Reviewerly/exHarmony huggingface dataset:
Building Related Words in Indonesian and English Translation of Al-Qur’an Vocabulary Based on Distributional Similarity

The Qur'an is the Muslim holy book as the main source and guide, consisting of 114 surahs, 30 juz and has 6200 fewer verses in it. The search for relationships or arrangements of meaning between words in the Qur'an takes a long time to find and summarize. Obtained from the dictionary, encyclopedia, or thesaurus of the Al-Qur'an vocabulary, which contains each word entry has links with other words. This final project discusses the interrelations and semantic correspondence between words in the Qur'an, which supports to help find inter-related words in it, using linking with distributions that involve important parts in the word embedding. Measurement of the relevance of the word measurement with semantic similarity which is one of the lessons learned in Natural Language Processing (NLP). Extraordinary similarity measures the proximity of a word vector using cosine similarity. The process of converting words in the form of vectors using the fasttext which is the development of the Word2vec algorithm. The dataset is used for translations of the word Al-Qur'an in English and Indonesian. This entry becomes an input into the system then produces a score that represents the interrelationship between words. Evaluation of system output results is to perform performance calculations using Pearson correlation involving the gold standard.
GEOMETRY-BASED REGULARISATION FOR DENSE IMAGE MATCHING VIA UNCERTAINTY-DRIVEN DEPTH PROPAGATION

Abstract. In the present work, an uncertainty-driven geometry-based regularisation for the task of dense stereo matching is presented. The objective of the regularisation is the reduction of ambiguities in the depth reconstruction process, which exist due to the ill-posed nature of this task. Based on cost and uncertainty information computed beforehand, pixels are selected, whose depth information can be determined correctly with a high probability. This depth information assumed to be of high confidence is initially used to construct a triangle mesh, which is interpreted as surface approximation of the imaged scene and allows to propagate the confident depth information of the triangle vertices within local neighbourhoods. The proposed method further computes confidence scores for propagated depth estimates, which are used to fuse this depth information with the previously computed cost information, introducing a regularisation into the data term of global optimisation methods. Furthermore, based on the propagated depth information the local smoothness assumption of global optimisation methods is adjusted. Instead of fronto-parallel planes, the method presumes planes, which are parallel to the propagated depth information. The performance of the proposed regularisation approach is evaluated in combination with a global optimisation method. For a quantitative and qualitative evaluation two commonly employed and well-established stereo datasets are used. The proposed method shows significant improvements in accuracy on both datasets and for two different cost computation methods. Especially in unstructured areas, artefacts in the disparity maps are reduced.
Multispectral and low-filling-factor superconducting nanowire single photon detector with high absorption efficiency

We propose a superconducting nanowire single photon detector (SNSPD) that can work efficiently at two target wavelengths and has a significant improvement in the counting rate due to the low-filling-factor nanowires in the silicon slots and the silver reflector. Numerical simulations show that the absorption efficiency can be over 90% at a single wavelength, whether the incident light wavelength is 1550 nm or 1064 nm, and over 80% when the SNSPD is designed to work at both wavelengths. In addition, the reset time is about 24% of the conventional SNSPDs. Our work presents a design for fabricating faster, larger-area, and multispectral SNSPDs with a high efficiency, which can be applied in applications such as quantum optics communication and multiwavelength sensing.
Workshop—Ground Truthing and Virtual Field Trips

This workshop will provide iLRN 2020 delegates with an opportunity to extend their knowledge of shared immersive virtual environments (IVEs) in the context of teaching and learning through co-creation. The workshop will begin by outlining methods for capturing digital media in-situ, establishing the ‘ground truth’ of a place as both teaching preparation and as a learning activity in its own right. The workshop will then demonstrate how data captured in physical locations can be incorporated into online 3D models that can be experienced as a ‘virtual field trip’ in social virtual reality (VR). This activity highlights the student’s constitutive role in the selection and capture of data representing their environment and encourages active learning through engagement with the creation of IVEs. Workshop participants will be taken on a virtual expedition into one such model to stimulate further discussion about how these environments can be used to promote active learning. While the session focuses on the use of captured media to establish the ground truth of a location, the session will also consider the further potential of social IVEs as opportunities for storytelling, repositories of shared experience, and as a means for imagining and experimenting with counter-factual histories and alternative futures.
Convolutional Neural Network and Topic Modeling based Hybrid Recommender System

In today's personalized business environment, organizations are providing bulk of information regarding their products and services.Recommender system has various accomplishment on exploiting auxiliary information in matrix factorization.To handle data sparsity problem most recommender systems utilized deep learning techniques for in-depth analysis of item content to generate more accurate recommendations.However, these systems still have a research gap on how to handle user reviews effectively.Reviews that were written by users contain a large amount of information that can be utilized for more accurate predictions.This paper proposes a Hybrid Model to address the sparsity problem, convolutional neural network and topic modeling for recommender system, which extract the contextual features of both items and users by utilizing Deep Learning Convolutional Neural Network (CNN) along with Topic Modeling (Lda2vec) technique to generate latent factors of user and item.Topic Modeling is used to capture important topics from side information and deep learning is used to provide contextual information.To demonstrate the effectiveness of the research, an extensive experimental sets were performed on four public datasets (Amazon Instant Video, Kindle store, Health and Personal Care, Automotive).Results demonstrate that the proposed model outperformed the other state of the art approaches.
Analysis of student activity on the e-learning course based on «OpenEdu» platform logs

A lot of people nowadays use online education platforms. Most of them run on the free «Open edX» open-source software platform. Using the logs that the platform provides us, we can get psychometrics of students, which can be used to improve the presentation of material or other things, which can increase the quality of online courses. We provide a ready-to-use tool that will help figure out how and for what purpose you can analyze the log files of platforms based on «Open edX».
Squeezing in the quantum Rabi model with parametric nonlinearity

The squeezing effect arising in the interacting qubit-oscillator system is studied with the presence of a parametric oscillator in the Rabi model. To solve the system, we employ the generalized rotating wave approximation which works well in the wide range of coupling strength as well as detuning. The analytically derived approximate energy spectrum portrays good agreement with the numerically determined spectrum of the Hamiltonian. For the initial state of the bipartite system, the dynamical evolution of the reduced density matrix corresponding to the oscillator is obtained by partial tracing over the qubit degree of freedom. The oscillator’s reduced density matrix yields the nonnegative phase space quasi-probability distribution known as Husimi Q-function which is utilized to compute the quadrature variance. It is noticed that the squeezing produced in the oscillator sector gets reduced with increasing coupling strength. We demonstrate the role of the parametric term to obtain adequate squeezing in the strong coupling regime. We also study the revival–collapse phenomenon and the generation of squeezed coherent state.
Cybercrime and Pakistan

Cybercrime or electronic crime is a very diverse and expanding phenomenon. It has no boundaries. For people from different walks of life, its really hard to understand it. No exact definition of cybercrime can be given. Cybercrime in Pakistan has immensely increased in the past two decades. We had no digital forensic and information data experts. No latest system was available. Previously we had no legislation on cybercrimes until when Prevention of Electronic Crimes Act (PECA) 2016, was passed by the government. This research paper analyzes cybercrime, its meaning and origination, different difficulties, laws of cybercrime and its punishment given under the PECA. Some deficiencies along with few suggestions have been highlighted in the current setup of cybercrime and policy making at the end.
The book market during digitalization epoch: What should the libraries know. &lt;i&gt;(Based on the materials of 2019 London Book Fair)&lt;/i&gt;

The paper continues to review ideas, technologies and solutions discussed at the London Book Fair 2019. There are two through lines of technology development. First, our “new” technology is derivative of earlier technologies, similar to Darwinian evolution. Second, each of those earlier technologies resulted in upheaval, uncertainty, and unrest. The blockchain platform is already taking a transformative role in supply chain management, music licensing, recordkeeping. Rights transactions for creative works happen in myriad ways. First you have a book deal, then video deal, then e-book licensing deal or a video game deal or a merchandising deal. With blockchain, all those deals can be united in a “smart contract”. The launch by the European Research Council of the Coalition S consortium with the goal of making “full and immediate Open Access to research publications a reality” was a major development in open access last year. Open access must be based on a sustainable model. Copyright was created to encourage and reward creativity. We should not expect copyright to constantly contort itself into something for which it was not intended. Policy focus is now on "democratizing knowledge” and ensuring as many people as possible can tap into this rich natural resource of mankind.
USING SEMANTICALLY PAIRED IMAGES TO IMPROVE DOMAIN ADAPTATION FOR THE SEMANTIC SEGMENTATION OF AERIAL IMAGES

Abstract. Modern machine learning, especially deep learning, which is used in a variety of applications, requires a lot of labelled data for model training. Having an insufficient amount of training examples leads to models which do not generalize well to new input instances. This is a particular significant problem for tasks involving aerial images: often training data is only available for a limited geographical area and a narrow time window, thus leading to models which perform poorly in different regions, at different times of day, or during different seasons. Domain adaptation can mitigate this issue by using labelled source domain training examples and unlabeled target domain images to train a model which performs well on both domains. Modern adversarial domain adaptation approaches use unpaired data. We propose using pairs of semantically similar images, i.e., whose segmentations are accurate predictions of each other, for improved model performance. In this paper we show that, as an upper limit based on ground truth, using semantically paired aerial images during training almost always increases model performance with an average improvement of 4.2% accuracy and .036 mean intersection-over-union (mIoU). Using a practical estimate of semantic similarity, we still achieve improvements in more than half of all cases, with average improvements of 2.5% accuracy and .017 mIoU in those cases.
AUDIT SISTEM INFORMASI ABSENSI PADA KEJAKSAAN NEGERI KOTA BANDUNG MENGGUNAKAN FRAMEWORK COBIT 5

Kejaksaan Negeri Kota Bandung is a unit of the state institutions that carries out state power, particularly in the field of prosecution. Kejaksaan Negeri Kota Bandung implemented an information system to intensify operational activities. Using a computerized tool that is FaceUnlock to record the attendance list of employees. The system must be able to manage, convey, and maintain information security properly. Then, an audit is needed to evaluate the governance of the current information system. In this research, the capability model is used as a measurement of respondent's answers from a questionnaire based on the COBIT 5 framework with the MEA (Monitor, Evaluate, and Assess) domain as a reference. Respondents who were involved in filling out the questionnaire were employees of Kejaksaan Negeri Kota Bandung who carried out SIMPEG operations on daily. Based on the recapitulation of the answers from the respondents, the value of the current capability level is 2.4 in the range 1-4. To get the expected results, there some alternative to intensify SIMPEG's performance at Kejaksaan Negeri Kota Bandung in the future.
Proposal for a Software Architecture as a Tool for the Fight Against Corruption in the Regional Governments of Peru

This paper covers the problem of corruption inPeru, with an emphasis on regional governments, and presents a proposal for an anti-corruption software application architecture for those levels of government.The design of the proposal starts from the analysis of corruption encompassing statistical studies, trust evolution, government management, legal situation and incidents in information technology.Also, aspects of the budget allocation, crime data, political party financing data, management resources, contracting processes, integration systems and citizen participation are presented, for the subsequent presentation of the data structure and resources for the software application architecture.The methodology used is of an exploratory documentary type.In addition, a systemic approach and development are considered in three layers: data persistence, logical process, and presentation; considering the interrelationships that must exist between them for the development of the proposed architecture.
Ontology-based Course Teacher Assignment within Universities

Educational institutions suffer from the enormous amount of data that keeps growing continuously.These data are usually scattered and unorganised, and it comes from different resources with different formats.Besides, modernization vision within these institutions aims to reduce human action and replace it with automatic devices interactions.To have the full benefit from these data and use it within the modern systems, they have to be readable and understandable by machines.Those data and knowledge with semantic descriptions make an easy way to monitor and manage decision processes within universities to solve many educational challenges.In this study, an educational ontology is developed to model the semantic courses and academic profiles in universities and use it to solve the challenge of assigning the most appropriate academic teacher to teach a specific course.
Implementation of Digital Video Watermarking Scheme Based on FPGA

Objective of this scheme is to develop low power, robust and secure watermarking system for authentication of video.Here we present an FPGA based implementation of an invisible watermarking encoder.It consists of a watermark generator module and watermark insertion module.The system is initially simulated and tested for various attacks in MATLAB/ Simulink® and then prototyped on VERTEX-6 FPGA using VHDL.The watermarked video is same as that of original video with an average Peak-Signal-to-Noise Ratio (PSNR) of 46 dB.
Text documents clustering using modified multi-verse optimizer

In this study, a multi-verse optimizer (MVO) is utilised for the text document clus- tering (TDC) problem. TDC is treated as a discrete optimization problem, and an objective function based on the Euclidean distance is applied as similarity measure. TDC is tackled by the division of the documents into clusters; documents belonging to the same cluster are similar, whereas those belonging to different clusters are dissimilar. MVO, which is a recent metaheuristic optimization algorithm established for continuous optimization problems, can intelligently navigate different areas in the search space and search deeply in each area using a particular learning mechanism. The proposed algorithm is called MVOTDC, and it adopts the convergence behaviour of MVO operators to deal with discrete, rather than continuous, optimization problems.&#x0D; For evaluating MVOTDC, a comprehensive comparative study is conducted on six text document datasets with various numbers of documents and clusters. The quality of the ﬁnal results is assessed using precision, recall, F-measure, entropy accuracy, and purity measures. Experimental results reveal that the proposed method performs competitively in comparison with state-of-the-art algorithms. Statistical analysis is also conducted and shows that MVOTDC can produce signiﬁcant results in comparison with three well-established methods.
ANALISIS KEMAMPUAN MENULIS PANTUN SISWA KELAS V SD

AbstrakPenelitian ini bertujuan untuk mendeskripsikan aspek-aspek kemampuan menulis pantun siswa kelas V SD. Metode yang digunakan peneliti adalah metode deskriptif kualitatif.Sumber data dalam penelitian ini adalah jawaban tes perbuatan pantun karya siswa kelas V SD. Teknik analisis data yang digunakan adalah validitas data dan realibilitas data. Analisis data diperoleh dari aspek-aspek kemampuan menulis pantun siswa kelas V SD yang menunjukkan bahwa (1)8 siwa yang mendapatkan kategori penilaian sangat baik dengan hasil persentase 30,76%, (2) 6 siswa yang mendapatkan kategori penilaian baik dengan hasil persentase 23,07%, (3) 2 siswa yang mendapatkan kategori penilaian cukup dengan hasil persentase 07,69%, (4) 6 siswa yang mendapatkan kategori penilaian kurang dengan hasil persentase 23,07%, dan (5) 4 siswa yang mendapatkan kategori penilaian gagal dengan hasil persentase 15,38%. Kesimpulan dari penelitian ini bahwa rata-rata nilai siswa dalam menulis pantun yaitu 70 dengan kategori baik. Kata kunci : analisis, menulis pantunAbstractThis study aims to describe aspects of the ability to write rhymes for fifth-grade elementary school students. The method used by researchers is a qualitative descriptive method. The source of the data in this study is the answer to the act of rhyme test by the fifth-grade elementary school students. Data analysis techniques used are data validity and data reliability. Data analysis was obtained from aspects of the ability to write rhymes for fifth-grade elementary school students which showed that (1) 8 students who received very good assessment categories with the percentage results of 30.76%, (2) 6 students who got good assessment categories with percentage results of 23, 07%, (3) 2 students who get enough assessment categories with percentage results 07.69%, (4) 6 students who get less assessment categories with percentage results of 23.07%, and (5) 4 students who get assessment categories failed with a percentage of 15.38%. The conclusion from this study that the average value of students in writing rhymes is 70 with a good category. Keywords: analysis, poetry writing
Effect of Solar Tilt Angles on Photovoltaic Module Performance: A Behavioral Optimization Approach

In the present study, performance analyses of a solar module are made through the optimal variation of solar tilt angle, pertaining to the maximum generation of solar energy. The work has been carried out for a particular location at Tripura, in India, considering three different cases on an annual basis. An intelligent behavioural based algorithm, known as artificial bee algorithm (ABC), has been implemented for finding the optimal orientation of solar angle in analysing the performance. The result shows marginal differences are obtained in producing yearly maximum solar energy for different orientations of the PV module. It has been observed that the maximum average solar energy is obtained for the case where continuous adjustment is made by rotating the plane about the horizontal east-west axis within 20° to 30° tilt angle. The computed maximum and minimum of the monthly average efficiency is 10.9% and 8.7%, respectively. Further, a comparative study has been performed in generating average solar energy through optimal tilt angle by the implementation of Perturb &amp; Observe method (P&amp;O). The monthly average solar power computed by P&amp;O method resulted better in a range of 2% to 15% in comparison to that obtained by ABC. While on the other hand, the efficiency computed by ABC algorithm was 15% to 19% better than that evaluated by P&amp;O method for all the cases studied in the present work.
UNDERWATER IMAGE ENHANCEMENT BASED ON POLARIZATION IMAGING

Abstract. The need of high-quality underwater imaging is obviously required in many underwater applications. For example, underwater archaeology, underwater ecological research, underwater object detection and tracking. This paper presents a joint enhancing and denoising scheme for an image taken in underwater conditions. Conventional image enhancing methods may amplify the noise depending on the distance and density of the particles in the water. To suppress the noise and improve the enhancement performance, an imaging model is modified by adding the process of amplifying the noise in underwater conditions. This model offers depth-chromaticity compensation regularization for the transmission map and chromaticity-depth compensation regularization for enhancing the image. The proposed iterative underwater image enhancing method with polarization uses these two joint regularization schemes and the relationship between the transmission map and enhanced irradiance image. The transmission map and irradiance image are used to promote each other. To verify the effectiveness of the algorithm, polarizing images of different scenes in different conditions are collected. Different algorithms are applied to the original images. Experimental results demonstrate that the proposed scheme increases visibility in extreme conditions without amplifying the noise.
Factors influencing on extra budgetary income of leading Russian universities: econometric analysis

This study is aimed to study the mechanisms that contribute to increasing the economic independence of universities, which in conditions of high dependence on-budget funding is exceptionally relevant in Russia. Even though the share of extra-budgetary revenues is growing at most Russian universities, this growth is heterogeneous and partly due to the “low base effect”. To determine the factors of economic independence of universities, we used data on the 49 largest universities in Russia over four years, taken from monitoring the effectiveness of higher educational institutions. The primary research method is regression analysis using panel data models. The analysis reveals that the share of extra-budgetary funds of the university is positively influenced by factors such as the number and quality of publications, the number of journals published by the university, and the ratio of employees to the regional average. These dependencies are important in the long-term comprehensive planning of university activities.