Reviewerly/exHarmony huggingface dataset:
Voice Assistance and Big Data Financial Management Based on High-Resolution Imaging Algorithm

With the combination of information technology and economic fields, the amount of data has been greatly increased, and big data has begun to be valued by modern enterprises. As a new IT technology, it has had a huge impact on enterprise management, financial and management models, and business processes. Big data will surely become the basis of enterprise competition and management, and the use of information will have a decisive impact on the operating efficiency of enterprises. Big data sets put forward new requirements for corporate financial management. This article is the research goal of voice assistance and big data financial management based on high-resolution imaging algorithms. This paper establishes the specific process of the speech recognition model and high-resolution imaging algorithm based on the genetic algorithm of big data, and compares the experimental data of this paper with the data obtained from the reference literature and the Internet. Big data puts forward new requirements for financial management. It integrates high-resolution imaging algorithms and voice assistance into financial management based on big data, and studies the academic value and practical application value of financial management based on big data. Combined with actual data practice, it proves the feasibility and practicability of the research direction of this article. According to the experimental research in this article, the voice assistance and big data financial management based on the high-resolution imaging algorithm proposed in this article, adding voice assistance to the financial management can make the financial management run better, and the customers can obtain better data. The changes to the management staff can get management errors in a more timely manner, so that they can be modified in a more timely manner. In the use of genetic algorithms based on big data to optimize speech acquisition and recognition, experimental data shows that the highest recognition rate of optimized speech assistance is 98% close to 100%.
Class Semantic Enhancement Network for Semantic Segmentation

Existing semantic segmentation methods favor class semantic consistency by extracting long-range contextual features through multi-scale and attention strategies. These methods ignore the relations between feature channels and classes, which are essential to represent consistent class semantics. To this end, we propose the Class Semantic Channel Graph Module (CSCG), which captures inter-dependencies among channels, further strengthening the channel-class relation. Meanwhile, the Class Prior Fully Convolution (CPFC) is introduced to utilize the above relations as class priors to refine the segmentation results. Based on the above modules, we build a Class Semantic Enhancement Network (CSENet) in a coarse-to-fine manner. Extensive experiments have demonstrated that our proposed CSENet is able to learnÂ discriminative feature representations and achieves state-of-the-art performance on three famous semantic segmentation datasets including PASCAL Context, ADE20K and COCO Stuff.
Book Genre Classification Based on Reviews of Portuguese-Language Literature

Automatic book genre classification is a hard task as it requires the whole book’s content or a high-quality summary, which is challenging to write automatically. On the other hand, online reviews are an accessible resource for readers to evaluate a book or even get a general sense about it, including its genre. As the amount of book reviews is always increasing, using such information to genre classification needs a robust solution to deal with high volumes of data. In such a context, we introduce a model for automatically classifying book genres by analyzing online text reviews. We build a dataset of compiled texts from online book reviews. Then, we use multiple machine learning algorithms to categorize a book into a specific genre. Such a process enables to compare algorithms and detect the best classifiers. Hence, the most efficient machine learning algorithm completed the task with an accuracy of 96%; i.e., the proposed model is convenient for various information retrieval systems due to its high certainty and efficiency.
An optimized novel public cloud system to secure the medical record from third parties

Abstract Nowadays, modern technologies are growing rapidly by advancing their application day by day. Moreover, the Internet of Things (IoT) is well utilized in many applications for sensing and information gathering purposes. Considering this, medical applications have used the advances of IoT facilities in sensing the disease rate in humans. After sensing, the sensed data is stored in the cloud system, so securing the cloud system is the more required task to maintain the privacy of the patient's data. But, in front of malicious activities, securing the data is not easy. So, the present research has proposed a novel African buffalo‐based International Data Encryption (ABIDE) public cloud system to secure the medical database during the data transmission. The developed technique is utilized in a public cloud system. The main objective of this research is to secure medical data during transmission. Moreover, the implementation of the proposed ABIDE algorithm is done in the MATLAB environment. Consequently, the performance of the developed technique is compared with existing methods in terms of resource usage, encryption time, decryption time and so forth. Therefore, the proposed model has attained the finest confidential rate of 98.98%, throughput 97.86% and less processing time of 4 ms.
A Ceramic Artwork Retrieval System via HSH-SIFT Feature

This paper studies the problem of resource location and retrieval of ceramic artworks in the ceramic VR applications, which solves the key technologies such as the retrieval system architecture, feature library design, the 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rd</sup> party Resource Retrieval Service Interface, and the UI generator in the ceramic artwork retrieval systems. Achievements described have been applied to the image retrieval system of ceramic artworks in this research successfully, and the retrieval system was applied in the ceramic artwork VR system "CArtSo".
The Use of Virtual Reality as an Advertising Tool for Brand Experience in Peru

Recently, the advertising industry has implemented new methods to improve the relationship between brands and their consumers. To understand how to achieve this effect in a positive way, several studies have turned to the examination of advertising actions aimed at boosting brand experience. However, there are few studies that deepen the knowledge of the importance of creating brand experiences through innovative communication technologies such as virtual reality (VR). The present research explores how the use of VR in advertising helps in the generation of brand experiences. A qualitative methodology of phenomenological design was used with advertising professionals in the city of Lima. In addition, the exploratory scope of the analysis followed a descriptive coding process. It was evidenced that the properties of VR, such as immersion, presence, and its sensory capabilities are crucial to promote brand experiences with greater intensity. This category of virtual brand experiences influences brand image, affects purchase intentions and, above all, builds brand loyalty.
A Brief Analysis on Teaching Chinese as a Foreign Language with the Assistance of New Media Technology under the New Crown Epidemic

Since the outbreak of the new crown, the Chinese as a foreign language education industry has been greatly affected, a large number of overseas Confucius Institutes have been suspended from classes, and domestic Chinese volunteers can not be sent out. The epidemic situation makes the development of Confucius Institutes and Chinese international education major face great challenges. In the face of such a dilemma, Confucius Institute launched the call to stop classes, break through the traditional teaching mode, and move the Chinese as a foreign language classroom from offline to online, thus giving birth to a new mode of education. Chinese as a foreign language teaching under the new mode thanks to the rapid development of the Internet industry, the emergence of the “Internet +” era makes the application of new media technology in teaching Chinese as a foreign language become a reality. With the rapid development of network information technology and the application of 5 G technology, new media, as a new means of content dissemination, has changed the teaching method and teaching mode of Chinese as a foreign language. Focusing on the development of teaching Chinese as a foreign language under the condition of Newcastle disease, this paper analyzes the application of new media technology in teaching Chinese as a foreign language from the mode of “Internet +” and the new challenges and opportunities brought to the Chinese international education industry.
Using Relational Graph Convolutional Networks to Assign Fashion Communities to Users

Community detection is a well-studied problem in machine learning and recommendation systems literature. In this paper, we study a novel variant of this problem where we assign predefined fashion communities to users in an Ecommerce ecosystem for downstream tasks. We model our problem as a link prediction task in knowledge graphs with multiple types of edges and multiple types of nodes depicting the intricate Ecommerce ecosystems. We employ Relational Graph Convolutional Networks (R-GCN) on top of this knowledge graph to determine whether a user should be assigned to a given community or not. We conduct empirical experiments on two real-world datasets from a leading fashion retailer. Our experiments demonstrate that the proposed graph-based approach performs significantly better than the non-graph-based baseline, indicating that higher order methods like GCN can improve the task of community assignment for fashion and Ecommerce users.
Visualizing Human Interactions in a Workspace Setting and Maintaining Privacy

To better understand how humans interact in office setting, it is necessary to provide sufficiently understandable tooling for interaction analysis. At the same time there is a requirement to maintain the balance between privacy and utility. Using interaction measurement framework developed by the MIT, we utilized network analysis for determining an approximation of location, voice activity detection (VAD) for determining discussions, and spatio-temporal analysis (STA) for creating a visualization. We aimed to provide tooling for evasive analysis of workplace interactions, which would be intuitive enough to be understandable by non-technically oriented actors. Here we detail implementation details and decision reasoning.
Linux Storage IO Important Parameters Filtering Model Based on Random Forest

There are many key configuration parameters in the Linux storage IO module that affect the performance of the server system. The parameters will affect each other in a complicated way, and it becomes challenging to adjust the parameters to obtain high performance manually. The emergence of an automatic tuning framework alleviates this problem, but there are many parameters in storage IO, and the automatic tuning framework is usually based on machine learning algorithms. It is not efficient to explore such a vast parameter space. It is necessary to repeat the work after replacing the system. We believe that evaluating the utility of the parameters in the storage IO and pre-filter important parameters in advance for automatic tuning is necessary. The important parameters filtering method based on the random forest algorithm to filter the storage IO parameters shows that only the important parameters tuning can almost achieve the high performance obtained by all parameter tuning. In addition, the operating system is close to the hardware. Compared with the parameter optimization of the application layer program, the optimization of the operating system storage IO parameters can fundamentally maximize the IO performance of the server.
Software Defect Evaluation Methods for Embedded Real-Time Control System Based on Transfer Learning

In order to reduce the impact of environmental changes on software defect evaluation, an embedded real-time control system software defect evaluation method based on transfer learning is proposed. Firstly, the defect software measurement index is selected. On this basis, the relevant index features are divided into the same cluster by using the feature clustering technology. According to the distribution similarity of the features between the two items, the relevant features are found and the features with large distribution differences are removed. Select high-quality features from the source project to build the training data set, and select better evaluation data from the source project through weight adjustment to realize the accurate evaluation of software defects. The mapping results have clear objectives, and the error rate of the evaluation results of the design method is within 8.7, which has a good evaluation effect.
Image Style Transfer with Feature Extraction Algorithm using Deep Learning

Image style transfer, as one of significant image editing tasks, uses existing artistic works for the purpose of image recreation. This imaging technology can integrate well a given stylistic image into the given content image, so as to create new artworks with certain characteristics. In recent years, the emergence of deep learning neural network, especially the convolutional neural network (CNN), has greatly accelerated the development of image style transfer algorithm, making it one of the most widely used in artworks creation, font style transfer, movie special effects rendering and mobile device photo rendering. This paper introduces a new algorithm based on the concept of deep learning in achieving image style transfer by merging content image and style image to obtain the stylized image.
Benefits of Interactive Augmented Reality in Education

Globally, the Fourth Industrial Revolution is a means of pushing boundaries, providing support for development and research in every aspect of life, and education is one of them. The augmented reality learning system is an effective key to an overall understanding of the lectured content, as it allows you to interactively enter, view, enlarge or otherwise process the displayed data. The interaction evokes increased attention and a naturally growing curiosity to explore the details, which the participant learns and at the same time has the experience of the depicted holograms. Reality as such cannot in fact be spread, only its perception. The possibility of achieving an extended space of digital illusions depends on several factors, the basic ones being the observation phase and the image reconstruction phase. The space in which it is to be displayed also plays an important role in creating augmented reality. The most used AR display methods include QR code display, which is enabled by all display devices with a built-in camera and HDM devices such as HoloLens. In education, both have their strong representation, which results in innumerable advantages but also certain disadvantages. Interactive Augmented Reality should also be applicable as supporting technology for UAV path visualization in various scenarios.
A Blind Signature Scheme Based on Bilinear Pairings

Bilinear pairings have been used to design protocols such as identity-based encryption, one-round three-party key agreement, and aggregate signatures. Several authors have proposed many secret sharing schemes using elliptic curves and bilinear pairings. In 2004, Lee introduced bilinear self-pairing. Blind signature schemes have been the most significant obligation since the beginning of electronic money. In this article, we propose a blind signature scheme based on elliptic curves and a slightly modified Lee’s self-pairing. We provide an explicit example for the illustration purpose. We also show that the signature scheme avoids well-known attacks by M. Tian, Y. Zhu, and Z. Chen related to the blind signature scheme proposed by K. Chakraborty and J. Mehta. The security aspects of the proposed blind signature scheme have been discussed in this article.
A Method for Fault Diagnosis of Equipment of Train Control System

The train control system is the key technical equipment to ensure the safe and efficient operation of high-speed railroads. The safety of the train control system equipment is the premise to ensure the transport efficiency and quality as well as the safety of train traffic. Timely detection and correct identification of the train control system equipment failure is the most important work. Traditional fault diagnosis needs to rely on manual daily inspection to discover hidden faults or find fault premises. The manual inspection cannot find the fault in time, and there are video fatigue and missed inspection in manual inspection. In order to improve the timeliness of fault detection, method of diagnosis and identification of equipment faults in railroad train control center based on extreme Learning Machined (ELM) optimized by dragonfly algorithm (DA) is presented, which can automatically analyze and locate faults. The system can correctly locate the key indicators of equipment faults, automatically fuse color features and texture features as fault features. The DA is used to optimize the initial input weights and biases in ELM, which effectively improves the correct rate of diagnosis with a correct rate of 85.71% and provides an idea for the diagnosis of critical faults in railroad train control center equipment.
Study on the Personalized Recommendation Technology of Meteorological Network Course Resources∗

This study collects, summarizes and analyzes a large number of user data on the Meteorological Distance Education Platform of the CMA Training Centre of China Meteorological Administration. According to the data of user attribute information, course resource information and user learning behavior of the Meteorological Distance Education Platform, this study proposes a personalized recommendation algorithm for meteorological network courses, and realizes the personalized course resource recommendation function of the Meteorological Distance Education Platform. Through the realization of this function, learners can understand their own learning status, find out and fill vacancies in time, and choose personalized learning paths, thereby ultimately improving the training effect of meteorological network courses.
Incremental Vocabularies in Machine Translation Through Aligned Embedding Projections

The vocabulary of a neural machine translation (NMT) model is often one of its most critical components since it defines the numerical inputs that the model will receive. Because of this, replacing or modifying a model's vocabulary usually involves re-training the model to adjust its weights to the new embeddings. In this work, we study the properties that pre-trained embeddings must have in order to use them to extend the vocabulary of pre-trained NMT models in a zero-shot fashion. Our work shows that extending vocabularies for pre-trained NMT models to perform zero-shot translation is possible, but this requires the use of aligned, high-quality embeddings adapted to the model's domain.
Statistical Visualization of Big Data Through Hadoop Streaming in RStudio

Data Visualization enables visual representation of the data set for interpretation of data in a meaningful manner from human perspective. The Statistical visualization calls for various tools, algorithms and techniques that can support and render graphical modeling. This chapter shall explore on the detailed features R and RStudio. The combination of Hadoop and R for the Big Data Analytics and its data visualization shall be demonstrated through appropriate code snippets. The integration perspective of R and Hadoop is explained in detail with the help of a utility called Hadoop streaming jar. The various R packages and their integration with Hadoop operations in the R environment are explained through suitable examples. The process of data streaming is provided using different readers of Hadoop streaming package. A case based statistical project is considered in which the data set is visualized after dual execution using the Hadoop MapReduce and R script.
Telemetry Parameter Prediction of Spacecraft Power System Based on Concept Drift

In view of the characteristics of the dynamic distribution of space big data with super long time series, the traditional time series model is difficult to effectively approximate or completely realize the state prediction and trend analysis of telemetry data covering the whole period. In this paper, the concept drift is combined with the autoregressive theory in time series analysis, and the hidden rules in telemetry data are mined from the perspective of multi-level and multi time scale, and a new solution to telemetry sequence prediction is proposed. This paper designs the prediction algorithm of telemetry sequence based on concept drift. By detecting the concept drift point of telemetry sequence, and on this basis, the multivariable autoregressive model is used to predict the future concept and the duration of telemetry sequence, so as to realize the prediction of telemetry sequence. The experimental results show that the proposed method has more accurate prediction effect.
A Game Theoretic Approach for Quality Assurance in Software Systems Using Antifragility-Based Learning Hooks

The use of software in mission critical applications poses greater quality needs. Quality assurance activities are aimed at ensuring such quality requirements of the software system. Antifragility is a property of software that increases its quality as a result of errors, faults, and attacks. Such antifragile software systems proactively accepts the errors and learns from these errors and relies on test-driven development methodology. In this article, an innovative approach is proposed which uses a fault injection methodology to perform the task of quality assurance. Such a fault injection mechanism makes the software antifragile and it gets better with the increase in the intensity of such errors up to a point. A software quality game is designed as a two-player game model with stressor and backer entities. The stressor is an error model which injects errors into the software system. The software system acts as a backer, and tries to recover from the errors. The backer uses a cheating mechanism by implementing software Learning Hooks (SLH) which learn from the injected errors. This makes the software antifragile and leads to improvement of the code. Moreover, the SLH uses a Q-Learning reinforcement algorithm with a hybrid reward function to learn from the incoming defects. The game is played for a maximum of K errors. This approach is introduced to incorporate the anti-fragility aspects into the software system within the existing framework of object-oriented development. The game is run at the end of every increment during the construction of object-oriented systems. A detailed report of the injected errors and the actions taken is output at the end of each increment so that necessary actions are incorporated into the actual software during the next iteration. This ensures at the end of all the iterations, the software is immune to majority of the so-called Black Swans. The experiment is conducted with an open source Java sample and the results are studied selected two categories of evaluation parameters. The defect related performance parameters considered are the defect density, defect distribution over different iterations, and number of hooks inserted. These parameters show much reduction in adopting the proposed approach. The quality parameters such as abstraction, inheritance, and coupling are studied for various iterations and this approach ensures considerable increases in these parameters.