Reviewerly/exHarmony huggingface dataset:
Vision Aids for Colour Deficiency in Development of Contactless Colour Recognition Device

Colour blind people face many difficulties in everyday life which normally sighted people just are not aware of. Problems can arise in even the simplest of activities including choosing and preparing food, gardening, sport, driving a car and selecting which clothes to wear. This research is proposed to develop a wearable device that can assist colour blind people to recognize colour they cannot see. The objective of this project are to develop a device to help the colour blind people by notify and guide them the correct colours and to investigate the colour contrast of colour blind people for the proposed device colour detection accuracy. The device used TCS3200 colour sensor as input and Arduino Nano as controller. A portable and wearable device of colour recognition system is developed with the low cost and affordable to be used by them. It is validated that the 90% of the user is able to recognise the true colour.
Measurement of Skeletal Muscle Pennation Angle

Pennation angle (PA) is an important indicator for skeletal muscle activity, and automatic calculation of PA has high practical value in fields such as sports medicine and rehabilitation engineering. This chapter mainly introduces the automatic calculation methods used in PA measurement, especially focusing on those related to Hough transform and Radon transform, and their derivative versions designed to be more robust and accurate as well. Today, on different datasets, these methods have generally demonstrated calculation accuracy of PA measurement comparable to traditional manual methods, but are faster and more objective.
A Course Redesign Project

In the summer of 2019, a cooperative team of Biology faculty and a principal investigator worked to develop a solid set of aligned student learning outcomes across the sections of first semester (BIOL 1305) and second semester (BIOL 1306) of introductory Biology. Additionally, the group worked on course objectives alignment within the scope and sequence of the courses, as well as aligned syllabi. A full course redesign was initiated over the summer, where the goal was to align student learning outcomes (SLOs), assessments, and develop a shared set of syllabi for six sections across two courses of introductory biology. &#x0D; At UTEP, the overall goal was to integrate adaptive courseware technology tools, open education resources (OER) and active learning strategies within a course redesign in our Learning Management System (LMS), Blackboard, for a number of sections in Biology 1305 and Biology 1306 beginning in the spring of 2020. This is challenging, as much of adaptive courseware technology is not as strong in content as the Biology faculty would like for these classes, although it can help to substantially reduce the costs for students. The information that follows defines the case study for integrating adaptive courseware within the course redesign process for a series of high enrollment introductory Biology courses
A novel temporal frequent subgraph based mining algorithm using spark

Generally, big interaction networks keep the interaction records of actors over a certain period. With the rapid increase of these networks users, the demand for frequent subgraph mining on a large database is more and more intense. However, most of the existing studies of frequent subgraphs have not considered the temporal information of the graph. To fill this research gap, this article presents a novel temporal frequent subgraph-based mining algorithm (TFSBMA) using spark. TFSBMA employs frequent subgraph mining with a minimum threshold in a spark environment. The proposed algorithm attempts to analyze the temporal frequent subgraph (TFS) using a Frequent Subgraph Mining Based Using Spark (FSMBUS) method with a minimum support threshold and evaluate its frequency in a temporal manner. Furthermore, based on the FSMBUS results, the study also tries to compute TFS using an incremental update strategy. Experimental results show that the proposed algorithm can accurately and efficiently compute all the TFS with corresponding frequencies. In addition, we also apply the proposed algorithm on a real-world dataset having artificial time information that confirms the practical usability of the proposed algorithm.
Time-Dependent Item Embeddings for Collaborative Filtering

Collaborative filtering recommender systems are essential tools in many modern applications. Their main advantage compared with the alternatives is that they require only a matrix of user-item interactions to recommend a subset of relevant items for a given user. However, the increasing volume of the data consumed by these systems may lead to a representation model with very high sparsity and dimensionality. Several approaches to overcome this problem have been proposed, neural embeddings being one of the most recent. Since then, many recommender systems were made using this representation model, but few consumed temporal information during the learning phase. This study shows how to adapt a pioneering method of item embeddings by adding a sliding window over time, in conjunction with a split in the user’s interaction history. Results indicate that considering temporal information when learning neural embeddings for items can significantly improve the quality of the recommendations.
A Novel Framework for Authority Management Based on Knowledge Base Completion of the Graph Neural Network

Big data is massive and heterogeneous, along with the rapid increase in data quantity, and the diversification of user access, traditional database, and access control methods can no longer meet the requirements of big data storage and flexible access control. To solve this problem, an entity relationship completion and authority management method is proposed. By combining the weighted graph convolutional neural network and the attention mechanism, a knowledge base completion model is given. On this basis, the authority management model is formally defined and the process of multilevel trust access control is designed. The effectiveness of the proposed method is verified by experiments, and the authority management of knowledge base is more fine-grained and more secure.
Learning progress in university students associated with disruptive innovation of metacognitive strategies in times of COVID-19

The use of information and communication technologies, have become a necessary resource for teaching and learning in the current era, this article aims to present the results of the relationship between metacognitive strategies and the progress of learning in university students, in a social interaction based on digital technology resources in times of COVID-19. The study sample was made up of 200 students from the Faculty of Education Sciences and Humanities of the public University of the city of Ica - Peru, selected through non-probabilistic sampling. The methodology that was used was descriptive correlational, with a non-experimental design; In data collection, the MAI "Metacognitive Awareness Inventory" instrument was applied, which consists of 52 items and the document analysis technical sheet for the students' learning progress. Likewise, to find the association of the variables, statistical analysis was used which, according to the results obtained, it was shown that there is a statistically significant relationship between metacognitive strategies and the learning progress of university students.
LibGuides: Information Technology Help: Typing

Resources to help Central Penn College students navigate the technology required in their courses.
Relation‐Level User Behavior Modeling for Click‐Through Rate Prediction

Abstract Many recent user behavior based click‐through rate models adopt a similar item‐level paradigm: learn the user representation from a list of item representations via a sequence model and/or a pooling mechanism. However, sequence models are usually sensitive to the exact order of the behavior sequence, while item‐level pooling mechanisms simply neglect the chronological information. In this paper, we balance the two approaches by decomposing the long item sequence into a group of extremely short sequences (item pairs) and conducting relational reasoning on them. More specifically, the relational reasoning mechanism consists of two parts, which are designed for capturing various transitional patterns in the behavior sequences. An attentive pooling layer is employed to emphasize those relation‐level signals that are highly related to the target item. Therefore, our approach is essentially a middle ground between the previous two approaches. To verify the effectiveness of our method, we conduct extensive experiments on three public datasets. Experimental results show that our methods achieve better performance than others. Besides, we explore the properties of our model and verify the effectiveness of each component by controlled experiments. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
Medical Device Insurance Fund Management and Risk Prevention and Control Based on Feature Matching Algorithm

For a long time, the improvement of medical security level can not be separated from the important role of medical insurance fund. With the continuous improvement and promotion of the national medical insurance system, the medical insurance level and coverage rate in China have been greatly improved. However, in the face of the rapid development of science and technology and people's growing medical needs, the management system of medical device insurance fund is relatively backward, coupled with various problems in the promotion process, the degree is slow and the efficiency is low. This paper presents an improved SIFT feature matching algorithm based on image transformation. The improved algorithm makes straight lines in different directions in the sampling area of SIFT feature points of images, and uses Radon transform of straight lines as SIFT feature vector descriptors, thus reducing the dimension of SIFT feature vector and improving the efficiency of feature matching. Comprehensive experimental comparison and analysis show that the improved algorithm is effective.
A State-of-the-Art Survey on Deep Learning Methods and Applications in Bioinformatics

Deep learning (DL) methods have impacted machine learning-based bioinformatics applications as these methods provide the ability to learn complex non-linear relationships between features. DL methods also allow information leveraging from unlabeled data that does not belong to the problem under study. This paper's main objective is to provide a state-of-the-art survey on deep learning (DL) methods and applications in bioinformatics. Various DL methods, including feed-forward neural networks (FNNs), recurrent neural networks (RNNs), bidirectional recurrent neural networks (BRNNs), and convolutional neural networks (CNN), are presented. Deep learning methods are presented along with a review of the state-of-the-art applications in the bioinformatics domain.
Approximate Gradient Coding for Heterogeneous Nodes

In distributed machine learning (DML), the training data is distributed across multiple worker nodes to perform the underlying training in parallel. One major problem affecting the performance of DML algorithms is presence of stragglers. These are nodes that are terribly slow in performing their task which results in under-utilization of the training data that is stored in them. Towards this, gradient coding mitigates the impact of stragglers by adding sufficient redundancy in the data. Gradient coding and other straggler mitigation schemes assume that the straggler behavior of the worker nodes is identical. Our experiments on the Amazon AWS cluster however suggest otherwise and we see that there is a correlation in the straggler behavior across iterations. To model this, we introduce a heterogeneous straggler model where nodes are categorized into two classes, slow and active. To better utilize training data stored with slow nodes, we modify the existing gradient coding schemes with shuffling of the training data among workers. Our results (both simulation and cloud experiments) suggest remarkable improvement with shuffling over existing schemes. We perform theoretical analysis for the proposed models justifying their utility.
Design and Realization of General Platform for Computer Game

This paper studies the current situation of computer game platforms and focuses on the rational use and expansion of existing game technology to build a complete general-purpose computer game platform that can load different game engines. The front-end and back-end decoupling architecture of the game platform was designed. The front-end calls the Native API at the back-end through WebSocket and uses JSON data for interaction. Realize the role of the game platform in the game rule determination and input–output interaction interface, and provide higher execution efficiency, more convenient operation mode and necessary rule evaluation for game participants. Let computer games be more fair, just and efficient. Experiments show that the system is feasible and effective, and it has practical significance for the future study of computer game.
Machine Learning Approach to Classify Toxic Comments on Social Media Platforms

Gupta, Sakshi Goel, Mugdha Rathee, NishaSocial networking has become a significant component of life for all. With increasing accessibility to the Internet, there is a huge amount of hatred and toxicity globally on online platforms. People tend to post toxic comments, hiding behind anonymous identities. Therefore, it is very much required to filter out the toxicity and avoid unnecessary hate on online social media platforms, so that the actual purpose of these platforms, which is to facilitate effective conversations among communities and expressing oneself freely, can be fulfilled. The research work aims to build and train various classification models, namely SVM, logistic regression, naive Bayes, XGBoost, and bidirectional LSTM (long short-term memory), which assist in the classification of toxic comments in different subcategories. A comparative evaluation of these classification models using two datasets is done based on their accuracy. With these classification models’ help, we can prevent online harassment and abuse and create a safer Internet environment.
Improvement of fidelity on quantum blind signature with imprecise quantum measurement

Quantum signature is used for verifying authenticity, integrity, and non-repudiation of message in communication. Many different quantum signature are proposed, however, almost all of them merely work under the assumption of ideal conditions. At present, the non-idealities in the actual system have attracted great attention of researchers, and they are constantly exploring how to overcome or reduce the adverse effects and constraints. However, the existing results have not thoroughly revealed the problem of "how inaccurate quantum physical operations affect and restrict the performance of quantum communication", which is the point of this project. In this paper, the description of imprecise quantum physical operation is proposed, the fidility of quantum blind signature with imprecise quantum measurement is discussed. Finally, our results show that the phase paremeter &Phi; and &Phi; <sub>&epsilon;</sub> has no effect on the fidelity of quantum blind signature and the higher fluctuation of impercise quantum measurement parameter, the lower fidelity will be. We hope that the results of this study will help improve the ability of quantum blind signature experiments in actual quantum systems to resist the influence of inaccurate measurements.
Multi-Receptive-Fields Convolutional Network for Remote Sensing Images Super-Resolution

Recently, single image super-resolution (SISR) has been widely applied in the field of remote sensing image processing and obtained remarkable performance, focusing on restoring the high-resolution (HR) image from a low-resolution (LR) image. However, we observe that the existing CNN-based SISR methods mainly focus on wider or deeper architecture design, neglecting to exploit features at global receptive field. Moreover, the LR inputs and features contain abundant low-frequency information, which are perceived equally in the same receptive field, hence limiting the representational ability of CNNs. To solve these problems, we propose a Multi-Receptive-Fields Super Resolution Network (MRFSR) for remote sensing image reconstruction. The proposed network employs non-local neural network to enhance low-level complex features by expanding the receptive field of the shallow convolution layer. Moreover, we propose the multi-branch up- and down-sampling modules to deal with LR features in multiple receptive fields, which can enhance the high-frequency components and learn abstract feature representations in multiple scales, respectively. Extensive experiments on NPU-RESISC45 dataset shows that the proposed MRFSR can provide state-of-the-art or even better performance in both quantitative and qualitative measurements.
Design a Game of Charged Particles Moving in a Uniform Magnetic Field Based on Unity3D–Take "the Wise Snake through the Pass" as an Example

Educational games are hot spots in the field of education, and they are widely concerned by scholars. Educational games can prompt students to understand and grasp knowledge in the process of participating in the game. Since Unity3d is the mainstream game development software, educators should think about how to use it to design a game that aims at the current teaching difficulties to supplement daily teaching. The motion of charged particles in uniform magnetic field is the focus of physics course in senior high school. Most of the students said that they felt inadequate when solving problems that required them to depict the topic of the electric particle movement trajectory. In view of the actual situation of the above students, this paper uses the Unity engine, C # programming language and the relevant education theory related to education game designing to design a 2D game. The purpose is to make students understand the trajectory of charged particles in the uniform magnetic field in the process of participating in the game, and then learn the relevant knowledge of electromagnetic part better.
Decentralising Personal Credit Score

The current process of securing a loan involves a cumbersome know-your-customer (KYC) process. The process also raises a question about the ownership of credit scores. In this chapter, the authors propose a solution based on a combination of decentralized identifier (DID) W3C blockchain and cryptographic wallet to make it possible to make credit scores possible. A decentralized identifier to enable a loan applicant to assert who he/she is without relying on a centralized identity issuer is key to enabling loan applicants to own his/her own credit score. The use of blockchain is to enable loan applicants to have his/her identity recorded immutably on a store that is trusted by all parties. Finally, the use of a cryptographic wallet is to enable loan applicants to assert identities on demand and prove his/her assertion.
Improving Steganography using Image Compression JPEG

Due to digitization and the rapid evolution of tech-nologies, cryptography is no longer sufficient to ensure a secure digital life, hence the need for steganography. In this article, the best-known methods are explained and a new steganography method is proposed and compared to other techniques to assess its performance.
Prediction method of high-rise building foundation settlement based on GPS Baseline Solution

The traditional foundation settlement prediction method of high-rise buildings cannot achieve average distribution in the distribution of unit weight ratio, resulting in poor accuracy of foundation settlement prediction calculation. In order to improve the prediction accuracy, a foundation settlement prediction method of high-rise buildings based on GPS baseline solution is proposed. The expression of three-phase system composed of soil particles, pore water and pore gas is constructed, and the variation trend of density pressure between foundation surface water and soil is analyzed; Based on the GPS baseline solution, the unit weight ratio is allocated to improve the detection accuracy of cycle slip, accurately calculate the ionospheric residuals before and after epochs, reset the proportion and redistribute the weight of a posteriori variance component estimation; The Poisson curve expression is established to calculate the main consolidation settlement of high-rise buildings, and the foundation settlement prediction model is established. A comparative experiment is designed to test the accuracy of the three prediction methods. In the prediction test of untreated foundation settlement and the prediction test of foundation settlement after filling treatment, the algorithm is better than the other two algorithms. It can be seen that the prediction result of GPS baseline solution algorithm is more accurate.
Beat Time: Dance Game with Motion Detection and Face Recognition

Currently, the electronic leisure and entertainment sector has been growing steadily and new technologies ensure an increase in entertainment values by improving the gaming experience. One of them is the remote interaction, which is presented as new ways to control video games, becoming more and more popular exergame type games. Therefore, the objective of this work has been to develop a video game on the Unity 3D game engine, whose interface works remotely with the movement of the hands, offering an immersive experience. In addition to this, differential factors such as face recognition and motion detection with the Kinect v2.0 depth camera have been used, to be subsequently evaluated by a group of players. Hence, demonstrating the applicability of agile methodologies and the use of Game Development using limited resources.
New End-Stopped Complex Cell Model Applicable in Convolutional Neural Networks

This article is dedicated to modeling the end-stopped neuron. This type of neuron gives the maximum response at the end of the line and is used to refine the edge. The article provides an overview of different models of end-stopped neurons. I have proposed a simpler and more accurate model of an end-stopped neuron based on the use of Gabor filters in antiphase. For this purpose, the models of simple and complex cells whose output is used in the proposed model are also described. Simple cells are based on the use of a Gabor filter, the parameters of which are also described in this article. The proposed model has shown its effectiveness.
Research on Interaction Design of Anti-addiction for Minor Games Based on Flow Theory

With the development of Internet technology, minors’ addiction to games has become more and more serious. How to improve this problem has become a focus of social attention. This paper introduces the theory of flow, and through its positive and negative applications, it takes the premise of causing as little rebelliousness as possible in minors, and subtly reduces the time they devote to online games, so as to achieve the purpose of preventing minors from getting addicted to games, and finally establishes the interaction model of preventing minors from getting addicted to games. Firstly, through literature research method, inductive method and user interview method, we analyze the main causes of minors’ game addiction, combine them with nine features of flow theory, and summarize the inspiring principles of flow theory in the interaction design of minors’ game anti-addiction. The interaction design model of anti-addiction interaction for minors’ games is proposed by applying the flow theory in reverse and positive directions while minimizing the impact on the game experience, so that it can be a reference for the interaction design of anti-addiction interaction model for minors’ games in several mobile game apps in the future.