Reviewerly/exHarmony huggingface dataset:
Automatic Tajweed Rules Recognition using K-Nearest Neighbour (K-NN)

Tajweed refers to a pronunciation rule for Al-Quran recitation in Islam. It acts as guidance for Muslims in reciting the Al-Quran in a correct manner. Yet, Tajweed rules could be complicated as it consists of various types of laws. It could also be confusing, and difficult to remember particularly for the people who have less knowledge in Tajweed rules. Thus, a study on automatic tajweed rules recognition using image processing technique is proposed. The scope of this study is limited to Idgham laws only. Initially, the input image went through the pre-processing process which includes four sub-processes which are greyscale conversion, binary conversion, thinning and flip, and word segmentation. Next, six attributes of shape descriptor which are major axis length, minor axis length, eccentricity, filled area, solidity, and perimeter were extracted from each input image. A technique of k-Nearest Neighbour (k-NN) is employed to recognize the two types of Idgham Laws which are Idgham Maal Ghunnah and Idgham Bila Ghunnah. The performance of the proposed study is evaluated to 180 testing images which returned 84.44% of classification accuracy. The outcome of this study is expected to recognize the Tajweed rules automatically and may assist the user on a proper recitation of Al-Quran.
Facilitating and Improving Speaking Accuracy through Self-correction and Self-awareness in TOEFL Independent Speaking Tasks

Assessing the speaking skills of language learners has always been in the agenda considering the fact that speaking correspondingly involves comprehension, and interaction is almost impossible without comprehension in the target language.During the independent speaking tasks of the TOEFL-IBT test, candidates often make grammar mistakes, and since they have limited time for self-correction, they cannot answer the questions fully, and eventually they run out of time finishing the task completely.These mistakes directly affect candidates' speaking negatively, and they often seek ways to make fewer mistakes so that they can focus more on supporting their ideas and fluency.The purpose of this study is to find out if a TOEFL test candidate is likely to use the target language more efficiently in the successive and subsequent trials based on self-correction and selfawareness of errors.For this purpose, a Turkish learner of English preparing to take TOEFL-IBT test in a private English language school was assessed on an 8-step speaking test trial for the 14 speaking questions for the independent speaking tasks of the TOEFL-IBT test.After each trial, the participant's reflections were analyzed through coding and under three themes: lexis, elaboration and structure.The results of this study indicated that, through awareness raising and self-correction, the participant's speaking skills improved in terms of vocabulary range, elaboration, using discourse linkers based on the raters' judgments, however, there was no noticeable improvement regarding sentence structure.The participant also reported that self-correction was an effective way to remember the errors and considered the raters' feedback useful in becoming more aware of certain areas.The interview results revealed that the learner preferred discovery learning rather than rote.
Deadline Constrained based Resource Allocation in Cloud Environment

Cloud computing faces a challenge of handling huge amounts of data. The users keep on pushing the data without knowing the challenge in increased storage. Task Scheduling deals with allocating the task to a respective resource pool on a demand basis. Approaches have been built that handle requests from users with deadlines on the amount of request that can be handled. It is important to understand that the mechanism is available to handle the deadlines. The experimental results show that the proposed algorithm produces remarkable performance improvement rate on the total execution cost and total transfer time under meeting the deadline constraint. In view of the experimental results, the proposed algorithm provides a better-quality scheduling solution that is suitable for scientific application task execution in the cloud computing environment.
Military Database Security via Justapose Database Shuffling

This paper aims for Database security which expresses the need for preventing data leak from military databases which often leads to sensitive data leaks which bring the defence machinery of the nation at peril. Cyber-Attacks and Large-Scale Phishing from neighbouring nations have brought the immediate need of securing the database which concerns the confidentiality of the defence proceedings and hence the paper proposes some techniques and a unique project proposal to overcome the drawbacks of the existing technologies which can henceforth be secured to find a secure environment for the data. Lack of awareness regarding the gravity of the confidential data and what harm it might bring upon the defence set-up can lead to essential data being compromised and hence our paper proposes a novel solution in Database security by employing RSM method which invokes data dredging and mining.
Confidentiality Preserving and Data Portability for Secure Cloud Computing using LCC-AP and SP-GA

Cloud computing (CC) allows obtaining computing resources on-demand and also allows storing avalanche of data with a high fault tolerance level.Security is extremely vital when storing an avalanche of data on the cloud for resolving a number of issues.Though several existing works were there to store data on the cloud, still there are some problems that the data owner has to suffer, such as lack of confidentiality, time delay in storing, and also data being attacked because of insider troubles.Thus, this paper strives to strengthen the sensitive Data Confidentiality (DC) in private as well as public cloud storage by proposing a new data portability and access policy scheme to resolve these afore-mentioned problems.A novel algorithm termed Sandpiper Genetic Algorithm (SP-GA) is launched to maintain proper portability between the cloud-severs.Then, Length Caesar Cipher based Access Policy (LCC-AP) technique is utilized to achieve DC.The proposed techniques guarantee DC, support portability, and also provide secured sharing of files among users.Experimental outcomes show the potential efficiency of SP-GA and LCC-AP
Роль академического инбридинга в реализации научно-исследовательского потенциала студентов из социально уязвимых слоев населения

The article analyzes the features of academic inbreeding, its types and risks identified by Russian and foreign researchers (Н.&nbsp;Horta, F.M.&nbsp;Veloso, Ph.&nbsp;Altbach, M.&nbsp;Yudkevich, M.&nbsp;Seeber and others). These risks include the poor quality of university research and teaching due to bias in the selection of candidates. Moreover, it depends on the type of academic inbreeding: pure inbreds, silver-corded, mobile inbreds, adherents (noninbreds). In contrast, a model of «healthy» inbreeding of students with high research potential is proposed. The article is based on the concept of the research potential of students as an integral set of cognitive, motivational, behavioral characteristics of a person, providing the ability to solve research problems effectively. Academic inbreeding can be viewed as a tool for implementing the «third mission» of universities and corporate social responsibility programs, within the framework of which it is supposed to promote the development of the potential of students from socially vulnerable groups of the population. The article concludes with general recommendations to stimulate «healthy» academic inbreeding: 1)&nbsp;meritocratic selection of candidates; 2)&nbsp;competitive selection; 3)&nbsp;openness of the selection procedure; 4)&nbsp;comprehensive assessment of achievements; 5)&nbsp;independent evaluation of candidates; 6)&nbsp;early search for talented young researchers using psychological testing and sociological survey methods.
Cognitive mapping links human factors to corporate strategies

Human factors (HF) can improve business performance. Our objective is to harness individual tacit knowledge from senior directors about human factors as it relates to strategic goals and to make explicit their shared managerial thinking with an aim to identifying improvement opportunities using HF. Individual cognitive maps were drawn during one-hour interviews with seven senior directors of a large electronics firm. The maps were then merged on a common strategic goal of 'improving quality' into a group map containing 221 concepts and 900 loops. In a two hour workshop with the directors, reducing fatigue, improving systems design, and reducing repetitive activities were concepts that emerged as critical-to-quality. Workshop discussions identified untapped improvement opportunities.Directors viewed the maps as a dynamic indicator of their HF performance. Making the connection between HF and strategic goals explicit can help an organisation identify opportunities to improve human, and therefore business, performance.
An Analysis on Object Recognition Using Convolutional Neural Networks

The global development and progress in scientific paraphernalia and technology is the fundamental reason for the rapid increasein the data volume. Several significant techniques have been introducedfor image processing and object detection owing to this advancement. The promising features and transfer learning of ConvolutionalNeural Network (CNN) havegained much attention around the globe by researchers as well as computer vision society, as a result of which, several remarkable breakthroughs were achieved. This paper comprehensively reviews the data classification, history as well as architecture of CNN and well-known techniques bytheir boons and absurdities. Finally, a discussion for implementation of CNN over object detection for effectual results based on their critical analysis and performances is presented
Solving the Rating Scarcity Issue using an Enhanced Memory-based Collaborative Filtering Method

In Service Oriented Architecture (SOA), reputation-oriented web service discovery has gained popularity in finding the optimal service from a pool of services having similar functionality. Almost all reputation-oriented discovery mechanisms make use of the feedback ratings reported by the users in order to assess service reputations. However, there are certain factors which, if not addressed carefully, may affect the process of precise service reputation evaluation. One such factor is the issue regarding rating scarcity. When the percentage of users who rate the web services compared to the percentage of users who avail the web services is low, the issue of missing feedback rating arises which leads to incomplete rating matrix. Since all users, after availing services, may not report their satisfaction levels in the form of feedback ratings, it is obvious that the system will encounter incompleteness in rating information while evaluating service reputations. In this paper, an approach to solve the rating scarcity issue in reputation-oriented service discovery is proposed using an enhanced memory-based collaborative filtering method. Experiments are performed and the results are reported in this paper.
An Effective System to Detect Fake Research

Detection of spam review is an important operation for present e-commwebsites and apps.We address the issue on fake review detection in user reviews in e-commerce application, which wasimportant for implementing anti-opinion spam.First we analyze the characteristics of fake reviews and we apply the machine learning algorithms on that data. Spam or fake reviews of the itemsreducing the reliability of decision making and competitive analysis.The presence of fake reviews makes the customer cannot make the right decisions of sellers, which can also causes the goodwill of the platform decreased. There is a chance of leaving appraisals via web-based networking media systems whether states or harming by spammers on specific item, firm alongside their answers by recognizing these spammers just as in like manner spams so as to understand the assessments in the interpersonal organizations sites, we exist a stand-out structure called Netspam which uses spam highlights for demonstrating tribute datasets as heterogeneous subtleties systems to guide spam location treatment directly into gathering issue in such systems.
Encryption and Decryption Technique Involving Finite Fields

The object of this paper is to produce a technique for encryption and decryption Involving Finite Fields. In this paper we consider the elements of finite fields GF(2m) and logical operator XOR to analyze the encryption and decryption technique. Here we also uses the non-singular matrix and a key matrix.
Computação Serverless: Conceitos, Aplicações e Desafios

The book Short Courses of the 38th Symposium on Computer Networks and Distributed Systems comprises the short courses selected for presentation at the 38th Brazilian Symposium on Computer Networks and Distributed Systems (SBRC), held online between December 7 and 10, 2020. The SBRC Short Courses Book has traditionally been used as high quality study material by undergraduate and graduate students as well as by IT professionals who work on computer networking and distributed systems. The short-courses presentations sessions are also an important opportunity to update the knowledge of the scientific community and to complement the attendees training. The main objective of the SBRC Short Courses is to offer short-term training and updating on topics not normally covered in the curriculum and to make both the students and professionals more interested in the area. &nbsp
Signal Processing

This chapter summarizes sampling and reconstruction using the concrete one-dimensional example of digital audio. It aims to present the basic mathematics and algorithms that underlie sampling and reconstruction in one and two dimensions. The digital audio recording chain can serve as a concrete model for the sampling and reconstruction processes that happen in graphics. The same kind of under-sampling and reconstruction artifacts also happens with images or other sampled signals in graphics, and the solution is the same: filtering before sampling and filtering again during reconstruction. There is a deeper mathematical theory of sampling with a history reaching back to the first uses of sampled representations in telecommunications. Sampling theory answers many questions that are difficult to answer with reasoning based strictly on scale arguments. Following the frequency domain analysis to its logical conclusion, a filter that is exactly a box in the frequency domain is ideal for both sampling and reconstruction.
Beyond photon pairs: Nanophotonic photon number difference squeezing

We demonstrate over 1 dB of photon number difference correlations from two-mode squeezed states having mean photon number above 5, generated with a silicon nitride ring resonator and measured using photon number-resolving transition edge sensors.
Local news poverty in Canadian communities: presentation to the House of Commons Heritage Committee

This is an updated version of the brief submitted to the House of Commons Heritage Committee on Oct. 6, 2016 as part of the committee’s study on Communities and Local Media. Residents of Canada’s largest municipalities can obtain news from multiple sources, but it’s a different story elsewhere in the country. People who live in smaller cities and towns, suburban communities and rural areas have fewer options to begin with, and in recent years their choices have become even more limited. Traditional news outlets have been hit by cutbacks, consolidations and closures, while digital first news sites often struggle to stay afloat. Does any of this matter? The answer is “yes,” according to a report by the U.S.-based Knight Commission on the Information Needs of Communities in a Democracy. The commission’s report concluded that information is “as vital to the healthy functioning of communities as clean air, safe streets, good schools, and public health (Knight Commission, 2009, xiii).” It went on to argue that in addition to helping communities develop a sense of connectedness, access to information is essential in terms of holding public officials to account and making it possible for community members to work together to solve problems. While local journalism is the subject of increasingly intensive scrutiny by scholars in the United States – Duke University’s Philip Napoli, for instance, is launching a project that will investigate the state of local news in 100 U.S. communities (Napoli, 2016) - there is much we don’t know about the Canadian situation and the extent to which the critical information needs of rural areas, towns and smaller cities in particular are being addressed. As Carleton University professor Dwayne Winseck warned committee members during his testimony earlier this year, there are “severe” shortages of information on changes to the media landscape overall. Moreover, he cautioned, there are “a lot of opinions and little data to act upon” (Standing Committee on Canadian Heritage 2016, 5) .
A Signature Verification System with Ensemble Classifier

Handwritten signature is considered as one of the established authentication process to study the behavioral nature of a person. This paper focuses on verification of offline handwritten signatures (for English scripts) as either genuine or forgery. Here the considered samples are genuine, skilled and simple forgeries. The verification is carried out by ensembling the three base classifiers Naive Bayes (NB), K-Nearest Neighbor (KNN) and Kmeans classifiers. The accuracies for skilled and simple forgeries are obtained as 86 % and 92 % respectively.
ANALISIS PENYUSUNAN ANGGARAN PADA SMK NEGERI 5 SURABAYA JAWA TIMUR

The research aims to examne the budgeting that applied in the public sector. The sample used was one of the public schools there in Surabaya. In this research, the type of data used is primary data which is obtained directly from informants and supported by secondary data in the from of budget reports and school budget realization. The data analysis technique used is the reduction and validity test of the data obtained through field research, interviews and documentation studies. That the making and realization of budgets on 5 Surabaya education schools have been running. Will do well by obeying the rules set by the governor of east Java.&#x0D;
A Research on Similarity Measure to Identify Effective Similar Users in Recommender Systems

In recent years there is a drastic increase in information over the internet. Users get confused to find out best product on the internet of one’s interest. Here the recommender system helps to filter the information and gives relevant recommendations to users so that the user community can find the item(s) of their interest from huge collection of available data. But filtering information from the users reviews given for various items seems to be a challenging task for recommending the user interested things. In general similarities between the users are considered for recommendations in collaborative filtering techniques. This paper describes a new collaborative filtering technique called Adaptive Similarity Measure Model [ASMM] to identify similarity between users for the selection of unseen items. Out of all the available items most similarities would be sorted out by ASMM for recommendation which varies from user to user
PENERAPAN METODE ITERATIVE DICHOTOMIZER 3 (ID 3) UNTUK MENENTUKAN BEASISWA BERPRESTASI PADA SMP PGRI CARINGIN SUKABUMI

Scholarships are assistance from the government to students / students who are less able or have the ability in the academic and non-academic fields that are given individually to reduce the burden in terms of material. Frequently stalling time in selection, the number of students who apply for scholarships, the number of students whose homes are far from school, the number of students who race to come early as one of the criteria eligible to receive scholarships as well as the most scholarship applicants feel disadvantaged by unfavorable decisions. Iterative Dichotomizer 3 (ID3) algorithm is the most basic decision tree learning algorithm (decision tree learning algorithm). This algorithm conducts a thorough search on all possible decisions. In this research, it will be analyzed the application of the iterative dichotomizer 3 method in the case of determining achievement scholarships. In order to make decisions quickly and accurately. From 708 scholarship candidates including 28 eligible and 680 scholarship recipients, 136 scholarship recipients were obtained from ID3 algorithm with 3 eligible and 133 who had not, and obtained an accuracy rate of 97.75% so that it could be concluded that good and can help the school.
DEVELOPMENT OF INDONESIAN LANGUAGE AND LITERATURE TEACHING MATERIALS WITH TECHNOLOGY INITIATIONS FOR GENERATION Z TEACHERS

One way to create good quality learning is to design methods according to students' cognitive and social conditions. Students can be categorized as generation Z. The purpose of this study is to design teaching materials for Indonesian Language and Literature learning methods which are initiated by technology for generation Z students. This type is research development. Data obtained from the results of expert testing with learning methods experts and Indonesian Language and Literature learning experts. Based on the results of expert tests, teaching materials can be categorized as feasible to be implemented, but with some revisions.
Emerging Trends in Virtual Reality for Gaming: an assessment of best practices from research and development in the gaming industry

Virtual reality is a new and rapidly changing medium, with best practices still emerging at various locations across the industry. This white paper summarizes industry research and development focusing on player experience and comfort, particularly interventions that seek to mitigate the effects of Simulator Sickness. In order to better collate, evaluate and understand the variety of approaches and practices across the gaming industry, Phantom Compass partnered with the Ryerson’s Responsive Ecologies Lab to develop and playtest three prototypes that employ the current best practices in an effort examine lessons learned and expand current VR design.
Implementation of Vedic Multiplier and Floating Point Matrix Multiplier in Image Compression Applications

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