Publications 2018-11-06T10:15:37+00:00

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. 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
  2. J. Sidorova, O. Rosander, L. Sköldand 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 
  3. 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. doihttps://doi.org/10.1155/2018/7274141 
  4. 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, Springer, 1-22 (published August 6, 2018) https://link.springer.com/article/10.1007/s42001-018-0022-0

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. XX-YY, 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. NardelliV. Cardelliniand E. CasalicchioMulti-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. YavariabdiH. KusetogullariE. 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. MF., Demir, A. Cankirli, B. Karabatak, A. Yavariabdi, H. Kusetogullariand E. Mendi, Real-Time Resistor Color Code Recognition using Image Processing in Mobile Devices, 9th IEEE International Conference on Intelligent SystemsSeptember 2018, Madeira, PortugalAccepted, to appear. 
  9. E. Ventocilla, T.  Helldin, M. Riveiro, J. Bae, V. BoevaG. Falkman and N. Lavesson, “Towards a Taxonomy for Interpretable and Interactive Machine Learning”, Workshop on Explainable AI (XAI 2018), IJCAI 2018 Workshopaccepted. 
  10. C. NordahlV. Boeva, H. Grahn and M. Netz. “OrganizingVisualizing and Understanding Households Electricity Consumption Data through Clustering Analysis”. ARIEL 2018, IJCAI 2018 Workshopaccepted. 
  11. L. Lundberg, H. Lennerstad, E. Maria Garcia-Martin, N. LavessonV. Boeva. “Hyperplane Folding – a Way to Increase the Margin in Support Vector Machines.” GiML 2018, IJCAI 2018 Workshop, accepted. (abstract) 
  12. 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.
  13. 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

Journals

  1. MBoldt, ABorg, M. Svensson, JHildeby, ”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. XieQoE 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. XieA Glance at the Dagstuhl Manifesto ‘QoE Vadis?’, in Proc. 10th Int. Conf. On Quality of Multimedia ExperienceQoE Management Workshop, Pula, Italy, May/June 2018. 

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 AnalyticsSpringer, 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. 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
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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)
  10. 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.
  11. 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
  12. 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
  13. 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.
  14. 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
  15. 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.
  16. 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.
  17. 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.
  18. V. BoevaL. Lundberg, S. M. H. Kota, L. Skold, Analysis of Organizational Structure through Cluster Validation Techniques, in Proc. of ICDM 2017 Workshop on Data Science for Human Capital Management (DSHCM 2017)pp. 170-176, New Orleans, USANov. 2017
  19. V. Boeva, M. Angelova, E. Tsiporkova, N. Lavesson. “Evolutionary Clustering Techniques”, WiML2017, 12th Women in Machine Learning Workshop located with NIPS 2017, Long Beach, USADec. 2017
  20. 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
  21. 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.
  22. 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 
  23. V. Boeva, L. Lundberg and M. Angelova,Outlier Mining in Supervised Classification Problems, SweDS 2017 – 5th Swedish Workshop in Data ScienceGothenburg, SwedenDec. 2017. 
  24. 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–YYNew Orleans, USANov. 2017 
  25. 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, USADecember 2017. Accepted for poster presentation. 
  26. 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, SwedenDec.  2017. (poster) 
  27. 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, SwedenDec.  2017
  28. 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, SwedenDec.  2017. 
  29. 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, SwedenDecember 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 MethodEntropy, 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 DasariV.Boeva, JWallNLavesson 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 Letters85:8-14. 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. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. 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
  13. 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.
  14. 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.
  15. 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.
  16. 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.
  17. S. Abghari, H. Grahn, and N. Lavesson, “Market Share Prediction Based on Scenario Analysis Using a Naive Bayes Model,” in Proc. of the 4th Swedish Workshop on Data Science (SweDS 2016), Skövde, Sweden, November 2016.
  18. 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.
  19. 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.
  1. 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.
  2. 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.
  3. S. Shirinbab, L. Lundberg. “Performance Implications of Resource Over-Allocation During the Live Migration,” IEEE Cloud Com conference, pp. 1-6, Luxembourg, 2016.
  4. C. Niyizamwiyitira, L. Sköld, L. Lundberg, J. Sidorova, “Analytic Queries on Telenor Data”, HPI Future SOC Lab Day, Potsdam, Germany, April 2016.
  5. 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.
  6. E. Casalicchio “Autonomic Orchestration of Containers: Problem Definition and Research Challenges” (Position Paper), INFQ Workshop at 10th EAI International Conference on Performance Evaluation Methodologies and Tools.
  7. 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 MediaAdvances 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 LearningEntropy. 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, 4(2016) 15-29.
  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.