Reviewerly/exHarmony huggingface dataset:
A Security Protection Technology Based on Multi-factor Authentication

Today, with the rapid development of the information society and the increasingly complex computer network environment, multi-factor authentication, as one of the security protection technologies, plays an important role in both IT science and business. How to safely complete multi-factor authentication without affecting user experience has attracted extensive attention from researchers in the field of business security protection and network security. The purpose of this paper is to apply multi-factor authentication technology to enterprise security protection systems, develop and design a security protection technology based on multi-factor authentication dynamic authorization, and provide enterprises with unified identity management and authority management methods. The cornerstone of trust and security to ensure uninterrupted and stable operation of users. The original master key k is subjected to secondary multi-factor processing, which enhances the user’s authentication ability and effectively avoids the risk of easy password theft and disguised identity. In order to meet the given VoIP security requirements, a SIP multi-factor authentication protocol is proposed for the VoIP environment by using the multi-factor authentication technology to solve the security problem. The performance test results show that due to the influence of data encryption and decryption, the response time of the encrypted database is 100s longer than that of the unencrypted one, but the growth rate is 10% smaller than that of the unencrypted one. Therefore, the performance of this scheme is better when the amount of data is larger.
Gamification of Cyber Security Training - EnsureSecure

There is an ever-growing fear of cyber-attacks in the modern workplace and these attacks are increasingly being linked to the human element. Securing this weak point is essential and this can be done through investment into workplace training. The current basic training methods have many issues that discourage the retention of the information learnt during the training. An example of one such issue is the level of engagement that the basic cyber security training provides the non-experienced participants. Low engagement means inefficient knowledge transfer and retention. This paper presents the results of a study aimed at improving cyber security knowledge retention of participants through the use of a conceptual physical card game that could be developed into an e-learning resource. This paper will document the preliminary research, design process and evaluation of the game.
Issues and Future Challenges of Sentiment Analysis for Social Networks- A Survey

Sentiment Analysis is a approach that uses Natural Language Processing, Machine Learning, and Deep Learning to computationally identify, categorize, classify, and recover human emotions from unstructured text. The most efficient and popular method for learning from and training social media datasets is deep learning. In a range of applications, including audio, picture, and natural language processing, it has been demonstrated to be more successful. A text block is evaluated using the sentiment technique to determine whether it is positive, negative, or neutral. Not all members of the public express their feelings in the same way; as a result, some do so through comments and ratings while others do so through texts that don't reflect the right frame of mind. This evaluation lists the most typical and well-liked sentiment analysis algorithms for social media data. The classification of positive, negative, and neutral reviews using "LSTM, Knearest method, Random Forest, Support Vector Machine, RNN, and MaLSTM" is explored and examined in this work. All of these classification methods were evaluated along with their drawbacks and difficulties.
Design and Analysis of an Adaptive QoS Aware Approach for Supporting Multiple Services using Meta Heuristic Enhanced Whale Optimization Algorithm over Hybrid Cloud Environment (EWOC)

Though numerous research works have discussed about approaches towards providing QoS over cloud environment for variable services, research community suggest that the need for an adaptive QoS approach with quantifiable resources is a challenge. Hybrid cloud is currently adopted by industry with need for consistent QoS and variable security features. Variable number of users connected over cloud demand consistent resources such as network link capacity, distributed storage and dynamic processing capabilities for execution of service based on variable cloud. This paper focuses on identification and provisioning of demandable resources over a session for a specific task. The proposed EWOC approach works on an enhanced whale optimization algorithm for QoS supportive sessions in use. This paper models an adaptive resource assignment over PSO, SA scheduling algorithms and their QoS aspects.
Splicing Image Forgery Detection by Deploying Deep Learning Model

The use of digital images has become more important in numerous industries, including journalism, medical diagnosis, law enforcement, and forensics. Images could be easily modified due to the availability of photo-altering tools and software, which can distort the original information. The availability of low-cost and simple image-editing software has also drastically decreased the time and money needed to engage in image tampering. This destroys the purity of the image, making it vulnerable to misuse by anyone. Copy-move, splicing, and enhancement falsification are prominent methods used to create fake images. Using a combination of two or more images, a new image is created called splicing, which may be shared across many online channels and used to spread information and impact target audiences. The discovery could have beneficial as well as negative results. Consequently, it is important to provide a method that can identify the splicing forgery in an image. The study used a Deep Learning (DL) model named MobileNet to distinguish between authentic and manipulated images. Both the training and testing of the DL model make use of the CASIA data. Resizing and enhancing methods are used to process the collected data. Accuracy, F1 score, and recall are employed to test the reliability of the created DL model.
Prediction of Solar Energy using Time Series Methods

The utilization of solar energy as a source of electricity is increasing day by day, raising interest in prediction of solar irradiation. A successful integration of solar energy sources with existing grid system is the biggest challenge due to volatile and unpredictable behaviour of solar energy. To date, several approaches are proposed to analyse and predict solar irradiation as well as to improve forecast accuracy. The present study concentrates on hourly to monthly forecasting of solar irradiation through various statistical methods, namely AR, MA, ARMA, ARIMA, and Holt Winter’s technique. From the decomposition of time series data, we found that the dataset exhibits seasonality and randomness. The adequacy of the models is assessed from the Root Mean Square Error (RMSE). We note that the model performance improves with the increase of time horizon (from hourly to monthly), probably due to enhanced clarity in seasonality. In case of ARIMA, the RMSE value turns out to be 124.21 in hourly forecasting, whereas this value reduces to 15.66 in monthly forecasting. A similar change has been observed for other models as well.
Data Privatization and Security using Bit Plane Complexity Segmentation

Advances in digital communication play an important role in our daily lives in today’s world. In general, information security systems fall into two categories: encryption and information hiding. The storage and transmission of sensitive personal data have become a part of everyday life for professional and personal well-being. Therefore, the issue of secure storage and transmission of confidential information is interest to many researchers. Steganography is a collective method to hide sensitive data in unobtrusive digital media such as video, audio, and images. One of the biggest challenges in developing steganography systems is finding the right balance between metrices like reliability, security, and data privacy. This project provides information about how the Bit Plane Complexity Steganography (BPCS) technology is applied to both grayscale and color imaging techniques and assesses performance parameters such as data concealment, security and stability based on the peak signal-to-noise ratio (PSNR) and Mean Square Error (MSE) with various test cases to explain how it works for different files and images.
A Technical Analysis on the Current Implementation of Online Examination System and the Factors Influencing the Issue

An examination is an essential aspect of learning. The traditional method of examination is when the exam taker takes an assessment in a room or a huge space with other candidates while being supervised by the exam monitor. Although the term ‘Online Examination’ has existed for a while, however, it has been taking off recently due to the global nation closing as a precautionary measure to prevent the spreading of the Covid-19 virus. Many institutions have their way of conducting online examinations, which prompt many responses from the exam takers. While it is undeniable that online examination could increase efficiency in conducting an examination, multiple factors could influence the success of conducting a smooth online examination. Hence, this paper will study exam taker’s sentiments towards online examinations based on the current online exam implementation in their institutions. A questionnaire survey was conducted throughout 67 exam takers above the age of 18, where their responses are gathered and analyzed in identifying their concerns and obstacles when taking an online examination. The results would be helpful for institutions to formulate a method for creating an online examination method that would be both efficient and convenient for both students, lecturers, and staff of the institution.
Prediction of YouTube View Count using Supervised and Ensemble Machine Learning Techniques

The social media platform named YouTube is an American based online video sharing and it is headquartered in California. It also provides various services to users such as watching and uploading their own videos through their laptop, mobile and PC’s. The goal of this research work is to analyze the YouTube view count for five different countries namely India, Britain, Russia, Canada and United states. The key goal is to investigate the view count of the video with influencing factors up on YouTube such as likes, dislikes, published date, trending date, Country, Category of the video and other ten variables. This has also indicated the relationship between dependent variable "view count "with all other independent variable by the regression analysis. This data analysis helps the users for better understand of their video, channel performance and reports in YouTube. Through the results of YouTube analysis, it is helpful for the users to identify the key metrics such as video content, duration of the video and liked or disliked. These metrics helps the users to make their video trending. The data is collected from the Kaggle repository, where the data will be updated on the daily basis. Various machine learning regression models such as Multiple Linear Regression (MLR), Random Forest Regressor (RFR), Decision Tree Regressor (DTR), XGBoost Regressor (XGB), Gradient Boost Regressor (GBR) has been used to predict the view count of the video. The results of each of these algorithms are noted and compared in order to determine which method is best suited for the view count prediction. The experimental results inferred that the Random Forest technique performs better than the other machine learning models.
Picture In Picture Detection For Mobile Captured Digital Video

Along with the popularization of high-speed internet and high-performance digital cameras, the consumption prosperity of high-resolution videos and pictures appears. However, high-performance cameras bring about content infringes through illegal videotaping, which greatly damages the privileges and benefits of the content owners. Multimedia forensics is one of the most important copyright protection tools. In present forensic technologies, picture-in-picture detection is an important pre-processing step. In this study, a three-step picture-in-picture detection method is designed to effectively find the region of recaptured video. The region location is simply obtained from the I-frames after a rough detection and a fine-tuning step. Experimental results proved the effectiveness of the proposed work.
An End-to-End Speech Recognition System Based on Shared Encoder

With the development of streaming media, automatic speech recognition (ASR) has been widely used in online education, live broadcast and other fields. However, for a better recognition effect in the real scenario, it is necessary to combine various technologies, such as front-end voice endpoint detection and back-end language model. In order to filter sensitive words in real scenarios, we require good online recognition and decoding methods. This paper presents an End-to-End speech recognition system, which unifies stream and non-stream speech recognition based on a shared encoder, and contains an additional CTC structure in the middle layer. Based on the monosyllable feature of mandarin, we calculate the probability distribution of syllables in the middle layer. The results show that our method is reliable for recognition in educational scenarios. We have achieved good results on aishell-l and audio in real scenarios provided by the company. At the same time, this system provides accurate syllable information to analyze sensitive words further.
A contribution towards the regulation of anonymised datasets within the framework of GDPR

The European Union’s General Data Protection Regulation legal framework outlines data anonymisation as an effective method to help data processors meet their legal obligations. Once personal data has been anonymised, GDPR no longer recognises it as personal data and it becomes free to be processed unregulated. The potential capacity for re-identification of any anonymised dataset brings into question the value of anonymisation and whether it should be regulated to ensure the privacy of data subjects. This paper identifies and reviews some of these problems, challenges the current set of regulations, and recommends maintaining regulation over anonymised datasets, and enforcing systems of traceability and erasure of datasets over time, which would increase the value of utilising anonymisation and its capacity to be a reliable system for data privacy.
Cybercrime Incident Reporting System

This paper presented a research project to study the cyber-crime incidents in Indonesia and identify cyber-crime attacks and hackers to detect and help internet users towards cyber-crime incidents by using a web-based application. Internet users can report through the system regarding cybercrime incidents such as Online Scams, Phishing, Malware, Identity Theft, etc. Besides, the proposed web application will be developed to increase the cyber-security awareness toward the internet users in Indonesia by providing cyber-security content within the web application such as: how to protect their devices, social media account, data, how to prevent the cyber-crime attacks.
DWT Algorithm for Macro &amp; Micro Block based Multiple Histogram Shifting for Video Data Hiding

With the help of video compression and compressive sensing technology, this study demonstrates how to conceal data. Video data was concealed using a linear sensing technique for greater quality. By reducing changes across pixels to obtain discrete micro-block features while keeping their statistical properties, micro-block residuals can be assessed using CS. In order to create many histograms, micro blocks with related features are grouped together using CS. The seamless integration of data into video is the project’s main objective. The picture quality ratio and PSNR rate of the image are improved using this method. This method uses a micro-block-based approach to increase PSNR and improve security. In order to eliminate shifting distortions, data embedding is used on each of these histograms to extend the outermost bins while maintaining the size of the inner bins. Numerous RDH strategies based on multiple HS (MHS) based on spatial correlations of multimedia are offered to protect copyright, in which several histograms are constructed using some strict criteria. Compared to photographs, video has more three-dimensional and temporal linkages, and it’s simple to obtain histograms with greater clarity. Reduced embedding distortions enable lossless restoration of the cover thanks to the reversible data hiding (RDH) technology. Histogram shifting is one RDH strategy that is widely employed (HS).
Behavioral Recognition Based on Representation Learning in Video Scenes

With the construction of smart cities in China, surveillance cameras have become ubiquitous, which has also caused an unprecedented expansion of surveillance video data. Using the intelligent video surveillance system developed by behavior recognition technology to replace human beings for video surveillance, which can reduce human resources and time resources, and has great practical value. This paper mainly explores the algorithm principle of the video behavior recognition method based on representation learning. By constructing a dual-stream network, we extract the appearance information and action information in the video respectively, and then fuse the two kinds of information to classify the behavior on the fusion results. Compared with the single-stream, the dual-stream network broadens the dimension of network learning and partly enriches the capture of information in video sequences, but it lacks the extraction of long-distance information in video sequences. Therefore, a two-stream network structure based on temporal fragment fusion is improved based on the ordinary two-stream network. The network first divides the overall behavior into multiple action segments according to the optical flow intensity, and then classifies the temporal features and modal features of the different segments after fusion.
None-Negative Graph Contrastive Learning for Knowledge-Driven Zero-Shot Learning

Negative samples are usually used in previous contrastive learning methods, but such methods often have major defects in graph contrastive learning. In the knowledge graph, the potential relationship between nodes is mainly represented by the edge relationship between nodes. However, in the previous contrastive learning method, the distance between positive and negative samples is infinitely expanded, so this potential relationship is destroyed. Therefore, inspired by the state-of-the-art contrastive learning methods without negative examples, we propose a None-Negative Graph Contrastive Learning method (NGCL) for generalized zero-shot learning. NGCL uses the dropout graph augmentation method to make a graph node have its corresponding positive sample, and the two augmentation graphs are compared with positive samples after graph convolution and MLP. Experimental results on real-world datasets without predefined attributes demonstrate the effectiveness of our method.
A Public Key Cryptography based Mechanism for the Secure Transmission of RGB Images using Elliptic Curve based Hill Cipher and Magic Square Concept

The use of image data in multimedia communication based applications like military applications and medical images security applications are increasing every day and the secrecy of the image data is extremely important for such applications. A number of methods and techniques for securely transmitting images are proposed in the literature based on image encryption and steganography approaches. A novel mechanism for transmitting color images securely is proposed in this paper mainly based on public key cryptography mechanism also by combining the advantage of simplicity of symmetric schemes. The technique combines the strengths of Elliptic Curve Cryptography and the classical symmetric cryptographic mechanism called Hill Cipher encryption method. The technique also includes the concept of Magic Square for jumbling the pixels yielding maximum diffusion in the image pixels. In the performance evaluation, the proposed method proved that the new system works pretty well. The method is proved to be effective in maintaining the confidentiality of the image in transit and also for resisting security attacks.
A Comprehensive Survey on Cloud Security Mechanisms

Cloud computing indicates a tremendous promise for providing on-demand solutions to customers with increased flexibility even at a lower cost over the internet. Cloud service companies efficiently supply their consumers with a variety of storage options. Cloud users are permitted to utilize cloud storage to store their data on the cloud server, which lessens the need for local computer storage and retrieval. Cloud computing is the set of the resources as well as the services that are offered to users through the Internet. There are several issues as well as challenges raised from the adoption of the computational paradigm which includes privacy, security, authentication and association. This paper aims to introduce new data security ideas in cloud computing. In this paper, we have described the specifics of cloud computing, addressed the principal security issues and discussed primary cloud operations that need to be secured and also discussed the data security-based model for cloud computing. Although advancing towards to the paradigm of on-demand operation, dynamic resource, and transferring it all to a cloud system, security is the main roadblock towards this grand approach of computational capacity. Additionally, various critical aspects such as information security practices, firewall-based protection, and access control procedures in the clouds also analyzed in depth. This study will benefit research and developing network algorithms to address recognized safety concerns, as well as businesses producing gadgets to monitor such dangers and potential vulnerabilities.
We live in a Cyberspace invented by Writers

The article uses the example of new information technologies to examine examples of writers’ predictions of future technical systems with high accuracy and strikingly precise detail. It is shown that literary descriptions are not always visionary, sometimes they are based on scientific predictions or pseudo-scientific descriptions. Nevertheless, as one delves into literary history, one finds ample evidence of writers’ foresight of future technological development. Although the extension of the findings to other fields of technology remains beyond the scope of this article, there is considerable scope for analysing the mechanism of literary-technical foresight.