| 1. |
- Arvidsson, Åke, et al.
(författare)
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Tracking user terminals in a mobile communication network
- 2011. - 7
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Patent (populärvet., debatt m.m.)abstract
- There is provided a method of tracking user terminals in a mobile communication network. The method comprising, at a tracking node, determining that a user terminal is located in a tracking area, storing data associated with the tracking area, the data comprising a number of observations of all user terminals at the tracking area at a first time, receiving a page response from the user terminal located in one of the tracking area and a further tracking area, and in the event that the user terminal remains located at the tracking area, updating the data to include the number of page responses received at the tracking area after a first time interval, and in the event that the user terminal is located at the further tracking area, updating the data to include the number of page responses received at the further tracking area after the first time interval.
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| 2. |
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| 3. |
- Boman, Magnus, et al.
(författare)
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Learning Machines
- 2018
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Ingår i: <em>Learning, Inference and Control of Multi-Agent Systems</em>. ; , s. 610-613
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Konferensbidrag (refereegranskat)
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| 4. |
- Boman, Magnus, et al.
(författare)
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Learning Machines for Computational Epidemiology
- 2014
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Ingår i: Proceedings - 2014 IEEE International Conference on Big Data. - Washington DC : IEEE conference proceedings. ; , s. 1-5
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Konferensbidrag (refereegranskat)abstract
- Resting on our experience of computational epidemiologyin practice and of industrial projects on analytics ofcomplex networks, we point to an innovation opportunity forimproving the digital services to epidemiologists for monitoring,modeling, and mitigating the effects of communicable disease.Artificial intelligence and intelligent analytics of syndromicsurveillance data promise new insights to epidemiologists, butthe real value can only be realized if human assessments arepaired with assessments made by machines. Neither massivedata itself, nor careful analytics will necessarily lead to betterinformed decisions. The process producing feedback to humanson decision making informed by machines can be reversed toconsider feedback to machines on decision making informed byhumans, enabling learning machines. We predict and argue forthe fact that the sensemaking that such machines can perform intandem with humans can be of immense value to epidemiologistsin the future.
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| 5. |
- Boman, Magnus, et al.
(författare)
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Learning machines in Internet-delivered psychological treatment
- 2019
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Ingår i: Progress in Artificial Intelligence. - : Springer Verlag. - 2192-6352 .- 2192-6360. ; 8:4, s. 475-485
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 6. |
- Bozic, Nina, et al.
(författare)
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Integrated AI and Innovation Management : The Beginning of a Beautiful Friendship
- 2020
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Ingår i: Technology Innovation Management Review. - : Talent First Network. ; 10:11
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Tidskriftsartikel (refereegranskat)abstract
- There is a growing consensus around the transformative and innovative power of Artificial Intelligence (AI) technology. AI will transform which products are launched and how new business models will be developed to support them. Despite this, little research exists today that systematically explores how AI will change and support various aspects of innovation management. To address this question, this article proposes a holistic, multi-dimensional AI maturity model that describes the essential conditions and capabilities necessary to integrate AI into current systems, and guides organisations on their journey to AI maturity. It explores how various elements of the innovation management system can be enabled by AI at different maturity stages. Two key experimentation stages are identified, 1) an initial stage that focuses on optimisation and incremental innovation, and 2) a higher maturity stage where AI becomes an enabler of radical innovation. We conclude that AI technologies can be applied to democratise and distribute innovation across organisations.
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| 7. |
- Bozic, Nina, et al.
(författare)
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Integrated ai and innovationmanagement : The beginning of a beautiful friendship
- 2021
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Ingår i: Technology Innovation Management Review. - : Carleton University. - 1927-0321. ; 10:11, s. 5-18
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Tidskriftsartikel (refereegranskat)abstract
- There is a growing consensus around the transformative and innovative power of Artificial Intelligence (AI) technology. AI will transform which products are launched and how new business models will be developed to support them. Despite this, little research exists today that systematically explores how AI will change and support various aspects of innovation management. To address this question, this article proposes a holistic, multi-dimensional AI maturity model that describes the essential conditions and capabilities necessary to integrate AI into current systems, and guides organisations on their journey to AI maturity. It explores how various elements of the innovation management system can be enabled by AI at different maturity stages. Two key experimentation stages are identified, 1) an initial stage that focuses on optimisation and incremental innovation, and 2) a higher maturity stage where AI becomes an enabler of radical innovation. We conclude that AI technologies can be applied to democratise and distribute innovation across organisations.
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| 8. |
- Dokoohaki, Nima, et al.
(författare)
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Predicting Swedish elections with Twitter : A case for stochastic link structure analysis
- 2015
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Ingår i: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015. - New York, NY, USA : Association for Computing Machinery, Inc. - 9781450338547 ; , s. 1269-1276
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Konferensbidrag (refereegranskat)abstract
- The question that whether Twitter data can be leveraged to forecast outcome of the elections has always been of great anticipation in the research community. Existing research focuses on leveraging content analysis for positivity or negativity analysis of the sentiments of opinions expressed. This is while, analysis of link structure features of social networks underlying the conversation involving politicians has been less looked. The intuition behind such study comes from the fact that density of conversations about parties along with their respective members, whether explicit or implicit, should reflect on their popularity. On the other hand, dynamism of interactions, can capture the inherent shift in popularity of accounts of politicians. Within this manuscript we present evidence of how a well-known link prediction algorithm, can reveal an authoritative structural link formation within which the popularity of the political accounts along with their neighbourhoods, shows strong correlation with the standing of electoral outcomes. As an evidence, the public time-lines of two electoral events from 2014 elections of Sweden on Twitter have been studied. By distinguishing between member and official party accounts, we report that even using a focus-crawled public dataset, structural link popularities bear strong statistical similarities with vote outcomes. In addition we report strong ranked dependence between standings of selected politicians and general election outcome, as well as for official party accounts and European election outcome.
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