Reviewerly/exHarmony huggingface dataset:
Research and Design of Digital Library Based on Virtual Reality

With the development of information technology, virtual reality been widely used in military, commercial, education and entertainment industries. By applying virtual reality technology to traditional library, this paper established a virtual digital library system. The system takes the library of NJUPT as the designing and modeling object, uses Unity3D as the virtual reality engine to develop the virtual library system, realizes the virtual library's scene construction, the virtual roaming, the scene interaction and the book loaning inquiry and other functions. This paper presents a method for selecting books based on the method of "Ray", and designed a Web Service interface to realize communication between virtual reality environment and library management system. The users can get immersive experiences in the virtual library, and also can get the location and the situation of the books in the virtual library by VR glasses, PCs and Smart Mobile Phones.
Multiple information fusion and watermarking based on DWT and Yang-Gu mixture amplitude-phase retrieval algorithm in Fresnel transform domain

This paper proposes a new multiple-information fusion and watermarking, which utilizes DWT, Yang-Gu mixture amplitude-phase retrieval algorithm (YGAPRA) and Fresnel transform. In this method, LL sub-bands of four images are merged to construct an input color image. The R, G, B channels of an input image are encrypted by using YGAPRA in Fresnel transform domain in which coupled logistic maps provide remarkably sensitive decryption keys. The gray-scale cover image is decomposed by using DWT. Now HL, LH, and HH sub-bands of cover image are substituted for encrypted R, G, B channels and performed inverse DWT to produce a watermarked image. Simulations and security analyses validate the efficiency and practicability of the scheme.
Geometry Models for the Calculation of Land Usage of PV Systems

It is very important to be able to calculate the optimum land usage of PV systems. If PV system is poorly designed, it would result in either a waste of land due to too large an area, or the loss of power generation because of shading between arrays. Optimum land usage of PV plants can make efficient use of land and maximize the power generation of PV by avoiding shading, so that the PV project can get maximize benefits. PV array has various types, fixed arrays, single axis tracking and double axis tracking. The geometry method of calculating the land usage of PV systems is provided in this paper. The contents of this paper has been published in IEC TR 63149-2018 [1].
SECURE ONLINE VOTING SYSTEM USING VISUAL CRYPTOGRAPHY: A COMPARATIVE ANALYSIS

13Jan 2020 SECURE ONLINE VOTING SYSTEM USING VISUAL CRYPTOGRAPHY: A COMPARATIVE ANALYSIS Meher Gayatri Devi Tiwari and Kakelli Anil Kumar SCOPE, Vellore Institute of Technology, Vellore, TN, India - 632014 Associate Professor, SCOPE, Vellore Institute of Technology, Vellore, TN, India - 632014
Challenges and Perspectives in Creating a Digital Epigraphy Collection Using TEI XML and Front-end Services

The article presents the main tools and methods applied in the creation of the Telamon database of the ancient Greek inscriptions from Bulgaria encoded in TEI XML. The work so far on the project is reported, the modifications to the existing services are enumerated and some future perspectives are discussed.
PP-ind: A Repository of Industrial Pair Programming Session Recordings.

PP-ind is a repository of audio-video-recordings of industrial pair programming sessions. Since 2007, our research group has collected data in 13 companies. A total of 57 developers worked together (mostly in groups of two, but also three or four) in 67 sessions with a mean length of 1:35 hours. In this report, we describe how we collected the data and provide summaries and characterizations of the sessions.
The digitalization of the incunabula of Italy: the Central National Library of Rome from IGI to the Polonski project

Since 1932 the Central National Library of Rome has developed a specific vocation for the census and study of incunabula in a national perspective. In fact, it is the owner of the direction of IGI - General Index of the Incunabula of Italy which, published from 1943 to 1981, constituted one of the most innovative and complete European projects for the re-cataloging of the bibliographic heritage of the century of the origins of the press. The Library was followed by the Italian contact person for the ISTC - Incunaula Short-Title catalog and Incipit projects of the British Library. Recently, in collaboration with the CERL and thanks to the contribution of the Polonski Foundation in New York, it  has launched a new project for the digitalization of incunabula starting from the conspicuous collection of the S. Scholastica di Subiaco library, the cradle of the Italian press, through methods and procedures. innovative products that allow a full-scale research on the specimens treated, through a specific portal and participation in other related projects, such as MEI, Text-Inc and 15cIllustration.
The determinants and efficiency of auditing in the Republic of Kosovo

The aim of this paper is to investigate the auditors' efficiency and its determinants in public and the private sector in the Republic of Kosovo. An ordered probit model and its marginal effect are applied in order to measure auditors' efficiency in the public and private sector. The results show that employees with high level of education, experienced ones, and those in good relationship with the personnel and clients increase the performance of auditing. Moreover, the results show that training/courses have positive impact on auditor's efficiency. The paper provides empirical evidence about the audit's efficiency and the results are at most recommended to the government and policymakers in transition economies as a course of action to take institutional improvement and increase the audit's efficiency both in public and private sector. These will in turn lead to lower corruption in these countries.
USING LITERATURE-BASED APPROACH WITH TALES WITH GIGI APPLICATION TO IMPROVE LITERACY ACHIEVEMENT OF THE ELEVENTH GRADERS OF SMA NEGERI 1 TANJUNG BATU

Literature-based approach is also named as a whole language. A whole language is an approach to learn that sees language as a whole entity, and writing, speaking, reading, and listening should be integrated when learned. This study focused on reading and writing as literacy. The aims of this study were to find out whether or not (1) there was a significant difference in literacy achievement before and after the treatment by using a literature-based approach with Tales with Gigi , (2) there was a significant difference in students’ each sub-skill literacy achievement before and after the treatment, (3) there was a significant contribution of each sub-skill of reading comprehension and writing narrative paragraph. This study applied time series design and involved 30 eleventh graders of SMA Negeri 1 Tanjung Batu as the sample. The researchers used test in collecting the data. The finding of the study revealed that 1). The mean differences between pretest and posttest were 22.5 for reading and 20.4 for writing at the significance level of p 2.023) for reading and (18.333>2.023) for writing, it meant that H01 was rejected and there was a significant improvement in literacy achievement after they were taught by using literature-based approach, 2) the mean difference of each sub-skill of literacy achievement between pretest and posttest showed that each sub-skill was improved, and 3) the highest contribution of literacy sub-skills was given by detail (62.8%) in reading and then it was followed by the organization (41.7%) in writing. In short, this study showed a significant improvement and contribution to students’ literacy achievement by using a literature-based approach with Tales with Gigi .
Enabling Test Case Prioritization For Component Based Software Development

Frequent evolution of modern software systems increases complexity and system failure. Therefore, to overcome these issues the component-based system was developed that consists of integrated reuse components that work together to perform specific tasks or new application development. Mostly component based systems use configuration capabilities to adopt the changes and external uncertainties. Quality of these systems can be assessed through customer satisfaction after modification to verify the performance during maintenance activities. Software engineers for high-reliability, overall requirements functionality needs to verify before release of new product. One of the common ways to evaluate system quality in a sequence of releases is regression testing. Software quality engineers use to ensure that no faults introduced after changes. The purpose of this research work to identify the limitations in existing studies that create complexity and increase efforts during testing component based software and eliminate those factors through the proposed approach. The mixed research methodology will be followed in this context comprising qualitative and quantitative methods. To investigate the potential benefits of the proposed approach; will be performed a case study and experiment. Preliminary results indicated that the proposed approach significantly improve faults detection rate after changes alongwith less effort and cost in component based system development.
GAN Network Solves The Problem of Image Segmentation Across Data

Since FCN [1] added deep neural network to image segmentation technology, the image segmentation effect has been significantly improved in recent years. For example, the VOC2012 data set has been improved from 40% to more than 80%, and the effect has doubled. . However, the improvement of the effect is only for the training set and the verification set of the same data set. When the crossdata set is verified, the accuracy rate will drop sharply. In response to this problem, we propose to apply the CycleGAN [2] network to image segmentation to learn the style features between different datasets. It turns out that the processing can improve the effect of image segmentation between different datasets.
Coupled discriminant mappings for heterogeneous face recognition

Previous efforts on heterogeneous face recognition typically assume each subject has multiple training samples. However, this assumption may not hold in some special cases such as law-enforcement where only a Single Sample Per Person (SSPP) exists in the training set. For face recognition in SSPP scenario, it often suffers from overfitting and singular matrix problems. To solve this problem, we propose a novel learning-based algorithm called Coupled Discriminant Mapping (CDM) for heterogeneous face recognition. The CDM method finds a common space and learns a couple of discriminant projections for two different modalities without depending on the intra-class scatters. In the common space ,images of the same person are pulled into close proximity even if they come through different modalities meanwhile all the image under the same modality are pushed apart since each image belongs to a distinct class. The performance of CDM method is evaluated in two tasks: visual face image vs. near infrared face image and conventional face recognition. Experiments are conducted on two widely studied databases to show the effectiveness and consistence of the proposed CDM method.
IMPACT OF WEB QUALITY ON EMPLOYEES’ SATISFACTION IN INDONESIA STATE EMPLOYMENT DEPARTMENT

The State Employment Department (BKN) had applied information technology in employee data management since 2011. However, flaws were found in the system, such as occurrence of inaccurate data and complicated navigation. This study was aimed at analyzing the satisfaction of BKN staffs in using DMS web. Web quality was assessed using 3 dimensions: usability, information quality and service information quality. These 3 dimensions were then associated with the satisfaction of BKN staffs as the users. Data was collected through questionnaires to 206 staffs with authorized accesses to the web. The results showed that the quality of three dimensions of the web influenced users’ satisfaction.
Occluded Traffic Signs Recognition

Traffic sign recognition is very important in the intelligent driving. It can remind drivers to react properly to the road condition and increase the driving safety. One of the challenges in recognizing traffic sign is occlusion. In this paper, we focus on this problem particularly in Taipei and the vicinity including Taipei and New Taipei City. We propose a convolution neural network equipped with the regional masks to solve the occlusion traffic sign recognition. Traffic sign images of Taipei and New Taipei City are collected mainly from Google Maps for training and testing. Finally, the proposed method is tested both on our own dataset and German public dataset GTSRB. The experimental results demonstrated the occlusion problem is being greatly alleviated and the result is very promising.
Machine Learning Approach to Unveil Time Delay Signature of Electro-Optical Chaotic System with Strong Nonlinearity

We demonstrate a time delay signature extraction method for optoelectronic chaotic system based on convolutional neural network. The method shows effectiveness under strong nonlinearity, and the amount of data required is less than traditional methods.
Likelihood Induced by Moment Functions using Particle Filter: A Comparison of Particle GMM and Standard MCMC Methods

Particle filtering is a useful statistical tool which can be used to make inference on the latent variables and the structural parameters of state space models by employing it inside MCMC algorithms (Flury and Shephard, 2011). It only relies on two assumptions (Gordon et al, 1993): a: The ability to simulate from the dynamic of the model; b: The predictive measurement density can be computed. In practice the second assumption may not be obvious and implementations of particle filter can become difficult to conduct. Gallant, Giacomini and Ragusa (2016) have recently developed a particle filter which does not rely on the structural form of the measurement equation. This method uses a set of moment conditions to induce the likelihood function of a structural model under a GMM criteria. The semiparametric structure allows to use particle filtering where the standard techniques are not applicable or difficult to implement. On the other hand, the GMM representation is less efficient than the standard technique and in some cases it can affect the proper functioning of particle lter and in turn deliver poor estimates. The contribution of this paper is to provide a comparison between the standard techniques, as Kalman filter and standard bootstrap particle filter, and the method proposed by Gallant et al (2016) in order to measure the performance of particle filter with GMM representation.
The Use of Social Media as Learning Media among High School Student: A Case study of Pangkajene and Islands

This study aims to investigate the use of social media as an alternative media in student education.It employs qualitative method and data was collected through direct interviews, focus group discussions and daily observations.The study was conducted in Pangkajene Regency.The results showed that there were differences in social media use between students from Senior High School 1 Liukang Tupabiring Utara and Senior High School 2 Pangkajene.These differences can be associated to geographical factors leading to a discrepency in internet accessibility that ultimately affected social media usage behavior.Data shows that the majority of students of Senior High School 1 Liukang Tupabbiring Utara, which is located on the small island of Sagara roughly 4.5 kilometers from the Makassar coastline, did not use social media for educational purposes with many claiming to have never sought out educational information through social media.This is in stark contrast to the social media usage behavior of students in Senior High School 2 Pangkajene, which is several kilometers inland on the South Sulawesi mainland, who were far more likely to use social media for learning.
The User-Pleasant Video Skimming by Multi-Sources-Indices Analysis

Video skimming is the process to extract the most significant content of the video and represent it in a concise form. Previous research of video skimming generation could not make full use of the multi-modal information and thoroughly consider the user preferences. In this paper, we propose a novel approach of video skimming through exploiting the fusion of video temporal information and semantic information, supported by audio and text classification and keyframe extraction. By means of analysis on multi-modal video information including audio, text and visual indices, one video skim is generated for one original video. In addition, we introduce the brand-safe filtering and sentiment analysis in order to only reserve the user-pleasant content. Experiment conducted on Youtube-8M dataset has proved that our method highly outperforms the approaches which only consider partial information in video. Moreover, with brand-safe filtering, the unpleasant content has been successfully removed.
Performance of Discrete Meyer wavelet Transform on Encoding Levels using Computed Tomography Lung Image

The image transmits and storage capability could be a broad appliance in compression. Image compression techniques engage the appropriate and successful transforms and encoding methods to attain the aim. In this work, discrete Meyer transform based image compression algorithm is used for decomposing the image. The effects of different Encoder loops are described based on the values of peak signal to noise ratio (PSNR), compression ratio (CR), means square error (MSE) and bits per pixel (BPP). The finest encoding loop level for compression is also found based on the results.
Parallel Training for Large-Scale Echo State Networks via Alternating Direction Method of Multipliers

Large-scale neural networks have been widely used in data processing applications. As a special type of neural network, the recurrent neural network (RNN) is equipped with additional recurrent connections. This unique architecture enables the RNN to memory the processed information and makes it an expressive model for nonlinear sequence processing tasks. However, the large computation complexity makes it difficult to effectively train an RNN. In this paper, we aim to develop parallel approaches for a particular type of RNN, known as echo state network (ESN). We first decompose the training problem of a large-scale ESN into a number of smaller subproblems. Next, we introduce the alternating direction method of multipliers (ADMM) to solve the optimization problem. Then, two parallel algorithms are proposed to train the ESN across the learning agents, which restricting communication to the fusion of achieving an overall decision. The experiment results of the prediction of the Mackey-Glass chaotic time-series demonstrate that the proposed algorithms for a large-scale ESN are able to reach a comparable accuracy achieved by a single ESN on the same dataset.
A novel and improved developer rank algorithm for bug assignment

Analytical studies on automatic bug triaging approach have the main objective to recommend appropriate developer for bug report with reduced bug tossing length, time and effort in bug resolution. In bug triaging process, if the first recommended developer cannot fix a bug, it is tossed to another developer and the tossing process is continued till the bug gets assigned and resolved. Existing approaches to the best of our knowledge have not considered developer's contributions and performance assessment metrics for bug triaging process. In this paper, we proposed a novel and improved two phase bug triager that involves a developer profile creation and assignment phases. In this, developer profile is built by using individual contributions (IC) and performance assessment (PA) metrics. Contribution and performance of a developer in pre-fixed bug reports are analysed to calculate a developer's weighted score. This score indicates the level of expertise to fix and resolve a newly reported bug. This approach is tested on two open source projects - Eclipse and Mozilla. Empirical results show that proposed approach has achieved a significantly higher F-score up to 90% for both projects and has effectively reduced bug tossing length up to 11.8% as compared to existing approaches.
UCC Library: Research Data Service: Website Archiving

We are a university wide resource which supports and promotes best practice in data management.
TRIADE A Three-Factor Trace Segmentation Method to Support Program Comprehension

Trace analysis allows software engineers to gain insights into the behavior of the systems they maintain, and thus serves as an essential tool to aid in multiple tasks that require an understanding of complex systems, including security analysis, debugging and maintenance. However, the considerable size of execution traces can hinder the effectiveness of trace analysis. There exist techniques that extract higher level abstractions from a lengthy trace by automatically segmenting a trace into a number of cohesive segments, allowing software engineers to focus only on the segments of interest. In this paper, we improve on related work on segmenting traces of method calls by considering three factors: method names, method calling relationship, and method parameters. We show experimentally that this approach is more effective for the purpose of dividing a trace in a manner concordant with the underlying behavior of the program than existing algorithms. We also examine the issue of key element extraction from a trace, and again demonstrate experimentally that traces segmented using our method can more readily be subjected to this analysis
Rehabilitation Seat Cushion System

A rehabilitation seat cushion system is developed for wheelchair user in this work. The system includes a sitting posture tilt reminder, a slide warning and a real-time feedback rehabilitation training software. The sensor of the system can be battery-powered. This system can detect whether there is a tilt in the sitting position of the wheelchair user and also can give an immediate warning notice when the user slips off the wheelchair. When the user is leaning to left or right for more than a certain period of time, the system will send a warning signal to remind the user to adjust the sitting posture. In addition, a software is provided for users to conduct related training, where the training intensity can be increased to achieve better effects of rehabilitation.
Stacked Particle Size Measurement Method Based on Data Processing

In order to obtain accurate measurement results of particle size, the watershed algorithm is used to segment the image after steps including the median filtering, binaryzation, expansion and corrosion, distance transformation and seed point marking. Then the segmented image is data filtered and errors and data with large deviations are eliminated. The result with data filtering is compared with result without data filtering. The result with data filtering is verified. Finally, the precise particle size is obtained. The experiments show the proposed method can accurately measure the size of overlapped particles.