research

A Quantitative Evaluation of the Relative Status of Journal and Conference Publications in Computer Science

This page has some summaries of the raw data that was used/generated for our citation analysis research, which led to the following publications

This page was (mostly) automatically generated from our Citation Analysis Scripts (FYI: the original automatically generated page is here). Our study gathered data from a total of 8764 papers (15 conferences with 3258 papers and 17 journals with 5506 articles).

Please get in contact with the authors if you want more information on this work.

Correlation between ISI Impact Factor and Google Scholar Impact Factor for Selected Journals

There is a strong correlation between citation counts computed from Google Scholar data and comparable data from the ISI Web of Knowledge index, validating the use of our new Google Scholar Impact Factor as an alternative citation-based evaluation metric applicable to both journals and conferences. The Pearson correlation coefficient between the two is 0.86. The table below shows the raw values.

ISI and Google Scholar Impact Factors for Selected Journals
Category Journal Name ISI Impact Factor GS Impact Factor
A* Journal of the ACM 2.9 35
B Pattern Analysis Applications 0.4 8
A* Transactions on Knowledge and Data Engineering 2.1 15
A Information Retrieal 1.7 9.5
A* Artificial Intelligence 2.3 22
B AI EDAM 0.4 5
A Computational Intelligence 1.4 9
A* IEEE Trans. on Pattern Analysis and Machine Intelligence 4.3 34
B AI Communications 0.5 4
A AI in Medicine 1.6 12
B International Journal of Pattern Recognition 0.5 4
A Decision Support Systems 1.2 17.5
A* Machine Learning 2.6 32.5
A* International Journal of Computer Vision 6 27
A Data and Knowledge Engineering 1.4 8

Scatterplot of Available Rejection Rates versus Google Scholar Impact Factors

Below is a scatter plot of GS impact factor against conference rejection rates for 23 conferences across the 15 conference series we evaluated; note the data points reflect a subset of the full set of conferences, namely those for which we were able to obtain. Reliable rejection rates could not be obtained for all of the conferences for which we had data. More details on the rejection rates we used are below. While our study indicate some correlation between GS impact factor and rejection rates, the related Pearson correlation score of 0.53 was not very convincing and reflects considerable variation when it comes to the relationship between conference rejection rates and a conference paper’s ability to attract citations. There are conferences with similar rejection rates but very different Google Scholar impact factors. There are also conferences with very different rejection rates that still manage to achieve similar GS impact factors. For instance, AAAI achieves a Google Scholar impact factor of 20, with a rejection rate of 65%–75%. Its European counterpart, ECAI the European Conference on Artificial Intelligence, is just as selective but achieves a median GS impact factor of only 7 (see the section on regional bias below).

Another example of a lack of correlation between paper acceptance rate and citation count is the 2002 European Conference on Case-Based Reasoning (ECCBR), which had a rejection rate of approximately 0.33 33% and Google Scholar impact factor of 7, achieving a citation rate better than the European Conference on Machine Learning?? 2000 and ECAI 2002, which had twice its rejection rate (see the Section below on open-world versus closed-world bias).

To further test the strength of the correlation between GS impact factor and rejection rate we examined the available data for three conference series that took place between 2000-2003; UAI, ICML, and ECML were the only conferences that occurred and had published rejection rates available in every year of the study. Table 5 outlines the GS impact factors and rejection rates for each year in the study. What is interesting here is there is no significant correlation between rejection rate and GS impact factor. The Pearson score for UAI is was?? only 0.27, for ICML 0.24, and for ECML 0.42. This suggests that, at least for these conferences, the yearly changes in rejection rates had little bearing on expected citation count.
These results highlight the assumption?? the fact?? that any assumed relationship between conference rejection rates and a conference’s ability to attract citations is at best weak, so other factors play a more critical role when it comes to influencing future citations.

Scatterplot of Available Rejection Rates versus Google Scholar Impact Factors

Scatter Plot Values
Conference Rejection Rate Google Scholar Impact Factor
UAI 2000 0.64 17.00
UAI 2001 0.60 11.00
UAI 2002 0.66 16.00
UAI 2003 0.66 10.00
ECAI 2002 0.72 6.00
NIPS 2001 0.70 13.00
ECAI 2000 0.69 9.00
NIPS 2003 0.72 9.50
AAAI 2000 0.67 24.50
AAAI 2002 0.74 14.00
ECCV 2002 0.62 16.00
ICML 2001 0.68 16.00
ICML 2000 0.56 15.00
ICML 2003 0.68 14.00
ICML 2002 0.67 11.00
IJCAI 2001 0.75 20.00
IJCAI 2003 0.79 15.00
ECML 2001 0.62 10.50
ECML 2000 0.57 6.00
NIPS 2002 0.69 13.00
ECCBR 2002 0.30 7.00
GECCO 2003 0.31 4.00
ICCBR 2003 0.45 4.00

International versus Local Venues

We found evidence of strong regional bias between similar conferences, with international (mainly U.S.-centric) conferences attracting much higher citation counts that their similarly selective though less-well-cited European counterparts. Both the AAAI and ECAI conferences target the same research area and attract submissions from a similar community of researchers in a way that is equally selective. Yet the U.S.-centric AAAI enjoys an expected citation count (computed from the product of the median citation count and the rejection rate of the conference) more than twice that of ECAI.

Google Scholar Rates for the AAAI Conference on Artificial Intelligence versus the European Conference on Artificial Intelligence between 2000-2003
Year AAAI ECAI
2000 24.5 9
2001 None None
2002 14 6
2003 None None

This apparent regional bias is also evident in another pair of related conferences: the International Conference on Machine Learning (ICML) and ECML. Once again, a more U.S.-centric ICML conference series attracts far more citations—twice as many—than a similarly selective Euro-centric ECML conference series. There are several possible explanations: One is that non-European researchers are likely to miss publications at European venues (such as ECAI and ECML), so papers at these conferences pick up references only from European researchers. Another is that a pre-selection process allows researchers to keep their best work for the international conference.

Google Scholar Rates for the International Conference on Machine Learning versus the European Conference on Machine Learning between 2000-2003
Year ICML ECML
2000 15 6
2001 16 10.5
2002 11 7
2003 14 5

There does not appear to be a case for bias between the International and European Conferences on Case Based Reasoning, although these conferences are held on alternative years, and in ICCBR was held in Norway in 2003. This table is here only because we have the data to show it.

Google Scholar Rates for the International Conference on Case Based Reasoning versus the European Conference on Case Based Reasoning between 2000-2003
Year ICCBR ECCBR
2000 None 8
2001 9 None
2002 None 7
2003 4 None

Open versus Closed World Venues

The table below shows a breakdown of the GS Impact Factor count of ECCBR (formally known as EWCBR) versus ECAI. The European Conference on Case Based Reasoning (CBR) ranked almost as highly in 2000 and 2002 as the European Conference on AI conference, which has a much higher rejection rate. This suggests that there is some merit in targeting one's own community and publishing in niche conferences rather than the larger conferences with a more general audience. However, there is not enough evidence here to draw strong conclusions.

Google Scholar Rates for the European Conference on Case Based Reasoning versus the European Conference on Artificial Intelligence between 2000-2003
Year ECCBR ECAI
2000 8 9
2001 None None
2002 7 6
2003 None None

Google Scholar Impact Factors

This table shows a breakdown of the Google Scholar Impact factor that were calculated for each venue in each available year.

Google Scholar Impact Factors for each available year
Venue 2000 2001 2002 2003 Google Scholar Impact Factor
TKDE 23 14 15 14 15
PE 9 11 9 5 8.5
PAMI 43 32 36 26 34
PAA 11 8 9 7 8
ML 35.5 32 36 29 32.5
JACM 32.5 59 51.5 24.5 35
IR 15 8 6.5 5 9.5
INFFUS 10 10.5 10 6.5 9
IJPRAI 4 5.5 5 3 4
IJCV 36.5 13.5 27 21 27
DSS 17 18 18 17 17.5
DKE 10 10.5 8 6 8
CI 7.5 8 17 9
ARTMED 15 9.5 10 12.5 12
AIEDAM 5 4 6 3 5
AICOM 1.5 4 7.5 2.5 4
AI 27 22 22.5 17 22
UAI 17 11 16 10 13
TREC 10 10 6 6 8
NIPS 14 13 13 9.5 12
IJCAI 20 15 17
ICML 15 16 11 14 14
ICCBR 9 4 6
ICANN 3 3 2 3
GECCO 7 6 4 5
ECCBR 8 7 8
ECML 6 10.5 7 5 7
ECCV 11.5 16 13.5
ECAI 9 6 7
COLING 10 8 9
AH 19.5 16 17
AAAI 24.5 14 20

Table of Available Acceptance Rates

These are the available Acceptance rates and their sources. These acceptance rates were used to generate the scatterplot above. The rejection rate is calculated as 100% - acceptance rate. If you have further acceptance rates please send them to us and we will add new points to our Scatterplot.

Table of available acceptance rates and the source of each value
Conference Acceptance Rate Number Of Submissions Number of Accepted Papers Notes Source
UAI 2000 0.36 84.0 30 The Computational Intelligence Repository's Conference Acceptance Ratio Statistics
UAI 2001 0.4 84.0 0 no figures for the number of accepted papers quoted The Computational Intelligence Repository's Conference Acceptance Ratio Statistics
UAI 2002 0.34 192.0 66 The Computational Intelligence Repository's Conference Acceptance Ratio Statistics
UAI 2003 0.336 229.0 77 25 papers accepted for oral presentation (10.9% acceptance rate) & 52 accepted for poster presentation The Computational Intelligence Repository's Conference Acceptance Ratio Statistics
ECAI 2002 0.277 505.0 140 from Pádraig Cunningham
NIPS 2001 0.302 650.0 196 The Computational Intelligence Repository's Conference Acceptance Ratio Statistics
ECAI 2000 0.314 443.0 139 from Pádraig Cunningham
NIPS 2003 0.276 717.0 198 The Computational Intelligence Repository's Conference Acceptance Ratio Statistics
AAAI 2000 0.332 431.0 143 AAAI Website
AAAI 2002 0.258 469.0 121 AAAI Website
ECCV 2002 0.379 600.0 226 45 papers accepted for oral presentation (7.5% acceptance rate) & 181 accepted for poster presentation (30.2% poster acceptance rate) The Computational Intelligence Repository's Conference Acceptance Ratio Statistics
ICML 2001 0.321 249.0 80 The Computational Intelligence Repository's Conference Acceptance Ratio Statistics
ICML 2000 0.443 349.0 151 The Computational Intelligence Repository's Conference Acceptance Ratio Statistics
ICML 2003 0.321 371.0 119 The Computational Intelligence Repository's Conference Acceptance Ratio Statistics
ICML 2002 0.33 261.0 86 The Computational Intelligence Repository's Conference Acceptance Ratio Statistics
IJCAI 2001 0.25 796.0 197 The Computational Intelligence Repository's Conference Acceptance Ratio Statistics
IJCAI 2003 0.207 913.0 189 189(regular= 20.7%) **63(poster)** The Computational Intelligence Repository's Conference Acceptance Ratio Statistics
ECML 2001 0.375 240.0 50 The acceptance rate quoted here is calculated on pkdd/ecml where 90 papers were submitted. However our database only contains 50 papers. This acceptance rate is therefore an approximation (90 from 240) from the combined figures for ECML/PKDD from Pádraig Cunningham
ECML 2000 0.43 100.0 43 43 papers accepted (20 + 23) from 100 from Pádraig Cunningham
NIPS 2002 0.311 710.0 221 The Computational Intelligence Repository's Conference Acceptance Ratio Statistics
ECCBR 2002 0.703 64.0 45 64 submissions (18+5+14+8) accepted from a mail from Susan Craw to Pádraig Cunningham
GECCO 2003 0.686 417.0 286 194 papers accepted for oral presentation (46.5% acceptance rate) & 92 accepted for poster presentation The Computational Intelligence Repository's Conference Acceptance Ratio Statistics
ICCBR 2003 0.554 92.0 51 51(19+32) from 92 from Pádraig Cunningham

Paper conference/type Breakdown

This table shows a breakdown of how many papers of each type are in the database for each conference. If you have more up-to-date values for any of these conferences we will update the table. We used only full papers in our calculations, when that information was available.

Breakdown of the numbers of different types of paper for each conference, where available
Conference Type Number Of Papers Google Scholar Impact Factor
AAAI 2000 Application Paper 18 9
AAAI 2000 DC Paper 10 0
AAAI 2000 Demo Paper 12 2.5
AAAI 2000 Invited Paper 8 4
AAAI 2000 Robot Competition and Exibition Paper 1 None
AAAI 2000 Student Abstract Paper 36 0
AAAI 2000 Technical Paper 143 24.5
AAAI 2002 Application Paper 18 10.5
AAAI 2002 DC Paper 13 0
AAAI 2002 Demo Paper 10 0
AAAI 2002 Invited Paper 2 0
AAAI 2002 Student Abstract Paper 17 4
AAAI 2002 Technical Paper 120 14
AH 2000 DC Paper 4 3.5
AH 2000 Full Paper 22 19.5
AH 2000 Invited Paper 1 3
AH 2000 Short Paper 31 5
AH 2002 Full Paper 33 16
AH 2002 Invited Paper 3 33
AH 2002 Poster Paper 35 3
AH 2002 Short Paper 23 3
COLING 2000 Not Specified 174 10
COLING 2002 Not Specified 198 8
ECAI 2000 Not Specified 141 9
ECAI 2002 Not Specified 140 6
ECCV 2000 Not Specified 116 11.5
ECCV 2002 Not Specified 226 16
ECML 2000 Full Paper 43 6
ECML 2000 Invited Paper 2 18
ECML 2001 Full Paper 50 10.5
ECML 2001 Invited Paper 5 2
ECML 2002 Full Paper 41 7
ECML 2002 Invited Paper 4 19
ECML 2003 Full Paper 40 5
ECML 2003 Invited Paper 4 1
ECCBR 2000 Application Paper 16 6.5
ECCBR 2000 Invited Paper 2 4
ECCBR 2000 Research Paper 26 11.5
ECCBR 2002 Application Paper 14 4.5
ECCBR 2002 Invited Paper 2 8.5
ECCBR 2002 Research Paper 31 8
GECCO 2000 Full Paper 119 9
GECCO 2000 Poster Paper 63 3
GECCO 2002 Not Specified 230 6
GECCO 2003 Full Paper 200 5
GECCO 2003 Poster Paper 85 2
ICANN 2001 Invited Paper 3 6.5
ICANN 2001 Not Specified 171 3
ICANN 2002 Not Specified 221 3
ICANN 2003 Not Specified 140 2
ICCBR 2001 Application Paper 14 4.5
ICCBR 2001 Invited Paper 3 14
ICCBR 2001 Research Paper 36 13.5
ICCBR 2003 Full Paper 51 4
ICCBR 2003 Invited Paper 3 0.5
ICML 2000 Not Specified 150 15
ICML 2001 Not Specified 80 16
ICML 2002 Not Specified 87 11
ICML 2003 Not Specified 116 14
IJCAI 2001 Invited Paper 3 5
IJCAI 2001 Not Specified 196 20
IJCAI 2003 Computers and Thought Award Paper 1 29
IJCAI 2003 Full Paper 189 15
IJCAI 2003 Intelligent Systems Demonstrations 9 0
IJCAI 2003 Invited Speakers 10 16.5
IJCAI 2003 Poster Paper 87 3
NIPS 2000 Not Specified 153 14
NIPS 2001 Not Specified 196 13
NIPS 2002 Not Specified 207 13
NIPS 2003 Not Specified 198 9.5
TREC 2000 Not Specified 74 10
TREC 2001 Not Specified 84 10
TREC 2002 Not Specified 100 6
TREC 2003 Not Specified 100 6
UAI 2000 Not Specified 75 17
UAI 2001 Not Specified 71 11
UAI 2002 Not Specified 69 16
UAI 2003 Not Specified 77 10

Acknowledgements

This work has been was supported by Science Foundation Ireland through grants 07/CE/I1147, 04/RPI/1544, 03/CE2/I303 1, and 05/IN.1/I24.

We gathered and generated data using Python scripts, taking the details of accepted papers from DBLP and citation figures from Google Scholar. Matlab was used to generate the scatterplots.

Learn to Play like Minnesota Fats - Augmented Reality in the Pool Hall - Deirdre Dalton

Deirdre Dalton prototyped a pool trainer as her ODCSSS project in summer of 2009 under the supervision of Lorcan Coyle. The aim of the project was to analyse the game of pool with the view to implementing some simple instructions to help tutor or teach a player.

A camera and sensor were used to take in information from the pool table. The camera was mounted directly above the pool table to capture ball colour and position. A SHIMMER, (Sensing Health with Intelligence, Modularity, Mobility, and Experimental Reusability) developed by Intel, was strapped to the base of the cue. Each SHIMMER contains a MicroSD - card slot, a rechargeable lithium-polymer battery, a 3-Axis accelerometer (1.5 6G) and a 3-Axis gyroscope (500 o/s). The accelerometer was of particular interest to this project as it was designed to react to very small changes in movement. It relayed the movements of the cue and was used to identify the strength of each shot.

The Shimmer is attached to the cue handle

A shot appeared as a large spike in the accelerometer data. The magnitude of this spike was calculated, and a strong correlation between the distance the ball travelled following this shot and the acceleration was determined. After much experimenting, a general equation was extracted which can determine how far the ball will travel, the moment it is struck by the cue.

This graph shows the correlation between peak-to-trough acceleration and distance that the ball travelled

Using this results it is possible to identify if a player is striking the ball with enough force to travel a certain distance, e.g., to pot a ball or roll up to a cushion for a safety shot. The work presented here is a strong starting point for future experimentation and analysis with a view to fully instrumenting a pool hall and providing teaching aids to a player to improve his/her game.

Further Information

There are more images from ODCSSS 2009 on Flickr. If you are interested in following up on this work or need further information contact Lorcan.

Learn to Play like Minnesota Fats - Augmented Reality in the Pool Hall - Deirdre Dalton

Deirdre Dalton prototyped a pool trainer as her ODCSSS project in summer of 2009 under the supervision of Lorcan Coyle. The aim of the project was to analyse the game of pool with the view to implementing some simple instructions to help tutor or teach a player.

A camera and sensor were used to take in information from the pool table. The camera was mounted directly above the pool table to capture ball colour and position. A SHIMMER, (Sensing Health with Intelligence, Modularity, Mobility, and Experimental Reusability) developed by Intel, was strapped to the base of the cue. Each SHIMMER contains a MicroSD - card slot, a rechargeable lithium-polymer battery, a 3-Axis accelerometer (1.5 6G) and a 3-Axis gyroscope (500 o/s). The accelerometer was of particular interest to this project as it was designed to react to very small changes in movement. It relayed the movements of the cue and was used to identify the strength of each shot.

A shot appeared as a large spike in the accelerometer data. The magnitude of this spike was calculated, and a strong correlation between the distance the ball travelled following this shot and the acceleration was determined. After much experimenting, a general equation was extracted which can determine how far the ball will travel, the moment it is struck by the cue.

Using this results it is possible to identify if a player is striking the ball with enough force to travel a certain distance, e.g., to pot a ball or roll up to a cushion for a safety shot. The work presented here is a strong starting point for future experimentation and analysis with a view to fully instrumenting a pool hall and providing teaching aids to a player to improve his/her game.

Further Information
There are more images from ODCSSS 2009 on Flickr. If you are interested in following up on this work or need further information contact Lorcan.

Connecting Families by Sharing the Minutiae of their Lives - Poornima Hanumara

Poornima Hanumara developed the Near Dear project as her ODCSSS project in summer of 2008 under the supervision of Lorcan Coyle. The original project title was Connecting Families by Sharing the Minutiae of their Lives - it called for an ambient/pervasive/ubiquitous technology for helping family members keep in touch with each other using micro-blogging tools (e.g., using Twitter or Jaiku). Originally we looked at using Nabaztags (thanks Matt), but eventually settled on using Chumbys.

The motivation for Near Dear was that members of a family have different degrees of familiarity and comfort with using technology. While some members of the family do not have access to a computer very often and are not familiar with micro-blogging, for others Internet is the main medium for keeping-in-touch. Near Dear bridges this gap by using Chumby, which sits in an accessible place at home and makes microblogging easy and convenient for computer-novices. We completed a small user study shows that the Near Dear widget is intuitive and serves the purpose of making Twittering more convenient. The Near Dear Chumby widget was released as a beta on the Chumby Network in July 2008 and is available to install on any Chumby. The project is documented on the Near Dear website. The project is being maintained and if anyone wants to collaborate on it going forward we'd love to hear from you :-)

In the News

Before leaving Texas, Pegasus News ran an article on Poornima's success at winning a place in ODCSSS 2008 and wished her luck. During the project, one of the Dublin newspapers, the Herald, wrote an article on Near Dear at an early stage of the project's development when we were playing with Nabaztags. The project was also mentioned in a DCU news release after the mid-term review day, and in science.ie, a popular Irish science website.

Further Information

Pictured below is Poornima being presented with a Hamilton silver coin, her prize for best report in 2008 (the award was presented by one of the heads of ODCSSS, Dr. Aaron Quigley). Poornima's final report was published as a UCD technical report and is available for download here. She still maintains a blog that contains detailed progress of her project. There are more images from ODCSSS 2008 on Flickr . If you are interested in following up on this work or need further information contact Lorcan.

Lost and Found at the Kilkenny Arts Festival 2007

Lost and Found was a collaboration between Science and Art that ran two sell-out shows at the Kilkenny Arts Festival in August 2007. The goal of the project was to engage children in the potential of technology. Lost and Found followed brought its audience and participants on a journey through four magical realms. Sensor technology is used to follow the location and movements of a dancer and map these to the projected visuals and sounds, and through these a story evolves. Active audience participation was required to move the story between the different realms, and children were invited on stage individually and in groups as the show progressed.

The Technology Behind Lost and Found

Lost and found included a diverse set of technologies, including sensor systems based on pressure pads, microprocessor programming to interpret sensor readings from pressure pad sensors, processing to recognise and deal with patterns in behaviour, and flash animation to drive the display.

The Cast

  • Paddy Nixon oversaw the project.
  • Tara Carrigy came up with the vision and produced the show.
  • Megan Kennedy was the dancer/choreographer.
  • Jo Timmons directed Lost and Found.
  • Lorcan Coyle headed the technology team and managed the technology interfaces.
  • Emerson Loureiro worked on the interface and built the technology to react to the dancers' movements.
  • Hui Zhang was responsible for the electronics behind the sensor system.

Press

Reviews of the show appeared in the Irish Times and some of UCD's internal publications. An incomplete list is here:

Case Based Markup Language (CBML)

Introduction to CBML

Hayes et al. (1999) describe imposing a standard case-based view on the information system of an application in order to retrieve case-knowledge. They anticipated that a standard way of representing case based reasoning (CBR) information will make this easier to achieve and proposed a case representation language that would facilitate this. Without such a standard means of representing case data it is up to the application developer to shape the case data from the available knowledge base. The manipulation of case data is dependent on the representation format chosen by the developer. This limits transformation of the data into a format suitable for the presentation layer, or its movement to another back-end component or between distributed CBR components. Hayes et al. proposed a standard case representation language called Case-Based Mark-up Language (CBML) in 1998. Our work in the field of CBR representation is a continuation of that work.

My Chumby

I got a Chumby in June 2008 and here's the virtual view of what channels I'm watching at the moment:

Using RFID to Remember - Colin Smith

Project Summary

The ReFInDer system was a final year project undertaken by Colin Smith under the supervision of Lorcan Coyle. The aim of the project was to design and implement a memory aid using RFID technology. This memory aid takes the form a lost and found application. The system records data about user's interactions with everyday objects, such as a wallet or phone, and presents the user with this data through a lost and found website.

Lorcan Coyle - Research

This page contains a summary of my research.

I am employed as a software engineer with EgoNav Analytics, based in the INSIGHT Centre in University College Dublin in Ireland.

I have a few web presences associated with my work/research, including:

Past Biography

Dr. Coyle's previous position was as a research fellow position in Lero—the Irish Software Engineering Research Centre in the University of Limerick (since March 2009). Prior to that he worked as a postdoctoral researcher in the School of Computer Science and Informatics in University College Dublin between April 2005 and March 2009. He was conferred with his PhD at Trinity College Dublin in 2005. He also holds a Bachelors degree in Computer Engineering from Trinity College (BA, BAI 2001). His research interests include Software Engineering, Pervasive and Ubiquitous Computing, Context-Aware Systems, Machine Learning, Personalisation technologies, and Electronic Voting. Dr Coyle has published at a number of international journals and conferences including IEEE Computer, the Communications of the ACM, the Knowledge Engineering Review, the journal on Knowledge-Based Systems, the IEEE International Conference on Pervasive Computing and Communications (PerCom), the ACM Conference on Pervasive Services (ICPS), the International Conference on Intelligent User Interfaces (IUI), the European Conference on Smart Sensing & Context (EuroSSC), Artificial Intelligence Review, and the International Symposium on Location and Context Awareness (LoCA).

Dr Coyle served as general co-chair at the 20th Irish Conference on Artificial Intelligence and Cognitive Science (AICS 2009); served as volunteers chair at Pervasive 2006; and as Publications Co-Chair at Pervasive in 2007. He is a member of the editorial board for the International Journal of Ambient Computing and Intelligence and serves or has served on a number of Internationally peer reviewed symposia and workshops in the area of context-awareness and ubiquitous computing, including the International Symposium on Location and Context Awareness, the International Conference on Autonomic and Autonomous Systems, the IEEE International Symposium on Ubiquitous Computing and Intelligence, the International IEEE Workshop on Management of Ubiquitous Communications and Services, the International Workshop on Modelling and Reasoning in Context, and the International Workshop on Ubiquitous Systems Evaluation. He has also served as reviewer for The Knowledge Engineering Review, ACM TAAS, Pervasive and Mobile Computing, as well as many of the premier venues for Ubiquitous Computing, including Ubicomp, PerCom, Pervasive, CHI, IUI, and the Internet of Things conference.

Research Projects

Conference and Workshop Organisation

Program Committee Membership

I am/was a member of the following program committees:

I am also a member of the editorial review board of the International Journal of Ambient Computing and Intelligence (IJACI)

Publications

A List of my publications, along with bibtex and links to download is here. My current h-index is ≥ 14.

Student Supervision

ODCSSS Students

Since 2007 I have been one of the Project Supervisors on the Online Dublin Computer Science Summer School (ODCSSS). An ODCSSS internship provides a foundation of basic research skills to 2nd and 3rd year undergraduate interns which will aid them in transforming this research experience into a long term plan for final year research or subsequent research career options. There is more information on the OCCSSS website.

Final Year Project Students

In the Computer Science and Informatics in UCD fourth year computer science students are required to undertake a substantial project. The purpose of the project is to introduce the student to a particular field of Computer Science and to give them an opportunity to learn how to undertake a major project, taking it from problem specification through to problem solution. I have supervised the following students' final year projects:

* I was Associate Supervisor to these students.

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