Sok: Towards The Science Of Security And Privacy In Machine Learning,
2018
Singapore Management University
Sok: Towards The Science Of Security And Privacy In Machine Learning, Nicolas Papernot, Patrick Mcdaniel, Arunesh Sinha, Michael Wellman
Research Collection School Of Computing and Information Systems
Advances in machine learning (ML) in recent years have enabled a dizzying array of applications such as data analytics, autonomous systems, and security diagnostics. ML is now pervasive—new systems and models are being deployed in every domain imaginable, leading to rapid and widespread deployment of software based inference and decision making. There is growing recognition that ML exposes new vulnerabilities in software systems, yet the technical community’s understanding of the nature and extent of these vulnerabilities remains limited. We systematize recent findings on ML security and privacy, focusing on attacks identified on these systems and defenses crafted to date. We …
Demo Abstract: Simultaneous Energy Harvesting And Sensing Using Piezoelectric Energy Harvester,
2018
Singapore Management University
Demo Abstract: Simultaneous Energy Harvesting And Sensing Using Piezoelectric Energy Harvester, Dong Ma, Guohao Lan, Weitao Xu, Mahbub Hassan, Wen Hu
Research Collection School Of Computing and Information Systems
With the capability to harvest energy from low frequency motions or vibrations, piezoelectric energy harvesting has become a promising solution to achieve battery-less wearable system. Recently, many works have convincingly demonstrated that PEH can also act as a self-powered sensor for detecting a wide range of machine and human contexts, which suggests that energy harvesting and sensing can be performed concurrently. However, realization of simultaneous energy harvesting and sensing (SEHS) is challenging as the energy harvesting process distorts the sensing signal. In this demo, we propose a novel SEHS architecture prototyped in the form factor of an insole, which combines …
Artificial Intelligence: An Analysis Of Alan Turing’S Role In The Conception And Development Of Intelligent Machinery,
2018
Southeastern University - Lakeland
Artificial Intelligence: An Analysis Of Alan Turing’S Role In The Conception And Development Of Intelligent Machinery, Erika L. Furtado
Selected Honors Theses
The purpose of this thesis is to follow the thread of Alan Turing’s ideas throughout his decades of research and analyze how his predictions have come to fruition over the years. Turing’s Computing Machinery and Intelligence is the paper in which the Turing Test is described as an alternative way to answer the question “can machines think?” (Turing 433). Since the development of Turing’s original paper, there has been a tremendous amount of advancement in the field of artificial intelligence. The field has made its way into art classification as well as the medical industry. The main concept researched in …
Compressed Sensing For Few-View Multi-Pinhole Spect With Applications To Preclinical Imaging,
2018
Marquette University
Compressed Sensing For Few-View Multi-Pinhole Spect With Applications To Preclinical Imaging, Benjamin Michael Rizzo
Dissertations (1934 -)
Single Photon Emission Computed Tomography (SPECT) can be used to identify and quantify changes in molecular and cellular targets involved in disease. A radiopharmaceutical that targets a specific metabolic function is administered to a subject and planar projections are formed by imaging emissions at different view angles around the subject. The reconstruction task is to determine the distribution of radioactivity within the subject from the projections. We present a reconstruction approach that utilizes only a few view angles, resulting in a highly underdetermined system, which could be used for dynamic imaging applications designed to quantify physiologic processes altered with disease. …
Methods Of Reverse Engineering A Bitstream For Field Programmable Gate Array Protection,
2018
Air Force Institute of Technology
Methods Of Reverse Engineering A Bitstream For Field Programmable Gate Array Protection, Daniel J. Celebucki
Theses and Dissertations
Field Programmable Gate Arrays (FPGAs) are found in numerous industries including consumer electronics, automotive, military and aerospace, and critical infrastructure. The ability to be reprogrammed as well as large computational power and relatively low price make them a good fit for low-volume applications that cannot justify the Non-Recurring Engineering (NRE) costs associated with producing Application-Specific Integrated Circuits (ASICs). FPGAs however, have seen a variety of security issues stemming from the fact that their configuration files are not inherently protected. This research assesses the feasibility of reverse engineering the bitstream format for a previously unexplored FPGA, as well as the utilization …
Special Perturbations On The Jetson Tx1 And Tx2 Computers,
2018
Air Force Institute of Technology
Special Perturbations On The Jetson Tx1 And Tx2 Computers, Tyler M. Moore
Theses and Dissertations
Simplified General Perturbations Number 4 (SGP4) has been the traditional algorithm for performing Orbit Determination (OD) onboard orbiting spacecraft. However, the recent rise of high-performance computers with low Size, Weight, and Power (SWAP) factors has provided the opportunity to use Special Perturbations (SP), a more accurate algorithm to perform onboard OD. This research evaluates the most efficient way to implement SP on NVIDIA’s Jetson TX series of integrated Graphical Processing Units (GPUs). An initial serial version was implemented on the Jetson TX1 and TX2's Central Processing Units (CPUs). The runtimes of the initial version are the benchmark that the runtimes …
Rss-Based Device-Free Passive Detection And Localization Using Home Automation Network Radio Frequencies,
2018
Air Force Institute of Technology
Rss-Based Device-Free Passive Detection And Localization Using Home Automation Network Radio Frequencies, Tiffany M. Phan
Theses and Dissertations
This research provided a proof of concept for a device-free passive (DfP) system capable of detecting and localizing a target through exploitation of a home automation network’s radio frequency (RF) signals. The system was developed using Insteon devices with a 915 MHz center frequency. Without developer privileges, limitations of the Insteon technology like no intrinsic received signal strength (RSS) field and silent periods between messages were overcome by using software-defined radios to simulate Insteon devices capable of collecting and reporting RSS, and by creating a message generation script and implementing a calibrated filter threshold to reduce silent periods. Evaluation of …
Integrity Monitoring For Automated Aerial Refueling: A Stereo Vision Approach,
2018
Air Force Institute of Technology
Integrity Monitoring For Automated Aerial Refueling: A Stereo Vision Approach, Thomas R. Stuart
Theses and Dissertations
Unmanned aerial vehicles (UAVs) increasingly require the capability to y autonomously in close formation including to facilitate automated aerial refueling (AAR). The availability of relative navigation measurements and navigation integrity are essential to autonomous relative navigation. Due to the potential non-availability of the global positioning system (GPS) during military operations, it is highly desirable that relative navigation can be accomplished without the use of GPS. This paper develops two algorithms designed to provide relative navigation measurements solely from a stereo image pair. These algorithms were developed and analyzed in the context of AAR using a stereo camera system modeling that …
Mitigating The Effects Of Boom Occlusion On Automated Aerial Refueling Through Shadow Volumes,
2018
Air Force Institute of Technology
Mitigating The Effects Of Boom Occlusion On Automated Aerial Refueling Through Shadow Volumes, Zachary C. Paulson
Theses and Dissertations
In flight refueling of Unmanned Aerial Vehicles (UAVs) is critical to the United States Air Force (USAF). However, the large communication latency between a ground-based operator and his/her remote UAV makes docking with a refueling tanker unsafe. This latency may be mitigated by leveraging a tanker-centric stereo vision system. The vision system observes and computes an approaching receiver's relative position and orientation offering a low-latency, high frequency docking solution. Unfortunately, the boom -- an articulated refueling arm responsible for physically pumping fuel into the receiver -- occludes large portions of the receiver especially as the receiver approaches and docks with …
Stereo Vision: A Comparison Of Synthetic Imagery Vs. Real World Imagery For The Automated Aerial Refueling Problem,
2018
Air Force Institute of Technology
Stereo Vision: A Comparison Of Synthetic Imagery Vs. Real World Imagery For The Automated Aerial Refueling Problem, Nicholas J. Seydel
Theses and Dissertations
Missions using unmanned aerial vehicles have increased in the past decade. Currently, there is no way to refuel these aircraft. Accomplishing automated aerial refueling can be made possible using the stereo vision system on a tanker. Real world experiments for the automated aerial refueling problem are expensive and time consuming. Currently, simulations performed in a virtual world have shown promising results using computer vision. It is possible to use the virtual world as a substitute environment for the real world. This research compares the performance of stereo vision algorithms on synthetic and real world imagery.
Assessment Of Structure From Motion For Reconnaissance Augmentation And Bandwidth Usage Reduction,
2018
Air Force Institute of Technology
Assessment Of Structure From Motion For Reconnaissance Augmentation And Bandwidth Usage Reduction, Jonathan B. Roeber
Theses and Dissertations
Modern militaries rely upon remote image sensors for real-time intelligence. A typical remote system consists of an unmanned aerial vehicle, or UAV, with an attached camera. A video stream is sent from the UAV, through a bandwidth-constrained satellite connection, to an intelligence processing unit. In this research, an upgrade to this method of collection is proposed. A set of synthetic images of a scene captured by a UAV in a virtual environment is sent to a pipeline of computer vision algorithms, collectively known as Structure from Motion. The output of Structure from Motion, a three-dimensional model, is then assessed in …
Cora: Commingled Remains And Analytics – An Open Community Ecosystem,
2018
University of Nebraska at Omaha
Cora: Commingled Remains And Analytics – An Open Community Ecosystem, Nicole Mcelroy, Ryan Ernst
UNO Student Research and Creative Activity Fair
Anthropologists at organizations such as the DPAA (Defense POW/MIA Accounting Agency) have the tough job of sorting through commingled remains of fallen soldiers. Under the direction of Professor Pawaskar at the College of IS&T, Ryan Ernst and I are currently developing a web application for the DPAA that will help them inventory the bones and record all the appropriate associations. After the inventory web application is built we will begin the analysis process using graph theory and other mathematical algorithms. This will ultimately help organizations like the DPAA get closer to the end goal of identifying fallen soldiers from commingled …
Virtualized Cloud Platform Management Using A Combined Neural Network And Wavelet Transform Strategy,
2018
California State University – San Bernardino
Virtualized Cloud Platform Management Using A Combined Neural Network And Wavelet Transform Strategy, Chunyu Liu
Electronic Theses, Projects, and Dissertations
This study focuses on implementing a log analysis strategy that combines a neural network algorithm and wavelet transform. Wavelet transform allows us to extract the important hidden information and features of the original time series log data and offers a precise framework for the analysis of input information. While neural network algorithm constitutes a powerfulnonlinear function approximation which can provide detection and prediction functions. The combination of the two techniques is based on the idea of using wavelet transform to denoise the log data by decomposing it into a set of coefficients, then feed the denoised data into a neural …
Target Detection Using Convolutional Neural Networks,
2018
Air Force Institute of Technology
Target Detection Using Convolutional Neural Networks, Robert P. Loibl
Theses and Dissertations
This research explores the use of Convolutional Neural Networks (CNNs) to classify targets of interest within satellite imagery. Methods were specifically devised for the classification of airports within Landsat-8 scenes. A novel automated dataset generation technique was developed to create labeled datasets from satellite imagery using only coordinate metadata. Using this approach a very large dataset of over 132,000 labeled images was created without human input. This dataset was used to evaluate the effects of color and resolution on airport classification accuracy. Two experiments were run with the first experiment classifying large airports with 96.8% accuracy, and the second classifying …
Cryptosystems Using Subgroup Distortion,
2018
l'Universite de Nice
Cryptosystems Using Subgroup Distortion, Indira Chatterji, Delaram Kahrobaei, Ni Yen Lu
Publications and Research
In this paper we propose cryptosystems based on subgroup distortion in hyperbolic groups. We also include concrete examples of hyperbolic groups as possible platforms.
Relating Justification Logic Modality And Type Theory In Curry–Howard Fashion,
2018
CUNY Graduate Center
Relating Justification Logic Modality And Type Theory In Curry–Howard Fashion, Konstantinos Pouliasis
Dissertations, Theses, and Capstone Projects
This dissertation is a work in the intersection of Justification Logic and Curry--Howard Isomorphism. Justification logic is an umbrella of modal logics of knowledge with explicit evidence. Justification logics have been used to tackle traditional problems in proof theory (in relation to Godel's provability) and philosophy (Gettier examples, Russel's barn paradox). The Curry--Howard Isomorphism or proofs-as-programs is an understanding of logic that places logical studies in conjunction with type theory and -- in current developments -- category theory. The point being that understanding a system as a logic, a typed calculus and, a language of a class of categories constitutes …
Sparse Passive-Aggressive Learning For Bounded Online Kernel Methods,
2018
Singapore Management University
Sparse Passive-Aggressive Learning For Bounded Online Kernel Methods, Jing Lu, Doyen Sahoo, Peilin Zhao, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
One critical deficiency of traditional online kernel learning methods is their unbounded and growing number of support vectors in the online learning process, making them inefficient and non-scalable for large-scale applications. Recent studies on scalable online kernel learning have attempted to overcome this shortcoming, e.g., by imposing a constant budget on the number of support vectors. Although they attempt to bound the number of support vectors at each online learning iteration, most of them fail to bound the number of support vectors for the final output hypothesis, which is often obtained by averaging the series of hypotheses over all the …
Two Birds With One Stone: Classifying Positive And Unlabeled Examples On Uncertain Data Streams,
2018
Singapore Management University
Two Birds With One Stone: Classifying Positive And Unlabeled Examples On Uncertain Data Streams, Donghong Han, Shuoru Li, Fulin Wei, Yuying Tang, Feida Zhu, Guoren Wang
Research Collection School Of Computing and Information Systems
An important feature characteristic of the data streams in many of today's big data applications is the intrinsic uncertainty, which could happen for both item occurrence and attribute value. While this has already posed great challenges for fundamental data mining tasks such as classification, things are made even more complicated by the fact that completely-labeled examples are usually unavailable in such settings, leaving researchers the only option to learn classifiers on partially-labeled examples on uncertain data streams. Furthermore, there will be concept drift on evolving data streams. To address these challenges, this paper therefore focuses on the study of learning …
Gradient Estimation For Attractor Networks,
2018
CUNY Graduate Center
Gradient Estimation For Attractor Networks, Thomas Flynn
Dissertations, Theses, and Capstone Projects
It has been hypothesized that neural network models with cyclic connectivity may be more powerful than their feed-forward counterparts. This thesis investigates this hypothesis in several ways. We study the gradient estimation and optimization procedures for several variants of these networks. We show how the convergence of the gradient estimation procedures are related to the properties of the networks. Then we consider how to tune the relative rates of gradient estimation and parameter adaptation to ensure successful optimization in these models. We also derive new gradient estimators for stochastic models. First, we port the forward sensitivity analysis method to the …
R3: Reinforced Ranker-Reader For Open-Domain Question Answering,
2018
Singapore Management University
R3: Reinforced Ranker-Reader For Open-Domain Question Answering, Shuohang Wang, Mo Yu, Xiaoxiao Guo, Zhiguo Wang, Tim Klinger, Wei Zhang, Shiyu Chang, Gerald Tesauro, Bowen Zhou, Jing Jiang
Research Collection School Of Computing and Information Systems
In recent years researchers have achieved considerable success applying neural network methods to question answering (QA). These approaches have achieved state of the art results in simplified closed-domain settings such as the SQuAD (Rajpurkar et al. 2016) dataset, which provides a pre-selected passage, from which the answer to a given question may be extracted. More recently, researchers have begun to tackle open-domain QA, in which the model is given a question and access to a large corpus (e.g., wikipedia) instead of a pre-selected passage (Chen et al. 2017a). This setting is more complex as it requires large-scale search for relevant …
