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关键词： Computer engineering   Web studies   Computer science  

摘要： Web frameworks are some of the most popular technologies used for the development of web applications. There are many frameworks written in many languages including PHP, Javascript, Java, and Python. With the growing popularity of Python in web development, it is important to understand the performance of Python frameworks. The research into Python frameworks is still in its infancy and more work needs to be completed to determine which frameworks perform the best. A comparison study of the Python micro frameworks, Flask and Pyramid, is completed. A lessons registration system is developed using the two frameworks and are tested for their efficiency and performance using the tools Locust and Radon. They are also tested using Nessus, to scan for any vulnerabilities that exist in the frameworks without the use of security extensions.

关键词： Computer engineering   Electrical engineering   Computer science  

摘要： The thesis presents methods to enable commercial off-the-shelf (COTS) datacom transceivers for use in optical circuit-switching applications. These applications require fast or (burst-mode) locking characteristics. This goal requires research in two areas. First, the burst-mode characteristics of (COTS) datacom transceivers were characterized as a function of the mean received power, the power offset between the channels being switched, and the transition time of the optical switch. Second, a high-speed board that can be used for variable optical attenuation was developed and tested to mitigate the power offset. This substantially improves the burst-mode lock times. The culmination of this work is a calibration procedure that can improve the locking characteristics of COTS transceivers used in a circuit-switched optical network.

关键词： Computer science  

摘要： Nowadays breast cancer is the leading cause of death. This is a disease in which some cells of the breast multiply abnormally. These abnormal cells destroy other surrounding cells and prevent their normal functioning. It can spread throughout the human body. In some forms, it spreads within days. Therefore, the diagnosis of breast cancer during early stages of the disease is extremely important. Identification of genetic and environmental factors is very important in developing novel methods to detect and prevent breast cancer. The objective of the research was to develop an approach for expeditiously and accurately by diagnosis of breast cancer. To achieve this objective, it is important to identify the features of a mammogram which are most relevant to the diagnosis. A new approach to identify the most important features based on Support-Vector-Machines was developed. These features were then used to develop a decision function based on Support-Vector-Machines, which functioning as a classifier providing the diagnosis. The proposed approach is specified in algorithmic form. Future work consists of developing executable code from these algorithms and testing the proposed approach with real-world data to ascertain its accuracy.

关键词： Information science   Computer science  

摘要： Social media are increasingly used by both the public and emergency management in disasters. In disasters arising from weather-related hazards such as hurricanes, social media are especially used for communicating about risk in the pre-disaster period when the nature of the hazard is uncertain. This dissertation explores the sociotechnical aspects of hurricane risk communication, especially information diffusion, interpretation, and reaction, as it occurs on social media between members of the public and authoritative weather experts. I first investigate the kinds of hurricane risk information that were shared by authoritative sources on social media during the 2017 Atlantic hurricane season and how different kinds of information diffuse temporally. This motivated a deeper qualitative analysis in a second study on the interactions between laypeople and experts around authoritative yet uncertain risk representations and how the mediation of the representations on social media impacts people’s risk interpretations. Risk communication, however, is not only concerned with what authorities/experts see as the important message to get out and how people respond to those messages, but also about how people conceive of risk more independently. The issues that lay people are concerned about, as understood from their social media posts, are not necessarily the issues that authoritative sources are concerned about or would share in their risk messages. Thus, the third study takes this perspective and investigates what people do under highly constrained situations while preparing for and experiencing a multi-hazard hurricane, revealing the liminality of such experiences and the supportive role of social media. Throughout the research, social media is both a tool used by stakeholders for risk information sharing and communication purposes, but also a source of data that allows for analysis of these activities in situ, an affordance that not many other methods of data collection o

关键词： Computer science  

摘要： Cropping and Scaling are the most common image resizing techniques but neither of these considers the content of the image. Seam carving is a content-aware image resizing technique that removes or duplicates the least visible pixels from an image, thereby making it more effective than Cropping and Scaling. For large batches of images, it may be unrealistic to do Seam carving on one processing element due to memory constraints. To solve this problem, a parallel approach to image resizing that helps to ease data transfer between processing elements needs to be considered. Partitioned Global Address Space (PGAS) programming models have been attracting attention as a parallel computing model and it is often used to implement one-sided Remote Memory Access (RMA) from multi-host systems, such as computer clusters. OpenSHMEM is a distributed, PGAS programming model that has light-weight semantics and high performance RMA and atomic memory operations. In this thesis, we parallelize Seam carving using Pthreads, OpenSHMEM and MPI. We evaluate the relative performance gained with multiple threads and processing elements (PEs).

关键词： Artificial intelligence   Computer science  

摘要： MATLAB® is a multi-paradigm proprietary programming language and numerical computing environment developed by MathWorks. Within MATLAB® Integrated Development Environment (IDE) you can perform Computer-aided design (CAD), different matrix manipulations, plotting of functions and data, implementation algorithms, creation of user interfaces, and has the ability to interface with programs written in other languages1. Since, its launch in 1984 MATLAB® software has not particularly been associated within the field of data science. In 2013, that changed with the launch of their new data science concentrated toolboxes that included Deep Learning™, Image Processing™, Computer Vision™, and then a year later Statistics and Machine Learning™. The main objective of my thesis was to research and explore the field of data science. More specifically pertaining to the development of an object recognition application that could be built entirely using MATLAB® IDE and have a positive social impact on the deaf community. And in doing so, answering the question, could MATLAB® be utilized for development of this type of application? To simultaneously answer this question while addressing my main objectives, I constructed two different object recognition protocols utilizing MATLAB_R2019 with the add-on data science tool packages. I named the protocols ASLtranslate (I) and (II). This allowed me to experiment with all of MATLAB® data science toolboxes while learning the differences, benefits, and disadvantages of using multiple approaches to the same problem. The methods and approaches for the design of both versions was very similar. ASLtranslate takes in 2D image of American Sign Language (ASL) hand gestures as an input, classifies the image and then outputs its corresponding alphabet character. ASLtranslate (I) was an implementation of image category classification using machine learning methods. ASLtranslate (II) was implemented by using a deep learning method called transfer learning,

关键词： Computer science  

摘要： Cyber-attacks and security breaches are occurring more frequently against web applications on the Internet. The success of these malicious attacks stems from vulnerabilities in the web application that were not rectified before they were deployed to the Internet. As companies release more innovative web applications within short turnaround times, detecting and fixing security vulnerabilities is deemed critical, but not a priority. Consequently, cyber-attacks and security breaches continue to rise unabated. The purpose of this exploratory study was to explore security process improvements for integrating security tools within a software application development methodology. This exploratory study addressed the problem through a conceptual framework, exploring security process improvements for integrating security tools within a software application methodology that could detect and fix vulnerabilities early and help build secure software. Participants were purposively selected from the population of professionals who were members of the software security group and software developer groups in the LinkedIn professional site. Data for the study were collected from 11 participants who were software engineers, software security engineers, and software architects. Each participant answered 10 semi-structured, open-ended interview questions conducted through online Zoom conferencing. Thematic content analysis was the method used for analyzing the data, which involved closely examining data to identify common themes. The common themes were further synthesized into 5 major themes that included detect and fix vulnerabilities, educate software engineers on secure coding, empower software engineers to learn application security, automate security testing, and perform threat modeling.

关键词： Computer science  

关键词： Computer science  

摘要： This dissertation focuses on the design and analysis of approximation and online algorithms for scheduling problems arising in large datacenters and cloud computing *** recent advancement of science and engineering crucially relies on computing platforms processing large data sets, and modern datacenters provide such platforms. We address four different scheduling settings motivated by modern computing environments; speed scaling setting, co-flow setting, batch scheduling setting, and unrelated machines setting. Speed scaling setting: Modern processors typically allow dynamic speed-scaling offering an effective trade-off between high throughput and energy efficiency. In a classical model, a processor/machine runs at speed s when consuming power sα where α > 1 is a constant. We study the problem of completing all jobs before their deadlines non-preemptively with the minimum energy on parallel machines with dynamic speed-scaling capabilities. This problem is crucial in datacenters that are estimated to use energy as much as a city. Co-flow setting: Coflow has recently been introduced to abstract communication patterns that are widely observed in the cloud and massively parallel computing frameworks. Coflow consists of several flows that each represent data communication from one machine to another. A coflow is completed when all of its flows are completed. We consider coflow for the objective of maximizing partial throughput. This objective seeks to measure the progress made for partially completed coflows before their deadlines. Batch scheduling setting: This setting is used to model client-server systems like multiuser operating systems, web servers, etc. In this setting, there is a server that stores different pages of information and receives requests from clients over time for specific pages. The server can transmit at most one page at each time to satisfy a batch of requests for that page, up to a certain capacity. We study the maximum flow time min

关键词： Computer science  

摘要： Over the years, Software Engineering, as a discipline, has recognized the potential for engineers to make mistakes and has incorporated processes to prevent such mistakes from becoming exploitable vulnerabilities. These processes span the spectrum from using unit/integration/fuzz testing, static/dynamic/hybrid analysis, and (automatic) patching to discover instances of vulnerabilities to leveraging data mining and machine learning to collect metrics that characterize attributes indicative of vulnerabilities. Among these processes, metrics have the potential to uncover systemic problems in the product, process, or people that could lead to vulnerabilities being introduced, rather than identifying specific instances of vulnerabilities. The insights from metrics can be used to support developers and managers in making decisions to improve the product, process, and/or people with the goal of engineering secure software. Despite empirical evidence of metrics' association with historical software vulnerabilities, their adoption in the software development industry has been limited. The level of granularity at which the metrics are defined, the high false positive rate from models that use the metrics as explanatory variables, and, more importantly, the difficulty in deriving actionable intelligence from the metrics are often cited as factors that inhibit metrics' adoption in practice. Our research vision is to assist software engineers in building secure software by providing a technique that generates scientific, interpretable, and actionable feedback on security as the software evolves. In this dissertation, we present our approach toward achieving this vision through (1) systematization of vulnerability discovery metrics literature, (2) unsupervised generation of metrics-informed security feedback, and (3) continuous developer-in-the-loop improvement of the feedback. We systematically reviewed the literature to enumerate metrics that have been proposed and/or evaluated