Reviewerly/exHarmony huggingface dataset:
Managing Query Processing on Encrypted Database on the Cloud Efficiently

Abstract- Many advantages have been provided by Cloud computing to organizations and computer users. It allows many services and applications to distribute as services in an economical way to organizations and users, Therefore, these organizations and users have begun using Cloud computing. On the other hand, they are concerned about their information when they store it on the Cloud Computing. In this paper, a technique has been proposed to encrypt the data on the client site and send it to the Cloud, and the SQL queries execute and run on the Cloud over encrypted data. The technique begins by studying Where statements that are used with the SELECT statement and define all the attributes that are used with the Where statement. In addition, Encoding codes have been generated to use with SQL statements on the Cloud instead of using conditions with main attributes. Moreover, to achieve efficiency, exact records or data that are requested from the Cloud are returned to the client. The client site has to decrypt the data to prevent any leakage at the Cloud or during transmission of the data. This method improves query processing performance while protecting database tables on the Cloud and helps in reducing the entire processing time.
Towards Fully Automated Face Verification

Automatic face verification under uncontrolled environment is a challenging task due to various crucial applications, such as surveillance, access control, and security. Despite the immense research in the past few decades, it is still an open issue due to factors, such as non-uniform illuminations, pose variation, low-resolution, expression, and occlusion. Owing to the aforementioned issues, this paper presents an adhoc study on three different poses and occlusion towards automated face verification. This study comprises of two face detectors, which are Dual Shot Face Detector (DSFD) and the Viola Jones algorithm. The verification methods investigated in this study are the AdaBoost-LDA, the LBP, and the PCA based algorithm. Simulations on face detection reveal that the DSFD method yields higher accuracy at the cost of higher computations. Whereas, face verification experiments reveal that both the PCA and LBP perform well under occlusion and pose variation than the AdaBoost-LDA. The AdaBoost-LDA is computationally efficient than the compared methods. In addition, all the three verification algorithms are near real-time.
Addressing algorithmic discrimination

Considering the intersection of technical design and civil rights when building and using classification algorithms.
Image Restoration of Landslide Photographs Using SRCNN

In the proposed work, a CNN-based Super-Resolution Convolutional Neural Network (SRCNN) framework for landslide photographs is used for resolution improvement. SRCNN method is based on a deep convolutional neural network, and it is a fast-moving field with several practical applications in different areas. The main aim of SRCNN is to construct a high-resolution image from low-resolution landslide images. For conducting this work, free landslide photographs are used from various search engines. By using Peak Signal to Noise Ratio (PSNR), Mean squared error (MSE), and Structural Similarity (SSIM) index, performance of the proposed algorithm is evaluated on landslide photographs in a meaningful way. The proposed work involves following steps: (i) Pre-processing: Upscaling low-resolution (LR) landslide image resized to the desired size using bilinear interpolation, (ii) Activation Feature Extraction: Set of convolved feature map is extracted from the upscaled LR landslide image, (iii) Multidimensional Scaling: Maps the activation map representing LR landslide image to high-resolution (HR) image, (iv) Reconstruction: Producing the HR landslide image from HR patches. In this work for quantitative evaluation of super-resolution quality, two most commonly used parameters PSNR and SSIM are evaluated. Higher values of PSNR and SSIM suggest that the reconstructed photographs are of enhanced quality. The experimental results on landslide photographs demonstrate that using bilinear interpolation for low-resolution landslide photographs to intensify its target size and then utilizing three CNN layers to do nonlinear mapping significantly improve the reconstruction quality of landslide photographs. In terms of PSNR and SSIM values, satisfactory reconstruction result is achieved as compared to the traditional methods. The obtained improved resolution landslide photographs manifest noticeable qualitative enhancement.
A semi-decentralized security framework for Connected and Autonomous Vehicles

With the fast evolution of vehicle networks, Connected and Autonomous Vehicles (CAVs) are mobile devices that are not only used for transportation but also form part of other promising technologies such as the internet of vehicles and cyber-physical systems. Despite the benefits of CAVs, exposing in-vehicle networks to external devices can lead to different privacy and security challenges. Due to scalability, efficiency, and dynamic communication requirements, the security between in-vehicle networks with external devices cannot fully rely on the vehicle's external third parties. In this paper, we propose a semi-decentralized fine-grained security framework based on Ciphertex-Policy Attribute-Based Encryption (CP-ABE) to secure the communication of in-vehicle network's Electronic Control Units (ECUs) with vehicle's external connected device. Our approach's fine-grained access control is based on the attributes of external devices connected to different subsystems inside the vehicle. Each subsystem inside the vehicle has full control of the access policy during the vehicle's operation. Therefore, our proposed approach has a flexible and dynamic key management suitable for future CAVs. Finally, the experiment of our framework validates its feasibility for implementation.
Self-calibrated dense 3D sensor using multiple cross line-lasers based on light sectioning method and visual odometry

Among various 3D capturing systems, since the system with line lasers based on the light sectioning method is simple and accurate, it has widely attracted many developers and used for many purposes. In addition, there is no need to synchronize the camera and the laser and also the configuration of the camera and the lasers is flexible, and thus, the system can be used for extreme conditions, such as underwater. There are two open problems for the system. The first problem is a low density of the 3D shape obtained from a single image, i.e., just several curves. The second problem is the accuracy of line detection in the wild. In this paper, we propose a self-calibration method using visual odometry (VO) to bundle a large number of frames to increase the density to solve the first problem. We also propose a robust line detection algorithm using CNN to solve the second problem. Comparative experiments prove the effectiveness of our proposed method. In addition, the system was tested in the extreme condition for demonstration.
Sentiment Analysis of Comment Texts on Converged Media Platforms based on BERT Model

The development of mobile Internet, smart terminals and cloud computing has promoted the advent of the era of media convergence. And Converged Media Centers in various regions had gradually been established, assuming the responsibility for disseminating news and current affairs and guiding and supervising public opinion. The study of comment texts under the converged media platforms is more targeted. In addition, this research is of great significance to the supervision and guidance of local public. This paper applies the BERT (Bidirectional Encoder Representation from Transformers) model to the sentiment analysis of comment texts on a converged media platform. In this experiment, the data used are the comment texts collected by our laboratory under the videos, which were posted by a part of the Converged Media Center accounts on the Douyin platform. By fine-tuning the pre-trained BERT model, and then the results obtained are classified and processed through the constructed neural network model. The accuracy of the validation set is 85.83%. Compared with other models, this method has a significant improvement in classification accuracy. When it is applied to a converged media platform, it is very possible to obtain correct classification results.
CPU-GPU Collaborative Acceleration of Bulletproofs - A Zero-Knowledge Proof Algorithm

Zero-knowledge proofs help to protect the privacy and security of blockchains by keeping the transaction information private. Bulletproofs are the state-of-the-art zero-knowledge technologies that perform confidential transactions without a trusted setup. However, Bulletproofs are still computationally inefficient to be applied in blockchain applications, and it is of great significance to parallelize Bulletproofs on GPUs. In this paper, we present a CPU-GPU collaborative framework to accelerate the inner-product arguments of Bulletproofs. To our best knowledge, it is the first time that Bulletproofs are implemented in a CPU-GPU hybrid system. The experiments show that our implementation achieves an average speedup ratio of 3.7x. The results also demonstrate that the CPU-GPU collaborative acceleration of Bulletproofs has properties of small size, high efficiency, and high scalability.
Performance Analysis of Post-Quantum Algorithms

This article reports on post-quantum algorithms as a next step in improving the security of data transmitted on the internet. The effectiveness of these algorithms in computing operations over commonly applied cryptography algorithms today is an important consideration. We conducted experiments to compare the performance and efficiency of new post-quantum algorithms with some commonly used algorithms and describe in detail our method of evaluation.
Supervised Neural Network for Offline Forgery Detection of Handwritten Signature

Authorized high security systems for legal contracts is need of the hour for an automatic authentication system. This study investigates the off-line signature verification system to identify skilled forgery based on Writer-Independent system approach. This research advances our understanding of layer architecture in deep convolutional neural network (CNNs) to perform specific task using optimized learning parameters. Four layered architecture with Adam optimizer produces significant accuracy of 88.39% for self-generated off-line handwritten signatures. The proposed model efficiently classifies forged signatures from genuine ones.
ICU admission and mortality classifiers for COVID-19 patients based on subgroups of dynamically associated profiles across multiple timepoints

The coronavirus disease 2019 (COVID-19) which is caused by severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) is consistently causing profound wounds in the global healthcare system due to its increased transmissibility. Currently, there is an urgent unmet need to identify the underlying dynamic associations among COVID-19 patients and distinguish patient subgroups with common clinical profiles towards the development of robust classifiers for ICU admission and mortality. To address this need, we propose a four step pipeline which: (i) enhances the quality of multiple timeseries clinical data through an automated data curation workflow, (ii) deploys Dynamic Bayesian Networks (DBNs) for the detection of features with increased connectivity based on dynamic association analysis across multiple points, (iii) utilizes Self Organizing Maps (SOMs) and trajectory analysis for the early identification of COVID-19 patients with common clinical profiles, and (iv) trains robust multiple additive regression trees (MART) for ICU admission and mortality classification based on the extracted homogeneous clusters, to identify risk factors and biomarkers for disease progression. The contribution of the extracted clusters and the dynamically associated clinical data improved the classification performance for ICU admission to sensitivity 0.83 and specificity 0.83, and for mortality to sensitivity 0.74 and specificity 0.76. Additional information was included to enhance the performance of the classifiers yielding an increase by 4% in sensitivity and specificity for mortality. According to the risk factor analysis, the number of lymphocytes, SatO2, PO2/FiO2, and O2 supply type were highlighted as risk factors for ICU admission and the percentage of neutrophils and lymphocytes, PO2/FiO2, LDH, and ALP for mortality, among others. To our knowledge, this is the first study that combines dynamic modeling with clustering analysis to identify homogeneous groups of COVID-19 patients towards the development of robust classifiers for ICU admission and mortality.
A predictor-corrector affine scaling method to train optimized extreme learning machine

Optimized extreme learning machine (OELM) has been shown to achieve high performance on classification problems due to its simple dual form. This paper presents a predictor-corrector affine scaling interior point method to exploit the dual problem of OELM. This method aims to combine a predictor step with a corrector step for determining the descent Newton direction. At each iteration, the predictor step focuses on the complementarity gap reduction and computes an affine scaling direction to estimate the extent of the reduction of complementarity gap, while the corrector step traces the central path towards the optimal solution by high order approximation, and computes the corresponding center direction. Then, the Newton direction is combined by using both two directions, and the iteration sequence of interior feasible points converges to the optimal solution. Extensive experimental evaluations on various benchmark datasets show that the proposed algorithms outperform other interior point-based or active set-based algorithms. Moreover, they are able to converge in fewer iterations, which are independent of kernel type, dataset size and dimensionality.
Improved Facial Expression Recognition Method Based on GAN

Recognizing facial expressions accurately and effectively is of great significance to medical and other fields. Aiming at problem of low accuracy of face recognition in traditional methods, an improved facial expression recognition method is proposed. The proposed method conducts continuous confrontation training between the discriminator structure and the generator structure of the generative adversarial networks (GANs) to ensure enhanced extraction of image features of detected data set. Then, the high-accuracy recognition of facial expressions is realized. To reduce the amount of calculation, GAN generator is improved based on idea of residual network. The image is first reduced in dimension and then processed to ensure the high accuracy of the recognition method and improve real-time performance. Experimental part of the thesis uses JAFEE dataset, CK + dataset, and FER2013 dataset for simulation verification. The proposed recognition method shows obvious advantages in data sets of different sizes. The average recognition accuracy rates are 96.6%, 95.6%, and 72.8%, respectively. It proves that the method proposed has a generalization ability.
A Survey on Pedestrian Detection System Using Computer Vision and Deep Learning

An important research topic in computer vision is pedestrian detection. Research in this topic has wide applications using deep learning methods that are capable of learning high-level representation of features necessary for automatic detection of the pedestrians. The objective of this paper is to study various systems based on vision-based pedestrian detection using deep learning frameworks. The paper considers the performance of some methods over the dataset Caltech, INRIA and KITTI in the span of study. In the extensive study, it has been found out ExtAtt method has gained maximum average precision rate on KITTI dataset, while PCN method has achieved lower log average miss rate on both Caltech and INRIA dataset.
Multi-person Daily Activity Recognition with Non-contact Sensors based on Activity Co-occurrence

Recognizing the activities of daily living (ADLs) for a multi-person residence is necessary for the provision of smart services, such as health care and behavior change support in “real-world” home environments. Especially, activity recognition by non-contact sensors, such as infrared sensors, is required for monitoring elderly people who are reluctant to wear devices or install cameras. However, an ADL recognition system for a multi-person environment using non-contact sensors has not been proposed due to the difficulty of identifying individual residents. In this study, we analyzed the data collected from ordinary homes with multiple residents with our ADL sensing system, consisting of only non-contact sensors, and developed a method for recognizing ADLs in multi-person environments. In a multi-person environment, multiple residents often simultaneously perform the same activity, such as sleeping, but they also frequently perform different mutually related activities at the same time, such as one person eating while another person is cooking. Hence, to recognize multi-person ADLs, we used co-occurrence relationships between the multi-person ADLs in each home as knowledge, and developed a machine learning model that used the co-occurrence of each resident’s activity history as a feature. To evaluate the proposed method, we conducted a one-month ADL sensing experiment in four ordinary homes under conditions of multi-person and non-scripted living. The results of the analysis with a deep neural network showed that the method of considering co-occurrence of multi-person ADLs improved the recognition accuracy by 5%, giving a weighted F-measure of 66.7%.
Why would you buy an electric car on Jetski Friday? Or, a critique of financial markets from an options trading room

Abstract This article presents a close, dialogue-based ethnographic account of a group of contemporary options market makers making a decision about pricing options in Tesla, Inc. Careful attention to their deliberations reveals how the rise of algorithms and automation on financial markets have rendered traders alienated and estranged from the markets they work on for their livelihood. This alienation arises, in part, due to novel cascade effects between futures and underlying equities, which algorithmic and automated trading seems to afford, and which also relate to news events as well as the actions of politicians and prominent business people. Emerging from this alienation, traders produce a critique of how highly automated financial markets allocate capital and how ripe they are for political manipulation.
The Involvement of Jupyter Notebooks as an Educational Tools: A Case Study

The paper presents a case study of involvement Jupyter notebooks as an educational tool. We described implementing a notebook for one task - exploratory data analysis - in the context of the win-day-one pedagogical pattern. Notebook implements the workflow of exploratory analysis, and Python scripts implement measurement scale identification, descriptive statistics calculation, frequency distribution visualization, and correlation analysis. We also examined the properties of notebooks as an educational tool and ways to mitigate the pedagogical and technical challenges.
Image Processing of Insulator and Vibration Damper by YOLO Algorithm

Image processing is widely used in many fields, including electronics, computer science, energy, construction, medicine, and self-driving cars. Deep learning based object detection methods have also evolved to detect in real-time. Depending on the nature of the object or image to be processed, the performance of image processing methods will vary from application to application. Therefore, researchers test different types of algorithms in each case and compare performance results. The study aims to detect and report selected objects, such as insulators and vibration dampers. This video was recorded by using an UAV in a power transmission line. The research aims to identify the insulator from the input data processed by image processing, create an image database, and then report the visible physical damages. This study summarizes the results of a study in which video insulators and vibration dampers were detected using the DL youonly look once algorithm.
Applying Deep Learning and Natural Language Processing in Cancer: A Survey

Artificial intelligence (AI) tools significantly bolstered and facilitated the complexity of forecasting dangers, catching cancer earlier, and predicting the survival after treatment. Nowadays, they are emerging as a major adjunct for clinical and medical approaches. In this paper, the main idea concentrates on using deep learning (DL) and natural language processing (NLP) to scan, diagnose, and reduce any future negativities of cancer and oncology. In addition to giving strong variety of structural, systematic analysis of some reviewed studies. The primary aim of this survey is that it includes and represents a helpful guidance for researchers who need such direct evaluation of existed techniques for the purpose of more effective maintenance and development.
Transfer Learning for the Russian Language Speech Synthesis

In this article, we suggest a method for creating a high-quality model of speech synthesis for the Russian language. Even though the task of speech synthesis is widespread, only large corporations can create high-quality models of speech synthesis since this requires a large amount of well-processed data recorded by the voice of one person. The proposed method allows the creation of a speech synthesis model, using about 30 minutes of target speech and about 20 hours of speech from different people from open sources. This model will be comparable in quality to the models of large companies that were trained on large amounts of data.
Analysis and Prediction of COVID-19 with Image Super-Resolution Using CNN and SRCNN-Based Approach

The advancement of technology has created a huge scope and requirement for transforming the image into a high-visibility image. Here, transforming an image into a high-visibility image means to convert a low-visibility image into a high-visibility image. Super-resolution has many applications worldwide like in medical industries, surveillance, satellite photography, the study of the galaxy, etc. Also, COVID-19 is a monstrous threat to earth. Doctors are predicting whether the patient is having the coronavirus or not via X-Rays and CT-Scans. These scans sometimes miss little details because of the blurriness/low-visibility of the image. This problem can be overcome by using the Super-resolution convolutional neural network (SRCNN). The purpose of the study is the classification of whether the person has the coronavirus or not becomes very accurate by using the SRCNN model. For the transformation of a low-visibility image into a high-visibility image, SRCNN and for classifying whether the person is having coronavirus or not a convolutional neural network (CNN) is used. Our models are trained and tested on four datasets, which are Set5, Set14, COVID-chest-X-Ray dataset, and chest-X-Ray-pneumonia. Our results depict that after applying super-resolution on the X-Rays or the CT-Scans, the classification of COVID-19 attained an accuracy of 95.31% which is higher if compared to the classification of COVID-19 without image super-resolution that attained an accuracy of 92.19%. These were the results after running the model for 20 epochs. Hence, with the help of the SRCNN model, the classification of COVID-19 is much easier and accurate as compared to without the image super-resolution technique.