Reviewerly/exHarmony huggingface dataset:
Factor Scoring and Machine Learning algorithm to Predict Student Counselling

Personal realization is one of the best things for a successful life. Sometimes, one needs help to realize about bad habits, career goals and accomplish mental health as well as to overcome other problems. This help is generally known as “Counselling”. To ensure effectiveness of counselling service, prime concern is to find out the target group of instances. Many researchers worked with student performance prediction based on academic attributes moreover students’ counselling is also needed to increase their performance. We addressed this issue for this paper work. Here, a model is proposed to predict a student who needs counselling. This study was mainly motivated by two main steps. The first was to investigate university students who feels an urge about having counselling for psychological help from their circumstances and second was to predict efficiently which group of students really needs counselling. This paper work was established with 498 instances and each comprised of 6 attributes. In the case of evaluate the result, paper shows superiority over state- of-the-art methods to predict student counselling through machine learning and factor scoring method. We applied 10 fold cross-validation and 66% dataset splits evaluation method to find out better algorithm among selected 5 algorithms which are Ibk, Naive Bayes, Multilayer, SMO and Random Forest. Weka 3.8.0 have been used for machine learning algorithms where Ibk (Instance Based Learning) was found best for our approach with 95.38% accuracy.
Hierarchically Local Tasks and Deep Convolutional Networks

The main success stories of deep learning in visual perception tasks starting with ImageNet, has relied on convolutional neural networks, which on certain tasks perform significantly better than traditional shallow classifiers, such as support vector machines. Is there something special about deep convolutional networks that other learning machines do not possess? Recent results in approximation theory have shown that there is an exponential advantage of deep convolutional networks (DCN) over fully connected networks (FCN) in approximating functions with hierarchical locality in their compositional structure. These mathematical results, however, do not say which tasks are expected to have input-output functions with hierarchical locality. Here we explore a set of hierarchical and non-hierarchical visual tasks in which we study how network performance is affected by disrupting locality through scrambling in the input image space. In particular our experiments consist of having 2 distinct networks that vary in their computation: a fully connected network that has no locality prior, and a deep convolutional network that has a locality prior. These networks performed 3 tasks: Color Estimation (that does not require locality), Object Classification and Scene Gist. We verify that fully connected networks that possess no locality prior stay stable (albeit weaker) in performance even as the images are scrambled, across all the 3 tasks --with the exception of the color estimation task where although performance does not change, and FCNs achieve superior performance to that of DCNs. However, when the images are fully scrambled, we find that deep convolutional networks can perform worse than fully connected networks across all 3 tasks --with little variation for a non-hierarchical task. Finally, we show that small departures from the locality bias grow during learning with gradient descent even on a hierarchical task, suggesting that this bias cannot be purely learned from data without additional constraints.
Radical Composition Network for Chinese Character Generation

Recently, the generation of Chinese characters attracts many researchers. Many excellent works only focus on Chinese font transformation, which is, Chinese character can be transformed into another font style. However, there is no research to generate a Chinese character of new category, which is an interesting and important topic. This paper introduces a radical combination network, called RCN, to generate new Chinese character categories by integrating radicals according to the caption which describes the radicals and spatial relationship between them. The proposed RCN first splits the caption into pieces. A self-recurrent network is employed as an encoder, aiming at integrating these caption pieces and pictures of radicals into a vector. Then a vector which represents font/writing style is connected with the output from encoder. Finally a decoder, based on deconvolution network, using the vector to synthesize the picture of a Chinese character. The key idea of the proposed approach is to treat a Chinese character as a composition of radicals rather than a single character class, which makes the machine play the role of Cangjie who invents Chinese characters in ancient legend. As a kind of important resource, the generated characters can be reused in recognition tasks.
Individualized Inlay Design of Abnormal Amber Based on 3D Scanning Technique

In view of the current design status of abnormal amber, this paper innovatively combines 3D digital technology with personalized inlay design of abnormal amber. This experiment selects three different abnormal amber samples by classifying shape and texture of common abnormal amber in the market. Based on the shape and defect position that displayed by the 3D scanning point cloud of the sample, the corresponding individualized inlay was designed. After creating the 3D digital model, the inlay was printed by selective laser melting (SLM) 3D printer. The 3D printed inlay position is more accurate and the design is no longer restricted by handicraft. The experiment results show that the combination of 3D scanning technology and inlay design not only reduces labor costs and saves raw materials, but also improves the firmness and aesthetics of the inlay. With the continuous exploration, the application of 3D digital technology will provide more convenience for jewelry production.
Image Segmentation Method for an Illumination Highlight Region of Interior Design Effects Based on the Partial Differential Equation

The saliency calculation model based on the principle of partial differential equations sometimes highlights areas with high contrast in the background, and the salient targets obtained occasionally have holes. The above problems can be solved by combining the improved convex hull calculation center saliency map. This paper designs a single-target color image segmentation algorithm based on partial differential equations. First, we calculate the basic saliency map according to the uniqueness of the color and the spatial distribution of the color; second, we then use the superpixel to improve the convex hull and calculate the central saliency map according to the principle; finally, the basic saliency map and the central saliency map are calculated. The weighted fusion is used to obtain the comprehensive saliency map, and the threshold method is used to segment the comprehensive saliency map to obtain the final target image. This paper designs an evaluation standard suitable for the segmentation of the illuminated highlight area of the effect image. It compares the experimental results of the segmentation method in this paper with the SLIC (Simple Linear Iterative Clustering) method and the traditional superpixel method to segment the illuminated highlight area. The segmentation method is applied to the image enhancement experiment. Based on the fuzzy means clustering algorithm, a fuzzy clustering objective function including brightness, color, and distance parameters is designed, which improves the weight of the brightness value in the clustering and improves the edge fit of the segmentation of the lighting highlight area of the rendering. The segmentation method produced by combining the clustering method with the superpixel biased clustering method can improve the output effect of the illuminated highlight area of the effect image after segmentation. We perform color equalization processing on the image to be segmented to reduce the impact of light, then set the closed value of the brightness information component, perform segmentation judgment, and expand the long and short axes of the ellipse model in the high-brightness area to further reduce the impact of light. The experimental results prove that the above method has a better segmentation effect than the traditional ellipse model and can accurately segment the gesture image. Compared with the existing mainstream saliency calculation models, this algorithm is closer to the true value image in terms of visual effects and has obvious advantages in terms of accuracy.
Conv-Reluplex : A Verification Framework For Convolution Neural Networks (S)

In recent years, machine learning has demonstrated impressive performance in many real-world tasks, especially in computer vision and natural language processing.However, to apply them in safety-critical systems one needs formal guarantees on the neural network outputs.The Reluplex tool is proposed to verify the safety of deep neural networks (DNNs), and in case the DNN fails to give a correct output, can generate adversarial examples.Since the tool can only handle DNNs, it is necessary to extend the tool to process image data.Therefore, in this paper, we propose the Conv-Reluplex framework, which is designed to verify the convolutional layer and pooling layer in convolutional neural networks(CNNs), and generate adversarial examples when classification is misguided.We conduct several experiments on MNIST to evaluate our approaches.The results show that the original CNN is improved using the adversarial examples generated by our tool, and the precision of classification can be increased significantly.
Feature-Oriented Clone and Pull for Distributed Development and Evolution

Product line engineering aims at quickly delivering individual solutions to customers by customizing and evolving products based on a common platform. Engineers commonly follow a distributed and feature-oriented process, supported by version control systems, to track implementation-level changes. For instance, feature branches are widely used to add new or modify existing features. However, when merging back features to the product line, the information how features map to code is usually lost. Furthermore, the granularity of merging is limited to branches, making it hard to transfer individual features from one product to another. This paper thus presents feature-oriented clone and pull operations for the distributed development and evolution of product lines, which are implemented in the FORCE2 platform. Our evaluation uses the ArgoUML product line to assess the correctness and performance of our approach. The results show that the feature-oriented operations work with high precision and recall for different cases of feature interactions. The performance measurements demonstrate that the clone and pull operations can be integrated in typical workflows of engineers.
Blockchain-Based Access Control and Data Sharing Mechanism in Cloud Decentralized Storage System

Access control is a major factor in enhancing data security in the cloud storage system. However, the existing data sharing and the access control method have privacy data leakage and key abuse, which is a major challenge in the research community. Therefore, an effective method named Blockchain-based access control and data sharing approach is developed in the cloud storage system to increase data security. The proposed Blockchain-based access control and data sharing approach effectively solve single-point failure in the cloud system. It provides more benefits by increasing the throughput and reducing the cost. The Data user (DU) makes the registration request using the ID and password and forwards it to the Data Owner (DO), which processes the request and authenticates the Data user. The information of the data owner is embedded in the transactional blockchain using the encrypted master key. The Data owner achieves the data encryption process, and encrypted files are uploaded to the Interplanetary File System (IPFS). Based on the encrypted file location and encrypted key, the Data owner generates the ciphertext metadata and is embedded in the transactional blockchain. The proposed Blockchain-based access control and data sharing approach achieved better performance using the metrics, like a better genuine user detection rate of 95% and lower responsiveness of 25sec with the blockchain of 100 sizes.
Novel image enhancement approaches for despeckling in ultrasound images for fibroid detection in human uterus

Abstract Ultrasonography is an extensively used medical imaging technique for multiple reasons. It works on the basic theory of echoes from the tissues under consideration. However, the occurrence of signal dependent noise such as speckle destroys utility of ultrasound images. Speckle noise is subject to the composition of image tissue and parameters of image. It reduces the effectiveness of many image processing steps and decreases human perception of fine details form ultrasound images. In many medical image processing methods, despeckling is used as the preprocessing step before segmentation and feature extraction. Many speckle reduction filters are proposed but while combining many techniques some speckle diagnostic information should be preserved. Removal of speckle noise from ultrasound image by preserving edges and added features is a great challenging task in ultrasound image restoration. This paper aims at a comprehensive description and comparison of reduction of speckle noise of ultrasound fibroid image. Many filters are applied on ultrasound scanned images and the performance is marked in terms of some statistical measures. Even though several despeckling filters are there for speckle reduction, all are not good for ultrasound scanned images. A comparison of quality measures such as mean square error, peak signal-to-noise ratio, and signal-to-noise ratio is done in ultrasound images in despeckling.
Digital Literacy Skills as Prerequisite for Teaching and Learning in Higher Education Institutions

The advent of digital technologies has brought about new opportunities and challenges to the education system globally. The use of digital technologies by educators in Higher Education Institutions (HEIs) is a portal for innovative teaching and learning. While there is a gradual increase in the use of these innovative technologies by HEIs in South Africa, there is still a long way to transform the educational system fully. Effective use of digital technologies in teaching and learning needs a certain level of digital literacy. Digital literacy enhances teaching and learning by helping educators integrate and use digital technologies, while equipping students with skills to access the internet effectively. The impact of digital technology has beaconed the need of educators to acquire 21st-century skills. Digital literacy skills are critical in realising the potential and benefits of digital technologies. This paper investigates the importance of digital technologies in education and the impact of digital literacy skills on the effective use of these technologies in HEIs in South Africa. Data collection was primarily based on a critical review of literature relating to the application of mobile technologies in HEIs in South Africa. The results revealed various barriers inhibiting educators from adopting mobile technologies in teaching and learning, including lack of digital literacy skills, time constraints, unwillingness to change, lack of educator confidence, poor technological infrastructure, and lack of digital or mobile devices. The study provides recommendations to enable and inspire educators in HEIs to use mobile digital technologies effectively.
ONLINE COMMUNICATION OF TEACHERS AND STUDENTS IN THE HIGHER EDUCATION SYSTEM: PROBLEMS AND PROSPECTS

The relevance of the study is due, on the one hand, to the trends existing in modern education, on the other hand, to the forced situation of distance education due to the pandemic. The development of information and communication technologies, the definition of the content and direction of modern policy in the field of education, the establishment of feedback channels with students – all these are tools to improve the effectiveness of modern higher education. At this stage, there is an urgent need to transform universities with a partial transition to an online environment. Modern communications, on the one hand, provide new opportunities for interaction for teachers and students, on the other hand, they face formalism and lack of readiness (technical, moral, etc.) for full-fledged application. The purpose of our research is to analyze the current state and potential of online communication between teachers and students in the higher education system. The article discusses the features of the use of new communication technologies in the activities of university teachers in interaction with students. The possibilities of modern communication tools for organizing effective interaction and functioning of the educational process in crisis situations are investigated. The article considers the problems and opportunities of using online education in crisis situations. The article analyzes the readiness of teachers to interact with students through modern communication technologies, and examines the process of developing the necessary competencies. Using survey methods, students’ and teachers’ satisfaction with online interaction and distance education in general is revealed. The conclusions identify the problematic and promising aspects of modern online communications, their impact on improving the effectiveness of distance education, and predict possible options for further use.
Automatic Evaluation of Eye or Head Movements for Visual Information Selection

This chapter explains the methodical aspects of the measurement of eye movements and on experiments with a variable field of view. Viewing time is physically determined by the flight parameters and the optical parameters of the sensor. Because the on-line evaluation places high demands on the TV operator, it is felt that knowledge of the strategies used in target acquisition may give some clues for improvements of data presentation. The manual evaluation of the recorded eye movements is very time consuming and is feasible only for small data sets. In a semi-automatic process, the visual field to be checked is divided into separate fields, each field being represented by a key. The semi-automatic methods of evaluating the eye movements—as described before—facilitate evaluation work in that the storage of the results and the time course are taken over by a digital computer.
COVID-19 & Cyber Security Challenges US, Canada & Korea

Globalization and ubiquitous Internet have made significant contribution to mankind while bringing business, education and health closer to every home on the planet. However, in the process of dynamic development and innovation to the information communication technologies (ICT) and Internet, in conjunction with the fast developments in computing industry and artificial intelligence, the cyber security has become of critical importance to governments, businesses, industries, health and academia worldwide. Given current spread of COVID-19 with its impact on economies, business, industries, health and nations security in US, Canada and South Korea the importance of cyber security has become paramount. Millions of people lost their jobs, many people died, many are seriously ill, and the morale is quite low worldwide. It is in time like these that criminals and outlaws are exploiting any possible vulnerability that could give an opportunity for effective cyber-attack(s). The principal author, lived, worked and studied in South Korea, Canada and US, working with co-authors in times of earlier and current cyber security related issues in South Korea, Canada and US. Past twenty years may have brought most valuable and fascinating technologies to mankind worldwide, but at the same time the global mobility, seamless connectivity and free sharing of information and goods among the nations worldwide also promoted new dimension of computational challenges, such as cyber security.
Research of Solar Energy Potential of Photovoltaic Installations on Enclosing Structures of Buildings

Under conditions of limited space for the placement of photovoltaic modules, exploitation of rooftop and facade photovoltaic power plants are highly relevant. One of their main advantages is the possibility to integrate them into both existing and new consumer infrastructures, regardless of their configuration, using the building envelope. On the example of the specific tilt angles relative to the horizon of the receiving surfaces of the modules when they are placed on the enclosing structures of the building for southern spatial orientation, analysis of the utilization level of solar energy potential of the region is carried out. The inclination angles of the enclosing structures, which are characterized by the closest electric energy generation to the generation by modules of optimal spatial orientation, are revealed. There were also determined spatial orientations of the enclosing structures, for which the installation of modules of photovoltaic power plants is not recommended.
Students Utilization on Quipper LMS: A Mixed Method

COVID-19 is a global concern affecting the educational system in Higher Education Institution.As such Quipper-LMS became a resource in delivering quality education in the new normal learning.This study paved to examine students acceptance in Quipper-LMS as new way to deliver learning among students in UM Panabo.Results showed that there is a high acceptance of Quipper-LMS in terms of social influence, performance expectancy, effort expectancy and facilitating conditions.This is also affirmed from the qualitative responses of students, however sensitivity of the system be address.
A Comprehensive Feature Selection Approach for Machine Learning

In machine learning, it is required that the underlying important input variables are known or else the value of the predicted outcome variable would never match the value of the target outcome variable. Machine learning tools are used in many applications where the underlying scientific model is inadequate. Unfortunately, making any kind of mathematical relationship is difficult, and as a result, incorporation of variables during the training becomes a big issue as it affects the accuracy of results. Another important issue is to find the cause behind the phenomena and the major factor that affects the outcome variable. The aim of this article is to focus on developing an approach that is not particular-tool specific, but it gives accurate results under all circumstances. This paper proposes a model that filters out the irrelevant variables irrespective of the type of dataset that the researcher can use. This approach provides parameters for determining the quality of the data used for mining purposes.