Conception and implementation of a secure engineering and key exchange mechanism for the open source PLC Beremiz using a test driven approach

Computerized control systems play a vital role in modern critical infrastructure. These systems are designed to provide better functionality and performance without concerning of security. This leaves them extremely vulnerable to cyber attacks which may lead to serious consequences. Therefore, it is of utmost importance to analyze the vulnerabilities of such system to protect them against various threats. In this thesis, several vulnerabilities of the open source automation system Beremiz were analyzed while considering several attack vectors that may affect the control system. To resolve some of the existing flaws, a secure communication protocol and an authentication system was implemented. The total development process was done using Acceptance Test Driven Development method and was tested with an automated testing framework "Twister"

Gaussian Process in Computational Biology: Covariance Functions for Transcriptomics

In the field of machine learning, Gaussian process models are widely used families of stochastic process for modelling data observed over time, space or both. Gaussian processes models are nonparametric, meaning that the models are developed on an infinite-dimensional parameter space. The parameter space is then typically learnt as the set of all possible solutions for a given learning problem. Gaussian process distributions are distribution over functions. The covariance function determines the properties of functions samples drawn from the process. Once the decision to model with a Gaussian process has been made the choice of the covariance function is a central step in modelling. In molecular biology and genetics, a transcription factor is a protein that binds to specific DNA sequences and controls the flow of genetic information from DNA to mRNA. To develop models of cellular processes, quantitative estimation of the regulatory relationship between transcription factors and genes is a basic requirement. Quantitative estimation is complex due to various reasons. Many of the transcription factors' activities and their own transcription level are post transcriptionally modified; very often the levels of the transcription factors' expressions are low and noisy. So, from the expression levels of their target genes, it is useful to infer the activity of the transcription factors. Here we developed a Gaussian process based nonparametric regression model to infer the exact transcription factor activities from a combination of mRNA expression levels and DNA-protein binding measurements. Clustering of gene expression time series gives insight into which genes may be coregulated, allowing us to discern the activity of pathways in a given microarray experiment. Of particular interest is how a given group of genes varies with different conditions or genetic backgrounds. In this thesis, we developed a new clustering method that allows each cluster to be parametrized according to the behaviour of the genes across conditions whether they are correlated or anti-correlated. By specifying the correlation between such genes, we gain more information within the cluster about how the genes interrelate. Our study shows the effectiveness of sharing information between replicates and different model conditions while modelling gene expression time series

Change in Adaptability of Residential Architecture: Spatial Analysis on Traditional and Contemporary Houses of Bangladesh

This study focused on spatial analysis to identify the changes in adaptability over the last five decades. The features influencing adaptability were selected from the reference study. An appropriate method was used to analyse these features through spatial analysis. Six distinctive typologies of rural houses were selected from six regions. Unlike the traditional houses, the contemporary houses in the same area reflected a different character. Urban houses built since the early and mid-20th century were compared with contemporary houses. After analysing the openness, generality, flexibility, depth, typicality, construction technique, involvement of end-users, and the feedback from the inhabitants, the study identified a significant decrease in contemporary houses' adaptability. Spatial analysis was used to quantify the different features and compare between traditional and contemporary houses. Though the adaptability had been reduced over time, the latest houses started to achieve better flexibility in some features due to government policy and implementation of statutory building regulations. Further recommendations were provided to enhance the residential architecture's adaptability in future. The study samples were selected from different regions of Bangladesh. Still, the result and policy recommendations can be helpful for other countries, especially with high population density and a developing economy

Car Parking Availability Prediction: A Comparative Study of LSTM and Random Forest Regression Approaches

Drivers spend an enormous amount of time searching for parking spots every year. Waste of time, emission of carbon and air pollution have been issues in hunting for parking spots without proper prediction. In this paper, we have proposed to build a framework based on Recurrent Neural Network (RNN) using Long Short Term Memory (LSTM) and Random Forest Regression model to provide prediction of parking availability and compared results afterwards. A real-world case of parking spots availability consisting of 5,500 parking spots in Kuala Lumpur City Centre (KLCC), Malaysia, has been used for regression implementation in this comparative analysis. The results showed that random forest outperformed LSTM approach based on performance metrics

Selenium-Enriched Spirulina (SeE-SP) Enhance Antioxidant Response, Immunity, and Disease Resistance in Juvenile Asian Seabass, Lates calcarifer

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A Recommender System for Adaptive Examination Preparation using Pearson Correlation Collaborative Filtering

Distance learning is any type of far-off instruction where the understudy isn't actually present for the exercise. It is blasting gratitude to the force of the Internet. Distance learning plays a vital role for examination preparation where multiple choice questions can be utilized to evaluate the performance of students. Multiple Choice Question (MCQ) is a type of question used in the examination to evaluate the performance of students accordingly where usually four options are given along with the question, and one has to choose the correct answer. This research includes a simulation model that has been built to keep the learners continue to learn the subjects they might be weak in. We have developed a methodology that may guide a student to update his/her area of weakness by using a recommender system based on Pearson Correlation Collaborative Filtering approach. The paper describes a recommender system that will keep track of a learner's profile and create an adaptive training mechanism using the performance matrix

Effect of Corpora on Classification of Fake News using Naive Bayes Classifier

At the present world, one of the main sources of the news is an online platform like different websites and social media i.e. Facebook, Twitter, Linkedin, Youtube, Instagram and so on. However, due to the lack of proper knowledge or deliberate activity of some cunning people, fake news is spreading more than ever. People in general, struggling to filter which news to trust and which one to discard. Even the sly people take advantage of the situation by spreading false news and misleading the people. Natural Language Processing, one of the major branch of Machine Learning, the wealth of research is remarkable. However, new challenges underpinning this development. Here in this work, Naive Bayes Classifier, a Bayesian approach of Machine Learning algorithm has applied to identify the fake news. We showed, besides the algorithms, how the wealth of corpora can assist to improve the performance. The dataset collected from an open-source, has been used to classify whether the news is authenticated or not. Initially, we achieved classification accuracy about 87% which is higher than previously reported accuracy and then 92% by the same Naive Bayes Algorithm with enriched corpora

Effects of n-3 HUFA-enriched Artemia on growth, biochemical response, skeletal morphology and stress resistance of Asian sea bass (Lates calcarifer) larvae reared at high temperature

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Increasing Power by Sharing Information from Genetic Background and Treatment in Clustering of Gene Expression Time Series

هذا البحث يطوير طريقة تجميع جديدة تسمح لكل مجموعة لتكون بارامتريسد وفقا لما إذا كان سلوك الجينات عبر الظروف مترابطة أو غير مترابطة. من خلال تحديد الارتباط بين هذه الجينات، والمزيد من المعلومات هو كسب داخل المجموعة حول كيفية الجينات المترابطة. التصلب الجانبي الضموري (ألس) هو اضطراب عصبي لا رجعة فيه يقتل الخلايا العصبية الحركية ويؤدي إلى الموت في غضون 2-3 سنوات من بداية الأعراض. سرعة التقدم لمرضى مختلفة غير متجانسة مع تباين كبير. أظهرت الفئران المعدلة وراثيا SOD1G93A من خلفيات مختلفة (129Sv و C57) الاختلافات الظواهر ثابتة لتطور المرض. التسلسل الهرمي للعمليات الغوسية المستخدمة لتشكيل نموذجية محددة وجينات محددة التباين المشترك بين الجينات. وأظهرت هذه الدراسة حول العثور على بعض ملامح التعبير الجيني هامة ومجموعات من تعبيرات الجينات المرتبطة أو المشتركة معا من أربع مجموعات من البيانات (SOD1G93A و نتغ من 129Sv و C57 الخلفيات). وتظهر دراستنا فعالية تبادل المعلومات بين المكررات وظروف نموذج مختلفة عند النمذجة الجينات سلسلة الوقت التعبير. المزيد من الجينات إثراء تحليل النتيجة وتحليل مسار الأنطولوجيا من بعض المجموعات المحددة لمجموعة معينة قد يؤدي نحو تحديد الميزات الكامنة وراء سرعة التفاضلية تطور المرض.Clustering of gene expression time series gives insight into which genes may be co-regulated, allowing us to discern the activity of pathways in a given microarray experiment. Of particular interest is how a given group of genes varies with different conditions or genetic background. This paper develops a new clustering method that allows each cluster to be parameterised according to whether the behaviour of the genes across conditions is correlated or anti-correlated. By specifying correlation between such genes,more information is gain within the cluster about how the genes interrelate. Amyotrophic lateral sclerosis (ALS) is an irreversible neurodegenerative disorder that kills the motor neurons and results in death within 2 to 3 years from the symptom onset. Speed of progression for different patients are heterogeneous with significant variability. The SOD1G93A transgenic mice from different backgrounds (129Sv and C57) showed consistent phenotypic differences for disease progression. A hierarchy of Gaussian isused processes to model condition-specific and gene-specific temporal co-variances. This study demonstrated about finding some significant gene expression profiles and clusters of associated or co-regulated gene expressions together from four groups of data (SOD1G93A and Ntg from 129Sv and C57 backgrounds). Our study shows the effectiveness of sharing information between replicates and different model conditions when modelling gene expression time series. Further gene enrichment score analysis and ontology pathway analysis of some specified clusters for a particular group may lead toward identifying features underlying the differential speed of disease progression

Nutritional Profiling of Wild (Pangasius pangasius) and Farmed (Pangasius hypophthalmus) Pangasius Catfish with Implications to Human Health

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