Reviewerly/exHarmony huggingface dataset:
Adaptation of Modern Agile Practices in Global Software Engineering

Software engineering has been an active working area for many decades. It evolved in a bi-folded manner. First research and subsequently development. Since the day of its inception, the massive number of variants and methods of software engineering were proposed. Primarily, these methods are designed to cater the time-varying need of modern approach. In this connection, the Global Software Engineering (GSE) is one of the growing trends in the modern software industry. At the same time, the employment of Agile development methodologies has also gained the significant attention in the literature. This has created a rationale to explore and adopt agile development methodology in GSE. It gained rigorous attention as an alternative to traditional software development methodologies. This paper has presented a comprehensive review on the adaptation of modern agile practices in GSE. In addition, the strength and limitation of each approach have been highlighted. Finally, the open area in the said domain is submitted as one of the deliverables of this work.
Network Anomaly Sequence Prediction Method Based on LSTM and Two-layer Window Features

Abstract To solve the over-fitting problem in the prediction algorithm caused by the small number of features that arise during the network anomaly prediction process, an LSTM algorithm for network anomaly predictions based on two-layer time window features was proposed. Firstly, the network alarm data sequence was divided according to the observation time window and prediction time window. Secondly, considering that the time series of the anomaly alarm data can be somewhat periodic, a time window sequence dataset was created with the periodic features and statistical features in the two-layer windows. Finally, one-shot and feedback models of the LSTM algorithm were employed to predict network anomalies. The experiment showed that the best prediction accuracy for this method is over 80% with both one-shot and feedback models, when the prediction time window is 12h.
Performance Metrices of Different Machine Learning Algorithms

E-mail is one of the most significant components or applications of the internet. Without e-mail, the internet would be useless. E-mail is a convenient and speedy method of communication. Email is a common way for people to exchange personal and professional information. Spam emails and non-spam emails are the two forms of emails. The purpose of spam email detection is to distinguish spam from valid e-mail. Spam is typically defined as uninvited or unwelcome email messages. The majority of spam emails contain viruses or other dangerous software that can cause computer and network malfunctions, use network bandwidth and storage space, and cause email servers to slow down. Because of the increase in the abundance of information, spam has become a big issue for internet users in recent years. Email phishing is detected and prevented using text mining techniques. Phishers trick people into providing personal information by sending fake emails and visiting fraudulent websites. Because of the increase in the volume of unwanted emails, there is an urgent need for the development of more dependable and robust email systems. Every day, email users receive hundreds of spam emails with unique content from anonymous addresses generated by robot software agents. This paperwork focusses to compare the accuracy, precision, recall, and F1 of the following algorithms: KNN, SVM. Nave bias and Random forest using emails as input.
Modern educational technologies as an innovative resource of a higher school teacher's activity

The relevance of the research is conditioned by the growing interest for the use of modern educational technologies in the educational process of higher education, due to the implementation of new federal state educational standards for higher education, as well as forced isolation due to the spread of coronavirus infection. The experience gained while using modern educational technologies needs to be generalized, systematized and extrapolated in order to increase the effectiveness of the educational process. The research problem is as follows: what modern educational technologies can be used in the professional activities of a university teacher as an innovative resource? The purpose of the research is to describe the experience of using modern educational technologies for the activities of a university teacher. The research objectives are to reveal the category of "modern educational technologies"; to present the experience of using modern educational technologies on the example of a digital service in the process of teaching the discipline "Physical Culture and Sports"; to identify the benefits of using a digital service in teaching physical education. The research methods used are generalizations, comparisons, visual presentation. The results of the study are the following: the relevance of the use of modern educational technologies as an innovative resource for the activity of a university teacher has been confirmed; the experience of using a digital service in the process of teaching the discipline "Physical culture and sports" has been described; the advantages of a digital service in teaching physical education as a means of resource and information support have been revealed. Key conclusions: modern educational technologies represent a set of technical human resources that act as a means of acquiring knowledge; relevant educational technology in modern conditions is a digital service; in teaching the discipline "Physical Culture and Sports" the electronic information environment of the university and the Zoom online platform can be used; the use of electronic educational technologies makes it possible to make the educational process more comfortable and effective without interrupting it in conditions of self-isolation.
Construction of power network operation and maintenance cost prediction model based on data information mining

It is very important to scientifically plan the asset operation and maintenance investment scale and formulate scientific and reasonable operation and maintenance capital investment strategies to meet the requirements of fine management development and deal with the complex and severe internal and external situation. At present, the reasonable scale of power grid asset operation and maintenance investment lacks certain, monitoring and evaluation means, and the traditional extensive asset operation and maintenance investment management mode has certain deficiencies. This paper is based on data information mining, combined with the current status of power grid operation and maintenance cost investment management and management characteristics, the system identifies total fixed assets, average operating life of equipment, equipment defect rate, equipment failure rate, power supply reliability rate, and electricity sales as key influences. Factors; then combined with the analysis of influencing factors, an intelligent prediction model based on neural network theory is constructed; finally, through empirical analysis, it is verified that the prediction error of the model is 10.35%.
Comparison of Various Classifiers for Movie Data

Sentimental analysis is a subset of opinion mining where we study the emotion/feeling/opinion of the sentence of the people about specific matters from structured,. In this, we try to analyze on IMDB movie review dataset. we inspect the sentiment manner to classify the polarity of the movie review on a range of 0 to 1 i.e. 0 beings (highly disliked) to 1(highly liked) by excluding the stop word. Since human opinion amplifies the coherence of a product, the overall performance of a movie depends on how people react to it which would be in the form of reviews. Due to the variety of sentiments available in the movie scripts we choose this as our dataset, the term "emotional charge'' is best suitable for the movies as they tend to emotionally charge a person’s mindset. Though we didn’t take a large dataset to define the purpose it best suited the agenda to compare a variety of algorithms.
Blockchain grounded electronic record for healthcare monitoring system

The introductory conception of using Blockchain database in order to manage and maintain electronic health records of the case used for covering the health and to give better services and to store and save the data pertaining to the patient health and to make it public to the sphere experts it's veritably important that the data has to be stored in a secured database, so that only the authorize druggies can have the access to the same. So, in order to safe guard the data and to maintain sequestration of the data it's always recommended to have a database which can not be shifted. So, the significance of blockchain is vital to store the data safely. Principally, blockchain is a distributed database where data is participated among the bumps in a network. This database stores information in the electronically digital form. This particular database is more popular to store data pertaining to cryptocurrency systems. Blockchain will help us in furnishing complete security of the records stored and will help in generating trust factor. Further over the blockchain also provides dedication of the data stored.
Building a Meta-agent for Human-Machine Dialogue in Machine Learning Systems

One of the main challenges in the practical application of machine learning is to explain its solutions to a human specialist. We propose an approach to explaining the local solution of a machine, based on comparing the case under study with the “nearby” case to it when the machine makes an alternative solution. We are looking at various possibilities for determining “nearby” to find these cases. Then we propose building a meta-agent that first analyzes the human expert’s solution using machine learning and then offers tools to help the expert analyze the machine learning solution. We present examples to illustrate our approach.
A Softened Similarity with Global Attention Method for Unsupervised Person Re-Identification

There are many factors that affect the accuracy of the algorithm in the field of unsupervised person re-identification. This article focuses on two of them. One is the hard quantization loss caused by the clustering algorithm, and the other is the interference problem caused by the noise information in the image background. In this paper, we propose a method to solve the above problems. Our method consists of two parts. One is to use a non-clustering algorithm that softens similarity to process tags to minimize the impact of hard quantization losses. The other is to add global relational attention to calculate the degree of association between each feature point and the whole world. In this way, the attention value is calculated, and the foreground and background of the image are separated from the space and the channel, so that the algorithm focuses on the foreground information and try to avoid the interference caused by the background noise information. We use market-1501 and Duke data sets to conduct experiments. The experimental results show that our algorithm is significantly better than other advanced algorithms.
Systemization of Vulnerability Information by Ontology for Impact Analysis

Vulnerabilities are reported in the vulnerability information database one after another, and these can be targets of cyber attacks. We analyzed 5 years of data published in a vulnerability information database and found that more than half of the registered products have multiple vulnerabilities and 47% of these products have at least one high-risk vulnerability. We also observed that some vulnerabilities were common to multiple products, and more than 65% of them were of high risk. To address such high-risk vulnerabilities without overlooking them, we systematized the vulnerability information using ontology and extracted multiple related vulnerability information from specific data using the relationships defined by the ontology. The result of the comparison with existing methods indicate that all the desired information can be obtained in one search, rather than searching for an average of six times.
Sequence image mosaic method in 3D reconstruction based on contour features

In this paper, a stitching technique based on contour features to improve the signal-to-noise ratio of color 3D reconstruction active sequence images is proposed. Firstly, the information adaptation of color 3D reconstruction active sequence image is enhanced, then the template feature of color 3D reconstruction active sequence image is extracted, and the spatial feature matching of color 3D reconstruction active sequence image is realized by using the color reconstruction regional background information enhancement algorithm. Then the information enhancement and filtering of color 3D reconstruction active sequence images are realized, and the quality is optimized to realize the high signal-to-noise ratio repair and stitching of color 3D reconstruction active sequence images. Experimental results show that this method can improve the signal-to-noise ratio and image reliability of color 3D reconstruction active sequence images.
Cluster Division in Wind Farm Based on DTW and KL-GMM

In order to improve the contour precision of large-scale wind farms and widen the adaptability of various working conditions, this paper proposed a two-stage framework which calculates the clustering indexes of generators inside the wind farm based on dynamic time warping (DTW) and divides generator groups on the basis of Gaussian Mixture Model (GMM). Specifically, it first calculate the DTW distances of the active power time series between generators as the clustering index. Then it improves GMM by proposing a soft target distribution and utilizing the Kullback-Leibler (KL) divergence to implement adaptive and unsupervised generator clustering. Finally, it employs Matlab®/Simulink® to structure a simulated model. In this way, a grid-connection wind farm could be constructed. The results verify the effectiveness and considerable application value of the proposed equating approach by setting a fault three-phase short-circuit.
TRIPLEx: Triple Extraction for Explanation

Transformer-based models are used to solve a va-riety of Natural Language Processing tasks. Still, these models are opaque and poorly understandable for their users. Current approaches to explainability focus on token importance, in which the explanation consists of a set of tokens relevant to the prediction, and natural language explanations, in which the explanation is a generated piece of text. The latter are usually learned by design with models traind end-to-end to provide a prediction and an explanation, or rely on powerful external text generators to do the heavy lifting for them. In this paper we present TRIPLEX, an explainability algorithm for Transformer-based models fine-tuned on Natural Language Inference, Semantic Text Similarity, or Text Classification tasks. TRIPLEX explains Transformers-based models by extracting a set of facts from the input data, subsuming it by abstraction, and generating a set of weighted triples as explanation.
Analysis and Development of a Methodological Approach to Structuring the List of Documents Regulating Science and Technology, Formed through the Activities of Federal Executive Bodies and Organisations Subordinate to Them

Introduction.The target of our research is the methodology of creating government lists of documents generated by the activities of federal executivebodiesandorganisationssubordinatetothem.Thegoal of the study is to analyse and formulate a methodological approach to structuring a list of documents resulting from the activities of the Ministry of Science and Higher Education of the Russian Federation, as well as various science and technology organisations subordinate tothis Ministry.Methods.Theoretical (analysis, synthesis, analogy) and empirical (document analysis, generalisation, classification) research methods have been used to achieve the research goal specified. The theoretical foundation of our research was formed by the works of Russian authors on the problems of archive management and archival studies. The empirical foundation, in turn, is made out of the regulatory legal acts that assign a specific set of functions to the Ministry of Science and Higher Education of the Russian Federation, acts that control the creation of document lists reflecting the activities of federal executive bodies, approved and valid departmental lists, and decrees by the Ministry.ResultsandDiscussion.The study results in suggestions on structuring a list of documents generated by the activities of federal executive bodies and organisations subordinate to them, with mentions of storage terms relevant to science and technology. Methodologically, the creation of a structure for such a list is based on the results of analysing the lists of documents associated with other government institutions, analysing the functions of the Ministry of Science and Higher Education of the Russian Federation, as specified in Decree No. 682 of the Government of the Russian Federation of 15.06.2018 (amended on 19.06.2021), and analysing the activities of organisations subordinate to the Ministry. To split the list into interconnected segments, we have developed a special principle for distributing the science and technology functions of the Ministry of Science and Higher Education of the Russian Federationacrosstiersandspecialisations.Conclusion.Ourstudy results in the formulation of a structural and segmentation diagram that will facilitate the creation of a list of documents resulting from the activities of the Ministry of Science and Higher Education of the Russian Federation and its subordinate organisations, including storage terms relevant to regulating science and technology. The structural diagram of this part of the list has three sections, while the segmentation diagram has five elements. With the framework thus established, the list can be filled with content (specific documents).
MySQL vs MongoDB: A Preliminary Performance Evaluation Using the YCSB Framework

Information systems manage and process data for the fundamental tasks and specific requirements of each business. A database is thus an essential component of an information system. Database research involves two approaches, namely: relational and non-relational. A relational database is defined by rigid structures that provide consistency and referential integrity. However, this causes delays in response times and affects the availability of the schema. On the other hand, a non-relational database is schemaless and provides great flexibility for organizing and retrieving data; however, this characteristic does not guarantee strict consistency. An analysis of the performance of both database approaches is necessary to support the implementation of information systems. This research focuses on two datastores: MySQL (a relational database), and MongoDB (a non-relational database). Thus, the aim of this paper is to evaluate and compare the performance of both databases. For this, six evaluation scenarios were defined and executed using Yahoo! Cloud Serving Benchmark. This document highlights the best and worst performance scenarios for both databases based on three parameters: overall runtime, latency, and throughput. Finally, conclusions and future works are provided at the end of the paper.
Image Processing Methods for Face Recognition using Machine Learning Techniques

The face is one of the simplest ways to distinguish one another's personal image. Face recognition is a personal identification system which uses a person's personal features to recognize the identity of the individual. Human facial identification is basically a two-phase procedure, including face detection, where the process is carried out very rapidly in people, whereas the second is the implementation of environments that classify the face as persons, when the eye is positioned within a short distance. Stage is then repeated and established to be one of the most researched biometric strategies and established by experts for facial expression recognition. In this study, we implemented the area of face detection and face recognition image processing MTCNN techniques while utilizing the VGG face model dataset. In this initiative, python framework is the program necessity.
Elaborative Investigation of Blockchain Technology in Intelligent Networks

The fifth generation (5G) network advancements focus to help mixed upright applications by associating heterogeneous gadgets and machines with extreme upgrades regarding high quality of administration, extended organization limit, and improved framework throughput regardless of significant difficulties like decentralization, straightforwardness, dangers of information interoperability, network protection, and security weaknesses. The challenges and limitations of other intelligent 5G intelligent internet of networks (5G IoTs) are also to be met by using blockchain technology with the integration of cloud computing and edge computing technologies. In this chapter, the authors render an elaborated analytics of the empowering of blockchain technology in intelligent networks that includes 5G networks and 5G-based IoT. The solutions for the spectrum management, data sharing, security, and privacy in 5G networks will also be analyzed. It is believed that the chapter would be useful for researchers in the field of blockchain in intelligent networks.
Optimal Trajectory of Manipulator Torque Based on Two Population Cuckoo Algorithm

Aiming at the energy consumption of joint torque of manipulator, a dual population cuckoo (DCS) algorithm is proposed to optimize the energy consumption of joint torque. Firstly, the trajectory of the manipulator joint space is planned by using the quintic polynomial interpolation method; Secondly, the sum of the joint torques of the manipulator is taken as the objective function in the optimization process of the DCS algorithm. Under the constraints of angle, angular velocity and angular acceleration, the DCS algorithm is combined with the quintic polynomial interpolation method to plan a joint torque and the optimal trajectory.
Extending E Prover with Fully Use Binary Clauses Algorithm Based on Standard Contradiction Separation Rule

After nearly two decades of development, E prover has made great progress and is a state-of-the-art automated theorem prover for first-order logic in the world. E has routinely come in as one of the top three provers in all CASC competitions since 1999. However, up to now, E still cannot resolve or effectively resolve many problems of the latest benchmarks database TPTP. And the inference rule of E essentially is binary resolution method which lacks the synergetic processing ability for multi-clause. Standard contradiction separation (S-CS) inference rule is a recently proposed theoretical method to overcome the limitation of binary resolution inference rule. Based on this theory, this paper proposes a novel S-CS dynamic deduction algorithm that fully uses binary clauses (in short FUBC). While maintaining the advantages of E, we implement FUBC algorithm on E, forming an extending E prover, denoted as E_FUBC. Then we use the latest benchmarks database TPTP (such as problems of FOF division from CASC-J10 and CASC-27) to evaluate E_FUBC. Experimental results show E-FUBC outperforms E itself and FUBC algorithm effectively improves E prover’s ability.