Reviewerly/exHarmony huggingface dataset:
The application of data fusion method in the analysis of ocean and meteorology observation data

Data fusion is a new technology of data processing for multi source ocean meteorology data In order to clearly understand how to apply this technology in the data processing of ocean meteorology observation the author first divides the fusion methods into three categories i e pixel level fusion feature level fusion and decision making level fusion Then the corresponding methods of each data fusion are discussed in detail Afterward the SST fusion data from China Meteorological administrative is analyzed in order to explain that the fusion data is applied in numerical forecast model on the China rsquo s Bohai sea region Finally the authors believe that the data fusion technology of multi source observation data has a good application value when the observation data is processed into grid data of ocean meteorology which can be used as the grid truth value of numerical model inspection In addition the fusion technology at decision making level can also develop with the intelligent technology and deeply mine the application of data fusion technology to the ocean meteorology observation data
Introduction to Second Life® As a Virtual Training Environment: Perception of University Teachers

In January 2020, two three-hour workshops on an introduction to Second Life&reg; as an online educational platform were held inside a virtual world. The workshops were dedicated to medical university teachers with the main objective being to let them get to know Second Life&reg; and its formative possibilities. The format of this experience was well received by the participants. Everyone who answered the questionnaire agreed that the environment was useful and interesting and that they would repeat a similar experience again. Ten out of 23 participants (43.5%) declared that they were willing to carry out a teaching activity in Second Life&reg;. This kind of action allows for the promotion of other future in-world actions directed to the training of trainers.
Predicting Cards Using a Fuzzy Multiset Clustering of Decks

Search-based agents have shown to perform well in many game-based applications.In the context of partially-observable scenarios agent's require the state to be fully determinized.Especially in case of collectible cards games, the sheer number of decks constructed by players hinder an agent to reliably estimate the game's current state, and therefore, renders the search ineffective.In this paper, we propose the use of a (fuzzy) multiset representation to describe frequently played decks.Extracted deck prototypes have shown to match human expert labels well and seem to serve as an efficient abstraction of the deck space.We further show that such deck prototypes allow the agent to predict upcoming cards with high accuracy, therefore, allowing more accurate sampling procedures for search-based agents.
A novel method for digital data encoding-decoding

Cryptography is one of the paramount and most vital data treatment processes, it allows us to be secure in our electronic transactions. The process of cryptography protects our valuable data such as private account numbers and transaction amounts, electronic signatures replace handwritten signatures or credit card authorizations, and public-key encryption provides confidentiality. The objective of data encryption is to keep digital data confidentiality save as it is stored on computer systems and transferred using the internet or other computer networks. In this paper we will focus in enhancing security level of the encryption-decryption process by introducing a novel method, which uses any digital color image to encode-decode secret message, the using of a special key to encrypt-decrypt the encoded-decoded message, the color image will be known only by the transmitter and receiver to keep the process of data treatment confidential, the obtained experimental results by the proposed method will be analyzed to prove the enhancement in process efficiency and confidentiality.
A Fast High Precision Skew Angle Estimation of Digitized Documents

In this paper, we treated the problem of automatic skew angle estimation of scanned documents. The skew of document occurs very often, due to incorrect positioning of the documents or a manipulation error during scanning. This has negative consequences on the steps of automatic analysis and recognition of text. It is therefore essential to verify, before proceeding to these steps, the presence of skew on the document to be processed and to correct it. The difficulty of this verification is associated to the presence of graphic zones, sometimes dominant, that have a considerable impact on the accuracy of the text skew angle estimation. We also noted the importance of preprocessing to improve the accuracy and the calculation cost of skew estimation approaches. These two elements have been taken into consideration in our design and development of a new approach of skew angle estimation and correction. Our approach is based on local binarization followed by horizontal smoothing by the Run Length Smoothing Algorithm (RLSA) method, detection of horizontal contours and the Hierarchical Hough Transform (HHT). The algorithms involved in our approach have been chosen to guarantee a skew estimation: accurate, fast and robust, especially to graphic dominance and real time application. The experimental tests show the effectiveness of our approach on a representative database of the Document Image Skew Estimation Contest (DISEC) contest International Conference on Document Analysis and Recognition (ICDAR)
Oversampling Method on Classifying Hypertension Using Naive Bayes, Decision Tree, and Artificial Neural Network

Oversampling is a technique to balance the number of data records for each class by generating data with a small number of records in a class, so that the amount is balanced with data with a class with a large number of records. Oversampling in this study is applied to hypertension dataset where hypertensive class has a small number of records when compared to the number of records for non-hypertensive classes. This study aims to evaluate the effect of oversampling on the classification of hypertension dataset consisting of hypertensive and non-hypertensive classes by utilizing the Naïve Bayes, Decision Tree, and Artificial Neural Network (ANN) as well as finding the best model of the three algorithms. Evaluation of the use of oversampling on hypertension dataset is done by processing the data by imputing missing values, oversampling, and transforming data into the same range, then using the Naïve Bayes, Decision Tree, and ANN to build classification models. By dividing 80% of data as training data to build models and 20% as validation data for testing models, we had an increase in classification performance in the form of accuracy, precision, and recall of the oversampled data when compared without oversampling. The best performance in this study resulted in the highest accuracy using ANN with 0.91, precision 0.86 and recall 0.99.
Balancing Dual Roles in the Face of Missing On-Site Child Care Arrangements: Lived Experiences of Breast-Feeding Student Mothers at Kyambogo University

Through the use of a phenomenological approach this study explored lived experiences of breast-feeding student mothers in the face of missing institutionalized on-campus child care arrangements at Kyambogo University (KyU). Specifically, the study sought to; identify the missing institutionalized on-campus child care arrangements at the university, establish the dilemmas breast feeding student mothers experience in undertaking the dual roles of mother and student hood due to the missing institutionalized on campus child care arrangements at KyU and examine the effect of the missing institutionalized on campus child care arrangements on undertaking of academic activities by breast-feeding student mothers. A total of 10 breast feeding student mothers were interviewed and data was analyzed using themes. Results indicated that the following institutionalized on-campus child care arrangements were missing at Kyambogo University; adequately furnished baby care rooms, baby playgrounds, immunization and pediatric medical services, professional nannies, child care responsive; teaching time tables, teaching sessions, time frames for course work assignments, tests and examinations’ undertakings, and child care responsive-remedial lessons. Dilemmas of breast feeding student mothers’ in this scenario included choosing between; baby-sitting vs attending lectures, completing a lecture vs breastfeeding a crying hungry baby, undertaking child bonding activities vs undertaking academic activities, completing an exam or a test vs breast feeding a crying hungry baby, baby care vs attending academic group discussion and baby care vs completing a coursework assignment in time. Student mothers at KyU have devised means of coping and have learnt how to resolve the tension of combining motherhood and education in an academic environment without on-campus childcare arrangements so as to pave a brighter career and family future. Such strategies included; receiving child care support from family and student friends, use of privately hired nannies, ignoring one of the two roles, time tabling the activities for the two roles, creating extra time to read, and getting help from spouses. The voices of the interviewees revealed that when the two roles were in conflict, they would rather miss academic activities than abandon their babies especially in critical times like when the babies were sick. It was therefore recommended that KyU considers putting in place various instututionalised on campus arrangements as a critical resource that will enhance academic undertakings of breast-feeding student mothers in their pursuit of Higher Education.
Viterbi optimization for crime detection and identification

In this paper, we introduce two types of hybridization. The first contribution is the hybridization between the Viterbi algorithm and Baum Welch in order to predict crime locations. While the second contribution considers the optimization based on decision tree (DT) in combination with the Viterbi algorithm for criminal identification using Iraq and India crime dataset. This work is based on our previous work [1]. The main goal is to enhance the results of the model in both consuming times and to get a more accurate model. The obtained results proved the achievement of both goals in an efficient way.
PENERAPAN MODEL PEMBELAJARAN KOOPERATIF TIPE STAD (STUDENT TEAM ACHIEVEMENT DIVISION) DALAM KETERAMPILAN MENULIS KALIMAT SEDERHANA BAHASA JERMAN

Abstract. This study aims to determine the application of the cooperative learning model type STAD. Planning, process and results of the STAD (Student Team Achievement Division) cooperative learning model in writing simple sentence skills in German for class X students of SMAN 2 Bulukumba. This research is a Classroom Action Research conducted with 2 cycles. The research data consists of two types, namely: qualitative data and quantitative data. Qualitative data were obtained through observation, while quantitative data were obtained through the test results of writing simple sentence skills in cycle I and cycle II. Data were analyzed using percentage techniques. The results showed that the skill of writing simple sentences with the percentage of scores obtained by students in the first cycle reached 56.55%, and the second cycle reached 64.62%. These results indicate that the application of the STAD (Student Team Achievement Division) cooperative learning model improves the simple sentence writing skills of class X students of SMAN 2 Bulukumba, Bulukumba Regency.
Recognition of hand gestures in real time

Gesture recognition plays an important role in the interaction of humans with machines because of their natural and friendly semantic expression.To use this technology, machines must quickly and accurately identify them so that users feel comfortable and are ready to interact with the machines.Gesture recognition remains a challenge due to the variety of gestures, the similarity of the forms of gestures and the complexity of the application scenarios.Recently, more and more research in the field of computer vision is being conducted.The most common ones are approaches to recognizing gestures using infrared sensors or neural networks.In this paper, we analyze the existing solutions that allow us to solve the problem of gesture recognition using computer vision.This topic is relevant since gesture control can be used in places where it is not possible to use voice commands.Also, this type of control is intuitive to humans.In this paper, we analyze the existing methods for recognizing gestures using neural networks or infrared sensors.The advantages and disadvantages of the analyzed methods are investigated and the proprietary architecture of the convolutional neural network to classify gestures is proposed.Also, the accuracy of the network was evaluated depending on the distance between the camera and the hand, as well as depending on the complexity of the gesture.
Noise Elimination and Contour Detection Based on Innovative Target Image Contour Coding Algorithm

At present, the image mining is mainly based on its local and key features, which focuses on its texture and statistical grayscale features, but it focuses on its edge and shape features rarely. However, the contour is also an important feature for image shape recognition. In this paper, a good target image contour coding algorithm was adopted, and an LCV segmentation model with good image boundary acquisition capability that can reflect the target image contour features was selected for the original image contour segmentation. The detailed features analysis of the contour coding algorithm was carried out through the experiments; the experimental results showed that the algorithm was a significant technological breakthrough in image feature extraction and recognition.
Self-Regulated Learning Strategy Instructions in Reading Comprehension Skill Learning During Outbreak Era

&lt;em&gt;The online learning should be limited by principles and apply proper strategies. One of them is Self-Regulated Learning (SRL) strategy. Applying this strategy must be guided by proper instructions and procedures. In reading skill teaching and learning, this strategy is considered suitable to make students learn independently without letting them lost in the processes. This study aims to; 1) develop tasks instructions on reading comprehension skill to help students learn independently during outbreak era, 2) get students’ perspectives on how SRL works on them. The method is research and development cycle. The students’ perspectives are measured using Likert scale. The results showed that there need to be specific and clear instructions stating time, purposes of the tasks, giving students log to monitor their learning process, encouraging them to get help from lecturers or peers, and encouraging themselves to evaluate the process to find proper strategy for them.&lt;/em&gt;
Research on the Development of China University Library Alliances

With the continuous development of information technology, the university library alliances (ULA) have gradually changed from "entity alliance" in the last century to the "entity body combined with digitizing brain" in the present.In the near future, smart libraries (SL) and cloud service platforms (CSP) will be the real protagonists of the library alliances.In this process, the use of artificial intelligence (AI) and cloud computing (CC) technology will become an important trend.The continuous development of China University Library Alliance (CULA) is the result of using emerging information technology to continuously satisfying the needs of readers.In the era of big data, the analysis of mass document data will become a new topic in the development of ULA, and its service focuses will gradually be transferred from traditional business to document data.With the development of AI and CC, focusing on the intelligent, specialization and individuality of the service is the inevitable trend.This article mainly adopts the research method of comparative library science and historical analysis to explain the important characteristics of different development stages of CULA, to analyze the problems of "entity alliances" and "hybrid alliances", and then to explain why cloud libraries of big data (BD) and smart libraries will be the mainstream of CULA.
Computational alignment of on-machine deflectometry

Accurate system calibration remains an area of active improvement in deflectometry. Since deflectometry requires the geometry information of all participating hardware to be well known, miscalibration can mar the accuracy of surface reconstruction especially in lower order shapes. To uphold reconstruction fidelity, extra measuring instruments (i.e. coordinate measuring machines, laser trackers, metering rods) or reference features (i.e. fiducial points or reference mirror) to find out the positions of a camera, a screen, and a unit under test are used. These methods provide reliable calibration but are resource-intensive. In this paper, we introduce an alignment algorithm to calibrate the geometry of a deflectometry configuration. We leverage the concept of alignment algorithm which uses a sensitivity model. With the aid of ray tracing simulation, the relationship between camera pixels and screen pixels of a deflectometer is quantitatively established. This pixel-to-pixel relationship enables us to generate computational imaging of screen and characterize the tendency of misalignments of the deflectometer. On top of that, we can calculate and make multiplexed patterns of screen which highlight the effect of misalignments. We set specific indices and corresponding screen patterns for each alignment parameters to build the sensitivity model. The initial simulation result shows that the algorithm can estimate misalignment status. We believe that this algorithm can be an alternative and efficient calibration process for the deflectometry system, especially when the usage of extra measuring devices is limited.
Feature Selection in Big Image Datasets

In computer vision, current feature extraction techniques generate high dimensional data. Both convolutional neural networks and traditional approaches like keypoint detectors are used as extractors of high-level features. However, the resulting datasets have grown in the number of features, leading into long training times due to the curse of dimensionality. In this research, some feature selection methods were applied to these image features through big data technologies. Additionally, we analyzed how image resolutions may affect to extracted features and the impact of applying a selection of the most relevant features. Experimental results show that making an important reduction of the extracted features provides classification results similar to those obtained with the full set of features and, in some cases, outperforms the results achieved using broad feature vectors.
Exploring the Meaning of “Usable Security”

While there are many examples of incidents that make the need for more work around the human aspects of security apparent, the literature makes it obvious that usable security can mean many different things and usable security is a complex matter. This paper reports on a structured literature review that analyzed what the research community considers to be included in the term “usable security”. Publications from the past five years were analyzed and different perceptions of usable security were gathered. The result is a listing of the different aspects that are discussed under the term “usable security” and can be used as a reference for future research of practitioners who are developing security functions with usability in mind .
VIRTUAL TERRESTRIAL LASER SCANNER SIMULATOR FOR DIGITALISATION OF TEACHING ENVIRONMENT: CONCEPT AND FIRST RESULTS

Abstract. We are presenting a concept and first results of a Terrestrial Laser Scanner simulator - a tool, which could be a valuable educational tool for geomatics and engineering students. The main goal of the VirScan3D project is to cover engineering digitisation by two major project parts: 1) Development of a virtual environment for creation and processing of digital 3D scan data; 2) Digital teaching and e-learning material with interactive tools and practical experiences. Part 1 will be solved through the development of a virtual system that allows users to create realistic data in the absence of a real measuring device. At the time of writing (April 2020) all higher education teaching and learning worldwide has been changed to online delivery due to the Covid pandemic. However, this has affected the courses that involve working with equipment and – consequently – require the presence of students and devices. In this project, the development of such equipment simulator proposes a real and feasible solution, which allows undisturbed continuing the teaching.The virtual scanner will be based on existing software packages. It will be part of a general e-learning concept that will be implemented in partner universities. Part 2 is addressed through an integrated teaching concept that includes interactive learning tools (for both teachers and students) and practical exercises (for example project weeks, summer schools) where the theoretical and virtual education is connected to practical experience with real instrumentation and data. One outcome of the experimental work is additional e-learning material based on the experiences and results of the project work.The prototype implementation of the virtual laser scanner is realized within a game engine, which allows for fast and easy 3D visualisation and navigation. Within this environment, the user can freely navigate and define suitable scanning positions/stations. At each scanning station a simulated scan is performed, which is based on the technical specifications of a real scanner. The mathematical solution is based on 3D line intersection with the virtual 3D surface including noise and colour as well as an intensity simulation. As a result, 3D point clouds for each station are generated, which will be further processed for registration and modelling using standard software packages.