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Performance Comparison of Learning with Errors Cryptosystems
Get PDFDue to recent advancements in quantum computing, there has been great interest in finding quantum-resistant public key encryption algorithms. Much focus has been given to lattice-based cryptosystems, as certain lattice problems appear difficult even in the quantum setting. In particular, the Learning with Errors (LWE) problem, introduced by Regev, gives a means for constructing numerous public key cryptosystems with very strong proofs of security based on the hardness of finding a short vector in a lattice. We analyze and compare the performance, in terms of memory usage and speed, of four different Learning with Errors cryptosystems: basic LWE, normal-form LWE, amortized LWE, and Ring-LWE. We show that the Ring-LWE cryptosystem obtains the greatest performance in both speed and memory usage, while the amortized LWE obtains similar encryption and decryption speed to the Ring-LWE system, but requires a much larger public key that is slow to generate. We also show that the basic LWE and normal-form LWE cryptosystems have significantly slower encryption times than the amortized LWE and Ring-LWE systems, alongside comparable memory usage to the amortized system. Additionally we analyze the decryption error rates of the four cryptosystems. We find that the basic LWE cryptosystem has a lower rate of decryption errors than the other system, and that all four systems obtain a negligible error rate if the dimension parameter of the systems are chosen to be sufficiently large
Predictive Policing - Leveraging CCTV Data and AI for Crime Hotspots
Get PDFThis thesis explores fine-grained sentiment analysis in the context of police social media posts, leveraging hybrid approaches to manage the rapid flow of big data and mitigate its negative impact on youth. As social media becomes a primary medium for public interaction, understanding sentiment within police-related posts is crucial for law enforcement agencies to gauge public opinion and address community concerns effectively. The rapid dissemination of information and the potential for negative sentiments to spread swiftly pose significant challenges, particularly for young audiences. The research begins with a comprehensive review of existing sentiment analysis techniques and their applicability to large-scale social media data. The study then develops and evaluates hybrid models that combine lexicon-based methods with machine learning approaches to achieve a more nuanced understanding of sentiment in police-related posts. The proposed models are tested on a dataset of social media posts, demonstrating their ability to accurately classify sentiments while handling the complexities of big data. Results indicate that the hybrid approach not only improves sentiment classification accuracy but also effectively processes large volumes of data in real-time. The study further explores how these insights can be used to counteract the negative influence of social media on youth, proposing strategies for early detection and intervention. The findings of this research contribute to the field of sentiment analysis by offering a robust solution to the challenges posed by big data in social media. Additionally, the thesis provides practical recommendations for law enforcement agencies on how to utilize sentiment analysis to foster positive community relations and safeguard youth from harmful online content
Degradation of Anthropogenic Debris in Stormwater Infrastructure of the Lake Ontario Watershed
No full textThe accumulation of anthropogenic debris (AD) in the Great Lakes is a growing issue with largely unknown consequences for ecological and human health. To better constrain estimates of debris loading into Lake Ontario and elucidate the fate of AD accumulating upstream, an incubation experiment of the most commonly identified littered products was conducted in different stormwater infrastructure within the Lake Ontario Watershed. Chip bags, cigarette filters, and shopping bags were placed into storm drains, stormwater retention ponds, and along riparian zones of tributaries in December of 2022 and July of 2023 to test the spatial (type of stormwater infrastructure [SWI]) and temporal (season) impacts of AD entry into the environment. All Winter-deployed materials were aged for one, four, and 12 months; all Summer-deployed samples were aged for one month, with an additional set of cigarette filters collected after four months. Changes in material properties were evaluated using mass loss analysis for cigarette filters, Fourier transform infrared spectroscopy for chip and shopping bags, and optical microscopy, and tensile testing for all materials. Microbial community structure was assessed using 16S amplicon sequencing. Degradation varied by material, as cigarette filters rapidly degraded, especially during the summer deployment. Increased surface oxidation and changes to mechanical properties of shopping bags indicated degradation that varied across deployment times. Chip bags were resistant to degradation with no oxidation occurring, and differences in the mechanical properties between deployment seasons varied by SWI. Changes to the microbial community were driven by seasonal differences. Summer-deployed samples had site-specific communities. Changes to community structure over time were dependent on SWI. Identified microbial communities aligned with literature findings of bacteria associated with environmental plastics and some classes were known to degrade plastics
Design That Builds Communitites: An Evaluation Method for Analyzing, Assessing, and Improving the Way We Plan and Design the Places Around Us
No full textThe places around us shape and impact on the way we interact and live within our communities. As such, it is important to place greater emphasis on designing spaces which are meant to better respond to the needs and functions of a place. With an architectural design process this will result in the use of design strategies which will inform much of the design. In this research, multiple design strategies (Smart Growth, Placemaking, Place Value, and Street Experiments) are evaluated and compared using four regionally similar cities to analyze and assess how the different strategies impact the design process by placing emphasis on different focuses of a place. By evaluating all four strategies with all four cities, the results could be cross evaluated to determine the effectiveness and impact of using each strategy as well as the opportunities and constraints of using design strategies as a method of comparison
In Extremis: An international Perspective on Military Leadership Training in Extreme Situations
No full textThis comparative study explores how military leadership is developed and executed in extreme situations across diverse national contexts, including Switzerland, South Africa, India, Israel, and Canada. Each country adapts its leadership training and doctrine based on historical experiences, operational demands, and institutional culture. While some militaries, like Canada, emphasize values-based leadership aligned with democratic principles, others, such as India, prioritize cultural ethos and leading by example. Conscription-based systems like Israel’s and Switzerland`s rely heavily on early, experiential leadership, while professional forces like South Africa focus on psychological resilience and adaptability. Training approaches range from academic and doctrinal frameworks to immersive, high-stress simulations. Common across all contexts is the recognition that effective leadership under pressure requires a balance of technical competence, moral integrity, and emotional resilience. The findings underscore the importance of context-specific leadership training models tailored to modern security challenges
Reforming Military Curriculum in the Era of Big Data: Case Study of the Albanian Armed Forces Academy
No full textThe digital revolution and the emergence of Big Data have profoundly impacted modern military operations. As data becomes a strategic asset, military education must evolve to prepare officers not only for traditional combat but also for data driven decision-making.
This case study explores how the Albanian Armed Forces Academy can reform its military curriculum to meet the demands of the Big Data era. The analysis is based on a review of contemporary literature and a survey conducted among students enrolled in the technology program at the Faculty of Defense and Security.
Findings indicate that students perceive the integration of big data and AI as a highly valuable asset for practical training. However, military decision-making necessitates strong ethical principles that cannot be solely entrusted to algorithms. Therefore, the incorporation of technological innovations into military curricula must be accompanied by a clear understanding of data sources and appropriate methodologies for their application.
In conclusion, this study employs a comparative approach to explore how big data is reshaping military education, with the aim of modernizing curricula while addressing emerging challenges and ethical considerations