Reviewerly/exHarmony huggingface dataset:
Enhancing university's brand performance during the COVID-19 outbreak: The role of ICT orientation, perceived service quality, trust, and student's satisfaction

The purpose of this study is to examine the effect of information & communication technology (ICT) orientation on university's brand performance along with the mediating role of student's satisfaction and moderating role of trust and perceived service quality in times of COVID-19 To achieve this purpose, a quantitative methodological approach was adopted in which the data was collected online through 417 structured questionnaires from university students of Pakistan The data was analyzed using SPSS and AMOS in which structural equation modeling was performed for testing hypotheses of the study Results of the current study indicate that the ICT orientation has a significant positive effect on student's satisfaction and university's brand performance while the student's satisfaction significantly mediates the relationship between ICT orientation and university's brand performance It has been further found that the perceived service quality acts as a significant but negative moderator in the relationship between ICT orientation and student's satisfaction Results further reveal that the trust acts as a positive moderator in the relationship between student's satisfaction and university's brand performance The current study is expected to be an important contribution to the literature and practice as it provides useful implications that how universities can enhance and maintain their brand performance and student's satisfaction during the COVID-19 outbreak by adopting techniques and tools of ICT © 2020 Johar Education Society Pakistan
Quantum fluctuation and interference effect in a single atom–cavity QED system driven by a broadband squeezed vacuum

Squeezed vacuum, as a nonclassical field, has many interesting properties and results in many potential applications for quantum measurement and information processing. Here, we investigate a single atom–cavity quantum electrodynamics (QED) system driven by a broadband squeezed vacuum. In the presence of the atom, we show that both the mean photon number and the quantum fluctuations of photons in the cavity undergo a significant depletion due to the additional transition pathways generated by the atom–cavity interaction. By measuring these features, one can detect the existence of atoms in the cavity. We also show that two-photon excitation can be significantly suppressed by the quantum destructive interference when the squeezing parameter is very small. These results presented here are helpful in understanding the quantum nature of the broadband squeezed vacuum.
LPQ++: A discriminative blur-insensitive textural descriptor with spatial-channel interaction

Effective texture categorization plays an important role in effective visual recognition. Despite noticeable progress in this area, blurred-texture recognition remains a challenge. As a key reason for this, existing well-established visual descriptors (e.g., local binary patterns and deep convolutional feature) generally cannot ensure an insensitivity to blur, exhibiting a considerable decrease in performance under clear to blurring conditions. To alleviate this, we propose a discriminative blur-insensitive textural descriptor, referred to as local phase quantization plus plus (LPQ++). The main idea is to establish spatial-channel interactions between the normalized blur-insensitive feature maps yielded by a short-term Fourier transform (STFT) to enhance the descriptive power while maintaining the insensitivity to blur. In particular, spatial interactions executed within the specific STFT feature map capture the spatial correlations between neighboring points. Meanwhile, the column-wise channel interactions among the STFT feature maps help differentiate the edge and flat areas in the images; this is crucial for effective texture characterization under blurring conditions. To enable blurred texture description under dense sampling conditions, LPQ++ is extracted by calculating the spatial-channel gradient orientation histogram and embedding it into the Fisher vector. Experiments conducted on three difficult texture datasets demonstrate the effectiveness of LPQ++ for blurred-texture categorization. Our code is open-source and available at https://github.com/hustzhzhu/LPQplusplus.
A research on Quick-SIFT and ghosting elimination technique for UAV image mosaic

The practical value of unmanned aerial vehicles (UAVs) can be improved by using image mosaic to fill the gaps due to insufficient coverage of UAV aerial images. Research on UAV image mosaic is majorly focused on improving the operation speed and mosaic accuracy. To this end, this study first analyzes the UAV aerial image characteristics and causes of ghosting and blurring of images. A Quick-Scale-Invariant Feature Transform (Quick-SIFT) operator is constructed to reduce computer time by reducing the octaves and levels of the Gaussian pyramid and selecting the third level image in each octave for feature point extraction. Subsequently, the As-Natural-As-Possible (AANAP) algorithm is used for image registration and projection transformation. The difference region between adjacent sequence images is calculated by the frame difference method. The difference region thus obtained is subjected to the region growing algorithm to segment the moving objects by single sampling, the other regions are processed by linear weighted fusion, thereby eliminating ghosting effectively. Lastly, A special aerial images dataset for image mosaic is constructed, based on which the comparable experiments with state-of-art image mosaic methods are conducted. The experimental results indicate that the matching time with the proposed algorithm is improved by at least 78% compared to SIFT, thus realizing fast image mosaic processing. The proposed algorithm also effectively eliminates the motion ghosting in images and achieves stable, high quality results.
Public Sender Score System (S3) by ESPs for Email Spam Mitigation with Score Management in Mobile Application

Many businesses use email as a medium for advertising and they use emails to communicate with their customers. In the email world, the most common issue that remains unresolved even now is spamming or in other terms unsolicited bulk email. Currently, there is no common way to regulate the practices of an email sender. This proposed system is to formulate a protocol common for all the ESPs or inbox providers and a centralized system that will easily find the spammers and block them. By this method, the Email Service Providers (ESPs) or Inbox Providers need not wait for the sender behaviour and then take actions on the sender or sender domain or sender IP address. Instead, they can get the sender history of reputation from blockchain where the ESPs or Inbox Provider provides a score based on the emails they have received from the sender. The ESPs can get the Public Sender Score(S3) from the mobile application or web application which provides the score management user interface and APIs. The email marketers can also monitor their score through the application.
Open Access of research; which role in the process of evaluation? Experience by Agenzia Nazionale di Valutazione del sistema Universitario e della Ricerca (ANVUR)

This article focuses on Access and Research Evaluation, and the experience by the Italian National Agency for the Evaluation of Universities and Research Institutes (ANVUR). It is an updated version of the work presented at the Workshop Open Science: new models of scientific communication and research evaluation, organized by Virginia Valzano at the University of Salento, on January 30, 2019.
An Email to Readable Vietnamese Text Conversion Algorithm for Use in TTS Application

In recent years, the growth of corporates' businesses worldwide has driven the setups of call centers for supporting global customers 24/7 across the globe. However, many of the call centers are operated by human manually. Thus, there is need for developing automatic call center which is powered by artificial intelligence (AI), hence reducing operational costs through automation of calls, answering calls, conducting surveys, and receiving customer feedbacks. In this context, recently developed engines such as FPT.AI serves well local customers since it supports well the national language. However, it's text-to-speech (TTS), an essential part of the complete engine, is currently having difficulties in reading customer emails. Therefore, this work presents an email to readable Vietnamese text conversion algorithm for use in TTS application. The average processing time tested on 60 emails is 1.7 milliseconds (ms). By manually validating the dataset, it is found that the algorithm achieves accuracy of up to 86.67%.
Index Modulation Multiple Access via Deep Learning based Detection

In this paper, we propose a downlink index modulation multiple access (IM-MA) system using deep learning (DL) based detection. In the proposed IM-MA, a user transmits information by modulating either active antenna indexes or signal constellation points, unlike the conventional IM-MA, where each user sends information using both the antenna indexes and constellation points. Therefore, the proposed IM-MA can accommodate more users in a network. Further, we use the DL-based detector via deep neural network (DNN) models, for each user’s symbol detection to improve the proposed IM-MA system’s performance. The received signal is preprocessed by considering the system’s apriory knowledge before going into the DNNs. DNN models are trained offline via simulated data and then applied for online symbol detection. Simulation results show the effectiveness of DNN detectors in terms of symbol error rate performance over Rayleigh fading channels with a lower runtime and complexity as compared to optimal maximum-likelihood detection.
Legal And Pedagogical Aspects Of Students Adaptation To Distance Learning

Distance education based on computer telecommunications technologies has become increasingly popular in recent years. This process is quite logical, since the use of distance education technologies generates completely new forms of communication and relationships between the teacher and the student. At the same time, there should be regular and effective interactivity between the teacher and the student regardless of the medium where the teaching content is presented. Distance education defines new requirements for the organization of the educational process itself, and this in turn requires the teacher to take into account personal characteristics of students and their adaptation. The article considers and analyzes the normative legal acts that regulate public relations in the field of distance education. The authors pay attention not only to Federal laws, but also to bylaws that allow for more detailed regulation of this sphere of education in Russia. To a greater extent, the work covered the pedagogical aspects of students ' adaptation to distance education. The authors studied the process of adaptation to distance learning, identified the main tasks of the teacher in distance learning, and considered the forms of interaction between the teacher and students.
Digitalization Of Russian Higher Education: Expectations, Reality, Problems

The article clarifies the content of the concept of “digitalization of education” from the perspective of an interdisciplinary approach, its main interpretations, and the possibility of applying it in a wide and narrow sense are shown. In a broad sense “digitalization of education” is a process and tool for the transition to a new stage in the development of higher education and professional training of specialists, which should differ from previous stages with new technological capabilities that make it possible to make education personalized, flexible, practical and effective. In a narrow sense, it is a new educational format associated with the introduction of new digital technologies in the educational activities of higher education. The reasons for changing the educational paradigm and the transition to creating a new educational environment are formulated. Based on existing expert assessments, the ambiguity of the consequences of digitalization and its possible risks are shown. The problems that impede the introduction of digitalization in the educational processes of Russian higher education (little experience in the remote work of participants in the educational process; the need to modify educational content and new forms of its submission; the impossibility of a complete transition to online learning of disciplines that have a large amount of practical work, etc.), are identified. On the example of “digital volunteering” and “digital pedagogy” as a promising direction in pedagogical science, possible measures are presented that contribute to their solution.
Early anomaly detection in time series: a hierarchical approach for predicting critical health episodes

The early detection of anomalous events in time series data is essential in many domains of application. In this paper we deal with critical health events, which represent a significant cause of mortality in intensive care units of hospitals. The timely prediction of these events is crucial for mitigating their consequences and improving healthcare. One of the most common approaches to tackle early anomaly detection problems is through standard classification methods. In this paper we propose a novel method that uses a layered learning architecture to address these tasks. One key contribution of our work is the idea of pre-conditional events, which denote arbitrary but computable relaxed versions of the event of interest. We leverage this idea to break the original problem into two hierarchical layers, which we hypothesize are easier to solve. The results suggest that the proposed approach leads to a better performance relative to state of the art approaches for critical health episode prediction.
Ontology based Knowledge Modeling and Extraction of Power Equipment Supply Chain

Since the power equipment related data is distributed in different information systems of the power system, the required information to construct a complete power equipment supply chain is complex and disperse. The description of transportation and management in the equipment supply chain can be defined as multi-dimensional data. A series of entities with multidimensional data can establish a hierarchy to describe the affiliation relationship in management and geographic location in a supply chain. In the hierarchy ontology, each hierarchy forms a sequence of node data for internal information analysis and the cross-layer data can be queried through relationship links. In this study, a structural modeling method of equipment supply chain based on the ontology hierarchy framework is proposed. The multi-dimensional feature in the whole process of power equipment production, sales, transportation, and management can be used to establish and record an organized and sustainable transmission process. By comparing with the general intuitive ontology model in terms of construction complexity and query performance, the reliability and efficiency of the hierarchical structure are determined.
Image Denoising Methods for New TVL1 Models with Impulse Noisey

In this paper, we first propose two TVL1 variational problems for restoring images degraded by impulse noise, and then we propose two fixed-point-like methods for solving the new proposed TVL1 problems.Numerical experiments for several test images corrupted by impulse noise and several damaged images overlying with texts are provided to demonstrate the efficiency and reliability of the proposed fixed-point-like methods.Numerical results show that two fixed-point-like methods for solving the new TVL1 problems perform significantly better in the image quality measured by PSNR than a fixed-point method for solving an existing TVL1 problem.
Pengaruh Penggunaan Media Pembelajaran Pada Hasil Belajar Tema Peristiwa Dalam Kehidupan Siswa Kelas V SD Negeri 3 Muntoi

This study generally aims to determine the effect of the use of learning media on the learning outcomes of the "events in life" theme of Class V students of SD Negeri 3 Muntoi, and specifically aims to find out the differences in learning outcomes of groups of Class V students of SD Negeri 3 Muntoi who use video learning media compared to the learning outcomes of groups that did not use video learning media on the themes of "events in life" of the sub-themes "nationalism events of colonialism". This study uses an experimental method with dependent variable (Y) is the result of learning the theme of events in the lives of fifth grade elementary school students, and the independent variable (X) is the use of instructional media. Learning outcomes data were collected through learning outcomes tests. Learning outcomes tests are carried out in written form. Data analysis technique is hypothesis testing using a two-tailed test with the t-test formula with pooled variants. Based on the results of research and discussion, it can be concluded that there are differences in learning outcomes of groups of students who use computer-assisted learning media, in this case video compared to learning outcomes of groups of students who do not use computer-assisted learning media, where the average learning outcomes of groups of students in grade V SD in learning the themes of events in the life of the sub-nationalism event themes of colonialism using video learning media is higher than the average learning outcomes of groups of students in the class who do not use video learning.Keywords: learning outcomes, video learning media
Analisis Penurunan Gradien dengan Kombinasi Fungsi Aktivasi pada Algoritma JST untuk Pencarian Akurasi Terbaik

There are many training function methods for gradient descent (gradient descent) and activation functions (transfer functions) that can be used in the ANN algorithm, especially the backpropagation algorithm. Therefore the aim of this paper is to analyze the best gradient descent that can be used as a reference for use in the ANN algorithm, especially the backpropagation algorithm in data prediction, classification and pattern management problems. The gradient descent methods to be analyzed include; Gradient descent backpropagation (traingd), Gradient descent with momentum backpropagation (traingdm), Gradient descent with adaptive learning rate backpropagation (traingda), and Gradient descent with momentum and adaptive learning rate backpropagation (traingdx). The training function will be combined with the activation function (transfer function) of bipolar sigmoid (tansig), linear transfer (purelin) and binary sigmoid (logsig). The sample data used for the analysis process is the time-series data for the Human Development Index in Indonesia, which is obtained from the Central Bureau of Statistics (BPS). Architectural models used for gradient descent analysis include: 6-10-15-1, 6-15-20-1, 6-20-25-1 and 6-25-30-1. Based on the analysis results, the best training function is traingda with an architectural model of 6-15-20-1 which produces an accuracy rate of 91% and MSE testing is 0.000731529 (smaller than other methods)
On Offline Evaluation of Recommender Systems.

In academic research, recommender models are often evaluated offline on benchmark datasets. The offline dataset is first split to train and test instances. All training instances are then modeled in a user-item interaction matrix, and supervised learning models are trained. Many such offline evaluations ignore the global timeline in the data, which leads to leakage: a model learns from future data to predict a current value, making the evaluation unrealistic. In this paper, we evaluate the impact of leakage using two widely adopted baseline models, BPR and NeuMF, on MovieLens dataset. We show that accessing to different amount of future data may improve or deteriorate a model's recommendation accuracy. That is, ignoring the global timeline in offline evaluation makes the performance among recommendation models not comparable. Our experiments also show that more historical data in training set does not necessarily lead to better recommendation accuracy. We share our understanding of these observations and highlight the importance of preserving the global timeline. We also call for a revisit of recommender system offline evaluation.
Arabic tweeps dialect prediction based on machine learning approach

In this paper, we present our approach for profiling Arabic authors on twitter, based on their tweets. We consider here the dialect of an Arabic author as an important trait to be predicted. For this purpose, many indicators, feature vectors and machine learning-based classifiers were implemented. The results of these classifiers were compared to find out the best dialect prediction model. The best dialect prediction model was obtained using random forest classifier with full forms and their stems as feature vector.
Rule-based Recommendation System based on Semantic Web of Things

Social networking has certainly been the most apparent concept of virtual human life where all users may communicate with each other.Thus all users of social networks aim to handle and monitor the demands of their lives through their platforms for virtual lifestyle.And since the Internet of Things is now considered one of the latest technologies which connect everything around us together as objects in the form of an IP address that enables contact between them.On the other side, IoT also struggles from the variability of data obtained from various sensors.Addressing this problem includes the alignment of semantic web technology.We, therefore, aim to take advantage of the Internet of Things and Semantic Web technologies revolution by developing an integrated model based on the semantic web.Through our proposed research, we intend to create a rule-based recommendation framework that advises users to take the most convenient action that IoT applications should execute on their current social network activities utilizing the built-in definition logic rules.By virtue of this social recommender, Users of IoT applications can monitor their apps only by communicating their social status via their social media instead of using special IoT apps on their mobile devices.As a result, there is more integration between IoT resources and Social Network resources.
Privacy-Preserving in Big Data Analytics: State of the Art

In today's technology centric world, data volume is increasing exponentially from embedded devices (mobile phones, automobiles, and sensor data). Individual engagement with the Internet has caused tremendous data growth through e-commerce portals, e-governance, and various social media networks. This data growth brings potential benefits that are significant and useful, and some initial success is achieved from a technological perspective to handle such voluminous amount of data. Along with benefits, it brings many challenges with respect to the data including storage, data exchange, curation, transportation, analysis, visualization, information in the form of analytics and privacy. When it is readily accepted that privacy of data itself is very important, the privacy of data analytics based information should also be very crucial. Hence this research would like to explore the important topic of preserving the privacy of information which has been generated through big data analytics. This paper commences with the investigations on various forms of data privacy threats, including disclosure, monitoring, identification, and discrimination; etc. and analyze the availability of various privacy-preserving techniques that are available for big data platforms. Further we would like to identify their limitations, and discuss the future course of action towards achieving privacy in big data analytics.