Reviewerly/exHarmony huggingface dataset:
Analysis of CNN Architectures for Pose Estimation of Noisy 3-D Face Images

Convolutional neural networks (CNN) has been used in various applications, especially in computer vision field, due to its superiority compare with that of conventional artificial neural networks. In this paper, CNN is developed as head pose estimator for noisy three dimensional face images and analyzed the recognition accuracy for different architectural architecture of the networks, especially on the feature extraction part. Four different amount of layers are experimented, which resulting different input neurons to the estimator part. Experimental results show that the CNN could estimate the head pose with high enough recognition accuracy, and the CNN with 3 feature extraction layers could obtain the highest accuracy of 81.31% for normal face images.
DNN Based Adaptive Video Streaming Using Combination of Supervised Learning and Reinforcement Learning

Video streaming has emerged as a major form of entertainment and is more ubiquitous than ever before. However, as per the recent surveys, poor video quality and buffering continue to remain major concerns causing users to abandon streaming video. This is due to the conditional rule-based logic used by state-of-the-art algorithms, which cannot adapt to all the network conditions. In this paper, a Deep Neural Network (DNN) based adaptive streaming system is proposed, which is trained using a combination of supervised learning and reinforcement learning that can adapt to all the network conditions. This method aims to pre-train the model using supervised learning with a labelled data set generated using state-of-the-art rule based algorithm. This pre-trained model will be used as the base model and is trained with reinforcement learning, which aims to maximize quality, minimize buffering and maintain smooth playback. Training can happen on Personal Computer (PC) based server or edge server setup as well as On-Device, which can even be beneficial in providing user personalization based on network throughput collected on the device. It has been shown that this method will give users a superior video streaming experience, and achieve performance improvement of around 30% on QoE over the existing commercial solutions.
A Logical Framework of the Evidence Function Approximation Associated with Relevance Vector Machine

Relevance vector machine (RVM) is a Bayesian sparse kernel method for regression in statistical learning theory, which can avoid principal limitations of the support vector machine (SVM) and result in faster performance on test data while maintaining comparable generalization error. In this paper, we develop a logic framework of the evidence function approximation associated with RVM based on Taylor expansion instead of traditional technology called “completing the square.” While constructing the term of completing the square, we have to find the term of completing the square by making use of some skill, which in practice increases the difficulty in dealing with the evidence function approximation associated with RVM. The logical framework in this paper based on Taylor expansion shows some advantages compared with the conventional method of completing the square, which is easier to be enforced due to the clear logical framework and avoids the difficulty in looking for the term of completing the square intentionally. From symmetry of covariance in a multivariate Gaussian distribution and algebraic knowledge, we derive approximation and maximization of the evidence function associated with RVM, which is consistent with the previous result using the novel logical framework. Finally, we derive the EM algorithm for RVM, which is also consistent with the previous result except that we use the precision matrix as the covariance.
Compact Hybrid Domain based Human Recognition using Face Images

An augmented approach to detect persons based on face images captured under uncontrolled conditions is a challenging task.We propose Compact Hybrid Domain based Human Recognition using Face Images in this research.The three sets of features viz., Histogram Intensities (HI), Discrete Wavelet Transform (DWT), Double Density Dual Tree Discrete Wavelet Transform (DDDTDWT) are computed.The first set of features is extracted by HI and considered only the dominant 200 out of 256 coefficient values.The second set of features is extracted using DWT and considering only approximation band coefficients and the number of features are only 1/4th of the original size.The third set of features are extracted by DDDTDWT and considered fifth band coefficients as features.The concluding features are obtained by concatenating all the three features which are effective and compressed.The database and the test face image features are matched using Euclidian Distance (ED) to calculate performance parameters.The results of the projected model are superior to the current techniques and also anticipated that the speed of computation in a real-time system is high as the number of features are compressed.
Deep Generative Multi-view Learning

Deep generative networks has attracted proliferating interests recently. In this work, the linear generative multi-view model is extended to nonlinear multi-views model where the deep neural network is leveraged to model complex latent representation underlying the multi-view observation. The proposed deep multi-view model admits fast stochastic optimization for training the network and offers a model to infer the shared hidden representation and subsequently generate the second view based on the available primary view at the test time. Empirical results prove the merits of the proposed methods. Furthermore, it is shown that the proposed deep model can generate samples in the input space and suppress the background noise or other complex forms of distortions, the abilities that are not naturally available in CCA based methods.
Polotsk State University in the Educational Space of Polotsk Region

The article considers the role of university education in the regional space.It is proved that the regional university is faced with the task of working out a qualitatively new educational policy, as well as the fact that the mutual influence of the university and the region is one of the leading factors of regional development.The university provides an influx of young people into the region from other areas, leading to an increase in the number of young specialists who are actively involved in new areas of the economy.Polotsk State University is a major educational, scientific and cultural centre of the northern region of the Republic of Belarus.It has a direct impact on the improvement of the quality of life in Polotsk region, providing a wide and diverse range of professional, educational, cultural and leisure services to its population, especially young people, and organizing various types of vocational training and retraining of employees in accordance with the demand of the region for a particular profession and specialty.In addition, the university concentrates the bulk of scientific research of fundamental importance for the sustainable socio-economic and cultural development of the region.
Transformed Directional Tri Concomitant Triplet Patterns for Image Retrieval

Content-based image retrieval (CBIR) is an accurate characterization of visual information. In this paper, we have proposed a new technique entitled as Transformed Directional Tri Concomitant Triplet Patterns (TdtCTp) for CBIR. TdtCTp consist of three stages to obtain detail directional information about pixel progression. In first stage, structural rule based approach is proposed to extract directional information in various direction. Further, in second stage, microscopic information and correlation between each sub-structural elements are extracted by using concomitant conditions. Finally, minute directional intensity variation information and correlation between the sub-structural elements are extracted by integrating first two stages. Retrieval accuracy is estimated using traditional distance measure technique in terms of average retrieval precision and average retrieval rate on publicly available natural and medical image databases. Performance analysis shows that TdtCTp descriptor outperforms the existing methods in terms of retrieval accuracy.
Kernel Variants of Extended Locality Preserving Projection

In recent years, non-linear dimensionality reduction methods are getting popular for the handling image data due to non-linearity present in data. For the image recognition task, non-linear dimensionality reduction methods are not useful as it is unable to find the out-of-sample data representation in the reduced subspace. To handle non-linearity of the data, the kernel method is used, which find the feature space from higher dimensional space. One can find the reduce subspace representation by applying the linear dimensionality reduction techniques in the feature space. Extended Locality Preserving Projection (ELPP) tries to capture non-linearity by maintaining neighborhood information in the reduce subspace but fails to capture complex-nonlinear changes. So kernel variants of ELPP are proposed to handle non-linearity present in the data. This article addressed kernel variants of the ELPP which explored the complex non-linear changes of the facial expression recognition. The proposed kernel variants of the ELPP is applied for face recognition on some benchmark databases.
Non-uniform Deblurring from Blurry/Noisy Image Pairs

In this paper, we address the problem of recovering a sharp image from its non-uniformly blurred version making use of a known but noisy version of the same scene. The recovery process includes three main steps - motion estimation, segmentation and uniform deblurring. The noisy image is first denoised and then used as a reference image for estimating the motion occurred in the non-uniformly blurred image. From the obtained motion vectors, the blurred image is segmented into image blocks encountered with uniform motion. To deblur these uniformly blurred segments, we use a two step process where we first generate an unnatural representation under an $$l_{0}$$ minimization frame work followed by a hyper-Laplacian prior based non-blind deconvolution. The resulting deblurred segments are finally concatenated to form the output image. The proposed method gives better results in comparison with other state of the art methods.
A Fast and Rigid Copy Move Forgery Detection Technique Using HDBSCAN

Copy move forgery detection is a rapidly growing research area in the field of blind image forensics. Image forgery means fraction of image is copied and pasted within the same image to entrap the end users and distort the originality of the information. Methods introduced so far faces problems in detecting forged region present in large size image as well as images with post-processing attacks like noise, compression, rotation, scaling, translation, etc. We have introduced a new approach based on keypoint feature mapping. In this approach, Scale Invariant Feature Transform (SIFT) keypoint feature descriptors are computed followed by K-nearest neighbor searching and finally the copy move forgery is detected with the help of Hierarchical Density Based Scan (HDBSCAN) clustering technique. We have used several quantitative measures like true positive rate (TPR), false positive rate (FPR), precision, F-score, accuracy, negative likelihood (NL), positive likelihood (PL), etc. to compare the performance of our method with other existing methods. Our method detected forged images accurately and achieved highest F-score as well as low FPR, comparing with other existing methods.
A Reduced Graph Cut Approach to Interactive Object Segmentation with Flexible User Input

One of the major downsides of existing graph cut based interactive segmentation approaches is that the user has to give the input in a specified manner for a particular algorithm. In this paper, we propose an approach which provides the user with the flexibility to select the object he/she wants to segment in his/her own way. It may be a scribble, a rough contour, or simply a mouse click. Based on this input, the algorithm finds two bounding boxes, one lying completely inside the object and the other box will completely enclose the object inside it. Following this, graph cut is applied only on the pixels lying between these two boxes. Further refinement of the result is sometimes needed based on the complexity of the image. Experimental results demonstrate that our approach produces highly accurate segmentation while reducing significantly the size of the graph and the overall computational time.
Laser Marking of a Small Sized QR Code on a Plastic Surface

The development of telecommunication technologies is claims ever higher parameters of quality and reliability to communication cable connectors. Modern production of connecting cables has the need to identify connectors by their technical parameters. The proposed labelling system greatly simplifies the quality control system and increases productivity. Using laser marking technology, it is possible to mark the QR code on the surface of an object. This two-dimensional code contains hidden information related to the given object. The requirement of the producer is to label not only the product group but also the specific technical characteristics of each small product, which are unique for each product. Using the Rofin Powerline F20 Varia laser system, experiments were carried out where the goal was to find the necessary laser system parameters that would allow quality laser marking on the surface of an ABS plastic. The purpose of the experiment is to mark the QR code, the size of which must not exceed 3.5 mm and the information contained must be read by the help of a smartphone. In order to achieve the objective pursued, experiments were carried out in search of the optimal frequency-power relation, line step and frequency relation, and finding the optimal parameters experiment was carried out in cell sized symbols of the quantity of symbols by which the experimental markings of a size not exceeding 3.5 mm were defined, thus finding the maximum amount of symbols that could be encoded in QR code, while maintaining the ability to scan them using a smartphones camera.
Offline Signature Verification using Siamese Neural Networks

Handwritten signature verification is an emerging area, every person has his/her own unique signaturethat is used mainly for the purposes of personal identification and verification of important documents orlegal transactions. There are two kinds of signature verification: online and offline. Online signaturesconsist of sequences of point coordinates which are followed by the pen, and they are obtained byelectronic means when signing. Off-line signatures are images obtained after scanning a signeddocument and there is not any dynamic information from the act of signing Moreover, the off-lineverification of handwritten signatures still remains a challenging open problem.in the field of thefinancial, commercial and for the legal matters. Signature by any person considered as the approval forany work so the signature is the preferred authentication. In this paper signature verification is done bymeans of image processing, feature extraction and by using neural network technique.
A High Precision and High Recall Face Detector for Equi-Rectangular Images

$$360^{\circ }$$ cameras have recently become quite popular as it captures the entire surrounding environment in one frame. The captured image, however, suffers from spherical distortions because of the fish eye lenses used in these cameras. Equi-rectangular (ER) projections are the most popular and convenient 2D projections when it comes to dealing with $$360^{\circ }$$ images. However, these projections suffer from heavy deformations in the pole regions which distorts the parts of the image falling near pole regions, thus increasing the difficulty of face-detection in these regions. In this paper, we address face-detection in $$360^{\circ }$$ images in their ER projection. We discuss how a detector can be trained efficiently on ER images. We also introduce a deep learning pipeline which has high precision and high recall despite the presence of these spherical distortions.
Unsupervised Single-View Depth Estimation for Real Time Inference

Several approaches using unsupervised methods have been proposed recently to perform the task of depth prediction with higher accuracy. However, none of these approaches are flexible enough to be deployed in the real-time environment with limited computational capabilities. Inference latency is a major factor that limits the application of such methods to the real world scenarios where high end GPUs cannot be deployed. Six models based on three approaches are proposed in this work to reduce inference latency of depth prediction solutions without losing accuracy. The proposed solutions can be deployed in real-world applications with limited computational power and memory. The new models are also compared with the models recently proposed in literature to establish a state of the art depth prediction model that can be used in real-time.
Delegated authorization framework for EHR management using QR code encryption

The main goal of our project is to design a mobile application for patients through web based application management that acquires secure access to patient records through cloud storage service.admin can remotely store patient data to the cloud with safe and secure cloud storage through and patient can easily access the data which are stored in the cloud storage based on EHR service management.Remote data integrity of health record is proposed to guarantee the integrity of the secured data stored in the cloud.In some common cloud storage system cloud file might contain some sensitive information.Encrypting the whole file can realize the sensitive information hiding,but will make this shared file unable to be used by others.In this paper we propose remote document reference id automatically convert to qr code then just scan user module then download the particular documents integrity that realizes data sharing with sensitive information hiding.signatures are used to verify the files in the phase of integrity records.
FaceID: Verification of Face in Selfie and ID Document

Various activities in everyday life require us to verify our identity by demonstrating our ID document containing face images, for example, voter ID, passports, driver licence, to human administrators. However, this procedure is reluctant, unreliable and labor comprehensive. An automatic framework for verifying ID record photographs to live face pictures (selfies) progressively and with high precision is required. Cross-domain biometrics is another requirement, which represents se-veral additional challenges, including harsh illumination conditions, pose variations, noise, among others. In this paper, we propose an algorithm to meet this objective. We first extract faces from ID document and selfie using Multi-task Cascaded Convolutional Networks. To extract prominent features from the data, we apply a VGG face model which is a CNN-based transfer learning approach. Finally, we validate the methods using a novel FaceId-Selfie dataset comprising 600 individuals using cosine distance measure. Results show that 74% accuracy is achieved on FaceId-Selfie dataset.
STUDY ON IMPACT OF BLOCKCHAIN TECHNOLOGY ON INDIAN BANKING SECTOR

New technology has changed the commercial world in last three decades. To continue the same, the evolution of cryptocurrency emerged as the modern phenomena. This cryptocurrency concept took the whole world in awe due to its increasing market value. Even though the concept of cryptocurrency didn't lasted long in the world financial markets, the technology behind it need more study for its application in different areas. This new technology is called blockchain technology, which works on a universal software. This research paper focuses on the blockchain technology which acts as a universal database for all the transactions block by block in a chain. In India and in many other countries, several companies have adopted this blockchain technology for application in various sectors like banking, insurance, finance and other areas. This is a conceptual study of benefits and challenges faced by Indian banking sector in the application of blockchain technology in banking sector in India.
Improving Division Property Based Cube Attacks by Removing Invalid Monomials

Division property based cube attack was proposed by Todo et al. at CRYPTO 2017, which can exploit larger cube indices than traditional cube attacks. At CRYPTO 2018, Wang et al. introduced degree evaluation and flag technique to reduce the complexity of recovering the superpoly. Although division property based cube attacks that introducing these methods are powerful to analyze many stream ciphers, how to further reduce the complexity of determining possible monomials of the superpoly is still a problem. In this paper, we introduce some techniques to speedup the recovery of the superpoly. To verify our scheme, we apply the method to the initialization of the Grain128a. In the recovery of the superpoly of 106-round Grain128a, the number of possible monomials needs to be determined is reduced to $$5.56\%$$ of Wang et al.’s superpoly evaluations. The complexity of analysing 184-round Grain128a is smaller than $$57\%$$ of the current best complexity. In the recovery attack of 185 or higher rounds Grain128a, cube indices set that includes all non-constant public variables can be achieved according to the results of the filter technique.
Guidance-Based Motion Planning of Autonomous Systems

Motion planning is a significant stage in the control of autonomous systems. As an alternative method, guidance approach is proposed for the motion planning of those systems. In guided munitions, guidance laws determine the success of the guidance systems designed to steer systems such as missiles and guided bombs towards predefined targets. The guidance laws designated according to determinative agents such as the firing position of the munition, target type, and operational requirements try to provide the munition with arriving at the target point even under the disturbing effects. In this study, the applicability of the guidance laws to autonomous systems is investigated in a manner similar to the approach for the guided munitions. For this purpose, the motion planning of the selected robotic arm, tracked land vehicle, and quadrotor is tried to be performed in order to move them to predefined target points. Having designed the control systems compatible to the selected guidance laws for the considered systems, the corresponding guidance scheme is constructed. Eventually, after conducting the relevant computer simulations, it is observed that the desired target chase can be made in a successive manner for all cases.
Retrieval of Content-Based Images by Fuzzified HSV and Local Textural Pattern

An efficient recognition of texture color pattern information from the image under shifting condition is still an open problem. Quite a lot of features extraction methods have been proposed for this purpose, ranging from available descriptors to features extracted with machine learning tools. Upon this stage, it is not totally clear under what situations a feature behave batter than the others. In this paper, we propose combination of two methods under the region and present a novel descriptor to decide color and texture features using a structured fuzzy-color and Local Ternary pattern (LTP), which we call the Fuzzified Color Local Ternary Pattern Feature descriptor (FCLTP). In the suggested method, A combined of local ternary pattern feature descriptor with histograms obtained through Hue, Saturation and Value (illumination) channels, HSV color space, provides the entire coverage of global data.