Reviewerly/exHarmony huggingface dataset:
Deep Learning Based Network Traffic Analysis Using Modified Hybrid Methodology Comparing with SVM to Improve Accuracy

Users of networks are placing increased expectations on the speed and quality of the services provided by networks as a result of the fast advancement of network technology. As a result, one of the problems in the industry of network operation and maintenance management is to manage and regulate diverse network business traffic through efficient technological methods, differentiate between services, provide varied quality assurance, and fulfill the business demands of users. The identification of network traffic is a useful technological tool that may differentiate between the traffic generated by various applications. Through the processes of classifying, identifying, and distinguishing the application of network traffic, various types of traffic on the network may be provided with tailored network services, which in turn improves the quality of network services and the level of user satisfaction. The accurate identification of network traffic is not only a crucial foundation for the monitoring and data analysis of network traffic, but it is also the key to improving the overall quality of user service. Using a Hybrid Model that we've dubbed ANNSVM, the primary emphasis of this article is on doing an analysis of the data traffic on a 5G network. The acronym ANNSVM stands for Artificial Neural Network Support Vector Machine and combines the two terms. The term “artificial neural networks” (ANNs) refers to computer systems that utilize “learning algorithms,” which are programmes that can autonomously make modifications, or “learn,” when presented with new information. Because of this, they are a very useful tool for non-linear statistical data modeling, and SVM may function as a binary classifier. On the basis of the test data, the average classification accuracy is 98.8 percent, significantly exceeding other approaches that are already in use.
LEGAL ANALYSIS OF COPYRIGHT OF ARTIFICIAL INTELLIGENCE PRODUCTS IN THE ERA OF WEAK ARTIFICIAL INTELLIGENCE

At present, although artificial intelligence technology is still in the stage of weak artificial intelligence, it depends on the development of digital computing, which breaks the monopoly position of human beings in the field of literary creation, and then the legal subject and behavior of copyright have intelligent tendency, which puts forward new problems in judicial practice. Through the analysis of the nature of AI-generated works and the distinction between different categories, this paper makes a concrete analysis of the copyright of AI-generated works, and then draws the conclusion that the copyright of AI-generated works belongs to people. By using the criterion of "contact + substantial similarity", it is concluded that the self-editing articles produced by artificial intelligence based on deep learning do not belong to infringement. The expression and utilization of manuscript washing in artificial intelligence products constitutes infringement and does not belong to fair use. Whether the use of the original material by the ideological use of manuscript washing constitutes infringement requires a case judgment.
Combined Application of Various Techniques for Personalized Job Recommendation

The Personalized Job Recommender System is a subset of the custom recommendation system that provides a solution to the problem of information overload and is widely applied in numerous domains to solve a plethora of problems, such as unemployment and employment churn that we have seen emerging at higher rates in the COVID era. Furthermore, different jobs require divergent skill sets from their candidates to get hired. In this paper, we analyze the similarity techniques for Job Recommendation Systems based on the research done in the field of Job Recommendations. In our implementation, we have used three similarity measures: Tanimoto, Cosine (Orchini), and City Block similarity metrics. These techniques have been tested on a new Job Recommendation Systems Dataset taken from Kaggle. We have also analyzed the performance of similar techniques involving other distance measures, such as Euclidean distance. The performance of these similarity score-based techniques for generating the highest score-based recommendations is assessed using different evaluation metrics such as Accuracy, Precision, Recall, and F1-score respectively.
Lung Cancer Identification System to Improve the Accuracy Using Novel K Nearest Neighbour in Comparison with Logistic Regression Algorithm

The K Nearest Neighbor (KNN) algorithm is going to be compared against the logistic regression method in an effort to determine whether one has the potential to provide a lower false detection rate of lung cancer. Both the Techniques and the Materials: A total of 304 photos were taken using data from three different lung cancer datasets found on Kaggle. Group 1 is the representation of the KNN method, while Group 2 is the representation of the logistic regression technique. The G power was calculated using a significance level of 80% and an alpha value of 0.05. The first group, Group 1, and the second group, Group 2, each had 20 samples analyzed. The results showed that KNN had an accuracy of 89.56 percent, but the accuracy of the logistic regression approach was only 80.11 percent. The KNN technique reached a level of significance of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{p}=\mathbf{.042}$</tex> when it was applied using the logistic regression methodology. The results of this research reveal that the KNN technique is much more accurate than the Logistic Regression strategy when it comes to the detection of lung cancer in the datasets that were examined for this research.
EXPLORATION AND RESEARCH ON THE PATH OF SCHOOL-ENTERPRISE COOPERATION IN HIGHER VOCATIONAL EDUCATION IN THE NEW ERA—TAKING ZHEJIANG G HIGHER VOCATIONAL COLLEGE AS AN EXAMPLE

School-enterprise cooperation is an indispensable mode in the development of higher vocational colleges, and it is also the main way for higher vocational colleges to improve their self-competitiveness. It plays a crucial role in cultivating students' innovative and practical ability. This paper takes Zhejiang G higher vocational college as the research object, analyzes the problems of school-enterprise cooperation in this college, and puts forward a new path of school-enterprise cooperation in higher vocational colleges, which has certain practical significance for the development of higher vocational education in China.
STUDY ON THE CONSTRUCTION OF A PERSONALIZED TEACHING MODEL OF SECONDARY SCHOOL ENGLISH CLASSROOM BASED ON THE CULTIVATION OF CRITICAL THINKING ABILITY UNDER THE PERSPECTIVE OF BIG DATA

On the background of big data, the author applies constructivism learning theory and conducts research with the goal of developing secondary school students' English critical thinking skills. Teaching mode in the traditional secondary school still follows empiricism. In the era of rapid development of big data, secondary school teachers use big data to analyze a large amount of information, understand students' learning dynamics, adjust their teaching schemes according to their realities, and carry out studies based on students' characteristics. Secondary school teaching mode still follows empiricism. In the era of rapid development of big data, secondary school teachers use big data to analyze a large amount of information, understand students' learning dynamics, adjust their teaching schemes and carry out teaching according to students' characteristics to guide them to develop critical thinking skills in it.
Distributed Cloud Computing

The concept of distributed cloud computing is a part of cloud computing systems in which the computing resources and software applications are located at different cloud service providers or servers but they act under one cloud computing software for operating and managing the resources across multiple cloud platforms.
Blockchain Technology

Blockchain technology is a digital peer-to-peer network powered by the innovative, secure, reliable software platform for the management and processing of customers’ transactions without any centralized control and management mechanism.
Assessment and Decision Making for Grid Connection of Photovoltaic Systems in Residential Projects in Iraq

Urban growth leads to continuous demand for energy hence a negative impact on sustainability. Energy transition plays a role in mitigating the impacts of increasing the share of renewable energy and improving energy systems. As solar energy is growing fast, this study focused on the factors that contribute to its growth in developing countries. Grid connection is a crucial in achieving the maximum benefit from the outputs of photovoltaic systems (PVs) in residential projects. This study attempts to address this gap. Iraqâ€™s government initiatives to expand solar energy and establish new residential cities represent a promising opportunity to achieve stability in energy systems. A hybrid decision-making model, TES-AHP, was developed to select the grid connection type to accommodate the PV energy produced in residential projects depending on technical, economic, and social dimensions. The results indicate that distributed energy systems are the best option, and that social acceptance and experience of the technical staff are the most significant factors in this decision. Taking into consideration that all factors investigated in this study contribute to the success of the reforms in the energy sector, this study recommends that policymakers should initiate the expansion of solar energy installation by exploiting residential projects.
Design of Encrypted Steganography Double Secure Algorithm Using the Laguerre Transform

The excessive usage of imaging data has created the essential need to design security algorithms for copyright protection. As a consequence, merging of two image security standards for more robustness has become a critical task. This research focuses on combining the encryption method along with the steganography standard for providing double security. The Laguerre Transform (LT) domain stego embedding based on LSB embedding is proposed to combine with RC4 fast encryption method. The speedy recovery of RC4 is the prime reason for adopting it for data encryption. The method is implemented in 2D domain. It is recommended to evaluate the performance of both cases based on quantitative evaluation using PSNR and SSIM as the parameters. It is found that the modified double layer security can improve data authenticity.
Role of Blockchain and Smart Contracts in Healthcare -Challenges and Benefits

It is essential for healthcare 4.0, which includes patients and their care professionals, to have a dependable operating system to operate in a way that is free from the potential for danger. Items that are consistent with the current trajectory of economic progress when it comes to keeping a distributed ledger with several points of access honest, blockchain technology might be of assistance. This work investigates how technology may be used to raise the dependability of various types of systems. Further, a brief survey and comparative analysis of blockchain and smart contract-based development, operation, and maintenance of healthcare information systems are prepared in this work.
Generic Signcryption-based Certificate Less Security Scheme for Internet of Things

The generic signcryption scheme that addresses the certificate management and key escrow issues is utilized as signcryption scheme, and has a variety of application scenarios in the resource-constrained Internet of Things (IoT) environment. However, it is proved by specific attack methods that the scheme of Karati etc. cannot handle forgery attacks. This paper summarizes the reasons why the attacker successfully forged, and proposes a certificate-free generalized encryption scheme without bilinear pairing. The scheme's security is proved on the basis of the model's computational discrete logarithm and Diffie-Hellman problem. The experimental results show that the proposed scheme is advantages in terms of computational and communication overhead, and is more suitable for limited resources—secure data transmission between IoT devices as compared with the existing systems.
Giraph Dynamic Sized Structure Recurrent Subgraph Generation Algorithm for Frequent Subgraph Mining

Data Mining has a subpart called Frequent Subgraph Mining (FSM) and is a demanding area for the implementation of graph classification and graph clustering which is used in the area of the social network, chemical compounds, and biological datasets, enterprise world. Many research workers have been researching on how to produce an effective and optimized technique to extract the candidate subgraphs by eliminating duplicates for the last few decades. In the case of the Giraph distributed system, a different format for input and output classes is required to take graphs into memory and put graphs after completion of its operation, which leads to excessive memory exhaustion. In this paper, a novel methodology “Giraph Dynamic Sized Structure Frequent Subgraph Mining (GDSSFSM)” has been developed to reduce the memory necessity for FSM in a graph-distributed system. The proposed approach reorganizes the inner input format class (i.e. setEdgeInputFormatClass) without any changes. Hence, it can be used by default in a customized format. The experimental analysis is done on the different datasets with an existing algorithm based on execution time and memory requirements and concludes that it decreases up to on average 52% depending on the dataset and the graph (i.e., PageRank, Connected Components, and Simple Shortest Path) edge-centric algorithm. The proposed algorithm can be used in various fields of graph mining such as social networks, bioinformatics, and web data mining
A Survey of Diverse Segmentation Methods in Image Processing

The importance of image-processing techniques has increased across a wide range of applications as a result of advancements in computer technology.An important stage in the processing of images is segmenting of images.The process of breaking a picture into groups that each have their own distinct characteristics and similar characteristics, such as intensity, texture, class, culture and colour is known as segmentation.Image segmentation has been done with many different algorithms and methods.In this article, study reviews various image segmentation technologies.This paper covered many segmentation methods, including Threshold, Edge Detection, Region-based, Neural Network Image Segmentation and Clustering in any economy to increase the quality of life.This study is helpful in deciding how to best apply image segmentation techniques, as well as in enhancing their precision and effectiveness, in order to achieve the main goal of developing new algorithms.
Research on Path Planning of Intelligent Mowing System Based on Computer Vision

Among all kinds of lawn machinery, mower is the mainstream product, because the task of mowing is large and has strong repeatability. However, due to the backward technology, the traditional lawn mower consumes a lot of human and material resources. Therefore, the United States and other western countries put forward the concept of intelligent lawn mower robot. Intelligent mower is a very complex system, which integrates a variety of functions and technologies. Therefore, the research on Intelligent mower involves many aspects. This topic focuses on path planning and obstacle detection technology. Path planning technology is to plan a path without collision with obstacles between the starting point and the target point according to the perceived working environment information and some optimization index, and complete the expected task. Obstacle detection technology helps the mower to detect small or moving obstacles when walking along the planned path through sensors, so that the mower can avoid opening obstacles in time. This paper introduces the basic knowledge of path planning, which includes traditional point-to-point path planning and whole-area coverage path planning, analyzes and compares the differences between them, and deeply studies the whole-area coverage path planning according to the working requirements of lawn mowers.
Automatic Hospital Inspection System Based on Convolutional Neural Network

To solve the problem of timely replacement of hospital infusion bottle, first, ResNet and DenseNet were utilized as basic network to detect and locate their positions. Second, transfer learning algorithm was employed for the training. Third, part of the convolutional layers were trained while the front some layers were froze, this could reduce greatly the workload of training without significantly reducing the detection accuracy; Last, Canny algorithm was applied to detect the edge information of the sling, the lever position of the liquid medicine was determined by contrasting adjacent frames of dynamic change of edge data. Reminder mechanism was triggered as the liquid lever reached the given threshold.
“The great chain of being sure about things”: blockchain, truth, and a trustless network

Often associated with the volatile cryptocurrency Bitcoin, a blockchain is a distributed ledger, an additive record of digitally networked transactions. On the assumption that technologies are cultural practices as much as they are instrumental, this essay contributes to the special issue on big data in communication by examining texts that constitute blockchain in a shared political imaginary and, in so doing, participate in blockchain's future. The essay analyzes how materials produced by IBM orient blockchain within contemporary ideals such as transparent access, immutability, and a single source of truth. Drawing on Sean Phelan's study of neoliberal politics and global media, including the naturalization of ideological values, the essay proposes that public discourses about blockchain evidence fundamental human desires to access truth and establish an infrastructure immune to corruption. The study finds that blockchain's decentralized infrastructure comports with a historical moment in which sociality itself is under intense pressure, and wherein the idea and practice of interconnected yet distrustful nodes make sense in the absence of functional governance. The essay concludes that in the routinized social practices of the blockchain's executive protocols, political potential is undercut by the absence of prospective and fundamental change.
Research on Pedestrian Detection and Recognition Based on Improved YOLOv6 Algorithm

Pedestrian detection is a technology that uses computer vision to determine whether there are pedestrians passing by in the video sequence or pictures, and realizes the positioning of pedestrians. It is an important task in manless driving, automobile intelligence and intelligent monitoring. Aiming at the problems of low efficiency of pedestrian detection and slow running speed on small devices, a YOLOV6-SE (Yolo Look Only Once-SE) model is constructed to detect pedestrian targets。the mAP detected by pedestrians reaches 85.1%, which is 7.3% points higher than that of the original YOLOv6 without adding attention mechanism. The three-layer channel attention module is added to backbone(RepVGG) adopted by YOLOv6, which enables the backbone network to extract more feature information, thus improving the accuracy of the network. After experimental comparison, Good detection performance is achieved.
Maximising Rural Education Stakeholder Participation through the Use of Information Communication

There are multiple references in the relevant education regulations globally to explain why stakeholder participation is necessary for schools. South Africa’s democracy through the South African Schools Act (RSA, 1996) has created a need for stakeholders to participate in the decision-making processes in the schools in the country. However, rural schools seem to be less favoured by government education policies, leading to reluctance in stakeholder participation. Isa et al. (2019) posit that the lack of limited stakeholder participation in rural schools is the result of a lack of the necessary infrastructure, such as teaching aids, a conducive atmosphere, and other necessary components to improve teaching and learning. The effects of Covid-19, especially in the rural communities, influenced the writing of this chapter. We identified the inability of rural stakeholders to participate in their usual activities such as meetings because only virtual collaborations were possible due to Covid-19 national lockdown. In line with the identified inability, this chapter problematises the gap created by digital illiteracy in the rural school communities and argues that information and communications technology (ICT) has the potential to maximise rural education stakeholder participation. Activities of school governing bodies (SGB s) and representations by teacher unions in almost all rural schools demonstrate the willingness of stakeholders to participate in rural education; however, this participation is insufficient. This chapter employs Donaldson and Preston’s (1995) stakeholder theory to explore the role of ICT in maximising the participation of stakeholders in rural education. We established an ICT stakeholder participation monitoring framework to assist rural education stakeholders to maximise their participation.
Random variables and their quantitative characteristics

In the second chapter random variables are introduced and investigated in the framework of axiomatic of Kolmogorov. It is shown a connection of probability distributions and distributions of random variables as well as their distribution functions. The notion of the Lebesgue integral is given in context of definition of moments of random variables (see [1], [7], [10], [15], [19], [21], [40], and [45]).
ON-BOARD THREE-AXIS VIDEO STABILIZATION SYSTEM USING DIRECT DRIVE TECHNOLOGY

The evolution of technology in the field of unmanned aerial vehicles (UAVs) has led to the miniaturization of the equipment on board these aircraft. The most used equipment mounted on a UAV is still the video sensor, with the help of which the video image from the aerial vehicle can be processed for dedicated applications (facility inspection, surveying and mapping, operational oversight, traffic control, etc.). In this paper, we present the prototyping of a three-axis gimbal using the direct drive technology to replace the current two-axis gimbal payload equipping our Hirrus UAV. The results demonstrate notable improvements both mechanically, by excluding the gearing of the transmission belt (generating friction and elasticity in the system), and in terms of video resolution, maintaining quality especially at high zoom levels (over 5x).
INFORMATION SUPPORT FOR THE DEVELOPMENT OF PARENTS OF DIFFERENTLY-ABLE PERSONS IN WEST MEDINIPUR DISTRICT, WEST BENGAL (INDIA)

The parents of differently-able persons are very much unaware about the information needed for their children in particularly West Bengal. These parents are in need of information for the development of their children. In most cases they do not know how and where to get information properly and correctly. Darkness in their knowledge led the differently-able persons to a worst situation and led them far away from leading normal life like other human being instead of living on everyone’s kindness and begging. Modern science and technologies are developing day by day in the field of artificial limbs by the scientists and technicians. History shows that proper information, guidance and training can make them self-sufficient.