Master Level Theses

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Here you can find proposals for master thesis (20 CFU for each student). See Project Proposals for other kinds of projects and theses.



Evolutionary Optimization and Stochastic Optimization


Title: Combinatorial optimization based on stochastic relaxation
Stochastic.jpg
Description: The project will focus on the study, implementation, comparison and analysis of different algorithms for the optimization of pseudo-Boolean functions, i.e., functions defined over binary variables with values in R. These functions have been studied a lot in the mathematical programming literature, and different algorithms have been proposed [1]. More recently, the same problems have been faced in evolutionary computations, with the use of genetic algorithms, and in particular estimation of distribution algorithms [2,3]. Estimation of distribution algorithms are a recent meta-heuristic, where classical crossover and mutation operators used in genetic algorithms are replaced with operators that comes from statistics, such as sampling and estimation.

The focus will be on the implementation of a new algorithm able to combine different approaches (estimation and sampling, from one side, and exploitation of prior knowledge about the structure of problem, on the other), together with the comparison of the results with existing techniques that historically appear in different (and often separated) communities. Good coding (C/C++) abilities are required. Since the approach will be based on statistical models, the student is supposed to be comfortable with notions that come from probability and statistics courses. The project could require some extra effort in order to build and consolidate some background in math, especially in Bayesian statistics and MCMC techniques, such as Gibbs and Metropolis samplers [4].

The project can be extended to master thesis, according to interesting and novel directions of research that will emerge in the first part of the work. Possible ideas may concern the proposal of new algorithms able to learn existing dependencies among the variables in the function to be optimized, and exploit them in order to increase the probability to converge to the global optimum.

Picture taken from http://www.ra.cs.uni-tuebingen.de/

Bibliography
  • [1] Boros, Endre and Boros, Endre and Hammer, Peter L. (2002) Pseudo-boolean optimization. Discrete Applied Mathematics .
  • [2] Pelikan, Martin; Goldberg, David; Lobo, Fernando (1999), A Survey of Optimization by Building and Using Probabilistic Models, Illinois: Illinois Genetic Algorithms Laboratory (IlliGAL), University of Illinois at Urbana-Champaign.
  • [3] Larrañga, Pedro; & Lozano, Jose A. (Eds.). Estimation of distribution algorithms: A new tool for evolutionary computation. Kluwer Academic Publishers, Boston, 2002.
  • [4] Image Analysis, Random Fields Markov Chain Monte Carlo Methods
Tutor: Matteo Matteucci ([1]), Luigi Malagò (email)
Start: Anytime
Number of students: 1-2
CFU: 20-40



BioSignal Analysis

Analysis of the Olfactory Signal


Title: Computational Intelligence techniques to analyse the olfactory signal acquired by an electronic nose for cancer diagnosis
Description: The electronic nose is an instrument able to detect and recognize odors, that is the volatile substances in the atmosphere or emitted by the analyzed substance. This device can react to a gas substance by providing signals that can be analyzed to classify the input. It is composed of a sensor array (MOS sensors, in our case) and a pattern classification system based on machine learning techniques. Each sensor reacts in a different way to the analyzed substance, providing multidimensional data that can be considered as a unique olfactory blueprint of the analyzed substance. We have already tested the use of the electronic nose as diagnostic tool for lung cancer; boosted from the very satisfactory results that we have achieved by these analysis, we want to investigate the possibility of diagnosing other types of cancer and to improve the current computation intelligence techniques.

The project is done in collaboration with the Istituto dei Tumori, Milano.

Tools and instruments
Matlab
Bibliography 
BLATT R., BONARINI A, CALABRÒ E, DELLA TORRE M, MATTEUCCI M, PASTORINO U. (2008). Pattern Classification Techniques for Early Lung Cancer Diagnosis using an Electronic Nose. In: Frontiers in Artificial Intelligence and Applications. European Conference on Artificial Intelligence - Prestigious Applications of Intetelligent Systems. Patras, Greece. 21-15 luglio 2008. (vol. 178, pp. 693-697). ISBN/ISSN: 978-1-58603-891-5. IOS Press. File:PAIS.pdf
Tutor: Andrea Bonarini (email), Matteo Matteucci (email), Rossella Blatt (email)
Start: Anytime (a new acquisition phase will start in March)
Number of students: 1-2
CFU: 20


Sleep Staging


Title: Development of a computer-assisted CAP (Sleep cyclic alternating pattern) scoring method
CAP Sleep Staging.jpg
Description: In 1985, Terzano describes for the first time the Cyclic Alternating Pattern [2] during sleep and, nowadays, CAP is widely accepted by the medical community as basic analysis of sleep. The CAP evaluation is of fundamental importance since it represents the mechanism developed by the brain evolution to monitor the inner and outer world and to assure the survival during sleep. However, visual detection of CAP in polisomnography (i.e., the standard procedure) is a slow and time-consuming process. This limiting factor generates the necessity of new computer-assisted scoring methods for fast CAP evaluation. This thesis deals with the development of a Decision Support System for CAP scoring based on features extraction at multi-system level (by statistical and signal analysis) and Pattern Recognition or Machine Learning approaches. This may allow the automatic detection of CAP sleep and could be integrated, through reinforcement learning techniques, with the corrections given by physicians.
Tools and instruments
Matlab, C/C++
Bibliography
Mario Terzano, Liborio Parrino. Atlas, rules, and recording techniques for the scoring of cyclic alternating pattern (CAP) in human sleep, Sleep Medicine 2 (2001) 537–553. [3]
Tutor: Matteo Matteucci (email), Martin Mendez (email), Anna Maria Bianchi (email), Mario Terzano (Ospedale di Parma)
Start: Anytime
Number of students: 1-2
CFU: 20


Brain-Computer Interface


Title: Recognition of the user's focusing on the stimulation matrix
B p300 speller.jpg
Description: A P300-based BCI stimulates the user continuously, and the detection of a P300 designates the choice of the user. When the user is not paying attention to the interface, false positives are likely. The objective of this work is to avoid this problem; the analysis of the electroencephalogram (EEG) over the visual cortex (and possibly an analysis of P300s or of other biosignals) should tell when the user is looking at the interface.
Tools and instruments
Matlab, BCI2000, C++
EEG system
Bibliography
E. Donchin, K.M. Spencer, R. Wijesinghe. The Mental Prosthesis: Assessing the Speed of a P300-Based Brain-Computer Interface [4]
Tutor: Matteo Matteucci (email), Bernardo Dal Seno (email)
Start: Anytime
Number of students: 1-2
CFU: 20



Title: Creation of new EEG training by introduction of noise
Bci arch.png
Description: A BCI must be trained on the individual user in order to be effective. This training phase require recording data in long sessions, which is time consuming and boring for the user. The aim of this project is to develop algorithm to create new training EEG (electroencephalography) data from existing ones, so as to speed up the training phase.
Tools and instruments
Matlab, BCI2000
Knowledge of C++ may be useful
EEG system
Bibliography
J.R. Wolpaw et al. Brain-computer interfaces for communication and control [5]
Tutor: Matteo Matteucci (email), Bernardo Dal Seno (email)
Start: Anytime
Number of students: 1
CFU: 20



Title: Real-time removal of ocular artifact from EEG
B bci.jpg
Description: In a BCI based on electroencephalogram (EEG), one of the most important sources of noise is related to ocular movements. Algorithms have been devised to cancel the effect of such artifacts. The project consists in the in the implementation in real time of an existing algorithm (or one newly developed) in order to improve the performance of a BCI.
Tools and instruments
Matlab, BCI2000, C++
EEG-system
Bibliography
J.R. Wolpaw et al. Brain-computer interfaces for communication and control [6]
R.J. Croff, R.J. Barry. Removal of ocular artifact from the EEG: a review [7]
Tutor: Matteo Matteucci (email), Bernardo Dal Seno (email)
Start: Anytime
Number of students: 1
CFU: 10-20



Title: Aperiodic visual stimulation in a VEP-based BCI
Bci arch.png
Description: Visual-evoked potentials (VEPs) are a possible way to drive the a BCI. This projects aims at maximizing the discrimination between different stimuli.
Tools and instruments
Matlab, BCI2000, C++
EEG system
Bibliography
J.R. Wolpaw et al. Brain-computer interfaces for communication and control [8]
Tutor: Matteo Matteucci (email), Bernardo Dal Seno (email)
Start: Anytime
Number of students: 1
CFU: 20



Title: Driving an autonomous wheelchair with a P300-based BCI
LURCH wheelchair.jpg
Description: This project pulls together different Airlab projects with the aim to drive an autonomous wheelchair (LURCH) with a BCI, through the development of key software modules. The work will be validated with live experiments.
Tools and instruments
C++, C, BCI2000, Matlab
Linux
EEG system
Lurch wheelchair
Bibliography
R. Blatt et al. Brain Control of a Smart Wheelchair [9]
Tutor: Matteo Matteucci (email), Bernardo Dal Seno (email)
Start: November 2008
Number of students: 1
CFU: 5-20



Machine Learning


Title: Statistical inference for phylogenetic trees
Toloverview.jpg
Description: The project will focus on the study, implementation, comparison and analysis of different statistical inference techniques for phylogenetic trees. Phylogenetic trees [1, 2] are evolutionary trees used to represent the relationships between different species with a common ancestor. Typical inference task concern the construction of a tree starting from DNA sequences, involving both the choice of the topology of the tree (i.e., model selection) and the values of the parameters (i.e., model fitting). The focus will be a probabilistic description of the tree, given by the introduction of stochastic variables associated to both internal nodes and leaves of the tree.

The project will focus on the understanding of the problem and on the implementation of different algorithms, so (C/C++ or Matlab or R) coding will be required. Since the approach will be based on statistical models, the student is supposed to be comfortable with notions that come from probability and statistics courses.

The project is thought to be extended to master thesis, according to interesting and novel directions of research that will emerge in the first part of the work. Possible ideas may concern the proposal and implementation of new algorithms, based on recent approaches to phylogenetic inference available in the literature, as in [3] and [4]. In this case the thesis requires some extra effort in order to build and consolidate some background in math in oder to understand some recent literature, especially in (mathematical) statistics and, for example, in the emerging field of algebraic statistics [5].

Picture taken from http://www.tolweb.org/tree/


Bibliography
  • [1] Felsenstein 2003: Inferring Phylogenies
  • [2] Semple and Steel 2003: Phylogenetics: The mathematics of phylogenetics
  • [3] Louis J. Billera, Susan P. Holmes and and Karen Vogtmann Geometry of the space of phylogenetic trees. Advances in Applied Math 27, 733-767 (2001)
  • [4] Evans, S.N. and Speed, T.P. (1993). Invariants of some probability models used in phylogenetic inference. Annals of Statistics 21, 355-377.
  • [5] Lior Pachter, Bernd Sturmfels 2005, Algebraic Statistics for Computational Biology.
Tutor: Matteo Matteucci ([10]), Luigi Malagò (email)
Start: Anytime
Number of students: 1-2
CFU: 20-40



Title: Reinforcement Learning in Poker
PokerPRLT.png
Description: In this years, Artificial Intelligence research has shifted its attention from fully observable environments such as Chess to more challenging partially observable ones such as Poker.

Up to this moment research in this kind of environments, which can be formalized as Partially Observable Stochastic Games, has been more from a game theoretic point of view, thus focusing on the pursue of optimality and equilibrium, with no attention to payoff maximization, which may be more interesting in many real-world contexts.

On the other hand Reinforcement Learning techniques demonstrated to be successful in solving both fully observable problems, single and multi-agent, and single-agent partially observable ones, while lacking application to the partially observable multi-agent framework.

This research aims at studying the solution of Partially Observable Stochastic Games, analyzing the possibility to combine the Opponent Modeling concept with the well proven Reinforcement Learning solution techniques to solve problems in this framework, adopting Poker as testbed.

Tutor: Marcello Restelli (restelli-AT-elet-DOT-polimi-DOT-it)
Start: Anytime
Number of students: 1-2
CFU: 20-40



Title: Automatic generation of domain ontologies [[Image:|center|300px]]
Description: This thesis to be developed together with Noustat S.r.l., who are developing research activities directed toward the optimization of knowledge management services, in collaboration with another company operating in this field. This project is aimed at removing the ontology building bottleneck, long and expensive activity that usually requires the direct collaboration of a domain expert. The possibility of automatic building the ontology, starting from a set of textual documents related to a specific domain, is expected to improve the ability to provide the knowledge management service, both by reducing the time-to-application, and by increasing the number of domains that can be covered. For this project, unsupervised learning methods will be applied in sequence, exploiting the topological properties of the ultra-metric spaces that emerge from the taxonomic structure of the concepts present in the texts, and associative methods will extend the concept network to lateral, non-hierarchical relationships.
Tutor: Matteo Matteucci (email), Andrea Bonarini (email)
Start: before November 30th
Number of students: 1-2
CFU: 20



Computational Intelligence and Games


Title: AI in Racing Games
TORCS2.jpg
Description: TORCS is a state-of-the-art open source racing simulator that represents an ideal bechmark for machine learning techniques. At the same time, Machine Learning techniques could be used to improve the game experience in this kind of games. Several projects and theses are available on this topic, please contact us for additional information.
References
Tutor: Daniele Loiacono (loiacono-AT-elet-DOT-polimi-DOT-it)
Start: Anytime
Number of students: 1 to 2
CFU: 20



Title: Automatic Content Generation in Computer Games
Gar.jpg
Description: The generation of customized game content for each player is an attractive direction to improve the game experience in the next-generation computer games. In this scenario, Machine Learning could play an important role to provide automatically such customized game content. Several projects and theses are available on this topic, please contact us for additional information.

Picutre taken from [11]

references
Daniele Loiacono's AI&R Lab Projects
Tutor: Daniele Loiacono (loiacono-AT-elet-DOT-polimi-DOT-it)
Start: Anytime
Number of students: 1 to 2
CFU: 20



Title: Human-Like AI in Games
UT2004.png
Description: Developing a human-like AI is a challenging and fascinating problem from the point of view of the Artificial Intelligence research. At the same time, it is also a significative prolem for the computer games development: playing against humans is generally more exciting than playing against computers.

Our projects and theses on this topic involve two different games: Unreal Tournament 2004 and TORCS. Please contact us for additional information.

References
Tutor: Daniele Loiacono (loiacono-AT-elet-DOT-polimi-DOT-it)
Start: Anytime
Number of students: 1 to 2
CFU: 20



Title: Data Mining in Computer Games
QLive.png
Description: Today a lot of data can be extracted from popular games. The analysis of such data allow to discover a lot of interesting information about players, the game and the interaction between the game and different type of players.

Several theses and projects are available on this topic and involve different games: TORCS, Unreal Tournament and Quake Live. Please contact us for additional information.

References
Tutor: Daniele Loiacono (loiacono-AT-elet-DOT-polimi-DOT-it)
Start: Anytime
Number of students: 1 to 2
CFU: 20



Social Software and Semantic Web

Wiki Page: A firefox extension for semantic annotations
Title: A Firefox extension for semantic annotations
Description: Aim of this project is to develop a Firefox extension, to allow a community of users to annotate resources on the Web using a shared RDF vocabulary.

While browsing the Web, a user should be able to visualize the annotations relative to the page they are visiting, and to add new annotations as well.

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Start:
Students: 1 - 2
CFU: 5 - 20
Research Area: Social Software and Semantic Web
Research Topic: Semantic Annotations

Wiki Page: Annotation aggregators from social applications
Title: Annotation aggregators from social applications
Description: Annotations are metadata published about a resource, such as tags in del.icio.us, comments on stumbleupon.com, or twines on Twine.com. One of the main problems of these annotations is that they are not expressed in a standard format: thus, any tool trying to aggregate information from these sources should be able to access each one of them in a different way.

The purpose of this project is to develop translation tools for different social annotation systems, collect their data in a common format (expressed using an ontology), and show them through a unique user interface, able to display different annotations (i.e. geo coordinates, dates, tags, etc.) in different ways. Moreover, tests and evaluations should be performed on this aggregator to show how efficient the queries are when performed on-the-fly or from an intermediate knowledge base.

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Start:
Students: 1 - 2
CFU: 5 - 20
Research Area: Social Software and Semantic Web
Research Topic: Semantic Annotations

Wiki Page: Detecting patterns in ontology usage
Title: Detecting patterns in ontology usage
Description: When building a new knowledge base the reuse of existing, well known vocabularies is often desirable. However, sometimes it is not clear which ontology should be preferable or which term is best suited for a specific application. Aim of this project is to detect patterns in ontology usage by harvesting ontologies which use a given schema (i.e. Foaf or Dublin Core) and analysing how people are using them in practice. The resulting application should download ontologies from the main semantic search engines (such as http://watson.kmi.open.ac.uk/ and http://sindice.com/), parse them and calculate statistics about the terms used inside them. The tool should show these statistics, save them in an appropriate format and make them available through an API for use by external applications.
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Start:
Students: 1 - 2
CFU: 5 - 20
Research Area: Social Software and Semantic Web
Research Topic: Semantic Annotations

Wiki Page: Extending a search engine with semantic information
Velociraptor.png
Title: Extending a search engine with semantic information
Description: We are used to keyword-based search engines, where only documents matching the exact words in the query are retrieved. In a traditional search engine, if you submit the query "a dinosaur in a university in Lombardy" you won't probably find a document containing the phrase "a velociraptor in Politecnico di Milano", even though it's more or less what you were looking for.

Aim of this project is to expand a traditional search engine with semantic information, so that also documents containing words related to the ones in the query can be retrieved. Existing thesauri and ontologies can be used, as well as more dynamic and collaborative sources of knowledge such as user tags and wikipedia pages and categories.

Starting points for this work can be the projects "SeQuEx - Semantic Query Expansion" and "Enriching search results with semantic metadata".

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Start:
Students: 1 - 2
CFU: 5 - 20
Research Area: Social Software and Semantic Web
Research Topic: Semantic Search

Wiki Page: Mining wikipedia categories
Wikipedia categories.png
Title: Wikipedia category map
Description: Wikipedia articles are organized in a hierarchy of categories, manually assigned by users. This process can be considered a huge effort for the collective categorization of human knowledge; the result is a wide and disordered graph which can provide precious information for a variety of applications (natural language processing, information retrieval, ontology building...).

In the project "Wikipedia Category Map" a tool has been developed to extract the graph of Wikipedia categories, to store it in RDF format and to interactively visualize and explore it. Aim of this project is to analyze the resulting graph for the extraction of semantic relationships; for example it is possible to define metrics of distance between topics in the graph, which can be useful for various purposes in information retrieval.

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Start: 7 July 2009
Students: 1 - 2
CFU: 5 - 20
Research Area: Social Software and Semantic Web
Research Topic: Semantic Tagging

Wiki Page: Social Network Data Extraction from Online Communities
SocialNetworkDataExtraction.png
Title: Social Network Data Extraction From Online Communities
Description: With the growth of the Web and the emergence of online communities, a huge amount of data regarding social relationships is now available, that was unthinkable until a few years ago. The network of connections may unveil precious information about communities structures and dynamics and the spreading of information in the Web.

Aim of this project is to design and develop a software tool to extract this kind of information from a single social network platform (decided by the student). It may be required also some kind of analysis or visual representation of the collected data.

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Start:
Students: 1 - 2
CFU: 5 - 20
Research Area: Social Software and Semantic Web
Research Topic: Social Network Analysis

Wiki Page: Use case design and implementation for semantic annotations
Title: Use case design and implementation for semantic annotation
Description: Semantic annotations offer a variety of possibilities to enhance the user experience while browsing the Web. Aim of this project is to propose one scenario in which their usefulness is exploited for a specific community of users. In detail the project requires to design a simple ontology which describes some kind of domain to annotate resources on the Web and implement an interface to query it and insert assertions inside a semantic store (through SPARQL).

One possible example is the annotation of mp3 files available on the Web. They can be classified in genres or associated to datatype properties, such as rating, title, length and release date... also exploiting data already available in http://musicbrainz.org/

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Start:
Students: 1 - 2
CFU: 5 - 10
Research Area: Social Software and Semantic Web
Research Topic: Semantic Annotations

Wiki Page: Wikipedia Page Social Network
Title: Wikipedia Page Social Network
Description: Goal of this project is to study the social network of Wikipedia pages, where two pages are connected if they share at least one main contributor. This social network can be studied to reveal interesting information; for example, it is possible to extract clusters of pages which apparently have nothing in common. A metric of distance between pages in the network can be defined, and compared with other metrics, such as the distance in the category tree or in the hyperlink graph.
Tutor: [[DavidLaniado | ]] (, , , , , , , , , , , , , , , , , , … further resultswarning.png
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Start:
Students: 1 - 2
CFU: 5 - 20
Research Area: Social Software and Semantic Web
Research Topic: Social Network Analysis

Wiki Page: Wikipedia Tripartite Graph
Title: Wikipedia Tripartite Graph
Description: When a user edits a Wikipedia page, we can establish a link among her, the page and the categories to which the page belongs. A model to represent this information is a tripartite graph. Aim of this project is to build a tripartite graph from Wikipedia users, pages and categories, and mine the outcome network to extract emergent semantics.
Tutor: [[DavidLaniado | ]] (, , , , , , , , , , , , , , , , , , … further resultswarning.png
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Start:
Students: 1 - 2
CFU: 20 - 20
Research Area: Social Software and Semantic Web
Research Topic: Social Network Analysis, Semantic Tagging


Robotics


Title: Robot games
Robowii robot.jpg
Description: The goal of this activity is to develop an interactive game with robots using commercial devices such as the WII Mote (see the Robogames page)

Projects are available in different areas:

  • Design and implementation of the game on one of the available robots and extension of the robot functionalities
  • Design and implementation of the game and a new suitable robot
  • Evaluation of the game with users (in collaboration with Franca Garzotto)

These projects allow to experiment with real mobile robots and real interaction devices.

Parts of these projects can be considered as course projects. These projects can also be extended to cover course projects.

Tutor: Andrea Bonarini (bonarini-AT-elet-DOT-polimi-DOT-it)
Start: Anytime
Number of students: 1-2
CFU: 7.5-20