Narrow Results

Children suffering from Autism Spectrum Disorder (ASD) have impaired social communication, interaction and restricted and repetitive behaviors. ASD is caused by abnormal brain developments which give rise to the behavioral characteristics associated with ASD. The clinical diagnosis of ASD is performed on the basis of behavioral assessment and it causes a time delay in early intervention, as there is a time gap between abnormal brain developments and associated behavioral characteristics. Electroencephalography (EEG) is a technique which measures the electrical activity produced by the brain and it has been used to detect several neurological disorders. Studies have shown that there is a variation in the EEG signals of a normal subject and EEG signals of ASD subjects. In this study, we obtained scalograms of EEG signals by using Continuous Wavelet Transform (CWT). Pre-trained deep Convolutional Neural Networks (CNNs) such as GoogLeNet, AlexNet, MobileNet and SqueezeNet were used for extracting the features from scalograms and classification of obtained scalograms from EEG signals of normal and ASD subjects. We also used Support Vector Machine (SVM) algorithm and Relevance Vector Machine (RVM) for classification of the features extracted by the deep CNNs. The GoogLeNet, AlexNet, MobileNet and SqueezeNet deep CNNs achieved a validation accuracy of 75%, 75.84%, 79.45% and 82.98% in classifying the scalograms generated from EEG signals. The SVM achieved an accuracy of 71.6%, 74.76%, 70.70% and 81.47% using GoogleNet, Mobilenet, AlexNet and SqueezeNet for scalogram feature extraction. The RVM achieved an accuracy of 65.5%, 69.9%, 65.3% and 72.59% when used for classification using the features generated from GoogLeNet, AlexNet, MobileNet and SqueezeNet.The SqueezeNet deep CNN performed better than GoogLeNet, AlexNet and MobileNet for classification of the EEG scalograms. The feature extraction using SqueezeNet also resulted in better classification accuracy obtained by SVM and RVM. The results indicate that pre-trained models can be used for classifying the ASD using scalograms of the EEG signals.

Autism spectrum disorder (ASD) is a mental developmental disorder associated with social and communicational defects and Stereotypical Motor Movements (SMM). SMM is a set of repetitive motor activities associated with several mental developmental disorders like Autism. SMM has several forms like arm flapping, head banging, ear covering, and spinning with various degrees of severity that might lead to self-injury in severe cases.

Developing a computer-vision-based technology to detect noticeable SMM behaviors can help in the early diagnosis of autism. In this paper, a computer vision-based neural network model was proposed to detect and recognize repetitive motor behaviors. The proposed model went through three main stages: First, data preparation. Second, human body features extraction using deep learning pose estimation and the skeleton representation model, and finally, multiclass classification to distinguish between several classes of headbanging. The proposed solution was evaluated using the Self Stimulatory Behavior Dataset (SSBD) which is a public dataset of three classes of repetitive motor behaviors associated with autism. We also collected a set of 40 videos of autistic children exhibiting headbanging from public domains like YouTube. In addition to that, we captured 25 videos of typically developing subjects mimicking headbanging. The collected and the videoed videos were used to evaluate the proposed model. This work proves the applicability of diagnosing mental developmental syndrome symptoms using vision-based techniques in cooperation with neural networks. The produced results prove that the used techniques can operate well in real-world challenging applications. The proposed model achieved 85.5% accuracy on SSBD and 93% on the collected and recorded videos.

Currently, Extreme Programming, Scrum, and Kanban are the three most commonly used methods in agile software development (ASD) projects. Each method has different practices and shares a set of agile principles, where quality, time, and cost are the three project performance indicators. Companies may focus on and prioritize certain indicators based on industry or project differences. Therefore, choosing appropriate practices that fit the specific performance indicator is an important decision for organizations. This study utilizes a hierarchical consensus model to examine the correlation between four agile practice groups, six agile principle categories, and three project performance indicators. The modified Delphi method was applied to collect the pairwise comparison data, and the analytic hierarchy process was utilized to analyze the data. A Delphi panel of experts from both academia and industry was established to reach a consensus on the correlation priority using pairwise comparison matrices. The principle of cooperation between customer and developer is considered the most important principle related to project time and cost performance, while the technical excellence principle is the most important principle related to project quality performance. These results can assist organizations and practitioners in adopting the ASD practices that will best enhance their competitive advantage.

Functional Magnetic Resonance Imaging (fMRI), for many decades acts as a potential aiding method for diagnosing medical problems. Several successful machine learning algorithms have been proposed in literature to extract valuable knowledge from fMRI. One of these algorithms is the convolutional neural network (CNN) that competent with high capabilities for learning optimal abstractions of fMRI. This is because the CNN learns features similarly to human brain where it preserves local structure and avoids distortion of the global feature space. Focusing on the achievements of using the CNN for the fMRI, and accordingly, the Deep Convolutional Auto-Encoder (DCAE) benefits from the data-driven approach with CNN’s optimal features to strengthen the fMRI classification. In this paper, a new two consequent multi-layers DCAE deep discriminative approach for classifying fMRI Images is proposed. The first DCAE is unsupervised sub-model that is composed of four CNN. It focuses on learning weights to utilize discriminative characteristics of the extracted features for robust reconstruction of fMRI with lower dimensional considering tiny details and refining by its deep multiple layers. Then the second DCAE is a supervised sub-model that focuses on training labels to reach an outperformed results. The proposed approach proved its effectiveness and improved literately reported results on a large brain disorder fMRI dataset.

The purpose of this paper is to compare the design equation of Allowable Stress Design (ASD) with that of Load and Resistance Factor Design (LRFD) concerning the member stability for the economic design of cable-stayed bridges. Both elastic and inelastic buckling analyses are carried out for the cable-stayed bridges with the effective buckling lengths of the key members calculated. The axial-flexural interaction equations prescribed in ASD and LRFD are used to check the stability of main members in cable-stayed bridges. Parametric studies are performed for the bridges with a center span of 300, 600, 900, and 1200 m of different girder depths. Peak values of the interaction equations are calculated at the intersection between the girder and towers. Since the peak values of the interaction equations by inelastic buckling analysis are less than those by elastic buckling analysis, a more economical design of the girder and towers can be achieved using the inelastic buckling analysis. In addition, the use of LRFD specifications can result in a more economical design by about 20% on average than the ASD specifications for steel cable-stayed bridges.

There is an acknowledged global shortage in qualified and skilled cyber security practitioners, so much so that governments, employers and educational establishments are developing new routes and opportunities to encourage interest and applications from demographics that would not normally apply for cyber security roles. These demographics include women, young children and people on the autistic spectrum. The potential employment of people from this last demographic — people on the Autistic Spectrum — in cyber security roles will be the focus of this research. Two areas are of interest, the ethical considerations in employing people on the Autistic Spectrum and the identification of a strategy to support the employee and employer relationship. Both are discussed here. In Europe the shortfall is expected to be in the region of 350,000 employees by the year 2020 and in the United States the number is expected to be 1.2 million by the same date. This research looks at how specific autistic traits and strengths can be matched to cyber security vacancies and discusses ethical considerations and a potential support process. A qualitative research methodology was used to identify suitable traits and potential cyber security vacancies. Ethical principles and a proposed support process are put forward to allow potential employers and autistic employees to engage in equitable employment opportunities. The autistic demographic does indeed offer skilled and capable resources to help fill cyber security vacancies; however, work is needed to allow both sides to benefit from the opportunities.