Reviewerly/exHarmony huggingface dataset:
Blind noise estimation-based CT image denoising in tetrolet domain

Recently in medical imaging, various cases of cancers have been explored because of high dose radiation in computed tomography (CT) scan examinations. These high radiation doses are given to patients to achieve good quality CT images. Instead of increasing radiation dose, an alternate method is required to get high quality images for diagnosis purpose. In this paper, we propose a method where, the noise of CT images will be estimated using patch-based gradient approximation. Further, estimated noise is used to denoise the CT images in tetrolet domain. In proposed scheme, a locally adaptive-based thresholding in tetrolet domain and non-local means filtering have been performed to suppress noise from CT images. Estimation noise from proposed method has been compared from added noise in CT images and it was observed that noise is almost correctly estimated by proposed method. To verify the strength of noise suppression in proposed scheme, comparison with recent other existing methods have been performed. The PSNR and visual quality of experimental results indicate that the proposed scheme gives excellent outcomes in compare to existing schemes.
The Prediction Of Tourist Visiting With Average Based Fuzzy Time Series Method

This research aims to build a system which able to predict tourist visiting in the Province of East Kalimantan with Average Based Fuzzy Time Series method. Data collection in this research conducted by direct observation and interview with the Secretary on Duty of the East Kalimantan Province Tourism Department. This research uses the total numbers of tourist that visited the Province of East Kalimantan from 2008 through the year 2016 to predict tourist visit has the following years. In this research, value of MAPE is used to count the value of error or the imprecision of prediction result, so that the result of prediction will reach close to. The result of the building system is the prediction result for international tourist visiting with the MAPE value of 0,77375%, which is included in the criteria of ‘very good’, therefore it can be concluded that this system can be used to predict tourist visiting the Province of East Kalimantan
Face Acknowledgment using Principle Component Analysis (PCA) of Eigenfaces

Face acknowledgement is a biometric framework used to recognize or check an individual’s identity against a dataset of images. In recent times, there have been several different approaches utilized to try to achieve high accuracy rates. This paper presents a system that enables an individual’s identity to be determined based on a matching of their facial structure against a previously stored database. The matching compares the frontal view of the face with the two-dimensional images of the head already stored. In our system, the input image is sometimes enhanced using histogram equalization, before the matching takes place using the Euclidean distance between the face to be identified and those already stored. The developed acknowledgement system provides an accuracy of 97.5%.
Semantic Image Annotation using Ontology And SPARQL

Based on user’s interest or requirements, the search and retrieve images from large scale the databases, the content-based image retrieval (CBIR) technique has become the primary emerging area in research for digital image processing which makes the visual contents to use. Most promising tools for image searching are Google Images and Yahoo Image search. They are used for annotations based on textual of the images. In this, the images are annotated manually with the help of keywords and then the retrieval is carried by using various search methods based on text. Due to this method, the system performance is too low. Therefore, CBIR goal is to construct Image Ontology. The Ontology extracts the relevant images from the database by using low-level features like texture, shape and color. In multimedia technology, the challenging task is to retrieve the relevant images from an image database. For representation, organization and retrieving of images, the searching approaches based on semantic provide effective and efficient results by using image ontology. In this paper, protege software shows us how to create ontology and SPARQL query language provides semantic annotation for images. In addition to this, OntoViz and OntoGraph were used to generate Ontology in a graphical form for the relevant application.
Encryption and Decryption of a Message Involving Genetic Algorithm

The aim of this paper is to establish an algorithm for encryption and decryption of a message based on symmetric key cryptosystem involving Genetic Algorithm. In the proposed algorithm we use substitution algorithm, genetic crossover and mutation technique.
Research on evaluation model of oral English teaching quality based on cloud computing

Aiming at the problem of low evaluation accuracy of the existing evaluation model of oral English teaching quality, this paper proposes an evaluation model of oral English teaching quality based on cloud computing. The evaluation indicator is divided into expert's evaluation indicator, student's evaluation indicator and teacher's self-evaluation indicator and a comprehensive evaluation indicator set is obtained. The cloud weight in cloud computing model is described by three digital eigenvalues: weight value, entropy value and hyper-entropy value, and the corresponding evaluation cloud model is set up. Based on the cloud computing principle, the cloud evaluation model of oral English teaching quality is constructed. The experimental results show that the model can effectively evaluate the quality of oral English teaching. The selected evaluation indicators are highly representative and the evaluation results are accurate.
Multi Target Tracking Access with Data Association in Distributed Camera Networks

Data Association in Distributed Camera Network is a new method to analyse the large volume of video information in camera networking. It is an important step in multi camera multi target tracking. Distributed processing is a new paradigm to analyse the videos in camera network and each camera acts on its own and all cameras cooperatively work together to achieve a common goal, In this paper, we have addresses the problem of Distributed Data Association(DDA) to obtain the feet position of the object. These positions are shared with its immediate neighbours and find local matches using homography. By propagating these local matches across the network in order to obtain the global associations. In this proposed method DDA is less complex and improves the high accuracy compared to the centralized methods (STSPIE, EMTIC, JPDAEKCF, CSPIF, and CEIF).
LIVE MUSIC PERFORMANCES AND THE INTERNET OF THINGS

While the term Internet of Things (IoT) has become commonplace in both the vernacular of technologists and consumers alike the Internet of Musical Things (IoMusT) is an emerging field that promises to expand the landscape of music technologies.The internet of things provides additional possibilities for live music performances in particular.These possibilities can potentially increase markets for live music or substitute for traditional live performances during times when audience members may be unwilling to attend traditional concerts (for example, in a pandemic).Additionally, the technology could provide more interactive experiences for concert goers, affordable attendance options to a broader audience, and provide additional income streams for performers.We provide a model of critical considerations for integrating the internet of things with live concerts.The model framework has four parts: audience/musician interactions; technology; business models; and privacy and legal considerations.
Enhancing the JPEG image steganography security by RSA and attaining high payload using advanced DCT replacement method and modified quantisation table

Steganography deal with hiding information science, which offers an ultimate security in defence, profitable usages, thus sending the imperceptible information, will not be bare or distinguished by others. The aim of this paper is to propose a novel steganographic method in JPEG images to highly enrich a data security by RSA algorithm and attains higher payload by modified quantisation table. The goals of this paper are to be recognised through: 1) modify the quantisation table of the JPEG-JSTEG tool, hiding secret message with its middle frequency to offer great embedding capacity; 2) for challenge, secure RSA algorithm is used to prevent data from extraction. A broad experimental evaluation compares the performance of our proposed work with existing JSTEG was conducted. This algorithm resulted in greater PSNR values and steganogram histogram is more similar. Experimental results reported that the proposed system is a state-of-the model, contributing abundant payload and beating the statistical revealing. Besides, our method has better in all the parameters than JPEG-JSTEG method.
Crowd counting via scale-adaptive convolutional neural network in extremely dense crowd images

Crowd counting, a high accuracy and high-speed technology, has been applied in new retail, shopping mall, underground, rail station and vehicle surveillance systems. However, due to the inconsistent sizes of human heads, there are a lot of counting errors and instability of the crowd density estimation in extremely dense crowd images. Therefore, a scale-adaptive Convolutional Neural Network (CNN) architecture is proposed by introducing residual network on the basis of multi-column CNN. In the process of model training, joint learning is proposed in this paper. Through alternating training for residual network and multi-column CNN, network parameters with the best accuracy are selected after iteration. Joint learning helps to enhance the modelling ability for massive scale transformation and the scale self-adaptability of the network. The proposed method is experimented on public dense crowd data sets. Experimental results prove that scale-adaptive CNN shows higher counting capability than the current state-of-the-art method.
Assessing classification complexity of datasets using fractals

Supervised classification is a mechanism used in machine learning to associate classes with objects from datasets. Depending on the dimension and on the internal data structuring, classification may become complex. In this paper, we claim that the complexity level of a given dataset can be estimated by using fractal analysis. A novel fractal measure, called transition border, is proposed in order to estimate the chaos behind labelled points distribution. Their correlation with the success rate is tested by comparing it against results obtained from other supervised classification methods. Results suggest that this approach can be used to measure the complexity behind a classification task problem in real-valued datasets with three dimensions. The proposed method can also be useful for other science domains for which fractal analysis is applicable.
AI Assisted Human Activity Recognition (HAR)

Human Activity Recognition and assisting user on the basis of his context is attracting researchers since decade Researchers are working in the area to increase the accuracy of detection by various means. The challenging issue is to determine the correct supervised classifier for the detection purpose. This paper intent to examine the methodology used to recognize HAR and the impact of classifiers practiced in training and Testing. We have also tried to identify the suitable supervised machine learning model for HAR. Data of 30 Users with 561 features belonging to accelerometer and gyroscope sensor of smartphone from UCI repository is used for evaluation purpose. Nine different supervised machine learning Models are trained and tested on the dataset. The result concludes that HAR is a process which depends upon the classifiers used. It also conclude that out of 9 different Machine learning models ANN performs well and after that SVM, kNN, Random Forest and Extra Tree are equally good models for the purpose of HAR with Accuracy and execution time as the performance evaluation metric.
Blockchain for smart grid security: applications, trends, and challenges

The electric power grid is the world's largest engineering system, and its secure and reliable operation is vital to human activities. The introduction of intelligence in the electrical power grid through smart grids imposes challenges that require new techniques and approaches to provide cyber-physical security. In this article, we discuss the use of blockchain to provide security and reliability to smart grids. Blockchain allows untrusted nodes to correctly and verifiably interact with each other in a distributed peer-to-peer network, without any reliable intermediary. We explore smart contracts, codes resident in blockchain that automate multi-step processes, as a way to automatically trade electric energy. We also discuss initiatives, challenges, and research opportunities of blockchain technologies in the electrical sector.
Speech-based automatic personality trait prediction analysis

Automatic personality perception is the prediction of personality that others attribute to a person in a given situation. The aim of automatic personality perception is to predict the personality of the speaker perceived by the listener from nonverbal behaviour. Extroversion, conscientiousness, agreeableness, neuroticism, and openness are the speaker traits used for personality assessment. In this work, a speaker trait prediction approach for automatic personality assessment has been proposed. This approach is based on modelling the relationship between speech signal and personality traits. The experiments are performed over the SSPNet speaker personality corpus. For speaker trait prediction, support vector machines (SVM), multilayer perceptron (MLP), and instance-based k-nearest neighbour were analysed with multiple features. Various features have been analysed to find suitable feature for various speaker traits. The analyses have been conducted using pitch, formant, and mel frequency cepstral coefficients (MFCC) and the analysis results are presented. The accuracy of 100% has been obtained for MFCC features with 19 coefficients.
An O(m log n) algorithm for branching bisimilarity on labelled transition systems

ETAPS is a confederation of several conferences, each with its own Programme Committee and Steering Committee. ETAPS is the most important and visible annual European event related to software sciences. Altogether, more than 500 researchers participate in this event every year. The confederated conferences cover various aspects of software systems, ranging from theoretical foundations to programming language developments, compiler advancements, analysis tools, formal approaches to software engineering, and security. A gather.town room will be open during the whole conference, which can be found here: https://gather.town/app/nSgVv0DDYoCkmdkP/ETAPS2021 Password: Luxembourg2021 This can be used to meet in the coffee breaks. PDF's of the papers are available on https://etaps.org/2021/programme
Solar irradiance uncertainty management based on Monte Carlo-beta probability density function: case in Malaysian tropical climate

In recent years, solar PV power generation has seen a rapid growth due to environmental benefits and zero fuel costs. In Malaysia, due to its location near the equator, makes solar energy the most utilized renewable energy resources. Unlike conventional power generation, solar energy is considered as uncertain generation sources which will cause unstable energy supplied. The uncertainty of solar resource needs to be managed for the planning of the PV system to produce its maximum power. The statistical method is the most prominent to manage and model the solar irradiance uncertainty patterns. Based on one-minute time interval meteorological data taken in Pekan, Pahang, West Malaysia, the Monte Carlo-Beta probability density function (Beta PDF) is performed to model continuous random variable of solar irradiance. The uncertainty studies are needed to optimally plan the photovoltaic system for the development of solar PV technologies in generating electricity and enhance the utilization of renewable energy; especially in tropical climate region.
One-way steering of the optical fields with respect to the low-Q cavity via the thermal noise

Abstract We show that thermal noise could play a positive role in generating one-way Einstein–Podolsky–Rosen (EPR) steering of two cavity fields dispersively interacting with a single four-level atom, each of which is independently coupled to a thermal reservoir. In the low- Q and weak Raman transition regimes, two output fields exhibit asymmetric EPR steering and the steering direction could be easily controlled by adjusting the mean photon numbers of the thermal reservoirs and the decay rates of cavities. Striking features are present in our scheme. First, one-way EPR steering is achieved by using the couplings of the cavities with the heat baths, which sheds light on the quantum state manipulation in the presence of decoherence. Second, the obtainable EPR steering occurs between the cavity fields in the low- Q regime, which relaxes the constraints on the use of good cavities and so makes our scheme more practical. Third, the correlation properties of the output modes are considered, which can be measured more conveniently and will be more applicable to implement a range of quantum information processing.
Sentiment Analysis using Feature Based Support Vector Machine – A Proposed Method

Business decisions for any service or product depend on sentiments by the people. The mood of people towards any event, service and product are expressed in sentiments. The text sentiment contains different linguistic features of sentence. A sentiment sentence also contains other features which are playing a vital role in deciding the polarity of sentiments.The features like duplication of sentiment, unknown emotics may change the polarity of sentiment.If features selection is proper one can extract better sentiments for decision making. A directed preprocessing will feed filtered input to any machine learning approach. Support vector machine proved as a good tool of machine learning for better sentiment analysis.Better use of parts os speech (POS) folled by guided preprocessing and evaluation will provide less errorus polarity of sentiments
A Soft Computing Model to Predict the Rice Production in India

India, which has the most rice tillage area in the world, is one of the massive cultivators of this white crop. Besides, rice is the main staple food of many Indians. The main purpose of this study is to develop a predictive model on Indian rice production. In this, we have used different types of soft computing models like Fuzzy Logic, Statistical Equations, Artificial Neural Network (ANN) and Genetic Algorithm (GA) and developed a hybrid model to get the optimum result. The vital aspect of this predictive model is the accuracy of the future data prediction on the basis of past time series data. The Prediction performance has been assessed by using error finding equations like Mean Squared Error (MSE), Root Mean Square Error (RMSE) and Average Error.
Implementing Cryptographic Pairings on ARM dual-core Processors

In this paper, we explore the parallelization capabilities of the ARM processing system embedded in a Zynq device for a software implementation of the optimal Ate pairing. First, the use of the NEON coprocessor was evaluated. It was found that on ARM v7 Cortex-A9 processors the computation of the optimal Ate pairing based on NEON does not perform better than an optimized ARM-assembly equivalent implementation. Therefore, we moved to explore the parallelization of pairing computation using a dual-core processing approach. By organizing operations of line evaluation and point arithmetic formulas to have little data dependency, it was possible to schedule independent operations to be perfomed simultaneously in separate cores of an ARM dual-core Cortex-A9 processor. The same principle was applied in the arithmetic procedures of the extension fields. In this way, our software is able to perform 25.6% and 6.6% faster than the best two implementations previously reported on ARM Cortex-A9 processors.
Real-time human action recognition using depth motion maps and convolutional neural networks

This paper presents an effective approach for recognising human actions from depth video sequences by employing depth motion maps (DMMs) and convolutional neural networks (CNNs). Depth maps are projected onto three orthogonal planes, and frame differences under each view (front/side/top) are then accumulated through an entire depth video sequence generating a DMM. We build a model architecture of multi-view convolutional neural network (MV-CNN) containing multiple networks to deal with three DMMs (DMMf, DMMs, DMMt). The output of full-connected layer under each view is integrated as feature representation, which is then learned in the last softmax regression layer to predict human actions. Experimental results on MSR-Action3D dataset and UTD-MHAD dataset indicate that the proposed approach achieves state-of-the-art recognition performance and is appropriate for real-time recognition.
A Document Classification using NLP and Recurrent Neural Network

The classification technique is most important for supervised and semi supervised base machine learning task. Many classification algorithms has introduced already for existing systems. Class-label classification is an important machine learning task wherein one assigns a subset of candidate without label to an object. Classification of various document models based on short text, metadata, heading levels these are the existing techniques which are introduced in literature survey. Sometime whole data reading and processing might be take a much time for classification, so it increase the time complexity for entire system. We proposed a new document classification method based on deep learning using NLP and machine learning approach. In this work system has several attractive properties: it captures some metadata from entire abstract section and built the training set first. Once complete all document process, it deals with optimization algorithm. Recurrent Neural Network has used to categories the individual object according to their weights. And it provides final class label for entire test dataset. Based on the various experimental analysis system provides data classification accuracy as well as minimum time complexity than classical machine learning algorithms.
Laughter signature: a novel biometric trait for person identification

Laughter is a naturally occurring feature in speech and social interactions. Human intelligence can identify people by their laughter, but this has not been explored as a potential biometric in person identification systems. This study proposes a novel behavioural biometric based on individual laughter signatures. Mel frequency cepstral coefficients (MFCC) features were extracted and Kruskal-Wallis test was performed on each coefficient. A dynamic-average Mel frequency cepstral coefficients (DA-MFCC) was developed from the typical MFCC features for system training using Gaussian mixture model (GMM) and support vector machine (SVM). Test results showed an accuracy of 90%-person identification for SVM while the GMM was 65%. The use of GA-MFCC improved the accuracy of the system by 5.06% and 2.99% for GMM and SVM respectively. Laughter has thus been shown to be a viable biometric feature for person identification which can be embedded into artificial intelligence systems in diverse applications.