Reviewerly/exHarmony huggingface dataset:
FINE-TUNING DEEP LEARNING MODELS FOR STEREO MATCHING USING RESULTS FROM SEMI-GLOBAL MATCHING

Abstract. Deep learning (DL) methods are widely investigated for stereo image matching tasks due to their reported high accuracies. However, their transferability/generalization capabilities are limited by the instances seen in the training data. With satellite images covering large-scale areas with variances in locations, content, land covers, and spatial patterns, we expect their performances to be impacted. Increasing the number and diversity of training data is always an option, but with the ground-truth disparity being limited in remote sensing due to its high cost, it is almost impossible to obtain the ground-truth for all locations. Knowing that classical stereo matching methods such as Census-based semi-global-matching (SGM) are widely adopted to process different types of stereo data, we therefore, propose a finetuning method that takes advantage of disparity maps derived from SGM on target stereo data. Our proposed method adopts a simple scheme that uses the energy map derived from the SGM algorithm to select high confidence disparity measurements, at the same utilizing the images to limit these selected disparity measurements on texture-rich regions. Our approach aims to investigate the possibility of improving the transferability of current DL methods to unseen target data without having their ground truth as a requirement. To perform a comprehensive study, we select 20 study-sites around the world to cover a variety of complexities and densities. We choose well-established DL methods like geometric and context network (GCNet), pyramid stereo matching network (PSMNet), and LEAStereo for evaluation. Our results indicate an improvement in the transferability of the DL methods across different regions visually and numerically.
EVALUATION OF CRIME DOCUMENT USING RETRIEVAL TECHNIQUES

This paper presents the Deep Evaluation of a system that help in analysis and make available relevant of crime information extracted from the “Application of INFORMATION RETRIEVAL AND SCIENCE Like TEXT CATEGORIZATION and STRUCTURED Data(Extensible markup language). Clustering technique are used to Retrieve the Crime case Documents and analysis the identifying the crime proofs. The feature of a systematic review is the data collection, the assembling of meticulous, unbiased and indicators is performed using Neural Network where it’s a model to predict specifying types of crime using time information to predict a crime’s location. These neural models are able to predict the types of crimes being committed. Classification and Clustering algorithms are heavily used to retrieval the Document based search engines techniques. We can use automatically applying labels to database such as evidence, footwear prints or Images. Multinomial model: These model is overly simplistic, as it does not model the number of times that terms crimes evidence occurs in a Document. When crime cases are very short, it is unlikely that many evidences will occur more than one time, and therefore the multiple- Bernoulli model will be an accurate crime case model. However, The crime evidence document contain documents that are both short and long, and therefore it is important to take term frequency and, subsequently, document length into proof of Evidence. The aim of this paper is to Significate faster Database, gained through a systematic search how to guide for future evidence like Footwear prints and crime scene marks.
Perceptions and emotions of infection control team member during the COVID-19 pandemic in Germany

Background: We conducted an anonymous survey in compliance with German data protections regulations among participants of the annual infectious disease and control meeting in Freiburg, Germany, in October 2021. Methods: In total, 391 surveys were returned: 188 from nurse infection control practitioners (ICPs) and 66 from specially infection control trained physicians (STPs). We report the results of these 2 subgroups regarding their perceptions and emotions during the pandemic. Descriptive statistics and χ 2 test with P &lt; .05 were used when applicable. Results: Shortages of medical masks or FFP2 masks during the first pandemic wave in 2020 were reported by 48.5% STPs and 57.4% ICPs. STPs and ICPs relied equally on information provided by the Robert Koch Institute, the WHO, the ECDC and the CDC. Occupational health information was sought significantly more often by ICPs; only 17% of ICPs never used this source versus 51.5% of STPs ( P &lt; .001). Most ICPs (58%) and STPs (51%) described their relationship to local authorities as good as well as communication with institutional leaders (69.7%). Fewer ICPs (36.1%) felt frequently appreciated during the pandemic compared to 45.5% of STPs and more ICPs (25%) reported frustration than STPs (18.2%). However, the differences were not statistically significant. Rarely, ICPs (2.1%) or STPs (1.5%) felt unsafe at work and only 1.6% of ICPs and no STPs reported loss of motivation. In addition, 13.8% of ICPs and 12.1% of STPs often felt overwhelmed, but only 3.2% of ICPs and no STPs felt hopeless. Their self-reported competency was rated as high by 75% of ICPs and 69.7% of STPs. The 5 most frequent free-text comments regarding “lessons learned” pertained to better crisis communication, better supply chain management, precise regulations, “less talking more doing,” and mandatory vaccination. The most frequent free-text general comments pertained to maintain basic hygiene measures in private and public life because of the pandemic. Conclusions: Our survey results indicate a high level of resilience among members of infections control teams in German medical institutions despite obvious shortcomings in supplies during the first wave of the pandemic. There were no significant differences between physician and nurse members of infection control teams regarding their perceptions and emotions, indicating a homogenous situation within the teams. The high level of self-perceived competency has likely helped deal with the pandemic and prevented the feeling of loss of control implied in the question items “feeling overwhelmed” and “hopeless.” Funding: None Disclosures: None
On Quantiles Estimation Based on Stratified Sampling Using Multiplicative Bias Correction Approach

In the context of stratified sampling, we develop a nonparametric regression technique to estimating finite population quantiles in model-based frameworks using a multiplicative bias correction strategy. Furthermore, the proposed estimator’s asymptotic behavior is presented, and when certain conditions are met, the estimator is observed to be asymptotically unbiased and asymptotically consistent. Simulation studies were conducted to determine the proposed estimator’s performance for the three quartiles of two fictitious populations under varied distributional assumptions. Based on relative biases, mean-squared errors, and relative root-mean-squared errors, the proposed estimator can be extremely satisfactory, according to these findings.
A Contrastive Analysis of Affixation in English and Kirika

The English and Kirika languages are of different linguistic backgrounds and are characterized by differences in their affixation processes. These differences pose problems in second language learning. This study, therefore, investigates by describing the affixation features of English and kirika and tries to identify some areas of differences and similarities in both languages. Data for the study were collected and analyzed using the descriptive research method and are arranged in tables. The study hinges on the contrastive analysis theory as its framework. Findings in the study reveal that English and kirika use affixation (prefix, suffix and super fix or suprafix). It is discovered that sometimes where English uses a prefix as an affix, kirika realizes it as a suffix and where it is a suffix in English, kirika uses it as a prefix. There are no found cases of interfix, infix or even circumfix in both language. Also, stress and tone are used to create new words in English and kirika, respectively.
An Evaluation of the use of Audio Guidance in Augmented Reality Systems Implemented at Sites of Cultural Heritage

Recently, museums and historic sites have begun reaching out beyond their traditional audience groups, using more innovative digital display technology to find and attract a new audience. Virtual, mixed, and Augmented Reality (AR) technologies are becoming more ubiquitous in our society and “virtual history” exhibits are starting to be available to the public. There are numerous studies focusing on AR, however a scant amount of research is being done at historical sites. An initial experiment used repeated measures (ANOVA) to compare and rank three different types of AR devices used at a site of cultural heritage. A further experiment was then undertaken to observe participants using two different AR devices with and without sound to determine if which device used or the presence of sound impact the usability of the device, or the user’s satisfaction/preference of specific devices. Several surveys, including demographic and usability surveys, were provided in order to collect a range of user data. A two-way repeated measures (ANOVA) were used to analyze the quantitative data gathered. No significant effects were observed based on the quantitative data provided by the surveys, indicating that all devices were equally usable and satisfactory, and that sound did not have a significant impact in this instance. However, the qualitative data indicated that users may prefer using AR technology on a smartphone device and preferred to use this device paired with sound.
The Application of Social Media into Moodle for Teaching Writing Skills to Non-English Major Students at Dong Nai Technology University

&#x0D; Social media tools are a new type of media with a variety of properties and characteristics. Today, the use of social media is increasing at an alarming rate. Students can digitally meet up with their peers and teachers in this realm. They are able to discuss important topics that are connected to their learning process. This study aimed to see how social media tools in Moodle affected English writing skills in narrative paragraphs and whether social media tools in Moodle may help enhance English writing skills in the narrative paragraph. The current study used a mixed-method strategy that included quantitative and qualitative methodologies such as interviews and questionnaires, as well as an experimental research method. To investigate the impact of using social media platforms on boosting students’ writing, pre-and post-tests were used to assess students’ performance before and after the experiment. The three instruments, the questionnaire, the interview, and the quasi-experiment (pre-post-test), revealed how Moodle’s social media facilities affect students’ English writing skills. Using social media features in Moodle helped students improve their writing skills and enhanced their motivation when creating recount narratives. As a result of this research, it is recommended that social media be integrated into Dong Nai Technology University to increase the students’ other skills dramatically. &#x0D; &#x0D; &#x0D; &#x0D;
Neuroplasticity-Based Pruning Method for Deep Convolutional Neural Networks

In this paper, a new pruning strategy based on the neuroplasticity of biological neural networks is presented. The novel pruning algorithm proposed is inspired by the knowledge remapping ability after injuries in the cerebral cortex. Thus, it is proposed to simulate induced injuries into the network by pruning full convolutional layers or entire blocks, assuming that the knowledge from the removed segments of the network may be remapped and compressed during the recovery (retraining) process. To reconnect the remaining segments of the network, a translator block is introduced. The translator is composed of a pooling layer and a convolutional layer. The pooling layer is optional and placed to ensure that the spatial dimension of the feature maps matches across the pruned segments. After that, a convolutional layer (simulating the intact cortex) is placed to ensure that the depth of the feature maps matches and is used to remap the removed knowledge. As a result, lightweight, efficient and accurate sub-networks are created from the base models. Comparison analysis shows that in our approach is not necessary to define a threshold or metric as the criterion to prune the network in contrast to other pruning methods. Instead, only the origin and destination of the prune and reconnection points must be determined for the translator connection.
Smart Healthcare Management System

Abstract—Smart Healthcare Management System is an organized, computerized system which will be designed and programmed to deal with day to day operations and management of the hospital activities. The program can look after inpatients, outpatients, records, database treatments, status illness, billings in the pharmacy and labs. It also maintains hospital information such as ward id, doctors in charge and department administering. The purpose of the project entitled as "SMART HEALTHCARE MANAGEMENT SYSTEM” is to computerize the Front Office Management of Hospital to develop software which is user friendly, simple, fast, and cost effective. Traditionally, it was done manually, but now System input contains patient details, diagnosis details, while system output is to get these details on to the screen. Keywords—Healthcare; Admin; Doctor; Patient; Appointment
INFORMATION EXTRACTION USING NATURAL LANGUAGE PROCESSING

The expanding volume of data/Information creates a new challenge for Information Extraction techniques. The amount of unstructured data has increased in recent years. When we use this data in a clean and better way, we can extract wide variety of beneficial outcomes. Reduction of a text to its essential content, is a very complex problem which, despite the progress in the area thus far, poses many challenges to the scientific community. It is also a relevant application in today’s information society given the enormous amount of production and processing of data, leading to exponential growth of textual information online and the need to momentarily assess the contents of text collections. Information Extraction using Natural Language Text is used to process, or extract and encode information from unstructured data and produce desired output data according to the application. This review article is about how Information Extraction is done using NLP (Natural language Processing)
Otomind: A Text Mining Approach to Automatic Concept Mapping

Despite the linear use of PowerPoint presentation slides in classroom and lecture halls, the brain works primarily with key concepts that it links and integrates. This is a big contrast from the typical student behaviour of reading or memorizing the slides.This paper presents a new system that automatically generate a mind map based on a set of keywords and keyphrases extracted from PowerPoint presentation slides. The system takes a text mining approach and relies on a keyphrase extraction algorithm to extract the keywords and keyphrases. Survey results reflect a rising need of such application as part of educational technologies and the potential benefits are observed and discussed.
Deep Image Segmentation Using a Morphological Edge Operator

Background: Segmentation of deep images is a difficult, persistent problem in the computer vision field. This paper aimed to address the defects of traditional segmentation methods with deep images, presenting a deep image segmentation algorithm based on a morphological edge operator. Methods: Deep image edge features were first extracted using three traditional edge operators; the edge and tip type jump edges were then extracted via a morphological edge operator, which was used to make the boundary connection; finally, to obtain more accurate segmentation results, skeletonizing was used to refine the image. Results: Compared with traditional segmentation algorithms, the improved algorithm obtained smooth and continuous boundaries, protected edge information from blurring, and was slightly more efficient. When Mickey Mouse depth images were used as experimental subjects, the computational time was reduced by 12.62 seconds; when rabbit depth images were used, computational time was reduced by 17.53 seconds. Conclusion: Morphological edge operator algorithm proposed in this paper is much more effective than traditional edge detection operators algorithms for deep image segmentation; it can clearly divide Mickey Mouse's ears, eyes, pupils, nose, and mouth.
ORDINAL LOGISTIC REGRESSION MODEL AND CLASSIFICATION TREE ON ORDINAL RESPONSE DATA

Logistic regression (LR) is a model that associates the relationship between category-type response variables with quantitative or quantitative and qualitative predictor variables. The prediction of the LR model is in the form of probability. This research studied logistic regression (LR) models and Classification Trees in the case of ordinal response variable types. The data used in this research from The Central Statistics Agency (BPS). The research variables used are Human Development Index (HDI), gross enrollment rate for high school, percentage of poor people, open unemployment, and percentage of married age &lt;17 years and some of the related predictor variables in Central Java Province in 2018. The HDI data is categorized into three levels, namely very high, high, and moderate. The results of the ordinal LR model show that there are three factors that influence the HDI, they are the gross enrollment rate for high school (GER), the percentage of the poor, and the proportion of women who married at the age of less than 17 years. Comparison of the accuracy LR model and Classification Tree in classification analysis shows that if the training data used is 60%-70% the LR model is better than Classification Tree, while the training data used is more than 70% and less than 86% then the Classification Tree model is better than LR.
Modeling musician diffraction for artificially excited clarinet directivity measurements

Directivity measurements of musical instruments have many applications in musical, audio, and architectural acoustics. Typical measurement methods include artificially excited instruments and instruments played by live musicians. While recent advances in directivity measurement techniques enable higher resolutions for played instruments, the results are still limited in bandwidth and repeatability compared with directivity results from artificially excited instruments. However, artificially excited instruments typically neglect musician diffraction and absorption. This work compares possible approaches for representing musician diffraction in artificially excited clarinet measurements to improve their directivity results for room simulations or auralizations.
Ideal theory of semigroups based on ( 3 , 2 ) -fuzzy sets

In this paper, the notions of (3, 2)-fuzzy ideal, (3, 2)-fuzzy bi-ideal, (3, 2)-fuzzy interior ideal and (3, 2)-fuzzy (1, 2)-ideal of a semigroup are introduced and investigated their properties.The relation between ((3, 2)-fuzzy) ideal, bi-ideal, interior ideal and (1, 2)-ideal are given.We characterized (3, 2)-fuzzy (1, 2)-ideal in terms of f 3 -level α-cut and g 2 -level α-cut.A necessary and sufficient condition for a subset of a semigroup to be (1, 2)-ideal in terms of (3, 2)-fuzzy (1, 2)-ideal of a semigroup is given.
HUBUNGAN MINTA BACA DENGAN KEMAMPUAN MENGIDENTIFIKASI UNSUR-UNSUR PEMBANGUN TEKS PUISI KELAS VII SMP NEGERI 40 PADANG

This study aims to describe students' interest in reading, describe the ability to identify the building blocks of students' poetry texts, and to describe the relationship between reading requests and the ability to identify elements of poetry texts for class VII SMP Negeri 40 Padang. In this study, there were 2 classes as the research sample for class VII.1 as many as 29 students and another class as a test class for class VII.2 questionnaires with 27 students. The sampling technique was carried out using the purposive sampling technique. The instruments used in this research are questionnaires and objective tests. The results of this study are three things. First, the reading interest of class VII students of SMP Negeri 40 Padang obtained anaverage score of 74.24 in the range of 66-75% on a scale of 10, with more than adequate qualifications (LdC). Second, the ability to identify the elements of poetry building for seventh grade students of SMP Negeri 40 Padang obtained an average score of 82,34 in the range of values of 75-85% on a scale of 10, with good qualifications (B). Third, there is a significant relationship between reading interest and the ability to identify elements of poetry building for students of VII SMP Negeri 40 Padang. This shows that reading interest affects students in identifying the building blocks of poetry. This can be seen from the test results which prove that tcount &gt; from ttable, namely 5.40 &gt; 1.70.
Sentilyzer : Lexicon based Sentiment Analysis of Tweets

Sentiment analysis is a process of deriving emotions from texts. Twitter Sentiment Analysis is used to find market insights and trends or changes in people's perceptions contained in tweets mentioning particular product or service on Twitter which is a popular social media platform. As the world is slowly shifting towards digital currency, we have seen that social media is critical in establishing public perception about cryptocurrency and crypto market trends. The purpose of this study is to determine if there is any correlation between positive or negative perceptions of cryptocurrencies and their value, and whether its popularity has changed over time. We have used lexicon-based approach for analyzing the sentiments of tweets referring to either of the two most popular cryptocurrency namely Bitcoin and Dogecoin. We extracted tweets from Twitter from different geographical locations and different time periods in order to make our sample size as diverse as possible so that we could get precise results. For visualization of these results, we have created dashboard in tableau. These visualisations show popularity of BTC and Doge through total count of tweets, their polarity i.e. negative, positive or neutral, sentiment scores, etc. Our study has shown that though there are times when Dogecoin’s value has significantly shot up due to positive tweets especially by most influential figures like Elon Musk but the effect isn’t permanent and popularity of Bitcoin and Dogecoin remains volatile. Keywords— lexicon, tweets, opinion, analysis, map, time, location, emotion, sentiment, score, polarity, visualisation, tableau, etc.