Reviewerly/exHarmony huggingface dataset:
The Perceptions of Preservice Teachers on Creation and Use of Hologram Materials in Education

In the study, the use of 3D simple hologram material (3DHM) in pre-school education is evaluated with different aspects from pre-school pre-service teachers' perspectives. Forty pre-service teachers selected by the criterion sampling method were included in the study which was designed with triangulation design. At the end of the five-week study including 3DHM development and pre-school adaptation, interviews were conducted with the pre-service teachers. The quantitative data obtained were subjected to frequency analysis, and qualitative data were subjected to in vivo coding and attribute coding in the first step and axial coding in the second step. As a result of the research, according to pre-service teachers, 3DHM can be used as a teaching material and an activity that can be done with students. In addition, according to pre-service teachers, 3DHM could develop children's cognitive, psychomotor, and affective skills and also increase their motivation and interest and memorability.
Research on intelligent Terminal authentication Strategy based on block chain

In the face of huge Internet of Things (IoT) application scenarios, although trusted access and management of IoT devices can be realized through a single blockchain, there are problems of increased time consumption and low throughput. To solve the above problems, this paper proposes an IoT device authentication strategy based on master-slave chain structure. Firstly, the master-slave chain network structure of IoT device authentication is proposed based on blockchain technology. Secondly, the reputation evaluation mechanism is introduced to estimate the reputation value of slave chain nodes and IoT devices, so as to provide basis for the selection of consensus nodes. Thirdly, by selecting the consensus node with high reputation value and optimizing the consensus stage, the Practical Byzantine Fault Tolerance (PBFT) consensus algorithm is improved to increase the consensus throughput and improve the consensus efficiency of the authentication process. Finally, the authentication smart contract is designed to ensure fair and credible implementation of authentication rules. Security analysis shows that these strategies have high security against mainstream IoT attacks, reduce the delay of authentication request, and improve the efficiency and stability of authentication.
Incomplete Relationship in Sampling Inference Algorithm of Dynamic Uncertain Causality Graph

Dynamic Uncertain Causality Graph (DUCG) is a new generation of probability graph model (PGM). It uses virtual random events and casual relationship intensity to represent the uncertain causality, instead of the conditional probability tables (CPTs), so combination explosion problems of CPTs can be avoided in DUCG. Systems based on DUCG have been deployed in many areas such as nuclear power plants, spacecrafts and medical diagnoses. A basic sampling algorithm based on recursive reasoning method and cut-off estimation has been proposed to solve the combination explosion problem in DUCG inference process. In this paper, the sampling algorithm is adapted to support the incomplete expression, which is an importance characterize for DUCG. Because incompleteness expression can reduce the complexity of both construction and inference by ignoring some states of variables. Two new detailed schemes are proposed to provide this new feature. The first one is based on the numerical approximation and the second one is based on the mechanism of DUCG. A practical example based on practical viral hepatitis C is presented to verify the discussions.
The Xoshiro+ Pseudorandom Number Generator in a Computer Chess Program

Since computer chess programs can beat human capabilities, new ways are researched to further improve these programs. Pseudorandom number generators (PRNG) play an important role in computer chess. We implement Xoshiro+, a well-known and thoroughly tested PRNG, in Stockfish, the arguably most performant chess engine at the time. Stockfish itself uses Xorshift*. With the help of Cute Chess, a program allowing the automation of chess games, the new Stockfish variant Xoshirofish is tested against Stockfish. The results show a minute increase in the performance of Xoshirofish compared to Stockfish. However, further extensive testing is still required to assert the significance of this increase in performance.
Performance Analysis of Feature Detectors and Descriptors in Underwater and Polar Environments

Many scientific mapping surveys that deploy robotic platforms in underwater and polar environments perform Visual Simultaneous Localization and Mapping (VSLAM), Structure for Motion (SfM), and Image Mosaicking. These techniques heavily rely on robust and reliable feature-based vision front ends. The job of a vision front end is to provide correspondence information between different camera views which is then directly fed into a bundle adjustment step. Although the atomic steps involved in constructing a vision front end are well-known, many of the popular choices of the features and their parameters do not perform reliably in a variety of visually degraded underwater and polar environments that are characterized by low texture and contrast, and by unevenly lit and low-overlap imagery. In this paper, we develop novel metrics and quantitative analysis methods which can measure the impact of image pre-processing steps such as Contrast Limited Adaptive Histogram Equalization (CLAHE) on the improvement of vision front-end outputs. Our metrics and quantitative analysis can guide the selection between different feature detectors and descriptors to develop a reliable and robust vision front end that can operate in a wider range of underwater and polar environments. We showcase how CLAHE improves the saliency of features and feature track length on a visually degraded dataset underwater, resulting in a substantial increase in correspondence information for the SfM solution. Finally, we perform an end-to-end SfM analysis that shows reduced accumulated drift over the long term and improved accuracy.
Minimax nonparametric estimation of pure quantum states

In classical statistics, Pinsker's theorem provides an exact asymptotic minimax bound in nonparametric estimation, improving upon optimal rates of convergence results. We obtain a quantum version of the theorem by establishing asymptotic minimax results for estimation of the displacement vector in a quantum Gaussian white noise model, given by a sequence of shifted vacuum states. Analogous results are then obtained for estimation of a general pure state from an ensemble of identically prepared, independent quantum systems, using the recently established local asymptotic equivalence to the quantum Gaussian white noise model. Optimality holds with respect to the most fundamental distance measure for quantum states, that is, trace norm distance, and in a true quantum sense, allowing for all possible measurements. Adaptive estimators are also obtained for the above cases. As an application, we obtain asymptotic minimax adaptive estimators for Wigner functions of pure states.
An Efficient Blind Approach for Restoration of Atmospheric Turbulence Degraded Images

Image restoration is a technique to improve the quality of degraded image using the estimated amount of noise and blur involved in the image. The image may get degraded due to different atmospheric, environmental conditions, transmission medium or faulty devices. This paper focuses on restoration of atmospheric turbulence affected sequences. Lots of image registration techniques are used for turbulence mitigation which causes smoothness. This paper proposes a novel blind deconvolution approach. The proposed method includes central moments to estimate the Point Spread Function (PSF) blindly. The proposed method is computed on standard dataset (OTIS) and is compared with state of the art Adaptive kurtosis and kurtosis minimization approach. Experimental results showcases that the proposed approach achieves higher accuracy on standard parameters like PSNR and SSIM.
Sense of Presence in VR Mobile Application

Presence is one of the most important psychological constructs for understanding human-computer interaction. It is especially important for applications that use advanced techniques of iteration between humans and computers, such as Virtual Reality (VR) applications. The paper addresses the problem of difficulty for users to experience a sense of presence in VR applications, especially when the worlds they are transported to are unknown to them. It presents the experiments made for three selected mobile application together with their results and analysis.
Demography of Machine Learning Education Within the K12

The rapid increase of artificial intelligence and machine learning tools and technologies around us has led to a rise in the daily interaction between humans and these technologies. Children nowadays are very likely to interact with these tools in different contexts, such as at home, recreation centres, and schools. While some children are already exposed to and working with these technologies, others are still far behind in the digital world. In this paper, we use the 2020 Stack Overflow Developer Surveys dataset to examine the demography of K12 students who are already using machine learning tools at school or their workplace. Over 55% of the respondents are younger than 24 years. The finding shows that there is still a significant gender gap in the IT field, with only 2% of 138 respondents identified as female. Also, with only four African countries represented in the dataset, Africa is still behind regarding machine learning in K12.
Optimization of Electromagnetic Devices using a RSM Enhanced ACOR Algorithm

The present paper proposes and studies the efficiency of using a RSM enhanced ACOR algorithm for the optimization of electromagnetic devices. Different RSM methods, such as Box-Behnken, CCD and Doelhert, are applied to find most suitable parameters (optimal set) for the ACOR in order to solve the corresponding electromagnetic optimization problems. The parameters optimal set is found by building a metaheuristic function. In the same time, the optimal parameter set is searched and determined for each electromagnetic problems for different objective functions, the best and the average global best solution for a tests set. The electromagnetic devices to be optimized are the Loney’s solenoid and an energy storage device, as defined by the TEAM22 problem. Both electromagnetic problems are proposed benchmarks from electromagnetic community
Deep Set Conditioned Latent Representations for Action Recognition

In recent years multi-label, multi-class video action recognition has gained significant popularity. While reasoning over temporally connected atomic actions is mundane for intelligent species, standard artificial neural networks (ANN) still struggle to classify them. In the real world, atomic actions often temporally connect to form more complex composite actions. The challenge lies in recognising composite action of varying durations while other distinct composite or atomic actions occur in the background. Drawing upon the success of relational networks, we propose methods that learn to reason over the semantic concept of objects and actions. We empirically show how ANNs benefit from pretraining, relational inductive biases and unordered set-based latent representations. In this paper we propose deep set conditioned I3D (SCI3D), a two stream relational network that employs latent representation of state and visual representation for reasoning over events and actions. They learn to reason about temporally connected actions in order to identify all of them in the video. The proposed method achieves an improvement of around 1.49% mAP in atomic action recognition and 17.57% mAP in composite action recognition, over a I3D-NL baseline, on the CATER dataset.
Dissected Scene Character Recognition Using HOG Descriptors

Automatic scene text recognition is an interesting problem in computer vision and Internet of things. It may facilitate intelligent interaction between machines and mankind in today’s cloud-enabled civilization. In this paper, we present a method for dissected scene character recognition. At first, color images are converted into grayscale and then some noise removal and pre-processing operations are applied. Next, we normalize them to bring them to a uniform dimension and compute features for training and prediction. Experimenting on scene characters at three different levels of complexities i.e. relatively good images, relatively bad images, and combined images with multiple classifiers such as naïve Bayes, KNN, MLP, random forest and SVM, detail results are reported. Highest accuracies i.e. 74.48% for good images only, 59.13% on bad images only and 71.52% for overall images, are obtained with the SVM classifier. Comparison with similar state-of-the-art methods is also included and our method is found to outperform others.
A Simulation System for Scene Synthesis in Virtual Reality

Real-world scene synthesis can be realized through view synthesis or 3D reconstruction methods. While industrial and commercial demands emerge for real-world scene synthesis in virtual reality (VR) using head-mounted displays (HMDs), the methods in the literature generally do not target specific display devices. To meet the rising demands, we propose a simulation system to evaluate scene synthesis methods in VR. Our system aims at providing the full pipeline of scene capturing, processing, rendering, and evaluation. The capturing module provides various input dataset formulations . The processing and rendering module integrates three representative scene synthesis methods with a voluntary performance-aid option. Finally, the evaluation module supports traditional metrics as well as perception-based metrics. An experiment demonstrates the use of our system for identifying the best capturing strategy among the three degrees of foveation tested. As can be expected, the FovVideoVDP metric (based on a model of the human visual system) finds the highest degree of foveation giving best results. The three other quality metrics from the evaluation module (which use features to measure similarity) confirm that result. The synthetic scenes in the experiment can run in VR with an average latency of 5.9 ms for the two selected scenarios across the tested methods on Nvidia GTX 2080 ti.
Biomedical Data Classification Using Meta-learning: An Experimental Investigation

Supervised machine learning is a vast field, and meta-learning is one the sub parts of that which is applied on data of previous years. Numerous data mining problems and techniques are available to take out unseen information from huge databases. Different input data are classified into target classes through classification assignment. We are using various machine learning algorithms in our model. Our overall classification contributed by output of base classifier which are under meta-learning. There are many challenges in the real world problem can be explained by combining machine learning algorithm. Meta-learning algorithms like bagging, dabbing, rotation forest, filtered classifier and decorate are widely used. Meta-learning is based on the principle from preceding experience and learns to enhance data mining work. In our paper, we investigate the performance of bagging, logitboost and filtered classifiers on a biomedical dataset by some popular learning algorithms for meta-data approaches.
#hokerbeest

In small language communities like that of North Frisian, the limited communicative range of the small language is a major obstacle when it comes to computer mediated communication in social media networks. Although completely genuine Frisian content is rarely published in public postings on the Instagram network, users do use Frisian language hashtags. This paper shows how different types of hashtags can be classified and argues that the majority of Frisian hashtags will most likely appear in non-Frisian postings.
Silent Observation

With the growth of information communication technology, new technologies are emerging, and their applications in learning are increasing. Emerging technologies that may produce effective learning outcomes include 5G technology to have efficient and effective learning, artificial intelligence – imitating human-like behaviour, voice searches, augmented reality (AR), virtual reality (VR), and mixed reality (MR). Research in the field of emerging technologies reflects that they are able to engage learners and grab their attention. Learners must be assessed to measure learning gains. Learner assessment using Bloom's taxonomy has been proved effective. Algorithm “Silent Observation,” proposed to assess learners, is found to be effective in its idea of conception stage. The algorithm has sound footing in micro-research findings on formative assessment and emerging technologies. Assessment using this algorithm will be an iterative process and will refine itself using AI techniques. “Silent Observation” has a strong pedagogical framework interweaving emerging technologies.
Smart Communities to Reduce Earthquake Damage: A Case Study in Xinheyuan, China

Abstract Earthquakes frequently cause serious casualties and property loss in China. Building smart communities is an effective means of preventing damage and reducing disastrous outcomes from earthquakes. This article reports on the Chinese experience of building a smart community and the concept of smart service for preventing damage and mitigating disasters from earthquakes in Hubei province. The case study examined the Xinheyuan Smart Community, which is the first such community constructed in Hubei province, including its background development, system design and system functions. The disaster mitigation function was tested when a magnitude 4.9 earthquake occurred in Yingcheng earthquake.
Cyber-Physical Environment for Field Science Education and Research through Field Work Archiving

学術情報メディアセンターとフィールド科学教育研究センターの有志でフィールドに関するデータの蓄積・活用に関する検討を行ってきた．本稿では，その一つの試みとして，フィールドワークの体験を記録して教育・研究に活用する枠組みについて紹介する．また，高校生のような若い世代に仮想的なフィールドワークをしてもらう試み(サイバーフィールドワーク) とそこから得られた知見を紹介する．最後に，このようなデータの蓄積・共有・利活用のために必要なデータマネージメントについて考える．
Research on Neural Network Based Image and Video Denoising

Noises are inevitable in images and videos. Many denoising algorithms have been proposed. As the acquirement of image and video qualities gets higher, algorithms with high performances and easy implementation are the new trend. With the development of deep learning, neural network has been applied in denoising algorithms. These methods represent better performances and obtain video of high quality. In this paper, we will discuss several denoising methods for both images and videos on their architectures, analyze their features and comment. Finally, we will carefully forecast what neural network based denoising modules can be built in the future study.
A Novel Steganography Approach to Embed Secret Information into a Legitimate URL

Hiding confidential digital information in today&#x0027;s modern communication system is a very challenging task. Even though many algorithms have been developed to ensure the security of information delivered via the communication channel, many flaws have been discovered over time. Steganography is the science of hiding the existence of secret information. The concept of steganography is about embedding the secret data in a cover file in which the cover file could be an image, audio, video, text, or any other medium. Successfully formulating an algorithm that embeds the secret information in a cover file will produce a new file called the stego file. This research aims to develop a new methodology in which the secret information will be embedded into a legitimate URL link. Unlike the usual methods that hide the secret message in the content of another file, this method uses a valid form of the URL link to hide the secret message. Since the URL is a type of text that links users to a web page and is not an informative text to be read, it results in a secure method to pass secret information as no one pays attention to a URL text. This method combines and implements different techniques, abbreviates, searches, and matches bits and converts to allowed URL characters to encode the secret message. The results show that it works well with legitimate URLs, including the website&#x0027;s homepage URL.
Performance Comparison of Two Modified Krill Herd Algorithms

The krill herd algorithm is one of the heuristic search methods to find globally optimal solutions inspired by nature. This paper presents a comparison between the Chaotic Krill Herd (CKH) algorithm and Chaotic Particle-swarm Krill Herd (CPKH) algorithm as modifications of the Krill Herd algorithm. Performance measures of the three algorithms can be seen from the results obtained from the results of tests carried out on 14 benchmark functions. The best algorithm is the one that produces the minimum value for each benchmark function. The result of the performance of the CPKH Algorithm is that it has succeeded in obtaining the minimum point on thirteen benchmark functions. The results of the performance of the CKH Algorithm managed to get the minimum value on eleven benchmark functions. The results of the performance of the KH Algorithm managed to reach the minimum point on two benchmark functions. Based on these results, it can be concluded that the Chaotic Particle-swarm Krill Herd (CPKH) algorithm is more efficient than the Chaotic Krill Herd (CKH) and Krill Herd (KH) algorithms for finding the minimum point of the function.
Reconstructive Training for Real-World Robustness in Image Classification

In order to generalize to real-world data, computer vision models need to be robust to corruptions which may not generally be available in the traditional benchmark datasets. Real world data is diverse and can vary over time - sensors may become damaged, environments may change, or users may provide malicious inputs. While substantial research has focused separately on processing specific image distortions or on defending against types of adversarial attack, some real-world applications will require vision models to generalize to corruptions, while additionally maintaining image quality. We propose a simple training strategy to leverage image reconstruction, with similarities to a GAN training process, to reduce image data corruptions while maintaining the visual integrity of the image. Our approach is demonstrated on several corruptions for the task of image classification, and compared with established approaches, with qualitative and quantitative improvements. Code available at: https://github.com/UTSA-VAIL/ReconstructiveTraining
Implementation of Vertex Colouring Using Adjacency Matrix

Graph colouring is very useful when analysing graph traversal, degree of the vertex, analysing printed circuit boards, world map, mobile networks etc. In this article an attempt is made to analyse vertex colouring on small graphs, for that capital English alphabets are been considered which has different small shapes. English alphabets cover some possible shapes which can include the concepts of tree, parallel edges, loop and pendent vertex. Here adjacency matrixis used to analyse connectivity. There are different types of graphs like simple graphs which have no parallel edges and loops, trees, complete graphs etc. since it is difficult to consider all the variation capital alphabets of English are considered. Complex graphs are nothing but combination of simple graphs. Hence any methods implemented on simple graphs can be extended to complex graphs, so the capital English alphabets are considered.