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Kleyko, D., Osipov, E. & Wiklund, U. (2019). A Hyperdimensional Computing Framework for Analysis of Cardiorespiratory Synchronization during Paced Deep Breathing. IEEE Access, 7, 34403-34415
Open this publication in new window or tab >>A Hyperdimensional Computing Framework for Analysis of Cardiorespiratory Synchronization during Paced Deep Breathing
2019 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 7, p. 34403-34415Article in journal (Refereed) Published
Abstract [en]

Objective: Autonomic function during deep breathing (DB) is normally scored based on the assumption that the heart rate is synchronized with the breathing. We have observed individuals with subtle arrhythmias during DB where autonomic function cannot be evaluated. This study presents a novel method for analyzing cardiorespiratory synchronization: feature-based analysis of the similarity between heart rate and respiration using principles of hyperdimensional computing. Methods: Heart rate and respiration signals were modeled using Fourier series analysis. Three feature variables were derived and mapped to binary vectors in a high-dimensional space. Using both synthesized data and recordings from patients/healthy subjects, the similarity between the feature vectors was assessed using Hamming distance (high-dimensional space), Euclidean distance (original space), and with a coherence-based index. Methods were evaluated via classification of the similarity indices into three groups. Results: The distance-based methods achieved good separation of signals into classes with different degree of cardiorespiratory synchronization, also providing identification of patients with low cardiorespiratory synchronization but high values of conventional DB scores. Moreover, binary high-dimensional vectors allowed an additional analysis of the obtained Hamming distance. Conclusions: Feature-based similarity analysis using hyperdimensional computing is capable of identifying signals with low cardiorespiratory synchronization during DB due to arrhythmias. Vector-based similarity analysis could be applied to other types of feature variables than based on spectral analysis. Significance: The proposed methods for robustly assessing cardiorespiratory synchronization during DB facilitate the identification of individuals where the evaluation of autonomic function is problematic or even impossible, thus, increasing the correctness of the conventional DB scores.

Place, publisher, year, edition, pages
IEEE, 2019
Keywords
Deep breathing test, Deep breathing index, similarity analysis, heart rate variability, hyperdimensional computing
National Category
Computer Sciences
Research subject
Dependable Communication and Computation Systems
Identifiers
urn:nbn:se:ltu:diva-73196 (URN)10.1109/ACCESS.2019.2904311 (DOI)000463487400001 ()
Note

Validerad;2019;Nivå 2;2019-04-17 (oliekm)

Available from: 2019-03-14 Created: 2019-03-14 Last updated: 2019-04-24Bibliographically approved
Kleyko, D., Rahimi, A., Gayler, R. W. & Osipov, E. (2019). Autoscaling Bloom filter: Controlling trade-off between true and false positives. Neural computing & applications (Print)
Open this publication in new window or tab >>Autoscaling Bloom filter: Controlling trade-off between true and false positives
2019 (English)In: Neural computing & applications (Print), ISSN 0941-0643, E-ISSN 1433-3058Article in journal (Refereed) Epub ahead of print
Abstract [en]

A Bloom filter is a special case of an artificial neural network with two layers. Traditionally, it is seen as a simple data structure supporting membership queries on a set. The standard Bloom filter does not support the delete operation, and therefore, many applications use a counting Bloom filter to enable deletion. This paper proposes a generalization of the counting Bloom filter approach, called “autoscaling Bloom filters”, which allows adjustment of its capacity with probabilistic bounds on false positives and true positives. Thus, by relaxing the requirement on perfect true positive rate, the proposed autoscaling Bloom filter addresses the major difficulty of Bloom filters with respect to their scalability. In essence, the autoscaling Bloom filter is a binarized counting Bloom filter with an adjustable binarization threshold. We present the mathematical analysis of its performance and provide a procedure for minimizing its false positive rate.

Place, publisher, year, edition, pages
Springer, 2019
Keywords
Bloom filter, Counting Bloom filter, Autoscaling Bloom filter, True positive rate, False positive rate
National Category
Computer Sciences
Research subject
Dependable Communication and Computation Systems
Identifiers
urn:nbn:se:ltu:diva-75476 (URN)10.1007/s00521-019-04397-1 (DOI)
Funder
Swedish Research Council, 2015-04677
Available from: 2019-08-12 Created: 2019-08-12 Last updated: 2019-08-13
de Silva, D., Pang, Z., Osipov, E. & Vyatkin, V. (2019). Guest Editorial: Special Section on Developments in Artificial Intelligence for Industrial Informatics. IEEE Transactions on Industrial Informatics, 15(6), 3690-3692
Open this publication in new window or tab >>Guest Editorial: Special Section on Developments in Artificial Intelligence for Industrial Informatics
2019 (English)In: IEEE Transactions on Industrial Informatics, ISSN 1551-3203, E-ISSN 1941-0050, Vol. 15, no 6, p. 3690-3692Article in journal, Editorial material (Refereed) Published
Abstract [en]

The emergence of artificial intelligence (AI), empowered by robust computing infrastructure and abundance of data, maintains potential for radical transformation of human society, essentially a third phase in evolution. Numerous research endeavor, policy development, and thought-leadership are presently in progress aimed at discovering data-driven intelligent decision-making solutions for smart cities, smart grids, smart homes, and informed citizens as well as addressing potential risks posed by AI workplace automation. Joining this broad effort, this Special Section contributes six research articles that consolidate recent developments in AI for industrial informatics.

Place, publisher, year, edition, pages
IEEE, 2019
Keywords
Artificial intelligence, Informatics, Neural networks, Manufacturing
National Category
Computer Sciences
Research subject
Dependable Communication and Computation Systems
Identifiers
urn:nbn:se:ltu:diva-75161 (URN)10.1109/TII.2019.2913769 (DOI)000471725400054 ()2-s2.0-85067350748 (Scopus ID)
Available from: 2019-07-01 Created: 2019-07-01 Last updated: 2019-07-10Bibliographically approved
Kleyko, D., Rahimi, A., Rachkovskij, D. A., Osipov, E. & Rabaey, J. M. (2018). Classification and Recall With Binary Hyperdimensional Computing: Tradeoffs in Choice of Density and Mapping Characteristics. IEEE Transactions on Neural Networks and Learning Systems, 29(12), 5880-5898
Open this publication in new window or tab >>Classification and Recall With Binary Hyperdimensional Computing: Tradeoffs in Choice of Density and Mapping Characteristics
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2018 (English)In: IEEE Transactions on Neural Networks and Learning Systems, ISSN 2162-237X, E-ISSN 2162-2388, Vol. 29, no 12, p. 5880-5898Article in journal (Refereed) Published
Abstract [en]

Hyperdimensional (HD) computing is a promising paradigm for future intelligent electronic appliances operating at low power. This paper discusses tradeoffs of selecting parameters of binary HD representations when applied to pattern recognition tasks. Particular design choices include density of representations and strategies for mapping data from the original representation. It is demonstrated that for the considered pattern recognition tasks (using synthetic and real-world data) both sparse and dense representations behave nearly identically. This paper also discusses implementation peculiarities which may favor one type of representations over the other. Finally, the capacity of representations of various densities is discussed.

Place, publisher, year, edition, pages
IEEE, 2018
National Category
Computer Systems
Research subject
Dependable Communication and Computation Systems
Identifiers
urn:nbn:se:ltu:diva-68400 (URN)10.1109/TNNLS.2018.2814400 (DOI)000451230100008 ()29993669 (PubMedID)2-s2.0-85045214003 (Scopus ID)
Funder
Swedish Research Council, 2015- 04677
Note

Validerad;2018;Nivå 2;2018-12-05 (svasva)

Available from: 2018-04-18 Created: 2018-04-18 Last updated: 2019-03-04Bibliographically approved
Kleyko, D., Osipov, E., Papakonstantinou, N. & Vyatkin, V. (2018). Hyperdimensional computing in industrial systems: the use-case of distributed fault isolation in a power plant. IEEE Access, 6, 30766-30777
Open this publication in new window or tab >>Hyperdimensional computing in industrial systems: the use-case of distributed fault isolation in a power plant
2018 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 6, p. 30766-30777Article in journal (Refereed) Published
Abstract [en]

This paper presents an approach for distributed fault isolation in a generic system of systems. The proposed approach is based on the principles of hyperdimensional computing. In particular, the recently proposed method called Holographic Graph Neuron is used. We present a distributed version of Holographic Graph Neuron and evaluate its performance on the problem of fault isolation in a complex power plant model. Compared to conventional machine learning methods applied in the context of the same scenario the proposed approach shows comparable performance while being distributed and requiring simple binary operations, which allow for a fast and efficient implementation in hardware.

Place, publisher, year, edition, pages
IEEE, 2018
National Category
Computer Systems Computer Sciences
Research subject
Dependable Communication and Computation Systems
Identifiers
urn:nbn:se:ltu:diva-69034 (URN)10.1109/ACCESS.2018.2840128 (DOI)000437220700001 ()2-s2.0-85047613488 (Scopus ID)
Note

Validerad;2018;Nivå 2;2018-08-06 (rokbeg)

Available from: 2018-06-01 Created: 2018-06-01 Last updated: 2018-08-06Bibliographically approved
De Silva, D., Ranasinghe, W., Bandaragoda, T., Adikari, A., Mills, N., Iddamalgoda, L., . . . Bolton, D. (2018). Machine learning to support social media empowered patients in cancer care and cancer treatment decisions. PLoS ONE, 13(10), Article ID e0205855.
Open this publication in new window or tab >>Machine learning to support social media empowered patients in cancer care and cancer treatment decisions
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2018 (English)In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 13, no 10, article id e0205855Article in journal (Refereed) Published
Abstract [en]

BACKGROUND

A primary variant of social media, online support groups (OSG) extend beyond the standard definition to incorporate a dimension of advice, support and guidance for patients. OSG are complementary, yet significant adjunct to patient journeys. Machine learning and natural language processing techniques can be applied to these large volumes of unstructured text discussions accumulated in OSG for intelligent extraction of patient-reported demographics, behaviours, decisions, treatment, side effects and expressions of emotions. New insights from the fusion and synthesis of such diverse patient-reported information, as expressed throughout the patient journey from diagnosis to treatment and recovery, can contribute towards informed decision-making on personalized healthcare delivery and the development of healthcare policy guidelines.

METHODS AND FINDINGS

We have designed and developed an artificial intelligence based analytics framework using machine learning and natural language processing techniques for intelligent analysis and automated aggregation of patient information and interaction trajectories in online support groups. Alongside the social interactions aspect, patient behaviours, decisions, demographics, clinical factors, emotions, as subsequently expressed over time, are extracted and analysed. More specifically, we utilised this platform to investigate the impact of online social influences on the intimate decision scenario of selecting a treatment type, recovery after treatment, side effects and emotions expressed over time, using prostate cancer as a model. Results manifest the three major decision-making behaviours among patients, Paternalistic group, Autonomous group and Shared group. Furthermore, each group demonstrated diverse behaviours in post-decision discussions on clinical outcomes, advice and expressions of emotion during the twelve months following treatment. Over time, the transition of patients from information and emotional support seeking behaviours to providers of information and emotional support to other patients was also observed.

CONCLUSIONS

Findings from this study are a rigorous indication of the expectations of social media empowered patients, their potential for individualised decision-making, clinical and emotional needs. The increasing popularity of OSG further confirms that it is timely for clinicians to consider patient voices as expressed in OSG. We have successfully demonstrated that the proposed platform can be utilised to investigate, analyse and derive actionable insights from patient-reported information on prostate cancer, in support of patient focused healthcare delivery. The platform can be extended and applied just as effectively to any other medical condition.

Place, publisher, year, edition, pages
Public Library of Science, 2018
National Category
Computer Sciences
Research subject
Dependable Communication and Computation Systems
Identifiers
urn:nbn:se:ltu:diva-71302 (URN)10.1371/journal.pone.0205855 (DOI)000447701300063 ()30335805 (PubMedID)2-s2.0-85055075136 (Scopus ID)
Note

Validerad;2018;Nivå 2;2018-10-23 (johcin)

Available from: 2018-10-23 Created: 2018-10-23 Last updated: 2018-12-10Bibliographically approved
Osipov, E., Kleyko, D. & Papakonstantinou, N. (2017). Approximate Sensing with Vector Symbolic Architectures: The case of fault isolation in distributed automation systems. In: Proceedings: EWSN ’17 Proceedings of the 2017 International Conference on Embedded Wireless Systems and Networks. Paper presented at 2017 International Conference on Embedded Wireless Systems and Networks Uppsala, Sweden, February 20 - 22, 2017 (pp. 224-225). New york: ACM Digital Library
Open this publication in new window or tab >>Approximate Sensing with Vector Symbolic Architectures: The case of fault isolation in distributed automation systems
2017 (English)In: Proceedings: EWSN ’17 Proceedings of the 2017 International Conference on Embedded Wireless Systems and Networks, New york: ACM Digital Library, 2017, p. 224-225Conference paper, Poster (with or without abstract) (Refereed)
Abstract [en]

Due to the stochastic and imprecise nature of sensory data, the current (exact computational) algorithms for their processing introduce unnecessary computational overhead. One of the major trends in the development of computation al elements for processing of sensory data is low-power imprecise electronics and accompanying algorithmic solutions for approximate computing. This poster introduces the usage of hyper-dimensional computing and vector-symbolic architectures in the context of wireless embedded systems. A problem of fault isolation in a distributed automation system is considered as a showcase. The poster presents the performance of the associative sensing approach as well as challenges associated with the design of communication techniques and network protocols for exchanging of VSA information.

Place, publisher, year, edition, pages
New york: ACM Digital Library, 2017
National Category
Computer Sciences
Research subject
Dependable Communication and Computation Systems
Identifiers
urn:nbn:se:ltu:diva-64649 (URN)978-0-9949886-1-4 (ISBN)
Conference
2017 International Conference on Embedded Wireless Systems and Networks Uppsala, Sweden, February 20 - 22, 2017
Available from: 2017-06-29 Created: 2017-06-29 Last updated: 2018-03-28Bibliographically approved
Osipov, E., Kleyko, D. & Legalov, A. (2017). Associative Synthesis of Finite State Automata Model of a Controlled Object with Hyperdimensional Computing. In: Proceedings IECON 2017: 43rd Annual Conference of the IEEE Industrial Electronics Society. Paper presented at 43rd Annual Conference of the IEEE Industrial Electronics Society, IECON 2017, Bejing, China, 29 October - 1 November 2017 (pp. 3276-3281). Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Associative Synthesis of Finite State Automata Model of a Controlled Object with Hyperdimensional Computing
2017 (English)In: Proceedings IECON 2017: 43rd Annual Conference of the IEEE Industrial Electronics Society, Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 3276-3281Conference paper, Published paper (Refereed)
Abstract [en]

The main contribution of this paper is a study of the applicability of hyperdimensional computing and learning with an associative memory for modeling the dynamics of complex automation systems. Specifically, the problem of learning an evidence-based model of a plant in a distributed automation and control system is considered. The model is learned in the form a finite state automata. 

Place, publisher, year, edition, pages
Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2017
Series
IEEE Industrial Electronics Society, ISSN 1553-572X
National Category
Computer Systems
Research subject
Dependable Communication and Computation Systems
Identifiers
urn:nbn:se:ltu:diva-66594 (URN)10.1109/IECON.2017.8216554 (DOI)000427164803042 ()2-s2.0-85046676612 (Scopus ID)9781538611272 (ISBN)
Conference
43rd Annual Conference of the IEEE Industrial Electronics Society, IECON 2017, Bejing, China, 29 October - 1 November 2017
Available from: 2017-11-16 Created: 2017-11-16 Last updated: 2018-05-21Bibliographically approved
Kleyko, D., Osipov, E., Senior, A., Khan, A. & Sekercioglu, A. (2017). Holographic Graph Neuron: a Bio-Inspired Architecture for Pattern Processing (ed.). IEEE Transactions on Neural Networks and Learning Systems, 28(6), 1250-1262
Open this publication in new window or tab >>Holographic Graph Neuron: a Bio-Inspired Architecture for Pattern Processing
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2017 (English)In: IEEE Transactions on Neural Networks and Learning Systems, ISSN 2162-237X, E-ISSN 2162-2388, Vol. 28, no 6, p. 1250-1262Article in journal (Refereed) Published
Abstract [en]

This article proposes the use of Vector Symbolic Architectures for implementing Hierarchical Graph Neuron, an architecture for memorizing patterns of generic sensor stimuli. The adoption of a Vector Symbolic representation ensures a one-layered design for the approach, while maintaining the previously reported properties and performance characteristics of Hierarchical Graph Neuron, and also improving the noise resistance of the architecture. The proposed architecture enables a linear (with respect to the number of stored entries) time search for an arbitrary sub-pattern.

Place, publisher, year, edition, pages
IEEE, 2017
National Category
Computer Sciences
Research subject
Dependable Communication and Computation Systems
Identifiers
urn:nbn:se:ltu:diva-11974 (URN)10.1109/TNNLS.2016.2535338 (DOI)000401982100001 ()26978836 (PubMedID)2-s2.0-84960540059 (Scopus ID)b0709bbc-d372-4032-90cc-1dca4ff3a12d (Local ID)b0709bbc-d372-4032-90cc-1dca4ff3a12d (Archive number)b0709bbc-d372-4032-90cc-1dca4ff3a12d (OAI)
Note

Validerad;2017;Nivå 2;2017-06-01 (andbra)

Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-07-10Bibliographically approved
Kleyko, D., Khan, S., Osipov, E. & Yong, S.-P. (2017). Modality Classification of Medical Images with Distributed Representations Based on Cellular Automata Reservoir Computing. In: Proceedings - International Symposium on Biomedical Imaging: . Paper presented at 2017 IEEE International Symposium on Biomedical Imaging, Melbourne, Australia, 18-21 April 2017 (pp. 1053-1056). Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Modality Classification of Medical Images with Distributed Representations Based on Cellular Automata Reservoir Computing
2017 (English)In: Proceedings - International Symposium on Biomedical Imaging, Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 1053-1056Conference paper, Published paper (Refereed)
Abstract [en]

Modality corresponding to medical images is a vital filter in medical image retrieval systems. This article presents the classification of modalities of medical images based on the usage of principles of hyper-dimensional computing and reservoir computing. It is demonstrated that the highest classification accuracy of the proposed method is on a par with the best classical method for the given dataset (83% vs. 84%). The major positive property of the proposed method is that it does not require any optimization routine during the training phase and naturally allows for incremental learning upon the availability of new training data.

Place, publisher, year, edition, pages
Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2017
Series
Proceedings. IEEE International Symposium on Biomedical Imaging, E-ISSN 1945-7928
National Category
Medical Image Processing Computer Sciences
Research subject
Dependable Communication and Computation Systems
Identifiers
urn:nbn:se:ltu:diva-61558 (URN)10.1109/ISBI.2017.7950697 (DOI)000414283200243 ()2-s2.0-85023198723 (Scopus ID)9781509011711 (ISBN)
Conference
2017 IEEE International Symposium on Biomedical Imaging, Melbourne, Australia, 18-21 April 2017
Funder
Swedish Research Council, 2015-04677
Available from: 2017-01-20 Created: 2017-01-20 Last updated: 2018-07-10Bibliographically approved
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Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-0069-640x

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