Reviewerly/exHarmony huggingface dataset:
Implementation of ICT at University Level during Covid-19 Pandemic: An Evidence from Yemen

Integration of ICT in higher education has a significant role after the full transition to remote learning during the outbreak Covid-19 pandemic.This research aims to investigate the impact of the internet accessibility, management system, e-learning infrastructure, and organization ICT policy on the integration of ICT usage during the Covid-19 pandemic among the faculty members and students of Thamar University, Yemen.A quantitative approach was used to collect data by conducting online surveys.Participants of the surveys were 264 educators, staff, and students of Thamar University.Data were analyzed using different statistical techniques including descriptive statistics, exploratory factor analysis, correlation, and multiple regressions to determine the impact of interaction among variables.Results reveal evidence that internet accessibility, management system, and infrastructure have a significant positive impact on the integration of ICT in higher education.In addition, integration of ICT with the university's Policies has an insignificant negative impact on the usage of technology.Furthermore, these results give significant evidence of transition to virtual learning in developing countries generally.Practical implications contributed by this research include the practice of using ICT as an instruction tool during the pandemic by adopting ICT infrastructure, access to the internet, developing learning management systems, and implementing the right policies.Finally, both ministry of education, higher education institutions are recommended to take responsibility in facilitating and supporting e-learning systems continuously as long as this pandemic has yet lasted.
Digital Campus

Mobile is everywhere, changing the way we work, play, socialize, and learn. Students nowadays are immersed in a digital culture driven by mobile consumer experiences across a range of devices, from wearable to phones, tablets and virtual-reality platforms. Digital Campus is a website application that is in University Tun Hussien Onn Pagoh Campus. The purpose of this website is to search all locations located in Pagoh Campus such as laboratory, admin office, dean office, cafe and classroom. Digital campus website is embedded with Open Street Map. It is open databased licensing and it is a collaborative mapping. This website is developed using Joomla 3.8.13 with PHP version, 5.6.25. There are 204 locations were plotted using this link https://digitalcampus.uthm.edu.my/index.php. Digital campus based on Open Street Map can helps students, admin staff, lectures and visitors to find location in very easy way by using digital platform. Thus, this application facilities can optimization of administration work and promote university management to higher performances.Â Â
Definitions and methodological principles in theory of knowledge / trans. from Engl. A. B. Didikin, D. G. Trinitka

A translation of an article by the great British philosopher Bertrand Russell on the conceptualisation of the philosophical foundations of epistemology is presented. It formulates key definitions and justifies the methodological principles on the basis of which will be possible to construct theories consistent with the ideas of empiricism and the concept of knowledge by acquaintance. Drawing on concrete examples as well as the theses on the logical conditioning of the methods of cognition, Russell lays the foundation for the theory of neutral monism, which became the basis of his epistemological ideas in the early period of his work.
Collaboration Between ESPOL and Villanova University on the Development and Delivery of a Digital Literacy Program for Youth on the Galapagos Islands

1981 and 1984, respectively.Dr. Singh teaches courses at the undergraduate and graduate levels in the areas of semiconductor
Design and Application of Virtual Avatar Framework Based on E-commerce Live Streaming

With the rapid development of live streaming services, enterprises, as well as online celebrities, are increasingly in demand for 24-hour live streaming support to engage a broader audience. However, no person can broadcast twenty-four hours a day for a long period of time. Therefore, the virtual avatar is proposed to replace the real avatar for streaming. Based on the new thinking on the use scene of digital human, this paper designs a virtual avatar called Miebo. Through a series of experiments, it is found that the parameters that can produce the most effect on Miebo's emotion of “Sadness”, “Happiness”, “Surprise” and “Anger” is “Upset”, “Grin mouth”, “Open mouth big” and “Eyes closed” respectively, and the presentation results of Miebo's facial expression are optimized according to the experimental analysis. We also present a set of highly feasible virtual avatar application schemes and develop a special platform for virtual avatar design called “Virtualfull”, which allows ordinary users could create their own virtual avatars.
Exploring Public Responses to Government’s COVID-19 Pandemic Policies

The ongoing pandemic of coronavirus disease 2019 (COVID-19) has challenged governments worldwide and approaches to combating the spread and maintaining livelihoods have ranged significantly. The purpose of the study is to a) investigate whether social media, specifically Twitter, can be used to identify topics of discussion regarding governments' COVID-19 policies, and; b) whether those discussions can be interpreted in a way that can support governments in making policy decisions. Real-time public responses to these policies are a matter of interest, as understanding the content of discussions and the attitudes expressed towards government approaches can support the development of more grounded solutions for large-scale policy issues. Latent Dirichlet Allocation (LDA) is used to identify topics of discussion alongside Valence Aware Dictionary for sEntiment Reasoning (VADER) sentiment analyzer. Text data from two different jurisdictions are used and examined side by side. The Oxford COVID-19 Government Response Tracker (OxCGRT) is used to standardize policy discussions. The results of the study found that: a) Individuals tweeted most frequently and most passionately about case and death rates in their jurisdictions. Particularly about the rates with respect to vulnerable populations, such as those in long term care, nursing homes, and health workers, and b) Tweets expressed frustrations with the communication, length of implementation, or lack of rationale behind policies, suggesting the way the policy is communicated and delivered impacts individuals' sentiments.
Radar Recognition Using Pulse Repetition Interval Technology

Recognition of pulse repetition interval modulation (PRI) of a radar signal is important.It helps in identifying the radar's functional purpose and assists in emitter identification.This paper discusses two methods, the gate time pulse method and feature extraction, used for PRI modulation recognition.These features are based on both sequential and statistical information of the pulse intervals.The extracted features can be used for emitter recognition by feeding the information in a neural network.
Tingkat Pemahaman Mahasiswa antar Pembelajaran Online dan Offline dalam masa pandemi Covid-19 menggunakan metode Forward Chaining

Advances in science and technology have changed this world. One of the data technologies that function in the world of learning during the COVID-19 pandemic is online education. Online education is used as a liaison between lecturers and students in an internet network that is accessed anytime. However, student learning outcomes in online and offline learning have not shown maximum results. Various obstacles arise that affect student learning outcomes online and offline during the COVID-19 pandemic. This study aims to predict the level of student understanding in online and offline learning. So that it can also help Pelita Indonesia universities to take the right policies to improve the quality of learning in the future. To solve the various problems above, an expert system is needed to predict. In the expert system used is the forward chaining method. The forward chaining method is a method that performs forward tracking, starting from a collection of facts and ending at a conclusion. So that the accuracy value is obtained. With the results of the accuracy test, it determines the level of understanding between online and offline learning.
Discovering Sentiments and Latent Themes in the Views of Faculty Members towards the Shift from Conventional to Online Teaching Using VADER and Latent Dirichlet Allocation

This research primarily aimed at determining the frequently occurring words, the sentiment and the underlying latent themes in the responses of faculty members towards the shift from conventional to online teaching using data mining techniques. Orange data mining software was utilized to preprocess and analyze the data. VADER sentiment analysis and Latent Dirichlet Allocation Topic Modelling were used to generate the overall sentiment and the themes of the teachers’ responses. Results revealed that the most frequently occurring words in the responses of teachers were blended, online, students, teaching, teachers, learning, difficult, challenging, internet, and connectivity. Twenty-three (23) out of 37 (62%) responses were determined to have negative polarity making the general sentiment of faculty members towards the shift to online learning negative. The following themes were generated after the application of Latent Dirichlet Allocation Topic Modeling technique: unexpected shift from conventional to blended teaching, Mental and Physical Health Issues Related to the Implementation of Blended Learning, Online tools used in the conduct of Online Classes, Difficulties and Challenges in the Conduct of Blended/Online Learning, Slow Internet Connectivity as a Major Impediment in the Conduct of Online Teaching.
Action rate models for predicting actions in soccer

Abstract We present a data-driven approach to predict the next action in soccer. We focus on passing actions of the ball possessing player and aim to forecast the pass itself and when, in time, the pass will be played. At the same time, our model estimates the probability that the player loses possession of the ball before she can perform the action. Our approach consists of parameterized exponential rate models for all possible actions that are adapted to historic data with graph recurrent neural networks to account for inter-dependencies of the output space (i.e., the possible actions). We report on empirical results.
A Five-Year Review of the Outcomes of Breast Imaging Reporting and Data System 4 Lesions in Hospital Universiti Sains Malaysia

The Breast Imaging Reporting and Data System (BI-RADS) lexicon used in reporting breast imaging has several categories with specific positive predictive values for breast cancer. Among those, BI-RADS 4 is associated with a wider range of risk for breast cancer, which makes the decision for biopsy difficult. The study aim was to determine the malignancy rate and clinical outcomes of BI-RADS 4 lesions in Hospital Universiti Sains Malaysia (HUSM) for a period of five years.This was a retrospective study of patients diagnosed by mammographic or ultrasonographic findings with BI-RADS 4 breast lesions in HUSM, Kelantan from July 2015 to June 2020. Data were collected from the medical records and an electronic database. Patients with BI-RADS 4 lesions who underwent biopsy and had a known tissue diagnosis were included in this study. The data was used to calculate the malignancy rate and associated positive predictive factors for breast cancer associated with BI-RADS 4 lesions.From the mammography and ultrasonography performed at HUSM from July 2015 to June 2020, a total of 256 lesions were categorized as BI-RADS 4. However, only 198 BI-RADS 4 lesions underwent biopsy and were included in the study. Of these 198 lesions, 26.8% were malignant on histopathological examination of the biopsy samples. Simple logistic regression analysis showed that age, diabetes mellitus, hypertension, number of parity, and certain mammogram findings were significantly associated with breast cancer. Invasive breast cancer was the most common type. Fibrocystic disease was the most common benign pathology, followed by fibroadenoma.The malignancy rate of BI-RADS 4 lesions in HUSM was similar to previously reported rates. A thorough evaluation of positive predictive factors and careful selection of patients for biopsy in BI-RADS 4 lesions will minimize unwanted biopsies and associated patient anxiety, in addition to reducing the health care burden.
A Fully Pipelined FPGA Architecture for Multiscale BRISK Descriptors With a Novel Hardware-Aware Sampling Pattern

Binary descriptors have been shown to be faster than nonbinary descriptors while producing comparable results in image matching applications. In recent years, there have been many attempts to design hardware accelerators for extraction of binary descriptors to achieve higher processing rates. One of the well-known methods is the binary robust invariant scalable key point (BRISK) algorithm, which has shown outstanding results in various applications. In this work, we propose a multiscale field-programmable gate array (FPGA)-based hardware architecture for the BRISK descriptor. In addition, a new image sampling pattern for the BRISK algorithm is described which is shown to be more efficient than the original sampling pattern for hardware implementation. Our new sampling pattern decreases the size of the patches containing the key point to one-quarter of the size of that used in the original BRISK algorithm, which leads to a reduction in FPGA resource utilization while maintaining the accuracy of the image matching application. Our proposed design is fully pipelined and achieves a frame rate of 78 fps on images with full HD resolution.
CAPTION GENERATION OF IMAGES USING CNN AND LSTM

The contents of a picture are automatically created in Artificial Intelligence (AI), which combines computer vision and natural language processing (NLP) (Natural Language Processing). It is developed a regenerative neuronal model. Computer vision and machine translation are required. This model is used to produce natural-sounding phrases that describe the picture. Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) are used in this model (RNN). The CNN is used to extract features from images, while the RNN is used to generate sentences. The model has been trained in such a manner that when an input image is provided to it, it creates captions that almost accurately describe the image. On various datasets, the model's accuracy, smoothness, and command of language learned from picture descriptions are assessed. These tests reveal that the model typically provides correct descriptions of the input image.
Performance of coherent‐state quantum target detection in the context of asymmetric hypothesis testing

Due to the difficulties of implementing joint measurements, quantum illumination schemes that are based on signal-idler entanglement are difficult to implement in practice. For this reason, one may consider quantum-inspired designs of quantum lidar/radar where the input sources are semi-classical (coherent states) while retaining the quantum aspects of the detection. The performance of these designs could be studied in the context of asymmetric hypothesis testing by resorting to the quantum Stein’s lemma. However, here the authors discuss that, for typical finite-size regimes, the second- and third-order expansions associated with this approach are not sufficient to prove quantum advantage.
Realizing collision avoidance motion for link mechanism using nonlinear model predictive control

In this paper, we aim to develop a technology that can be commonly used for link mechanisms. We use nonlinear model predictive control to design a control system that enables trajectory generation and trajectory tracking using only a single controller. By using the nonlinear model predictive control, we can expect the versatility that does not depend on the hardware by changing the model, the scalability that can realize multiple objectives by the evaluation function, and the easiness that the trajectory generation and tracking can be realized by a single controller. As an example of a link mechanism, a hydraulic excavator was modeled as a link mechanism with four degrees of freedom, and the effectiveness of the proposed method was verified by numerical simulation. In this simulation, the hydraulic excavator performed the loading operation to load the excavated soil into the dump truck and the repositioning operation to move to the next excavation position. During these operations, the excavator needs to reach the target position while avoiding collision with the dump truck. From the simulation results, it was confirmed that by setting the evaluation function appropriately, the loading and repositioning operation in three-dimensional space can be performed while avoiding collision with obstacles without giving a target trajectory.
3D Object Detection from Images for Autonomous Driving: A Survey

3D object detection from images, one of the fundamental and challenging problems in autonomous driving, has received increasing attention from both industry and academia in recent years. Benefiting from the rapid development of deep learning technologies, image-based 3D detection has achieved remarkable progress. Particularly, more than 200 works have studied this problem from 2015 to 2021, encompassing a broad spectrum of theories, algorithms, and applications. However, to date no recent survey exists to collect and organize this knowledge. In this paper, we fill this gap in the literature and provide the first comprehensive survey of this novel and continuously growing research field, summarizing the most commonly used pipelines for image-based 3D detection and deeply analyzing each of their components. Additionally, we also propose two new taxonomies to organize the state-of-the-art methods into different categories, with the intent of providing a more systematic review of existing methods and facilitating fair comparisons with future works. In retrospect of what has been achieved so far, we also analyze the current challenges in the field and discuss future directions for image-based 3D detection research.
Knowledge-based System of Indian Culture using Ontology with Customized Named Entity Recognition

Indian culture is a blend of diversified ethics with different cultures and religions.There arise the sustainable challenges for the people of inter societies and religions to participate in the regional events with lack of cultural information like types of cuisines eaten; rituals followed etc.The existing search engines using a keyword-based approach make it difficult for people to find relevant information among millions of undesirable results.To overcome this problem, we applied the concept of the semantic web that facilitates unstructured data that is represented more precisely by using welldefined vocabularies such as Resource Description Framework (RDF), which is recognized by both people and machines.In this research, we have developed a Knowledge Base System (KBS) using an ontological approach (Ontology Development 101) integrating the concept of Customized Named Entity Recognition (CNER) that supports full-text query searching with structured information.To test the feasibility, several queries from our domain taken from different users were executed successfully.The precision of 0.96 justifies the efficacy of our framework.Finally, the evaluation results were compared with two previous methodologies which show that our methodology outperforms.
Factors of Composite 4n2+1 using Fermat’s Factorization Method

In this article, we factor the composite $4n^2 +1$ using Fermat's factorization method. Consequently, we characterized all proper factors of composite $4n^2 +1$ in terms of its form. Furthermore, the composite Fermat's number is considered in this study.
Arbitrary Font Generation by Encoder Learning of Disentangled Features

Making a new font requires graphical designs for all base characters, and this designing process consumes lots of time and human resources. Especially for languages including a large number of combinations of consonants and vowels, it is a heavy burden to design all such combinations independently. Automatic font generation methods have been proposed to reduce this labor-intensive design problem. Most of the methods are GAN-based approaches, and they are limited to generate the trained fonts. In some previous methods, they used two encoders, one for content, the other for style, but their disentanglement of content and style is not sufficiently effective in generating arbitrary fonts. Arbitrary font generation is a challenging task because learning text and font design separately from given font images is very difficult, where the font images have both text content and font style in each image. In this paper, we propose a new automatic font generation method to solve this disentanglement problem. First, we use two stacked inputs, i.e., images with the same text but different font style as content input and images with the same font style but different text as style input. Second, we propose new consistency losses that force any combination of encoded features of the stacked inputs to have the same values. In our experiments, we proved that our method can extract consistent features of text contents and font styles by separating content and style encoders and this works well for generating unseen font design from a small number of reference font images that are human-designed. Comparing to the previous methods, the font designs generated with our method showed better quality both qualitatively and quantitatively than those with the previous methods for Korean, Chinese, and English characters. e.g., 17.84 lower FID in unseen font compared to other methods.
Mobile Device Users’ Susceptibility to Phishing Attacks

The mobile device is one of the fasted growing technologies that is widely used in a diversifying sector. Mobile devices are used for everyday life, such as personal information exchange – chatting, email, shopping, and mobile banking, contributing to information security threats. Users' behavior can influence information security threats. More research is needed to understand users' threat avoidance behavior and motivation. Using Technology threat avoidance theory (TTAT), this study assessed factors that influenced mobile device users' threat avoidance motivations and behaviors as it relates to phishing attacks. From the data collected from 137 mobile device users using a questionnaire, the findings indicate that (1) mobile device users' perceived susceptibility and severity of phishing attacks have a significant correlation with a users' perception of the threat; (2) mobile device users' motivation to avoid a threat is correlated to a users' behavior in avoiding threat; and (3) a mobile device user's susceptibility to phishing attacks can be reduced by their perception of the threat. These findings reveal that a user's perception of threat increases if they perceive that the consequence of such threat to their mobile devices will be severe, thereby increasing a user's motivation and behavior to avoid phishing attack threats. This study is beneficial to mobile device users in personal and organizational settings.