Reviewerly/exHarmony huggingface dataset:
Digitalization of the Educational Process in the Higher Educational Process: Analysis of the Dynamics of Students' Attitude to Different Forms of Learning

The article is devoted to topical issues of digitalization of the educational process at the university. The development of digital technologies in the field of education is, on the one hand, a necessity, on the other hand, a rapid transformation of the education system and is supported by the state and society. In the course of the study, the authors analyzed and presented the theoretical aspects of the digitalization of the educational process, identified by domestic scientists and practitioners. The subject of research in this article is the digitalization of the educational process at the university. Purpose: to analyze the change in the attitude of students studying in the direction of training "Personnel Management" to various forms of conducting training sessions, namely, offline and online, to identify the existing opportunities and threats for the educational process at the university. The following research methods were used in the work: written survey, participant observation, comparative analysis, analysis of scientific works. The findings of the study and the conclusion are presented.
Data Encryption Using Image Processing: A Brief Study

We can see considerable growth in the field of science and technology over the past few decades. This growth has also brought an enormous threat to the data used by users. Encryption and decryption of data play an important role in safeguarding the data. Many authors have contributed much outstanding research on unauthorized access to user data. In this journal, I have proposed an algorithm for data encryption using images. My main aim is to provide a new and highly customizable approach to protecting data using images and eliminating the use of plaintext. In my research, I will use images to encrypt and decrypt data rather than using plaintext.
Development of the National Qualifications System: The Impact of Transhumanism

The article discusses the problems of developing new educational programs for universities and issues that need to be addressed by coordinating with various system institutions under the influence of transhumanism. The methods of analysis, generalization of the regulatory and methodological framework, and external expertise in interaction with employers were used. The scheme of interaction of elements of the national qualifications system in developing an educational program is presented. The hypothesis of the need to include all elements for the development of educational programs has been tested. The correlation dependence and percentage approximation results proved the need to use all components to develop educational programs. Several issues related to universities' development of educational programs and requiring further clarification are highlighted, including issues of harmonization of professional and educational standards under the influence of transhumanism. The work results determine the following directions for the further development of educational programs: 1. normative and methodological development, 2. the use of external expertise and evaluation, and 3. expanding opportunities for further development of unique programs with active cooperation with employers.
Waste and Vehicle Detection Using YOLO

Abstract: Automatic detection and classification of objects is an important functionality of image analysis. Due to the nature and size of objects and the varied visual features, it becomes challenging to detect and classify objects in aerial images. Manual detection of objects in these images is very time consuming due to the nature and that data captured in these images. It is desirable to automate the detection of various features or objects from these images. The conventional methods for object classification involve two stages: (i) Identify the regions with object presence in the image and (ii) Classify the objects in the regions. Additionally, detection of objects becomes challenging in presence of complexities in background, size, noise, and distance parameters. This work proposes a customized YOLO to detect and classify different objects such as garbage waste, plastic waste and vehicles in the images.
MediSmart

Health Care is one of the most primary aspects of one’s personal life, where it is once deteriorated can never be restored forever. In this pathetic situation the people face immense difficulty in managing their health-related reports, documents etc. Another emerging problem in the health life is despite providing all the necessary advice in the prescription, there are still many suggestions &amp; recommendations given by the doctor are not documented. Therefore, we propose a solution which demolishes the burden of carrying files or reports all the time to visit a doctor. This paper aims in implementing the MediSmart- An intelligent Medical Assistant, a system that comprises all the patients’ history, consolidated report along with most important information suggested by the doctor orally is recorded (voice prescription). Since the approach is patient-centric we intend to implement the MediSmart Document Collection System, which collects patient personal information, hospital specific information, EHR and Voice records of doctor’s advice. Later, the EHRs are summarized, and a consolidated medical report is presented using NLP techniques and simultaneously, the voice record is also summarized to help patients keep a track of the oral prescription provided by the doctor.
Hyperparameter Tuning on Classification Algorithm with Grid Search

Currently, machine learning algorithms continue to be developed to perform optimization with various methods to produce the best-performing model. In Supervised learning or classification, most of the algorithms have hyperparameters. Tuning hyperparameter is an architecture of deep learning to improve the performance of predictive models. One of the popular hyperparameter methodologies is Grid Search. Grid Search using Cross Validation provides convenience in testing each model parameter without having to do manual validation one by one. In this study, we will use a method in hyperparameter optimization, namely Grid Search. The purpose of this study is to find out the best optimization of hyperparameters against 7 machine learning classification algorithms. Validation of experimental results using the Mean Cross Validation. The experimental results show that the XGBoost model gets the best value while the Decision tree has the lowest value.
A Scalable Deep Network for Graph Clustering via Personalized PageRank

Recently, many models based on the combination of graph convolutional networks and deep learning have attracted extensive attention for their superior performance in graph clustering tasks. However, the existing models have the following limitations: (1) Existing models are limited by the calculation method of graph convolution, and their computational cost will increase exponentially as the graph scale grows. (2) Stacking too many convolutional layers causes the over-smoothing issue and neglects the local graph structure. (3) Expanding the range of the neighborhood and the model depth together is difficult due to the orthogonal relationship between them. Inspired by personalized pagerank and auto-encoder, we conduct the node-wise graph clustering task in the undirected simple graph as the research direction and propose a Scalable Deep Network (SDN) for graph clustering via personalized pagerank. Specifically, we utilize the combination of multi-layer perceptrons and linear propagation layer based on personalized pagerank as the backbone network (i.e., the Quasi-GNN module) and employ a DNN module for auto-encoder to learn different dimensions embeddings. After that, SDN combines the two embeddings correspondingly; then, it utilizes a dual self-supervised module to constrain the training of the embedding and clustering process. Our proposed Quasi-GNN module reduces the computational costs of traditional GNN models in a decoupled approach and solves the orthogonal relationship between the model depth and the neighborhood range. Meanwhile, it also alleviates the degraded clustering effect caused by the over-smoothing issue. We conducted experiments on five widely used graph datasets. The experimental results demonstrate that our model achieves state-of-the-art performance.
Open-set recognition for automatic target recognition: practical considerations for obtaining out of distribution examples

In most image classification applications, the task is assumed to be ”closed-set” in which the only classifications the model expects to make are of examples that it was originally trained on. However, the real world presents a much more complex ”open-set” in which a given model may encounter examples it was not trained to classify. Open-Set Recognition is the practice of enabling classifiers to recognize when they have encountered a given example that they were not previously trained to classify. Typically, these Open-Set Recognition techniques can be grouped into two categories: those that require a feature space, and those that learn a feature space. However, finding a suitable feature space is difficult and so it is often necessary that one is learned. To accomplish this, one can leverage ”Out of Distribution” examples, or examples that exist outside of the training data. This effort explores the various methods of obtaining Out of Distribution examples and how they compare. Additionally, based on our findings, we make practical recommendations for obtaining Out of Distribution examples to enable Open-Set Recognition techniques for overhead imagery and Synthetic Aperture Radar (SAR) applications.
Testing a proposed ROI for academic libraries

Purpose The main purpose of this study is to test and verify return on investment (ROI) model proposed for academic libraries in Arab countries. The study assessed the value of AAU digital library resources and identified the value of the digital library in supporting the funded research projects. Design/methodology/approach The proposed ROI model is based on two phases and two different data. In Phase I, the authors calculated ROI based on the total downloads of full text to determine the total financial returns of the database subscription costs for the academic year 2019–2020. In Phase II, the authors examined the citations drawn from the Scopus database on a sample of 30 funded research projects for the College of Engineering during the year 2019. Findings Although the application of the proposed model has some challenges, it is relevant in measuring the ROI of academic libraries in Arab countries. The results of the study in Phase 1 revealed that the AAU gained $0.95 for every $1 spent on subscriptions to online databases. For Phase 2, the findings indicated a negative ROI of $−0.70 for every $1 spent on library subscriptions to the IEEE database. Research limitations/implications This is a case study based on data collected from Al Ain University, United Arab Emirate (UAE). Therefore, the findings of the study may not be generalized, and other studies may find different results if more samples and data are used. Originality/value Although the proposed model has been cited by research papers indexed in the Scopus database, Web of Science, Google Scholar, Crossref and ResearchGate, none of these papers tested or verified the suggested ROI model. This study could be the first study testing the suggested model. The findings of the study may contribute to the application of the ROI model in the Arab countries, and particularly the academic libraries in the Arab world.
Memory Augmented State Space Model for Time Series Forecasting

State space model (SSM) provides a general and flexible forecasting framework for time series. Conventional SSM with fixed-order Markovian assumption often falls short in handling the long-range temporal dependencies and/or highly non-linear correlation in time-series data, which is crucial for accurate forecasting. To this extend, we present External Memory Augmented State Space Model (EMSSM) within the sequential Monte Carlo (SMC) framework. Unlike the common fixed-order Markovian SSM, our model features an external memory system, in which we store informative latent state experience, whereby to create ``memoryful" latent dynamics modeling complex long-term dependencies. Moreover, conditional normalizing flows are incorporated in our emission model, enabling the adaptation to a broad class of underlying data distributions. We further propose a Monte Carlo Objective that employs an efficient variational proposal distribution, which fuses the filtering and the dynamic prior information, to approximate the posterior state with proper particles. Our results demonstrate the competitiveness of forecasting performance of our proposed model comparing with other state-of-the-art SSMs.
Wool fabric image retrieval based on soft similarity and listwise learning

As a special case in content-based image retrieval, fabric retrieval has high potential application value in many fields. However, fabric retrieval has higher requirements for results, which makes it difficult for common retrieval methods to be directly applied to fabric retrieval. It is also a challenging issue with several obstacles: variety and complexity of fabric appearance, and high requirements for retrieval accuracy. To address this issue, this paper presents a novel method for fabric image retrieval based on soft similarity and pairwise learning. First, a soft similarity between two fabric images is defined to describe their relationship. Then, a convolutional neural network with compact structure and cross-domain connections is designed to learn the fabric image representation. Finally, listwise learning is introduced to train the convolutional neural network model and hash function. The generated hash codes are used to index the fabric image. The experiments are conducted on a wool fabric dataset. The experimental results show that the newly proposed method has a greater improvement than our previous work.
Ultra-weak variational formulation for heterogeneous Maxwell problem in the context of high performance computing

Electromagnetic simulations on large domains require a huge memory consumption. Domain decomposition methods, based on Trefftz methods, could be an answer to this issue. In this paper, we associate to heterogeneous three-dimensional Maxwell equations one variational formulation which can be obtained either by upwind fluxes or Riemann traces. We associate to this variational formulation an iterative Trefftz Krylov solver. The poor conditioning due to the use of plane wave basis functions is bypassed thanks to a compression strategy. Moreover, the developed iterative solver is accelerated thanks to a left preconditioner. The considered numerical cases illustrate the performance of this basis reduction, which leads to the consideration of an industrial case of more than 750 millions of degrees of freedom.
Trends in information-seeking behavior research at Airlangga University

Introduction. This study analyzes research trends in information-seeking behavior at Airlangga University, Indonesia. This study examines various aspects and models of information behavior using a systematic literature review. There has been an increase in research topics conducted by Airlangga University students, especially regarding information-seeking behavior.&#x0D; Data Collection Methods. The method used was a systematic literature review with a qualitative approach. Data sources were identified and accessed through the Airlangga University Repository (http://repository.unair.ac.id/). Items selected for review were 39 journal articles, 45 abstracts, and three theses.&#x0D; Data Analysis. The analysis process consist of planning, conducting, and reporting. 1) Creating research questions, 2) Conducting search process, 3) Deciding inclusion and exclusion criteria, 4) Conducting the quality assessment.&#x0D; Results and Discussion. Of all, there are 15 models of information-seeking behavior were examined. Among them, several frequently used models were those developed by Ellis; Leckie et al.; and Meho and Tibbo’s revision of Ellis.&#x0D; Conclusion. This research recommends the scientific development of information-seeking behavior, it is expected that research in Indonesia will create models of information-seeking behavior and be able to collaborate scientifically, increase innovation in knowledge and its application.
Decentralized trust

Design: This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context. Purpose: This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies. Findings: DAOs have the possibility of revolutionizing business models and strategies across the global economy. Offering decentralized transparent and trustworthy ledgers and community oversight, they can work to unburden organizations of their too heavy hierarchy. Originality: The briefing saves busy executives, strategists and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.
Transformer-based Question Text Generation in the Learning System

Question text generation from the triple in knowledge graph exists some challenges in learning system. One is the generated question text is difficult to be understood; the other is it considers few contexts. Therefore, this paper focuses on question text generation. Based on the traditional Bi-LSTM+Attention network model, we import Transformer model into question generation to get the simple question with some triples. In addition, this paper proposes a method to get the diverse expressions of questions (a variety of expressions of a question), that is, to take advantage of the semantic similarity algorithm based on Bi-LSTM with the help of a question database constructed in advance. Finally, a corresponding comparison experiment is designed, and the experimental results demonstrated that the accuracy of question generation experiment based on the Transformer model is 8.36% higher than the traditional Bi-LSTM + Attention network model.
Learning Insurance Benefit Rules from Policy Texts with Small Labeled Data

To protect vital health program funds from being paid out on services that are wasteful and inconsistent with medical practices, government healthcare insurance programs need to validate the integrity of claims submitted by providers for reimbursement. However, due the complexity of healthcare billing policies and the lack of coded rules, maintaining "integrity" is a labor-intensive task, often narrow-scope and expensive. We propose an approach that combines deep learning and an ontology to support the extraction of actionable knowledge on benefit rules from regulatory healthcare policy text. We demonstrate its feasibility even in the presence of small ground truth labeled data provided by policy investigators. Leveraging deep learning and rich ontological information enables the system to learn from human corrections and capture better benefit rules from policy text, beyond just using a deterministic approach based on pre-defined textual and semantic pattterns.
Paragraph-level attention based deep model for chapter segmentation

Books are usually divided into chapters and sections. Correctly and automatically recognizing chapter boundaries can work as a proxy when segmenting long texts (a more general task). Book chapters can be easily segmented by humans, but automatic segregation is more challenging because the data is semi-structured. Since the concept of language is prone to ambiguity, it is essential to identify the relationship between the words in each paragraph and classify each consecutive paragraph based on their respective relationships with one another. Although researchers have designed deep learning-based models to solve this problem, these approaches have not considered the paragraph-level semantics among the consecutive paragraphs. In this article, we propose a novel deep learning-based method to segment book chapters that uses paragraph-level semantics and an attention mechanism. We first utilized a pre-trained XLNet model connected to a convolutional neural network (CNN) to extract the semantic meaning of each paragraph. Then, we measured the similarities in the semantics of each paragraph and designed an attention mechanism to inject the similarity information in order to better predict the chapter boundaries. The experimental results indicated that the performance of our proposed method can surpass those of other state-of-the-art (SOTA) methods for chapter segmentation on public datasets (the proposed model achieved an F1 score of 0.8856, outperforming the Bidirectional Encoder Representations from Transformers (BERT) model’s F1 score of 0.6640). The ablation study also illustrated that the paragraph-level attention mechanism could produce a significant increase in performance.
Self-supervised learning based knowledge distillation framework for automatic speech recognition for hearing impaired

The use of speech processing applications, particularly speech recognition, has got a lot of attention in recent decades. In recent years, research has focused on using deep learning for speech-related applications. This new branch of machine learning has outperformed others in a range of applications, including voice, and has thus become a particularly appealing research subject. Noise, speaker variability, language variability, vocabulary size, and domain remain one of the most significant research difficulties in speech recognition. We investigated on self-supervised algorithm for the unlabelled data. In recent years, these algorithms have progressed significantly, with their efficacy approaching and supervised pre-training alternatives across a variety of data modalities such as image and video. The purpose of this research is to develop powerful models for audio speech recognition that do not require human annotation. We accomplish this by distilling information from an automatic speech recognition (ASR) model that was trained on a large audio-only corpus. We integrate Connectionist Temporal Classification (CTC) loss, KL divergence loss in distillation technique. We demonstrate that distillation significantly speeds up training. We evaluate our model with evaluation metric Word Error Rate (WER).
Peran Perpustakaan Kafe Literacy Coffee dalam meningkatkan minat baca pengunjung di Kota Medan

Introduction. To increase the interest in reading could be anywhere, not only in libraries. The purpose of this study is to examine the role of Literacy Coffee Cafe Library in increasing reading interest and visitors. &#x0D; Data Collection Methods. This study uses a qualitative research with a descriptive approach. Data collection techniques were conducted by using interviews, observations, and documentation studies. The sampling technique used was purposive sampling, consisting of two managers and two visitors to the Literacy Coffee Cafe Library.&#x0D; Data Analysis. Data analysis was carried out through data reduction, data presentation, conclusions and data verification.&#x0D; Results and Discussion. The role of the Literacy Coffee Cafe Library is to increase visitors’ interest by providing rooms, services, collection of library materials. It also conducts activities such as discussion and promotion.&#x0D; Conclusion and Recommendations. Cafe libraries which are educative, informative, and recreational may become triggers to increase visitors’ reading interest. To increase more interest in reading, it is recommended that the Literacy Coffee Cafe Library should be more active in having innovative activities.