Reviewerly/exHarmony huggingface dataset:
Security Use-Cases for Countering Threats of CLA and UAV in 6G

Recently1, 6G mobile communication technology has been actively researched and faces issues about security and privacy. The various technologies and applications to be used in 6G will bring new security issues. Among the technologies, CLA and UAV have a ripple effect that can cause severe damage to the entire network. In particular, in case of the UAV base station, there are various security points such as devices, software and communications to be considered. In this paper, the security use-cases and threats scenarios of both CLA and UAV in 6G environment are described. Initially, the major threats that can occur in each environment are described through system architecture and communication process. Afterwards, the security use-cases are described in consideration of the threats and characteristics of each environment respectively. Finally, the attacks and security use-cases of each environment are depicted as the respective scenarios.
Textual Posts Analysis Using Voting Classifier

Web-based media is a tremendous virtual space to communicate and impart singular insight. Despite the fact that there are numerous positive parts of web-based media, there are additionally chances. Strict and standing predisposition, religious and caste bias, sexual provocation, cyberbullying and sexting are at present netizen’s propensity via online media. This affects the humans round the globe. Bias identification is a perplexing cycle that involves a few measurements to be considered and it is interwoven with social, political and monetary issues. This necessitates the requirement of a model which is capable enough to segregate the posts in their initial phase and curb those posts which are threat or can lead to civil disobedience and mass agitation. The proposed model presented in this paper can recognize the biasedness of client’s post and characterize it into positive or negative. This characterization will help in blocking their further propagation. In the experimental study, the textual posts are gathered from different sources. The proposed model scored highest accuracy of 80.30%.
Overcoming misattribution to understand open data reuse in smart cities

Smart city governments face several barriers in adopting open data initiatives. Some of these barriers are related to the attribution rights of open licenses, as reusers often misattribute open data. As a result, publishers do not know which open data is reused and for what, thus ignoring the return on investment and hindering the sustainability of open data initiatives. In addition, reusers who do not fully comply the right of attribution of open data licences may face legal problems. To overcome these pitfalls, this paper envisions an approach that aims to extend open data publication standards with elements coming from open-source software development standards, to support reusers in achieving proper attribution of open data. Our approach is a first attempt to both (i) enabling publishers to gather information to understand open data reuse in smart cities, and (ii) supporting reusers to avoid lawsuit issues related to open data license violation.
Focus on Increasing the Number of Attendees

Special Events GaloreVolume 22, Issue 2 p. 8-8 Event Promotion Ideas Focus on Increasing the Number of Attendees First published: 08 January 2022 https://doi.org/10.1002/speg.31724Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume22, Issue2February 2022Pages 8-8 RelatedInformation
Blockchain Technology and its Applications and Appeals in Commercial World

Blockchain is an innovation that can raise considerable change in the current business world and can have a huge explosion on the resulting years. It can change the current understanding on the business movements, and can change the economy as well. Blockchain is considered as a conveyed record innovation, and since it is very hard to be altered or manufactured it reasonably guarantees straightforwardness, information security and respectability. A large portion of the current applications regarding Blockchain Technology is focusing on a space, cryptographic money, generally Bitcoin. Blockchain Technology is a decentralized innovation that can have shifted working in government, money and banking industry, bookkeeping and Business Process Management. Attributable to its particular highlights in security region, it has set up distinction in an exceptionally limited ability to focus time and thus parcel of studies are being done on taking advantage of Blockchain as fundamental administrations .Several organizations are moving towards Blockchain to improve the security elements and dependence in their organizations. Subsequently, this concentrate simply centers around investigating different freedoms and related difficulties looked in the current or future uses of Blockchain Technology. Consequently, countless exploration papers were painstakingly inspected, examined and researched dependent on their contributions to the Block chain's realities.
Probing Simple Factoid Question Answering Based on Linguistic Knowledge

Recent studies have indicated that existing systems for simple factoid question answering over a knowledge base are not robust for different datasets. We evaluated the ability of a pretrained language model, BERT, to perform this task on four datasets, Free917, FreebaseQA, SimpleQuestions, and WebQSP, and found that, like other existing systems, the existing BERT-based system also can not solve them robustly. To investigate the reason for this problem, we employ a statistical method, partial least squares path modeling (PLSPM), with 24 BERT models and two probing tasks, SentEval and GLUE. Our results reveal that the existing BERT-based system tends to depend on the surface and syntactic features of each dataset, and it disturbs the generality and robustness of the system performance. We also discuss the reason for this phenomenon by considering the features of each dataset and the method that was used to evaluate the simple factoid question answering task.
Text Difficulty Classification by Combining Machine Learning and Language Features

In recent years the application of deep learning algorithms has dominated this research field on document readability prediction. Traditional methods rely excessively on manual feature extraction, and modern deep learning algorithms are severely time consuming in terms of efficiency in deep feature extraction. On the other hand, there is a considerable lack of common datasets for readability studies in the relevant English language education field. In view of these problems, we proposed an improved approach for corpus construction and reconstructed a more reasonable dataset (English Teaching Texts in China, CETT) with five-level difficulty by purposefully integrating and adding some missing datasets. Based on CETT, we extracted three dimensions of text word frequency features, linguistic difficulty features and in-depth features. We compared the effectness of combining features of different features for difficulty prediction under multiple classifiers. The final experimental results show that the accuracy of fused linguistic difficulty features and in-depth features on the text difficulty assessment task reaches 0.9011, and the fused features have an overall significant improvement over using individual features.
A Behavior Recognition Algorithm Based on 3D-VGG-GAP Network

In view of the low accuracy of the current human behavior recognition algorithm and the large amount of calculation parameters, this paper proposes a behavior recognition algorithm based on 3D-VGG-GAP, which combines the improved VGG network and GAP layer. Firstly, the overall structure of CNN is optimized, and the BLOCK structure is adopted. Videlicet, multiple convolution kernels are stacked to extend the overall depth of the neural network and reduce the number of network parameters. In addition, the BN algorithm is introduced into the network, which greatly improves training speed. Subsequently, the dropout layer is added to Block structure, which significantly improves the ability to extract high-level abstract features. In the end, the experimental results indicated that the recognition accuracy of the network on the UCF-101 data set reached 92.4%, and the computing speed is 800 frames per second. The results show that the proposed method solves the problem of large amount of network parameters, accelerate training speed, and prone to over-fitting. It has the advantages of strong scalability and easy construction. It verifies the feasibility of the algorithm in practical applications.
Viewing Direction Weighted Bit Plane LSB Data Hiding in 360° Videos

In this paper, we propose a viewing direction weighted bit plane least significant bit (LSB) data hiding method for 360&#x00B0; videos. Normalized Gaussian mixture models (GMMs) of the distributions of the relative viewing direction frequency for latitude, longitude, and both latitude and longitude are used to describe the human viewing behavior of 360&#x00B0; videos on head-mounted displays (HMDs). Apart from formulating data hiding weight functions to control the amount of secret data to be hidden at given latitudes and longitudes, the GMMs are used in this work also to define viewing direction weighted bit plane functions. These viewing direction weighted bit plane functions specify the number of bit planes to be used for data hiding depending on the latitude and longitude. As humans pay more attention to the central front view of 360&#x00B0; videos when watched on HMDs, the number of engaged bit planes is decreased accordingly from the poles to the equator, and from the west and the east to the front view. A performance assessment of the proposed viewing direction weighted bit plane LSB data hiding method is conducted, showing the trade-offs between capacity and objective perceptual video quality. The results indicate that it is preferable to first utilize a given bit plane for data hiding in an entire 360&#x00B0; video frame before engaging the next higher bit plane subject to the viewing direction weighted bit plane functions.
A Novel Gradient Feature Importance Method for Neural Networks: An Application to Controller Gain Tuning for Mobile Robots

In the paper, a novel gradient-based feature importance method for neural networks is described. This method is compared to the existing feature importance method using a trained neural network, which predicts the optimal gains in real time, for a steering controller on a mobile robot. The neural network is trained using the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm, in order to minimize an objective function. From an analysis using the feature importance methods, key inputs are determined, and their contribution to the neural network’s prediction are observed. Furthermore, using a first-order Taylor approximation of the neural network, an improved control law is determined and tested based on the results of the gradient-based feature importance method. This analysis is then applied to an existing neural network using real-world experiments, in order to determine the behavior of the gains with respect to each input, and allows for a glimpse into the neural network’s inner workings in order to improve its explainability.
Model and Algorithms for User Identification by Network Traffic

This paper proposes a method of user identification by network traffic. We describe the information model created, as well as the implementation of each of the proposed problem solving stages. During the network traffic collection stage, a method of capturing network packets on the user's device using specialized software is used. The information obtained is further filtered by removing redundant data. During the object feature descriptor construction stage, we extract and describe the characteristics of network sessions from which the behavioral habits of users are derived. Classification of users according to the extracted characteristics of the network sessions is performed using machine learning techniques. When analyzing the test results, the most appropriate machine learning algorithms for solving the problem of user identification by network traffic were proposed, such as: logistic regression, decision trees, SVM with a linear hyperplane and the boosting method. The accuracy of the above methods was more than 95%. The results proved that it is possible to identify a particular user with a sufficiently high accuracy based on the characteristics of the data transmitted through the network, without examining the contents of the transmitted packets. Comparison of the developed model has shown that the proposed model of user identification by network traffic works as effectively as the existing analogues.
Application of Model Predictive Control Algorithm Used in Path Planning of the Unmanned Tractor

In this paper, the model predictive control algorithm based on the kinematics model of unmanned tractor is designed for the problem of precise tracking of unmanned tractor path. The adaptive predictive control algorithm is used to adaptively control the PID parameters [1]. Finally, the method of predicting the path tracking model in the actual road is compared with the traditional PID control result. The results show that compared with the traditional PID control algorithm, the overshoot and oscillation amplitude can be effectively suppressed by the model predictive control algorithm[2], the reference path can be accurately fitted, and the optimization of system dynamic performance and steady-state error performance can be optimized, Unmanned tractors got a more accurate path fit result. Realized to achieve more precise control.
Image Translation Method for Game Character Sprite Drawing

Two-dimensional (2D) character animation is one of the most important visual elements on which users’ interest is focused in the game field. However, 2D character animation works in the game field are mostly performed manually in two dimensions, thus generating high production costs. This study proposes a generative adversarial network based production tool that can easily and quickly generate the sprite images of 2D characters. First, we proposed a methodology to create a synthetic dataset for training using images from the real world in the game resource production field where machine learning datasets are insufficient. In addition, we have enabled effective sprite generation while minimizing user input in the process of using the tool. To this end, we proposed a mixed input method with a small number of segmentations and skeletal bone paintings. The proposed image-to-image translation network effectively generated sprite images from the user input images using the skeletal loss. We conducted an experiment regarding the number of images required and showed that 2D sprite resources can be generated even with a small number of segmentation inputs and one skeletal bone drawing.
Art Construction and Protective System Design of Northeast Residential Decoration Classification Patterns Based on Big Data

There are all kinds of traditional dwellings reflecting different national cultures, different historical periods, and different decorative patterns in our country. These dwellings carry a large number of excellent and valuable historical and cultural heritages. Based on big data, the artistic construction and protective system design of decorative classification patterns of northeast residential buildings are conducive to better development and protection of northeast residential buildings. The purpose of this article is to study the art construction and protective system design of northeast residential decoration classification pattern based on big data. This article summarizes and categorizes the decorative patterns of the different structures of the northeast residential houses in detail, and summarizes and analyzes the decorative patterns of the brick-wood structure, the brick-concrete structure and the brick-steel structure. Based on the background of the era of big data, this paper designs the protective system of the decorative pattern art of northeast residential decoration, and puts forward suggestions for the inheritance and development of decorative patterns in artistic construction. The survey data shows that among the decorative patterns of Northeast residential buildings, the theme of the decorative patterns of plants, flowers, animals, and traditional Chinese characters are 80%, 46.43%, and 35.71%, respectively. It can be seen that the northeastern residential houses use more plants, flowers, animals, and traditional Chinese calligraphy patterns.
Enhancing Model Adaptability Using Concept Drift Detection for Short-Term Load Forecast

Concept drift refers to the relation between input and target envolves over time in an online supervised learning scenario. With the development of smart grid and smart meter, mass data accessibility poses a huge challenge to learning model adaptability. To address such issue, this paper proposed a model adaptability enhancement approaches based on concept drift detection for short-term forecast model. It exploits canonical correlation analysis to measure mapping relation between input and output of the forecast model. Then the correlation coefficient vector sequence will be monitored over an adaptive window to detect concept drift. Model is updated on data over the sliding window only when a concept drift happens. Experimental results shows this method reduces memory assumption and computing resources remarkably meanwhile guarantees the forecast accuracy.
Status of Research on Privacy Protection of Location-Based Services

Location Based Service (LBS) is playing an increasingly important role in our daily life, which has greatly changed people's lifestyle, but at the same time, it also brings the problem of location privacy threat to users. Location privacy protection technology has received a lot of attention from researchers, and a large number of research results have emerged. This paper firstly introduces the privacy protection background of location-based services, including the origin of LBS, the current status of commercial applications, and the existence of threats; then introduces the LBS privacy protection architecture, and classifies and summarizes the LBS privacy protection.
Game Outlier Behavior Detection System Based on Dynamic Time Warp Algorithm

This paper proposes a methodology for using multi-modal data in gameplay to detect outlier behavior. The proposed methodology collects, synchronizes, and quantifies time-series data from webcams, mouses, and keyboards. Facial expressions are varied on a one-dimensional pleasure axis, and changes in expression in the mouth and eye areas are detected separately. Furthermore, the keyboard and mouse input frequencies are tracked to determine the interaction intensity of users. Then, we apply a dynamic time warp algorithm to detect outlier behavior. The detected outlier behavior graph patterns were the play patterns that the game designer did not intend or play patterns that differed greatly from those of other users. These outlier patterns can provide game designers with feedback on the actual play experiences of users of the game. Our results can be applied to the game industry as game user experience analysis, enabling a quantitative evaluation of the excitement of a game.
Utilization of RFID Technology in the Employee Mobility System of the Inspectorate Office of Merauke Regency

The Inspectorate of Merauke Regency has a role as an internal supervisory agency for the implementation of government in Merauke Regency. Mobility in the office has not been supported by an entry and exit permit book for employees during working hours. The impact is a lack of employee attendance information. In addition, the process of calculating the percentage of active working time also experiences problems because it is done conventionally. To solve this problem, an employee mobility system was designed in the office by utilizing Radio Frequency Identification (RFID) technology. The system was built using the prototyping method. The design of mobility devices using a limited microcontroller in the form of a prototype. Presentation of data and information from the prototype recording is displayed on a website. The website is built using PHP and javascript programming languages. The results of the study led to a new innovation in monitoring the attendance of employees at the office during working hours by the Receptionist Department. The results of this study are also able to present the calculation of earning an allowance called Tunjangan Khusus Pengawasan Daerah (TKPD) for employees in the form of a percentage of mobility activity during active working hours.
Detection and Classification of Cells in Colorectal Histology Images Utilizing U-Net Machine Learning Architecture

Cell detection and classification in hematoxylin and eosin stained (H & E stained) histology images are important and difficult steps in the analysis of these images. [1] In this paper, we propose a method for the detection and classification of individual cells in H & E stained routine colon cancer histology images that utilizes the unique advantages of the U-Net neural network architecture. The proposed method employs the U-Net architecture during cell detection to apply the architecture’s use of both global and local features in the image to make decisions. [2] Instead of performing the more difficult task of segmenting entirety of a cell, this detection model regresses on the probability that a pixel is within 4 pixels of the annotated center of a cell. For cell classification, our method uses a simpler convolutional neural network (CNN) to accurately predict the annotated cell type. We evaluated this detection and classification method using the publicly available CRCHistoPhenotypes dataset [3] which contains annotations for 29,756 cell centers and 22,444 annotated cell classifications. Our results show that this method is a viable and fast way to detect and classify individual cells in H & E stained colorectal histology images and has the potential to greatly benefit digital pathology and our understanding of colorectal cancer.
A method of AMGL coding technique on odd-even sum labeling

A technique of coding secret messages using odd-even sum labeling and AMGL code is developed through this paper. Illustrations for coding a text message using H-graph by odd-even sum labeling are studied.
Coxswain: Guided Data Analytics

Data science is driving the next economic revolution, though, it relies on efficient data exploration of data sources, is expensive, and is considered artisanal by data scientists and data analysts. This work introduces Coxswain, a novel data exploration assistant that guides the search of relevant models and provides valuable insights into the data. It mimics the steps of data scientists by selecting and analyzing exploration patterns used to suggest analytical steps in the form of scenarios. Coxswain then captures the customer decisions and adds this feedback for future suggestions. This paper concludes by presenting lessons learned when developing a proof of concept, and interactions with data practitioners.