Miracle-dz/UniFAR-FTUs huggingface dataset:
A Scoping Review Exploring the Feasibility of Virtual Reality Technology Use with Individuals Living with Dementia

The existing evidence base in relation to the feasibility of using Virtual Reality technology systems with individuals living with a dementia appeared limited and was therefore explored. The research was collected and reviewed in terms of the different types of Virtual Reality systems (equipment and levels of immersion) and feasibility of the technology within different stages of demen- tia as well as the methodological limitations. A systematic search of the literature was conducted using the healthcare databases advanced search (Medline, PsychINFO, and EMBASE) and snowballing methods. The participants had a dementia diagnosis and the feasibility of Virtual Reality in terms of its acceptability and practicality was discussed. Only five articles met the eligibility criteria. Four included semi-immersive Virtual Reality with participants in the early stages of dementia. One included fully- immersive Virtual Reality where dementia stage ranged from ‘mild’ to ‘severe’. Based on available demographic information, study participants resided in residential care homes, alone in the community or with their spouse. The existing literature sug- gests that both semi and fully-immersive Virtual Reality technology use can be feasible amongst individuals living within the ear- lier stages of dementia outside of a hospital environment, with it being viewed as a welcomed distraction that increased alert- ness and pleasure. However, Virtual Reality was also found to increase fear and anxiety in one study, raising important ethical implications around the safety of the user. The current evidence-base leaves a predominant gap in Virtual Reality technology system use for people within the moderate to later stages of dementia and those living in a hospital environment.
Relation of Country-of-Origin Effect, Culture, and Type of Product with the Consumer's Shopping Intention: An Analysis for Small- and Medium-Sized Enterprises

To obtain information about foreign markets is a very complex task for small- and medium-sized enterprises. This work’s aim is to find out how certain variables are related to the consumer’s shopping intention. It will help these firms to get a better knowledge of this intention. The work is specifically centred on products related to the gastronomy of a particular place. Through a sample of 154 tourists who visited this destination, we sought to know these products’ degree of acceptation. Via structural equation modelling, it has been possible to determine the relevance which the variables of country of origin, gastronomic culture, and type of product have regarding the shopping intention. The work’s novelty is to contribute a methodology of gathering information in the destination on the likes and behaviour patterns of foreign consumers without the need of costly research in their countries of origin.
Price Dynamics in an Order-Driven Market with Bayesian Learning

In this paper, we have developed a model of limit order book with learning mechanism and investigated its price dynamics. In this model, continuous Bayesian learning is introduced to describe the dynamics of self-adjusting learning mechanism of agents, which can result in some important stylized facts of limit order markets. This study also provides some behavioral explanations for these well-known stylized facts that are commonly observed in the financial markets.
Identifying and reducing individual's perceived risk in crowdfunding investment

The purpose of this study is to identify individual's perceived risk, and further, to investigate the impact of perceived risk and to seek risk relievers in a crowdfunding investment. Laboratory experiments were conducted to test the hypothesized relations among perceived risk, risk reliever and investment intention. Results indicate that informativeness of a project presentation could be deemed as an available risk reliever. Meanwhile, informativeness exerts a significantly positive influence on investment intention. Results also report that performance risk and psychological risk have significantly negative impacts on investment intention, while financial risk exerts a non-significant influence in the context of crowdfunding.
Language processing in visually and non-visually impaired individuals: the use of Auditory Event-Related Potentials

This study examines the electrical brain activity of visually impaired individuals. Its aim is to identify differences in latency and signal amplitude between visually and non-visually impaired individuals in order to improve the school experience of students with visual impairment. The experiment included five visually impaired and five non-visually impaired subjects. Auditory stimuli included words with and without semantic content, i.e. actual words and pseudo-words, and Auditory Evoked Potentials were recorded. Results show that visually impaired individuals process auditory stimuli faster, regardless if the words lack semantic content, in comparison to their non-visually impaired counterparts. The study also showed variations in signal amplitude between the two groups.
Exploring the effect of heuristic factors on the popularity of "best places to visit" recommendation in a Chinese outbound traveler community

Online "best places to visit" recommendations in an independent travelers' community are gaining more and more popularity due to the various and differentiated individual needs. Therefore, this research aims to identify heuristic factors affecting the popularity of "best places to visit" recommendations. The heuristic factors are derived from both recommender-related (recommender identity disclosure, recommender reputation, recommender experience, recommender location) and recommendation-related (recommendation helpfulness, recommendation comment, recommendation length, recommendation place) heuristic perspectives based on the heuristic-systematic model. The implications of this research are expected to contribute to build one more layer to the literature on both the destination recommendation and recommendation popularity, and practically, to make more favorable "best places to visit" recommendations in the context of Chinese outbound independent traveler communities.
Validity and Reliability Assessment of the Game Addiction Scale: An Empirical Finding from Indonesia

The game addiction scale (GAS) by Lemmens, Valkenburg, and Peter is a popular tool that is frequently used to evaluate game playing by adolescents. The scale that showed high reliabilities was tested on 644 gamers in the Netherlands. The GAS consists of 21 items that are divided into seven criteria, i.e., salience, tolerance, mood modification, relapse, withdrawal, conflict, and problems. This research attempted to investigate the scale in gamers in Indonesia in order to validate and to make a generalization of the GAS. A total of 360 respondents who are gamers have been chosen to participate in this study. They have to play the game for the last six months. Note that the massively multiplayer online role-playing game is chosen for this research since this type of game is very popular among the gamers in Indonesia. Results showed that the GAS demonstrated excellent internal consistency (Cronbach's alpha scores for all variables are more than 0.6). Furthermore, the factor analysis revealed good psychometric properties and fit the data well. These indicate that the GAS can be used to assess game addiction for Indonesian gamers due to its demonstrated psychometric validity.
Bridging the cross-cultural language divide through design

With the increasing trend of students going abroad to pursue their higher education, cross-cultural collaboration has become increasingly important. This study explores what considerations need to be taken into account when designing a pedagogical tool that helps enhance cross-cultural communication in a co-located context. We use a multi-stage approach that starts with a survey and observations to understand what are the key contextual aspects of cross-cultural groups. In the second stage, we design a technology probe Contributor, an interactive tabletop interface, which is deployed in a workshop involving university students with practical experience in Human Computer Interaction (HCI). Results show that key considerations when designing for co-located cross-cultural collaboration tools in the context of higher education are: social accountability and visibility of prompts and accessibility of devices; focus on maintaining participants' attention to enhance conversation dynamics; focus on data privacy when monitoring actions of participants; and providing a platform for participants that allows externalization of ideas through annotations and visualizations, as well as highlighting keywords relevant to the discussion.
Convolutional Attention Networks for Scene Text Recognition

In this article, we present Convoluitional Attention Networks (CAN) for unconstrained scene text recognition. Recent dominant approaches for scene text recognition are mainly based on Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), where the CNN encodes images and the RNN generates character sequences. Our CAN is different from these methods; our CAN is completely built on CNN and includes an attention mechanism. The distinctive characteristics of our method include (i) CAN follows encoder-decoder architecture, in which the encoder is a deep two-dimensional CNN and the decoder is a one-dimensional CNN; (ii) the attention mechanism is applied in every convolutional layer of the decoder, and we propose a novel spatial attention method using average pooling; and (iii) position embeddings are equipped in both a spatial encoder and a sequence decoder to give our networks a sense of location. We conduct experiments on standard datasets for scene text recognition, including Street View Text, IIIT5K, and ICDAR datasets. The experimental results validate the effectiveness of different components and show that our convolutional-based method achieves state-of-the-art or competitive performance over prior works, even without the use of RNN.
Inclusive education on stem subjects with the arduino platform

The aim of the present work is to propose a course on fields of Science, Technology,Engineeringand Mathematics (STEM) subjects which is very suitable for the inclusion of persons with disabilities in the classroom. We developed an interdisciplinary STEM project based on the Arduino platform. It has bearing on the constructivist theory and it considers the principle of the learning communities and the differentiated instruction. Its target group is an inhomogeneous groupof students from middle and high school and some students with hearing disabilities. The deaf students are using the Greek Sign Language (GSL). From literature, it becomes evident the low participation of students with disabilities in subjects related to STEM education. The designed course is based on the Arduino platform and aims at solving an authentic problem. We describe the difficulties that we confronted, especially concerning attitudes about STEM subjects and the necessary adaptations. One of the authors worked as an interpreter for the GSL. The students with hearing disabilities participated in equal terms with other students and achieved comparable results. This is a proof that they acquired the required skills and attitudes for such a complex process. In addition, it deemed necessary to enhance the GSL with new signs to facilitate the communication.
Model-based, Mutation-driven Test-case Generation Via Heuristic-guided Branching Search

This work introduces a heuristic-guided branching search algorithm for model-based, mutation-driven test-case generation. The algorithm is designed towards the efficient and computationally tractable exploration of discrete, non-deterministic models with huge state spaces. Asynchronous parallel processing is a key feature of the algorithm. The algorithm is inspired by the successful path planning algorithm Rapidly exploring Random Trees (RRT). We adapt RRT in several aspects towards test-case generation. Most notably, we introduce parametrized heuristics for start and successor state selection, as well as a mechanism to construct test cases from the data produced during the search. We implemented our algorithm in the existing test-case generation framework MoMuT. We present an extensive evaluation of the proposed heuristics and parameters of the algorithm, based on a diverse set of demanding models obtained in an industrial context. In total, we continuously utilized 128 CPU cores on three servers for several weeks to gather the experimental data presented. We show that branching search works well and the use of multiple heuristics is justified. With our new algorithm, we are now able to process models consisting of over 2,300 concurrent objects. To our knowledge, there is no other mutation-driven test-case generation tool that is able to process models of this magnitude.
Deploying new technology in residential aged care: staff members' perspectives

Residential aged care facilities (RACFs) provide full-time, permanent care for older adults who are no longer able to live at home independently. In these facilities, new technology such as tablets, virtual reality, and social robots are increasingly being deployed with the aim of providing engaging and fun activities for residents. Although HCI research has examined the design and use of technology in aged care, there is limited understanding of the role staff members play in its deployment in RACFs. We interviewed five workers from one Australian RACF about new technology use within their facility. We found that its implementation was part of a complex, decision-making hierarchy in which issues such as marketing potential and staff members' roles and capacity, created tensions about the use of new technology. Drawing on our findings, we identify issues HCI researchers should consider when introducing new technology in this complex environment.
Understanding online collectible card game players' motivations: a survey study with two games

Online collectible card games (OCCGs) are digital, networked contemporaries of collectible card games (CCGs) which combine the collection of trading cards with strategic deck building and competitive gameplay. Despite their popularity and unique mechanics, we know very little about OCCG players, their motivations and play habits. A survey study using the Trojan player typology[11] with 856 players from two popular OCCGs (Hearthstone and Eternal) revealed four main player motivations: immersion seeker, socializer, competition and smarty-pants. Competition and immersion seeking were main motivations to play OCCGs whereas socializing and strategizing (smarty-pants) were the least. These differ from the findings with CCGs where the main motivations were socialization and strategy development. We found Eternal players had higher satisfaction of their autonomy and competence needs than Hearthstone players. These findings contribute to our understanding of why individuals play different types of video games. It also helps game designers to target the needs and motivations of their audience.
The challenges in adopting assistive technologies in the workplace for people with visual impairments

There are many barriers to employment for people with visual impairments. Assistive technologies (ATs), such as computer screen readers and enlarging software, are commonly used to help overcome employment barriers and enable people with visual impairments to contribute to, and participate in, the workforce. However, recent research suggests it can be difficult for people with visual impairments to effectively adopt and implement ATs in the workplace. This paper reports insights from interviews with five professional workers with visual impairments. The interviews revealed that each participant had adopted different configurations of ATs, which they used in various ways to support their work. In addition, all participants followed a complex process to successfully select, learn, and integrate these tools into their work activities. During this process, they faced challenges, such as the need to educate colleagues to make shared documents more accessible. We conclude that despite progress in the development and use of ATs, there remains a need for future work that addresses these accessibility issues to support collaborative work between people with visual impairments and their sighted colleagues.
Interlinked SPH Pressure Solvers for Strong Fluid-Rigid Coupling

We present a strong fluid-rigid coupling for Smoothed Particle Hydrodynamics (SPH) fluids and rigid bodies with particle-sampled surfaces. The approach interlinks the iterative pressure update at fluid particles with a second SPH solver that computes artificial pressure at rigid-body particles. The introduced SPH rigid-body solver models rigid-rigid contacts as artificial density deviations at rigid-body particles. The corresponding pressure is iteratively computed by solving a global formulation that is particularly useful for large numbers of rigid-rigid contacts. Compared to previous SPH coupling methods, the proposed concept stabilizes the fluid-rigid interface handling. It significantly reduces the computation times of SPH fluid simulations by enabling larger time steps. Performance gain factors of up to 58 compared to previous methods are presented. We illustrate the flexibility of the presented fluid-rigid coupling by integrating it into DFSPH, IISPH, and a recent SPH solver for highly viscous fluids. We further show its applicability to a recent SPH solver for elastic objects. Large scenarios with up to 90M particles of various interacting materials and complex contact geometries with up to 90k rigid-rigid contacts are shown. We demonstrate the competitiveness of our proposed rigid-body solver by comparing it to Bullet.
Running with drones: desired services and control gestures

Due to their mobility, drones are in principle well-suited to support runners, but it is not yet clear, which services runners desire and how they would want to control them. We, therefore, conducted an online survey (N=22) to identify desired services and then asked runners to produce control gestures for those services in a realistic outdoor elicitation study (N=16). Our main contributions: (1) are a set of services that runners would want from a drone, such as taking a picture or calling the police; (2) a set of intuitive gestures for controlling flight actions and drone functions such as forming a square with both hands; and (3) insights into how runners propose gestures. We also evaluate and discuss the idea of modulating gestures on running movement as well as the tension between intuitiveness of a gesture and how much it interferes with the running movement.
Minimal-Variance Distributed Deadline Scheduling in a Stationary Environment

Many modern schedulers can dynamically adjust their service capacity to match the incoming workload. At the same time, however, variability in service capacity often incurs operational and infrastructure costs. In this paper, we propose distributed algorithms that minimize service capacity variability when scheduling jobs with deadlines. Specifically, we show that Exact Scheduling minimizes service capacity variance subject to strict demand and deadline requirements under stationary Poisson arrivals. We also characterize the optimal distributed policies for more general settings with soft demand requirements, soft deadline requirements, or both. Additionally, we show how close the performance of the optimal distributed policy is to that of the optimal centralized policy by deriving a competitive-ratio-like bound.
Towards the Decentralised Cloud

Cloud computing emerged as a centralised paradigm that made “infinite” computing resources available on demand. Nevertheless, the ever-increasing computing capacities present on smart connected things and devices calls for the decentralisation of Cloud computing to avoid unnecessary latencies and fully exploit accessible computing capacities at the edges of the network. Whilst these decentralised Cloud models represent a significant breakthrough from a Cloud perspective, they are rooted in existing research areas such as Mobile Cloud Computing, Mobile Ad hoc Computing, and Edge computing. This article analyses the pre-existing works to determine their role in Decentralised Cloud and future computing development.
Understanding the challenges and needs of knee arthroscopy surgeons to inform the design of surgical robots

Current surgical procedures need to be properly understood before designing robotic platforms for surgery, so that the strengths of robotic systems can cover and enhance the capabilities of the surgeon, which will create better patient outcomes. The aim of our research is to explore the potential of robotic assistance in arthroscopic knee surgery, which is one of the most common elective surgeries undertaken. We observed and video-recorded 15 arthroscopic knee surgeries performed by four orthopaedic surgeons and conducted interviews with two of these surgeons to gain further insight and clarification. Our findings highlight the challenges experienced by surgeons: (1) frustration with instruments, (2) visibility challenges, and (3) ergonomic, cognitive, social, and cooperative work demands. Furthermore, we identified the following needs: flexible distal end of instruments and better visualisation of instruments. With this knowledge we plan to develop a video card game, which, with the collaboration of orthopaedic surgeons, will be used to design an interface for a robotic surgical assistant to help alleviate these challenges.
Near Optimal Online Algorithms and Fast Approximation Algorithms for Resource Allocation Problems

We present prior robust algorithms for a large class of resource allocation problems where requests arrive one-by-one (online), drawn independently from an unknown distribution at every step. We design a single algorithm that, for every possible underlying distribution, obtains a 1−ε fraction of the profit obtained by an algorithm that knows the entire request sequence ahead of time. The factor ε approaches 0 when no single request consumes/contributes a significant fraction of the global consumption/contribution by all requests together. We show that the tradeoff we obtain here that determines how fast ε approaches 0, is near optimal: We give a nearly matching lower bound showing that the tradeoff cannot be improved much beyond what we obtain. Going beyond the model of a static underlying distribution, we introduce the adversarial stochastic input model, where an adversary, possibly in an adaptive manner, controls the distributions from which the requests are drawn at each step. Placing no restriction on the adversary, we design an algorithm that obtains a 1−ε fraction of the optimal profit obtainable w.r. t. the worst distribution in the adversarial sequence. Further, if the algorithm is given one number per distribution, namely the optimal profit possible for each of the adversary’s distribution, then we design an algorithm that achieves a 1−ε fraction of the weighted average of the optimal profit of each distribution the adversary picks. In the offline setting we give a fast algorithm to solve very large linear programs (LPs) with both packing and covering constraints. We give algorithms to approximately solve (within a factor of 1+ε) the mixed packing-covering problem with O(γ m log (n/δ)/ε2) oracle calls where the constraint matrix of this LP has dimension n× m, the success probability of the algorithm is 1−δ, and γ quantifies how significant a single request is when compared to the sum total of all requests. We discuss implications of our results to several special cases including online combinatorial auctions, network routing, and the adwords problem.