Reviewerly/exHarmony huggingface dataset:
Design and development of learning mathematics game for primary school using handheld augmented reality

Abstract The traditional learning methods have some drawbacks and limitation such as restricted learning space, high cost, and lack of student engagement. In the era of digitalization, traditional education starts to transform into a more dynamic and modern education with the influence of the 21st-century learning method. Moreover, due to the pandemic, schools and universities are moving to digital learning to contain the spread of Coronavirus Disease 2019 (COVID-19). Therefore, the aim of this project is to develop a Mathematics game using handheld Augmented Reality (HAR) for primary school children as an alternative method in learning Mathematics. There are three main phases have been carried out, first is to study the requirements of an educational game. The second phase involves the design and development including the game element, storyline, game characters as well as user interface design. Finally, the third phase is integrating the learning Mathematics game using HAR, followed by evaluation. The evaluation of the learning Mathematics game was carried out for usability and user acceptance. The usability results show that the users are satisfied with the prototype and feedbacks show the game is fun. Overall, this project has successfully produced a learning Mathematics game using HAR for primary school children.
White-Collar Criminality Within the Video Game Industry

Edwin Sutherland’s concept of white-collar crime remains incredibly influential in contemporary criminological research and thought. Sutherland’s work was critical of several of the most influential industries in the United States, highlighting both their criminal and unethical conduct. Today, the video game industry has garnered tremendous influence over society and until recently, has seemed to escape criminological critique (other than for that of the potential negative effects of violent games) and white-collar crime theoretical inquiry. This study aims to change that, as we analyze the video game industry for its involvement in many traditional (and some more contemporary) forms of white-collar crime. More specifically, we discuss overpromising, broken games, and price gouging.
Active Learning of SVDD Hyperparameter Values

Support Vector Data Description (SVDD) is a popular one-class classifier, and well-suited for outlier detection. However, the effectiveness of SVDD depends on selecting good hyperparameter values - a difficult problem that has received significant attention in the literature. Since SVDD is an unsupervised classifier, tuning of hyperparameter values is difficult. This has motivated several methods to estimate hyperparameter values based on data characteristics. But existing methods are purely heuristic, and the conditions under which they work well are largely unclear. This has created a situation where instead of selecting hyperparameter values, one has to choose among several, equally plausible heuristics.In this article, we make some strides towards a principled approach to estimate SVDD hyperparameter values. We propose LAMA (Local Active Min-Max Alignment), the first method to select SVDD hyperparameter values by active learning. The core idea is based on kernel alignment, which we adapt to active learning with small sample sizes. LAMA provides estimates for both of the SVDD hyperparameters. These estimates are evidence-based, i.e., rely on actual class labels, and come with a quality score. This eliminates the need for manual validation, an issue with current heuristics. LAMA outperforms state-of-theart competitors in extensive experiments on real-world data. In several cases, LAMA even yields results close to the empirical upper bound.
Educational mobile game for learning English words

Abstract English is a second language in Malaysia and English language skills are vital for Malaysians. Mobile technologies such as mobile phone can be a great tool for educational purposes since it can increase students’ engagement and is a popular tool for interactive digital media. This paper discusses the development of an educational mobile game called EWORD for learning English words, for primary school kids. There were four phases involved; preliminary analysis, game design, game development and game evaluation. In the first phase, the fundamental knowledge of English words and vocabulary, English language learning methods in Malaysia, traditional learning methods, and educational mobile game were studied to acquire adequate information about the requirements for educational mobile game. User Interface (UI), gameplay, and game mechanics were designed in the second phase, while the mobile game prototype was developed in the third phase. In the fourth phase, the evaluation was conducted to evaluate the usability of the educational mobile game prototype. Based on the results, the educational mobile game helps in boosting the learnability of English words among students and is capable to increase student engagement.
A finite element model of the Japanese koto constructed from computed tomography scans

This paper presents a method for using computed tomography (CT) scan images of a Japanese koto as the basis of a mesh of a finite element model. The CT images gave access to previously inaccessible internal geometry. Images of grain structure and orientation in all three dimensions of the koto's Paulownia wood was important to the model's development. Obtaining the wood's physical properties for the Voigt notation and using curvilinear coordinates to rotate it to account for the grain was also a critical step. Following the model construction, studies were conducted to investigate the koto's fundamental vibrational behavior. First, the model predicted the koto's mode shapes and frequencies. Simulations in the frequency and time domains also identified unreported aspects of the koto's acoustics. Second, five physical experiments were conducted including transducer studies, Chladni patterns, an acoustic camera tests, and the response of the koto when played. The acoustic camera in particular gave insights into the koto's sound radiation. The combined results advance the limited knowledge of the koto including identifying 100 Hz as the first eigenmode and 85 Hz as a major air mode.
A Survey On Graph Matching In Computer Vision

Graph matching (GM) which is the problem of finding vertex correspondence among two or multiple graphs is a fundamental problem in computer vision and pattern recognition. GM problem is a discrete combinatorial optimization problem. the property of this problem is NP-hard. Starting with a detailed introduction for modeling methods of graph matching. We walk through the recent development of two-graph matching and multi-graph matching. In two-graph matching, we focus on the continuous domain algorithms and briefly introduce the discrete domain algorithms. In the continuous domain method, we explain the method of transforming the problem from the discrete domain to the continuous domain and those state-of-the-arts algorithms in each type of algorithms in detail, including spectral methods, continuous methods, and deep learning methods. After two-graph matching, we introduce some typical multi-graph matching algorithms. In addition, the research activities of graph matching applications in computer vision and multimedia are displayed. In the end, several directions for future work are discussed.
Tripartite genuinely entangled states from entanglement-breaking subspaces

The determination of genuine entanglement is a central problem in quantum information processing. We investigate the tripartite state as the tensor product of two bipartite entangled states by merging two systems. We show that the tripartite state is a genuinely entangled state when the range of both bipartite states are entanglement-breaking subspaces. We further investigate the tripartite state when one of the two bipartite states has rank two. Our results provide the latest progress on a conjecture proposed in the paper [Yi Shen $\textit{et al}$, J. Phys. A 53, 125302 (2020)]. We apply our results to construct multipartite states whose bipartite reduced density operators have additive EOF. Further, such states are distillable across every bipartition under local operations and classical communications.
Laplacian Matrix for Dimensionality Reduction and Clustering

Many problems in machine learning can be expressed by means of a graph with nodes representing training samples and edges representing the relationship between samples in terms of similarity, temporal proximity, or label information. Graphs can in turn be represented by matrices. A special example is the Laplacian matrix, which allows us to assign each node a value that varies only little between strongly connected nodes and more between distant nodes. Such an assignment can be used to extract a useful feature representation, find a good embedding of data in a low dimensional space, or perform clustering on the original samples. In these lecture notes we first introduce the Laplacian matrix and then present a small number of algorithms designed around it for data visualization and feature extraction.
A time-efficient model for detecting fraudulent health insurance claims using Artificial neural networks

Health insurance has come in rescue for people, in reducing their medical expenditure, which otherwise would have taken a high toll on their income. There are both private and government-funded agencies serving in the health insurance sector. With soaring high demand among the public, healthcare is not safe from the fraudsters. The usage of computerized techniques has proved this area even more vulnerable. It has become highly essential to detect this fraud at the earliest, such that the impact of loss could be minimized. This paper throws light on a framework in detecting fraud with faster learning and identifying the maximum number of fraud instances. The usual problems, like data heterogeneity and imbalanced classification of classes, have also been discussed in this paper. As a part of developing an efficient framework for fraud detection, we applied several learners and optimization techniques. The framework has evaluated with claims dataset obtained from the CMS Medicare facility. We finally reached to a conclusion that the application of Multi-Layer Perceptron, a feed-forward Neural Network with genetic algorithm optimization had helped in enhancing the results and gain higher accuracy. PCA was also applied to pick the most significant variables. The use of PCA and other appropriate pre-processing techniques has also helped us in reducing the training time, thereby achieving efficiency in terms of accuracy and speed.
An integrated approach to solving influence diagrams and finite-horizon partially observable decision processes

We show how to integrate a variable elimination approach to solving influence diagrams with a value iteration approach to solving finite-horizon partially observable Markov decision processes (POMDPs). The integration of these approaches creates a variable elimination algorithm for influence diagrams that has much more relaxed constraints on elimination order, which allows improved scalability in many cases. The new algorithm can also be viewed as a generalization of the value iteration algorithm for POMDPs that solves non-Markovian as well as Markovian problems, in addition to leveraging a factored representation for improved efficiency. The development of a single algorithm that integrates and generalizes both of these classic algorithms, one for influence diagrams and the other for POMDPs, unifies these two approaches to solving Bayesian decision problems in a way that combines their complementary advantages.
Predicting Activities of Daily Living with Spatio-Temporal Information

The smart home has begun playing an important role in supporting independent living by monitoring the activities of daily living, typically for the elderly who live alone. Activity recognition in smart homes has been studied by many researchers with much effort spent on modeling user activities to predict behaviors. Most people, when performing their daily activities, interact with multiple objects both in space and through time. The interactions between user and objects in the home can provide rich contextual information in interpreting human activity. This paper shows the importance of spatial and temporal information for reasoning in smart homes and demonstrates how such information is represented for activity recognition. Evaluation was conducted on three publicly available smart-home datasets. Our method achieved an average recognition accuracy of more than 81% when predicting user activities given the spatial and temporal information.
Indoor and Outdoor Face Recognition for Social Robot, Sanbot Robot as Case Study

The interaction between human and robots is of paramount importance in comforting robot and human in the context of social demand. For the purpose of human-robot interaction, the robot should have the ability to perform a variety of actions including face recognition, path planning, etc. In this paper, face recognition has been implemented on the Sanbot robot. Since the Sanbot robot is intended to work in real environment, therefore indoor and outdoor environment is taken into account in proposing the corresponding face recognition algorithm. For each case a robust pre-processing algorithm should be designed and which can circumvent a challenging problem in face recognition, namely, different lighting conditions (light intensity, angle of radiation, etc.). In case of indoor environment, faces in an captured image by the robot HD camera are found using a Haar-cascade algorithm. Afterwards, a histogram equalization is applied to face images in order to standardize them. Then commonly practiced Deep convolutional neural network structures such as Inception and ResNet are used to design a model and trained end-to-end on a customized dataset with strong augmentation. Finally, by using a voting method, proper prediction is carried out on each face. In what concerns the outdoor environment, which has more challenges, upon applying histogram Equalization on the captured image, faces are found using a MultiTask Cascaded Convolutional Neural Network. Then face images are aligned as head orientation are corrected. Finally, cropped face image is fed to Siamese Network in order to extract face features and verifying individuals. From several practical results it has been inferred that the accuracy of the indoor method is nearly 93% without voting and with voting 97%, and the outdoor method is about 95%.
Relativistic Approach for Training Self-Supervised Adversarial Depth Prediction Model Using Symmetric Consistency

This article proposes a novel approach for predicting depth from a single image in a self-supervised manner, specifically using generative adversarial networks (GANs) with enhancements to improve training performance. We train our model to generate disparity maps or inverse depth maps using left/right stereo image supervision rather than direct supervision from depth ground truth. We avoid the problem of training instability in standard GANs by employing a relativistic discriminator that evaluates how realistic an input image is. Since the reconstructed image quality determines the disparity map's accuracy, we adopt the GMSD, one of the standout IQA algorithms, as a part of the image reconstruction loss function. A VGG-19 pre-trained model-based perceptual loss is used to optimize our model in a feature space instead of pixel space and to enhance reconstructed images' perceptual quality with the learning-based perspective. Also, we add another symmetric generator that takes the right images as input, which helps to maintain the consistency of left/right views and enrich our training data with reasonable overhead on the training time. Our network is trained to predict depths of 640 × 192 images using the KITTI stereo dataset, which shows an outstanding performance compared to state-of-the-art works in various evaluation metrics. We verify our method's modules by performing an ablation study and evaluate the generalization performance on CityScapes and Make3D datasets.
Sistema otsenivaniya kompetentsii komandnoj raboty budushhikh spetsialistov IT-sfery [The system for assessing the teamwork competency of future IT specialists]

The article highlights the need to prepare future IT specialists for teamwork. The system for assessing the teamwork competency is proposed. The system includes a set of indicators, which are divided into invariant and variable. Each indicator is characterized by a grading scale with three possible assessment options: “not mastered” — 0, “partially mastered” — 1, “mastered” — 2. In the context of “indicator — assessment”, the required standard of student behavior in the process of performing team activities was determined. Invariant indicators are used to assess the teamwork competency, regardless of the role played by the student. The invariant indicators are indicated: understanding the importance of the task for the team's activities, the distribution of roles in the team, team interaction, the assessment of the teamwork. To determine the variable indicators, an analysis of the existing methods for the distribution ofcommand roles was carried out, the types of activities of an IT specialist were studied. To solve problems related to the implementation of activities for the design, development and maintenance of software tools within the framework of teamwork, it is advisable to distinguish six roles: analyst, innovator, negotiator, designer, executor, coordinator. Based on these roles, variable indicators are defined. The division of indicators into invariant and variable implies different requirements for the level of development. Invariant indicators must be mastered by all, while variable indicators allow for partial mastering in some indicators. The use of the system for assessing the teamwork competency will reveal potential problems in the preparation of a future IT specialist, clearly define the directions of the student's development, and form recommendations for him regarding professional activities.
Long-Term Planning with Deep Reinforcement Learning on Autonomous Drones

In this paper, we study a long-term planning scenario that is based on drone racing competitions held in real life. We conducted this experiment on a framework created for “Game of Drones: Drone Racing Competition” at NeurIPS 2019. The racing environment was created using Microsoft's AirSim Drone Racing Lab. We have trained a reinforcement learning agent, simulated quadrotor in our case, with the Policy Proximal Optimization (PPO) algorithm. After training process it was successfully able to compete against another simulated quadrotor that was running a classical path planning algorithm. Agent observations consist of data from IMU sensors, GPS coordinates of drone obtained through simulation and opponent drone GPS information. Using opponent drone GPS information during training helps dealing with complex state spaces which serves as expert guidance. This approach allows efficient and stable training process. Our work fits into Clought's level 10 UAV autonomy categorization. All experiments performed in this paper can be found and reproduced with code at our GitHub repository.
An Exploratory Study of the Adoption of Blockchain Technology Among Australian Organizations: A Theoretical Model

Scholarly and commercial literature indicates several applications of Blockchain Technology (BCT) in different industries e.g. health, finance, supply chain, government, and energy. Despite abundant benefits reported and growing prominence, BCT has been facing various challenges across the globe, including low adoption by organizations. There is a dearth of studies that examined the organizational adoption of blockchain technology, particularly in Australia. This lack of uptake provides the rationale to initiate this research to identify the factors influencing the Australian organizations to adopt BCT. To achieve this, we conducted a qualitative study based on the Technology, Organization, Environment (TOE) framework. The study proposes a theoretical model grounded on the findings of semi-structured interviews of blockchain experts in Australia. The proposed model shows that the organizational adoption of blockchain is influenced by perceived benefits, compatibility, and complexity, organization innovativeness, organizational learning capability, competitive intensity, government support, trading partner readiness, and standards uncertainty.
Integrated spatial approaches for long-term monitoring of cadmium contamination caused by rainfall erosion: A case study of overland sediment in Mae Sot, Thailand

Mae Tao basin, Thailand has been reported as a remote cadmium (Cd) - contaminated area from 2002 to 2016 and a declaration on the contaminant source has not been accomplished. Howbeit, the determination of contaminant sources transmitted by overland flow is generally limited by various kinds of factors, data insufficiency in particular. In this study, the integrated spatial technology between remote sensing, digital mapping, GIS, and RUSLE were used to conduct the approach in deciding the source of contamination in the basin. According to the results, the significant anthropogenic activity which can release the highest cadmium's level was determined as the cornfield in the upstream. With the potential cadmium flux from erosion ranged from 279.86 to 703.73 t/ha/year and 0.77 correlation factor to the cadmium contamination level in the creeks, the soil surface and corn collected from the upstream were investigated using FAAS and Micro XRF for validation. The cadmium transported by upstream soil surface may not affect the Cd contamination in the downstream so that the contamination's source be from other anthropogenic activities. As a result, this integrated method demonstrates a capability in applying this set of applications to monitoring, investigating and managing contaminant sources in a remote area.
Digitalised, decentralised power infrastructures challenge blockchains

Electricity generation is decentralising quickly. Simultaneously, final energy use for residential customers electrifies in order to reduce carbon dioxide emissions. Together with ubiquitous digitalisation, this decentralisation and flexibility at the demand side paves the way towards local energy communities and – in its most distributed version – to peer-to-peer energy trading, where customers buy and sell electricity among each other. Although peer-to-peer energy trading is not yet legal everywhere, ‘citizen energy communities’ have been introduced as cornerstones of the energy transition by the European Commission in their ‘Clean Energy for All Europeans’ programme. This paper firstly discusses this digitalisation and decentralisation of the power infrastructure. These trends are supported by distributed information technologies, including peer-to-peer control paradigms. Distributed ledger technologies, such as blockchains, might be one such piece of the puzzle. The second part of the paper investigates whether blockchain technologies, and their associated smart contracts, offer advantages for larger-scale peer-to-peer energy-trading applications over a classic, centralised approach. Different blockchain implementations are investigated and qualitatively evaluated from a scalability, efficiency and trust perspective. The conclusion indicates that in the current state of the art, a trade-off between decentralised and more classical (hierarchically centralised) solutions suits larger-scale peer-to-peer energy-trading applications best.