A generic framework to create and use QR codes and a usage case in the field of access control under Android

This project describes the development of a framework for secure exchange of secret information based on QR codes. The framework is programmed to be platform-independent. A possible usage scenario in the field of access control is described and a program to fit said scenario is presented, which runs on Android. Various design considerations are discussed and a number of possible off-the-label uses are considered. At the end, a road map for future improvements is presented. The present document has been drawn up to show the steps in the development of the framework in detail.El presente proyecto describe el desarrollo de un framework para el intercambio seguro de información secreta basado en códigos QR. El framework se desarrolla independientemente de la plataforma operativa. Se describe un posible uso en el ámbito del control de acceso y se presenta un programa ejemplo de su uso bajo Android. Se sustenta el diseño elegido y se presentan algunos posibles usos en otros ámbitos. Al final, se presenta una posible vía de futura evolución de la plataforma. El presente documento tiene como finalidad la presentación detallada de todos los pasos en el desarrollo del framework.Ingeniería Técnica en Sistemas de Telecomunicació

Malware Pattern of Life Analysis

Many malware classifications include viruses, worms, trojans, ransomware, bots, adware, spyware, rootkits, file-less downloaders, malvertising, and many more. Each type may share unique behavioral characteristics with its methods of operations (MO), a pattern of behavior so distinctive that it could be recognized as having the same creator. The research shows the extraction of malware methods of operation using the step-by-step process of Artificial-Based Intelligence (ABI) with built-in Density-based spatial clustering of applications with noise (DBSCAN) machine learning to quantify the actions for their similarities, differences, baseline behaviors, and anomalies. The collected data of the research is from the ransomware sample repositories of Malware Bazaar and Virus Share, totaling 1300 live malicious codes ingested into the CAPEv2 malware sandbox, allowing the capture of traces of static, dynamic, and network behavior features. The ransomware features have shown significant activity of varying identified functions used in encryption, file application programming interface (API), and network function calls. During the machine learning categorization phase, there are eight identified clusters that have similar and different features regarding function-call sequencing events and file access manipulation for dropping file notes and writing encryption. Having compared all the clusters using a “supervenn” pictorial diagram, the characteristics of the static and dynamic behavior of the ransomware give the initial baselines for comparison with other variants that may have been added to the collected data for intelligence gathering. The findings provide a novel practical approach for intelligence gathering to address ransomware or any other malware variants’ activity patterns to discern similarities, anomalies, and differences between malware actions under study

SNP based literature and data retrieval

>Magister Scientiae - MScReference single nucleotide polymorphism (refSNP) identifiers are used to earmark SNPs in the human genome. These identifiers are often found in variant call format (VCF) files. RefSNPs can be useful to include as terms submitted to search engines when sourcing biomedical literature. In this thesis, the development of a bioinformatics software package is motivated, planned and implemented as a web application (http://sniphunter.sanbi.ac.za) with an application programming interface (API). The purpose is to allow scientists searching for relevant literature to query a database using refSNP identifiers and potential keywords assigned to scientific literature by the authors. Multiple queries can be simultaneously launched using either the web interface or the API. In addition, a VCF file parser was developed and packaged with the application to allow users to upload, extract and write information from VCF files to a file format that can be interpreted by the novel search engine created during this project. The parsing feature is seamlessly integrated with the web application's user interface, meaning there is no expectation on the user to learn a scripting language. This multi-faceted software system, called SNiPhunter, envisions saving researchers time during life sciences literature procurement, by suggesting articles based on the amount of times a reference SNP identifier has been mentioned in an article. This will allow the user to make a quantitative estimate as to the relevance of an article. A second novel feature is the inclusion of the email address of a correspondence author in the results returned to the user, which promotes communication between scientists. Moreover, links to external functional information are provided to allow researchers to examine annotations associated with their reference SNP identifier of interest. Standard information such as digital object identifiers and publishing dates, that are typically provided by other search engines, are also included in the results returned to the user.National Research Foundation (NRF) /The South African Research Chairs Initiative (SARChI

Transient processing and analysis using AMPEL: alert management, photometry, and evaluation of light curves

Context. Both multi-messenger astronomy and new high-throughput wide-field surveys require flexible tools for the selection and analysis of astrophysical transients. Aims. Here we introduce the alert management, photometry, and evaluation of light curves (AMPEL) system, an analysis framework designed for high-throughput surveys and suited for streamed data. AMPEL combines the functionality of an alert broker with a generic framework capable of hosting user-contributed code; it encourages provenance and keeps track of the varying information states that a transient displays. The latter concept includes information gathered over time and data policies such as access or calibration levels. Methods. We describe a novel ongoing real-time multi-messenger analysis using AMPEL to combine IceCube neutrino data with the alert streams of the Zwicky Transient Facility (ZTF). We also reprocess the first four months of ZTF public alerts, and compare the yields of more than 200 different transient selection functions to quantify efficiencies for selecting Type Ia supernovae that were reported to the Transient Name Server (TNS). Results. We highlight three channels suitable for (1) the collection of a complete sample of extragalactic transients, (2) immediate follow-up of nearby transients, and (3) follow-up campaigns targeting young, extragalactic transients. We confirm ZTF completeness in that all TNS supernovae positioned on active CCD regions were detected. Conclusions. AMPEL can assist in filtering transients in real time, running alert reaction simulations, the reprocessing of full datasets as well as in the final scientific analysis of transient data. This is made possible by a novel way of capturing transient information through sequences of evolving states, and interfaces that allow new code to be natively applied to a full stream of alerts. This text also introduces a method by which users can design their own channels for inclusion in the AMPEL live instance that parses the ZTF stream and the real-time submission of high-quality extragalactic supernova candidates to the TNS

Human action recognition with 3D convolutional neural networks

Convolutional neural networks (CNNs) adapt the regular fully-connected neural network (NN) algorithm to facilitate image classification. Recently, CNNs have been demonstrated to provide superior performance across numerous image classification databases including large natural images (Krizhevsky et al., 2012). Furthermore, CNNs are more readily transferable between different image classification problems when compared to common alternatives. The extension of CNNs to video classification is simple and the rationale behind the components of the model are still applicable due to the similarity between image and video data. Previous CNNs have demonstrated good performance upon video datasets, however have not employed methods that have been recently developed and attributed improvements in image classification networks. The purpose of this research to build a CNN model that includes recently developed elements to present a human action recognition model which is up-to-date with current trends in CNNs and current hardware. Focus is applied to ensemble models and methods such as the Dropout technique, developed by Hinton et al. (2012) to reduce overfitting, and learning rate adaptation techniques. The KTH human action dataset is used to assess the CNN model, which, as a widely used benchmark dataset, facilitates the comparison between previous work performed in the literature. Three CNNs are built and trained to provide insight into design choices as well as allow the construction of an ensemble model. The final ensemble model achieved comparative performance to previous CNNs trained upon the KTH data. While the inclusion of new methods to the CNN model did not result in an improvement on previous models, the competitive result provides an alternative combination of architecture and components to other CNN models

Deep Learning for Detection and Segmentation in High-Content Microscopy Images

High-content microscopy led to many advances in biology and medicine. This fast emerging technology is transforming cell biology into a big data driven science. Computer vision methods are used to automate the analysis of microscopy image data. In recent years, deep learning became popular and had major success in computer vision. Most of the available methods are developed to process natural images. Compared to natural images, microscopy images pose domain specific challenges such as small training datasets, clustered objects, and class imbalance. In this thesis, new deep learning methods for object detection and cell segmentation in microscopy images are introduced. For particle detection in fluorescence microscopy images, a deep learning method based on a domain-adapted Deconvolution Network is presented. In addition, a method for mitotic cell detection in heterogeneous histopathology images is proposed, which combines a deep residual network with Hough voting. The method is used for grading of whole-slide histology images of breast carcinoma. Moreover, a method for both particle detection and cell detection based on object centroids is introduced, which is trainable end-to-end. It comprises a novel Centroid Proposal Network, a layer for ensembling detection hypotheses over image scales and anchors, an anchor regularization scheme which favours prior anchors over regressed locations, and an improved algorithm for Non-Maximum Suppression. Furthermore, a novel loss function based on Normalized Mutual Information is proposed which can cope with strong class imbalance and is derived within a Bayesian framework. For cell segmentation, a deep neural network with increased receptive field to capture rich semantic information is introduced. Moreover, a deep neural network which combines both paradigms of multi-scale feature aggregation of Convolutional Neural Networks and iterative refinement of Recurrent Neural Networks is proposed. To increase the robustness of the training and improve segmentation, a novel focal loss function is presented. In addition, a framework for black-box hyperparameter optimization for biomedical image analysis pipelines is proposed. The framework has a modular architecture that separates hyperparameter sampling and hyperparameter optimization. A visualization of the loss function based on infimum projections is suggested to obtain further insights into the optimization problem. Also, a transfer learning approach is presented, which uses only one color channel for pre-training and performs fine-tuning on more color channels. Furthermore, an approach for unsupervised domain adaptation for histopathological slides is presented. Finally, Galaxy Image Analysis is presented, a platform for web-based microscopy image analysis. Galaxy Image Analysis workflows for cell segmentation in cell cultures, particle detection in mice brain tissue, and MALDI/H&E image registration have been developed. The proposed methods were applied to challenging synthetic as well as real microscopy image data from various microscopy modalities. It turned out that the proposed methods yield state-of-the-art or improved results. The methods were benchmarked in international image analysis challenges and used in various cooperation projects with biomedical researchers