Reviewerly/exHarmony huggingface dataset:
Graph Degree Linkage Clustering for Identify Student’s Performance on Kompetisi Sains Madrasah in Indonesia

Graph degree linkage (GDL) algorithm is a development of agglomerative clustering and graph. The clustering algorithm can be applied to various problems, such as student performance. Kompetisi Sains Madrasah (KSM) is one of the competitions by integrating science and Islam held in Indonesia. The final score of the competition will be used in this study to identify the students’ performance who participate in the competition. The main goal of the study is obtaining the results of the participant’s performance clusters based on the final score of KSM and obtain the information about the quality of students in schools that are participating in the competition. Based on the research, we obtain the best K-neighborhood value of three clusters is equal to 25. The research obtains the silhouette coefficient value for clustering evaluation. They are 0.5104, 0.4838, 0.6853, 0.5943, 0.6605, 0.8037, 0.6455, 0.6723, 0.6996, 0.5767, and 0.6695 in mathematics, natural science, mathematics, natural science, social science, mathematics, biology, physic, chemistry, economics, and geography subjects. The identification of school student performance in each subject tested shows that State Islamic School student performance in KSM has the best performance in elementary and middle-high grade. In senior grade, the best students’ performance in KSM is from Private Islamic School.
Generalized Krawtchouk polynomials and the composition scheme

In this article, the eigenmatrix of an association scheme called the composition scheme is obtained and expressed in terms of generalized Krawtchouk polynomials. It is established that generalized Krawtchouk polynomials can be expressed in terms of characters, and this result is used to generalize a previously known theorem about eigenmatrices of the Hamming scheme, whose eigenmatrices are given in terms of Krawtchouk polynomials.
Flood Hazard Risk Matrix for Urbanization Control Area of Flood Plain

Identification and understanding the potential flood risk area are important elements in disaster risk reduction through development planning control plan. The development growth in Merlimau area causes demand for new urbanization area for residential, commercial and public infrastructure. Therefore, the risk of flood should evaluate to identify the possible future flood hazard-risk area based on future land use in Merlimau. This paper describes an approach to produce flood hazard and risk matrix and map using a combination of flood numerical modelling and Geographical Information System (GIS) analysis in Merlimau River Basin. The result from this study is a flood hazard risk map that provides information on flood risk level in Merlimau basin, which further can contribute to future development planning. This matrix and map identified as the risk areas that suggested being an Urbanization Control Area (UCA) to control future development. Six locations identified as the UCA that involved about 26.27 hectares. The analysis also shows almost 80% or 21.03 hectares of UCA already been planned for residential area and about 18% or 4.78 hectares for industrial area. There are only about 2% or 0.46 hectare of UCA remain in the future development plan.
A Smart Document Converter: Conversion of Handwritten Text Document to Computerized Text Document

With the advancement of technologies many time-consuming tasks are automated using different software without direct involvement of human beings. Particularly in Educational Service industry, teachers and students are making so many good handwritten notes for future references. Even though they spent a good amount of time to prepare their notes, but they find it very difficult to update if required. At the same time if they want to share their notes among friends or students then the only option is either to make photo copies (Xerox) or by taking pictures and share as images. In both the cases, the note cannot be updated. Although many solutions are exists for converting text image to computerized text, but all are not efficient in terms user feedback. Hence, in this research article, a novel approach is presented to convert handwritten images to computerized text document. In this approach, first the handwritten characters are extracted from the input image using digital image processing techniques and then these characters are recognized by using machine learning techniques. In this paper convolution recurrent neural network (CRNN) technique is used.
Arquivista de dados: análise do perfil profissional em sites de bancos internacionais de trabalho

In the context of the data age, the world labor Market has been in need of professional increasingly qualified to work with data. Among existing professionals there is the professional data archivist, a professional almost unknown in Brazil. In this way, the overall objective was to analyze the job requirements of the data archivist on internacional job bank sites for five years. The specific objectives are: to describe the requirements of the position of data archivist regarding the categories: education, experience, knowledge, skills and competences. Compare and analyze the results of the research with the results of the research carried out by Lyon et al (2015). The methodological path consisted of bibliographic, exploratory, quanti-qualitative approach. Five referred bank sites were investigated: The Cronicle of Higher Education’s Vitae, ALA JobList, Jobs and IASSIST. As for the results, only IASSIST presented vacancies for data archivist, with five existing vacancies. The research pointed out a versatile, dynamic and analytical profile that is increasingly evident for the position of data archivist. As for the comparative study, there was a search for an Archive Science professional oriented to data curation and management mixed with excellence in organizational and interpersonal relationships.
Learning Deformable Kernels Predict Network for Burst Images Denoising

Mobile phones are becoming more common, people has higher requirement about the quality of the pictures that captured by handheld device, in order to reduce the noise in image, we studied the prediction network (KPN) denoising algorithm, to overcome the disadvantage of low efficiency of traditional convolution receptive field, a deformable kernel prediction network (DEF_KPN) algorithm for burst images denoising is proposed in this paper. The deformable convolution structure is used to preprocess the noise image, so that the kernel prediction network can generate a pixel-by-pixel filter core more suitable for the image structure, so as to make the image contour more clearer after denoised. We obtain bayer image by anti-ISP method, and then synthesizing the training data by jitter clipping and adding poissonian-gaussian noise. Applying Adam algorithm and annealing strategy to train neural network to convergence. The experiment results show that; the DEF_KPN algorithm proposed in this paper is superior to the KPN algorithm in both synthetic noise data and real noise data. In terms of synthetic noise data, the PSNR index is improved by about 0.5db. On the real noise data, PSNR index was increased by 0.5dB. The SSIM index has been improved by 0.005. Experiments on burst mobile phone bayer images illustrate that : The neural network trained by our method can successfully de-noise the multi-frame images taken by the phone.
Verifiable Secret Share for file storage with cheater identification

Verifiable Secret Share (VSS) is a branch of Secret Share (SS) that allow Verifiable Secret Share (VSS) is a branch of Secret Share (SS) that allows verifying the correctness of recomposed files. VSS techniques usually check the correctness of secrets at the end of the re-composition process, therefore, in case of errors, a lot of computational resources and time are wasted. In this paper, we propose an innovative VSS model that is able to verify the correctness of SS fragments before the ending of the recomposition task, thus increasing efficiency and response time. The basic idea behind the proposed approach is to make use of a Hash function to validate fragments, thus to decide if the recomposition task can be performed or not. The experimental result validates our model and proves its applicability in distributed storage systems.
A 3D digital Jordan–Brouwer separation theorem

We introduce and discuss a concept of connectedness induced by an n-ary relation ( $$n>1$$ an integer). In particular, for every integer $$n>1$$ , we define an n-ary relation $$R_n$$ on the digital line $$\mathbb {Z}$$ and equip the digital space $$\mathbb {Z}^3$$ with the n-ary relation $$R_n^3$$ obtained as a special product of three copies of $$R_n$$ . For $$n=2$$ , the connectedness induced by $$R_n^3$$ coincides with the connectedness given by the Khalimsky topology on $$\mathbb {Z}^3$$ and we show that, for every integer $$n>2$$ , it allows for a digital analog of the Jordan–Brouwer separation theorem for three-dimensional spaces. An advantage of the connectedness induced by $$R_n^3$$ ( $$n>2$$ ) over that given by the Khalimsky topology is shown, too.
Randomness Evaluation of the Improved 3D-Playfair (i3D) Cipher Algorithm

In cryptography, two things are highly valued to secure the exchange of communication. One is the secrecy of key generated by a cryptographic algorithm, and the other one is its ability to have an unpredictable sequence in the message. The i3D-Playfair is a symmetric block cipher algorithm designed to improve the existing 3D (4×4×4) Playfair cipher algorithm. The improvement focuses on securing the key and likewise enhancing the message encryption. Randomness determines the cryptographic strength of an algorithm. Evaluating the p-value in statistical tests identifies the binary sequence as random or non-random. This paper uses the five selected NIST Statistical Test Suites battery tests to get the probability p-value in determining the randomness of a binary sequence. NIST STS is a standard statistical test for randomness. The selected statistical tests are the following: frequency (monobit) test, frequency test within a block, runs test, the longest run test of 1's in a block, and cumulative sums test. The experimental result shows that all the statistical tests have a passing p-value that is greater than 0.01 (p-value≥ 0.01). Also, the p-value of p-values (POP) of all the statistical tests is greater than or equal to 0.0001, showing uniformity of p-values. The histogram of all the tests also shows that the p-value is normally distributed. Furthermore, results show that all the randomness tests conducted execute an acceptable proportion that is above 57 or was able to achieve a passing percentage above 95%. Thus, the proposed i3D-Playfair algorithm generates random numbers. It is then strongly suggested to implement i3D-Playfair Cipher in software applications and messaging to secure data transmission.
EFL Learners' Perceptions of Vlog Projects to Facilitate Group Collaboration and Learning Participation

This case study explored whether the implementation of applying video blog (vlog) projects facilitated learners' group collaboration skills and their learning participation in an EFL learning classroom. Sixty-two participants applied their smartphones to produce two vlog videos during the eight weeks of intervention. The participants filmed two three-minute vlogs on the teacher-assigned topics, which were related to the class textbook content. After completing two vlog projects, the students voluntarily completed a questionnaire of group collaboration and six of the 62 students participated in a focus-group interview at the end of the intervention period. Results revealed that the students increased the number of words when they spoke in the vlogs. The results indicated that 1) students enjoyed working in groups to complete the vlog projects in English learning from the questionnaire results; and 2) they enjoyed speaking English and group collaboration while filming vlogs from the focus-group interviews. In addition, the students also recognized the challenges of integrating vlog projects as learning activities in EFL learning. Limitations and future research suggestions are provided.
Some Designs and Normalized Diversity Product of n×n Lattice-Based Diagonal Space-Time Codes in Z[ζ<sub>m</sub>]

Space-time code designs have attracted considerable attentions. There have been several kinds of space-time block code designs, lattics based diagonal space-time block code designs using algebraic number theory. On the basis of the number theory, we construct several kinds of space-time codes about lattices by using the method of [14]. We give a class 2×2 lattice space-time code with a large normalized diversity product. The diagonal algebraic space-time code was more extensive than reported in reference. We also mainly design diagonal space-time block codes from the algebraic extension approach for three and four transmit antennas by using some of the best known high order algebraic extensions on Z[i] or Z[ζ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sub> ], and some determinants of the corresponding generating matrix of the complex lattice have smaller values.
M.I.R.A. Morandini, Architecture and Immersive Reality: A Realized Example of Shifting Immersive Reality from Representation to the Final Outcome

M.I.R.A. Morandini is the acronym for Augmented Reality Immersive Museum dedicated to the intellectual and poet Luciano Morandini. The M.I.R.A. Morandini is the first prototype of an immersive museum with both real contents and virtual ones, where the visitors can move freely. M.I.R.A. Morandini shifts the use of XR from a tool for visualization and/or representation to the final outcome. M.I.R.A. Morandini is a concrete example of how architects should be thinking about XR as not just a tool to design and visualize physical spaces, but as the final product, a space to design, with attention to the spatial experience.
Improving Bi-Real Net with block-wise quantization and multiple-steps binarization on activation

Quantization neural networks use low-precision lowbit for both weights and activations, which are used for portable, low-power devices. And binary neural networks (BNNs) is an extreme case of quantization network in which both weights and activations are in a 1-bit representation. Binary neural networks replace expensive convolutional operations with fast bitwise operations such as xnor and popcount. While being efficient in the inference task, training BNN is very difficult because the training process is easy to be stuck on bad local minima. Besides, BNNs also suffer a problem of severe accuracy degradation. So how to train binary neural networks more effectively with higher accuracy? We answer this question by proposing a new training strategy on state-of-the-art binary neural network Bi-Real net. Our proposed strategy uses 3-steps binarization for activations, which helps to gradually reduce quantization error in the training phase. Also, we apply block-wise binarization to quantize block by block when training. Finally, we add a new regularization term into loss, to drive weights to -1 or 1. The comprehensive experiments on ImageNet show that our proposed training algorithm surpasses other binarization methods.
Parallel generalized elliptical slice sampling with adaptive regional pseudo-priors

MCMC algorithm is well-known for having difficulty exploring distant modes when the target distribution is multi-modal. The reason is that a proposal state is likely to be rejected when traversing across low density regions. Focusing on this issue, we proposed parallel generalized elliptical slice sampling algorithm with adaptive regional pseudo-prior (RGESS). Different from the work of Fagan et al. [2016. Elliptical slice sampling with expectation propagation. In: UAI] and Nishihara et al. [Parallel mcmc with generalized elliptical slice sampling. J Mach Learn Res. 2014;15:2087–2112], different pseudo-priors are used at different regions to conduct the generalized elliptical slice sampling (GESS) algorithm. The rejection rate is modified to guarantee detailed balance condition. We also employ adaptive transition kernel and parallel computing to accelerate sampling speed. Experimental results on one synthetic and one real-world dataset show that the proposed algorithm has the following advantages: with the same starting points, the proposed algorithm spend less time to find the modes of the target distribution; after finding the modes, the proposed algorithm has less interactions between modes due to parameters adaption, which leads to lower rejection rate; when estimating the parameters of multi-modal posterior distributions, the samples generated by proposed algorithm can find different modes better.
GPU Based Angular Radial Transform for Image Analysis

Angular radial transform (ART) is a shape descriptor in MPEG-7 for image representation and image analysis. For real world applications, execution efficiency is always a significant challenge. With widespread use of Graphics Processing Unit (GPU) in image processing tasks, this paper presents GPU based angular radial transform (GART). Proposed parallel GART is based on mathematical properties of ART and optimization techniques of GPU. Optimal parameter selections for GPU execution are also evaluated. In our experiments, with same computation result GART is over 1800 times faster than ART. Wide range of applications that using ART will inspired from this work.
Building natural language processing tools for Runyakitara

Abstract This paper describes an endeavour to build natural language processing (NLP) tools for Runyakitara, a group of four closely related Bantu languages spoken in western Uganda. In contrast with major world languages such as English, for which corpora are comparatively abundant and NLP tools are well developed, computational linguistic resources for Runyakitara are in short supply. First therefore, we need to collect corpora for these languages, before we can proceed to the design of a spell-checker, grammar-checker and applications for computer-assisted language learning (CALL). We explain how we are collecting primary data for a new Runya Corpus of speech and writing, we outline the design of a morphological analyser, and discuss how we can use these new resources to build NLP tools. We are initially working with Runyankore–Rukiga, a closely-related pair of Runyakitara languages, and we frame our project in the context of NLP for low-resource languages, as well as CALL for the preservation of endangered languages. We put our project forward as a test case for the revitalization of endangered languages through education and technology.
Factors Promoting Software Project Escalation: A Systematic Review

A vast amount of resources is wasted on software projects delivering less than the planned benefits. The objective of this paper is to investigate the tendency to continue a project even when it is evident that it will not provide the expected benefits, often referred to as “project escalation” or “escalation of commitment.” We aim to identify factors that empirically have been found to promote software project escalation. We examined 1376 papers related to the phenomenon of escalating commitment to software projects and found that 44 of them included relevant empirical research. After reviewing these papers, we synthesized the results. We provide an overview of 46 factors that have been found to promote software project escalation. Thirteen of the factors were project-related, 20 were psychological factors, nine were social factors, and four were structural factors related to the project's contextual dimensions. We contribute to practice by systemizing empirical evidence of software project escalation that can be of help in avoiding it in the first place and uncovering already escalated situations. As most studies are investigations of only a few factors, we propose further research to study how factors potentially interplay as they contribute to escalation of software projects.
Dimension Detection of Varistor Based on Random Hough Transform

An improved method based on edge detection and random Hough transform is proposed for industrial detection of varistors. Firstly, using the improved edge detection to detect the contour, the protruding part can be smoothed effectively, making it more fit to the circular feature without changing the size. Then, the improved random Hough transform is used to accurately locate the circle, the properties of the circle are determined by using three points, three non-collinear pixel points are extracted from the edge point, the circle satisfying the conditions is found, and then the fourth point is randomly taken out. If the difference between the distance and radius from the point to the center of the circle is within the threshold 1, it may be true circle. Then, if the difference between the center and radius of the detected circle is greater than the threshold 2, the detected circle is determined to be true circle. Otherwise, the duplicate circle is deleted.
A Fair and Anonymous Payment System for the Onion Relays

Tor, aka ‘The Onion Router’, is a protocol that allows anonymous browsing of the web in the sense that the network address of the client is not known to a single relay or even relays provided by the same provider. The relay that knows the target does not know the source and vice versa. However, the current Tor relays are voluntary and unpaid, doing a social service to protect the identities of people who want to stay anonymous while browsing the web. We propose a modification of the Tor protocol so that the client can anonymously pay the Tor relays for their services through a smart-contract enabled cryptocurrency. Our design ensures that the Tor relays cannot be paid unless the client gets the data and that the client cannot get the data without paying the Tor relays.
Recursive Error Division Kalman Filter for Advanced Driver Assistance Radar Systems

This paper presents a time-domain analysis for digital Infinite Impulse Response filters used in tracking applications. It provides information on how such a filter may be used as discrete-time estimator, and even more so, offer valuable information on the rate of change of the input signal. An example of an application where the proposed acquisition-synchronous filter is useful, is in automotive radar-based driver assistance systems. The filter is responsible for estimating the velocity and acceleration of the tracked targets, which are then fed into a range- only Kalman Filter Tracker. The proposed algorithm fusion not only reduces computational complexity, but also improves performance. Unlike alpha-beta-gamma filters, the present method can also be extended to non-Gaussian distributions for Extended Kalman Filters.