Reviewerly/exHarmony huggingface dataset:
Reactive Power Optimization of Power Project Management System Based on K-Means Algorithm

With the continuous improvement of our country’s economic and technological level, the rapid expansion of the scale of the power system, and the continuous application of advanced power technology in the power system, which makes the complexity and urgency of reactive PO more prominent. Its connotation is also changing day by day, which increases the randomness, uncertainty, unpredictability and other factors in the reactive PO of the power system. It is precisely because of the emergence of these new challenges that it has always been one of the hot issues in the field of power system optimization. The reactive PO calculation method has been developed so far, and it is mainly divided into the bionic intelligent optimization algorithm and the traditional conventional mathematical algorithm. Conventional mathematical algorithms face the shortcomings of non-linear, discrete and continuous variable mixed, multi-constrained reactive PO inverse problems, it is easy to fall into the local optimal solution or even fail to find the optimal solution. When the bionic intelligent optimization method deals with the above problems, it is widely used in the field of power system reactive PO due to its high flexibility, high robustness, strong adaptability and other characteristics. This article explores the reactive PO of the power project management system based on the K-means algorithm. First, consult the relevant information to get a general understanding of the reactive PO of the power project management system, and then summarize the reactive PO methods of the power project management system based on the data. According to the method, the reactive PO steps of the power project management system based on the K-means algorithm are proposed, and then the experimental verification is carried out for the optimized power project management system. The experimental results show that the active power loss after optimization using the standard particle swarm algorithm is reduced from the initial 0.159 to 0.133, which is a 16.352% reduction; when the IPSO algorithm is used for optimization, the active power loss value is reduced to 0.131, which is a decrease of 17.610%; after adopting the K-means algorithm, the active power loss value drops to 0.125, and the reduction rate is 21.384%.
Separation of gates in quantum parallel programming

The number of qubits in current quantum computers is a major restriction on their wider application. To address this issue, Ying conceived of using two or more small-capacity quantum computers to produce a larger-capacity quantum computing system by quantum parallel programming ([M. S. Ying, Morgan-Kaufmann, 2016]). In doing so, the main obstacle is separating the quantum gates in the whole circuit to produce a tensor product of the local gates. In this study, we theoretically analyse the (sufficient and necessary) separability conditions of multipartite quantum gates in finite or infinite dimensional systems. We then conduct separation experiments with n-qubit quantum gates on IBM quantum computers using QSI software.
Enseñanza y aprendizaje en línea durante la pandemia de Covid-19: El caso de la educación superior pública portuguesa

All over the world, because of the widespread public health emergency caused by Covid-19, Higher Education Institutions were forced to review their practices and, over a short period of time, had to design strategies, establish regulations, and outline support plans capable of supporting a wider and widely generalized use of Information and Communication Technology (ICT) in teaching and learning environments. This article presents the results obtained from a national study whose main objective was to understand the changes that have affected the teaching and learning practice in Portuguese Public Higher Education Institutions (PPHEI), from the perspective of students and teachers whose face-to-face teaching activities had to be suspended. The main data collection tool was an online questionnaire, which was answered by 1908 students and 453 teachers between May 4th and June 24th, 2020. The results obtained revealed that the PPHEI provided information, mainly through the institution email, not only about the pandemic, but also about teaching activities. The use of online learning platforms and applications was reinforced. Most of participants showed they had resources and conditions which allowed them to properly work online.
System Combination for Grammatical Error Correction Based on Integer Programming

In this paper, we propose a system combination method for grammatical error correction (GEC), based on nonlinear integer programming (IP). Our method optimizes a novel F score objective based on error types, and combines multiple end-to-end GEC systems. The proposed IP approach optimizes the selection of a single best system for each grammatical error type present in the data. Experiments of the IP approach on combining state-of-the-art standalone GEC systems show that the combined system outperforms all standalone systems. It improves F0.5 score by 3.61% when combining the two best participating systems in the BEA 2019 shared task, and achieves F0.5 score of 73.08%. We also perform experiments to compare our IP approach with another state-of-the-art system combination method for GEC, demonstrating IP's competitive combination capability.
Relational Prior for Multi-Object Tracking

Tracking multiple objects individually differs from tracking groups of related objects. When an object is a part of the group, its trajectory is conditioned on the trajectories of the other group members. Most of the current state-of-the-art trackers follow the approach of tracking each object independently, with the mechanism to handle the overlapping trajectories where necessary. Such an approach does not take inter-object relations into account, which may cause unreliable tracking for the members of the groups, especially in crowded scenarios, where individual cues become unreliable. To overcome these limitations, we propose a plug-in Relation Encoding Module (REM). REM encodes relations between tracked objects by running a message passing over a spatio-temporal graph of tracked instances, computing the relation embeddings. The relation embeddings then serve as a prior for predicting future positions of the objects. Our experiments on MOT17 and MOT20 benchmarks demonstrate that extending a tracker with relational prior improves tracking quality.
Adversarial Attacks and Classifiers: A Review

Through the widespread distribution of equipment (ML) for a variety of programs including medical, military, multimedia, automotive, social networking sites, and genomic there is a high risk of injury from enemy attacks (AL). The study of the opposition machine and the research center examining the production and acquisition of contradictory models, which are efforts particularly designed to betray editors, then is widely deliberate especially in the field of duplicate recognition, where small changes in image photography are made. of black boxes and methods of white box attack. Also, the study expanded the effort and examined the black box attack in contradiction of the opposition. machine learning and in-depth learning, with a strong focus on Protection from student assault. After presenting the relevant terms and objectives and a list of possible details of the attackers and opponents.
ISP-Agnostic Image Reconstruction for Under-Display Cameras

Under-display cameras have been proposed in recent years as a way to reduce the form factor of mobile devices while maximizing the screen area. Unfortunately, placing the camera behind the screen results in significant image distortions, including loss of contrast, blur, noise, color shift, scattering artifacts, and reduced light sensitivity. In this paper, we propose an image-restoration pipeline that is ISP-agnostic, i.e. it can be combined with any legacy ISP to produce a final image that matches the appearance of regular cameras using the same ISP. This is achieved with a deep learning approach that performs a RAW-to-RAW image restoration. To obtain large quantities of real under-display camera training data with sufficient contrast and scene diversity, we furthermore develop a data capture method utilizing an HDR monitor, as well as a data augmentation method to generate suitable HDR content. The monitor data is supplemented with real-world data that has less scene diversity but allows us to achieve fine detail recovery without being limited by the monitor resolution. Together, this approach successfully restores color and contrast as well as image detail.
Servant Leadership Reality Practice and its Impact on Job Involvement – A field Study on Administrative Staff of King Abdul-Aziz University - Female Section in Sulaymaniyah –: واقع ممارسة القيادة الخادمة وأثرها على تحقيق الاستغراق الوظيفي – دراسة ميدانية على إداريات جامعة الملك عبد العزيز - شطر الطالبات بالسليمانية –

The aim of this research is to identify the role of servant leadership with its dimensions (empowering others- humility- accountability- acceptance-giving directions- Stewardship) in achieving job involvement for King Abdul-Aziz University female administrators. In order to fulfill the research objectives, descriptive analytical methodology was used, and questionnaire was utilized to collect the required data from (166) participants; the data were statistically analysesd. Several statistical functions were used on the collected date. The most important result is stated that there is an impact and a relationship between servant leadership and job involvement. Showing that, giving more attention to the practice of servant leadership behaviours may lead to the enhancement of KAU administrator's job involvement. Accordingly, this research introduces some recommendations, such as, giving leadership literature more concern by providing the organizations with new books and scientific journals about leadership types, competencies and traits; this will encourage employees to read them. Moreover, organization should focus on employee's job involvement; this may benefit the organization in a way that would give a higher impact on the employee's work desire, this in turn, can positively enhance organizational performance indicators such as; productivity, job turnover, job security, efficiency, effectiveness, and customer satisfaction. In addition, the organization should give more concern to provide a healthier workplace that could trigger the employees' enthusiasm, morality and job involvement; as a result, employees may gain a positive feeling and good impression towards achieving the organization goals.
NAS-FCOS: Efficient Search for Object Detection Architectures

Neural Architecture Search (NAS) has shown great potential in effectively reducing manual effort in network design by automatically discovering optimal architectures. What is noteworthy is that as of now, object detection is less touched by NAS algorithms despite its significant importance in computer vision. To the best of our knowledge, most of the recent NAS studies on object detection tasks fail to satisfactorily strike a balance between performance and efficiency of the resulting models, let alone the excessive amount of computational resources cost by those algorithms. Here we propose an efficient method to obtain better object detectors by searching for the feature pyramid network (FPN) as well as the prediction head of a simple anchor-free object detector, namely, FCOS [36], using a tailored reinforcement learning paradigm. With carefully designed search space, search algorithms, and strategies for evaluating network quality, we are able to find top-performing detection architectures within 4 days using 8 V100 GPUs. The discovered architectures surpass state-of-the-art object detection models (such as Faster R-CNN, Retina-Net and, FCOS) by 1.0% to 5.4% points in AP on the COCO dataset, with comparable computation complexity and memory footprint, demonstrating the efficacy of the proposed NAS method for object detection. Code is available at https://github.com/Lausannen/NAS-FCOS.
The Special Feature of Expert System of Policy Decision Analysis for Prevention and Treatment of Geological Hazards (GHPES)

The article briefly discusscd the main problems in developing and study of expert system and its structure and function, the stress is put on the introduction of main characteristics of development environment of expert system of policy decision analysis for prevention and treatment of geological disasters .A breakthrough was made in solving problem about coupling of expert system, information resources database(DBASE), map and image base(GIS), method and model base in the conditions of windows. A graphical user interface for multiobjectives, -tasks and -windows was established. In the process of inference, the data can be drawn from information resources data base. The images files can be acquired from map and image base. Various prompt information can be displayed. At the same time bitmaps could be displayed on the screen. The reasoning results could be shown dynamically. This greatly widened the usability of the system. The results of retrieval from GeoRef photo disk database by the Institute of Information of Geology and Mineral Resources in China showed that up to now analogous results of study are unknown at home and abroad.
From didactic game to educational gamification

The purpose of the article is to trace the continuity and establish the relationship of the use of traditional didactic games with modern trends in gamification in education and to build the author’s methodological system of gamification of the educational process. The article considers the problem of gamification in education, substantiates the relevance of research in this direction and formulates a contradiction that leads to the problem of finding optimal approaches to the introduction of gamification in the educational process of schools and universities. The results of a survey of teachers of various subject areas are presented in order to identify an understanding of the relationship between the concepts of “didactic game” and “gamification” and their place in their own professional activities. Based on the analysis of the essence of the concepts “didactic game” and “gamification”, the author’s approach to the construction of the methodological system” Educational gamification” is proposed and the key provisions are formulated. According to the results of the study, conclusions are formulated and prospects are outlined in the development of individual components of the author’s methodological system, its approbation in the real educational process and analysis of the results obtained.
The effectiveness of the Think Pair Share (TPS) method on explanatory text writing skills in terms of learning styles

This study aims to review the effectiveness of the think pair share method on explanatory text writing skills in terms of learning styles. This type of research is quantitative with the 2x2 factorial experimental method. The population in this study were eighth grade students of SMP Negeri 1 Kinali. Research data was collected using a questionnaire and performance test. Based on the results of data analysis, it can be concluded that (1) the explanatory text writing skills of students who study using the think pair share method are higher than students who study using conventional methods, (2) the explanatory text writing skills of students who have high learning styles who study with the think pair share method are higher than students who learn by conventional methods, (3) the explanatory text writing skills of students who have low learning styles who study with the think pair share method are higher than students who learn by conventional methods, and (4) there is an interaction between the think pair share method and learning styles in influencing the skills of writing explanatory texts.
Attention-Guided NIR Image Colorization via Adaptive Fusion of Semantic and Texture Clues

Near infrared (NIR) imaging has been widely applied in low-light imaging scenarios; however, it is difficult for human and algorithms to perceive the real scene in the colorless NIR domain. While Generative Adversarial Network (GAN) has been widely employed in various image colorization tasks, it is challenging for a direct mapping mechanism, such as a conventional GAN, to transform an image from the NIR to the RGB domain with correct semantic reasoning, well-preserved textures, and vivid color combinations concurrently. In this work, we propose a novel Attention-based NIR image colorization framework via Adaptive Fusion of Semantic and Texture clues, aiming at achieving these goals within the same framework. The tasks of texture transfer and semantic reasoning are carried out in two separate network blocks. Specifically, the Texture Transfer Block (TTB) aims at extracting texture features from the NIR image's Laplacian component and transferring them for subsequent color fusion. The Semantic Reasoning Block (SRB) extracts semantic clues and maps the NIR pixel values to the RGB domain. Finally, a Fusion Attention Block (FAB) is proposed to adaptively fuse the features from the two branches and generate an optimized colorization result. In order to enhance the network's learning capacity in semantic reasoning as well as mapping precision in texture transfer, we have proposed the Residual Coordinate Attention Block (RCAB), which incorporates coordinate attention into a residual learning framework, enabling the network to capture long-range dependencies along the channel direction and meanwhile precise positional information can be preserved along spatial directions. RCAB is also incorporated into FAB to facilitate accurate texture alignment during fusion. Both quantitative and qualitative evaluations show that the proposed method outperforms state-of-the-art NIR image colorization methods.
A Comprehensive Study on Asymmetric Cryptography Signatures and Smart Contract

ABSTRACTDigital signatures are a restrained technique is used to validate the secrete document without any tampering. The hand-written signatures and Digital signature are both rely on the fact that it is very hard to find two signatures with the same person. Nowadays People use public-key cryptography to compute digital signatures by connecting something unique with another person's signature. Sometimes this digital signature called an electronic signature used for acknowledging online transactions. These signatures are commonly used for financial transactions, online transactions, software distribution, and in some common cases these signs are used for security purposes. The digital signatures are using different algorithms and procedures for cryptocurrency and other applications. This paper performs security analysis and the understanding of Digital Signature.
DP-SSL: Towards Robust Semi-supervised Learning with A Few Labeled  Samples

The scarcity of labeled data is a critical obstacle to deep learning. Semi-supervised learning (SSL) provides a promising way to leverage unlabeled data by pseudo labels. However, when the size of labeled data is very small (say a few labeled samples per class), SSL performs poorly and unstably, possibly due to the low quality of learned pseudo labels. In this paper, we propose a new SSL method called DP-SSL that adopts an innovative data programming (DP) scheme to generate probabilistic labels for unlabeled data. Different from existing DP methods that rely on human experts to provide initial labeling functions (LFs), we develop a multiple-choice learning~(MCL) based approach to automatically generate LFs from scratch in SSL style. With the noisy labels produced by the LFs, we design a label model to resolve the conflict and overlap among the noisy labels, and finally infer probabilistic labels for unlabeled samples. Extensive experiments on four standard SSL benchmarks show that DP-SSL can provide reliable labels for unlabeled data and achieve better classification performance on test sets than existing SSL methods, especially when only a small number of labeled samples are available. Concretely, for CIFAR-10 with only 40 labeled samples, DP-SSL achieves 93.82% annotation accuracy on unlabeled data and 93.46% classification accuracy on test data, which are higher than the SOTA results.
FedPrune: Towards Inclusive Federated Learning

Federated learning (FL) is a distributed learning technique that trains a shared model over distributed data in a privacy-preserving manner. Unfortunately, FL's performance degrades when there is (i) variability in client characteristics in terms of computational and memory resources (system heterogeneity) and (ii) non-IID data distribution across clients (statistical heterogeneity). For example, slow clients get dropped in FL schemes, such as Federated Averaging (FedAvg), which not only limits overall learning but also biases results towards fast clients. We propose FedPrune; a system that tackles this challenge by pruning the global model for slow clients based on their device characteristics. By doing so, slow clients can train a small model quickly and participate in FL which increases test accuracy as well as fairness. By using insights from Central Limit Theorem, FedPrune incorporates a new aggregation technique that achieves robust performance over non-IID data. Experimental evaluation shows that Fed- Prune provides robust convergence and better fairness compared to Federated Averaging.
Towards an Understanding of Default Policies in Multitask Policy Optimization

Much of the recent success of deep reinforcement learning has been driven by regularized policy optimization (RPO) algorithms, with strong performance across multiple domains. In this family of methods, agents are trained to maximize cumulative reward while penalizing deviation in behavior from some reference, or default policy. In addition to empirical success, there is a strong theoretical foundation for understanding RPO methods applied to single tasks, with connections to natural gradient, trust region, and variational approaches. However, there is limited formal understanding of desirable properties for default policies in the multitask setting, an increasingly important domain as the field shifts towards training more generally capable agents. Here, we take a first step towards filling this gap by formally linking the quality of the default policy to its effect on optimization. Using these results, we then derive a principled RPO algorithm for multitask learning with strong performance guarantees.
Ginrai-Bot for Ordering and Recommending Healthy Food on Line Application

This article presents a Ginrai-Bot, an interactive system for food ordering services via Line application. This system focuses on a favorite customer group who pays attention on their health. The healthy dishes are screened for people with health problems focusing on common diseases in Thai people such as cancer, stroke, diabetes, blood pressure, and heart disease. The proposed system consists of 2 main parts. The first part is an automatic dialog, Chatbot on Line chat for contact between customers and restaurant which can answer both general questions and questions on healthy food items. Question set archives are taken from conversation between customers and stores for a month. All dialogs were classified into type of questions, who asked this question and character of answer. A natural language processing tool in this project is used the Dialogflow. The second part is the web system for collecting food items and forbidden diseases from experts in the JSON format using Firebase database. The system was experimented on 27 users and one user did conversation 2 times. The number of correctly ordering food is 38 orders. The experimental result provided the overall evaluation of 4.30 from 5.00 scores. The highest score is based on how easy to use the system was; it results with 4.40 from 5.00 scores. Follow by the answer ability of system to answer proper food menu for health and suggest good food for people who had health problem with score of 4.18 from 5.00.