Reviewerly/exHarmony huggingface dataset:
Social Presence in VR Empathy Game for Children: Empathic Interaction with the Virtual Characters

This paper discusses children&#x0027;s empathic interactions with the VR characters. The study and findings in this paper were derived from a larger research study that explored the design and evaluation of empathic experiences for young children (6&#x2013;9 years old) in VR environments. We found similarities between how children interacted with the VR characters and with real people (i.e., cognitive empathy and emotional contagion). This provides initial insight into children&#x0027;s experience of social presence with the VR characters. We suggest follow-up research on connection between empathy and social presence to explore the ways to create empathic VR experiences for children.
An English Pronunciation Error Detection System Based on Improved Random Forest

The existing English pronunciation error detection methods are more oriented to the detection of wrong pronunciation, and lack of targeted improvement suggestions for pronunciation errors. With the aim of solving this problem, the paper proposes an English pronunciation error detection system based on improved random forest. Firstly, a speech corpus is constructed along with the evaluation of the acoustic features. Then an improved random forest detection algorithm is designed. The algorithm inputs rare mispronunciation data into a GAN neural network to generate new class samples and improve the uneven distribution of mispronunciation data in the sample set. The distribution rules of the pronunciation data are extracted layer by layer by stacking deep SDAEs, and the coefficient penalties and reconstruction errors of each coding layer are combined to identify the features associated with the wrong pronunciation in the high-dimensional data. Furthermore, a forest decision tree is constructed using the reduced-dimensional feature-based data to improve the pronunciation detection accuracy. Finally, the extracted 39 Mel Frequency Cepstral Coefficient (MFCC) acoustic features are used as the input of the improved random forest classifier to construct a classification error detection model. The experimental results indicate that the designed system achieves a high accuracy of English pronunciation detection.
Using double attention for text tattoo localisation

Text tattoos contain rich information about an individual for forensic investigation. To extract this information, text tattoo localisation is the first and essential step. Previous tattoo studies applied existing object detectors to detect general tattoos, but none of them considered text tattoo localisation and they neglect the prior knowledge that text tattoos are usually inside or nearby larger tattoos and appear only on human skin. To use this prior knowledge, a prior knowledge-based attention mechanism (PKAM) and a network named Text Tattoo Localisation Network based on Double Attention (TTLN-DA) are proposed. In addition to TTLN-DA, two variants of TTLN-DA are designed to study the effectiveness of different prior knowledge. For this study, NTU Tattoo V2, the largest tattoo dataset and NTU Text Tattoo V1, the largest text tattoo dataset are established. To examine the importance of the prior knowledge and the effectiveness of the proposed attention mechanism and the networks, TTLN-DA and its variants are compared with state-of-the-art object detectors and text detectors. The experimental results indicate that the prior knowledge is vital for text tattoo localisation; The PKAM contributes significantly to the performance and TTLN-DA outperforms the state-of-the-art object detectors and scene text detectors.
Social Facilitation and Inhibition in Augmented Reality: Performing Motor and Cognitive Tasks in the Presence of a Virtual Agent

In this preliminary study, we set out to explore the social facilitation and inhibition effects in augmented reality (AR). To do so, participants performed cognitive and motor tasks in two levels of difficulty either alone or in the presence of a real human or a virtual agent. Tendencies towards social facilitation and inhibition could be observed.
KARLI: Kid-friendly Augmented Reality for Primary School Health Education

Acquiring health knowledge is essential and starts already in primary school. Augmented Reality (AR) helps to convey complex topics in a more understandable way. In this paper, we present the prototype of KARLI, the &#x201C;Kid-friendly Augmented Reality Learning Interface&#x201D;. This AR smartphone app for in-school use is designed for age level 8 to 10, enabling pupils to explore a 3D model of the human body based on the primary school curriculum. Underlining the importance of kid-friendly app development and testing, our evaluation results of 38 pupils and 3 teachers indicate that KARLI is suitable and helpful for health education in primary schools.
Cloudlet Selection Strategy According to the Types of Applications in Cloud Networks

The selection of the most suitable cloudlet that allows running users applications rapidly in the cloud is still an urgent problem. For the elimination of resource shortages, power consumption, and delays in communication channels on mobile devices, the remote cloud servers are placed adjacent to devices. The delays on communication channels and power consumption on mobile devices are reduced through cloud-based mobile computing. In the paper we propose a strategy for selecting a cloudlet with high computing productivity, which provides a fast solution, considering the complexity degree of the application (file type). Also, in the paper, the solution of the task in a cloudlet close to the user, the downloading of the task to the cloudlet, the sending of the outcome to the user, and the minimization of network interruptions are presented.
Learning Environments in AR: Comparing Tablet and Head-mounted Augmented Reality Devices at Room and Table Scale

This paper presents work to examine how presentation scale and device form factor may affect learning in augmented reality (AR) en-vironments. We conducted a 2 (form factor) x 2 (scale) experiment in which 131 participants explored an AR learning environment using either tablet AR or a Hololens2 crossed with either full room scale or at table scale. Dependent variables measured participants declarative knowledge about information acquired in the environ-ment as well as their understanding of spatial layout. Initial analysis suggests comparable outcomes across all manipulations with respect to acquiring declarative and spatial knowledge.
Detecting Examinees With Item Preknowledge on Real Data

Recently, Belov & Wollack (2021) developed a method for detecting groups of colluding examinees as cliques in a graph. The objective of this article is to study how the performance of their method on real data with item preknowledge (IP) depends on the mechanism of edge formation governed by a response similarity index (RSI). This study resulted in the development of three new RSIs and demonstrated a remarkable advantage of combining responses and response times for detecting examinees with IP. Possible extensions of this study and recommendations for practitioners were formulated.
Attendance Marking System based on Hog Descriptor Algorithm

The atmosphere of individuals in a workspace is an important part of the daily management. Attendance is however a method to keep the record of the presence and absence of the individuals. Students&#x0027; attendance has been taken using paper-based methods for decades. This attendance approach has been used for a long time. This strategy may be appropriate and practicable for a small group of attendees, but it is time and energy consuming for a bigger group. The drawbacks of this procedure are well-known and well-understood: it takes time, is prone to errors, and there is always the possibility of proxy attendance. We provide a practical and fashionable way for recording attendance using face recognition technology. The technology will recognize a person&#x0027;s face, and compare it to the database to determine whether they are present. Furthermore, the attendance sheet is kept with student information and time, which can be manually amended if necessary. Using this approach, the problem of fraudulent attendance and proxies can be solved.
Focal stack based image forgery localization

Image security is becoming an increasingly important issue due to advances in deep learning based image manipulations, such as deep image inpainting and deepfakes. There has been considerable work to date on detecting such image manipulations using improved algorithms, with little attention paid to the possible role that hardware advances may have for improving security. We propose to use a focal stack camera as a novel secure imaging device, to the best of our knowledge, that facilitates localizing modified regions in manipulated images. We show that applying convolutional neural network detection methods to focal stack images achieves significantly better detection accuracy compared to single image based forgery detection. This work demonstrates that focal stack images could be used as a novel secure image file format and opens up a new direction for secure imaging.
Perceptions of Colour Pickers and Companions in Virtual Reality Art-Making

Virtual reality art is reshaping digital art experiences but may elicit different first impressions across disparate age groups. We investigate first impressions of VR colour pickers and the impact of a virtual companion via an online survey with 63 adults and 24 older adults. The colour pickers differed significantly in perceived hedonic qualities. We found no statistical differences between perceptions of adults and older adults. The virtual companion had no significant effect on participants&#x2019; overall experiences. However, we found statistical trends where older adults rated the virtual companion higher in terms of companionship and making VR art more engaging.
User Sensitive Information Protection Scheme Based on Blockchain Technology

With the continuous development of mobile applications and location technology, <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>k</mi> </math> -anonymity technology is usually used to protect the privacy and security of users in location-based services that are used by mobile users. Due to the self-interest of users, in the cooperative construction of a <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>k</mi> </math> -anonymity privacy protection scheme, it is difficult to guarantee the honest cooperation behavior of the requesting and cooperating users. To solve the above problems, this paper proposes a distributed <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mi>k</mi> </math> -anonymous location privacy protection scheme based on blockchain. In this paper, we record the requesting users, cooperative users, and their location information as evidence, and we punish the users with location leakage and deception. An anonymous zone cannot be constructed when the user is a requesting user. An interactive record mechanism is designed to constrain the self-interest behavior of the requesting and cooperating users in the process of anonymous region construction. Security analysis and experiments show that this scheme can effectively protect the user’s location privacy, promote the honest cooperation between the requesting user and the cooperative user, and encourage more users to participate in the construction of an anonymous zone.
Prototype Regularized Manifold Regularization Technique for Semi-Supervised Online Extreme Learning Machine

Data streaming applications such as the Internet of Things (IoT) require processing or predicting from sequential data from various sensors. However, most of the data are unlabeled, making applying fully supervised learning algorithms impossible. The online manifold regularization approach allows sequential learning from partially labeled data, which is useful for sequential learning in environments with scarcely labeled data. Unfortunately, the manifold regularization technique does not work out of the box as it requires determining the radial basis function (RBF) kernel width parameter. The RBF kernel width parameter directly impacts the performance as it is used to inform the model to which class each piece of data most likely belongs. The width parameter is often determined off-line via hyperparameter search, where a vast amount of labeled data is required. Therefore, it limits its utility in applications where it is difficult to collect a great deal of labeled data, such as data stream mining. To address this issue, we proposed eliminating the RBF kernel from the manifold regularization technique altogether by combining the manifold regularization technique with a prototype learning method, which uses a finite set of prototypes to approximate the entire data set. Compared to other manifold regularization approaches, this approach instead queries the prototype-based learner to find the most similar samples for each sample instead of relying on the RBF kernel. Thus, it no longer necessitates the RBF kernel, which improves its practicality. The proposed approach can learn faster and achieve a higher classification performance than other manifold regularization techniques based on experiments on benchmark data sets. Results showed that the proposed approach can perform well even without using the RBF kernel, which improves the practicality of manifold regularization techniques for semi-supervised learning.
Dynamically producing asymmetric interferometric power under dephasing

Interferometric power, based on the minimal quantum Fisher information in a black-box interferometer, can be used to measure general quantum correlation in a bipartite system. This paper explores the interferometric power of a two-qubit system in a common bath. It is found that, for an initially separable state, the interferometric power of the system can be produced with the time evolution, even subject to the dephasing environment. Moreover, it is asymmetric with respect to the local evolution implementing on one subsystem or another. Our result would be useful to quantum information processing beyond entanglement, especially in one-sided device-independent task.
Fast asymmetric encryption and decryption of SimpleMatrix scheme for Internet of Things

Asymmetric cryptography plays an essential role in many areas, including cloud computing, big data, blockchain, and the Internet of Things (IoT). However, most of them are based on the difficulty of factorizing large numbers or discrete logarithm problems, which are not secure to quantum computer attacks. SimpleMatrix is a new multivariate encryption scheme based on simple matrix multiplications, which can resist quantum computer attacks. Because of the low speed and demands of large finite fields, SimpleMatrix is limited in applications that use small finite fields. As a result, it is critical to improve the efficiency of SimpleMatrix to make its applications broader. In this paper, we speed up the encryption and decryption of SimpleMatrix by building efficient small finite field arithmetics based on Field-Programmable Gate Arrays (FPGAs) technology. We propose a fast architecture for encryption and decryption of SimpleMatrix based on table look-up based composite field multiplications and inversions and fast Gauss–Jordan elimination for solving systems of linear equations in a composite field. We test and verify the hardware architecture of SimpleMatrix on an FPGA, and the experimental results confirm our estimates and comparisons show that our design is much faster than other implementations. Thus, the hardware architecture can be used in FPGA-based systems of cloud computing, IoT, etc., for accelerating encryption and decryption.
Universal Reconfigurable Hardware Accelerator for Sparse Machine Learning Predictive Models

This study presents a universal reconfigurable hardware accelerator for efficient processing of sparse decision trees, artificial neural networks and support vector machines. The main idea is to develop a hardware accelerator that will be able to directly process sparse machine learning models, resulting in shorter inference times and lower power consumption compared to existing solutions. To the author’s best knowledge, this is the first hardware accelerator of this type. Additionally, this is the first accelerator that is capable of processing sparse machine learning models of different types. Besides the hardware accelerator itself, algorithms for induction of sparse decision trees, pruning of support vector machines and artificial neural networks are presented. Such sparse machine learning classifiers are attractive since they require significantly less memory resources for storing model parameters. This results in reduced data movement between the accelerator and the DRAM memory, as well as a reduced number of operations required to process input instances, leading to faster and more energy-efficient processing. This could be of a significant interest in edge-based applications, with severely constrained memory, computation resources and power consumption. The performance of algorithms and the developed hardware accelerator are demonstrated using standard benchmark datasets from the UCI Machine Learning Repository database. The results of the experimental study reveal that the proposed algorithms and presented hardware accelerator are superior when compared to some of the existing solutions. Throughput is increased up to 2 times for decision trees, 2.3 times for support vector machines and 38 times for artificial neural networks. When the processing latency is considered, maximum performance improvement is even higher: up to a 4.4 times reduction for decision trees, a 84.1 times reduction for support vector machines and a 22.2 times reduction for artificial neural networks. Finally, since it is capable of supporting sparse classifiers, the usage of the proposed hardware accelerator leads to a significant reduction in energy spent on DRAM data transfers and a reduction of 50.16% for decision trees, 93.65% for support vector machines and as much as 93.75% for artificial neural networks, respectively.
Bot Undercover: On the Use of Conversational Agents to Stimulate Teacher-Students Interaction in Remote Learning

In this work, the use of an undercover conversational agent, acting as a participative student in a synchronous virtual reality distance learning scenario is proposed to stimulate social interaction between teacher and students. The outcome of an exploratory user study indicated that the undercover conversational agent is capable of fostering interaction, relieving social pressure, and overall leading to a more satisfactory and engaging learning experience without sacrificing learning performance.
A new document representation based on global policy for supervised term weighting schemes in text categorization

There are two main factors involved in documents classification, document representation method and classification algorithm. In this study, we focus on document representation method and demonstrate that the choice of representation methods has impacts on quality of classification results. We propose a document representation strategy for supervised text classification named document representation based on global policy (DRGP), which can obtain an appropriate document representation according to the distribution of terms. The main idea of DRGP is to construct the optimization function through the importance of terms to different categories. In the experiments, we investigate the effects of DRGP on the 20 Newsgroups, Reuters21578 datasets, and using the SVM as classifier. The results show that the DRGP outperforms other text representation strategy schemes, such as Document Max, Document Two Max and global policy.
Development of Real-Time Hand Gesture for Volume Control Application using Python on Raspberry Pi

Computer interaction is one of the significant challenges that elderly and disabled people face in today's computerized world. In recent years, hand gesture recognition has emerged as one of the most natural human-machine interactions in software development, particularly for facilitating friendly and flexible human-computer interaction. This article proposed a system architecture and software configuration for developing a real-time volume control hand gesture on a Raspberry Pi equipped with a camera programmed in Python using the Open-Source Computer Vision (OpenCV) library. The hand gesture recognition system's primary objective is to establish communication between humans and computerized systems in order to control volume. According to the experimental results, the hand gesture recognition system performs well when controlling volume. The system is capable of operating in real-time for any individual.
A Topic Modeling-Guided Framework for Aspect-Oriented Sentiment Analysis on Social Media

Social media platforms have incorporated more than half of the world's population, making it one of the most data-rich domains recently. The sentiments expressed by social media users hold great significance for various reasons, such as the identification of public opinion on a product or towards a governmental policy, to name a few. There are different domains where companies use social media sentiments to gather feedback from customers to provide them with better products and services. Only a few attempts have been reported on aspect-based sentiment analysis literature on sentiment analysis and opinion mining. This chapter proposes a framework for aspect-based sentiment analysis for social media using a topic modeling-powered approach. The experiments conducted on real-world datasets show that the proposed framework outperforms some existing works on aspect-oriented sentiment analysis.
The Application of Smart Grid Power System through Interconnection of Active Distribution Management in Better Performance of Network Management - A Correlation Analysis Approach

The advent of technologies has enabled in transforming the overall energy market in a sustainable manner, the usage of smart grids supports the users and energy companies in understanding the change in the overall behaviour related to the electricity rates, it incorporates the necessary equipment and behaviour in the grid design, support in enhancing the distribution management through interaction between users and providers, which enables in enhancing the performance of the network management effectively. The implementation of smart grids and innovative technologies in the disruption management supports the users, energy companies, government and communities to be more sustainable, analyse the future demand of the energy requirement and proactively take action. Moreover, the implementation of these grids also enables in switching between the different sources of energy, thereby optimizing the consumption in an effective manner. The major benefits of these tools results in enhanced distribution management at lower cost thereby enhancing financial benefits directly to the stakeholders, support in better power reliability, effective storage and manage the supply chain in an efficient manner, also it supports in increasing the virility of the unsecure situation and act in a more proactive manner. This study will enable in analyzing the critical determinants of smart grids in enhancing the overall performance of network management in the energy sector, the analysis reveals that the implementation of these technologies tends to be high supportive for all the stakeholders and enable in achieving sustainable growth and development.