Reviewerly/exHarmony huggingface dataset:
BarBam:a new arcophobia virtual reality game

Acrophobia is an extreme or inconsequent fear of heights. Acrophobia can reason for panic attacks and avoid a person who suffers from this phobia from daily works. These days Expensive and sophisticated systems are utilized in many applications such as airline pilots. With the appearance of entertainment industries because of the interest of virtual reality and also virtual environment helped to release low-cost virtual reality systems. Simulation of heights in VR used for over 30 years[1] the purpose of this study is to introduce new Phobia games based on Virtual Reality (VR). Heights with acceptable quality for the treatment of acrophobia is one of the example medical application which is well-developed for VR platform[2]. In the first part, we discuss advantages of virtual reality and in the second part we reviewed different games of VR developed for therapeutic purpose on acrophobia treatment, and also electronic games for medical reasons are named serious games and has a different purpose from entertain games. In the second part then we introduce BarBam new Acrophobia Game and in the last part we conclude and future works. For game evaluation we are tested that game on the 13 participants with acrophobia disorder and get their feedback and improve game based on that.
Strategies to Improve the Effectiveness of Business English Classroom Teaching Based on Intelligent Teaching System

This paper analyses the current teaching situation of Business English course, explores the connotation of classroom teaching effectiveness under the network environment, and considers and explores the effectiveness of classroom teaching of Business English based on the network from the aspects of cognitive tools, information-based teaching design and teaching mode. In the learning mode of Intelligent Teaching, student model is the basis of providing real intelligent guidance. This paper proposes a method of building cognitive student model in the network-based intelligent teaching system. The establishment of teaching system model, the application of data mining algorithm in teaching and the extraction of students' learning characteristics and students' characteristics are the main contents of the study. This paper introduces a web-based Business English Intelligent Teaching System based on this cognitive student model, and analyses the experimental results of the system.
Fast Incremental Spectral Clustering in Titanate Application via Graph Fourier Transform

In numerous applications the rapid increase of data size with time makes classical clustering algorithms too slow because of the high computational cost. In the present contribution, a novel method for online spectral clustering algorithm is introduced, which can be applied for simulation of structure and properties of titanate in chemical engineering. The proposed algorithm uses the recent results in the emerging field of graph signal processing. It avoids the costly computation of the eigenvectors by filtering random signals on the graph, and only a few derivative operations are needed to update the clustering result when a new data arrives. Initial simulations are presented using both simulated and real data sets, illustrating the relevance of the proposed algorithm.
Leveraging on Digital Footprints to Identify Potential Security Threats

The growing pervasiveness of the internet and the rise of social media have revolutionised how individuals communicate and interact with one another. Serving as an effective conduit for communication, these technological advancements have also been exploited by individuals with malicious intent (e.g., criminals, violent extremists). As the world witnesses an upward trend of such crime and security concerns in the online sphere, it places the ‘responsibility' on intelligence and law enforcement agencies to respond with the appropriate technological interventions. Thus, this article will discuss how digital footprints can be leveraged to identify potential security threats, particularly for crime and security issues that will result in negative repercussion at the national level, such as acts of violent extremism and hate crimes.
Construction of “Audio-Visual Japanese” Digital Course Based on “General Education Curriculum” Cloud Platform

Abstract In this paper, the development of new textbooks is taken as the first step, the modern information technology is used to establish a multidimensional stereoscopic teaching mode of “Cloud platform-assisted learning + students’ autonomous learning”, which has effectively improved the teaching effect of the Japanese audio-visual course. This paper is aimed to explore new ideas for building the digital course of “Audio-Visual Japanese’ based on the General Education Curriculum” cloud platform, to improve students’ Japanese listening and speaking skills effectively, and provide a reference for practical teaching and research in this field.
RPMSP: A Novel Replica Placement Method Inspired by Self-Similarity Propagation of Plants

Replica placement method determines replica layout directly and affects resource management of cloud storage indirectly. Since the imbalance of performance indicators in the issue of replica placement, this paper proposes a novel replica placement method inspired by self-similarity propagation of plants (RPMSP). Firstly, the issue of replica placement affected by multiple performance indicators is modeled as the objective space. Then, the method simulates the self-similarity propagation of plants to find the most profitable area and selects the optimal individual from it as the most suitable replica layout. Finally, it conducted several simulation experiments to evaluate the proposed method. The experimental results show that RPMSP can effectively find a suitable replica layout and balance the relationship among multiple performance indicators.
Clarifying Origin-Destination Flows Using Force-Directed Edge Bundling Layout

Origin-destination (OD) flows have time-varying characteristics and spatial heterogeneity. With the increasing amount of urban travel data, it is very challenging to clarify OD flows through traditional visualization methods. A novel approach based on the force-directed edge bundling (FDEB) algorithm is proposed to visualize OD flows and identify the main corridors in a city. First, we reduce the chaos of OD flows through vertex clustering, and then we use a modified FDEB algorithm to clarify the OD flows. Furthermore, to illustrate the validity of our approach from the perspective of traffic-representation ability, we design three evaluation metrics: local feature richness(LFR) and strength of spatial relationship (SSR), which measure the ability to express the spatial heterogeneity of OD flows, and time characteristic richness (TCR), which measures the ability to express time-varying characteristics in OD flows. Experiments are conducted on real-world automatic vehicle identification (AVI) data that are gathered from Xuancheng, China. The results show that our method can well enhance the representation of spatial heterogeneity and uncover the temporal characteristics hidden in OD flows.
From Publications to Knowledge Graphs

We address the task of compiling structured documentation of research processes in the form of knowledge graphs by automatically extracting information from publications and associating it with information from other sources. This challenge has not been previously addressed at the level described here. We have developed a process and a system that leverages existing information from DBpedia, retrieves articles from repositories, extracts and interrelates various kinds of named and non-named entities by exploiting article metadata, the structure of text as well as syntactic, lexical and semantic constraints, and populates a knowledge base in the form of RDF triples. An ontology designed to represent scholarly practices is driving the whole process. Rule -based and machine learning- based methods that account for the nature of scientific texts and a wide variety of writing styles have been developed for the task. Evaluation on datasets from three disciplines, Digital Humanities, Bioinformatics, and Medicine, shows very promising performance.
Research on Language Ability Assessment Method Based on Computer Contextualization

Abstract Language is the main way of human social communication, language ability in people’s life learning and social work has a very important role that is a basic ability one must have as well as be trained from a child. Language is the main tool for people to communicate with each other, it is the carrier of all kinds of information transmission. Language is one of the main ways to inherit culture, which is an important force to promote social progress and development. The cultivation and development of language ability affect everyone’s status and development prospect in society in the future. Therefore, great attention should be paid to the cultivation of language ability. To cultivate and develop language competence, we must first understand the current situation of language mastery and evaluate language competence.
Application of Cloud Teaching in Higher Vocational English Teaching—Take the MosoInk Cloud Class as an Example

Abstract Big data bring revolutionary changes to education in the Internet +education era. This paper analyzes the current teaching situation of higher vocational English teaching, and proposes Cloud teaching mode based on the MosoInk Cloud Class platform. The teaching practice shows that Cloud teaching mode can stimulate students’ interest in autonomous learning, transform passive learning into active learning, and make teaching fun, simple and efficient, improve the students’ language skills.
Automated Domain Model Learning Tools for Planning

Intelligent agents solving problems in the real world require domain models containing widespread knowledge of the world. Domain models can be encoded by human experts or automatically learned through the observation of some existing plans (behaviours). Encoding a domain model manually from experience and intuition is a very complex and time-consuming task, even for domain experts. This chapter investigates various classical and state-of-the-art methods proposed by the researchers to attain the ability of automatic learning of domain models from training data. This concerns with the learning and representation of knowledge about the operator schema, discrete or continuous resources, processes and events involved in the planning domain model. The taxonomy and order of these methods we followed are based on their standing and frequency of usage in the past research. Our intended contribution in this chapter is to provide a broader perspective on the range of techniques in the domain model learning area which underpin the developmental decisions of the learning tools.
A Case Study on Data Vulnerabilities in Software Development Lifecycle Model

Software security has become a very critical part of our lives. A software developer who has a fundamental understanding of software security can have an advantage in the workplace. In the massive Equifax breach that occurred in 2017 that exposed data of roughly 140 million people, attackers exploited a vulnerability in Apache Struts, CVE-2017-5638, which allows remote attackers to execute arbitrary commands when specially crafting user-controlled data in HTTP headers. Sensitive data exposure issues are essential to know to protect customer data. It is fascinating to understand how attackers can exploit application vulnerabilities to perform malicious activities. The authors also want the reader to be aware of the fact that we should always be thinking about how our applications handle user-controlled data so that we can put guards in place to minimize security issues in the development of new applications.
On Solving Nominal Disunification Constraints

This paper proposes an extension of first-order disunification problems by taking into account binding operators according to the nominal approach. In this approach, bindings are implemented through atom abstraction, and renaming of atoms is implemented via atom permutations. In the nominal setting, unification problems consist of equational questions (s≈α?t) considered under freshness constraints (a#?t) that restrict solutions by forbidding free occurrences of atoms in the instantiation of variables. In addition to equational and freshness constraints, nominal disunification problems include also nominal disunification constraints (s≉α?t), and their solutions consist of a substitution and additional freshness constraints such that under these constraints the instantiation of the equations, disequations and freshness constraints with the substitution hold. By re-using nominal unification techniques, this paper shows how to decide whether two nominal terms can be made different modulo α-equivalence. This is done by extending previous results on first-order disunification, and defining the notion of solutions with exceptions in the nominal syntax. A discussion on the semantics of disunification constraints is also given.
PT-MMD: A Novel Statistical Framework for the Evaluation of Generative Systems

Stochastic-sampling-based Generative Neural Networks, such as Restricted Boltzmann Machines and Generative Adversarial Networks, are now used for applications such as denoising, image occlusion removal, pattern completion, and motion synthesis. In scenarios which involve performing such inference tasks with these models, it is critical to determine metrics that allow for model selection and/or maintenance of requisite generative performance under pre-specified implementation constraints. In this paper, we propose a new metric for evaluating generative model performance based on p-values derived from the combined use of Maximum Mean Discrepancy (MMD) and permutation-based (PT-based) resampling, which we refer to as PT-MMD. We demonstrate the effectiveness of this metric for two cases: (1) Selection of bitwidth and activation function complexity to achieve minimum power-at-performance for Restricted Boltzmann Machines; (2) Quantitative comparison of images generated by two types of Generative Adversarial Networks (PGAN and WGAN) to facilitate model selection in order to maximize the fidelity of generated images. For these applications, our results are shown using Euclidean and Haar-based kernels for the PT-MMD two sample hypothesis test. This demonstrates the critical role of distance functions in comparing generated images against their corresponding ground truth counterparts as what would be perceived by human users.
Minimal residual ordinal loss hashing with an adaptive optimization mechanism

Abstract The binary coding technique has been widely used in approximate nearest neighbors (ANN) search tasks. Traditional hashing algorithms treat binary bits equally, which usually causes an ambiguous ranking. To solve this issue, we propose an innovative bitwise weight method dubbed minimal residual ordinal loss hashing (MROLH). Different from a two-step mechanism, MROLH simultaneously learns binary codes and bitwise weights by a feedback mechanism. When the algorithm converges, the binary codes and bitwise weights can be well adaptive to each other. Furthermore, we establish the ordinal relation preserving constraint based on quartic samples to enhance the power of preserving relative similarity. To decrease the training complexity, we utilize a tensor ordinal graph to represent quartic ordinal relation, and the original objective function is approximated by the one based on triplet samples. In this paper, we also assign different weight values to training samples. During the training procedure, the weight of each data is initialized to the same value, and we iteratively boost the weight of the data whose relative similarity is not well preserved. As a result, we can minimize the residual ordinal loss. Experimental results on three large-scale ANN search benchmark datasets, i.e., SIFT1M, GIST1M, and Cifar10, show that the proposed method MROLH achieves a superior ANN search performance in both the Hamming space and the weighted Hamming space over the sate-of-the-art approaches.
Mixed Word Embedding Method Based on Knowledge Graph Augment for Text Classification

This paper presents a self-training word embedding text classification model based on knowledge graph expansion for text classification. Current mixed word embedding methods are overly dependent on the Fasttext pre-training model and here is still a problem of missing words with rich semantic information are not mapped. First, we propose a method for extracting missing nouns based on shape near word filtering. Second, we design a self-training word embedding method based on knowledge graph that mixes with pre-training word embedding to obtain a high-quality mixed word vector with rich semantics and rich semantics. Third, we designed a GRU model based on improved mixed word embedding to improve the quality of text classification. Experiments conducted on multiple text classification datasets demonstrate that our methods can effectively improve the text classification accuracy.
The Multilayer Perceptron Vector Quantized Variational AutoEncoder for Spectral Envelope Quantization

Recently, deep generative learning has been introduced to replace the conventional mathematical models. In speech processing, the vector quantization was the effective compression method to reduce the amount of speech data before transmitting. In this paper, we propose The Multilayer Perceptron Vector Quantized Variational Autoencoder (MLP-VQ-VAE) to manage the flexibility of controlling the number of z-latent vectors to quantize and embedding space size efficiently. The MLP-VQVAE replaces the Convolutional Neural Network (CNN) with Multilayer Perceptron (MLP) in the encoder network and the decoder network of Vector Quantized Variational AutoEncoder (VQ-VAE) to receive the size of the effectively z-latent vectors for quantization and also the ability of dimensional reduction. In the experiments, the MLP-VQ-VAE is applied to quantize spectral envelope parameters from the 48 kHz high-quality vocoder named WORLD. The MLP-VQ-VAE reduces the memory sizes of the representation of z-latent or the length of vectors to quantize and embedding space size or codebook size around 1.6 times compared to the conventional vector quantization and 21.4 times for VQVAE. The proposed method decreases the Log Spectral Distortion around 1.1 dB lower than the conventional VQ and around 2.5 dB than the VQ-VAE.
A Dynamic Ensemble Selection Strategy for Improving Error Correcting Output Codes Algorithm

Error correcting output codes (ECOC) is an effective approach for the multiclass classification problem by decomposing a multiclass problem to a set of binary class problems. Up to now, most of proposed ECOC algorithms mainly focus on the generation of codematrices, while this study proposes a Dynamic Ensemble Selection strategy for improving the performance of the original ECOC algorithms. In this strategy, a codematrix is generated by an ECOC algorithm firstly, and then some feature selection algorithms are deployed to select feature subsets for each column in the codematrix. When an unknown sample arrives, the Fisher criterion is deployed to select the best feature subset from the candidate subsets to match a column. After determining the optimal feature subsets for various columns, the distance evaluation process is carried out to assign the sample to a class. The proposed strategy is tested on UCI data sets with three classical ECOC algorithms. Experiment results prove that our strategy can improve the performance of the original ECOC algorithms in most cases.
Experiment on Cognitive Load of Gesture Interaction Based on Semiotics

Abstract Objective: To explore the amount of information contained in gesture symbols and its relationship with cognitive load, and to provide theoretical guidance for gesture interaction design based on semiotics. Methods: Firstly, a user-defined method was combined with oral analysis to collect gestures and establish a corresponding semantic knowledge base. Based on the knowledge base, the icon algebra theory is used to redesign the gesture symbols. Then through experiments combined with the NASA-TLX scale to measure the user’s cognitive load, and through data analysis to explore the relationship between the amount of information contained in the gesture symbol and it. Conclusion: The amount of information contained in gesture symbols has a significant correlation with the user’s cognitive load and response time. As the amount of gesture symbol information increases, the user’s cognitive load and response time also increase accordingly. Based on this, a regression equation of user cognitive load and information amount of gesture symbols is established, which provides theoretical support for the design of gesture symbols based on semiotics.
Automatic Age Classification of Prospective Voters Using Deep Convolutional Neural Network

Age estimation from images of the face, has gained more attention in recent as it is favorable in some realworld applications. In this work, we address the problem of Under-Age registration/voting in Nigeria Electoral system, which has been a major menace, hindering a free and fair election in the country since the assumption of the Democratic system of Government. To this end, a pretrained VGG-16 Deep Convolutional Neural Network while comparing two optimization algorithms without image preprocessing is employed to both extract features from image(s) of prospective voters and classify same under the established age classification group, as eligible or not to exercise their civil right. In light of this, a classification accuracy of 77.67% is achieved with the model.
The Construction of a Mock-Up Test Building and a Statistical Analysis of the Data Acquired to Evaluate the Power Generation Performance of Photovoltaic Modules

Traditionally, studies on the power generation performance analysis of the photovoltaic (PV) modules used in building-integrated PV (BIPV) systems have been based on computer simulations and actual experiments with constraints, resulting in the results being inaccurate and limited. This paper proposes a two-step analysis method that results in a more versatile and reliable means of analysis. The steps are: (1) construction of a mock-up test building in the form of BIPV systems and the collection of a massive amount of operational data for one year; and (2) a statistical analysis of the acquired data using Minitab software (Version: 17, Manufacturer: Minitab Inc., State College, PA, USA) to examine the power generation performance. The constructed BIPV mock-up applies design elements such as material types (c-Si and a-Si) and various directions and angles for different module installations. Prior to the analysis, the reliability of the large database (DB) constructed from the acquired data is statistically validated. Then, from the statistical correlation analysis of the DB, several plots that visualize the performance characteristics governed by design elements, including contour plots that show the region of higher performance, are generated. Further, a regression model equation for power generation performance is derived and verified. The results of this study will be useful in determining whether a BIPV system should be adopted in a building’s architectural design and, subsequently, selecting design element values for an actual BIPV system.