Reviewerly/exHarmony huggingface dataset:
Improving Byzantine Fault Tolerance in Swarm Robotics Collective Decision-making Scenario via a New Blockchain Consensus Algorithm

Swarm robotics applies concepts of swarm intelligence to robotics. Discrete consensus achievement is one of the major behaviors found in swarm robotics. Various algorithms have been developed for discrete consensus achievement. However, existing discrete consensus achievement algorithms are vulnerable to Byzantine robots. Blockchain has been successfully used to mitigate the negative effect of Byzantine robots. Nevertheless, since the blockchain solution uses the Proof-of-Work blockchain consensus algorithm, it is vulnerable to the 51% attack. Besides, the swarm also takes longer to achieve consensus. This research proposes a novel blockchain consensus algorithm called Proof-of-Identity—which uses a private-public key pair and a swarm controller—to create a dynamically permissioned blockchain that would negate the 51%-attack problem associated with the Proof-of-Work algorithm while also reducing the consensus time. This proposed solution was tested against the classical solution and the existing blockchain solution using the collective perception scenario. Test results show that the Proof-of-Identity algorithm prevents the 51%-attack problem while improving the consensus time in comparison to the existing blockchain solution without affecting the exit probability.
The future is now? Consumers' paradoxical expectations of human-like service robots

The increasing adoption of human-like intelligent robots in various services has raised significant social and ethical concerns about their future implications. This study investigates how consumers' perceptions of the future development of anthropomorphic features in service robots influence their expectations and acceptance of this emerging technology. Focusing on human-like appearance and mind, we utilize the expectancy-value theory to propose a conceptual framework that delves into consumers' paradoxical expectations. Through a survey of 486 participants, we examine how these perceptions, combined with levels of technology anxiety, shape psychological expectations and subsequently impact the willingness to adopt service robots. Our findings highlight that consumers' perceptions and anxiety levels predict paradoxical expectations, which in turn influence acceptance. This study contributes by introducing a novel framework, exploring the human-like mind in robot anthropomorphism, and addressing the intricate interplay between consumers' perceptions and service robots.
Workshop on Advances of Mobile and Wearable Biometrics (WAMWB)

Biometrics is defined as the automated recognition of individuals based on their biological and behavioural characteristics. It represents a fundamental aspect of mobile Human-Computer Interaction (HCI), as mobile devices such as smartphones and wearables are designed to capture, process and transmit biometric data. While people benefit from the innumerable applications of biometric data in the context of HCI, new concerns have raised in relation with the performance, reliability, protection of privacy, bias, misuse, regulations, and their impact on society. The Workshop on Advances of Mobile and Wearable Biometrics (WAMWB) aims to highlight recent developments with respect to such challenges and risks.
A quantum algorithm for finding collision-inducing disturbance vectors in SHA-1

Abstract Modern cryptographic protocols rely on sophisticated hash functions to generate quasi-unique numbers that serve as signatures for user authentication and other security verifications. The security could be compromised by finding texts hash-mappable to identical numbers, forming so-called collision attack. Seeding a disturbance vector in the hash mapping to obtain a successful collision is that a major focus of cryptography study in the past two decades to improve hash protocols. We propose an algorithm that takes advantage of entangled quantum states for concurrent seeding of candidate disturbance vectors, out of which the one entailing collision is selected through a combination of quantum search, phase gating, diffusion gating, and information feedbacks from classical computing machinery. The complexity reduction is shown to be on the order of <?CDATA ${ \mathcal O }({2}^{n/2+1})$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi mathvariant="italic"></mml:mi> <mml:mo stretchy="false">(</mml:mo> <mml:msup> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> <mml:mrow> <mml:mi>n</mml:mi> <mml:mrow> <mml:mo stretchy="true">/</mml:mo> </mml:mrow> <mml:mn>2</mml:mn> <mml:mo>+</mml:mo> <mml:mn>1</mml:mn> </mml:mrow> </mml:msup> <mml:mo stretchy="false">)</mml:mo> </mml:math> where n is the number of qubits encoding addresses. We demonstrate the practicality of the proposed by an implementation scheme based on degenerate optical parametric oscillators.
It Is Time to Let Go of 'Virtual Reality'

Advocating standardized terminology and reporting guidelines for mixed reality research.
Designing for Diverse Populations in Mobile Research

Companies are adapting to fast-changing business environments and reduced product life cycle time with targeted scope and priorities. UX researchers working in tandem with product teams are also adapting by focusing on our core consumer populations with our study design and recruitment targets. Yet, our mobile user base is composed of many diverse populations. We describe how, by studying diverse samples with an inclusive design approach followed by an evaluative approach to quantify needs with core consumer samples, we meet the needs of all. We aim to demonstrate that inclusive design is complementary to supporting our business priorities in fast-paced environments, meaning we can design for all and achieve quality of findings within the constraints of reduced product life cycle time.
Transversal diagonal logical operators for stabiliser codes

Abstract Storing quantum information in a quantum error correction code can protect it from errors, but the ability to transform the stored quantum information in a fault tolerant way is equally important. Logical Pauli group operators can be implemented on Calderbank-Shor-Steane (CSS) codes, a commonly-studied category of codes, by applying a series of physical Pauli X and Z gates. Logical operators of this form are fault-tolerant because each qubit is acted upon by at most one gate, limiting the spread of errors, and are referred to as transversal logical operators. Identifying transversal logical operators outside the Pauli group is less well understood. Pauli operators are the first level of the Clifford hierarchy which is deeply connected to fault-tolerance and universality. In this work, we study transversal logical operators composed of single- and multi-qubit diagonal Clifford hierarchy gates. We demonstrate algorithms for identifying all transversal diagonal logical operators on a CSS code that are more general or have lower computational complexity than previous methods. We also show a method for constructing CSS codes that have a desired diagonal logical Clifford hierarchy operator implemented using single qubit phase gates. Our methods rely on representing operators composed of diagonal Clifford hierarchy gates as diagonal XP operators and this technique may have broader applications.
Individual versus simultaneous estimation of two parameters in a correlated noisy environment

Abstract Recently, the phase parametric estimation for a single qubit system driven by a phase noisy laser under non-Markovian dynamics has been reported [Annals of Physics 351, 952 (2014)]. We here generalize the single-parameter estimation to the two-parameter scenario where a two-qubit system is influenced by the classical correlated noisy laser, and compare the performances of two-parameter estimation in both individual and simultaneous strategies by using the quantum Fisher information and the quantum Fisher information matrix, respectively. Our results show the phase parametric estimation precision can be significantly improved due to the memory effect arising from successive applications of the noisy laser. With the memory coefficient μ increasing, the precision of parameter estimation becomes more accurate. Besides, the phase parameter estimation precision can be drastically enhanced by engineering the ratios of classical phase diffusion rate and system-environment coupling strength in both Markovian and non-Markovian regions. Remarkably, we find the simultaneous estimation of two-phase parameters is not always advantageous over the individual strategy.
Show It or Tell It? Text, Visualization, and Their Combination

When communicating information, language should be considered as co-equal with visualization.
FaceWard: Face Anonymization in Group Photos

Sharing photos on social media and messaging services often result in a vast amount of personal data being made public online. As a result, it has become increasingly vital to devise measures that ensure privacy protection, especially for people who want to maintain social boundaries by hiding their faces in group photos. In this paper, we propose FaceWard, an automatic system for face anonymization of people different from a target person. FaceWard is based on a pattern matching algorithm and supports different anonymization policies such as blur or smiley overlays. Taken together, FaceWard yields a very efficient solution, eliminating the need for computationally expensive training of complex machine learning models, thus offering a practical trade-off between prediction accuracy and data availability. FaceWard is publicly available as open source software.
Generating and Exploiting Automated Reasoning Proof Certificates

Moving toward a full suite of proof-producing automated reasoning tools with SMT solvers that can produce full, independently checkable proofs for real-world problems.
Representing microgestures interaction for wearable computing

Microgestures, i.e. fast and subtle movements of the fingers, offer always available and versatile interaction that makes them promising for wearable computing. While research has been done to define sets of microgestures and sensing systems, we do not know how to best represent microgestures and their associated commands which is essential for the adoption of microgesture interaction. Thus, this thesis focuses on how microgestures and their corresponding commands can be represented to users and discovered by them.
Exploring Real-Time Collaborative Heart Rate Displays for Cycling Partners

Training with a partner is beneficial for promoting enjoyment and persistence. However, there has been little research on real-time collaborative displays that enable co-located training partners to share data, such as heart rate (HR). We developed a prototype interface that enables cycling partners to view each others’ HRs both as beats per minute and as percentage of each user’s maximum HR. In a pilot study with six participants, we found that the real-time display may be particularly helpful for less experienced recreational cyclists, and highly experienced cyclists find it useful when road conditions are dynamic. We also observed that real-time collaborative data may facilitate more social dialogue. All participants indicated that they would use this app in the future. This study highlights the importance of future research on real-time interfaces for exercise, with the goal of enabling more users to integrate physical activity into their lives through successful collaboration.
Modelling human behavior during text entry

Mobile text entry, used in several contexts and done in a variety of ways, has been a topic of ongoing research. Research in mobile text entry has largely depended on empirical evaluations with human participants, where users are asked to complete specific tasks to measure their performance. Until recently, metrics used for study results were based on solely users’ touch input, while ignoring other factors involved in the process of text entry. We aim to develop novel metrics and models of behavior during text entry, which will incorporate aspects of human motor and cognitive processes, by adopting new technologies, to enhance current text entry research and meliorate our understanding of users’ text entry behaviors.
RoboType: Realistic Mobile Text Entry Evaluations with Synthetic Users

Mobile text entry research is contingent on user-based evaluations, which are frequently carried out in small-scale lab experiments. Recruitment and execution of experiments are costly in terms of time and effort required. Further, the results are hard to generalise, because of the small number of participants in the experiments. Studies are sometimes replicable, but not reproducible. RoboType is a realistic open-source simulator for mobile text entry, written in Python and based on the consolidation of state-of-the-art understanding of human behaviour during entry tasks. It aims to aid researchers to produce fast and accurate evaluations of mobile text entry ideas with fully reproducible results. It can be used to significantly reduce time required to explore the design space of new prototypes on various user bases, or as a benchmark to compare results with other researchers. This paper presents an early implementation of RoboType and demonstrate its promising potential.
Predicting Users’ Attention Breakpoints During Mobile Text Entry

During mobile text entry, users must shift their gaze from the keyboard to the entry area to check for errors. Many of these shifts are wasteful, since users have not committed any errors in the input stream. This waste might be reduced if, for example, on-keyboard visual feedback about the presence or absence of errors could be provided to users during text entry. However, constant feedback might result in visual clutter disruptive to the input process. We aim to address the challenge of predicting opportune moments (breakpoints) in user’s attention to the keyboard to deliver such feedback, utilizing open gaze data collected from 30 participants while typing a) using the index finger of user’s dominant hand and b) using both thumbs. We find that our model achieves promising results in predicting attention breakpoints under both typing conditions and, particularly, during text entry with both thumbs.
Mobility and Utility in Robot Mediated Interaction

In recent years virtual meetings have become the predominant alternative to face-to-face meetings. Ongoing efforts in the design of telepresence robots promise remote access to physical settings and a greater sense of presence, leading to improved remote collaboration. However, a comparable sense of physical presence and utility has yet to be achieved. Mobile telepresence still provides limited ways to interact with remote users (e.g., with the environment and other people). This workshop aims to re-imagine telepresence robots, moving away from the decades-old ‘iPad-on-a-stick’ paradigm. Using interactive activities involving existing telepresence robots and a hybrid workshop format, we hope to ideate ways of expanding the capabilities of mobile telepresence robots through a range of mechanisms (e.g., mobile and wearable technology, Augmented Reality, Internet of Things, etc.) and to inform the future design of these devices to provide additional affordances. In doing so, we plan to identify use cases for which mobile telepresence robots can provide additional value through their locomotive capabilities compared to current screen-based remote interactions. Lastly, we aim to identify scenarios for future research in Mobile HCI using use cases and affordances identified during the workshop to support more equitable participation for remote users.
EmoFlow: Visualizing Emotional Changes in Video Chat - Preliminary Study

We propose EmoFlow, a prototype service that can recognize the emotional changes of users in video chat and provide a text-based summary along with various visualizations. We present design concepts to represent users’ emotional flow on the mobile messenger application in three types: Emoji, Color, and Shape visualizations. Through a user study with 60 voluntary participants, we found that the Emoji type received the highest score in terms of conveying emotions and design preferences. Also, 65 percent of the participants responded they are willing to use the proposed service if several points are enhanced in the future. The survey results suggest that the proposed service would help users understand and manage their emotional status.
DianTea:Designing and Evaluating an Immersive Virtual Reality Game to Enhance Youth Tea Culture Learning

As an intangible cultural heritage of China, dian cha is a unique tea-drinking ritual in traditional Chinese tea culture. The ritual comprises various tea-drinking utensils and a specific tea art performance. Nowadays, many young Chinese increasingly ignore traditional Chinese cultural activities because of their high work pressures and fast-paced lives. To broaden the audience for traditional Chinese tea ceremonies and stimulate young people’s interest in learning tea culture, we propose “DianTea” as an immersive virtual reality tea-drinking game where remote players can practice the traditional dian cha ritual. The game design also includes a virtual teacher, multimodal interaction, social scene, and game-based learning. In this paper, we also perform an intersubject user study of the game’s interactive and browsing versions. The results show that the interactive game can significantly enhance learners’ knowledge acquisition, interactive experience, and sense of participation in the dian cha ritual.
Design Principles for AI UX Delivering User Value for Samsung Galaxy

In a collaboration between Samsung Electronics’ Mobile UX and Research Institutes of Sweden (RISE) we created a teaching material on how to design with AI. The material is aimed for UX-designer and consists of design principles, example designs, UX-values, ethics and a business proposition motivating why and where to use AI in the Galaxy UX.
Multi-hop question answering using sparse graphs

Multi-hop question answering (QA) across multiple documents requires a deep understanding of relationships between entities in documents, questions, and answer candidates. Graph Neural Networks (GNNs) have emerged as a promising tool for multi-hop QA tasks. These models often suffer from increasing computational and model complexity, which makes them inefficient for real-world applications with limited resources. In this paper, we propose a graph-based approach called Sparse Graph-based Multi-hop Question Answering system (SG-MQA), which provides a throughout examination of the mentioned challenges and presents appropriate measures to address them. We propose a novel approach based on the Relational Graph Convolutional Network (R-GCN) that reduces the model complexity and improves its performance. We have utilized various strategies and conducted multiple experiments to achieve this goal. We show the efficacy of the proposed approach by examining the results of experiments on two QA datasets, namely WikiHop and HotpotQA. The SG-MQA model outperforms all the state-of-the-art (SOTA) methods on WikiHop and increases the accuracy of the best previous approach from 74.4% to 78.3%. Additionally, it achieves acceptable performance on HotpotQA. Although, according to the F1 measure, the performance of SG-MQA is inferior to that of the SOTA model, it is comparable to that of all other approaches. On the other hand, based on the Exact Match (EM) measure, SG-MQA shows comparable performance to that of the SOTA model and outperforms all other approaches.
INTERACT: An authoring tool that facilitates the creation of human centric interaction with 3d objects in virtual reality

A widespread adoption of Virtual, Augmented, and Mixed Reality (VR/AR/MR), collectively referred to as Extended Reality (XR), has become a tangible possibility to revolutionize educational and training scenarios by offering immersive, interactive experiences. In this paper we present INTERACT, an authoring tool for creating advanced 3D physics-based Intelligent Tutoring Systems (ITS) by individual developers or small-scale development teams. INTERACT is based on a cutting edge physics engine allowing realistic interactions such as collision detection and ergonomic evaluations. We demonstrate the benefits of INTERACT by developing a set of training scenarios for a use case of a Laser cutting machine. The use case illustrates the numerous possibilities such as creating interaction with objects, ease of configuring a scenario and how to design the visual effects to the machine.
New Data Collection Approach to Investigate Touch Based Errors: A Case Study of Smartphone System Navigation

Studying touch based errors on mobile devices (e.g. swipes not registering or misregistering…) is challenging due to the difficulty of obtaining user intention along with high-fidelity touch event data. In a recent three-week longitudinal study investigating smartphone system navigation errors, we preinstalled custom data collection software on test phones. The software allowed 53 US-based participants to generate error reports containing their intention, outcome, screenshots, and touch data for the last three minutes. We adhered to strict ethical data collection practices. Participants could exit the study at any time and request all their data deleted. Only participants could generate error reports, could cancel when desired, and were warned of what data was being saved. Participants also submitted video diary entries. Both touch event and diary data revealed consistent and insightful findings on the causes of gesture errors. Our approach can help usability professionals meet similar challenges by analyzing touch event data and user intention to accurately identify the root causes of errors.
"I Can Feel What I Used": A Diary Study of Smartwatch Features and Emotional Experiences

This study focuses on the usability of the smartwatch device in long-term use. Five participants were asked to wear the smartwatch every day and write daily diary entries for 30 days. This study aims to understand how people use and interact with the smartwatch features and how this affects their emotional experience. We found a significant relationship between smartwatch features and emotional experience: smartwatch features are positively and negatively related to users’ emotional experience. The results show that participants primarily use mental health and well-being, and contextual alert features to enhance their performance and increase productivity. Participants respond to the renewal event with immediate response, body health monitoring, and physical activity tracking features. When participants experience burnout and discomfort, they take the smartwatch off. This study contributes to the evaluation of the design of further smartwatch technologies by improving the user experience and interaction.
V-ir-Net: A Novel Neural Network for Pupil and Corneal Reflection Detection trained on Simulated Light Distributions

Deep learning has shown promise for gaze estimation in Virtual Reality (VR) and other head-mounted applications, but such models are hard to train due to lack of available data. Here we introduce a novel method to train neural networks for gaze estimation using synthetic images that model the light distributions captured in a P-CR setup. We tested our model on a dataset of real eye images from a VR setup, achieving 76% accuracy which is close to the state-of-the-art model which was trained on the dataset itself. The localization error for CRs was 1.56 pixels and 2.02 pixels for the pupil, which is on par with state-of-the-art. Our approach allowed inference on the whole dataset without sacrificing data for model training. Our method provides a cost-efficient and lightweight training alternative, eliminating the need for hand-labeled data. It offers flexible customization, e.g. adapting to different illuminator configurations, with minimal code changes.