Reviewerly/exHarmony huggingface dataset:
Roles of Artificial Intelligence and Extended Reality Development in the Post-COVID-19 Era

This paper explores recent roles of artificial intelligence and extended reality development during the coronavirus pandemic and then predicts their significant roles in the post-COVID-19 era in an interdisciplinary manner. To begin with, we investigate roles of artificial intelligence in tackling coronavirus during the outbreak since 2020 until today. It has been effectively used for many ways, such as forecasting the spread of COVID-19 on multimodal data using data analytics, preliminary diagnosis the virus disease from specific symptoms using machine learning, and analyzing big data from social media platforms to accurately prevent the spread of virus. At the same time, due to rapid advancement in recent immersive technology and extended reality is a very popular research topic in computer science, we discuss roles of extended reality which has been extensively used during the virus outbreak for various purposes, such as supporting for businesses and education and helping the medical and health care workers. For instance, it can used for supporting psychological recovery from medical treatment for virus patients, reducing the face-to-face interactivity of physicians with the symptomatic patients, and helping people with the use of telemedicine. Next, we present a new summary of integrated roles of artificial intelligence and extended reality development in the post-COVID-19 era in an interdisciplinary perspective. Moreover, we suggest possible directions of artificial intelligence in extended reality which can be used to guide the design of the next-generation human-computer interaction applications in the future.
Students’ Immersive Experience in Initial Teacher Training in a Virtual World to Promote Sustainable Education: Interactivity, Presence, and Flow

The Virtual World is a technology that has created countless opportunities for teaching and learning, innovating traditional and online education, and promoting a more sustainable and accessible education. Through their avatars and digital representations, students can navigate, observe, and manipulate virtual objects, while interacting with their classmates inside the simulated 3D environment. This study examined how preservice teachers experience and participate in a VW that simulates a university campus, considering three main components: interactivity, sense of presence, and state of flow. A total of 103 pedagogy students, enrolled in an educational technology course, participated in the study. A postintervention survey was implemented, as well as a self-report about the immersive experience. The results show a high level of agreement with the survey’s affirmations, which allows for the determination of the favorable levels of interactivity, presence, and flow, as well as the meaningful and positive associations among these technological properties. Guidelines are argued to deepen the Virtual World’s potential and are given for the design of pedagogical activities in those environments.
Efficient Data Optimisation for Harmonic Inpainting with Finite Elements

Harmonic inpainting with optimised data is very popular for inpainting-based image compression. We improve this approach in three important aspects. Firstly, we replace the standard finite differences discretisation by a finite element method with triangle elements. This does not only speed up inpainting and data selection, but even improves the reconstruction quality. Secondly, we propose highly efficient algorithms for spatial and tonal data optimisation that are several orders of magnitude faster than state-of-the-art methods. Last but not least, we show that our algorithms also allow working with very large images. This has previously been impractical due to the memory and runtime requirements of prior algorithms.
Evaluating Wearable Tactile Feedback Patterns During a Virtual Reality Fighting Game

A two-study approach was used to explore the effects of tactile feedback patterns on virtual reality gaming experiences of participants, including presence, flow, usability, emotion, technology adoption, and tactile sensation. In our main study, participants were instructed to play a virtual reality fighting game. Five experimental conditions with different tactile feedback patterns (no-tactile, random, fixed, discrete, and realistic tactile feedback) were examined. The results of our study indicated that: (1) tactile feedback patterns have no significant effect on flow and emotion, and (2) the no-tactile feedback condition was rated significantly lower by the participants than other tactile feedback conditions in terms of presence, usability, technology adoption, and tactile sensation. A follow-up study was then conducted to understand the different effects of realistic feedback in active versus passive fighting games. The results of our follow-up study indicated that participants rated tactile sensation and usability higher when under the passive gaming condition. We discuss our findings along with the study limitations and future research directions.
Collusion-free for Cloud Verification toward the View of Game Theory

At present, clients can outsource lots of complex and abundant computation, e.g., Internet of things (IoT), tasks to clouds by the “pay as you go” model. Outsourcing computation can save costs for clients and fully utilize the existing cloud infrastructures. However, it is hard for clients to trust the clouds even if blockchain is used as the trusted platform. In this article, we utilize the verification method as SETI@home by only two rational clouds, who hope to maximize their utilities. Utilities are defined as the incomes of clouds when they provide computation results to clients. More specifically, one client outsources two jobs to two clouds and each job contains n tasks, which include k identical sentinels. Two clouds can either honestly compute each task or collude on the identical sentinel tasks by agreeing on random values. If the results of identical sentinels are identical, then client regards the jobs as correctly computed without verification. Obviously, rational clouds have incentives to deviate by collusion and provide identical random results for a higher income. We discuss how to prevent collusion by using deposits, e.g., bit-coins. Furthermore, utilities for each cloud can be automatically assigned by a smart contract. We prove that, given proper parameters, two rational clouds will honestly send correct results to the client without collusion.
OpenSquare: Decentralized Repeated Modular Squaring Service

Repeated Modular Squaring is a versatile computational operation that has led to practical constructions of timed-cryptographic primitives like time-lock puzzles (TLP) and verifiable delay functions (VDF) that have a fast growing list of applications. While there is a huge interest for timed-cryptographic primitives in the blockchains area, we find two real-world concerns that need immediate attention towards their large-scale practical adoption: Firstly, the requirement to constantly perform computations seems unrealistic for most of the users. Secondly, choosing the parameters for the bound (T) seems complicated due to the lack of heuristics and experience. We present OpenSquare, a decentralized repeated modular squaring service, that overcomes the above concerns. OpenSquare lets clients outsource their repeated modular squaring computation via smart contracts to any computationally powerful servers that offer computational services for rewards in an unlinkable manner.
Exploring Non-Autoregressive End-to-End Neural Modeling for English Mispronunciation Detection and Diagnosis

End-to-end (E2E) neural modeling has emerged as one predominant school of thought to develop computer-assisted pronunciation training (CAPT) systems, showing competitive performance to conventional pronunciation-scoring based methods. However, current E2E neural methods for CAPT are faced with at least two pivotal challenges. On one hand, most of the E2E methods operate in an autoregressive manner with left-to-right beam search to dictate the pronunciations of an L2 learners. This however leads to very slow inference speed, which inevitably hinders their practical use. On the other hand, E2E neural methods are normally data-hungry and meanwhile an insufficient amount of nonnative training data would often reduce their efficacy on mispronunciation detection and diagnosis (MD&D). In response, we put forward a novel MD&D method that leverages non-autoregressive (NAR) E2E neural modeling to dramatically speed up the inference time while maintaining performance in line with the conventional E2E neural methods. In addition, we design and develop a pronunciation modeling network stacked on top of the NAR E2E models of our method to further boost the effectiveness of MD&D. Empirical experiments conducted on the L2-ARCTIC English dataset seems to validate the feasibility of our method, in comparison to some top-of-the-line E2E models and an iconic pronunciation-scoring based method built on a DNN-HMM acoustic model.
An Evaluation of Virtual Reality for Fear Arousal Safety Training in the Construction Industry

Occupational Health and Safety is a significant area of concern for the construction industry, in which subcontractors work across multiple sites with varying safety induction and training. Prior work in applying immersive technologies for safety training often focuses on the simulation of working sites for hazard identification, demonstration of safety practice, and knowledge-based safety tests. However, it has been identified that current safety training is largely ineffective in improving workers&#x2019; attitudes towards safe work practices. We apply a fear-arousal approach to safety training by simulating the experience of different types of common safety accidents on a construction site in virtual reality. We conducted an evaluation with workers, contractors, and employees of a commercial construction company, where each participant experienced safety incidents on a virtual construction site. We applied pre- and post-testing measures on the impact on safety attitude and learning practice. We present the empirical evidence of fear arousal safety training in VR for improving the safety attitudes of construction workers, subcontractors, and employees. We suggested improvements and design considerations for other researchers, designers, and stakeholders in this domain based on our findings.
Improving the utility of locally differentially private protocols for longitudinal and multidimensional frequency estimates

This paper investigates the problem of collecting multidimensional data throughout time (i.e., longitudinal studies) for the fundamental task of frequency estimation under Local Differential Privacy (LDP) guarantees. Contrary to frequency estimation of a single attribute, the multidimensional aspect demands particular attention to the privacy budget. Besides, when collecting user statistics longitudinally, privacy progressively degrades. Indeed, the "multiple" settings in combination (i.e., many attributes and several collections throughout time) impose several challenges, for which this paper proposes the first solution for frequency estimates under LDP. To tackle these issues, we extend the analysis of three state-of-the-art LDP protocols (Generalized Randomized Response – GRR, Optimized Unary Encoding – OUE, and Symmetric Unary Encoding – SUE) for both longitudinal and multidimensional data collections. While the known literature uses OUE and SUE for two rounds of sanitization (a.k.a. memoization), i.e., L-OUE and L-SUE, respectively, we analytically and experimentally show that starting with OUE and then with SUE provides higher data utility (i.e., L-OSUE). Also, for attributes with small domain sizes, we propose Longitudinal GRR (L-GRR), which provides higher utility than the other protocols based on unary encoding. Last, we also propose a new solution named Adaptive LDP for LOngitudinal and Multidimensional FREquency Estimates (ALLOMFREE), which randomly samples a single attribute to be sent with the whole privacy budget and adaptively selects the optimal protocol, i.e., either L-GRR or L-OSUE. As shown in the results, ALLOMFREE consistently and considerably outperforms the state-of-the-art L-SUE and L-OUE protocols in the quality of the frequency estimates.
Multi-scale Edge-Based U-Shape Network for Salient Object Detection

Deep-learning based salient object detection methods achieve great improvements. However, there are still problems existing in the predictions, such as blurry boundary and inaccurate location, which is mainly caused by inadequate feature extraction and integration. In this paper, we propose a Multi-scale Edge-based U-shape Network (MEUN) to integrate various features at different scales to achieve better performance. To extract more useful information for boundary prediction, U-shape Edge Network modules are embedded in each decoder units. Besides, the additional down-sampling module alleviates the location inaccuracy. Experimental results on four benchmark datasets demonstrate the validity and reliability of the proposed method. Multi-scale Edge-based U-shape Network also shows its superiority when compared with 15 state-of-the-art salient object detection methods.
Classical Binding for Quantum Commitments

In classical commitments, statistical binding means that for almost any commitment transcript there is at most one possible opening. While quantum commitments (for classical messages) sometimes have benefits over their classical counterparts (e.g. in terms of assumptions), they provide a weaker notion of binding. Essentially that the sender cannot open a given commitment to a random value with probability noticeably greater than 1/2.
Distributed Linearly Separable Computation

This paper formulates a distributed computation problem, where a master asks <inline-formula> <tex-math notation="LaTeX">${\mathsf N}$ </tex-math></inline-formula> distributed workers to compute a linearly separable function. The task function can be expressed as <inline-formula> <tex-math notation="LaTeX">${\mathsf K}_{\mathrm{ c}}$ </tex-math></inline-formula> linear combinations of <inline-formula> <tex-math notation="LaTeX">${\mathsf K}$ </tex-math></inline-formula> messages, where each message is a function of one dataset. Our objective is to find the optimal tradeoff between the computation cost (number of uncoded datasets assigned to each worker) and the communication cost (number of symbols the master must download), such that from the answers of any <inline-formula> <tex-math notation="LaTeX">${\mathsf N}_{\mathrm{ r}}$ </tex-math></inline-formula> out of <inline-formula> <tex-math notation="LaTeX">${\mathsf N}$ </tex-math></inline-formula> workers the master can recover the task function with high probability, where the coefficients of the <inline-formula> <tex-math notation="LaTeX">${\mathsf K}_{\mathrm{ c}}$ </tex-math></inline-formula> linear combinations are uniformly i.i.d. over some large enough finite field. The formulated problem can be seen as a generalized version of some existing problems, such as distributed gradient coding and distributed linear transform. In this paper, we consider the specific case where the computation cost is minimum, and propose novel achievability schemes and converse bounds for the optimal communication cost. Achievability and converse bounds coincide for some system parameters; when they do not match, we prove that the achievable distributed computing scheme is optimal under the constraint of a widely used &#x2018;cyclic assignment&#x2019; scheme on the datasets. Our results also show that when <inline-formula> <tex-math notation="LaTeX">${\mathsf K}= {\mathsf N}$ </tex-math></inline-formula>, with the same communication cost as the optimal distributed gradient coding scheme proposed by Tandon <i>et al</i>. from which the master recovers one linear combination of <inline-formula> <tex-math notation="LaTeX">${\mathsf K}$ </tex-math></inline-formula> messages, our proposed scheme can let the master recover any additional <inline-formula> <tex-math notation="LaTeX">${\mathsf N}_{\mathrm{ r}}-1$ </tex-math></inline-formula> independent linear combinations of messages with high probability.
Secure and efficient parameters aggregation protocol for federated incremental learning and its applications

Federated Learning (FL) enables the deployment of distributed machine learning models over the cloud and Edge Devices (EDs) while preserving the privacy of sensitive local data, such as electronic health records. However, despite FL advantages regarding security and flexibility, current constructions still suffer from some limitations. Namely, heavy computation overhead on limited resources EDs, communication overhead in uploading converged local models' parameters to a centralized server for parameters aggregation, and lack of guaranteeing the acquired knowledge preservation in the face of incremental learning over new local data sets. This paper introduces a secure and resource-friendly protocol for parameters aggregation in federated incremental learning and its applications. In this study, the central server relies on a new method for parameters aggregation called orthogonal gradient aggregation. Such a method assumes constant changes of each local data set and allows updating parameters in the orthogonal direction of previous parameters spaces. As a result, our new construction is robust against catastrophic forgetting, maintains the federated neural network accuracy, and is efficient in computation and communication overhead. Moreover, extensive experiments analysis over several significant data sets for incremental learning demonstrates our new protocol's efficiency, efficacy, and flexibility.
Conversational Agents in Language Education: Where They Fit and Their Research Challenges

Conversational agents (or dialogue agents or Artificial Intelligent AI agents or chatbots) can provide a foreign language learner with otherwise hard-to-find conversational exposure to a new language. Such agents that teach languages differ significantly from their general-purpose counterparts in their goals, their approach, and their users' characteristics, thereby effectively creating a new interaction paradigm for which little literature exists in the HCI of Conversational Interfaces community. The difference from general-purpose agents comes from two themes highlighted in this work: the user is not expected to know the language of interaction, and the purpose of the conversation is language education through task completion rather than task completion itself. This paper highlights the role and the research challenges of interactions with dialogue agents that allow people to learn and practice new languages.
A Wideband Signal Recognition Dataset

Signal recognition is a spectrum sensing problem that jointly requires detection, localization in time and frequency, and classification. This is a step beyond most spectrum sensing work which involves signal detection to estimate "present" or "not present" detections for either a single channel or fixed sized channels or classification which assumes a signal is present. We define the signal recognition task, present the metrics of precision and recall to the RF domain, and review recent machine-learning based approaches to this problem. We introduce a new dataset that is useful for training neural networks to perform these tasks and show a training framework to train wideband signal recognizers.
The Case for Adaptive Deep Neural Networks in Edge Computing

Deep Neural Networks (DNNs) are an application class that benefit from being distributed across the edge and cloud. A DNN is partitioned such that specific layers of the DNN are deployed onto the edge and the cloud to meet performance and privacy objectives. However, there is limited understanding of: whether and how evolving operational conditions (increased CPU and memory utilization at the edge or reduced data transfer rates between the edge and cloud) affect the performance of already deployed DNNs, and whether a new partition configuration is required to maximize performance. A DNN that adapts to changing operational conditions is referred to as an ‘adaptive DNN’. This paper investigates whether there is a case for adaptive DNNs by considering four questions: (i) Are DNNs sensitive to operational conditions? (ii) How sensitive are DNNs to operational conditions? (iii) Do individual or a combination of operational conditions equally affect DNNs? (iv) Is DNN partitioning sensitive to hardware architectures? The exploration is carried out in the context of 8 pre-trained DNN models and the results presented are from analyzing nearly 8 million data points. The results highlight that network conditions affect DNN performance more than CPU or memory related operational conditions. Repartitioning is noted to provide a performance gain in a number of cases, but a specific trend is not noted in relation to the underlying hardware architecture. Nonetheless, the need for adaptive DNNs is confirmed.
Assessing Telepresence, Social Presence and Stress Response in a Virtual Reality Store

The development of immersive Virtual Reality (VR) has provided users around the globe with a highly realistic virtual world experience. Since its first use, extensive research has been conducted with the attempt to understand how human behaviour in virtual environments compares to the real world. Studies have shown that people exhibit similar behaviours and reactions in a number of scenarios including virtual shopping, thus making it a promising tool for researchers to study in-store shopper behaviour. This paper outlines ideas on how store atmospherics can affect the user experience, stress levels and behaviour in a virtual store environment, developed using the Unreal Game Engine. The presence/absence of avatars can be investigated as an important aspect of store atmospherics and a potential antecedent of perceived presence in a simulated retail environment. These insights will be useful for retailers in that they can guide development and improvement of virtual simulated shopping experiences, using elements of telepresence and social presence, to enhance the consumer shopping experience and their own retail strategy.
Enhanced Correlation Matching based Video Frame Interpolation

We propose a novel DNN based framework called the Enhanced Correlation Matching based Video Frame Interpolation Network to support high resolution like 4K, which has a large scale of motion and occlusion. Considering the extensibility of the network model according to resolution, the proposed scheme employs the recurrent pyramid architecture that shares the parameters among each pyramid layer for the optical flow estimation. In the proposed flow estimation, the optical flows are recursively refined by tracing the location with maximum correlation. The forward warping based correlation matching enables to improve the accuracy of flow update by excluding incorrectly warped features around the occlusion area. Based on the final bi- directional flows, the intermediate frame at arbitrary temporal position is synthesized using the warping and blending network and it is further improved by refinement network. Experiment results demonstrate that the proposed scheme outperforms the previous works at 4K video data and low-resolution benchmark datasets as well in terms of objective and subjective quality with the smallest number of model parameters.