Reviewerly/exHarmony huggingface dataset:
Secure Authentication Using One Time Contextual QR Code

Traditional methods of authentication are subject to a wide variety of attacks. There is a high demand to deploy necessary mechanisms while authenticating a user to safeguard him/her and the system from the vulnerable attacks. In this paper, a novel one time Quick Response (QR) code based solution has been proposed to counter various types of security breach during the authentication process. The QR code will facilitate context-based authentication. Some information is stored within the QR code which changes for each authentication of the user. Using this information the user needs to derive a one-time password corresponding to his/her actual password. The proposed scheme can be well and easily adapted in various existing and new systems. The experiment and analysis shows that it is more efficient than the existing algorithms in countering security threats.
Multi-platform track initiation method in dense clutter environment

The track initiation is the premise of target tracking. How to reduce the initiation of false track as much as possible under the condition of ensuring the normal initiation of the target, especially the effective reduction of false track in the dense clutter environment is an important direction of track initiation research. Aiming at the problem of track initiation in dense clutter environment, this paper proposes a multiple hypotheses, multiple rules, self-adaption, multi-platform track initiation algorithm combined with clutter filtering technology. The experimental results show that compared with the traditional algorithm, the algorithm has higher track initiation efficiency and clutter suppression effect.
Pedagogical activity of museum in Ukraine in modern conditions: possibility of stages

The author of the article analyzes various aspects of educational activity of museum institutions of Ukraine in the national educational space.He outlines the dynamics of changes in this activity in accordance with social and historical processes.The author traces that from the late 90's of the XX century.it is possible to observe certain systemic changes in the organization and carrying out of museum-pedagogical activity, which acquire new features and
Akurasi Keputusan dalam Penentuan Guru Berprestasi Dengan Menggunakan Metode Simple Additive Weighting (Studi Kasus Sekolah Menengah Kejuruan Muhammadiyah Batam)

This study aims to obtain decision accuracy in determining teacher achievement using the Simple Additive Weighting method. Problems in Batam Muhammadiyah Vocational High School in determining the achievement of teachers using assessment criteria with the weight of educational qualifications, discipline, neatness, character, personality and teaching methods. The ranking process stage determines the alternatives of selected teacher achievement using the input of criteria weights. The results of this study get an accuracy rate of 80% with an accurate, fast, objective value. In the future, this research will be used as a new standard in determining teacher achievement.
Implementation of Distributed Energy Resource of Photovoltaic Systems with LSTM Neural Network System

The use of solar power is rapidly increasing in today's power grid, because of the rapid depletion of non-renewable energy sources such as fossil fuels. This new source of power is coming up as independent units of solar panels all around the globe. However, the problem with solar power is its seasonal nature, due to which a fixed output can't be expected. Hence in the process of moving towards solar power it is important to be able to forecast the amount of power that will be produced by a certain photovoltaic plant in the next time horizon. The proposed technique is an attempt to implement a system that constantly monitors this power forecast obtained for multiple distributed energy sources, through means of neural network models and web-based applications and gives access to such predictions at the consumer level.
Analisis Hidrograf dengan Perbandingan Metode HSS ITB 2, SCS (HEC-HMS) dan Gama 1 pada Sungai Progo di Daerah Istimewa Yogyakarta

Progo River is the longest river in the Special Region of Yogyakarta. It covers many watersheds that causes a big flow rate, the changes in rainfall will also affect the maximum flow rate that occurs each year. This study was conducted to analyze the flow hydrograph in the Progo River, especially in the Pajangan Watersheds due to the changes in rainfall, so that the maximum discharge at each return period is known. In analyzing the unit hydrograph and calculating the maximum discharges that occur at each return period, the ITB 2 and SCS (HEC-HMS) methods are chosen. ITB 2 was chosen because it is the current method for calculating hydrographs, while SCS (HEC-HMS) was chosen because this program makes it easy to simulate rainfall that occurs in watersheds. The data used in this study were obtained from relevant agencies and literature studies that have been carried out. Based on the results of the hydrograph analysis on the Pajangan Watershed that has been done, the maximum flow rate generated from the ITB 2 at returning period are Q2 years = 1556,903 m3/s , Q5 years = 1923,949 m3/s , dan Q10 years = 2293,163 m3/s. The maximum flow rate generated from the calculation of SCS (HEC-HMS) when returning period are Q2 years = 1504,752 m3/s , Q5 years = 1858,255 m3/s , dan Q10 years = 2213,846 m3/s. And for the literature that we got from PT.Gracia Widyakarsa, the maximum flow rate generated from the calculation of Gama 1 when returning period are Q2 years = 1658,02 m3/s , Q5 years = 2107,23 m3/s , dan Q10 years = 2353,68 m3/s.
Symbiotic Organisms Search for Constrained Optimization Problems

Since constrained optimization algorithms are easy to fall into local optimum and their ability of searching are weak, an improved symbiotic organisms search algorithm with mixed strategy based on adaptive e constrained (e_SOSMS) is proposed in this paper. Firstly, an adaptive e constrained method is presented to balance the relationship between the constrained violation degrees and fitness. Secondly, the evolutionary strategies of symbiotic organisms search algorithm are improved as follows. Selecting different best individuals according to the proportion of feasible individuals and infeasible individuals to make evolutionary strategy more suitable for solving constrained optimization problems, and the individual comparison criteria is replaced with population selection strategy, which can better enhance the diversity of population. Finally, numerical experiments on 13 benchmark functions show that not only is e_SOSMS able to converge to the global optimal solution, but also it has better robustness.
Deep Learning Approach For Object Tracking Of RoboEye

RoboEye is a spherical 3RRR parallel robot which has been developed for its high precision. It can provide high speeds, so can be used for fast tracking tasks. To this end, in this paper proper deep learning approaches are combined with classical control methods. Deep learning algorithms are employed to detect an object of interest among various ones in a monocular image, and then obtain an estimatation of the distance to the camera. So, simultaneous depth estimation, and object detection with a monocular camera for real time implementation is proposed here. For fast calculations, also to overcome manufacturing uncertainties, inverse kinematic equations are computed by a multi-layer perceptron (MLP) network based on real data. Finally, a classical PID controller can perform a fast tracking of the object.
LibGuides: Editing and Proofreading: Quick resources (5-10 mins)

An online study guide from the Skills Centre, including guidance and electronic resources on how to efficiently edit and proofread your own work before submission.
LibGuides: Easy as ABC: Nonfiction Books

This guide focuses on alphabet picture books, and resources that can be used in conjunction with them.
SISTEM KONVERSI UCAPAN KATA KE TEKS MENGGUNAKAN SUPPORT VECTOR MACHINE : SPEECH WORD RECOGNITION TO TEXT CONVERTER USING SUPPORT VECTOR MACHINE

Artificial intelligence technology is developing very rapidly. Various fields have applied this technology to help human work. Speech recognition system is one of the artificial intelligence technologies that are widely applied in various fields. However, some research showed that it was still necessary to develop a method for a good speech recognition system. In addition, the development of speech recognition systems that can provide benefits needs to be developed, such as text recording. Based on this, the research focuses on developing a speech recognition system, in the form of spoken words and convert to text form. Speech words that have been recorded are then extracted features using linear predictive coding method. After that, the characteristic features of each sound are trained and tested using the Support Vector Machine (SVM) method for the process of recognition and convert it into text. Based on the evaluation results show that this system is able to recognize words with an accuracy rate of 71.875%. These percentages indicate that the system is able to recognize spoken words and transform them into text form properly.
LibGuides: Virtual Adventures (from Home): Virtual Science & Technology

A guide to free virtual experiences of places, museums, galleries, and adventures from around the world. Free digital resources available for the education and exploration of science and technology.
The Effect of Immediate and Delayed Error Correction on Accuracy Development of Intermediate EFL Learners’ Writing

This study investigated the immediate and delayed effects of error correction on the accuracy improvement of intermediate EFL learners’ writing. Through TOEFL test, the researcher selected 3 intact intermediate classes and assigned them into peer correction, self-correction and teacher correction groups. Before the main treatment, all participants were requested to write a composition on a topic, which served the purpose of pre-test. After writing the composition, the first group (peer), including two learners in each group, corrected the errors underlined by the teacher together. The learners in the second group, namely, self-correction, corrected the errors underlined by teacher individually and the third group received the correct forms written by the teacher on top of the errors. All the groups wrote 6 compositions on the topics during 6 sessions. The first topic served the purpose of immediate pre-test. This procedure was followed for 6 weeks and the 6th composition was regarded as immediate post-test and after a month (12th week), delayed post-test was conducted for all learners. The findings of ANOVA indicated that both peer correction and selfcorrection were influential in enhancing learners' writing accuracy; however, the group doing peer correction outperformed the self-correction one.
Education and business in the Industry 4.0

Planning and Teaching Engineering Staff for the Industrial and Economic Complex of the Region 2019: Proceedings of 2019 XVIII Russian Scientific and Practical Conference on Planning and Teaching Engineering Staff for the Industrial and Economic Complex of the Region (PTES)
Translation Techniques of Islamic Terminologies in Bilingual Children Story Books

The current research focuses on analyzing the translation techniques of Islamic terminologies in bilingual children storybooks from Indonesian into English. The source of the data is two selected bilingual children storybooks Seri Belajar Islam sejak Usia Dini. This study applied a descriptive qualitative approach to analyze the data, to explain the phenomena, and to withdraw the conclusion. The result showes that in Ayo Belajar Manasik Haji there are 33 Islamic terminologies and 6 translation techniques, while in Ayo Belajar Hadist there are 24 Islamic terminologies and 5 translation techniques.
Subject & Course Guides: Optical Character Recognition (OCR) - Getting Started: Other Tools

Basic overview of several tools (both open source such as Tesseract and commercial such as Adobe Acrobat) that perform optical character recognition (OCR).
LibGuides: RPGs: Dungeons and Dragons

This libguide is dedicated to tabletop roleplaying games. It is meant to be a resource for new players and existing players.
Online Handwriting Recognition Using Encoder-Decoder Model

In this paper we propose a system that is capable of recognizing raw online handwritten data. The system consists of an advanced type of neural network known as LSTM (Long Short Term Memory) Encoder-decoder combined with a customized attention mechanism layer. The attention mechanism has greatly enhanced the system performance from a low character level accuracy of 53% to an excellent accuracy of 96%. Moreover, the system involves a segmentation algorithm designed to divide the sentences into segments of lines. For the training and testing we employ the IAM On-Line Handwriting database, the source can be found here [1]. The accuracy can be improved even further by integrating our system with a language model to spell check the outputs.
Diachronic Analysis of Italian Opera Librettos

The purpose of this article is to find out which are the most typical words used in Italian opera librettos in each of historical periods of opera and to see the evolution of their use. A word which statistically occurs in a corpus of texts more often than expected is called a keyword. The article shows a new approach in diachronic linguistics, proposing a different examination of keywords found with the Log-Likelihood method in a corpus of Italian opera’s librettos. To get the keywords, the libretto’s corpus was compared with a reference corpus which includes the most representative works of Italian literature. Later the librettos’ corpus was divided diachronically in 5 sub-corpora according to some historical periods. A new analysis of these sub-corpora was performed using a software tool written by the author called CorpStat. It allows to analyze the evolution in the use of lexicon of opera’s librettos. Often the Log-Likelihood statistical method was criticized mainly because some words qualifies as keywords, even if they appear only in one or very few texts. With the software CorpStat it was possible to validate words as real keywords taking into consideration the percentage of number of texts in which they appear. The analysis brings to the conclusion that only 41 keywords out of a list of first 200 keywords could be validated as real keywords. However, the diachronic analysis shows that a word is a real keyword only in a certain historical period, but not necessarily in another. This can be due to the changes of libretto’s plots and language along the history.
Segmentation Algorithm of Road Scene based on Full Convolutional Network and Conditional Random Field

Traditional lane detection algorithms use lane features and mathematical models to detect the location of lane lines, which is sensitive to complex scenes and multiple interference conditions. Full Convolutional Neural Network (FCN) considers the full-connected layer in the traditional network as convolution layer, and connects the deconvolution layer. After upsampling, we can obtain the segmentation graph of the pixels. The feature extraction network is based on AlexNet, which reduces the receptive field by reducing the convolution kernel size and downsampling ratio of the first layer and introduces the conditional random field (CRF) to enhance the image edge constraints. Finally, the CRF is incorporated into the FCN, and we construct an end-to-end network that is categorized by pixel-by-pixel prediction. 367 road scenes in CamVid dataset were used in this experiment, and 11 types of pixels are classified. The accuracy rate of training is 90.1%, and the test result is good.
Konstmuseers arbete med digitalisering av sina samlingar : En semiotisk analys

The purpose of this thesis is to examine how six different art museums are working on digitizing their collections, and how they are made available to the public. To answer the purpose, three resea ...
Automated Scalability Assessment in DevOps Environments

In this extended abstract, we provide an outline of the presentation planned for WOSP-C 2020. The goal of the presentation is to provide an overview of the challenges and approaches for automated scalability assessment in the context of DevOps and microservices. The focus of this presentation is on approaches that employ automated identification of performance problems because these approaches can leverage performance anti-pattern[5] detection technology. In addition, we envision extending the approach to recommend component refactoring. In our previous work[1,2] we have designed a methodology and associated tool support for the automated scalability assessment of micro-service architectures, which included the automation of all the steps required for scalability assessment. The presentation starts with an introduction to dependability, operational Profile Data, and DevOps. Specifically, we provide an overview of the state of the art in continuous performance monitoring technologies[4] that are used for obtaining operational profile data using APM tools. We then present an overview of selected approaches for production and performance testing based on the application monitoring tool (PPTAM) as introduced in [1,2]. The presentation concludes by outlining a vision for automated performance anti-pattern[5] detection. Specifically, we present the approach introduced for automated anti-pattern detection based on load testing results and profiling introduced in[6] and provide recommendations for future research.
How to Apply Modeling to Compare Options and Select the Appropriate Cloud Platform

Organizations want to take advantage of the flexibility and scalability of Cloud platforms. By migrating to the Cloud, they hope to develop and implement new applications faster with lower cost. Amazon AWS, Microsoft Azure, Google, IBM, Oracle and others Cloud providers support different DBMS like Snowflake, Redshift, Teradata Vantage, and others. These platforms have different architectures, mechanisms of allocation and management of resources, and levels of sophistication of DBMS optimizers which affect performance, scalability and cost. As a result, the response time, CPU Service Time and the number of I/Os for the same query, accessing the similar table in the Cloud could be significantly different than On Prem. In order to select the appropriate Cloud platform as a first step we perform a Workload Characterization for On Prem Data Warehouse. Each Data Warehouse workload represents a specific line of business and includes activity of many users generating concurrently simple and complex queries accessing data from different tables. Each workload has different demands for resources and different Response Time and Throughput Service Level Goals. In this presentation we will review results of the workload characterization for an On Prem Data Warehouse environment. During the second step we collected measurement data for standard TPC-DS benchmark tests performed in AWS Vantage, Redshift and Snowflake Cloud platform for different sizes of the data sets and different number of concurrent users. During the third step we used the results of the workload characterization and measurement data collected during the benchmark to modify BEZNext On Prem Closed Queueing model to model individual Clouds. And finally, during the fourth step we used our Model to take into consideration differences in concurrency, priorities and resource allocation to different workloads. BEZNext optimization algorithms incorporating Graduate search mechanism are used to find the AWS instance type and minimum number of instances which will be required to meet SLGs for each of the workloads. Publicly available information about the cost of the different AWS instances is used to predict the cost of supporting workloads in the Cloud month by month during next 12 months.
Governance and Economic Growth Relationship : The Case of Tunisia (1996-2017)

The article aims to study the relationship between governance and economic growth in Tunisia. For this, we have integrated governance indicators into a growth model. Based on VAR modeling, the estimation of the model thus developed, using data from the World Bank for the period 1996-2017, made it possible to show that certain indicators of governance such as political stability, rule of law and regulatory quality have a positive influence on economic growth in Tunisia. Thus, the fight against corruption can further boost economic growth in countries rich in natural resources and applying democratic principles. On the other hand, other governance indicators such as government effectiveness and the voice of accountability do not produce expected effects on economic growth. As a result, political authorities need to improve governance indicators, including political stability, and control of corruption.
Methods of Corporate Surveillance: a Primer on Experimental Transparency Research

News headlines about privacy invasions, discrimination, and biases discovered in the platforms of big technology companies are commonplace today. The headlines – ranging from comprehensive profiling of users, to microtargeting of political messages, to discrimination based on gender, race, and age – show that big tech’s operations can cause real-world harm. However, big tech companies are reluctant to disclose how they operate and typically do not give out specific information, such as what data they collect or how their algorithms make decisions. This secretive operation counteracts ideals of transparency, openness, and accountability. This tutorial will present research methods and findings from the last 5-10 years that use large-scale experiments to systematically interact with the publicly accessible elements of big tech’s platforms. These experiments allow inferring details about big tech’s hidden operations – in essence conducting meta-surveillance against big tech. For example, findings from these experiments have documented how user tracking works, the extent of tracking on the web today, and how the collected information is used for algorithmic decision-making and ad targeting.
The CONNECT-UDP HTTP Method

This document describes the CONNECT-UDP HTTP method. CONNECT-UDP issimilar to the HTTP CONNECT method, but it uses UDP instead of TCP.Discussion of this work is encouraged to happen on the MASQUE IETFmailing list masque@ietf.org or on the GitHub repository whichcontains the draft: https://github.com/DavidSchinazi/masque-drafts.Discussion Venues This note is to be removed before publishing as anRFC. Source for this draft and an issue tracker can be found athttps://github.com/ietf-wg-masque/draft-ietf-masque-connect-udp.
LibGuides: General Information Management: Journals & Conference Proceedings

This guide is for the NCU specialization General Information Management. It contains resources available in the NCU Library.
LibGuides: General Information Management: Ask Us!

This guide is for the NCU specialization General Information Management. It contains resources available in the NCU Library.
Employee Involvement and Industrial Performance: Case of a multinational company based in Morocco - Casablanca

Managers are now faced with a weakening of employees’ involvement due to the difficulty to comply and adhere to the new visions generated by the various socio-economic changes. Our publication aims to identify and analyze the motives that enable employees to get involved in their work. More specifically, it will explore and identify the main factors pertaining to employee involvement, assesse their effects on staff behavior and attitude and finally show the impact of this behavioral change on the structure's performance.The data were collected using face-to-face questionnaires administered to executives, technicians, engineers and directors of a multinational company based in Morocco - Casablanca.
LibGuides: General Information Management: Websites

This guide is for the NCU specialization General Information Management. It contains resources available in the NCU Library.