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Approximation algorithms for orienteering and discounted-reward TSP
In this paper, we give the first constant-factor approximation algorithm for the rooted orienteering problem, as well as a new problem that we call the Discounted-Reward TSP, motivated by robotExpand
TDCS guided using fMRI significantly accelerates learning to identify concealed objects
These brain imaging and stimulation studies suggest that right frontal and parietal cortex are involved in learning to identify concealed objects in naturalistic surroundings and suggest that the application of anodal tDCS over these regions can greatly increase learning, resulting in one of the largest effects on learning yet reported. Expand
Graph-based malware detection using dynamic analysis
A novel malware detection algorithm based on the analysis of graphs constructed from dynamically collected instruction traces of the target executable, where the vertices are the instructions and the transition probabilities are estimated by the data contained in the trace. Expand
An Application of Machine Learning to Anomaly Detection
A machine learning approach to anomaly detection that builds user profiles based on command sequences and compares current input sequences to the profile using a similarity measure and demonstrates that this is a promising approach to distinguishing the legitamate user from an intruder. Expand
Temporal sequence learning and data reduction for anomaly detection
An approach that transforms temporal sequences of discrete, unordered observations into a metric space via a similarity measure that encodes intra-attribute dependencies and demonstrates that it can accurately differentiate the profiled user from alternative users when the available features encode sufficient information. Expand
Sequence Matching and Learning in Anomaly Detection for Computer Security
Two problems of importance in computer security are to 1) detect the presence of an intruder masquerading as the valid user and 2) detect the perpetration of abusive actions on the part of anExpand
A computational study of off-target effects of RNA interference
Off-target effects of RNAi are examined, employing the genome and transcriptome sequence data of Homo sapiens, Caenorhabditis elegans and Schizosaccharomyces pombe to suggest a direction for future in vivo studies that could both help in calibrating true off-target rates in living organisms. Expand
Nearly deterministic abstractions of Markov decision processes
Careful construction of macro actions allows us to effectively "hide" navigational stochasticity from the global routing problem and to approximate the latter with off-the-shelf combinatorial optimization routines for the traveling salesdroid problem, yielding a net exponential speedup in planning performance. Expand
Approximation Algorithms for Orienteering and Discounted-Reward TSP
In this paper, we give the first constant-factor approximation algorithm for the rooted Orienteering problem, as well as a new problem that we call the Discounted-Reward traveling salesman problemExpand
Hidden Markov Models for Human/Computer Interface Modeling
It is demonstrated that, for most of the user population, a singlestate model is inferior to the multi- state models, and that, within multi-state models, those with more states tend to model the pro led user more e ectively but imposters less than do smaller models, consistent with the interpretation that larger models are necessary to capture high degrees of user behavioral complexity. Expand