Reviewerly/exHarmony huggingface dataset:
Two-dimensional animation interactive demonstration teaching system based on multimedia technology---artificial intelligence and information technology

With the development and innovation of computer technology and the heavy research of media, A multimedia technology that can interact with computers in real-time information has appeared. It is the technology of industrial life and education. On the other hand, this promotes the extent of computer applications and combines multimedia technologies in education. This is new. The development of the world is a way of finding new factors and increasing learning. An animation technology as a product plays an incomplete role in the production of the multimedia cursors. As we all know, the multimedia materials developed by Flash software are known for their exquisite two-dimensional animations. However, it is more difficult to implement interactive two-dimensional molecular model demonstrations in Flash. Combining the current animation technology and database technology, this article has conducted an in-depth study on it, and designed a set of interactive multimedia teaching system based on two-dimensional animation technology. This article first introduces the basic principles of animation technology and the implementation of interactive information platforms. On this basis, the system's hierarchical structure and development process are analyzed, and the main work is divided into two key parts. On the one hand, it uses animation technology to design and t make vivid, feasible and interactive teaching courseware, on the other hand, to build an interactive information management teaching platform combined with database principles.
Introduction

Due to the increase in the availability of low cost and open source image handling softwares such as Photo-shop, Paint Shop, Photo-scape, Photo-plus, GIMP and Pixelmator, manipulation of digital images has become easier and a common practice. Using these powerful tools, it has become absolutely unfeasible to visually identify whether a given image is original or a manipulated version. Generating forged images by modifying the original image data is called digital image forgery. Generally, image forensics can be classified into two main categories: active and passive. Active forensic, for example, watermarking, has been used for copyright protection of digital media while other watermarking techniques, such as fragile and semi-fragile watermarks, have been used to detect and localize alteration to the media content. On the other hand, passive forensic tools are used to authenticate and verify the digital content and also detect any tampering applied to the media. The analysis is, therefore, performed on a blind basis.
Experimental analysis of automated negotiation agents in modeling Gaussian bidders

Automated negotiating agents are usually designed and implemented in a general way so that they can negotiate successfully in front of a vast variety of opponents. In the real world, most opponents are single-peaked. Gaussian agents that use such distribution function to rate the negotiation items are important sorts of such opponents. Modeling the opponents is of great importance since it enables us to adjust our next decisions accordingly. This can bring us short-time compromises, ideal eventual utility, more satisfaction, and so on. In negotiating with Gaussian opponents, the estimation of the opponent's peak point is the core. In this regard, we have paid particular attention to how accurate the existing automated agents attended in Automated Negotiating Agents Competition (ANAC) during 2010–2019 can model Gaussian bidders and showed the result of the experiments.
From Opinion Mining to Improvement Mining : Understanding Product Improvements from User Reviews

A valuable trove of information exists for product(s) or services online via user opinions like detailed reviews provided by customers on popular e-commerce websites. Users express their individual opinions in the form of overall product/service experiences, which may include explicit positive/negative feedback, preferences, concerns, and suggestions for the future. Such information can be valuable to product/service owners in helping them understand the improvement(s) that must be made to a particular product or service. The primary focus of opinion mining has been on understanding positive and negative aspects within the review effectively. Limited emphasis has been placed on finer topics like user suggestions or conflicting information from users. In this work, we describe a method to extract possible product / service improvements from opinionated text in the form of non-conflicting negative feedback, user tips, recommendations, product usage details, feature suggestions, and specific complaints.
Privacy-preserving Machine Learning Based on Homomorphic Conjugate Search Problem

With the development of computer technology and the rise of artificial intelligence, machine learning has achieved major breakthroughs in many fields. However, the accuracy of machine learning is usually proportional to the amount of training data, so a large amount of data needs to be collected. In the context of big data, both users and service providers have to bear the threaten of privacy disclosure since the attackers may abuse their data for illegal profits. On the other hand, it is necessary to prevent the leakage of model parameters in consideration of intellectual property protection. Therefore, it is imperative to solve privacy preservation for both data providers and trainers in machine learning. Without crippling the accuracy of machine learning, this paper proposed a neural network privacy training scheme in virtue of an efficient homomorphic encryption scheme to protect the privacy of data. The proposed scheme also realizes one-way security based on the Conjugate Search Problem (CSP). Specifically, our scheme uses ciphertext to participate in the operations for both machine training and data classification, which is capable of realizing homomorphic comparison without decryption, thus ensuring data privacy. The analysis illustrated that our method is provided with good portability in machine learning systems thanks to its efficiency and complete homomorphic operations.
Faiad: Feature Adaptive-Based Image Anomaly Detection

Image abnormality detection is a hot research topic in the field of data mining, and it has great application value in the fields of industrial appearance defect detection and medical image analysis. To address the problem of poor performance of anomaly detection models caused by incomplete feature extraction, we propose a feature-adaptive image anomaly detection model. FAIAD first trains the initial feature extraction model by pre-training the model. Then introduce feature adaptation methods to improve image feature extraction performance. The last step is to calculate the accuracy of image anomaly detection. In order to explore the feature extraction effects of different neural networks, this paper designs three kinds of backbone network comparison experiments. Experimenting on both Cifar-10 and Fashion-MNIST datasets, the accuracy of our model improved by 3.5\% and 2.3\%, respectively, compared to the baseline model. The experimental results show that combining pre-trained models with feature adaptation methods can effectively improve the performance of anomaly detection models.
Classically boosted network embeddings

Abstract Network embeddings are a popular and effective pre-processing step when performing machine learning with network data. We demonstrate that standard boosting techniques, AdaBoost and Real AdaBoost can be applied to network embedding techniques to increase performance, particularly in terms of link prediction on test data in a cross-validation context. These approaches produce results competitive with other state-of-the-art embedding approaches when applied to a number of empirical networks. Additionally, we show on simulated data that Real AdaBoost can de-aggregate some networks, wherein networks created by two independent latent features can have those separate latent features inferred by different boosted rounds. Further analysis of the performance of these boosted methods shows that they retain the characteristic robustness to over-fitting as boosting methods in classical settings.
Sentiment Analysis of YouTube Video Comments with the Topic of Starlink Mission Using Long Short Term Memory

The launch of the Starlink Satellite by a private company from the United States (SpaceX) has become a hot topic for conversation. SpaceX is a project that aims to provide satellite-based internet services worldwide with high performance and affordable prices even in remote places. The satellite launch video was shared on the YouTube platform. Many comments were on the satellite launch video, and various reactions were written on the video's comments column. Therefore, in this study, sentiment analysis will be carried out to analyze the responses of internet users through comments on YouTube to the launch of the Starlink satellite using deep learning. The YouTube comment data used in this study amounted to 22,000 comments. The model used is Long Short Term Memory (LSTM). This study will produce an accuracy value of the LSTM model by applying different activation and optimization functions. This study shows that the highest accuracy is 86% using the LSTM model, Softmax activation function, and Adam's optimization.
Culture and Multitude of Learning Models: Using Digital Educational Resources in Solving Applied Problems

An essential factor of high-quality mathematical training of students is their properly organized independent work. Its contents must consider multiple factors: Basic units of mathematical disciplines; the existing body of mathematics available to students; metasubject relations between the courses of general and special disciplines; and requirements for graduate papers and projects. Due to continuously decreasing contact academic hours allocated to study the higher mathematics units, teachers have only time to consider just very basic, key aspects of theory and practice. Therefore, students have to reinforce and perform further in-depth studies of the subject independently. It appears necessary to introduce the elements of scientific research activities in the learning process as early as possible in this context. Thus, for example, upon having introduced basic terms, we can introduce solving real practical problems into the learning process. Smart mobility management, solving logistic problems of traffic, optimization of transportation networks, queueing theory problems, automation matters, and performance evaluation of accomplished work are all problems that are very topical for modern cities and rural areas. This helps enhance self-discipline, expand creative potential, learn teamworking, organize communications with fellow students, adequately forecast and estimate the results of their activities, and correct the focus of their independent work.
A Multi-scale Binarized Neural Network Application Based on All Programmable System on Chip

Binary neural networks (BNNs) are particularly well-suited for low-power embedded devices with limited computational capabilities. Due to the binary weight parameters, it significantly reduces memory footprint and arithmetic logic unit operations. Nevertheless, one of the disadvantages of BNN is low accuracy and sharp optimization space. Several studies of BNNs have recently shown improved accuracy in various tests via more operations and more complicated topologies. This approach, however, is incompatible with the embedded BNN application since it requires complicated data type translation. Hence, We propose a novel approach for the BNN application on the embedded system with multi-scale neural network topology in this research from two optimization perspectives: hardware structure and BNN topology, which preserves more low-level information during the feed-forward process with few operations. Our network topology achieves 91.3% accuracy for the CIFAR-10 dataset, one of the highest recorded by BNN and can process 537 tiny pictures per second when deployed on an All programmable System on Chip (APSoc) device with 4.4W power consumption.
Face Mask Detection And Alert System Using Convolution Neural Networks

The present situation of the COVID-19 pandemic called for very strict safety norms to contain the virus. One of these mainly included wearing a mask at all places outside one’s home. However, many do not take that seriously. This paper deals with an approach to detect a face mask on an individual’s face, which if violated can be used in an alert system, and the like. The algorithm used to train the machine is convolution neural networks (CNN) for face(image) recognition in real-time. The results and inferences obtained are with respect to the model structure, algorithm, and variation in the dataset. The final model had an accuracy of 96.18% and was stable in real-time with no false-negative results. The final application alerts the concerned if the mask is not worn properly and captures an image to be sent to the concerned.
Research on Feature Extraction Technology of Japanese Corpus Resources Based on Rule Matching

Abstract Existing methods of relation extraction can be divided into pattern matching-based methods, dictionary-driven methods and machine learning-based methods, among which machine learning-based methods are the mainstream methods of relation extraction at present. Based on the analysis of Snort’s new features and existing rule matching methods, this paper effectively combines Snort algorithm with Japanese language features, and corrects keyword deviation caused by word frequency dependence by introducing multiple eigenvalues such as position factor and part-of-speech factor. Experimental results show that the introduction of part-of-speech factor and position factor is simple and efficient, and can effectively improve the extraction effect of Japanese keywords, which is suitable for short article keywords.
Sentiment Analysis of Customers Review Using Hybrid Approach

Sentiment analysis is utilized for expressing consumer materials such as internet and social media, examination and survey responses, and health material for applications from marketing to customers and medical services. Applications of sentiment analysis to examine Amazon goods are considered. The application of the hybrid technique Naïve Bayes, KNN, and LSTM mechanism are involved in work. Naïve Bayes offered a categorization solution. The data collection would be educated to deliver a greater precision solution utilizing the LSTM-based model. In order to do sentimental analysis, data set customer evaluation was considered. Present work is intended to alleviate the problems faced by earlier research during the analysis of sentiment. The simulation work has integrated a classification mechanism into the LSTM learning model in order to develop a sentiment analysis mechanism to review the Amazon product. The simulation results have concluded that the proposed work is capable to provide a more accurate & reliable solution. Moreover the comparison of three cases has been made for performance analysis where traditional work, naïve integrated approach and proposed work are considered.
Multiple projection Markov chain Monte Carlo algorithms on submanifolds

Abstract We propose new Markov chain Monte Carlo (MCMC) algorithms to sample probability distributions on submanifolds, which generalize previous methods by allowing the use of set-valued maps in the proposal step of the MCMC algorithms. The motivation for this generalization is that the numerical solvers used to project proposed moves to the submanifold of interest may find several solutions. We show that the new algorithms indeed sample the target probability measure correctly, thanks to some carefully enforced reversibility property. We demonstrate the interest of the new MCMC algorithms on illustrative numerical examples.
Toward Resilience Methods in Cloud Computing

Advances in cloud computing create feasible and cost-effective solutions while improving the enterprise systems' computational resources and analysis results [1] . The global cloud storage market is worth around $30 billion in 2020, and the public cloud market will reach $ 338 billion in 2021 cloud-based apps are expanding their number has nearly tripled between 2013 and 2016, from 545 to 1427 different services [2] . However, the diverse cloud service architecture and complex distributed components will make these systems susceptible to failure and faults. Therefore, Fault Tolerance (FT) is one of the critical approaches for cloud computing to improve the reliability of cloud systems. These FT techniques will improve both the efficiency and availability of cloud applications. There are three main categories for FT: reactive, proactive, and resiliency methods. Reactive methods attempt to get the device back up when the system reaches a defect condition. When a system failure occurs, the system is configured to start from a safe and active checkpoint. Proactive approaches introduce actions to prevent defect status in the system and minimize the impact of the defect. For example, the FT system monitors computing resources like CPU and RAM to take the appropriate step to avoid failure. Lastly, resiliency methods attempt to diminish the time the cloud system takes to find a failure, such as Byzantine Fault Tolerant (BFT) systems to withstand attacks and failures.
Data Security And Recovery Approach Using Elliptic Curve Cryptography

The transmission of various facilities and services via the network is known as cloud computing. They involve data storage, data centers, networks, internet, and software applications, among other systems and features. Cryptography is a technique in which plain text is converted into cipher-text to preserve information security. It basically consists of encryption and decryption. The level of safety is determined by the category of encryption and decryption technique employed. The key plays an important part in the encryption method. If the key is leaked, anyone can intrude into the data and there is no use of this encryption. When the data is lost and the server fails to deliver it to the user, then it is to be recovered from any of the backup server using a recovery technique. The main objective is to develop an advanced method to increase the scope for data protection in cloud. Elliptic Curve Cryptography is a relatively new approach in the area of cryptography. The degree of security provides higher as compared to other Cryptographic techniques. The raw data and it’s accompanying as CII characters are combined and sent into the Elliptic Curve Cryptography as a source. This method eliminates the need for the transmitter and recipient to have a similar search database. Finally, a plain text is converted into cipher-text using Elliptic Curve Cryptography. The results are oat aimed by implementing a C program for Elliptic Curve Cryptography. Encryption, decryption and recovery using suitable algorithms are done.
Dynamic Target Tracking and Geospatial Transformation in Place-Based on DNN

Video surveillance is critical for public safety. In open spaces such as urban places, how to conduct an intelligent analysis of surveillance video to obtain the dynamic target trajectory in the place is valuable for monitoring the behavior and motion situation of the dynamic target. According to the latest progress in the field of Deep Neural Network (DNN) and object detection and tracking, this paper aims to develop the method for dynamic Target Tracking and geographical transformation in the place. The detection model, YOLOv3 is used to extract dynamic target features, and dynamic target tracking is performed based on the DeepSORT method. The trajectories of the target are generated in the video frames and transformed into geographic space using the homography transformation method to visualize and analyze it based on GIS. As the experimental shows, the integrated DeepSORT and YOLOv3 models can quickly detect and track dynamic targets, and map target trajectories to geographic space, which could provide essential support for target trajectory analysis and motion situation awareness based on geospatial information in video surveillance.
Design of Intelligent Extraction System of Chemical Characteristics of Natural Drugs Based on Big Data

Considering the problems of low bit error rate, throughput and long response time in the traditional system of extracting natural drug chemical features, an intelligent extraction system of natural drug chemical features based on big data is designed in this paper. In the big data environment, the hardware design of the system is realized by designing the natural drug chemical feature extraction driving unit, user behavior management module, natural drug chemical feature extraction encoder and natural drug chemical feature extraction module. Then, by detecting the outliers of natural drug chemical features, mining natural drug chemical features and designing a natural drug chemical feature extraction algorithm, the software design of the system is completed. To date, the intelligent extraction of chemical characteristics of natural drugs has been realized. The test results show that the designed system can not only improve the bit error rate and throughput but also shorten the response time and achieve better performance.
An Ensemble Learning Approach of Multi-Model for Classifying House Damage

In the past decades, natural disasters have caused an immeasurable impact on the development of human society. The original way to measure the damage is to detect and quantify the number of damaged buildings, usually by driving around the affected area and noting down manually, which takes a lot of resources. Therefore, damage assessment is vital to the relief helpers and first responders so that resources and help can be planned and allocated appropriately. To tackle this issue, an ensemble learning model for classifying house damage is proposed in this paper. Firstly, to facilitate the processing of the model, a set of satellite images before and after the destruction of houses is preprocessed using data augmentation. Secondly, the basic models are first trained and used to predict test sets. Their running parameters were recorded to demonstrate their performances. Thirdly, using Bootstrapping to generate multiple sub-datasets and Bagging is adopted to ensemble the basic classifiers. Finally, the ensemble learning model is used to predict the test set, and the final result is determined by voting strategy to achieve the optimization effect. The experimental results on the public dataset show that the proposed model achieves an ideal effect on classification. Furthermore, by comparing the results of the basic classifiers with that of the model using the Bagging strategy, we find that Bagging is significantly helpful to improve the model effect.
Use of Embedding Spaces for Transferring Robot Skills in Diverse Sensor Settings

We propose a method for learning transferable skills among various state-action spaces for reinforcement learning using embedding spaces. Most existing approaches for robot skill transfer assume similar hardware settings between robots. However, in practical applications, identical hardware settings among different robots is not necessarily guaranteed. This study is an attempt to abstract and represent the characteristics of state-action space caused by differences in hardware, such as sensor settings and actuators, using an embedding space. Using this method, the skills become transferable between different hardware settings without requiring heuristic tuning by experts. In this study, we implement the proposed method on a mobile robot in a two-dimensional plane and demonstrate its effectiveness by performing a navigation task in a simulation environment.