Reviewerly/exHarmony huggingface dataset:
Optical Image Encryption Process Using Triple Deterministic Spherical Phase Masks Array

In this paper, we perform a study of optical image encryption using triple deterministic phase mask (TDPE) based on diffractive spherical phase masks array (SPMA). By combining the diffractive spherical phase masks array with the deterministic phase masks method we can improve the security level of an encryption scheme by increasing the key space and providing additional security parameters and also by overcoming the problem of the sensitivity to misalignment associated with an optical setup which is beneficial for practical implementation. Numerical simulation results indicate that the proposed method can reach a better effect in image encryption compared with the conventional double random phase encryption (DRPE).
Trends in the academic integrity development of future spesialists in European universities

In the article the trends of the academic integrity development among the future specialists during the professional training are defined and grouped by signs; emphasize adherence to European academic integrity standards and awareness of the values during the educational process. The methods and forms of mastering the principles of academic integrity are outlined, the basic tools for its promotion and implementation are proposed.
LibGuides: Remote Library Services for Online Learning: Databases

This guide offers links and answers questions for the online learning shift taking place in response to the COVID-19 outbreak.
Classification of Seismic BodyWave Phases Using Supervised Learning : Classification of Seismic BodyWave Phases Using Supervised Learning

The task of accurately distinguishing between arrivals of different types of seismic waves is a common and important task within the field of seismology. For data generated by seismic stations oper ...
Research Trends and Issues of Social Studies related STEAM Education

Integrated education has attracted a lot of attention both in Korea and globally. The Korean government has been implementing the STEAM education since 2011. Many teachers have practiced integrated education at all school levels. The purpose of this study was to investigate the trend of research outcomes related to social studies subject about integrated STEAM education and seek the direction of STEAM education for social studies. In this study, a meta-analysis was conducted for 109 papers related to STEAM education in social studies published as listed journals and dissertations from 2010 to 2018. Results of research trends analysis of social studies related STEAM education are as follows. First, research results of social studies related STEAM education have been published steadily every year. Most of these studies were master"s degree-related thesis. They were published evenly when analyzed by school grade level. Second, quantitative studies made up half of the research to see the implementation and effectiveness of the program. All studies measured the effect on the affective domain of students. Third, papers of STEAM education in social studies were limited to class in the classroom. The majority of them used an interdisciplinary approach. Main subjects applied in convergence with social subjects were music and art. In order to further develop STEAM education centering on social studies, there is a need to go beyond academic-oriented approaches and regular curriculum. In-depth research is needed to build social educational theories for fostering creative convergence talent beyond the development of learning programs.
Revisiting ECM on GPUs

Modern public-key cryptography is a crucial part of our contemporary life where a secure communication channel with another party is needed. With the advance of more powerful computing architectures – especially Graphics Processing Units (GPUs) – traditional approaches like RSA and Diffie-Hellman schemes are more and more in danger of being broken. We present a highly optimized implementation of Lenstra’s ECM algorithm customized for GPUs. Our implementation uses state-of-the-art elliptic curve arithmetic and optimized integer arithmetic while providing the possibility of arbitrarily scaling ECM’s parameters allowing an application even for larger discrete logarithm problems. Furthermore, the proposed software is not limited to any specific GPU generation and is to the best of our knowledge the first implementation supporting multiple device computation. To this end, for a bound of $$B_1 = {8 192}$$ and a modulus size of 192 bit, we achieve a throughput of 214 thousand ECM trials per second on a modern RTX 2080 Ti GPU considering only the first stage of ECM. To solve the Discrete Logarithm Problem for larger bit sizes, our software can easily support larger parameter sets such that a throughput of 2 781 ECM trials per second is achieved using $$B_1={50\, 000}$$ , $$B_2 = {5\, 000\, 000}$$ , and a modulus size of 448 bit.
Pengolahan Sinyal EOG Satu Kanal dengan Karakterisasi Vektor dan Deteksi Puncak

The more channels required in EOG signal recognition will also require more electrodes attached to the subject. With the end goal to develop an entertainment application, this research focuses in single-channel EOG which is simpler and less complicated, but is still functional. In this research, a characterization on each single-channel EOG signal which is viewed necessary as a part of a control scheme for a multimedia player is being pursued. The signals belong to the four main eye movements (up-down-left-right) and two eyeblinks, each belongs to a specific eye. Signal recognition was done by dividing operational voltage regions from the EOG signal receiver hardware into five separate regions, in which where each region will identify when a peak or valley of a signal happens. Therefore, each signal can be defined through a sequence of certain hill and peak which is relatively consistent for each signal. A signal recognition algorithm can afterwards be developed for each of these signals, and then implemented in a software which can eventually serve as one of the possible Human-Machine Interfaces (HMI) using EOG signals.
The trends of library science choice among students in the Universitas Islam Negeri Sumatera Utara (State Islamic University of North Sumatera)

The Library Science Study Program in Indonesia is developing in line with the current advances in information technology. This educational field has been around for quite a long time. However, it is so diverse that it differs from one university to the other. The field of library science produces "Librarians." Unfortunately, the profession does not have a high demand compared to others. However, the opening of this educational field at the Universitas Islam Negeri Sumatera Utara (UINSU) has sparked the interest of prospective students in the Library Science study program. These have further increased over the years. Therefore, this research aimed to examine the data regarding factors that encourage students to choose the Library Science study program at the UINSU. Meanwhile, the focused data analysis technique in the Focus Group Discussion (FGD) was used. The results showed that four factors influence students' choice of the Library Science program, which include; parents, relatives, friends, and themselves. Career-wise, most students study Library Science so that they can work as lecturers in the field. Furthermore, the students face certain obstacles which include; facilities and language. Therefore, the study program conducts curriculum updates to adapt to new developments in science. This is achieved by applying information technology and organizing training programs in languages.
Gaze Guidance for Captioned Videos for DHH Users

35th Annual Assistive Technology Conference Scientific/Research Proceedings, San Diego, 2020
Opposed transcriptomic gradients contribute to both the arealization of human visual cortex and the topological layout of its orthogonal maps

While recent findings demonstrate an unexpected coupling between functional and cytoarchitectonic regions relative to the folding of human visual cortex, a unifying principle linking these anatomical and functional features of cortex remains elusive. To fill this gap in knowledge, we summarize findings from two of our recent studies that examined two main questions: 1) Does differential gene expression among cytoarchitectonic areas contribute to the arealization of occipito-temporal cortex into a processing hierarchy? 2) Does differential gene expression also contribute to the topological layout of orthogonal functional maps within visual areas? Our multimodal approach revealed two main findings. First, there are two large-scale opposing gene expression gradients in human occipito-temporal cortex: one that contains a series of genes with expression magnitudes that ascend from posterior (e.g. areas hOc1, hOc2, etc.) to anterior cytoarchitectonic areas (e.g. areas FG1-FG4) and another that contains a separate series of genes that show a descending gradient from posterior to anterior areas. Using data from the living human brain, we show that each of these gradients correlates strongly with variations in measures related to either thickness or myelination of cortex, respectively. Second, additional transcription gradients exist within single visual field maps that capture the orthogonal functional maps of receptive field eccentricity and polar angle. The genes contributing to the fine-scale layout of maps within areas are largely distinct from those contributing to the large-scale expression gradients contributing to the arealization of human visual cortex. The combination of these findings suggest a new rule of cortical organization in which the adult brain employs opposed transcriptional gradients at multiple spatial scales. Altogether, these findings help pinpoint the genes contributing to healthy cortical development, as well as establish essential groundwork for understanding future work linking genetic mutations with the function and development of human visual cortex.
A Patient Feedback Driven, Stacked Machine-Learning Approach to At-Home COPD Triage

<b>Background:</b> COPD imposes a global burden of exacerbation risk and threat to patient longevity and wellness. As modern disease management devices continue to struggle with patient adoption and personalization, we developed a stacked machine learning (ML) methodology that incorporates clinician diagnostic criteria and patient feedback to yield personalized decision support. <b>Objective:</b> Develop ML-Triage predictions that incorporate patient feedback and stacked classifiers for optimized at-home exacerbation detection and triage. <b>Methods:</b> Triage algorithms were trained on simulated patient vignettes labelled by physicians and comprising distinct combinations of clinically relevant health variables. Accuracy was assessed based on algorithm concordance with physician consensus in statistically and clinically diverse vignettes. Three initial classifiers were trained to 1) identify excerbations, 2) deliver triage guidance, and 3) determine a generic need for medical attention. Algorithms were deployed in a mobile application in trials where patient feedback was collected on app accuracy and relevance. Data was analyzed and a new stacked classifier was trained and assessed for overall performance <b>Results &amp; Conclusions:</b> Pilot data showed 18% disagreement between patient intuition and initial classifier recommendations to call the MD. Remarkably, backtesting of the new stacked classifier on instances of patient disagreement showed a 43% improvement in algorithm agreement with patient intuition. The stacked classifier maintained accuracy of 88%, better than all 8 participating MDs. Overall, the study revealed an accurate, safe, and generalizable approach to at-home COPD triage via integrated clinician and patient inputs.
Application of image matching in pathological image diagnosis of breast masses

Among pathological images, there are usually some typical patient structures. For these typical images, template matching can be used for diagnosis, thereby the workload of doctors can be reduced. In this paper, pathological images of breast masses are studied in depth. A typical breast masses pathological template is established. Three template matching methods, correlation coefficient matching, correlation matching method as well as square difference matching method are used for experiments separately, and the timeliness and effectiveness of them are evaluated in terms of computing time and matching accuracy. The experimental results show that among these three template matching methods, the accuracy of correlation coefficient matching is the highest, and it is able to overcome the interference of angel rotation and noise. However, it is also consumes the longest time.
Image restoration parameters adaptive selection algorithm basing on sparse representation model

The SL0 algorithm is a sort of sparse reconstruction algorithm approximate to l0 norm, which has significant applications in the field of deblurring. In the SL0 algorithm, usually a number of important parameters need to be set to obtain deblurred images. This paper first introduces the basic of the SL0 algorithm, then it analyzes the operator, which has higher dipartite degree for blurred images in edge extraction algorithm, and choose the Roberts operator as the standard for judging parameter optimization. Finally, an algorithm for image restoration parameter adaptive selection is designed, and experiments are conducted. The experimental results show that comparing with the traditional SL0 algorithm, the algorithm in this paper has a great improvement in terms of repairing quality. The repairing effect of the algorithm in this paper is more natural, and the PSNR of images can be increased about 1.5dB.
The Role of Big Data in Political Campaigns (Case Study of Jakarta Special Region Province)

This research is about the role of Big Data in Indonesia in political campaigns.T he method used is a mix method that is quantitatively by means of computing with a Big Data based application called BitWafl Media Analytic.T hen using a qualitative method by means of narration in which with this method the data is drawn and explored thematically then narrated.Quantitative data collection by Data Mining represents the discovery of useful patterns or trends through a number of Big Data while qualitatively with open interviews.T he recommendations of the results of this study are that big data technology has a significant role in political campaigns, but the technology still requires an important role for humans to package campaign messages or as operators.
A Study on Exploring the Direction of Student Life Guidance through the Reward and Penalty Point System

본 연구는 학생생활지도의 어려움 속에서 여전히 활용되고 있는 상벌점제의 개선 방향과 향후 학생생활지도가 나아가야 할 방향성을 탐색하기 위해 실시되었다. 이를 위해 서울 지역 상벌점제 운영교를 대상으로 상벌점제 운영에 대한 교사와 학생, 학부모의 인식을 살펴보았다. 그 결과 현재 운영되는 상벌점제는 첫째, 학생들의 문제 행동 수정(M=3.22)과 바람직한 생활태도 형성(M=3.09)에 보통 수준으로 기여하고 있다. 둘째, 상벌점제는 최소한의 생활지도 수단으로서 필요하다(M=3.24). 셋째, ‘상벌점 주기의 비일관성과 불공정성’이 상벌점제의 가장 큰 문제점으로 나타났다. 넷째, ‘학생자치회, 상벌점제위원회 등을 통한 민주적 운영’이 상벌점제의 가장 시급한 개선방안으로 나타났다. 이를 토대로 상벌점제 운영의 개선 방향을 제시하면 다음과 같다. 첫째, 상벌점제는 바람직한 생활태도 형성을 기본 목적으로 하여 운영해야 한다. 둘째, 상벌점제 규정은 항목의 최소화, 적용 범위의 최소화를 통해 운영의 비일관성과 모호성을 해소해야 한다. 셋째, 상벌점제의 운영은 상벌점제위원회의 구성, 학생 선택의 벌점 경감 제도 마련, 상벌점제 정보 제공 방법의 개선, 교육공동체 간의 지속적인 소통 창구 마련을 통해 민주적으로 운영되어야 한다. 연구 결과에 터해 정책적으로 제언을 하면, 학생생활지도에 관한 교원 역량 강화 연수를 활성화하고, 시·도교육청 수준에서 학생생활지도 우수사례를 발굴·확산해야 한다.This study explored the direction for improvement of the reward and penalty point system, which is still used in times of difficulties. To this end, the perception of teachers, students, and parents about the operation of the reward and penalty point system were examined for schools in Seoul. As a result, the system currently being operated first, contributes to the correction of students problem behaviors(M=3.22) and formation of desirable life attitude(M=3.09) at a normal level. Second, the system is necessary as a minimum means of student guidance(M=3.24). Third, ‘inconsistency and unfairness in the reward and penalty cycle’ were the biggest problems with the system. Fourth, the “democratic operation through the Student Council and the Committee,” appeared requiring as the most urgent improvement plan for the system. Based on this, the directions for improving the operation of the system are as follows. First, the system should be operated with the basic purpose of forming a desirable life attitude. Second, the regulations should resolve the inconsistency and ambiguity of operation by minimizing parts and scope of application. Third, the system should be democratically operated through the composition of the committee, the provision of the system which students choose to reduce penalty points, improvement of the method of providing information on the system, and continuous communication channels between the educational members. If policy suggestions are made based on the research results, it is necessary to revitalize teacher competency training for student life guidance, and discover and distribute excellent examples of student life guidance in both metropolitan and provincial offices of education.
COST Action “European network for Web-centred linguistic data science” (NexusLinguarum)

We present the current state of the large “European network for Web-centred linguistic data science”. In its first phase, the network has put in place several working groups to deal with specific topics. The network also already implemented a first round of Short Term Scientific Missions (STSM), which was unfortunately hindered by the Covid-19 outbreak.
Visual memory recall for personally familiar objects in medial parietal cortex

Human ventral temporal cortex (VTC) contains distinct category-selective regions that are selectively recruited during visual processing of faces and scenes. In prior work (Silson et al., 2019), we found distinct functional connectivity between these VTC regions and parts of medial parietal cortex (MPC) with corresponding differences in the magnitude of activation in MPC during visual recall of personally familiar people and places. In addition to face- and scene- selective regions, VTC also contains object-selective regions. To investigate if there is also a separate region in MPC engaged during recall of personally familiar objects we collected functional magnetic resonance imaging (fMRI) data while participants (N = 21) visually recalled from memory personally familiar people (e.g. mom), places (e.g. bedroom), or objects (e.g. laptop, cell phone, jacket). Stimuli were personalized to each participant, and they reported 12 names for each condition prior to the scan session. We specifically chose objects that were not strongly associated with a specific context (e.g. toothbrush) to avoid any contextual effects. Regions of interest (ROI) were defined across the MPC using independent resting-state data, based on functional connectivity with VTC. While the data replicated our previous finding of separate regions for memory recall of people and places, there was little activation in MPC during the recall of personally relevant objects and no distinct subdivision of the MPC that was more selective for the recall of objects then people or places. Thus, the functional organization of MPC is not a complete reflection of the visual category-selective organization of VTC.
JBCA: Designing an Adaptative Continuous Authentication Architecture

Several continuous authentication methods provide very accurate ways of detecting the impersonation of a user . However, even though they should be access control chain’s links, they are commonly documented as algorithms unrelated to any system. In this paper, we propose a continuous authentication architecture to integrate several kinds of continuous authentication engines in a complete framework. The architecture covers different use cases where continuous authentication methods, placed in the user’s device or in the cloud, collaborate to determine if the user’s identity has changed. We use ensemble learning principles to combine the values generated by each CA model, and compute the trust level adaptively, addressing common errors caused by changes in the environments of continuous authentication systems.
Unsupervised Pre-training for Biomedical Question Answering.

We explore the suitability of unsupervised representation learning methods on biomedical text -- BioBERT, SciBERT, and BioSentVec -- for biomedical question answering. To further improve unsupervised representations for biomedical QA, we introduce a new pre-training task from unlabeled data designed to reason about biomedical entities in the context. Our pre-training method consists of corrupting a given context by randomly replacing some mention of a biomedical entity with a random entity mention and then querying the model with the correct entity mention in order to locate the corrupted part of the context. This de-noising task enables the model to learn good representations from abundant, unlabeled biomedical text that helps QA tasks and minimizes the train-test mismatch between the pre-training task and the downstream QA tasks by requiring the model to predict spans. Our experiments show that pre-training BioBERT on the proposed pre-training task significantly boosts performance and outperforms the previous best model from the 7th BioASQ Task 7b-Phase B challenge.
Opponent Center-Surround Contrast: Colour to Grey Conversion

Vision is the science that informs us about the biological and evolutionary algorithms that our eyes, opticnerves and brains have chosen over time to see. This article is an attempt to solve the problem of colour to grey conversion, by borrowing ideas from vision science. We introduce an algorithm that measures contrast along the opponent colour directions and use the results to combine a three dimensional colour space into a grey. The results indicate that the proposed algorithm competes with the state of art algorithms.
A Novel Method to Improve Security and Imperceptibility of Image Steganography

Nowadays when photos, images and selfies are very popular, when we cannot imagine our lives without them in our daily life, security of images is our priority. Over the last year, the messenger Snapchat, which is popular among teenagers has become the target of hackers three times. And we also remind you about the hacking of the anti-doping administration and management system (ADAMS). Hackers gained access to the athletes’ personal medical images and made publicly available images, the results of their doping tests, which indicated that athletes were taking illegal substances. Securing images can be accomplished by cryptography and steganography. The combination of both techniques can enhance image security. The Arnold‘s transformation (ACM) in this paper has been combined with Elliptic Curve cryptography (ECC) based on new mapping method, which proposed to convert the pixels of the plain image into the coordinates on the curve. In addition, this paper also suggests combining ACM with ECC and subsequently integrating the result into a cover image with Least Significant Bit (LSB) steganography. After a simulation test, efficiency of the proposed system is evaluated using peak signal to noise ratio (PSNR), histogram analysis, and entropy as parameters for obtaining quality of stego image and quality of cipher image. The proposed method recorded the highest PSNR of 59.22 dB, the highest entropy of stego image and cover images equaled 7.76. The simulation results show that a high level of security is offered by the proposed algorithm.