Reviewerly/exHarmony huggingface dataset:
Framework for Creating Outdoors Augmented and Virtual Reality

In this article we propose the architecture of a system in which its central objective is focused on creating a complete framework for creating outdoor environments of Augmented Reality (AR) and Virtual Reality (VR) allowing its users to digitize reality for hypermedia format. Subsequently, there will be an internal process with the objective of merging / grouping these 3D models, thus enabling clear and intuitive navigation within infinite virtual realities (based on the captured real world). In this way, the user is able to create points of interest within their parallel realities, being able to navigate and traverse their new worlds through these points.
Basic principles of the methodology of research of mass media of political parties in library and information activity

The basic principles of the methodology of research of mass media of social and communicative nature are considered. Emphasis is placed on the peculiarities of the methodological basis for the study of party media as an object of library and information activities. Various methodological approaches and methods have been studied, as well as their effectiveness has been analyzed, which gives grounds for concluding on the effectiveness of using the communicative component of the methodological complex in library research, which is based on the dialectical method. Different research concepts are considered.
Standards and Protocols of Blockchain

In the present digital world, the day-to-day activities of human life is managed digitally, and often the scenario of “anything, anytime, and anywhere” holds. The three-dimensional buzz words which revolve globally are Internet of Things, Industry 4.0, and Smart Cities. Smart cities are feasible today because of the advancements in technologies such as ubiquitous sensing, heterogeneous network, intelligent sensing, and processing of information and control systems. Security is a major concern when all the daily chores are managed digitally. Therefore, more research has to be focused on security aspects to ensure authentication, privacy, and trust. Blockchain is providing a significant contribution to achieving a secured platform for distributed transaction management. There is a stringent need to discuss in detail the various standards and protocols of blockchain. This chapter explains the standards and categorization of blockchain and its protocols. It includes the protocols of network and data organization, distributed consensus protocols, the framework of autonomous organization that relies on smart contracts executed in distributed virtual machines, and the human–machine interface implementations. There are three main categories of consensus protocols namely, Proof of Work, Proof of Stake, and Byzantine Fault Tolerant which are categorized based on the applications.
Blockchain Model of Sensitive Information Identification in Social Online Games

This paper studies the sensitive information recognition and processing system in social online games.A real-time data mining framework for sensitive information on online games in the blockchain mode is proposed.A large-scale social online game chat room database is selected for testing, with SKLearn library based on Python is used for data preprocessing.The model training and verification are implemented through tensorflow.The results show that the accuracy of TextCNN is over 5% higher than that of Naive Bayes and CNN models for short text recognition in online game context, so TextCNN model can meet the requirements for high efficiency and accuracy of sensitive information recognition in large-scale online games.The conclusion proves that the blockchain-based online game sensitive information mining framework has the advantage of alliance chain hierarchical management, the characteristics of the distributed ledgers leaving traces in the whole process of information dissemination, and enhanced contract enforcement through the smart contracts and consensus mechanisms.
Application of Pseudo-random electrical exploration method in mineral exploration: a case study of skarn-type ore body

Abstract Skarn-type ore body containing rich mineral resources, which is a type of deposit with important industrial significance and a wide distribution, is formed by metasomatism of mineral gas-bearing hydrothermal fluids on or near the contact zone between intermediate-acid-medium-basic intrusive rocks and carbonate rocks. It is hard to obtain stable data by using conventional electrical prospecting method because of strong electromagnetic interference. A new pseudo-random method is used to do geological survey work in Northeast China for interference avoidance.
Compression for population genetic data through finite-state entropy

We improve the efficiency of population genetic file formats and GWAS computation by leveraging the distribution of samples in population-level genetic data. We identify conditional exchangeability of these data, recommending finite state entropy algorithms as an arithmetic code naturally suited for compression of population genetic data. We show between [Formula: see text] and [Formula: see text] speed and size improvements over modern dictionary compression methods that are often used for population genetic data such as Zstd and Zlib in computation and decompression tasks. We provide open source prototype software for multi-phenotype GWAS with finite state entropy compression demonstrating significant space saving and speed comparable to the state-of-the-art.
On the Effective Application of New Media Platform in Student Management

With the continuous development of network information technology, the student management of the school is also facing new opportunities and challenges.In order to further improve the scientificity of student management, we should effectively grasp the development trend of new media.Based on this, this paper studies the application of new media platform in student management, starts with the impact of new media environment, and discusses the links of class management, school management and daily affairs management.
Data Secure Communication System Based on OpenABE

In the information era, data security is important.At first, it is an identity-based encryption mechanism and then it extends various branches and deformations.A policy combined with attribute based access control is known as Attribute Based Access Control came into existence.The two classes of Attribute based Encryption Scheme, such as CP-ABE (Ciphertext-Policy Attribute-based Encryption) and KP-ABE (Key Policy Attribute-based Encryption) concepts are utilized to data security communication system.In this paper, we build a data security communication system based on OpenABE library.We use the basic system call of network communication send() and recv() to send or receive message.Then, the OpenABE encrypts or decrypts for the message.In ABE system, the public key and private key are not one-to-one, and one public key can correspond to multiple private keys.No matter how many users share the data, it is only necessary to encrypt it once.When encrypting the information, the encryption party does not need to know who is decrypted, and the decryption party can decrypt it which only needs to meet the corresponding conditions, so as to achieve data access control while encrypting.Besides, this paper also briefly introduced the ABE and the OpenABE.
Distantly Supervised Semantic Text Detection and Recognition for Broadcast Sports Videos Understanding

Comprehensive understanding of key players and actions in multiplayer sports broadcast videos is a challenging problem. Unlike in news or finance videos, sports videos have limited text. While both action recognition for multiplayer sports and detection of players has seen robust research, understanding contextual text in video frames still remains one of the most impactful avenues of sports video understanding. In this work we study extremely accurate semantic text detection and recognition in sports clocks, and challenges therein. We observe unique properties of sports clocks, which makes it hard to utilize general-purpose pre-trained detectors and recognizers, so that text can be accurately understood to the degree of being used to align to external knowledge. We propose a novel distant supervision technique to automatically build sports clock datasets. Along with suitable data augmentations, combined with any state-of-the-art text detection and recognition model architectures, we extract extremely accurate semantic text. Finally, we share our computational architecture pipeline to scale this system in industrial setting and proposed a robust dataset for the same to validate our results.
Image Splice Detection based on Illumination Inconsistency Principle and Machine learning Algorithms for Forensic Applications

Image tampering is a technique by which the image and visual properties are altered for many reasons like for enjoyment, for creating false evidence, for publicity etc. This can be done by the application of hardware, software and editing tools. A performance comparison and a novel approach for verifying the image authenticity through the exploration of the illuminant based transformed spaces which is given by Illuminant Maps (IM) is discussed in this paper. A method is proposed for detecting the image splice. For acquiring statistical-based information two algorithms are applied namely Generalized Grayworld Estimates (GGE) and Inverse Intensity Chromaticity (IIC). The various features thus obtained through image descriptors like color, shape and texture features. The proposed method provides a solution for detecting the image tampering. Two different data sets DA1 and DA2 are considered for the experiments and are tested. The feature extraction algorithms are tested namely SVM, KNN, GLCM and proposed method using GLCM and SURF. The proposed work produced a better result of 83.50%, 82.55% and 84.50% in terms of accuracy, sensitivity and specificity respectively comparing to the classical methods of image forgery detection.
The Effect of Professor Support on the Quality of Life of Middle-aged Female Adult Learners: Focusing on the Mediating Effect of Satisfaction with University Life

Lifelong education has growing importance due to various factors such as increased life expectancy and diversification of the industrial structure. In Korea, lifelong education policies are expanded as part of a national project and more studies on adult learners are conducted. This study intends to examine the effect of professor support in professor-learner relationships on the quality of life of middle-aged female adult learners. In particular, this study aims to investigate the role of satisfaction with university life in relation to the two variables and provide useful information for the formulation of lifelong education policies and in the lifelong education field. To this end, a survey was conducted on middle-aged women attending two-year college and four-year universities located in Gyeongsangnam-do. A total of 255 copies of questionnaire were used for analysis. The conclusions of this study are as follows. First, it was found that support from professors had a statistically significant positive effect on middle-aged female learners’ satisfaction with university life. Second, support from professor was found to have a statistically significant positive effect on the quality of life of middle-aged female learners. Third, it was found that middle-aged female learners’ satisfaction with university life had a mediating effect on the relationship between professor support and quality of life. Based on the analyzed results, suggestions were made to promote lifelong education and improve the quality of life of middle-aged women.
AutoDrop: Training Deep Learning Models with Automatic Learning Rate  Drop

Modern deep learning (DL) architectures are trained using variants of the SGD algorithm that is run with a $\textit{manually}$ defined learning rate schedule, i.e., the learning rate is dropped at the pre-defined epochs, typically when the training loss is expected to saturate. In this paper we develop an algorithm that realizes the learning rate drop $\textit{automatically}$. The proposed method, that we refer to as AutoDrop, is motivated by the observation that the angular velocity of the model parameters, i.e., the velocity of the changes of the convergence direction, for a fixed learning rate initially increases rapidly and then progresses towards soft saturation. At saturation the optimizer slows down thus the angular velocity saturation is a good indicator for dropping the learning rate. After the drop, the angular velocity "resets" and follows the previously described pattern - it increases again until saturation. We show that our method improves over SOTA training approaches: it accelerates the training of DL models and leads to a better generalization. We also show that our method does not require any extra hyperparameter tuning. AutoDrop is furthermore extremely simple to implement and computationally cheap. Finally, we develop a theoretical framework for analyzing our algorithm and provide convergence guarantees.
Design and Development of Access Control and Face Mask Detector in Real Time Using Deep Learning to Prevent COVID-19

After the breakout of this worldwide pandemic situation COVID-19, there arises a severe need for protection mechanisms, wearing a face mask being the primary one. The main aim of the project is to detect the presence of a face mask on human faces on real-time live streaming video. The proposed model is developed using MobileNetV2 which is a deep learning algorithm. The architecture takes the image as input, assigns weights to various objects in the image, differentiates one from another and the neural network output which tells us whether there is a mask or not, and the result is given to the Arduino module by using PY serial software. This model gives an accuracy of 99.9%, and it is connected to the servo-motor which is attached to the Arduino and acts as an automatic sensor door present at various public places. The door will be opened or remains closed based on the output value given to Arduino by the mask detection model designed in proposed study. The door opens only when a person is wearing a mask; otherwise, it remains closed.
Implementing a self Driven Edge Avoiding Robot ------------ Using Arduino

Robot navigation requires the guidance of a mobile robot through the desired path to the desired goal avoiding obstacles and hazards encountered in an unknown environment. Detection and avoidance of obstacles, collisions and hazardous situations are in the first place. However, path planning and arrival at the desired goal is also an essential part of the reliable and secure navigation of mobile robots. Planning the optimal path requires optimization of specific navigation performance, such as the minimum time until the robot reaches desired goal with a minimum of control, but also requires to comply with certain restrictions in robot motion, such as avoiding obstacles with the maximum speed of the robot. Now, the requirement for avoiding the collision with obstacles in the environment, the robots should be equipped with a sensor system that will enable the detection of these obstacles. Information obtained from sensors is used in obstacle avoidance strategies or in strategies for finding the best solution in case of the robot's emergency stop before the collision. In this research paper authors have tried to develop an edge avoiding robot and explained clearly the usefulness of the newly developed robot.
Criteria and Analysis for Human-Centered Browser Fingerprinting Countermeasures

Browser fingerprinting is a surveillance technique that uses browser and device attributes to track visitors across the web.Defeating fingerprinting requires blocking attribute information or spoofing attributes, which can result in loss of functionality.To address the challenge of escaping surveillance while obtaining functionality, we identify six design criteria for an ideal spoofing system.We present three fingerprint generation algorithms as well as a baseline algorithm that simply samples a dataset of fingerprints.For each algorithm, we identify trade-offs among the criteria: distinguishability from a non-spoofed fingerprint, uniqueness, size of the anonymity set, efficient generation, loss of web functionality, and whether or not the algorithm protects the confidentiality of the underlying dataset.We report on a series of experiments illustrating that the use of our Partially-Dependent algorithm for spoofing fingerprints will avoid detection by machine learning approaches to surveillance.
Enhanced Data Security Using Integrated Secret Key With Panagram Series

In this fast moving world, communication among several systems via networks plays a vital role. It is significant to secure Data for a classified transmission. It becomes indispensable to protect information from unconstitutional users. The Existing system uses a common value shared between sender and the receiver. Parent string is arranged in ASCII order, therefore same cipher value is generated for a single character at every instance making brute force attack possible. The proposed system aims at the secured transmission of data in text format between the sender and the receiver. The system uses synchronous stream cipher for the secured transmission of data. The system uses algorithm that is integrated with a series of keys adding to which the message can be encrypted and by using this algorithm the cipher text is decrypted on the receiver end. A pangram is chosen as parent string, so that random cipher values are generated at every instance thereby making brute force attack impossible. The key value is generated by Tribonacci multiplication which uses a prime factor impossible for the hacker to trace the key using traffic analysis. The system remains secured as the secret key of the existing system is replaced with a series of hash value that are self generated by the built-in module in the sender and receiver.
Error Handling

Abstract Agatha Christie wrote in 1969 that “human error is nothing to what a computer can do if it tries.” It is no mystery that we, as programmers, get to clean up the mess. The mechanisms for error handling could catch programmer errors that others may make. Since we do not plan on making mistakes ourselves, we can focus on using error handling to handle conditions that may occur in the real world from other causes.
Analisis Keamanan Sistem Informasi Akademik (SIAKAD) Universitas XYZ Menggunakan Metode Vulnerability Assessment

&#x0D; &#x0D; &#x0D; The use of academic information systems (siakad) has become mandatory for universities in providing user convenience in online academic administrative activities. However, sometimes college siakad has security holes that irresponsible people can take advantage of by hacking. This study aims to identify security vulnerabilities at XYZ Siakad University.&#x0D; The method used in this study is a vulnerability assessment method. A university syakad will conduct an initial vulnerability assessment by doing footprinting to get information related to XYZ syakad after that a vulnerability scan is carried out using vulnerability assessment tools to identify vulnerabilities and the level of risk found.&#x0D; Based on the vulnerability of the XYZ university's vulnerabilities, it is quite good, with a high risk level of 1, a medium risk level of 6 and a low risk level of 14. Researchers provide recommendations for improvements related to the findings of security holes in XYZ university Siakad from XSS (Cross Site Scripting) attacks, Clickjacking, Brute Force, Cross-site Request Forgery (CSRF) and Sniffing.&#x0D; &#x0D; &#x0D;
An adaptive threshold policy for host overload detection in cloud data centre

Cloud computing technology exploits virtualization with the help of the internet and remote servers to provide hassle free services to its users.With the help of virtualization, the resources of a physical server are divided into multiple Virtual Machines (VMs), where each VM seems like an independent entity having its own capacity for performing computations and processing the data.The demand for cloud computing services has risen exponentially in the last decade because of its ability to easily host applications anytime and anywhere.This exponential growth leads to an enormous increase in energy consumption.Also, the pandemic situation occurred due to coronavirus has emphasized the online work environment for almost all organizations and institutions.This has taken the energy consumed by data centers to the next level.This raised energy consumption is a source of increased emissions contributing to global warming.
Manajemen Edukasi Preventif dan Penatalaksanaan Covid-19 Berbasis Home Learning System Pada Remaja

Background: The high number of Novel Coronavirus Diseases (Covid-19) cases in Indonesia has caused the public to need to receive proper education regarding the prevention and management of Covid 19, especially teenagers. Teenagers are part of the community who need to be equipped with good knowledge about Covid management in order to maintain healthy living behaviors. This preventive management education using the home learning system method is very suitable for use during the pandemic. The purpose of this community service was to increase the knowledge and skills of adolescents in taking preventive measures and the management of Covid 19. Methods: This activity includes 2 stages of providing education and demonstration using online media. The Results: Based on the results of the post-test it was found that almost all teenagers had scores of knowledge scores at 8-10 intervals after a preventive education and covid 19 management based on home learning system. The results of the skills indicator show that almost all teenagers are able to knowledge again with a score of 8-10. Conclusion: The application of the Home Learning System is very suitable for students to increase their knowledge and abilities in taking preventive and curative actions independently.
Classification of Iris Flower by Random Forest Algorithm

With the introduction of artificial intelligence into our lives, artificial intelligence researches and applications in different fields such as agriculture, health, military and engineering applications have become very popular iris flower was classified using the popular Random Forest, support vector machine and Artificial neural network machine learning classifiers with high accuracy rates. As a result of the classification, the performance of the trained models was evaluated according to the confusion matrix, sensitivity, specificity, accuracy, F1 score, ROC curve and AUC evaluation criteria. The random forest algorithm was the most successful among the trained algorithms with an accuracy rate of 97%.