Reviewerly/exHarmony huggingface dataset:
Why Don't We See More of Augmented Reality in Schools?

Previous research has shown the potential of Augmented Reality (AR) in education, however, its use is still not widespread in the classroom setting. This study aims to understand what is the current maturity level regarding AR use in schools, as well as what is preventing schools to reach higher levels of maturity. We aim to discuss the current use of AR in schools and reflect on ways AR technology can evolve and adapt to support more meaningful and effective learning practices. 106 teachers answered an online survey in order to help us understand those issues. Results have shown that lack of infrastructure and authoring tools are the two biggest problems hindering AR use in classrooms. Evidence suggests the need to focus on authoring tools that support collaboration and creativity in the educational settings, thus, enabling schools to use AR technology in more effective ways and achieving higher levels of maturity. We conclude by listing features for designing AR applications in relation to the maturity levels identified.
Evolutionary Classifier and Cluster Selection Approach for Ensemble Classification

Ensemble classifiers improve the classification performance by combining several classifiers using a suitable fusion methodology. Many ensemble classifier generation methods have been developed that allowed the training of multiple classifiers on a single dataset. As such random subspace is a common methodology utilized by many state-of-the-art ensemble classifiers that generate random subsamples from the input data and train classifiers on different subsamples. Real-world datasets have randomness and noise in them, therefore not all randomly generated samples are suitable for training. In this article, we propose a novel particle swarm optimization-based approach to optimize the random subspace to generate an ensemble classifier. We first generate a random subspace by incrementally clustering input data and then optimize all generated data clusters. On all optimized data clusters, a set of classifiers is trained and added to the pool. The pool of classifiers is then optimized and an optimized ensemble classifier is generated. The proposed approach is tested on 12 benchmark datasets from the UCI repository and results are compared with current state-of-the-art ensemble classifier approaches. A statistical significance test is also conducted and an analysis is presented.
8‐bit serialised architecture of SEED block cipher for constrained devices

This study presents an 8-bit serialised architecture of SEED block cipher for constrained devices. The circuit utilises 356 FPGA slices and 447 1-bit registers flip-flops (FFs) in the BASYS3 board, operates with an 8-bit datapath and is aimed for use on area constraints devices. In order to keep the usage of hardware resources to a minimum but, at the same time, achieve a high level of security, the key generation process of SEED is implemented through an on-the-fly procedure. In addition, the necessary S-boxes are implemented using composite field arithmetic without using any block RAMs, resulting in a very compact implementation. The proposed architecture achieves a maximum frequency equal to 125 MHz with a total latency of 280 clock cycles and a throughput up to 57.1 Mbps for encryption or decryption.
GAN-based Image Compression Using Mutual Information Maximizing Regularization

Recently, image compression systems based on convolutional neural networks that use flexible nonlinear analysis and synthesis transformations have been developed to improve the restoration accuracy of decoded images. A method using a framework called a generative adversarial network [1] has been reported as one of the methods aiming to improve the subjective image quality [2][3]. It optimizes the distribution of restored images to be close to that of natural images; thus it suppresses visual artifacts such as blurring, ringing, and blocking. However, since methods of this type are optimized to focus on whether the restored image is subjectively natural or not, components that are not correlated with the original image are mixed in the coding features obtained from the encoder. Thus, even though the appearance looks natural, it may be subjectively seen as a different object from the original image or the impression may be changed. In this paper, we describe a method we have developed to maximize mutual information between the coding features and the restored images. This method, which we call "regularization", makes it possible to develop image compression systems that suppress appearance differences with subjective naturalness.
Vision Model-Based Real-Time Localization of Unmanned Aerial Vehicle for Autonomous Structure Inspection under GPS-Denied Environment

UAVs have been widely used in visual inspections of buildings, bridges, and other structures. In either outdoor autonomous or semi-autonomous flights missions strong GPS signal is vital for UAV to locate its own positions. However, strong GPS signal is not always available, and it can degrade or fully loss underneath large structures or close to power lines, which can cause serious control issues or even UAV crashes. Such limitations highly restricted the applications of UAV as a routine inspection tool in various domains. In this paper a vision-model-based real-time self-positioning method is proposed to support autonomous aerial inspection without the need of GPS support. Compared to other localization methods that requires additional onboard sensors, the proposed method uses a single camera to continuously estimate the inflight poses of UAV. Each step of the proposed method is discussed in detail, and its performance is tested through an indoor test case.
Customer recommendation based on profile matching and customized campaigns in on-line social networks

We propose a general framework for the recommendation of possible customers (users) to advertisers (e.g., brands) based on the comparison between On-Line Social Network profiles. In particular, we associate suitable categories and subcategories to both user and brand profiles in the considered On-line Social Network. When categories involve posts and comments, the comparison is based on word embedding, and this allows to take into account the similarity between the topics of particular interest for a brand and the user preferences. Furthermore, user personal information, such as age, job or genre, are used for targeting specific advertising campaigns. Results on real Facebook dataset show that the proposed approach is successful in identifying the most suitable set of users to be used as target for a given advertisement campaign.
A Survey on Privacy Concerns in Blockchain Applications and Current Blockchain Solutions to Preserve Data Privacy

Due to the offering of many benefits, blockchain is a useful environment to build many applications in multiple fields such as cryptocurrency, Internet of Things (IoT), mobile applications and healthcare industry. However, it has a problem in ensuring the privacy of user data due to its public nature. Data on a blockchain is public and can reveal someone's personal data or transactions. Many applications that venture in blockchain such as financial applications, Internet of Things (IoT) and healthcare applications can fall victim to revelation of personal data and linking attack. Many solutions such as automated access-control protocol that does not require a third party, a local miner operating in a local private blockchain that enforces users' access control policy, a cryptographic protocol that lets contractual parties interact by using zero-knowledge proof, and computation of data query in a distributed way by using secure multi-party computation have been proposed to tackle this privacy issue in using blockchain. Hence, this paper provides a review on the current solutions that addresses the privacy issues of blockchain in different platforms. However, these solutions have some limitations that cannot be overlooked and proper measures must be taken to make blockchain to be able to better protect the privacy of its users. This paper also discusses the limitations of the solutions and looks into the aspects that need to be addressed.
Optimizing Project Delivery through Augmented Reality and Agile Methodologies

The construction sector, which has a long history to use visualisation to envisage proposed designs and project delivery, is beginning to see the benefits of augmented reality and agile project management methodologies. This study investigated the benefits of augmented reality and agile project management methodologies. Convergent design method was considered valuable and the most straightforward for this study, as different types of quantitative and qualitative data were required to be collected and analysed. The participants drawn from the construction sector revealed a number of augmented and agile determinants that facilitated the delivery of construction and integration of project teams. The participants suggested that the proposed ARGILE framework increases client understanding of the tasks output, increases client involvement and collaboration with the project team. It was further established that the proposed ARGILE framework enhances project time management, embeds the client and empowers multidisciplinary team, increases collaboration and communication.
Mobile Application Development for Tourist Guide in Pekanbaru City

Abstract Pekanbaru City is a place that has a huge tourism potential. Pekanbaru is very strong with the Malay culture that is proven that Pekanbaru has many Malay style tourism buildings. The culture of Pekanbaru city has different attraction for tourists. Various information about tourism developments, supporting facilities, tourism objects, visitor information about the locals is important information to know. However, many unknown places are worth a visit but people do not know about their existence, because of the lack of public information. The purpose of this research is to design an application on an Android smartphone for Tour Guide Pekanbaru. The system will use GPS to find the user point and can provide information about tourist attractions, hotels, restaurants and shopping so that they can use the time in Pekanbaru effectively. The recommendation would be to use the neighboring K-nearby algorithm (KNN) to determine the closest place. The result of this tour guide is the user can detail information about tourist attractions, hotels, places to eat and shopping centers in the city of Pekanbaru as desired by the user. Tourist attraction information and directions with minimal GPS accuracy error 10 meters distance and maximum error 40 meters. The speed of the application in determining the user position depends on strong network signal and the condition of the surroundings
HIGS: Hand Interaction Guidance System

In this paper, we introduce a hand interaction guidance system (HIGS) which can automatically author video guidance from 'one-shot' expert demonstration recording, and decompose the step instructions and deliver feedback according to the users' operation. The system observes hand-object interactions with a RGB-D camera from an egocentric view. In the guidance recording stage, HIGS is able to detect the global positions and moments of hand-object interactions and recover the 3D-hand poses during the operation. In the guidance stage, the recorded guidance video is automatically progressed as the user performs the task and the steps are used to assess task progress and completion. Our system operates in real time for both authoring and for monitoring and providing guidance. We see this work as a step towards fully automated learning and guidance systems, in mixed reality settings.
Experimental analysis of a game-theoretic formulation of target tracking

Optimal trajectories for two platforms with similar dynamics are calculated using a game theoretic formulation. Each platform makes noisy observations of the kinematic state of the other. The objective of each is to maximise observable information about the other while minimising the information the other is able to acquire about it. That is to say, each platform maximises the mutual information between the expected future measurement of the opposing platform and the current likelihood of the state whilst minimising the estimated mutual information between potential measurements of itself by the other and its actual state. The multi-objective optimisation problem for each platform is converted to a single optimisation using the Pareto parameter to weigh the relative importance of the two information measures. The relationship between the two Pareto parameters, and different initial track initialisations is investigated. Remarkably this complex coupled system of two platforms exhibits, for suitably chosen values of the Pareto parameters, interesting cyclical behaviours that are worthy of further exploration.
Optimization of Tilt Angle for Intercepting Maximum Solar Radiation for Power Generation

In this study the novelty is determination of optimum tilt angles $$ (\upbeta_{\text{opt}} ) $$ for photovoltaic system at 11 different sites for Gujarat in India. The $$ \upbeta_{\text{opt}} $$ is searched for maximum incident solar radiation (SR). For calculation SR values given by National Aeronautics and Space Administration (NASA) is utilized. It was found that the optimum tilt angle varies between 1° and 57° throughout the year in Gujarat, India. The monthly optimum tilt angle is maximum in December for different sites in Gujarat India. This study is useful for industry and researcher to install PV system in India to generate maximum power.
A Model of Emotional Speech Generation Based on Conditional Generative Adversarial Networks

This paper presents a speech generation technology based on Conditional Generative Adversarial Networks (Conditional GAN). By introducing emotional conditions and learning emotional information in the speech database, a new voice with specified emotions can be generated independently. Generative Adversarial Networks consists of a Discriminator (D) and a Generator (G). Tensorflow is used as a learning framework, Conditional GAN model is used to train a large number of emotional voice, and voice generation network G and generation network D are used to form a dynamic "game process" to learn and observe the conditional distribution of emotional voice data better. The generated samples are close to the natural speech signals of the original learning content, which are rich in diversity and can approximate the real emotional speech. The proposed solution is evaluated on the Interactive Emotional Dyadic Motion Capture (IEMOCAP) corpus and student emotional database, and shows more accurate results than existing emotional speech generation algorithms.
Consolidating the Research Agenda of Augmented Reality Television with Insights from Potential End-Users

We examine the research agenda proposed for Augmented Reality Television (AR-TV) through the perspective of the findings of an online study, where 172 respondents shared their understanding of Augmented Reality (AR) and their preferences for AR-TV scenarios.
Telugu Movie Review Sentiment Analysis Using Natural Language Processing Approach

In this modern age of digitization, everyone uses online services for various day-to-day activities like to know about product functionality or about movie-related information by numerous blogs. This short description about a movie is nothing but the review of a movie, which gives the opinion about the movie by several authors/authenticated persons. Opinion provides the sentiment of a movie, i.e., whether the movie is good or bad or normal/routine. Here in the sentiment, good resembles positive, and bad resembles negative. These reviews are very useful to improve and determine the pros and cons of movie. In India, there has been requirement for provincial dialects interfaces for better comprehension about the web substance and make it more easy to use to local clients. Here, we propose a framework to sort film surveys which are utilized transliteration plot, i.e., Telugu to English into positive, negative and nonpartisan for better basic leadership about the motion picture. The system is based on rule-based (natural language processing (NLP)) and machine learning approach. Our contributions in this approach are preprocessing (tokenization, sentence segmentation), tagging, feature extraction and opinion extraction. Feature extraction is based on tags mainly nouns. Opinion extraction is based on verb, adjacent and adverbs. It involves the concepts of natural language processing, machine learning for the development of the system. Our contributions in this paper are to create a seed list words based on category. We are finding the polarity of a sentence as positive, negative or neutral.
CONVUL: An Effective Tool for Detecting Concurrency Vulnerabilities

Concurrency vulnerabilities are extremely harmful and can be frequently exploited to launch severe attacks. Due to the non-determinism of multithreaded executions, it is very difficult to detect them. Recently, data race detectors and techniques based on maximal casual model have been applied to detect concurrency vulnerabilities. However, the former are ineffective and the latter report many false negatives. In this paper, we present CONVUL, an effective tool for concurrency vulnerability detection. CONVUL is based on exchangeable events, and adopts novel algorithms to detect three major kinds of concurrency vulnerabilities. In our experiments, CONVUL detected 9 of 10 known vulnerabilities, while other tools only detected at most 2 out of these 10 vulnerabilities. The 10 vulnerabilities are available at https://github.com/mryancai/ConVul.
BNU-Net: A Novel Deep Learning Approach for LV MRI Analysis in Short-Axis MRI

This work presents a novel deep learning architecture called BNU-Net for the purpose of cardiac segmentation based on short-axis MRI images. Its name is derived from the Batch Normalized (BN) U-Net architecture for medical image segmentation. New generations of deep neural networks (NN) are called convolutional NN (CNN). CNNs like U-Net have been widely used for image classification tasks. CNNs are supervised training models which are trained to learn hierarchies of features automatically and robustly perform classification. Our architecture consists of an encoding path for feature extraction and a decoding path that enables precise localization. We compare this approach with a parallel approach named U-Net. Both BNU-Net and U-Net are cardiac segmentation approaches: while BNU-Net employs batch normalization to the results of each convolutional layer and applies an exponential linear unit (ELU) approach that operates as activation function, U-Net does not apply batch normalization and is based on Rectified Linear Units (ReLU). The presented work (i) facilitates various image preprocessing techniques, which includes affine transformations and elastic deformations, and (ii) segments the preprocessed images using the new deep learning architecture. We evaluate our approach on a dataset containing 805 MRI images from 45 patients. The experimental results reveal that our approach accomplishes comparable or better performance than other state-of-the-art approaches in terms of the Dice coefficient and the average perpendicular distance.
I Know What You Are Doing With Remote Desktop

Remote desktop enables users to remotely access their computers via the Internet, which is widely used as a basic tool in areas such as remote work, remote assistance and remote administration. However, existing remote desktop is designed to work in the mode of updating user's real-time command and remote screen's state interactively for a better user experience, such working mode may cause serious side-channel information leakage problem in spite of encryption of the traffic, as revealed in this paper. We carry out an experimental research to assess the side-channel information leakage of six most popular remote desktop softwares in Windows 10 & 7 platforms: Anydesk, ConnectWise, MicroRDS, RealVNC, Teamviewer, and Zoho Assist. With the help of machine learning techniques including logistic regression, support vector machine, gradient boosting decision tree, random forest as well as statistic features of flow burst, we observe that an adversary can excellently uncover (top at 99.26% TPR, 0.57% FPR, 97.17% F1-score) 5 rough kinds of daily activities covering editing documents, reading documents, surfing webs, watching videos and installing softwares and even worse precisely classify 4 fine activities predefined as editing documents with Microsoft Office Word and the other three edit tools with high true positive rate and low false positive rate. Our results prove the fact for remote desktop traffic encryption mechanism is nothing sufficient to prevent side-channel information leakage and both users and providers of remote desktop should pay more attention to such serious privacy leakage problem.
A Preliminary Exploration of Montage Transitions in Cinematic Virtual Reality

Cinematic Virtual Reality allows viewers to watch films without the limitation of screen edges, whilst controlling their own viewpoint. The loss of screen edges and camera control leads to the problem that filmmakers cannot precisely edit each shot using traditional techniques, as the viewer's direction and field of view is dynamic. As such, many established film making methods should be reconsidered. In this paper, we present our initial exploration for the implementation of montage in Virtual Reality (VR) films, focusing on the investigation of transition effects. A pilot study is presented, which compared three transition effects. Two popular existing transition effects (cut and fade) were applied, along with a third method (VR portal). The VR Portal was designed and selected specifically to meet the requirements for montage in VR films. We present the preliminary results and our insights, concluding with future plans.
Automatic segmentation of true color sectioned images using FMRIB Software Library: First trial in brain, gray matter, and white matter

Abstract Three‐dimensional (3D) models of the brain made from magnetic resonance images (MRI) are used in various medical fields. 3D models assembled from grayscale color and low‐resolution can be complemented with true color sectioned images of the Visible Korean. The purpose of this study is to apply the MRI automatic segmentation technique to the sectioned images. 3D models of the sectioned images, which have true color and high resolution, can be produced without manual segmentation. The Brain Extraction Tool and the Automated Segmentation Tool of the FMRIB Software Library (FSL) were chosen for automatic segmentation. Using those tools, true color sectioned images were reconstructed from gray 3D models of brain, gray matter, and white matter. Color 3D models of those structures were generated from the gray 3D models using MRIcroGL. The color 3D models made from the sectioned images revealed details of brain anatomy that could not be observed on the 3D models from MRI. This trial suggests that convergence of the MRI segmentation technique with color sectioned images is a time‐efficient method for producing color 3D models of various structures. In future, the method of this study will be used for various sectioned images of cadavers. The resulting color sectioned images and 3D models will be made available to other researchers.
Systematic Methods of Constructing Bent Functions and 2-Rotation Symmetric Bent Functions

In this paper, we first present two systematic constructions of bent functions by modifying the truth tables of Rothaus's bent function and Maiorana-McFarland's bent function respectively. The number of the newly constructed bent functions by modifying the truth table of Rothaus's bent function is also determined. The methods of constructing self-dual bent functions are then given after the dual functions of these bent functions being determined. Finally, as an application, two constructions of 2-rotation symmetric bent functions are presented in this paper.