Reviewerly/exHarmony huggingface dataset:
High Resolution Headband Document Scanner and Transmitter for the Disabled People

We attempt to find ways for people who are paralyzed and are in wheelchairs to be able to use a scanner, but the same way everyone can use them. As some individuals may be paralyzed from the neck down, we build a system for them to still be able to scan documents and be part of the office workplace. We employed open-source computer vision techniques in order to develop and design the scanner system software. In addition, machine learning based text recognition open-source tool called Tesseract are used for the OCR module of the system. For the hardware implementation, we mount the components onto a headband including the microcontroller, camera, a flash circuit, and a power supply circuit to charge the battery. The camera component is developed to be able provide a live video feed instead of needing to create a database where the data will be stored. The live video is transmitted wirelessly trough microcontroller to a smart phone application. This application can work on both Apple IOS and Android operating system, and was built using Flutter, a crossplatform app development software using a unique language named Dart. The proposed system can improve the working conditions of the disabled people by providing them an assistive technology to participate in the office workplace.
Uloga školske biblioteke u kontekstu digitalne transformacije obrazovanja

The first part of the paper examines the role of school in creating a stimulat- ing learning environment, emphasizing the importance of multimedia instruction and a con- structivist approach to learning. In this context, school libraries are presented as multimedia learning centers. Further, the paper analyzes classroom equipment that should be in line with the constructivist approach to learning, which brings up the current issues of the use of digital media. The relationship between students’ and teachers’ competencies in terms of the use of ICT is considered, and the importance of pedagogy (and didactics) of the media in integrating media into interdisciplinary competencies is emphasized. The second segment is dedicated to the role of the school library in the modern educa- tional process, to its importance, function, organization and increasingly important position in multimedia and digitally transformed teaching, as well as its importance in the cognitive process and lifelong learning; competencies of library and information professionals are discussed as well.
An AI-based, automated workflow for identification and scoring of invasive tumors in Ki-67 stained breast cancer specimens.

e15108 Background: Understanding the rate of tumor cell growth in breast cancer specimens may be indicative of disease aggressiveness, a tumor characteristic which can be used to make an informed treatment decision. The nuclear protein Ki-67 is increased in cells as they prepare to divide or proliferate and is therefore widely used as a proliferation marker for tumor progression. This degree of tumor cell proliferation, or the proliferative index, is commonly detailed in pathology reports shared with the patient care team. Methods: In this study, we utilized the Ki-67 [K2] immunohistochemistry (IHC) assay to stain 10 breast cancer specimens. Stained slides were imaged using the AT2 scanner (Leica Biosystems, Buffalo Grove, IL) and analyzed using the Visiopharm Image Analysis platform. Results: Previous efforts to assess Ki-67 positivity utilizing image analysis have relied on the use of a secondary stain or manual effort by the pathologist to exclude non-invasive tumor regions. These antiquated methods are costly to the lab as they require additional materials or valuable pathologist time. Our novel image analysis approach utilizes artificial intelligence (AI) to automatically denote non-invasive verses invasive tumor regions, which can then be used to quantify the Ki-67 proliferative index. Conclusions: This valuable tool will allow for greater accuracy, cost-savings, and time efficiency when analyzing breast cancer samples compared to traditional methods.
Diagnostic accuracy of the CxPREVENTIVE breast model for early detection of breast cancer.

e13578 Background: Despite different available methods for breast cancer (BC) screening (mainly mammographies for ages between 50 and 65 yr.), and their proven benefits, morbidity, and mortality of this malignancy are still high, partly due to technical limitations and low compliance with screening. The aim of the present study was to assess the diagnostic accuracy for early detection of BC using a modeling algorithm based on multiple blood and urine biomarkers. Methods: In this retrospective cohort study, clinical and blood and urine markers-based algorithms with predefined cutoff values were applied to a generally healthy asymptomatic outpatient cohort of women aged 23 years or older undergoing mammography and histopathology for routine breast cancer screening. We used a modeling algorithm based on two different frameworks (Multiple Biomarkers Disease Activity Algorithm MBDAA [Bioprognos], and Artificial Intelligence Recursive Algorithm AIRA [Mintlot] both in Barcelona, Spain). Both algorithms compute information from a wide number of analytes, tumor markers (CA 15.3, CEA, EGFR, NGAL, NSE and 8-OHdG), and additional clinical data (age, weight, height, and BMI). MBDAA includes Barcelona Criteria (four criteria to correctly interpret the cause of the increase in tumor markers, benign or malignant, defined by the Spanish Society of Clinical Biochemistry and Molecular Pathology, Commission of Biological Markers of Cancer). To assess the association between the modelling algorithm and downstream diagnosis we used logistic regression to compute receiver operating characteristic curves (ROC). Exact binomial confidence limits were calculated to test accuracy, sensitivity, and specificity. Results: 123 women (median age 48 yr. [IQR 40-63]) who underwent mammography at the Juan Ramón Jiménez Hospital (Huelva, Spain), were included in this analysis. Based on mammography and histopathologic findings, BC was diagnosed in 61 (49.5%) women, and absence of pathological findings (control group) was seen in 62 (51.5%). The diagnostic intervention for BC yielded an area under the curve (AUC) of 1 among the entire specimen set, accuracy 1 (95% CI 0.990,1), Se 1 (0.991, 1) and Sp 1 (0.991, 1). Conclusions: Using mammography as a gold standard, our data suggest that an innovative newly designed non-invasive blood and urine-based biomarker algorithm CxPREVENTIVE Breast may provide a timely diagnosis of BC with very high accuracy, especially for those women under 50 and over 65yr., where up to 60% of all BC are concentrated. Our findings warrant further investigation, and we are planning large-scale (n 10,000), fully powered study to inform clinical practice.
Assessment of Risk Types Confronted by Agriprenuers in North Central, Nigeria

This study evaluated the types of risks faced by agripreneurs in North Central Nigeria. Multistage random sampling technique was adopted in the selection of 300 respondents from Nasarawa, Niger and Kogi States used for the study. Well -structured questionnaires were used to elicit the required data from the respondents. The significant economic and financial risk facing the input agripreneurs were exchange rate fluctuations (mean=3.84), high interest rate (mean=4.82) and high level of debts (3.20). The significant production risks facing the production agripreneurs were inadequate storage and processing facilities (mean=4.25), power supply interruption (mean=4.56), outdated technology and lack of expertise (mean=4.34) inaccessibility of input (mean=3.94). The significant production risk facing the processing agripreneurs were inadequate processing and storage facilities (mean=3.42), instability in power supply (mean=4.54), outdated technology and lack of expertise (mean=4.41) and inadequate and improve inputs (mean=3.42). The Major risks faced by marketing agripreneurs include frequent price fluctuation (mean=4.04), high level of inflation (mean=4.66), lack of product variety (mean=3.12), past failure (mean=3.4.07) and too many competitors (mean= 3.97). The significant economic and financial risk facing the support agripreneurs were limited ways of raising fund (mean=4.95), exchange rate fluctuation (mean=4.42) and high interest on loans (mean=4.53). Programs and policies meant to promote capital access of the agripreneurs should be promoted and executed judiciously. Agripreneurs should be enlightened that risk is essential part of entrepreneurial success.
PRICE COMPARISON WEBSITE USING OBJECT RECOGNITION

The aim of this product is to check the product costs of various sellers like Flipkart.com, Amazon.in, Croma.com etc. And come back the worth of product in every web site that helps the client to make a decision and get the minimum priced product. The system provides list of value of a product by comparison with different websites. Unidentified product will be simply recognized by seeing simply by uploading the image. These sites offer the simplest value by uploading the image of the product and supply the simplest value whereas avoiding spare advertisements and knowledge. value Comparison web site for on-line searching Project is meant in python language. Seeing is employed to spot product. to get the knowledge concerning the product, the API’s of various E-commerce sites area unit taken.
Differential Evolution Algorithm Based on Adaptive Rank Exponent and Parameters

Differential evolution algorithm is a very useful and impactful method for handling global numerical optimization issue in the evolutionary algorithm family. However, there are still some shortcomings. Such as, the performance of DE is depend on its mutation strategy and parameters setting. In this article, we present a new fashione differential evolution algorithm which called AFP-DE with adaptive rank exponent and parameters. Compared with the update variant DE algorithms, the experiment shows that performance of AFP-DE is better than them with good performance.
Correction: Using verb morphology to predict subject number in L1 and L2 sentence processing: A visual-world eye-tracking experiment

This article details a correction to: Koch, E. M., Bulté, B., Housen, A., &amp; Godfroid, A. (2021). Using verb morphology to predict subject number in L1 and L2 sentence processing: A visual-world eye-tracking experiment. <em>Journal of the European Second Language Association, 5</em>(1), 115–132. DOI: <a href="http://doi.org/10.22599/jesla.79" target="_blank">http://doi.org/10.22599/jesla.79</a>.
ResConvE: Deeper Convolution-Based Knowledge Graph Embeddings

Link prediction on knowledge graphs (KGs) is an effective way to address their incompleteness. ConvE and InteractE have introduced CNN to this task and achieved excellent performance, but their model uses only a single 2D convolutional layer. Instead, we think that the network should go deeper. In this case, we propose the ResConvE model, which takes reference from the application of residual networks in computer vision, and deepens the neural network, and applies a skip connection to alleviate the gradient explosion and gradient disappearance caused by the deepening of the network layers. We also introduce the SKG-course dataset from Scholat for experiments. Through extensive experiments, we find that ResConvE performs well on some datasets, which proves that the idea of this method has better performance than baselines. Moreover, we also design controlled experiments setting different depths of ResConvE on FB15k and SKG-course to demonstrate that deepening the number of network layers within a certain range does help in performance improvement on different datasets.
Discrimination of seismic events (2006 to 2020) in North Korea using P/Lg amplitude ratios from regional stations and a bivariate discriminant function.

and triggered earthquakes that followed the 3 September 2017 nuclear explosion. We were able to separate the cavity collapse from the population of nuclear explosions. However, based on P/Lg ratios, the distinction between the earthquakes and the cavity collapse is ambiguous. The performed discriminant analyses suggest that combining Pg/Lg and Pn/Lg ratios results in improved discriminant power compared with any of the ratio types alone. We used the two ratio types jointly in a quadratic discriminant function and successfully classified the six declared nuclear tests and the triggered earthquakes that followed the September 2017 explosion. Our analyses also confirm that the recent southern events of June 2019 and May 2020 are both tectonic earthquakes that occurred naturally.
Gaming Research for Alliance Network Dynamics.

between anattacker and a provider, using the sum-form and product-form utility functions, each composed of a survival probability term and a cost term. We derive Nash Equilibrium conditions which provide expressions for individual system survival probabilities, and also the expected capacity specified by the total number of operational components. These expressions differ only in a single term for the sum-form and product-form utilities, despite their significant differences.We apply these results to simplified models of distributed cloud computing infrastructures.
Scalable3-BO: Big Data meets HPC - A scalable parallel high-dimensional Bayesian optimization framework on supercomputers.

To address these issues, we recently developed a multi-user Big Data analytics framework for HPC log data at Oak Ridge National Laboratory (ORNL). This paper introduces three in-progress data analytics projects that leverage this framework to assess system status, mine event patterns, and study correlations between user applications and system events. We describe the motivation of each project and detail their workflows using three years of log data collected from ORNL's Titan supercomputer.
Introduction of Application Cases of “Five Whys Cardgame” for Project Design Education

Kanazawa Institute of Technology applies PBL to the backbone of its curriculum, and names it Project Design Program to grow up students into independently minded and actively engaged engineers. Through the Project Design courses for freshmen and sophomores, students construct teams and carry out problem-finding and problem-solving activities. To solve their problem, they need to find out the true cause of the problem. Therefore we have introduced “Five Whys” into the Project Design courses to find out about it efficiently. Furthermore, we developed “Five Whys Cardgame” to make it easier to understand the concept of “Five Whys” . And we introduce application cases of “Five Whys Cardgame” for the Project Design course for freshmen.
Educational Effect of “Measurement of Light Spectrum” by Using a Tablet Device

The possibility of using tablet devices is increasing as ICT (Information and Communication Technology) equipment. Tablet devices can also enhance a learning effect on students. For this reason, we introduced an experiment named “Measurement of Light Spectrum” in “Experiments in Physics” at Kagoshima KOSEN. The new experiment requires tablet devices and enables the students to understand the light spectrum. They operate a spectrophotometer with tablet devices and measure light spectrum of various light sources. They find characteristics of the light spectrums while viewing and operating tablet devices. Our follow-up survey after the experiment showed that most of the students were satisfied with the experiment. We also confirmed that it can increase the level of students’ understanding of the light spectrum.
Click-Through Rate (Ctr) Prediction for Online Advertising

Click-Through Rate (CTR) prediction is used to estimate the probability of a user to click on an item in an online advertising. CTR provides insights in supporting effective online advertising that generates huge advertising revenues and also improve users’ satisfaction. Researchers use various features in their proposed CTR prediction models, and they can be generally classified as advertisement feature, user feature and ad-context feature. Advertisement feature explains “How” an ad serving is, user feature illustrates “Who” is browsing the web pages and lastly the ad-context feature describes “What” the product is, “Where” will an ad displayed and the “Mood” of the web contents from users’ comments. In recent years, there is a transformation on CTR models from the traditional methodologies into deep CTR models. Different studies have emphasized certain features and there is no consensus on the features used in the CTR prediction models to achieve better performance. This paper examines the features used by various CTR prediction models in the online advertising and the various opportunities that are available for future research. Keywords: Ad-context, Click-Through Rate, Deep Interest Evolution Network, Deep Interest Network, Deep Session Interest Network
Multi-Emotion Detection in Social Media Post using Lexicon-Driven Approach

Popular social media sites such as Facebook, Instagram and Twitter have become important tools in fostering relationships between companies and customers. When using social media sites, organisations are often keen to maximise engagements from their target audiences. Companies tend to create posts to which their audience will reply, retweet, comment, share and like. However, different types of contents from posts can impact on three interaction metrics: likes, comments and shares. In this paper, we investigate the factors that characterise the posts with which an audience engages. The results showed that media and content type of posts have a positive significant impact on customer engagement. The study also demonstrated the importance of an active online participation and communication among consumers will ultimately impact their purchase behaviour in reality. Keywords: Social Media Sites, customer engagement, Facebook, content post, media post
A Comparative Study on the Time Series Models for Forecasting Facebook Reactions

The Facebook reactions were used over 300 billion times during their first year of existence. Research on reaction activity is essential especially for the digital marketing purpose. The market needs to understand how Facebook reactions fluctuate to forecast the best period to post advertisements on Facebook that yields the highest number of reactions. In this study, several time-series models are used to forecast the number of Facebook reactions over a certain period for different domains. A comparative study is done to evaluate the performance of each model, in terms of strengths and weaknesses. Keywords: Forecasting, Facebook reactions, time series model, ARIMA, SARIMA
Sentiment Analysis on Adaptive Lexicon-Based Approach and Statistical Lexicon-Based Approach

Social media has been playing a huge role towards the society nowadays, mainly because of the community express their opinions and thoughts mostly on their own social media. Therefore, it is difficult to figure out the emotion of each sentences on social media as the words used carries different meaning individually, and when used in a sentence. A method has been created from existing static lexicon (staticLex) to improve the performance of determining the emotion on message-level sentiment analysis, called adaptive lexicon (adaLex) [2]. Data source is from Plutchik’s emotion annotated data. Keywords: sentiment analysis, adaptive lexicon, static lexicon
Enhancement Of Gale-Shapley algorithm with imbalanced sets for hiring and job finding applications

In this paper, &lt;span&gt;we discuss a modification to the Gale Shapley algorithm in this study. We concentrated on how the modified algorithm would operate if the data sets for men and women were not equal. We showed how to measure the average energies of men and women with equal and unequal sets, how the new design of the algorithm will benefit both parties by duplicating sets of data to fill the unequal sets of data, resulting in an equal set of pairings, and how to measure the average energies of men and women of the new algorithm in this paper. Having stated that, each couple will have a stable partner and/or will be queued to the algorithm's suggested stable partner.&lt;/span&gt;
Analysis of Strategies and Skills of English Translation Based on Coverage Mechanism

In order to alleviate the problem of over translation and missing translation in NMT, based on the consistency and complementarity of information stored in different covering models, a multicoverage fusion model is proposed, which uses coverage vector and coverage score to guide the attention mechanism at the same time. First, the concept level definitions of words are covered. Then, two kinds of translation history information stored in the cover vector and cover score are used to guide the calculation of the attention score at the same time. Finally, the dual attention decoding method based on the fusion coverage mechanism is adopted. The experimental results show that the multicoverage fusion model can improve the translation quality of NMT.
Exploring the Acceptance of 360 Videos in Virtual Reality Settings for Teaching and Learning

Virtual Reality (VR) is a technology that has been widely adopted in supporting learning and educational purposes over recent years. It has shown enormous potential to pioneer education in the future, mainly through its immersive learning environments provided in 360° settings. We explore the possibility of using VR technology in supporting teaching and learning experience in class. This paper reports the perception of trainee teachers towards the acceptance of the use of 360° videos in VR settings for teaching and learning. Forty trainee teachers were grouped to create learning content using available 360° videos and rendering and processing them into a VR format. They viewed and experienced the content they developed using the ClassVR headset. The findings showed positive feedback on the use of 360° videos in facilitating immersive learning experiences.
The The Effect of SABELS and GRPQ on The Students’ Reading Comprehension at SMA Swasta RK Bintang Timur Pematangsiantar

The aim of this research is to find out the effect of Scientific Approach-Based on English LearningStrategy (SABELS) and Guided Reading Peer Questioning Strategy (GRPQ) on students’ readingcomprehension at grade X of SMA Swasta RK Bintang Timur Pematang Siantar. The researchersused quantitative method. The mean of GRPQ strategy is 77.50, Standard Deviation is 7.75, andsignificance value is .002 and number of students is 36. The mean in SABELS strategy is 80.00and standard deviation is 9.25, and significance value is 0.38 and number of students is 36. Theclass who got conventional strategy, it has mean 75.00, standard deviation is 7.50, and significancevalue is .000 and number of students is 37. Using t-test in analyzing data, is found that t-value(2,36) is higher that t-table (1,667) at level of significant 5 %. This means using SABELS iseffective to develop students’ ability in reading comprehension. According to the researchfindings, it is concluded that SABELS is more significant than GRPQ Strategy in teaching readingcomprehension at tenth grade students at SMA Swasta RK Bintang Timur Pematangsiantar.