Spatial Evolutionary Generative Adversarial Networks

Generative adversary networks (GANs) suffer from training pathologies such as instability and mode collapse. These pathologies mainly arise from a lack of diversity in their adversarial interactions. Evolutionary generative adversarial networks apply the principles of evolutionary computation to mitigate these problems. We hybridize two of these approaches that promote training diversity. One, E-GAN, at each batch, injects mutation diversity by training the (replicated) generator with three independent objective functions then selecting the resulting best performing generator for the next batch. The other, Lipizzaner, injects population diversity by training a two-dimensional grid of GANs with a distributed evolutionary algorithm that includes neighbor exchanges of additional training adversaries, performance based selection and population-based hyper-parameter tuning. We propose to combine mutation and population approaches to diversity improvement. We contribute a superior evolutionary GANs training method, Mustangs, that eliminates the single loss function used across Lipizzaner's grid. Instead, each training round, a loss function is selected with equal probability, from among the three E-GAN uses. Experimental analyses on standard benchmarks, MNIST and CelebA, demonstrate that Mustangs provides a statistically faster training method resulting in more accurate networks

Christmas Tree Sales

This year’s Christmas tree sales were an overwhelming success. Four hundred trees were sold in seven days, bringing in more than one thousand dollars profit

Nonexistence of Entanglement Sudden Death in High NOON States

We study the dynamics of entanglement in continuous variable quantum systems (CVQS). Specifically, we study the phenomena of Entanglement Sudden Death (ESD) in general two-mode-N-photon states undergoing pure dephasing. We show that for these states, ESD never occurs. These states are generalizations of the so-called High NOON states, shown to decrease the Rayleigh limit of lambda to lambda/N, which promises great improvement in resolution of interference patterns if states with large N are physically realized. However, we show that in dephasing NOON states, the time to reach V_crit, critical visibility, scales inversely with N^2. On the practical level, this shows that as N increases, the visibility degrades much faster, which is likely to be a considerable drawback for any practical application of these states.Comment: 4 pages, 1 figur

Learning Race and Racism While Learning: Experiences of International Students Pursuing Higher Education in the Midwestern United States

Researchers have documented how race and racism influence the college experiences of U.S. citizens. However, research on the ways that race and racism affect international students warrants similar attention. This qualitative study explored how international students learned about U.S. concepts of race and racism and how such concepts shaped their college experiences. The participating international college students learned about U.S. concepts of race and racism through media, relationships, formal education, and lived experiences. They defined these concepts in varying ways and had varying racial ideologies

Unleashing AI in Ethical Hacking: A Preliminary Experimental Study

This technical report details an experimental study aimed at evaluating the integration of AI, specifically ChatGPT, into ethical hacking. Conducted in a controlled virtual environment using a MacBook Pro host with VirtualBox 7, the study focused on assessing ChatGPT’s efficacy in aiding the penetration testing of target virtual machines, including one running Windows. This experiment was carried out to validate the claims made in the companion position paper, "Unleashing AI in Ethical Hacking". The primary aim was to explore ChatGPT’sutility in enhancing various stages of ethical hacking, such as Reconnaissance,Scanning, Gaining Access, Maintaining Access, and Covering Tracks. This technical report comprehensively documents the laboratory experiment and will be used to support the position paper, which is being prepared for conference presentation. The results underscore ChatGPT’s highly effective and remarkably helpful role in supporting and streamlining the penetration testing process

AutoPass:An automatic password generator

Text password has long been the dominant user authentication technique and is used by large numbers of Internet services. If they follow recommended practice, users are faced with the almost insuperable problem of generating and managing a large number of site-unique and strong (i.e. non-guessable) passwords. One way of addressing this problem is through the use of a password generator, i.e. a client-side scheme which generates (and regenerates) site-specific strong passwords on demand, with the minimum of user input. This paper provides a detailed specification and analysis of AutoPass, a password generator scheme previously outlined as part of a general analysis of such schemes. AutoPass has been designed to address issues identified in previously proposed password generators, and incorporates novel techniques to address these issues. Unlike almost all previously proposed schemes, AutoPass enables the generation of passwords that meet important real-world requirements, including forced password changes, use of pre-specified passwords, and generation of passwords meeting site-specific requirements.Comment: 22 page

Cow Bells

https://digitalcommons.library.umaine.edu/mmb-vp/1247/thumbnail.jp