Reviewerly/exHarmony huggingface dataset:
Convroberta: Identification of Fake Reviews Using Sequential and Weighted Non-Sequential Features in Multimodal Fusion

Nowadays, e-commerce platforms have become a popular source for purchases. Most of the buyers read the reviews of the products available on these platforms before deciding to purchase from them. To influence buyers’ purchasing decisions, some unscrupulous merchants employ professional writers (fake reviewers) to write fake reviews. Therefore, it is an important area of research to detect fake reviews and reviewers. Existing studies mainly focused on linguistic or behavioural features at random to detect fake reviews and reviewers using traditional machine learning. So, they have not used the best combination of linguistic or behavioural features. Therefore, We propose the ConvRoBERTa model to detect fake reviews using an amalgamation of sequential and Non-sequential features. The ConvRoBERTa model uses CNN and RoBERTa for weighted non-sequential and sequential features, respectively and after concatenation of these features, fed into a classification algorithm to detect fake reviews. In this paper, the research results on identifying fake reviews have proved the effectiveness of the algorithm proposed and provided new research ideas and methods.
ChatGPT: Virtual Creative Model

We offer ChatGPT, an autoregressive language model that employs deep learning to generate sentences that resemble those written by humans, and assess them using the preceding distinction. We discuss the nature of ChatGPT based on a review of its output. Also, we reviewed some details about the different versions of ChatGPT. We show, based on the resulting ChatGPT outputs, which the recently released AI-enabled ChatGPT can be of great help to daily life. We showed the full benefit behind ChatGPT.
Evaluation of Semantic Relations Impact in Query Expansion-Based Retrieval Systems

Intelligent systems operating in different user contexts criticize the correct interpretation of the user-need. The most effective applications are in the fields of NLP and semantic expansion. Such techniques are useful for extrapolating the meaning of words contained in user questions. Classic NLP approaches employ different semantic relations without justifying one over another. The aim of this paper is to demonstrate which semantic relation impacts the most, identifying the best tradeoff between accuracy and noise introduction when combining such relations. This is achieved generating textual resources for tuning a classifier, combining different relations. The system can reformulate user questions as labels of a given taxonomy, making use of the generated resources. The proposed evaluation employs a wide and varied taxonomy as a use-case, exploiting its labels as basis for the expansions. To build the knowledge resources several corpora have been produced and integrated as gazetteers into the NLP infrastructure with the purpose of estimating the pseudo-queries corresponding to the taxonomy labels, considered as the possible intents.
The “Scholarly Map of Russia” as a Research Project: An Attempt of Systematization of Musicology

The published material contains a description of some of the approaches that formed the basis of the project “Scholarly Map of Russia”). This project has become one of the most important sections of the “Priority 2030” academic strategic leadership program, in which the Gnesins’ Russian Academy of Music is included as one of five higher educational institutions. The article contains an overview related to the experience of creating various electronic databases. The attempt to create a universal information resource “Scholarly Map of Russia”, initiated by the Ministry of Education and Science in 2013, is examined. The article analyzes some of the reasons that led to an unsuccessful completion of the project. This has made it possible to arrive at two important conclusions: the task should be observable, and it should be carried out not by automatic systems, but by specialists who are well-informed in a particular field of scholarship and able to keep the resource up to date. The article also presents a panorama of some of the musicological experiments that have been consistently undertaken throughout the 20th and 21st centuries. These include the creation of musical dictionaries, indexes and encyclopedias in the 1920s and 1930s; the development of the Scholarly bibliographic index “Music”; bibliographic publications of individual musicologists – both from the Soviet period and contemporary; the formation of the regional information resource “The Musical Culture of Siberia”, etc. It is noted that Russian musicology is concentrated around conservatory research centers and, accordingly, the geographical factor plays a key role in its formation. This is the reason for the concept of “map”, which in the title of the project is not metaphorical, but literal.
Research on financial sharing framework system based on blockchanin technilogy

The utilization of blockchain technology, which features a one-of-a-kind distributed architecture model, has substantially raised the bar for the reliability and traceability of information. Additionally, this technology shares many conceptual and practical parallels with the financial sharing system This topic utilizes blockchain as the underlying data technology platform, adhering to the prerequisites of decentralization, intelligence, intensification, and adaptability. The paper proposes a five-level architecture system for a financial sharing center, comprised of business activities, data collection, consensus contract, decentralized network architecture, and a sharing center. The framework system discusses the implementation and optimization of key technologies such as dynamic verification of consensus mechanisms, intelligent integration of standardized rules, and efficiency expansion.
VulMiningBGS: Detection of overflow vulnerabilities based on graph similarity

The increasing number of software vulnerabilities pose serious security attacks and lead to system compromise, information leakage or denial of service. It is a challenge to further improve the vulnerability detection technique. Nowadays most applications are implemented using C/C++. In this paper we focus on the detection of overflow vulnerabilities in C/C++ source code. A novel scheme named VulMiningBGS (Vulnerability Mining Based on Graph Similarity) is proposed. We convert the source code into Top N-Weighted Range Sum Feature Graph (TN-WRSFG), and graph similarity comparisons based on source code level can be effectively carried on to detect possible vulnerabilities. Three categories of vulnerabilities in the Juliet test suite are used, i.e., CWE121, CWE122 and CWE190, with four indicators for performance evaluation (precision, recall, accuracy and F1_score). Experimental results show that our scheme outperforms the traditional methods, and is effective in the overflow vulnerability detection for C/C++ source code.
Community epidemic prevention statistical equipment based on stm32

With the development of globalization and urbanization, the population is constantly moving and gathering. China is facing more and more health threats. A series of major epidemic diseases, major food safety, and other public health emergencies have occurred around. In 2003, China responded to a major public health emergency event - the SARS epidemic for the first time. From this, the government realized that public health events greatly threatened the health of the people, related to economic and social development, and even affected the political stability of the country. Living under the epidemic situation, the work of epidemic prevention has a long way to go <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</sup> . Epidemic prevention in densely populated communities has greatly increased the burden of staff on physical and mental health and safety. This paper designs a statistical device for epidemic prevention in the community that can automatically identify people entering the community, measure temperature and record, and displays the status on the PC in real-time. The whole design system uses an STM32 chip as the microcontroller to drive different functional modules under different communication protocols, such as the display of human thermal imaging, the collection of temperature dot matrix, the storage of data, the connection of wireless communication, and the real-time detection on the upper computer. Finally, the community epidemic prevention statistical equipment based on STM32 is realized, which effectively improves the epidemic prevention efficiency.
Image Generation Model Based on Ls-Capsgan Network

In the last few years, generative adversarial networks(GANs) based on the concept of game theory have become one of the key research in the field of image generation. Nevertheless, the CNN network architecture in GANs cannot effectively utilize spatial information, which is meaningful for the classification problem. The capsule network proposed in 2017 can solve the problems of CNN to a certain extent, which replaces individual neurons in a neural network with a vector of neurons that are wrapped together and form a capsule and can encode several feature attributes and spatial information of an image. Consequently, the capsule network is combined with GANs, the least squares loss is used to optimize and strengthen the network, and the LS-CapsGAN image generation model is proposed, which makes the training safer and the generated image quality higher. In this paper, the advantages of LS-CapsGAN in image data distribution modeling on MNIST, Fashion-MNIST, and CIFAR-10 datasets are expounded through three groups of comparative experiments, which verify that LS-CapsGAN has a certain potential in the field of image generation.
A Lightweight Network for Small Object in UAV Images

Small objects and model lightweighting are inherent challenges in Unmanned Aerial Vehicles (UAVs) image detection. To address these challenges, we propose an improved YOLOV4 network called Lightweight YOLOV4(L- YOLOV4). Specifically, we first combine part of the MobileNetV3 backbone network with CSPDarkNet53 to form a new backbone network called Mobile-CSPDarkNet53. Mobile-CSPDarkNet53 network can effectively reduce the amount of network computation and improve detection efficiency without any significant degradation in detection accuracy. Secondly, we use the Mixed Pooling Module (MPM) structure to capture long-range dependent features while improving the network perception field. Finally, we incorporate a residual structure in PANet, which allows more valid information to be retained and reduces object misses or false detections. Our full experiments on the aerial vehicle dataset CARPK show that the number of L-YOLOV4 network parameters is only 40.1% of that of YOLOv4, but the small object detection accuracy (AP) is improved by 11.6% and the speed is increased by 4.6 FPS. The experimental results show that our proposed model can effectively improve the detection accuracy and speed of detecting small targets.
Research on pedestrian target trajectory detection of mobile robot in a complex scene

In the complex road environment, given the decline in accuracy and poor real-time performance of pedestrian detection and recognition under the conditions of illumination intensity and occlusion, this paper proposes a multi-target walking tracking method using YOLOV5 and deep sort, which combines the two modules of walking identification and standard walking tracking under different conditions, and improves the Kalman filter on this basis, to realize the function of walking trajectory prediction. First, the YOLOV5 method is used to mark the pedestrians in the video through the recorded video number, pedestrian number, and other location data, to obtain the correct location of all pedestrians in the video; Then, pedestrian tracking is realized by the deep sort method through information.
Design of Monocular Distance Measurement System based on Face Recognition

Combined with the practical needs of daily life, the article designs a monocular distance measurement system based on face recognition. The system first uses the camera to capture video images, thresholds the face part of the image through image processing, trains a strong classifier based on the improved Adaboost algorithm and cascades it to achieve face detection and obtains the width information of the face image, determines the coordinate relationship of the target position relative to the camera, and uses the geometric similarity principle of small-aperture imaging according to the construction of a distance measurement model that meets the demand. The distance between the target person and the camera is calculated using the pixel information of the face image and the actual face width relationship, and finally, the obtained detection information is presented through the GUI visualization interface. The experimental results show that the system has high accuracy and good stability, and has good feasibility and application prospects.
Research on the incentive mechanism of Energy IoT based on DAG blockchain

In the era of Internet of everything, Energy Internet of Things(IoT), as a typical application of Internet of things technology, has been widely concerned and studied. At the same time, blockchain technology and Energy IoT can be coordinated and complementary. Considering that the Energy IoT is usually characterized by diversification and high transaction demand, discussing the impact of the energy IoT environment on the performance of blockchain consensus algorithm and ensuring the stability of blockchain in the Energy IoT environment has become a problem worth studying. Therefore, this paper proposes an incentive mechanism based on Stackelberg game model to simulate the trade behavior between blockchain users and miners. Among them, followers of blockchain users submit transactions, their demand is lower response latency, miners are leaders, pay computing and storage resources to verify the user's transactions. By solving the game model between blockchain users and miners, the goal of reducing user response time and maximizing mining benefits of miners is achieved, and the influence of wireless communication environment on the game process is considered. The simulation results show that the proposed scheme can effectively ensure the benefits of blockchain users and miners, and improve the security and stability of the blockchain-based Energy IoT system.
Convolutional neural networks news classification based on gating mechanisms

Aiming at the problem that a single convolutional neural network often easily ignores the semantic information of the context. A convolutional neural network based on a gating mechanism (gated Linear Unit Convolutional Neural Networks) is proposed and applied for news classification. GLU-CNN model introduces a gating mechanism after the convolution layer and controls the feature information after the convolutional layer through the gating mechanism, which can reduce the gradient dispersion and also retain the nonlinear ability of the model. The experimental results show that compared with traditional convolutional neural networks, the number of classifications of different news types has increased in the same news dataset. The gating mechanism of convolutional neural networks has a certain improvement in classification effect. The model's accuracy in news classification reached 95.97%.
Understanding Blockchain

One of the hottest technologies of late is blockchain. But what exactly is a blockchain? And how does it actually work? In this chapter, you will explore the concept of blockchain, how the concept was conceived, and what problems it aims to solve. By the end of this chapter, the idea and motivation behind blockchain will be crystal clear.
A Smart Video Streaming Web Portal

Mining data is a technique for extracting required data from a massive knowledge base. Nowadays, multimedia data is increasing exponentially and is widely utilized in many major applications like medicine, security and surveillance, entertainment, education, and sports. Nowadays, content-related video retrieval is extremely necessary. The prevailing data processing techniques can’t be directly applied to multimedia, i.e., video. The proposed system utilizes data mining techniques for getting content-based videos from huge datasets. The videos uploaded are classified based on categories by the admin, and the videos are automatically migrated to the cloud server. To save the users’ time, the video’s content is given in the form of a small summary or snippet to the users. By looking at the summary, they will find out the relevance of the video that they want. Though it is provided in YouTube videos nowadays, that content is not sufficient enough to reveal the video content, so we are designing a system where we can provide a good summary that reveals the video content and that saves the time of the user when searching for the particular video that they desire. Our system has also been designed to analyze the likes of videos. If the percentage of likes is below 30%, the system automatically deletes videos after 40 days. Due to this, the cloud server space will be optimally used. This is not provided on other video-sharing platforms. Registered users can also make sharing of various videos with other registered users without using the third-party platform.
Creating Your Tokens

If you have been following the previous chapters, you should have a pretty good understanding of Ethereum smart contracts and how to interact with them through the web3.js APIs.
A Deep Learning-Based Approach for Channel Estimation and Equalization for Cognitive Radio Systems

Cognitive radio systems have emerged as a pivotal enabler in 5G and upcoming 6G systems. Future generation wireless networks in order to improve upon the quality of service (QoS) parameters need to avail the channel state information. Off late, machine learning-based approaches are being used to analyze the channel state information (CSI) owing to the fact that the CSI is generally extremely complex in nature for real-time critical applications. In this proposed approach, a cognitive-OFDM system is designed under Gaussian noise conditions. The channel sensing information is applied to a gradient descent-based deep neural network (DNN) which is used for channel estimation. The deep learning-based approach design is applied with 70% data for training, 15% for testing, and 15% for validation, respectively. The channel estimation information is utilized for frequency domain equalization and is performed with and without addition of pilot bits. The performance of the proposed gradient descent-based deep learning approach is evaluated in terms of the training epochs, time, mean square error, and gradient. The truncation of the estimation is governed by the stabilization of error during training or the elapse of maximum number of iterations which is 1000 in this case. The mean square error for channel estimation and the bit error rate (BER) have been chosen as the primary performance metrics. It has been shown that the proposed system outperforms existing systems in terms of BER and MSE.
Content Management Systems

A content management system (or CMS) is a software application that enables you to create, edit, and store digital content. In other words, a CMS is a piece of software that runs on a web server and allows you to easily create, edit, and manage content published on a website.
Introduction to HTML

In this chapter, we’ll take a look at the basics of an HTML document.
Cascading Style Sheets

Cascading Style Sheets (CSS) are used to define and customize the styles and layouts for your web pages. This means you can create style sheets to alter the design, layout, and responsiveness to different screen sizes on various devices from computers to smartphones.
Empirical analysis of the impact of country-level corporate governance on starting a business in Asian countries

The purpose of this study is to investigate the impact of corporate governance on starting a business in some Asian countries over a period from 2012 to 2017. Nine Asian countries on the basis of availability of data for the research period were selected for this study. Corporate governance is measured by the Worldwide Governance Indicators of the World Bank and starting a business is measured by the score of starting business reported by the Ease of Doing Business Report of the World Bank Group. The findings of the present study reveal that voice and accountability, political stability and absence of violence and regulatory quality have statistically significant effect on starting a business in the selected countries. While both political stability and absence of violence and regulatory quality are found to have statistical significant positive influence on starting a business, voice and accountability exhibits a significant negative effect on starting a business. However, the results reported that both government effectiveness and control of corruption exhibit an insignificant negative effect on starting a business, rule of law is found to have an insignificant positive impact on starting a business.
A Comparative Study of the Advantages and Disadvantages of Using Authentic Materials and Created materials for English Language Teaching

This study investigates and contrasts the advantages and disadvantages of using generated materials (made by teachers) instead of natural resources for teaching English. This inquiry was conducted inside a library for the most part. The data for the study come from academic articles and books that discuss forged and genuine sources of information. According to the study's findings, one of the components of practical English as a second language instruction is using authentic or original content such as books, images, videos, and other media not produced to serve as educational tools are examples of natural resources. "Planned materials" refers to books and other goods developed mainly for classroom use. In the real world, lecturers and instructors use educational strategies such as adapting and adopting in two separate ways depending on the context. It is permissible to make alterations to and use as raw material any textbook, even those acquired from retail bookstores. Utilizing authentic content drawn from various sources written in natural language is another viable alternative. It is possible to blend these two kinds of resources in a language lesson to more effectively satisfy the needs of the students and cater to their interests. However, lecturers and teachers must weigh the advantages and disadvantages of inventors and substantial resources in their lessons (teacher-made materials). In order to better the quality of learning, instructors and students alike need access to a variety of different teaching tools. Without instructional resources, it will be difficult for instructors to improve their pupils' learning, and it will be difficult for students to keep up with the learning process in the classroom.