Publications

Publications (2018-2020)

International Journals:

  1. S. Jiang, M. Kaiser, S. Kollias, N. Krasnogor, “A Scalable Test Suite for Continuous Dynamic Multiobjective Optimization”, IEEE Transactions on Cybernetics, vol. 50, pp. 2814-2826, 2020.
  2. U. Abideen, M. Ghafoor, K. Munir, M. Saqib, A. Ullah, T. Zia, S. A. Tariq, G. Ahmed, and A. Zahra, “Uncertainty Assisted Robust Tuberculosis Identification with Bayesian CNNs”, IEEE Access, vol. 8, pp. 22812-22825, 2020, doi: 10.1109/ACCESS.2020.2970023.
  3. J. Wingate, I. Kollia, L. Bidaut, S. Kollias, “A Unified Deep Learning Approach to Prediction of Parkinson’s Disease”, IET Image Processing, pp. 1-10, to appear, May/June 2020.
  4. M. Junaid, M. Ghafoor, A. Hassan, S. Khalid, S. A. Tariq, G. Ahmed, and T. Zia, “Multi-Feature View-Based Shallow Convolutional Neural Network for Road Segmentation”, IEEE Access, vol. 8, pp. 36612-36623, 2020, doi: 10.1109/ACCESS.2020.2968965.
  5. M. Thota, S. Kollias, M. Swainson, D. Leontidis, “Multi-Source Deep Domain Adaptation for Quality Control in Retail Food Packaging”, Computers in Industry, accepted for publication July 2020.
  6. M. Shafiq, I. A. Taj, M. Ghafoor, S. A. Tariq, A. Abbas, A. Y. Zomaya, “Accelerating fingerprint identification using FPGA for large-scale applications”, Journal of Parallel and Distributed Computing., vol. 141, pp. 35-48, July 2020.
  7. B. Alhnaity, S. Kollias, G. Leontidis, S. Jiang, B. Schampb, S. Pearson, “An autoencoder wavelet based deep neural network with attention mechanism for multi-step prediction of
    plant growth”, Information Sciences, accepted for publication, December 2020.
  8. M. Ghafoor, S. A. Tariq, I. A. Taj, N. M. Jafri, “Robust palmprint identification using efficient enhancement and two-stage matching technique”, IET Image Processing, March 2020.
  9. F. Ribeiro, F. Caliva, M. Swainson, K. Gudmundsson, G. Leontidis, S. Kollias, “Deep Bayesian Self-Training”, Neural Computing and Applications, vol. 32, pp. 4275-4291, 2019.
  10. A. Chatzitofis, D. Zarpalas, S. Kollias, P. Daras, “Deep Optical Motion Capture Using Multiple Depth Sensors and Retro-Reflectors”, Sensors, 19(2), pp. 282-307, 2019.
  11. B. Chen, J. Huang, S. Kollias, S. Yue, Y. Huang, “Combining Guaranteed and Spot Markets in Display Advertising: Selling Guaranteed Page Views with Stochastic Demand’, accepted for publication, European Journal of Operational Research, 2019.
  12. M. Ghafoor, S. A. Tariq, T. Zia, I. A. Taj, A. Abbas, Ali Hassan, A. Y. Zomaya, “Fingerprint Identification with Shallow Multifeature View Classifier”, IEEE Transactions on Cybernetics, Dec. 2019. doi: 10.1109/TCYB.2019.2957188.
  13. W. Ahmad, M. Ghafoor, S. A. Tariq, A. Hassan, M. Sjöström, R. Olsson, “Computationally Efficient Light Field Image Compression Using a Multiview HEVC Framework”, IEEE Access, vol. 7, pp. 143002 – 143014, Sep. 2019.
  14. N. Papanelopoulos, Y. Avrithis, S. Kollias, “Revisiting the Medial Axis for Planar Shape Decomposition”, Computer Vision and Image Understanding, pp. 66-78, vol. 179, February 2019.
  15. B. Alhnaity, S. Pearson, G. Leontidis, S. Kollias, “ Using Deep Learning to Predict Plant Growth and yield in Greenhouse Environments”, Act Horticuturare, 2019.
  16. T. Zia, M. Ghafoor, S. A. Tariq, I. A. Taj. “Robust Fingerprint Classification with Bayesian Convolutional Networks”, IET Image Processing, Feb 2019.
  17. U. Zafar, M. Ghafoor, T. Zia, G. Ahmed, S. Jabbar, A. M. Sharif, “Face Recognition with Bayesian Convolutional Networks for Robust Surveillance Systems”, EURASIP Journal on Image and Video Processing – Springer, Jan 2019.
  18. K. Raftopoulos, S. Kollias, D. Sourlas, M. Ferecatu, “On the Beneficial Effect of Noise in Vertex Localization”, International Journal of Computer Vision, vol. 126, no 1, pp. 111-139, 2018.
  19. D. Kollias, A. Tagaris, A.-G. Stafylopatis, S. Kollias, G. Tagaris, “Deep Neural Architectures for Prediction in Healthcare”, Complex Intelligent Systems, vol. 4, no. 2, pp. 119–131, 2018.
  20. M. Ghafoor, S. Iqbal, S. A. Tariq, N. M. Jafri, I. A. Taj, “Efficient Fingerprint Matching Using GPU”, IET Image Processing, vol. 12, no 2, pp. 274 – 284, Feb 2018.
  21. A. Tagaris, D. Kollias, A.-G. Stafylopatis, G. Tagaris, S. Kollias, “Machine Learning for Neurodegenerative Disorder Diagnosis: Survey of Practices and Launch of Benchmark Dataset”, International Journal on Artificial Intelligence Tools, vol. 27, no. 3, 1850011, 2018.

International Conferences:

  1. F. Ribeiro, G. Leontidis, S. Kollias, “Introducing Routing Uncertainty in Capsule Networks”, NeurIPS, December 2020.
  2. D. Kollias, M. Seferis, V. Brillakis, Y. Vlaxos, J. Wingate, L. Soukissian, S. Kollias, “‘Deep Transparent Prediction through Latent Representation Analysis”, European Conference on Artificial Intelligence (ECAI 2020), 1st TAILOR Workshop on Foundations of Trustworthy AI – Integrating Learning, Optimization and Reasoning, Santiago de Compostela, 29-30/8/2020.
  3. F. Ribeiro, G. Leontidis, S. Kollias, “Capsule routing via variational bayes”, in Proceedings of the 34th AAAI Conference on Artificial Intelligence, New York, USA, 8-12/2/2020.
  4. C. Demazière, A. Mylonakis, P. Vinai, A. Durrant, F. Ribeiro, J. Wingate, G. Leontidis, S. Kollias, “Neutron Noise-based Anomaly Classification & Localization Using ML”, PHYSOR 2020, Cambridge, UK, 29/3-2/4/2020.
  5. A. Durrant, G. Leontidis, S., “3D CNN – RNNs for reactor perturbation unfolding and anomaly detection, EPJ Nuclear Sci. Technol. 5, 20, 2019; also in FISA, Pitesti, Romania, 4-7/6/2019 (paper award).
  6. I. Kollia, A. Stafylopatis, S. Kollias, “Predicting Parkinson’s Disease using Latent Information extracted from Deep Neural Networks”, IEEE International Joint Conference on Neural Networks, Budapest, Hungary, 15-19 July 2019.
  7. B. Alhnaity, S. Pierson, G. Leontidis, S. Kollias, “Using Deep Learning to Predict Plant Growth and Yield in Greenhouse Environments”, GreenSys 2019, International Symposium on Advanced Technologies and Management for Innovative Greenhouses, Angers, France, 16-20 June 2019.
  8. I. Kollia, A. Stafylopatis, S. Kollias, “Artificial Intelligence and Machine Learning for Cultural Heritage Content Enrichment and Search on the Web”, Heritage Dot Conference, 3-5 June 2019.
  9. J. Wingate, I. Kollia, L. Bidaut, S. Kollias, “Healthcare Prediction using Latent Information extracted from Deep Neural Networks. Advances in Data Science, Manchester, UK, 20-21 May 2019.
  10. F. Ribeiro, F. Caliva, D. Chionis, A. Dokhane, A. Mylonakis, C. Demaziere, G. Leontidis and S. Kollias, “Towards a Deep Unified Framework for Nuclear Reactor Perturbation Analysis”, IEEE Symposium Series on Computational intelligence, Bangalore, India, 18-21 November 2018.
  11. I. Kollia, S. Kollias, “A Deep Learning Approach for Load Forecasting of Power Systems”, IEEE Symposium Series on Computational Intelligence, Bangalore, India, 18-21 November 2018.
  12. F. Ribeiro, L. Gong, F. Caliva, M. Swainson, K. Gudmundsson, M. Yu, G. Leontidis, X. Ye and S. Kollias, “An end-to-end deep neural architecture for optical character verification and recognition in retail food packaging”, 25th IEEE International Conference on Image Processing, 7-10 October 2018.
  13. F. Caliva, F.   S.  Ribeiro,  A.  Mylonakis,  C.  Demaziere,  P.Vinai, G.  Leontidis,  and  S.  Kollias,  “A  deep  learning  approach  to  anomaly detection  in  nuclear  reactors,”  IEEE International  Joint  Conference on Neural Networks, Rio de Janeiro, Brazil, 8-13 July 2018.
  14. J. McGinty, G. Leontidis, S. Kollias, “Optimising Remedial Outcomes in Gas Turbines through Data Analysis”, Intern. Conf. on Industrial Maintenance & Reliability, Manchester, 13-15 June 2018.
  15. F.Ribeiro, F. Caliva, M. Swainson, K. Gudmundsson, G. Leontidis, S. Kollias, “An adaptable deep learning system for optical character verification in retail food packaging”, IEEE EAIS Conference, Rhodes, 25-27 May 2018.

 

 

Publications (2014-2018)

International Journals:

  1. G.Leontidis, “A new unified framework for the early detection of the progression to diabetic retinopathy from fundus images”, Computers in Biology and Medicine, Elsevier, 90, 98-115, 2017.
  2. V.Cutsuridis , A. Moustafa, “Computational models of Alzheimer’s disease”, Scholarpedia, 12(1):32144, 2017.
  3. K. Raftopoulos, S. Kollias, D. Souras, M. Ferecatu, “On the benecial effect of Noise in Vertex Localization”, International Journal of Computer Vision, accepted for publication, 2017.
  4. F. Caliva*, G. Leontidis*, P. Chudzik, A. Hunter, L. Antica, V. Al-Diri, “Hemodynamics in the retinal vasculature during the progression of diabetic retinopathy” Journal for Modeling in Ophthalmology, 1 (4), 6-15, 2017 (*co-first authors).
  5. X. Xu, M. Zhong, C. Guo. “A Hyperplane Clustering Algorithm for Estimating the Mixing Matrix in Sparse Component Analysis”, Neural Processing Letters, 1-16, 2017.
  6. J. Du, M. Zhong. ”Pseudo-marginal Markov Chain Monte Carlo for Nonnegative Matrix Factorization.” Neural Processing Letters, vol. 45, no. 2, pp. 553-562, 2017.
  7. M. Yu, W. Chen and J. Chambers, “State dependent multiple model-based particle filtering for ballistic missile tracking in a low-observable environment”, Aerospace Science and Technology, Vol.67, pp. 144–154, 2017.
  8. V. Cutsuridis, “Behavioral and computational varieties of response inhibition in eye movements”,  Philos Trans R Soc Lond B, 372: 20160196, 2017.
  9. A. Moustafa, M. Hassan, D. Hewedi, J. Garami, H.  Alashwal, N. Zaki, S. Seo, V. Cutsuridis et al., “Genetic Underpinnings in Alzheimer’s disease – a review”, Reviews in the Neurosciences, accepted for publication, 2017.
  10. G. Leontidis, B. Al-Diri, A. Hunter “Summarising the retinal vessel calibres in healthy, diabetic and diabetic retinopathy eyes”, Computers in Biology and Medicine, Elsevier, 72, 65-74, 2016.
  11. V.Cutsuridis, A. Moustafa, “Multiscale Models of Pharmacological, Immunological and Neurostimulation Treatments in Alzheimer’s Disease”, Drug Discov Today: Dis Model http://dx.doi.org/10.1016/j.ddmod.2016.12.001. 2016
  12. C. Varytimidis, K. Rapantzikos , Y. Avrithis, S. Kollias, “α-shapes for local feature detection”, Pattern Recognition, vol. 50, pp. 56-73, February 2016.
  13. B. Chen, J. Wang, I. Cox, and M. Kankanhalli. Multi-keyword multi-click advertisement option contracts for sponsored search. ACM Transactions on Intelligent Systems and Technology, vol 7, no. 1, 2015.
  14. M. Khan, M. Yu (Corresponding author), P. Feng L. Wang and J. Chambers, “An Unsupervised Acoustic Fall Detection System Using Source Separation for Sound Interference Suppression”, Signal Processing, Vol. 110, pp. 199—201, 2015.
  15. V. Saravanan, D. Arabali, A. Jochems, A.Cui, L. Gootjes-Dreesbach, V. Cutsuridis, M. Yoshida, “Transition between encoding and consolidation/replay dynamics via cholinergic modulation of CAN current: A modeling study”, Hippocampus, 25(9):1052-70, 2015.
  16. C. Varytimidis, K. Rapantzikos , Y. Avrithis, S. Kollias, “Dithering-based sampling & weighted a-shapes for local feature detection”, IPSJ Transactions Computer Vision & Applications, vol. 7, pp.189-200, 2015.
  17. B. Chen and J. Wang, “A lattice framework for pricing display advertisement options with the stochastic volatility underlying model”, Electronic Commerce Research and Applications, vol 14, no. 6, pp. 465-479, 2015.
  18. G. Siolas, G. Caridakis, S. Kollias, A. Stafylopatis, “Context-Aware User Modeling and Semantic Interoperability in Smart Home Environments”, Int. Journal Virtual Communities & Social Networking, vol. 7, no 3, pp. 17-50, 2015.
  19. V. Cutsuridis, , “Neural Competition via Lateral Inhibition between Decision Processes and Not a STOP Signal Accounts for the Antisaccade Performance in Healthy and Schizophrenia Subjects”, Front. Neurosci., 9:5, 2015.
  20. V. Cutsuridis, P. Poirazi, , “A computational study on how theta modulated inhibition can account for the long temporal delays in the entorhinal-hippocampal loop”, Neurobiology of Learning and Memory, 120: 69-83, 2015
  21. S. Asteriadis, K. Karpouzis, S. Kollias, “Visual Focus of Attention in Non-calibrated Environments using Gaze Estimation”, International Journal of Computer Vision, Springer, vol. 107, no 3, pp. 293-316, 2014.
  22. G. Leontidis, B. Al-Diri, A. Hunter, “Diabetic retinopathy: current and future methods for early screening from a retinal hemodynamic and geometric approach”, Expert Review of Ophthalmology, 9(5), 431-442, 2014.
  23. G. Tolias, Y. Kalantidis, Y. Avrithis, S. Kollias, “Towards Large Scale Image Indexing by Feature Selection”, Computer Vision and Image Understanding, vol. 120, no 3, pp. 31 – 45,  2014.
  24. Ch. Hondrou, E. Tsalapati, A. Raouzaiou, G. Marandianos, K. Karpouzis, S. Kollias, “A Player Specific Conflict Handling Ontology”, International Journal of Serious Games, vol 1, no 3, pp. 61-74, 2014.

Books:

  1. N. Simou, A. Chortaras, G. Stamou, S. Kollias, “Enriching and Publishing Cultural Heritage as Linked Open Data”, in Mixed Reality and Gamification for Cultural Heritage, Springer, 2017.
  2. V. Cutsuridis, “Computational Models of Memory Formation in Healthy and Diseased Microcircuits of the Hippocampus”, in: A. Moustafa (ed.), Computational models of brain and behavior. Wiley, UK, 2017.
  3. V. Cutsuridis , “Simplified compartmental models of CA1 pyramidal cells of theta-modulated inhibition effects on spike timing-dependent plasticity”, in: Cutsuridis V, Graham BP, Cobb S, Vida I (eds), Hippocampal Microcircuits: A computational modeller’s resource book, 2nd edition, Springer, USA, 2017.
  4. V. Cutsuridis , “Models of Rate and Phase Coding of Place Cells in Hippocampal Microcircuits”, in: Cutsuridis V, Graham BP, Cobb S, Vida I (eds), Hippocampal Microcircuits: A computational modeller’s resource book, 2nd edition, Springer, USA, 2017.
  5. G. Leontidis, B. Al-Diri B., A. Hunter, “Exploiting the retinal vascular geometry in identifying the progression to diabetic retinopathy using penalized logistic regression and random forests”, in Studies in Computational Intelligence Book Series, Springer International Publishing, pp. 381-400, 2016.
  6. A. Chortaras, S. Kollias, K. Rapantzikos, G. Stamou, “Semantic Representation, Enrichment and Retrieval of Audiovisual Film Content”, in Trends in DSP, Pan Stanford Series, 2015.

International Conferences:

  1. F.DeSousaRibeiro, L.Gong, F.Caliva, M.Swainson, K.Gudmundsson, M.Yu, G.Leontidis, X.Ye, S.Kollias, “An End-to-End Deep Neural Architecture for Optical Character Verification and Recognition in Retail Food Packaging”, In: Image Processing, International Conference on (ICIP), 2018.
  2. F.Caliva, F.DeSousaRibeiro, A.Mylonakis, C.Demaziere, P.Vinai, G.Leontidis, S.Kollias, “A Deep Learning Approach to Anomaly Detection in Nuclear Reactors”, In: Neural Networks, International Joint Conference on (IJCNN), 2018.
  3. F.DeSousa Ribeiro, F.Caliva, M.Swainson, K.Gudmundsson, G.Leontidis, S.kollias, “An Adaptable Deep Learning System for Optical Character Verification in Retail Food Packaging”, In: Evolving and Adaptive Intelligent Systems, IEEE Conference on, 2018.
  4. D.Kollias, M.Yu, A.Tagaris, G.Leontidis, S.Kollias, A.G. Stafylopatis, “Adaptation and contextualization of deep neural network models”, In: Computational Intelligence, 2017 IEEE Symposium Series on, 1-8, 2017.
  5. X.Chen, B. Chen, MS. Kankanhalli, “Optimizing trade-offs among stakeholders in real-time bidding by incorporating multimedia metrics”, Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2017.
  6. X.Chen, B. Chen, MS. Kankanhalli, “MM2RTB: bringing multimedia metrics to real-time bidding”,  Proceedings of the 10th International Workshop on Data Mining for Online Advertising (AdKDD, in conjunction with SIGKDD), 2017
  7. A. Vlaxostergiou, G. Marandianos, S. Kollias, “From Conditional Random Field (CRF) to Rhetorical Structure Theory(RST): incorporating Context information in sentiment analysis”, Semantic Sentiment Analysis Workshop, 14th ESWC 2017, Portoroz, Slovenia, May 28, 2017.
  8. A. Vlaxostergiou, G. Marandianos, S. Kollias, “From HCI and Affective Computing to Sentiment Analysis: extending the pool of context-aware features in Affective-aware systems”, 8th Intern. Conf. on Applied Human Factors & Ergonomics, LA, CA, USA, July 17-21, 2017.
  9. L. Gong, M. Yu and T. Gordon, Online codebook modeling based background subtraction with a moving camera, accepted by International Conference on Frontiers of Signal Processing (ICFSP), Paris, France, 2017.
  10. L. Gong and M. Yu, A NEW INTERACTING MULTIPLE MODEL PARTICLE FILTER BASED BALLISTIC MISSILE TRACKING METHOD, accepted by International Conference on Frontiers of Signal Processing (ICFSP), Paris, France, 2017.
  11. V. Cutsuridis, “A neural accumulator model of antisaccade performance of healthy controls and obsessive-compulsive disorder patients”, ICCM 2017, Warwick, July 21-25, 2017.
  12. A. Vlaxostergiou, G. Marandianos, S. Kollias, “Context Incorporation Using Context – Aware Language Features”, EUSIPCO 2017, Kos, Greece, August 28-September 2, 2017.
  13. G. Goudelis, G. Tsatiris, K. Karpouzis, S. Kollias, “3D Cylindrical Trace Transform based feature extraction for effective human action classification”, IEEE Intern. Conf. on Computational Intelligence in Games, New York, August 22-25, 2017.
  14. G. Leontidis, B. Al-Diri, A. Hunter, “Is the arteriovenous ratio a biomarker of progression from diabetes to diabetic retinopathy?” Investigative Ophthalmology and Visual Science, ARVO Conference, Seattle, USA, 2016.
  15. C. Varytimidis, K. Rapantzikos, G. Tsatiris and S. Kollias, ”A systemic approach to automatic metadata extraction from multimedia content”, IEEE Symposium Series on Computational Intelligence, Athens, Greece, 2016.
  16. B. Chen, “Risk-aware dynamic reserve prices of programmatic guarantee in display advertising”, Proceedings of the 16th IEEE International Conference on Data Mining Workshops (ICDMW), pp. 511-518, 2016.
  17. C. Varytimidis, K. Rapantzikos, G. Tsatiris, S. Kollias, “The Mecanex system for Multimedia Content Annotation”, IEEE Digital Media Industry and Academia Forum, 2016.
  18. A. Chortaras, S. Kollias, K. Rapantzikos, G. Stamou, “A Semantic Approach to Film Content Analysis and Retrieval”, SETN, 2016.
  19. G. Stratogiannis, A. Vlachostergiou, G. Siolas, G. Caridakis, Ph. Mylonas, A. Stafylopatis, S. Kollias, “User and home appliances pervasive interaction in a sensor driven Smart Home environment: the SandS approach”, 10th International Workshop on Semantic and Social Media Adaptation and Personalization, Trento, Italy, November 2015.
  20. A. Vlachostergiou, G. Caridakis, A. Raouzaiou, S. Kollias, “HCI and Natural Progression of Context – Related Questions”, 17th International Conference on Human – Computer Interaction, Los Angeles, CA, USA, August 2015,
  21. G. Goudelis, G. Tsatiris, K. Karpouzis, S. Kollias, “Fall detection using History Triple Features”, PETRA 2015, Affective Computing for Biological Activity Recognition in Assistive Environments Workshop, Corfu, July 2015.
  22. M. Zhong, N. Goddard, C. Sutton, “Latent Bayesian melding for integrating individual and population models”, In Advances in Neural Information Processing Systems (NIPS) 28, 2015. (Spotlight presentation; the acceptance rate is 4.46% with 1838 submissions.)
  23. S. Hammer, A. Seiderer, E. André, T. Rist, S. Kastrinaki, C. Hondrou, A. Raouzaiou, K. Karpouzis, S. Kollias, “Design of a Lifestyle Recommender system for the Elderly: Requirement Gatherings in Germany and Greece”, PETRA 2015, Affective Computing for Biological Activity Recognition in Assistive Environments Workshop, Corfu, July 2015.
  24. V. Cutsuridis, G. Efstathiou, M. Kokkinidis, “Protein Function Prediction by an ARTMAP Neural Network. Proc MLCB/MLSB worskshop”, NIPS 2015, Montreal, Canada, Dec 12-13, 2015.
  25. G. Leontidis, B. Al-Diri, J. Wigdahl, A. Hunter, “Evaluation of geometric features as biomarkers of diabetic retinopathy for characterizing the retinal vascular changes during the progression of diabetes”, Engineering in Medicine and Biology Society (EMBC), 37th Annual International Conference of IEEE, 5255-5259, 25-29 August, 2015.
  26. G. Leontidis, J. Wigdahl, B. Al-Dir, A. Ruggeri, A. Hunter, , “Evaluating tortuosity in retinal fundus images of diabetic patients who progressed to diabetic retinopathy”, Engineering in Medicine and Biology Society (EMBC),  37th Annual International Conference of IEEE, 25-29 August 2015.
  27. J. Wigdahl, P. Guimarães, G. Leontidis, A.  Triantafyllou, A. Ruggeri, “Automatic gunn and salus sign quantification in retinal images”, Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of IEEE, 5251-5254, 25-29 August 2015.
  28. G. Leontidis, B. Al-Diri, A. Hunter, “Retinal vascular geometry: examination of the changes between the early stages of diabetes and first year of diabetic retinopathy”,  IEEE Science and Information conference, pp. 709-713, London, UK, 2015.
  29. G. Leontidis, B. Al-Diri, A. Hunter, “Study of the retinal vascular changes between the early stages of diabetes and first year of diabetic retinopathy”, Investigative Ophthalmology and Visual Science 56(7), ARVO conference, Denver, USA, 2015.
  30. G. Leontidis, B. Al-Diri, A. Hunter, “Study of the retinal vascular changes in the transition from diabetic to diabetic retinopathy eye”, 36th International Conference of IEEE in Engineering in Medicine and Biology (EMBC), Chicago, USA, 2014
  31. M. Zhong, N. Goddard, C. Sutton, “Signal aggregate constraints in additive factorial HMMs, with application to energy disaggregation”, In Advances in Neural Information Processing Systems (NIPS) 27, 2014.
  32. P. Feng, M. Yu, S. Naqvi, J. Chambers, ”Deep learning for posture analysis in fall detection.” IEEE 19th International Conference on Digital Signal Processing (DSP), Hong Kong, 2014.
  33. A. Vlachostergiou, G. Caridakis, S. Kollias, “Investigating context awareness of Affective Computing systems: A critical approach”, 6th Intern. Conference on Intelligence Human Computer Interaction (IHCI), 2014.
  34. C. Varytimidis, K. Rapantzikos, Y. Avrithis and S. Kollias, “Improving local features by dithering-based image sampling”, Asian Conference on Computer Vision, 2014.
  35. B. Chen, S. Yuan, J. Wang, “A dynamic pricing model for unifying programmatic guarantee and real-time bidding in display advertising”, Proceedings of the 8th International Workshop on Data Mining for Online Advertising (AdKDD, in conjunction with SIGKDD), pp 1–9, 2014. (Best Paper Award)
  36. S. Yuan, J. Wang, B. Chen, P. Mason, S. Seljan. “An empirical study of reserve price optimisation in real-time bidding”, Proceedings of the 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (SIGKDD), pp. 1897–1906, 2014.
  37. A. Vlachostergiou, G. Caridakis, S. Kollias, “Context in Affective Multiparty and Multimodal Interaction: Why, Which, How and Where?”, Workshop on Understanding and Modeling Multiparty, Multimodal Interactions, 2014.

Leave a Reply

Your email address will not be published.