Reviewerly/exHarmony huggingface dataset:
The Educational Potential of Augmented Reality Technology: Experiences of Instructional Designers and Practitioners

In this study, the educational potential of the augmented reality (AR) technology was investigated in the context of the instructional designers and practitioners' experiences. Within this scope, intrinsic case study design was used in this study. 42 instructional designers and 10 practitioners participated in the research. The data were collected using open-ended questionnaire from instructional designers. Interviews were held with the practitioners through the semi-structured interview form created from the open-ended questionnaire. The qualitative data were analyzed with the content analysis method, using Nvivo 8.0 software. The participants suggested that AR would be more influential in the educational fields of science education, social studies, and health education. Among the most important instructional materials to be developed with AR technology are 3D materials, videos, and animations. The participants suggested that AR could be useful in the development of special applications for other fields of education. For example, AR could be used to create magic books, 3D demonstrations related to different subjects, and experimental simulations in the field of science education. Moreover, the participants suggested that AR could provide system design enhancements to better attract users’ attention, to improve retention, to increase user motivation, and to “concretize” virtually presented information by creating a fuller sense of reality in terms of educational. Lastly, in order to develop an AR application, several points must be considered in the stages of analysis, design, development, and implementation. We present these points for consideration in detail. The results obtained in this study can be used to guide future research studies on AR technology.
Typed Meta-interpretive Learning of Logic Programs

Meta-interpretive learning (MIL) is a form of inductive logic programming that learns logic programs from background knowledge and examples. We claim that adding types to MIL can improve learning performance. We show that type checking can reduce the MIL hypothesis space by a cubic factor. We introduce two typed MIL systems: Metagol $$_{T}$$ and HEXMIL $$_{T}$$ , implemented in Prolog and Answer Set Programming (ASP), respectively. Both systems support polymorphic types and can infer the types of invented predicates. Our experimental results show that types can substantially reduce learning times.
The Impact of Artificial Intelligence on the Accounting Industry

With the development of artificial intelligence technology, artificial intelligence has entered the accounting field more and more deeply, which plays an important role in improving business efficiency, reducing work errors, preventing and controlling enterprise risks, improving enterprise competitiveness, and improving human resource efficiency. Artificial intelligence technology is like a double-edged sword. While promoting the development of accounting work, it will also cause accountants to face the crisis of unemployment in the workplace. Based on the concept of artificial intelligence technology and its application in the accounting industry, this paper discusses the impact of artificial intelligence on the accounting industry, and puts forward the challenges of the artificial intelligence technology accounting industry. It should take the lead in completing the skills upgrade; realize the transformation of the comprehensive management accounting role. Develop strategies such as designers and supervisors of artificial intelligence accounting systems.
HCFS: A Density Peak Based Clustering Algorithm Employing A Hierarchical Strategy

Clustering, which explores the visualization and distribution of data, has recently been widely studied. Although current clustering algorithms such as DBSCAN, can detect the arbitrary-shape clusters and work well, the parameters involved in these methods are often difficult to determine. Clustering using a fast search of density peaks is a promising technique for solving this problem. However, the current methods suffer from the problem of uneven distribution within local clusters. To solve this problem, we propose a new density peak based clustering algorithm employing a hierarchical strategy, namely, HCFS, which consists mainly of two stages. In the first stage, the HCFS estimates the density and distance of each point. The points with higher density and distance are selected as candidate centers, and then subclusters centered on them are further obtained. In the second stage, considering that adjacent subclusters based on certain candidate centers are highly similar and connected within the same cluster, we propose a new mechanism for measuring dissimilarity and connectivity between the subclusters. Those highly similar and connected subclusters are merged to increase the dissimilarity between different clusters and to obtain the final clustering results. The experiments conducted on a large number of datasets show that our method can effectively identify unevenly distributed clusters and yield better or comparable performance for different datasets.
Heterogeneous Face Recognition Based on Multiple Deep Networks With Scatter Loss and Diversity Combination

Due to the gap between sensing patterns of different domains and a lack of sufficient training sample, heterogeneous face recognition (HFR) is still a challenging issue in the computer vision community.In this paper, we propose a novel method called multiple deep networks with scatter loss and diversity combination (MDNDC) for solving the HFR problem.As we know, the performance of deep models is affected by data, network structure, and loss function, so we devote much effort to improve the HFR performance from all these three aspects.First, to reduce the intra-class variations and increase the inter-class variations, the scatter loss (SL) is used as an objective function that can bridge the modality gap while preserving the identity information.Second, we design a multiple deep networks (MDN) structure for feature extraction and propose a joint decision strategy called diversity combination (DC) to adaptively adjust the weights of each deep network and make a joint classification decision.Finally, instead of using only one publicly available dataset, we make full use of multiple datasets to train the networks, which can further improve the HFR performance.The extensive experiments are carried out on two challenging NIR-VIS HFR datasets, CASIA NIR-VIS 2.0 and Oulu-CASIA NIR-VIS, demonstrating the superiority of the proposed method.
Cryptocurrency as disruptive technology: theoretical insights

Bitcoin, Cryptocurrencies and Blockchain technologies are widely discussed nowadays. The Bitcoin market value is discussed in top magazines, the names of people who earned money from cryptocurrencies are on the Richest People list. The Blockchain technology is said to be a one of disruption pioneers on the one hand, and cryptocurrency is said to be an illegal phenomenon on the other hand. The Fourth Industrial Revolution, which is based on digitalized industry is changing the power centres. Will cryptocurrency disrupt the financial sector? Or perhaps it will disrupt the retail market or global monetary policy. Disruptive technologies together with the Internet, Mobile Internet and Internet of Things are changing our world. While society has gained simple and reachable comfort for a cheaper price on the one hand, many people have lost their jobs on the other. Consequently, the authors presuppose discussion about the phenomenon of cryptocurrency through the context of social phenomena and legal regulation: divulge definitions, principles, relating to the topic; identify possible tendencies according to legal regulation and its practical realization.
An Automated Fall Detection System Using Recurrent Neural Networks

Falls are the most common cause of fatal injuries in elderly people, causing even death if there is no immediate assistance. Fall detection systems can be used to alert and request help when this type of accident happens. Certain types of these systems include wearable devices that analyze bio-medical signals from the person carrying it in real time. In this way, Deep Learning algorithms could automate and improve the detection of unintentional falls by analyzing these signals. These algorithms have proven to achieve high effectiveness with competitive performances in many classification problems. This work aims to study 16 Recurrent Neural Networks architectures (using Long Short-Term Memory and Gated Recurrent Units) for falls detection based on accelerometer data, reducing computational requirements of previous research. The architectures have been tested on a labeled version of the publicly available SisFall dataset, achieving a mean F1-score above 0.73 and improving state-of-the-art solutions in terms of network complexity.
Impact of BIM-based augmented reality interfaces on construction projects: protocol for a systematic review

Building Information Modelling (BIM) has been adopted in construction projects to increase efficiency. In general, improving project management and monitoring, reducing time and cost, strengthen the collaboration between stakeholders, in addition to Risk and injury reduction. To assist the BIM tools, Augmented Reality (AR) is being implemented, as a promising technology that clarifies the construction process. To evaluate the usability and effectiveness of AR tools in the Architectural, Engineering, Construction and Operations (AECO) sector, a protocol for a systematic review is proposed, adapting the PRISMA Statement Protocol (PRISMA-P). The search will be carried out in the reference databases for the field. One of the main outcomes of this study is to assess the effectiveness and usability of AR tools implemented in construction projects. Furthermore, this study identifies the BIM dimensions covered and the related tools and techniques. In addition, to the assessment and evaluation methods adopted by the authors to evaluate their studies, the reliability of the tools and their impact were also analysed. The secondary outcomes are objective oriented, the study points out why the AR technology was implemented and at which construction stage. Moreover, it assesses the technology’s capability of replacing traditional approaches in the construction projects, by mentioning the time and cost consumption and the role of the stakeholders.
Active Learning Using Digital Smart Board to Enhance Primary School Students’ Learning

Digital smart board is one of interactive technology tools which has been used widely in schools to facilitate teaching and learning. Empirical evidence shows that digital smart board allows students to construct their knowledge while mastering more advanced thinking skills. Thus integration of digital smart board facilitates active learning, which is fundamental to mastery of skills. Active learning using smart board program is a program which integrates smart board in Data Handling so that primary school students can learn Data Handling concept actively and meaningfully. Therefore, this study was conducted to identify the effectiveness of active learning using smart board program to enhance primary school students’ learning. Five experts in teaching Mathematics Education and thirty year five students were involved in this study. The students were selected based on purposive sampling. This study used both quantitative and qualitative research approach. A set of interview protocol and a set of questionnaire were used during the data collection phase. Findings from the questionnaire were analysed based on descriptive and inferential statistics by using Winsteps Version 3.72.3. In addition, thematic analysis was used to analyse the qualitative data. Findings from the study indicate that active learning using smart board program has the attributes which can effectively enhance primary school students’ learning. It is hoped that the results from this study can contribute towards development of active learning strategies, methods or activities using smart board in order to enhance primary students’ learning in school.
Efficient Graph Cut Optimization for Full CRFs with Quantized Edges

Fully connected pairwise Conditional Random Fields (Full-CRF) with Gaussian edge weights can achieve superior results compared to sparsely connected CRFs. However, traditional methods for Full-CRFs are too expensive. Previous work develops efficient approximate optimization based on mean field inference, which is a local optimization method and can be far from the optimum. We propose efficient and effective optimization based on graph cuts for Full-CRFs with quantized edge weights. To quantize edge weights, we partition the image into superpixels and assume that the weight of an edge between any two pixels depends only on the superpixels these pixels belong to. Our quantized edge CRF is an approximation to the Gaussian edge CRF, and gets closer to it as superpixel size decreases. Being an approximation, our model offers an intuition about the regularization properties of the Guassian edge Full-CRF. For efficient inference, we first consider the two-label case and develop an approximate method based on transforming the original problem into a smaller domain. Then we handle multi-label CRF by showing how to implement expansion moves. In both binary and multi-label cases, our solutions have significantly lower energy compared to that of mean field inference. We also show the effectiveness of our approach on semantic segmentation task.
Neural Name Translation Improves Neural Machine Translation

In order to control computational complexity, neural machine translation (NMT) systems convert all rare words outside the vocabulary into a single unk symbol. Previous solution (Luong et al. [1]) resorts to use multiple numbered unks to learn the correspondence between source and target rare words. However, testing words unseen in the training corpus cannot be handled by this method. And it also suffers from the noisy word alignment. In this paper, we focus on a major type of rare words – named entity (NE), and propose to translate them with character level sequence to sequence model. The NE translation model is further used to derive high quality NE alignment in the bilingual training corpus. With the integration of NE translation and alignment modules, our NMT system is able to surpass the baseline system by 2.9 BLEU points on the Chinese to English task.
Improving Differentiable Neural Computers Through Memory Masking, De-allocation, and Link Distribution Sharpness Control.

The Differentiable Neural Computer (DNC) can learn algorithmic and question answering tasks. An analysis of its internal activation patterns reveals three problems: Most importantly, the lack of key-value separation makes the address distribution resulting from content-based look-up noisy and flat, since the value influences the score calculation, although only the key should. Second, DNC's de-allocation of memory results in aliasing, which is a problem for content-based look-up. Thirdly, chaining memory reads with the temporal linkage matrix exponentially degrades the quality of the address distribution. Our proposed fixes of these problems yield improved performance on arithmetic tasks, and also improve the mean error rate on the bAbI question answering dataset by 43%.
Implementation of an Additional Factor for Secure Authentication in Online Transactions

To eradicate financial fraud, governments encourage the digitization of financial transactions, which is also reinforced by the digital economy paradigm. Recently, there has been an exponential increase in the number of e-transactions, and the incidence of cyber crimes related to online transaction fraud has also been increasing. To prevent online transaction fraud, the stakeholders of financial-transaction-related companies have implemented various secured authentication and authorization practices at all levels. In this paper, an additional factor for secure authentication for online transactions has been proposed. A third authentication factor, in addition to Personal Identification Number (PIN) and one time password (OTP), has been proposed, which is based on the global positioning system (GPS) location of the user who initiates the transaction. The strategy is to approve / decline the transaction based on a specified distance constraint between the transaction device and the user’s mobile device; this distance is used as an additional authentication factor (third factor) to verify the online transaction. The main objective of this study is to prevent fraudsters from performing online transactions from devices that do not belong to the user and are not currently in the possession of the user. The simulation results show that a high detection rate, i.e., 98.55%, is obtained using the proposed method.
Danger from the Deep: A Gap Affordance Study in Augmented Reality

It is an open question as to whether people perceive and act in augmented reality environments in the same way that they do in real environments. The current work investigated participants' judgments of whether or not they could act on an obstacle portrayed with augmented reality. Specifically, we presented gaps of varying widths and depths to participants in augmented reality using the Microsoft HoloLens. We asked users to indicate whether or not they believed that they could step across the virtual gaps given their widths and depths. Averaging across changes in width and depth, users generally underestimated their abilities to cross gaps. However, this underestimation was significantly greater when the gaps were deep. Thus, our findings suggest that users in augmented reality respond with more conservative judgments when presented with risky stimuli-a response that mimics real world behavior. Their altered reactions to deeper gaps may provide early evidence for augmented reality's capacity to evoke a sense of realism or immersion and its use in evaluating perception and action.
Multimode lasing in wave-chaotic semiconductor microlasers

We investigate experimentally and theoretically the lasing behavior of dielectric microcavity lasers with chaotic ray dynamics. Experiments show multimode lasing for both $\mathsf{D}$-shaped and stadium-shaped wave-chaotic cavities. Theoretical calculations also find multimode lasing for different shapes, sizes, and refractive indices. While there are quantitative differences between the theoretical lasing spectra of the stadium and $\mathsf{D}$-cavity, due to the presence of scarred modes with anomalously high-quality factors, these differences decrease as the system size increases, and are also substantially reduced when the effects of surface roughness are taken into account. Lasing spectra calculations are based on steady-state ab initio laser theory, and indicate that gain competition is not sufficient to result in single-mode lasing in these systems.
Discriminability Tests for Visualization Effectiveness and Scalability

The scalability of a particular visualization approach is limited by the ability for people to discern differences between plots made with different datasets. Ideally, when the data changes, the visualization changes in perceptible ways. This relation breaks down when there is a mismatch between the encoding and the character of the dataset being viewed. Unfortunately, visualizations are often designed and evaluated without fully exploring how they will respond to a wide variety of datasets. We explore the use of an image similarity measure, the Multi-Scale Structural Similarity Index (MS-SSIM), for testing the discriminability of a data visualization across a variety of datasets. MS-SSIM is able to capture the similarity of two visualizations across multiple scales, including low level granular changes and high level patterns. Significant data changes that are not captured by the MS-SSIM indicate visualizations of low discriminability and effectiveness. The measure's utility is demonstrated with two empirical studies. In the first, we compare human similarity judgments and MS-SSIM scores for a collection of scatterplots. In the second, we compute the discriminability values for a set of basic visualizations and compare them with empirical measurements of effectiveness. In both cases, the analyses show that the computational measure is able to approximate empirical results. Our approach can be used to rank competing encodings on their discriminability and to aid in selecting visualizations for a particular type of data distribution.
The DKU System for the Speaker Recognition Task of the 2019 VOiCES from a Distance Challenge

In this paper, we present the DKU system for the speaker recognition task of the VOiCES from a distance challenge 2019. We investigate the whole system pipeline for the far-field speaker verification, including data pre-processing, short-term spectral feature representation, utterance-level speaker modeling, back-end scoring, and score normalization. Our best single system employs a residual neural network trained with angular softmax loss. Also, the weighted prediction error algorithms can further improve performance. It achieves 0.3668 minDCF and 5.58% EER on the evaluation set by using a simple cosine similarity scoring. Finally, the submitted primary system obtains 0.3532 minDCF and 4.96% EER on the evaluation set.
Efficient average-case population recovery in the presence of insertions and deletions

Several recent works have considered the \emph{trace reconstruction problem}, in which an unknown source string $x\in\{0,1\}^n$ is transmitted through a probabilistic channel which may randomly delete coordinates or insert random bits, resulting in a \emph{trace} of $x$. The goal is to reconstruct the original string~$x$ from independent traces of $x$. While the best algorithms known for worst-case strings use $\exp(O(n^{1/3}))$ traces \cite{DOS17,NazarovPeres17}, highly efficient algorithms are known \cite{PZ17,HPP18} for the \emph{average-case} version, in which $x$ is uniformly random. We consider a generalization of this average-case trace reconstruction problem, which we call \emph{average-case population recovery in the presence of insertions and deletions}. In this problem, there is an unknown distribution $\cal{D}$ over $s$ unknown source strings $x^1,\dots,x^s \in \{0,1\}^n$, and each sample is independently generated by drawing some $x^i$ from $\cal{D}$ and returning an independent trace of $x^i$. Building on \cite{PZ17} and \cite{HPP18}, we give an efficient algorithm for this problem. For any support size $s \leq \smash{\exp(\Theta(n^{1/3}))}$, for a $1-o(1)$ fraction of all $s$-element support sets $\{x^1,\dots,x^s\} \subset \{0,1\}^n$, for every distribution $\cal{D}$ supported on $\{x^1,\dots,x^s\}$, our algorithm efficiently recovers ${\cal D}$ up to total variation distance $\epsilon$ with high probability, given access to independent traces of independent draws from $\cal{D}$. The algorithm runs in time poly$(n,s,1/\epsilon)$ and its sample complexity is poly$(s,1/\epsilon,\exp(\log^{1/3}n)).$ This polynomial dependence on the support size $s$ is in sharp contrast with the \emph{worst-case} version (when $x^1,\dots,x^s$ may be any strings in $\{0,1\}^n$), in which the sample complexity of the most efficient known algorithm \cite{BCFSS19} is doubly exponential in $s$.