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Publications (10 of 17) Show all publications
Ståhl, N., Falkman, G., Karlsson, A., Mathiason, G. & Boström, J. (2019). Improving the use of deep convolutional neural networks for the prediction of molecular properties. In: Florentino Fdez-Riverola, Mohd Saberi Mohamad, Miguel Rocha, Juan F. De Paz, Pascual González (Ed.), Practical Applications of Computational Biology and Bioinformatics, 12th International Conference: . Paper presented at PACBB2018: International Conference on Practical Applications of Computational Biology & Bioinformatics (pp. 71-79). Springer
Open this publication in new window or tab >>Improving the use of deep convolutional neural networks for the prediction of molecular properties
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2019 (English)In: Practical Applications of Computational Biology and Bioinformatics, 12th International Conference / [ed] Florentino Fdez-Riverola, Mohd Saberi Mohamad, Miguel Rocha, Juan F. De Paz, Pascual González, Springer, 2019, p. 71-79Conference paper, Published paper (Refereed)
Abstract [en]

We present a flexible deep convolutional neural network method for the analyse of arbitrary sized graph structures representing molecules. The method makes use of RDKit, an open-source cheminformatics software, allowing the incorporation of any global molecular (such as molecular charge) and local (such as atom type) information. We evaluate the method on the Side Effect Resource (SIDER) v4.1 dataset and show that it significantly outperforms another recently proposed method based on deep convolutional neural networks. We also reflect on how different types of information and input data affect the predictive power of our model. This reflection highlights several open problems that should be solved to further improve the use of deep learning within cheminformatics.

Place, publisher, year, edition, pages
Springer, 2019
Series
Advances in Intelligent Systems and Computing, ISSN 2194-5357, E-ISSN 2194-5365 ; 803
Keywords
drug discovery, graph convolutional neural network, molecular property prediction, bioinformatics, convolution, neural networks, open source software, open systems, cheminformatics, convolutional neural network, deep convolutional neural networks, graph structures, molecular charge, molecular properties, predictive power, deep neural networks
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-16230 (URN)10.1007/978-3-319-98702-6_9 (DOI)2-s2.0-85052956812 (Scopus ID)978-3-319-98701-9 (ISBN)978-3-319-98702-6 (ISBN)
Conference
PACBB2018: International Conference on Practical Applications of Computational Biology & Bioinformatics
Available from: 2018-09-25 Created: 2018-09-25 Last updated: 2018-10-01
Ståhl, N., Falkman, G., Mathiason, G. & Karlsson, A. (2018). A self-organizing ensemble of deep neural networks for the classification of data from complex processes. In: : . Paper presented at 17th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Jun 11 - Jun 15, 2018, Cadiz, Spain (pp. 248-259). , 855
Open this publication in new window or tab >>A self-organizing ensemble of deep neural networks for the classification of data from complex processes
2018 (English)Conference paper, Published paper (Refereed)
Abstract [en]

We present a new self-organizing algorithm for classification of a data that combines and extends the strengths of several common machine learning algorithms, such as algorithms in self-organizing neural networks, ensemble methods and deep neural networks. The increased expression power is combined with the explanation power of self-organizing networks. Our algorithm outperforms both deep neural networks and ensembles of deep neural networks. For our evaluation case, we use production monitoring data from a complex steel manufacturing process, where data is both high-dimensional and has many nonlinear interdependencies. In addition to the improved prediction score, the algorithm offers a new deep-learning based approach for how computational resources can be focused in data exploration, since the algorithm points out areas of the input space that are more challenging to learn.

Series
Communications in Computer and Information Science, ISSN 1865-0929
Keywords
artificial neural networks, complex processes, ensemble methods, self organisation
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifiers
urn:nbn:se:his:diva-15008 (URN)10.1007/978-3-319-91479-4_21 (DOI)2-s2.0-85048061876 (Scopus ID)
Conference
17th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Jun 11 - Jun 15, 2018, Cadiz, Spain
Available from: 2018-04-04 Created: 2018-04-04 Last updated: 2018-07-27Bibliographically approved
Atif, Y., Stylianos, S., Demetrios, S. & Mathiason, G. (2017). A Cyberphysical Learning Approach for Digital Smart Citizenship Competence Development. In: WWW '17: Proceedings of the 26th International Conference on World Wide Web Companion. Paper presented at International World Wide Web Conference, Perth, Australia, April 3–7, 2017 (pp. 397-405). ACM Digital Library
Open this publication in new window or tab >>A Cyberphysical Learning Approach for Digital Smart Citizenship Competence Development
2017 (English)In: WWW '17: Proceedings of the 26th International Conference on World Wide Web Companion, ACM Digital Library, 2017, p. 397-405Conference paper, Published paper (Refereed)
Abstract [en]

Smart Cities have emerged as a global concept that argues for the effective exploitation of digital technologies to drive sustainable innovation and well-being for citizens. Despite the large investments being placed on Smart City infrastructure, however, there is still very scarce attention on the new learning approaches that will be needed for cultivating Digital Smart Citizenship competences, namely the competences which will be needed by the citizens and workforce of such cities for exploiting the digital technologies in creative and innovative ways for driving financial and societal sustainability. In this context, this paper introduces cyberphysical learning as an overarching model of cultivating Digital Smart Citizenship competences by exploiting the potential of Internet of Things technologies and social media, in order to create authentic blended and augmented learning experiences.

Place, publisher, year, edition, pages
ACM Digital Library, 2017
Keywords
Digital Smart citizenship, smart city, smart citizenship competences, cyberphysical systems, learning design, social networks, learning technology, smart grid, collaborative learning, Internet of Things
National Category
Learning Computer and Information Sciences
Research subject
Distributed Real-Time Systems; INF302 Autonomous Intelligent Systems
Identifiers
urn:nbn:se:his:diva-13400 (URN)10.1145/3041021.3054167 (DOI)978-1-4503-4913-0 (ISBN)
Conference
International World Wide Web Conference, Perth, Australia, April 3–7, 2017
Projects
Kraftsamling Smarta Nät
Funder
Region Västra Götaland, dnr MN 39-2015
Available from: 2017-02-18 Created: 2017-02-18 Last updated: 2018-06-01Bibliographically approved
Rose, J., Berndtsson, M., Mathiason, G. & Larsson, P. (2017). The advanced analytics Jumpstart: definition, process model, best practices. Journal of Information Systems and Technology Management, 14(3), 339-360
Open this publication in new window or tab >>The advanced analytics Jumpstart: definition, process model, best practices
2017 (English)In: Journal of Information Systems and Technology Management, ISSN 1809-2640, E-ISSN 1807-1775, Vol. 14, no 3, p. 339-360Article in journal (Refereed) Published
National Category
Information Systems, Social aspects
Research subject
Information Systems; Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-14768 (URN)10.4301/S1807-17752017000300003 (DOI)
Projects
Big Data Fusion (BISON)
Available from: 2018-02-22 Created: 2018-02-22 Last updated: 2018-04-25Bibliographically approved
Steinhauer, H. J., Helldin, T., Karlsson, A. & Mathiason, G. (2017). Topic Modeling for Situation Understanding in Telecommunication Networks. In: 2017 27th International Telecommunication Networks and Applications Conference (ITNAC): . Paper presented at 27th International Telecommunication Networks and Applications Conference (ITNAC), 22-24 November 2017, Melbourne, Australia (pp. 73-78). IEEE
Open this publication in new window or tab >>Topic Modeling for Situation Understanding in Telecommunication Networks
2017 (English)In: 2017 27th International Telecommunication Networks and Applications Conference (ITNAC), IEEE, 2017, p. 73-78Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
IEEE, 2017
Series
International Telecommunication Networks and Applications Conference (ITNAC), E-ISSN 2474-154X
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifiers
urn:nbn:se:his:diva-14591 (URN)10.1109/ATNAC.2017.8215362 (DOI)000427574400013 ()2-s2.0-85046642703& (Scopus ID)978-1-5090-6796-1 (ISBN)978-1-5090-6795-4 (ISBN)978-1-5090-6797-8 (ISBN)
Conference
27th International Telecommunication Networks and Applications Conference (ITNAC), 22-24 November 2017, Melbourne, Australia
Available from: 2017-12-18 Created: 2017-12-18 Last updated: 2018-09-03Bibliographically approved
Steinhauer, H. J., Karlsson, A., Mathiason, G. & Helldin, T. (2016). Root-Cause Localization using Restricted Boltzmann Machines. In: 2016 19th International Conference on Information Fusion Proceedings: . Paper presented at 19th International Conference on Information Fusion, Heidelberg, Germany - July 5-8, 2016 (pp. 248-255). IEEE Computer Society
Open this publication in new window or tab >>Root-Cause Localization using Restricted Boltzmann Machines
2016 (English)In: 2016 19th International Conference on Information Fusion Proceedings, IEEE Computer Society, 2016, p. 248-255Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
IEEE Computer Society, 2016
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-12884 (URN)000391273400034 ()2-s2.0-84992092150 (Scopus ID)9780996452748 (ISBN)978-1-5090-2012-6 (ISBN)
Conference
19th International Conference on Information Fusion, Heidelberg, Germany - July 5-8, 2016
Available from: 2016-09-07 Created: 2016-09-07 Last updated: 2018-04-12Bibliographically approved
Ding, J., Lindström, B., Mathiason, G. & Andler, S. F. (2015). Towards Threat Modeling for CPS-based Critical Infrastructure Protection. In: Snjezana Knezic & Meen Poudyal Chhetri (Ed.), Proceedings of the International Emergency Management Society (TIEMS), 22nd TIEMS Annual Conference: Evolving threats and vulnerability landscape: new challenges for the emergency management. Paper presented at The 22nd International Emergency Management Society (TIEMS) Annual Conference, Rome, Italy, 30th September – 2nd October 2015. Brussels: TIEMS, The International Emergency Management Society, 22
Open this publication in new window or tab >>Towards Threat Modeling for CPS-based Critical Infrastructure Protection
2015 (English)In: Proceedings of the International Emergency Management Society (TIEMS), 22nd TIEMS Annual Conference: Evolving threats and vulnerability landscape: new challenges for the emergency management / [ed] Snjezana Knezic & Meen Poudyal Chhetri, Brussels: TIEMS, The International Emergency Management Society , 2015, Vol. 22Conference paper, Published paper (Refereed)
Abstract [en]

With the evolution of modern Critical Infrastructures (CI), more Cyber-Physical systems are integrated into the traditional CIs. This makes the CIs a multidimensional complex system, which is characterized by integrating cyber-physical systems into CI sectors (e.g., transportation, energy or food & agriculture). This integration creates complex interdependencies and dynamics among the system and its components. We suggest using a model with a multi-dimensional operational specification to allow detection of operational threats. Embedded (and distributed) information systems are critical parts of the CI where disruption can lead to serious consequences. Embedded information system protection is therefore crucial. As there are many different stakeholders of a CI, comprehensive protection must be viewed as a cross-sector activity to identify and monitor the critical elements, evaluate and determine the threat, and eliminate potential vulnerabilities in the CI. A systematic approach to threat modeling is necessary to support the CI threat and vulnerability assessment. We suggest a Threat Graph Model (TGM) to systematically model the complex CIs. Such modeling is expected to help the understanding of the nature of a threat and its impact on throughout the system. In order to handle threat cascading, the model must capture local vulnerabilities as well as how a threat might propagate to other components. The model can be used for improving the resilience of the CI by encouraging a design that enhances the system's ability to predict threats and mitigate their damages. This paper surveys and investigates the various threats and current approaches to threat modeling of CI. We suggest integrating both a vulnerability model and an attack model, and we incorporate the interdependencies within CI cross CI sectors. Finally, we present a multi-dimensional threat modeling approach for critical infrastructure protection.

Place, publisher, year, edition, pages
Brussels: TIEMS, The International Emergency Management Society, 2015
Keywords
Critical infrastructure protection (CIP), threat modeling, threat cascading, threat mitigation
National Category
Computer and Information Sciences
Research subject
Natural sciences; Technology
Identifiers
urn:nbn:se:his:diva-11622 (URN)978-94-90297-13-8 (ISBN)
Conference
The 22nd International Emergency Management Society (TIEMS) Annual Conference, Rome, Italy, 30th September – 2nd October 2015
Available from: 2015-10-22 Created: 2015-10-22 Last updated: 2018-01-11Bibliographically approved
Mathiason, G. (2009). Virtual Full Replication for Scalable Distributed Real-Time Databases. (Doctoral dissertation). Linköping University
Open this publication in new window or tab >>Virtual Full Replication for Scalable Distributed Real-Time Databases
2009 (English)Doctoral thesis, monograph (Other academic)
Abstract [en]

A fully replicated distributed real-time database provides high availability and predictable access times, independent of user location, since all the data is available at each node. However, full replication requires that all updates are replicated to every node, resulting in exponential growth of bandwidth and processing demands with the number of nodes and objects added. To eliminate this scalability problem, while retaining the advantages of full replication, this thesis explores Virtual Full Replication (ViFuR); a technique that gives database users a perception of using a fully replicated database while only replicating a subset of the data.

We use ViFuR in a distributed main memory real-time database where timely transaction execution is required. ViFuR enables scalability by replicating only data used at the local nodes. Also, ViFuR enables flexibility by adaptively replicating the currently used data, effectively providing logical availability of all data objects. Hence, ViFuR substantially reduces the problem of non-scalable resource usage of full replication, while allowing timely execution and access to arbitrary data objects.

In the thesis we pursue ViFuR by exploring the use of database segmentation. We give a scheme (ViFuR-S) for static segmentation of the database prior to execution, where access patterns are known a priori. We also give an adaptive scheme (ViFuR-A) that changes segmentation during execution to meet the evolving needs of database users. Further, we apply an extended approach of adaptive segmentation (ViFuR-ASN) in a wireless sensor network - a typical dynamic large-scale and resource-constrained environment. We use up to several hundreds of nodes and thousands of objects per node, and apply a typical periodic transaction workload with operation modes where the used data set changes dynamically. We show that when replacing full replication with ViFuR, resource usage scales linearly with the required number of concurrent replicas, rather than exponentially with the system size.

Place, publisher, year, edition, pages
Linköping University, 2009. p. 213
Series
Linköping Studies in Science and Technology, ISSN 0345-7524 ; 1281
Keywords
Scalability, Flexibility, Adaptiveness, Database Replication, Resource Management, Distributed Database, Real-time Database
National Category
Computer and Information Sciences Computer Sciences
Research subject
Technology
Identifiers
urn:nbn:se:his:diva-3533 (URN)978-91-7393-503-6 (ISBN)
Public defence
(English)
Available from: 2010-01-19 Created: 2010-01-04 Last updated: 2018-01-12Bibliographically approved
Mathiason, G., Andler, S. F. & Kang, W. (2008). Exploring a Multi-Tiered Whiteboard Infrastructure for Information Fusion in Wireless Sensor Networks. In: H. Boström, R. Johansson, Joeri van Laere (Ed.), Proceedings of the second Skövde Workshop on Information Fusion Topics (SWIFT 2008): . Paper presented at The Second Skövde Workshop on Information Fusion Topics, University of Skövde, Sweden, 4−6 November 2008 (pp. 63-66). Skövde: University of Skövde
Open this publication in new window or tab >>Exploring a Multi-Tiered Whiteboard Infrastructure for Information Fusion in Wireless Sensor Networks
2008 (English)In: Proceedings of the second Skövde Workshop on Information Fusion Topics (SWIFT 2008) / [ed] H. Boström, R. Johansson, Joeri van Laere, Skövde: University of Skövde , 2008, p. 63-66Conference paper, Published paper (Refereed)
Abstract [en]

 It is important for the life time of a wireless sensor network (WSN) to reduce the amount of data transferred through the network. As a typical approach, sensor data is filtered before propagating updates, to a node at the edge of a network, where it can be fused. Information Fusion inside the network can reduce the amount of data propagated, by fusing data before and in propagation, without losing the information value in it. We explore infrastructures for distributed fusion, with fusion nodes located at strategic nodes inside the network, as an approach of structured distributed fusion for WSNs. We propose an infrastructure for a white-board approach that uses a distributed real-time database with virtual full replication. With such an approach, both raw and fused data are logically available at all nodes and physically available where used, such that only used data will be propagated and use resources. The actual resource usage will be relative to the actual demand for data, rather than to the amount of data published at the white-board. We present an exploration of such an infrastructure, and points out future key research questions for such a white-board approach.

Place, publisher, year, edition, pages
Skövde: University of Skövde, 2008
Series
Skövde University Studies in Informatics, ISSN 1653-2325 ; 2008:1
Research subject
Technology
Identifiers
urn:nbn:se:his:diva-2924 (URN)978-91-633-3697-3 (ISBN)
Conference
The Second Skövde Workshop on Information Fusion Topics, University of Skövde, Sweden, 4−6 November 2008
Available from: 2009-04-01 Created: 2009-04-01 Last updated: 2018-09-26Bibliographically approved
Mathiason, G., Andler, S. F. & Son, S. H. (2008). Virtual Full Replication for Scalable and Adaptive Real-Time Communication in Wireless Sensor Networks. In: Proceedings of the Second International Conference on Sensor Technologies and Applications (SENSORCOMM 2008) (pp. 55-64). IEEE Computer Society
Open this publication in new window or tab >>Virtual Full Replication for Scalable and Adaptive Real-Time Communication in Wireless Sensor Networks
2008 (English)In: Proceedings of the Second International Conference on Sensor Technologies and Applications (SENSORCOMM 2008), IEEE Computer Society , 2008, p. 55-64Conference paper, Published paper (Refereed)
Abstract [en]

 

Sensor networks have limited resources and often support large-scale applications that need scalable propagation of sensor data to users. We propose a white-board style of communication in sensor networks using a distributed real-time database supporting Virtual Full Replication with Adaptive Segmentation. This allows mobile client nodes to access, transparently and efficiently, any sensor data at any node in the network. We present a two-tiered wireless architecture, and an adaptation protocol, for scalable and adaptive white-board communication in large-scale sensor networks. Sensor value readings at nodes of the sensor tier are published at nodes of the database tier as database updates to objects in a distributed real-time database. The search space of client nodes for sensor data is thus limited to the number of database nodes. With this scheme, we can show scalable resource usage and short adaptation times for several hundreds of database nodes and up to 50 moving clients.

 

Place, publisher, year, edition, pages
IEEE Computer Society, 2008
Research subject
Technology
Identifiers
urn:nbn:se:his:diva-3619 (URN)10.1109/SENSORCOMM.2008.74 (DOI)978-0-7695-3330-8 (ISBN)
Available from: 2010-02-01 Created: 2010-02-01 Last updated: 2017-11-27
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-7106-0025

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