Reviewerly/exHarmony huggingface dataset:
A novel Siamese deep hashing model for histopathology image retrieval

Content-based histopathology image retrieval can be a useful technique for help in diagnosing various diseases. The process of retrieving images is often time-consuming and challenging due to the need for high-dimensional features when trying to model complex content. Hashing methods can therefore be employed to resolve the challenge by producing binary codes of different lengths. Deep hashing methods are frequently superior to traditional machine learning approaches but are affected by the size of training sets. In addition, back-propagation learning can further complicate the generation of binary values. Hence, this paper proposes a novel Siamese deep hashing model, named histopathology Siamese deep hashing (HSDH), for histopathology image retrieval. Two designed deep hashing models with shared weights and structures are used to generate hash codes. A Hamming distance layer is then applied to evaluate the similarity of the generated values. A highly effective loss function is also introduced that incorporates a modified version of the standard contrastive loss function with an error estimation term to improve both the training and retrieval phases. In the retrieval phase, the trained model compares a query image with all the training images and ranks the most similar images. According to the experimental results on two publicly available databases, BreakHis and Kather, the HSDH model outperforms other state-of-the-art hashing-based methods in histopathology image retrieval.
VMP-A3C: Virtual machines placement in cloud computing based on asynchronous advantage actor-critic algorithm

Virtualization technology represented through Virtual Machines (VMs) is recognized as a key infrastructure in cloud computing. This technology is developing rapidly and cloud data centers face challenges such as Virtual Machine Placement (VMP) for energy efficiency. VMP is defined as the efficient allocation of VMs to Host Machines (HMs) to achieve various objectives such as reducing energy consumption, load balancing and avoid Service Level Agreement Violations (SLAV). In this paper, VMP is addressed using a Deep Reinforcement Learning (DRL) based strategy to determine the best mapping between VMs and HMs. We present VMP-A3C, an effective strategy to solve VMP using Asynchronous Advantage Actor-Critic (A3C) algorithm as a new DRL approach. VMP-A3C aims at load balancing in HMs without SLAV, where energy consumption is reduced as much as possible. VMP-A3C learns to dynamically consolidate VMs using migration techniques to a minimum number of HMs. We believe that there is scope for improvements in shutting down little-workload HMs through VMs migration. The effectiveness of the proposed algorithm has been evaluated from various aspects such as the deployment rate, energy consumption, SLAV, the number of shutdown HMs and the number of migrated VMs. The main difference in terms of energy consumption and the number of required HMs between VMP-A3C and the best existing state-of-the-art method is 2.54% and 7.14%, respectively.
Demonstrating Fluito: A playful floatation tank experience

The use of technologies for interactions in bodies of water has fostered the WaterHCI field. However, the interactive systems proposed for water activities have, so far, primarily focused on supporting instrumental and performance aspects. In contrast, the use of interactive technology for experiential purposes, such as water play, appears to be underexplored. We designed Fluito, a playful floatation tank experience, to expand discovery potentials. Fluito has a unique combination of technology (a floatation tank, a virtual reality headset, a heart rate sensor, and a pneumatic system) that leverages water affordances to create and amplify different experiences, such as play, relaxation, and bodily illusions. The CHI community will benefit from our prototype through the novel experiences it affords, difficult to describe in words, and the first-hand accounts it can facilitate, which is useful for inspiring the design community towards future playful water experiences.
Exploring Values of Energy Justice: A Case Study of a Burgeoning Energy Community

We increasingly see local community energy initiatives unfold to support sustainable energy transitions. The notion of energy communities may aid these initiatives as HCI researchers, practitioners, and political organizations argue for their potential benefits. However, envisionments of energy communities carry assumed expectations of a just energy future for community members. This paper presents a case study of a burgeoning energy community where diverse stakeholders reflect on their expectations of a newly established Danish energy cooperative. Through a value-sensitive design study, we identify ten values reflecting social-technical expectations of how the community may be organized and supported by technology in the future. We structure the values into three tenets of energy justice to discuss value tensions regarding the; i) distribution of energy community benefits and threats, ii) enabling energy community engagement, and iii) recognizing the energy community. Lastly, we discuss how HCI may steer technology design toward a just energy future.
Versatile Immersive Virtual and Augmented Tangible OR – Using VR, AR and Tangibles to Support Surgical Practice

Immersive technologies such as virtual reality (VR) and augmented reality (AR), in combination with advanced image segmentation and visualization, have considerable potential to improve and support a surgeon's work. We demonstrate a solution to help surgeons plan and perform surgeries and educate future medical staff using VR, AR, and tangibles. A VR planning tool improves spatial understanding of an individual's anatomy, a tangible organ model allows for intuitive interaction, and AR gives contactless access to medical images in the operating room. Additionally, we present improvements regarding point cloud representations to provide detailed visual information to a remote expert and about the remote expert. Therefore, we give an exemplary setup showing how recent interaction techniques and modalities benefit an area that can positively change the life of patients.
Detecting Backdoors in Collaboration Graphs of Software Repositories

Software backdoors pose a major threat to the security of computer systems. Minor modifications to a program are often sufficient to undermine security mechanisms and enable unauthorized access to a system. The direct approach of detecting backdoors using static or dynamic program analysis is a daunting task that becomes increasingly futile with the attacker's capabilities. As a remedy, we introduce an orthogonal strategy for the detection of software backdoors. Instead of searching for concealed functionality in program code, we propose to analyze how a software has been developed and locate clues for malicious activities in its version history, such as in a Git repository. To this end, we model the version history as a collaboration graph that reflects how, when and where developers have committed changes to the software. We develop a method for anomaly detection using graph neural networks that builds on this representation and is able to detect spatial and temporal anomalies in the development process. % We evaluate our approach using a collection of real-world backdoors added to Github repositories. Compared to previous work, our method identifies a significantly larger number of backdoors with a low false-positive rate. While our approach cannot rule out the presence of software backdoors, it provides an alternative detection strategy that complements existing work focused only on program analysis.
Impact of Privacy Protection Methods of Lifelogs on Remembered Memories

Lifelogging is traditionally used for memory augmentation. However, recent research shows that users' trust in the completeness and accuracy of lifelogs might skew their memories. Privacy-protection alterations such as body blurring and content deletion are commonly applied to photos to circumvent capturing sensitive information. However, their impact on how users remember memories remain unclear. To this end, we conduct a white-hat memory attack and report on an iterative experiment (N=21) to compare the impact of viewing 1) unaltered lifelogs, 2) blurred lifelogs, and 3) a subset of the lifelogs after deleting private ones, on confidently remembering memories. Findings indicate that all the privacy methods impact memories' quality similarly and that users tend to change their answers in recognition more than recall scenarios. Results also show that users have high confidence in their remembered content across all privacy methods. Our work raises awareness about the mindful designing of technological interventions.
"This machine is for the aides": Tailoring Voice Assistant Design to Home Health Care Work

This paper explores how the design of interactive voice assistants (IVAs) might be tailored to support home health aides' important work in complex home care contexts. We designed two custom IVAs: one that looks like an aide's medical kit and one that blends into the home environment. We also designed a voice-based application that provides aides with guidance for day-to-day tasks and for performing a medical assessment. Via a lab-based study with 25 aides and seven patients, we explore how tailoring the IVAs' design to home health care might impact its acceptability as a work device, enabling cooperative work among aides and clients, while potentially causing conflict that will require IVA designers to decide whose values to prioritize. We also highlight limits in aides' power to control IVAs in clients' homes. Finally, we discuss implications for designing privacy-preserving IVAs, including leveraging IVAs' physical design to enact privacy mechanisms and opportunities to build 'always on' IVAs for privacy-sensitive contexts like home health care.
Building Community Capacity: Exploring Voice Assistants to Support Older Adults in an Independent Living Community

Voice assistants (VAs) such as Siri or Amazon Alexa can benefit older adults by offering accessible and convenient information options and aiding living independently. Therefore, researchers have considered how VAs might support older adults. Yet, few studies have explored opportunities for VAs to support communities and the implications of this integration. This study investigates older adults' perceptions of VAs and the potential to extend VA capabilities to support an independent living community. We invited independent living residents to virtual community forums and interviews to discuss their experiences with VAs, expectations, and concerns for VA integration to support information exchange, wellness, and social connections in their community. We found that residents desired additional VA capabilities to address unique community needs, including building on existing community capacities to support VA adoption and use. We discuss VA design implications for independent living communities and recommendations to support VA sustainability in a community environment.
Volumetric Mixed Reality Telepresence for Real-time Cross Modality Collaboration

Mixed-reality telepresence allows local and remote users feel as if they are present together in the same space. In this paper we report on a mixed-reality volumetric telepresence system that is adaptable, multi-user and cross-modal, i.e. combining augmented and virtual reality technologies with face-to-face interactions. The system extends state-of-art by creating full-body and environmental volumetric renderings in real-time over local enterprise networks. We report findings of an evaluation in a training scenario which was adapted for remote delivery and led by an industry professional. Analysis of interviews and observed behaviours identify varying attitudes towards virtually mediated full-body experiences and highlight the impact of volumetric mixed-reality telepresence to facilitate personal experiences of co-presence and to ground communication with interlocutors.
multiverse: Multiplexing Alternative Data Analyses in R Notebooks

There are myriad ways to analyse a dataset. But which one to trust? In the face of such uncertainty, analysts may adopt multiverse analysis: running all reasonable analyses on the dataset. Yet this is cognitively and technically difficult with existing tools—how does one specify and execute all combinations of reasonable analyses of a dataset?—and often requires discarding existing workflows. We present multiverse, a tool for implementing multiverse analyses in R with expressive syntax supporting existing computational notebook workflows. multiverse supports building up a multiverse through local changes to a single analysis and optimises execution by pruning redundant computations. We evaluate how multiverse supports programming multiverse analyses using (a) principles of cognitive ergonomics to compare with two existing multiverse tools; and (b) case studies based on semi-structured interviews with researchers who have successfully implemented an end-to-end analysis using multiverse. We identify design tradeoffs (e.g. increased flexibility versus learnability), and suggest future directions for multiverse tool design.
DRAVA: Aligning Human Concepts with Machine Learning Latent Dimensions for the Visual Exploration of Small Multiples

Latent vectors extracted by machine learning (ML) are widely used in data exploration (e.g., t-SNE) but suffer from a lack of interpretability. While previous studies employed disentangled representation learning (DRL) to enable more interpretable exploration, they often overlooked the potential mismatches between the concepts of humans and the semantic dimensions learned by DRL. To address this issue, we propose Drava, a visual analytics system that supports users in 1) relating the concepts of humans with the semantic dimensions of DRL and identifying mismatches, 2) providing feedback to minimize the mismatches, and 3) obtaining data insights from concept-driven exploration. Drava provides a set of visualizations and interactions based on visual piles to help users understand and refine concepts and conduct concept-driven exploration. Meanwhile, Drava employs a concept adaptor model to fine-tune the semantic dimensions of DRL based on user refinement. The usefulness of Drava is demonstrated through application scenarios and experimental validation.
Children with ADHD and their Care Ecosystem: Designing Beyond Symptoms

Designing for children with ADHD has been of increasing interest to the HCI community. However, current approaches do not adequately involve all relevant stakeholders, and primarily focus on addressing symptoms, following a medical model of disability that is extrinsic to neurodivergent interests. To address this, we employed a multi-step, multi-stakeholder approach (N=31). First, we conducted 1) interviews with children with ADHD and their care ecosystem followed by 2) a co-design pilot with one child with ADHD and his therapists and an interview with a UX designer and an occupational therapist. We then employed 3) co-design sessions with neurotypical children and children with ADHD, and 4) a focus group with their therapists. We identified communication and reflection as key concepts for empowering and promoting the well-being of children with ADHD and their care ecosystem. We contribute design implications for future systems aiming to promote the overall well-being of this population.
OpenMic: Utilizing Proxemic Metaphors for Conversational Floor Transitions in Multiparty Video Meetings

Turn-taking is one of the biggest interactivity challenges in multiparty remote meetings. One contributing factor is that current videoconferencing tools lack support for proxemic cues; i.e., spatial cues that humans use to enact their social relations and intentions. While more recent tools provide support for proxemic metaphors, they often focus on approach and leave-taking rather than turn-taking. In this paper, we present OpenMic, a videoconferencing system that utilizes proxemic metaphors for conversational floor management by providing 1) a Virtual Floor that serves as a fixed-feature space for users to be aware of others' intention to talk, and 2) Malleable Mirrors, which are video and screen feeds that can be continuously moved and resized for conversational floor transitions. Our exploratory user study found that these system features can aid the conversational flow in multiparty video meetings. With this work, we show potential for embedding proxemic metaphors to support conversational floor management in videoconferencing systems.
Trust, Comfort and Relatability: Understanding Black Older Adults’ Perceptions of Chatbot Design for Health Information Seeking

Conversational agents such as chatbots have emerged as a useful resource to access real-time health information online. Perceptions of trust and credibility among chatbots have been attributed to the anthropomorphism and humanness of the chatbot design, with gender and race influencing their reception. Few existing studies have looked specifically at the diversity of chatbot avatar design related to both race, age, and gender, which may have particular significance for racially minoritized users like Black older adults. In this paper, we explored perceptions of chatbots with varying identities for health information seeking in a diary and interview study with 30 Black older adults. Our findings suggest that while racial and age likeness influence feelings of trust and comfort with chatbots, constructs such as professionalism and likeability and overall familiarity also influence reception. Based on these findings, we provide implications for designing text-based chatbots that consider Black older adults.
"I Am a Mirror Dweller": Probing the Unique Strategies Users Take to Communicate in the Context of Mirrors in Social Virtual Reality

Increasingly popular social virtual reality (VR) platforms like VRChat created new ways for people to interact with each other, generating dedicated user communities with unique idioms of socializing in an alternative world. In VRChat, users frequently gather in front of mirrors en masse during online interactions. Understanding how user communities deal with the mirror's unique interactions can generate insights for supporting communication in social VR. In this study, we investigated the mirror's synergistic effect with avatars on behaviors and dedicated user conversational performance. Qualitative findings indicate that avatar-mediated communication through mirrors provides functions like ensuring synchronization of incarnations, increasing immersion, and enhancing idealized embodiment to express bolder behaviors anonymously. Quantitative studies show that while mirrors improve self-perception, it has a potentially adverse effect on conversational performance, similar to the role of self-viewing in video conferencing. Studying how users interact with mirrors in an immersive environment allows us to explore how digital environments affect spatialized interactions when transported from physical to digital domains.
Tesseract: Querying Spatial Design Recordings by Manipulating Worlds in Miniature

New immersive 3D design tools enable the creation of spatial design recordings, capturing collaborative design activities. By reviewing captured spatial design sessions, which include user activities, workflows, and tool use, users can reflect on their own design processes, learn new workflows, and understand others' design rationale. However, finding interesting moments in design activities can be challenging: they contain multimodal data (such as user motion and logged events) occurring over time which can be difficult to specify when searching, and are typically distributed over many sessions or recordings. We present Tesseract, a Worlds-in-Miniature-based system to expressively query VR spatial design recordings. Tesseract consists of the Search Cube interface acting as a centralized stage-to-search container, and four querying tools for specifying multimodal data to enable users to find interesting moments in past design activities. We studied ten participants who used Tesseract and found support for our miniature-based stage-to-search approach.
EquityWare: Co-Designing Wearables With And For Low Income Communities In The U.S.

Wearables are a potentially vital mechanism for individuals to monitor their health, track behaviors, and stay connected. Unfortunately, both price and a lack of consideration of the needs of low-SES communities have made these devices inaccessible and unusable for communities that would most substantially benefit from their affordances. To address this gap and better understand how members of low-SES communities perceive the potential benefits and barriers to using wearable devices, we conducted 19 semi-structured interviews with people from minority, high crime rate, low-SES communities. Participants emphasized a critical need for safety-related wearable devices in their communities. Still, existing tools do not yet address the specific needs of this community and are out of reach due to several barriers. We distill themes on perceived useful features and ongoing obstacles to guide a much-needed research agenda we term 'Equityware': building wearable devices based on low-SES communities' needs, comfortability, and limitations.
Personalised But Impersonal: Listeners' Experiences of Algorithmic Curation on Music Streaming Services

The consumption of music is increasingly reliant on the personalisation, recommendation, and automated curation features of music streaming services. Using algorithm experience (AX) as a lens, we investigated the user experience of the algorithmic recommendation and automated curation features of several popular music streaming services. We conducted interviews and participant-observation with 15 daily users of music streaming services, followed by a design workshop. We found that despite the utility of increasingly algorithmic personalisation, listeners experienced these algorithmic and recommendation features as impersonal in determining their background listening, music discovery, and playlist curation. While listener desire for more control over recommendation settings is not new, we offer a number of novel insights about music listening to nuance this understanding, particularly through the notion of vibe.
Investigating Wrist Deflection Scrolling Techniques for Extended Reality

Scrolling in extended reality (XR) is currently performed using handheld controllers or vision-based arm-in-front gestures, which have the limitations of encumbering the user's hands or requiring a specific arm posture, respectively. To address these limitations, we investigate freehand, posture-independent scrolling driven by wrist deflection. We propose two novel techniques: Wrist Joystick, which uses rate control, and Wrist Drag, which uses position control. In an empirical study of a rapid item acquisition task and a casual browsing task, both Wrist Drag and Wrist Joystick performed on par with a comparable state-of-the-art technique on one of the two tasks. Further, using a relaxed arm-at-side posture, participants retained their arm-in-front performance for both wrist techniques. Finally, we analyze behavioral and ergonomic data to provide design insights for wrist deflection scrolling. Our results demonstrate that wrist deflection provides a promising method for performant scrolling controls while offering additional benefits over existing XR interaction techniques.