Reviewerly/exHarmony huggingface dataset:
Analisis Kesulitan Pelafalan Kosakata Bahasa Inggris Siswa Kelas V SDN Pondok Makmur

The importance of English to be learned in this era of globalization, it would be better if English is taught early on. The earlier you get to know a foreign language, the easier it is to develop your skills. The focus of this research is some students who have difficulty pronouncing English vocabulary in class V SDN Pondok Makmur. The purpose of this study was to describe the difficulty of pronouncing English vocabulary for the fifth grade students of SDN Pondok Makmur. The type of research method used is a type of qualitative descriptive analysis research method, namely the researcher will describe the difficulty of pronouncing English vocabulary for the fifth grade students of SDN Pondok Makmur, Tangerang City. Data collection techniques using tests, observations and documentation. Students found difficulty in pronouncing vocabulary from the results of this research test, there were errors of consonants as many as 4 words, vowels 3 words, intonation 10 words, and word stress 27 words. The most dominant errors are on word stress, second intonation, third consonants, and lastly on vowels.
Pattern Discovery in Conceptual Models Using Frequent Itemset Mining

Patterns are recurrent structures that provide key insights for Conceptual Modeling. Typically, patterns emerge from the repeated modeling practice in a given field. However, their discovery, if performed manually, is a slow and highly laborious task and, hence, it usually takes years for pattern catalogs to emerge in new domains. For this reason, the field would greatly benefit from the creation of automated data-driven techniques for the empirical discovery of patterns. In this paper, we propose a highly automated interactive approach for the discovery of patterns from conceptual model catalogs. The approach combines graph manipulation and Frequent Itemset Mining techniques. We also advance a computational tool implementing our proposal, which is then validated in an experiment with a dataset of 105 UML models.
More secure on the DL-encryption schemes using the TFM function

In this work, the fuzzy mask of the discrete logarithm (DL) encryption schemes has been proposed. These schemes are the Diffie-Hellman key exchange and the El-Gamal public key cryptosystem. The fuzzy mask for these schemes is computed using the triangular fuzzy membership (TFM) function. The purpose of this mask is to increase the security level of the DL encryption schemes with the moderate values of the parameters in compare with the security of the original DL encryption schemes that are need the huge values of the parameters. Some new small experimental results are discussed. The fuzzy masking on the DL encryption schemes considers as a new insight for more secure communications.
Requirements Engineering for Safety-Critical Molecular Programs

The field of cyber-molecular systems is growing rapidly. In these nanotechnology applications the computational logic is encoded by developers into the molecules themselves. Many planned applications are safety-critical, including bio-compatible sensors, pollution trackers, and targeted drug-delivery devices. Requirements engineering (RE) activities and artifacts are essential to assuring the safety of molecular programs. However, molecular programmed devices offer challenges to traditional RE activities. Molecular programmed systems are nanoscale, so hard to monitor; execute at scale, typically 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">10</sup> devices in solution at once; and have probabilistic behavior. Toward safe molecular programs, we propose a new framework, RE4DNA, for their safety requirements discovery, specification, and verification. Its contribution is to bridge the cyber and the molecular in the requirements engineering process. Further, use of RE4DNA identifies building blocks that can contribute to a preliminary safety case. In this paper we introduce RE4DNA, describe how it handles some particular challenges of molecular programming, illustrate its use on a benchmark molecular program, and discuss future work.
The National Solar Radiation Database (NSRDB): Current Status

The National Solar Radiation Database (NSRDB) has evolved significantly since the first release of the point source database in 1992. The NSRDB has become the industry standard for public long-term time-series solar resource data for performance and economic analysis of solar energy projects. NREL continuously updates the scientific methods and spatiotemporal scale of the NSRDB data based on new research and improvements in satellite technology. Currently the NSRDB covers most of the globe and is a publicly available database of high-resolution solar resource data in the U.S. with over 150,000 users annually. NSRDB represents the state of the art in satellite-based estimation of solar resource information and uses a unique physics-based modeling approach that allows improvements in accuracy with the deployment of the next-generation geostationary satellites. This paper will cover the evolution of the NSRDB, current status of the database including estimated uncertainty and spatiotemporal scale and variability, and the advancement in modeling approach.
PerceptSent - Exploring Subjectivity in a Novel Dataset for Visual Sentiment Analysis

Visual sentiment analysis is a challenging problem. Many datasets and approaches have been designed to foster breakthroughs in this trending research topic. However, most works scrutinize only subsymbolic models through visual attributes of the evaluated images, paying less attention to the subjectivity of viewers’ perceptions as a basis for neuro-symbolic systems. Aiming to fill this gap, we present PerceptSent, a novel dataset for visual sentiment analysis that spans 5,000 images shared by users on social networks. Besides the sentiment opinion (positive, slightly positive, neutral, slightly negative, negative) expressed by every evaluator about each image analyzed, the dataset contains evaluator's metadata (age, gender, socioeconomic status, education, and psychological hints) as well as perceptions observed by the evaluator about the image — such as the presence of nature, violence, lack of maintenance, etc. Deep architectures and different problem formulations are explored using our dataset to combine visual and extra attributes (external knowledge) for automatic sentiment analysis. We show evidence that evaluator's perceptionss, when correctly employed, are crucial in visual sentiment analysis, improving the F-score performance from 61% to an impressive rate above 97%. Although, at this point, we do not have automatic approaches to capture these perceptions, our results open up new investigation avenues.
Access Control System for Covid19 Using Computer Vision and Deep Learning Techniques: A Aystematic Review

After the emergence of the Coronavirus disease (Covid19) in December, 2019 the virus was confirmed by the World Health Organization (WHO) to be a dangerous virus that spread through airborne and droplets. One of the most effective way of preventing the spread of this virus is the use of facemask in public places such as banks, shopping mall, schools, offices and the likes. For this reasons people are advised to use facemask whenever they go out for their daily activities. However, some people have refused to wear the facemask in public places, thereby increasing the rate at which the virus spreads. Hence, there is a need to design an access control system for use in public places to grant access to people complying with the facemask regulations or deny access to those faulting them. Literature has shown that the use of computer vision and deep learning technology can play important role in the deployment of such access control system for face detection and facemask detection. The computer vision and deep learning techniques used in face recognition and facemask detection are describes in this systematic literature review.
OAuth2

In the last chapter, we looked at authentication and authorization. A widely used authorization protocol is OAuth2. It is a large topic with many use cases and options, so we have given it its own chapter.
THE CORRELATION BETWEEN STUDENTS’ EMOTIONAL INTELLIGENCE AND READING COMPREHENSION ACHIEVEMENT

The objective of this report was to see if there was a correlation between emotional intelligence and reading comprehension achievement among eighth-grade students at SMPN 2 Kayuagung. A correlational research design was utilized. Purposive sampling was applied to choose 140 students for the study. A questionnaire was utilized to check students’ emotional intelligence and a writing test in the form of multiple choice about recount text was employed to measure students’ reading achievement. Before administering the test to the samples, the validity and reliability of the test were assessed on non-sample students at SMPN 2 Kayuagung. The Pearson Product Moment was used to examine the data. The correlation coefficient was found to be 0.240 based on the results at a p-value of 0.05. It was found that r-obtained (02.40) was higher than r-table (0.166). It meant students' emotional intelligence and reading comprehension had a positive relationship. Students who had good emotional intelligence had better reading achievement.
Towards Generic 3D Tracking in RGBD Videos: Benchmark and Baseline

Tracking in 3D scenes is gaining momentum because of its numerous applications in robotics, autonomous driving, and scene understanding. Currently, 3D tracking is limited to specific model-based approaches involving point clouds, which impedes 3D trackers from applying in natural 3D scenes. RGBD sensors provide a more reasonable and acceptable solution for 3D object tracking due to their readily available synchronised color and depth information. Thus, in this paper, we investigate a novel problem: is it possible to track a generic (class-agnostic) 3D object in RGBD videos and predict 3D bounding boxes of the object of interest? To inspire research on this topic, we newly construct a standard benchmark for generic 3D object tracking, ‘Track-it-in-3D’, which contains 300 RGBD video sequences with dense 3D annotations and corresponding evaluation protocols. Furthermore, we propose an effective tracking baseline to estimate 3D bounding boxes for arbitrary objects in RGBD videos, by fusing appearance and spatial information effectively. Resources are available on https://github.com/yjybuaa/Track-it-in-3D .
A Gyrovector Space Approach for Symmetric Positive Semi-definite Matrix Learning

Representation learning with Symmetric Positive Semi-definite (SPSD) matrices has proven effective in many machine learning problems. Recently, some SPSD neural networks have been proposed and shown promising performance. While these works share a common idea of generalizing some basic operations in deep neural networks (DNNs) to the SPSD manifold setting, their proposed generalizations are usually designed in an ad hoc manner. In this work, we make an attempt to propose a principled framework for building such generalizations. Our method is motivated by the success of hyperbolic neural networks (HNNs) that have demonstrated impressive performance in a variety of applications. At the heart of HNNs is the theory of gyrovector spaces that provides a powerful tool for studying hyperbolic geometry. Here we consider connecting the theory of gyrovector spaces and the Riemannian geometry of SPSD manifolds. We first propose a method to define basic operations, i.e., binary operation and scalar multiplication in gyrovector spaces of (full-rank) Symmetric Positive Definite (SPD) matrices. We then extend these operations to the low-rank SPSD manifold setting. Finally, we present an approach for building SPSD neural networks. Experimental evaluations on three benchmarks for human activity recognition demonstrate the efficacy of our proposed framework.
Quality-Oriented Federated Learning on the Fly

With federated learning, a large number of edge devices are engaged to train a global model collaboratively using their local private data. To train a high-quality global model, however, recent studies recognized that the quality of model contributions from local training on different edge devices are substantially different. Existing mechanisms for quantifying such model quality are intuitively based on the training loss or model parameters, and fail to capture the effect of highly variable data and heterogeneous resources available on participating edge devices. In this article, we propose a new aggregation mechanism that uses deep reinforcement learning to dynamically evaluate the quality of model updates, with accommodations for both data and device heterogeneity as the training process progresses. By dynamically mapping the quality of local models to their importance during model aggregation, the global training process is able to converge toward the direction of better effectiveness and generalization. We show that our proposed mechanism outperforms its state-of-the-art counterparts, achieving faster convergence and more stable learning progress. Further, the LSTM-TD3 architecture and state representation design in our mechanism allows it to adapt to various unseen federated learning environments with an arbitrary number of local updates.
A Comparative Study of DWT-HOG Based Face Recognition with Other Similar Techniques

The Discrete Wavelet Transform (DWT) is one of the efficient tools used in many image and pattern recognition applications. In this research, the DWT technique is combined with the Histogram of Oriented Gradients (HOG) to build a face recognition system. The proposal is to design and test the performance of the DWT-HOG based face recognition system and then compare the recognition rate results with other similar published techniques. To extract some useful sets of features from the addressed face image, the DTW technique is used first to extract the frequency content and provide the sub-bands for each of the selected face images. The HOG technique is then applied to each sub-band to extract the useful features. The performance of this new suggested system is measured by using the standard public face databases, ORL, AR and CMU-PIE. The experimental results proved that the proposed method is effective and relatively more accurate than other known published similar systems.
Phishing Frauds Detection Based on Graph Neural Network on Ethereum

Blockchain, as an emerging technology, has vulnerabilities that make the blockchain ecosystem rife with many criminal activities. However, existing technologies of phishing fraud detection heavily rely on shallow machine learning, leading to low detection precision. To solve this problem, in this paper, we construct a graph classification network model TransDetectionNet. Particularly, we propose a node embedding algorithm named Edge-sampling To Node Vector (Esmp2NVec) that can effectively extract the features hiding in the directed transaction network. Then, we use graph convolutional neural networks (GraphSAGE and GCN) to learn the topological space structure between nodes for further extraction of node features, where the nodes represent Ethereum accounts. To evaluate the method, a series of transaction data from the real Ethereum system is leveraged to train the graph classification model, and several experiments are designed to measure the phishing accounts identification performance comparison between our method and the other related works. The final results of those experiments show that our method can effectively detect phishing accounts from the Ethereum system.
Unsupervised confidence for LiDAR depth maps and applications

Depth perception is pivotal in many fields, such as robotics and autonomous driving, to name a few. Consequently, depth sensors such as LiDARs rapidly spread in many applications. The 3D point clouds generated by these sensors must often be coupled with an RGB camera to understand the framed scene semantically. Usually, the former is projected over the camera image plane, leading to a sparse depth map. Unfortunately, this process, coupled with the intrinsic issues affecting all the depth sensors, yields noise and gross outliers in the final output. Purposely, in this paper, we propose an effective unsupervised framework aimed at explicitly addressing this issue by learning to estimate the confidence of the LiDAR sparse depth map and thus allowing for filtering out the outliers. Experimental results on the KITTI dataset highlight that our framework excels for this purpose. Moreover, we demonstrate how this achievement can improve a wide range of tasks.
Education Of Children With Dyslexia In Croatia: The Role Of Easy Language

Dyslexia is a language-based disorder that can have serious consequences on the learning needs of individuals with this specific learning disorder. In Croatia, most students diagnosed with dyslexia are taught according to the regular prescribed curriculum while using an individualized approach. However, teachers often find it difficult to implement an individualized approach, particularly when it comes to adapting the level of linguistic complexity of teaching material. In order to address this challenge, the concept of easy language is increasingly being incorporated into the education of students with dyslexia in Europe. Easy language refers to language adaptation of a text in order to facilitate both reading and comprehension abilities. Although there is a history of the use of easy language in the Croatian context, this approach has not yet been fully developed in practice. The aim of this study was to analyze whether the level of language complexity of e-testing exams in natural science subjects is suitable for elementary school students with dyslexia. The materials analyzed in this study were natural science exam questions from a single unit of instruction (one thematic unit) prepared by teachers in the e-testing format (for online instruction during a pandemic) for children from 1st to 6th grade. All questions were analyzed at the level of lexical, syntactic, and morphological criteria, as well as language at the content (pragmatic) level. In addition, graphic adjustments were examined as a function of language. All materials analyzed in the study were adapted according to the guidelines of easy language. The results of our analysis indicate the need for a high proportion of (morpho)syntactic adaptations (51%), followed by language adaptations made at the content (pragmatic) level (36%), and a low proportion of lexical adaptations (13%). These findings clearly indicate that significant adaptations are required for children with dyslexia to be able to actively participate in daily instruction. The implications of this study are directly related to education in the context of dyslexia. Our results highlight the importance of easy language in order to help children with dyslexia reach their full potential. In addition, the importance of speech and language pathologists in the school team is recognized, since they are experts in language and graphic adjustment, and they can provide the required support to teachers who teach children with dyslexia.
Convolutional and Dense ANN for Cloud Kinetics Forecasting Using Satellite Images

Intermittency of solar radiation over earth surface due to cloud presence poses a great challenge for power production at photovoltaic fields. Prediction of cloud kinetics may help to deal with variations of energy production and their compensation to satisfy electricity demand. Nowadays, there are huge amounts of satellite information available about clouds at most regions of the world. That information could be useful to characterize cloud kinetics and to calculate solar radiation ahead of time. In this paper, we propose two methods to predict cloud kinetics by using artificial neural networks (ANN) and satellite images. First method is mainly graphical and uses a convolutional ANN to predict the cloud satellite image at time k + 1 from the set of previous n cloud satellite images. Then, the cloud position vector, that represents the predicted image, is obtained. Second method is mainly numerical and uses a dense ANN to directly predict the cloud position vector at time k + 1 from the set of previous m position vectors of satellite images. The predicted cloud position vectors are compared to the real ones when the corresponding satellite image becomes available. Time series of predicted cloud position and velocity vectors are compared through the RMSE index to quantify the precision of predictions. We find that even though both methods offer a short-term forecasting with good accuracy, the method using dense ANN provides more accurate results though its performance should be checked under less simplified assumptions.
Missing Data Reconstruction Method of Distribution Network based on RES-AT-UNET

When the power monitoring equipment in distribution network is affected by extreme events such as typhoon, thunderstorm and strong electromagnetic pulse, the measurement data is missing, the evaluation of equipment observability and availability cannot be realized effectively. The traditional data reconstruction method adopts linear interpolation method, which ignores the change rule of power system measurement data and context constraints, the reconstruction accuracy is very low. In this paper, a data missing value reconstruction method based on Residual Attention UNET(RES-AT-UNET) network is proposed. Considering the characteristics of distribution network and avoiding complex explicit modeling, the proposed method adopts the end-to-end model training method, which can still maintain the accuracy of data reconstruction in the case of missing large interval time series data. The experimental results show that the root mean square error of the data reconstructed by the proposed method is the smallest compared with the actual data, and the reconstruction model has strong applicability to the data under different missing rates.
THE EFFECTIVENESS OF COOPERATIVE LEARNING IN DEVELOPING GRAMMAR SKILLS

The article presents the results of the experimental enquiry aimed at studying the impact of cooperative learning on improving grammar skills. The research focused on two interaction modes – pair work and group work, which were compared in terms of their potential to address accuracy. The experiment was carried out in the first term of the 2020-2021 academic year (September-October). The participants included 23 third-year students of the Foreign Philology Faculty of H. S. Skovoroda Kharkiv National Pedagogical University and constituted two academic groups, with one being offered pair-work activities and the other working in small groups. The experimental study comprised three phases: pre-experiment testing, teaching, post-experiment testing. Both quantitative and qualitative methods were used. The results demonstrate the validity of the assumption about the efficacy of pair work and group work in promoting accuracy, at the same time indicating no statistically significant difference between the two modes. The presented evidence suggests that grammar instruction can clearly benefit from incorporating both pair work and group work into classroom practices, however further research might be advisable to maximize the effectiveness of these interaction patterns by refining the procedure involved and improving students’ cooperative learning skills.