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Publications (10 of 43) Show all publications
Boman, M., Ben Abdesslem, F., Forsell, E., Gillblad, D., Görnerup, O., Isacsson, N., . . . Kaldo, V. (2019). Learning machines in Internet-delivered psychological treatment. Progress in Artificial Intelligence
Open this publication in new window or tab >>Learning machines in Internet-delivered psychological treatment
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2019 (English)In: Progress in Artificial Intelligence, ISSN 2192-6352Article in journal (Refereed) Epub ahead of print
Abstract [en]

A learning machine, in the form of a gating network that governs a finite number of different machine learning methods, is described at the conceptual level with examples of concrete prediction subtasks. A historical data set with data from over 5000 patients in Internet-based psychological treatment will be used to equip healthcare staff with decision support for questions pertaining to ongoing and future cases in clinical care for depression, social anxiety, and panic disorder. The organizational knowledge graph is used to inform the weight adjustment of the gating network and for routing subtasks to the different methods employed locally for prediction. The result is an operational model for assisting therapists in their clinical work, about to be subjected to validation in a clinical trial.

Place, publisher, year, edition, pages
Springer Verlag, 2019
Keywords
Ensemble learning, Gating network, Internet-based psychological treatment, Learning machine, Machine learning, Decision support systems, Learning systems, Conceptual levels, Decision supports, Learning machines, Machine learning methods, Operational model, Organizational knowledge, Psychological treatments, Patient treatment
National Category
Natural Sciences
Identifiers
urn:nbn:se:ri:diva-39062 (URN)10.1007/s13748-019-00192-0 (DOI)2-s2.0-85066625908 (Scopus ID)
Available from: 2019-06-26 Created: 2019-06-26 Last updated: 2019-07-31Bibliographically approved
Harz, D. & Boman, M. (2019). The scalability of trustless trust. In: Part of the                 Lecture Notes in Computer Science                book series (LNCS, volume 10958): . Paper presented at International Conference on Financial Cryptography and Data Security FC 2018: Financial Cryptography and Data Security 26 February 2018 through 2 March 2018 (pp. 279-293). Springer Verlag
Open this publication in new window or tab >>The scalability of trustless trust
2019 (English)In: Part of the                 Lecture Notes in Computer Science                book series (LNCS, volume 10958), Springer Verlag , 2019, p. 279-293Conference paper, Published paper (Refereed)
Abstract [en]

Permission-less blockchains can realise trustless trust, albeit at the cost of limiting the complexity of computation tasks. To explain the implications for scalability, we have implemented a trust model for smart contracts, described as agents in an open multi-agent system. Agent intentions are not necessarily known and autonomous agents have to be able to make decisions under risk. The ramifications of these general conditions for scalability are analysed for Ethereum and then generalised to other current and future platforms. Finally, mechanisms from the trust model are applied to a verifiable computation algorithm and implemented in the Ethereum blockchain. We show in experiments that the algorithm needs at most six semi-honest verifiers to detect false submission.

Place, publisher, year, edition, pages
Springer Verlag, 2019
Keywords
Agent, Blockchain, Distributed ledger, Ethereum, Multi-agent system, Scalability, Smart contract, Trustless trust
National Category
Natural Sciences
Identifiers
urn:nbn:se:ri:diva-38468 (URN)10.1007/978-3-662-58820-8_19 (DOI)2-s2.0-85063467913 (Scopus ID)9783662588192 (ISBN)
Conference
International Conference on Financial Cryptography and Data Security FC 2018: Financial Cryptography and Data Security 26 February 2018 through 2 March 2018
Available from: 2019-05-10 Created: 2019-05-10 Last updated: 2019-05-10Bibliographically approved
Borlenghi, S., Boman, M. & Delin, A. (2018). Modeling reservoir computing with the discrete nonlinear Schrödinger equation. Physical review. E, 98(5), Article ID 052101.
Open this publication in new window or tab >>Modeling reservoir computing with the discrete nonlinear Schrödinger equation
2018 (English)In: Physical review. E, ISSN 2470-0045, E-ISSN 2470-0053, Vol. 98, no 5, article id 052101Article in journal (Refereed) Published
Abstract [en]

We formulate, using the discrete nonlinear Schrödinger equation (DNLS), a general approach to encode and process information based on reservoir computing. Reservoir computing is a promising avenue for realizing neuromorphic computing devices. In such computing systems, training is performed only at the output level by adjusting the output from the reservoir with respect to a target signal. In our formulation, the reservoir can be an arbitrary physical system, driven out of thermal equilibrium by an external driving. The DNLS is a general oscillator model with broad application in physics, and we argue that our approach is completely general and does not depend on the physical realization of the reservoir. The driving, which encodes the object to be recognized, acts as a thermodynamic force, one for each node in the reservoir. Currents associated with these thermodynamic forces in turn encode the output signal from the reservoir. As an example, we consider numerically the problem of supervised learning for pattern recognition, using as a reservoir a network of nonlinear oscillators.

Keywords
Encoding (symbols), Oscillators (mechanical), Pattern recognition, Broad application, Neuromorphic computing, Non-linear oscillators, Physical realization, Process information, Reservoir Computing, Thermal equilibriums, Thermodynamic forces, Nonlinear equations
National Category
Natural Sciences
Identifiers
urn:nbn:se:ri:diva-36439 (URN)10.1103/PhysRevE.98.052101 (DOI)2-s2.0-85056391374 (Scopus ID)
Note

Funding details: Energimyndigheten, STEM P40147-1; Funding details: NSC; Funding details: Vetenskapsrådet, VR, VR 2016-05980; Funding details: Vetenskapsrådet, VR, VR 2016-01961; Funding details: Vetenskapsrådet, VR, VR 2015-04608; Funding details: Kungliga Tekniska Högskolan, KTH, HPC2N; Funding details: Umeå Universitet; Funding details: Linköpings Universitet,

Available from: 2018-11-22 Created: 2018-11-22 Last updated: 2018-11-22Bibliographically approved
Boman, M. & Kruse, E. (2017). Supporting global health goals with information and communications technology. Global Health Action, 10, Article ID 1321904.
Open this publication in new window or tab >>Supporting global health goals with information and communications technology
2017 (English)In: Global Health Action, ISSN 1654-9716, E-ISSN 1654-9880, Vol. 10, article id 1321904Article in journal (Refereed) Published
Abstract [en]

The objective of this study is to critically assess the possible roles of information and communications technology (ICT) in supporting global health goals. This is done by considering privilege and connectibility. In short, ICT can contribute by providing health information via four different kinds of access, each with its own history and prospective future. All four are analyzed here, in two perspectives: business-as-usual and disruptive. Health data analytics is difficult since the digital representation of past, current, and future health information is lacking. The flow of analytics that may prove beneficial to the individual and not just meet abstract population-level goals or ambitions is analyzed in detail. Sensemaking is also needed, to meet the minimum requirement of making prospective future services understandable to policymakers. Drivers as well as barriers for areas in which policy decisions have the potential to drive positive developments for meeting the Sustainable Development Goals are identified. © 2017 The Author(s).

Keywords
Connectibility, Health data, Health data analytics, Precision medicine, Privilege, Sensemaking, driver, global health, human, medical information, personalized medicine, sustainable development
National Category
Natural Sciences
Identifiers
urn:nbn:se:ri:diva-33199 (URN)10.1080/16549716.2017.1321904 (DOI)2-s2.0-85028928632 (Scopus ID)
Available from: 2018-01-31 Created: 2018-01-31 Last updated: 2019-07-01Bibliographically approved
Boman, M. & Sanches, P. (2015). Sensemaking in Intelligent Health Data Analytics (6ed.). Künstliche Intelligenz, 29(2), 143-152
Open this publication in new window or tab >>Sensemaking in Intelligent Health Data Analytics
2015 (English)In: Künstliche Intelligenz, ISSN 0933-1875, E-ISSN 1610-1987, Vol. 29, no 2, p. 143-152Article in journal (Refereed) Published
Abstract [en]

A systemic model for making sense of health data is presented, in which networked foresight complements intelligent data analytics. Data here serves the goal of a future systems medicine approach by explaining the past and the current, while foresight can serve by explaining the future. Anecdotal evidence from a case study is presented, in which the complex decisions faced by the traditional stakeholder of results—the policymaker—are replaced by the often mundane problems faced by an individual trying to make sense of sensor input and output when self-tracking wellness. The conclusion is that the employment of our systemic model for successful sensemaking integrates not only data with networked foresight, but also unpacks such problems and the user practices associated with their solutions.

Keywords
Artificial intelligence, Massive data, Health data, Intelligent data analytics, Syndromic surveillance, Sensemaking
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:ri:diva-24403 (URN)10.1007/s13218-015-0349-0 (DOI)
Available from: 2016-10-31 Created: 2016-10-31 Last updated: 2019-07-09Bibliographically approved
Moore, H., Sanches, P. & Boman, M. (2014). Ethnographies of Practice, Visioning, and Foresight (6ed.). In: : . Paper presented at Proc XXV ISPIM Conf, Innovation for Sustainable Economy & Society.
Open this publication in new window or tab >>Ethnographies of Practice, Visioning, and Foresight
2014 (English)Conference paper, Published paper (Refereed)
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:ri:diva-24407 (URN)
Conference
Proc XXV ISPIM Conf, Innovation for Sustainable Economy & Society
Available from: 2016-10-31 Created: 2016-10-31 Last updated: 2018-08-13Bibliographically approved
Moore, H., Sanches, P. & Boman, M. (2014). Ethnographies of Practice, Visioning and Foresight in Future-Oriented Technology Analysis (6ed.). In: : . Paper presented at Proc Future-Oriented Technology Analysis Conference: Engage today to shape tomorrow.
Open this publication in new window or tab >>Ethnographies of Practice, Visioning and Foresight in Future-Oriented Technology Analysis
2014 (English)Conference paper, Published paper (Refereed)
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:ri:diva-24408 (URN)
Conference
Proc Future-Oriented Technology Analysis Conference: Engage today to shape tomorrow
Available from: 2016-10-31 Created: 2016-10-31 Last updated: 2018-08-13Bibliographically approved
Boman, M. & Gillblad, D. (2014). Learning machines for computational epidemiology (6ed.). In: : . Paper presented at IEEE Big Data Workshop on Computational Epidemiology (pp. 1-5).
Open this publication in new window or tab >>Learning machines for computational epidemiology
2014 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Resting on our experience of computational epidemiology in practice and of industrial projects on analytics of complex networks, we point to an innovation opportunity for improving the digital services to epidemiologists for monitoring, modeling, and mitigating the effects of communicable disease. Artificial intelligence and intelligent analytics of syndromic surveillance data promise new insights to epidemiologists, but the real value can only be realized if human assessments are paired with assessments made by machines. Neither massive data itself, nor careful analytics will necessarily lead to better informed decisions. The process producing feedback to humans on decision making informed by machines can be reversed to consider feedback to machines on decision making informed by humans, enabling learning machines. We predict and argue for the fact that the sensemaking that such machines can perform in tandem with humans can be of immense value to epidemiologists in the future.

National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:ri:diva-24405 (URN)
Conference
IEEE Big Data Workshop on Computational Epidemiology
Available from: 2016-10-31 Created: 2016-10-31 Last updated: 2018-08-17Bibliographically approved
Heger, T. & Boman, M. (2014). Networked foresight—The case of EIT ICT Labs (5ed.). Technological Forecasting and Social Change, 101, 147-164
Open this publication in new window or tab >>Networked foresight—The case of EIT ICT Labs
2014 (English)In: Technological Forecasting and Social Change, Vol. 101, p. 147-164Article in journal (Refereed) Published
Abstract [en]

The objective of this article is to explore the value of networked foresight: foresight conducted in innovation networks for the benefit of the network and its partners with active contributions from the partners. Strategic management, specifically the dynamic capabilities approach and vast literature on corporate and strategic foresight argue that deficiencies like one-dimensionality, narrow-sightedness and myopia of closed corporate processes are remedied by incorporating external sources. A broad knowledge base promises to especially benefit foresight in multiple ways. Thus, created an analytical framework that integrates the dynamic capabilities approach with existing results on potential value contributions of foresight, enriched with existing findings in networked foresight and organizational design in the light increasing importance of inter-organizational networks. We conducted a series of interviews and a survey among foresight practitioners in a network to explore the perceived value proposition of networked foresight for the network partners and the network itself. The analysis is based on data drawn from the EIT ICT Labs network of large industry corporations, small-and-medium sized companies, and academic and research institutes. Our study shows that network partners use the results primarily for sensing activities, i.e. data collection and to a lesser extend activity initiation. More sensitive and fundamental organizational aspects such as strategy and decision-making or path-dependency are less affected. Especially SMEs may benefit substantially from network approaches to foresight whereas MNEs are more confident in their existing corporate foresight processes and results. The value for the network itself is substantial and goes beyond value creation potential for companies as discussed in literature. The development of a shared vision—relatable to organizational learning and reconfiguration capabilities—was identified as particularly valuable for the network.

National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:ri:diva-24402 (URN)10.1016/j.techfore.2014.02.002 (DOI)2-s2.0-84949099852 (Scopus ID)
Available from: 2016-10-31 Created: 2016-10-31 Last updated: 2019-08-09Bibliographically approved
Boman, M. (2014). Speedwriting in Networked Foresight (6ed.). In: : . Paper presented at Proc XXV ISPIM Conf, Innovation for Sustainable Economy & Society.
Open this publication in new window or tab >>Speedwriting in Networked Foresight
2014 (English)Conference paper, Published paper (Refereed)
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:ri:diva-24406 (URN)
Conference
Proc XXV ISPIM Conf, Innovation for Sustainable Economy & Society
Available from: 2016-10-31 Created: 2016-10-31 Last updated: 2018-08-13Bibliographically approved
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-7949-1815

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