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Agent-based modeling is a paradigm of modeling dynamic systems of interacting agents that are individually governed by specified behavioral rules. Training a model of such agents to produce an emergent behavior by specification of the emergent (as opposed to agent) behavior is easier from a demonstration perspective. Without the involvement of manual behavior specification via code or reliance on...
The problem of Learning from Demonstration is targeted at learning to perform tasks based on observed examples. One approach to Learning from Demonstration is Inverse Reinforcement Learning, in which actions are observed to infer rewards. This work combines a feature-based state evaluation approach to Inverse Reinforcement Learning with neuroevolution, a paradigm for modifying neural networks based...
Electroencephalogram (EEG) data is used for a variety of purposes, including brain-computer interfaces, disease diagnosis, and determining cognitive states. Yet EEG signals are susceptible to noise from many sources, such as muscle and eye movements, and motion of electrodes and cables. Traditional approaches to this problem involve supervised training to identify signal components corresponding to...
Neural networks have attracted significant interest in recent years due to their exceptional performance in various domains ranging from natural language processing to image identification and classification. Modern deep neural networks demonstrate state-of-the-art results in complex tasks such as epileptic seizure detection [1] and time series classification [2]. The internal architecture of these...
Traditional approaches to building a large scale knowledge graph have usually relied on extracting information (entities, their properties, and relations between them) from unstructured text (e.g. Dbpedia). Recent advances in Convolutional Neural Networks (CNN) allow us to shift our focus to learning entities and relations from images, as they build robust models that require little or no pre-processing...
We propose a simple but strong baseline for time series classification from scratch with deep neural networks. Our proposed baseline models are pure end-to-end without any heavy preprocessing on the raw data or feature crafting. The proposed Fully Convolutional Network (FCN) achieves premium performance to other state-of-the-art approaches and our exploration of the very deep neural networks with...
Entity linking is the task of identifying entities like people and places in textual data and linking them to corresponding entities in a knowledge base. In this paper we solve a visual equivalent of this task called visual entity linking. The goal is to link regions of images to corresponding entities in knowledge bases. Visual entity linking will enable computers to better understand visual content...
Feature selection is a form of both data reduction and attribute prioritization. It is modeled in existing work as a game between agents (buyers and sellers of information) in a corporate environment where information is accessible at a price. However, the interactions are typically moderated by a trusted third-party agent. Extending that work, we observe behavior in an unmoderated environment, integrated...
Agent-based modeling is a paradigm of modeling dynamic systems of interacting agents that are individually governed by specified behavioral rules. Training a model of such agents to produce an emergent behavior by specification of the emergent (as opposed to agent) behavior is easier from a demonstration perspective. While many approaches involve manual behavior specification via code or reliance...
This paper describes a data driven approach to studying the science of cyber security (SoS). It argues that science is driven by data. It then describes issues and approaches towards the following three aspects: (i) Data Driven Science for Attack Detection and Mitigation, (ii) Foundations for Data Trustworthiness and Policy-based Sharing, and (iii) A Risk-based Approach to Security Metrics. We believe...
This work introduces adversarial feature selection, a game between a feature selection agent and its adversary. The adversarial approach is drawn from existing work on adversarial classification. The feature selection algorithm selects a subset of features from the original set based on their utility towards classification accuracy. A cost is incurred based on features selected. The adversary modifies...
In this paper we explore the use of a variety of machine learning algorithms for designing a reliable and low-power, multi-channel EEG feature extractor and classifier for predicting seizures from electroencephalographic data (scalp EEG). Different machine learning classifiers including k-nearest neighbor, support vector machines, naïve Bayes, logistic regression, and neural networks are explored...
This brief presents a low-power, flexible, and multichannel electroencephalography (EEG) feature extractor and classifier for the purpose of personalized seizure detection. Various features and classifiers were explored with the goal of maximizing detection accuracy while minimizing power, area, and latency. Additionally, algorithmic and hardware optimizations were identified to further improve performance...
Most common and complex diseases, such as diabetes and cancer, are influenced at some level by variation in the genome. To truly address the goal of translational research, genetic variation must be taken into consideration. Research done in public health genetics, specifically in the area of single nucleotide polymorphisms (SNPs), is the first step to understanding human genetic variation. In addition,...
Signatures are the single most widely used method of identifying an individual but they carry with them an alarmingly significant number of vulnerabilities, implying the need for an effective and robust method of precisely identifying an individual's signature. The signature of an individual is visually acquired by using a pen-based tracking system [1], [2]. This paper considers the possibility of...
Standard Symbolic Aggregation Approximation (SAX) is at the core of many effective time series data mining algorithms. Its combination with Bag-of-Patterns (BoP) has become the standard approach with state-of-the-art performance on standard datasets. However, standard SAX with the BoP representation might neglect internal temporal correlation embedded in the raw data. In this paper, we proposed time...
The goal of meta-analysis is to synthesize results from a collection of studies in order to identify patterns that have broader applicability. In many of the global change sciences, these synthesis studies attempt to bring together results of local case studies to make claims about global patterns. In order to substantiate claims of generality, it is crucial to establish that the collected case studies...
Constructing an image classification system using strong, local invariant descriptors is both time consuming and tedious, requiring many experimentations and parameter tunings to obtain an adequately performing model. Furthermore training a system in a given domain and then migrating the model to a separate domain will likely yield poor performance. As the recent Boston Marathon attacks demonstrated,...
In this paper, we review recent advances in Reinforcement Learning (RL) in light of potential applications to robotics, introduce the basic concepts of RL and Markov Decision Process (MDP), and compare different RL algorithms such as Q-learning, Temporal Difference learning, the Actor Critic, and the Natural Actor Critic. We conclude that policy gradient methods are more suitable for solving continuous...
More than a decade of research has produced numerous representations and similarity measures to support time series classification and clustering. Yet most of the work in the field is so focused on the representation or similarity measure that it ignores the possibility of improving performance using ensembles of representations or classifiers. This paper explores ways of exploiting representational...
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