Reviewerly/exHarmony huggingface dataset:
Research on the Prediction of Highly Cited Papers Based on PCA-BPNN

With the increase in scientific research investment, the number of papers has increased significantly, and the evaluation of the impact of papers has received extensive attention from scholars. The citation frequency is the most convenient and widely used index to measure the academic influence of papers. Still, the citation frequency can only measure the real impact of papers some period of time after those have been published. Therefore, to be able to identify highly cited papers at the early stage of publication, this paper collects data on 1025 academic papers published under the library and information discipline of the Web of Science library in 2007 and then extracts 24 predictive characteristics from three aspects: papers, authors, and journals. On this basis, 7 principal component vectors are constructed by feature screening based on PCA. Also, combined with the BP neural network model, the PCA-BPNN highly-cited paper classification prediction model is constructed and finally compared with the other 5 models. The results show that the PCA-BPNN model built in this paper has better prediction performance and provides an effective model for the prediction of paper influence.
A Computer Vision Assisted Yoga Trainer for a Naive Performer by Using Human Joint Detection

It is a well-known proverb that a healthy mind lives in a healthy body, and yoga is one such means for connecting the body to the mind. However, yoga should be performed under professional supervision and in a regulated manner, as it can be harmful to one's health if done incorrectly. Moreover, it is difficult for beginners to identify the incorrect portions of their yoga postures on their own. In this research article, we present a user-friendly python-flask based web application that assists its registered users to perform every pose accurately. We have used computer vision techniques as it can perform various visual data frame related operations in real time. Our method consists of two main components: a hand gesture component that records video using hand gestures and a pose estimation component that detects body joint coordinates. The system then compares the angles obtained from the instructor's pose and the users for feedback generation and provides correction if the difference is larger than a certain threshold. With this inherent capability of pose feedback generation, the proposed system thus enables the naive performers to evaluate their poses and correct it when it deviates from the correct pose sequence. The method was evaluated in real-time on people of varied age groups and gender for four different asanas, and it was proven that it recognizes incorrect portions of the performed asanas for all the test cases. The experimental findings in terms of feedback generated using the user videos gave a functional validation of the proposed procedure and its usability in modern day human life.
FUELING GLOCAL: OPTIMIZATION-BASED PATH PLANNING FOR INDOOR UAVS IN AN AUTONOMOUS EXPLORATION FRAMEWORK

Abstract. Exploration is a fundamental problem in robotics that requires robots to navigate through unknown environments to autonomously gather information about their surroundings while executing collision-free paths. In this paper, we propose a method for producing smooth paths during the exploration process in indoor environments using UAVs to improve battery efficiency and enhance the quality of pose estimation. The developed framework is built by merging two approaches that represent the state of the art in the field of autonomous exploration with UAVs. The overall exploration logic is given by GLocal, a paper that introduces an hybrid, i.e. both sampling-based and frontier-based, framework that is able to cope with the issue of odometry drift when exploring indoor environments due to the absence of absolute localization, e.g. through GNSS. The second paper is FUEL, which introduces a frontier-based exploration methodology which computes the drone’s path as an optimized non-uniform B-Spline. The framework described in this paper borrows the optimized B-Spline trajectory generation from FUEL and implements it in GLocal. The presented system is evaluated in two different simulated environments, which show the pros and the cons of such method.
Extended Reality for Health Care Systems: Recent Advances in Contemporary Research

Having worked with patient simulator systems, and at this point having used an oculus system to explore various virtual reality (VR) recordings for several months, this reviewer thought the above title might be worth reading and reviewing for the readers of IEEE Pulse. Per the publisher’s website advertising, the readership of this text might be “Extended reality technologists and engineers, students in biomedical engineering and extended reality, Instrumentation and measurement technologist and engineers, Computer Scientist; Health care professionals with an interest in using extended reality in surgery.” From the preface, we are promised an “elaborate introduction to extended reality,” a discussion of “future research, potential impacts, and benefits and benefits of XR adoption in health care” (XR is here defined as extended reality), as well as cases and case studies relating to the technology. The text comprises 15 chapters contributed by 34 individuals, two of whom are among the four named editors. A brief review of the chapters <xref ref-type="fn" rid="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</xref> follows.
Impact of Technological, Organizational, Product, and Process Innovation on Employee Turnover Intention

The study aimed at investigating the predictive relationship between innovation and employee turnover intention in the telecom sector of Pakistan; particularly Mobilink Microfinance Bank, Khushali Bank, U-Bank&amp; Telenor Microfinance Bank. The impact of technological, organizational, product, and process innovation was measured on employee turnover intention by using the adapted version of The Denison Organizational Culture Survey (1990). The sample comprised 150 employees with the inclusion criteria of only those working at the Microfinance Banks. A purposive sampling technique was used and the survey was conducted in person as well as on Google Docs. The method of regression analysis was used to analyze the data. The results suggested the predictive role of innovation in decreasing employee turnover intention.
The God of Alexander Zinoviev

The novel “Go to Golgotha” by A.A. Zinoviev occupies a special place in the literary heritage of the thinker. It is not just a “sociological” novel written in the genre invented by the author himself, but also a novel in the traditional – psychological genre. For a philosopher and logician, an extremely rational and fundamentally atheistic person to create such a novel about the spiritual world of a man is an extraordinary choice. And a novel where the theme of God is touched upon may be viewed as exceptional. Even though Zinoviev called himself a “believing atheist”. Nevertheless, this paradoxical novel fully complies with the general context of Zinoviev’s thought that allowed the existence of mutually exclusive assertions. For example, such as: “God exists” and “God does not exist”. The paradox consists in the fact that logic can penetrate into the most irrational states of human soul, – if only it’s real logic. And “Go to Golgotha” novel serves as brilliant proof to this theory.
The Logic Doctrine of A.A. Zinoviev

The article is devoted to the analysis of the main provisions of A.A. Zinoviev’s logic doctrine. The author shows Zinoviev’s evolution from the traditional view of the formal logic as knowledge of the laws and regulations of correct thinking to the understanding of it as the science that deals with the aspects of natural language that make it the instrument of thinking and comprehension. According to Zinoviev, the logic singles out and abstracts in language phenomena certain structural components that form the structure of knowledge irrespective of whether this knowledge pertains to the sphere of physics, biology, history, sociology etc. The laws and regulations prescribed by it are not discovered by people, but are invented by them in the process of cognition methods perfecting. The author characterizes Zinoviev’s innovative contribution to the development of non-standard logics, and above all, multivalued logic, succession theory, logic principles of the natural and social science methodology, as well as to the creation of complex logic. The author shows the actual importance of Zinoviev’s logic and methodological legacy for the development of scientifi c perception and for the resistance to the phenomenon that the thinker referred to as the threatof mass stupefaction.
Designing Of Efficient Model for Facial Feature Identification and Classification Using a Sparse Fingerprint Algorithm

By leveraging the geometrical and textural data of the retrieved local characteristics to align the probing partial face to gallery faces throughout this process, we present a novel partial face identification technique. Our fundamental assumption is that the cost function of our alignment approach ought to be reduced if the probing partial face patch and the gallery face picture are using the exact same individual. The similarity between the partial probing patch also the gallery photos across the identified facial feature points is also computed using a point-set distance metric that we provide. The usefulness of the suggested technique is demonstrated by experimental findings on four popular face datasets. This method focuses on choosing localised features from facial detection photos to distinguish between classes depending upon regression results, or partial F-test. The results demonstrate that traditional procedures are more resilient in terms of appropriate feature selection and categorization. The most noticeable characteristics were chosen by introducing a reliable method termed stepwise linear discriminant analysis, which concentrates on choosing the localised features from the face frames and classifying them relied upon regression values. A feature extraction procedure's goal is to extract the localised characteristics from faces that the earlier feature extraction methods were unable to analyse.
Two major attacks on the Ethereum smart contract application layer

Ethereum is a decentralized open-source, public blockchain platform with smart contract functionality. As of December 2021, Ether is the second highest cryptocurrency in the world by market capitalization, after Bitcoin. Also, Ether is one of the most used blockchain platforms. As a second-generation blockchain platform, Ethernet supports a new computing paradigm - decentralized applications (DApps) running on top of the blockchain. While DApps bring rich and flexible application semantics and functions, they also inevitably bring more security risks and vulnerabilities. And Ethereum, as a blockchain that carries a large amount of value in virtual currency, has attracted a large number of unsuspecting attackers to target Ethereum security vulnerability attacks for profit. Among them, there have been numerous attacks against the application layer of Ether in the past few years. This article will review several serious attacks on Ethereum at the application level in the past few years and analyse the vulnerabilities exploited by these attacks, the methods of attack, and the improvements made by Ethereum after the attacks in order to provide some insight into the future security of virtual currencies and blockchains.
Ethereum cybersecurity mechanisms, issues and countermeasures

This paper entails the mechanisms of Ethereum (ETH) blockchain in term of security aspects. Some basic definitions will be covered to help to understand the structure of mechanisms that keep the blockchain extending and increasing blocks and transactions, such as PoW, PoS, mining. The hierarchical layer model of ETH and the structure of the blockchain itself will be discussed in detail at the beginning, and the corresponding theoretical model and structure diagram will be shown. Specific mechanisms that have and would be introduced in detail, including the beginning structure of ETH while constructed, the issues caused the development of mechanisms, how to make the transition from PoW to PoS, what the shape of the mechanism after the end of the transition and after the upgrade look like. Several key concepts that have been implemented into the Ethereum blockchain are detailed as well. These concepts are “extensions” within the blockchain to improve the security, automation capability and efficiency transactions and mining and Smart Contract will be the example that be introduced in this case. In addition, some features, vulnerabilities, and countermeasures regarding forking happening in the blockchain will also be discussed at the end of the paper. The forking will be explained in two main scenarios, and officially driven hard forking and a natural occurring soft forking.
Secrets behind virtual gold: basics of blockchain and cryptocurrencies

In recent years, blockchain and cryptocurrencies have become popular topics. The significant increase in the cryptocurrency market attracts more people devoted to these fields. Many people consider cryptocurrencies a revolutionary invitation or a path to wealth; some believe it is a colossal fraud. Although some research has been conducted, cryptocurrency's primary mechanism and concept are still confusing for ordinary people. Thus, in the research, we dig into blockchain technology and provide a lucid explanation of some fundamental mechanisms. The research has chosen four popular cryptocurrencies (Bitcoin, Litecoin, Dogecoin, and Ethereum) as examples, and some of their essential features will be introduced. Then, the article focuses on the consensus mechanisms and why people always talk about "mining" new coins. Finally, we analyze the usability of blockchain technology and the discussion about the future of cryptocurrencies.
GHOST protocol used in Ethereum compared with protocols used in bitcoin

Bitcoin's volatile price in recent years hasn't stopped it from attracting new users. As of July 2022, more than 83 million people around the world had registered their Bitcoin wallets with the Bitcoin system. That's about 9 million more users than in 2022; and about 43 million more than in 2019. The explosive growth of the number of users and transaction volume has brought about the problems of slow transactions and huge resource consumption. To address these issues, the younger Ethereum system offers a new set of mechanisms to provide solutions. such as Greedy Heaviest-Observed Sub-Tree (GHOST) protocol. However, since Ethereum has a higher throughput compared to Bitcoin, the longest rule is no longer suitable for the Ethereum system. As a result, one need a new smart protocol like GHOST.GHOST can be used to address some critical problems in the longest rule used in Bitcoin. This paper will mainly introduce the basic definition of GHOST, its advantages compared to the protocol used in Bitcoin, its disadvantages of GHOST, and the security analysis of GHOST.
Research on dense crowd counting method combining attention and hybrid dilated convolution

To solve the problems of background interference, noise and occlusion in crowd counting in complex scenes, a CA-HDC crowd counting method was proposed. The network consists of the front end and the back end. The front end selects the first 10 layers of the VGG-16 network. In the back end, the SE attention mechanism is fused with the hybrid cavity convolution layer that alternately uses the expansion rate. The head features are weighted by the attention mechanism, and the receptive field of the network is expanded by the hybrid cavity convolution to eliminate the influence of background interference and noise. Experiments are carried out on two public data sets. Compared with other crowd counting networks, the proposed network achieves better results.
A load statistics algorithm basing on statistical priority-based multiple access protocol

With the development of Unmanned Aerial Vehicle (UAV), UAV networks are beginning to exhibit more dynamic characteristics, and it plays an important role in multi-task coordination. MAC layer provides transmission quality assurance for the message transmission. In this paper, A period adaptive load statistics algorithm based on MAC layer traffic (PA-LSBM) is proposed for multi-task priority. Finally, we build a simulation model by Opnet Modeler. The experimental results show that the PA-LSBM has excellent performance in timeliness. Ensuring that the success rate of one transmission of the highest priority data is above 99%, End-to-End delay is less than 2ms.
A new approach to detecting and analyzing monkey activities using the improved YOLOv5

With the development of science and technology, video equipment has become popular in laboratories. Researchers using animals (e.g., macaques) as models often collect a large number of animal behavior videos for the research aims. Researchers often need to watch numerous videos to record and analyze animal movements and behaviors for subsequent analysis. Manual analysis of videos is time-consuming and inefficient. To resolve this problem, we developed an approach to detect macaques in video and then capture information about their location. Using this information, we can quickly analyze animal behaviors, such as movement preferences and behavior patterns. The method was developed based on the YOLOv5 algorithm and had excellent accuracy and robustness. In addition, this method can automatically process videos and generate statistical results and analysis charts. Furthermore, all settings are manually configurable and easy to use to meet the different requirements of researchers. We believe that this new approach will benefit many researchers.
A spam classification method based on NB and SVM

The dataset used in this project is derived from the SMS spam classification dataset in the UCI Dataset Repository, and it is necessary to understand what the dataset looks like before pre-processing the data. The first step is text clean-up, and the second step is text feature extraction. This paper investigates and compares the performance of classifiers combining different dimensionality reduction methods on spam datasets to provide a reference for related classification studies. The project then uses the scikit-learn machine learning library to train the classifier, dividing the dataset into 75% training sets and 25% test sets, and introducing classifiers such as NB, IR, SVM for training. After classifier training is complete, test the result of the model on the test set. Use trained classification models to predict the category of a message (regular mail or spam) The result shows that the best performer among the various classifiers is the SVM.
Pyramid input-based network for single image dehazing

Images taken in hazy weather are susceptible to the effects of haze, resulting in blurred images and low contrast to the extent that important information is lost in the image. Therefore, this is necessary to dehaze haze images, process the image information and ensure the normal operation of other computer vision tasks. Traditional deep learning-based image dehazing methods often suffer from uneven haze removal, colour bias and loss of detail. To solve this problem, this paper proposes a single image dehazing method (IMNet) based on pyramidal input for image dehazing. The network is divided into three modules: an intensive feature extraction module, a pyramid input branch and a detail deepening module. This paper uses two loss functions in combination, which can help preserve texture details more effectively. Experimental results have shown that IMNet outperforms other dehazing algorithms in terms of metrics and visual effects.
Grasping perception method of space manipulator for complex scene task

Aiming at the problems such as illumination variation, occlusion interference, complex background and diversity of objects in the grasping process of space manipulator, a grasping perception method of space manipulator which can be used for real-time calculation was proposed. A hybrid algorithm strategy of long-distance gray feature search, lightweight detection network classification and lightweight segmentation network was adopted to obtain the centroid/position/attitude. First, when the end is far away from the target, the simple gray distribution feature is used in the field of view to detect whether the target is there or not, so as to quickly judge the area where the target is concerned. Then, the target area is extracted, and the lightweight deep convolution detection and recognition network is used in the process of close movement to carry out continuous recognition and classification of the target, determine the key parts to be captured, determine whether the target is an effective target, and constantly adjust to ensure that the target stays in the middle of the camera. Finally, when a relatively short distance is reached, the lightweight segmentation convolutional network is used to conduct semantic segmentation of the target, and the shape binarization data of multiple angles are obtained. In this way, the position and attitude information of the target are obtained according to the binocular stereo vision, and the feedback control computer is used to complete the fetching task. Compared with previous designs, the identification accuracy of the proposed method is increased by 1.4%, and the calculation speed is reduced by 58%. The simulation results show the effectiveness and adaptability of the proposed method.
Killing Them Softly: Virtual Reality Training in Project Termination

&lt;p&gt;The use of virtual reality (VR) as an innovative learning tool in higher education has been rising steadily, as being actively engaged in a learning activity has repeatedly been shown to be beneficial for learning (Price et al. 2003). This major research project explores the potential use of VR for ‘soft skills’ training by addressing two main questions: 1) How can we train individuals for complex work environments without exposing vulnerable students to potentially harmful situations?; and 2) how can we create these environments with a role-player simulation? This research paper builds on how VR combined with digital storytelling can be used to build on communication skills training. It suggests that by creating a prototype for an impactful VR experience students can improve their communication skills, and demonstrate higher levels of goal completion required to successfully bring a project to fruition in the 21st-century creative workplace. Mirroring real-world engagements, users assume the role of the manager while working through these virtual challenges in three distinct steps; preparation, delivery, and transition. This novel research suggests that there is a way to effectively combine traditional role-playing techniques while adhering to the new digital standards.&lt;br&gt; &lt;/p&gt;