Reviewerly/exHarmony huggingface dataset:
An IoT aware nature inspired Multilayer Hybrid Dropout Deep-learning paradigm for waste image classification and management

Abstract In this manuscript, the combination of IoT and Multilayer Hybrid Dropout Deep-learning Model for waste image categorization is proposed to categorize the wastes as bio waste and non-bio waste. The input captured images are pre-processed and remove noises in the captured images. Under this approach, a Nature inspired Multilayer Hybrid Dropout Deep-learning Model is proposed. Multilayer Hybrid Dropout Deep-learning Model is the consolidation of deep convolutional neural network and Dropout Extreme Learning Machine classifier. Here, deep convolutional neural network is used for feature extraction and Dropout Extreme Learning Machine classifier for categorizing the waste images. To improve the classification accurateness, Horse herd optimization algorithm is used to optimize the parameter of the Dropout Extreme Learning Machine classifier. The objective function is to maximize the accuracy by minimize the computational complexity. The simulation is executed in MATLAB. The proposed Multilayer Hybrid Dropout Deep-learning Model and Horse herd optimization algorithm attains higher accuracy 39.56% and 42.46%, higher Precision 48.74% and 34.56%, higher F-Score 32.5% and 45.34%, higher Sensitivity 24.45% and 34.23%, higher Specificity 31.43% and 21.45%, lower execution time 0.019(s) and 0.014(s) compared with existing waste management and classification using convolutional neural network with hyper parameter of random search optimization algorithm waste management and classification using clustering approach with Ant colony optimization algorithm. Finally, the proposed method categorizes the waste image accurately.
Curvature of the Boolean majority function

Abstract The Boolean majority function and the generalized Boolean majority function of an even number n of variables are considered. For these functions exact values of the Walsh coefficients and the curvature are calculated.
Properties of proper families of Boolean functions

Abstract We show that triangular families of Boolean functions comprise an exponentially small fraction of proper families of a given order. We prove that if F is a proper family of Boolean functions, then the number of solutions of an equation F ( x ) = A is even. Finally, we describe a new class of proper families of Boolean functions.
Space-variant Shack–Hartmann wavefront sensing based on affine transformation estimation

The space-variant wavefront reconstruction problem inherently exists in deep tissue imaging. In this paper,we propose a framework of Shack-Hartmann wavefront space-variant sensing with extended source illumination. The space-variant wavefront is modeled as a four-dimensional function where two dimensionsare in the spatial domain and two in the Fourier domain with priors that both gently vary. Here, the affinetransformation is used to characterize the wavefront space-variant function. Correspondingly, the zonaland modal methods are both escalated to adapt to four-dimensional representation and reconstruction.Experiments and simulations show double to quadruple improvements in space-variant wavefront reconstruction accuracy compared to the conventional space-invariant correlation method.
Cultural Mapping in the Digital Economy

The article discusses issues related to the development and prospects of applying the cultural mapping technology in the Russian Federation. This technology, based on the integration of creative industries and advanced information and communication developments, involves the comprehensive identification, inventory and updating of tangible and intangible cultural resources with their subsequent inclusion in strategic plans for territorial development. The relevance of the article is connected with the need to search for new resources to develop regions, and Russia as a whole, in the conditions of global crises and fluctuations in the conjuncture of traditional commodity markets, the importance of identifying promising areas for the country’s economy transformation during the transition to the post-industrial era. The turn to innovative development that accompanies the processes of socio-cultural modernization in Russia is due not only to the level of development of modern technologies in society. First of all, it is associated with the modernization of the cultural sphere, understood as a broad interdepartmental phenomenon and the basis for positive transformations in the constituent entities of the Russian Federation. The author notes that the further construction of a modern society in the context of the development trends of the world economic and cultural space is impossible in Russian regions without the formation of an infrastructure, the key elements of which are cultural, intellectual and information resources, as well as institutions and advanced sociocultural technologies that ensure their storage, transmission and provision to users.
An Automatic Method for Geometric and Morphological Information Extraction and Archiving of Ceramic Finds

The study of potteries is still today almost entirely performed manually by archaeologists. The primary limits of the traditional approach are lack of repeatability in the results, the time required for the analysis and difficulty in exchanging information between researchers. Taking advantage of the previous research results obtained by the Authors in this field, a fully automated procedure for the analysis and cataloguing of potteries is presented in this paper. The procedure allows performing the geometric and semantic analysis of sherds, starting from their 3D scanned model. The method can also determine a set of meaningful measurements of the analyzed sherds and classify them according to the analysis results. Finally, the results are collected into a public and web-based application, which can be interacted with by interested people.
Sensitivity Support in Data Privacy Algorithms

Personal data privacy is a great concern by governments across the world as citizens generate huge amount of data continuously and industries using this for betterment of user centric services. There must be a reasonable balance between data privacy and utility of data. Differential privacy is a promise by data collector to the customer’s personal privacy. Centralised Differential Privacy (CDP) is performing output perturbation of user’s data by applying required privacy budget. This promises the inclusion or exclusion of individual’s data in data set not going to create significant change for a statistical query output and it offers -Differential privacy guarantee. CDP is holding a strong belief on trusted data collector and applying global sensitivity of the data. Local Differential Privacy (LDP) helps user to locally perturb his data and there by guaranteeing privacy even with untrusted data collector. Many differential privacy algorithms handles parameters like privacy budget, sensitivity and data utility in different ways and mostly trying to keep trade-off between privacy and utility of data. This paper evaluates differential privacy algorithms in regard to the privacy support it offers according to the sensitivity of the data. Generalized application of privacy budget is found ineffective in comparison to the sensitivity based usage of privacy budget.
Ontology Construction and Knowledge Graph for Cross Domain Unstructured Text

Ontology construction takes a lot of effort and time. Semantic web extract accurate knowledge from large databases. In this paper, an ontology construction process is proposed for cross domain data. The amazon and flip kart reviews are taken to construct ontology for unstructured text data . The data is pre-processed to clean and remove noise. The combined approach of cosine similarity and TF-IDF has been used to find similarity. Further, K means clustering is applied to identify topics. The hierarchical clustering is implemented to represent ontology. The accuracy, precision and recall are calculated by applying different classifier algorithms like Decision Tree Classifier, Gaussian NB, Random Forest Classifier, Support vector classifier and, K Neighbors Classifier. Support vector classifiers show excellent results comparative to other classifier algorithms. Support vector classifier performance shows accuracy - 0.70%, precision- 0.83% , recall- 0.70% and F1-score - 0.73%.
Human-Face Image Retrieving Based Texture Feature Extraction Method

The face image retrieval solutions are widely studied, Although it deal with different facial features and difficulties to retrieve facial images due to the similarity of these features. This paper introduces a solution of human face-images retrieval based on a combination feature extraction methods where there are some challenges related with human-face image detection and then retrieving. Human face-images retrieving used in various fields like justification, Criminal Evidence and law inspections and robotic intelligence. The research-proposal contributes to conquer on a number of confronts in images of human-face detections then accurate retrieving in acceptable time. So this work deals with getting higher rate of recognition using a combination method of Gray-Level-Co-occurrence-Matrix(GLCM) and Local-Binary-Patterns (LBP) as feature-descriptors and classifiers to develop face-image recognition. First of all some previous processing techniques of image to detect the centre of face image then GLCM calculation method involves process of gray image, after that a number of statistical-texture attributes and 2nd order-attributes are obtained. The LBP technique acts as feature extraction after the representation of a human-face image, and finally the classification. The histograms are finding of blocks of an image of human-face. Then retrieve human-face image based minimum difference between attributes of a strange human-face image with the features of familiar images. All findings of this work were evaluate using MSE, Chi-square test and PSNR. Olivetti Research Laboratory ORL human-face images dataset used in this proposal. The experiments showed that the combination technique detecting the human-faces grows accuracy rate, and effectiveness. The results show increase in recognition exactitude to be 98%.
A Hybrid watermarking using Genetic Algorithm

Watermarking is a growing field used in various fields such as copyright protection, Asset Assertion, broadcast monitoring and secured communication. This paper present a hybrid combination of stationary wavelet transform( SWT2) and Singular Value Decomposition (SVD) for inserting the water-mark. The watermarking metrics are optimised by executing the Genetic Optimisation Algorithm(GOA). The obtained results of the simulation exhibit that the proposed algorithm is not just extremely imperceptible but also highly robust and has high capacity.
Analysis of CNN Digit Classifier Parameters

Convolutional Neural Networks (CNN’s) are widely being used for various image processing applications such as denoising, classification, de-hazing and super-resolution. In this paper, CNN image classifier to classify the digits is designed with the convolutional units, batch normalization units and the rectified linear units. The classification accuracy variation by changing different CNN parameters such as learning rate, convolutional filters, convolutional layers and training images is being analyzed. The accuracy saturates or degrades with the increment in number of convolutional layers. Selection of number of filters and learning rate are important hyper parameters impacting classifier accuracy.
Malaria Cell Image Classification using Autoencoder

Over the years, many researchers have sought to use Deep Learning techniques to detect the malaria-infected cells in blood sample images. Although extremely dangerous, the spread of malaria can be restricted when treated in the early stages. This serves as an impetus for implementing an accurate solution for the detection of malaria that can replace the traditional manual process. The manual process consists of visually examining the blood samples and counting the parasitized and non-parasitized red blood cells. This process is extremely time-consuming, requires the presence of trained medical personnel, and is susceptible to human errors. With these aspects in mind, we aimed to develop a solution that could be used by medical staff with minimal training, thereby saving on time and labour. Having studied various research papers related to the use of Deep Learning techniques for the detection of malaria, we have proposed a model that addresses the gaps we identified in these systems while not compromising on the accuracy of the results. Our proposed model comprises a pre-processing module, the frozen Encoder of an Autoencoder model, a few dense CNN layers, and the classifier (Softmax).
Support Vector Regression based Forecasting of Solar Irradiance

PV power is being increasingly popular in terms of distributed energy source and derives its energy from irradiation of the sun. This irradiation differs demographically and needs to be accurately modelled for optimizing the dispatch of the source. Many methods are already in use to forecast the sun irradiation primarily based on Neural Networks and Machine learning techniques. In this paper, Support Vector based prediction is implemented and verified on a set of data. Support Vector Regressor (SVR) is a method of shifting the data points to a hyperplane and finding the correlation between the data samples. Different Kernel functions are used to define the hyperplane and their performance compared. Various combinations of input data is used to obtain the output from the regressor. Prediction metrics are used to determine the efficacy of the algorithm and based on the metrics the worst and best models for forecasting are presented.
Low Light Image Contrast Enhancement using Blending of Histogram Equalization Based Methods DSIHE and MMBEBHE

Image contrast is the quantity of color variation that exists within the features of the image and the surrounding objects. The contrast enhancement process increases the brightness levels of the image overall. Low light image has its consequences of poor visibility and lesser dynamic range. The very first method of contrast enhancement is Histogram-Equalization (HE). As per the adaptive nature of science various new methods of contrast enhancement have been discovered where Histogram-Equalization is still been used with some variations in it, being an old method Histogram-Equalization still produces a good results.The flaw of HE is that the brightness level of an image increases in such a way that it creates unpleasant effect in certain cases and doesn’t preserve the average information content(AIC) of the image as well. There are numerous more advanced versions of histogram equalization(HE) methods which take care of issues related to brightness preservation and preservation of image details. The paper primarily looks into the study of DSIHE and MMBEBHE methods and the image fusion of these two methods based on experimental studies of different image quality metrics such as BRISQUE Blind / Referenceless Image Spatial Quality Evaluator and Entropy which gives better results compared to the stand alone methods with reference to both contrast-enhancement and brightness preservation.
Improving Few-Shot Text Classification with Task-aware Contrastive Learning

Recent years have proven Pre-trained Language Models (PLMs) as the State-of-the-Art (SoA) over an assortment of downstream natural language understanding tasks. They do so by first pre-training over a large pre-training dataset, usually via a self-supervised auxiliary objective that is somewhat associated with the task in question, and then re-training with a task-specific classification head with appropriate evaluation metrics using the de facto cross-entropy loss. Although prevalent throughout literature, cross-entropy loss is problematic: fine-tuning often yields unstable results and the fine-tuned models lack generalizability. This is especially apparent in data-scarce settings, where the availability of labelled data is limited. Inspired by recent works that incorporate task descriptions in a few-shot setting and the way humans learn from limited labelled examples, we propose a task-aware contrastive learning framework for few-shot learning. We introduce an entirely new data augmentation strategy and a hybrid weighted training objective that blends the traditional cross-entropy loss with a contrastive term. Our approach does not require any unlabelled data access or architectural modifications and significantly enhances performance over four diverse natural language understanding benchmarks when compared to strong baselines and the current SoA. Further, we empirically show how our approach increases stability and generalization performance while transferring to related downstream tasks.
An Approach Towards Data Security Based on DCT and Chaotic Map

Currently, the rapid development of digital communication and multimedia has made security an increasingly prominent issue of communicating, storing, and transmitting digital data such as images, audio, and video. Encryption techniques such as chaotic map based encryption can ensure high levels of security of data and have been used in many fields including medical science, military, and geographic satellite imagery. As a result, ensuring image data confidentiality, integrity, security, privacy, and authenticity while transferring and storing images over an unsecured network like the internet has become a high concern. There have been many encryption technologies proposed in recent years. This paper begins with a summary of cryptography and image encryption basics, followed by a discussion of different kinds of chaotic image encryption techniques and a literature review for each form of encryption. Finally, by examining the behaviour of numerous existing chaotic based image encryption algorithms, this paper hopes to build new chaotic based image encryption strategies in the future.
An Inner Round Pipeline Architecture Hardware Core for AES

The following article presents an inner round architecture for the AES Encryption Scheme suitable for implementation on FPGAs and as ASICs. The uniformity between the encryption and decryption hardware makes them suitable for implementation as separate or co-existing blocks as required. The modular approach of our architecture allows for different encryption/decryption core configurations providing a compact, scalable implementation that is suitable for applications that may demand compact yet high performant hardware. The architecture employs a combinational S-Box forming a crucial step in the parallel operation of the hardware. For an operating frequency of 278.5 MHz, the hardware achieves a high throughput of about 3.5 gigabits per second (GBps).
Non-genital warts response to Cryotherapy and Immunotherapy treatment using Gradient Boosting Prediction Models

Non-genital warts are skin diseases caused by the Human Papillomavirus (HPV) the two most common types of which are common, often seen on hands and plantar, often seen on soles of feet. The two most common methods of treatment for these non-genital warts are Cryotherapy and Immunotherapy which are very different from each other. In this study, Scikit-Learn’s Gradient Boosting, Categorical Boosting, and Extreme Gradient Boosting are used to predict the response to treatment of patients with common, plantar, and both types of warts with Cryotherapy and Immunotherapy treatment. This study used the Cryotherapy and Immunotherapy dataset which are donated and are publicly available on the University of California, Irvine Machine Learning repository. Scikit-Learn’s Gradient Boosting proved to achieve the highest accuracy rate of 100% for the Cryotherapy dataset and 96.3% for the Immunotherapy dataset. The results of this study can serve as a ground for an expert system for the non-genital warts treatment selection method.
Robust Copy Move Forgery Detection using Guided Filtering and Geometric Invariant Features

Because of the Technical Advancements of many Android applications, image fraud is becoming a vital study problem for many academics. It is a typical threat employed in everyday life to further unlawful goals. Image fraud has been classified into distinct forms based on their forging pattern, with copymove forgery being the most common danger. The identification and localization of precise copy-move image forgeries are two needs of digital forensic technologies. Several computer vision approaches have been developed to identify copy-move forgeries. However, detecting copy-move forgeries with improved accuracy and fewer computing resources remains a research challenge. By deploying the more functional approaches of the computer vision coupled with objective of resolving the above challenge new technique is proposed for the CMFD. Initially on the digital image preprocessing is implemented by lightweight and effective guided filters. The filtered image is further divided into non- overlapping blocks. The moment invariant features are extracted from each block. Those extracted feature values are subjected for lexical sorting.. The candidate blocks are selected using the predefined threshold value. and then forged blocks are identified using the Euclidian Distance . After detection of all the forged blocks, we re- arrange the blocks to rebuild the forged detected image as the outcome. The efficiency of the proposed algorithm is analyzed on different datasets and various block size parameters.
System for Identification of References

Citations and references are made in almost every kind of document. Most documents tend to make direct or indirect reference to past events or articles. Such references help us obtain holistic picture of current events by picking up information from earlier works. Identifying these references improves the reading experience & provides a clear overall picture. Manual identification of such references and linkage creation for associated historical events is not only tedious but also a gross misutilization of human resources.This work reports a system that can identify & locate textual statements containing reference entities in an article. BERT is utilized to encode the sentence tokens which subsequently are classified with various standard and ensemble algorithms. Random Forest, an ensemble algorithm, at 92% accuracy & F1 score, is observed to be the most efficient in identifying the sentences with reference entities.
Detection of Cyberbullying Using Machine Learning and Deep Learning Algorithms

Use of digital technologies lead to the development of cyberbullying and social media has become a major source for it compared to mobile phones, platforms such as gaming and messaging. Cyberbullying can take several forms that includes sexual remarks, threats, hate mails and posting false things about someone which can be seen and read by millions of people. Compared to traditional bullying, cyberbullying has a longer lasting effect on the victim which can affect them physically or emotionally or mentally or in all the forms. Number of suicides due to cyberbullying has increased in recent years and India is one among the four countries that has more number of cases in cyberbullying. Prevention of cyberbullying has become manda-tory in universities and schools due to rising cases since 2015. This paper aims to detect cyberbullying comments automatically using Machine learning and Deep learning techniques. Metrics such as accuracy, precision, recall and F1-score used to evaluate the model performance. It is found that Gated Recurrent Unit, a deep learning technique outperformed all the other techniques which are considered in this paper with an accuracy of 95.47%.