Barış Kurt

A Collaborative Multi-robot Localization Method without Robot Identification

This paper introduces a method for multi-robot localization which can be applied to more than two robots without identifying each of them individually. The Monte Carlo localization for the single robot case is extended using negative landmark information and shared belief state in addition to perception. A robot perceives a teammate and broadcasts its observation without identifying the teammate, and whenever a robot receives an observation, the observation is processed only if the robot decides that the observation concerns itself. The experiments are based on scenarios where it is impossible for a single robot to localize precisely and the shared information is ambiguous. We demonstrate successful robot identification and localization results in different scenarios.

Probabilistic sequence clustering with spectral learning

Abstract In this paper, we derive two novel learning algorithms for time series clustering; namely for learning mixtures of Markov Models and mixtures of Hidden Markov Models. Mixture models are special latent variable models that require the usage of local search heuristics such as Expectation Maximization (EM) algorithm, that can only provide locally optimal solutions. In contrast, we make use of the spectral learning algorithms, recently popularized in the machine learning community. Under mild assumptions, spectral learning algorithms are able to estimate the parameters in latent variable models by solving systems of equations via eigendecompositions of matrices or tensors of observable moments. As such, spectral methods can be viewed as an instance of the method of moments for parameter estimation, an alternative to maximum likelihood. The popularity stems from the fact that these methods provide a computationally cheap and local optima free alternative to EM. We conduct classification experiments on human action sequences extracted from videos, clustering experiments on motion capture data and network traffic data to illustrate the viability of our approach. We conclude that the spectral methods are a practical and useful alternative in terms of computational effort and solution quality to standard iterative techniques such as EM in several sequence clustering applications.

Combined perception and control for timing in robotic music performances

Interaction with human musicians is a challenging task for robots as it involves online perception and precise synchronization. In this paper, we present a consistent and theoretically sound framework for combining perception and control for accurate musical timing. For the perception, we develop a hierarchical hidden Markov model that combines event detection and tempo tracking. The robot performance is formulated as a linear quadratic control problem that is able to generate a surprisingly complex timing behavior in adapting the tempo. We provide results with both simulated and real data. In our experiments, a simple Lego robot percussionist accompanied the music by detecting the tempo and position of clave patterns in the polyphonic music. The robot successfully synchronized itself with the music by quickly adapting to the changes in the tempo.

A probabilistic SIP network simulation system

Experimenting with large-scale real world data is crucial for the development of network protocol and investigate their performance. However, collecting such data from real networks, and especially to annotate them with ground truth proves to, if not impossible, too tedious. In such cases use of simulated data, generated for various network scenarios, becomes a plausible alternative. To this purpose, we have developed a SIP network simulation system that employs probabilistic network model and we have demonstrated its use for the performance analysis of statistical (D)DOS attack detectors.

Attack detection in VOIP networks using Bayesian multiple change-point models

One of the most commonly used network protocols in Internet telephony services is SIP. As the popularity of the protocol increases, SIP networks have become targets of DDoS attacks more frequently. In this work, we propose a Bayesian change point model to detect anomalies due to such attacks. The model monitors the network and alarms when a change in the network traffic occurs. We test the model with a data set generated by network traffic and attack simulators.

A Bayesian change point model for detecting SIP-based DDoS attacks

Abstract Session Initiation Protocol (SIP), as one the most common signaling mechanism for Voice Over Internet Protocol (VoIP) applications, is a popular target for the flooding-based Distributed Denial of Service (DDoS) attacks. In this paper, we propose a DDoS attack detection framework based on the Bayesian multiple change model, which can detect different types of flooding attacks. Additionally, we propose a probabilistic SIP network simulation system that provides a test environment for network security tools.

A Network Monitoring System for High Speed Network Traffic

Monitoring network statistics is important for the maintenance and infrastructure planning for the network service providers. In this demonstration, we will showcase an initial analysis of a general purpose network monitoring platform for high speed mobile networks. The developed platform is the basis for performing complex real-time analysis such as application usage behaviour, security analysis, infrastructure planning. We have used the platform for real-time flow size and length monitoring with packet sampling.

Comparison of sampling strategies for flow length estimation

The importance of the analysis and understanding of the network traffic has constantly been increasing due to insights that this provides towards determination of user behaviour and resource usage. The data analyses in order to determine the related parameters are performed by selection of a small subset of the complete flow data due to data privacy and heavy computational/memory load issues. That is, sampling is required in order to detect the properties of the complete data set. In this work, four distinct sampling schemes, namely the packet based uniform sampling, time-slot based uniform sampling, packet based random sampling and time-slot based random sampling are investigated from which packet flow length distributions are estimated and compared with the actual data. No major differences are observed amongst the strategies based on the analysed data.

Multiple change point analysis in random graph series

Graphs are important mathematical tools for modelling processes. An important issue in this area is to infer the changes that occur in the underlying generative process. In this work, inference of multiple change points in stochastic block graph time series is studied. A well-known algorithm for inference in time series is the forward-backward algorithm. In order to decrease computational complexity of this algorithm in graphical models, backward smoothing part is replaced with backward Monte Carlo sampling. With the experiments, it is observed that modified algorithm gives result in accordance with the real data.