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Publications 2020-12-09T12:49:40+01:00

Publications 2020

Book chapters

  1. J. Kohstall, V. Boeva, L. Lundberg, and M. Angelova, “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. Studies in Computational Intelligence, Volume 864, 2020, Pages 71-98.

Journals

  1. E. Casalicchio and S. Iannucci, “The State-of-the-Art in Container Technologies: Application, Orchestration and Security,” Concurrency and Computation: Practice and Experience, Wiley DOI: 10.1002/cpe.5668.
  2. S. Shirinbab, L. Lundberg, and E. Casalicchio, “Performance evaluation of containers and virtual machines when running Cassandra workload concurrently”, 2020,DOI: 10.1002/cpe.5693
  3. V. Boeva, J. Kohstall, L. Lundberg and M. Angelova. “Combining Cluster Validation Indices for Detecting Label Noise.” Archives of Data Science Journal, Series A, in press.
  4. A. Cheddad, “On Box-Cox Transformation for Image Normality and Pattern Classification,” Accepted for publication in IEEE Access, 2020,  IF: 3.745.
  5. F. Westphal, H. Grahn, and N. Lavesson, “Representative Image Selection for Data Efficient Word Spot- ting,” in 14th IAPR Int’l Workshop on Document Analysis Systems (DAS 2020), pp. 383-397, July 2020.
  6. Abghari, V. Boeva, J. Brage, and H. Grahn, “A Higher Order Mining Approach for Analysis of Real- world Datasets,” Energies, 13(21):5781, 2020. doi: 10.3390/en13215781. Published online November 2020, https://www.mdpi.com/1996-1073/13/21/5781

Conference/workshop

  1. A. Borg, J. Ahlstrand, and M. Boldt, “Predicting E-mail Response Time in Corporate Customer Support,” 22nd International Conference on Enterprise Information Systems (ICEIS), 2020, Prague, Czech Republic.
  2. S. Abghari, V. Boeva, J. Brage, and H. Grahn, “Multi-view Clustering Analyses for District Heating Sub-stations,” in 9th International Conference on Data Science, Technology and Applications (DATA 2020), pp. XX-YY, July 2020, Lieusaint-Paris, France.
  3. V.S. Vineeth, H. Kusetogullari, and A. Boone, “Forecasting Sales of Truck Components: A Machine Learning Approach”, 10th IEEE International Conference on Intelligent Systems (IS2020), August 2020 (accepted)
  4. A. Cheddad, “Machine Learning in Healthcare”. Accepted for oral presentation in the Road Mapping Infrastructures for Artificial Intelligence Supporting Advanced Visual Big Data Analysis workshop, co-located with the International Conference on Advanced Visual Interfaces (AVI 2020). Springer Lecture Notes in Computer Science [LNCS] Series.
  5. V. M. Devagiri, V. Boeva, E. Tsiporkova, “Split-Merge Evolutionary Clustering for Multi-View Streaming Data.”, 24th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems KES 2020, accepted.
  6. Benhamza, A. Djeffal and A. Cheddad, “A review of image forgery detection,” Accepted for oral presentation at the International Conference on Control, Automation and Diagnosis (ICCAD’20), IEEE, October 7-9, 2020 at Paris, France.
  7. S.K. Dasari, A. Cheddad, J. Palmquist,”Melt-pool Defects Classification for Additive Manufactured Components in Aerospace Use-case,” Accepted for oral presentation in 7th Intl. IEEE Conference on Soft Computing & Machine Intelligence (ISCMI 2020), Stockholm, Sweden November 14-15, 2020.
  8. A. Eghbalian, S. Abghari, V. Boeva and F. Basiri. Multi-view “Data Mining Approach for Behaviour Analysis of Smart Control Valve.” 19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020, accepted.
  9. M. Dhont, E. Tsiporkova, V. Boeva. “Layered Integration Approach for Multi-view Analysis of Temporal Data.” AALTD 20, Workshop of ECML/PKDD 2020, accepted.
  10. Dhont, R. Verbeke, C. Droutsas, V. Boeva, M. Verbeke, A. Murgiaand E. Tsiporkova. Advanced exploration of wind fleet data through operating mode labelling, Renewable Energy Sources – Research and Business, RESRB 2020, September, Belgium, accepted.
  11. C.D. Lekamlage, F. Afzal, E. Westerberg and A. Cheddad, “Mini-DDSM: Mammography-based Automatic Age Estimation,” Accepted for oral presentation at the 3rd International Conference on Digital Medicine and Image Processing (DMIP 2020), Kyoto, Japan, November 06-09, 2020. 

Journals

Conference/workshop

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. 

Journals

  1. Boldt and K. Rekanar, “On the analysis and binary classification of privacy policies from both rogue and top 100 Fortune global companies”, in International Journal of Information Security and Privacy, Volume 13, Issue 2, 2019.

Conferences/workshops

  1. Gualandi, E.Casalicchio, (2019) A Self-protecting Control Application for IIoT, IEEE FAS* Workshops series, IEEE International Conference on Autonomic Computing, Umeå, June 2019
  2. Bustamante, A.A.,  Cheddad, A.,  and Rodriguez-Garcia, A.  “Digital Image Processing and Development of Machine Learning Models for the Discrimination of Corneal Pathology: An Experimental Model”.  American Academy of Ophthalmology’s annual meeting (AAO 2019) San Francisco Oct, 2019.

Publications 2018

Book chapter

  1. J. Sidorova, O. Rosander, L. Skold, H. Grahn, L. Lundberg, “Finding a healthy equilibrium of geodemographic segments for a telecom business”, Machine Learning Paradigms – Advances in Data Analytics, Springer, Intelligent Systems Reference Library book series, Eds. G. Tsihrintzis et al. Accepted.

Journals

  1. E. Casalicchio, V. Cardellini, G. Interino, and M. Palmirani, “Research Challenges in Legal-rule and QoS- aware Cloud Service Brokerage,” Future Generation Computer Systems, Volume 78, Part 1, pp. 211-223, January 2018 (published online in 2016), Elsevier, DOI: 10.1016/j.future.2016.11.025
  2. F. Westphal, H. Grahn, and N. Lavesson, “Efficient Document Image Binarization Using Heterogeneous Computing and Parameter Tuning,” International Journal on Document Analysis and Recognition (IJDAR), pp. 1–18. Published online January 2018. doi: https://doi.org/10.1007/s10032-017-0293-7
  3. J. Sidorova, O. Rosander, L. Sköld, and L. Lundberg,“Optimizing utilization in cellular radio networks using mobility data” Optimization and Engineering, pp. 1-28, 2018. doi.org/10.1007/s11081-018-9387-4 
  4. H. Kusetogullari and A. Yavariabdi, “Unsupervised Change Detection in Landsat Images with Atmospheric Artifacts: A Fuzzy Multiobjective Approach,” Mathematical Problems in Engineering, 2018: 1-16, May 2018. doi: https://doi.org/10.1155/2018/7274141 
  5. V. Boeva, L. Lundberg, S. M. H. Kota, L. Sköld, “Evaluation of Organizational Structure through Cluster Validation Analysis of Email Communications,” Journal of Computational Social Science, 1(2) (2018) 327-347, Springer, (published August 6, 2018) https://link.springer.com/article/10.1007/s42001-018-0022-0
  6. H. Kusetogullari, A. Yavariabdi, “Evolutionary Multiobjective Multiple Description Wavelet Based Image Coding in the Presence of Mixed Noise in Images”, Applied Soft Computing, vol. 73, pp. 1039-1052, December 2018.

Conferences/workshops

  1. V. Boeva, M. Angelova, N. Lavesson, O. Rosander, E. Tsiporkova. Evolutionary clustering techniques for expertise mining scenarios.10th International Conference on Agents and Artificial Intelligence ICAART, pp.523-530. 2018 (Funchal, Madeira, Portugal, January 2018), 
  2. F. Westphal, N. Lavesson, and H. Grahn, “Document Image Binarization Using Recurrent Neural Net- works,” in 13th IAPR International Workshop on Document Analysis Systems (DAS 2018), pp. 263-268, April 2018, Vienna, Austria.
  3. M. Angelova, V. Manasa Devagiri, V. Boeva, P. Linde, N. Lavesson. “An Expertise Recommender System based on Data from Institutional Repository (DIVA)”. ElPub 2018 (Toronto, Canada, June 2018), accepted.
  4. C. Nordahl, V. Beova, H. Grahn, and M. Persson, “Organizing, Visualizing and Understanding Households Electricity Consumption Data through Clustering Analysis,” in 2nd Workshop on AI for Aging, Rehabilitation and Independent Assisted Living (ARIAL@IJCAI 2018), July 2018, Stockholm, Sweden.
  5. V. Boeva, L. Lundberg, J. Kohstall, and M. Angelova, “Label Noice Filtering based on Cluster Validation Measures”, European Conference on Data Analysis, ECDA 2018, (Paderborn, Germany, July 2018), accepted (abstract) 
  6. M. Nardelli, V. Cardellini, and E. Casalicchio, “Multi-level Elastic Deployment of Containerized Applications in Geo-distributed Environments,“ IEEE 6th International Conference on Future Internet of Things and Cloud (FiCloud-2018), August 2018, Barcelona, Spain.
  7. A. Yavariabdi, H. Kusetogullari, E. Mendi, B. Karabatak, “Unsupervised Change Detection using Thin Cloud-Contaminated Landsat Images”, 9th IEEE International Conference on Intelligent Systems, (Madeira, Portugal, September 2018) (accepted)  
  8. M. F., Demir, A. Cankirli, B. Karabatak, A. Yavariabdi, H. Kusetogullari, and E. Mendi, “Real-Time Resistor Color Code Recognition using Image Processing in Mobile Devices,“ 9th IEEE International Conference on Intelligent Systems, September 2018, Madeira, Portugal. Accepted, to appear. 
  9. E. Ventocilla, T.  Helldin, M. Riveiro, J. Bae, V. Boeva, G. Falkman and N. Lavesson, “Towards a Taxonomy for Interpretable and Interactive Machine Learning”, Workshop on Explainable AI (XAI 2018), IJCAI 2018 Workshop, accepted. 
  10. L. Lundberg, H. Lennerstad, E. Maria Garcia-Martin, N. Lavesson, V. Boeva. “Hyperplane Folding – a Way to Increase the Margin in Support Vector Machines.” GiML 2018, IJCAI 2018 Workshop. 
  11. V. Boeva, L. Lundberg, M. Angelova and J. Kohstall. Cluster Validation Measures for Label Noise Filtering, The 9th IEEE International Conference on Intelligent Systems IS’18, (Madeira Island, Portugal, September 25-27, 2018), accepted.
  12. F. Westphal, H. Grahn, and N. Lavesson. ” User Feedback and Uncertainty in User Guided Binarization”. In: IEEE International Conference on Data Mining Workshops. 2018, to appear
  13. Garcia-Martin, N. Lavesson, H. Grahn, E. Casalicchio, and V. Boeva, “How to Measure Energy Consumption in Machine Learning algorithms,” in 1st Int’l Workshop on Energy Efficient Data Mining and Knowledge Discovery (GreenDataMining), ECML PKDD workshop, September 2018, Dublin, Ireland. https://greendatamining.github.io/
  14. J. Sidorova, L. Sköld, H. Lennerstad, and L. Lundberg, “The Use of Fuzzy Logic in Creating a Visual Data Summary of a Telecom Operator’s Customer Base,” 1st International Conference on Intelligent Technologies and Applications, INTAP 2018, pp. 301-312, Oct. 2018.
  15. Abghari, V. Boeva, N. Lavesson, H. Grahn, S. Ickin, and J. Gustafsson, “A Minimum Spanning Tree Clustering Approach for Mining Sequence Datasets” in 6th Swedish Workshop on Data Science (SweDS 2018), Umeå University, November 2018,
  16. Abghari, V. Boeva, N. Lavesson, H. Grahn, J. Gustafsson, and J. Shaikh, “Outlier detection for video session data using sequential pattern mining”. In ACM SIGKDD: Workshop on Outlier Detection De-constructed, 2018, London, UK, together with Ericsson Research, Stockholm.
  17. Nordahl, V. Boeva, H. Grahn, and M. Netz, “Detecting abnormal behavior of elderlies by analyzing energy consumption of individual households,” in 6th Swedish Workshop on Data Science (SweDS 2018), Umeå University, November 2018, Umeå, Sweden.
  18. Nordahl, V. Boeva, H. Grahn and M. Persson Netz. “Organizing, Visualizing and Understanding Households Electricity Consumption Data through Clustering Analysis”. ARIAL 2018, IJCAI 2018 Workshop.
  19. Ventocilla, T. Helldin, M. Riveiro, J. Bae, V. Boeva, G. Falkman and N. Lavesson, “Towards a Taxonomy for Interpretable and Interactive Machine Learning”, Workshop on Explainable AI (XAI 2018), IJCAI 2018 Workshop.
  20. Abghari, V. Boeva, N. Lavesson, H. Grahn, S. Ickin, and J. Gustafsson, “A minimum spanning tree clustering approach for outlier detection in event sequences”. In 17th IEEE International Conference on Machine Learning and Applications (ICMLA): Special Session on Machine Learning Algorithms, Systems and Applications, December 2018, Orlando, Florida, USA, together with Ericsson Research, Stockholm.
  21. Garcia-Martin, N. Lavesson, H. Grahn, E. Casalicchio, and V. Boeva, “Hoeffding Trees with nmin adaptation,” in 5th IEEE International Conference on Data Science and Advanced Analytics (DSAA 2018), pp. XX–YY, October 2018, Turin, Italy.
  22. Westphal, H. Grahn, and N. Lavesson, “User Feedback and Uncertainty in User Guided Binarization”, 1st International Workshop on Developmental Learning (DELL), November 2018, Singapore
  23. S. Shirinbab, L. Lundberg, and E. Casalicchio, “Performance Comparison between Horizontal Scaling of Hypervisor and Container Based Virtualization using Cassandra NoSQL Database,” 3rd International Conference on Virtualization Application and Technology (ICVAT 2018), Nov. 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

Book chapter

  1. E. Garcia-Martin, N. Lavesson, and H. Grahn, “Energy Efficiency Analysis of the Very Fast Decision Tree Algorithm,” in Trends in Social Network Analysis: Information Propagation, User Behavior Modelling, Forecasting, and Vulnerability Assessment, Lecture Notes in Social Networks, editors R. Missaoui, T. Abdessalem, and M. Latapy, pp. 229–252, April 2017. DOI: 10.1007/978-3-319-53420-6_10 
  2. J. Sidorova, H. Grahn, O. Rosander, L. Skold, L. Lundberg, “Finding a healthy equilibrium of geo-demographic segments for a telecom business”, Machine Learning Paradigms – Advances in Data Analytics, Springer, Intelligent Systems Reference Library book series, Eds. G. Tsihrintzis et al. Accepted.

Journals

  1. A. Cheddad, “Structure Preserving Binary Image Morphing using Delaunay Triangulation,” in Pattern Recognition Letters, Elsevier, 85(): 8-14, January 2017. DOI: 10.1016/j.patrec.2016.11.010
  2. A. Yavariabdi and H. Kusetogullari, “Change Detection in Multispectral Landsat Images Using Multi-Objective Evolutionary Algorithm”, in IEEE Geoscience and Remote Sensing Letters, 14(3), pp. 414-418, March 2017. doi: 10.1109/LGRS.2016.2645742.
  3.  E. Casalicchio, L. Lundberg, S. Shirinbab, “Energy-aware Auto-scaling Algorithms for Cassandra Virtual Data Centers”, in Cluster Computing, 20(3), pp 2065–2082, ISSN 1386-7857, EISSN 1573-7543, Springer-Verlag New York, September 2017.
  4. S. Abghari, E. García-Martín, C. Johansson, N. Lavesson, and H. Grahn, “Trend Analysis to Automatically Identify Heat Program Changes,” in Energy Procedia, 116:407–415, June 2017. DOI: 10.1016/j.egypro.2017.05.088. Also published at DHC-2016.
  5. E. Casalicchio, V. Cardellini, G. Interino and M. Palmirani “Research Challenges in Legal-rule and QoS-aware Cloud Service Brokerage,” in Future Generation Computer Systems, Elsevier, DOI: 10.1016/j.future.2016.11.025
  6. V. Boddapati, A. Petef och L. Lundberg, “Classifying environmental sounds using image recognition networks,” in Procedia Computer Science, Elsevier, 112:2048–2056, September 2017. DOI: 10.1016/j.procs.2017.08.250

Conferences/workshops

  1. E. Casalicchio and V. Perciballi. “Measuring Docker Performance: What a Mess!!!”, In Proceedings of the 8th ACM/SPEC on International Conference on Performance Engineering Companion (ICPE ’17 Companion). ACM, New York, NY, USA, 11-16. DOI: https://doi.org/10.1145/3053600.3053605
  2. V.M. Devagiri and A. Cheddad, “Splicing Forgery Detection and the Impact of Image Resolution,” in Proc. 9th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2017, pp. 1-6, January 2017.
  3. E. Garcia-Martin, N. Lavesson, and H. Grahn, “Identification of Energy Hotspots: A case study of the Very Fast Decision Tree Algorithm,” in Recent Advances in Green, Pervasive and Cloud Computing – 12th International Conference on Green, Pervasive and Cloud Computing (GPC 2017), pp. 267–281, May 2017, Cetara, Italy. https://link.springer.com/chapter/10.1007/ 978-3-319-57186-7_21
  4. E. Garcia-Martin, N. Lavesson, H. Grahn, and V. Boeva, “Energy Efficiency in Machine Learning: A position paper,” in 30th Annual Workshop of the Swedish Artificial Intelligence Society (SAIS 2017), pp. 68–72, May 2017, Karlskrona, Sweden.
  5. S. Sagar, L. Lundberg, L. Skold, J.  Sidorova, “Segmentation of mobile user groups based on trajectory patterns in place of geodemographic segmentation (MOSAIC) in business analytics”, International Symposium on Advances in Smart Big Data Processing (SBDP-2017) in conjunction with the 3rd IEEE International Conference on Smart Data (SmartData-2017), Jun 21, 2017 – Jun 23, 2017, Exeter, UK. In (IEEE) press.
  6. S. Sagar, L.  Lundberg, L.  Skold, and J. Sidorova, “Trajectory segmentation for a recommendation module of a customer relationship management system,” Joint 10th IEEE Int’l Conf. on Internet of Things, iThings 2017, 13th IEEE Int’l Conf. on Green Computing and Communications, GreenCom 2017, 10th IEEE Int’l Conf. on Cyber, Physical and Social Computing, CPSCom 2017 and the 3rd IEEE Int’l Conf. on Smart Data, Smart Data 2017, pp. 1150-1155, June 2017.
  7. J. Sidorova, L.Skold, O. Rosander, L. Lundberg, “Recommendations for Marketing Campaigns in Telecommunication Business based on the footprint analysis”, the 1st International Workshop on Data Science: Methodologies and Use-Cases (DaS 2017) at 21st European Conference on Advances in Databases and Information Systems (ADBIS 2017). 28-30 Larnaca, Cyprus, August 2017 LNCS. In press.
  8. A. Cheddad, H. Kusetogullari, and H. Grahn, “Object Recognition using Shape Growth Pattern” in 10th Int’l Symp. on Image and Signal Processing Analysis (ISPA 2017), pp. 47-52, September 2017, Ljubljana, Slovenia.
  9. A. Yavariabdi and H. Kusetogullari, “Change detection in multispectral landsat images using multi-objective evolutionary algorithms”, IEEE Geoscience and Remote Sensing Letters, 14(3):414-418, 2017, DOI: 10.1109/LGRS.2016.2645742.
  10. Podapati, L. Lundberg, L. Skold, O. Rosander, J. Sidorova, “Fuzzy Recommendations in Marketing Campaigns”, the 1st International Workshop on Data Science: Methodologies and Use-Cases (DaS 2017). Nicosia, Cyprus on September 24, 2017, LNCS. In press.
  11. M. Fiedler, K. De Moor, H. Ravuri, R. Tanneedi, and M. Chandiri, “Users on the move: On relationships between QoE ratings, data volumes and intentions to churn,” in IEEE LCN 11th Workshop On User MObility and VEhicular Networks, (IEEE LCN ON-MOVE 2017), pp. XX–YY, October 2017, Singapore. (accepted, to appear)
  12. H. Kusetogullari, H. Grahn, N. Lavesson, “Handwriting Image Enhancement using Local Learning Windowing and Gaussian Mixture Model, ” in Proc. of the 16th IEEE International Symaposium on Signal Processing and Information Technology, pp. 305-310, Paphos, Larnaca, 2017.
  13. S. Abghari, E. Garcia-Martin, C. Johansson, N. Lavesson and H. Grahn, “Trend Analysis to automation identify heat program changes”, Energy Procedia, 116: 407-415, 2017
  14. E.García-Martín, N. Lavesson. “Is it ethical to avoid error analysis” . 2017 Workshop on Fairness, Accountability, and Transparency in Machine Learning (FAT/ML 2017). arXiv preprint arXiv:1706.10237
  15. T. Hossfeld, M. Fiedler, and J. Gustafsson, “Betas: Deriving Quantiles from MOS-QoS Relations of IQX Models for QoE Management,” in Proc. of the 1st IFIP/IEEE Int. Workshop on Quality of Experience Management (QoE-Management 2017), May 2017, Lisbon, Portugal. Not yet available on IEEExplore.
  16. M. Fiedler, S. Möller, P. Reichl, and M. Xie, “QoE Vadis? (Dagstuhl Perspectives Workshop 16472),” Dagstuhl Reports 6(11):129—141, 2017. DOI: 10.4230/DagRep.6.11.129
  17. V. Boeva, M. Angelova, E. Tsiporkova. “Data-Driven Techniques for Expert Finding”. 9th International Conference on Agents and Artificial Intelligence ICAART 2017 (Porto, Portugal, February 24-26, 2017), 535-542.
  18. M. Angelova, V. Boeva and E. Tsiporkova. “Advanced Data-driven Techniques for Mining Expertise”. 30th Annual Workshop of the Swedish Artificial Intelligence Society SAIS 2017 (Karlskrona, Sweden, May 15-16, 2017), 45-52.
  19. V. Boeva, M. Angelova, E. Tsiporkova. “Identifying Subject Experts through Clustering Analysis”. The Annual Machine Learning Conference of the Benelux Benelearn 2017 (Eindhoven, Netherlands, June 9-10, 2017), 95-97.
  20. V. Boeva, M. Angelova, E. Tsiporkova, N. Lavesson. “Evolutionary Clustering Techniques”, WiML2017, 12th Women in Machine Learning Workshop located with NIPS 2017, Long Beach, USA, Dec. 2017
  21. S. Shirimbab, L. Lundberg, E.Casalicchio “Performance Evaluation of Container and Virtual Machine Running Cassandra Workload”, Proc. of The 3rd International Conference on Cloud Computing Technologies and Applications (IEEE CloudTech17), Rabat, Marocco Oct. 2017
  22. M. Fiedler, K. De Moor, H. Ravuri, P. Tanneedi, and M. Chandiri, “On relationships between QoE ratings, data volumes and intentions to churn,” in Proc. of the 2017 IEEE 42nd Conference on Local Computer Networks Workshops (LCN Workshops), pp. 97-102, Singapore, Oct. 2017.
  23. E.Casalicchio and V.Perciballi, “Auto-scaling of Containers: The Impact of Relative and Absolute Metrics”, Foundations and Applications of Self* Systems (FAS*W), 2017 IEEE 2nd International Workshops on, Tucson, AZ, Sept. 2017 DOI: 10.1109/FAS-W.2017.149 
  24. V. Boeva, L. Lundberg and M. Angelova,”Outlier Mining in Supervised Classification Problems,” SweDS 2017 – 5th Swedish Workshop in Data Science, Gothenburg, Sweden, Dec. 2017. 
  25. C. Nordahl, M. Persson, and H. Grahn, “Detection of Residents’ Abnormal Behaviour by Analysing Energy Consumption of Individual Households,” in 1st Workshop on Data mining for Aging, Rehabilitation and Independent Assisted Living (ARIAL 2017), in conjunction with ICDM 2017, pp. XX–YY, New Orleans, USA, Nov. 2017.  
  26. V. Boeva, L. Lundberg, S. M. H. Kota, and L. Sköld, “Analysis of Organizational Structure Through Cluster Validation Techniques: Evaluation of Email Communications at an Organizational Level,” in Proc. of ICDM 2017 Workshop on Data Science for Human Capital Management (DSHCM 2017), pp. 170-176, USA, Nov. 2017.
  27. E. Garcia-Martin, N. Lavesson, H. Grahn, E. Casalicchio, and V. Boeva, “Adaptive Very Fast Decision Tree, preliminary results,” in 12th Women in Machine Learning Workshop (WiML 2017), in conjunction with NIPS 2017, pp. XX–YY, Long Beach, USA, December 2017. Accepted for poster presentation. 
  28. S. Abghari, V. Boeva, N. Lavesson, J. Gustafsson, J. Shaikh, and H. Grahn, “Anomaly Detection in Video Data Sessions,” in 4th Swedish Workshop on Data Science (SweDS 2017), Gothenburg, Sweden, Dec.  2017. (poster) 
  29. E. Garcia-Martin, N. Lavesson, H. Grahn, E. Casalicchio, and V. Boeva, “Adaptive Very Fast Decision Tree — Preliminary Results,” in 4th Swedish Workshop on Data Science (SweDS 2017), Gothenburg, Sweden, Dec.  2017
  30. C. Nordahl, M. Persson, and H. Grahn, “Detecting abnormal behavior of elderlies by analyzing energy consumption of individual households,” in 4th Swedish Workshop on Data Science (SweDS 2017), Gothenburg, Sweden, Dec.  2017. 
  31. F. Westphal, N. Lavesson, and H. Grahn, “Document Image Binarization Using Recurrent Neural Net- works,” in 4th Swedish Workshop on Data Science (SweDS 2017), Dec. 2017, Gothenburg, Sweden, December 2017 (poster). 

Datasets

  1. E. Lopez-Rojas, “Synthetic Financial Datasets For Fraud Detection,” https://www.kaggle.com/ntnu-testimon/paysim1

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. E. G. Martín, N. Lavesson, and M. Doroud, “Hashtags and followers,” Social Network Analysis and Mining, Springer 6(1):1-15, December, 2016. DOI: 10.1007/s13278-016-0320-6
  2. A. Cheddad, “Structure Preserving Binary Image Morphing using Delaunay Triangulation,” Pattern Recognition Letters, 85:8-14. Elsevier. An official publication of the International Association for Pattern Recognition. 2017
  3. E. Garcia-Martin, N. Lavesson, and H. Grahn. “Energy efficiency Analysis of the Very Fast Decision Tree Algorithm”. Trends in Social Network analysis – Information Propagation, User Behavior Modelling, Forecasting and Vulnerability Assessment. Springer International Publishing, 2016. To appear.
  4. E. Casalicchio, V. Cardellini, G. Interino, M. Palmirani, “Research Challenges in Legal-rule and QoS-aware Cloud Service Brokerage“, Future Generation Computer Systems, Elsevier, DOI: 10.1016/j.future.2016.11.025
  5. C. Niyizamwiyitira, L. Lundberg, “Real-Time Systems Scheduling of Multiple Virtual Machines”, International Journal of Computers and Their Applications, to appear, June 2017.

Conferences/workshops

  1.  M. Danielsson, T. Sievert, H. Grahn, and J. Rasmusson, “Feature Detection and Description using a Harris- Hessian/FREAK Combination on an Embedded GPU,” in Proc. of the 5th Intl Conf. on Pattern Recognition Applications and Methods (ICPRAM 2016), pp. 517-525, Rome, Italy, February, 2016.
  2. C. Niyizamwiyitira, L. Lundberg, J. Sidorova, “Performance Evaluation Of Trajectory Queries On Multiprocessor And Cluster”, proc of the Int Conf on Data Mining and Database (DMDB). Austria. Venna. 2016. In press.
  3. F. Westphal, H. Grahn, and N. Lavesson, “A Binarization Pipeline for Historical Handwritten Documents,” in Proc. of the Family History Technology Workshop (FHTW), Provo, Utah, USA. Electronic proceedings at http://fhtw.byu.edu/program , February 2016.
  4. S. Shirinbab, L. Lundberg, J. Håkansson, “Comparing Automatic Load Balancing using VMware DRS with a Human Expert“, published in IEEE Int. Conf. on Cloud Engineering Workshop (IC2EW), pp 239-246, Berlin, Germany, April 2016.
  5. M. Akser, B. Bridges, G. Campo, A. Cheddad, et al., “SceneMaker: Creative Technology for Digital StoryTelling,” Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, 196, pp. 29-38, May 2016.
  6. D. Ammar, P. Heegaard, M. Xie, K. De Moor, and M. Fiedler, “Revealing the Dark Side of WebRTC Statistics Collected by Google Chrome,” in Proc. QoMEX, Lisbon, Portugal, June 2016.
  7. A. Cheddad, “Towards Query by Text Example for Pattern Spotting in Historical Documents,” 7th International Conference on Computer Science and Information Technology (CSIT’16), Amman, Jordan, IEEE Computer Society, July 2016.
  8. E. Casalicchio, L. Lundberg, S. Shirinbab, “Optimal adaptation for Apache Cassandra”, SoSeMC workshop at 13th IEEE International Conference on Autonomic Computing, Würzburg, Germany, July 2016.
  9. E. Casalicchio, L. Lundberg, S. Shirinbab, “An energy-aware adaptation model for Big Data platforms” (Poster) 13th IEEE International Conference on Autonomic Computing, Würzburg, Germany, July 2016.
  10. D. Ammar, K. De Moor, M. Xie, M. Fiedler, and P. Heegaard, “Video QoE Killer and Performance Statistics in WebRTC-based Video Communication,” in Proc. ICCE, Ha Long Bay, Vietnam, July 2016.
  11. S. Sagar, J. Sidorova. “Sequence Retriever for Known, Discovered, and User-Specified Molecular Fragments”. Proc. in the 10th International Conference on Practical Applications of Computational Biology & Bioinformatics 2016 Springer International Publishing. (pp. 51-58), Sevilla, Spain, July, 2016.
  12. S. Abghari, E. G. Martin, C. Johansson, N. Lavesson, and H. Grahn “Trend analysis to automatically identify heat program changes,” in Proc. of the 15th International Symposium on District Heating and Cooling, Seoul, Korea, September 2016.
  13. H. Kusetogullari, “Unsupervised Text Binarization in Handwritten Historical Documents using k-means Clustering”, in Proc. IEEE International Science and Information Conference on Intelligent Systems, London, September, 2016
  14. C. Johansson, M. Bergkvist, D. Geysen, and N. Lavesson, “Operational demand forecasting in district heating systems using ensembles of online machine learning algorithms,” in Proc. of the 15th Int’l Symp. on District Heating and Cooling (DHC), Seoul, Korea. The paper won the Excellence in Research award at the conference. September 2016.
  15. M. Khambhammettu and M. Persson, “Analyzing a decision support system for resource planning and surgery scheduling” in Hcist 2016 (International conference on Health and Social Care Information Systems and Technologies), Porto, Portugal.  October 2016.
  16. E. Casalicchio, “Autonomic Orchestration of Containers: Problem Definition and Research Challenges,” in Proc. of the 10th EAI Int’l Conf. on Performance Evaluation Methodologies and Tools, pp. 287-290, October 2016.
  17. A. Lopez-Rojas, and S. Axelsson. “A Review of Computer Simulation for Fraud Detection Research in Financial Datasets”. In: Future Technologies Conference, San Francisco, USA. 2016.
  18. A. Lopez-Rojas, A. Elmir, and S. Axelsson. “PaySim: A financial mobile money simulator for fraud detection”. In: The 28th European Modeling and Simulation Symposium – EMSS 2016, Larnaca, Cyprus.
  19. E. García Martín, H. Grahn, and N. Lavesson, “Energy Efficiency in Machine Learning,” in Proc. of 4th the Swedish Workshop on Data Science (SweDS 2016), Skövde, Sweden November 2016.
  20. F. Westphal, H. Grahn, and N. Lavesson, “Efficient Parameter Tuning for Image Binarization,” in Proc. of the 4th Swedish Workshop on Data Science (SweDS 2016), Skövde, Sweden, November 2016.
  21. S. Abghari, N. Lavesson, and H. Grahn “Market Share Prediction Based on Scenario Analysis Using a Naive Bayes Model,” [abstract] The 4th Swedish Workshop on Data Science (SweDS 2016), Skövde, Sweden, November 2016.
  22. S. Petersson, H. Grahn, and J. Rasmusson, “Color Demosaicing using Structural Instability,” in Proc. of IEEE International Symposium on Multimedia (ISM-2016), pp. 541-544, San Jose, CA, USA, December. 2016.
  23. S. Shirinbab, L. Lundberg. “Performance Implications of Resource Over-Allocation During the Live Migration,” IEEE Cloud Com conference, pp. 1-6, Luxembourg, 2016.
  24. C. Niyizamwiyitira, L. Sköld, L. Lundberg, J. Sidorova, “Analytic Queries on Telenor Data”, HPI Future SOC Lab Day, Potsdam, Germany, April 2016.
  25. J. Sidorova, L. Lundberg, L. Sköld, “Optimizing the Utilization in Cellular Networks using Telenor Mobility Data and HPI Future SoC Lab Hardware Resources”, HPI Future SOC Lab Day, Potsdam, Germany, November 2016.
  26. E.Casalicchio, L.Lundberg, S.Shirinbab, “Energy-aware adaptation in managed Cassandra datacenters”, IEEE International Conference on Cloud and Autonomic Computing (ICCAC 2016), Augsburg, Germany, September 12-16, 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. Sidorova, J. Garcia,  “Bridging from Syntactic to Statistical Methods: Classification with Automatically Segmented Features from Sequences,” Pattern Recognition, 48(11):3749-3756, November 2015.
  2. M. Forsman, A.Glad, L. Lundberg, D. Ilie, Algorithms for automated live migration of virtual machines. Journal of Systems and Software 101: 110-126 (2015

Conferences/workshops

  1. L. Lundberg, H. Grahn, D. Ilie, and C. Melander, “Cache Support in a High Performance Fault-tolerant Distributed Storage System for Cloud and Big Data,” in Proc. of the High Performance Big Data and Cloud Computing Workshop (HPBC), pp. XX-YY, May 2015, Hyderabad, India.
  2. E. García Martín, N. Lavesson, and H. Grahn, “Energy Efficiency in Data Stream Mining,” in Proc. of the Int’l Symp. on Foundations and Applications of Big Data Analytics (FAB 2015), pp. XX-YY, August 2015, Paris, France.
  3. E. A. Lopez-Rojas, S.Axelsson, (2015). Using the RetSim Fraud Simulation Tool to set Thresholds for Triage of Retail Fraud. The 20th Nordic Conference on Secure IT Systems. Stockholm, Sweden 19-21 October. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-10868
  4. E. A Lopez-Rojas, (2015). Extending the RetSim Simulator for Estimating the Cost of fraud in the Retail Store Domain. The 27th European Modeling and Simulation Symposium. Bergeggi, Italy. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-10869
  5. C. Niyizamwiyitira, L. Lundberg, and H. Lennerstad. (2015). “Utilization-based Schedulability Test of Real-time Systems on Virtual Multiprocessors.” In 2015 44th International Conference on Parallel Processing Workshops, (Beijing: IEEE), pp. 267-276.
  6. C. Niyizamwiyitira, L. Lundberg, “Period Assignment in Real-time scheduling of Multiple Virtual Machines” in Proceedings of the 7th International Conference on Management of Emergent Digital EcoSystems, MEDES’15, October 25-29, 2015, Caraguatatuba/Sao Paulo, Brazil. Copyright © 2015 ACM 978-1-4503-3480-8
  7. S. Shirinbab, L. Lundberg, Performance Implications of the Number of Virtual CPUs in a Large Telecommunication Application, International conference on Networks, Computers, and Communications, 2015, pp 1-6.
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
  1. Anton Borg, Martin Boldt, Niklas Lavesson, Ulf Melander, Veselka Boevac, “Detecting serial residential burglaries using clustering,” Expert Systems with Applications, Volume 41, Issue 11, September 2014, Pages 5252-5266, doi:10.1016/j.eswa.2014.02.035.
  2. Mattias Forsman, Andreas Glad, Lars Lundberg, Dragos, Ilie, “Automated Live Migration of Virtual Machines,” Journal of Systems and Software, 2014.
  3. Jan Kasper Martinsen, Håkan Grahn, and Anders Isberg, “Heuristics for Thread-Level Speculation in Web Applications,” IEEE Computer Architecture Letters, Vol. 13, No. 2, pp. 77-80, June-December 2014, doi 10.1109/L-CA.2013.26. 2 [3]
  4. Rafid Siddiqui and Siamak Khatibi, “Robust visual odometry estimation of road vehicle from dominant surfaces for large-scale mapping,” IET Intelligent Transport Systems, 9, pp, 26 September 2014, doi: 10.1049/iet-its.2014.0100.
  5. A.A. Beyene, T. Welemariam, M. Persson, and N. Lavesson, “Improved concept drift handling in surgery prediction and other applications,” Knowledge and Information Systems, 2014, Springer London, doi: 10.1007/s10115-014-0756-9.
  6. Florian Westphal, Stefan Axelsson, Christian Neuhaus, Andreas Polze, “VMI-PL: A monitoring language for virtual platforms using virtual machine introspection,” Digital Investigation: The International Journal of Digital Forensics & Incident Response, Volume 11, pp. S85-S94, August 2014, Elsevier, doi:10.1016/j.diin.2014.05.016
  7. Martin Boldt and Anton Borg, “En ny metod för registrering och automatisk analys av mängdbrott”, in proceedings of The 5th Biennial Nordic Police Research Seminar, 2014.
  8. Anton Borg, “On Descriptive and Predictive Models for Serial Crime Analysis,” Blekinge Institute of Technology Doctoral Dissertation Series, 2014, ISBN: 978-91-7295-288-1.
  9. Fredrik Erlandsson, “On social interaction metrics: social network crawling based on interestingness,” Licentiate dissertation, Blekinge Institute of Technology, 2014, ISBN: 978-91-7295-287-4.