Reviewerly/exHarmony huggingface dataset:
ViGather: Inclusive Virtual Conferencing with a Joint Experience Across Traditional Screen Devices and Mixed Reality Headsets

Teleconferencing is poised to become one of the most frequent use cases of immersive platforms, since it supports high levels of presence and embodiment in collaborative settings. On desktop and mobile platforms, teleconferencing solutions are already among the most popular apps and accumulate significant usage time---not least due to the pandemic or as a desirable substitute for air travel or commuting. In this paper, we present ViGather, an immersive teleconferencing system that integrates users of all platform types into a joint experience via equal representation and a first-person experience. ViGather renders all participants as embodied avatars in one shared scene to establish co-presence and elicit natural behavior during collocated conversations, including nonverbal communication cues such as eye contact between participants as well as body language such as turning one's body to another person or using hand gestures to emphasize parts of a conversation during the virtual hangout. Since each user embodies an avatar and experiences situated meetings from an egocentric perspective no matter the device they join from, ViGather alleviates potential concerns about self-perception and appearance while mitigating potential 'Zoom fatigue', as users' self-views are not shown. For participants in Mixed Reality, our system leverages the rich sensing and reconstruction capabilities of today's headsets. For users of tablets, laptops, or PCs, ViGather reconstructs the user's pose from the device's front-facing camera, estimates eye contact with other participants, and relates these non-verbal cues to immediate avatar animations in the shared scene. Our evaluation compared participants' behavior and impressions while videoconferencing in groups of four inside ViGather with those in Meta Horizon as a baseline for a social VR setting. Participants who participated on traditional screen devices (e.g., laptops and desktops) using ViGather reported a significantly higher sense of physical, spatial, and self-presence than when using Horizon, while all perceived similar levels of active social presence when using Virtual Reality headsets. Our follow-up study confirmed the importance of representing users on traditional screen devices as reconstructed avatars for perceiving self-presence.
Exploring Opportunities for Multimodality and Multiple Devices in Food Journaling

Digital food journaling can support personal goals, such as weight loss and developing healthy eating behaviors. However, traditional manual tracking demands great effort, often leading to lapses or abandonment. We explore opportunities for journaling with multiple input modalities and devices, leveraging people's daily interactions with a range of technologies. We report on an extended analysis of 15 participants' experiences with ModEat, a prototype supporting journaling with several input modalities on phone, computer, and voice assistants. Participants' modality and device preferences were largely influenced by their goals, but they frequently deviated from those preferences depending on device availability, perceived affordances, and characteristics of foods eaten. Participants rarely combined input modalities in entries, but some described that doing so allowed for more detailed journaling or serve as a placeholder for later. We discuss advantages and drawbacks of multimodal tracking and potential strategies for improving interactions.
UnifiedSense: Enabling Without-Device Gesture Interactions Using Over-the-shoulder Training Between Redundant Wearable Sensors

Wearable devices allow quick and convenient interactions for controlling mobile computers. However, these interactions are often device-dependent, and users cannot control devices in a way they are familiar with if they do not wear the same wearable device. This paper proposes a new method, UnifiedSense, to enable device-dependent gestures even when the device that detects such gestures is missing by utilizing sensors on other wearable devices. UnifiedSense achieves this without explicit gesture training for different devices, by training its recognition model while users naturally perform gestures. The recognizer uses the gestures detected on the primary device (i.e., a device that reliably detects gestures) as labels for training samples and collects sensor data from all other available devices on the user. We conducted a technical evaluation with data collected from 15 participants with four types of wearable devices. It showed that UnifiedSense could correctly recognize 5 gestures (5 gestures × 5 configurations) with an accuracy of 90.9% (SD = 1.9%) without the primary device present.
GeShort: One-Handed Mobile Text Editing and Formatting with Gestural Shortcuts and a Floating Clipboard

GeShort is a novel method for one-handed text editing and formatting on mobile devices. It uses simple rules to facilitate direct cursor positioning, gestural shortcuts inspired by keyboard hotkeys for editing and formatting, and a floating clipboard to enable delayed, repeated, and block editing. A comparison between GeShort and the default Google keyboard revealed that users perform editing and formatting tasks about 11% and 22% faster, respectively, with GeShort. This is achieved by significantly reducing selection time by 11% and action time by 17%. A second study comparing the clipboard features of the two methods revealed that users perform advanced editing tasks 34% faster with GeShort. Besides, participants find GeShort less onerous in mental demand, physical demand, and effort, which likely contribute to the overall performance gain. They also perceive GeShort as faster and easier to use, feel that its functions are better integrated, thus want to keep using it on their devices.
Single-tap Latency Reduction with Single- or Double- tap Prediction

Touch surfaces are widely utilized for smartphones, tablet PCs, and laptops (touchpad), and single and double taps are the most basic and common operations on them. The detection of single or double taps causes the single-tap latency problem, which creates a bottleneck in terms of the sensitivity of touch inputs. To reduce the single-tap latency, we propose a novel machine-learning-based tap prediction method called PredicTaps. Our method predicts whether a detected tap is a single tap or the first contact of a double tap without having to wait for the hundreds of milliseconds conventionally required. We present three evaluations and one user evaluation that demonstrate its broad applicability and usability for various tap situations on two form factors (touchpad and smartphone). The results showed PredicTaps reduces the single-tap latency from 150--500 ms to 12 ms on laptops and to 17.6 ms on smartphones without reducing usability.
A First Exploration on the Use of Head-Mounted Augmented Reality in the Context of the Portuguese Military

In this paper, we present the design and implementation of a first iteration of an augmented reality (AR) system for dismounted soldiers in the Portuguese military. We started the work via a survey of 86 members of the military to better understand their experience, needs, and preferences with current Command &amp; Control (C2) systems. We then assessed the effects of our prototype on the performance, situational awareness, and perceived usability and workload of 13 participants from a local Commando Regiment. We compared our results to a representative baseline using a paper map and radio in a hostage extraction simulation and found that our first AR iteration, despite a short practice session, increased the quality of the information available and decreased the complexity, temporal demands, and effort required to complete the study tasks; leading to an overall decrease in perceived workload. Overall, participants described the AR experience as more user-friendly. We conclude our case study with research ideas for further iterations of our prototype.
Print Book and e-Book Collections in Academic Libraries in Malawi

Arrival of e-books on the scene has had a significant effect on the collection development activities of many academic libraries. Whilst some libraries have made the radical decision to ditch print books in favor of e-books, others are cautiously navigating the new terrain by acquiring both. The current study used the survey research design to study the collection development trends of print books and e-books in academic libraries in Malawi. The study found that academic libraries in Malawi are acquiring both print books and e-books. The trend has resulted from collection development decisions, practices, and budgetary constraints.
Evaluation methodology for deep learning imputation models

&lt;p&gt;There is growing interest in imputing missing data in tabular datasets using deep learning. Existing deep learning–based imputation models have been commonly evaluated using root mean square error (RMSE) as the predictive accuracy metric. In this article, we investigate the limitations of assessing deep learning–based imputation models by conducting a comparative analysis between RMSE and alternative metrics in the statistical literature including qualitative, predictive accuracy, statistical distance, and descriptive statistics. We design a new aggregated metric, called reconstruction loss (RL), to evaluate deep learning–based imputation models. We also develop and evaluate a novel imputation evaluation methodology based on RL. To minimize model and dataset biases, we use a regression imputation model and two different deep learning imputation models: denoising autoencoders and generative adversarial nets. We also use two tabular datasets from different industry sectors: health care and financial. Our results show that the proposed methodology is effective in evaluating multiple properties of the deep learning–based imputation model’s reconstruction performance.&lt;/p&gt;
A Dynamic Drilling Sampling Method and Evaluation Model for Big Streaming Data

The big data sampling method for real-time and high-speed streaming data is prone to lose the value and information of a large amount of discrete data, and it is not easy to make an efficient and accurate evaluation of the value characteristics of streaming data. The SDSLA sampling method based on mineral drilling exploration can evaluate the valuable information of streaming data containing many discrete data in real-time, but when the range of discrete data is irregular, it has low sampling accuracy for discrete data. Based on the SDSLA algorithm, we propose a dynamic drilling sampling method SDDS, which takes well as the analysis unit, dynamically changes the size and position of the well, and accurately locates the position and range of discrete data. A new model SDVEM is further proposed for data valuation, which evaluates the sample set from discrete, centralized, and overall dimensions. Experiments show that compared with the SDSLA algorithm, the sample sampled by the SDDS algorithm has higher evaluation accuracy, and the probability distribution of the sample is closer to the original streaming data, with the AOCV indicator being nearly 10% higher. In addition, the SDDS algorithm can achieve over 90% accuracy, recall, and F1 score for training and testing neural networks with small sampling rates, all of which are higher than the SDSLA algorithm. In summary, the SDDS algorithm not only accurately evaluates the value characteristics of streaming data but also facilitates the training of neural network models, which has important research significance in big data estimation.
Don't Forget to Disinfect: Understanding Technology-Supported Hand Disinfection Stations

The global COVID-19 pandemic created a constant need for hand disinfection. While it is still essential, disinfection use is declining with the decrease in perceived personal risk (e.g., as a result of vaccination). Thus this work explores using different visual cues to act as reminders for hand disinfection. We investigated different public display designs using (1) paper-based only, adding (2) screen-based, or (3) projection-based visual cues. To gain insights into these designs, we conducted semi-structured interviews with passersby (N=30). Our results show that the screen- and projection-based conditions were perceived as more engaging. Furthermore, we conclude that the disinfection process consists of four steps that can be supported: drawing attention to the disinfection station, supporting the (subconscious) understanding of the interaction, motivating hand disinfection, and performing the action itself. We conclude with design implications for technology-supported disinfection.
Drawing Connections: Designing Situated Links for Immersive Maps

We explore the design of situated visual links in outdoor augmented reality (AR) for connecting miniature buildings on a virtual map to their real-world counterparts. We first distill design criteria from prior work, then conduct two user studies to evaluate a set of proposed link designs to better understand users' preferences for different design choices of the links. In two user studies we evaluated, respectively, a set of link geometries in a virtual environment and a refined AR prototype in two different outdoor environments. The studies reveal that links help in identifying buildings in the environments. Participants prefer straight rather than curved links, simple and thin links to avoid information occlusion, and links and maps aligned with their direction of view. We recommend using a consistent color with a strong contrast to the background color for all links in a scene. To improve visibility, the diameter of links should grow with distance to the viewer and optional animated stripes can be placed on links. The findings of this study have the potential to bolster the development of various situated visualization applications, such as those used in urban planning, tourism, smart agriculture, and other fields.
Entanglement statistics of randomly interacting spins

We investigate the entanglement in the ground state of systems comprising two and three qubits with random interactions. Since the Hamiltonians also contain deterministic one-body terms, by varying the interaction strength, one can continuously interpolate between deterministic separable eigenstates and fully random entangled eigenstates, with nontrivial intermediate behavior. Entanglement strongly depends on the underlying topology of the interaction among the qubits. Since W states correspond to a zero-measure set as compared to the set of Greenberger-Horne-Zeilinger (GHZ) states, in all investigated cases the ground states are of the latter type. However, for a certain class of interactions (nonseparable collective potential) high GHZ entanglement is produced, while for fully separable pairwise interactions the marginal GHZ ground states concentrate in the vicinity of W states.
A Naive Prover for First-Order Logic: A Minimal Example of Analytic Completeness

Abstract The analytic technique for proving completeness gives a very operational perspective: build a countermodel to the unproved formula from a failed proof attempt in your calculus. We have to be careful, however, that the proof attempt did not fail because our strategy in finding it was flawed. Overcoming this concern requires designing a prover. We design and formalize in Isabelle/HOL a sequent calculus prover for first-order logic with functions. We formalize soundness and completeness theorems using an existing framework and extract executable code to Haskell. The crucial idea is to move complexity from the prover itself to a stream of instructions that it follows. The result serves as a minimal example of the analytic technique, a naive prover for first-order logic, and a case study in formal verification.
Proof-Theoretic Semantics for Intuitionistic Multiplicative Linear Logic

Abstract This work is the first exploration of proof-theoretic semantics for a substructural logic. It focuses on the base-extension semantics (B-eS) for intuitionistic multiplicative linear logic ( $$\mathrm IMLL$$ ). The starting point is a review of Sandqvist’s B-eS for intuitionistic propositional logic (IPL), for which we propose an alternative treatment of conjunction that takes the form of the generalized elimination rule for the connective. The resulting semantics is shown to be sound and complete. This motivates our main contribution, a B-eS for $$\mathrm IMLL$$ , in which the definitions of the logical constants all take the form of their elimination rule and for which soundness and completeness are established.
MLCM: Multi-Label Confusion Matrix

&lt;p&gt;Concise and unambiguous assessment of a machine learning algorithm is key to classifier design and performance improvement. In the multi-class classification task, where each instance can only be labeled as one class, the confusion matrix is a powerful tool for performance assessment by quantifying the classification overlap. However, in the multi-label classification task, where each instance can be labeled with more than one class, the confusion matrix is undefined. Performance assessment of the multi-label classifier is currently based on calculating performance averages, such as hamming loss, precision, recall, and F-score. While the current assessment techniques present a reasonable representation of each class and overall performance, their aggregate nature results in ambiguity when identifying false negative ( FN ) and false positive ( FP ) results. To address this gap, we define a method of creating the multi-label confusion matrix (MLCM) based on three proposed categories of multi-label problems. After establishing the shortcomings of current methods for identifying FN and FP , we demonstrate the usage of the MLCM with the classification of two publicly available multi-label data sets: i) a 12-lead ECG data set with nine classes, and ii) a movie poster data set with eighteen classes. A comparison of the MLCM results against statistics from the current techniques is presented to show the effectiveness in providing a concise and unambiguous understanding of a multi-label classifier behavior. &lt;/p&gt;
RingVKB: A Ring-Shaped Virtual Keyboard Using Low-Cost IMU

Wearable devices have been important components for ubiquitous computing. However, text input remains challenging on wearables due to the lack of a physical keyboard. In this paper, we propose a novel ring-shaped virtual keyboard system named RingVKB for convenient text input using low-cost IMUs available on any wearables. At the core of RingVKB are two novel designs: 1) A circular keyboard layout with 12 equal sectors, which assembles all common keys on classical keyboards while allowing users to type with only one finger effectively, and 2) an error control algorithm that calculates the relative displacement of keystrokes from the noisy IMU sensor data. The two components, coupled together, enable high-accuracy and efficient text input for ubiquitous scenarios.We implement RingVKB using a small device consisting of a microcontroller and a MEMS sensor, which can be attached to the user's index finger.Experimental results show that RingVKB can effectively improve the relative displacement estimation accuracy, and achieves an overall keystroke recognition accuracy of 93% for 25 key positions. A user study also shows that RingVKB is easy to learn and use. Using only low-cost IMU sensors, RingVKB provides a virtual keyboard solution that can be widely adopted on wearables.
Systematic effects in the search for a muon electric dipole moment using the frozen-spin technique

Abstract At the Paul Scherrer Institute we are developing a high precision instrument to measure the electric dipole moment (EDM) of the muon. The salient feature of the experiment is the use of the frozen-spin technique to suppress the anomalous precession of the muon spin, allowing for a sensitivity that cannot be achieved with conventional g-2 muon storage rings. With this technique, the expected statistical sensitivity for the EDM after one year of data taking is 6 × 10 -23 e · cm with a p = 125 MeV/c muon beam available at PSI. Reaching this goal necessitates a comprehensive analysis on spurious effects that mimic the EDM signal. This work discusses a quantitative approach to study systematic effects for the frozen-spin technique when searching for the muon EDM with a focus on the kinematics of decay positrons in the context of this experiment, which operates at muon momenta well below the ultrarelativistic limit.
An alternative intervention for juvenile hackers? A qualitative evaluation of the Hack_Right intervention

While traditional crime rates are decreasing, cybercrime is on the rise. As a result, the criminal justice system is increasingly dealing with criminals committing cyber-dependent crimes. However, to date there are no effective interventions to prevent recidivism in this type of offenders. Dutch authorities have developed an intervention program, called Hack_Right. Hack_Right is an alternative criminal justice program for young first-offenders of cyber-dependent crimes. In order to prevent recidivism, this program places participants in organizations where they are taught about ethical hacking, complete (technical) assignments and reflect on their offense. In this study, we have evaluated the Hack_Right program and the pilot interventions carried out thus far. By examining the program theory (program evaluation) and implementation of the intervention (process evaluation), the study adds to the scarce literature about cybercrime interventions. During the study, two qualitative research methods have been applied: 1) document analysis and 2) interviews with intervention developers, imposers, implementers and participants. In addition to the observation that the scientific basis for linking specific criminogenic factors to cybercriminals is still fragile, the article concludes that the theoretical base and program integrity of Hack_Right need to be further developed in order to adhere to principles of effective interventions.
NaCanva: Exploring and Enabling the Nature-Inspired Creativity for Children

Nature has been a plentiful source of materials, replenishment, inspiration, and creativity. Nature collage, as a crafting technique, is a fun and educational activity for children to explore nature and engage their creativity. However, the raw material collection is limited to static things such as leaves, ignoring inspiration from nature sounds and dynamic elements such as babbling creeks. Using a mobile application, we hope to encourage children's creativity by renewing collage materials collection and careful observation in nature. To explore this, we conducted a formative study with children (N=20) and a design workshop with experts (N=6) to formulate NaCanva, an AI-assisted multi-modal collage creation system for children. Drawing on the interactivity between children and nature, NaCanva enables the multi-modal material collection, including images, sound, and videos, which differs our system from traditional collages. We validated this system with a between-subject user study (N=30), and the results suggested that NaCanva unleashes children's creativity in nature collage creation by enhancing children's multidimensional observation and engagement in nature.