Reviewerly/exHarmony huggingface dataset:
Identification of Critical Nodes and Accident Chains of Power Systems under the Effect of Geomagnetic Storms

Geomagnetically induced currents (GICs) generated by geomagnetic disturbances (GMD) during strong magnetic storms may trigger chain failures in power systems. In this paper, a critical node identification method based on the improved LeaderRank algorithm considering the effects of geomagnetic storms is proposed. A chain fault identification process based on the fragility theory of complex systems is proposed to determine the initial fault line by critical nodes and simulate the fault development process to obtain the accident chain set. The effectiveness of the method is verified by using the IEEE RTS79 node system as an example. The research results show that the method can reflect the critical nodes and accident chain set of power systems under different geomagnetic storm effects, and the research results can provide a reference for geomagnetic storm disaster quantification, assessment, and prevention.
Arabic handwritten alphabets, words and paragraphs per user (AHAWP) dataset

This article presents a handwritten Arabic alphabets, words and paragraphs dataset (AHAWP). The dataset contains 65 different Arabic alphabets (with variations on begin, end, middle and regular alphabets), 10 different Arabic words (that encompass all Arabic alphabets) and 3 different paragraphs. The dataset was collected anonymously from 82 different users. Each user was asked to write each alphabet and word 10 times. A userid uniquely but anonymously identifies the writer of each alphabet, word and paragraph. In total, the dataset consists of 53199 alphabet images, 8144 words images and 241 paragraphs images. This dataset can be used for multiple purposes. It can be used for optical handwriting recognition of alphabets and words. It can also be used for writer identification (or verification) of handwritten Arabic text. It is also possible to evaluate difference in writing styles of isolated alphabets as compared to the same alphabet written as part of the word or in paragraph by the same user using this dataset. The dataset is publicly available at https://data.mendeley.com/datasets/2h76672znt/1.
DECISIVE ANALYSIS OF MULTIPLE LOGISTIC REGRESSION APROPOS OF HYPER-PARAMETERS

Machine learning based predictive models are playing a vital role in every domain; however, to get the best performance of any model, hyperparameters of the underlying algorithm need to be fine-tuned.Tuning these hyperparameters is an extensive task in terms of resources and time.In this study, we tried to generalize the hyperparameters of the Multiple Logistic Regression model using the iterative method applied on multiple datasets to obtain the best hyperparameters values.In most cases, Newton-cg was found as the best solver with ridge regression.LBFGS and Liblinear were found suitable for a few datasets with L2 regularization irrespective of the linear separability of the data.This work gives the behavioral analysis of hyperparameters of the underlying dataset with respect to Multiple Logistic Regression Classification.It will help the researchers to come up with a more robust generalized tool for fine-tuning of hyperparameters to save the resources.
ITA: The Improved Throttled Algorithm of Load Balancing on Cloud Computing

Cloud computing makes the information technology industry boom. It is a great solution for businesses who want to save costs while ensuring the quality of service. One of the key issues that make cloud computing successful is the load balancing technique used in the load balancer to minimize time costs and optimize costs economically. This paper proposes an algorithm to enhance the processing time of tasks so that it can help improve the load balancing capacity on cloud computing. This algorithm, named as Improved Throttled Algorithm (ITA), is an improvement of Throttled Algorithm. The paper uses the Cloud Analyst tool to simulate. The selected algorithms are used to compare: Equally Load, Round Robin, Throttled and TMA. The simulation results show that the proposed algorithm ITA has improved the processing time of tasks, time spent processing requests and reduced the cost of Datacenters compared to the selected popular algorithms as above. The improvement of ITA is because of selecting virtual machines in an index table that is available but in order of priority. It helps response times and processing times remain stable, limits the idling resources, and cloud costs are minimized compared to selected algorithms.
Commonsense Knowledge-Aware Prompt Tuning for Few-Shot NOTA Relation Classification

Compared with the traditional few-shot task, the few-shot none-of-the-above (NOTA) relation classification focuses on the realistic scenario of few-shot learning, in which a test instance might not belong to any of the target categories. This undoubtedly increases the task’s difficulty because given only a few support samples, this cannot represent the distribution of NOTA categories in space. The model needs to make full use of the syntactic information and word meaning information learned in the pre-training stage to distinguish the NOTA category and the support sample category in the embedding space. However, previous fine-tuning methods mainly focus on optimizing the extra classifiers (on top of pre-trained language models (PLMs)) and neglect the connection between pre-training objectives and downstream tasks. In this paper, we propose the commonsense knowledge-aware prompt tuning (CKPT) method for a few-shot NOTA relation classification task. First, a simple and effective prompt-learning method is developed by constructing relation-oriented templates, which can further stimulate the rich knowledge distributed in PLMs to better serve downstream tasks. Second, external knowledge is incorporated into the model by a label-extension operation, which forms knowledgeable prompt tuning to improve and stabilize prompt tuning. Third, to distinguish the NOTA pairs and positive pairs in embedding space more accurately, a learned scoring strategy is proposed, which introduces a learned threshold classification function and improves the loss function by adding a new term focused on NOTA identification. Experiments on two widely used benchmarks (FewRel 2.0 and Few-shot TACRED) show that our method is a simple and effective framework, and a new state of the art is established in the few-shot classification field.
Multi-Objective Optimization of a Task-Scheduling Algorithm for a Secure Cloud

As more and more power information systems are gradually deployed to cloud servers, the task scheduling of a secure cloud is facing challenges. Optimizing the scheduling strategy only from a single aspect cannot meet the needs of power business. At the same time, the power information system deployed on the security cloud will face different types of business traffic, and each business traffic has different risk levels. However, the existing research has not conducted in-depth research on this aspect, so it is difficult to obtain the optimal scheduling scheme. To solve the above problems, we first build a security cloud task-scheduling model combined with the power information system, and then we define the risk level of business traffic and the objective function of task scheduling. Based on the above, we propose a multi-objective optimization task-scheduling algorithm based on artificial fish swarm algorithm (MOOAFSA). MOOAFSA initializes the fish population through chaotic mapping, which improves the global optimization capability. Moreover, MOOAFSA uses a dynamic step size and field of view, as well as the introduction of adaptive weight factor, which accelerates the convergence and improves optimization accuracy. Finally, MOOAFSA applies crossovers and mutations, which make it easier to jump out of a local optimum. The experimental results show that compared with ant colony (ACO), particle swarm optimization (PSO) and artificial fish swarm algorithm (AFSA), MOOAFSA not only significantly accelerates the convergence speed but also reduces the task-completion time, load balancing and execution cost by 15.62–28.69%, 66.91–75.62% and 32.37–41.31%, respectively.
Actively Learning Gaussian Process Dynamical Systems Through Global and Local Explorations

Usually learning dynamical systems by data-driven methods requires large amount of training data, which may be time consuming and expensive.Active learning, which aims at choosing the most informative samples to make learning more efficient is a promising way to solve this issue.However, actively learning dynamical systems is difficult since it is not possible to arbitrarily sample the state-action space under the constraint of system dynamics.The state-of-the-art methods for actively learning dynamical systems iteratively search for an informative state-action pair by maximizing the differential entropy of the predictive distribution, or iteratively search for a long informative trajectory by maximizing the sum of predictive variances along the trajectory.These methods suffer from low efficiency or high computational complexity and memory demand.To solve these problems, this paper proposes novel and more sample-efficient methods which combine global and local explorations.As the global exploration, the agent searches for a relatively short informative trajectory in the whole state-action space of the dynamical system.Then, as the local exploration, an action sequence is optimized to drive the system's state towards the initial state of the local informative trajectory found by the global exploration and the agent explores this local informative trajectory.Compared to the state-of-the-art methods, the proposed methods are capable of exploring the state-action space more efficiently, and have much lower computational complexity and memory demand.With the state-of-the-art methods as baselines, the advantages of the proposed methods are verified via various numerical examples.
6. Fibonacci lattices have minimal dispersion on the two-dimensional torus

We study the size of the largest rectangle containing no point of a given point set in the two-dimensional torus, the dispersion of the point set. A known lower bound for the dispersion of any point set of cardinality n ≥ 2 in this setting is 2/n. We show that if n is a Fibonacci number then the Fibonacci lattice has dispersion exactly 2/n meeting the lower bound. Moreover, we completely characterize integration lattices achieving the lower bound and provide insight into the structure of other optimal sets. We also treat related results in the nonperiodic setting.
A core region captioning framework for automatic video understanding in story video contents

Due to the rapid increase in images and image data, research examining the visual analysis of such unstructured data has recently come to be actively conducted. One of the representative image caption models the DenseCap model extracts various regions in an image and generates region-level captions. However, since the existing DenseCap model does not consider priority for region captions, it is difficult to identify relatively significant region captions that best describe the image. There has also been a lack of research into captioning focusing on the core areas for story content, such as images in movies and dramas. In this study, we propose a new image captioning framework based on DenseCap that aims to promote the understanding of movies in particular. In addition, we design and implement a module for identifying characters so that the character information can be used in caption detection and caption improvement in core areas. We also propose a core area caption detection algorithm that considers the variables affecting the area caption importance. Finally, a performance evaluation is conducted to determine the accuracy of the character identification module, and the effectiveness of the proposed algorithm is demonstrated by visually comparing it with the existing DenseCap model.
THE INTERACTION OF THE SUBJECTS OF EDUCATIONAL PROCESS IN POLY-ETHNICAL UNIVERSITY ENVIRONMENT

The article is devoted to the theoretical analysis of the problem of pedagogical interaction described in domestic and foreign studies in the field of philosophy, sociology and pedagogy.The article reveals the origin of interaction as a social phenomenon and subsequently as a communicative action, considered in the work of foreign philosophers and sociologists.The author analyzes the idea of dialogical nature of interaction described in the work of ancient thinkers, philosophers of IX and XX centuries, as well as modern researchers in the field of education.Particular emphasis is placed on the current approaches to pedagogical interaction as a purposeful interaction of the participants, involving mutual development, creative activity, transformation of personal and social qualities, increase of motivation and self-esteem.In order to promote effective subject-subject interaction modern teachers actively use a variety of forms and methods of interactive learning, whose active role was determined by the trend of computerization of all areas of the modern world including education.The actualization of the problem of pedagogical interaction in a multi-ethnic educational environment of the university was caused by the promotion of educational services offered by Russian higher educational establishments to the international market, which led to a significant increase in the number of foreign students in higher education institutions in Russia.In addition, changes in the Federal State Educational Standard of higher education determine the need to revise the content of pedagogical interaction.In this regard, the article proposes a working definition of pedagogical interaction provided in the process of teaching a foreign language in a multi-ethnic educational environment of the university, which will be used in our further research.
Large Comparative Study of Recent Computational Approach in Automatic Hate Speech Detection

Social media has become a constant in our everyday life. However, its steady growth has increased the hate speech and hostile content problem. To curb this, hate speech detection and recognition is required, but it is faced to two major challenges - laws and enforcement, and automatic computerized hate speech detection. Although many studies are already implemented in detecting hate content, many of these are done in a single setting showing a single dataset in comparison to machine learning or deep learning models. Thus, there is no comparison between previous approaches and recent inventions such as transformer model. Therefore, in this work we explored and compared recent advanced approaches in automatic hate speech detection. Our aim is to analyze the influence different approaches in detecting hate content and its applicability in the real world. Several experiments were conducted on eight real hate speech datasets from recent studies. We present the results of each comparison which shows that the recent transformer model approach is able to outmatch many of the previous hate speech recognition models by significant G-Means and F1 scores. To the author’s knowledge, this paper is the first attempt to present a large comparative study of approaches in hate speech detection.
A Simple Framework for Robust Out-of-Distribution Detection

Out-of-distribution (OOD) detection, i.e., identifying whether a given test sample is drawn from outside the training distribution, is essential for a deep classifier to be deployed in a real-world application. The existing state-of-the-art methods of OOD detection tackle this issue by utilizing the internal feature of the classification network. However, we found that such detection methods inherently struggle to detect hard OOD images, i.e., drawn near from the training distribution: a naive softmax-based baseline even outperforms them. Motivated by this, we propose a simple yet effective training scheme for further calibrating the softmax probability of a classifier to achieve high OOD detection performance under both hard and easy scenarios. In particular, we suggest to optimize consistency regularization and self-supervised loss during training. Our experiments demonstrate the superiority of our simple method under various OOD detection scenarios.
Nearest Descent, In-Tree, and Clustering

Clustering aims at discovering the natural groupings in a dataset, prevalent in many disciplines that involve multivariate data analysis. In this paper, we propose a physically inspired graph-theoretical clustering method, which first makes the data points organized into an attractive graph, called In-Tree, via a physically inspired rule, called Nearest Descent (ND). The rule of ND works to select the nearest node in the descending direction of potential as the parent node of each node, which is fundamentally different from the classical Gradient Descent. The constructed In-Tree proves a very good candidate for clustering due to its particular features and properties. In the In-Tree, the original clustering problem is reduced to a problem of removing the inter-cluster edges from this graph. Pleasingly, those inter-cluster edges are usually so distinguishable that they can be easily determined by different automatic edge-cutting methods. We also propose a visualized strategy to validate the effectiveness of the automatic edge-cutting methods. The experimental results reveal that the proposed method is superior to the related clustering methods. The results also reveal the characteristics of different automatic cutting methods and the meaningfulness of the visualized strategy in increasing the reliability of the clustering results in practice.
Research on Word Vector Training Method Based on Improved Skip-Gram Algorithm

Through the effective word vector training method, we can obtain semantic-rich word vectors and can achieve better results on the same task. In view of the shortcomings of the traditional skip-gram model in coding and modeling the processing of context words, this study proposes an improved word vector-training method based on skip-gram algorithm. Based on the analysis of the existing skip-gram model, the concept of distribution hypothesis is introduced. The distribution of each word in the word context is taken as the representation of the word, the word is put into the semantic space of the word, and then the word is modelled, which is better modelled by the smoothing of words and the semantic space of words. In the training process, the random gradient descent method is used to solve the vector representation of each word and each Chinese character. The proposed training method is compared with skip gram, <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mtext>CWE</mtext> <mo>+</mo> <mi>P</mi> </math> , and SEING by using word sense similarity task and text classification task in the experiment. Experimental results showed that the proposed method had significant advantages in the Chinese-word segmentation task with a performance gain rate of about 30%. The method proposed in this study provides a reference for the in-depth study of word vector and text mining.
A Digital Feature Recognition Technology Used in Ballet Training Action Correction

In order to improve the effect of ballet training, this paper combines digital technology to improve the motion recognition process, analyzes the imaging process, analyzes the system process combined with the human node model, and combines digital feature recognition technology to construct a ballet training motion correction system. Moreover, this paper inputs the ballet training action recognition results into the system and compares the standard actions to judge the rationality of the actions and builds a ballet training action correction system based on digital feature recognition. In addition, this paper designs experiments to conduct ballet training movements in the system effect evaluation. The experimental research verifies that the digital feature recognition technology proposed in this paper can play an important role in ballet movement recognition and has a good action correction effect.
Increased Robustness of Object Detection on Aerial Image Datasets using Simulated Imagery

Machine learning-based models for object detection rely on large datasets of labeled images, such as COCO or ImageNet. When models trained on these datasets are applied to aerial images recorded on Unmanned Aerial Vehicles (UAVs), the problem arises that the conditions under which the training images were created (for example, light, altitude, or angle) may be different in the environment where the UAVs are put into practice, leading to failed detections. This problem becomes even more pressing in safety critical applications where failures can have huge negative impacts and also constitutes an obstacle for certification of cognitive components in UAVs. Along a case study on car detection in low-altitude aerial imagery, we show that using, both, artificial and real images for model training has a positive effect on the performance of object detection algorithms when the trained model is applied on images from another domain. Since simulated images are easy to create and object labels are inherently given, the presented approach shows a promising direction for scenarios where adequate datasets are difficult to obtain, as well as for targeted exploration of weak points of object detection algorithms.
Implementasi CRISP-DM Model Menggunakan Metode Decision Tree dengan Algoritma CART untuk Prediksi Curah Hujan Berpotensi Banjir

Indonesia is part of a tropical climate with high rainfall intensity. High rainfall intensity can potentially cause flooding. To minimize this, accurate weather predictions are needed to be able to anticipate beforehand. This research was conducted with the aim of classifying based on the rain category with the dichotomy of heavy rain and very heavy rain using data mining techniques with the CRISP-DM methodology. The algorithm used in the classification technique is CART (Classification And Regression Tree) with Confusion Matrix test parameters. Based on the results of the model evaluation, it shows that the CART algorithm has a fairly good performance in classifying with an accuracy value of 89.4%.
Problem Transformation as a Gateway to the Wider Use of Basic Computational Algorithms

The problem transformation method is based on the idea that if we cannot solve the given problem directly, we will transfer it to a situation in which we know how to solve it. The basic feature of the method is the division of the problem into subtasks. Furthermore, it is the division of the problem solution into the solution of partial tasks that will allow the use of already learned algorithms outside the set of problems in which they were taught. The use of the method of transformation develops the necessary students’ transformation skills, and, at the same time, it enables the greater use of ICT in mathematics teaching.