Reviewerly/exHarmony huggingface dataset:
Improving Just-In-Time Comment Updating via AST Edit Sequence

Code comments are valuable for program comprehension and software maintenance. However, comments can be inconsistent or out-of-date after code changes. To tackle this problem, Just-In-Time (JIT) comment updating aims to automatically update comments with code changes. Existing approaches for this task use edit sequences of source code to model code changes. Meanwhile, recent researches indicate that neural models based on abstract syntax trees (AST) can help represent source code. In this paper, we propose a new method to learn code changes by combining code edit sequences with AST edit sequences, so that the generated new comment can be more accurate. Our approach utilizes three encoders to encode code edit sequences, AST edit sequences and old comment token sequences, respectively. The outputs of the encoders are then decoded into a sequence of edit actions, which is parsed to generate a new comment. The proposed method is evaluated on a public dataset using seven metrics, and the experimental results show that our approach outperforms the baselines. Furthermore, when the new comment has a larger edit distance than the old one, our model shows better performance.
PSYCHOLOGICAL FEATURES OF THE SUCCESS OF THE LEADER IN PUBLIC ADMINISTRATION

The general characteristics of the head of public administration has been justified. The main components of the psychology of success of the head of a modern public institution has been considered. Foreign experience in the psychology of success of the head of a modern state institution has been studied. Psychological features of the success of the leader has been identified. The main recommendations on the main problems of success of a modern leader in public administration has been developed.
Multi-label Algorithm Based on the Second and Higher Order Strategies

At present, the time complexity of multi-label algorithm based on second-order and higher-order strategies is usually increased due to the increase of the size of the label set. To solve this problem, this chapter proposes a Multi-label algorithm by mining local label correlations (MLMC) with efficiency as the optimization objective. Firstly, from the broad dimension of the correlation between labels, the correlation between labels is used to optimize the classification model. Finally, the nearest neighbor rough set theory is used to consider the positive and negative dependencies between the labels. Experiments show that the MLMC has better accuracy and efficiency than the classical methods.
SPECIALIZED TOOLS FOR VISUALIZING DATA MINING RESULTS

The relevance of the use of visualization technology in interpreting the results of data analysis is substantiated. Practical aspects of visualization of Data Mining results are considered. Classifications of analytical methods and existing practical tasks are given. The classification of visualization tools in the context of analytical methods and data analytics tasks is proposed. An overview of modern analytical systems allowing to get a visual representation of the data is made. The choice of analytical systems, the visual components of which are used in the study, is justified. A brief description of the tasks of analytics is given. The analysis of the built-in visualizers for various analytical systems is carried out taking into account the methods and tasks of analytics. The similarities and differences in the built-in visualization tools used for each of the systems are considered. The problem of the lack of visual components for interpreting the results of the "associative rules" model is indicated. Modern visualization tools are noted on the example of interactive panels "Cluster Profiles" in the Loginom package. The analysis of specialized tools for visualization of metrics in the considered Data Mining models is carried out. Conclusions are formulated about the possibility of supplementing the built-in tools of a specific analytical system with alternative components of other systems.
Setup

Part I of this book dealt with question answering on structured knowledge bases. Now we turn our focus to the other side of the curated Web: large volumes of unstructured textual content in collections like Wikipedia. Although knowledge bases are excellent sources of objective world knowledge, efforts toward their creation and maintenance limits their information coverage at any given point of time. On the other hand, textual sources arguably contain a higher coverage of up-to-date factual knowledge essential for addressing typical questions asked by an average Web user. Unlike canonicalized knowledge bases, text sources often offer substantial redundancy in information. Such redundancy can often be a blessing for boosting confidence in candidate answers derived by the QA system. This chapter focuses on the standard task setups in question answering over text data.
An Imprecise Label Ranking Method for Heterogeneous Data

Learning to rank is an important problem in many sectors ranging from social sciences to artificial intelligence. However, it remains a rather difficult task to perform. Therefore, in some cases, it is preferable to perform cautious inference. For this purpose, we look into the possibility of an imprecise probabilistic approach for the Plackett-Luce model, a popular probabilistic model for label ranking. We aim at extending current Bayesian inference techniques for the Plackett-Luce model to an imprecise probabilistic setting so that we can deal with heterogeneous data by means of cautious mixture modelling. To achieve this, we perform a robust Bayesian analysis over a set of imprecise Dirichlet priors, which allows us to perform cautious label ranking. Finally, we use a synthetic dataset to illustrate our imprecise estimation method.
Penalized Estimation of a Finite Mixture of Linear Regression Models

Finite mixtures of linear regressions are often used in practice in order to classify a set of observations and/or explain an unobserved heterogeneity. Their application poses two major challenges. The first is about the maximum likelihood estimation, which is, in theory, impossible in case of Gaussian errors with component specific variances because the likelihood is unbounded. The second is about covariate selection. As in every regression model, there are several candidate predictors and we have to choose the best subset among them. These two problems can share a similar solution, and here lies the motivation of the present paper. The pathway is to add an appropriate penalty to the likelihood. We review possible approaches, discussing and comparing their main features.
A demonstration of the effects of the number of fuzzy states on a control system

In this paper, we describe a fuzzy logic demonstrator that uses heart rate and skin impedance values as inputs to predict a hypoglycaemia index. The demonstrator was created using JFS v2.00 fuzzy logic freeware and is freely available on the author's website at http://healthcybermap.org/FL/index.htm. We investigate the effects of varying the number of fuzzy states (adjectives or membership functions) in our demonstrator. Iterative experimentation and tuning are always necessary to determine the ideal number and configuration of fuzzy sets for solving a particular clinical problem. The exercise presented in this paper is meant for educational purposes, to provide new students of this important subject with some fundamental insights into the inner working of fuzzy logic systems in their quest to simulate the decision-making process of a human expert.
INTELIGÊNCIA ARTIFICIAL NA JUSTIÇA: SEGUNDA REVOLUÇÃO TECNOLÓGICA NO JUDICIÁRIO

Q uality in patient safety has been discussed for a long time, because even if human care brings benefits, errors and damages arising from this care are still present, bringing consequences to patients, in this context arise the International Patient Safety Goals that were determined by the World Health Organization together with the Joint Commission International.Considering the relevance of this thematic approach to the academic community and to the practice of health care, the objective of this work is to present the international patient safety goals of the World Health Organization.An integrative review was carried out with searches for articles in "Periódicos CAPES" and "Google Academic", as well as regulations and protocols related to the subject in question, the research evidenced the six International Goals of Patient Safety: Patient Identification; Improve Communication between Health Professionals; Improve Safety in Prescription, Use and Administration of High Surveillance Medicines; Ensure Surgeries with Correct Intervention Site, Correct Procedure and Correct Patient; Wash your hands to prevent infections; Decrease the Risk of Injury to the Patient Resulting from a Fall and Pressure Injury.It was possible to conclude that the implementation of the International Patient Safety Goals are fundamental in the prevention of adverse events and harm to patients through monitoring and prevention actions in problem areas in patient care.
Unsupervised Document Binarization Via Disentangled Representation

Salman, K. H. Bhagvati, ChakravarthyBinarization of document is considered the first key step in many document processing tasks. In this paper, we try to reformulate the problem as an image-to-image translation. Most of the existing learning methods for document binarization make use of supervised approach, but obtaining ground truth for binarized documents is difficult. Here we developed an unsupervised adversarial training procedure for binarization. We use disentangling of style and content from a binarized document and transfer the binarized style to the input document. Our results indicate that this approach works on par with many other results published in literature.
Illumination Insensitive Video Cut Detection Using Phase Congruency

Kar, T. Kanungo, P. Jha, VinodShot boundary detection is the first and the most crucial step towards video content management applications including indexing, retrieval and summerisation. In this paper, an abrupt transition detection algorithm has been proposed based on phase congruency feature of the frames. The phase congruency feature is insensitive to illumination variation, change in contrast and scale. Besides this, it captures edges, corners and structural information of the frames. Motivated by this, a PC-based similarity measure is proposed for illumination insensitive video cut detection. The proposed approach is experimentally validated with standard algorithms available in the literature using TRECVid data set and other publicly available videos. The favourable results are in agreement with the proposed model.
Forward Reasoning of Owl Rule Set Based on SPARQL Query

In recent years, in the context of the vigorous development of knowledge graphs, the data scale of the related Semantic Web has also shown an explosive trend. This makes the connection between semantic data more abundant. The RDFS rules and OWL rules currently used in the mainstream of data reasoning in the Semantic Web. When reasoning on large-scale data, the general method is to use forward flow parallel reasoning. In this process, due to the limitation of the reasoning rule set, the more common OWL Horst rule set often makes the content of reasoning insufficient. In this paper, the standard query language SPARQL of the Semantic Web is used to realize the design and implementation of a conversion method corresponding to OWL axioms and OWL Horst rule sets, so as to expand the forward flow reasoning ability based on OWL Horst rules. Through data LUBM and existing Experiments on the reasoning algorithm of this method have verified the feasibility of this method.
The design and development of university teaching management information system based on Asp.Net

The rapid development of information technology to promote economic production and life style of a new change, and accelerate the speed of urban construction innovation. Building electronic government as public administrative reform under the new era and evaluate the important symbol of the levels of national urban competition, in the current social environment, information technology has been a national office daily office affairs, one of the biggest factors affect whether email or file sharing and so on all the authority unit of file management has brought the profound influence. Information publishing technology also has a unique application role in archives management. Therefore, based on the understanding of the application characteristics of the information construction project of the comprehensive archives room, this paper designs and implements the dynamic information release system with Briwser/Server structure as the core under the NET framework, which is mainly used to optimize the operation of file submission, system management and permission browsing. Combined with B/S distributed structure, the functions of file submission and online filing are optimized. At the same time, this paper summarizes the system after scientific adjustment, to meet the requirements of other e-commerce information release management.
Is energy the weak link in the UK’s critical national infrastructure?

The UK’s energy sector has been identified as a major target for ransomware attacks, a concerning issue given its place as part of the nation’s critical national infrastructure (CNI). This refers to assets and industries that are deemed essential to the functioning of a society and economy by the Government. Effective cyber security measures in the energy industry have never been more important and the Government, and its respective bodies, have an important role to play in improving cyber security for CNI.
Industrial Control Network Security Situation Assessment Based on SAE-RBF

With the in-depth development of &#x201C;two integration&#x201D;, industrial control network security situation is more and more serious, industrial control network security protection work is more and more important. Aiming at the characteristics of sparse and complex dimensions of industrial control network security data, combining with the conceptual model of network security situation awareness, this paper proposes a network security situation assessment method, which uses stack self-encoder to process sparse data and uses radial basis neural network to fit complex nonlinear characteristics. It effectively extracts the data features, realizes the analysis and understanding of the data, and completes the security situation assessment, which provides a new method for the security situation assessment of the industrial control network.
Weakly-Supervised Audiovisual Network For Video Saliency Estimation

Existing video saliency estimation methods typically learn from pixel-level annotations. Though the annotation can provide multi-modality information for model training, it requires more labeling effort from human beings. And the commonly used datasets or the videos obtained from the Internet usually have incomplete annotations. The high cost of pixel-level annotations makes it appealing to train video saliency estimation models with weak supervision. Therefore, we expect to reduce the cost of the annotation while preserving the multidimensional features in videos. To this end, we propose an audio-visual network using incomplete visual fixation labels to deal with the video saliency estimation and appropriately learn to localize sound sources in weakly-supervised settings. Our network includes two independent branches which are used to extract auditory and visual features respectively. A connection module will fuse the two features into an audiovisual correspondence in order to obtain the final saliency map. We conduct extensive experiments on six different datasets. Evaluation results indicate that our method performs better than other state-of-art methods as well as achieves excellent performance in weakly-supervised video saliency estimation.
Towards Better Pedestrian Detection Using Multi-Scale CSPN and Dual Attention

Pedestrian detection is a significant research direction in the computer vision, but the detection performance of existing pedestrian detection algorithms is inadequate. Therefore, this article proposes a novel algorithm to improve the anchor-free pedestrian detection algorithm. First, the multi-scale CSPN module is used to deepen the network depth, further extract semantic information on multiple scales, and improve detection performance. Moreover, the dual attention module based on feature fusion is used to effectively fuse features of different scales, assigning new weights to the fused features in the two dimensions of space and channel. Experiments show our method reduces MR<sup>&#x2212;2</sup> by 0.10&#x0025;, 2.60&#x0025; and 0.98&#x0025; on the Reasonable, Heavy Occlusion and ALL of the Caltech pedestrian dataset, which is better than the existing algorithms.
Hubungan Dukungan Orang Tua Dan Efikasi Diri Dengan Motivasi Belajar Siswa Kelas VIII Sekolah Menengah Pertama

This study aims to determine the relationship between parental support and self-efficacy with learning motivation of eighth grade students of SMP Shafiyyatul Amaliyyah Medan, with a population of 103 students. The research sample amounted to 26 students. The method used in this research is quantitative method with sampling technique using Random Sampling Technique . The instrument used is a Likert scale model questionnaire. The data collection technique used three scales consisting of the Parental Support, Self-Efficacy and Learning Motivation scales. The analysis technique used is Multiple Regression. Based on the results of the study it was concluded that: 1) there was a significant positive relationship between parental support and learning motivation with a correlation coefficient (rx1y) of 0.698 with p = 0.000, R2 = 0.487 with a contribution of 48.7%. 2) There is a significant positive relationship between self-efficacy and student learning motivation with the results of the correlation coefficient (rx2y) of 0.715 with p = 0.000, R2 = 0.512 with a contribution of 51.2%. 3) There is a significant positive relationship between parental support and self-efficacy with learning motivation, this result is indicated by the linearity coefficient value of F = 16.919 and p = 0.000, p &lt;0.05 and R2 = 0.595, with a contribution of 59.5%. These results indicate that there are still 40.5% contributions from other factors that can affect learning motivation. From these results it can be said that the higher the parental support and self-efficacy, the higher the learning motivation, and conversely the lower the parental support and self-efficacy, the lower the students' learning motivation.
Unsupervised Lightweight Face 3D Reconstruction From a Single Uncalibrated Image

Reconstruct 3D model from 2D image is an important task in the field of deep learning, which aims to make computers have the ability to perceive the 3D world like human-beings. In this paper, a lightweight method is proposed for 3D face reconstruction from a single image without any supervision. Specifically, our method employs encoder-decoder architectures to extract depth map, light condition, transformation matrix and albedo from input image. According to the principle of rendering, we can obtain the connection between 2D image coordinates and 3D model vertices coordinates, using the above elements, we can get the projection image of the reconstructed model. Then the reconstruction loss can be used to optimize the network parameters. Experiments show that our method surpasses the previous work in reconstruction speed and the size of model.
Robot Step Length Automatic Control Method based on Virtual Reality Technology

Based on virtual reality, this paper proposes a robot step length automation planning algorithm based on a linear inverted pendulum model. The algorithm first obtains the position of the center of mass when the support leg switches according to the step length to be achieved; for multi-robot groups, a hierarchical system is adopted Structure, hierarchical structure combines the advantages of centralized structure and distributed structure. In general, there is a master robot to master global information, and locally controlled robots can exchange information with each other. This structure makes group robots suitable for complex and changeable Work under the environment. So that it can reach the required footing position at the right time to switch the supporting feet. The algorithm can make the biped robot change the step length during the movement, which can improve its walking flexibility to 6.7&#x0025;.
Perma Model for 21st Century Student Success Motivation

The study follows the process of setting up the first newly established Bulgarian public school in the 21st century and the development of an innovative educational model that addresses the challenges of personal development in the context of digital transformation and the professions of the future. The design of educational solutions takes into account the current trends in soft skills and an integrated set of knowledge aimed at identifying and solving problems in the micro-(individual) and macro (global) context of the person and community in a period of dynamic digitization and robotization of personal and social parameters and their consolidation into business intelligent systems. In solving the above-mentioned challenges we apply the PERMA model of positive psychology and present the ways in which it is realized in: Organizational structure of the school; Building the relationship between stakeholders; Integrated approach to presenting learning content; The set of hardware and software solutions that ensure the implementation of the above two indicators and position the valuebased approach in the context of the digital society, expanding the parameters of personal development of professionals, students and their parents into the realms of virtual existence, adding new parameters to the established interpersonal practices and poses new challenges for the administration, training and mental (cognitive and emotional) growth of the context of augmented reality.
OPPORTUNITIES TO DECREASE LONELINESS IN THE WORKPLACE

Leader to LeaderVolume 2022, Issue 105 p. 57-59 FROM THE FRONT LINES OPPORTUNITIES TO DECREASE LONELINESS IN THE WORKPLACE First published: 27 April 2022 https://doi.org/10.1002/ltl.20651Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume2022, Issue105Summer 2022Pages 57-59 RelatedInformation