Reviewerly/exHarmony huggingface dataset:
Human Action Recognition using BlazePose Skeleton on Spatial Temporal Graph Convolutional Neural Networks

The trend in multimedia transmission in social media has increased tremendously during the last decade and it is expected to continue growing during the next. Therefore, the need for new tools with the capacity of analyzing this kind of data grows accordingly. In this work, we implement the BlazePose skeleton topology into the ST-GCN model for action recognition. We test our experiments on the UCF-101 and HMDB-51 datasets. These are the first experiments of action recognition using the BlazePose skeleton upon these benchmarks. Moreover, we present an improved skeleton topology based on BlazePose that can enhance the performance achieved by its predecessor. By using the Enhanced-BlazePose topology presented in this study, we improved the results of the ST-GCN model on the UCF-101 benchmark more than 13% in accuracy performance. Finally, we have released the BlazePose skeleton data of the UCF-101 and HMDB-51 from our experiments to contribute future studies in the research community.
Audio and image encryption scheme based on QR decomposition and random modulus decomposition in Fresnel domain

In this paper, an asymmetric audio and image encryption mechanism using QR decomposition and random modulus decomposition (RD) in the Fresnel domain is proposed. The audio file is recorded as a vector and converted to a two-dimensional array to act as an image or a sound map. This sound map is encrypted using the image encryption algorithm proposed in this paper. The proposed cryptosystem is validated for both audios and grayscale images. Fresnel parameters and the two private keys obtained from QR decomposition and random modulus decomposition (RD) form the key space. Computer-based reproductions have been carried out to prove the validity and authenticity of the scheme. Simulation results authenticate that the scheme is robust and efficient against various attacks and is sensitive to input parameters.
Learning Player Tailored Content From Observation: Platformer Level Generation from Video Traces using LSTMs

A touted use of Procedural Content Generation is generating content tailored to specific players. Previous work has relied on human identification of player profile features which are then mapped to level generator features. We present a machine-learned technique to train generators on Super Mario Bros. videos, generating levels based on latent play styles learned from the video. We evaluate the generators in comparison to the original levels and a machine-learned generator trained using simulated players.
Status report on the third round of the NIST post-quantum cryptography standardization process

In recent years, there has been steady progress in the creation of quantum computers. If large-scale quantum computers are implemented, they will threaten the security of many widely used public-key cryptosystems. Key-establishment schemes and digital signatures based on factorization, discrete logarithms, and elliptic curve cryptography will be most affected. Symmetric cryptographic primitives such as block ciphers and hash functions will be broken only slightly. As a result, there has been an intensification of research on finding public-key cryptosystems that would be secure against cryptanalysts with both quantum and classical computers. This area is often called post-quantum cryptography (PQC), or sometimes quantum-resistant cryptography. The goal is to design schemes that can be deployed in existing communication networks and protocols without significant changes. The National Institute of Standards and Technology is in the process of selecting one or more public-key cryptographic algorithms through an open competition. New public-key cryptography standards will define one or more additional digital signatures, public-key encryption, and key-establishment algorithms. It is assumed that these algorithms will be able to protect confidential information well in the near future, including after the advent of quantum computers. After three rounds of evaluation and analysis, NIST has selected the first algorithms that will be standardized as a result of the PQC standardization process. The purpose of this article is to review and analyze the state of NIST's post-quantum cryptography standardization evaluation and selection process. The article summarizes each of the 15 candidate algorithms from the third round and identifies the algorithms selected for standardization, as well as those that will continue to be evaluated in the fourth round of analysis. Although the third round is coming to an end and NIST will begin developing the first PQC standards, standardization efforts in this area will continue for some time. This should not be interpreted as meaning that users should wait to adopt post-quantum algorithms. NIST looks forward to the rapid implementation of these first standardized algorithms and will issue future guidance on the transition. The transition will undoubtedly have many complexities, and there will be challenges for some use cases such as IoT devices or certificate transparency.
PENERAPAN MODEL PEMBELAJARAN KOOPERATIF TIPE NUMBER HEADS TOGETHER UNTUK MENINGKATKAN AKTIVITAS DAN PRESTASI BELAJAR SISWA

The purpose of this research is to apply the number heads together (NHT) learning method to increase student activity and achievement. This research has been carried out with the Classroom Action Research (CAR) method which will be followed by a quasi experiment. The subject of this research is SD Negeri 3 Lahat which has 20 people, consisting of 10 men and 10 women. The technique of collecting data was by means of assessment sheets and tests, while data analysis was carried out using the t-test. Based on the data analysis of this study, using number heads together can increase student activity and learning achievement in mathematics learning in grade V SD Negeri 3 Lahat. The implication of the results of this study is that if teachers want to improve student achievement in social studies subjects, a number heads together type of cooperative learning model can be used
Automated Personalized Exposure Therapy Based on Physiological Measures Using Experience-Driven Procedural Content Generation

Our research focuses on personalized virtual reality exposure therapy (VRET) based on the Experience-Driven Procedural Content Generation (EDPCG) framework. There are existing approaches for personalized VRET; however, they are subjective and require hand-authored and predefined rules that may not generalize to all subjects. We propose a framework to personalize VRET based on predicting subjects' experiences via physiological sensors and machine learning algorithms. The framework then automatically adapts exposure parameters based on the subject's physiological response using a PCG method. We intend to conduct two human subject studies for arachnophobia and fear of public speaking.
CURRENT TRENDS OF ART THERAPY (BIBLIOMETRIC ANALYSIS)

The article presents the results of bibliometric analysis of papers in periodicals of the Scientific Electronic Library with titles containing the concept of “art therapy” for the period from 2017 to 2021. For the analysis and visualization of a data array 348 papers were chosen, the overwhelming number of which — 45.6% and 38.4%, respectively, related to the topic “psychology” and “public education and pedagogy”, and only 10% to the topic “medicine”. We use the free VOS viewer program for concept map making. As a result of the analysis, the only one topical issue was revealed — “art therapy in the rehabilitation of children and adolescents”. It is shown that the concept map, created on the basis of the analysis of the titles of articles published in journals included in the Higher Attestation Commission list, the Scopus database (11.2%), as well as their annotations, differs from the concept map of the entire sample, namely, related to the authors' consideration problems of the effectiveness of art therapy, its advantages in the development of children. Moreover, few articles published in the journals of the Scopus database (2% of the whole sample) are related to medical topics, organizational psychology and others discrepant from psychological and pedagogical sphere. It is concluded that the interest of researchers in the problem of the effectiveness of art therapy in the development and rehabilitation of children and adolescents, as a leading trend in periodicals for 2017–2021, is proved by social demand.
Machine Learning Approach on Cyberstalking Detection in Social Media Using Naive Bayes and Decision Tree

Social media has increased the chance to communicate through many things such as video calls and can be connected globally. But there is also a growth in the vulnerability of the system. With this advancement, some threat is bound to happen. Cyberbullying is one of the social issues that users deliberately and tenaciously misuse social media. It became an issue because most cases affect the victim's mental health. Before, detecting these crimes only has traditionally used linguistic features, but cyberbullying on social media has more than that. Therefore, technologies today may play an important role in detecting cyberstalking on social media by using Machine Learning (ML). In this paper, cyberbullying detection will use the ML algorithm, which is Naïve Bayes and Decision Tree, and compare which algorithm is better to detect. ML has a wide range of methods that allow systems to quickly access the data and learn from it to make decisions for complicated problems. Cyberstalking has been concerning as it psychologically affects the victims. An experimental result indicates that Naïve Bayes algorithms achieve the best accuracy, which is 0.958.
Assessment of higher education system for region’s sustainable development

Higher education plays an important role in promoting the concept of sustainable development and achieving the 2030 UN goals. In modern society, higher education institutions act as educational and research centers and impact the economy, politics, and social sphere of the region. At the same time, unlike the world's leading universities, Russian higher education institutions demonstrate a predominantly low level of involvement in the implementation of sustainable development. In this regard, the assessment of regional universities for sustainable development becomes relevant. Scientific novelty lies in the development of a system of assessment indicators and methods for the regional system of higher education for sustainable development. The methodology evaluates five functions of the higher education system by twenty-four indicators. An information base serves to be available statistical data for six years (2015–2020). The methodology is tested with the Volga Federal District regions. The study revealed that the higher education system in the Republic of Tatarstan achieves the highest level in its performance, while the higher education system in Kirov region and Udmurt Republic shows the lowest level. Perm Krai demonstrates an interfunctional imbalance and an insufficient level of development of the higher education system for the region’s sustainable development. Correlation analysis detected a statistically significant relationship between the performance of the higher education system in the region and its sustainable development. The article will be of interest to researchers of sustainable development problems of territories, students and graduate students examining this topic; state and municipal employees involved in the implementation of the region’s sustainable development.
Complex-valued time series based solar irradiance forecast

A new method for short-term probabilistic forecasting of global solar irradiance from complex-valued time series is explored. The measurement defines the real part of the time series while the estimate of the volatility is the imaginary part. A complex autoregressive model (capable to capture quick fluctuations) is then applied with data gathered on the Corsica island (France). Results show that even if this approach is easy to implement and requires very little resource and data, both deterministic and probabilistic forecasts generated by this model are in agreement with experimental data (root mean square error ranging from 0.196 to 0.325 considering all studied horizons). In addition, it exhibits sometimes a better accuracy than classical models such as the Gaussian process, bootstrap methodology, or even more sophisticated models such as quantile regression. Many studies and many fields of physics could benefit from this methodology and from the many models that could result from it.
AI for automated code updates

Most modern code bases extensively rely on external libraries to provide robust functionality out of the box. When these libraries are updated they can sometimes introduce breaking changes in the process, which require extensive developer maintenance. To mitigate this we propose to use artificial intelligence to parse the text of release notes to capture code deprecations in structured form. This, in turn, enables us to develop an IDE plugin that can automatically detect deprecated library usages in live code bases and even suggest recommended fixes. We evaluated our system on over 30 internal projects within J.P. Morgan.
Biomedical Word Sense Disambiguation Based on Graph Attention Networks

Biomedical words have many semantics. Biomedical word sense disambiguation (WSD) is an important research issue in biomedicine field. Biomedical WSD refers to the process of determining meanings of ambiguous word according to its context. It is widely applied to process, translate and retrieve biomedical texts now. In order to improve WSD accuracy in biomedicine, this paper proposes a new WSD method based on graph attention neural network (GAT). Words, parts of speech, and semantic categories in context of ambiguous word are used as disambiguation features. Disambiguation features and the sentence are used as nodes to construct WSD graph. GAT is used to extract discriminative features, and softmax function is applied to determine semantic category of biomedical ambiguous word. MSH dataset is used to optimize GAT-based WSD classifier and test its accuracy. Experiments show that average accuracy of the proposed method is improved. At the same time, majority voting strategy is adopted to optimize GAT-based WSD classifier further.
AN EFFICIENT LOAD SCHEDULING TECHNIQUE USING OPPOSITIONAL SPARROW SEARCH ALGORITHM FOR CLOUD COMPUTING ENVIRONMENT

Cloud computing (CC) environment is commonly applied for provisioning effectual services and offers diverse benefits, along with some challenges like load balancing and load scheduling. The load scheduling can be defined as the procedure of assigning and balancing the load like tasks or cloudlets to virtual machines (VMs) in the cloud platform. A main goal of the load scheduling algorithm is to lessen the transfer time and total cost incurred in the system. This paper presents a new oppositional sparrow search algorithm (OSSA) for efficient load scheduling in CC environment. The classical sparrow search algorithm (SSA) is based on the group wisdom, foraging and anti-predation behaviour of sparrows. To improve the convergence speed of SSA, Opposition based learning (OBL) concept is incorporated to it. The proposed OSSA based load scheduler has the ability of scheduling the load effectively in the CC platform. An extensive set of simulations were carried out to ensure the goodness of the OSSA and the results are investigated under several aspects. The experimental results indicated the superior nature of the OSSA over the compared methods on all the applied scenarios.
USE OF TAM FOR EVALUATION OF INTERNSHIP INFORMATION SYSTEM

This study aims to evaluate the internship information system at a Vocational High School. To achieve this goal, it is necessary to perform statistical analysis and calculations on the influence of the relationship between each variable in the Technology Acceptance Model (TAM) with the addition of one external variable, namely self-efficacy variable. The approach used in this research is quantitative. The source of the data used is primary data, derived from the users of the internship information system through a questionnaire containing eighteen questions with five alternative answers using a Likert scale as a point determination. Respondents targeted in this study amounted to 202 respondents, but one other thing was constrained, only 180 respondents filled out the questionnaire. refers to the analysis obtained using WarpPLS, data obtained that: (1) the convenience factor has an influence on the benefits, in using the information system, (2) the use benefit factor has a real influence on the intensity and use of the information system, (3) the attitude of use has influence on the intensity of the use of information systems, (4) the intensity has an effect on the use of information systems in real or implementation. (5) self-efficacy has an influence on the ease of use of information systems.
Multi-Label Classification via Adaptive Resonance Theory-Based Clustering

This article proposes a multi-label classification algorithm capable of continual learning by applying an Adaptive Resonance Theory (ART)-based clustering algorithm and the Bayesian approach for label probability computation. The ART-based clustering algorithm adaptively and continually generates prototype nodes corresponding to given data, and the generated nodes are used as classifiers. The label probability computation independently counts the number of label appearances for each class and calculates the Bayesian probabilities. Thus, the label probability computation can cope with an increase in the number of labels. Experimental results with synthetic and real-world multi-label datasets show that the proposed algorithm has competitive classification performance to other well-known algorithms while realizing continual learning.
An Enhanced Video Coding Technique Leveraging on Edge Aware Motion Modeling and Frame Super Resolution

To capture motion homogeneity between successive frames, the edge position difference (EPD) measure based motion modeling (EPD-MM) has shown good motion compensation capabilities. The EPD-MM technique is underpinned by the fact that from one frame to next, edges map to edges and such mapping can be captured by an appropriate motion model. However, for higher resolution video sequences, the baseline EPD-MM approach, may fail to approximate the underlying motion field accurately. This is due to the difficulty in fitting motion model parameters by incorporating significantly large number of moving edge pixels. Observing the fact that in lower resolution version of the current frame <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$C$</tex> , the same scene structure is present although scaled down moving objects contain smaller number of edge pixels; in this paper we propose to carry out the EPD-MM over lower resolution form of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$C$</tex> . The resultant edge motion compensated prediction is then upsampled back to the original resolution of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$C$</tex> employing single image super resolution (SISR) technique. Experimental results show an improved prediction PSNR of 1.71 dB from the proposed approach compared to that of the baseline EPD-MM. Moreover, if this predicted frame is employed as an additional reference frame to encode <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$C$</tex> , bit rate savings of up to 8.48% is achievable over a HEVC reference and 0.2% is achievable over a VVC reference.
THE EFFECT OF GAME-BASED LEARNING MODEL ASSISTED BY BAMBOOZLE ON THE MULTIPLICATION SKILLS OF ELEMENTARY SCHOOL STUDENTS

Students’ skills in performing multiplication operations are low. The purpose of the study in this article is to determine the effect of the game-based learning model assisted by "Bamboozle" on the multiplication skills of the second-grade students at Islamic Elementary School in Al Hidayah in the academic year of 2021/2002. This study used experimental quantitative research with a One Group Pretest Posttest design. This study used a saturated sample as the sampling technique and tests as the data collection method. The results showed that the pre-treatment mean score of 63.33 was lower than the post-treatment mean score of 91.90. By using the Wilcoxon test data analysis method, the values of 21 samples increased from pre-test to post-test with an average increase of 11.00. In addition, by analyzing the Asymp value. Sig. (2-tailed) 0.000 &lt; 0.05, it could be concluded that there was a significant effect of the game-based learning model assisted by the application of "Bamboozle" on the multiplication operation skills of the second-grade students at SD Islam Al-Hidayah in the academic year 2021/2022.
Quality Evaluation of Arbitrary Style Transfer: Subjective Study and Objective Metric

Arbitrary neural style transfer is a vital topic with great research value and wide industrial application, which strives to render the structure of one image using the style of another. Recent researches have devoted great efforts on the task of arbitrary style transfer (AST) for improving the stylization quality. However, there are very few explorations about the quality evaluation of AST images, even it can potentially guide the design of different algorithms. In this paper, we first construct a new AST images quality assessment database (AST-IQAD), which consists 150 content-style image pairs and the corresponding 1200 stylized images produced by eight typical AST algorithms. Then, a subjective study is conducted on our AST-IQAD database, which obtains the subjective rating scores of all stylized images on the three subjective evaluations, i.e., content preservation (CP), style resemblance (SR), and overall vision (OV). To quantitatively measure the quality of AST image, we propose a new sparse representation-based method, which computes the quality according to the sparse feature similarity. Experimental results on our AST-IQAD have demonstrated the superiority of the proposed method. The dataset and source code will be released at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Hangwei-Chen/AST-IQAD-SRQE</uri>