Enhanced Version Control for Unconventional Applications

The Extensible Markup Language (XML) is widely used to store, retrieve, and share digital documents. Recently, a form of Version Control System has been applied to the language, resulting in Version-Aware XML allowing for enhanced portability and scalability. While Version Control Systems are able to keep track of changes made to documents, we think that there is untapped potential in the technology. In this dissertation, we present novel ways of using Version Control System to enhance the security and performance of existing applications. We present a framework to maintain integrity in offline XML documents and provide non-repudiation security features that are independent of central certificate repositories. In addition, we use Version Control information to enhance the performance of Automated Policy Enforcement eXchange framework (APEX), an existing document security framework developed by Hewlett-Packard (HP) Labs. Finally, we present an interactive and scalable visualization framework to represent Version-Aware-related data that helps users visualize and understand version control data, delete specific revisions of a document, and access a comprehensive overview of the entire versioning history

Quasi Real-Time Intermodulation Interference Method: Analysis and Performance

Establishing interference-free wireless networks has become essential requirement with theadvent of 5G networks as wireless carriers becoming eager to start transmitting high volume of voice anddata to meet enterprise demands and consumers for dispersed information. Indeed, reduction of interferencelevel now emerges as one of the most viable solutions for the service provider networks, as 5G networksuse is increasingly on the rise. Therefore, a comprehensive interference solution is needed to enable serviceproviders (e.g., cellular, PCS, Wi-Fi and Broadband, and LTE wireless LAN/WAN services) to identify andresolve network interference quickly and reliably. However, currently existing methods and tools, used toconduct interference detection and analysis lack the necessary performance required in the RF engineeringand spectrum optimization fields, and hence, fast, and reliable networks. This paper presents a new highperformancemethod for detecting and mitigating intermodulation interference in various wireless networks.Clearly, reducing the level of interference in wireless communication contributes directly to having animproved and reliable signal to noise ratio (C/I). We will discuss the complexity of the intermodulationinterference problem. We will further compare the performance of the new approach with different existinginterference detection methods. We will show that the presented method intermodulation complexity isreduced to a near linear one, compared to the current non-linear methods

Solvability of a system of integral equations in two variables in the weighted Sobolev space W(1,1)-omega(a,b) using a generalized measure of noncompactness

In this paper, we deal with the existence of solutions for a coupled system of integral equations in the Cartesian product of weighted Sobolev spaces E = Wω1,1 (a,b) x Wω1,1 (a,b). The results were achieved by equipping the space E with the vector-valued norms and using the measure of noncompactness constructed in [F.P. Najafabad, J.J. Nieto, H.A. Kayvanloo, Measure of noncompactness on weighted Sobolev space with an application to some nonlinear convolution type integral equations, J. Fixed Point Theory Appl., 22(3), 75, 2020] to applicate the generalized Darbo’s fixed point theorem [J.R. Graef, J. Henderson, and A. Ouahab, Topological Methods for Differential Equations and Inclusions, CRC Press, Boca Raton, FL, 2018]

A Software Evolution Process Model: Analysis of Software Failure Causes

This paper presents a study on the degree of impact of several components on the evolvability of software systems. In particular, it focuses on failure rates, testing, and other factors which force the evolution of a software system. Also, it studies the evolution of software systems in the presence of various failure scenarios. Unlike previous studies based on the system dynamic (SD) model, this study is modeled on the basis of actor-network theory (ANT) of software evolution, using the system dynamic environment. The main index used in this study is the destabilization period after the recovery from any failure scenario. The results show that more testing and quick recovery after failure are keys to a fast system return to stability

A Software Evolution Process Model: Analysis of Software Failure Causes

This paper presents a study on the degree of impact of several components on the evolvability of software systems. In particular, it focuses on failure rates, testing, and other factors which force the evolution of a software system. Also, it studies the evolution of software systems in the presence of various failure scenarios. Unlike previous studies based on the system dynamic (SD) model, this study is modeled on the basis of actor-network theory (ANT) of software evolution, using the system dynamic environment. The main index used in this study is the destabilization period after the recovery from any failure scenario. The results show that more testing and quick recovery after failure are keys to a fast system return to stability

Eye Tracking-Based Diagnosis and Early Detection of Autism Spectrum Disorder Using Machine Learning and Deep Learning Techniques

Eye tracking is a useful technique for detecting autism spectrum disorder (ASD). One of the most important aspects of good learning is the ability to have atypical visual attention. The eye-tracking technique provides useful information about children’s visual behaviour for early and accurate diagnosis. It works by scanning the paths of the eyes to extract a sequence of eye projection points on the image to analyse the behaviour of children with autism. In this study, three artificial-intelligence techniques were developed, namely, machine learning, deep learning, and a hybrid technique between them, for early diagnosis of autism. The first technique, neural networks [feedforward neural networks (FFNNs) and artificial neural networks (ANNs)], is based on feature classification extracted by a hybrid method between local binary pattern (LBP) and grey level co-occurrence matrix (GLCM) algorithms. This technique achieved a high accuracy of 99.8% for FFNNs and ANNs. The second technique used a pre-trained convolutional neural network (CNN) model, such as GoogleNet and ResNet-18, on the basis of deep feature map extraction. The GoogleNet and ResNet-18 models achieved high performances of 93.6% and 97.6%, respectively. The third technique used the hybrid method between deep learning (GoogleNet and ResNet-18) and machine learning (SVM), called GoogleNet + SVM and ResNet-18 + SVM. This technique depends on two blocks. The first block used CNN to extract deep feature maps, whilst the second block used SVM to classify the features extracted from the first block. This technique proved its high diagnostic ability, achieving accuracies of 95.5% and 94.5% for GoogleNet + SVM and ResNet-18 + SVM, respectively

Automatic and early detection of Parkinson’s Disease by analyzing acoustic signals using classification algorithms based on recursive feature elimination method

Parkinson’s disease (PD) is a neurodegenerative condition generated by the dysfunction of brain cells and their 60–80% inability to produce dopamine, an organic chemical responsible for controlling a person’s movement. This condition causes PD symptoms to appear. Diagnosis involves many physical and psychological tests and specialist examinations of the patient’s nervous system, which causes several issues. The methodology method of early diagnosis of PD is based on analysing voice disorders. This method extracts a set of features from a recording of the person’s voice. Then machine-learning (ML) methods are used to analyse and diagnose the recorded voice to distinguish Parkinson’s cases from healthy ones. This paper proposes novel techniques to optimize the techniques for early diagnosis of PD by evaluating selected features and hyperparameter tuning of ML algorithms for diagnosing PD based on voice disorders. The dataset was balanced by the synthetic minority oversampling technique (SMOTE) and features were arranged according to their contribution to the target characteristic by the recursive feature elimination (RFE) algorithm. We applied two algorithms, t-distributed stochastic neighbour embedding (t-SNE) and principal component analysis (PCA), to reduce the dimensions of the dataset. Both t-SNE and PCA finally fed the resulting features into the classifiers support-vector machine (SVM), K-nearest neighbours (KNN), decision tree (DT), random forest (RF), and multilayer perception (MLP). Experimental results proved that the proposed techniques were superior to existing studies in which RF with the t-SNE algorithm yielded an accuracy of 97%, precision of 96.50%, recall of 94%, and F1-score of 95%. In addition, MLP with the PCA algorithm yielded an accuracy of 98%, precision of 97.66%, recall of 96%, and F1-score of 96.66%

Effective early detection of epileptic seizures through EEG signals using classification algorithms based on t-distributed stochastic neighbor embedding and K-means

Epilepsy is a neurological disorder in the activity of brain cells that leads to seizures. An electroencephalogram (EEG) can detect seizures as it contains physiological information of the neural activity of the brain. However, visual examination of EEG by experts is time consuming, and their diagnoses may even contradict each other. Thus, an automated computer-aided diagnosis for EEG diagnostics is necessary. Therefore, this paper proposes an effective approach for the early detection of epilepsy. The proposed approach involves the extraction of important features and classification. First, signal components are decomposed to extract the features via the discrete wavelet transform (DWT) method. Principal component analysis (PCA) and the t-distributed stochastic neighbor embedding (t-SNE) algorithm were applied to reduce the dimensions and focus on the most important features. Subsequently, K-means clustering + PCA and K-means clustering + t-SNE were used to divide the dataset into subgroups to reduce the dimensions and focus on the most important representative features of epilepsy. The features extracted from these steps were fed to extreme gradient boosting, K-nearest neighbors (K-NN), decision tree (DT), random forest (RF) and multilayer perceptron (MLP) classifiers. The experimental results demonstrated that the proposed approach provides superior results to those of existing studies. During the testing phase, the RF classifier with DWT and PCA achieved an accuracy of 97.96%, precision of 99.1%, recall of 94.41% and F1 score of 97.41%. Moreover, the RF classifier with DWT and t-SNE attained an accuracy of 98.09%, precision of 99.1%, recall of 93.9% and F1 score of 96.21%. In comparison, the MLP classifier with PCA + K-means reached an accuracy of 98.98%, precision of 99.16%, recall of 95.69% and F1 score of 97.4%

Interactive Data Framework and User Interface for Wisconsin’s Oversize-Overweight Vehicle Permits

With continuing increases in the number of Oversize-Overweight (OSOW) vehicle permits issued in recent years, the management and analysis of OSOW permit data is becoming more inefficient and time-consuming. Large quantities of archived OSOW permit data are held by Departments of Transportation (DOTs) across the United States, and manual extraction and analysis of this data requires significant effort. In this paper, the authors present a new framework for analyzing Wisconsin’s historic OSOW permit program data. This framework provides an interactive, web-based interface to query the OSOW permit data, link OSOW records to geospatial data features, and dynamically visualize query results. The web-based interface offers scalability and broad accessibility to the data across different DOT divisions, and use cases. Furthermore, a user survey and heuristic evaluation of the interface demonstrate the project’s utility, and identify goals for future system development