# University of Cyprus

## Unmanned Aerial Vehicles – Innovation and Challenges

The IEEE and the Cyprus Computer Society present the state of art of drone technologies and applications.

3 October 2017
University of Cyprus
Building KOD 07, Room 10
Starts at 17.00
Free Food and Drinks

Presentations:

• Presentations on the state of art of drone technologies & applications
• Drone Piloting
• Prize draw for IEEE student members:
One-day piloting course worth €250, by DJI Cyprus

### Program

• 17:00-17:25: “UAV in Emergency Response: Research and Innovation Challenges”
Dr. Panayiotis Kolios, KIOS Research and Innovation Center of Excellence
• 17:25-18:15: “Demonstration of UAV automated functionalities”
Mr. Petros Petrides, KIOS Research and Innovation Center of Excellence
• 18:15-18:30: Coffee break
• 18:30-18:50: “Regulations on drone aviation in Cyprus”
Mr. Marios Louka, DJI Cyprus
• 18:50-19:10: “Monitoring Power Systems Lines using Drones”
Mr. Costas Stasopoulos, EAC, IEEE Region 8 Past-Director
• 19:10-19:15: “Contribution of Cyprus Computer Society (CCS) in Cyprus”
Mr. Costas Agrotis, CCS Chairman
• 19:15-19.30: “Introduction of IEEE: Its Vision and Role”
Mr. Nicos Michaelides, CYTA, IEEE Cyprus Section Chair
• 19:30: Drinks and snacks @ U-Pub – Prize draw for IEEE student members

## Really rough notes on compiling source code on Fedora 25 for STM32F767 Nucleo-144 (Nucleo-F767ZI)

#eclipse with support for C/C++
sudo dnf install -y eclipse-cdt;
#cross-compiler for arm
sudo dnf install -y arm-none-eabi-gcc arm-none-eabi-gdb arm-none-eabi-binutils arm-none-eabi-newlib arm-none-eabi-gcc-cs-c++;
#manually installing openocd from the repository as the version in the repositories does not support our board (STM32F767 Nucleo-144 (Nucleo-F767ZI))
git clone http://openocd.zylin.com/openocd;
cd openocd/;
./bootstrap;
./configure;
make;
sudo make install;

#install using from menu “Help” > “Install New Software…” > “Add…” > “Archive…”. Find “en.stsw-stm32095.zip” and press OK. Tick new repo and click next.

# st_nucleo_f7.cfg copy it with the rest of the configuration files e.g. /usr/local/share/openocd/scripts/board/
sudo cp st_nucleo_f7.cfg /usr/local/share/openocd/scripts/board/

Create a new st 7x project and add 2048 of memory

create a C/C++ run application run

create new openosd run to run the elf created by above run and add parameter

-f /usr/local/share/openocd/scripts/board/st_nucleo_f7.cfg

to config options in debugger tab

sudo usermod -a -G root george;
#if you get error on opening the usb device (really ugly hack)

Needed packages:

• sudo dnf install -y arm-none-eabi-gcc arm-none-eabi-gdb arm-none-eabi-binutils arm-none-eabi-newlib
• Do not install openocd from the repositories, clone the git server as it has a later version which supports our board.
git clone http://openocd.zylin.com/openocd
then build it

stm32f7x.cfg (compressed) (52 downloads) copy it where you have the rest of the target files
e.g. /usr/share/openocd/scripts/board/st_nucleo_f7.cfg

st_nucleo_f7.cfg (compressed) (59 downloads)   copy it with the rest of the configuration files
e.g. /usr/share/openocd/scripts/target/stm32f7x.cfg

The locations for the above files depend on your configuration

Extract it.

Navigate to a ready project like the GPIO_IOToggle in STM32Cube_FW_F7_V1.6.0/Projects/STM32F767ZI-Nucleo/Examples/GPIO/GPIO_IOToggle

Compile each .c file using the following command, but fix the paths !!! You also might need ton include the Inc directory of the project
e.g.
arm-none-eabi-gcc -Wall -mcpu=cortex-m7 -mlittle-endian -mthumb -ISTM32Cube_FW_F7_V1.6.0/Drivers/CMSIS/Device/ST/STM32F7xx/Include -ISTM32Cube_FW_F7_V1.6.0/Drivers/CMSIS/Include -ISTM32Cube_FW_F7_V1.6.0/Drivers/STM32F7xx_HAL_Driver/Inc -I. -ISTM32Cube_FW_F7_V1.6.0/Drivers/BSP/STM32F7xx_Nucleo_144 -DSTM32F767xx -Os -c system_stm32f7xx.c -o system_stm32f7xx.o

Merge all .o files into an .elf file

arm-none-eabi-gcc -mcpu=cortex-m7 -mlittle-endian -mthumb -DSTM32F767xx -TSTM32Cube_FW_F7_V1.6.0/Projects/STM32F767ZI-Nucleo/Templates/SW4STM32/STM32F767ZI_Nucleo_AXIM_FLASH/STM32F767ZITx_FLASH.ld -Wl,–gc-sections system_stm32f7xx.o main.o stm32f7xx_it.o -o main.elf

Convert the .elf file to a .hex

arm-none-eabi-objcopy -Oihex main.elf main.hex
Start openocd to attach to the board

sudo ../src/openocd -f /usr/share/openocd/scripts/board/st_nucleo_f7.cfg

Use telnet to control the board

telnet localhost 4444

Flash the board

reset halt
reset run

DONE

sudo dnf install eclipse-cdt-sdk;

sudo dnf install -y arm-none-eabi-gcc-cs-c++;

create new openosd run and add parameter

-f /usr/share/openocd/scripts/board/st_nucleo_f7.cfg

to config options in debugger tab

add   RCC_OscInitStruct.PLL.PLLR = 7; to _initialize_hardware.c

I hope I did not forget anything

Anyhow, this post will be updated soon

## 1. INTRODUCTION

Page 2

Machine learning algorithms must be trained using a large set of known data and then tested using another independent set before it is used on unknown data.

The result of running a machine learning algorithm can be expressed as a
function y(x) which takes a new x as input and that generates an output vector y, encoded in the same way as the target vectors. The precise form of the function y(x) is determined during the training phase, also known as the learning phase, on the basis of the training data. Once the model is trained it can then determine the identity of new elements, which are said to comprise a test set. The ability to categorize correctly new examples that differ from those used for training is known as generalization. In practical applications, the variability of the input vectors will be such that the training data can comprise only a tiny fraction of all possible input vectors, and so generalization is a central goal in pattern recognition.

The pre-processing stage is sometimes also called feature extraction. Note that new test data must be pre-processed using the same steps as the training data. The aim is to find useful features that are fast to compute, and yet that also preserve useful discriminatory information. Care must be taken during pre-processing because often information is discarded, and if this information is important to the solution of the problem then the overall accuracy of the system can suffer.

Page 3

Applications in which the training data comprises examples of the input vectors along with their corresponding target vectors are known as supervised learning problems. Cases such as the digit recognition example, in which the aim is to assign each input vector to one of a finite number of discrete categories, are called classification problems. If the desired output consists of one or more continuous variables, then the task is called regression. An example of a regression problem would be the prediction of the yield in a chemical manufacturing process in which the inputs consist of the concentrations of reactants, the temperature, and the pressure.

In other pattern recognition problems, the training data consists of a set of input
vectors x without any corresponding target values. The goal in such unsupervised learning problems may be to discover groups of similar examples within the data, where it is called clustering, or to determine the distribution of data within the input space, known as density estimation, or to project the data from a high-dimensional space down to two or three dimensions for the purpose of visualization.

Finally, the technique of reinforcement learning (Sutton and Barto, 1998) is concerned with the problem of finding suitable actions to take in a given situation in order to maximize a reward. Here the learning algorithm is not given examples of optimal outputs, in contrast to supervised learning, but must instead discover them by a process of trial and error. Typically there is a sequence of states and actions in which the learning algorithm is interacting with its environment. In many cases, the current action not only affects the immediate reward but also has an impact on the reward at all subsequent time steps.

The reward must then be attributed appropriately to all of the moves that led to it, even though some moves will have been good ones and others less so. This is an example of a credit assignment problem. A general feature of reinforcement learning is the trade-off between exploration, in which the system tries out new kinds of actions to see how effective they are, and exploitation, in which
the system makes use of actions that are known to yield a high reward.

### 1.1 Example: Polynomial Curve Fitting

Page 5

We fit the data using a polynomial function of the form:

$y(x, w) = w_{0}x^{0} + w_{1}x^{1} + w_{2}x^{2} + . . . + w_{M}x^{M} = \sum_{j =0}^{M}w_{j}x^{j}$

where M is the order of the polynomial.

The values of the coefficients will be determined by fitting the polynomial to the
training data. This can be done by minimizing an error function that measures the misfit between the function y(x, w), for any given value of w, and the training set data points.

Our error function: Sum of the squares of the errors between the predictions $y(x_{n} , w)$ for each data point $x_n$ and the corresponding target values $t_n$.

$E(w) = \frac{1}{2}\sum_{n=1}^{N}\{y(x_{n},w) - t_{n}\}^2$

Much higher order polynomial can cause Over-Fitting : the fitted curve oscillates wildly and gives a very poor representation of the function.

We can obtain some quantitative insight into the dependence of the generalization performance on M by considering a separate test set comprising 100 data points generated using exactly the same procedure used to generate the training set points but with new choices for the random noise values included in the target values.

Root Mean Square: $E_{RMS} = \sqrt{2E(w*)/N}$

The division by N allows us to compare different sizes of data sets on an equal footing, and the square root ensures that $E_{RMS}$ is measured on the same scale (and in the same units) as the target variable t.

## OTHER

### To study

Curve Fitting

Legend:

• TP = True Positive
• TN = True Negative
• FP = False Positive
• FN = False Negative
$correct\ rate (accuracy) = \frac{TP + TN}{TP + TN + FP + FN}$ $sensitivity = \frac{TP}{TP + FN}$ $specificity = \frac{TN}{TN + FP}$

In statistics, a receiver operating characteristic (ROC), or ROC curve, is a graphical plot that illustrates the performance of a binary classifier system as its discrimination threshold is varied. The curve is created by plotting the true positive rate (sensitivity) against the false positive rate (1  – specificity) at various threshold settings.

Assuming we have a system where changing its configuration we get the above results, we would pick the configuration that has the smallest Euclidean Distance from the perfect configuration. The perfect configuration can be found at point (0,1) where both Specificity and Sensitivity are both equal to one.

Non – Parametric

## 1st Workshop on Conformal Prediction and its Applications (CΟPA 2012)

==============================
1st Workshop on Conformal Prediction and its Applications (CΟPA 2012) to be held in conjunction with the 8th IFIP Conference on Artificial Intelligence Applications & Innovations (AIAI 2012)
Halkidiki, Greece, September 27-30, 2012
http://delab.csd.auth.gr/aiai2012/
===========================

Workshop Theme:
================

Quantifying the uncertainty of the predictions produced by classification and regression techniques is an important problem in the field of Machine Learning. Conformal Prediction is a recently developed framework for complementing the predictions of Machine Learning algorithms with reliable measures of confidence. The methods developed based on this framework produce well-calibrated confidence measures for individual examples without assuming anything more than that the data are generated independently by the same probability distribution (i.i.d.). Since its development the framework has been combined  with many popular techniques, such as Support Vector Machines, k-Nearest Neighbours, Neural Networks, Ridge Regression etc., and has been successfully applied to many challenging real world problems, such as the early detection of ovarian cancer, the classification of leukaemia subtypes, the diagnosis of acute abdominal pain, the assessment of stroke risk, the recognition of hypoxia in electroencephalograms (EEGs), the prediction of plant promoters, the prediction of network traffic demand, the estimation of effort for software projects and the backcalculation of non-linear pavement layer moduli. The framework has also been extended to additional problem settings such as  semi-supervised learning, anomaly detection, feature selection, outlier detection, change detection in streams and active learning. The aim of this workshop is to serve as a forum for the presentation of new and ongoing work and the exchange of ideas between  researchers on any aspect of Conformal Prediction and its applications.

The workshop welcomes submissions introducing further developments and extensions of the Conformal Prediction framework and describing its application to interesting problems of any field.

Submission
==========
Authors are invited to submit original, English-language research contributions or experience reports. Papers should be no longer than 10 pages formatted according to the well-known LNCS Springer style. Papers should be submitted either in a doc or in a pdf form to: [email protected]

Publication
===========
Submitted papers will be refereed for quality, correctness, originality, and relevance. Notification and reviews will be communicated via email. Accepted papers will be presented at the workshop and published in the Proceedings of the main event (by Springer). They will also be considered for potential publication in the Special Issues of the
Conference.

Important Dates
===============
Full paper submission due: April 29, 2012
Notification of acceptance: May 26, 2012
Camera-ready paper submission: June 4, 2012

Honorary Chairs
===============
Vladimir Vapnik NEC, USA & Royal Holloway, University of London, UK
Alexei Chervonenkis Russian Academy of Sciences, Russia & Royal Holloway, University  of London, UK

Program Chairs
==============
Frederick University, Cyprus
Email: [email protected]

Alex Gammerman
Royal Holloway, University of London, UK
Email: [email protected]

Royal Holloway, University of London, UK
Email: [email protected]

Program Committee
=================
Vineeth Balasubramanian, Arizona State University, USA
Anthony Bellotti, Imperial College London, UK
David R. Hardoon, SAS Singapore
Mohamed Hebiri, Universite de Marne-la-Vallee, France
Shen-Shyang Ho, Nanyang Technological University, Singapore
Zakria Hussain, University College London, UK
Yuri Kalnishkan, Royal Holloway, University of London, UK
Matjaz Kukar, University of Ljubljana, Slovenia
Antonis Lambrou, Royal Holloway, University of London, UK
Rikard Laxhammar, University of Skovde, Sweden
Yang Li, Chinese Academy of Sciences, China
Zhiyuan Luo, Royal Holloway, University of London, UK
Andrea Murari, Consorzio RFX, Italy
Ilia Nouretdinov, Royal Holloway, University of London, UK
Savvas Pericleous, Frederick University, Cyprus
David Surkov, Egham Capital, UK
Jesus Vega, Asociacion EURATOM/CIEMAT para Fusion, Spain
Fan Yang, Xiamen University, China

## 2nd Workshop on Artificial Intelligence Applications in Biomedicine (AIAB 2012)

==============================================

2nd Workshop on Artificial Intelligence Applications in Biomedicine (AIAB 2012) to be held in conjunction with the 8th IFIP Conference on Artificial Intelligence Applications & Innovations (AIAI 2012)
Halkidiki, Greece, September 27-30, 2012
http://delab.csd.auth.gr/aiai2012/
==============================================

Workshop Theme:
===============

Recent technological advances in computer science and biomedicine facilitated the development of complex biomedical systems including sophisticated medical imaging, signal processing systems and computer based decision support tools, assisting diagnosis for better delivery of health care services. Meanwhile, applications of Machine Learning, Neural Computing, Expert Systems, Fuzzy Logic and Evolutionary Computing in biomedicine are continuously emerging. Therefore AI tools and techniques are a vital part  of modern computer based systems that handle medical data. The aim of this workshop is to serve as a forum for the presentation of new and ongoing work and the exchange of ideas between researchers interested in the application of AI in any aspect of biomedicine and electronic healthcare.

The subject areas of the workshop include, but are not limited to, the following:

* Clinical decision support systems
* Medical imaging
* Medical signal processing
* Medical knowledge engineering
* Knowledge-based and agent-based systems
* Medical text analysis
* Computational intelligence in bio- and clinical medicine
* Data mining on medical data and records
* Intelligent medical information systems
* Clinical expert systems
* Modelling and simulation of medical processes
* Drug discovery
* Intelligent analysis of genomic and proteomic data
* Personalised medicine
* Intelligent devices and instruments
* Automated reasoning and metareasoning in medicine
* AI in medical education

Submission
==========
Authors are invited to submit original, English-language research contributions or experience reports. Papers should be no longer than 10 pages formatted according to the well-known LNCS Springer style. Papers should be submitted either in a doc or in a pdf form to: [email protected]

Publication
===========
Submitted papers will be refereed for quality, correctness, originality, and relevance. Notification and reviews will be communicated via email. Accepted papers will be presented at the workshop and published in the Proceedings of the main event (by Springer). They will also be considered for potential publication in the Special Issues of the Conference.

Important Dates:
===============
Full paper submission due: April 29, 2012
Notification of acceptance: May 26, 2012
Camera-ready paper submission: June 4, 2012

Workshop co-chairs:
==================

Frederick University, Cyprus
Email: [email protected]

Efthyvoulos Kyriacou
Frederick University, Cyprus
Email: [email protected]

Ilias Maglogiannis
University of Central Greece
Email: [email protected]

George Anastassopoulos
Democritus University of Thrace
Email: [email protected]

Program Committee
==================
Ioannis Anagnostopoulos, University of Central Greece, Greece
Panagiotis Bamidis, Aristotle University of Thessaloniki, Greece
Nikolaos Bourbakis, Wright State University, USA
Aristotle Chatziioannou, National Hellenic Research Foundation, Greece
Charalampos Doukas, University of Aegean, Greece
Alex Gammerman, Royal Holloway, University of London, UK
Ioannis K. Hatzilygeroudis, University of Patras, Greece
Vangelis Karkaletsis, NCSR Demokritos, Greece
Dimitrios Kosmopoulos, NCSR Demokritos, Greece
Antonis Lambrou, Royal Holloway, University of London, UK
Christos Loizou, Intercollege, Cyprus
Dimitris Lymberopoulos, University of Patras, Greece
Fillia Makedon, University of Texas at Arlington, USA
Konstantina Nikita, National Technical University of Athens, Greece
Costas Pappas, Aristotle University of Thessaloniki, Greece
Constantinos Pattichis, University of Cyprus, Cyprus
Vassilis Plagianakos, University of Central Greece, Greece
Christos N. Schizas, University of Cyprus, Cyprus
Ioannis Seimenis, Democritus University of Thrace, Greece
Vladimir Vovk, Royal Holloway, University of London, UK

## FP7 STREP Eurocloud: Research position at the University of Cyprus

FP7 STREP Research Program: Eurocloud

Research position opening at the University of Cyprus

Applications are invited for research positions (Special Scientist) in the Xi-Computer Architecture Group of the Department of Computer Science at  the University of Cyprus (UCY) (www.cs.ucy.ac.cy/carch/xi/). The successful applicant will  join the EU Framework 7 project Eurocloud (http://www.eurocloudserver.com/).

Eurocloud is a 3-year (Jan 2010 – Dec 2012) STREP project that aims to deliver a next generation energy-conscious 3D Server-on-Chip for Green Cloud Services. The project is lead by ARM and the consortium includes Nokia, IMEC, EPFL and UCY. University of Cyprus is leading the Reliability and Fault Tolerance work-package and is contributing to the other work-packages: Physical Prototype, Workload Characterization, 3D Architecture Design and Power Management, and On-chip Hierarchies and Interconnect.

We are seeking one motivated researcher with BSc in Computer Science, Computer Engineering or Electrical Engineering. Graduate degree will be considered an advantage. Applicants should have extensive knowledge in one of the following areas: Computer Architecture and Fault Tolerance. Previous related research experience will be considered an advantage. The post can be combined with graduate study at UCY.

The posts are until the end of the 2012 (extension possibility can be discussed). The salary will depend on qualifications.

Informal enquiries may be made by email to [email protected].

The applicants should submit:

1. a detailed CV,
2. contact info for at least two recommendations, and
3. one page research statement

in electronic format (PDF) by email with subject “EUROCLOUD Special Scientist @ UCY” to [email protected] Applications will be evaluated on a rolling basis starting March 12th until the positions are filled.

Please note that the positions are open to highly motivated 3rd and 4th year CS undergraduate students with exceptional academic record.

## Ημερίδα Πληροφορικής – Εισαγωγή στο Προγραμματισμό με Linux

Καλησπέρα,

Σαν Όμιλος Πληροφορικής διοργανώσαμε μια σειρά συναντήσεων πάνω σε θέματα που ενδιαφέρουν του φοιτητές πληροφορικής  και φοιτητές που τώρα ξεκινούν να προγραμματίζουν.
Τις ημερομηνίες 2 Μαρτίου, 16 Μαρτίου, 23 Μαρτίου και 6 Απριλίου στην αίθουσα του Ομίλου στο κτίριο Κοινωνικών Δραστηριοτήτων θα βρίσκονται εκεί για κάποιες ώρες άτομα του ομίλου και φοιτητές του τμήματος Πληροφορικής για να λύσουν απορίες, να ενημερώσουν τους φοιτητές για διάφορα θέματα που τους ενδιαφέρουν και να παρουσιάσουν κάποια θέματα για τα οποία έχουν προετοιμαστεί.
Αντί για επίσημες παρουσιάσεις θα έχουμε χαλαρές συζητήσεις όπως και στη βραδιά των Ομίλων. Η διάρκεια κάθε συνάντησης θα είναι 6 – 7 ώρες (10π.μ – 17μ.μ), εκτός από την αυριανή που λόγο άλλων υποχρεώσεων θα είναι από τις 10π.μ μέχρι τις 1:30μμ, όπου θα υπάρχει τουλάχιστον ένα άτομο διαθέσιμο για συζήτηση. Ο κόσμος θα μπορεί να πηγαινοέρχεται όποτε θέλει αφού δεν θα έχουμε αυστηρό πρόγραμμα παρουσιάσεων. Στην αίθουσα θα υπάρχει Internet.
Οι εθελοντές που θα βοηθήσουν στις ημερίδες συμπεριλαμβανομένου του εαυτού μου είναι οι ακόλουθοι:

• Αντρέας Χατζηδημητρίου (Μεταπτυχιακό 2ο)
• Κατερίνα Τορτούτη (Μεταπτυχιακό 2ο)
• Λευτέρης Ελευθεριάδης (Ειδικός Επιστήμονας)
• Μαρία Ευθυμίου (Μεταπτυχιακό 2ο)
• Φίλιππος Χατζημιχαήλ (Μεταπτυχιακό 1ο)
• Χάρης Μαραγκός (Προπτυχιακό 4ο)

Όλοι οι κάτοχοι πτυχίου είναι απόφοιτοι του τμήματος Πληροφορικής.

Τα θέματα που έχουμε κάνει προετοιμασία είναι τα ακόλουθα:

• Proper Java Programming & Design Patterns
• Συμβουλές για τις ροές μαθημάτων / μαθήματα
• Visualizing Linux σε Windows με το VirtualBox
• Λειτουργικά Ubuntu Linux / Linux Mint
• IDE CodeBlocks / Eclipse / NetBeans / Visual Studio
• Compilers GNU GCC / GNU GPP / GNU JavaC
• Τμήμα UCy CS
• Οργανισμοί ACM / CCS / IEEE

Παράλληλα θα προσφέρουμε mini-workshops για την χρήση των πιο πάνω προγραμμάτων και θα δώσουμε έτοιμο image Linux με τα βασικά προγράμματα για προγραμματισμό εγκατεστημένα.

Αν θέλετε περισσότερες πληροφορίες για τα θέματα ή να εισηγηθείτε κάποιο, παρακαλώ επικοινωνήστε μαζί μου.

Με εκτίμηση,
Γιώργος Μιχαήλ

## CFP: 2nd International Workshop on Computational Intelligence in Software Engineering (CISE 2012)

CFP: 2nd International Workshop on Computational Intelligence in Software Engineering (CISE 2012)

(Apologies if you have received multiple CFPs)
Call For Papers
===============================================================
CISE 2012: 2nd International Workshop on
Computational Intelligence in Software Engineering
September 27-30, 2012
Khalkidhiki (Halkidiki), Greece
===============================================================
http://delab.csd.auth.gr/aiai2012/workshops_computational_intelligence_software_engineering.html

The CISE workshop focuses on theoretical and applied research related to the utilization of Computational Intelligence techniques in Software Engineering, targeting the provision of alternative, interdisciplinary approaches for tackling problems found in Software Engineering.

The aim of the workshop is to host research papers that present practical solutions to emerging Software Engineering issues by applying Computational Intelligence methods. The workshop is associated with research and development advances in many fields of Software Engineering and particularly the study, analysis, design, modelling, implementation and application of Computational Intelligence techniques that tackle significant Software Engineering problems. The topics of interest call, especially, for papers with theoretical and practical importance, while research papers reporting emerging and innovative ideas are also highly desirable.

In particular the topics of the workshop include but are not limited to the following:

Clustering and Classification applied to Requirements Engineering
Decision Support Software Architectures
Machine Learning Software Methodologies
Data Mining Software Design
Text Mining & Retrieval Software Performance
Fuzzy Logic & Systems Software Quality Modelling & Assessment
Probabilistic Reasoning Software Reliability Modelling & Forecasting
Model Learning Software Maintenance
Recommender Systems Software Testing, Verification & Validation
Expert Systems Software Metrics
Artificial Neural Networks Software Reuse
Evolutionary Algorithms Software Project Management
Ranking Algorithms Object-Oriented Development
Cognitive Processes Open Source Software
Evolutionary Computing Agile Software Development
Swarm Intelligence Software Repository Management
Artificial Immune Systems Mobile Software Development
Dempster-Shafer Theory Software Risk Analysis & Modelling
Chaos Theory Configuration Management
Multi-valued Logic Component-Based Software Development
Ensemble Techniques Cloud Computing
Hybrid Approaches Web Engineering
Search-Based Software Engineering

Submission:
Papers should not exceed 10 pages in length and must be formatted according to the LNCS Springer publication style found here (http://www.springer.com/computer/lncs?SGWID=0-164-6-793341-0). In addition, papers should be submitted either in Microsoft Word or PDF format via email to one of the Workshop Chairs.
Each paper will reviewed by at least 2 academic referees. Papers reporting industrial applications will be reviewed by at least 1 industrial referee.

Important Dates:
Paper submission: April 27, 2012
Notification of acceptance/rejection: May 26, 2012
Early registration: June 04, 2012
Workshop dates: September 27-30, 2012

Publication:
Accepted papers will be presented at the workshop (approx. 20 minutes allocated time) and will be published in the Proceedings of the main event.
The papers of the workshop will be also considered for potential selection for publication in special issues of the journals “Artificial Intelligence Review”, “Engineering Intelligent Systems” and “Fuzzy Sets and Systems”.

Note that at least one author of each accepted paper is required to register and attend the workshop to present the paper.

Workshop Chairs:
Andreas S. Andreou
Department of Computer Engineering and Informatics,
Cyprus University of Technology, Cyprus
email: [email protected]
URL: http://www.cut.ac.cy/staff/andreas.andreou/

&

Efi Papatheocharous
Department of Computer Science
University of Cyprus, Cyprus
email: [email protected]