Reviewerly/exHarmony huggingface dataset:
Digital university: Approaches to the concept definition

Introduction. The modern period of development of higher education is directly related to the ongoing digital transformation of public relations. The changes that occur in universities, in their external interactions and functions, basic principles and value-normative foundations, require methodological analysis and research. Aim. The aim of the research is to analyse the theoretical approaches to disclosing the concept and essence of digital universities. Methodology and research methods. The mix-method was used as a research design: qualitative analysis (studying the content of theoretical sources and context, analysing the content of a concept) and quantitative analysis (scientometric analysis of publications and keywords in databases). Results. The study showed that in the methodological terms, there is a problem of defining the concept of “digital university”: there are several theoretical approaches based on different understanding of the phenomenon and its role in the digital development of the university. The methodological problem is complicated by the urgent need to systematise the practical activities of modern universities, which classify themselves in one way or another in the “digital” format; therefore, this is much more difficult to do without a coordinated approach to the concept itself. Scientific novelty lies in an attempt to systematise theoretical approaches to defining the concept of “digital university” and the problem of the absence of a holistic interdisciplinary approach has been identified. Practical significance of the study is associated with the need to develop a regulatory model of a digital university based on a holistic concept for the subsequent analysis of the activities of universities. The results of the analysis can be used in scientific discussion to further define the concept of “digital university” and develop a general model of a digital university.
Attention-guided video super-resolution with recurrent multi-scale spatial–temporal transformer

Abstract Video super-resolution (VSR) aims to recover the high-resolution (HR) contents from the low-resolution (LR) observations relying on compositing the spatial–temporal information in the LR frames. It is crucial to propagate and aggregate spatial–temporal information. Recently, while transformers show impressive performance on high-level vision tasks, few attempts have been made on image restoration, especially on VSR. In addition, previous transformers simultaneously process spatial–temporal information, easily synthesizing confused textures and high computational cost limit its development. Towards this end, we construct a novel bidirectional recurrent VSR architecture. Our model disentangles the task of learning spatial–temporal information into two easier sub-tasks, each sub-task focuses on propagating and aggregating specific information with a multi-scale transformer-based design, which alleviates the difficulty of learning. Additionally, an attention-guided motion compensation module is applied to get rid of the influence of misalignment between frames. Experiments on three widely used benchmark datasets show that, relying on superior feature correlation learning, the proposed network can outperform previous state-of-the-art methods, especially for recovering the fine details.
An Exploration of Student Satisfaction with Online Learning: A Systematic Review

Abstrak: Kepuasan belajar online merupakan konstruk yang dapat digunakan untuk mengevaluasi keefektifan layanan pembelajaran online. Tujuan penelitian ini untuk mengeksplorasi faktor-faktor kepuasan belajar online siswa. Sistematika literature review ini menganalisis 34 jurnal yang dikumpulkan dari dua situs, yaitu Google Scholar dan Semantic Scholar. Pada bagian pertama studi ini didiskusikan isu akademik pembelajaran online, kemudian model kepuasan belajar online yang digunakan para peneliti, dan terakhir menjelaskan prediktor yang dapat mempengaruhi kepuasan belajar online siswa. Hasil analisis menunjukkan bahwa faktor eksternal yang paling signifikan adalah learner content dan interaksi guru dengan siswa. Oleh karena itu, untuk meningkatkan kepuasan belajar siswa, guru diharapkan dapat menyajikan materi digital yang menarik serta meningkatkan kualitas komunikasi yang positif dikelas virtual.Â Abstract:Â Online learning satisfaction can be used to assess how well online learning programs work. This research can help education professionals in their efforts to develop adaptive online learning in dealing with future educational disruptions and challenges through the analysis of various literatures. This systematic literature review explored 34 journals from two sites: Google Scholar and Semantic Scholar. The first part of this study discusses the academic issue of online learning, the second section describes the online learning satisfaction model used by the researchers, and the last part describes the predictors that can affect students' online learning satisfaction. Learner content interaction and teacher-student interactions are the most primary external determinants on online learning satisfaction, according to the analysis' findings. Therefore, teachers are expected to be able to deliver engaging digital contents and enhance positive communication in online setting to increase studentsâ€™ learning satisfaction.
Application of DeLone and McLean Methods to Determine Supporting Factors for the Successful Implementation of Electronic Medical Records at Bali Mandara Eye Hospital

Information technology in the health sector is currently an important factor in providing health services, especially hospitals. Bali Mandara Eye Hospital has implemented information technology in the registration process and bill issuance from 2015. Electronic Medical Record is a new application that will be implemented in 2021 at Bali Mandara Eye Hospital. Electronic Medical Record is a computerized system for recording patient health history. The implementation of electronic medical records at the Bali Mandara Eye Hospital for 1 year has many problems both in terms of regulations, systems and users. Therefore, the DeLone and McLean methods are used to determine the success rate of the implementation of electronic medical records. In this method, research is conducted on 6 variables, namely System Quality, Information Quality, Service Quality, Intention of Use, User Satisfaction and Net Benefits. From each variable, the researcher combines indicators from other researchers that are more appropriate for measuring electronic medical records. The results obtained that all statements submitted are valid. The success rate of electronic medical records at the Bali Mandara Eye Hospital was obtained at 3.22 or 80.50%. To be able to increase this value, it is recommended to improve the Information Quality, System Quality and User Satisfaction
Efficient System for Delimitation of Benign and Malignant Breast Masses

In this study, a high-performing scheme is introduced to delimit benign and malignant masses in breast ultrasound images. The proposal is built upon by the Nonlocal Means filter for image quality improvement, an Intuitionistic Fuzzy C-Means local clustering algorithm for superpixel generation with high adherence to the edges, and the DBSCAN algorithm for the global clustering of those superpixels in order to delimit masses’ regions. The empirical study was performed using two datasets, both with benign and malignant breast tumors. The quantitative results with respect to the BUSI dataset were JSC≥0.907, DM≥0.913, HD≥7.025, and MCR≤6.431 for benign masses and JSC≥0.897, DM≥0.900, HD≥8.666, and MCR≤8.016 for malignant ones, while the MID dataset resulted in JSC≥0.890, DM≥0.905, HD≥8.370, and MCR≤7.241 along with JSC≥0.881, DM≥0.898, HD≥8.865, and MCR≤7.808 for benign and malignant masses, respectively. These numerical results revealed that our proposal outperformed all the evaluated comparative state-of-the-art methods in mass delimitation. This is confirmed by the visual results since the segmented regions had a better edge delimitation.
A Mismatched Group of Items That I Would Not Find Particularly Interesting: Challenges and Opportunities with Digital Exhibits and Collections Labels

Objective – The authors sought to identify link language that is user-friendly and sufficiently disambiguates between a digital collection and digital exhibit platform for users from a R1 institution, or a university with high research activity and doctoral programs as classified in the Carnegie Classification of Institutions of Higher Education. Methods – The authors distributed two online surveys using a modified open card sort and reverse-category test via university electronic mailing lists to undergraduate and graduate students to learn what language they would use to identify groups of items and to test their understanding of link labels that point to digitized cultural heritage items. Results – Our study uncovered that the link terms utilized by cultural heritage institutions are not uniformly understood by our users. Terms that are frequently used interchangeably (i.e., Digital Collections, Digital Project, and Digital Exhibit) can be too generic to be meaningful for different user groups. Conclusion – Because the link terms utilized by cultural heritage institutions were not uniformly understood by our users, the most user-friendly way to link to these resources is to use the term we—librarians, curators, and archivists—think is most accurate as the link text based on our professional knowledge and provide a brief description of what each site contains in order to provide necessary context.
Privacy-Enhanced Federated Learning: A Restrictively Self-Sampled and Data-Perturbed Local Differential Privacy Method

As a popular distributed learning framework, federated learning (FL) enables clients to conduct cooperative training without sharing data, thus having higher security and enjoying benefits in processing large-scale, high-dimensional data. However, by sharing parameters in the federated learning process, the attacker can still obtain private information from the sensitive data of participants by reverse parsing. Local differential privacy (LDP) has recently worked well in preserving privacy for federated learning. However, it faces the inherent problem of balancing privacy, model performance, and algorithm efficiency. In this paper, we propose a novel privacy-enhanced federated learning framework (Optimal LDP-FL) which achieves local differential privacy protection by the client self-sampling and data perturbation mechanisms. We theoretically analyze the relationship between the model accuracy and client self-sampling probability. Restrictive client self-sampling technology is proposed which eliminates the randomness of the self-sampling probability settings in existing studies and improves the utilization of the federated system. A novel, efficiency-optimized LDP data perturbation mechanism (Adaptive-Harmony) is also proposed, which allows an adaptive parameter range to reduce variance and improve model accuracy. Comprehensive experiments on the MNIST and Fashion MNIST datasets show that the proposed method can significantly reduce computational and communication costs with the same level of privacy and model utility.
Hybrid Attention-Based 3D Object Detection with Differential Point Clouds

Object detection based on point clouds has been widely used for autonomous driving, although how to improve its detection accuracy remains a significant challenge. Foreground points are more critical for 3D object detection than background points; however, most current detection frameworks cannot effectively preserve foreground points. Therefore, this work proposes a hybrid attention-based 3D object detection method with differential point clouds, which we name HA-RCNN. The method differentiates the foreground points from the background ones to preserve the critical information of foreground points. Extensive experiments conducted on the KITTI dataset show that the model outperforms the state-of-the-art methods, especially in recognizing large objects such as cars and cyclists.
Exploring EFL Students’ Speaking Anxiety in Microsoft Teams Classroom Discussion Learning

This case study reported on how students experienced speaking anxiety in online EFL classrooms. The data was obtained through observation, questionnaires on a google form, and interviews. The data analysis technique used was thematic analysis. The participants consisted of second-year students from one vocational school in Indonesia determined as many as 14 (fourteen) students, 4 representative students were chosen using FLCAS for the Interview, and 14 students for the FLCAS Questionnaire. The questionnaire sheet obtained feedback on students' types of speaking anxiety are (a) test anxiety, (b) fear of negative judgment, and (c) communicative apprehension. Data analysis showed that speaking anxiety can be influenced by several factors including fear speak in front of peers, fear of negative feedback, and fear of failure. In addition, the analysis discovered that (1) preparation, (2) relaxation, (3) positive thinking, (4) peer thinking, (5) positive outlook, and (6) using useful tools were strategies used by students to overcome speaking anxiety.
Rough set method-cloud internet of things: a two-degree verification scheme for security in cloud-internet of things

The quick development of innovations and increasing use of the internet of things (IoT) in human life brings numerous challenges. It is because of the absence of adequate capacity resources and tremendous volumes of IoT information. This can be resolved by a cloud-based architecture. Consequently, a progression of challenging security and privacy concerns has emerged in the cloud based IoT context. In this paper, a novel approach to providing security in cloud based IoT environments is proposed. This approach mainly depends on the working of rough set rules for guaranteeing security during data sharing (rough set method-cloud IoT (RSM-CIoTD)). The proposed RSM-CIoTD conspire guarantees secure communication between the user and cloud service provider (CSP) in a cloud based IoT. To manage unauthorized users, an RSM-CIoTD scheme utilizes a registered authority which plays out a two-degree confirmation between the network substances. The security and privacy appraisal techniques utilize minimum and maximum trust benefits of past communication. The experiments show that our proposed system can productively and safely store the cloud service while outperforming other security methods.
A hybrid approach based on personality traits for hate speech detection in Arabic social media

&lt;p&gt;In recent years, as social media has grown in popularity, people have gained the ability to freely share their views. However, this may lead to users' conflict and hostility, resulting in unattractive online environments. Hate speech relates to using expressions or phrases that are violent, offensive, or insulting to a minority of people. The number of Arab social media users is quickly rising, and this is being followed by an increase in the frequency of cyber hate speech in the area. Therefore, the automated detection of Arabic hate speech has become a major concern for many stakeholders. The intersection of personality learning and hate speech detection is a relatively less studied niche. We suggest a novel approach that is focused on extracting personality trait features and using these features to detect Arabic hate speech. The experimental results show that the proposed approach is superior in terms of the macro-F1 score by achieving 82.3% compared to previous work reported in the literature.&lt;/p&gt;
Cloud server design for heavy workload gaming computing with Google cloud platform

Cloud servers are generally used for data storage and remote office activities, but it can be applied for gaming purposes, where cloud servers can be paired with virtual machines and gaming platform that can be accessed by users via an internet connection. This makes the device used by user no longer needs to process resources because the workload is carried out by virtual machines on cloud server. The author designs a cloud gaming system using Google cloud platform as a cloud server and parsec as an optimizer that is attached to a virtual machine for game computing purposes. Author takes measurements of the cloud gaming system using 2 test games varying from low to middle specifications. Resource testing on central processing unit (CPU) and random-access memory (RAM) usage on the user side is below 40% when running game 1 and below 44% when running game 2, while on the system it reaches a capacity above 40% for CPU and RAM and 99% maximum on graphics processing unit (GPU). Quality of service testing of the system is carried out at bandwidths of 5, 10, and 30 Mbps with a minimum bandwidth of 10 Mbps. In general, there are a little difference that occurred between test game and different bandwidths.
Automatic modulation classification based deep learning with mixed feature

&lt;span lang="EN-US"&gt;The automatic modulation classification (AMC) plays an important and necessary role in the truncated wireless signal, which is used in modern communications. The proposed convolution neural network (CNN) for AMC is based on a method of feature expansion by integrating I/Q (time form) with r/Ɵ (polar form) in order to take advantage of two things: first, feature expansion helps to increase features; the second is that converting to polar form helps to increase classification accuracy for higher order modulation due to diversity in polar form. CNN consists of six blocks. Each block contains symmetric and asymmetric filters, as well as max and average pooling filters. This paper uses DeepSig: RadioML which is a dataset of 24 modulation classes. The proposed network has outperformed many recent papers in terms of classification accuracy for 24 modulation types, with a classification accuracy of up to 96.06 at an SNR=20 dB.&lt;/span&gt;
Automated-tuned hyper-parameter deep neural network by using arithmetic optimization algorithm for Lorenz chaotic system

&lt;p&gt;Deep neural networks (DNNs) are very dependent on their parameterization and require experts to determine which method to implement and modify the hyper-parameters value. This study proposes an automated-tuned hyper-parameter for DNN using a metaheuristic optimization algorithm, arithmetic optimization algorithm (AOA). AOA makes use of the distribution properties of mathematics’ primary arithmetic operators, including multiplication, division, addition, and subtraction. AOA is mathematically modeled and implemented to optimize processes across a broad range of search spaces. The performance of AOA is evaluated against 29 benchmark functions, and several real-world engineering design problems are to demonstrate AOA’s applicability. The hyper-parameter tuning framework consists of a set of Lorenz chaotic system datasets, hybrid DNN architecture, and AOA that works automatically. As a result, AOA produced the highest accuracy in the test dataset with a combination of optimized hyper-parameters for DNN architecture. The boxplot analysis also produced the ten AOA particles that are the most accurately chosen. Hence, AOA with ten particles had the smallest size of boxplot for all hyper-parameters, which concluded the best solution. In particular, the result for the proposed system is outperformed compared to the architecture tested with particle swarm optimization.&lt;/p&gt;
Digital image enhancement by brightness and contrast manipulation using Verilog hardware description language

A foggy environment may cause digitally captured images to appear blurry, dim, or low in contrast. This will impact computer vision systems that rely on image information. With the need for real-time image information, such as a plate number recognition system, a simple yet effective image enhancement algorithm using a hardware implementation is very much needed to fulfil the need. To improve images that suffer from low exposure and hazy, the hardware implementations are usually based on complex algorithms. Hence, the aim of this paper is to propose a less complex enhancement algorithm for hardware implementation that is able to improve the quality of such images. The proposed method simply combines brightness and contrast manipulation to enhance the image. In order to see the performance of the proposed method, a total of 100 vehicle registration number images were collected, enhanced, and evaluated. The evaluation results were compared to two other enhancement methods quantitatively and qualitatively. Quantitative evaluation is done by evaluating the output image using peak signal-to-noise ratio and mean-square error evaluation metrics, while a survey is done to evaluate the output image qualitatively. Based on the quantitative evaluation results, our proposed method outperforms the other two enhancement methods.
Autonomous UAV object Avoidance with Floyd-warshall differential evolution approach

Unmanned Aerial Vehicles (UAVs) are recently focused with significant research attention from commercial to military industries. Due to its wide range of applications such as traffic monitoring, surveillance, aerial photograph and rescue mission, many research studies were conducted related to UAV development. UAV are commonly called as ‘drones’ used to suit dull, dangerous and dirty missions that can be suited by manned aircraft. UAV can be controlled either remotely or using automation approaches so that it can be travelled into predefined path. To make the autonomous UAV, the most complex issue that is faced by UAV is obstacle / object avoidance. Obstacle detection and avoidance are important for UAV and it is the complex problem to solve due to the payload restriction. This will limit the sensor count mounted on the vehicle. Radar was used to find the distance between the object and vehicle. This can help to detect and track the moving objects speed and direction towards the vehicle. This paper considered the object avoidance problem as path planning problem. There were many path planning methods related to UAV which formulates the path planning as an optimization problem to avoid the obstacles. With the consideration, this paper proposed an efficient and optimal approach called Floyd Warshall- Differential evolution (FWDE) approach to detect the frontal obstacles of UAV. Finally, statistical analysis of the simulated environment reveals that the proposed evolutionary method can efficiently avoid both static and dynamic objects for UAVs. This efficient avoidance algorithm for UAV can be experimented with simulation environment with three kinds of scenarios having different number of cells. The obtained accuracy and recall value of the proposed system is 95.21% and 91.56%.
clusTransition: An R package for monitoring transition in cluster solutions of temporal datasets

Clustering analysis’ primary purpose is to divide a dataset into a finite number of segments based on the similarities between items. In recent years, a significant amount of study has focused on the spatio-temporal aspects of clustering. However, clusters are no longer regarded as static objects since changes influence them in the underlying population. This paper describes an R package implementing the MONIC framework for tracing the evolution of clusters extracted from temporal datasets. The name of the package is clusTransition, which stands for Cluster Transition. The algorithm is based on re-clustering cumulative datasets that evolve at successive time-points and monitoring the transitions experienced by the clusters in these clustering solutions. This paper’s contribution is to demonstrate how the package clusTransition is developed in the R programming language, and its workflow is discussed using hypothetical and real-life datasets.