Reviewerly/exHarmony huggingface dataset:
The development of Iqra' Lampung script teaching materials for primary school levels in Bandar Lampung city

The Lampung Provincial Government requires all primary and secondary schools to provide Lampung language lessons and introduction to Lampung script. Some students only recognize the Lampung Khaganga script, which amounts to 20 characters, while the combination of the main character and 12 characters that produces 560 syllables of the Lampung script is not yet fully understood. In fact, knowledge of Lampung script is still lacking, even some students do not understand Lampung script, it can be said that learning Lampung script at elementary school level has not been successful. This could be due to a lack of modules (teaching materials) that support learning. The research aims to develop Lampung language teaching materials in Lampung script material in the form of Iqra' Lampung script as teaching material for elementary school level. This study adopted a research and development (R&amp;D) method proposed by Gall, Borg &amp; Gall (2007). The work step procedure includes: 1) preliminary study, 2) product development, 3) product validation test and revision. Based on the research that has been done, it can be concluded that the teaching material (module) is feasible because the average value is 4.588 from the teacher validator and 4.446 from the lecturer validator. If it is based on a scale of 5 it can be concluded that the Iqra' Lampung script teaching material has a very good value of teachers and good grades from expert lecturers in terms of material aspects, language, presentation, the effect of teaching materials on learning strategies, and overall appearance feasibility. Modules are basically teaching materials that are systematically using language that is easily understood by students according to their level of knowledge and age so that they can learn independently or without minimal guidance from teachers.
Digital Certificates Using Blockchain: An Overview

India is a nation with the largest student body in the world which comprises around 315 million students. A numerous number of students pass out every year with various qualifications. During graduation, the students all kinds of excellent performance certificates, score transcripts, degree’s, diplomas, etc., will become an important reference for admitting to new institutions or new workplaces. With such a big figure of graduate students every year it is very difficult to determine whether the degree or any educational document is valid or not tampered at all as there is no such authority which verifies or validates any educational document as well as become an institution that generates it. With the help of blockchain, we are proposing a system which will generate digital certificates which cannot be forged and can be later used for downloading if misplaced and for verification while going for admission or job process.
Predicting Expository Text Processing: Causal Content Density as a Critical Expository Text Metric

In this investigation, we examine the contribution of intrinsic content density (ICD) to measures of expository text processing. In Studies 1 and 2, the factor structure of select text density metrics was examined and refined using two text samples (Ns = 150) randomly selected from an expository text corpus. Scores on the ICD measure based on the entire text sample (N = 300) explained unique variance in readability and text easability. In Study 3, ICD predicted adults’ text ratings of interest and ease of comprehension above and beyond established easability measures. Participants’ text familiarity moderated the relation between ICD and ease of comprehension, revealing a density-facilitative effect for participants more familiar with the text content. Finally, in Study 4, measures of text difficulty, processing, and comprehension were obtained from adult readers using 10 researcher-constructed science texts; evidence of descriptive density effects on each measure was obtained. Implications for future research are discussed.
EvalMe: Exploring the Value of New Technologies for In Situ Evaluation of Learning Experiences

Tangible interfaces have much potential for engendering shared interaction and reflection, as well as for promoting playful experiences. How can their properties be capitalised on to enable students to reflect on their learning, both individually and together, throughout learning sessions? This Research through Design paper describes our development of EvalMe, a flexible, tangible tool aimed at being playful, enjoyable to use and enabling children to reflect on their learning, both in the moment and after a learning session has ended. We discuss the insights gained through the process of designing EvalMe, co-defining its functionality with two groups of collaborators and deploying it in two workshop settings. Through this process, we map key contextual considerations for the design of technologies for in situ evaluation of learning experiences. Finally, we discuss how tangible evaluation technologies deployed throughout a learning session, can positively contribute to students’ reflection about their learning.
Graph Based Transforms based on Graph Neural Networks for Predictive Transform Coding

This paper introduces the GBT-NN, a novel class of Graph-based Transform within the context of block-based predictive transform coding using intra-prediction. The GBT-NNis constructed by learning a mapping function to map a graph Laplacian representing the covariance matrix of the current block. Our objective of learning such a mapping functionis to design a GBT that performs as well as the KLT without requiring to explicitly com-pute the covariance matrix for each residual block to be transformed. To avoid signallingany additional information required to compute the inverse GBT-NN, we also introduce acoding framework that uses a template-based prediction to predict residuals at the decoder. Evaluation results on several video frames and medical images, in terms of the percentageof preserved energy and mean square error, show that the GBT-NN can outperform the DST and DCT.
The game of Cops and Eternal Robbers

We introduce the game of Cops and Eternal Robbers played on graphs, where there are infinitely many robbers that appear sequentially over distinct plays of the game. A positive integer t is fixed, and the cops are required to capture the robber in at most t time-steps in each play. The associated optimization parameter is the eternal cop number, denoted by ct∞, which equals the eternal domination number in the case t=1, and the cop number for sufficiently large t. We study the complexity of Cops and Eternal Robbers, and show that the game is NP-hard when t is a fixed constant and EXPTIME-complete for large values of t. We determine precise values of ct∞ for paths and cycles. The eternal cop number is studied for retracts, and this approach is applied to give bounds for trees, as well as for strong and Cartesian grids.
Fidelity of Statistical Reporting in 10 Years of Cyber Security User Studies

Studies in socio-technical aspects of security often rely on user studies and statistical inferences on investigated relations to make their case. They, thereby, enable practitioners and scientists alike to judge on the validity and reliability of the research undertaken.To ascertain this capacity, we investigated the reporting fidelity of security user studies.Based on a systematic literature review of 114 user studies in cyber security from selected venues in the 10 years 2006–2016, we evaluated fidelity of the reporting of 1775 statistical inferences using the R package statcheck. We conducted a systematic classification of incomplete reporting, reporting inconsistencies and decision errors, leading to multinomial logistic regression (MLR) on the impact of publication venue/year as well as a comparison to a compatible field of psychology.We found that half the cyber security user studies considered reported incomplete results, in stark difference to comparable results in a field of psychology. Our MLR on analysis outcomes yielded a slight increase of likelihood of incomplete tests over time, while SOUPS yielded a few percent greater likelihood to report statistics correctly than other venues.In this study, we offer the first fully quantitative analysis of the state-of-play of socio-technical studies in security. While we highlight the impact and prevalence of incomplete reporting, we also offer fine-grained diagnostics and recommendations on how to respond to the situation.
Creating Something Different: Similarity, Contrast, and Representativeness in Categorization

The ability to generate new concepts and ideas is among the most fascinating aspects of human cognition, but we do not have a strong understanding of the cognitive processes and representations underlying concept generation. In this paper, we study the generation of new categories using the computational and behavioral toolkit of traditional artificial category learning. Previous work in this domain has focused on how the statistical structure of known categories generalizes to generated categories, overlooking whether (and if so, how) contrast between the known and generated categories is a factor. We report three experiments demonstrating that contrast between what is known and what is created is of fundamental importance for categorization. We propose two novel approaches to modeling category contrast: one focused on exemplar dissimilarity and another on the representativeness heuristic. Our experiments and computational analyses demonstrate that both models capture different aspects of contrast’s role in categorization.
ILS-SUMM: Iterated Local Search for Unsupervised Video Summarization

In recent years, there has been an increasing interest in building video summarization tools, where the goal is to automatically create a short summary of an input video that properly represents the original content. We consider shot-based video summarization where the summary consists of a subset of the video shots which can be of various lengths. A straightforward approach to maximize the representativeness of a subset of shots is by minimizing the total distance between shots and their nearest selected shots. We formulate the task of video summarization as an optimization problem with a knapsack-like constraint on the total summary duration. Previous studies have proposed greedy algorithms to solve this problem approximately, but no experiments were presented to measure the ability of these methods to obtain solutions with low total distance. Indeed, our experiments on video summarization datasets show that the success of current methods in obtaining results with low total distance still has much room for improvement. In this paper, we develop ILS-SUMM, a novel video summarization algorithm to solve the subset selection problem under the knapsack constraint. Our algorithm is based on the well-known metaheuristic optimization framework - Iterated Local Search (ILS), known for its ability to avoid weak local minima and obtain a good near-global minimum. Extensive experiments show that our method finds solutions with significantly better total distance than previous methods. Moreover, to indicate the high scalability of ILS-SUMM, we introduce a new dataset consisting of videos of various lengths.
An inexact Newton method for unconstrained total variation-based image denoising by approximate addition

The Inexact Newton method using the conjugate gradient has been widely used to solve large-scale unconstrained optimization problems, such as the total variation-based image denoising. To improve energy and latency, this paper initially proposes an additional step length parameter (), such that the required number of iterations (and therefore its energy dissipation) decreases. Then, a floating-point adder (32-bits) made of approximate or truncated cells is utilized to reduce the energy dissipation in each iteration. These two techniques are finally combined to reduce the total processing time and energy dissipation for image denoising. The results show that significantly reduces the number of iterations; the proposed technique is tested on a set of images taken from a public domain library and is found that when 1.39<<1.45, the number of iterations is the lowest. Moreover, the energy dissipation of the denoising algorithm decreases by applying an approximate adder at a very small loss of accuracy and quality of the output image; the number of iterations remains constant when the number of approximate or truncated cells in the least significant positions (given by so-called NAB) is below 10.
Effect of Context and Distance Switching on Visual Performances in Augmented Reality

Augmented reality may lead the user to repeatedly look at different environments (real/virtual) and at different distances to process information. We studied how context and distance switching could (together or separately) affect users' performances. 29 participants (16 video game players) performed two tasks that required to switch between two screens (visual search and target detection task). These screens could be virtual (using HoloLens2) or real and placed at 1.5 or 2 meters. Distance switching had an impact only on visual search performances. Participants' levels of experience with video games modified the effect of context switching.
Hide Medical Images in a Speech Signal using DNA Coding and Fuzzy C-Means

Medical images possess great privacy, as the patient's information must be completely confidential if it is transferred from one hospital to another via a hospital intranet or the internet to take medical consultations by specialized doctors. This paper presents a safe new method to transfer medical images by hiding them within a speech signal file, and this is done at three levels: First, a scrambling algorithm proposal to ensure a change in the original medical image based on the binary representation of the image values and using the Zaslavsky map, the second level is encryption the scrambled image relying on DNA coding and a series of keys generated by a proposed hybrid algorithm based on (Tinkerbelle map and 3D Henon) that provided us with good image distortion and provided difficulty in retrieving it by any intruder. The last level is to hide the contents of encrypted medical images in a speech signal depending on (integer wavelet transform and LSB algorithm), then using the proposed algorithm based on (fuzzy c-means clustering algorithm and short-time energy) to determine the locations of speech to hide in it. The results of this paper showed that using statistical measures of hiding and encryption were good in maintaining the confidentiality of medical image information compared to previous researches. Consequently, the use of encryption and hiding techniques together has provided double protection against the problems of malicious manipulation and privacy leakage on the internet as well as lack the hospital's intranet to security tools.
Extending the Reverse JPEG Compatibility Attack to Double Compressed Images

The reverse JPEG compatibility attack has recently been introduced as a very accurate and universal steganalysis algorithm for JPEG images with quality 99 or 100. The limitation to these two largest qualities appears fundamental as the prior work on this topic suggests. In this paper, we provide mathematical analysis and demonstrate experimentally that this attack can be extended to double compressed images when the first compression quality is 93 or larger and the second quality equal or larger than the first quality. Comparisons with state-of-the-art deep convolutional neural networks as well as detectors built in the JPEG domain show the merit of this work.
Semantic Segmentation of Breast Ultrasound Image with Pyramid Fuzzy Uncertainty Reduction and Direction Connectedness Feature

Deep learning approaches have achieved impressive results in breast ultrasound (BUS) image segmentation. However, these methods did not solve uncertainty and noise in BUS images well. Meanwhile, they did not involve the context information of BUS images, either. To address this issue, we present a novel deep learning structure for BUS image semantic segmentation by analyzing the uncertainty using a pyramid fuzzy block and generating a novel feature based on connectedness. There are three major contributions in this paper: (1) the structure of pyramid fuzzy block; (2) a novel membership function based on multi-convolution layers; and (3) a novel context feature based on connectedness. The proposed methods are applied to two datasets: a BUS image benchmark with two categories (background and tumor) and a five-category BUS image dataset with fat layer, mammary layer, muscle layer, background, and tumor. The proposed method achieves the best results on both datasets compared with eight state-of-the-art deep learning-based approaches.
Geological structure, ore-metasomatic and mineralogical geochemical zoning of Au-Mo-Cu porphyry Kyzyk-Chadr deposit, Tyva republic

Major geological structure features, ore and metasomatic composition, ore-metasomatic and isotope-geochemical zoning are considered for Kyzyk-Chadr deposit within the namesake ore cluster of Ozhinsky ore district, Altai-Sayany mineragenic province. A set of geological and prospecting evidence (elements of the deposit geological and prospecting model) was defined; it can be used to improve the efficiency of further exploration within Sayany-Altai region prospects.
Visual Multi-Face Tracking Applied to Council Proceedings

Face recognition, face measurement, camera control, measurement of expressions and other tasks can benefit from online visual multi-face tracking. Given the availability of high quality general purpose detectors and tracking-by-detection frameworks, we provide guidance on how to develop a multi-face tracker out of standard components. In this paper, we train common object detectors specifically on faces to understand how well these detectors perform and evaluate different classifier loss functions. Our specific case study tracks faces in the context of council meetings and in parliamentary settings such as the Canadian House of Commons for which we create an annotated video set as a benchmark (see Fig. 1). These meetings in a parliamentary setting are often recorded from multiple cameras with participants and audiences walking around. Fast camera switching and zooming lead to significant scale changes of faces. Therefore, these settings can be characterized as tracking in unconstrained video. This will negatively impact the tracking accuracy and increase the likelihood of identity switches (IDS) between face labels. However, being able to track in unconstrained video enables a wider range of measurement applications. We find that while online tracking based on combining state-of-the-art methods can lead to high-quality tracking results, there is still a large gap between offline and online methods. The discussed method can be adapted to other tracking tasks for which large image databases are available.
A novel centroid initialization in missing value imputation towards mixed datasets

Currently, many databases contain missing values, especially in medical data. Statistical and data mining approaches often require complete data conditions, where these two approaches will not provide adequate performance if the data contains missing values. Several techniques have been made to overcome missing values, one of which is by deleting data containing missing values. However, this approach will omit a lot of information if the data found includes many missing values. This study used an imputation approach (filling in the missing attributes) with a clustering approach. One of the most common clustering approaches is K-Means Clustering. In K-means clustering, the value of the centroid gets from the closest observed value. In this study, we propose updating the centroid value based on the harmonic average of the distance across all observations per centroid. This method is known as K-Harmonic Means Clustering (KHM). We proposed a new program approach for a mixed dataset on three scenarios for missing values of 10%, 20%, and 30%. From the experiments conducted on experimental data sets containing missing values, we get a small proportion of missing values (10%) with a small number of clusters or K, which gives a smaller RMSE value compared to other scenarios.
Searching a quantum database with Grover's search algorithm

The development of programming languages for quantum computing has increased rapidly over the last few years, making it practical for a hands-on approach in teaching quantum computation. In this paper, I introduce the standard textbook example of searching for one item out of four using Grover's search algorithm and extend it by including a quantum database. In addition to explaining how to include a quantum database using a quantum circuit model, I give a complete program implementing the algorithm written in the quantum computing language Qiskit from IBM. If presented in the classroom, this extension of Grover's search algorithm alleviates some unsatisfying aspects of the standard textbook example. If given as an exercise to students, it allows them to extend the standard example and provides experience in developing quantum algorithms. I also outline searching for one (or more) items out of eight with a quantum database, which could be used for student projects.
Dually Distribution Pulling Network for Cross-Resolution Person Reidentification

Person reidentification (Re-ID) aims at recognizing the same identity across different camera views. However, the cross resolution of images [high resolution (HR) and low resolution (LR)] is unavoidable in a realistic scenario due to the various distances among cameras and pedestrians of interest, thus leading to cross-resolution person Re-ID problems. Recently, most cross-resolution person Re-ID methods focus on solving the resolution mismatch problem, while the distribution mismatch between HR and LR images is another factor that significantly impacts the person Re-ID performance. In this article, we propose a dually distribution pulling network (DDPN) to tackle the distribution mismatch problem. DDPN is composed of two modules, that is: 1) super-resolution module and 2) person Re-ID module. They attempt to pull the distribution of LR images closer to the distribution of HR images from image and feature aspects, respectively, through optimizing the maximum mean discrepancy losses. Extensive experiments have been conducted on three benchmark datasets and the results demonstrate the effectiveness of DDPN. Remarkably, DDPN shows a great advantage when compared to the state-of-the-art methods, for instance, we achieve rank-1 accuracy of 76.9% on VR-Market1501, which outperforms the best existing cross-resolution person Re-ID method by 10%.
Comparison of two Deep Learning Methods for Classification of Dataset of Breast Ultrasound Images

Abstract Breast cancer is the prominent cancer types which account to high mortality. Early detection could serve to improve clinical outcomes. Ultrasonography is a digital imaging technique which is used for differentiating benign and malignant tumors. Recently a dataset of breast ultrasound image has been released which gives opportunity to apply machine learning methods over it and enable one to automatically classify them in the respective tumor class. In the present study, I compared the performance of two deep learning methods: AlexNet, and MobileNet for the classification of breast ultrasound images using an augmented data set of 12000 images (6000 each of benign, and malignant) and received an F1-score of 0.914 and 0.974 respectively so herewith concluded that the later deep learning approach performs better then the former in classifying the ultrasound images of breast tissues in benign and malignant cancer types.