Reviewerly/exHarmony huggingface dataset:
Information security and privacy in healthcare records: threat analysis, classification, and solutions

Information security and privacy in healthcare information system is a very critical matter. The increasing acceptance of information systems by various healthcare institutions and increasing security and privacy concerns among patients, all point to the need for proper frameworks of information security and privacy in place. In this paper, we have analyzed numerous categories of threats to a health information system and have identified threats that are unique to a health information system. Later, we have explored various models and algorithms that may be utilized to alleviate these threats. Finally, we have proposed future directions that may encourage further research into this field.
Zero-Shot Visual Recognition via Semantic Attention-Based Compare Network

Zero-shot visual recognition aims to classify images whose classes are not have been seen in the training stage. Most of current approaches first project the attribute semantic feature and image feature into a common space, then the manually defined similarity measures or linear classifiers are used to recognize the unseen image. Different from the existing study, in this paper, we propose a novel Semantic Attention-based Compare Network (SACN) which is comprised of a visual feature extraction module, a feature fusion module and a similarity compare module. Our SACN has several advantages: (1) In visual feature extraction module, a convolutional neural network (CNN) with multi-losses is introduced to extract more distinguishing visual features. (2) In feature fusion module, an attribute semantic attention mechanism is proposed to associate visual feature and class semantic representation. (3) In similarity compare module, a distance compare network is presented to predict the similarities of the test image with all unseen classes. To evaluate the proposed model, we have conducted extensive experiments on two widely used benchmarks, and both qualitative and quantitative evaluation results have demonstrated the effectiveness of the proposed SACN. More importantly, the proposed method is the 2nd place solution to the AI Challenger 2018 (Global AI Contest).
Emerging Named Entity Recognition on Retrieval Features in an Affective Computing Corpus

Affective Computing (AC) is a relatively new, dynamic and interdisciplinary research field. Numerous contributions from fields like computer science, psychology, cognitive science, sociology, physiology and medical science have been made. Consequently, it is difficult to track all recently published trends for early insight utilisation in practise or as basis for innovative research. Even if this fact holds true for many other research fields, AC in this respect is stimulating, due to its dynamic and interdisciplinary characteristics. However, Emergent Entities Recognition is a new concept introduced for early detection and prediction of developing professional terminology. Initial software developments have been completed and briefly analysed in general databases (e.g. MEDLINE). Here, we are interested in its evaluation for AC. In this respect, we have created and used a new Benchmark for Emergent Entities recognition specifially for the field of AC and show evaluation results in comparison to state of the art trained named entity recognition models and to a generic corpus (MEDLINE).
A Multi-sensor Image Matching Method Based on KAZE-HOG Features

This paper presents a multi-sensor image matching algorithm based on KAZE-HOG features. First, the multi- sensor images to be matched are preprocessed by bilateral filtering so as to remove noise. Then, a novel feature descriptor, namely KAZE-HOG is designed to extract as well as describe the feature points. Third, a nearest neighbor algorithm is used to match the obtained feature points of the images. Finally, a M-estimator sample consensus (MSAC) algorithm is utilized to remove the mismatches, so that the precise matching results can be gotten. Experimental results show that the proposed method has strong robustness, and it has good capability to resist scale and rotation transformations. As a result, it can achieve precise matching results for multi-sensor images.
Exploring the Use of Word Embedding and Deep Learning in Arabic Sentiment Analysis

In the past couple of years, improving Arabic sentiment Analysis systems has been one of the important fields of research. There are several challenges and issues facing existing systems, especially, handling multiple dialects and feature extraction. Most of those systems are generated using linear classification models and traditional bag-of-word features. In this work, we explore the use of word embedding as a modern feature representation, and Convolutional Neural Networks as a Deep Neural Network in a sentiment classification of Arabic texts. The application of our model on five benchmark datasets has yielded results that outperform previous works on 4 out of 5 datasets.
Introducing Blockchain for Smart City Technologies and Applications

A smart city is described as far from being an unambiguous concept, changing with each iteration of a much smarter version of residents living or economic activity. However, in recent years the governments around the world have been trying hard to provide the most advanced form of life to residents that their resources can afford. This means that there is no pre-defined definition and any city that is deemed to provide value to its citizens as well as generate benefit for the global society shall be considered smart. The motive of this paper is to provide a comprehensive review on Blockchain technology based smart city and enterprises their motivation, challenges and applications, in order to overcome their services, and production costs. This chapter will further explain about use cases of Blockchain technology for smart city application.
A Hybrid Method for Spectral Translation Equivalent Boolean Functions

The equivalence of Boolean functions with respect to five invariance (aka translation) operations has been well considered with respect to the Rademacher-Walsh spectral domain. In this paper, we introduce a hybrid approach that uses both the Reed-Muller and the Rademacher-Walsh spectra. A novel hybrid algorithm that maps a Boolean function to a representative function for the equivalence class containing the original function is presented. The algorithm can be used to determine a sequence of translations that maps one function to an equivalent function. We present experimental results that show the hybrid algorithm can determine the equivalence classes for 5 variables much more efficiently than before. We also show that for 6 variables where there are 150,357 equivalence classes, 8 are very difficult, a further 58 are difficult and the remainder are straightforward in terms of the CPU time required by the hybrid algorithm.
English and Chinese Neural Metonymy Recognition Based on Semantic Priority Interruption Theory

Metonymy is one of the types of common figurative languages and often used in human conversation without any difficulties.However, metonymy recognition in NLP requires a deep semantic/contextual processing to interpretation because it is highly related to the discourse of the contexts.Moreover, the fact that few available datasets of figurative languages make it more problematic.Motivated by the shortcomings of metonymy recognition, we develop several new data sets, including the Chinese version of the data, and design an end-to-end neural network metonymy recognizer.Our framework is based on the semantic priority interrupt theory and additional knowledge is introduced which makes to learn contexts effectively.Through a series of experiments, we show that our method is comparable to the state-of-the-art metonymy recognition method, especially we verified that metonymy trigger words information contributes to performance improvement in our model.
Exposing Presentation Attacks by a Combination of Multi-intrinsic Image Properties, Convolutional Networks and Transfer Learning

Nowadays, adoption of face recognition for biometric authentication systems is widespread, mainly because this is one of the most accessible biometric characteristic. Techniques intended on deceive these kinds of systems by using a forged biometric sample, such as a printed paper or a recorded video of a genuine access, are known as presentation attacks. Presentation Attack Detection is a crucial step for preventing this kind of unauthorized accesses into restricted areas or devices. In this paper, we propose a new method that relies on a combination of the intrinsic properties of the image with deep neural networks to detect presentation attack attempts. Exploring depth, salience and illumination properties, along with a Convolutional Neural Network, proposed method produce robust and discriminant features which are then classified to detect presentation attacks attempts. In a very challenging cross-dataset scenario, proposed method outperform state-of-the-art methods in two of three evaluated datasets.
Encasing Computation

This paper introduces initial attempts to bridge the worlds of digital fabrication and do-it-yourself wearable electronics. It introduces a selection of microcontrollers that are anticipated to work well in a wearable context and provides an overview of five prototypes: Folding Felt Photon, Photon Sleepers, Circuit Playground Aurora Hat, Feather Belt, and Feather Shoes. These prototypes use laser cutting and/or 3D printing to produce microcontroller enclosures that can be worn on the body. Rigid and flexible materials are used alone and in combination to achieve qualities such as conformability, comfort, and device protection. Digital fabrication techniques facilitate rapid and repeatable production of prototypes for testing while allowing precise modification of fit, material thickness and machine settings. The intent is to demonstrate this approach and to share initial designs for digitally fabricated encasements that allow researchers, designers, and artists to better integrate small computational systems into clothing or other wearables.
Design of Decision Support System with C4.5 Algorithm Methods in Determining Results Vaname Shrimp Cultivation

Water quality has a very important role in the success of shrimp farming. Water as a living medium for shrimp, has a direct effect on the health and growth of shrimp. Water quality determines the existence of various types of organisms in the pond ecosystem, both for shrimp and other biota. Water quality that is far from the optimal value can cause cultivation failure, while optimal water quality can support shrimp growth. This study aims to build a decision support system to help companies manage water quality to get good shrimp farming results. The attributes used are water height, salinity, DO, pH, temperature, alkalinity and target size attributes of shrimp. The data used were 93 data partitioned into 76 training data and 17 testing data. Based on the classification results using the C4.5 algorithm, accuracy was 76.47%, precision was 72.72% and recall was 88.88%. From this study it was proven that the decision support system with the C4.5 algorithm can be used to determine the results of vaname shrimp farming.
Natural Images Enhancement Using Structure Extraction and Retinex

Variational Retinex model-based methods for low-light image enhancement have been popularly studied in recent years. In this paper, we present an enhanced variational Retinex method for low-light natural image enhancement, based on the initial smoother illumination component with a structure extraction technique. The Bergman splitting algorithm is then introduced to estimate the illuminance component and reflectance component. The de-block processing and illuminance component correction are used for the enhanced reflectance as the ultimate enhanced image. Moreover, the estimated smoother illumination component can make enhanced images preserve edge details. Experimental results with a comparison demonstrate the present variational Retinex method can effectively enhance image quality and maintain image color.
Algorithm Diagnosis of Anemia on the basis of the Method of the Synthesis of the Decisive Rules

Anemia is a common pathology among diseases of the hematopoietic organs. In the framework of this study, a system of support for medical decision-making in determining the morphological classification of anemia based on computational algorithms for diagnosing clinical and hematological symptoms is proposed. The article presents a functional model for diagnosing clinical and hematological symptoms, describing the exact specification of all operations and actions carried out in the process of diagnosis, as well as the nature of the relationships between them A computational algorithm for the morphological classification of clinical and hematological symptoms is proposed, which makes it possible to quantitatively characterize their belonging to various types of anemia. The architecture of the decision support system for diagnosing clinical and hematological symptoms is proposed, as well as the software implementation of the medical diagnostic system is presented.
Model-Based Risk Analysis and Evaluation Using CORAS and CVSS

The consideration of security during software development is an important factor for deploying high-quality software. The later one considers security in a software development lifecycle the higher the effort to address security-related incident scenarios. Following the principle of security-by-design, we aim at providing methods to develop secure software right from the beginning, i.e. methods for an application during requirements engineering. The level of risk can be used to prioritize the treatment of scenarios, thus spending the required effort in an efficient manner. It is defined as the likelihood of a scenario and its consequence for an asset. The higher a risk level, the higher the priority to address the corresponding incident scenario. In previous work, we proposed a method that allows to semi-automatically estimate and evaluate risks based on the Common Vulnerability Scoring System using a template-based description of scenarios. In the present paper, we show how the method can be integrated into an existing risk management process like CORAS. To relate the CORAS diagrams and the template, we provide a metamodel. Our model-based approach ensures consistency and traceability between the different steps of the risk management process. Furthermore, we enhance the existing method with a questionnaire to improve the assessment of an incident scenario’s likelihood.
TESTING THE OPTIMIZATION-BASED RECURSIVE FILTERING ALGORITHM TO SUPPRESS HARMONICS ON MODEL AND FIELD CORRELOGRAMS

The performance of the algorithm for removing harmonic noise distortions of the vibroseis wavelet on the synthetic and the field data is studied. At the first stage, the noise is predicted. At the second stage, its adaptive subtraction from the original correlograms is performed. The algorithm demonstrates good performance, most clearly its advantages are manifested in slip-sweep data processing.
Basic investigation of sign language motion classification by feature extraction using pre-trained network models

This paper presents a method of classifying sign language motion using the feature elements extracted by using pre-trained networks. Good results for the image recognition were obtained using this approach. Sign language motions are diverse and complex, so it is difficult to manually extract appropriate feature elements from them. Furthermore, it is not realistic to collect a lot of sign language motion data for applying to deep learning. Therefore, it is thought that the possibility of sign language recognition system will be greatly enhanced if a pre-trained network model can be used. Feature elements of 25 types of sign language motions were extracted using the pre-trained network models including AlexNet. Trained models of sign language motions were created by Long Short Time Memory (LSTM) using feature element data, and the classification performance was evaluated. The results confirmed that an average classification rate 70.6% can be obtained with feature elements using the VGG-16 network model and the trained model created by LSTM.
Assessment of Quality of Program Based on Static Analysis

In today's world of automation, there is a growing trend towards automating even the simplest tasks. One area where this trend has not caught up is in the assignment evaluation tasks area in the education sector. Even today, many programs are manually evaluated in tests and exams. Even popular platforms like HackerRank and HackerEarth fall short in many cases as they perform checking only based on the test-cases irrespective of the quality of the code written. This may lead students to learn improper coding practices. In this paper, we present a tool that not only tests a student's program based on test cases dynamically but also evaluates a program statically based on certain rules given by the teacher, thereby automating the process of evaluation and removing any possible bias and error. The main idea of this paper is to demonstrate the application of static analysis in program evaluation based on rules created by the teacher to encourage higher coding standards.
Neural Spelling Correction for Azerbaijani Language

Spelling correction is one of the most needed steps in many natural language processing tasks. It is the operation of correcting orthographical mistakes. For languages such as English, spelling correction is usually performed by dictionary lookup for words with minimum edit distance to the given word which is not feasible for agglutinative languages such as Azerbaijani (ISO 639-3:aze). Thanks to their rich morphological structure, languages such as Azerbaijani can have quite complex words with several derivational and inflectional affixes. Indeed, agglutination of the affixes in the new word and meaning formation while adhering to the certain linguistic rules such as vowel harmony is what makes the neural models suitable for the correction of the certain classes of the errors in such languages, helping understand the patterns that the agglutination brings with it. In this paper, the possibility of building such a model for the mentioned purpose is explored and the results are reported.
A Novel Face Recognition System Based on Gabor and Zernike Features

The extraction of invariant features is the core of face recognition systems (FR). In this chapter, we propose a new and efficient facial image representation based on Gabor energy filters (GFs) and Complex Zernike moments (ZMs) in which GFs are used to extract texture features while ZMs are employed to extract the shape features. Most existing methods only use magnitude component of the ZMs (respectively GFs) as features in recognition task. Recently, it has become well-known that the phase component of moments (respectively Gabor Filters) also captures useful information for image representation. Next, the extracted features vectors are projected onto a low-dimensional subspace using Kernel Fisher Analysis (KFA) technique. Then, a comprehensive performance evaluation of these approach is achieved on the most popular benchmark FERET Database for face identification scenarios.
Evaluating the Accuracy of Various Irradiance Models in Detecting Soiling of Irradiance Sensors

We evaluate the feasibility of using various clear-sky models or purchased satellite data for estimating the soiling of a reference cell irradiance sensor. We find results to be more accurate for models that consider local meteorological conditions. We conclude that given the data sets considered, and depending on the requirements of the data analyst, choosing to use purchased satellite irradiance data from Solargis to estimate the soiling of a reference cell sensor tends to yield more accurate results, although there are instances where a clear-sky model performs better. The SOLIS clear-sky model in PVLIB with variable P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">wat</sub> provided useful soiling results, implying that the general method of using a clear-sky model with local meteorological data may provide a low-cost tool for detecting soiling of irradiance sensors.
Securing and Hiding the Destination of Confidential Medical Information with Domain Fronting

E-Health is a developed infrastructure, which usage becomes important to control access to personal patients' data, as well as to various medical resources. The need for confidentiality of personal medical data and control of access is necessitated by the safety concerns. This paper shows Domain Fronting technology application method that significantly decreases unauthorized accesses and illegal actions into the E-health system. It uses a method of hiding visits to a resource by redirecting received requests to arbitrary addresses.
Intra-day Solar Irradiance Forecasting Based on Artificial Neural Networks

Accurate solar irradiance forecasting is important for improving forecasting precision of photovoltaic (PV) power. In this study, an intra-day (i.e. 1 to 6 hours ahead) machine learning model based on an artificial neural network (ANN) was implemented for forecasting the intra-day incident solar irradiance (GI). The methodology included the implementation of the optimal ANN topology which was trained and validated on historical yearly datasets. The forecasting results demonstrated a normalised root mean square error (nRMSE) in the range of 4.23% to 9.51%. The lowest nRMSE of 4.23% was achieved for the hour-ahead forecast while the highest nRMSE of 9.51% was observed when forecasting at a horizon of 6 hours ahead. Finally, the mean absolute percentage error (MAPE) varied from 4.10% to 8.19% for the 1 hour to 6 hours ahead forecasts respectively.
Study of the Effect of Air Pollution on Solar Power Generation Using Sun Simulator

The basic objective of this project is to study the solar radiation pattern at different instants of time and produce such a pattern using an LED matrix and thereby calculate the power generated in each case. The second objective is studying the effect of level of air contamination or presence of pollutants in air, on solar power generated, using solar panel of a given size, and thereby evaluating the future prospects of solar energy as a substitute of non-renewable energy sources. This project helps us to calculate the size of solar panel required to generate given amount of power at different pollution levels.
Polynomial Invariant Theory and Shape Enumerator of Self-Dual Codes in the NRT-Metric

In this paper we consider self-dual NRT codes, that is, self-dual codes in the metric space endowed with the Niederreiter-Rosenbloom-Tsfasman metric (NRT metric) and their shape enumerators as defined by Barg and Park. We use polynomial invariant theory to describe the shape enumerator of a binary self-dual NRT code, even self-dual NRT code, and weak doubly even self-dual NRT code in M <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n,2</sub> (F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ). Motivated by these results, we describe the number of invariant polynomials that we must find to describe the shape enumerator of a self-dual NRT code in M <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n,s</sub> (F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ). We define the ordered flip of a matrix A ∈ M <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k,ns</sub> (F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> ) and present some constructions of self-dual NRT codes over F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> . We further give an application of ordered flip to the classification of self-dual NRT codes of dimension two.