Reviewerly/exHarmony huggingface dataset:
Boosted Hybrid Privacy Preserving Data Mining (BHPPDM) Technique to Increase Privacy and Accuracy

In the current scenario, large volumes of data are generated by different organizations. Data Mining (DM) applications provide suitable patterns that lead to business growth, improved health care and improved services to users etc. The Humongous data volumes may also contain some confidential information about individuals. So, this data may be exploited for identity theft, identity disclosure etc. In this situation, data mining methodologies that provide confidentiality are useful. Though Privacy-Preserving Data Mining (PPDM) methodologies like randomization, perturbation, anonymization etc., provide privacy, but when applied individually, they are ineffective. Henceforth, this article proposes a combination of these techniques called Boosted Hybrid Privacy Preserving Data Mining (BHPPDM). The proposed technique provides more privacy of data than the current methodologies whilst providing better classification accuracy as demonstrated by investigational results.
Influence of Cross Histology Transfer Learning on the Accuracy of Medical Diagnostics Systems

Transfer learning is a basic method in computer vision that helps extract features from images. In this research, we created a private data set for stomach cancer recognition and tested the various transfer learning techniques in an application to solution the binary classification problem. The obtained results show that it is not always necessary to use pretrained weights of similar nature (histology) or ImageNet-based models. Given enough data, randomly initialized weights can show more promising results. Meanwhile, the goal of the network pretrained with data set of different type of cancer is relatively high precision of the network in comparison with the network pretrained with ImageNet or the randomly initialized network.
Deep Learning based Automatic Radiology Report Generation

The diagnostic x-ray examination is carried out using the chest x-ray. It is the responsibility of the radiologist to analyze the x-rays and draw conclusions from them to prescribe the proper care. Obtaining comprehensive medical reports from these x-rays is frequently time-consuming. Images of the heart, lungs, airways, spine, and chest bones can be seen in a chest x-ray. A radiologist may see thousands of x-ray images in populous nations. The goal of this project is to present a collection of the best deep-learning techniques for producing medical reports from X-ray images automatically. Deep learning algorithms have been used with models to handle this difficult task and produce correct results. Therefore, a lot of work and time can be saved if a properly trained deep learning model can generate these medical reports automatically. In this research, the text report is produced using an encoder and decoder with an attention model, while the image features are obtained using a pretrained CheXnet model. The BLEU score is used to evaluate the resulting text report.
Implementation of a Secured Document Classifier

In the present smart digitization era, every document is prepared, presented and shared through electronic media effortlessly. Classifying these documents to everyone’s advantage is gaining important insight. Adapting Machine Learning (ML) algorithms for classification, is an efficient and easiest way of classifying documents. It requires document to be presented in understandable form. Classifying documents available online using ML smart techniques aid everyone in effectively utilizing time and energy. The existing challenge in document classification is the security of the uploaded document. The paper focuses on types of features depending on which a document can be classified. The features may include information present in the document. Classifying and grouping document for various applications help in sorting and ordering data in required format. The Documents are also secured using Rivest Shamir Adleman (RSA) cipher and a case study depicting the same are considered here.
A Comprehensive Study of Different Security Features in eBanking

In recent years, banking through mobile banking and usage of debit and credit cards for transfer of money and purchases of goods has increased exponentially due to ecommerce and the introduction of Unified Payments Interface (UPI) by the government of India which allows user easy to use facility with minimal to no charges. To work this mechanism properly, a secure framework is required to avoid the risk of cyber fraud. there has been a great shortfall of security in banking applications in this type of transactions. This article presents a comprehensive study on banking security in e-banking by various techniques like blockchain, data encryption, voice recognition. This article explores the various security and technologies recommended by various researchers for safe and secure use of online banking.
Malpractice Detection in Online Proctoring using Deep Learning

One of the most effective image processing applications, face recognition is crucial in the technical world. An ongoing problem with authentication is the ability to recognize a person's face, particularly when it comes to tracking students’ movements in an examination. It is a pupil identification method that employs face biostatistics based on multiple technologies. The creation of this system aims to digitally replace physical invigilation with the digital view. The manual recording makes it harder to concentrate on numerous students. HOG classifiers, CNN, SVM, Generative Adversarial Networks, and Gabor filters are used in the suggested system. Face detection systems have grown over years and the industry has paid close attention to this topic. Numerous studies have been conducted on face and eye identification. Reports will be generated and saved following face recognition. The system is tested under different conditions, which include head movements, and changing distances between the person and the cameras. Overall accuracy is determined after testing. This system is efficient to reduce malpractice in less time. The created system is inexpensive and requires little installation.
Single Shot Detector Algorithms for Hardware Design on FPGAs for Fast Object Detection

Object identification and analysis take place throughout the process of detecting YOLOv2-based objects. It entails determining the path that an item takes through space and time by identifying that object's location in each object. A YOLOv2-based object detection algorithm is developed by using FPGA in this work. The detection of objects that are in motion is a difficult job. Recent research has shown that convolutional neural networks, often known as CNNs, perform better than traditional methods, and a number of different object detectors that are based on CNNs have been developed. In order to implement the proposed model, the CNN algorithm is employed to distinguish individual items against a backdrop image for reference. MATLAB and an FPGA board are used to carry out the experiment.
3D Game Design Aided by Multi-Dimensional Collision Detection Algorithm with GPU Optimization

This paper addresses the problem of the 3D game design aided by multi-dimensional collision detection algorithm with GPU optimization. For the designed model, our task will be focused on the 3D game design, the GPU performance on the image computing and neural network computing will be essential, hence, the graph neural network algorithm is considered. This study firstly considers the GPU optimization with shared memory model. Shared memory can be used to design the memory access mode in order to realize the combination of the memory access which improves the efficiency. Then, this study considers the multi-dimensional collision detection algorithm design. In order to achieve the unique representation of matrix translation and also spatial points, this paper then adopts the homogeneous coordinate system in matrix transformation. That improves the traditional model. Furthermore, the 3D game design steps are clarified. Based on the experiment under different scenarios, the game design sample code, the GPU performance and algorithm performance are all tested.
Business activity of modern students in the context of the state management system development.

In order to effectively interact in modern society, a person must have the most up-to-date information and a new system of behavior. Market relations generate competition, the struggle for a vital resource and the possibility of self-preservation among others in almost all spheres of life. In the course of a sociological study of student youth, the foundations of business activity, initiative, entrepreneurship, characteristic features of economic behavior and economic consciousness were revealed. One of the most important indicators of the level of human viability in conditions of uncertainty is business activity. People's behavior and social reforms are in constant interaction. The pace of change in modern societies is so fast that people's business activity and their willingness to productively change the world around becomes one of the key factors in the development of society. At previous historical stages, society has always had a more or less clear idea of «what kind of society is this?», «where did it come from?», and «what is its purpose?». The question «who are we?» in the social sense was obvious. Today, the identity of Russians is vague and undefined. It is easy to answer the question who we were. But the answers to the questions «who are we?», and even more so, «who will we be tomorrow?» seem far from so obvious. In modern socio-economic conditions, the assessment of the business qualities of student youth is not only vital for the youth themselves, but also crucial for the formation of state youth policy.
Privacy Protection Against Reverse Image Search

The user wants to include their image in their résumé, LinkedIn profile, or any other relevant place and doesn't want their personal life to be viewed using a reverse image search, which can trace back their professional image to their social ID based on facial image. They may not want to be evaluated as social media account may expose sensitive information or personal life. Various techniques such as synthetic modification exist to protect the privacy of an individual but they mostly tend to distort the image to such an extent that figure is not identifiable as a human or some other person is represented.This article provides a technique that enables people to protect their images against the misuse of facial recognition software enabling humans to identify the person but machines fail to recognize the person. The proposed method would help the users to edit their images at the pixel level and removing the metadata before sharing them. These "cloaked" images yield functional models that repeatedly lead to incorrect identification of typical images of the user when used to train facial recognition models. When combined with metadata removal, it highly reduces the possibility of image traceback to a person’s social ID.
Sentiment Analysis of Movie Review using Hybrid Optimization with Convolutional Neural Network in English Language

There seems to be a growing amount of user-generated material online as more people become familiar with the Internet. Understanding hidden thoughts, emotions, and attitudes in tweets, emails, comments, and reviews is difficult yet essential for market analysis, brand tracking, social media tracking, and customer support. Sentiment Analysis (SA) identifies the emotional undertone of a string of words and also might basically be employed to comprehend a user’s attitude, thoughts, and emotions. The Harris Hawks Optimization - Sparrow S earch Algorithm with Convolutional Neural Network i.e., (HH-SSA-CNN) proposed in this study is an innovative SA algorithm. Pre-processing, sentiment categorization, and feature extraction make up the procedure. The preprocessing phase removes the unwanted info from input text evaluations using NLP algorithms. A hybrid technique that combines review-related features and aspect-related features has been presented for efficiently retrieving the features. This method creates unique composite features for every review. The created HH- SSA-CNN is used to accomplish sentiment categorization. This approach has been used in the IMDb dataset. To assess the model’s efficacy, the outcomes of the HH-SSA-CNN model are contrasted with those of alternative methodologies. The result indicates that the developed model accurately classifies the sentiments while compared to other existing methods.
Maintaining the Integrity of Online Exams using Computer Vision and Image Processing

Due to the growing popularity of online learning, there is a need for efficient methods to assure the validity of online tests, especially multiple-choice exams (MCQs). The application of computer vision and picture pre-processing techniques is one strategy that has shown potential in this situation. With the use of these methods, cheating may be identified and stopped by analysing photos of the test-taker and their surroundings. In the context of online MCQ tests, this article addresses the possible uses of computer vision and picture pre-processing, including the use of facial recognition algorithms and image segmentation to detect and extract particular items or characteristics. The employment of these strategies offers enormous potential for maintaining the integrity of online education, even if there are still issues to be resolved, such as privacy concerns and the requirement for precise and trustworthy algorithms.
Fundamentals of countering threats to the information and communication environment

In the article, the author investigates the basics of countering the threats to the information and communication environment. Currently, the information and communication environment continues to form, which determines the presence of certain threats classified by the author into regulated through socio-cultural mechanisms (information inequality, cyber disorders) or legal mechanisms (the use of destructive information, information warfare, information crime, information terrorism). The development of legal means of countering these threats is becoming a necessary direction of the law-making activity of the Russian Federation today. It is shown that the legal regulation of relations on the Internet is carried out on the basis of the norms of the information legislation of Russia. Part of the legal relations is regulated by the norms of international law, which is related to the global nature of this computer network. The vital activity of a person, society, and the state is likely to be significantly more vulnerable. Today, it is necessary to develop legal means not to counteract, but to counter threats to the information and communication environment. Such legal means should be able to protect the interests of a person, society, and the state as effectively as possible.
Design of Reconfigurable Reversible Low Power Adders

One of the most well-known technologies that is making a bigger contribution to current technological environment is DSP systems. With multiple applications, such as readily transportable electronic gadgets, the system that accepts films and audios as input, etc., embedded systems have gained popularity all over the world. The paper mainly focuses on low power, efficient, and programmable adders. Reversible logic gates, which are mostly employed for designing, provide the basis of this adder. The adders are developed and implemented using the cadence tool. These adders can be used in aerospace applications. There was a comparison made between the current and proposed methods in that the proposed way resulted in a reduction of 2005.34 nanowatts compared to the existing method.
A Semi-Supervised GAN Architecture for Video Classification

In recent years, several supervised deep-learning architectures have achieved state-of-the art accuracies in video-classification. However, they demand a considerable amount of annotated data which can be both cost and resource intensive. This study proposes a Semi-Supervised GAN architecture to efficiently perform classification on video datasets with a small percentage of labelled data. While the Generative Adversarial Network (GAN) architecture is known for its generative ability, we harness the discriminative property of this network instead for the classification of videos. The proposed model leverages the features extracted from the unlabelled data to classify the labelled videos. Results show that the proposed approach achieves 46% accuracy with just 5% labelled videos, reaching up to 62% when 50% of the videos are labelled. These results are a significant improvement over a standard supervised approach and show a promising aspect in the field of Semi-Supervised Learning domain.
Bio-Inspired Feature Selection Techniques for Sentiment Analysis – Review

In the recent times, data mining has been the most sought-after area of research. Coming to the text classification domain, lot of research is going into the sentiment analysis, wherein numerous approaches and strategies are used to obtain information about the customer's sentiments. In the past, only supervised, lexicon-based approaches and unsupervised machine learning-based techniques were used to process data. But nowadays, there is a rise in the application of nature-based algorithms. These algorithms are inspired by various social animals who use novel techniques to hunt down their preys or collect food. The very same approach is being simulated into a numerical optimization technique. To understand the same, this paper presents a systematic review of three different bio-mimicking procedures that are currently employed particularly in the arena of sentiment analysis namely Firefly Algorithm (FFA), Cuckoo Search Algorithm (CSA) and Chicken Swarm Optimization Algorithm (CSOA) from the years 2015-2022.
RMSLRS: Real-Time Multi-terminal Sign Language Recognition System

Sign language is commonly used by deaf or speech-impaired people to convey meaning. Sign language recognition (SLR) aims to help users learn and use sign language by recognizing the signs from given videos. Although sign language has gained social acceptance, few sign language recognition systems have been developed with educational purposes. Current SLR system has a single form of expression and lacks real-time interaction and feedback with an expensive cost. And videos captured in real scenes are complex, which has impact on the performance of models. To this end, this paper has proposed a novel real-time multi-terminal sign language recognition system (RMSLRS). Specifically, a lightweight sign language recognition model based on MediaPipe Holistic is proposed to perform sign language inference by sensing multi-dimensional information such as pose, facial expression, and hand tracking, achieving near real-time performance on mobile and desktop devices. Then, a novel pre-processing module is proposed to reduce the adverse effects of background noise in videos through the YOLO model and OpenCv. Furthermore, a novel technical architecture for front-end and back-end separation and multi-terminal deployment is designed, including WeChat applet, desktop application and website. Finally, this system has been deployed with gratifying success in practice.
Proving Ownership of Privacy-Protected Cloud Storage Devices

Systems for cloud storage is growing in popularity. Deduplication, a promising procedure collects only a single replica of recurring data there by reduces cost of cloud storages. Client-side deduplication makes an effort to locate existing deduplication opportunities prior to uploading at the client and avoid using server capacity to upload duplicates of already-existing data. This work, provides mechanism to overcome vulnerabilities and take advantage of client-side deduplication, giving an attacker access to other users' files of any size based on relatively weak hash signatures. More specifically, an attacker who is aware of a file's hash signature can persuade a storage service that it is the rightful owner of the file, at which point the intruder is allowed to acquire the entire file by the server. Proofs-of-ownership (PoWs), that allow a user to effectively prove to a host that the user genuinely owns a file instead of only giving a short summary of it. This work defines the principle of proof-of-ownership under strong security standards and strict efficiency constraints. Then, relying on popular encryption techniques and hash function, this study provides and assess security-related methodologies.
Prediction of Future Market Share of Cryptocurrencies using Real-Time Investments

The use of currency has a long history, predating the individual being referred to by 1000 years. Currently, currency is the primary means of exchange globally. This individual has introduced a new, untested type of money in the form of cryptocurrency. The purpose of this study is to use conjoint analysis to understand the factors that influence investors' decision-making process when considering investing in cryptocurrency. The research indicates that various characteristics of cryptocurrencies play a role in investor decision-making. This study aims to identify the different classes of consideration that investors take into account when evaluating their expectations for cryptocurrency investments. In this context, the investors are the customers and cryptocurrency are the new product being considered. The results of the study reveal that profitability, accounting practices, and security are crucial factors that impact investors' expectations for cryptocurrencies. Among these characteristics, profitability appears to be the most crucial factor for investors, based on the research findings. The survey data also confirms that a significant portion of investors anticipate Elevated returns from cryptocurrency investments. This study aims to use conjoint analysis to understand the factors that influence investors' decision-making process when considering investing in cryptocurrency. The research indicates that various characteristics of cryptocurrencies play a role in investor decision-making and the study aims to identify the different classes of consideration that investors take into account when evaluating their expectations for cryptocurrency investments. Additionally, a significant portion of investors anticipate elevated returns from cryptocurrency investments. The challenges faced in the study include understanding the factors that influence investors' decision-making process, identifying the different classes of consideration, addressing the lack of historical data and understanding of cryptocurrency, addressing the volatility and uncertainty of the cryptocurrency market and addressing the lack of standard regulations, laws and infrastructure for cryptocurrency. The study uses techniques such as conjoint analysis, surveying investors, data visualization and a combination of quantitative and qualitative research methods to provide a comprehensive understanding of the topic.