Reviewerly/exHarmony huggingface dataset:
Pola pencarian informasi mahasiswa Program Studi Perpustakaan dan Sains Informasi UKSW menggunakan teori Ellis

Satya Wacana Christian University (SWCU) Library and Information Science Study Program students have studied information science at an early study stage, so they consistently seek information. This study aimed to investigate the information-seeking patterns of the SWCU Library and Information Science Study Program students using Ellis' theory. This study used descriptive quantitative research methods. Determination of respondents used quota sampling for active students from class 2015 to 2019 using Google forms. Based on the study results, respondents searched for information according to information-seeking behavior: starting, chaining, browsing, differentiating, monitoring, and extracting activities. Respondents in starting activities can already determine the topic of information before seeking the information needed, with a value of 82.6%. Respondents in chaining activities could relate the information obtained through the initial search, with a value of 83.4%. Respondents in browsing activities could search for information in a structured manner, with a value of 78.4%. Respondents in differentiating activities could categorize the information needed, with a value of 83%. Respondents in monitoring activities always made notes after obtaining the required information development, with a value of 79.1%. Respondents in extracting activities could ensure the accuracy of the information obtained through scanning techniques, with a value of 77.9%. However, there was a shift in the Ellis model of information-seeking patterns because some respondents had several stages of information-seeking. The conclusion is that the development of information technology has influenced the student's information-seeking pattern.
From Theory to Practice: Operational Implementation of Telemetry Outlier Detection at EUMETSAT

In order to better support the Telemetry monitoring of both current and future missions at EUMETSAT, flight control teams have developed semi-supervised Outlier Detection algorithms which compliment traditional monitoring techniques such as Out of Limit Monitoring. This Outlier Detection has now been in use for several years. Through the process of developing and utilising the Outlier Detection algorithms, it has been found that the main challenges are not related to the choice, tuning or development of the algorithm. Instead, they are related to the more practical aspects surrounding the presentation of results, choice of parameters to be monitored, filtering of results and the labelling of Nominal Data. Moreover, these practical problems are largely algorithm independent and so once an adequate algorithm was developed, it was realised that chasing a better algorithm before these practical had been addressed would simply lead to a delay in the use of this technique. This paper discusses the Outlier Detection algorithm and its development, but the main focus is on the lessons learned from this process which led to the identification of practical problems and solutions to them. In particular, the Outlier Detection results are presented in a way which allows users to understand why outliers have been flagged, which minimises the “Black Box” effect. This, however, implies limiting the number of dimensions used when performing Outlier Detection. Another issue observed was floods of outliers being reported during non-nominal operations or anomalies. Not only does this mask real issues, but being since Outlier Detection is a relatively new technique, it also serves to erode trust in the process. This has mainly been resolved by a conditional Outlier Detection method which has been developed by categorising parameters hierarchically as Influencers and Followers, and also by careful selection of parameters. Finally, the main sticking point for any semi-supervised Outlier Detection techniques is keeping up to date with the labelling of Nominal Data. Again, this is partially addressed through careful selection of parameters in order to avoid having too many datasets to maintain, however it is the one area where development is still on-going within EUMETSAT, and so this paper describes the future plans to facilitate the labelling of Nominal Data.
CBC Mode Based Image Encryption Technique Using Arnold Transformation

This article focuses on a new CBC mode-based image encryption algorithm where a grayscale standard image has been taken as an input. The input image is scrambled using Arnold transformation and then applies the CBC mode to finally encrypt the image. The Full form of CBC is 'Cipher Block Chaining Mode'. In this algorithm, a series of Cipher blocks have been created and each of the successor blocks has been connected with each of the predecessor blocks. Besides of it, a series of strongest random secret keys has been created with respect to each of the successor cipher blocks. These secret keys and the CBC block apply to a standard grayscale image and finally create the desired encryption image. This scheme has been examined by different statistical tools such as NPCR, UACI, Entropy, Histogram analysis, and Correlation coefficients. The different experimental results state that this scheme is secure from different types of attacks and also proved the straightness properties of this proposed scheme.
Lossless compression of biomedical images using block-based arithmetic encoding employing resolution independent gradient edge detector

The radiological modalities such as CT scanners, MRI, X-ray, etc., produce a tremendous amount of data which needs to be stored in hospitals due to legal reasons. Compressing medical images will result in efficient storage and transmission of the data over a communication channel. Lossy compression techniques have high coding efficiency but they are not suitable for quality critical biomedical image compression. Lossless coding is essential in such case. In this paper, a new technique is presented for lossless coding using resolution independent gradient edge detector (RIGED) and block adaptive arithmetic coding (BAAE) for different modalities of volumetric medical images. RIGED is used for pixel prediction and coding redundancy is removed using BAAE. The proposed approach performs better as compared to the state-of-the-art compression algorithms in terms of bits-per-pixel (BPP). Experimentation results showed that the proposed technique outperforms CALIC and the JPEG-LS by 0.18% and 6.65%.
Level of Socio-Political Stability, Income Distribution and Democracy in Africa

[enteThe question of the links between inequality and democraty is still the subject of an intense academic debate. We revisit this question by taking explicitly into account the interactions between the level of of socio-political stability and inequality in order to measure their impact on democraty in Africa. More precisely, we use the dynamic panel GMM-system technique on a sample of 30 african countries for the period 1996-2019. It appears, in accordance with the theory of modernity, that the level of wealth of a country has a positive impact on democratization. Inequalities negatively impact the level of democracy. However, this impact is mitigated by the level of sociopolitical stability. From a certain level of socio-political stability, inequalities have a positive impact on democratization. Similarly, socio-political stability negatively impacts democratization. The greater the inequalities in a country, the more this effect will be attenuated until it becomes positive. The sociopolitical acceleration variable has a positive effect on democracy. For countries with a low level of inequality, the effect of political instability is negative.r Abstract Body]
An Efficient Biometric Approach of the Attendance Monitoring System for Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA)

The main idea of this research paper is to reduce one of the problems faced by the workers under MGNREGA in villages. The problem is that every day a group of 30–40 members goes to some work, which is based on MGNREGA (Mahatma Gandhi National Rural Employee Guarantee Act). This work is assigned by their leader, who takes attendance on every working day. In this process, if anyone is absent on working day, his/her will sign on the attendance sheet on next following day. By this, for those days that are absent to work also get money, and the people who worked on that day will lose their money. To store the details of the workers, we are using MYSQL data servers and HTML, CSS, PHP codes for creating the Web pages. Node MCU module is used to access the fingerprint of the worker. After getting finger, it checks for validation and displays the login details of the worker. By using the biometric, we can reduce the corruption in the MGNREGA Act. By doing this way, every worker in this act will be benefited and satisfied for their work and money.
Formation of professional competence in future teachers

To understand the characteristics of the pedagogical profession and the special requirements of this profession, to clarify the attitude to the chosen profession, to form the ability to use one or another method of teaching and learning, taking into account the psychophysiological characteristics of students., is required to operate as a mature staff. This article examines aspects of shaping professional competence in future teachers.
On Assisting Diagnoses of Pareidolia by Emulating Patient Behavior

The pareidolia phenomenon is a discriminating characteristic of psychiatric disorders, expressed through visual illusions seen by patients. Typically, it can be diagnosed through the noise pareidolia test, which is time-consuming to both patients and experts. In this research, we propose a novel computer-assisted method to identify pareidolia phenomenon. The idea is to emulate patient behavior in face detection models to get a similar behavior in noise pareidolia tests as patients. Unlike most medical image analysis methods, for psychiatric disorders the ground-truth varies from patient to patient, making this challenging. For a set of training patients, we fine-tune reference models to detect noise pareidolia test responses in the same way as each individual patient. Then, a new test patient is identified by comparing their behavior to the reference models using a distance function in a trained embedding space. In the experiments, the effectiveness of the proposed method is demonstrated. Further, we can show that our method can improve the efficiency of the clinical noise pareidolia test by reducing the number of necessary test images while reaching a comparable high accuracy.
Adding an Inception Network to Neural Network Open Information Extraction

This article presents a method to resolve tuples from plain text by adding an inception network, and dependence path embedding to existing neural network methods of open information extraction (Open IE). Inception networks are used in analysis of computer vision, and dependence path embedding in text processing, but neither has been reported with Open IE. Performance was measured on benchmark datasets using two existing Open IE deep learning methods, one using bidirectional long short-term memory and BIO tagging (RnnOIE-verb), and another using a span-based model (SpanOIE). RnnOIE-verb was compared with RnnOIE-verb plus inception network and/or dependence path embedding. SpanOIE was compared with SpanOIE plus inception network. Performance slightly increased with the addition of inception network to RnnOIE-verb (before AUC 0.45, F1 0.59; after AUC 0.46, F1 0.60) and inception network to SpanOIE (before AUC 0.63, F1 0.748; after AUC 0.64, F1 0.764). The performance gain was minor but potentially relevant to an iterative process of improvement.
Research on Grain Pile Temperature Prediction Based on CNN-GRU Neural Network

The temperature of grain while in storage in a silo is an important indicator used to determine food security. Therefore, monitoring and reasonable prediction of grain temperature can safeguard grain to a great extent. For the characteristics of grain pile temperature with nonlinear sequence, this paper adopts a hybrid neural network algorithm based on Convolutional Neural Network (CNN) and Gated Recurrent Unit Network (GRU) for grain pile temperature prediction model, which extracts the vector features of input data by CNN model, then learns the input features by GRU model, and finally predicts the grain pile temperature. In this paper, we build a model based on the existing grain bin data, train and test the model, and set up experimental comparisons. The experimental results show that the RMSE of the model is 0.049 and the mean absolute error MAE is 0.036. The temperature prediction is more accurate and has less error than other methods.
Studying teaching staff climate and motivation

The article presents the need of studying management mechanisms in order to create a favorable psychological climate for the teaching staff. The article describes the need for applied research to solve emerging problems that hinder effective work of a teacher. The article identifies the problem of the study that is to solve the contradiction between the need for effective activities of educational organizations and the lack of preparedness of the managers for managerial activities. The article describes the study of the climate and motivation of teaching staff in preschool educational organizations and secondary schools and their impact on the socio-psychological climate in educational organizations. The results of the study of the socio-psychological climate of two pedagogical teams are presented, problem areas are identified and rational forms and methods of team management are determined, conclusions are formulated. The practical significance of the conducted research is to identify the features of the psychological climate of specific teams, making it possible to determine the prospects for further activities of managers of organizations aimed at harmonizing relations in the team and improving the effectiveness of professional activities.
The Impact of COVID-19 on the Dispersion of Immersive Technologies and Human Behavior Research and Implementation

VR (virtual reality) technologies can elicit in participants the illusion of being present in a simulated reality. Throughout history, science and technology have co-evolved in complementary processes. Scientific discoveries have helped develop new technologies, and at the same time, new technologies allowed non-cumulative breakthroughs in research. We suggest that VR may revolutionize human behavior research and teaching, almost in the same way that the telescope advanced astronomy and the microscope advanced biological research. During the COVID-19 pandemic, these immersive technologies have scaled in use and adaptation. Academic papers and commercial applications are evaluated in this review. Furthermore, expert individuals are interviewed to account for the true nature of the advancement of immersive technologies due to the pandemic. We show that significant advancements were achieved in the educational framework using immersive technologies. Furthermore, we discuss the elevation in collaborative work using VR, either in large quantities in conferences or smaller groups, as a collaboration framework for coworkers. Next, we discuss the changes and opportunities in behavioral research that result in the necessity to exit the lab and create a global ecosystem to supply participants with remote evaluation methodologies named remote XR. Moreover, we discussed conceptual changes that accrued within the clinical communities that are believed to affect the technological adaptation of immersive technologies as part of clinical work and patient treatment plans and evaluation procedures in the future.
Supplemental Material: Quantifying the growth of continental crust through crustal thickness and zircon Hf-O isotopic signatures: A case study from the southern Central Asian Orogenic Belt

Data sources, lithology and geochemical characteristics, and calculation parameterization of the net volume of juvenile continental crust.
Breast Cancer Detection and Analysis Using Transfer Learning Algorithm

One of the most common and life threatening carcinomas in women is breast cancer. It is diagnosed by detecting the malignancy of the cells of breast tissue. Early diagnosis of breast cancer can be so important in increasing the survival rate of the patient. Breast cell shapes in histopathology images have to be examined carefully by pathologist to diagnose breast cancer. However, the histopathological analysis of breast cancer is labor-intensive, subjective and which might lead to dissent among different pathologist. An automatic diagnostic system could help pathologists to classify histopathological images quicker. This paper presents a transfer learning method for classifying images into malignant and benign tumor, and compares the performance of different convolution neural network (CNN) models, namely VGG16, VGG19, InceptionV3, Xception, InceptionResNetV2, ResNet101V2 and MobileNetV2.
Streaming Data in the Background

Up to this point, we have learned how to stream data from the Kinect device. We fully understand how to properly open the cameras and acquire color, depth, body, and IMU information.
One-Share-One-Vote and Dual-Class Shares in the Laboratory

We test the seminal Grossman and Hart (1988) model on the optimality of the one-share-one-vote share structure against the dual-class share structure in a laboratory experiment. Our result shows qualitative support to their theoretical prediction asserting that the more efficient contender of control (incumbent or raider) is more likely to win the takeover contest under one-share-one-vote than under dual-class shares. It is interesting to note that contenders generally submit tender-offer prices higher than their maximum willingness to pay predicted by theory in all our treatments. However, the price deviation from the fundamental value is smaller under one-share-one-vote than under dual-class shares. Overall, our results show supportive evidence for better allocation and information efficiency of one-share-one-vote and draw attention to some practical issues when the theory is applied to real market settings.
INTERRELATION OF PSYCHO-EMOTIONAL CHARACTERISTICS AND VALUE ORIENTATIONS OF STUDENTS IN THE PERCEPTION OF CINEMA IN A PANDEMIC

The issues of psycho-emotional characteristics and value orientations of students in the perception of cinema in a pandemic are presented in the article. The authors focused on psycho-emotional characteristics, value system of orientations and perception of cinema in a pandemic. Purpose of the research: to study the relationship between psycho-emotional characteristics and value orientations of students, taking into account the preference for a certain genre of film in a pandemic. Student age opens up opportunities for identifying the direction of value orientations and the realization of personal potential. The article shows that values and cinema are closely interrelated, since cinema is a very powerful tool in the socio-psychological development of a person. The analysis of scientific literature carried out by us has shown that value orientations have a very strong effect on people’s lives, on their choice in life situations. An empirical study was carried out and the results of the study confirm the hypotheses put forward, which consisted in the assumption that students have a relationship between the preference for a certain film genre and the indicator of “self-development”, and also the hypothesis was confirmed that the modality of emotions is associated with the level of spiritual satisfaction.
Enhancing Digital Competency: Validation of the Training Proposal for the Development of Teaching Digital Competence according to DigCompEdu

The technologicalization of society has been a great challenge for education in the XXI century, seeing this in the need to face it in order to provide quality digital literacy. These accelerated changes have repercussions in various areas, among them the professionalization of teachers, due to the difficulties presented when updating their knowledge and adapting to the dizzying technological pace. Teaching Digital Competence (TDC) as safe and critical use of technologies is considered one of the key competencies, guarantors of educational success, within a new approach in virtual education. Following this line, the present research focuses on the design and subsequent validation of the structure, contents, and activities of a training program aimed at improving and developing the digital competence of non-university teachers according to the model of the DigCompEdu Framework belonging to the European Union. This research is part of a larger one and, as a previous step to a larger study, a Delphi-type validation design is established using an expert coefficient that has the participation of 50 people. The results demonstrate the validity of the training proposal, as well as the uniformity of the experts' criteria. In this sense, the application and benefits of this training action for skills development are discussed.
RNN-BD: an approach for fraud visualisation and detection using deep learning

The evolution of banking information systems considerably increases fraud activities, which can have a negative impact on banking financial services. The use of credit cards has increased significantly due to electronic funding, electronic services and e-commerce activities. Massive amounts of data from credit card transactions can result in big data. Researchers are now using machine learning algorithms to detect and analyse fraud in online transactions. One of the major concerns of the banking industry is the visualisation and detection of credit card fraud. Machine learning techniques only work well when the dataset is small and does not have complex models. Deep learning, on the other hand, processes large and complex data sets. The objective of this paper is to visualise and detect credit card fraud by incorporating deep learning and dimensionality reduction techniques. A real dataset is used to assess the effectiveness of the intended work. The results show that our proposed model is more efficient in identifying fraudulent transactions to reduce fraud and income loss. We found that our deep learning model can be used to identify fraudulent transactions and reduce fraud losses to protect customer interests.
3D Reconstruction of the Colon from Monocular Sequences Evaluation by 3D-printed Phantom Data

Image based documentation of diagnostic findings in screening colonoscopy is currently achieved by capturing single images. Nevertheless, these lack precise information about their location in the colon. Creating a panorama map of the lumen of the colon during the examination, which shows detected lesion in their context, can support the endoscopist during the documentation process. Moreover, such a panoramic map also provides information about the completeness of an examination. An important step towards such a panoramic model is a robust 3D reconstruction of the colon in a first step. Nevertheless, as colonoscopy provides only monocular image data, 3D reconstruction of the colon is challenging. Therefore, we created a 3D reconstruction pipeline, consisting of a DCNN to estimate the depth for a single video frame and the concatenation and fusion of the depth maps to a 3D model based on feature consensus. As with real colonoscopic data, ground truth information regarding the exact extension and geometry of the colon is not available,we produced a modular 3D printed phantom of the colon to evaluate the proposed reconstruction method. The phantom was examined with standard withdrawal motions using two different colonoscopes resulting in endoscopic video streams. From these sequences the3Dreconstruction was computed, and the results were aligned and compared with the ground truth obtained from CAD-blueprint of the phantoms. In all cases, the achieved quality was highly sufficient.