Florida Polytechnic University

About: Florida Polytechnic University is a education organization based out in Lakeland, Florida, United States. It is known for research contribution in the topics: Computer science & Catalysis. The organization has 302 authors who have published 538 publications receiving 6549 citations. The organization is also known as: Florida Poly.

A Survey on Side Channel Attacks in Processor-Memory Chip Stack

Abstract: Side channel attacks use the process information to compromise either the process, system, or relevant data. These attacks can also be used on the shared resources to perform cross exploits on other parts of a system. Throughout this paper, we discuss what side channel attacks are, how they are used, and how they can be prevented through the use of obfuscation techniques. To accomplish this, we introduce the findings of a number of relevant researchers. These include successful attacks on systems using novel variations of side channel attacks and preventative measures against these attacks. We also provide a rundown of Intel SGX. Intel SGX offers a safe way to run the code on a potentially compromised system. We show that SGX is also vulnerable to a number of different side channel attacks.

Ultraviolet to mid-infrared gas-filled anti-resonant hollow-core fiber lasers

Abstract: We will present our recent work using noble and Raman-active gas-filled anti-resonant hollow-core fiber (ARHCF) technology. First, we will present the generation of supercontinuum spanning from 200 nm to 4000 nm based on nonlinear effects of soliton self-compression and phase-matched deep‑ultraviolet (DUV) dispersive wave (DW) emission in Argon (Ar)-filled ARHCFs pumped at 2.46 μm wavelength with 100 fs pulses and ~8μJ pulse energy. Then we will discuss our recent work on stimulated Raman scattering (SRS) effect in a hydrogen (H2)-filled ARHCF, to achieve near- and MIR Raman lasers. By employing the single-stage vibrational SRS effect, a 4.22 μm Raman laser line is directly converted from a linearly polarized 1.53 μm pump laser. A quantum efficiency as high as 74% was achieved, to yield 17.6 µJ pulse energy. The designed 4.22 μm wavelength is well overlapped with the strongest CO2 absorption, therefore constituting a promising way for CO2 detection. In addition, we report a multi-wavelength Raman laser based on the cascaded rotational SRS effect. Four Raman lines at 1683 nm, 1868 nm, 2100 nm, and 2400 nm are generated, with pulse energies as high as 18.25 µJ, 14.4 µJ, 14.1 µJ, and 8.2 µJ, respectively. The energy of these Raman lines can be controlled by tuning the H2 pressure from 1 bar to 20 bar.

Energetic Blue-shifted DW Emission in Multi-mode Gas-filled Hollow-core Fibers

Abstract: The dispersive wave emission can be enhanced and blue-shifted by selectively exciting high-order-modes (HOMs) in gas-filled hollow-core anti-resonant fiber pumping at 1030 nm with pulses of 30 fs duration and $12\ \mu \mathrm{J}$ energy. Multi-octave supercontinuum spanning 200–1400 nm has been demonstrated by exciting LP02 mode. © 2021 The Author(s)

A Framework for Intelligent Navigation Using Latent Dirichlet Allocation on Reddit Posts About Opiates

Abstract: Many people look to the internet for support and assistance when faced with issues in life, particularly when these issues are related to behaviors or conditions that are stigmatized within society, generally making open discussion difficult. In this study, we utilize the unique characteristics of the news aggregation and discussion internet forum, reddit, to demonstrate the potential for text mining as an intelligent content filtering and navigation framework; we use online discussion surrounding opiates as a case study. Topic modeling is used as a text mining approach to organize and discover hidden semantic structures within reddit posts, developing a representation of a post through the topics and the words which comprise them. These characterizations may act as an intelligent navigation system of an online community, providing users the ability to actively navigate through similar posts and identify dissimilar ones based on their specific interests.

Machine Learning-based Wait-time Prediction for Autonomous Mobility-on-Demand Systems

Abstract: The development of more sophisticated autonomous course-determination mechanisms for Autonomous Mobility-on-Demand systems is an active area of research and development. In the case of traditional ridesharing systems, there are various factors to be considered such as efficient use of vehicular assets, minimizing passenger wait times and selecting course of travel. In this paper we consider the use of machine learning to aid the selection of a destination that is predicted to result in a lower wait time until the next rideshare ride request will occur in the vicinity of a trip’s destination. We draw upon a real-world ridesharing dataset to build and evaluate predictive machine learning models to provide an exploratory analysis of the utility of this approach to destination selection and demonstrate promising performance.