Reviewerly/exHarmony huggingface dataset:
Empirical Evaluation of Texture Features and Classifiers for Liver Disease Diagnosis

The liver cancer is one of the most common fatal diseases worldwide, and its early detection through medical imaging is a major contributor to the reduction in mortality from certain cancer. This paves the way to work on diagnosing liver diseases effectively. An accurate diagnosis of liver disease in CT image requires an efficient description of textures and classification methods. This paper performs comparative analysis on proposed texture feature descriptor with the different existing texture features with various classifiers to classify six types of diffused and focal liver diseases. The classification of liver diseases is done in two stages. In first stage, features like segmentation based fractal texture analysis, counting label occurrence matrix, local configuration pattern, eXtended center-symmetric local binary pattern and the proposed local symmetric tetra pattern are used for extracting information from the CT liver structure and classifiers like support vector machine, [Formula: see text]-nearest neighbor, and naive Bayes are used for classifying the pathologic liver. When pathologic conditions are detected, the best feature descriptors and classifiers are used to classify the results into any of six exclusive pathologic liver diseases, in second stage. The experiments are carried out in medically validated liver datasets containing normal and six-disease category of liver. The first experiment is analyzed using sensitivity, specificity, and accuracy. The second experiment is evaluated using precision, recall, BCR, and F-measure. The results demonstrate that the local symmetric tetra pattern with [Formula: see text]-nearest neighbor classifier culminates in a state-of-the-art performance for diagnosing liver diseases.
Subpixel image registration regularised by and norms

In this study, the authors propose a subpixel image registration framework that detects and matches feature points. Rigid and nonrigid registration models are employed to solve the problem of subpixel image registration problem. A rigid registration model based on the norm is proposed to regularise the rotation coefficients using the indicator function to estimate the rigid transformation parameters. The latter estimation simplified is made easy by the reduction in the rigid transformation from two dimensions to one dimension. Furthermore, a non-rigid registration model based on the and norms is proposed to estimate the elastic coefficients of the compact support radial basis functions. Due to the linear representation of the transformation function, the rigid and nonrigid subpixel image registration models can be solved efficiently using the fast iterative shrinkage-thresholding algorithm. Experiments on a demosaicing data set, the ocean of remote sensing data set, a brain data set and the fundus image registration data set show that the proposed rigid and non-rigid registration models can accurately perform subpixel image registration.
A fuzzy dynamic approach to manage optimally the cloud resources

One of the main challenges in cloud computing is how to manage optimally the cloud resources in response of execution needs; so that both the cost and the time of running users applications are reduced. However, prices may change over time. In this paper, we propose an optimization-based solution which combines a Dynamic Genetic Algorithm (DGA) approach and fuzzy logic. First, we presented the DGA which provides a fair consideration of objectives. Then, we introduced a model based on possibility theory where each cost is taken as a fuzzy variable. For that, we used both possibility and necessity measures. In order to validate our solution, we tested it over some real applications using a set of cloud resources available on the market. The obtained results demonstrate clearly that our algorithms succeeded to find reasonable solutions that fulfill successfully our objectives function.
The Wave Concept Iterative Process Method in Spherical Coordinates

In this paper, we present a global development of the iterative method WCIP in spherical coordinates, also some special parameters that we will interpreted.
Finding Structure in Large Data Sets of Particle Distribution Functions Using Unsupervised Machine Learning

The raw data generated by simulation codes on supercomputers can be so large that it requires data reduction methods to allow scientists to understand it. Physics-based reductions are often used, for example, taking moments of particle distribution functions. It must be realized, however, that there will be a loss of information in these reductions. Here, we explore the use of unsupervised machine learning algorithms to see if patterns and structure can be learned and discovered directly in the data itself, before any reductions, and to give researchers further insights into areas of interest. This has the potential benefit of discovering kinetic structure that would be lost by some physics-based reductions. We utilize the 5-D, gyrokinetic distribution function in simulations from the full-f code X-point Gyrokinetic Code (XGC1). We find that in spatial regions of “blobby” turbulence in the edge, the electron distribution function has a very distinct signature, with higher energy regions varying across space separately from the lower energy component and higher energy regions showing a distinction near passed/trapped boundaries.
Health Data Information Retrieval For Improved Simulation

In this paper we propose an architecture specifically devoted to the analysis of huge natural language biomedical textual collections, with the purpose of searching for semantic similarity in order to obtain useful hints for effective simulation that could help physicians in diagnosis tasks. We leverage Word Embedding models trained with word2vec algorithm and a Big Data architecture for their processing and management. We performed some preliminary analyses using a dataset extracted from the whole PubMed library and we developed a web front-end to show the usability of this methodology in a real context.
Semi-Supervised Outlier Detection and Deep Feature Extraction for Detecting Cyber-Attacks in Smart Grids Using PMU Data

Smart grids are facing many challenges including cyber-attacks which can cause devastating damages to the grids. Existing machine learning based approaches for detecting cyber-attacks in smart grids are mainly based on supervised learning, which needs representative instances from various attack types to obtain good detection models. In this paper, we investigated semi-supervised outlier detection algorithms for this problem which only use instances of normal events for model training. Data collected by phasor measurement units (PMUs) was used for training the detection model. The semi-supervised outlier detection algorithms were augmented with deep feature extraction for enhanced detection performance. Our results show that semi-supervised outlier detection algorithms can perform better than popular supervised algorithms. Deep feature extraction can significantly improve the performance of semi-supervised algorithms for detecting cyber-attacks in smart grids.
A Quantum Cognition Model For Learning By Difference

This paper presents a quantum logic model for learning by behaviour by operant conditioning. We describe several variants of operant conditioning based on the notions semantic anchor and behaviour-meaning pair. If an agent has at its disposal a finite set mutually exclusive behaviours, the mathematical frame for the quantum logic model constructed here is a scalar product space whose dimension is exactly the cardinality of this finite set. We conclude that this type of quantum logic models is especially suitable for applications in cognitive infocommunication.
Recent Trends in Modelling and Simulation with Machine Learning

In recent years, the increased accessibility of large computing power, together with the availability of big data and the relevant progress in algorithms, led to an exponential increment of Machine Learning (ML) applications to predictive tasks concerning complex systems. In general, ML automatically builds a model of the system under study by exploiting an available large dataset of input features coupled to the corresponding expected outputs. While automatically learning models from data is an extremely powerful approach, in the case of complex systems' dynamics the generalization ability of ML models can easily be limited, that is, the predictions can be inaccurate once the model is applied beyond the bounds of previously observed data. On the other hand, a traditional field that deals with predicting the dynamics of complex systems is represented by Modelling and Simulation (M&S), in which, thanks to the knowledge of sufficient details concerning interactions and processes within the system under study, an abstract representation of it is developed and used for artificially reproducing its behaviour. Compared to ML models, when the M&S approach can be based on a sound theory about the functioning of the system, it can enable, in the line of principle, a more reliable forecasting of the system's dynamics beyond the bound defined by the observed historical behaviour. In spite of the outlined conceptual distance between the ML and M&S approaches, a recent and promising research trend encompasses their synergic combination for producing better data-driven simulation models. On the basis of an analysis of the relevant and recent scientific literature, the paper discusses new ideas and directions concerning M&S approaches based on advanced ML techniques.
Software Product Line Development Based on Reusability at Subdomain Level

Reusability is nowadays a highly valued attribute within the software engineering area. In particular, software product line (SPL) developments are focused on identifying similarities and variabilities within particular domains in order to be reused when new products are developed. It is also possible to reuse outside a domain, especially when the domains are related to each other. This fact allows us to develop SPLs by reusing assets that are hierarchically structured as domain/subdomain taxonomies. In this paper, we describe the process for developing new SPLs by reusing those most suitable assets. We exemplify the process for a case, and show similarity calculations that might facilitate systematic reuse.
Graph Composer: music composition from graph design

The Graph Composer is an app that allows the user to compose music through the design of a graph. You can create or modify an existing graph, listen and modify the composition in real time. Insert new nodes and connect them, change the corresponding note by clicking over the node and selecting a new one from the scale, define it’s duration over time and select a decoration to change the sound sequence.
Does foreign direct investments in financial services induce financial development? Lessons from emerging economies

Abstract We employ panel Vector Error Correction Models (VECM) and cointegration framework to identify the existence and direction of the causal association between foreign direct investment (FDI) in financial services and financial development for 26 emerging economies for the period 2003–2015. Our results show that there exists a long‐run cointegrating relationship between financial development and FDI in financial services after incorporating the extent of heterogeneity among emerging economies. We find long run unidirectional causality from financial development to financial services FDI. Using fully modified OLS (FMOLS) estimation, we estimate the long run elasticities between financial services FDI and financial development. Our results show that financial development has a positive and significant impact on FDI in financial services, which implies that a country with well‐developed financial markets tend to attract larger amounts of FDI in financial services.
Investigating the Impact of Developers Sentiments on Software Projects

Several areas of knowledge are subject to the interference of social aspects in their processes. Sentiment Analysis uses Data Science techniques to support automated or semi-automated identification of human behavior and has been widely used to characterize the perception of issues from different areas from Politics to E-commerce. The objective of this paper is to analyze the impact of developers’ sentiments on open source software projects based on evidence from the literature. To achieve this goal, we selected papers from Google Scholar reporting the impact of sentiments on software practices and artifacts. We have found studies that analyzed this impact based on extracted data from different sources. Productivity, collaboration, and the software product quality can be impacted by developers’ sentiments.
Research on Trajectory Anonymous Query using Network Voronoi Graph over Road Networks

The privacy protection method based on location service in the road network only focuses on the location privacy of users. When the query content proposed by the anonymous set mostly belongs to the same category, it is prone to homogeneity attack. Aiming at this problem, this paper proposes a privacy protection algorithm called V-L based on the (K, L) anonymous model. The algorithm first uses voronoi diagram to process the road network into independent voronoi diagram unit or known as V diagram unit, and then selects the anonymous set that satisfies (K, L) anonymous model in the current V diagram unit for the user, and finally adds false user to the anonymous set that does not satisfy (K,L) anonymous model, and randomly selects the location of the current V diagram unit to replace the real location of the user. The algorithm is verified by the data of Lanzhou road network. Experimental results show that on the basis of query cost slightly higher than traditional algorithms, the anonymous success rate of the proposed algorithm can be guaranteed to be 100%, and the average anonymous time is about 1ms. It is proved that the algorithm we proposed achieves a good balance between privacy protection intensity and algorithm performance.
Segmentation of cardiac left ventricle from MRI images using a method based on FCM and convexity preserving level set

Segmentation of the left ventricle (LV) is of great significance in diagnosis and treatment of cardiac diseases. In this paper, a rapid method is proposed for segmentation of left ventricle from MRI images. Specifically, each MRI image is divided into two clusters, the background and the foreground, using a pixel-based fuzzy c-mean (FCM) method firstly. Then each connected region is assigned with different connected component label (CCL) so that the label most similar to the left ventricle is selected based on the multiple attributes such as center distance, eccentricity and area size. Finally, a convexity preserving level set method is used to optimize and extract the left ventricular region. Experiment results show that the proposed method is accurate and effective in segmentation of the left ventricle with a fast average computational time of 1.02 seconds per image, which is superior to some current advanced methods.
An Analysis of Students’ Perception towards User Involvement in a Software Engineering Undergraduate Curriculum

Developing soft skills as well as other non-technical issues is essential for a successful career in software engineering.Educators, practitioners and researchers are paying more attention to this matter as they understand its importance to a software development context.Even the IEEE/ACM software engineering guidelines has already pointed out the importance of working with real-world projects in order to develop such skills.Being technically competent is not enough; students should have opportunities to go beyond coding and experience interactions with real users in order to better prepare themselves for their future.In this sense, this paper presents a software engineering undergraduate program that connects students with real projects throughout its curriculum.In order to evaluate whether this program helps students into understanding the importance of connecting and interacting with real stakeholders, we performed a survey with 111 students from this program.Our results indicate that providing a structure throughout the program in which students actually work on real projects is beneficial for their soft skills development.one of her studies, Shaw (Shaw, 2009) argued that problems faced by software engineers in the following ten years will be more "situated in complex social contexts, and delineating the problems' boundaries is increasingly difficult".This fact impacts on how software engineering should be taught.
Mortar fire direction with multiplatform mobile application

Abstract Mortar is one of the weapons that is still used for warfare. In determining the direction of mortar firing there are still using conventional method with plotting board. Then it takes a portable calculator that is quick and accurate in determining the angle of elevation and angle of azimuth. So it is proposed that this mortar fire direction is a mobile application with multiplatform development method that can run on Windows, Android, and iOS operating systems. The result of this research is a prototype mobile application that provides a report of elevation angles and azimuth angles rather than mortar quickly and accurately to the intended goal.
A Novel Percentage Split Distribution Method for Image Thresholding

Traditional image thresholding methods are found to be effective for bi-level thresholding, but computationally expensive for multi-level thresholding. Most of these methods do not precisely segment images with zero root mean square error. Nevertheless these methods fail to select quality thresholds with the increased number of segmentations. To overcome these issues, this paper proposes a novel and fastest image thresholding method ‘percentage split distribution’ to segment images into regions of relevant groups. The method effectively computes the globally optimized thresholds through percentage-wise segmentation of image pixels. To analyze the performance of the proposed method, nine bench mark grayscale images are taken and validated against SSIM, PSNR and time complexity measures. The experimental results show that the proposed method is superior to other thresholding based image segmentation methods in terms of high PSNR and SSIM values resulted with reduced time complexity. The experimental results also proclaim that the thresholds generated by the proposed method are effective and are of equal intervals. Moreover, the proposed method is found to be effective, simple, easy to implement and can be effectively used for real-time applications.
A recommendation dashboard utilising micro-filter visualisations: An evaluation and investigation of applicability on mobile devices

One classic issue associated with being a researcher nowadays is the multitude and magnitude of search results for a given topic. Recommender systems can help to fix this problem by directing users to the resources most relevant to their specific research focus. However, sets of automatically generated recommendations are likely to contain irrelevant resources, making user interfaces that provide effective filtering mechanisms necessary. This problem is exacerbated when users resume a previously interrupted research task, or when different users attempt to tackle one extensive list of results, as confusion as to what resources should be consulted can be overwhelming. The presented recommendation dashboard uses micro-visualisations to display the state of multiple filters in a data type-specific manner. This paper describes the design and geometry of micro-visualisations and presents results from an evaluation of their readability and memorability in the context of exploring recommendation results. Based on that, this paper also proposes applying micro-visualisations for extending the use of a desktop-based dashboard to the needs of small-screen, mobile multi-touch devices, such as smartphones. A small-scale heuristic evaluation was conducted using a first prototype implementation.
Online Extracting Sessions of Frequent Users

Web usage mining aims to discover interesting user access patterns from the data derived from interactions of users. The primary source data for web usage mining approach is user sessions extracted from web server logs. However, logs are usually very large and contain sessions of users with different behaviors. To expedite mining processes and enhance mining results, we consider extracting sessions of frequent users. In this paper, we investigate the problem of finding all frequent users as well as obtaining their sessions. Instead of discovering frequent users over static web log files, we present an online algorithm named FUSMiner to solve the mining task in a streaming environment. The experimental results show that our proposed algorithm is both efficient and effective.
The use of SIMPEG-based data mining techniques in measuring employee performance in Islamic higher education

Abstract This article intends to provide an overview of: 1) employee performance patterns based on Employ Information Systems (SIMPEG) data; 2) important characteristics of employee performance based on Employee Information System (SIMPEG) data; 3) employee performance classification based on Employee Information System (SIMPEG) data. The study was conducted using a qualitative approach, where data collection is done through a process of observation, study of documents, and interviews. The results of the research show that: 1) employee performance at IAIN Kendari tends to be stagnant, which is reflected in the daily performance report (LKH); 2) important characteristics of the performance of IAIN Kendari employees are to focus on routine, based on tasks and functions; 2) the performance of IAIN Kendari employees is divided into high, medium and low categories.
Improve the Efficiency of the Classifiers Using Resample Technique on Image Segmentation Dataset

One of the most popular data mining techniques is classification which assigns collection items to classes. The main goal of classification is to accurately predict the target class for each data cases. One of the popular application areas of machine learning is IMAGE SEGMENTATION. The trained classifiers are used to extract relevant features of the target region [1]. In this paper, experiments are conducted using WEKA (Waikato Environment for Knowledge Analysis) tool. It is an open source. It contains the collection of machine learning algorithms for data mining purpose. We performed preprocessing with or without resample filter on the imbalanced image segmentation dataset and experiments are conducted with the most popular classifiers namely J48(C4.5), Naive Bayes, Random Forest and SMO on this resultant dataset [2]. The resample filter will adds feature subset of data samples to the imbalanced dataset. Finally, our methodology improves the performance of the classifiers with the resample filter.