Reviewerly/exHarmony huggingface dataset:
Educating Adults with a Location-Based Augmented Reality Game: A Content Analysis Approach

With the exception of Pokémon Go, relatively little is known about other Mobile Augmented Reality (MAR) games, and the attitudes of middle-aged and elderly adults towards them are hitherto poorly examined. The aim of this research is to examine the opinions of young, middle-aged, and elderly adults about the location-based MAR game Ingress. The responses to questions related to Ingress were collected from 24 adult players aged 20–60 from Greece and subsequently were analyzed by means of content analysis, both qualitatively and quantitatively, using the Jaccard index of similarity. Our findings showed that opinions of young people (20–35) agree more with those of elderly adults (&gt;52) than with those of the intermediate age group of 36–51. It was also revealed that knowledge of geography facilitates the ability to play the game better, the game’s scenario is very interesting to adults, and the feeling of playing locally in a global game is also appreciated by all age groups. Along with these findings, with this research, it was shown that (a) content analysis is a valuable method for exploring opinions and attitudes of adult users towards MAR games and (b) Jaccard indices can be used to quantitatively explore themes emerging from content analysis.
Probabilistic Inference of Simulation Parameters via Parallel Differentiable Simulation

Reproducing real world dynamics in simulation is critical for the development of new control and perception methods. This task typically involves the estimation of simu-lation parameter distributions from observed rollouts through an inverse inference problem characterized by multi-modality and skewed distributions. We address this challenging problem through a novel Bayesian inference approach that approximates a posterior distribution over simulation parameters given real sensor measurements. By extending the commonly used Gaus-sian likelihood model for trajectories via the multiple-shooting formulation, our gradient-based particle inference algorithm, Stein Variational Gradient Descent, is able to identify highly nonlinear, underactuated systems. We leverage GPU code gen-eration and differentiable simulation to evaluate the likelihood and its gradient for many particles in parallel. Our algorithm infers nonparametric distributions over simulation parame-ters more accurately than comparable baselines and handles constraints over parameters efficiently through gradient-based optimization. We evaluate estimation performance on several physical experiments. On an underactuated mechanism where a 7-DOF robot arm excites an object with an unknown mass configuration, we demonstrate how the inference technique can identify symmetries between the parameters and provide highly accurate predictions. Website: https://uscresl.github.io/prob-diff-sim
Automatic Generation of Random Step Environment Models for Gazebo Simulator

Negotiating rough terrain obstacles is a key task of a mobile terrestrial robot. One of the most popular standards in rough terrain modeling is the NIST Random Step Environment (RSE) or Random Stepfield, which allows constructing a broad variety of debris-like environments. This paper proposes a virtual RSE models’ automatic generator LIRS-RSEGen for the Gazebo simulator. We analyzed typical for RSEs obstacles and structures, determined parameters that are required in order to generate a RSE and implemented a generator, which constructs RSE Gazebo worlds that could be further edited or directly used within the Gazebo. Constructed by our generator worlds were validated in the Gazebo using virtual models of TurtleBot3 wheeled robot and Servosila Engineer crawler robot.
Disentangling Multi-Facet Social Relations for Recommendation

Social networks have proven to be effective for high-quality item recommendation. Most social recommenders, however, are insufficient to capture the user preferences over items, as they largely neglect the multi-facet social relations latent in the social network, such as classmates, colleagues, and families. We, therefore, propose a novel disentangled social recommendation (DSR) framework to exploit the multifacet social relations for enhanced item recommendation. Specifically, DSR explicitly disentangles the social relations into multiple facets, and encodes the social influence under each facet into disentangled user embeddings in the social network. The multiple user embeddings are then aggregated via a facet-level attention mechanism, which distinguishes the effective facets for better inferring user interests over items. Extensive experiments show the superiority of DSR against the state-of-the-art methods and its potential in alleviating the data sparsity issue.
Multisegment Mapping Network for Massive MIMO Detection

The massive multiple-input multiple-output (MIMO) technology is one of the core technologies of 5G, which can significantly improve spectral efficiency. Because of the large number of massive MIMO antennas, the computational complexity of detection has increased significantly, which poses a significant challenge to traditional detection algorithms. However, the use of deep learning for massive MIMO detection can achieve a high degree of computational parallelism, and deep learning constitutes an important technical approach for solving the signal detection problem. This paper proposes a deep neural network for massive MIMO detection, named Multisegment Mapping Network (MsNet). MsNet is obtained by optimizing the prior detection networks that are termed as DetNet and ScNet. MsNet further simplifies the sparse connection structure and reduces network complexity, which also changes the coefficients of the residual structure in the network into trainable variables. In addition, this paper designs an activation function to improve the performance of massive MIMO detection in high-order modulation scenarios. The simulation results show that MsNet has better symbol error rate (SER) performance and both computational complexity and the number of training parameters are significantly reduced.
Revisiting the Nexus of FDI-Led Growth Hypothesis and Economic Development in Rwanda: a Johansen-ARDL Approach to Cointegration

Poverty reduction and economic progress and development accentuate sustainability and growth. This study explores a new model that specifies the FDI-led growth theory for the Rwandan economy. An annual time series data from 1970 to 2018 was obtained from the World Bank. The Johansen cointegration and ARDL approaches were used after realizing a varied order of integration from the stationarity test by adopting unit root tests. All variables were established to wield a positive impact on economic development except financial development from the financial sector which was significant in the short run but insignificant in the long run. Financial development from the private sector, exchange rate, consumer price index, gross domestic product, and population wield significant influence on FDI inflow for economic development, which implies that an improvement in these factors will equitably support economic growth. In principle, 1% improvement in the financial development from the private sector and exchange rate will produce a corresponding 397% and 78% increase advancement in FDI inflows in the long run. Averagely, financial development from the private sector, exchange rate, consumer price index, gross domestic product, and population are valuable to the economy of Rwanda. Therefore, we recommend that the relevant authority expand regional and global bilateral and partnership to enhance the economy to fully reap the benefits of engaging in globalization.
Support Estimation with Sampling Artifacts and Errors

The problem of estimating the support of a distribution is of great importance in many areas of machine learning, computer science and molecular biology. Almost all of the existing work in this area has used perfectly accurate sampling assumptions, which is seldom true in practice. Here we introduce the first known theoretical approach to support estimation in the presence of sampling artifacts, where each sample is assumed to be observed through a Poisson channel that simultaneously captures repetitions and deletions. The proposed estimator is based on regularized weighted Chebyshev approximations, with weights governed by evaluations of Touchard (Bell) polynomials. The supports in the presence of sampling artifacts are calculated via discretized semi-infinite programming methods. The newly proposed estimation approach is tested on synthetic and textual data, as well as on GISAID data for the purpose of estimating the mutational diversity of genes in the SARS-Cov-2 viral genome. For all experiments performed, we observed significant improvements of our integrated method compared to adequately modified known noiseless support estimation methods. A full version of this paper is accessible at: https://arxiv.org/pdf/2006.07999.pdf
First-Person Hand Action Recognition Using Multimodal Data

Extensive studies have been conducted on human action recognition, whereas relatively few methods have been proposed for hand action recognition. Although it is very natural and straightforward to apply a human action recognition method to hand action recognition, this approach cannot always lead to state-of-the-art performance. One of the important reasons is that both the between-class difference and the within-class difference in hand actions are much smaller than those in human actions. In this article, we study first-person hand action recognition from RGB-D sequences. To explore whether pretrained networks substantially influence accuracy, eight classic pretrained networks and one pretrained network designed by us are introduced for extracting RGB-D features. A Lie group is introduced for hand pose representation. Ablation studies are conducted to compare the discriminative power of the RGB modality, depth modality, pose modality, and their combinations. In our method, a fixed number of frames are randomly sampled to represent an action. This temporal modeling strategy is simple but is proven more effective than both the graph convolutional network (GCN) and the recurrent neural network (RNN), which are widely adopted by conventional methods. Evaluation experiments on two public data sets demonstrate that our method markedly outperforms recent baselines.
Evaluating BERT-based Rewards for Question Generation with Reinforcement Learning

Question generation systems aim to generate natural language questions that are relevant to a given piece of text, and can usually be answered by just considering this text. Prior works have identified a range of shortcomings (including semantic drift and exposure bias) and thus have turned to the reinforcement learning paradigm to improve the effectiveness of question generation. As part of it, different reward functions have been proposed. As typically these reward functions have been empirically investigated in different experimental settings (different datasets, models and parameters) we lack a common framework to fairly compare them. In this paper, we first categorize existing rewards systematically. We then provide such a fair empirical evaluation of different reward functions (including three we propose here for QG) in a common framework. We find rewards that model answerability to be the most effective.
A Decentralised Authentication and Access Control Mechanism for Medical Wearable Sensors Data

Recent years have seen an increase in medical big data, which can be attributed to a paradigm shift experienced in medical data sharing induced by the growth of medical technology and the Internet of Things.The evidence of this potential has been proved during the recent covid-19 pandemic, which was characterised by the use of medical wearable devices to help with the medical data exchange between the healthcare providers and patients in a bid to contain the pandemic.However, the use of these technologies has also raised questions and concerns about security and privacy risks.To assist in resolving this issue, this paper proposes a blockchain-based access control framework for managing access to users' medical data.This is facilitated by using a smart contract on the blockchain, which allows for delegated access control and secure user authentication.This solution leverages blockchain technology's inherent autonomy and immutability to solve the existing access control challenges.We have presented the solution in the form of a medical wearable sensor prototype and a mobile app that uses the Ethereum blockchain in a real data sharing control scenario.Based on the empirical results, the proposed solution has proven effective.It has the potential to facilitate reliable data exchange while also protecting sensitive health information against potential threats.When subjected to security analysis and evaluation, the system exhibits performance improvements in data privacy levels, high security and lightweight access control design compared to the current centralised access control models.
Eunomia: Efficiently Eliminating Abnormal Results in Distributed Stream Join Systems

With the emergence of big data applications, stream join systems are widely used in extracting valuable information among multi-source streams. However, providing completeness of processing results in a large-scale distributed stream join system is challenging because it is hard to guarantee the consistency among all instances. We show through experiments that the abnormal result can make the quality of achieved data unacceptable in practice.In this paper, we propose Eunomia, a novel distributed stream join system which leverages an ordered propagation model for efficiently eliminating abnormal results. We design a light-weighted self-adaptive strategy to adjust the structure in the model according to the dynamic stream input rates and workloads. It can improve the scalability and performance significantly. We implement Eunomia and conduct comprehensive experiments to evaluate its performance. The results show that Eunomia eliminates abnormal results to guarantee the completeness, improves the system throughput by 25% and reduces the processing latency by 74% compared to state-of-the-art designs.
Exploring Variability of Visual Accessibility Options in Operating Systems

Digital technologies are an opportunity to overcome disabilities, provided that accessibility is ensured. In this paper, we focus on visual accessibility and the way it is supported in Operating Systems (OS). The significant variability in this support has practical consequences, e.g., the difficulty to recommend or select an OS, or migrate from one OS to another. This suggests building a variability model for OS that would classify them and would serve as a reference. We propose a methodology to build such a variability model with the help of the Formal Concept Analysis (FCA) framework. In addition, as visual accessibility can be divided into several concerns (e.g., zoom, or contrast), we leverage an extension of FCA, namely Relational Concept Analysis. We also build an ontology to dispose of a standardized description of visual accessibility options. We apply our proposal to the analysis of the variability of a few representative operating systems.
Opportunities and Scaffolds for Critical Reflection on Ethical Issues in an Online After School Biowearable Workshop for Youth

The rapid adoption of biowearables, such as smartwatches, raises ethical issues as youth are increasingly being tracked, monitored and given feedback on a growing number of measures. To address this pressing need, we investigated how to support youth to understand and explore these ethical issues grounded in the processes of prototyping during an afterschool online critical making workshop. The main contribution of this paper is our critical reflection framework, consisting of three interrelated components: ethical issues, technical opportunities, and reflection scaffolds. We focus on ethical issues related to the potential for biowearables to negatively impact six constructs taken from child development. We describe how we created a biowearable-tangible prototyping kit that has under-determined design decision points, creating technologically-mediated opportunities for reflection during the iterative prototyping process. Third, we present a set of critical reflection cards created to support youth to explore the ethical issues related to those decision points. We provide two scenarios from a pilot study that illustrate our framework in action, providing preliminary validation for our approach in an online environment.
Quantum Computing at an Inflection Point: Are we Ready for a New Paradigm

Quantum computers leverage quantum mechanics to enable complex processing power enabling computation within a finite timeframe. As the industry reaches a peak of uncertainties in classical computing, researchers see an opportunity in quantum computing to resolve these uncertainties by unraveling the quantum mechanism that quantum physicists built for over a century. Organizations and academics have been working in tandem to conceptualize the quantum world through quantum computers. This article evaluates the tipping points of the quantum computers in the organization, the barrier of adoption, and a mitigation strategy. The article involves an extensive literature review of the current state and future application expectations from quantum computers. As part of this article, the authors interviewed senior industry technology experts to explore quantum computers’ introduction in the industry, and discussed the barriers to adopting quantum computing.
Deep Learning-based Face Mask Detection Using Automated GUI for COVID-19

The COVID-19 pandemic has caused a global health crisis. In response, the World Health Organization (WHO) has suggested wearing a face mask in public for effective protection. While much of the global population has adhered to these recommendations, some continue to wear the face mask improperly or refuse to wear the mask at all. It is essential that face masks are properly worn in public. To address this, we implemented computer vision, a recent advanced technology, to detect the status of face masks on individuals in crowded public places. Our research is intended to aid in minimizing the spread of coronavirus by developing technology for authorities to discern if face masks are being worn properly. We collected data from the Internet and increased it synthetically by augmentation. Two publicly available datasets were merged: the face mask detection dataset and the MASKEDFACE-NET dataset. Our data was annotated manually and then made into a graphical user interface (GUI) for semi-automatic annotation. The multiple object detection networks were trained for three states of face mask wearing: with_mask, without_mask, and mask_weared_incorrect. Four two-stage object detection models were trained and tested during the experiment. The results are compared based on the mean average precisions and scores. The networks achieved above 91% accuracy in both mean average precisions and scores for the three classes of object. We applied these object detectors to our annotation tool for quick semi-supervised annotation. The proposed mask status detection system can aid in reducing the spread of COVID-19 if deployed in a real-world scenario. Our data labeling tool with annotation, augmentation, and automatic suggestion can help further research into these types of technologies.
Automatic Monitoring and Analysis of Brands Using Data Extracted from Twitter in Romanian

We present a novel framework for brand monitoring and analysis, based on the available data of Twitter in Romanian. The framework uses a sentiment analysis text classifier for distinguishing Twitter posts between positive or negative, which was trained and tested using a novel dataset of tweets in Romanian, labelled by the authors. We created and compared four adapted preprocessing pipelines, that generated four sets of data, on which we trained several machine learning models. Based on the evaluation metrics, results show that a neural network using fastText has the best F1-score and accuracy, thus this model was further used for our proposed framework for brand monitoring and analysis. Our application creates various reputation scores, based on which it generates three kind of reports: reputation report of a single brand, reputation report of an industry and comparative reputation report of two companies, in a desired time frame.
An Ontology for Smart Home Design

Smart homes are becoming very fashionable. However, the construction of smart homes is still a developing industry. Thus, there are currently no standard criteria for defining smart homes. And since there is also no standard design model for smart homes, this study develops an ontology to formalise the design of smart homes. The aim of this smart home ontology is to provide useful information to DIY enthusiasts all the way up to architects to model a Smart Home. An iterative process was used to develop this smart home ontology. Existing ontologies and literature were used to gain a better understanding of how to model the ontology. The resulting smart home ontology can help to understand better the structure or framework of a smart home and how all the devices are interconnected and how smart homes can make peoples' everyday lives easier as well as reducing the carbon footprint.
The Discrepancy Attack on Polyshard-ed Blockchains

Sharding, i.e. splitting the miners or validators to form and run several subchains in parallel, is known as one of the main solutions to the scalability problems of blockchains. The drawback is that as the number of miners expanding each subchain becomes small, it becomes vulnerable to security attacks. To solve this problem, a framework, named as Ployshard, has been proposed in which each validator verifies a coded combination of the blocks introduced by different subchains, thus helping to protect the security of all subchains. In this paper, we introduce an attack on Ployshard, called the discrepancy attack, which is the result of malicious nodes controlling a few subchains and dispersing different blocks to different nodes. We show that this attack undermines the security of Polyshard and is undetectable in its current setting.
Generating Empathetic Responses with a Pre-trained Conversational Model

Conversational agents can be perceived to be more human-like if they possess empathy or the ability to understand and share feelings with their users. Studies have also showed improvement in user engagement on systems that can exhibit emotional skills. Empathy can be expressed through language. In this paper, a pre-trained neural conversational language model named DialoGPT and a new collection of empathetic dialogues tagged with emotions are used in order to investigate the ability of the model in learning and generating more empathetic responses. Using DialoGPT’s small model size, the model was fine-tuned on the EmpatheticDialogues dataset which was intentionally collected from emotional situations. Automatic evaluation using the perplexity metric, and manual evaluation based on performance and user preference, were conducted. The fine-tuned model achieved good performance in generating empathetic responses, with perplexity value of 12.59, which correlated with the ratings from human evaluators.