Reviewerly/exHarmony huggingface dataset:
Fable Reborn: Investigating Gameplay Experience between a Human Player and a Virtual Dungeon Master

Dungeons & Dragons (D&D) is a role-playing game typically played face-to-face with participants seated around a tabletop. Online technologies have enabled the conduct of live sessions on virtual platforms, allowing players to continue enjoying the game despite geographic distances. Playing D&D relies heavily on a dungeon master who is tasked with preparing the fictional world and facilitating the interaction between players who each control a single story character. In this paper, we investigate the impact of employing AI techniques in designing a virtual dungeon master (VDM) on the gameplay experience of D&D players. The VDM utilizes Open AI's GPT2 model to generate story prompts in combination with an input plausibility checker, a dice rolling feature, and a candidate story prompt scorer. Feedback from D&D players revealed that the VDM is capable of producing believable fantasy stories but lacks consistency in the quality of its generated stories. Our findings point to the potential abilities of an artificially intelligent agent in engaging human D&D players in role-playing games; however, the VDM is still not capable of truly replicating the experience of playing with a human DM.
Regulating Responsibility: Environmental Sustainability, Law, and the Platformisation of Waste Management

The scope of Sustainable HCI research is expanding to include the broad sociotechnical and ecological contexts of computing. We examine the intersection of environmental sustainability, technology, and the law. By studying the legal dispute between a platform service that facilitates crowd-sourced waste disposal and the local government's regulation of waste management, we step through an evolving debate on the meaning of care and responsibility for the environment. When faced with the municipality's claimed monopoly on responsibility for waste management, the platform argues for the paradigms of individual responsibility, designing for user needs, and personalised and on-demand digital services. In arguing against this framing, the municipality highlights the gap between the law, its interpretation, and the idealistic values of technology-driven environmental care. We contribute to the framing of environmental care within Sustainable HCI as a locally constructed, regulated, and contested aspect of technology design and appropriation.
Virtual Reconstruction with the Augmented Reality Technology of the Cultural Heritage Components that have Disappeared: The Ayazini Virgin Mary Church

Augmented reality (AR) method could be used in the context of being able to provide for the transfer of the architectural and tectonic attributes related to the period to which a historical building belongs, and under conditions where the reconstruction activities could produce damage to the integrity of the building or in situations where the financial resources remain insufficient for making reconstruction. Ayazini Village was selected as the study area. Ayazini Village is connected to Afyonkarahisar Province and is a region that is an extension of the Phrygian Valley, which has a 6,000-year-old past. The Ayazini Region is composed of layers, which produced the First-Degree Archaeological, Urban Archaeological, and First-Degree Natural Site areas. The rock settlements, which form the tectonic structure of Ayazini Village, were produced with the cutting of the tufa rocks that are described as Ayazini rock. The interior spaces and building components of the rock settlements have been damaged or disappeared in time. It is thought from this aspect that there are difficulties in the context of materials, cost, and labor for the reconstruction activities of the areas where these interior spaces were damaged. Within the scope of the research, a virtual restoration model was applied in the interior space of the Virgin Mary Church located in Ayazini Village by using the AR technology. The Virgin Mary Church is a unique historical building remaining from the Byzantine Middle Period when Christianity was accepted. The interior space of the Virgin Mary Church was damaged as the result of natural disasters and human influences over time. In this context, within the scope of the virtual restoration model in this research, the sections of the arches set on the columns in the interior space of the church that were damaged were treated in the integration of the interior space with the sign-based AR technology and reconstruction. The surfaces of the lower parts of the columns that were damaged were mapped with a laser scanner. The parts of the Quick Response (QR) code columns, which were damaged, were presented in simulations. These simulations contain the modeling of the columns from the aspect of shape, color, and fabric. The columns bearing the arches located in the interior space of the church were simulated in a computer environment by determining the colors, fabric, shape, and construction characteristics through the original photographs and based on the data in the literature and with the color pixelization and fabric characteristic mapping of the surfaces. At the conclusion of all these procedures, the data was transferred to the mobile application called Augment. The QR code assigned provided for the aligning under the columns by using the on-site test method and a laser scanner. The smart equipment, through the Augment application, showed the original condition and experienced on-site simultaneously the damaged parts of the column that were completed with the arch by perceiving the QR code.
A Higher-Order Language for Markov Kernels and Linear Operators

Abstract Much work has been done to give semantics to probabilistic programming languages. In recent years, most of the semantics used to reason about probabilistic programs fall in two categories: semantics based on Markov kernels and semantics based on linear operators. Both styles of semantics have found numerous applications in reasoning about probabilistic programs, but they each have their strengths and weaknesses. Though it is believed that there is a connection between them there are no languages that can handle both styles of programming. In this work we address these questions by defining a two-level calculus and its categorical semantics which makes it possible to program with both kinds of semantics. From the logical side of things we see this language as an alternative resource interpretation of linear logic, where the resource being kept track of is sampling instead of variable use.
We are the Data: Challenges and Opportunities for Creating Demographically Diverse Anthropographics

Anthropographics are human-shaped visualizations that aim to emphasize the human importance of datasets and the people behind them. However, current anthropographics tend to employ homogeneous human shapes to encode data about diverse demographic groups. Such anthropographics can obscure important differences between groups and contemporary designs exemplify the lack of inclusive approaches for representing human diversity in visualizations. In response, we explore the creation of demographically diverse anthropographics that communicate the visible diversity of demographically distinct populations. Building on previous anthropographics research, we explore strategies for visualizing datasets about people in ways that explicitly encode diversity—illustrating these approaches with examples in a variety of visual styles. We also critically reflect on strategies for creating diverse anthropographics, identifying social and technical challenges that can result in harmful representations. Finally, we highlight a set of forward-looking research opportunities for advancing the design and understanding of diverse anthropographics.
Self-Supervised Training of Speaker Encoder With Multi-Modal Diverse Positive Pairs

We study a novel neural speaker encoder and its training strategies for speaker recognition without using any identity labels. The speaker encoder is trained to extract a fixed dimensional speaker embedding from a spoken utterance of variable length. Contrastive learning is a typical self-supervised learning technique. However, the contrastive learning of the speaker encoder depends very much on the sampling strategy of positive and negative pairs. It is common that we sample a positive pair of segments from the same utterance. Unfortunately, such a strategy, denoted as poor-man's positive pairs (PPP), lacks the necessary diversity. In this work, we propose a multi-modal contrastive learning technique with novel sampling strategies. By cross-referencing between speech and face data, we find diverse positive pairs (DPP) for contrastive learning, thus improving the robustness of speaker encoder. We train the speaker encoder on the VoxCeleb2 dataset without any speaker labels, and achieve an equal error rate (EER) of 2.89%, 3.17% and 6.27% under the proposed progressive clustering strategy, and an EER of 1.44%, 1.77% and 3.27% under the two-stage learning strategy with pseudo labels, on the three test sets of VoxCeleb1. This novel solution outperforms the state-of-the-art self-supervised learning methods by a large margin, at the same time, achieves comparable results with the supervised learning counterpart. We also evaluate our self-supervised learning technique on the LRS2 and LRW datasets, where speaker information is unavailable. All experiments suggest that the proposed neural architecture and sampling strategies are robust across datasets.
Decorative, Evocative, and Uncanny: Reactions on Ambient-to-Disruptive Health Notifications via Plant-Mimicking Shape-Changing Interfaces

Ambient Information Systems (AIS) have shown some success when used as a notification towards users' health-related activities. But in the actual busy lives of users, ambient notifications might be forgotten or even missed, nullifying the original notification. Could a system use multiple levels of noticeability to ensure its message is received, and how could this concept be effectively portrayed? To examine these questions, we took a Research through Design approach and created plant-mimicking Shape-Changing Interface (S-CI) artifacts, then conducted interviews with 10 participants who currently used a reminder system for health-related activities. We report findings on acceptable scenarios to disrupting people for health-related activities, and participants' reactions to our design choices, including how using naturalistic aesthetics led to interpretations of the uncanny and morose, and which ways system physicality affected imagined uses. We offer design suggestions in health-related notification systems and S-CIs, and discuss future work in ambient-to-disruptive technology.
Be Our Guest: Intercultural Heritage Exchange through Augmented Reality (AR)

This paper explores how interactive applications can help mitigate the adversity of facing cultural differences between migrants and the host community, and between migrants of diverse backgrounds to foster intercultural exchange. Based on literature about situated cognition, immersive theater, and affordance, we designed and built Be Our Guest: an augmented reality application where a user is invited to the houses of people from different cultures and is asked to help with one of their cultural rituals around simple everyday objects. We detail the various phases we took to collect the cultural stories and construct the application. We then report the results of a user study with the developed application. Our findings show that participants were easily immersed in the augmented space due to the app's narrative, visuals, and interactive nature. Moreover, they enjoyed exploring cultural rituals, including their own, and felt more confident connecting with people from other cultures.
How Instructional Data Physicalisation Fosters Reflection in Personal Informatics

The ever-increasing number of devices quantifying our lives offers a perspective of high awareness of one's wellbeing, yet it remains a challenge for personal informatics (PI) to effectively support data-based reflection. Effective reflection is recognised as a key factor for PI technologies to foster wellbeing. Here, we investigate whether building tangible representations of health data can offer engaging and reflective experiences. We conducted a between-subjects study where n = 60 participants explored their immediate blood pressure data in relation to medical norms. They either used a standard mobile app, built a data representation from LEGO® bricks based on instructions, or completed a free-form brick build. We found that building with instructions fostered more comparison and using bricks fostered focused attention. The free-form condition required extra time to complete, and lacked usability. Our work shows that designing instructional physicalisation experiences for PI is a means of improving engagement and understanding of personal data.
Exploring Mixed Reality in General Aviation to Support Pilot Workload

Pilots in non-commercial aviation have minimal access to digital support tools. Equipping aircraft with modern technologies introduces high costs and is labor intensive. Hence, wearable or mobile support, such as common 2D maps displayed on standard tablets, is often the only digital information source used by pilots. Yet, they fail to adequately capture the 3D airspace and its surroundings, challenging the pilot's workload. This work explores how mixed reality can support pilots by projecting supportive elements into their fields of view. Considering the design of a preliminary mixed reality prototype, we conducted a user study with twelve pilots in a full-sized flight simulator. Our measures show that the prototype positively influenced the participants' situational awareness and overall landing routine efficiency, who also had generally favorable views regarding mixed reality in the cockpit. This work shows the utility of mixed reality technologies while emphasizing future research directions in general aviation.
Exploring Co-located Interactions with a Shape-Changing Bar Chart

Data-physicalizations encode data and meaning through geometry or material properties, providing a non-planar view of data, offering novel opportunities for interrogation, discovery and presentation. This field has explored how single users interact with complex 3D data, but the challenges in the application of this technology to collaborative situations have not been addressed. We describe a study exploring interactions and preferences among co-located individuals using a dynamic data-physicalization in the form of a shape-changing bar chart, and compare this to previous work with single participants. Results suggest that co-located interactions with physical data prompt non-interactive hand gestures, a mirroring of physicalizations, and novel hand gestures in comparison to single participant studies. We also note that behavioural similarities in participants between interactive tabletop studies and data-physicalizations may be capitalised upon for further development of these dynamic representations. Finally, we consider the implications and challenges for the adoption of these types of platforms.
All-in-One Print: Designing and 3D Printing Dynamic Objects Using Kinematic Mechanism Without Assembly

The field of Human-Computer-Interaction (HCI) has been consistently utilizing kinematic mechanisms to create tangible dynamic interfaces and objects. However, the design and fabrication of these mechanisms are challenging due to complex spatial structures, step-by-step assembly processes, and unstable joint connections resulting from the inevitable matching errors within separated parts. In this paper, we propose an integrated fabrication method for one-step FDM 3D printing (FDM3DP) kinematic mechanisms to create dynamic objects without additional post-processing. We describe the Arch-printing and Support-bridges method, which we call All-in-One Print, that compiles given arbitrary solid 3D models into printable kinematic models as G-Code for FDM3DP. To expand the design space, we investigate a series of motion structures (e.g., rotate, slide, and screw) with multi-stabilities and develop a design tool to help users quickly design such dynamic objects. We also demonstrate various application cases, including physical interfaces, toys with interactive aesthetics and daily items with internalized functions.
InstruMentAR: Auto-Generation of Augmented Reality Tutorials for Operating Digital Instruments Through Recording Embodied Demonstration

Augmented Reality tutorials, which provide necessary context by directly superimposing visual guidance on the physical referent, represent an effective way of scaffolding complex instrument operations. However, current AR tutorial authoring processes are not seamless as they require users to continuously alternate between operating instruments and interacting with virtual elements. We present InstruMentAR, a system that automatically generates AR tutorials through recording user demonstrations. We design a multimodal approach that fuses gestural information and hand-worn pressure sensor data to detect and register the user's step-by-step manipulations on the control panel. With this information, the system autonomously generates virtual cues with designated scales to respective locations for each step. Voice recognition and background capture are employed to automate the creation of text and images as AR content. For novice users receiving the authored AR tutorials, we facilitate immediate feedback through haptic modules. We compared InstruMentAR with traditional systems in the user study.
Less About Privacy: Revisiting a Survey about the German COVID-19 Contact Tracing App

The release of COVID-19 contact tracing apps was accompanied by a heated public debate with much focus on privacy concerns, e.g., possible government surveillance. Many papers studied people's intended behavior to research potential features and uptake of the apps. Studies in Germany conducted before the app's release, such as that by Häring et al., showed that privacy was an important factor in the intention to install the app. We conducted a follow-up study two months post-release to investigate the intention-behavior-gap, see how attitudes changed after the release, and capture reported behavior. Analyzing a quota sample (n=837) for Germany, we found that fewer participants mentioned privacy concerns post-release, whereas utility now plays a greater role. We provide further evidence that the results of intention-based studies should be handled with care when used for prediction purposes.
Adding Domain-Specific Features to a Text-Editor to Support Diverse, Real-World Approaches to Time Management Planning

Many tools are designed to support users in maintaining or developing strong time management practices. Abandonment of these specialized tools is common, in favor of returning to a more general-purpose unstructured tool. How can designs leverage the familiarity of general-purpose tools and the advantages of specialized ones? We explore if applying a time-management-specific understanding of conventions and interactions within unstructured plaintext can be a successful approach to designing support for these tasks. We report the results of two field deployments (combined n=29) of "Plan" - a mobile application with a notes-application-based interface designed to support the practice of Time Management Planning. We show that modest, domain-specific modifications of general-purpose designs can facilitate users' pre-existing workflows and nudge them towards better practices while leaving interfaces familiar and flexible. However, those with minimal planning experience desired additional structure.
UEyes: Understanding Visual Saliency across User Interface Types

While user interfaces (UIs) display elements such as images and text in a grid-based layout, UI types differ significantly in the number of elements and how they are displayed. For example, webpage designs rely heavily on images and text, whereas desktop UIs tend to feature numerous small images. To examine how such differences affect the way users look at UIs, we collected and analyzed a large eye-tracking-based dataset, UEyes (62 participants and 1,980 UI screenshots), covering four major UI types: webpage, desktop UI, mobile UI, and poster. We analyze its differences in biases related to such factors as color, location, and gaze direction. We also compare state-of-the-art predictive models and propose improvements for better capturing typical tendencies across UI types. Both the dataset and the models are publicly available.
Eye-Perspective View Management for Optical See-Through Head-Mounted Displays

Optical see-through (OST) head-mounted displays (HMDs) enable users to experience Augmented Reality (AR) support in the form of helpful real-world annotations. Unfortunately, the blend of the environment with virtual augmentations due to semitransparent OST displays often deteriorates the contrast and legibility of annotations. View management algorithms adapt the annotations' layout to improve legibility based on real-world information, typically captured by built-in HMD cameras. However, the camera views are different from the user's view through the OST display which decreases the final layout quality. We present eye-perspective view management that synthesizes high-fidelity renderings of the user's view to optimize annotation placement. Our method significantly improves over traditional camera-based view management in terms of annotation placement and legibility. Eye-perspective optimizations open up opportunities for further research on use cases relying on the user's true view through OST HMDs.
Ignoring As a Moderation Strategy for Volunteer Moderators on Twitch

Content moderation is a crucial aspect of online platforms, and it requires human moderators (mods) to repeatedly review and remove harmful content. However, this moderation process can lead to cognitive overload and emotional labor for the mods. As new platforms and designs emerge, such as live streaming space, new challenges arise due to the real-time nature of the interactions. In this study, we examined the use of ignoring as a moderation strategy by interviewing 19 Twitch mods. Our findings indicated that ignoring involves complex cognitive processes and significant invisible labor in the decision-making process. Additionally, we found that ignoring is an essential component of real-time moderation. These preliminary findings suggest that ignoring has the potential to be a valuable moderation strategy in future interactive systems, which highlights the need to design better support for ignoring in interactive live-streaming systems.
Collaborating with a Text-Based Chatbot: An Exploration of Real-World Collaboration Strategies Enacted during Human-Chatbot Interactions

A central problem for chatbots in the customer care domain revolves around how people collaborate with the agent to achieve their own situated goals. The majority of the previous research, however, relied on experiments within artificial settings, rather than on observation of real-world interactions. Moreover, such research mostly analyzed users’ responses to communication breakdowns, rather than the wider collaboration strategies utilized during a conversation. In this paper, we qualitatively analyzed 12,477 real-world exchanges with a task-based chatbot using a Grounded Theory approach as a rigorous coding method to analyze the data. We identified two main aspects of collaboration, behavioral and conversational, and for each aspect we highlighted the different strategies that users perform to “work together” with the agent. These strategies may be utilized from the very beginning of the conversation or in response to misunderstandings in the course of ongoing interactions and may show different evolving dynamics.