Reviewerly/exHarmony huggingface dataset:
Web scraping with excel

Web scraping is a process that is particularly important in fields such as Business Intelligence in the modern age. Web scrapping is a technology that allow us to extract structured data from text such as HTML. Web scrapping is extremely useful in situations where doesisn’t provided in readable format such as JSON or XML. The use of web scrapping to gather data allows us to gather prices in near real time from retail store sites and provide further details, web scrapping can also be used to gather intelligence of illicit businesses such as drug marketplaces in the darknet to provide law enforcement and researchers valuable data such as drug prices and varieties that would be unavailable with conventional methods. It has been found that using a web scraping program would yield data that is far more thorough, accurate, and consistent than manual entry Web scraping with Excel is the process of automatically extracting data from websites and importing it into Excel for analysis and reporting purposes. Excel's built-in web scraping tool, Power Query, allows users to connect to a website, select the data they want to extract, and transform the data into a usable format. However, it's important to be aware of the potential legal and ethical issues surrounding web scraping, and to use discretion when extracting data from websites KEYWORDS Scraping, Excel, Data, structure.
How authors evaluate the novelty of their articles: A comparative analysis of highlights in research articles

Due to the growing capacities of research articles and journals, the tasks of selecting interesting articles and quickly identifying interesting sections of articles have become primary challenges faced by researchers. Therefore, the notion of Highlights, a novel introductory section included in academic publications, has been proposed to directly emphasise the novelty and value of research articles to improve article retrieval and knowledge dissemination. In this article, we developed a classification schema featuring five categories to analyse the content and explore the features of sentences contained in the Highlights sections of articles. Subsequently, we conducted an experiment by using the fields of Library and Information Science (LIS) and Computer Science (CS) as examples to statistically analyse domain differences in the arrangement of Highlights sections. The experiment focused on both the sentence level and the article level and emphasised differences in research paradigms and principles of evaluation. In particular, we discovered that LIS is relatively ‘result-heavy’, while CS is ‘method-heavy’; furthermore, in self-evaluated contributions, LIS authors concentrated more on academic contributions and applications, while CS authors preferred to demonstrate the value of their articles by comparing their research with previous studies.
The Development of Tracking Systems in Mobile Application for Trail Run

Currently, mobile applications are an important medium, particularly for their use in assisting various activities, including extreme ones.For instance, mobile applications can assist with activities requiring GPS tracker functions, emergency calls, and health monitoring during the execution of activities.However, every mobile application that is created must undergo a development process that adheres to stringent standards.User Interface (UI) and User Experience (UX) testing is also essential for the efficiency and usability of an application.Using a tracking system based on the Design thinking development model, this study focuses on the creation of mobile applications.Next, the application's mobile usability test is tested and set.
Cryptanalysis and improvement of Q. Peng et al.’s PVSS scheme

A publicly verifiable secret sharing (PVSS) scheme based on the m-multilinear Diffie-Hellman hard assumption was proposed by Q. Peng et al., where the public values published by a dealer are verifiable before reconstructing a secret. In addition, the scheme has the advantages that the shares’ cheating/modification by some dishonest participants and dealer’s malicious behavior in shares’ generation and/or distribution can be detected. In this work, we cryptanalyze the scheme and find that a well-known attack called man-in-the-middle (MITM) is possible if the users’ secret shares are modified because they remain undetected and, as a result, a modified common secret is reconstructed by a group of any 1 < t ≤ n participants. We address this issue and propose some modifications of the PVSS scheme with (m+1) multilinear operations and two additional public values such that the MITM attack is infeasible and the scheme becomes secure. In addition, the proposed improvement is made adeptly without hampering performance, i.e., the computation cost has been marginally increased in our proposed improvement.
Prediction of behavior of a noncommunicative robot based on observations of its movement

Abstract An adversary robot can move around in a free space “patrolling an area” appearing to have a random “behavior” if observed through a “lens.” The “behavior” of the robot is associated with its observed/internal state. Stochastic techniques or machine learning techniques, which can be used to predict the future states of an adversary robot demand high computation and thus, power. Advanced RNN techniques like long short‐term memory, which can make predictions using long‐term dependencies of the sequence data, also have extremely high computing and memory requirements. Thus, the systems involved in using these former techniques are not appropriate to be deployed in fields which require inconspicuous monitoring. In this work, we present two novel methods using the “behavior” to predict the future states of an adversary robot which can be deployed using a low computing device/small single board computer/robot in a non‐GPS environment without any internet connection. The proposed two methods are based on a list and a graph, respectively. Unlike the most available literature, where the prediction is based on “time,” our model predicts the future state using the states of the system. These methods work for both single and multiple‐looped paths taken by the adversary robot. The results show that the prediction error which depends on the predicted move and the actual move taken by the robot decreases as a logarithmic function and is comparatively lower when the adversary robot follows a path having multiple loops.
DFS: Joint data formatting and sparsification for efficient communication in Distributed Machine Learning

Efficient communication is crucial to Distributed Machine Learning (DML). In this work, we propose an approach jointing Data Formatting and Sparsification (DFS) to optimize the communication in DML systems based on the parameter server framework. By doing so, we can reduce the time to transmit (aggregated) gradients between the parameter server and workers, and consequently the time to complete training jobs. More specifically, in DFS, every worker first tries to derive as many blocks with all-zero gradients as possible via sparsification, and transmits gradients block by block in a streaming fashion. By skipping blocks with all-zero gradients, we can reduce the communication cost for gradient transmission. Different from previous works on optimizing the communication in DML systems, DFS has three distinct features: (i). it dynamically determines the gradient block size; (ii). it takes into consideration both the data transfer from workers to the parameter server and that from the parameter server to workers; and (iii). it jointly optimizes the data formatting and sparsification. In other words, it performs sparsification in the way that helps form more (or larger) all-zero blocks and save more communication cost. By implementing DFS on a real testbed, we find that it can reduce the time to train a ResNet-18 model by 74.12%. Through extensive simulations, we demonstrate that DFS outperforms the state-of-the-art technique, i.e., OmniReduce (Fei et al., 2021), by up to 87.17% in terms of reducing communication cost in DML systems.
Yapay Zeka Tabanlı Türkçe Dİl İşleme, Tanı Öneri Sistemi Projesi

MD-Advisor is the abbreviation of “medical doctor – advisor” which is an artificial intelligence-based recommendation system in healthcare. Moreover, the health-based recommender system is a decision-making tool that makes recommendations for appropriate healthcare information to patients and clinicians. MD-Advisor project was developed in order to speed up the procedures that doctors follow when diagnosing patients and to present all possible conditions to the doctor in a short time. With this project, the processes of diagnosing the patient and then recommending the examination are completed very quickly. Thus, the patient is directly transferred to the treatment phase. Based on the data obtained from patient complaints which indicates the current health status of the patient; data preprocessing, labeling and deep learning modeling techniques are used. The diagnostic codes used as labels for the diagnosis recommendation were obtained as output from the Recurrent Neural Networks model. As a result of the study, the diagnosis proposal for the patient's complaints was successfully predicted with the applied recurrent neural networks (RNN) model approach.
Crypto-Influencers

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Multi-Object Tracking based on RGB-D Sensors

The accuracy of the multi-object tracking (MOT) based on the 2D camera without depth info is usually poor. In this paper, we propose a MOT method based on sensors composed of the camera and the ultra-wide band (UWB) radar, which are similar to the depth camera (RGB-D camera). First, we establish a backbone network to extract feature maps from video frames captured by a camera. Then, we combine Faster R-CNN with a re-ID branch to detect objects including the category, coordinate and ID. To track objects, we construct a similarity matrix to calculate the data association between the objects and their historical trajectories. The matrix's elements are calculated by the intersection over union (IoU) between the objects and their related two types of trajectories, which are based on the image data and the UWB localization data separately. Finally, the trajectories are updated by the two types of trajectories, and the recognition network is updated by the localization loss. The experimental results show that our method achieves multi-object recognition and tracking, and outperforms previous methods by a large margin on several public datasets.
Using Discretization and Numerical Methods of Problem 1D-3D-1D Model for Blood Vessel Walls with Navier-Stokes

This article describes a three-dimensional model of fluid flow a mathematical model of the circulatory system for the cardiovascular system and provides a basic framework for the mathematical representation of cumulative medical parameters such as total vascular area, blood volume, self-regulation, and effects on the upper and inner heart. This article presents a mathematical model of the circulatory system for the cardiovascular system and is the basis for a mathematical view of aggregated medical parameters such as total vascular area, blood volume, self-regulation, and effects on the upper and inner state of the heart. Concepts are given. Linear dependence of mathematical concepts, differential, integral differential, as well as logical-dynamic equations, Navier-Stokes problems, and mathematical apparatus for their practical application are given and the principle of operation of the program based on this mathematical model has used UML diagrams which consist of results of Navier-Stokes. The program given is the numerical results of Navier-Stokes 2D and 3D integral and differential equations on the basic diagram U, V, W, and parameters. In mathematical terms, linear dependencies, differential, integral, and differential equations are used.
What is the State-Of-The-Art Contribution of the Higher Education System to the Digital Economy: A Systematic Mapping Study on Changes and Challenges

The rise of the economy's productivity depends on the number of educated employees since the efficiency of performing tasks is enhanced by skillful workers. This paper analyzes the relevance and state-of-the-art performance of educational services and programs for the higher education system's higher contribution to the digital economy. It reviews changes in three common aspects of education concerning digital economy challenges: funding mechanisms for education; changes in the duration of learning; changes in how and what we learn. The systematic mapping study aims to structure the most recent knowledge in higher education to contribute to the digital economy by identifying, classifying, and evaluating the current state of the art in the target research area. The research process followed common steps based on the guidelines of similar systematic mapping studies. A classified portfolio of key publications is recommended, each of which proposes its own sets of educational change solutions in reaction to digital economy challenges. The most commonly cited change in education relevant to the main challenge (integrated skills) in the digital economy is the STEAM movement, which is useful for educators and academic managers to know. Finally, we will show the future research agenda, weaknesses of the study, and observed trends.
Artificial Intelligence Software Architecture in the Field of Cardiology and Application in the Cardio Vessel Project Using CJM and Customer Development Methods

The article is devoted to the task of automating tasks for the Cardio Vessel project, on the basis of which a platform is designed—an independent model of object-oriented software architecture within the framework of a model-oriented approach for the Cardio Vessel project in UML and the CJM and customer development projects is proposed. The article formalizes the problem of UML class diagrams, sequence diagrams, and precedents and offers mathematical models based on the Navier—Stokes method for defining the function of structural semantics of UML class diagrams and descriptions of semantically equivalent transformations. Algorithms for transforming Star UML class diagrams and calculating object-oriented metrics are also proposed. The paper presents a UML software tool that allows analysis using UML class diagrams. In conclusion, the CJM and customer development methodology for UML class diagrams using the Star UML software tool have been developed.
How Blockchain and Artificial Intelligence influence Digital Sovereignty

Digital Sovereignty is an ascending field that is viewed from a society, politics and enterprise perspective. When considering the situation from an economic standpoint, it is still unclear how to understand the issue of what businesses can do to strengthen their Digital Sovereignty. Artificial Intelligence and Blockchain are disruptive technologies that have significant impact on Digital Transformation. This article studies the relationship between Digital Sovereignty and novel technologies, such as Artificial Intelligence and Blockchain. A quantitative survey with 163 respondents is the empirical basis of the analysis. We propose seven measures to strengthen Digital Sovereignty: preserve Data Sovereignty, address concerns and create awareness, define responsibilities, co-create transformation, expand expertise, promote freedom of choice and measurement criteria. The proposed measures support formulating a Digital Sovereignty strategy to ensure the vision, goals and requirements for balancing heteronomy and autarky in a self-determined manner.
Small object in the dark light scene using dual-branch channel and spatial

Object detection is an enabling technology of computer vision for locating instances and categorizing classes of objects in images or videos. It has made enormous strides with significant growing of deep learning research over the last decade. However, there are severe challenges on small object detection in complex scenes as they appear with low resolution and have not enough contrast from the background information. This disturbance may cause missed detection and detection accuracy decline for small objects. We propose a small object detection network in the dark light scene based on improved YOLOv5. The network takes YOLOv5 as the baseline and incorporates a channel and spatial dual-branch backbone module, which enhances the details by fusing the features of the two branches. We also introduce a densely linked feature fusion network before detection layers with receptive field block. The fusion network integrates deep features with shallow ones across different scales. A data augmentation module is used to enhance the brightness and limit the contrast for small object detection in the dark light scene. Experiments are carried out on the Dark Face and darkened Vis Drone dataset. The results show that the evaluation index of the proposed method is better than that of the comparison methods. From the detected images, it is obvious that the undetected frame of the small object in the dark light scene decreases and the detection accuracy improves. All of the results show that our model has better performance than some existing methods for small object detection in the dark light scene.
Research on Network Security Evaluation Model Based on AHP and BP Neural Network

The Internet's sharing and openness have made information interaction more vulnerable to security risks. As a result, a comprehensive evaluation of the security of computer network systems has become a more effective means of preventing various network security problems. In recent years, there have been many network security evaluation methods proposed to address this issue, but not all of them are effective. Therefore, this paper analyzes existing network security evaluation methods and proposes a new model based on BP neural network and AHP jointly. The proposed model combines the advantages of BP neural network and hierarchical analysis (AHP) to provide a comprehensive and accurate evaluation of network security. The BP neural network is used to evaluate the risk level of each security factor, while AHP is used to calculate the weights of each security factor. The weights reflect the relative importance of each factor in determining the overall security level of the network. To verify the applicability of the proposed model, empirical research is conducted. The results demonstrate that the model can effectively evaluate network security comprehensively. The model's accuracy and effectiveness make it a promising approach to evaluate the security of computer network systems. Additionally, it can help in developing strategies to enhance network security by identifying potential vulnerabilities and assessing the effectiveness of security measures implemented. In conclusion, the model provides a useful tool for organizations to manage network security effectively.
Network Autonomous Learning Monitoring System Based on SVM Algorithm

The network autonomous learning monitoring system is a subsystem of the learning quality monitoring system in the network education platform. Based on the training objectives of network education and the course learning objectives of learners, and on the basis of educational evaluation theory, it makes value judgments on learners' attitudes, knowledge and ability development level in the whole learning process. Through the planning, inspection, evaluation, feedback, control and adjustment of learners' learning activities, timely guide learners to correct their learning attitude, adjust their learning strategies, and effectively use learning resources and modern information technology means to carry out autonomous learning, so as to achieve the expected learning goals. The network self-learning monitoring system is based on the database created by SQL Server platform, supports C/S structure, has good scalability and usability, and is used to extract and analyze data. SVM algorithm is used to extract system features, which has the advantages of low system load, low response delay and good performance. An accurate network autonomous learning monitoring system model is constructed. After system test, the network autonomous learning monitoring system based on SVM algorithm has high data analysis ability, easy to understand, easy to maintain, reasonable structure and easy to use, which meets the needs of learners. Using SVM algorithm for feature extraction, the evaluation performance of the algorithm is improved by more than 3.2%. When learners learn in the system, the system load is small, the response delay is low, and the performance is good. It is an accurate network autonomous learning monitoring system.
Obtaining mathematical equations for energy, exergy and electrical efficiency: A machine learning approach

In this study, in which the energy, exergy, and electrical efficiency values of the photovoltaic thermal panel are modeled with different machine learning algorithms, mathematical equations that can calculate the efficiency values have been obtained as an innovative approach. Data sets consisting of environmental parameters (temperature, wind speed, solar radiation, humidity) of the environment in which the experiments were carried out were used in the models. Thus, the effects of environmental parameters on collector efficiency values were observed, and mathematical equations were produced using these parameters with the help of the decision tree algorithm and Pace regression. In addition, environ-economic analyzes of the panels were made and the coefficient of performance values were examined. In the experiments, two data sets were obtained. With one of these data sets, the efficiency values were modeled with machine learning algorithms, and the accuracy of the mathematical equations obtained with the other data set was proven. The mean absolute percentage error values of the energy, exergy and electrical efficiency models created with the decision tree are 8.04%, 1.76%, and 1.43%, respectively. Similarly, Pace model error values are 3.83%, 2.54%, and 2.1%. The high accuracy values of the obtained efficiency equations under different experimental conditions show that these equations can be used under different conditions and in different solar energy systems.
Omicron virus emotions understanding system based on deep learning architecture

Emotions understanding has acquired a significant interest in the last few years because it has introduced remarkable services in many aspects regarding public opinion mining and recognition in the field of marketing, seeking product reviews, reviews of movies, and healthcare issues based on sentiment understanding. This conducted research has utilized the issue of Omicron virus as a case study to implement a emotions analysis framework to explore the global attitude and sentiment toward Omicron variant as an expression of Positive feeling, Neutral, and Negative feeling. Because since December 2021. Omicron variant has gained obvious attention and wide discussions on social media platforms that revealed lots of fears and anxiety feeling, due to its rapid spreading and infection ability between humans that could exceed the Delta variant infection. Therefore, this paper proposes to develop a framework utilizes techniques of natural languages processing (NLP) in deep learning methods using neural network model of Bidirectional-Long-Short-Term-Memory (Bi-LSTM) and deep neural network (DNN) to achieve accurate results. This study utilizes textual data collected and pulled from the Twitter platform (users' tweets) for the time interval from 11-Dec.-2021 to 18-Dec.-2021. Consequently, the overall achieved accuracy for the developed model is 0.946%. The produced results from carrying out the proposed framework for sentiment understanding have recorded Negative sentiment at 42.3%, Positive sentiment at 35.8%, and Neutral sentiment at 21.9% of overall extracted tweets. The acquired accuracy using data of validation for the deployed model is 0.946%.