Robot Introspection with Bayesian Nonparametric Vector Autoregressive Hidden Markov Models

Robot introspection, as opposed to anomaly detection typical in process monitoring, helps a robot understand what it is doing at all times. A robot should be able to identify its actions not only when failure or novelty occurs, but also as it executes any number of sub-tasks. As robots continue their quest of functioning in unstructured environments, it is imperative they understand what is it that they are actually doing to render them more robust. This work investigates the modeling ability of Bayesian nonparametric techniques on Markov Switching Process to learn complex dynamics typical in robot contact tasks. We study whether the Markov switching process, together with Bayesian priors can outperform the modeling ability of its counterparts: an HMM with Bayesian priors and without. The work was tested in a snap assembly task characterized by high elastic forces. The task consists of an insertion subtask with very complex dynamics. Our approach showed a stronger ability to generalize and was able to better model the subtask with complex dynamics in a computationally efficient way. The modeling technique is also used to learn a growing library of robot skills, one that when integrated with low-level control allows for robot online decision making.Comment: final version submitted to humanoids 201

Adtributor: Revenue Debugging in Advertising Systems

Abstract Advertising (ad) revenue plays a vital role in supporting free websites. When the revenue dips or increases sharply, ad system operators must find and fix the rootcause if actionable, for example, by optimizing infrastructure performance. Such revenue debugging is analogous to diagnosis and root-cause analysis in the systems literature but is more general. Failure of infrastructure elements is only one potential cause; a host of other dimensions (e.g., advertiser, device type) can be sources of potential causes. Further, the problem is complicated by derived measures such as costs-per-click that are also tracked along with revenue. Our paper takes the first systematic look at revenue debugging. Using the concepts of explanatory power, succinctness, and surprise, we propose a new multidimensional root-cause algorithm for fundamental and derived measures of ad systems to identify the dimension mostly likely to blame. Further, we implement the attribution algorithm and a visualization interface in a tool called the Adtributor to help troubleshooters quickly identify potential causes. Based on several case studies on a very large ad system and extensive evaluation, we show that the Adtributor has an accuracy of over 95% and helps cut down troubleshooting time by an order of magnitude

HoSNN: Adversarially-Robust Homeostatic Spiking Neural Networks with Adaptive Firing Thresholds

Spiking neural networks (SNNs) offer promise for efficient and powerful neurally inspired computation. Common to other types of neural networks, however, SNNs face the severe issue of vulnerability to adversarial attacks. We present the first study that draws inspiration from neural homeostasis to develop a bio-inspired solution that counters the susceptibilities of SNNs to adversarial onslaughts. At the heart of our approach is a novel threshold-adapting leaky integrate-and-fire (TA-LIF) neuron model, which we adopt to construct the proposed adversarially robust homeostatic SNN (HoSNN). Distinct from traditional LIF models, our TA-LIF model incorporates a self-stabilizing dynamic thresholding mechanism, curtailing adversarial noise propagation and safeguarding the robustness of HoSNNs in an unsupervised manner. Theoretical analysis is presented to shed light on the stability and convergence properties of the TA-LIF neurons, underscoring their superior dynamic robustness under input distributional shifts over traditional LIF neurons. Remarkably, without explicit adversarial training, our HoSNNs demonstrate inherent robustness on CIFAR-10, with accuracy improvements to 72.6% and 54.19% against FGSM and PGD attacks, up from 20.97% and 0.6%, respectively. Furthermore, with minimal FGSM adversarial training, our HoSNNs surpass previous models by 29.99% under FGSM and 47.83% under PGD attacks on CIFAR-10. Our findings offer a new perspective on harnessing biological principles for bolstering SNNs adversarial robustness and defense, paving the way to more resilient neuromorphic computing

Improving Long Term Stock Market Prediction with Text Analysis

The task of forecasting stock performance is well studied with clear monetary motivations for those wishing to invest. A large amount of research in the area of stock performance prediction has already been done, and multiple existing results have shown that data derived from textual sources related to the stock market can be successfully used towards forecasting. These existing approaches have mostly focused on short term forecasting, used relatively simple sentiment analysis techniques, or had little data available. In this thesis, we prepare over ten years worth of stock data and propose a solution which combines features from textual yearly and quarterly filings with fundamental factors for long term stock performance forecasting. Additionally, we develop a method of text feature extraction and apply feature selection aided by a novel evaluation function. We work with investment company Highstreet Inc. and create a set of models with our technique allowing us to compare the performance to their own models. Our results show that feature selection is able to greatly improve the validation and test performance when compared to baseline models. We also show that for 2015, our method produces models which perform comparably to Highstreet\u27s hand-made models while requiring no expert knowledge beyond data preparation, making the model an attractive aid for constructing investment portfolios. Highstreet has decided to continue to work with us on this research, and our machine learning models can potentially be used in actual portfolio selection in the near future

Non-parametric Methods for Correlation Analysis in Multivariate Data with Applications in Data Mining

In this thesis, we develop novel methods for correlation analysis in multivariate data, with a special focus on mining correlated subspaces. Our methods handle major open challenges arisen when combining correlation analysis with subspace mining. Besides traditional correlation analysis, we explore interaction-preserving discretization of multivariate data and causality analysis. We conduct experiments on a variety of real-world data sets. The results validate the benefits of our methods

Profiling relational data: a survey

Profiling data to determine metadata about a given dataset is an important and frequent activity of any IT professional and researcher and is necessary for various use-cases. It encompasses a vast array of methods to examine datasets and produce metadata. Among the simpler results are statistics, such as the number of null values and distinct values in a column, its data type, or the most frequent patterns of its data values. Metadata that are more difficult to compute involve multiple columns, namely correlations, unique column combinations, functional dependencies, and inclusion dependencies. Further techniques detect conditional properties of the dataset at hand. This survey provides a classification of data profiling tasks and comprehensively reviews the state of the art for each class. In addition, we review data profiling tools and systems from research and industry. We conclude with an outlook on the future of data profiling beyond traditional profiling tasks and beyond relational databases

Propagating Monsters: Conjoined Twins in Popular Culture

This study analyzes representations of conjoined twins in the United States to illustrate how historical images are in conversation with biographies, medical documents, sideshows, and contemporary film and television shows about conjoined twins, both fictional and nonfictional. The recycling of established tropes and the privileging of science over humanity results in limited understandings of the fluidity of conjoined twin identity. Separation and individuality are favored, relegating conjoined twins to disabled people that need fixing. Studying biographical artifacts of Millie-Christine McKoy\u27s and Daisy and Violet Hilton\u27s careers illuminates the interrelationship between biographies, images, and rights. Although born into slavery, Millie-Christine overcame social challenges and were afforded rights beyond what most people of African descent had during the 1800s. Daisy and Violet, however, were born decades later yet were owned for over twenty years and never fully wrested themselves from their tabloid images. The motion pictures they made, Tod Browning\u27s Freaks and Chained for Life, however, started creating narrative space for conjoined twins in film, and both allow for female conjoined twin sexuality, something no film has done since. Freaks visually and narratively accommodates those with unusual bodies, while Chained for Life lays the groundwork for later films that privilege separation. Building on this history, this study analyzes conjoined twins in fiction and nonfiction film and television, specifically fictional two-headed monsters --one body with two heads--and full-bodied conjoined twins who remain connected. These narratives insist upon separation if conjoined twins desire romance, or play out a good twin/bad twin pattern, and they favor easily assimilated bodies. Conjoined twins in nonfictional television shows generally become spectacle or specimen via the highlighting of scientific discovery, separation, and independence, while medical knowledge is favored at the expense of conjoined twins. However, several programs about Lori and George Schappell or Abigail and Brittany Hensel endeavor to disrupt medical narratives, overturn stereotypes, and widen perspectives. These offer a first step toward broadening the identity spectrum to account for fluctuating identities and notions of individuality, which could help redefine conjoined twins outside of singleton terms

2008 Program

University of Missouri-St. Louis Undergraduate Research Symposium Progra