Reviewerly/exHarmony huggingface dataset:
A Siamese tracker with “dynamic–static” dual-template fusion and dynamic template adaptive update

In recent years, visual tracking algorithms based on Siamese networks have attracted attention for their desirable balance between speed and accuracy. The performance of such tracking methods relies heavily on target templates. Static templates cannot cope with the adverse effects of target appearance change. The dynamic template method, with a template update mechanism, can adapt to the change in target appearance well, but it also causes new problems, which may lead the template to be polluted by noise. Based on the DaSiamRPN and UpdateNet template update networks, a Siamese tracker with "dynamic-static" dual-template fusion and dynamic template adaptive update is proposed in this paper. The new method combines a static template and a dynamic template that is updated in real time for object tracking. An adaptive update strategy was adopted when updating the dynamic template, which can not only help adjust to the changes in the object appearance, but also suppress the adverse effects of noise interference and contamination of the template. The experimental results showed that the robustness and EAO of the proposed method were 23% and 9.0% higher than those of the basic algorithm on the VOT2016 dataset, respectively, and that the precision and success were increased by 0.8 and 0.4% on the OTB100 dataset, respectively. The most comprehensive real-time tracking performance was obtained for the above two large public datasets.
Research on the Design of Automatic Accompaniment System of Piano Teaching Audio Database Under the Background of the Internet

With the rapid development of Internet technology, China has entered the era of Internet plus. In order to comply with the trend, colleges and universities have also established a relatively perfect campus network system, which provides strong technical support for teaching, scientific research and various types of activities. The interactive teaching theory of education cloud provides a solid theoretical support for the implementation of the integrated piano teaching mode of “teaching, scientific research and artistic practice”, which makes piano teachers and students get all-round development.
The Application of Hierarchical Teaching Mode Based on Hybrid Criterion Fuzzy Algorithm in Higher Vocational English Education

This paper studies the English Teaching in Higher Vocational Colleges Based on the hybrid criterion fuzzy algorithm, which must adapt to the current new situation and actively carry out the teaching reform. In English teaching, the hierarchical teaching strategy is adopted, that is, teachers teach students according to their aptitude, teaching should be targeted, different teaching objectives should be formulated according to the individual differences of students, and different teaching requirements should be put forward, It can make students have a strong interest in learning, enhance their learning confidence and sense of achievement, and promote their healthy development.
Evaluation Method of English Teaching Quality Based on SOFM Neural Network

With the globalization of world economy, culture, science and technology, international cooperation and exchanges are becoming more and more frequent, and English will occupy a more and more important position. As a public compulsory basic course for Non-English Majors in Colleges and universities in China, College English plays a very important role in Expanding College Students’ knowledge, improving foreign language comprehensive quality and cultivating language application ability. Therefore, the quality of College English teaching is also included in one of the important indicators of college curriculum construction evaluation. This paper analyzes four problems existing in College English teaching, and points out that emphasizing summative assessment and neglecting formative assessment are the main reasons affecting the quality of English teaching. Therefore, this paper proposes an English teaching quality evaluation method based on SOFM neural network to strengthen the monitoring of the formation process of teaching quality, so as to improve teaching quality.
Study on the Interactive Education System of Motion Evaluation Algorithm

This research is about the interactive education system of motion evaluation algorithm. The main goal of this work is to create an educational tool that can be used in the classroom environment, which will allow students to learn how to program using Python as their main language. To achieve this goal, we have developed a prototype application that allows students to interact with animations created by our team. The main purpose of this study is to study the effectiveness of using computer-based interactive education system in teaching basic algebraic operations and their applications. The main hypothesis to be tested in this study is: “using the computer-based interactive education system, students can learn basic algebraic operations and their applications more effectively than the traditional way. This hypothesis will be tested through three independent variables: 1) students’ motivation; 2) students’ ability; 3) students’ interest.
Analysis of Cryptocurrency Using a Genetic Algorithm (GA)

Finance, in particular, is tightly correlated with information-technology development. Digital currency or cryptocurrency is the outcome of the advancement of financial technology. As a means of payment over the internet, digital currencies are beginning to be widely used. This currency was developed in order to facilitate and secure transactions. Transaction fees have decreased as a result of the adoption of blockchain technology. Because it has a negative effect on the financial system, monetary stability, and payment system, government policy forbids transactions involving cryptocurrencies. Understanding of the stochastic and genetic algorithm models used in cryptocurrency is provided by this research. Cryptocurrency is a financial technological advancement that is part of the technological package and it enables banknotes to be replaced in financial transactions. A genetic algorithm (GA) achieved an MSE value of 0.0863 and an MPE of 0.0084 with data on cryptocurrencies. The aim of this research was that, in the future, the government would support cryptocurrency technology so that the laws that are passed preserve the Indonesian market while not forbidding anything outright.
Feature alignment as a generative process

Reversibility in artificial neural networks allows us to retrieve the input given an output. We present feature alignment, a method for approximating reversibility in arbitrary neural networks. We train a network by minimizing the distance between the output of a data point and the random output with respect to a random input. We applied the technique to the MNIST, CIFAR-10, CelebA, and STL-10 image datasets. We demonstrate that this method can roughly recover images from just their latent representation without the need of a decoder. By utilizing the formulation of variational autoencoders, we demonstrate that it is possible to produce new images that are statistically comparable to the training data. Furthermore, we demonstrate that the quality of the images can be improved by coupling a generator and a discriminator together. In addition, we show how this method, with a few minor modifications, can be used to train networks locally, which has the potential to save computational memory resources.
Construction of Large-Scale Chinese-English Bilingual Corpus and Sentence Alignment

With the development of computer and Internet, applications based on bilingual (or multilingual) parallel corpora are increasing in the field of natural language processing. In addition to the application of machine translation, the construction of parallel corpus is also of great value for bilingual dictionary compilation, word meaning disambiguation and cross language information retrieval. At present, the bilingual corpus of word alignment and sentence alignment has a large scale, and the related alignment algorithms are also relatively mature. In contrast, the chunk level alignment algorithm remains to be studied, and the chunk level alignment corpus required by the alignment algorithm is quite lacking. The construction of bilingual corpus and its automatic alignment are of great significance to the development of computational linguistics. At present, the existing bilingual corpora at home and abroad, especially Chinese-English bilingual corpora, are not large, the processing standards are not unified, and there is no general bilingual corpus that can be used publicly. It has laid a solid foundation for the large-scale establishment of bilingual language information and knowledge base with unified standards and norms, multi fields, multi genres and sentence level alignment.
Application of Improved Association Rule Algorithm in E-commerce English

The application of the improved association rule algorithm in E-business English is to find the relationship between the two terms. It can be used to find out the similarities between the two projects, and also to find out the differences between the two projects. In this case, we use a set of rules as input data, and then apply these rules to specific projects or project groups that are similar or different from each other according to the rules. The result will be a list with some similarities and some differences. Frequent set mining is a key step of association rule mining. It determines the efficiency of association rule mining to a great extent. This paper first introduces the basic process of data mining, then introduces the association rule algorithm in E-commerce English, and focuses on how to use the improved association rule algorithm to mine the paths and page interests frequently visited by users in pattern recognition, which provides a basis for the personalized recommendation system model, This proves the application of the improved association rule algorithm in E-commerce English.
Research Progress in the Treatment of Motion Sickness

Motion sickness is a disease characterized by vertigo, nausea, vomiting and other symptoms in traditional transportation and visual information technology. Signs of motion sickness occur in humans and animals when they are at sea, traveling by car or plane, and in space. In addition, some other special conditions, such as simulators, movie theaters, and video games, have also been described as causing pseudo disease. Treatment is directed towards reducing conflicting sensory inputs, speeding up the acclimation process, and controlling nausea and vomiting. At present, there are various clinical treatment methods, taking drugs to relieve vomiting is the main treatment method, but the effect varies from person to person. Therefore, this paper reviews the relevant data published in recent years, aiming to summarize the treatment methods of motion sickness, and provide a reference for the development of new treatment methods with small side effects and good efficacy to reduce the proportion of patients with motion sickness.
AUTO: supervised learning with full model search and global optimisation

The AUTO algorithm is presented to incrementally build models and solve supervised learning problems. AUTO uses traditional derivative-free optimisation, like genetic algorithms, to search the problem space of arbitrary functions. With reinforcement learning, AUTO learns actions to guide its search process and gradually improve performance. A comparative analysis is presented exploring supervised learning performance and interpretability of AUTO models. Results indicate AUTO outperforms genetic programming and rivals multilayer perceptrons. AUTO automatically builds models to closely match datasets without the limited search space of traditional supervised learning. With enough time and computational resources, AUTO can generate any function.
A Financial Handwritten Digit Recognition Model Based on Artificial Intelligence

Handwritten digit recognition is a kind of image information classification problem through optical character analysis. Its essence is to represent the pixel points of digital images as gray values and then replace the pixel matrix with a numerical matrix. The computer can deal with numerical problems converted from handwritten Arabic numeral recognition problems through feature extraction and classification. With the rapid development of science and technology, this technology has dramatically reduced the cost of identification and human consumption, making identification more efficient and having a specific use value. However, the current handwritten digit recognition technology will cause problems such as abnormal recognition and recognition errors, reducing recognition accuracy. This will not only increase the cost of human recognition but also increase unnecessary risks. Based on the broad application prospect of handwritten digit recognition in finance, this paper focuses on the research and analysis of the handwritten digit recognition model for its insufficient accuracy, low performance, and other problems. Based on traditional data analysis, this paper adopts deep learning and control variable methods to conduct multiple groups of experiments to explore the impact of different parameters on the accuracy of experimental results. Summarize the best recognition accuracy and achieve the best model performance.
Algorithms for Path Planning in a Group of Mobile Robots in an Environment with Obstacles with a Given Template

A method is proposed for solving the problem of planning the movement of a group of ground-based robotic platforms (UGR) with the requirement to maintain a given formation of the system in the presence of stationary obstacles and sources of disturbances. The task of calculating the trajectory of the leading UGR, coupled with the use of a displacement planner and subsequent smoothing of the resulting trajectory according to the method considered in the first part of this work, is highlighted. The trajectories of the slaved elements of the group are determined by constructing offset spatial curves along which these elements should move, taking into account a given configuration or the requirements of preserving some average kinematic parameters of the elements along their trajectory. To solve the problem of evading the group from the influence of sources of disturbances, the method considered in the previous works of the authors is proposed. It is based on the calculation of the probabilities of successful passage of the elements of the group of their trajectories. These probabilities can be found after evaluating the parameters of the characteristic probability functions of the sources describing the nature of their impact on moving objects over small time intervals. In this article, this method is modified by additional optimization of the resulting spatial trajectory along the length for each UGR, taking into account a given degree of permissible deviation from the original curve. A technique has been developed that allows to find the target trajectories of the leading and driven UGR of the group, the probability of successful passage of which exceeds the specified target value. The methodology is generalized to the case when the optimization criterion is the probability of successful completion of only part of the UGR group. Simulation results confirms the effectiveness of the proposed method of planning the trajectories of robots forming a group in the field of repeller sources.
Smart Diagnosis of Adenocarcinoma Using Convolution Neural Networks and Support Vector Machines

Adenocarcinoma is a type of cancer that develops in the glands present on the lining of the organs in the human body. It is found that histopathological images, obtained as a result of biopsy, are the most definitive way of diagnosing cancer. The main objective of this work is to use deep learning techniques for the detection and classification of adenocarcinoma using histopathological images of lung and colon tissues with minimal preprocessing. Two approaches have been utilized. The first method entails creating two CNN architectures: CNN with a Softmax classifier (AdenoCanNet) and CNN with an SVM classifier (AdenoCanSVM). The second approach corresponds to training some of the prominent existing architecture such as VGG16, VGG19, LeNet, and ResNet50. The study aims at understanding the performance of various architectures in diagnosing using histopathological images with cases taken separately and taken together, with a full dataset and a subset of the dataset. The LC25000 dataset used consists of 25,000 histopathological images, having both cancerous and normal images from both the lung and colon regions of the human body. The accuracy metric was taken as the defining parameter for determining and comparing the performance of various architectures undertaken during the study. A comparison between the several models used in the study is presented and discussed.
A Cost-Effective Multilayer Authentication Framework for Large-Scale IoT Deployments

Security is a fundamental requirement for Internet of Things (IoT) devices to avoid threats. Authentication is one common security control used to prevent and mitigate threats. Different authentication frameworks for various IoT environments exist. Some IoT applications require mutual authentication to enable two-way trust. Supporting mutual authentication in a cost-effective and practical way for IoT environment is a challenge. The primary objective of this paper is to compare the most efficient authentication techniques in IoT and propose a new cost-effective mutual authentication framework. The proposed framework includes several advantages, such as multi-layer authentication, two-way authentication between IoT devices and servers, protection against replay attacks, protection against message modification attacks, and behavior-based authentication. The proposed framework is suitable for small-scale and large-scale IoT applications.
Adoption of Augmented Reality To Enhance Durban University of Technology's Learning Management System

The Durban University of Technology's (DUT) e-learning system is used as a case study in the research as it investigates the various learning management systems in higher education. due to the expanding technological trends and the requirement to support students who belong to “Generation Z.” This study examines how augmented reality can be used to transform the DUT E-learning system from a three-standalone system into a unified system. The study illustrates how the Business Analysis Core Concept Model, a conceptual framework for business analysis, would be employed to analyze the proposed augmented reality system and the current DUT e-learning model. Additionally, the use cases of the three standalone platforms that make up the current DUT E-learning are contrasted with a single augmented reality E-learning system.
A novel mechanism to detect Cooperate Data Leakages via Digital Watermarking

One of the most significant assets of an organization is its sensitive data. Such sensitive data is usually digitally controlled, which explains the exponential rise in security breaches in which data is tampered with or stolen. Seeking to solve this problem, a Digital Watermarking Prototype developed using a hybrid data leak detection mechanism is proposed that allows for an organization to watch over its information and detect in case of a Data leakage. The main objectives are to provide legitimacy, prove ownership of files and identify the individual who the leaked data. While there are other proposed ways to detect leaked data, they fail when it comes to characteristics such as robustness and effectiveness and when it comes to identifying the person behind the data leak. Keeping the product unknown to other parties is also a challenge many faces. Many proposed theories have failed to prove the effectiveness using a product or prototype as well. The effectiveness of the proposed Prototype will be shown using several attacks by the end of the research.
A Content-Based Collaborative Filtering Movie Recommendation System using Keywords Extractions

The main focus of this research is to develop a Content-Based Collaborative Filtering model that uses different automated keyword extraction techniques to recommend movies to a user. Recommender systems predict consumers’ preferences for products and provide proactive suggestions for items they would enjoy. Collaborative filtering, content-based, and hybrid recommendation models are the most common types of recommendation models. Collaborative filtering generates suggestions based on previous interactions between the user and the item, whereas the majority of content-based recommendations are based on item comparisons. The majority of hybrid recommender systems are made up of a mix of collaborative filtering and content-based recommender models. The Content-Based method was used as the main model in this study, with Term Frequency - Inverse Document Frequency (TF-IDF) and Rapid Automatic Keyword Extraction (RAKE) algorithms serving as keyword extractors. A total of 244 movies were recommended using keywords from each extractor, with the highest average of 33% of the movies recommended from each being identical. Taking comparable movies into account, we can propose them to a user.
Comparing Spatio-Temporal Models for Aggregate PV Power Nowcasting

The photovoltaic (PV) power fluctuations caused by passing clouds have become a major concern for grid operators. Consequently, utilities are requiring proper treatments to limit the intermittent PV generation. On this point, solar nowcasting provides a remedy by enabling the transition from reactive control to proactive, which often offers remarkable reliability to PV systems. Sensor networks that utilize spatio-temporal models are considered promising for solar nowcasting. However, current studies on sensor network nowcasting have dedicated much to point nowcasts, where the geographic smoothing effect that occurs in aggregate PV systems is generally left out. In this context, this paper presents a comparing study on spatio-temporal models for aggregate PV power nowcasting. Through empirical studies, the forecast skill of spatio-temporal models is found to decrease for a larger PV aggregation. In addition, the spatio-temporal regression shows competitive performance in various scenarios, yielding a priority for practical use.
Myeloma, Melanoma, Lung, Breast, Colon and Brain Cancer Detection Using Deep Learning in Web Based Application

Cancer is a terminal disease that is caused by the assemblage of abnormal cells in the human body. Various pathological changes, and at times inherited problems cause such malignancies-the frequent being melanoma, myeloma, colon, lung, and breast cancers. There are several ways for identifying these cancers, however the process is very expensive and require experts. This project aims to create a web-based tool to detect these multiple malignancies. The project was split into two sections. In the first section, a detection model was created for each of the cancers. Then employed pre-trained convolutional neural network models to identify cancers using picture input. In the second part, these models were deployed on a website for detection. This website will help patients and doctors worldwide to diagnose these diseases without any advanced technology, which is very costly. We have merged all the cancer dataset and made a hybrid dataset for detection of all the cancer with only one model. The trained model gave 93% accuracy (3 classes with below 80% accuracy) on the test dataset without K-Fold and 93% accuracy (no individual class accuracy less than 80%) with K-Fold Cross Validation.