Publications

Publications 2019

Book chapter

1. Kohstall, J., Boeva, V., Lundberg, L., Angelova, M. ”Ensembles of Cluster Validation Indices for Label Noise Filtering.” R. Goncalves, V. Sgurev, V. Jotsov, J. Kacpzyk (Eds.): Intelligent Systems: Theory, Research and Innovation in Applications. Springer book series Studies in Computational Intelligence, accepted.
2. Boeva, V., Angelova, M., Manasa Devagiri, V., Tsiporkova, E.”Bipartite Split-Merge Evolutionary Clustering“, J. van den Herik et al. (Eds.): ICAART 2019, Springer Nature book: Agents and Artificial Intelligence, LNAI 11978 no. 11 (2019) 1-20, DOI:10.1007/978-3-030-37494-5_11
3. Angelova, M., Manasa Devagiri, V., Boeva, V., Linde, P., Lavesson, N. ”An Expertise Recommender System based on Data from Institutional Repository (DIVA)”. Leslie Chan and Pierre Mounier (Eds.): Connecting the Knowledge Commons – from projects to sustainable infrastructure. OpenEdition Press (2019) p. 135-149.

Journals

1. Sidorova J. , Carlsson S., Rosander O., Moreno-Torres I., and Berthier M., “Towards automatic assessment of emotional competence in neurological patients,” IEEE Transactions on Affective Computing, 2019. Accepted.
2. Kusetogullari, H., Yavariabdi, A., Cheddad, A., Grahn, H. and Hall, J. 2019, “ARDIS: A Swedish Historical Handwritten Digit Dataset,” Neural Computing and Applications, March 2019, Springer. DOI: 10.1007/s00521-019-04163-3.
3. Boldt M., Borg A., Ickin S., Gustafsson J., “Anomaly Detection of Event Sequences using Multiple Temporal Resolutions and Markov Chains,” Knowledge and Information Systems, 2019, Springer. Accepted.
4. Casalicchio, E., “A Study on Performance Measures for Auto-scaling CPU-intensive Containerized Applications,” Cluster Computing, Springer, 2019. DOI: 10.1007/s10586-018-02890-1
5. E. Garcia-Martin, C. Rodrigues, G. Riley, and H. Grahn, “Estimation of Energy Consumption in Machine Learning,” Journal of Parallel and Distributed Computing, 134:75–88, December 2019. Published online August 2019, https://doi.org/10.1016/j.jpdc.2019.07.007
6. E. Garcia-Martin, N. Lavesson, H. Grahn, E. Casalicchio, and V. Boeva, “Energy-Aware Very Fast Decision Tree,” International Journal of Data Science and Analytics, September 2019. Accepted.
7. Dasari, S. K., Cheddad, A., Andersson,P. “Predictive Modelling to Support Sensitivity Analysis for Robust Design in Aerospace Engineering.” Accepted for publication in Structural and Multidisciplinary Optimization, 2019, Springer. DOI: 10.1007/s00158-019-02467-5.

Conferences/workshops

1. Boeva,V. , Angelova, M., Tsiporkova, E. “A Split-Merge Evolutionary Clustering Algorithm“, 11th International Conference on Agents and Artificial Intelligence ICAART 2019 (Prague, Czech Republic, February 1, 2019) vol 2, 337-346.
2. L. Lundberg, H. Lennerstad, V. Boeva, and E. García-Martín, “Handling non-linear relations in support vector machines through hyperplane folding,” 11th International Conference on Machine Learning and Computing, ICMLC 2019, pp. 137-141, Feb. 2019.
3. Dasari, S.K., Cheddad, A. and Andersson, P., “Random Forest Surrogate Models to Support Design Space Exploration in Aerospace Use-case,” 15th International Conference on Artificial Intelligence Applications and Innovations (AIAI’19). 24-26 May 2019, Crete, Greece. Springer IFIP AICT (LNCS) Series.
4. S. Shirinbab, L. Lundberg, and E. Casalicchio, “Performance Comparision between Scaling of Virtual Machines and Containers using Cassandra NoSQL Database,” Tenth International Conference on Cloud Computing, GRIDs, and Virtualization, Cloud Computing 2019, pp. 93-98, May 2019.
5. Boeva, V., Angelova, M., Tsiporkova, E.. “A Bipartite-Graph Based Approach for Split-Merge Evolutionary Clustering”. European Conference on Data Analysis, ECDA 2019 (Bayureth, Germany, March, 2019) p. 18.
6. Bergenholtz, E., Ilie, D., Moss. A., Casalicchio, E., “Finding a needle in a haystack – A comparative study of IPv6 scanning methods”, IEEE Int. Symposium on Networks, Computer and Communication (ISNCC 2019), June 2019, Istanbul, Turkey
7. Nordahl, C., Boeva, V., Grahn, H. and Netz. M., “Profiling of Household Residents’ Electricity Consumption Behavior using Clustering Analysis.” International Conference on Computational Science ICCS 2019, Lecture Notes in Computer Science, vol 11540, pp. 779–786, Faro, Algarve, Portugal, June 2019.
8. Fiedler, M., Chapala, U. and Peteti, S. “Modeling Instantaneous Quality of Experience Using Machine Learning of Model Trees.” 2019 11th Int. Conf. On Quality of Multimedia Experience (QoMEX), Berlin, Germany, June 2019.
9. V. Boeva, M. Angelova, V. Manasa Devagiri, E. Tsiporkova, “A Split-Merge Framework for Evolutionary Clustering,” 31th Swedish AI Society Workshop SAIS 2019, Umeå, Sweden, June 2019.
10. Abghari, S., Boeva, V., Brage, J., Johansson, C., Grahn, H. and Lavesson, N. “Monitoring District Heating Substations via Clustering Analysis”, 31th Swedish AI Society Workshop SAIS 2019, Umeå, Sweden, June, 2019.
11. Nordahl, C., Boeva, V., Grahn H. and Netz, M. “Monitoring Household Electricity Consumption Behavior for Mining Changes.” ARIEL 2019, IJCAI 2019 Workshop, Macao, China, August 2019.
12. Fiedler, M. “Performance Analytics by Means of the M5P Machine Learning Algorithm.” 31st International Teletraffic Congress (ITC), Budapest, Hungary, August 2019.
13. F. Westphal, N. Lavesson, and H. Grahn, “A Case for Guided Machine Learning,” in International IFIP Cross-Domain Conference for Machine Learning and Knowledge Extraction (CD-MAKE 2019), Eds. A. Holzinger, P. Kieseberg, A. Tjoa, and E. Weippl, Lecture Notes in Computer Science, vol 11713. Springer, Cham. pp. 353-361, August 2019, Canterbury, UK. doi: https://doi.org/10.1007/978-3-030-29726-8_22
14. Abghari, S., Boeva, V., Johansson, C., Brage, J., Grahn, H., Lavesson N., “Data Analysis Techniques for Monitoring District Heating Substations.” 5th International Smart Energy Systems Conference 2019, Copenhagen, Denmark, September 2019.
15. Westphal, F., Lavesson, N. and Grahn, H. “Learning Character Recognition with Privileged Information,” in International Conference on Document Analysis and Recognition (ICDAR), pp. 1163–1168, September 2019, Sidney, Australia.
16. Boeva, V., Angelova, M., Manasa Devagiri, V., Tsiporkova, E.”Patient Profiling Using Evolutionary Clustering.” ACM Celebration of Women in Computing: womENcourage 2019, Rome, Italy, September 2019.
17. Ammar, D. De Moor, K. Skorin-Kapov, L. Fiedler, M. and Heegaard, P.E. “Exploring the Usefulness of Machine Learning in the Context of WebRTC Performance Estimation,” 44th Annual IEEE Conference on Local Computer Networks (LCN 2019), Oct. 2019, Osnabrück, Germany.
18. Borg A., Boldt M., Svensson J., “Using conformal prediction for multi-label document classification in e-mail support systems” 32nd International Conference on Industrial, Engineering & Other Applications of Applied Intelligent Systems, 2019, to appear.
19. Gualandi, G., Casalicchio, E., “Use of Redundancy in the Design of a Secure Software Defined Industrial Control Application”, 6th IEEE International Conference on Software Defined Systems (SDS2019), Rome, Italy, 2019.
20. Qian, W., and Cheddad, A. “Segmentation-based Deep Learning Fundus Image Analysis,” Accepted for oral presentation at the 9th International Conference on Image Processing Theory, Tools and Applications IPTA 2019. Nov, 2019, Istanbul, Turkey.
21. Abghari, S., Boeva, V., Brage, J. and Johansson, C. “District Heating Substation Behaviour Modelling for Annotating the Performance.” UMCit 2019, ECML & PKDD 2019 Workshop Würzburg, Germany, September, 2019.
22. Boeva, V., and Nordahl. C. “Modelling Evolving User Behaviour via Sequential Clustering. UMCit 2019, ECML & PKDD 2019 Workshop Würzburg, Germany, September, 2019.
23. Abghari, S., Boeva, V., Brage, J., Johansson, C., Grahn, H. and Lavesson, N. “Higher Order Mining for Monitoring District Heating Substations.” DSAA 2019 Washington DC, USA, October, 2019.
24. Krantz and F. Westphal, “Cluster-based Sample Selection for Document Image Binarization,” in WML 2019, 2019 International Conference on Document Analysis and Recognition Workshops (ICDARW), Sydney, Australia, September 2019, pp. 47-52.
25. Ickin,S. Vandikas, K. and Fiedler, M. “Privacy preserving QoE modeling using collaborative learning,” 4th ACM Workshop on QoE-based Analysis and Management of Data Communication Networks (Internet-QoE 2019, with ACM MOBICOM 2019), Oct. 2019, Los Cabos, Mexico.

Dataset

1. Kusetogullari H. and Cheddad A., The ARDIS Datasets of Handwritten Digits available freely from https://ardisdataset.github.io/ARDIS/ In collaboration with our partner company Arkiv Digital AB.

Publications 2018

Journals

1. M. Boldt, A. Borg, M. Svensson, J. Hildeby, ”Using predictive models on crime scene data to estimate burglars’ risk exposure and level of pre-crime preparation”, Intelligent Data Analysis, Vol. XX, no. X, Pages XX, XX, XX.
2. M. Boldt and K. Rekanar, “On the analysis and binary classification of privacy policies from both rogue and top 100 Fortune global companies”, to appear in International Journal of Information Security and Privacy, Special Issue on Machine Learning Techniques for Information Security and Data Privacy, 2018.
3. M. Boldt, “An evaluation of the efficiency and quality of structured crime reports”, in Nordic Journal of Policing Studies, 2018.
4. R., Bouhennache, T., Bouden, A., Taleb-Ahmed & A., Cheddad, (2018). “A new spectral index for the extraction of built-up land features from Landsat 8 satellite imagery.” Geocarto International, Taylor & Francis. https://doi.org/10.1080/10106049.2018.1497094
5. SP Josyula, JT Krasemann, L Lundberg, “A parallel algorithm for train rescheduling”, Transportation Research Part C: Emerging Technologies 95, 545-569, 2018. https://doi.org/10.1016/j.trc.2018.07.003

Conferences/workshops

1. M. Fiedler, S. Möller, P. Reichl, and M. Xie, QoE Vadis? (Dagstuhl Perspectives Workshop 16472), Dagstuhl Manifestos, 7(1):30–51, Schloss Dagstuhl–Leibniz-Zentrum fuer Informatik, Dagstuhl, Germany, 2018, DOI: 10.4230/DagMan.7.1.30, http://drops.dagstuhl.de/opus/volltexte/2018/8683
2. M. Fiedler, S. Möller, P. Reichl, and M. Xie, A Glance at the Dagstuhl Manifesto ‘QoE Vadis?’, in Proc. 10th Int. Conf. On Quality of Multimedia Experience, QoE Management Workshop, Pula, Italy, May/June 2018.
3. Erlandsson, A. Borg, and M. Boldt, “Visualizing modus operandi similarity between burglaries in a city”, NetCrime 2018 3nd Symposium on the Structure and Mobility of Crime, Paris, France, extended abstract
4. Erlandsson, P. Bródka, and A. Borg, “Seed selection for information cascade in multilayer network”, NetSci 2018 International School and Conference on Network Science, June 2018, Paris, France, extended abstract accepted for poster session
5. Erlandsson, P. Bródka, M. Boldt, and H. Johnson, “Do We Really Need To Catch Them All? A New User-guided Social Media Crawling Method”, International Conference on Computational Social Science IC2S2, July 2018, Chicago, USA. extended abstract accepted for poster session
6. Danielsson, H. Grahn, T. Sievert, and J. Rasmusson, “Comparing Two Generations of Embedded GPUs Running a Feature Detection Algorithm,” Computing Research Repository (CoRR), arXiv:1806.04859 [cs.DC], June 2018, http://arxiv.org/abs/1806.04859.

Publications 2017

Journals

1. O. Spjuth, A. Karlsson, M. Clements, K. Humphreys, E. Ivansson, J. Dowling, M. Eklund, A. Jauhiainen, K. Czene, H. Grönberg, P. Sparén, F. Wiklund, A. Cheddad, þ. Pálsdóttir, M. Rantalainen, L. Abrahamsson, E. Laure, J.-E. Litton, and J. Palmgren. “E-Science technologies in a workflow for personalized medicine using cancer screening as a case study.” Journal of the American Medical Informatics Association, 0(0), 2017, 1–8. Oxford University Press. DOI: 10.1093/jamia/ocx038. Impact Factor: 3.428.
2. J.K. Martinsen, H. Grahn, and A. Isberg, “Combining Thread-Level Speculation and Just-In-Time Compilation in Google’s V8 JavaScript Engine,” Concurrency and Computation: Practice and Experience, 29(1), January 2017 (online May 2016). DOI: 10.1002/cpe.3826.
3. M. Persson, H. Hvitfeldt-Forsberg, M Unbeck, O.G Sköldenberg, A Stark, P Kelly-Pettersson, P Mazzocato, “Operational strategies to manage non-elective orthopaedic surgical flows: a simulation modelling study”, BMJ Open, 2017;7:e013303. DOI: 10.1136/bmjopen-2016-013303
4. F. Erlandsson, P. Bródka, M. Boldt and H. Johonson. “Do We Really Need To Catch Them All? : A New User-Guided Social Media Crawling Method”. Entropy, vol. 19(12), MDPI, 2017, DOI:10.3390/e19120686.

Conferences/workshops

1. V.M. Devagiri and A. Cheddad, “Splicing Forgery Detection and the Impact of Image Resolution,” in 5th Int’l Workshop on Systems Safety and Security (IWSSS 2017), IEEE, pp. XX–YY, June 2017, Targoviste, România. (accepted, to appear).
2. M. Boldt, A.Borg and V. Boeva, ”Multi-expert estimations of burglars’ risk exposure and level of pre-crime preparation using on crime scene data”, to appear in proceedings of the 30th Annual Workshop of the Swedish Artificial Intelligence Society (SAIS), 2017.
3. M. Boldt and A. Borg, ”A statistical method for detecting significant temporal hotspots using LISA statistics”, to appear in proceedings of the 8th European Intelligence and Security Informatics Conference (EISIC), 2017.
4. A. Borg, M. Boldt and J. Eliasson, ”Detecting crime series based on route estimations and behavioral similarity”, to appear in proceedings of the 8th European Intelligence and Security Informatics Conference (EISIC), 2017.
5. F. Erlandsson, P. Bródka and A. Borg. “Seed Selection for Information Cascade in Multilayer Networks”, Complex Networks & Their Applications VI : Proceedings of the 6th International Workshop on Complex Networks and Their Applications (COMPLEX NETWORKS 2017), 2017, DOI: 10.1007/978-3-319-72150-7_35
6. F. Erlandsson “Human Interactions on Online Social Media : Collecting and Analyzing Social Interaction Networks” [PhD dissertation]. Karlskrona; 2018. (Blekinge Institute of Technology Doctoral Dissertation Series). Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:bth-15503
7. S. Krishna Dasari, V.Boeva, J, Wall, N, Lavesson and P, Andersson. “ Data Integration Analysis for Supporting Decisions in Engineering Design.” The 5th Swedish Workshop on Data Science. pp.26-27 Gothenburg, December 2017 (link: https://schlieplab.org/Static/Downloads/SweDS2017-Abstracts.pdf).

Publications 2016

Journals

1. J.K. Martinsen, H. Grahn, and A. Isberg, “Combining Thread-Level Speculation and Just-In-Time Compilation in Google’s V8 JavaScript Engine,” Concurrency and Computation: Practice and Experience, February 2016. (accepted for publication, to appear)
2. F. Erlandsson, A. Borg, H. Johnson, & P. Bródka, (2016, January). Predicting User Participation in Social Media. Advances in Network Science. in collection, Cham: Springer International Publishing.doi:10.1007/978-3-319-28361-6_10
3. F. Erlandsson, P. Bródka, A. Borg, & H. Johnson, (2016). Finding Influential Users in Social Media Using Association Rule Learning. Entropy. article. doi:10.3390/e18050164
4. B. Brik, N. Lagraa, A. Lakas and A. Cheddad “DDGP: Distributed Data Gathering Protocol for vehicular networks,” Vehicular Communications, Elsevier, Volume 4, pp. 15-29, April 2016.
5. M. Boldt and A. Borg “Evaluating temporal analysis methods using residential burglary data”, in International Journal of Geo-Information – Special Issue “Frontiers in Spatial and Spatiotemporal Crime Analytics”, 2016, Impact factor: 0.651, DOI: 10.3390/ijgi5090148
6. A. Borg and M. Boldt “Clustering residential burglaries using modus operandi and spatiotemporal information”, in International Journal of Information Technology & Decision Making, World Scientific, 2016, Impact factor: 1.406, DOI: http://www.worldscientific.com/doi/10.1142/S0219622015500339
7. F. Strand, K. Humphreys, A. Cheddad, et, al. (2016) “Novel mammographic image features differentiate between interval and screen-detected breast cancer: a case-case study.” Breast Cancer Research (2016) 18:100. DOI 10.1186/s13058-016-0761-x. Springer.

Conferences/workshops

1. H.Kusetogullari and A. Yavariabdi, “Self-Adaptive Hybrid PSO-GA Method for Change Detection Under Varying Contrast Conditions on Satellite Images”, IEEE Int. Science and Information Conf. on Computing, pp. 361-368, London, July 2016.
2. S. Sagar and J. Sidorova, “Transparent Statistical Adapter with Flexible Machinery and Sequence Retriever”, Conference on Practical Applications of Computational Biology & Bioinformatics (PACBB’16). LNCS. Sevilla. Spain. Accepted.
3. F. Erlandsson, (2016). Finding Influential Users in Social Media Using Association Rule Learning.doi:10.3390/e18050164
4. D. Ilie and V. V. K. Sai Datta, “On Designing a Cost-Aware Virtual CDN for the Federated Cloud“, in Proceedings of IEEE COMM, Bucharest, Romania, June 2016.
5. S. K. Dasari, N. Lavesson, P. Andersson, and M. Persson, “Tree-Based Response Surface Analysis.” In Proc. Machine Learning, Optimization, and Big Data, pp. 118-129, Lecture notes in Computer Science, Volume 9432, 2015.
6. M. Boldt and J. Bala, “Filtering estimated crime series based on route calculations on spatiotemporal data”, to appear in proceedings of the 7th European Intelligence and Security Informatics Conference (EISIC), 2016.
7. B. Shao, N. Lavesson, V. Boeva, and R.K. Shahzad, “A mixture-of-experts approach for gene regulatory network inference,” Data Mining and Bioinformatics. Vol. 14. No. 3, 2016.

Publications 2015

Journals

1. J.K. Martinsen, H. Grahn, and A. Isberg, “The Effect of Parameter Tuning in Thread-Level Speculation in JavaScript Engines,” ACM Transactions on Architecture and Code Optimization, 11(4):46:146:25, January 2015, doi:10.1145/2686036.
2. A. Borg and M. Boldt, “Clustering residential burglaries using multiple heterogeneous variables”, accepted for publication in International Journal of Information Technology & Decision Making, World Scientific, 2015.
3. M. Unterkalmsteiner, T. Gorschek, R. Feldt, N. Lavesson, “Large-scale Information Retrieval in Software Engineering – An Experience Report from Industrial Application,” Empirical Software Engineering, Accepted for publication, Springer, 2015.
4. B. Shao, N. Lavesson, V.Boeva, R. K. Shahzad, “A Mixture-of-Experts Approach for Gene Regulatory Network Inference”, International Journal of Data Mining and Bioinformatics, Accepted for publication, Inderscience, 2015.
5. J.Törnquist Krasemann, (2015) “Computational decision-support for railway traffic management and associated configuration challenges: An experimental study”, Journal of Rail Transport Planning & Management, Elsevier (in press, available online 9 October 2015).
6. E. Andersson, A. Peterson, J. Törnquist Krasemann, (2015), “Reduced Railway TrafficDelays using a MILP Approach to Increase Robustness in Critical Points”, Journal of RailTransport & Planning, Elsevier.
7. E. Kocaguneli, T. Menzies, E. Mendes, “Transfer learning in effort estimation,” Empirical Software Engineering 20(3): 813-843 (2015)
8. Jacobsson, A., Boldt, M and Carlsson, B “A risk analysis of a smart home automation systems”, in Journal of Future Generation Computer Systems, Elsevier, 2015, Impact factor: 2.786, DOI: http://www.sciencedirect.com/science/article/pii/S0167739X15002812

Conferences/workshops

1. E. Nilsson, D. Aarno, E. Carstensen, and H. Grahn, Accelerating Graphics in the Simics Full-system Simulator, in Proc. of the 23rd IEEE Int’l Symp. on Modeling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS), pp. XX-YY, October 2015, Atlanta, GA, USA.
2. R. K. Shahzad, N. Lavesson, “Consensus Voting in Random Forests,” Proc. International Workshop on Machine Learning, Optimization, and Big Data, Accepted / To appear, 2015.
3. S. K. Dasari, N. Lavesson, P.Andersson, M. Persson, “Tree-Based Response Surface Analysis,” Proc. International Workshop on Machine Learning, Optimization, and Big Data, Accepted / To appear, 2015.
4. O. Isaksson, M. Bertoni, S.Hallstedt, N.Lavesson, “Model Based Decision Support for Value and Sustainability in Product Development,” Proc. 20th International Conference on Engineering Design, Accepted / To appear, 2015.
5. J. Törnquist Krasemann, (2015). “Configuration of an optimization-based decision support for railway traffic management in different contexts”, conference paper from IAROR RailTokyo, March 23-26, 2015.
6. J Holmgren och M Persson, “An optimization model for sequence dependent parallel operating room scheduling”, Second International Conference on Health Care Systems Engineering, Lyon 29-29 May France. To appear in Springer Proceedings in Mathematics and Statistics 2016.
7. M. Usman, E.Mendes, J. Börstler, “Effort estimation in agile software development: a survey on the state of the practice,” EASE 2015: 12:1-12:10
8. R. Britto, E. Mendes, J.Börstler, “An Empirical Investigation on Effort Estimation in Agile Global Software Development,” ICGSE 2015: 38-45
9. E. Mendes, B. Turhan, P. Rodríguez, V. Freitas, “Estimating the Value of Decisions Relating to Managing and Developing Software-intensive Products and Projects,” PROMISE 2015: 7
10. L.Minku, F. Sarro, E. Mendes, F.Ferrucci, “How to Make Best Use of Cross-Company Data for Web Effort Estimation?” Proceedings ESEM, 2015.
11. T. Wang, F. Erlandsson, &S. F. Wu, (2015). Mining User Deliberation and Bias in Online Newsgroups: A Dynamic View. Proceedings of the 2015 ACM on Conference on Online Social Networks, COSN ’15. inproceedings, New York, NY, USA: ACM. doi:10.1145/2817946.2817951
12. F. Erlandsson, R. Nia, M. Boldt, H. Johnson, & S. F. Wu, (2015, September). Crawling Online Social Networks. Network Intelligence Conference (ENIC), 2015 Second European. inproceedings. doi:10.1109/ENIC.2015.10
13. Baca, D., Boldt, M., Carlsson, B and Jacobsson, A “A security-focused Agile software development process”, in proceedings of the 10th International Conference on Availability, Reliability and Security (ARES), Lecture Notes in Computer Science, 2015.

Publications 2014

1. Edgar Alonso Lopez-Rojas and Stefan Axelsson, “BankSim: A Bank Payments Simulator for Fraud Detection Research,” In Proceedings of the 26th European Modelling & Simulation Symposium (EMSS 2014), 10-13 Sept. Bordeaux, France, 2014.
2. Edgar Alonso Lopez-Rojas and Stefan Axelsson, “Social Simulation of Commercial and Financial Behaviour for Fraud Detection Research,” In Proceedings of the Social Simulation Conference (SSC 2014), The 10th Conference of the European Social Simulation Association, Sep. 2014, Barcelona, Spain.
3. Stefan Petersson and Håkan Grahn, “Improving Image Quality by SSIM Based Increase of Run-Length Zeros in GPGPU JPEG Encoding,” in Proc. of the 48th Asilomar Conference on Signals, Systems & Computers, November 2014, Pacific Grove, U.S.A. (invited paper)
4. Spyridon Provatas and Niklas Lavesson, Christian Johansson, “An Online Machine Learning Algorithm for Heat Load Forecasting in District Heating Systems,” In Proc. 14th International Symposium on District Heating and Cooling, 2014.
5. Sogand Shirinbab, “Performance Aspects in Virtualized Software Systems,” Licentiate dissertation, Blekinge Institute of Technology, 2014, ISBN: 978-91-7295- 290-4