Reviewerly/exHarmony huggingface dataset:
Analysis and reduction of error caused by tested object using fringe projection technique with wavelet transform

In this paper the effects of the shape and surface gray of the tested objects on the measurement precision is studied using fringe projection technique by wavelet transform. For researching the effects of the tested object shape on the measurement precision, the profiles of different cylinders are tested in order to research the effects of height and gradient (height variation) on the measurement precision. For improving the measurement precision, the different grating frequencies are used to measure the profiles in order to acquire the reasonable grating frequencies for different cylinders. Then, random speckles are added on the cylindrical surfaces, and the profiles under the conditions with different speckle numbers and sizes are measured for revealing the effects of the object surface on the measurement precision. And different filtering methods are applied to improve the test accuracy. In conclusion, this study finds the effects of tested objects on the test accuracy using fringe projection technique by wavelet transform and proposes some effective schemes for its improvement, which are helpful to the application. And different cylinders are used to realize quantitative characterization of shape with height and gradient. Random speckles of different speckle numbers and sizes are used to realize quantitative characterization of surface gray. It provides the basis for the influence research of the tested object on the test accuracy.
Dynamic user modeling for expert recommendation in community question answering

Community Question Answering (CQA) websites has become an important channel for people to acquire knowledge. In CQA, one key issue is to recommend users with high expertise and willingness to answer the given questions, i.e., expert recommendation. However, a lot of existing methods consider the expert recommendation problem in a static context, ignoring that the real-world CQA websites are dynamic, with users’ interest and expertise changing over time. Although some methods that utilize time information have been proposed, their performance improvement can be limited due to fact that they fail they fail to consider the dynamic change of both user interests and expertise. To solve these problems, we propose a deep learning based framework for expert recommendation to exploit user interest and expertise in a dynamic environment. For user interest, we leverage Long Short-Term Memory (LSTM) to model user’s short-term interest so as to capture the dynamic change of users’ interests. For user expertise, we design user expertise network, which leverages feedback on users’ historical behavior to estimate their expertise on new question. We propose two methods in user expertise network according to whether the dynamic property of expertise is considered. The experimental results on a large-scale dataset from a real-world CQA site demonstrate the superior performance of our method.
Visuo-Haptic Mixed Reality Simulation Using Unbound Handheld Tools

Visuo-haptic mixed reality (VHMR) adds virtual objects to a real scene and enables users to see and also touch them via a see-through display and a haptic device. Most studies with kinesthetic feedback use general-purpose haptic devices, which require the user to continuously hold an attached stylus. This approach constrains users to the mechanical limits of the device even when it is not needed. In this paper, we propose a novel VHMR concept with an encountered-type haptic display (ETHD), which consists of a precision hexapod positioner and a six-axis force/torque transducer. The main contribution is that the users work with unbound real-life tools with tracking markers. ETHD’s end-effector remains inside the virtual object and follows the tooltip to engage only during an interaction. We have developed a simulation setup and experimentally evaluated the relative accuracy and synchronization of the three major processes, namely tool tracking, haptic rendering, and visual rendering. The experiments successfully build-up to a simple simulation scenario where a tennis ball with a fixed center is deformed by the user.
E-Government, Information and Communications Technology Support and Paperless Environment in Nigerian Public Universities: Issues and Challenges

The move toward a paperless environment has become the driving force behind sustainable development and e-government usage in many public sector institutions. It is equally at the heart of the government campaign to make service delivery in public institutions cost-effective, seamless, and efficient. The universities are supposed to be at the front-line of this campaign due to their operations which involve the heavy usage of papers at huge costs. Thus, making it important for university management to provide ICT support in order to promote paperless exchange of information and presentations. It is, however, understood recently, that there are issues which surround the low usage of ICT among university management and ultimately paperless environment. The purpose of this paper, therefore, is to conceptually discuss the mode of university operations and how paperless environment can be attained. The paper highlights the cyclical order of document generation, document management and document sharing as the process in which a paperless environment can take place within both the academic and the administrative settings in the university. The paper further discusses the challenges hindering the attainment of paperless environment among which are infrastructural gap, inadequate ICT support and attitudinal challenge. It is recommended that for the smooth operation of a paperless environment, the government must bridge the infrastructure gap especially power as well as train and retrain staff on the path ICT usage.
Transformation of University Libraries During the Digital Era

The paper considers the transition period of society development, based on the use of digital information and communication technologies. Now the digital economy covers all the spheres of society, so the question of its study is relevant. The transition to a new digital economy model contributes to improving the life quality of the population. In higher education institutions educational paths are changing, a rapid transition from physical to virtual reality is observed. However, the educational environment lags far behind the pace of industrial production based on modern technologies. In such situation, approaches to modernizing the training system need to be developed. This case university strategies, aimed at creating a full environment for the development of education and science, information and knowledge from the intellectual global environment, are being developed. In the context of the transition to digital technologies in education, the library, as one of the main structural divisions of the university, should also become digital. The paper reveals the different viewpoints of the library community that show the strategy and direction of library development for the digital environment. Libraries are now actively developing information and communications technology providing services to their users, while maintaining traditional forms and methods of working with readers. This symbiosis meets the needs of different groups of readers and users. On the basis of theoretical research of formation processes and development of electronic library, problems of transition period are considered, strategy of digital library development is identified, technologies of creation and introduction of information resources into university educational environment are determined. The purpose of the research was to study the activity of using information resources in the educational process and research activities, in order to improve the quality of service to library users. It will identify the attitudes of students towards information resources and library services. The relevance and importance of the transition to the digital library is noted, the impact of this process on the development of all library activities is analysed, while maintaining the continuity of its main functional purpose. In addition, the emergence of new social and technological tasks is indicated, the specifics of working with digital users are distinguished. The library works with both virtual users and real readers. Book loan and electronic resources to users are analyzed. The library’s activities are covered in educational and leisure activities with university students and teachers. In the article, the recommendations presented will improve the efficiency of the library
Differentially Private Accelerated Optimization Algorithms

We present two classes of differentially private optimization algorithms derived from the well-known accelerated first-order methods. The first algorithm is inspired by Polyak's heavy ball method and employs a smoothing approach to decrease the accumulated noise on the gradient steps required for differential privacy. The second class of algorithms are based on Nesterov's accelerated gradient method and its recent multistage variant. We propose a noise dividing mechanism for the iterations of Nesterov's method in order to improve the error behavior of the algorithm. The convergence rate analyses are provided for both the heavy ball and the Nesterov's accelerated gradient method with the help of the dynamical system analysis techniques. Finally, we conclude with our numerical experiments showing that the presented algorithms have advantages over the well-known differentially private algorithms.
Augmented Reality Application to Develop a Learning Tool for Students: Transforming Cellphones into Flashcards

Flashcards are one of the most popular and optimized ways to learn factual knowledge and improve memory performance. Students of modern age, who use smart technology and mobile devices in their daily lives, often lack the time and motivation to create and use flashcards effectively. We aim to use the inseparable relationship between university students and their smartphones to create new options for higher education, converting their cellphones into flashcards. We have used this new technology to develop a simple application (app) to convert the smart mobile devices of students into flashcards.We have developed an augmented reality (AR) flashcard application using Unity3D, which requires the user to identify a target image. Once the target image is identified, it can be replaced by any other digital output, i.e., 2D image, 3D models, or videos. We used images of histological sections of oral mucosa, which dentistry students study as a part of an oral biology course.The AR flashcard application worked on both iOS and Android systems. It was able to detect the target image and replace it with the output image on the device screen.Using this application, students will be able to independently learn and self-test their learning at their own convenience. Instructors can use the application to provide additional study aids for the students. Our application, which is being developed as a pilot project, will be expanded and applied as a learning tool for students studying dentistry at the University of Alberta.
ANALISIS SENTIMEN PANDEMI COVID19 PADA STREAMING TWITTER DENGAN TEXT MINING PYTHON

The impact of the novel coronavirus (COVID-19) is widespread and will likely shape community behavior for months to come. And while the humanitarian and safety-related aspects of this outbreak are top of mind globally, it’s unquestionable that social distancing, quarantining, and staying home will have a significant effect on media consumption, which could rise up to 60%, according to recent research from Nielsen’s U.S. media team. Social media, now a part of everyday life for most consumers engaged with the world digitally, became the primary source for buzz about all things COVID-19 as worries and news intensified. Sentiment analysis is applied in this study to analyze the opinions, feelings, and interests of individuals in the COVID-19. The purpose of this study is to analyze sentiment based on an opinion by classifying individual feelings such as sadness, happiness, or panic in facing a COVID-19 into sentiment level that is negative, positive or, neutral. In this paper, an open-source approach is presented where we have collected tweets from the Twitter API and then reprocessing, analyzing and, visualizing these tweets using python. Furthermore, Twitter data streaming will be processed and cleaned to parse data that can be classified based on opinion with a text mining algorithm using text blob Python. Feature extraction is done for the relationship between words by the Bigram and N-gram methods.
Exploring the impact of cloud computing on existing South African regulatory frameworks

Background: The use of cloud computing services raises policy and regulatory challenges globally, more specifically on data security and privacy, amongst other issues. There is a concern on whether South African information and communications technology (ICT) policies and regulatory frameworks are sufficient to address emerging cloud computing regulatory challenges. Therefore, this necessitates a review to determine the extent to which existing regulatory frameworks are applicable to cloud computing and the challenges thereof. Objectives: The study determines the impact of cloud computing on existing South African ICT policies and regulatory frameworks and ascertains whether they are sufficient to address challenges regarding the use of cloud computing services. Method: The study followed an interpretivism philosophical stance. A multi-method qualitative research approach was employed and a thematic analysis was applied in which the data was collected through interviews, questionnaires and document analysis. Results: The existing policies and regulatory frameworks as applicable to computing are playing 'catch-up' where technology has a footprint. Moreover, some existing policies use a blanket approach and are not specific to the subject matter. As a result, there is a need for policies that are future-proof and specific to the subject matter. Conclusion: The emergence of cloud computing has exposed the existing ICT policies and regulatory laws as being inadequate to address cloud computing developments, complexities and challenges, especially challenges related to public confidence regarding the use of cloud computing services and cloud competitiveness.
Clustering by Detecting Density Peaks and Assigning Points by Similarity-First Search Based on Weighted K-Nearest Neighbors Graph

This paper presents an improved clustering algorithm for categorizing data with arbitrary shapes. Most of the conventional clustering approaches work only with round-shaped clusters. This task can be accomplished by quickly searching and finding clustering methods for density peaks (DPC), but in some cases, it is limited by density peaks and allocation strategy. To overcome these limitations, two improvements are proposed in this paper. To describe the clustering center more comprehensively, the definitions of local density and relative distance are fused with multiple distances, including K-nearest neighbors (KNN) and shared-nearest neighbors (SNN). A similarity-first search algorithm is designed to search the most matching cluster centers for noncenter points in a weighted KNN graph. Extensive comparison with several existing DPC methods, e.g., traditional DPC algorithm, density-based spatial clustering of applications with noise (DBSCAN), affinity propagation (AP), FKNN-DPC, and K-means methods, has been carried out. Experiments based on synthetic data and real data show that the proposed clustering algorithm can outperform DPC, DBSCAN, AP, and K-means in terms of the clustering accuracy (ACC), the adjusted mutual information (AMI), and the adjusted Rand index (ARI).
System Identification of Neural Signal Transmission Based on Backpropagation Neural Network

The identification method of backpropagation (BP) neural network is adopted to approximate the mapping relation between input and output of neurons based on neural firing trajectory in this paper. In advance, the input and output data of neural model is used for BP neural network learning, so that the identified BP neural network can present the transfer characteristics of the model, which makes the network precisely predict the firing trajectory of the neural model. In addition, the method is applied to identify electrophysiological experimental data of real neurons, so that the output of the identified BP neural network can not only accurately fit the neural firing trajectories of neurons participating in the network training but also predict the firing trajectories and spike moments of neurons which are not involved in the training process with high accuracy.
A Practical Analysis of Mobile Data Collection Apps

&lt;p&gt;Nowadays, data collection has become an activity inherent in the emergence of any organization. The digital age has enabled the development of mobile data collection apps that are becoming increasingly common around the world. But faced with the growing number of apps offered, Data Managers are often challenged by with the choice of the solution that best suits their case.&lt;/p&gt;&lt;p&gt;This study meets this need by providing clear, precise and verified information on each of the selected solutions. The study presents, analyzes and compares four mobile data collection solutions. To achieve an effective comparison, we first chose to collect and select papers on each of the solutions, and then to install and test each of them by executing a data collection process, all the way from the form creation to the visualization of collected data. The comparison presented in this paper was based on technical aspects but also on other important aspects to help users make a good decision.&lt;/p&gt;
Trusted computing theory and technology in innovation-driven development

Trusted computing is based on a hardware security mechanism establishing a trusted computing environment and comprehensively enhances the system and network trust from the architectural perspective. With the development of information technology and continuous emergence of new application scenarios, security threats in the cyberspace are becoming increasingly serious; hence, trusted computing is actively researched in both academia and industry to find solutions against such treats. This paper summarizes the development process of trusted computing theory from the perspective of innovation and development. The study centers around one of the authors research results in trusted computing over the past 20 years. It proposes a trusted computing technology architecture that covers two method foundations, three trust cores, and four key technologies. Furthermore, the paper summarizes important research problems in mobile trusted computing, quantum-resistant trusted computing, trusted Internet of Things (IoT), trusted cloud, and trusted blockchain, elaborating on the integration and development of trusted computing in these fields. In mobile trusted computing, the design and implementation of a trusted execution environment architecture with software/hardware co-design is the focus of research. Another two important research issues in mobile trusted computing are the runtime security isolation and protection of the mobile operating systems kernel and trusted execution environment-based mobile application security protection. Due to the characteristics of embedded environments and limitation of resources, the construction of lightweight trusted roots, efficient and secure software attestation, practical secure code update mechanism, and swarm device attestation are important issues for further research in trusted IoT. In new scenarios such as quantum-resistant trusted computing, trusted cloud, and trusted blockchain, trusted computing is also constantly expanding its application boundaries and playing an increasingly important role. Finally, this paper looks ahead and discusses the development trends in trusted computing.
Adversarial Training and Provable Robustness: A Tale of Two Objectives

We propose a principled framework that combines adversarial training and provable robustness verification for training certifiably robust neural networks. We formulate the training problem as a joint optimization problem with both empirical and provable robustness objectives and develop a novel gradient-descent technique that can eliminate bias in stochastic multi-gradients. We perform both theoretical analysis on the convergence of the proposed technique and experimental comparison with state-of-the-arts. Results on MNIST and CIFAR-10 show that our method can consistently match or outperform prior approaches for provable l∞ robustness. Notably, we achieve 6.60% verified test error on MNIST at ε = 0.3, and 66.57% on CIFAR-10 with ε = 8/255.
Upsampling Real-Time, Low-Resolution CCTV Videos Using Generative Adversarial Networks

Video super-resolution has become an emerging topic in the field of machine learning. The generative adversarial network is a framework that is widely used to develop solutions for low-resolution videos. Video surveillance using closed-circuit television (CCTV) is significant in every field, all over the world. A common problem with CCTV videos is sudden video loss or poor quality. In this paper, we propose a generative adversarial network that implements spatio-temporal generators and discriminators to enhance real-time low-resolution CCTV videos to high-resolution. The proposed model considers both foreground and background motion of a CCTV video and effectively models the spatial and temporal consistency from low-resolution video frames to generate high-resolution videos. Quantitative and qualitative experiments on benchmark datasets, including Kinetics-700, UCF101, HMDB51 and IITH_Helmet2, showed that our model outperforms the existing GAN models for video super-resolution.
Competency-based approach and competencies in higher education: a theoretical review

Economic changes not only in the country, but also on the world labor market explain the growing demands on young professionals. New requirements for the model and quality of the graduate appear, and new approaches to their competitiveness and effectiveness appear. The authors analyzed the concepts of a competency-based approach, competence, developed characteristic features and the field of implementation of these definitions in higher education. It has been established that a qualitatively new mission, tasks and content of modern education in the new conditions should be focused not only on fundamental knowledge, but also on the formation of practically oriented skills and competencies.
An Education for Life: The Process of Learning the Alexander Technique

The Alexander technique is an educational self-development self-management method with therapeutic benefits. The primary focus of the technique is learning about the self, conceptualized as a mind–body unity. Skills in the technique are gained experientially, including through hands-on and spoken guidance from a certified Alexander teacher, often using everyday movement such as walking and standing. In this article the authors summarize key evidence for the effectiveness of learning the Alexander technique and describe how the method was developed. They attempt to convey a sense of the unique all-encompassing and fundamental nature of the technique by exploring the perspectives of those engaged in teaching and learning it and conclude by bringing together elements of this account with relevant strands of qualitative research to view this lived experience in a broader context.