Reviewerly/exHarmony huggingface dataset:
Predicting Mouse Positions Beyond a System’s Latency Can Increase Throughput and User Experience in Linear Steering Tasks

Latency is present in all interactive systems and decreases user experience and performance. Previous work developed approaches that predict user actions and show these predictions to reduce latencies’ negative effects. While this can increase user experience and performance, it is unclear if predicting beyond a system’s latency results in further improvements. Therefore, we investigated the effects of predicting beyond a system’s latency. We collected data from 60 participants performing Steering Law tasks to systematically train an artificial neural network (ANN) that predicts 100ms into the future. We integrated the ANN into the Steering Law task and buffered users’ inputs to simulate latency between 50ms and -50ms. A study with 30 participants showed that decreasing latency beyond the system’s latency increases throughput up to -50ms. Subjective measures improved up to -16.67ms without negative effects on agency. Overall, we show that predicting beyond a system’s latency can increase performance and user experience.
Patient Journey Value Mapping: Illustrating values and experiences along the patient journey to support eHealth design

This paper introduces patient journey value mapping – an approach to capture experiences, emotions and values implicated in patients’ care delivery. As patients’ values (i.e., what’s important to them in their lives) may change along their patient journeys, our approach aims to support designers to respond to patients’ changing needs in the (re)design of eHealth, by mapping patients’ values and their prioritisations over time. To substantiate the creation of the map, we propose two preceding data collection phases comprising complementary empirical methods. First, important care-related events and associated values are collected retrospectively through interviews, and in-situ through diary studies. Subsequently, the data are analysed to develop materials to elicit values and value tensions through deepening discussions in an interactive workshop based on which the maps are finalised. The approach is illustrated through discussions and reflections on its application in a case study investigating patient values in eHealth for rehabilitation care.
Applying a Feature-Oriented Software Development Approach to Model Interaction Diversity

This research introduces a novel modelling approach based on methods from feature-oriented software development, aimed at enhancing accessibility and diversity in interactive systems. The method integrates user requirements, particularly accessibility and sensitivity to diversity, into software family development. Utilizing a user model subtree, it allows for customization based on users' needs, constraints, and preferences. A prototypical demonstration is shown through a voice user interface of an assistance robot. Despite an overall satisfying success rate of 96%, results suggest the quality of configuration slightly decreases with an increasing number of user constraints. This innovative approach offers significant potential, especially given the growing need for personalized human-computer interaction in our ageing society. However, it also prompts further research questions, such as its adaptability to non-software family systems and quality of configuration via smart AI models.
Enacted Selves in Technological Activities – Framework and Case Study in Immersive Telementoring

The incorporation of new technology into existing human activities can be challenging. Numerous models have been proposed in human-computer interaction (HCI) to guide research and analyze effects. However, bridging the gap between experimental data and real-world applications often proves to be difficult. In the last decades, post-cognitivistic approaches have been developed to explain human cognition and the relation between humans and their environment. In this paper, we present a novel framework to systematically describe and analyze challenges in the context of HCI from multiple perspectives. It extends Cultural-Historical Activity Theory (CHAT) and is enriched by contemporary philosophical perspectives (enactivism, pattern theory of self and post-phenomenology). The proposed framework is further illustrated by applying it to an immersive telementoring prototype system.
Accessible Text Tools for People with Cognitive Impairments and Non-Native Readers: Challenges and Opportunities

Many people have problems with reading, which limits their ability to participate in society. This paper explores tools that make text more accessible. For this, we interviewed experts, who proposed tools for different stakeholders and scenarios. Important stakeholders of such tools are people with cognitive impairments and non-native readers. Frequently mentioned scenarios are public administration, the medical domain, and everyday life. The tools proposed by experts support stakeholders by improving how text is compressed, expanded, reviewed, and experienced. In a survey of stakeholders, we confirm that the scenarios are relevant and that the proposed tools appear helpful to them. We provide the Accessible Text Framework to help researchers understand how the different tools can be combined and discuss how individual tools can be implemented. The investigation shows that accessible text tools are an important HCI+AI challenge that a large number of people can benefit from.
Interactive Soundscape Tabletop to Promote Immersive Sound Experiences for People Living With Dementia and Their Caregivers

Music interventions have been proven effective in activating people living with dementia, even in the advanced stages of the disease. Alongside music, sound-based activities are vital for the care of those affected. However, the use of sound-based activities in the daily care of people living with dementia is challenging and lacks research. To address this gap, sound-related interactive media can play a significant role. Currently, existing systems rarely allow individuals with dementia to create personalized sound experiences. In the DIDEM project, we are exploring digital solutions in a participatory development process to improve everyday sound experiences for people with dementia. In this demonstration, we introduce a prototype of an interactive soundscape tabletop designed specifically for collaborative activities with people living with dementia within institutional settings.
VRoom – Development of a Virtual Reality learning environment for the utilization of color in an architectural context

Usage of Virtual Reality (VR) applications in an education setting has specific needs. The high number of parallel users create atypical time and space requirements. Interaction with the system needs to be intuitive, easy to learn and quick to understand. While intuition is approachable in simple applications, with an increasing system complexity this principle is becoming harder to implement. In the development of VRoom we used a User Centered Design approach. Regarding the process we used informal tests and implemented different methods of user interaction in VR. We examined different placements and alignments of the User Interface (UI) elements. Depending on the qualitative user feedback the functionality of the VR application was adapted and resulted in a combination of different UI elements with the goal to learn the utilization of color in different interior environments.
Supporting Software Developers Through a Gaze-Based Adaptive IDE

Highly complex systems, such as software development tools, constantly gain features and, consequently, complexity and, thus, risk overwhelming or distracting the user. We argue that automation and adaptation could help users to focus on their work. However, the challenge is to correctly and promptly determine when to adapt what, as often the users’ intent is unclear. To assist software developers, we build a gaze-adaptive integrated development environment using the developers’ gaze as the source for learning appropriate adaptation. Beyond our experience of using gaze for an adaptive user interface, we also report first feedback from developers regarding the desirability of such a user interface, which indicated that adaptations for development tools need to strike a careful balance between automation and user control. Nonetheless, the developers see the value in a gaze-based adaptive user interface and how it could improve software development tools going forward.
"Virtual reality nature as our next retreat?"

In this article, we present the initial user experience test results of an experiment conducted in an interactive natural environment created in virtual reality (VR). The computer-generated (CG) VR application, entitled Zenctuary VR, was designed with the intention of giving the users a pleasant experience with the benefits of easing anxiety symptoms, boosting positive feelings and lowering negative ones, thus having a restorative effect. For this, we designed a graphically high-resolution, natural environment with playful interactions. The testing was conducted with adults diagnosed with attentional deficit and hyperactivity disorder (ADHD) and with a neurotypical control group in order to investigate whether the ADHD group experiences the app differently from the latter group and to enhance its effectiveness specifically for individuals with ADHD. While the assessed scales for presence, immersion, feeling of flow, gained skills, usability, and judgment did not differ between the two groups, overall their evaluations of the experience and the software application were favorable. We also gathered insightful comments on the application's shortcomings, which will enable us to improve it and make it more enjoyable and ultimately more beneficial for the users.
HoloBoard: A gamified balance board experience

In this demo, we present HoloBoard, a HoloLens2-based Augmented Reality application, which gamifies traditional balance board training to make it more engaging and enjoyable. Ankle injuries are among the most common injuries in many ball sports. Balance training, such as training programs which use a balance board, can help stabilize foot joints and prevent injuries. Traditional balance training is often inadequate because there is no feedback, and the "monotonous" movements are perceived as "boring". Our application is based on the Unreal game engine and utilizes the HoloLens2 to engage the player in various ball sports related game types such as catching, heading and dodging, thereby requiring various body movements similar to traditional balance board training programs.
Meta pseudo labels for anomaly detection via partially observed anomalies

General anomaly detection has been an important research due to its broad and significant applications. Those algorithms that are based on weakly supervised or partially observed anomalies have attracted particular interest currently. However, most such algorithms treat the unlabeled set as a substitute for normal samples and ignore the potential anomalies in it, which on the one hand causes the noise of the normal set, and on the other hand, fails make full use of the abnormal supervision information. To address this issue, we propose a meta-pseudo-label based framework for anomaly detection (MPAD). The framework strives to obtain effective pseudo anomalies from the unlabeled samples to supplement the observed anomaly set. Specifically, a teacher network is improved based on the feedback of a student network on a validation set, thereby generating more conducive pseudo anomalies to assist the student network while incurring less confirmation bias. In addition, we also design a model roll-back scheme to guarantee the performance of the student network. Extensive experiments show that the proposed MPAD algorithm outperforms current popular algorithms on five real datasets, and the framework can effectively improve the utilization of unlabeled samples while maintaining an advanced degree of robustness. In addition, the results of ablation experiments confirm the effectiveness of each module of our MPAD. Parameter experiments provide sufficient reference for parameter selecting.
Robust image hashing based on multi-view dimension reduction

In the field of multimedia security, image hashing is an effective technology for solving image content identification. Image hashing algorithm aims to map the high dimensional input features onto perceptual hashing values in terms of their perceptual content, in which visually identical images are mapped to similar digital representations. In this paper, we focus on multi-view dimensionality reduction based hashing generation, and propose a novel method, called Multi-view Dimensionality Reduction based Robust Image Hashing (MDRIH). Our MDRIH scheme maps the multi-view features into a compact binary space by considering the complementarity and similarity between image features across these different views. For preprocessing, bilinear interpolation is applied on the host image to adjust to a standard size, and ensure the different images with same hash size. Specifically, in order to improve the robustness of the algorithm against content-preserving operations, multi-view features, including structural features, edge features, and color features are extracted. After that, multi-view dimension reduction is adopted to obtain meaningful low-dimensional information from multi-feature fusion. Finally, the data in low-dimensional subspace is encoded into a compact binary sequence. The experimental results indicate that the proposed hashing method not only preserves the correlation between multiple views and the similarity among different data points within each view, but also achieves a better balance between robustness and discriminability compared to existing hashing methods.
Laundromats: Community-Based Partnerships to Increase Reproductive Health Literacy Outreach

Laundromats are a regularly visited, highly localized community venue, especially in underserved communities. Few health literacy and health-promotion programs have taken place in laundromats, and there have been no efforts to apply community-based participatory research approaches in this setting. Literacy programs and cancer-prevention initiatives have been held in laundromats, but little data exist on the empirical outcomes of such initiatives or whether the programs have been fashioned as a collaboration between community and researchers. In this paper, we present a brief overview of literacy and cancer-prevention initiatives that have taken place in laundromats and introduce our Wash and Spin Toward Health/Washington Avanzando la Salud Hispana (W.A.S.H.) project. We describe how we applied community-based participatory research principles to launch this effort and identify both benefits and challenges of this approach. We hope this project will stimulate greater interest in laundromat settings for outreach and education efforts, especially those addressing disparities in health literacy and access.
Comparing the Performance of AR Technology, Video Display, and Paper Instruction in Maintenance

Abstract—Augmented reality (AR) smart glasses are one of innovative device which is available implementation in maintenance task operation. The aim of this paper is to compare the effectiveness of three instructional tools (AR smart glasses, computer-based, and paper-based) in terms of time to completion, number of errors, mental workload, and satisfaction to system. Twelve healthy adults, including 6 of females and 6 of males, with average age 20.08 (SD=1.50) years old. All of them had a normal vision without color deficiency. They performed the computer assembly and disassembly tasks following the job instructions presented by paper menu, video displayed on a laptop computer, and on a set of AR glasses. The time to complete the tasks, the NASA-Task Load Index (TLX), and system usability scales (SUS) were collected. The three-way ANOVA results indicated that gender, instructional tools, and task types (dis/assembly) was all significant on time to complete the task (p<0.05). Both the NASA-TLX and SUS scores was significant different on gender. The AR glasses provided a lower number of errors compared to those of the computer video and paper menu. However, it led to longer time to complete the task for both the disassembly and assembly tasks.
Single View Scene Scale Estimation using Scale Field

In this paper, we propose a single image scale estimation method based on a novel scale field representation. A scale field defines the local pixel-to-metric conversion ratio along the gravity direction on all the ground pixels. This representation resolves the ambiguity in camera parameters, allowing us to use a simple yet effective way to collect scale annotations on arbitrary images from human annotators. By training our model on calibrated panoramic image data and the in-the-wild human annotated data, our single image scene scale estimation network generates robust scale field on a variety of image, which can be utilized in various 3D understanding and scale-aware image editing applications.
Relational Space-Time Query in Long-Form Videos

Egocentric videos are often available in the form of uninterrupted, uncurated long videos capturing the camera wearers' daily life activities. Understanding these videos requires models to be able to reason about activities, objects, and their interactions. However, current video benchmarks study these problems independently and under short, curated clips. In contrast, real-world applications, e.g. AR assistants, require bundling these problems for both model development and evaluation. In this paper, we propose to study these problems in a joint framework for long video understanding. Our contributions are three-fold. First, we propose an integrated framework, namely Relational Space-Time Query (ReST), for evaluating video understanding models via templated spatiotemporal queries. Second, we introduce two new benchmarks, ReST-ADL and ReST-Ego4D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> The latest version of our benchmark and models will be available here., which augment the existing egocentric video datasets with abundant query annotations generated by the ReST framework. Finally, we present a set of baselines and in-depth analysis on the two benchmarks and provide insights about the query tasks. We view our integrated framework and benchmarks as a step towards comprehensive, multi-step reasoning in long videos, and believe it will facilitate the development of next generations of video understanding models.
Video Compression with Entropy-Constrained Neural Representations

Encoding videos as neural networks is a recently proposed approach that allows new forms of video processing. However, traditional techniques still outperform such neural video representation (NVR) methods for the task of video compression. This performance gap can be explained by the fact that current NVR methods: i) use architectures that do not efficiently obtain a compact representation of temporal and spatial information; and ii) minimize rate and distortion disjointly (first overfitting a network on a video and then using heuristic techniques such as post-training quantization or weight pruning to compress the model). We propose a novel convolutional architecture for video representation that better represents spatio-temporal information and a training strategy capable of jointly optimizing rate and distortion. All network and quantization parameters are jointly learned end-to-end, and the post-training operations used in previous works are unnecessary. We evaluate our method on the UVG dataset, achieving new state-of-the-art results for video compression with NVRs. Moreover, we deliver the first NVR-based video compression method that improves over the typically adopted HEVC benchmark (x265, disabled b-frames, “medium” preset), closing the gap to autoencoder-based video compression techniques.
More Communication Does Not Result in Smaller Generalization Error in Federated Learning

We study the generalization error of statistical learning models in a Federated Learning (FL) setting. Specifically, there are K devices or clients, each holding an independent own dataset of size n. Individual models, learned locally via Stochastic Gradient Descent, are aggregated (averaged) by a central server into a global model and then sent back to the devices. We consider multiple (say $ \in {{\mathbb{N}}^{\text{*}}}$ ) rounds of model aggregation and study the effect of R on the generalization error of the final aggregated model. We establish an upper bound on the generalization error that accounts explicitly for the effect of R (in addition to the number of participating devices K and dataset size ). It is observed that, for fixed $\left({n,K}\right)$, the bound increases with R, suggesting that the generalization of such learning algorithms is negatively affected by more frequent communication with the parameter server. Combined with the fact that the empirical risk, however, generally decreases for larger values of R, this indicates that R might be a parameter to optimize to reduce the population risk of FL algorithms. The results of this paper, which extend straightforwardly to the heterogeneous (non-i.i.d.) data setting, are also illustrated through numerical examples.
Generalization Bounds for Neural Belief Propagation Decoders

Machine learning based approaches are being increasingly used for designing decoders for next generation communication systems. One widely used framework is neural belief propagation (NBP), which unfolds the belief propagation (BP) iterations into a deep neural network and the parameters are trained in a data-driven manner. NBP decoders have been shown to improve upon classical decoding algorithms. In this paper, we investigate the generalization capabilities of NBP decoders. Specifically, the generalization gap of a decoder is the difference between empirical and expected bit-error-rate(s). We present new theoretical results which bound this gap and show the dependence on the decoder complexity, in terms of code parameters (blocklength, message length, variable/check node degrees), decoding iterations, and the training dataset size. Results are presented for both regular and irregular parity-check matrices. To the best of our knowledge, this is the first set of theoretical results on generalization performance of neural network based decoders. We present experimental results to show the dependence of generalization gap on the training dataset size, and decoding iterations for different codes.
On the Optimal Bounds for Noisy Computing

We revisit the problem of computing with noisy information considered in Feige et al. [1], which includes computing the OR function from noisy queries, and computing the MAX, SEARCH, and SORT functions from noisy pairwise comparisons. For K given elements, the goal is to correctly recover the desired function with probability at least 1 – δ when the outcome of each query is flipped with probability p. We consider both the adaptive sampling setting where each query can be adaptively designed based on past outcomes, and the non-adaptive sampling setting where the query cannot depend on past outcomes. The prior work provides tight bounds on the worst-case query complexity in terms of the dependence on K. However, the upper and lower bounds do not match in terms of the dependence on δ and p. We improve the lower bounds for all the four functions under both adaptive and non-adaptive query models. Most of our lower bounds match the upper bounds up to constant factors when either p or δ is bounded away from 0, while the ratio between the best prior upper and lower bounds go to infinity when p → 0 or p → 1/2. On the other hand, we also provide matching upper and lower bounds for the number of queries in expectation, improving both the upper and lower bounds for variable-length query model.