Reviewerly/exHarmony huggingface dataset:
Technology of formation and coding of marker arrays of sequences of clustered transformants of sufficiently informative image segments

The subject of research is the transmission of video data in information and telecommunications networks. The subject of research is the processes of processing informative segments of the video image using the transformant orthogonal transformation dkp. The aim is to build a technology for coding the sequence of transformants of sufficiently informative image segments while maintaining the efficiency of its delivery at a given informative probability. This will allow, while maintaining structural and statistical legitimacy and controlled loss of quality, to ensure the gain in time to prove compressed video images, the required level of availability and accessibility in the transmission of video data. The solution of the scientific problem aimed at developing methods to reduce the computational complexity of transformations (compression and decompression) of static video images in the equipment for processing signals of visual information, which will increase the efficiency of information delivery. An improved technology for reducing the informative intensity of sufficiently informative image segments has been proposed and studied. It is substantiated that the promising direction of improvement of such technology is the restructuring of dkp transformant components of these segments into several clusters. The technology of processing marker arrays of clustered transformants of sufficiently informative image segments is presented. This provides an opportunity to take into account their dynamic range and statistical redundancy, reduced using the Huffman algorithm. This approach reduces the time of compression and recovery of images; reduce additional statistical redundancy.
Monophone and Triphone Acoustic Phonetic Model for Kannada Speech Recognition System

The automatic Speech Recognition system (ASR) is the most widely used application in the speech domain. ASR systems generate text data from spoken utterances without manual intervention. In this work, we build an ASR system for the Kannada language. For building the proposed system, we extract Mel Frequency Cepstral Coefficients (MFCC) features from the audio data, and the Kannada language model is developed using corresponding labels. The dictionary generation and phonetic labelings are automated. Recognition performance is compared for both monophonic and triphone models. The word error rate of 15.73 &#x0025; and the sentence error rate of 55.5 &#x0025; are achieved for the triphone model. Comparatively, the triphone model gives a better performance than the monophonic model.
Low Restriction 3D Reconstruction Based on Uniaxial MEMS Fringe Projection Profilometry

The equipment of a conventional 3D reconstruction system is expensive and bulky, and the layout of the projector and camera also has strict restrictions. Micro-Electro-Mechanical System (MEMS) scanning is increasingly popular in 3D surface measurement with the merits of the compact structure and high frame rate. In this paper, we build a more practical and economical 3D reconstruction system by using a uniaxial MEMS-based projector and a common camera module. In order to overcome the strict restrictions layout, a corresponding theoretical model is proposed. Our model uses the pinhole imaging principle to transfer the conventional strict layout restrictions to the planeness of the work-plate, which is more controllable and combines the inverse linear method to solve the calibration problem of the uniaxial MEMS-based projector. Experiments demonstrated that our method could achieve low restriction and high accuracy 3D reconstruction.
Development of Multilingual Speech Database for Speaker Recognition in Indian Languages

In this paper, we describe the collection of speech samples to develop a database for speaker recognition in the Indian scenario in the office environment and named VIT-Indian Language Speech Corpus (VIT-ILSC) speech database. Presently, we developed the Phase &#x2212;1 database of speech samples from 50 speakers. The speech samples were collected in the office environment. Most of the speech samples collected are in English and other Indian languages in reading style, using two digital voice recorders. This work aims to develop a speech corpus database for a speaker recognition system in Indian languages, including English. Traditional Mel-frequency cepstral coefficients (MFCC) and Gaussian Mixture Model (GMM) was used to evaluate the collected phase-1 database. The phase-1 database has been evaluated on a speaker verification system. We considered both clean and noise backgrounds for initial studies and showed the impact of mismatch in training and testing samples.
Comparative Study of Content-Based Phishing Email Detection Using Global Vector (GloVe) and Bidirectional Encoder Representation from Transformer (BERT) Word Embedding Models

Emails are the simplest way to communicate between users. But increasing rate of cybercrime activities require shrewd use of email. Phishing email is one of the most perilous threats nowadays. Through phishing email, attackers want to steal the sensitive information of the email users. This sensitive information includes username, password, credit card details etc. These may cause excessive financial loss. Many anti-phishing methods exist in literature to detect phishing emails. But criminals use new methods day by day, which causes challenge to build anti-phishing methods to prevent phishing emails. In this paper the performance of two deep learning models for detection of phishing email have been compared. The first model uses convolution neural network with Global Vector (GloVe) word embedding where Bidirectional Encoder Representations from Transformers (BERT) model with fine tuning has been used in second model. The proposed method detects the phishing email by analyzing the content of the email. Some widely used datasets (lingSpam, enronSpamSubset, completeSpamAssassin, Jose Nazario's phishing dataset and Enron email dataset) are merged together and applied to measure the performance of the model. It is found that the GloVe word embedding achieves better accuracy (98%) than BERT model (96%) in detection of phishing emails in the present context.
Implementation of Computer Aided System for Automated Bone Fracture Detection using Digital Geometry

A computer-aided detection and diagnosis (CADD) system must include automated fracture identification, which can lead to improve the treatment of orthopedics. This article provides a unified approach for detecting and evaluating orthopedic fractures in digital X-ray images of different types of long bones. Initially, the test images are applied to the gaussian filtering, which performs the image pre-processing, noise removal, artifact removal and also performs the image enhancements. Then, the preprocessed images are applied to the watershed segmentation approach. Then, the digital geometry-based line extraction operation is performed on the segmented images, which draws the lines on the fracture location. Finally, back propagated artificial neural network (BP-ANN) is applied on the fractured region, which identifies the test image status as fractured or not. The simulation results shows that the proposed method resulted in better performance as compared to the conventional approaches.
Comparative Performance Analysis of Spatial Domain Filters for Removing Speckle Noise in SAR images

In synthetic aperture radar (SAR) images, speckle noise is common, and SAR data is handled coherently. Multiplicative noise is another name for speckle. The purpose of this paper is to compare several approaches for reducing speckle noise. These techniques will be used to demonstrate trends and numerous different approaches that have evolved over the years. The technical aspects of the various adaptive spatial domain filters were discussed in this paper, and they were summarised for use in removing speckle noise from SAR images. ENL, SSI, and SSIM are the performance parameters that have been quantitatively and qualitatively analysed. It indicates that the adaptive filters with varied window sizes can be used to eliminate speckle and that noise suppression is more effective in SAR images. It may be enhanced to incorporate several machine learning techniques to optimise the result in order to improve various performance parameters. The experimental results show that the structural details are better preserved while speckle noise is suppressed.
Continual Wavelets with Basis of Distributions

We study wavelet decompositions of information flows connected with trajectories in the space of distributions. We obtain an embedding criterion and the wavelet decomposition for the space of distributions and the space of dipoles. We show that it is possible to pass from the continual to the discrete case.
Multiparty Data Publishing via Blockchain and Differential Privacy

Data are distributed between different parties. Collecting data from multiple parties for analysis and mining will serve people better. However, it also brings unprecedented privacy threats to the participants. Therefore, safe and reliable data publishing among multiple data owners is an urgent problem to be solved. We mainly study the problem of privacy protection in data publishing. For a centralized scenario, we propose the LDA-DP algorithm. First, the within-class mean vectors and the pooled within-class scatter matrix are perturbed by the Gaussian noise. Second, the optimal projection direction vector with differential privacy is obtained by the Fisher criterion. Finally, the low-dimensional projection data of the original data are obtained. For distributed scenarios, we propose the Mul-LDA-DP algorithm based on a blockchain and differential privacy technology. First, the within-class mean vectors and within-class scatter matrices of local data are perturbed by the Gaussian noise and uploaded to the blockchain network. Second, the projection direction vector is calculated in the blockchain network and returned to the data owner. Finally, the data owner uses the projection direction vector to generate low-dimensional projection data of the original data and upload it to the blockchain network for publishing. Furthermore, in a distributed scenario, we propose a correlated noise generation scheme that uses the additivity of the Gaussian distribution to mitigate the effects of noise and can achieve the same noise level as the centralized scenario. We measure the utility of the published data by the SVM misclassification rate. We conduct comparative experiments with similar algorithms on different real data sets. The experimental results show that the data released by the two algorithms can maintain good utility in SVM classification.
Intelligibility Assessment of Dysarthric Speech Using Extreme Learning Machine

Artificial neural networks are known for their superior performance in many applications. Weights obtained during training are fine-tuned to the training data used. As a result the performance is reduced when tested with unseen data. Also, the use of backpropagation algorithms used in conventional neural networks is time consuming. Extreme learning machine classifiers are known for better generalization and quicker training compared to the neural networks with back propagation. In this work, the performance of the above mentioned classifiers on both seen and unseen data when used with cepstral coefficients is compared.
Media files to ISL: GAN based Indian Sign Language Interpreter

Indian Sign Language (ISL) is used as the mode of communication among deaf people in India. Currently videos, audio files, and any other synchronized or time-based media are not accessible to deaf or hard of hearing. The proposed Generative adversarial network (GAN) based system will automatically convert text or transcriptions from audio/video file to sign language in avatar form will aid them to understand the content in the media files. It is achieved by breaking down the conversion into two sub-processes. Text is converted to gloss sequences using Neural Machine Translation (NMT), and then it is converted to ISL pose sequences using GAN followed by generating corresponding avatar. Experimental results showed that the proposed sign language interpretation provides richer and more equivalent access to synchronized media
Steganography Method Using Effective Combination of RSA Cryptography and Data Compression

In this modern era, technology is increasing at a rapid rate and making new developments. The growing use of the Internet amongst people around the world and availability of digital data and its sharing has driven researchers and professionals to pay particular attention to information security. Data security is of great need. The emergence of digital media has taken this to a complete new level. Users frequently need to store, send, or receive private information and the information needs to be protected against unauthorized access and attacks from intruder. The process of hiding data and information is known to be steganography it is done to provide information security. By using steganography, one can hide thousands of words even in an average sized image. Cryptography method enhances the security level. This proposed paper uses combination of RSA cryptography, data compression and Hash-LSB steganography technique to make the data secure. RSA cryptography algorithm is implemented as receiver will have to use private key to decrypt message because the confidential message has been encrypted using public key. The secret message is compressed using Huffman coding algorithm and the cover image is compressed using Discrete wavelet transform (DWT). The cover image lossy compression is to reduce the cover image's dimensions. Then Hash LSB algorithm is applied to the RSA encrypted data to put it into the cover image. The proposed algorithm is implemented in MATLAB. At last PSNR, SSIM, Compression ratio and MSE are calculated. Our proposed method is secure for digital data transmission and also reduces the time of sending data over the Internet.
The evolutionary learning method of Bayesian network structure based on expert knowledge

Bayesian Network, which has been widely used for its significant advantages in causal inference, has great potential in product design. However, it is difficult to learn a reasonable network structure facing the problem with small data during the design process. In order to solve this problem, this paper introduces expert knowledge and Genetic Algorithm based on matrix coding into the process of Bayesian Network structure learning. Integrating the expert knowledge and data is a decent way to make up for the problem of insufficient data, and Genetic Algorithm is used to improve traditional structure learning algorithms, so as to obtain a more suitable structure conforming to the knowledge and data characteristics. The solutions illustrate that the Genetic Algorithm has some advantages compared with traditional structure learning method, and the use of expert knowledge can improve the rationality of the learned structure.
Variational Channel Distribution Pruning and Mixed-Precision Quantization for Neural Network Model Compression

This paper presents a model compression frame-work for both pruning and quantizing according to the channel distribution information. We apply the variational inference technique to train a Bayesian deep neural network, in which the parameters are modeled by probability distributions. According to the characteristic of the probability distribution, we can prune the redundant channels and determine the bit-width layer by layer. The experiments conducted on the CIFAR10 dataset with the VGG16 show that the number of parameters can be saved by 58.91x. The proposed compression approach can help implement hardware circuits for efficient edge and mobile computing.
The Perils of Being Unhinged: On the Accuracy of Classifiers Minimizing a Noise-Robust Convex Loss

van Rooyen, Menon, and Williamson (2015) introduced a notion of convex loss functions being robust to random classification noise and established that the "unhinged" loss function is robust in this sense. In this letter, we study the accuracy of binary classifiers obtained by minimizing the unhinged loss and observe that even for simple linearly separable data distributions, minimizing the unhinged loss may only yield a binary classifier with accuracy no better than random guessing.
The Student Journey Map as a Tool for Effective University Communication Strategy Development in Digital Environment

The world-wide growing competition for talented applicant to universities and digital transformation of the world forces public relation departments of education institutions to develop effective communication strategies, tactics and tools. Digitalization of communication helps to collect and analyze data from students on different "stages": when they are applicants, students, or alumni.The article introduces a Student Journey Map (SJM) as a tool for effective university communication strategy development in digital environment as far as it is learners’ experience oriented.
Domain-Agnostic Outlier Ranking Algorithms—A Configurable Pipeline for Facilitating Outlier Detection in Scientific Datasets

Automatic detection of outliers is universally needed when working with scientific datasets, e.g., for cleaning datasets or flagging novel samples to guide instrument acquisition or scientific analysis. We present Domain-agnostic Outlier Ranking Algorithms (DORA), a configurable pipeline that facilitates application and evaluation of outlier detection methods in a variety of domains. DORA allows users to configure experiments by specifying the location of their dataset(s), the input data type, feature extraction methods, and which algorithms should be applied. DORA supports image, raster, time series, or feature vector input data types and outlier detection methods that include Isolation Forest, DEMUD, PCA, RX detector, Local RX, negative sampling, and probabilistic autoencoder. Each algorithm assigns an outlier score to each data sample. DORA provides results interpretation modules to help users process the results, including sorting samples by outlier score, evaluating the fraction of known outliers in n selections, clustering groups of similar outliers together, and web visualization. We demonstrated how DORA facilitates application, evaluation, and interpretation of outlier detection methods by performing experiments for three real-world datasets from Earth science, planetary science, and astrophysics, as well as one benchmark dataset (MNIST/Fashion-MNIST). We found that no single algorithm performed best across all datasets, underscoring the need for a tool that enables comparison of multiple algorithms.
A Novel Deep Learning Approach Based Multilingual Opinion Mining

Deep learning is an emerging technique in natural language processing applications. In today's internet world a large amount of data is generated every day. This massive amount of data generated over the internet attracted researchers, industries, decision-makers for investigating the people's thoughts and views on different aspects. Opinion Mining is considered to be a great tool for analyzing people's views and thoughts regarding the products, services, individuals, entities, etc. The decision-makers, companies, business persons, service providers consider opinion mining as the best tool for improvement of their services as it analyzes the people's perspectives. The main objective of this paper is to propose a novel deep learning approach for multilingual opinion mining. A country like India is diversified in languages. People express their views in different languages. The multilingual Opinion Mining technique plays an important role in analyzing the reviews in different languages. Multilingual Opinion Mining is the technique of analyzing people's opinions in different languages. The proposed Deep Learning-based hybrid SMITH algorithm with Adam Optimizer has proven to be efficient in multilingual opinion mining.
Understanding Reinforcement Learning Control in Cyber-Physical Energy Systems

The possibility of modeling a renewable energy system as a Cyber-Physical Energy System (CPES) offers new possibilities in terms of control. More precisely, this document discusses the applicability of Reinforcement Learning (RL) techniques to CPES. By considering a benchmark algorithm, we focus on conceptual and implementation details and on how such details relate to the problem of interest. In this case, we simulate how a RL model can optimize the energy storage control in order to reduce energy costs. The work also discusses the issues that arise in RL models and the possible approaches to these difficulties. Specifically, we propose investigating a better exploitation of the memory mechanism.
Two-factor Authentication System Using Audio Signal Analysis

The article deals with the issues of authentication and authorization of users of systems operating on the Internet, considers the problem of reliable access and options to improve and enhance the security and safety of user data. The paper proposes a solution to the problem of improving reliability through an authentication system developed on the basis of sound signal analysis. It has been shown that the problem of protecting user accounts is becoming increasingly important with the spread of Internet technology in the lives of ordinary people.
Dynamic facial animation of Chinese Shaanxi Xi’an dialect talking head

Xi'an is the capital city of Shaanxi Province, the core city of the Guanzhong Plain urban agglomeration, an important central city in western China approved by the State Council, and an important national scientific research, education and industrial base. So creating Chinese Shaanxi Xi’an Dialect talking head has significant research value. In addition, Chinese Shaanxi Xi'an Dialect is also a precious cultural heritage which has great research value not only in linguistic field but also in speech analysis. We decided to take up this challenge to develop a facial animation system for this dialect lip and tone synchronization. The static viseme could be displayed by a stationary human facial image. However when a phoneme was articulated, the movement of our vocal organs，such as tongue or lips are more like a dynamic process than a static state. So we introduce the concept of dynamic tongue model here, which represents the entire process of the vocal organ movement during the articulation of a certain phoneme. The dynamic viseme model is a combination of dominance and parameter value based on dominance grade. I have already created a dynamic viseme model which is a dynamic articulation model based on dominance classification concept of the visemes for the Chinese Shaanxi Xi’an Dialect. It can be used in speech assistant systems for hard-of-hearing children or the people who wants to learn this dialect and provided a method to be used in speech assistant systems for other languages.
Two-Stage Hand Gesture Recognition based on Hand Keypoints Localization

Hand gesture is an important component of non-verbal communication, and the appropriate categorization of the gestures is quintessential for fruitful communication. Hand gestures are used in many human-computer interfaces for their natural and simplistic contactless way of conveying instruction to the interface. However, the recognition of hand gestures is complicated by numerous factors. This paper addresses a few issues by proposing a two-stage recognition framework that uses a hand joint localization technique. Firstly, the proposed method predicts hand keypoints that localize the region of interest by encompassing the hand region through a bounding box. Subsequently, this region of interest is used in gesture recognizing. The proposed work uses only one input modality-RGB image and performs phenomenally despite background clutter and illumination variation.