligent applications based on OpenKG-COVID19 for further development are also described.A KG is useful for intelligent question-answering, semantic searches, recommendation systems, visualization analysis, and decision-making support. Research related to COVID-19, biomedicine, and many other communities can benefit from OpenKG-COVID19. Furthermore, the 10 KGs will be continuously updated to ensure that the public will have access to sufficient and up-to-date knowledge.
Supporting the Contact Tracing Process with WiFi Location Data: Opportunities and Challenges

Contact tracers assist in containing the spread of highly infectious diseases such as COVID-19 by engaging community members who receive a positive test result in order to identify close contacts. Many contact tracers rely on community member's recall for those identifications, and face limitations such as unreliable memory. To investigate how technology can alleviate this challenge, we developed a visualization tool using de-identified location data sensed from campus WiFi and provided it to contact tracers during mock contact tracing calls. While the visualization allowed contact tracers to find and address inconsistencies due to gaps in community member's memory, it also introduced inconsistencies such as false-positive and false-negative reports due to imperfect data, and information sharing hesitancy. We suggest design implications for technologies that can better highlight and inform contact tracers of potential areas of inconsistencies, and further present discussion on using imperfect data in decision making.
Systematic comparison of path planning algorithms using PathBench

Path planning is an essential component of mobile robotics. Classical path planning algorithms, such as wavefront and rapidly exploring random tree, are used heavily in autonomous robots. With the recent advances in machine learning, development of learning-based path planning algorithms has been experiencing a rapid growth. A unified path planning interface that facilitates the development and benchmarking of existing and new algorithms is needed. This paper presents PathBench, a platform for developing, visualizing, training, testing, and benchmarking of existing and future, classical and learning-based path planning algorithms in 2D and 3D grid world environments. Many existing path planning algorithms are supported, e.g. A*, Dijkstra, waypoint planning networks, value iteration networks, and gated path planning networks; integrating new algorithms is easy and clearly specified. The benchmarking ability of PathBench is explored in this paper by comparing algorithms across five different hardware systems and three different map types, including built-in PathBench maps, video game maps, and maps from real world databases. Metrics, such as path length, success rate, and computational time, were used to evaluate algorithms. Algorithmic analysis was also performed on a real-world robot to demonstrate PathBench's support for Robot Operating System. PathBench is open source1.
Discovering Representative Attribute-stars via Minimum Description Length

Graphs are a popular data type found in many domains. Numerous techniques have been proposed to find interesting patterns in graphs to help understand the data and support decision-making. However, there are generally two limitations that hinder their practical use: (1) they have multiple parameters that are hard to set but greatly influence results, (2) and they generally focus on identifying complex subgraphs while ignoring relationships between attributes of nodes. Graphs are a popular data type found in many domains. Numerous techniques have been proposed to find interesting patterns in graphs to help understand the data and support decision-making. However, there are generally two limitations that hinder their practical use: (1) they have multiple parameters that are hard to set but greatly influence results, (2) and they generally focus on identifying complex subgraphs while ignoring relationships between attributes of nodes. To address these problems, we propose a parameter-free algorithm named CSPM (Compressing Star Pattern Miner) which identifies star-shaped patterns that indicate strong correlations among attributes via the concept of conditional entropy and the minimum description length principle. Experiments performed on several benchmark datasets show that CSPM reveals insightful and interpretable patterns and is efficient in runtime. Moreover, quantitative evaluations on two real-world applications show that CSPM has broad applications as it successfully boosts the accuracy of graph attribute completion models by up to 30.68% and uncovers important patterns in telecommunication alarm data.
More than a bit of coding: (un-)Grounded (non-)Theory in HCI

Grounded Theory Methodology (GTM) is a powerful way to develop theories where there is little existing research using a flexible but rigorous empirically-based approach. Although it originates from the fields of social and health sciences, it is a field-agnostic methodology that can be used in any discipline. However, it tends to be misunderstood by researchers within HCI. This paper sets out to explain what GTM is, how it can be useful to HCI researchers, and examples of how it has been misapplied. There is an overview of the decades of methodological debate that surrounds GTM, why it's important to be aware of this debate, and how GTM differs from other, better understood, qualitative methodologies. It is hoped the reader is left with a greater understanding of GTM, and better able to judge the results of research which claims to use GTM, but often does not.
Universal approach to sending-or-not-sending twin field quantum key distribution

Abstract We present a universal approach to sending-or-not-sending (SNS) protocol of twin-field quantum key distribution with the method of actively odd parity pairing. In this improved protocol, the code bits are not limited to heralded events in time windows participated by pulses of intensity μ z and vacuum. All kinds of heralded events can be used for code bits to distill the final keys. The number of intensities (3 or 4) and the kinds of heralded events for code bits are automatically chosen by the key rate optimization itself. Numerical simulation shows that the key rate rises drastically in typical settings, up to 80% improvement compared with the prior results. Also, larger intensity value can be used for decoy pulses. This makes the protocol more robust in practical experiments.
Auditory and Olfactory Stimuli-Based Attractors to Induce Reorientation in Virtual Reality Forward Redirected Walking

Redirected walking (RDW) visually manipulates the virtual environment to imperceptibly redirect the walkers to keep them in the tracking area, and offers a larger space than physical space. An attractor is a redirected walking technique that captures the walker's attention and manipulates the walker's trajectory through rotational gain. However, the attractor visually manipulates the walker's virtual environment using a predefined rotational gain, and having to constantly gaze at the attractor or the attractor frequently appearing until the walker's direction matches the desired direction, are problems limiting the application of visual attractors. Moreover, when the walker is unable to recognize or ignores the attractor, reorientation fails. In this study, we designed a human-sense-stimulating attractor that utilizes the auditory and olfactory senses to improve the rotational gain, naturalness, and immersion and decrease the chance of reorientation failure. Although sound and scents are invisible, they can be detected through direction; however, humans cannot recognize the accurate direction of a sound or scent. Based on these characteristics, auditory and olfactory attractors are proposed. We measured the amount of reorientation induced by the auditory and olfactory attractors and calculated the reorientation success rate. Additionally, the naturalness and immersion of the attractor were evaluated. The auditory attractor has a high reorientation success rate, naturalness, and immersion. The olfactory attractor induces more turn changes in the walker than other attractors, and a high number of turn changes leads to a larger rotational gain. Auditory and olfactory-based attractors have the potential to overcome the shortcomings of visual attractors such as the frequent interventions.
Multi-task Data Augmentation Method Joint Object Detection and Semantic Segmentation

Data augmentation is a commonly used method for training networks in deep learning. For object detection tasks, data augmentation methods include random flip, random crop, etc. For semantic segmentation tasks, data enhancement includes horizontal flip, random saturation transformation, etc. With the application of deep learning technology in autonomous driving, many multi-task networks based on deep learning have proposed. These multi-task networks can simultaneously process target detection tasks and semantic segmentation tasks in autonomous driving. For the multi-task data augmentation, the existing methods can only take some simple methods. This paper proposed an effective data augmentation method, which can combine the data augmentation of object detection and semantic segmentation at the same time.
A Study of the Effects of Network Latency on Visual Task Performance in Video Conferencing

We study the effects of network latency and jitter on visual task performance between participants in video conferencing. Users of video conferencing tools commonly report a phenomenon known as Zoom fatigue. We postulate that this is a consequence of the effort required to maintain visual communication in the face of network latency and jitter. Understanding the relationship between network latency, jitter, and Zoom fatigue can inform the design of visual computing methods to mitigate the effects. To study the causes of Zoom fatigue, we analyzed a visual communication task via video conferencing through a user study employing a game of Charades since it relies on highly visual communication. Our findings demonstrate a direct correlation between user performance while playing the game and network latency and that the level of user engagement in the game plays a vital role in indicating adaptation to the latency when user engagement levels are high.
Physical Journey Maps: Staging Users’ Experiences to Increase Stakeholders’ Empathy towards Users

Customer Journey Mapping is a widespread service design tool that synthesizes and communicates user research insights to stakeholders. In its common form, a journey map is a synthetic (typically non-interactive) visualization of the key steps of the users' experience with a service or product. By decomposing the elements of a journey map and staging them under the form of a physical and interactive installation, we intend to leverage the power of journey mapping to break silos and prompt employees within an organization to discover end-users journeys in a compelling and empathic way. This aims to support the user-centered maturity of the organization by developing employees' curiosity and empathy towards users. We illustrate this approach through a case study on railway passengers' experiences. We explore the value of richer transfers of user research insights through physical journey maps and discuss design processes and mediums enabling journey maps to come to life.
Immersive Analytics 2.0: Spatial and Embodied Sensemaking

Immersive Analytics is now a fully emerged research topic that spans several research communities, including Human-Computer Interaction, Data Visualisation, Virtual Reality and Augmented Reality. Immersive Analytics research has identified and validated benefits of using embodied, 3D spatial immersive environments for visualisation and have shown benefits in the effective use of space and 3D interaction to explore complex data. As of today, most studies in Immersive Analytics have focused on exploring novel visualisation techniques in 3D embodied immersive environments. Thus far, there is a lack of fundamental study in this field that clearly compares immersive versus non immersive platforms for analytics purposes, and firmly delineates the benefits of immersive environments for analytic tasks. We feel that it is time to establish an agenda to assess the benefits and potential of immersive technologies, spatial interfaces, and embodied interaction to support sensemaking, data understanding, and collaborative analytics. This workshop will aim at putting this agenda together, by gathering international experts from Immersive Analytics and related fields to define which studies need to be conducted to assess the effect of embodied interaction on cognition in data analytics.