Classification of motor imaginary EEG signals using machine learning

Brain Computer Interface (BCI) is a term that was first introduced by Jacques Vidal in the 1970s when he created a system that can determine the human eye gaze direction, making the system able to determine the direction a person want to go or move something to using scalp-recorded visual evoked potential (VEP) over the visual cortex. Ever since that time, many researchers where captivated by the huge potential and list of possibilities that can be achieved if simply a digital machine can interpret human thoughts. In this work, we explore electroencephalography (EEG) signal classification, specifically for motor imagery (MI) tasks. Classification of MI tasks can be carried out by using machine learning and deep learning models, yet there is a trade between accuracy and computation time that needs to be maintained. The objective is to create a machine learning model that can be optimized for real-time classification while having a relatively acceptable classification accuracy. The proposed model relies on common spatial patter (CSP) for feature extraction as well as linear discriminant analysis (LDA) for classification. With simple pre-processing stage and a proper selection of data for training the model proved to have a balanced accuracy of +80% while maintaining small run-time (milliseconds) that is opted for real-time classifications

Legality of Autonomous Weapons: Where to Draw the Line?

Inspired by Koskenniemi’s work, From Apology to Utopia, this paper attempts to engage in the discussion on the legality of autonomous weapons by showing the conflicting arguments presented by advocates of each side of the debate. The paper does not aim at finding the answer to whether autonomous weapons can be lawfully deployed or not, but rather its main interest is to highlight the indeterminacy within international law that allows both advocates and opponents of banning autonomous weapons to hold to their arguments and legally defend them on basis of the same legal rules used by their adversaries to refute their arguments and to build conflicting arguments. The paper will also be investigating the efforts made to define autonomous weapons and how definitions play an important role in giving international law this indeterminate character

Systems Biology Approach to Identifying Host Interactive Pathways Modulating the Severity of Streptococcal Sepsis

Clinical outcomes of infectious diseases are controlled by complex interactions between the host and the pathogen. Epidemiological, genetic and molecular studies in my mentor’s laboratory provided evidence that in invasive Group A streptococcal (GAS) infections, genetic variations in both bacteria and patients influenced the severity of GAS sepsis. Allelic variations in class II human leukocyte antigens (HLA) contributed significantly to differences in the severity of group A streptococcal sepsis caused by the same virulent strain of the bacteria. HLA class II molecules present streptococcal superantigens (SAgs) to T cells, and variations in HLA class II molecules can strongly influence SAg responses. However, the bacteria produce a very large number of additional virulence factors that participate in the pathogenesis of this complex disease, and it is likely that host genes besides HLA class II molecules are also participating in modulating the severity of GAS sepsis. The main focus of this Ph.D. project was to identify additional host genes and pathways that may be modulating the severity of GAS sepsis. To achieve this goal I applied a systems genetics approach, involving genome wide association studies (GWAS) of GAS sepsis in the Advanced Recombinant Inbred (ARI) panel of BXD mouse strains. We used this panel of ARI-BXD strains as a genetically diverse reference population to study differential severity of GAS sepsis as ARI-BXD strains diversity mimics the genetic diversity of human population. We assessed several traits associated with differential host responses to GAS sepsis, and analyzed variations in these traits in the context of mice genotypic variability, using genome-wide scans and the sophisticated analysis tools of WebQTL. This allowed us to map quantitative trait loci (QTL) associated with modulating susceptibility to severe GAS sepsis on chromosome (Chr) 2 and Chr X. The mapped QTLs strongly predicted disease severity (accounting for 25–30% of variance), and harbored highly polymorphic genes known to play important roles in innate immune responses. Based on linkage analyses, gene ontology, co-citation networks, and variations in gene expression, we identified interleukin 1 (IL1) and prostaglandin E (PGE) pathways as prime candidates associated with modulating the severity of GAS sepsis. To further investigate mechanisms underlying differential host susceptibility, we analyzed genome-wide differential gene expression in blood and spleens of uninfected vs. infected mice belonging to highly resistant or susceptible BXD strains, at selected times post infection. Our transcriptional analyses revealed common pathways between susceptible and resistant strains associated with innate immune response, e.g. Interferon signaling pathway. Since our data has pointed to a strong association of differential response to GAS with innate immune responses, we explored if differences in the numbers of relevant immune cells among the BXD strains played a role in their differential susceptibility to GAS. We found no significant differences in numbers or percentages of immune cell populations between susceptible and resistant strains under normal, uninfected conditions. However, depletion of neutrophils and/or macrophages significantly increased the severity of GAS sepsis in both resistant and susceptible strains. Taken together, our data suggested that differences in mobilization and /or function of these cells between susceptible and resistant strains might play a role in modulating differential severity of GAS sepsis. In conclusion, we found that variations in the severity of GAS sepsis have a strong genetic component that is complex and multigenic. Different combinations of genetic variants influenced theonset, progression, and severity of GAS sepsis and disease and ultimate outcome. Our overall approach of systems genetics, where we systematically dissected genetic, molecular, cellular and functional differences that may be associated with differential host susceptibility to GAS provided us with tremendous insight into disease mechanism. The knowledge gained can help the development of better diagnostics and means to predict disease severity based on a set of genetic and prognostic biomarkers to help customize patient care, to apply effective and more targeted therapeutic interventions and improve disease outcomes in septic patients

Solar Diodes: Novel Heterostructured Materials for Self-Powered Gas Sensors

The integration and correlation of multiple nanomaterial components and junctions in a singular device can open exciting new avenues for more advanced functionalities in nanodevices. One of the key challenges is to achieve controlled and reproducible synthetic protocols of such complex heterostructures with optimal material combinations and geometries. Due to the current global challenges including growing energy demand, limitation of natural resources, as well as envi-ronmental issues, research efforts have been devoted to the development of self-powered nanodevices that are capable of harvesting renewable energies such as solar and mechanical energies. Nevertheless, the current concept of self-powered nanodevices is based on coupling an external energy harvesting unit, such as a solar cell or piezo-electric nanogenerator, with the functional nanodevices. In this work, an innovative approach, named solar diode sensor (SDS), has been developed to realize an autonomously operated gas sensor with no additional need of coupling it to a powering devices. The SDS based on a CdS@n-ZnO/p-Si nanosystem unifies gas sensing (CdS@n-ZnO) and solar energy harvesting (n-ZnO/p-Si) functionalities in one single device. A novel sensing mechanism (change of open circuit voltage, ∆Voc), in comparison to the well-known conductometric sensors (∆R), was demonstrated. It was explained in terms of modulated polarization of the nanoparticles/nanowire interface, gas-material surface interactions and the subsequent changes in the donor density of ZnO (ND), which is manifested in the varia-tion of Voc in CdS@n-ZnO/p-Si. The fabricated sensors were capable of detecting oxidizing (e.g. oxygen) and reducing gases (such as ethanol and methane) with reproducible response at room temperature and with no need of any other energy source except solar light illumination to deliver a self-sustained gas sensor signal. The generality of the new concept was demonstrated by extending the approach to other nanomaterial geometries including radial heterojunctions of CdS@ZnO/p-Si nanowires and thin-film planar heterojunction. Additionally, the fabrication of stand-alone single nanowire devices was employed to study the inherent intrinsic electrical and functional properties of single coaxial heterostructures. In this work, the electrical characterization, the photovoltaic and gas sensing performances of a heterojunction device based on a single coaxial n-ZnO/p-Si nanowire were preliminary assessed

Role of deep learning techniques in non-invasive diagnosis of human diseases.

Machine learning, a sub-discipline in the domain of artificial intelligence, concentrates on algorithms able to learn and/or adapt their structure (e.g., parameters) based on a set of observed data. The adaptation is performed by optimizing over a cost function. Machine learning obtained a great attention in the biomedical community because it offers a promise for improving sensitivity and/or specificity of detection and diagnosis of diseases. It also can increase objectivity of the decision making, decrease the time and effort on health care professionals during the process of disease detection and diagnosis. The potential impact of machine learning is greater than ever due to the increase in medical data being acquired, the presence of novel modalities being developed and the complexity of medical data. In all of these scenarios, machine learning can come up with new tools for interpreting the complex datasets that confront clinicians. Much of the excitement for the application of machine learning to biomedical research comes from the development of deep learning which is modeled after computation in the brain. Deep learning can help in attaining insights that would be impossible to obtain through manual analysis. Deep learning algorithms and in particular convolutional neural networks are different from traditional machine learning approaches. Deep learning algorithms are known by their ability to learn complex representations to enhance pattern recognition from raw data. On the other hand, traditional machine learning requires human engineering and domain expertise to design feature extractors and structure data. With increasing demands upon current radiologists, there are growing needs for automating the diagnosis. This is a concern that deep learning is able to address. In this dissertation, we present four different successful applications of deep learning for diseases diagnosis. All the work presented in the dissertation utilizes medical images. In the first application, we introduce a deep-learning based computer-aided diagnostic system for the early detection of acute renal transplant rejection. The system is based on the fusion of both imaging markers (apparent diffusion coefficients derived from diffusion-weighted magnetic resonance imaging) and clinical biomarkers (creatinine clearance and serum plasma creatinine). The fused data is then used as an input to train and test a convolutional neural network based classifier. The proposed system is tested on scans collected from 56 subjects from geographically diverse populations and different scanner types/image collection protocols. The overall accuracy of the proposed system is 92.9% with 93.3% sensitivity and 92.3% specificity in distinguishing non-rejected kidney transplants from rejected ones. In the second application, we propose a novel deep learning approach for the automated segmentation and quantification of the LV from cardiac cine MR images. We aimed at achieving lower errors for the estimated heart parameters compared to the previous studies by proposing a novel deep learning segmentation method. Using fully convolutional neural networks, we proposed novel methods for the extraction of a region of interest that contains the left ventricle, and the segmentation of the left ventricle. Following myocardial segmentation, functional and mass parameters of the left ventricle are estimated. Automated Cardiac Diagnosis Challenge dataset was used to validate our framework, which gave better segmentation, accurate estimation of cardiac parameters, and produced less error compared to other methods applied on the same dataset. Furthermore, we showed that our segmentation approach generalizes well across different datasets by testing its performance on a locally acquired dataset. In the third application, we propose a novel deep learning approach for automated quantification of strain from cardiac cine MR images of mice. For strain analysis, we developed a Laplace-based approach to track the LV wall points by solving the Laplace equation between the LV contours of each two successive image frames over the cardiac cycle. Following tracking, the strain estimation is performed using the Lagrangian-based approach. This new automated system for strain analysis was validated by comparing the outcome of these analysis with the tagged MR images from the same mice. There were no significant differences between the strain data obtained from our algorithm using cine compared to tagged MR imaging. In the fourth application, we demonstrate how a deep learning approach can be utilized for the automated classification of kidney histopathological images. Our approach can classify four classes: the fat, the parenchyma, the clear cell renal cell carcinoma, and the unusual cancer which has been discovered recently, called clear cell papillary renal cell carcinoma. Our framework consists of three convolutional neural networks and the whole-slide kidney images were divided into patches with three different sizes to be inputted to the networks. Our approach can provide patch-wise and pixel-wise classification. Our approach classified the four classes accurately and surpassed other state-of-the-art methods such as ResNet (pixel accuracy: 0.89 Resnet18, 0.93 proposed). In conclusion, the results of our proposed systems demonstrate the potential of deep learning for the efficient, reproducible, fast, and affordable disease diagnosis

[TEACHING VOCAL SKILLS TO NON-ARABIC SPEAKING PRESCHOOL CHILDREN AND PRINCIPLES OF DEVELOPING APPROPRIATE LANGUAGE EDUCATIONAL PROGRAMS FOR THEM] تعليم المهارات الصوتية لأطفال ما قبل المدرسة الناطقين بغير العربية وأسس بناء البرامج التعليمية اللغوية المعدة

The field of teaching Arabic to speakers of other languages suffers from insufficient educational curricula and research that address the problems and difficulties facing those learners. Therefore, the study aimed to identify the characteristics and problems of the pre-school stage in teaching Arabic as a foreign language, as well as the most important vocal skills and the principles of developing language educational programs for preschool learners. The study used the descriptive analytical approach in the collection and discussion of data, through reviewing some previous studies. Some of the findings the study reached, included: The pre-school stage is one of the most essential stages in acquiring a foreign language, therefore, teaching vocal skills to children at this stage is a vital step due to the distinctive characteristics such learners possess. The study provided a list of vocal skills appropriate for children at this stage, and a set of principles and criteria that have to be met to ensure the success of the language educational programs developed. The study recommended the development of programs and textbooks for teaching Arabic to non-Arabic speaking children based on their needs and in light of international standards. يعاني مجال تعليم اللغة العربية للأطفال الناطقين بغيرها من قلة المناهج التعليمية، وقلة البحوث التي تتناولالمشكلات والصعوبات التي تواجههم؛ ولذلك هدفت الدراسة إلى التعرف على خصائص ومشكلات مرحلةما قبل المدرسة في تعليم اللغة العربية لغة أجنبية، وتحديد أهم المهارات الصوتية وأسس بناء البرامج التعليميةاللغوية المناسبة لهم؛ ولتحقيق هذه الأهداف اعتمدت الدراسة المنهج الوصفي التحليلي في جمع البياناتومناقشتها، من خلال عرض بعض الدراسات السابقة، وتم التوصل إلى بعض النتائج منها: أن مرحلة ما قبلالمدرسة من أهم المراحل في اكتساب اللغة الأجنبية، وتعليم المهارات الصوتية للأطفال في هذه المرحلة خطوةضرورية لما لهم من صفات مميزة، وقدمت الدراسة قائمة بالمهارات الصوتية المناسبة للأطفال في هذه المرحلة،واستنتجت مجموعة من الأسس والمعايير لبناء البرامج التعليمية اللغوية لابد من اتباعها لنجاح البرنامجالتعلمي، وأوصت الدراسة ببناء برامج وكتب تعليم العربية للأطفال الناطقين بغيرها في ضوء احتياجاتهم، وفيضوء المعايير العالمية

Crisis Management Art from the Risks to the Control: A Review of Methods and Directions

A crisis is an exceptional event that causes damage and negative impacts on organizations. For this reason, crisis management is considered as a significant action needed to follow crisis causes and consequences for preventing or avoiding these exceptional events from occurring again. Studies have devoted their efforts to proposing methods, techniques, and approaches in the crisis management direction. As a result, it is critical to provide a consolidated study that has an integrated view of proposed crisis management methods, crisis impacts, and effective response strategies. For this purpose, this paper first highlights the proposed techniques used in crisis management and presents the main objective behind each technique. Second, the risks and impacts resulting from a crisis are highlighted. Finally, crisis response strategies are discussed. The major contribution of this study is it can guide researchers to define research gaps or new directions in crisis management and choose the proper techniques that cope with their research problems or help them discover new research problems

TraceAll: A Real-Time Processing for Contact Tracing Using Indoor Trajectories

The rapid spread of infectious diseases is a major public health problem. Recent developments in fighting these diseases have heightened the need for a contact tracing process. Contact tracing can be considered an ideal method for controlling the transmission of infectious diseases. The result of the contact tracing process is performing diagnostic tests, treating for suspected cases or self-isolation, and then treating for infected persons; this eventually results in limiting the spread of diseases. This paper proposes a technique named TraceAll that traces all contacts exposed to the infected patient and produces a list of these contacts to be considered potentially infected patients. Initially, it considers the infected patient as the querying user and starts to fetch the contacts exposed to him. Secondly, it obtains all the trajectories that belong to the objects moved nearby the querying user. Next, it investigates these trajectories by considering the social distance and exposure period to identify if these objects have become infected or not. The experimental evaluation of the proposed technique with real data sets illustrates the effectiveness of this solution. Comparative analysis experiments confirm that TraceAll outperforms baseline methods by 40% regarding the efficiency of answering contact tracing querie