Reviewerly/exHarmony huggingface dataset:
Understanding Students' Identification and Use of Patterns While Writing SQL Queries

SQL (Structured Query Language) is a standard language to communicate with a relational database system. SQL is a core competency for people in the data management field and is, therefore, a foundational skill taught to users in information systems. This research analyzes data from the group of students with two different problem sets. The data set consisted of student activity, log data, captured by the database management system as students executed queries. The primary goal of analyzing data in this research is to observe the exact sequence of attempts while students formulate SQL queries, understand the patterns students use and how these patterns help them learn SQL for the purpose of finding better ways to teach these skills. This research will try to improve students? learning processes and how they can be taught more efficiently about the SQL language and its usage.
LARQ: Learning to Ask and Rewrite Questions for Community Question Answering

Taking advantage of the rapid growth of community platforms, such as Yahoo Answers, Quora, etc., Community Question Answering (CQA) systems are developed to retrieve semantically equivalent questions when users raise a new query. A typical CQA system mainly consists of two key components, a retrieval model and a ranking model, to search for similar questions and select the most related, respectively. In this paper, we propose LARQ, Learning to Ask and Rewrite Questions, which is a novel sentence-level data augmentation method. Different from common lexical-level data augmentation progresses, we take advantage of the Question Generation (QG) model to obtain more accurate, diverse, and semantically-rich query examples. Since the queries differ greatly in a low-resource code-start scenario, incorporating the QG model as an augmentation to the indexed collection significantly improves the response rate of CQA systems. We incorporate LARQ in an online CQA system and the Bank Question (BQ) Corpus to evaluate the enhancements for both the retrieval process and the ranking model. Extensive experimental results show that the LARQ enhanced model significantly outperforms single BERT and XGBoost models, as well as a widely-used QG model (NQG).
Security Issue Detection and Mitigation Patterns for Product Line Resource Variation

ABSTRACT Product Line Engineering (PLE) builds upon an Agile Architectural Pattern—with reusable resources, evolving resource variations, and a standardized interconnect and sustainment infrastructure. Commercial PLE systems attract alternative resource suppliers, such as automotive parts. Defense PLE systems typically result from acquirers encouraging Open System Architectures to enable alternative resource suppliers. Alternative resource suppliers are a major resource variation source in product line engineered systems. Product line engineered systems are systems of systems with potential for complex interactions and unintended emergent behaviors. This article focuses on PLE cyber‐physical‐social system products, the security issues resource variation can introduce, and security patterns for detecting and mitigating these security issues. Resource variations may cause security issues unintentionally, but intentional introduction is also possible by malicious alternative resource suppliers, supply chain interdiction, and insiders. This article assumes malicious intent in resource variation as its security issue base line, as patterns for effective detection and mitigation of malicious variation intent encompass unintentional occurrences.
Impact of Memory Control on Batch Learning in Human Activity Recognition Scenario in Comparison to Data Stream Learning

Human Activity Recognition (HAR) has gained attention in many studies in the ubiquitous computing area. Due to its high demand in recent years, various researchers use the sensors data stream to infer activities. In general, state-of-the-art literature uses batch machine learning approaches for constructing inference models. One disadvantage of batch learning is that many algorithms may present high memory consumption in the training phase. On the other hand, adaptive algorithms based on data stream learning can also be used. However, its disadvantage is that the model may need a high volume of data to converge. Thus, in this work, we compare the behavior of batch learning and online learning algorithm for constructing inference models in HAR scenarios. For this, we used in our methodology a memory control module for restricting memory consumption in batch learning. Our results show that models constructed using batch learning are much sensitive to memory consumption.
Extraction of Website Navigation Label Using A Multiple Web Crawler. A Case Study on 14 University Websites in Indonesia

Labeling system is designed for a website to find information easily. Labeling system in a website is required to represent the information contents. One of methods to design labeling system is to compare a website with its competitors' websites. The benefit of comparing labels is to get common labels therefore it will make users to easily find and use the labeling system. Labels are extracted using a web crawler. To make web crawler, it must consider the structure of targeted website. The problems arise when there are several different target websites that will be compared. That means, it is necessary to create some unique and different web crawler program codes, so it takes a long time. This research proposes and analyzes multiple web crawler. The step to make multiple web crawler is, first, start to collect targeted website based on 14 top Indonesia's University (based on Indonesia Higher Education's University Ranking). Next step is analyzing the pattern of structure navigation labels. Then the results are used to make a multiple web crawler. The result of this research is a multiple web crawler that can extract navigation label of several different target websites automatically without writing another program code of different web crawler for each crawled website.
On Design of Blockchain-based Transport Scheme

Block chain is a revolutionary new technology proposed in recent years. It has the characteristics of decentralization, non-tampering with transactions, and extremely low transaction costs. These characteristics coincide with the decentralization and market structure of distributed transportation system. In this paper, we give the concrete structure of a blockchain-based transportation scheme, including process of journalizing, registration, transaction process. In addition, the correctness and security analysis of the scheme are also given in this paper.
Big Data Platform for Faults Prediction Diagnosis of CBI

Safety and reliability have always been the most concerned research of computer based interlocking system(CBI). With the rapid development of computer technology, information network technology, new material technology and digital technology, CBI system itself produces massive and complex data, which has become an important resource for innovation and development of CBI system. How to use these massive data to help the system complete automatic or semi-automatic fault diagnosis, and then ensure the safe operation of the system, has become the focus for various interlocking manufacturers. This paper introduces Fail-Safe, several fault model analysis methods, and introduces a big data platform which can be used for fault prediction and diagnosis analysis methods.
Detecting Distracted Driving from Images by Processing Relative Locations of Objects of Interest Inside Vehicles

Distracted driving on roads is a problem that is common across the world now. With increasing use of smarter and connected devices, coupled with their miniature form factors, humans are now increasingly using these devices under mobility. When operating a vehicle, using smart devices can pose serious threats to road safety. Another contributing factor to distracted driving today stems from the urge to eat, drink and fall asleep while driving. In this paper, we use a popular and publicly available image dataset captured from embedded cameras inside cars that indicate instances of distracted driving or not. Different from existing works that look at the entire image to classify distracted driving, we first localize objects within the image that impact distracted driving. There are three broad categories we localize, namely, external entities (smartphones and bottles); entities within the car (steering wheel) and human-centered entities (left and right hand). Our approach to localize objects is based on Regional-Convolutional Neural Networks (R-CNNs). Once we localize these objects, we then design simpler machine learning techniques to process the relative locations of these objects within the image to detect instances of distracted driving. Our resulting performance evaluations demonstrate the validity of our approach. To the best of our knowledge, our work in this paper is unique, and we believe, provides more contextual relevance towards detecting instances of distracting driving, and could possibly yield newer approaches to educate drivers on safe driving.
Trajectory Tracking of Manipulator Based on Iterative Learning Control

With the development of technology of automated production and robot, robots, which are at high levels of automation, are widely used in all aspects of production. The application of manipulator in production enhances the production efficiency and product quality, and slash the production cost. The research and design of manipulator motional control system, with low cost and high performance, has very important research value and significance. The iterative learning control is particularly effective for the repetitive round-trip motion control system, which is looking forward to meet the requirements of rapid response and the repeatability of the manipulator's work and then the manipulator reaches the predetermined space position and completes the given work content. This paper takes the double-joint manipulator as the research object, analyzes its kinematics by Lagrange dynamics equation, and establishes the kinematics model of the manipulator. Besides, the iterative learning control was applied to the tracking of the manipulator, and its convergence was tested. Three learning methods of d-type iterative learning control, improved d-type iterative learning control and pd-type iterative learning control are compared. And during this experiment the Simulink model was established. The final results of simulation manifest that the improved pd-type iterative learning control has better convergence and control accuracy than the previous two methods.
Methodology for evaluating the focus of educational programs in IT sphere

Abstract The growing IT market in Russia challenges the education system, requiring it to train highly qualified personnel who are ready to work in the IT industry after graduation. The article contains a research of educational programs in the IT areas of bachelor’s, master’s and specialist degree, purpose of identifying the focus on the development of professional competencies of an IT specialist. A methodology has been developed for evaluating the focus of educational programs in IT sphere to extend the professional skills of an IT specialist. It includes the research of the share of academic hours devoted to the development of professional competencies, which gives an understanding of the level of graduate’s belonging to the IT industry. Results of approbation of method are presented on a basis of educational programs of universities of the Perm region. It’s region where the growth of the IT sector market run ahead of the performance for the Russian Federation. The implementation of the methodology will allow comparing the educational programs of different universities, simplifying the assessment and selection process for job applicants and employers. In addition, the methodology can be used by university methodologists to modernization and maintain the relevance of curricula.
High-resolution video inpainting based on spatial structure and temporal edge information

Video inpainting is a very challenging task. Directly using the image inpainting method to repair the damaged video leads to the inter-frame contents flicker due to temporal discontinuities. In this paper, we introduce spatial structure and temporal edge information guided video inpainting model to repair the missing regions in high-resolution video. The model uses a convolutional neural network with residual blocks to fix up the missing contents in intra-frame according to spatial structure. At the same time, temporal edge of reference frame is introduced in the temporal domain, which has a large guiding effect on improving the texture and reducing the inter-frame flicker. We train the model with regular and irregular masks on the YouTube high resolution video datasets, and the trained model is qualitatively and quantitatively evaluated on the test set, and the results show our method is superior to the previous methods.
PEMBUATAN MEDIA PROMOSI E-CATALOGUE MELALUI INSTAGRAM DENGAN MENGGUNAKAN CORELDRAW UNTUK MENINGKATKAN PENJUALAN DI SUKUN JAYA MALANG

The aim of this study is to create desain e-catalogue to upload this Sukun Jaya Malang. This study was an Action Research, The data collection method deployed interviews, documentation, observation and questionnaire The effectiveness of the design development was measured by the EPIC. The questionnaires were distributed to 15 respondents which were 3 computer experts, 3 marketing experts, 1 owner, 3 employees, and 5 prospective customers. the study showed that the created e-catalog was very effective. The average value of persuasion dimension was obtained 4.6; the average of impact dimension was 4.49 and communication dimension was 4.47. The average EPIC rate is 4.5. From the results of this research, It can be concluded that  e-catalog can be used as a promotional medium for the company. recommended that Sukun Jaya Malang frequently updates information about its new products.
Bot-Human Twitter Messages Classification

Bots identification has gained relevance within social networks due to its ability to influence the opinion of users on political, consumer and ideological issues. This is why research related to bot identification has grown in recent years. Various models have been proposed for the identification of bots, but this is an issue that has not been resolved yet. In this article, a model is proposed that, through the use of specific preprocessing and a four-layer neural network, improves the bot-human classification accuracy of Twitter messages, reaching a precision of 0.9462, which represents an advance with respect to what is presented in the state of the art with the same corpus.
No-reference Image Quality Assessment Based on Ensemble Convolutional Neural Network

We propose a no-reference image quality assessment based on ensemble convolutional neural network. Firstly, the distorted image is cut into image patches, and the image patches are pre-processed by performing local contrast normalization. Then use convolutional neural networks to extract features of image patches, which eliminates the trouble of manually extracting features, four convolutional neural network models with different structures are designed. Finally ensemble the four network models to improve accuracy of image quality assessment. Experimental results on LIVE and CSIQ database show that the proposed method can predict the image quality well and has good generalization performance.
Efficient Lifelong Relation Extraction with Dynamic Regularization

Relation extraction has received increasing attention due to its important role in natural language processing applications. However, most existing methods are designed for a fixed set of relations. They are unable to handle the lifelong learning scenario, i.e. adapting a well-trained model to newly added relations without catastrophically forgetting the previously learned knowledge. In this work, we present a memory-efficient dynamic regularization method to address this issue. Specifically, two types of powerful consolidation regularizers are applied to preserve the learned knowledge and ensure the robustness of the model, and the regularization strength is adaptively adjusted with respect to the dynamics of the training losses. Experiment results on multiple benchmarks show that our proposed method significantly outperforms prior state-of-the-art approaches.
FFT wavefront reconstruction algorithm with periodical extension for lateral shearing interferometry

A new FFT wavefront reconstruction algorithm is proposed in this work, which can retrieve the test wavefront without spectrum leakage error. Since only part of the test wavefront interferes with its copy wavefront in the lateral shearing interferometer, the sizes of the sheared wavefronts are usually smaller than the test wavefront. To solve the issues introduced by the incompatible sizes of the sheared wavefronts, we used the measured information in the sheared wavefronts to compensate the missing parts based on the periodicity of the Fast Fourier Transform. This procedure can be applied under an arbitrary shear amount. Since no estimation is required during the extension, the accuracy of the reconstructed wavefront can be improved. We also provide an algorithm to estimate the lost information due to the illposed problem of reconstructing wavefront from the sheared interferometric data base on the proposed algorithm. The detail of the FFT processing and the sheared wavefront extension algorithm is given in this work. Some test is performed to evaluate the performance of the proposed algorithm. The test result shows that this algorithm is capable of reconstructing the test wavefront without introduces extra error. The estimation of the lost information based on our algorithm is convenient to be applied in the frequency. When the shear ratio is less than 25%, the estimation can always give a better result. The accuracy of the reconstructed wavefront is improved by up to 14.3% after estimation.
Research on Mongolian-Chinese Machine Translation Based on Dual-Learning

The Deep Learning method sits on the field of machine translation by virtue of its ability to understand semantics, especially in the field of large languages. However, for low-resource languages, a difficult problem is the lack of large-scale bilingual corpus which leads to over-fitting of the model. In this paper, for languages with few data resources, Round-Trip Translation(RTT) is combined to expand the scale of pseudo-parallel corpus, while dual learning method is used to semi-supervised the model in two directions: source-target language and target-source language, then the updating of model parameters is guided by reward feedback. In addition, in order to reduce the "noise" effect of pseudo-corpus, an iterative attenuation method is proposed to refine the training data. Then, the CWMT2018 Mongolian-Chinese translation task is used to test the model. The results show that the BLEU value of the model is 2.1 higher than that of the traditional method, and the validity of the method is fully proved.
Implementation of the Framework to Heritage Education Supported in Augmented Reality

Heritage education is a process that traditionally has been implemented in a similar way to how schools work, a teacher transmits certain knowledge to a student. Nevertheless, lately, there have been new experiences that integrate Augmented Reality into the processes of heritage education. This paper describes an experience of an Augmented Reality application called “Social Heritage” that supports heritage education processes, and is based on a framework called “Framework to Heritage Education supported in Augmented Reality”. This work presents the results of the application of the framework in the city of Cartagena - Colombia, where positive results were obtained with citizens and visitors. These Results are related to positive experiences and motivation when users use Augmented Reality technologies to develop heritage education processes.
Panoramic video assessment based on cascaded network using saliency map

Virtual reality (VR) refers to a technology that allows people to experience the virtual world in an artificial environment. As one of the most important forms of VR media content, panoramic video can provide viewers with 360-degree free viewing angles. However, the acquisition, stitching, transmission and playback of panoramic video may damage the video quality and seriously affect the viewer's quality of experience. Therefore, how to improve the display quality and provide users with a better visual experience has become a hot topic in this field. When watching the videos, people pay attention to the salient areas, especially for the panoramic videos that people can choose the regions of interest freely. Considering this characteristic, the saliency information needs to be utilized when performing quality assessment. In this paper, we use two cascaded networks to calculate the quality score of panoramic video without reference video. First, the saliency prediction network is used to compute the saliency map of the image, and the patches with higher saliency are selected through the saliency map. In this way, we can exclude the areas in the panoramic image that have no positive effect on the quality assessment task. Then, we input the selected small salient patches into the quality assessment network for prediction, and obtain the final image quality score. Experimental results show that the proposed method can achieve more accurate quality scores for the panoramic videos compared with the state-of-the-art works due to its special network structure.
Design of ISAR Image Annotation System Based on Deep Learning

Imaging radar can obtain excellent images of non-cooperative moving targets, which is an essential means of target detection. According to the scattering point and time-frequency characteristics of the ISAR image, it is possible to determine the physical properties such as the target’s component structure. Combining with other radar electromagnetic scattering characteristics, it is the primary technical approach to carry out the full recognition of the target. Manually captioning of the target components and the main structure is a traditional and straightforward method that lacks a quantitative standard, requires heavy workload, and the captioning result may be affected by the subjective judgment. With the development and application of deep learning technology in recent years, remarkable achievements have been made in the fields of image classification, image detection, and machine translation, and the performance of automatic image captioning has been greatly improved. Through intelligent segmentation of ISAR images and feature extraction of segmented regions, we build a database of image features and generate the corresponding text description according to the characteristics of different parts of the image applying the text generation network. Based on the above theory, this paper brings out an ISAR image annotation system design scheme based on deep learning.