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Error: #f88
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ol ol {list-style-type:lower-alpha;}
ol ol ol {list-style-type:lower-roman;}
ol ol ol ol {list-style-type:decimal;}
ol ol ol ol ol {list-style-type:lower-alpha;}
ol ol ol ol ol ol {list-style-type:lower-roman;}
ol ol ol ol ol ol ol {list-style-type:decimal;}

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#contentWrapper .chkOptionInput {border:0;}

.externalLink {text-decoration:underline;}

.indent {margin-left:3em;}
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code.escaped {white-space:nowrap;}

.tiddlyLinkExisting {font-weight:bold;}
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/* the 'a' is required for IE, otherwise it renders the whole tiddler in bold */
a.tiddlyLinkNonExisting.shadow {font-weight:bold;}

#mainMenu .tiddlyLinkExisting,
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#sidebarTabs .tiddlyLinkExisting {font-weight:bold; font-style:normal;}

.header {position:relative;}
.header a:hover {background:transparent;}
.headerShadow {position:relative; padding:4.5em 0 1em 1em; left:-1px; top:-1px;}
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.tagClear {clear:both;}

.footer {font-size:.9em;}
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.annotation {padding:0.5em; margin:0.5em;}

* html .viewer pre {width:99%; padding:0 0 1em 0;}
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.zoomer div {padding:1em;}

* html #backstage {width:99%;}
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#backstageArea {display:none; position:relative; overflow: hidden; z-index:150; padding:0.3em 0.5em;}
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.whenBackstage {display:none;}
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/*}}}*/
/***
StyleSheet for use when a translation requires any css style changes.
This StyleSheet can be used directly by languages such as Chinese, Japanese and Korean which need larger font sizes.
***/
/*{{{*/
body {font-size:0.8em;}
#sidebarOptions {font-size:1.05em;}
#sidebarOptions a {font-style:normal;}
#sidebarOptions .sliderPanel {font-size:0.95em;}
.subtitle {font-size:0.8em;}
.viewer table.listView {font-size:0.95em;}
/*}}}*/
/*{{{*/
@media print {
#mainMenu, #sidebar, #messageArea, .toolbar, #backstageButton, #backstageArea {display: none !important;}
#displayArea {margin: 1em 1em 0em;}
noscript {display:none;} /* Fixes a feature in Firefox 1.5.0.2 where print preview displays the noscript content */
}
/*}}}*/
<!--{{{-->
<div class='header' macro='gradient vert [[ColorPalette::PrimaryLight]] [[ColorPalette::PrimaryMid]]'>
<div class='headerShadow'>
<span class='siteTitle' refresh='content' tiddler='SiteTitle'></span>&nbsp;
<span class='siteSubtitle' refresh='content' tiddler='SiteSubtitle'></span>
</div>
<div class='headerForeground'>
<span class='siteTitle' refresh='content' tiddler='SiteTitle'></span>&nbsp;
<span class='siteSubtitle' refresh='content' tiddler='SiteSubtitle'></span>
</div>
</div>
<div id='mainMenu' refresh='content' tiddler='MainMenu'></div>
<div id='sidebar'>
<div id='sidebarOptions' refresh='content' tiddler='SideBarOptions'></div>
<div id='sidebarTabs' refresh='content' force='true' tiddler='SideBarTabs'></div>
</div>
<div id='displayArea'>
<div id='messageArea'></div>
<div id='tiddlerDisplay'></div>
</div>
<!--}}}-->
<!--{{{-->
<div class='toolbar' macro='toolbar [[ToolbarCommands::ViewToolbar]]'></div>
<div class='title' macro='view title'></div>
<div class='tagging' macro='tagging'></div>
<div class='viewer' macro='view text wikified'></div>
<div class='tagClear'></div>
<!--}}}-->
<!--{{{-->
<div class='toolbar' macro='toolbar [[ToolbarCommands::EditToolbar]]'></div>
<div class='title' macro='view title'></div>
<div class='editor' macro='edit title'></div>
<div macro='annotations'></div>
<div class='editor' macro='edit text'></div>
<div class='editor' macro='edit tags'></div><div class='editorFooter'><span macro='message views.editor.tagPrompt'></span><span macro='tagChooser excludeLists'></span></div>
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To get started with this blank [[TiddlyWiki]], you'll need to modify the following tiddlers:
* [[SiteTitle]] & [[SiteSubtitle]]: The title and subtitle of the site, as shown above (after saving, they will also appear in the browser title bar)
* [[MainMenu]]: The menu (usually on the left)
* [[DefaultTiddlers]]: Contains the names of the tiddlers that you want to appear when the TiddlyWiki is opened
You'll also need to enter your username for signing your edits: <<option txtUserName>>
These [[InterfaceOptions]] for customising [[TiddlyWiki]] are saved in your browser

Your username for signing your edits. Write it as a [[WikiWord]] (eg [[JoeBloggs]])

<<option txtUserName>>
<<option chkSaveBackups>> [[SaveBackups]]
<<option chkAutoSave>> [[AutoSave]]
<<option chkRegExpSearch>> [[RegExpSearch]]
<<option chkCaseSensitiveSearch>> [[CaseSensitiveSearch]]
<<option chkAnimate>> [[EnableAnimations]]

----
Also see [[AdvancedOptions]]
<<importTiddlers>>
1999 - 2000
''Automatic Acquisition of ~Named-Entity Recognition Grammars for Greek''
Relevant publications
*''S. Petridis'', G. Paliouras and S. J. Perantonis: Allen's hourglass: Probabilistic Treatment of Interval Relations, 17th International Symposium on Temporal Representation and Reasoning (TIME'10), pp.87-94, ([[bib|publications/2010-petridis-time10.bib]], [[pdf|publications/2010-petridis-time10.pdf]])
*''S. Petridis'', C.A. Psomas, Probabilistic extension of Allen’s relations using the Hourglass model, 24th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 12), pp.87-94, ([[bib|publications/2012-petridis-ictai.bib]], [[pdf|publications/2012-petridis-ictai.pdf]])
<html>
<center>
<object data="presentations/allensHourglass.svg" width="500" height="500" type="image/svg+xml"/>
</center>
</html>

Note: If your browser does not support svg you may download the figure in [[pdf|presentations/allensHourglass.pdf]]
Rename this tiddler to 'ColorPalette' to enable this color scheme

Background: #ffc
Foreground: #000
PrimaryPale: #fc8
PrimaryLight: #f81
PrimaryMid: #b40
PrimaryDark: #410
SecondaryPale: #ffc
SecondaryLight: #fe8
SecondaryMid: #db4
SecondaryDark: #841
TertiaryPale: #e88
TertiaryLight: #c66
TertiaryMid: #944
TertiaryDark: #633
Aminess is a project funded from the Greek General Secretariat for Research and Technology. Detailed information can be found at the official [[site|http://www.aminess.eu]].

The goal of the AMINESS project is to contribute in the safety, management and monitoring of the sea environment and the Aegean Sea in particular.
*[[Winner|awards/2010-icmla-award.pdf]] of the [[ICMLA 2010 speaker clustering challenge|http://www.icmla-conference.org/icmla10/CFP_Challenge1_files/CFP_Challenge1.html]]. Relative publication:  T. Giannakopoulos and ''S. Petridis'', Unsupervised Speaker Clustering in a Linear Discriminant Subspace, 9th International Conference on Machine Learning and Applications (ICMLA), 2010. ([[bib|publications/2010-petridis-icmla.bib]], [[pdf|publications/2010-petridis-icmla.pdf]])
[<img[BOEMIE Logo|logoboemie.gif]]''Bootstrapping Ontology evolution using Multimedia Information Extraction''. 

BOEMIE is a european ~FP6 co-funded project. Analytical information can be found at the official [[site|http://www.boemie.org]].

My research focuses on devising multimedia analysis algorithms that can 
* allow for //analyzing// content, //learning// from annotated corpora and  self-//expanding// their domain of application.
* allow to recursively analyzing multimedia information (''webpages'', ''audiovisual streams''), by spitting their content into their embedded elements and then fuse the analysis results
* combine ''ontological representation'' of the domain with ''machine learnable'' classification and feature extraction techniques.
 

One of the great strengths of TiddlyWiki is the way that its interface can be customised by editting various shadow tiddlers. However, a problem that has consistently emerged is the difficulty of reconciling the needs of authors, who need access to the full array of TiddlyWiki features, and the needs of ordinary readers, who generally benefit from seeing a restricted set of functionality more suited to their needs.

The new backstage area in release 2.2 offers a solution to this conundrum by providing a consistent way of accessing authoring functionality that is independent of the interface customisations (so, even if you blast away the contents of your PageTemplate, you can still access the backstage area).

The backstage area is only available when a TiddlyWiki is edittable - typically meaning that it's been opened off of a {{{file://}}} URL. It appears as an unobtrusive link in the topright corner of the page. Clicking it reveals the backstage toolbar consisting of commands like {{{saveChanges}}} and drop-down tasks like ImportTiddlers, SyncMacro, PluginManager and Tweak (which provides access to the new OptionsMacro).
BioASQ is a European ~FP7 co-funded project.  It initiates a series of challenges on biomedical semantic indexing and question answering (QA). My involvement in this project focuses on the organisation of the challenge platform and the definitinon of evaluation metrics.

Relevant Links:
* [[BioASQ Project Official Site|http://www.bioasq.org]]
* [[BioASQ Participants Area|http://bioasq.lip6.fr/]]
* [[BioASQ Documents Repository|https://lshtc.iit.demokritos.gr:8443/BioASQ/]]
''Computer Aided Semantic Annotation of Multimedia''

CASAM is a European ~FP7 co-funded project.  Analytical information can be found at the official [[site|http://www.casam-project.eu]].

Members of the CASAM project have also access to an alfresco server located [[here|http://www.iisa-innov.com:8080/alfresco/faces/jsp/login.jsp]]. 

My research focuses on devising multimedia analysis algorithms (audio,video) with the following properties,
* capable of  ''exploiting knowledge'' in various degrees, in order to improve the analysis accuracy,
* ''adaptable'' to new domains of application, given annotated or semi-annotated corpora,
* capable of balancing ''accuracy vs. speed'', in order to allow on-line interaction with the end-user.

Here is my CV in pdf format [[in English|petridis-cv-en.pdf]] and [[in Greek|petridis-cv-gr.pdf]]
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*[[Societes]]
*[[Journals]]
*[[Conferences]]
Recent conferences I have reviewed articles for
* 19th International Conference on Pattern Recognition  [[(ICPR 2008|http://www.icpr2008.org]])
* 5th European Semantic Web Conference ([[ESWC 2008|http://www.eswc2008.org/]])
* 9th International Conference on Document Analysis and Recofnition ([[ICDAR 2007|http://www.icdar2007.org/]])
* 17th European Conference on Machine Learning ([[ECML 2007|http://www.ecmlpkdd2006.org]])
* 4th Hellenic Conference on Artificial Intelligence ([[SETN 2006|http://www.setn06.gr/]])
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!!!!email
petridis [at] iit.demokritos.gr
!!!!tel / fax
+(30) 2106503175 / +(30) 2106532175
!!!! postal address
National Center For Scientific Research "Demokritos", 
Institute of Informatics and Telecommunications

Patriarchou Grigoriou & Neapoleos,
Ag. Paraskevi 15310,
P.O. BOX 60228,
Greece
* Presenting [[USEFIL]] at the [[5th Panhellenic Conference on Biomedical Engineering|http://elevit.org.gr/index.php?option=com_content&view=article&id=95&Itemid=142&lang=el]]
* Program committee member at the 2nd International Workshhop on Artificial Intelligence and Netmedicine ([[NetMed'13|http://netmed2013.dii.univpm.it]])
* Extracting health-related physiological paramteres from video ([[USEFIL]] project)
* Organising challenges on biomedical semantic indexing and question answering ([[BioASQ]] project)
* Finding environmentally optimal navigation routes ([[Aminess]] project)
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[[Current and Recent Activities]][[Contacts]]
!!!!Related Publications
* T. Giannakopoulos and ''S. Petridis''. [[Fisher Linear Semi-discriminant Analysis for Speaker Diarization]]. Audio, Speech, and Language Processing, IEEE Transactions on , vol.20, no.7, pp.1913-1922, Sept. 2012, doi: 10.1109/TASL.2012.2191285 ([[bib|publications/2012-petridis-flsd.bib]], [[pdf|publications/2012-petridis-flsdpdf]])
> Given an audio signal with an unknown number of people speaking, speaker diarization aims to automatically answer the question “who spoke when.” Crucial to the success of diarization is the distance metric between speech segments, a factor depending on the choice of the feature space: distances should be low for segments of the same speaker and high for segments of different speakers. Starting from an Mel-frequency cepstrum coefficient (MFCC)-based feature space, an algorithm is proposed that finds a Fisher near-optimal linear discriminant subspace, adapted to the particular speakers which exist in the audio signal. The proposed approach relies on a semi-supervised version of Fisher linear discriminant analysis (FLD), leveraging information from the sequential structure of the audio signal as a substitute for unknown speaker labels. The resulting algorithm is completely unsupervised; therefore, the need for speaker labels in the provided or an independent set is dismissed. The eigenvalue perturbation theory is applied in order to provide optimality bounds with respect to FLD, showing the effectiveness of the approach under the assumption that speakers do not significantly modify the characteristics of their voice. A complete diarization system is then proposed, using fuzzy clustering, a non-parametric K-nearest neighbors classifier and a hidden Markov model. The experimental results show a major improvement of speaker diarization accuracy when using the optimal subspace found by the proposed approach with respect to using the initial MFCC feature space or subspaces found by competitive approaches.
* T. Giannakopoulos and ''S. Petridis'', Detection and Clustering of Musical Audio Parts Using Fisher Linear Semi - Discriminant Analysis, in Proceedings of the 20th European Signal Processing Conference (EUSIPCO), 2012.  ([[bib|publications/2012-petridis-eusipco.bib]], [[pdf|publications/2012-petridis-eusipco.pdf]])
> In this paper we deal with the problem of segmentation and detection of music clusters in audio streams, i.e., dividing a music signal into non-overlapping segments of homogeneous content and clustering these segments. As with all clustering tasks, crucial to the success of the present problem is the distance metric between the music segments, which depends on the choice of the audio features. The desired response of a feature space is to provide distances that are low for segments of the same music part and high for segments of different music parts. In this work, we adopt an initial feature space composed from several types of audio features (e.g., chroma-based, MFCCs, etc.). Then, we focus on the estimation of a music-discriminant feature subspace. Towards this end, we use the information from the sequential structure of audio signals, in order to estimate a Fischer subspace with music-related discriminant characteristics. After the estimation of the discriminant feature space, we propose a complete segmentation-clustering system based on fuzzy clustering, Hidden Markov Models and k-Nearest Neighbour probability estimation. The experimental results show a major performance improvement when adopting the Fisher subspace with respect to using the original feature space.
2003 - 2004
''Internal Waters Integrated surveillance methods for pollution measurement and risk assessment''
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* SaveBackups gives the option of whether to generate backup files 
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* RegExpSearch allows more complex search expressions
* CaseSensitiveSearch does as its name implies
I have recently reviewed articles for: 
* [[IEEE Computer Magazine|http://www.computer.org/portal/site/computer/index.jsp]]
* [[IEEE Transactions on Neural Networks|http://ieee-cis.org/pubs/tnn/]]
* [[IEEE Transactions on Pattern Analysis and Machine Intelligence|http://www.computer.org/portal/site/transactions/menuitem.802944db300bb678c4f34b978bcd45f3/index.jsp?&pName=tpami_home]]
* [[IEEE Intelligent Systems|http://www.computer.org/portal/site/intelligent/]]
* [[IEEE Transactions on Knowledge and Data Engineering|http://www.computer.org/portal/site/transactions/menuitem.a66ec5ba52117764cfe79d108bcd45f3/index.jsp?&pName=tkde_home&]]
* [[Signal Processing|http://www.elsevier.com/wps/find/journaldescription.cws_home/505662/description?navopenmenu=-2]]
* [[International Journal of Artificial Intelligence Tools|http://www.journals.wspc.com.sg/ijait/ijait.shtml]]
* [[Information Sciences|http://www.elsevier.com/wps/find/journaldescription.cws_home/505730/description#description]]
* [[Pattern Recognition Letters|http://ees.elsevier.com/patrec/default.asp]]
2004 - 2005
''Human Network in Knowledge Management and Discovery Technologies from Multimedia Content''
[img[Sergios Petridis photo|photo.jpg]]
!!! CV links
[[CV in pdf|CV]]
[[Research]]
[[Teaching|Teaching Experience]]
[[Studies]]
[[Publication List|Publications]]
[[Awards]]

!!! Shortcuts
[[Fisher Linear Semi-Discriminant Analysis]]
[[Allen's Hourglass]]
[[Seminars at NCSR/IIT]]
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Last Updated: 2008-06-12  /%<<today YYYY-0MM-DD>>%/
''S. Petridis'' and S. J. Perantonis: [[On the relation between discriminant analysis and mutual information for supervised linear feature extraction]]. Pattern Recognition, 37:857–874, 2004. ([[bib|publications/2004-petridis-patrec.bib]], [[pdf|publications/2004-petridis-patrec.pdf]])

This paper provides a unifying view of three discriminant linear feature extraction methods: Linear Discriminant Analysis, Heteroscedastic Discriminant Analysis and Maximization of Mutual Information. We propose a model-independent reformulation of the criteria related to these three methods that stresses their similarities and elucidates their differences. Based on assumptions for the probability distribution of the classification data, we obtain sufficient conditions under which two or more of the above criteria coincide. It is shown that these conditions also suffice for Bayes optimality of the criteria. Our approach results in an information-theoretic derivation of Linear Discriminant Analysis and Heteroscedastic Discriminant Analysis. Finally, regarding Linear Discriminant Analysis, we discuss its relation to Multidimensional Independent Component Analysis and derive suboptimality bounds based on information theory.
;ejf;af
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|Standard Periodic Table (ref. Wikipedia)|c
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|!2|bgcolor(#ff6666): Li |bgcolor(#ffdead): Be |>|>|>|>|>|>|>|>|>|>||bgcolor(#cccc99): B |bgcolor(#a0ffa0): C |bgcolor(#a0ffa0): @@color(red):N@@ |bgcolor(#a0ffa0): @@color(red):O@@ |bgcolor(#ffff99): @@color(red):F@@ |bgcolor(#c0ffff): @@color(red):Ne@@ |
|!3|bgcolor(#ff6666): Na |bgcolor(#ffdead): Mg |>|>|>|>|>|>|>|>|>|>||bgcolor(#cccccc): Al |bgcolor(#cccc99): Si |bgcolor(#a0ffa0): P |bgcolor(#a0ffa0): S |bgcolor(#ffff99): @@color(red):Cl@@ |bgcolor(#c0ffff): @@color(red):Ar@@ |
|!4|bgcolor(#ff6666): K |bgcolor(#ffdead): Ca ||bgcolor(#ffc0c0): Sc |bgcolor(#ffc0c0): Ti |bgcolor(#ffc0c0): V |bgcolor(#ffc0c0): Cr |bgcolor(#ffc0c0): Mn |bgcolor(#ffc0c0): Fe |bgcolor(#ffc0c0): Co |bgcolor(#ffc0c0): Ni |bgcolor(#ffc0c0): Cu |bgcolor(#ffc0c0): Zn |bgcolor(#cccccc): Ga |bgcolor(#cccc99): Ge |bgcolor(#cccc99): As |bgcolor(#a0ffa0): Se |bgcolor(#ffff99): @@color(green):Br@@ |bgcolor(#c0ffff): @@color(red):Kr@@ |
|!5|bgcolor(#ff6666): Rb |bgcolor(#ffdead): Sr ||bgcolor(#ffc0c0): Y |bgcolor(#ffc0c0): Zr |bgcolor(#ffc0c0): Nb |bgcolor(#ffc0c0): Mo |bgcolor(#ffc0c0): Tc |bgcolor(#ffc0c0): Ru |bgcolor(#ffc0c0): Rh |bgcolor(#ffc0c0): Pd |bgcolor(#ffc0c0): Ag |bgcolor(#ffc0c0): Cd |bgcolor(#cccccc): In |bgcolor(#cccccc): Sn |bgcolor(#cccc99): Sb |bgcolor(#cccc99): Te |bgcolor(#ffff99): I |bgcolor(#c0ffff): @@color(red):Xe@@ |
|!6|bgcolor(#ff6666): Cs |bgcolor(#ffdead): Ba |bgcolor(#ffbfff):^^*1^^|bgcolor(#ffc0c0): Lu |bgcolor(#ffc0c0): Hf |bgcolor(#ffc0c0): Ta |bgcolor(#ffc0c0): W |bgcolor(#ffc0c0): Re |bgcolor(#ffc0c0): Os |bgcolor(#ffc0c0): Ir |bgcolor(#ffc0c0): Pt |bgcolor(#ffc0c0): Au |bgcolor(#ffc0c0): @@color(green):Hg@@ |bgcolor(#cccccc): Tl |bgcolor(#cccccc): Pb |bgcolor(#cccccc): Bi |bgcolor(#cccc99): Po |bgcolor(#ffff99): At |bgcolor(#c0ffff): @@color(red):Rn@@ |
|!7|bgcolor(#ff6666): Fr |bgcolor(#ffdead): Ra |bgcolor(#ff99cc):^^*2^^|bgcolor(#ffc0c0): Lr |bgcolor(#ffc0c0): Rf |bgcolor(#ffc0c0): Db |bgcolor(#ffc0c0): Sq |bgcolor(#ffc0c0): Bh |bgcolor(#ffc0c0): Hs |bgcolor(#ffc0c0): Mt |bgcolor(#ffc0c0): Ds |bgcolor(#ffc0c0): Rg |bgcolor(#ffc0c0): @@color(green):Uub@@ |bgcolor(#cccccc): Uut |bgcolor(#cccccc): Uuq |bgcolor(#cccccc): Uup |bgcolor(#cccccc): Uuh |bgcolor(#fcfecc): @@color(#cccccc):Uus@@ |bgcolor(#ecfefc): @@color(#cccccc):Uuo@@ |

| !Lanthanides^^*1^^|bgcolor(#ffbfff): La |bgcolor(#ffbfff): Ce |bgcolor(#ffbfff): Pr |bgcolor(#ffbfff): Nd |bgcolor(#ffbfff): Pm |bgcolor(#ffbfff): Sm |bgcolor(#ffbfff): Eu |bgcolor(#ffbfff): Gd |bgcolor(#ffbfff): Tb |bgcolor(#ffbfff): Dy |bgcolor(#ffbfff): Ho |bgcolor(#ffbfff): Er |bgcolor(#ffbfff): Tm |bgcolor(#ffbfff): Yb |
| !Actinides^^*2^^|bgcolor(#ff99cc): Ac |bgcolor(#ff99cc): Th |bgcolor(#ff99cc): Pa |bgcolor(#ff99cc): U |bgcolor(#ff99cc): Np |bgcolor(#ff99cc): Pu |bgcolor(#ff99cc): Am |bgcolor(#ff99cc): Cm |bgcolor(#ff99cc): Bk |bgcolor(#ff99cc): Cf |bgcolor(#ff99cc): Es |bgcolor(#ff99cc): Fm |bgcolor(#ff99cc): Md |bgcolor(#ff99cc): No |

*Chemical Series of the Periodic Table
**@@bgcolor(#ff6666): Alkali metals@@
**@@bgcolor(#ffdead): Alkaline earth metals@@
**@@bgcolor(#ffbfff): Lanthanides@@
**@@bgcolor(#ff99cc): Actinides@@
**@@bgcolor(#ffc0c0): Transition metals@@
**@@bgcolor(#cccccc): Poor metals@@
**@@bgcolor(#cccc99): Metalloids@@
**@@bgcolor(#a0ffa0): Nonmetals@@
**@@bgcolor(#ffff99): Halogens@@
**@@bgcolor(#c0ffff): Noble gases@@

*State at standard temperature and pressure
**those in @@color(red):red@@ are gases
**those in @@color(green):green@@ are liquids
**those in black are solids
!!!Thesis
*''Sergios Petridis'', Transform Learning in Classification Problems, 2006 ([[pdf, in greek|publications/2006-petridis-uoa.pdf]], [[pdf, extended synopsis in english|publications/2006-petridis-uoa-en.pdf]]).
!!!Journal Articles
*R. Hendley et all, CASAM: collaborative human-machine annotation of multimedia. Mutltimedia Tools and Applications, 2013, Publisher: Springer US ([[bib|publications/2013-petridis-mta.bib]],[[pdf|http://dx.doi.org/10.1007/s11042-012-1255-1]])
*T. Giannakopoulos and ''S. Petridis''. [[Fisher Linear Semi-discriminant Analysis for Speaker Diarization]]. Audio, Speech, and Language Processing, IEEE Transactions on , vol.20, no.7, pp.1913-1922, Sept. 2012, doi: 10.1109/TASL.2012.2191285 ([[bib|publications/2012-petridis-flsd.bib]], [[pdf|publications/2012-petridis-flsd.pdf]])
*E. Zavitsanos, G. Paliouras, G. Vouros, ''S. Petridis''.  Learning Subsumption Hierarchies of Ontology Concepts from Texts. Web Intelligence and Agent Systems: An International Journal, (8):1, pp. 37-51, IOS Press, 2010. ([[bib|publications/2010-petridis-wias.bib]], [[pdf|publications/2010-petridis-wias.pdf]])
* B. Gatos, K. Ntzios,  I. Pratikakis, ''S. Petridis'', T. Konidaris and S. J. Perantonis, "An Efficient Segmentation-free Approach to Assist Old Greek Handwritten Manuscript OCR ", Pattern Analysis and Applications (PAA), Vol. 8, Num. 4, pp. 305-320, 2006 ([[bib|publications/2006-petridis-paa.bib]], [[pdf|publications/2006-petridis-paa.pdf]])
* ''S. Petridis'' and S. J. Perantonis: [[On the relation between discriminant analysis and mutual information for supervised linear feature extraction]]. Pattern Recognition, 37:857–874, 2004. ([[bib|publications/2004-petridis-patrec.bib]], [[pdf|publications/2004-petridis-patrec.pdf]])
!!!Book Chapters
* ''S. Petridis'' and S. J. Perantonis,  Semantics extraction from multimedia data: an ontology-based machine learning approach, in "Perception-action cycle: Models, architectures and hardware", Eds. Vassilis Cutsuridis and Amir Hussain, Springer, 2011  ([[bib|publications/2010-petridis-prac.bib]], [[pdf|publications/2010-petridis-prac.pdf]])
!!!Conference Articles
*''S. Petridis'', C.A. Psomas, Probabilistic extension of Allen’s relations using the Hourglass model, 24th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 12), pp.87-94, ([[bib|publications/2012-petridis-ictai.bib]], [[pdf|publications/2012-petridis-ictai.pdf]])
* T. Giannakopoulos and ''S. Petridis'', Detection and Clustering of Musical Audio Parts Using Fisher Linear Semi - Discriminant Analysis, in Proceedings of the 20th European Signal Processing Conference (EUSIPCO), 2012.  ([[bib|publications/2012-petridis-eusipco.bib]], [[pdf|publications/2012-petridis-eusipco.pdf]])
* ''S. Petridis'' //et al.//, Joint analysis of audio and text data in CASAM, 5th International Conference on Semantic and Digital Media Technologies (SAMT 2010), 2010. ([[bib|publications/2010-petridis-samt.bib]], [[pdf|publications/2010-petridis-samt.pdf]])
* ''S. Petridis'' and T. Giannakopoulos, A multi-class method for detecting audio events in news broadcasts, Advances in Artificial Intelligence: Theories, Models, and Applications, Proceeding of the 6th Hellenic Conference on Artificial Intelligence (SETN 2010), Lecture notes in Artificial Intelligence, vol. 6040, Springer, 2010, pp. 399-404. ([[bib|publications/2010-petridis-setn.bib]], [[pdf|publications/2010-petridis-setn.pdf]])
* T. Giannakopoulos and ''S. Petridis'', Unsupervised Speaker Clustering in a Linear Discriminant Subspace, in Proceedings of the 9th International Conference on Machine Learning and Applications (ICMLA), 2010, pp.1005-1009. ([[bib|publications/2010-petridis-icmla.bib]], [[pdf|publications/2010-petridis-icmla.pdf]])
*''S. Petridis'', G. Paliouras and S. J. Perantonis: [[Allen's Hourglass]]: probabilistic treatment of interval relations, 17th International Symposium on Temporal Representation and Reasoning (TIME'10), pp.87-94, ([[bib|publications/2010-petridis-time10.bib]], [[pdf|publications/2010-petridis-time10.pdf]])
* E. Zavitsanos, ''S. Petridis'', G. Paliouras, G. Vouros. Learning Ontologies of Appropriate Size. In Proceedings of Panhellenic Conference on Artificial Intelligence, LNAI: Theories, Models and Applications. pp. 327-338, 2008. ([[bib|publications/2008-petridis-aitma.bib]],[[pdf|publications/2008-petridis-aitma.pdf]])
* E. Zavitsanos, ''S. Petridis'', G. Paliouras, G. Vouros. Determining Automatically the Size of Learned Ontologies. European Conference on Artificial Intelligence (ECAI) 2008. ([[bib|publications/2008-petridis-ecai.bib]], [[pdf|publications/2008-petridis-ecai.pdf]])
* G. Vamvakas, B. Gatos, ''S. Petridis'', and N. Stamatopoulos: An efficient feature extraction and dimensionality reduction scheme for isolated greek handwritten character recognition. In 9th International Conference on Document Analysis and Recognition (~ICDAR2007), 2007 ([[bib|publications/2007-petridis-icdar.bib]], [[pdf|publications/2007-petridis-icdar.pdf]])
* N. Tsapatsoulis, ''S. Petridis'' and S. J. Perantonis: On the use of spatial relations between objects for image classification. In Artificial Intelligence and Innovations: from Theory to Applications, 2007, pages 347 - 356,  Springer Boston ([[bib|publications/2007-petridis-smap.bib]], [[pdf|publications/2007-petridis-smap.pdf]])
* E. Zavitsanos, G. Paliouras, G. Vouros, and ''S. Petridis'',  "Discovering Subsumption Hierarchies of Ontology Concepts from Text Corpora",  IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, Nov 2-5, 2007 ([[bib|publications/2007-petridis-webintelligence.bib]], [[pdf|publications/2007-petridis-webintelligence.pdf]])
* D. Kosmopoulos, ''S. Petridis'', I. Pratikakis, B. Gatos, S. J. Perantonis, V. Karkaletsis and G. Paliouras, "Knowledge Acquisition from Multimedia Content using an Evolution Framework," In Proceedings of the IFIP Conference on Artificial Intelligence Applications and Innovations (AIAI), IFIP International Federation for Information Processing Series,  n. 204, pp. 557-565, Springer Boston, 2006. ([[bib|publications/2006-petridis-aiai.bib]], [[pdf|publications/2006-petridis-aiai.pdf]])
* E. Charou, ''S. Petridis'', M. Stefouli, O. D. Mavrantza, and S. J. Perantonis: Innovative feature selection used in multispectral imagery, classification for water quality monitoring. In Proceedings of the 2004 ISPRS Congress, Istanbul, 2004. ([[bib|publications/2004-petridis-isprs.bib]], [[pdf|publications/2004-petridis-isprs.pdf]])
* B. Gatos, K. Ntzios,  I. Pratikakis, ''S. Petridis'', T. Konidaris, and S. J. Perantonis: A segmentation-free recognition technique to assist old greek handwritten manuscript ocr. In Proceedings of Document Analysis Systems, volume 3163 of Lectures Notes in Computer Science, pages 63–74, 2004. ([[bib|publications/2004-petridis-das.pdf]], [[pdf|publications/2004-petridis-das.pdf]])
* ''S. Petridis'' and S. J. Perantonis: Feature deforming for improved similarity based learning. In Proceedings of the 3rd Hellenic Conference on Artificial Intelligence, volume 3025 of Lecture Notes in Artificial Intelligence, pages 201–209, 2004. ([[bib|publications/2004-petridis-setn.bib]], [[pdf|publications/2004-petridis-setn.pdf]])
* ''S. Petridis'', E. Charou and S. J. Perantonis: Non-redundant feature selection of multi-band remotely sensed images for land cover classification. In Proceedings of the 2003 Tyrrhenian International Workshop on Remote Sensing, 2003. ([[bib|publications/2003-petridis-tiwrs.bib]], [[pdf|publications/2003-petridis-tiwrs.pdf]])
* G. Petasis, ''S. Petridis'', G. Paliouras, V. Karkaletsis, S.J. Perantonis, and C.D. Spyropoulos,   Symbolic and Neural Learning of Named - Entity Recognition and Classification Systems in Two Languages. In Advances in Computational intelligence and Learning: Methods and Applications H. Zimmermann, G. Tselentis, M. v. Someren, and G. Dounias, Eds. Intelligent Technologies Series. Kluwer B.V., Deventer, The Netherlands, 193-210, 2002 ([[bib|publications/2002-petridis-coil.bib]], [[pdf|publications/2002-petridis-coil.pdf]])
* S. J. Perantonis, N. Ampazis, V. Virvilis, and ''S. Petridis'': Open issues in feedforward neural network training. In LFTNC (Siena, Italy), 2001. ([[bib|publications/2001-petridis-lftnc.bib]], [[pdf|publications/2001-petridis-lftnc.pdf]])
* S. J. Perantonis, ''S. Petridis'', and V. Virvilis. Supervised principal component analysis using a smooth classifier paradigm. In Proceedings of the 15th International Conference on Pattern Recognition, volume 2, pages 109–112, 2000. ([[bib|publications/2000-petridis-icpr.bib]], [[pdf|publications/2000-petridis-icpr.pdf]])
* G. Petasis, ''S. Petridis'', G. Paliouras, and V. Karkaletsis: Symbolic and neural learning for named-entity recognition. In Proceedings of European Best Practice Workshops and Symposium on Computational Intelligence and Learning, 2000. ([[bib|publications/2000-petridis-coil.bib]], [[pdf|publications/2000-petridis-coil.pdf]])
* A. Agogino, J. Gosh, S. J. Perantonis, V. Virvilis, ''S. Petridis'', and P.J.G. Lisboa: The role of multiple, linear-projection based visualization techniques in rbf-based classification of high dimensional data. In Proceedings of the International Joint Conference on Neural Networks, volume 3, 2000. ([[bib|publications/2000-petridis-icnn.bib]], [[pdf|publications/2000-petridis-icnn.pdf]])
!!! Techincal Reports
S. Petridis, The [[D-graph|The D-graph structure]] structure, Techinal Report, NCR "Demokritos",  ([[bib|publications/2010-petridis-tr-dgraphs.bib]], [[pdf|publications/2010-petridis-tr-dgraphs.pdf]])
Here is a calendar of conferences related to my research interests. Entries refer to the article submission deadlines.

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I' m a collaborating researcher at  the National Center For Scientific Research Demokritos ([[NCSR "D"|http://www.demokritos.gr]]),  Institute of Informatics and Telecommunications ([[II&T|http://www.iit.demokritos.gr]]), 

Take a look at
* my research [[interests|Research Interests]],
* research [[projects|Research Projects]] I have  participated
* [[people|Research Associates]] I work with, and 
*  [[articles|Publications]] I have published.
* Theodoros Giannakopoulos  ([[link| http://cgi.di.uoa.gr/~tyiannak]])
* Stavros Perantonis ([[link|http://www.iit.demokritos.gr/~sper]])
* Georgios Paliouras  ([[link|http://www.iit.demokritos.gr/~paliourg]])
* Basilis Gatos ([[link|http://www.iit.demokritos.gr/~bgat]])
* Elias Zavitsanos ([[link|http://www.iit.demokritos.gr/~izavits]])
* Nikos Tsapatsoulis  ([[link|http://www.cs.ucy.ac.cy/~nicolast/]])
* Eleni Charou ([[link|http://www.iit.demokritos.gr/~exarou]])
* Vangelis Karkaletsis ([[link|http://www.iit.demokritos.gr/~vangelis]])
and the  Uber Admin ([[link|http://www.iit.demokritos.gr/~vatikiot]])
* Speech/Audio analysis
* Computer Vision
* Optimal Feature Transforms ([[phd|Studies]])
*[[Aminess]]
*[[BioASQ]]
*[[USEFIL]]
*[[CASAM]]
*[[BOEMIE]]
*[[TECHNOLANGUE]]
*[[MULTIMINE]]
*[[INTERWATPOL]]
*[[AUTONOMA]]
Take a look at the calendar of scheduled seminars at NCSR/II&T. More details can be found [[here|http://www.iit.demokritos.gr/index.php?option=com_content&view=article&id=125&Itemid=80〈=en]].


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home page
Sergios Petridis&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
http://www.iit.demokritos.gr/~petridis
* Member of the Hellenic Association of Artificial Intelligence ([[EETN|http://www.eetn.gr/english/main_frame_page_en.htm]])
!!!1999 – 2006
''~PhD'' in Artificial intelligence and Pattern recognition.
National and Kapodistrian University of Athens ([[UOA|http://www.di.uoa.gr/en/]]) - National Center for Scientific Research ([[“Demokritos''|http://www.iit.demokritos.gr]])
Supervisors: [[Pr. S. Theodoridis|http://cgi.di.uoa.gr/~stheodor/]], [[Pr. N. Kalouptsidis|http://cgi.di.uoa.gr/~kalou/]], [[Dr. S. J. Perantonis|http://iit.demokritos.gr/~sper]].

''Thesis'': """Classification-Optimal""" Transforms ([[in Greek|publications/2006-petridis-uoa.pdf]] / [[extended synopsis in English|publications/2006-petridis-uoa-en.pdf]]).
!!!1996 – 1997
''Master'' Degree in Artificial intelligence and Pattern Recognition  (DEA IARFA).
University Pierre et Marie Curie ([[Paris 6|http://www.lip6.fr]]),

''Thesis'': Speaker Language Identification using Hidden Markov Models

!!!''1991 – 1996''
''Degree'' in Electrical and Computer Engineering
National Technical University of Athens ([[NTUA|http://www.ece.ntua.gr/index.php?lang=en]])

''Thesis'': Finding key in music using fuzzy neural networks. 
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2005 - 2006
Integration entre les langages de la planification et de l'information en vue de l'`elaboration de cartes interactives d' espace ~Medocc-Meda et des systemes de transport correspondants (~Interreg-III-B)
!!!2007 – 2010
''Lecturer'', University of [[Peloponnese|http://www.uop.gr]],  [[Telecommunication Department|http://depts.uop.gr/departments/depart2/]]  .
:Here is the list of courses I lecture:
** Undergraduate Course on ''Speech Processing and Recognition''
** Undergraduate Course on ''Digital Image Processing''
!!!2006 – 2007
''Lecturer'', Educational Institute of Certified Public Accountants (IESOEL)  
:I participated in the post-graduate studies conducted by the Greek institute IESOEL as a lecturer teaching the module of Computer Applications in Accounting. This course module focuses on ''computational methods for accounting and financial analysis''.
!!! 2003 – 2004
''Teaching Assistance'', National and Kapodistrian University of Athens
:During my PhD, I was a member of the Digital Signal Processing Laboratory of the department of Informatics. The laboratory course objective was to enable students to learn ''applied signal processing through Matlab''.
!!!1998 – 1999
''Teaching Assistance'', Technical University of Montreuil in Paris ([[IUT|http://www.iut.univ-paris8.fr/]])
:Course:  ''Database Management Systems''.
!!!1997 – 1998
''Teaching Assistance'', University "Pierre et Marie Curie ([[Paris 6|http://www.upmc.fr/FR/info/00]]). 
:Course:  ''Introduction to Algorithms''

D-graph is a structure that enables connecting edges to edges.

Take a look at this technical report.
S. Petridis, The D-graph structure, Techinal Report, NCR "Demokritos",  ([[bib|publications/2010-petridis-tr-dgraphs.bib]], [[pdf|publications/2010-petridis-tr-dgraphs.pdf]])
USEFIL is a European ~FP7 co-funded project. Detailed information  can be found at the official [[site|http://www.usefil.eu]].

My research focuses on devising multimedia analysis algorithms (audio,video) for
* tracking humans behavior and detect anomalities
* monitoring physiological parameters of humans, based on multimedia cues
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  }
 
}}}
I have used [[Mocha|http://saq.unamesa.org/]] [[TiddlyWiki|http://www.tiddlywiki.com]] for the design and functionality of this web page.
//{{{
//This ensures that the footer sticks to the bottom of the screen when there are no tiddlers open. If that is not desirable, it can be deleted.
function setFooter() {
         if (document.getElementById && document.getElementById("contentFooter") ) {
            var windowHeight=findWindowHeight();
         if (windowHeight>0) {
            var contentHeight= document.getElementById('mainMenu').offsetHeight + document.getElementById("header").offsetHeight + document.getElementById("contentFooter").offsetHeight;
            var menu= document.getElementById('mainMenu');
            if (windowHeight-(contentHeight)>=0) {
               menu.style.position='relative';
               menu.style.marginBottom=(windowHeight-(contentHeight))+'px';
               }
            else {
                 menu.style.position='';
                 menu.style.marginBottom='';
                 }
            }
         }
}
window.onresize = function() {
  setFooter();
}

Story.prototype.refreshTiddler_footerhack=Story.prototype.refreshTiddler;
Story.prototype.refreshTiddler = function (title,template,force)
{    
var theTiddler = Story.prototype.refreshTiddler_footerhack.apply(this,arguments);
setFooter();
   return theTiddler;}

//}}}