Reviewerly/exHarmony huggingface dataset:
Prior language knowledge and intercomprehension at the first encounter of Italian as an additional language. A translation task

The aim of the present study is to examine and describe Swedish upper secondary students’ use of their background languages while translating a text from Italian, a language unknown to them, into either their L2 English or their L3 French or Spanish. The assumption here is that searching for similarities between these languages is a natural feature of language learning and that intercomprehension can lead to at least limited understanding of an unknown language. The written translations were analysed quantitatively by calculating translation accuracy in the different languages and qualitatively by means of a retrospective questionnaire on the translation process. A psychotypology questionnaire was also included to examine the participants’ perceptions of the similarities between the languages involved. The majority of the participants stated that they perceived Spanish to be more similar to Italian than any of the other languages involved in the study. Moreover, the results show that the students in the group that translated into Spanish translated the text more accurately than those who translated into French and English. The comments in the retrospective questionnaire show that the students reflected on similarities between the languages on a lexical level, but also on structural and phonological similarities. Contextual cues were also important for the participants’ inferences and translations. (Less)
CNN Texture Synthesis for High-Resolution Image Inpainting

In most modern manufacturing processes surface structure is stored separate from the basic geometry of the surface. A suitable counterpart from computer graphics are displacement maps which store a displacement on actual surface points to enrich models with detail. While displace-ment maps can have arbitrary depth resolution they are often represented as 8-bit grayscale images.
Origin and evolution of the Vindhyan Basin: a geochemical perspective

The Indian Peninsula hosts several Proterozoic sedimentary basins, popularly known as the Purana basins.The Vindhyan Basin of central India, comprising ~5 km thick sediment pile, is the largest among the Purana basins.The sedimentary units comprise mostly of undeformed and unmetamorphosed siliciclastic and carbonate rocks.With an initiation age of late Paleoproterozoic, the sedimentation in the Vindhyan Basin continued up to late Mesoproterozoic to early Neoproterozoic.The undeformed and unmetamorphosed nature of the sediments with a prolonged extent of sedimentation makes the Vindhyan Basin an excellent archive for geochemical and isotopic studies.The present study provides an overview of the existing geochemical, geochronological and isotopic data for the sedimentary and volcanic rocks exposed in the Vindhyan Basin and their implications in terms of basin evolution as well as global events.
A New Variation of Singular Value Decomposition

This paper presents a new variation of Singular Value Decomposition (SVD) computation for image compression. It modifies the SVD operation with two additional pre-processing steps, i.e. Pixel Interleaving and Linearization process. An input image is firstly scrambled by Pixel Interleaving operation. This interleaved image is further linearized before applying SVD calculation. As documented in the Experimental Results Section, this simple approach yields a promising result in the image compression task compared to that of the classical SVD-based technique.
Adaptive Vehicle Detection and Classification Scheme for Urban Traffic Scenes Using Convolutional Neural Network

A large number of digital cameras have been installed at intersections in urban areas to help monitor traffic conditions. Making better use of scenes captured by these traffic surveillance cameras facilitates the construction of advanced Intelligent Transportation Systems (ITS). This research aims at developing an adaptive vehicle detection and classification scheme for urban traffic scenes, which collects roadside surveillance videos from publicly available sources. The proposed scheme consists of two main phases; the first phase is to collect some traffic surveillance images for training a general model using Faster R-CNN. The second phase utilizes background subtraction to extract vehicle proposals. A sufficient number of vehicles are collected by comparing proposals with the detection results by the general model. Collected vehicles are superimposed on the constructed background in an appropriate order to achieve semiautomatic generation of annotated training data. The training data are used to acquire a second-phase adaptive model. The experimental results show that the proposed scheme performs well and can handle vehicle occlusion problems.
Intra-consortia data sharing platforms for interdisciplinary collaborative research projects

Abstract As the importance of data in today’s research increases, the effective management of research data is of central interest for reproducibility. Research is often conducted in large interdisciplinary consortia that collaboratively collect and analyse such data. This raises the need of intra-consortia data sharing. In this article, we propose the use of data management platforms to facilitate this exchange among research partners. Based on the experiences of a large research project, we customized the CKAN software to satisfy these needs for intra-consortia data sharing.
Sistem Perbaikan Kata Tidak Baku Bahasa Indonesia Menggunakan Metode Crowdsourcing

Most languages have two form of words: standard and not-standard. Standard words usually used in formal conversation, while not-standard words used in informal conversation. The massive use of non-standard words in the text-based communication through social media application causing a new problem for its text-processing features such as language translator or artificial intelligence (AI) conversation program (chatbots). Our research purpose is to create a system that can convert any non-standard Indonesian word into standard Indonesian word using crowdsourcing method, a community-based method to create database that contain non-standard Indonesian words and its associated standard words. We hope this system can help people who working on text-mining based application, especially in Indonesian words, to improve their performance in text-processing.
Performance Effects of Running Container-Based Open-MPI Cluster in Public Cloud

The vast majority of HPC users are heavily leveraging MPI middleware for their applications needs. For almost two decades, providing an infrastructure that constantly answers the increasing demand, security standards, software interoperability, and the ineffective resources utilization are the challenges placing HPC administrators between the overhead of the virtualization and the manual tuning of the performance pick, forcing them to find the edge of the consensus on their own. Recently, developments like Linux Containers, Infrastructure as Code, Public Cloud opened a new horizon in front of the industry, by redefining the engineering roles and providing tools and practices for solving some major caveats from the past. This paper presents three architectures for setting up Open-MPI cluster in a Linux Container-Based environment and explains how these architectures can be implemented across private and public cloud with the main focus of the performance effects in the public cloud.
Service-oriented Situational Method Engineering (SOSME) Model and Architecture

This paper proposes a model and architecture of service-oriented situational method engineering. The model and the architecture are designed as part of service-oriented situational method engineering (SME) framework, which is proposed as a solution to increase SME application on a software project. By applying SME, a project-specific or situational software development method can be composed from several method chunks. Currently, method engineers maintain their own method chunks in a method base built for a specific CAME tool. It would be better if the concept of service-oriented is applied to provide sharing mechanism of method chunks between method engineers. The SOSME model consists of three participants adopted from Service-oriented Architecture (SOA) model: method chunk provider (MCP), method chunk registry (MCR), and method chunk client (MCC). MCPs publish their method chunks to the MCR. Method engineers can build a new (situational) method by using a CAME tool as the MCC which retrieves a list of suitable method chunks from the MCR. Once the method chunks are found, the MCC can retrieve method chunks description from the MCP. The proposed model also includes method chunk representation and searching mechanism from the MCR, method chunk representation and retrieving mechanism from the MCP, as well as method construction and method enactment mechanism at the MCC. The proposed architecture is designed based on the proposed model. The proposed architecture consists of three components: Method Base Management System (MBMS) as part of the MCP, UDDI-like MCR, and Computer Aided Method Engineering (CAME) as a Service (CMaaS) and Computer Aided Software Engineering (CASE) as a Service (CSaaS) as parts of the MCC. Each component is designed based on an existing CAME and CASE architecture, which is modified to apply SOA and Software as a Service (SaaS) concepts.
Center extraction for non-uniform line structured light stripe with wide view field

When line structured light is used in 3D measuring with wide view field, the extraction of light stripe center becomes difficult because of the complex image background and non-uniform illumination. A line structured light center extraction method based on peak intensity is proposed in the paper. The peak intensity value of every pixel is calculated and the image is segmented according to the threshold related to the peak intensity, thus the structured light stripe center is extracted. Line structured light stripe images with wide view field have been processed by the proposed method. The experimental results indicate that our proposed method extracts the line structured light stripe center accurately when the background is complex, the illumination is non-uniform and the view field is wide.
XOR-ed Based Friendly-Progressive Secret Sharing

This paper presents a simple approach for secret sharing under the bitwise eXclusive-OR (XOR) framework. The proposed method extends the effectiveness of Progressive Secret Sharing (PSS) into the Friendly Secret Sharing (FSS). The PSS produces a set of shared images in noisy-like form, while FSS generates shared images into more-visually-friendly appearance. The proposed method offers lossless ability in the recovery result of secret image. At the same time, it shows superiority compared to the former existing scheme in the secret sharing task.
A Knowledge Discovery Method for Characteristics Extraction of Power Consumption Based on Cluster Analysis

Remote cost control is a new type of cost control that comes along with the construction of integrated service platform in power supply enterprises. It manages cost control information in a centralized way. However, the application of cost control strategy in remote cost control mode faces weak user-targeting of cost control strategy. To solve the problem mentioned above, we propose a knowledge discovery method for characteristics extraction of power consumption based on cluster analysis. The data of users' historical power consumption is modeled as matrix object set. K-means algorithm and matrix theory are adopted to obtain the similarity of two matrix by calculating the Euclidean norm of their differences. Moreover, the fine granularity classification of the types of users' power consumption characteristic is carried out. Finally, the effectiveness of the method is verified by using four individual-to-whole approaches.
Aggregation of LARK Vectors for Facial Image Classification

Face recognition is prevailing to be a key aspect wherever there is a need for interaction between humans and machines. This can be achieved by containing a set of sketches for all the possible individuals and then cross-validating at necessary circumstances. We propose a mechanism to fulfil this task which is centred on locally adaptive regression kernels. A comparative study has been presented at encoding stages as well as at the classification stages of the pipeline. The results are cautiously examined and analyzed to deduce the best mechanism out of the proposed methodologies. All the ideologies have been tested for multiple iterations on benchmark datasets like ORL, grimace and faces 95. The vectorized descriptors have been subjected to encoding using slightly refined methods of feature aggregation and clustering to assist classifiers in imputing the test subjects to their respective classes. The encoded vectors are classified using Gaussian Naive Bayes, Stochastic Gradient Descent classifier, linear discriminant analysis and K Nearest Neighbour to accomplish face recognition. An inference on sparse nature of locally adaptive regression kernels was made from the experimentation. A rigorous study regarding the discrepancies of the performance of LARK descriptors is reported.
Analysis on Factors Affecting Performance of Industry-Academia Cooperation Based Workforce Training and Development

본 연구는 중소기업 산학협력 인력양성사업의 성과에 영향을 미치는 요인을 정리하고, 개선이 요구되는 요인의 우선순위를 파악하여 시사점을 도출하는 것을 목적으로 한다. 선행연구를 기반으로 학생 개인적, 조직 내부적, 조직간 상호작용, 환경요인 등 4개 분야에서 23개의 세부 성과 영향요인을 도출하였다. 사업 관련 주체인 중소기업, 특성화고, 대학의 관계자들을 대상으로 성과 영향요인의 중요도와 현재수준에 대해 설문하고, 이를 바탕으로 중요도-수행도분석(IPA)을 수행하였다. 본 연구에서는 기존 IPA 방법의 한계를 보완하기 위해 중요도(I)와 현재수준(P) 외에 둘 사이의 격차(I-P)도 우선순위 산정에 반영하는 개선된 방법을 적용하였다. 분석결과, 중소기업과 특성화고, 그리고 대학 세 그룹에서 공통적으로 나타나는 특징이 존재했으며, 그룹 간 차이도 발견할 수 있었다. 첫째, 학생과 직·간접적으로 관련 있는 성과 영향요인에 대한 개선 요구가 두드러지게 나타났다. 학생들의 역량, 흥미, 적성 등 개인적 특성들과 함께 학교와 기업 간 지리적 근접성, 기업의 작업환경 등의 우선순위가 높게 나타났다. 둘째, 사업의 외형적 발전과 양적 확대 못지않게 질적인 개선이 필요하다는 사실을 확인할 수 있었다. 본 연구의 대상이 산학협력에 기반한 인력양성사업이라는 점을 감안할 때, 협력 대상과의 커뮤니케이션 및 중소기업으로의 취업연계를 강화할 필요가 있다. 향후 연구에서는 개선이 필요하다고 도출된 요인들에 대한 세부 추진과제 제시와 본 연구에서 사용한 IPA 방법에 대한 추가적인 보완을 기대한다.The purpose of this study is to classify the factors that affect the performance of industry-academia cooperation based workforce training and development, and to draw implications by identifying the priorities of them that require improvements. Based on extant literature, 23 factors that is categorized as 4 groups(student, organizational, inter-organizational, environmental) were derived. The respondents from 300 SMEs, 155 specialized vocational high schools and 51 universities were asked about the importance and current level of each factor. Based on this, importance-performance analysis(IPA) was performed. In particular, this study uses the additional information of the difference between importance and current level(called gap) to overcome the limitations of existing studies. As a results, the demand for improvement of factors that are directly or indirectly related to the students was prominent. In addition to personal characteristics such as students’ competencies, interests and aptitudes, geographic proximity between schools and firms as well as working environment show high priorities for improvements. It was also confirmed that solving the inherent problems take precedence over quantitative expansion and external development of the programs. Given that it is based on industry-academia cooperation, it is necessary to strengthen communication with the partner and strengthen employment connections. Further studies should provide a specific plans to improve factors and supplement the extended IPA method.
Desain Basis Data Non Relasional Nosql Mongodb pada Website Sistem Informasi Akademik

Non relational data bases differ from the relational database model that has been used so far. The NoSQL database is very useful in continuously developing data, where the data is so complex that a relational database can no longer accommodate.The Academic System aims to support the implementation of education, so that universities can provide better and more effective information services to their environment, both inside and outside the university through the internet. Various needs in the field of education as well as regulations that surround it are so high that the management of academic data is a work that is very time-consuming, energy and mind.MongoDB does not have tables, columns, and rows. In MongoDB there are only collections and documents. Documents contained in mongoDB can have different attributes than other documents even though they are in one collection.
Automatic Text Summarization Using Fuzzy Extraction

Text summarization is an approach to summarize the original document with better efficiency than human. Mining prominent words from the original document and connecting those to make a semantically correct sentence in the best way are, the ultimate goal. This paper focuses on making the summary more reliable and understandable with the techniques of fuzzy logic. The extractive method of text summarization and the fuzzy logic method along with algorithms have enhanced the power of unsupervised learning which is proved by comparison with the algorithms working alone.
The Implementation of Objects Detection and Analysis Using Deep Learning with GPU

In the application of Artificial Intelligence (AI), image recognition is an important part. It is a combination of artificial intelligence and Machine Learning. Many jobs that require humans to spend time on processing can be more productive with the help of computers. For example, object detection can be applied to the police image system; the number of people on each occasion can be accurately calculated. Also, it can handle related cases in traffic control. In this paper, we propose the implementation of object detection and analysis using deep learning and GPU. First, we design the system architecture of object detection using Azure and GPU. Second, we deploy a system for identifying the presence of objects and identification the rectangular boundary surrounding each object.
Progressive Web Apps are a Game-Changer! Use Active Learning to Engage Students and Convert Any Website into a Mobile-Installable, Offline-Capable, Interactive App

Learn how to design and create powerful new progressive web apps (PWA). This workshop introduces the computer science principles that enable modern web sites to be converted into installable apps. This course starts with an example web site (built with HTML/CSS/JavaScript) and shows in just two easy steps how to create an engaging app that can be installed on mobile devices, including Android phones, iPhones, iPads, tablets, and more. The course illustrates the principles by converting an open source, cloud-hosted, client-side app. It introduces manifest files and JSON, then introduces the powerful new JavaScript service workers. The course employs Google's free Workbox libraries and shows how service workers enable web apps to interact with device APIs and alter requests before and after sending. You can easily install (and uninstall) the apps on mobile devices - and even use an app while your device is offline. The course illustrates how engaging tools like Lighthouse provide critical feedback on performance, accessibility, best practices, and more. As a bonus, a PWA syllabus is provided that can be customized to your courses. Skills for developing these apps are in demand and quite accessible to students. Laptop Required. Presenters are most familiar with Windows, but another OS should be fine. Internet access and the ability to create and edit files is required. Chrome browser is required to run the Lighthouse audits. All software and libraries are free and open source.
Domain Mixture: An Overlooked Scenario in Domain Adaptation

An image based object classification system trained on one domain usually shows decreased performance for other domains if data distributions differ significantly. There exist various domain adaptation approaches that improve generalization between domains. However, those approaches consider during transfer only the restricted setting where supervised samples of all competing classes are available from the source domain. We investigate here the more open and so far overlooked scenario, where during training only a subset of all competing classes is shown in one domain and another subset in another domain. We show the unexpected tendency of a deep learning classifier to use the domain origin as a prominent feature, which is resulting in a poor performance when testing on samples of unseen domain-class combinations. With an existing domain adaptation method this issue can be overcome, while additional unsupervised data of all unseen domain-class combinations is not essential. First results of this overlooked scenario are extensively discussed on a modified MNIST benchmark.
Chaotic Maps for Image Encryption: An Assessment Study

Digital images, which are transmitted over different networks nowadays, need a high level of security. Protecting these images is a big challenge. Image encryption is one of the most important methods in securing digital images. In recent years, chaotic maps have proved the efficiency in image encryption. This efficiency is due to excellent properties such as unpredictability and high sensitivity to their initial condition and control parameters. In this chapter, a performance analysis of the chaotic maps-based methods for image encryption is presented. In this comparative study, chaotic maps are used in the permutation and diffusion operations to encrypt the plain image. Moreover, evaluation criteria are selected carefully to evaluate the performance of the image encryption methods in terms of NPCR, UACI, PSNR, correlation coefficient, Local Shannon entropy and computational time. Experiments are performed where the obtained results are used to analyze the performance of the chaotic maps-based image encryption methods.
Reading Environment and Reading Interests of Public Sector Secondary School Students

This study explored the reading environment, reading interests and available reading facilities for Public Sector students in Secondary Schools. A Structured questionnaire and a reading interest inventory were used as survey instruments on 9th and 10th class (N= 3600) students in Southern Punjab, Pakistan. The findings of the study highlighted that parents, friends, teachers, and examinations influence the reading interests of students positively. Parents and teachers both can encourage nourishing the minds of adolescence towards reading. Significantly a greater number of students viewed their teachers and examinations to be playing a motivational role for reading. The majority of the students asserted that magazines and newspapers were available at their schools and homes as the reading facility. Furthermore, the findings of the study revealed that the majority of the students appeared to be interested in reading magazines