Reviewerly/exHarmony huggingface dataset:
Data Preprocessing Technology in Network Traffic Anomaly Detection

Computer network plays a very important role in today’s world. With the development of network technology and the increasing popularity of network topology, network management is facing huge challenges. Among them, finding network traffic anomalies is one of the most important tasks in network monitoring, and is an important technology in network intrusion, security monitoring, and performance maintenance. This paper aims to study data preprocessing techniques in network traffic anomaly detection. This paper firstly introduces the network traffic measurement characteristics completely, and then selects and applies the data preprocessing algorithm, constructs a complete data preprocessing framework, and conducts experiments for the abnormal detection of network multi-dimensional characteristic traffic and the abnormal detection of unit-level characteristic traffic. The superiority of the data preprocessing technology in this paper, by reducing the data, determines the data preprocessing threshold of this paper to ensure that the data preprocessing is maximized. Experiments show that the data preprocessing technology constructed in this paper has about 20% more data filtering than traditional data filtering, which shortens the amount of detection data. The threshold is around 0.6, and data preprocessing can maximize the reduction of data without destroying the integrity of the attacked data.
Moving Image Processing Technology and Method Based on Neural Network Algorithm

With the convenience brought by the rapid development of information, teaching reform has also set off a heat wave. The emergence of Internet+ , multimedia technology, virtual reality technology and artificial intelligence has made education continue to develop towards informatization, and mobile learning has become a way for students to learn. This paper provides a reference for the reform of exercise classes in colleges and universities, enriches the teaching methods, improves the learning quality of students, and makes the action image processing technology better applied to physical education classrooms. In this paper, according to the characteristics of the human motion system, the training actions of sports are collected. After the 3D action data is properly calibrated and normalized, the motion vector is used as the training input method. This method conforms to the laws of the human motion system. In terms of temporal feature extraction, this paper proposes a multi-level long short-term memory neural network structure for action recognition. The action image processing technology improves the accuracy of movements more significantly, and proves that it has a certain promotion effect on the self-editing ability and innovation ability of students'. The final results of the research show that the accuracy of action recognition has been improved to a certain extent after image data processing. The accuracy of action recognition in images has increased from 67.13% to 91.23%, and the accuracy rate has remained above 90% after action image processing which proves the effectiveness of the neural network algorithm processing method.
Elastic Monte Carlo Tree Search

Strategy games are a challenge for the design of artificial intelligence agents due to their complexity and the combinatorial search space they produce. State abstraction has been applied in different domains to shrink the search space. Automatic state abstraction methods have gained much success in the planning domain and their transfer to strategy games raises a question of scalability. In this article, we propose elastic Monte Carlo tree search (MCTS), an algorithm that uses automatic state abstraction to play strategy games. In elastic MCTS, tree nodes are clustered dynamically. First, nodes are grouped by state abstraction for efficient exploration, to later be separated for refining exploitable action sequences. Such an elastic tree benefits from efficient information sharing while avoiding using an imperfect state abstraction during the whole search process. We provide empirical analyses of the proposed method in three strategy games of different complexity. Our empirical results show that in all games, elastic MCTS outperforms MCTS baselines by a large margin, with a considerable search tree size reduction at the expense of small computation time.
Design of Regional Economic Information Sharing Based on Blockchain Technology

We have developed a new data sharing platform by using blockchain technology to realize the exchange of economic information on this platform. On the other hand, the architecture of monitoring function is built to realize the functions of location sharing, information updating and storage, and enhance the storage capacity and information security. In terms of software, blockchain technology is used to build data interaction data links between regions.
Distributed 3D Interior Design System Based on Intelligent VR Technology

Intelligent virtual reality technology (VRT) is a very new art form, which gradually matures with the progress of science. As one of the most popular research topics in the twenty-first century, VRT has been integrated into various industries. This paper studies the distributed 3D interior design system based on intelligent VRT for the application of VRT in ID and the requirements of distributed 3D ID. The research results show that the ID can fully apply VRT to conduct a comprehensive prospective analysis of the early design scheme. It has greatly improved the quality of engineering design, reduced expenditure, improved the efficiency of scheme design and approval, gradually changed the way of modern ID, and formed a virtuous circle of modern interior and exterior design mode. In the ID scheme, various details can be presented to people more realistically, so that the experiencer can fully and carefully understand the intention of the designer.
Consistency Comparison of Machine Vision Images Based on Improved ORB Algorithm

With the rapid development of the computer field, machine vision recognition technology is often used to process image recognition and analysis. Machine vision is a key technology in the field of artificial intelligence, which can greatly improve the cognitive ability and perception ability of robots. Identifying and converting images into simple text information can focus on the important content of the image and make relevant expressions and statements about the content in the image. In this paper, the improved ORB algorithm is used to compare and study the consistency of machine vision images. Machine vision technology has played a great role in improving detection efficiency and reducing labor costs. The machine vision image processing method is the basis for the application of machine vision technology. and key. This paper aims to study the consistency of visual images and improve the consistency of machine vision image comparison. As an image matching feature point algorithm, ORB algorithm has good image fusion adaptability, which is of great help to improve the accuracy of machine vision image recognition and image registration. The final results of the research show that the extraction times of the three algorithms in the third extraction result are 2.459 s, 1.686 s, and 1.458 s, respectively. It can be seen in turn that the improved ORB algorithm used in this paper can significantly improve the processing time of visual images. ORB The algorithm is better than Hough transform and RLD algorithm.
Research on Target Detection and Anti-Attack Method Based on Neural Network

The well trained neural networks have performed much better than human beings in many daily task, like object detection and image classification. The security has also been an important issue while neural network makes our daily life much more convinient. Adversarial attack can attack on the input with small perturbation to let network make wrong prediction. Based on the information that attacker can get during the attack, we can divide the attack methods into white box attack and black box attack. Currently, all the black box attack methods are designed for image classification, and there is no black box attack for object detection. In this paper, we successfully apply the Boundary Attack which was originally designed for image classification on object detection task.
Visceral Leishmaniasis Detection Using Deep Learning Techniques and Multiple Color Space Bands

Leishmaniasis is a group of neglected popular parasitic diseases typical of tropical and subtropical countries, and its most serious form is Visceral Leishmaniasis (VL). Every year, it’s 700,000 to 1 million cases are recorded worldwide, leading to the death of 26,000 to 65,000 people. The diagnostic, performed through parasitological examination, is very tiring and error-prone; simultaneously, it is a step with a great capacity for automation. Therefore, this work aims to develop an automatic system based on computer vision capable of diagnosing patients infected with VL through medical images. We compared the results obtained in this study with related works, where it was possible to observe that the methodology implemented here proved superior and more efficient, reaching an Accuracy of 99%. In this way, we demonstrated that the deep learning models, trained with images of the patient’s bone marrow’s biological material, can help specialists accurately and safely diagnose patients with VL.
Complex SPARQL Queries Based on Ontology and RDF

In order to explore the methods of complex SPARQL query based on ontology and RDF, this paper first constructs a computer ontology and formally represents it. After that, the RDF dataset is modeled and described serially using RDF/XML. Finally, the query application of complex SPARQL is carried out, including: simple query, multiple matching query, filter conditional query and optional query.
A Meta-analytical Comparison of Naive Bayes and Random Forest for Software Defect Prediction

Is there a statistical difference between Naive Bayes and Random Forest in terms of recall, f-measure, and precision for predicting software defects? By utilizing systematic literature review and meta-analysis, we are answering this question. We conducted a systematic literature review by establishing criteria to search and choose papers, resulting in five studies. After that, using the meta-data and forest-plots of five chosen papers, we conducted a meta-analysis to compare the two models. The results have shown that there is no significant statistical evidence that Naive Bayes perform differently from Random Forest in terms of recall, f-measure, and precision.
Inventiveness of Text Extraction with Inspiration of Cloud Computing and ML Using Python Logic

Data is being spawned at an astounding rate and volume in present era of internet and online services. Scientists, engineers, data analyst often work with relational or tabular data. The data in these tabular data columns might be numerical or categorical. Video, audio, picture and text are just few of the structures that may be found in generated data. Unstructured textual data is generated by online activities such as social posts, blogs entries, and website content. To effectively build their company, corporations and businesses must evaluate textual data to comprehend consumer conducts, opinions, and comments. Text analytics is evolving at a quicker rate than ever before to compete with large textual data. In this research paper, the text mining and language processing modules that have been proposed for improvisation of text extraction process in very prominent way through python.
An Approach to Identify DeepFakes Using Deep Learning

Deepfakes are the manipulated images and videos that are created by performing a face swap using various Artificial Intelligence tools. This creates an illusion that someone either said something that they did not say or are someone they are not. This incorrect way of using the deepfake technology leads to severe consequences. Some scenarios like creating political tension, fake terrorism events, damaging image and dignity of people are some of the negative impacts created by these deep fakes. It is difficult for a naked human eye to detect the results of these deepfake technology. Thus, in this work, a deep learning-based method that can efficiently differentiate deepfake videos is developed. The proposed method uses Res-Next pretrained Convolutional neural network for extracting features from the frames that are divided from the input video. The extracted features are then utilized to train a Long Short Term Memory (LSTM) network to differentiate whether the input video taken from the user is subject to any manipulation or not. To make the model perform better on real time data, it is trained with a balanced dataset. The dataset used to train the model is Deepfake detection challenge dataset. The results of the proposed model can predict the output with a better accuracy. This method can thus encounter the threats and danger produced from the deepfake technology to the society.
ML Classifier Using Multiple Neural Networks Trained by Linear Programming

This paper presents a novel approach for designing a ML classifier using multiple neural networks trained using Linear Programming (LP). In the paper “A Novel Approach for Training Neural Network using Linear Programming” it is demonstrated that we can train a neural network using linear programming algorithms like simplex method. In this paper we propose further improvements to the same algorithm by fixing several of its shortcomings and aim to design a novel method which is similar to commonly used neural network based algorithms in terms of achievable accuracy. In our method we make use of multiple improved LP trained neural networks along with a genetic algorithm to further optimize the training settings variables. We also include the test results for both industry generated data and synthetic data with our model and compare it against other commonly used neural network based algorithms.
Blockchain Computing Resource Allocation and Benefit Sharing Based on Artificial Intelligence Technology

With the rapid development of Internet of Things technology, the emergence of more and more Internet of Things devices has brought more application requirements, which has also promoted the development of cloud computing and edge computing, making servers with sufficient computing power (such as Cloud servers, edge servers, etc.) provide services for computing needs. The purpose of this article is to study blockchain computing resource allocation and revenue sharing based on artificial intelligence technology. The common problems of optimizing revenue sharing and the proportion of computing resources are studied, and the optimal computing power and revenue sharing scheme is proposed to maximize the total net revenue of users and servers and ensure the fairness of the two. Based on the idea of revenue sharing, this paper introduces the revenue sharing ratio into the problem, and establishes a problem model related to the total net revenue of users and servers and the distribution of computing power and the distribution ratio. Aiming at the non-convex optimization feature of the problem, we decompose the problem vertically and propose a hierarchical algorithm to solve it. The simulation results show that the solution results of the algorithm proposed in this paper meet the target requirements and are consistent with the linear search calculation results, and the calculation performance has been significantly improved.
Stereoscopic Visual Effect Simulation of Film and Television Works Based on 3D Technology

The development of 3D technology has greatly broadened the scope of film and television creation. It not only has a strong visual impact in form, but also has a great tension in content, which has greatly enriched the plot of the film and greatly enhanced the artistic charm of the film. Today, we can use 3D technology to depict real life scenes, and thus bring a strong three-dimensional effect. Therefore, this paper studies the simulation of 3D technology to realize the stereoscopic visual effect (VE) of film and television works (FATW). The principle of stereoscopic vision and the influence of 3D technology shooting skills on SE are discussed. The 3D technology is used to simulate the stereoscopic VE of FATW, so as to strengthen the 3D spatial hierarchy and highlight the sense of spatial existence; It effectively avoids the problems of different lens focus and parallax in the shooting process. It ensures the stability and excellence of the 3D effect, so that the audience can always enjoy the film in a comfortable three-dimensional experience.
Development of Virtual Imaging Technology for Engineering Surveying and Mapping Based on Genetic Algorithm

The rapid development of big data technology and the rapid progress of virtual imaging technology for engineering surveying and mapping have contributed new tools and new methods to surveying and mapping work. The acquisition of engineering surveying and mapping data is developing in the direction of digitization, real-time and multi-dimensional. The purpose of this article is to study the virtual imaging technology of engineering surveying and mapping based on genetic algorithm, which is of great significance to the design, construction and operation of engineering projects. This paper designs a virtual imaging system for engineering surveying and mapping based on genetic algorithm, designs a set of online surveying and mapping management process through B/S architecture, divides the responsibilities of office administrators and field surveyors, and transfers the surveying and mapping work to the browser side. It also provides a visual construction interface to conduct surveying, mapping and stakeout operations in a more intuitive and convenient way. The system has a built-in algorithm conversion module, which uses the calculated spatial coordinates of the rover as the source data, and automatically converts it into geodetic coordinates, plane coordinates, and construction coordinates, avoiding manual intervention, improving the convenience of surveying and mapping and ensuring data reliability. Taking the hydropower engineering surveying and mapping work in the third district of A High School as the test application scenario, it can be found by comparing the test results that the measurement errors in the horizontal and vertical directions are within 0.5 cm and 1 cm in the vertical direction, respectively.
Word Sense Disambiguation in the Biomedical Domain: Short Literature Review

Word Sense Disambiguation (WSD) is a subfield of Natural Language Processing that discerns which meaning of a given term is used in a specific context. Exclusively in the biomedical domain, automatic processing of medical documents containing biomolecular entities, diseases, proteins, and genes, for instance, is summoning namely because many of the terminologies are ambiguous. Word Sense Disambiguation algorithms work mainly on identifying the correct sense of a given vague word in its specific context. This paper runs through the existing methods and datasets in WSD in the biomedical domain. Various methods were applied in this endeavour ranging from knowledge based methods to supervised and unsupervised machine learning methods, applied to different types of datasets such as PubMed and PubMed Central, MIMIC III, Medline, etc. The main findings in our study include the following: (i)The lack of large testing datasets containing medical ambiguities available for public use limited the application of WSD in the biomedical domain. (ii)Whilst the unsupervised methods rely mainly on the UMLS Metathesaurus and can be applied widely, the restricted use of a manually annotated dataset containing both ambiguous words and their definitions, along with supervised learning methods, have given promising results in terms of providing the best definition for the given entity. (iii)Automatic analysis of massive health related corpora would be liable to err without accurate word sense disambiguation approaches.
Organizational Network Monitoring and Security Evaluation Using Next-Generation Firewall (NGFW)

The most Bangladeshi government and non-government scientific organizations have comprised both wired and wireless system network infrastructure. When such network infrastructure has been designed and developed at these organizations, ensuring network security has become a highly challenging issue. For this purpose, Next-Generation Hardware Firewall (NGFW) has been implemented by most scientific organizations. This hardware firewall is a very effective technology to perform network security. In this paper, we have chosen Atomic Energy Research Establishment (AERE), Savar under Bangladesh Atomic Energy Commission (BAEC) as an experimental zone. To facilitate research and services, Internet-based network infrastructure has been established at AERE, BAEC. A Next-Generation CISCO-based network firewall has been integrated with the implementation of AERE network infrastructure to enhance and ensure network security. We have experimented and collected data from October 09, 2020 to October 09, 2021 at the network system of AERE. By collecting and evaluating these statistical data in terms of different network security parameters, we have investigated AERE’s network data like controlling ingress and egress traffic, monitoring traffic flow, malware threat analysis, web content and application filtering, URL filtering, decrypting traffic results, etc. In this paper, we have illustrated how network and application security is being achieved by the NGFW firewall and as a resulting factor, the summarization of scale up the network security for a scientific organization is enlightened.
Model of Knowledge-Oriented Adaptive Enterprise Management System

Automated control systems are a background of modern production processes. However, various models of system construction are used to manage organizational and technological processes. Such systems are focused generally on managing objects’ tasks with a known structure and control algorithms with the difference in data only. If the structure of the control object differs from the implemented one, especially to a large extent, “individual” development is needed. That is rather expensive. For example, the management object in the agricultural sector, with the same structure of management tasks can be changed every year accordingly to the crop rotation. This requires either system redundancy or support for a manual control mode at the recommendations stage and event recording. The paper solves the problem of developing a model applicable to all automated control systems. For this, the features of the MVC pattern, the framework organization and the knowledge representation based on ontologies are considered and analyzed. This features makes it possible to propose a model synthesizing the ACS structure based on the knowledge that describes the subject area in the form of ontologies. For automated control system implementation, a distributed microservice architecture is proposed. These services implement links between ontology entities and corresponding data transformation.