Reviewerly/exHarmony huggingface dataset:
Research on Key Technologies of Software Configuration Management in Development of Large-scale Software System

Software configuration management is a technology throughout the whole software life cycle. Its main function is to control software changes in the software life cycle and reduce the impact of various changes. With the increasing complexity of software system, user requirements and frequent software updates, software configuration management has gradually become an important control process of software life cycle. Based on the analysis of the current situation of software configuration management system, this paper puting forward a configuration management model based on process and cooperation, and introduces the key problems in the design and implementation of the software configuration management system based on this model. Based on the analysis of the development process of large-scale software system, this paper summarizes some factors that will affect configuration management, and analyzes how these factors affect configuration management, and then puting forward the method of establishing configuration management system of whole life cycle.
Localization and Transformation Reconstruction of Image Regions: An Extended Congruent Triangles Approach

Most of the existing methods to localize (sub) image relations - a subclass of near-duplicate retrieval techniques - rely on the distinctiveness of matched features of the images being compared. These sets of matching features usually include a proportion of outliers, i.e. features linking non matching regions. In approaches that are designed for retrieval purposes only, these false matches usually have a minor impact on the final ranking. However, if also a localization of regions and corresponding image transformations should be computed, these false matches often have a more significant impact. In this paper, we propose a novel outlier filtering approach based on the geometrical information of the matched features. Our approach is similar to RANSAC, but instead of randomly selecting sets of matches and employ them to derive the homography transformation between images or image regions, we exploit in addition the geometrical relation of feature matches to find the best congruent triangle matches. Based on this information we classify outliers and determine the correlation between image regions. We compare our approach with state of the art approaches using different feature detectors and descriptors and various benchmark data sets (panorama, painting or aerial images with affine transformation, adding blur, noise or scale change). The results indicate that our approach is more robust than the state of the art approaches and is able to detect correlation even when most matches are outliers. Moreover, our approach reduces the pre-processing time to filter the matches sianificantly.
WeChat Forensics Method of Android Smart Phone Based on Improved NBC

At present, in the investigation stage, public security organs, procuratorial organs and people's courts often need to conduct electronic data forensics analysis on criminals' WeChat and other social software to obtain sensitive information. This paper proposes a forensic analysis method based on chat history data of Wechat on Android smart phone. By using an Improved Naive Bayes Algorithm with redefinable the weights of the feature words, an improved Naive Bayes Classifier (NBC) model is established. The classifier is used to classify the chatting texts in WeChat, in order to find the sensitive information in the chatting texts efficiently. Experiments show that this model can improve the efficiency of acquiring sensitive information in WeChat, and has obvious effects on saving evidence collection time and improving the efficiency of criminal detection.
Research of urban waterfront space planning and design based on children-friendly idea

Abstract The natural environment is vital for the healthy growth of children. The absence of exposure to Nature in cities is an important cause of physical and psychological health problems in children. With the objective of a healthy growth environment for children, this paper states the active role of the urban waterfront space, analyses the relationship between children and urban waterfront space, summarises the characteristics of children’s outdoor activities, proposes the principle and methods of planning and designing a children-friendly urban waterfront space, and provides some improved design strategies.
Expediting discovery in Neural Architecture Search by Combining Learning with Planning

In our previous work, we introduced NASIL as an automated neural architecture search method with imitation learning. Time to discover optimal structures is a key concern in many AML solutions including NASIL. Here, we proposed an extended version called "GNASIL" to speed up the process. Similar to NASIL, GNASIL takes advantage of imitation learning to discover neural architectures for a given device specification. Unlike NASIL that used deep deterministic policy gradient method, GNASIL uses the soft-actor-critic to predict an optimal layer during its search. Furthermore, GNASIL employs a set of probing options and combines learning and planning options to sweep the search space faster. We investigated impact of such deliberative planning on decision making process on a speech recognition task. Reported results demonstrate that probing options in presence of imitation learning enables GNASIL algorithm to automatically learn suitable network structures with very competitive performance both in terms of speed of finding the optimal architectures and their accuracy while keeping computational footprint restrictions into consideration.
Enhanced Movie Reviews Classification Using Precise Combination of PCA with LSTM Model

As everything is becoming online day by day, a large number of comments or reviews are recorded for various types of products, movies, photographs, etc. Due to its large number, it is very difficult and time taking for someone to read all those comments and then classify each comment as positive or negative. So, in this work, my area of concern is to extract the sentiment from the text and to classify it into suitable categories. In this work, long short-term memory (LSTM) and recurrent neural network (RNN) in addition to hybrid model of (LSTM and PCA) are developed to analyze and classify reviews of the movie. Here, principle component analysis (PCA) is used to get the principle components or we can say for extracting the features which can be used as predictors. In this work, we are using IMDB movie survey dataset, so that it arranges a survey depends upon the neural system in LSTM with PCA. For movie description, it takes useful keywords from reviews of the movie. By this we could physically choose a film is positive reviews or negative. Utilizing the AI approach, we are arranging the film audits to such an extent it is easily decided that movie is positive or negative. Results have shown that the hybrid model of LSTM with PCA have outperformed the singular LSTM and RNN networks. LSTM with PCA have reported the accuracy of 88.2% as it gives higher accuracy as compared to traditional approaches.
Structure-functional analyses of psychological readiness for specialists in physical culture and sport sphere

Among the many competences to determine the readiness level of specialist in the field of physical culture and sport sphere (FCS), the most important include those which constitute psychological readiness. We have developed and implemented a new structural-functional approach to the study and formation of psychological readiness. The practice of training specialists in the physical culture and sports sphere dictates the need to supplement the variety of competences reflected in Russia’s federal standards with superprofessional competences in order to achieve full psychological readiness of the specialist. We present a complex model of competences embedded in the structural components of psychological readiness – cognitive, motivational, operational, value-moral, reflexive. Structural and functional analysis of the model by the method of "competent judges" – employers allowed us to assess the functional significance of each competence in each of the components of readiness for the main specialties of the physical culture and sports sphere. The results of the study revealed a specific and differentiated influence of individual competences on the level of psychological readiness of graduates and specialists of different specialisations of a sports higher education institution.
Identification of Nighttime Rear Vehicle Images

The identification of vehicles from the images recorded by security cameras is now one of the most important investigative techniques. In the past, there were not variations in the shape of the side marker lights and the brake light so much, and it wasn't easy to distinguish the vehicle type from a nighttime rear image. Recently, however, LED-based devices have become popular, and the devices with unique shapes are increasing. For this reason, we focus on the nighttime rear vehicle images, and clarify how much the identification can be done. Nighttime rear vehicle images of 100 models, which is equivalent to 5% of the about 2,000 envisaged models, were used for the identification. The identification was performed with fine-tuning using ResNet-34 and ResNeXt-50-32x4d on PyTorch. The accuracy was 0.88 for the former and 0.90 for the latter. These results would be a first step to squeeze the vehicle type in the criminal investigation.
Numerical simulation of high-temperature superconductivity and artificial intelligence-assisted visual analysis

Numerical simulation of the high-temperature superconductivity and the artificial intelligence-assisted visual analysis is conducted in this paper. High-temperature superconducting power technology is a high-tech technology developed in recent decades. It has very important significance and the development prospects in many fields. To better visualize the testing performance, Artificial Intelligence [AI] has been applied and the data mining models are incorporated to construct the efficient numerical simulation platform. The system is implemented and tested based on the data, and the efficient is validated. Compared with the novel methods, the proposed framework can reflect the test results with interactives.
The Monsoon Pattern Analyses on the Irrigation Water Balance

Abstract The water balance for irrigation is affected by conditional monsoon patterns, including the beginning of the season and the duration. In a technical irrigation service, the actual dynamic of monsoon patterns may influence the crop pattern schedule and the standard operating rule of irrigation intake. Some significant impacts on the water balance characteristics can enlarge the water deficit during drought periods and reduce the efficiency of crop intensity. Its significance can be detected by conducting hydrological simulations of water availability and water demand in certain seasonal periods. Hence, the updated crop pattern schedule and the standard operating rule of irrigation intake can be adjusted correspondingly. Moreover, the social conflict during the water distribution can be diminished, and water efficiency can be increased as well as the crop intensity. This study is conducted at Tibu Kuning irrigation area, West Lombok Regency, West Nusa Tenggara Province using 15 daily rainfall data for 25-year from 1993 to 2017. The results show that the monsoon shift pattern in the study area has an interval of 3-year with the maximum shifting period is about one month. Moreover, the monsoon shift pattern has affected the reliability of irrigation water services. In this situation, an adaptive crop pattern schedule needs to be updated to maintain agricultural productivities. Based on the results obtained in this study, updating of the crop pattern schedule and the standard operating rule of irrigation intake can be be adjusted for the upcoming periods.
A VR-based Learning Material for Anti-drug Education: Evaluation from Aspects of Motivation, Satisfaction, and Self-efficacy

Anti-drug education is one of the important and necessary educational activities for young people. In this paper, a virtual reality based (VR-based) material for learning anti-drug skills is proposed. By using the learning material, learners are exposed in a virtual unsafe drug situation, and can feel the dangerousness seriously to learn the harms and risks of drug abuse while improving their abilities to refuse drugs. In order to evaluate the proposed material, it was compared with a video-based learning material which has the same contents with the VR-based learning material. Experiments were conducted and the results were analyzed from the aspects of effect on the users’ attitudes, users’ satisfaction, and anti-drug self-efficacy. As the result, it became clear that learners can learn effectively with the VR-based learning material more than with the video-based learning material.
WITHDRAWN: Two-dimensional predictive model: Data analysis using regression algorithm

The learning cycle dependent on which machines are prepared, or educated to machines, utilizing all around organized information. That is, machines are prepared with information with the right responses to the inquiry, and these are made to be repeated in the memory of such machines. In this way the machine takes in a couple of subtleties from the preparation information previously gave and afterward applies that information to the test information. One area is utilized to prepare the model and another to test or assess the model, haphazardly isolating the arrangement of information. Direct backslide is a technique for showing the association between a scalar ward variable y and in any event one intelligent factor (or free factor) implied X. The example of one enlightening variable is called fundamental straight backslide.
RANSAC Optimization for Category-level 6D Object Pose Estimation

The performance of category-level 6D object pose estimation is insufficient although the RANSAC algorithm has been adopted to remove outliers from the 3D-3D correspondence. In this paper, we optimize the Preemptive RANSAC algorithm to progressively find an inlier subset that has the smallest reprojection error. Experiments demonstrate that the optimized algorithm gets a more precise 6D pose in which hypotheses can be measured against each other and be revised to estimate a more precise pose. For the 10° &10cm metric on the REAL275 dataset, it achieves 43.8% and exceeds 64.0% of the NOCS method. On the Context-Aware MixEd ReAlity(CAMERA25) dataset it achieves 63.2% which exceeds 1.6% of NOCS.
Local Adjustment Block Net for Object Detection

Recently, object detectors based on Receptive Fields (RFs) in human visual systems have stronger feature extraction capabilities and have achieved great detection performance, such as Inception, ASPP and RFBNet. However, while having capabilities to extract more contextual information, these detectors also capture redundant information, which will reduce the precision of detection. In this paper, we propose a novel and lightweight block based on spatial attention mechanism to solve this problem effectively. Compared with RFB, it can better capture effective contextual information in the feature map and suppress redundant information. Moreover, we propose a local enhancement strategy, which can sparsely locate regions that contains rich feature information and enhance them locally. Experimental results show that our proposed method gains 0.6% mAP improvement on the PSACAL VOC dataset and 0.5% mAP improvement on the COC02019 test-dev set. Keywords: Local Adjustment Block; Receptive Field; Spatial attention mechanism.
Identification of Key Opinion Leaders in Pharmaceutics Using Network Analysis

The term Key Opinion Leader in marketing is not new. Key Opinion Leaders (KOLs) commonly known as thought leaders who play a crucial role in the life science industry. We through this project intend to implement the concept of identifying key opinion leaders using weighted Social Network Analysis (SNA). We intend to use European PubMed Central dataset for creating a weighted social Network of authors who have healthcare and medicine related publications and apply different centrality measures on it. In order to collect the data, we will be using one of the web scraping methods and predefined libraries like scrapy. After fetching and processing the data we intend to form a network of authors using python’s NetworkX library. This network will then be subjected to various centrality measures which in turn will give prominent opinion leaders as the output.
On Overfitting in Discrete Super-Resolution Recovery

This paper studies the overfitting in discrete super-resolution problem. In particular, we solve for the estimate that simply overfits the noisy measurements. By doing this, we no longer require the prior knowledge of additive noise to set the parameter of sparse reconstruction algorithm to ensure the feasibility of target signal. The analysis of overfitting is based on a new proof of the quotient property of deterministic Fourier measurement matrix as well as a novel insight of the widely used interpolation-based proof technique in super-resolution literature. Our theoretical result shows that a similar stability guarantee holds for the overfitting algorithm as those for nonoverfitting ones. The derived error bound is demonstrated by the numerical experiments.
Shape and Texture Aware Graph Processing

Neural network training is time-consuming and often application specific. While pre-trained convolutional neural networks can be fine-tuned for other applications, the lower levels are often affected by changes in rotation and scale, since the approach is purely appearance-based. However, hybrid approaches that take both shape and appearance into account have shown success in general computer vision, so in this work we propose a graph network architecture that captures shape and texture information. Our approach separates shape and texture within a graph neural network in a manner analogous to an active appearance model (AAM), offering potential robustness to shape and scale. To test the effectiveness of this approach, we perform a series of experiments in both unsupervised and supervised settings on the MNIST and CIFAR datasets. First, we perform experiments using unsupervised image data and node position reconstruction; we also perform a cross-database experiment. As part of this work, we introduce an extension to the spectral graph convolution approach GCN by automatically generating weights based on a local and global representation in an effort to make the convolutions more flexible and less application or data specific. We call this new approach Generative GCN (GenGCN). Second, we develop approaches to allow grouping and ungrouping of triangle nodes within the graph. Specifically, we propose a Similarity Matrix generation approach to correctly identify similar triangle nodes. We also introduce a variant of the DIFFPOOL approach that allows us to reverse the grouping process. We performed experiments on MNIST with respect to triangle node grouping and ungrouping. Finally, we perform classification tasks on MNIST and CIFAR, experimenting on a variety of architectural variations and parameters.
AN ANALYSIS OF STUDENTS' GRAMMATICAL ERRORS IN WRITING CONVERSATION TEXT

The objective of this study was to identifyand to categorize, grammatical error types inwritingEnglish at second grade of SMK-SMTI Banda Aceh. The researcherused a descriptive qualitative method. The subject of this research is limited to 10 students. The data was obtained from the students' writingsheet. So, the instrument of this researh is students' worksheet. Based on the finding of this research, there are 11 categories of grammatical errors that students made. The most grammatical error that students produced was in using verb which consist of 8 students with the total number of errors were 15 words.The second highest number of grammatical errors that students produced was error in using tenses that consist of 7 students with the total number of errors were 13 words. Furthermore, it followed by the errors in using adjectives and singular/plural which consist of 5 words, errors in using preposition consist of 3 words, errors in using to be consist of 2 words, while the least errors is in using article, modal, noun, objective pronoun, and phrasal verb that consist of 1 word. In conclusion, there are 48 total number of errors that students produced in their writing sheet.
SCR-Graph: Spatial-Causal Relationships Based Graph Reasoning Network for Human Action Prediction

Technologies to predict human actions are extremely important for applications such as human robot cooperation and autonomous driving. However, a majority of the existing algorithms focus on exploiting visual features of the videos and do not consider the mining of relationships, which include spatial relationships between human and scene elements as well as causal relationships in temporal action sequences. In fact, human beings are good at using spatial and causal relational reasoning mechanism to predict the actions of others. Inspired by this idea, we proposed a Spatial and Causal Relationship based Graph Reasoning Network (SCR-Graph), which can be used to predict human actions by modeling the action-scene relationship, and causal relationship between actions, in spatial and temporal dimensions respectively. Here, in spatial dimension, a hierarchical graph attention module is designed by iteratively aggregating the features of different kinds of scene elements in different level. In temporal dimension, we designed a knowledge graph based causal reasoning module and map the past actions to temporal causal features through Diffusion RNN. Finally, we integrated the causality features into the heterogeneous graph in the form of shadow node, and introduced a self-attention module to determine the time when the knowledge graph information should be activated. Extensive experimental results on the VIRAT datasets demonstrate the favorable performance of the proposed framework.
TEACHING MATERIAL DEVELOPMENT BASED CONSTRUCTIVISM APPROACH OF READING DESCRIPTIVE TEXT

This research method is used to produce a products i.e teaching material of reading desceriptive text based constructivist approach. Data collection techniques used were questionnaires, and learning outcomes tests. The data analysis technique was carried out by analyzing the questionnaire which was seen from the results of the high and low percentage of the needs desired by students and the next teacher. Development of teaching materials follows the procedure: 1) preliminary study or need assessment; 2) preparation of prototype teaching material design; 3) draft product preparation; 4) draft product trials on a limited scale; 5)draft product revision; 6) testing the revised product on a large scale; 7) writing final product. Development of teaching materials based constructivism by Needham’s 5 fase. The research instrument was in the form of a questionnaire addressed to the team of experts, users, and audience. The cloze test instrument is used to measure the readability of the reading text and the main description of teaching materials. The product trial is carried out in seventh grade of junior high school of Lingga and East Lingga district, Lingga regency, Riau Island province which is not exposed the Covid-19. The conclusion of this study found that the print instructional materials Curriculum 2013, which has been made, deserves to be used in learning activities.. This development resulted in a teaching material product which is believed to be able to contribute to the development of science in the field of education.