Detecting Flow Anomalies in Distributed Systems

Deep within the networks of distributed systems, one often finds anomalies that affect their efficiency and performance. These anomalies are difficult to detect because the distributed systems may not have sufficient sensors to monitor the flow of traffic within the interconnected nodes of the networks. Without early detection and making corrections, these anomalies may aggravate over time and could possibly cause disastrous outcomes in the system in the unforeseeable future. Using only coarse-grained information from the two end points of network flows, we propose a network transmission model and a localization algorithm, to detect the location of anomalies and rank them using a proposed metric within distributed systems. We evaluate our approach on passengers' records of an urbanized city's public transportation system and correlate our findings with passengers' postings on social media microblogs. Our experiments show that the metric derived using our localization algorithm gives a better ranking of anomalies as compared to standard deviation measures from statistical models. Our case studies also demonstrate that transportation events reported in social media microblogs matches the locations of our detect anomalies, suggesting that our algorithm performs well in locating the anomalies within distributed systems

Grounding robot motion in natural language and visual perception

The current state of the art in military and first responder ground robots involves heavy physical and cognitive burdens on the human operator while taking little to no advantage of the potential autonomy of robotic technology. The robots currently in use are rugged remote-controlled vehicles. Their interaction modalities, usually utilizing a game controller connected to a computer, require a dedicated operator who has limited capacity for other tasks. I present research which aims to ease these burdens by incorporating multiple modes of robotic sensing into a system which allows humans to interact with robots through a natural-language interface. I conduct this research on a custom-built six-wheeled mobile robot. First I present a unified framework which supports grounding natural-language semantics in robotic driving. This framework supports learning the meanings of nouns and prepositions from sentential descriptions of paths driven by the robot, as well as using such meanings to both generate a sentential description of a path and perform automated driving of a path specified in natural language. One limitation of this framework is that it requires as input the locations of the (initially nameless) objects in the floor plan. Next I present a method to automatically detect, localize, and label objects in the robot’s environment using only the robot’s video feed and corresponding odometry. This method produces a map of the robot’s environment in which objects are differentiated by abstract class labels. Finally, I present work that unifies the previous two approaches. This method detects, localizes, and labels objects, as the previous method does. However, this new method integrates natural-language descriptions to learn actual object names, rather than abstract labels

Social group discovery using using co-location traces

Social information can be used to enhance existing applications and services or can be utilized to devise entirely new applications. Examples of such applications include recommendation systems, peer-to-peer networks, opportunistic data dissemination in ad hoc networks, or mobile friend finder. Social information can be collected from either online or mobile sources. This thesis focuses on identifying social groups based on data collected from mobile phones. These data can be either location or co-location traces. Unfortunately, location traces require a localization system for every mobile device, and users are reluctant to share absolute location due to privacy concerns. On the other hand, co- location can be collected using the embedded Bluetooth interface, present on almost all phones, and alleviates the privacy concerns as it does not collect user location. Existing graph algorithms, such as K-Clique and WNA, applied on co-location traces achieve low group detection accuracy because they focus on pair-wise ties, which cannot tell if multiple users spent time together simultaneously or how often they met. This thesis proposes the Group Discovery using Co-location (GDC) algorithm, which leverages the meeting frequency and meeting duration to accurately detect social groups. These parameters allow us to compare, categorize, and rank the groups discovered by GDC. This algorithm is tested and validated on data collected from 141 active users who carried mobile phones on our campus over the duration of one month. GDC received ratings that were 30% better than the K-Clique algorithm

Quantifying Membrane Topology at the Nanoscale

Changes in the shape of cellular membranes are linked with viral replication, Alzheimer\u27s, heart disease and an abundance of other maladies. Some membranous organelles, such as the endoplasmic reticulum and the Golgi, are only 50 nm in diameter. As such, membrane shape changes are conventionally studied with electron microscopy (EM), which preserves cellular ultrastructure and achieves a resolution of 2 nm or better. However, immunolabeling in EM is challenging, and often destroys the cell, making it difficult to study interactions between membranes and other proteins. Additionally, cells must be fixed in EM imaging, making it impossible to study mechanisms of disease. To address these problems, this thesis advances nanoscale imaging and analysis of membrane shape changes and their associated proteins using super-resolution single-molecule localization microscopy. This thesis is divided into three parts. In the first, a novel correlative orientation-independent differential interference contrast (OI-DIC) and single-molecule localization microscopy (SMLM) instrument is designed to address challenges with live-cell imaging of membrane nanostructure. SMLM super-resolution fluorescence techniques image with ~ 20 nm resolution, and are compatible with live-cell imaging. However, due to SMLM\u27s slow imaging speeds, most cell movement is under-sampled. OI-DIC images fast, is gentle enough to be used with living cells and can image cellular structure without labelling, but is diffraction-limited. Combining SMLM with OI-DIC allows for imaging of cellular context that can supplement sparse super-resolution data in real time. The second part of the thesis describes an open-source software package for visualizing and analyzing SMLM data. SMLM imaging yields localization point clouds, which requires non-standard visualization and analysis techniques. Existing techniques are described, and necessary new ones are implemented. These tools are designed to interpret data collected from the OI-DIC/SMLM microscope, as well as from other optical setups. Finally, a tool for extracting membrane structure from SMLM point clouds is described. SMLM data is often noisy, containing multiple localizations per fluorophore and many non-specific localizations. SMLM\u27s resolution reveals labelling discontinuities, which exacerbate sparsity of localizations. It is non-trivial to reconstruct the continuous shape of a membrane from a discrete set of points, and even more difficult in the presence of the noise profile characteristic of most SMLM point clouds. To address this, a surface reconstruction algorithm for extracting continuous surfaces from SMLM data is implemented. This method employs biophysical curvature constraints to improve the accuracy of the surface

Local and Global Explanations of Agent Behavior: Integrating Strategy Summaries with Saliency Maps

With advances in reinforcement learning (RL), agents are now being developed in high-stakes application domains such as healthcare and transportation. Explaining the behavior of these agents is challenging, as the environments in which they act have large state spaces, and their decision-making can be affected by delayed rewards, making it difficult to analyze their behavior. To address this problem, several approaches have been developed. Some approaches attempt to convey the $\textit{global}$ behavior of the agent, describing the actions it takes in different states. Other approaches devised $\textit{local}$ explanations which provide information regarding the agent's decision-making in a particular state. In this paper, we combine global and local explanation methods, and evaluate their joint and separate contributions, providing (to the best of our knowledge) the first user study of combined local and global explanations for RL agents. Specifically, we augment strategy summaries that extract important trajectories of states from simulations of the agent with saliency maps which show what information the agent attends to. Our results show that the choice of what states to include in the summary (global information) strongly affects people's understanding of agents: participants shown summaries that included important states significantly outperformed participants who were presented with agent behavior in a randomly set of chosen world-states. We find mixed results with respect to augmenting demonstrations with saliency maps (local information), as the addition of saliency maps did not significantly improve performance in most cases. However, we do find some evidence that saliency maps can help users better understand what information the agent relies on in its decision making, suggesting avenues for future work that can further improve explanations of RL agents

SystemC Through the Looking Glass : Non-Intrusive Analysis of Electronic System Level Designs in SystemC

Due to the ever increasing complexity of hardware and hardware/software co-designs, developers strive for higher levels of abstractions in the early stages of the design flow. To address these demands, design at the Electronic System Level (ESL) has been introduced. SystemC currently is the de-facto standard for ESL design. The extraction of data from system designs written in SystemC is thereby crucial e.g. for the proper understanding of a given system. However, no satisfactory support of reflection/introspection of SystemC has been provided yet. Previously proposed methods for this purpose %introduced to achieve the goal nonetheless either focus on static aspects only, restrict the language means of SystemC, or rely on modifications of the compiler and/or parser. In this thesis, approaches that overcome these limitations are introduced, allowing the extraction of information from a given SystemC design without changing the SystemC library or the compiler. The proposed approaches retrieve both, static and dynamic (i.e. run-time) information

Deep learning for video game playing

In this article, we review recent Deep Learning advances in the context of how they have been applied to play different types of video games such as first-person shooters, arcade games, and real-time strategy games. We analyze the unique requirements that different game genres pose to a deep learning system and highlight important open challenges in the context of applying these machine learning methods to video games, such as general game playing, dealing with extremely large decision spaces and sparse rewards

Advanced Radio Frequency Identification Design and Applications

Radio Frequency Identification (RFID) is a modern wireless data transmission and reception technique for applications including automatic identification, asset tracking and security surveillance. This book focuses on the advances in RFID tag antenna and ASIC design, novel chipless RFID tag design, security protocol enhancements along with some novel applications of RFID