Reviewerly/exHarmony huggingface dataset:
Efficient Task Scheduling in a Cloud Environment based on Dynamic Priority and Optimized Technique

Cloud computing is an emerging field process with an enormous task of handling resources. From an application perspective, the study of task scheduling mechanisms for data transfer in large cloud computing environments needs to be performed better. Unbalanced scheduling leads to traffic load, energy loss, and hardware control failure. In addition, residents' devices are not considered to reduce power consumption delay. So, the Internet of things (IoT) rules the modern trends of the Internet. The enormous number of things (objects), which are associated with the Internet, produces a large amount of information that needs a ton of exertion and task preparation to make it valuable. To resolve this problem, we propose a dynamic multi-level task scheduling (DMLTS) based on cascade shrink priority (CSP) to allocate the task to optimize the scheduling. With intent, a Tactical Load Balancer (TLB) and The Preemptive Flow Manager (PFM) are responsible for applying a load balancing strategy based on the mixed load balancing algorithm to improve the task allocation better to balance the load to improve the response time. Experimental results have been demonstrated concerning better load balancing, lower power rate, and time consumption rate in both phase and random uniform propagation. Simulated results performance of this process can reduce data processing time and achieve load neutralization.
Path Planning of Mobile Robot Based on Multi-agent

With the development of robot technology, more and more robots are used to replace human beings to complete simple, repetitive and dangerous work. Intelligent machines facilitate the construction and implementation of robotic systems by increasing autonomy, interaction with the environment and society, and modeling processes. The purpose of this paper is to study the planning of moving paths for multiagent robots. First, the research history and importance of the proposed multi-task approach. Secondly, establish the research structure of standard projects represented by domestic and foreign. The state of the control algorithm representing the model was simulated and analyzed using the SRIsim simulator. The results show that using this method to plan the mobile robot path, triangles, right angles and right angles between 10s, 16s and 25s can be achieved and kept safe. The distance between them is the distance between the plane and the direction. The conditions in the actual work can meet the requirements within the specified time.
Big Data Association Rule Algorithm for Encryption of Accounting Data

As the scale of enterprises continues to expand, the complexity of management within enterprises continues to increase and the amount of data generated is becoming larger and larger. For accounting information systems, how to ensure data security, visualize data and ensure analysis and mining of data are important issues for enterprises to consider. This paper will discuss the encryption of accounting information systems in relation to the actual situation such as the internal management of accounting systems in the era of big data (BD). In this paper, firstly, the existence of irreversible security risks in AD is analysed. Secondly, the data is analysed and mined, and the accounting data (AD) is encrypted through association rule algorithms (ARA). Finally, data information that occurs in the enterprise information system in the midst of financial activities and is even directly related to financial operations is extracted and processed for association analysis to improve the security and visualisation of the financial information system in the whole system. In this paper, we analyse the causes and solutions of the security risks in the process of acquiring data from the accounting system and put forward suggestions for improvement after analysing the encryption process of AD.
An Optimized Job Scheduling Mechanism for Mapreduce Framework Using DIW-WOA in Big Data

For processing, storing, and managing huge amounts of data, big data has turned out to be famous. The most popular map reduce programming model is the open-source Hadoop. It executes the MapReduce (MR) programming model to process big data with MR jobs. Nevertheless, scheduling MR jobs across multiple nodes has been regarded as an optimization issue in spite of current endeavors toward ameliorating MR's performance. Hence, to carry out an MR Job scheduling centered on Completion Time (CT) and cost whilst meeting the security constraints, a new optimization algorithm like Decreasing Inertia Weight induced Whale Optimization Algorithm (DIW-WOA) is proposed. At first, for big data clustering, a Butterfly Optimization-centered K-Means clustering Algorithm (BO-KMA) is applied. At last, for scheduling the client's tasks or jobs to the optimized scheduler. The whale optimization algorithm is hybridized with inertia weight concept and finally DIW-WOA is employed. When weighed against the other well-known algorithms, the experimental outcomes exhibit that the proposed algorithms for data clustering, DS and Task Scheduling (TS) achieve better performance in terms of make-span and throughput. It can be incorporated into the Hadoop environment.
MRI Image Enhancement for Brain Tumor Detection using Hybridization of Contrast Enhancement Techniques

Magnetic Resonance Imaging also known as MRI is a significant technology used in medical image processing that provides important information on the structure of human soft tissue. As MRI is used to diagnose numerous brain-related severe illnesses, such as brain tumors and spinal cord tumors, image enhancement of the collected brain images is considered to be important by a large number of medical researchers and doctors. Despite the progress of technology, every image has some deficiencies, such as noise, low contrast, low brightness. To solve these issues related to MRI images, various histogram equalization techniques for example HE, CLAHE, BBHE, DSIHE, RMSHE, and Fuzzification and their hybridizations are applied to different MRI images. Metrics such as entropy, PIQE, and BRISQUE are used to evaluate the findings.
Age Detection based on Facial Features Using Support Vector Machine

Face aging has been studied for decades. Determining age from a facial shot is key to our technique for diagnosing abnormal behavior. Security monitoring, forensics, biometrics, and Human-Computer Interface (HCI) use facial age estimates. We only look at adults 1–75 in the UTK Face database, which covers 0 to 116 years. The database contains 23,708 face photos with age, gender, and ethnicity annotations. In work, preprocessing, feature extraction, feature selection, and age categorization are involved. Preprocessing adjusts images. Computer vision uses Local Binary Pattern (LBP) and Histogram of Oriented Gradient (HOG) as visual descriptors, whereas fscmrmr is utilized for classification. Support Vector Machines (SVM) improve classification accuracy in highdimensional areas. The chosen characteristics are concatenated and passed to a multiclass SVM classifier to classify the images with 95.69% success.
Design of Digital Film and Television Projection System Based On Blockchain Technology

Digital Rights Management (DRM) is widely used to protect digital content and has made a major contribution to protecting digital content. However, traditional DRM technology has shortcomings, such as centralized and opaque copyright and transaction information. In order to solve the security problem of centralized management of digital movies, a digital movie distribution system architecture based on blockchain technology is proposed. Provide authenticity and traceability of data and business logic in film trading, license certification, projection control and other fields. It is developed through the decentralized consensus mechanism and decentralized IPFS storage mechanism using smart contract scripts in the blockchain. Create efficient and accurate implementation and reduce manual intervention and authorization to ensure traffic control throughout the working day. Our mechanism allows rights holders to set prices for different content usage rules according to their own wishes. Customers have the flexibility to choose the usage rules they want to purchase. We have also developed a licensing framework based on blockchain, which is close to the current DRM standard and easy to promote.
Research on English Learning Resource Query System Based on Collaborative Filtering Recommendation Algorithm

The collaborative filtering algorithm can comprehensively consider the historical evaluation information of the users in the system, calculating the corresponding users, and then finding the resources that the users may be interested in through the evaluation of teaching resources by similar users. Compared with the content-based recommendation algorithm, the collaborative filtering algorithm can find the new interests of users, which can achieve leaping recommendations. This article focuses on the present setting of modern English education and the particular application strategies of applying the latest techniques to improve English learning resource query, reviewing the existing strategies for the improvement of the reliability and validity of language study for online English learning, and looking forward to the direction of follow-up research and practice of online resource query system for virtual language study in the era of big data, which has a certain practical reference meaning for the improvement of English teaching theory under the context of the continuous maturity in the area of AI.
Network Operation Status Evaluation Monitoring System Based on Machine Learning Algorithm

After decades of steady development, the Internet industry has become an important driving force for global economic and social development, and the scale of the network is growing. The evaluation and monitoring system of network operation status plays a vital role in ensuring the normal operation of the network, diagnosing network faults in time, and meeting the service needs of different users. The purpose of this paper is to study the network operation status evaluation and monitoring system based on machine learning algorithm, mainly to expound the background and importance of this topic, as well as the development status at home and abroad, mainly to study the software architecture of the network operation monitoring system in the IP network environment And monitoring architecture design and implementation, further theoretical research on background server load balancing technology, and a load balancing mechanism based on machine learning algorithm is proposed. The database business simulation results show that the database query time of the load balancer based on the genetic algorithm is about 3.3 seconds, while the load balancer based on the machine learning algorithm only needs 2.2 seconds, a reduction of nearly 0.9 seconds. It can be seen that machine learning algorithms also have great advantages in database query business.
Digital Association Rules Algorithm in Accounting Information Distortion Recognition System

With the continuous development of digital technology, traditional data processing methods can no longer meet people's requirements for information security in the identification of information distortion. In order to improve the analysis accuracy of various indicators in the accounting information system, reduce misjudgments and correct deviations, it is necessary to use association rule algorithms. Therefore, this paper studies the application of digital association rule algorithm in accounting information distortion identification system. This paper mainly uses the experimental method, the survey method and so on to carry on the related research on the accounting information distortion identification system. The experimental results show that the accuracy of the digital association rule algorithm in the system is over 90%, which can meet the system requirements.
Construction of Mass Cultural and Sports Activity Knowledge Graph based on the Fusion of MultiSource Data

With the advent of the era of big data, people are increasingly demanding for queries of mass cultural and sports activities, which have been given high attention. However, due to the challenges of such activities and the outstanding performance of knowledge graphs in recent years, in this paper we solve the problem from the perspective of knowledge graph. We first present a method to construct a knowledge graph of mass cultural and sports activities based on the fusion of multisource data for user activity query, then present a hierarchical modeling method combining ontology and taxonomy, design a BERT-based pipeline model to extract activity entities, solve the extraction of unstructured text data, and use a rule-based method to extract the relationship between activities and labels. Finally, the knowledge graph is constructed using the real activity data of Beijing region, and the multi-condition query of the activity is realized. The experimental results show that this method can effectively provide personalized activity query for users with different needs, and solve the problem of wide activity information and low matching between activities and users. A detailed experimental analysis was carried out to construct a data set using real activity data in Beijing, and the validity of the experiment was evaluated.
To Detect Spammer from Image-Based Spam E-mail through Adjacent-Duplicate Technique

An Email facility proves to be efficient and more convenient communication way. Now a day, an internet service grows continuously, so information available to user is transformed from text to enriched multimedia. Where, spammer uses a new approach in which text is embedded in images. Widely known image detection algorithm such as optical character recognition (OCR) algorithm is defeated by spam image building techniques where randomization method is used to make sure the entire image is look different to each other. Spam image e-mails are mostly forwarded in a group that have visual similarity but dissimilar due to randomization technique used. The proposed system uses adjacent-duplicate detection technique to identify image-based spam. The system uses conventional spam detection technique to identify image-based spam and then further, use of numerous image-based spam filter to identify all the images which have similarity among them. The system is implemented to gain high identification rate where other side have false positive rate (FPR) is less than 0.001%.
Construction of English Grammar Knowledge Base System Based on DB2 Database

The development of information technology has made the knowledge stored by human grow exponentially. Due to the organic combination of artificial intelligence and database computer technologies, knowledge base systems have been generated and developed. In order to help English learners and researchers to master systematic grammar knowledge, this paper is based on DB2 database. First, the characteristics and advantages of DB2 database are analyzed, and the structure of DB2 database instance is defined. Next, the hierarchical structure of grammar knowledge is designed. Then, the logical storage structure of grammar knowledge is designed. Finally, the methods of knowledge acquisition are studied, including acquisition from books, web crawler technology acquisition and teachers' acquisition in practice. The research content of this paper makes use of the advantages of the DB2 database management system to improve the breadth and richness of the knowledge base.
Design of Mathematics Teaching System Based on Computer Network Multimedia Technology

With the continuous development of network technology, the relationship between multimedia computers and traditional computers is gradually being paid attention to, and has been widely used in various fields. In the Internet era, the effective management and utilization of network information is an inevitable requirement of the current trend of informationization, digitalization and intelligence of society. The effective combination of traditional computers and digital computers can greatly enrich multimedia teaching resources and improve students' learning efficiency. This paper first analyzes the existing network multimedia teaching system and its problems, and analyzes the functions of the computer network multimedia teaching system that needs to be improved, and puts forward the relevant concepts of computer network multimedia technology, and then analyzes mathematics teaching based on mathematics teaching system. Finally, this paper uses MATLAB software to build out a complete and efficient digital multimedia system. This system combines DX9 technology to realize the function of intercepting player screen, and provides practical functions such as remote tutoring, file transfer, online information, screen recording, etc. These functions make it easy for learners to learn through network multimedia technology, and provide a more convenient learning environment for students.
Financial Data Intelligent Analysis System Based on Big Data

This paper is a financial information intelligent system based on big data analysis technology, including data acquisition module, data preprocessing template, big data analysis module, data analysis module and financial data display template; Obtain all financial data information from the source, and transfer the collected results to the data preprocessing template; the data analysis module is used to obtain the information integrated in the data preprocessing system, and extract and record the effective content of the obtained information., and output the obtained effective information to the data analysis module; the statistical analysis module is used to collect the useful information obtained from the data analysis model, conduct statistical analysis on the obtained information, and output the results of the statistical analysis to the information display model. This paper solves the chaotic phenomenon of enterprise financial management in the existing technology, and the problems of single function and low intelligence in the existing computer management financial management.
An Efficient Pelican optimization based CNN-BiLSTM to Detect and Classify 3D Objects

The lack of appropriate shape representation makes it complex to recognize the 3D shapes accurately and it is a hot topic in the field of Computer Vision (CV). This paper presents a Pelican optimized Convolutional Neural Network (CNN)-Bidirectional Long Short Term Memory (BiLSTM) to recognize the different objects in a particular scene. The CNN-BilSTM architecture is formed by placing two BiLSTM architectures below the CNN network and integrating the outputs via a fully connected layer. The pelican optimization algorithm is mainly incorporated to optimize the different hyperparameters associated with the CNN-BiLSTM architecture such as number of layers, batch size, number of layers, dropout, etc. The experiments are conducted using the ScanNet dataset which comprises both 2D and 3D data along with the labeled voxels. The proposed methodology offers improved results when compared with the existing techniques in terms of confusion matrix, accuracy, precision, and recall.
Solar Powered Multi-functional Agricultural Robot

More than 40% of the world's population selects agribusiness as their primary occupation. Agribusiness has recently seen growth in the development of autonomous vehicles, including robots. When using a traditional farming strategy, the equipment used to carry out different tasks is expensive and poorly suited for handling. In this manner, Farmers must use cutting-edge machinery to conduct farming procedures. The proposed project intends to strengthen the robot. The robot is capable of seeding, ploughing, planting, mowing the lawn, and watering. The suggested robot uses solar energy for electricity. The need for any photovoltaic panels is an unnecessary external power source. The robot will reduce the cost of the work, speed up the process, and also improves the accuracy of the work. The energy utilized by the device is less when compared to tractors or other farming machinery devices such as electric pumps.
Terminal Software Defect Detection Method Based on Association Rules

The research of terminal software defect detection method based on association rules is a research topic in the field of computer science. The main purpose of this study is to find out how much accuracy can be achieved using this type of method. This question has been studied for many years, and there is no clear answer so far. Terminal software defect detection methods have been developed since the beginning of computerization. In order to detect defects in a program before it is released, it is necessary to develop effective methods to help us determine whether there are errors in a given code. The main idea is to use information about how often certain events occur together and predict that they will occur again. Association rules are used for this purpose. A rule is a mathematical statement that describes a relationship between two variables (in our case, different parts of a program). When a variable changes its value, it may trigger another variable to change; In other words, each event involves some causes and effects.
Quantum Programming Taxonomy

By boosting computer processing capability, quantum computing will transform digital usage in the upcoming years. The development of the quantum computer and the advancement to the several thousand Qubits involved in this technological breakthrough are opening new doors for a whole new generation of professionals and developers to work on previously unsolvable challenges. To put quantum solutions and algorithms into action, we need tools and platforms that allow us to communicate with a quantum system. This article presents the existing tools while mentioning the important points of each one a taxonomy that presents the existing tools in the world of quantum programming is proposed, as well as some challenges that face this field.
Using AI and IoT to assess the efficacy of English-language curricula in higher education: A Proposed Method

The world has entered the Internet + era as a result of the rapid development of information technology. To solve the current education crisis, universities and colleges must update their English curricula to reflect the demands of their students. Strengthening Internet + era awareness and reconstructing Internet + era resource advantages and technology advantages are both necessary given the Internet + era's characteristics. The current English teaching mode in colleges and universities has resulted in a number of positive changes, including a revitalization of college English instruction, the establishment of college English instruction that is better suited for the new era, and a significant increase in the efficiency with which college English instruction is carried out. The evaluation of English teachers in higher education classrooms is a hot topic right now. As a first step, it's important to note that both teachers and students care deeply about students' performance on the mandatory English examination. The rapid growth of distance network teaching using computers as its backbone has made it possible to present a wealth of video and audio teaching resources to students via the internet; this has helped to broaden the scope of online education and, in turn, advance the goals of digitalization, lifelong learning, and other reforms in the field of education. To begin, this article provides a concise summary of the issues of low efficacy, and inconsistent competence, as well as the discordant relationship between evaluation and instruction in contemporary college English.