Reviewerly/exHarmony huggingface dataset:
Review of Smart Home Privacy-Protecting Strategies from a Wireless Eavesdropping Attack

Increasing concerns about the potential for privacy breaches cast doubt on the future of smart homes. Specifically, wireless snooping-based attacks that target home networks have demonstrated their capacity to illegitimately infer daily activities within the home. This paper reviews the fundamental strategies for safeguarding the personal data of the home residents and evaluates the efficacy of existing privacy-protecting solutions that are built upon the reviewed strategies. The study will show that, while some solutions established a reliable level of home data privacy protection, their negative effects on other system characteristics are significant, emphasizing the need for an ideal compromise between these elements. These factors are the provided privacy rate, impact on the system’s response time, and energy consumption of privacy-protecting approaches. This overview of current research will aid in understanding the existing drawbacks and indicate potential avenues for future research.
Cars tracking based on YOLO for feature extraction

Object tracking is regarded as one of the significant topics in the scope of computer vision, which led to rapid development in the practical field through enhancing the reliable tools available in such a field. Recently, the appearance of Artificial Neural Networks (ANNs) resulted in new methods to the identification and recognition of objects. In this research a noble method is suggested for tracking objects inside video files, the method was used to detect cars on the streets through extracting the features of these cars by using You Only Look Ones (YOLO v3) Our method was able to process the extracted features of the cars at detection phase. Also, it exploited the extracted feature in an effective way in order to obtain precise detections. The findings show that our proposed method performs better than the other methods available in the field, as it is able to produce better predictions by using less computation which resulted in reducing the time that such process usually takes. The evaluation results show that our method was able to process an average of 207.6 frames per second to track objects with 67.6% Multi-Object Tracking Accuracy (MOTA) and 89.1% Multi-Object Tracking Precision (MOTP).
Reducing the Cost of Machine Learning Differential Attacks Using Bit Selection and a Partial ML-Distinguisher

In a differential cryptanalysis attack, the attacker tries to observe a block cipher’s behavior under an input difference: if the system’s resulting output differences show any non-random behavior, a differential distinguisher is obtained. While differential cryptanlysis has been known for several decades, Gohr was the first to propose in 2019 the use of machine learning (ML) to build a distinguisher. In this paper, we present the first Partial Differential (PD) ML distinguisher, and demonstrate its effectiveness on cipher SPECK32/64. As a PD-ML-distinguisher is based on a selection of bits rather than all bits in a block, we also study if different selections of bits have different impact in the accuracy of the distinguisher, and we find that to be the case. More importantly, we also establish that certain bits have reliably higher effectiveness than others, through a series of independent experiments on different datasets, and we propose an algorithm for assigning an effectiveness score to each bit in the block. By selecting the highest scoring bits, we are able to train a partial ML-distinguisher over 8-bits that is almost as accurate as an equivalent ML-distinguisher over the entire 32 bits (68.8% against 72%), for six rounds of SPECK32/64. Furthermore, we demonstrate that our obtained machine can reduce the time complexity of the key-averaging algorithm for training a 7-round distinguisher by a factor of $$2^5$$ at a cost of only 3% in the resulting machine’s accuracy. These results may therefore open the way to the application of (partial) ML-based distinguishers to ciphers whose block size has so far been considered too large.
Algebraic Preliminaries

In this chapter, we review some facts from abstract algebra. Since we assume that the reader is familiar with basic abstract algebra, we put the emphasis on the concepts that are particularly important in this book, such as the ring of polynomials, modules over this ring, algebraic and inseparable field extensions, finite fields, and central simple algebras.
Discouraged Arrivals Queueing System in Interval Valued Type-2 Fuzzy Environment

We provide a survey in this article about cost and attainment stratagems for discouraged arrivals Queueing System in the interval valued type-2 fuzzy environment by centroid of centroids ordering technique. This technique is very remarkable for defuzzification and it helps to convert the fuzzy numerals to crisp (classic) numerals. This propounded ordering technique is very effective and provides the accurate verdicts.
Learning a simple multilayer perceptron with PSO

In this study, we attempt to learn the parameters of a multilayer perceptron (MLP) using the particle swarm optimization (PSO) method, which is an approximate solution method for optimization problems without requiring the derivative information of the evaluation function. We used the gradient method and PSO to learn to classify a linearly inseparable dataset with an MLP in the middle layer with a few neurons. We experimentally confirmed that PSO outperformed gradient-based learning.
Mammography images classification system based texture analysis and multi class support vector machine

Breast cancer is the greatest common reason of loss women in the world and the additional important cause of cancer losses world-wide. Classification and Detection of breast cancer are very significant since it offers body information of abnormal and normal soft tissue which supports in primary treatment planning and patient’s situation follow-up, which is critical for woman’s excellence in her life. X-ray mammography is the chief check used within quick diagnosis and screening, mammography is using in the medical imaging, and its exploration and processing are thesolutions for improving this tumor or cancer prognosis, several computer_aided finding structures have been advanced to provide support radiologists and internists for their diagnosis. In this article, an method is proposed to efficiently analyze digital mammograms based on texture segmentation to the detection for first stage tumors and there are a number of methods for medical image classification. The proposed algorithm was Multi Class Support Vector Machine and system accuracy of (98%).
Construction of Library Database Security System Supported by SQL Server Technology

With the development of electronic books and materials, modern libraries have changed from traditional paper data to electronic data. The core of library service is data service. Therefore, a safe, stable and powerful database is the key to library development. SQL Server technology can help build library database security system, in order to achieve this goal, this paper will carry out related research. The research first introduces the basic concept of SQL Server technology and the current situation of library database security, and then carries on the security system design based on SQL Server technology. Through the research, the SQL Server library database security system is successfully constructed, which can protect the database security from many angles.
Artificial Bee Colony Optimized Precompensated Interval Type-2 Fuzzy Logic Controller for a Magnetic Levitation System

Magnetic Levitation System (MLS) is highly nonlinear and unstable complex system wherein the load disturbance and external noise adversely affect the system’s performance. Therefore, controller design for controlling the ball position is a complex task for researchers. In this chapter, a novel two-layer interval type-2 fuzzy logic controller (TL-IT2FLC) approach is presented for MLS to suspend a steel ball in air without any mechanical support. For optimal controller design, well known optimization technique i.e., artificial bee colony (ABC) is employed to obtain the required control parameters. In addition, the MLS is also tested in presence of disturbance, varying reference positions, and random noise to show the efficacy of the proposed method. Finally, the simulation results clearly show that the performance of TL-IT2FLC approach is superior to a two-layer type-1 fuzzy logic controller (TL-T1FLC), and a conventional T1FLC.
Athlete Action Recognition in Sports Video: A Survey

The world of sports is growing in rapid and precise shifts which are more demanding for coaches and trainers to track an athlete’s actions. Athlete action recognition is now trending as an ordinary and necessary practice in sports video analysis. The Goals of the sports team are to identify the weakness of the opposite team and to improve its own-potential strength. In such cases, athlete action recognition of a particular player helps in increasing the player’s performance, enhances coach training, and reduces injury risk. It provides a rich set of opportunities for athlete tracking which helps the trainers, coaches and competitors. The existing studies of the athlete action recognition based on team sports videos and where the particular player’s identity issues lie are not adequate. To overcome the existing issues this paper focuses on reviews about athlete action recognition, on particular athlete tracking, and also on the sports video datasets. The paper focuses on some of the common issues like frequent occlusion, background clutter, out of view, motion blur and Inter-class similarity. In this paper the review parts are divided into two categories: Athlete tracking and sports video action recognition, where the athlete tracking parts studies the player’s identity and the sports video action recognition part reviews the action performed by the player, using learning algorithms. Finally, the future direction of the athlete action recognition on particular athlete tracking in sports video is recommended based on current technologies.
THE IMPLEMENTATION OF PROVOCATIVE PEDAGOGY TECHNOLOGIES IN TEACHING A FOREIGN LANGUAGE FOR UNDERGRADUATE STUDENTS AT THE UNIVERSITY

The chapter deals with the organization of the educational process in a technical university for master’s students in the discipline «Foreign language in the professional field» with the introduction of technologies of provocative pedagogy; the advantages of using these technologies in order to increase the interest of master’s students in the material being studied, the degree of assimilation of educational material, motivation and their effective interaction and compensation for the lack of a language environment have been revealed. The paper substantiates the need to use these technologies, techniques and teaching aids, depending on the level of foreign language proficiency of students; examples of tasks for the proposed technologies were given and an analysis of the effectiveness of the developed tasks was carried out.
Sports Video Image Analysis System Based on SIFT Algorithm

At present, in the field of computer vision and the application field of computer video image acquisition and processing, the motion trajectory analysis method of human body is highly valued by many scientific and technological workers because of its wide range of technical application and research, wide application fields, good and broad market application and broad prospects. The analysis of human motion parameters will play an important mathematical role in the analysis of clinical medical results and human-computer interaction simulation. Even in the planning of some busy modern central cities in some big cities in China, the passenger flow analysis of local statistics such as the location of planned road intersections and urban subway entrances can give full play to the statistical role. Therefore, this paper designs and studies the sports video image system based on SIFT algorithm. Firstly, this paper introduces the relevant background and significance, and then expounds the relevant technical theory. Then, based on the theory and technology, build the system model, and finally debug it. Finally, the test results show that the denoising effect of the proposed algorithm on various noise density images is significantly better than other algorithms.
A Secure Method for Data Storage and Transmission in Sustainable Cloud Computing

Cloud computing is a technology that provides secure storage space for the customer’s massive data and gives them the facility to retrieve and transmit their data efficiently through a secure network in which encryption and decryption algorithms are being deployed. In cloud computation, data processing, storage, and transmission can be done through laptops and mobile devices. Data Storing in cloud facilities is expanding each day and data is the most significant asset of clients. The important concern with the transmission of information to the cloud is security because there is no perceivability of the client’s data. They have to be dependent on cloud service providers for assurance of the platform’s security. Data security and privacy issues reduce the progression of cloud computing and add complexity. Nowadays; most of the data that is stored on cloud servers is in the form of images and photographs, which is a very confidential form of data that requires secured transmission. In this research work, a public key cryptosystem is being implemented to store, retrieve and transmit information in cloud computation through a modified Rivest-Shamir-Adleman (RSA) algorithm for the encryption and decryption of data. The implementation of a modified RSA algorithm results guaranteed the security of data in the cloud environment. To enhance the user data security level, a neural network is used for user authentication and recognition. Moreover; the proposed technique develops the performance of detection as a loss function of the bounding box. The Faster Region-Based Convolutional Neural Network (Faster R-CNN) gets trained on images to identify authorized users with an accuracy of 99.9% on training.
Machine learning modelling based on smartphone sensor data of human activity recognition

Smartphone sensors produce high-dimensional feature vectors that can be utilized to recognize different human activities. However, the contribution of each vector in the identification process is different, and several strategies have been examined over time to develop a procedure that yields favorable results. This paper presents the latest Machine Learning algorithms proposed for human activity classification, which include data acquisition, data preprocessing, data segmentation, feature selection, and dataset classification into training and testing sets. The solutions are compared and thoroughly analyzed by highlighting the respective advantages and disadvantages. The results show that the Support Vector Machine (SVM) algorithm achieved an accuracy rate of 95%.
Scalability improvement of simplified, secure distributed processing with decomposition data

Much research on secure and safe AI analysis methods for users has been conducted. They are 1) secure multiparty computation (SMC), 2) quasi-homomorphic encryption, 3) federated learning (FL), and so on. It is known that both utility and confidentiality are essential for machine learning using confidential data. However, there is a trade-off between them. In the previous paper, we proposed a learning method for secure distributed processing using decomposition data and parameters. The characteristic feature of this method is to achieve high confidentiality through distributed processing with decomposed data and parameters. On the other hand, the realization of machine learning through the distribution and integration of decomposition data leads to an increase in computational complexity and a deterioration of computational accuracy. In particular, while increasing the number of servers improves confidentiality, this problem becomes more pronounced. In this paper, we propose scalability improvement of simplified, secure distributed processing methods of BP and NG methods to suppress the computational complexity associated with an increase in the number of servers and demonstrate its effectiveness through a numerical simulation.
FB-Net: Dual-Branch Foreground-Background Fusion Network With Multi-Scale Semantic Scanning for Image-Text Retrieval

As a fundamental branch in cross-modal retrieval, image-text retrieval is still a challenging problem largely due to the complementary and imbalanced relationship between different modalities. However, existing works have not effectively scanned and aligned the semantic units distributed in different granularities of images and texts. To address these issues, we propose a dual-branch foreground-background fusion network (FB-Net), which is implemented by fully exploring and fusing the complementarity in semantic units collected from the foreground and background areas of instances (e.g., images and texts). Firstly, to generate multi-granularity semantic units from images and texts, multi-scale semantic scanning is conducted on both foreground and background areas through multi-level overlapped sliding windows. Secondly, to align semantic units between images and texts, the stacked cross-attention mechanism is used to calculate the initial image-text similarity. Thirdly, to further adaptively optimize the image-text similarity, the dynamically self-adaptive weighted loss is designed. Finally, to perform the image-text retrieval, the similarities between multi-granularity foreground and background semantic units are fused to obtain the final image-text similarity. Experimental results show that our proposed FB-Net outperforms representative state-of-the-art methods for image-text retrieval, and ablation studies further verify the effectiveness of each component in FB-Net.
Evaluation of Extractive and Abstract Methods in Text Summarization

Text summarization has become very essential tool to record important points and has been used by several websites and applications to lessen length, difficulty, and to preserve the vital information of the original file. The requirement on well-organized and useful text summarization of the website content, news feed and other kinds of legal documents with judgments and predilection is the demand of the present requirement. Hence several attempts have been made to automate the summarizing process. The recent development and state of the art models in natural language processing demonstrated outstanding results in text summarization, however major focus of these analysis was on large dataset with large parameters. This study’s primary purpose is to evaluate the performance of ensemble abstractive and extractive models on text summarization. Combined core of BERT and PEGASUS models’ output were applied to LexRank model on News Summary dataset to evaluate the performance through ROUGE metric. The results showed the performance of combined and ensemble model is better than individual performance.
[Paper] Compressing Light Field as Multiplane Image

A light field contains a large amount of data because it is represented as a dense set of multi-view images. We propose a method of compressing a light field as a newly emerging representation called a multiplane image (MPI), a graphics-oriented representation composed of a stack of semi-transparent images. Our method was constructed on a deep convolutional neural network (CNN), which was trained to generate an MPI from the given light field. To draw out the potential of this representation, we trained the CNN to be overfitted to the target light field. We also encouraged spatial smoothness to make the MPI easier to compress. Despite being in the early stage of development, our method has already achieved promising rate-distortion performance.
Identifying Counterexamples Without Variability in Software Product Line Model Checking

Product detection based on state abstraction technologies in the software product line (SPL) is more complex when compared to a single system. This variability constitutes a new complexity, and the counterexample may be valid for some products but spurious for others. In this paper, we found that spurious products are primarily due to the failure states, which correspond to the spurious counterexamples. The violated products correspond to the real counterexamples. Hence, identifying counterexamples is a critical problem in detecting violated products. In our approach, we obtain the violated products through the genuine counterexamples, which have no failure state, to avoid the tedious computation of identifying spurious products dealt with by the existing algorithm. This can be executed in parallel to improve the efficiency further. Experimental results show that our approach performs well, varying with the growth of the system scale. By analyzing counterexamples in the abstract model, we observed that spurious products occur in the failure state. The approach helps in identifying whether a counterexample is spurious or genuine. The approach also helps to check whether a failure state exists in the counterexample. The performance evaluation shows that the proposed approach helps significantly in improving the efficiency of abstraction-based SPL model checking.
A survey of deep learning techniques in the field of sentiment analysis for the hindi language

In the domain of natural language processing, sentiment analysis is an important field for any review. Nowadays Indian languages are more popular for any product review. In North India, Hindi is the most widely used language. People having Hindi as their mother tongue can easily express their opinions and thoughts through that language. In the field of research, Hindi language has many challenges due to very less research work, limited data for analysis and less size of corpus data. Deep learning techniques are currently used for predicting feelings. Recurrent Neural Network (RNN), particularly the Long Short Term Memory (LSTM) and Convolution Neural Network (CNN), are two generally used deep learning approaches. Depending on the domain area of application, the strategies are utilized in combinations or as stand-alone procedures. This review paper emphases on the numerous flavours of deep learning approaches employed in various applications of sentiment analysis at the sentence and aspect levels.
Application of Cloud Computing in Data Processing of Audit System

In order to solve the problem of data processing in audit system, the author proposes a research method of cloud computing. First of all, from the perspective of the concept, core content, features, functions and advantages of the audit system, it comprehensively expounds the audit theory, analyzes the development process of the audit system data, and analyzes the necessity and feasibility of the construction and application of audit, provide relevant theoretical basis for the application and promotion of cloud audit. Secondly, research the construction of cloud audit system from the aspects of system deployment mode, platform architecture and platform characteristics, and then design a feasible audit system, provide guidance for audit units to use cloud audit. Finally, considering the low degree and narrow scope of application of audit system data in the audit field, the author puts forward reasonable and feasible risk prevention and control measures on the basis of the risk sources of the audit system, which will help the audit units to use cloud computing, build and apply.