| 1. |
- 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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| 2. |
- 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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| 3. |
- 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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| 4. |
- Görnerup, Olof, et al.
(författare)
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Domain-agnostic discovery of similarities and concepts at scale
- 2017
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Ingår i: Knowledge and Information Systems. - London : Springer. - 0219-1377 .- 0219-3116. ; 51:2, s. 531-560
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Tidskriftsartikel (refereegranskat)abstract
- Appropriately defining and efficiently calculating similarities from large data sets are often essential in data mining, both for gaining understanding of data and generating processes and for building tractable representations. Given a set of objects and their correlations, we here rely on the premise that each object is characterized by its context, i.e., its correlations to the other objects. The similarity between two objects can then be expressed in terms of the similarity between their contexts. In this way, similarity pertains to the general notion that objects are similar if they are exchangeable in the data. We propose a scalable approach for calculating all relevant similarities among objects by relating them in a correlation graph that is transformed to a similarity graph. These graphs can express rich structural properties among objects. Specifically, we show that concepts—abstractions of objects—are constituted by groups of similar objects that can be discovered by clustering the objects in the similarity graph. These principles and methods are applicable in a wide range of fields and will be demonstrated here in three domains: computational linguistics, music, and molecular biology, where the numbers of objects and correlations range from small to very large.
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| 5. |
- Görnerup, Olof, et al.
(författare)
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Knowing an Object by the Company It Keeps: A Domain-Agnostic Scheme for Similarity Discovery
- 2015
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Ingår i: 2015 IEEE International Conference on Data Mining. - 9781467395045 ; , s. 121-130
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Konferensbidrag (refereegranskat)abstract
- Appropriately defining and then efficiently calculating similarities from large data sets are often essential in data mining, both for building tractable representations and for gaining understanding of data and generating processes. Here we rely on the premise that given a set of objects and their correlations, each object is characterized by its context, i.e. its correlations to the other objects, and that the similarity between two objects therefore can be expressed in terms of the similarity between their respective contexts. Resting on this principle, we propose a data-driven and highly scalable approach for discovering similarities from large data sets by representing objects and their relations as a correlation graph that is transformed to a similarity graph. Together these graphs can express rich structural properties among objects. Specifically, we show that concepts -- representations of abstract ideas and notions -- are constituted by groups of similar objects that can be identified by clustering the objects in the similarity graph. These principles and methods are applicable in a wide range of domains, and will here be demonstrated for three distinct types of objects: codons, artists and words, where the numbers of objects and correlations range from small to very large.
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| 6. |
- Görnerup, Olof, et al.
(författare)
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Streaming word similarity mining on the cheap
- 2018
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Accurately and efficiently estimating word similarities from text is fundamental in natural language processing. In this paper, we propose a fast and lightweight method for estimating similarities from streams by explicitly counting second-order co-occurrences. The method rests on the observation that words that are highly correlated with respect to such counts are also highly similar with respect to first-order co-occurrences. Using buffers of co-occurred words per word to count second-order co-occurrences, we can then estimate similarities in a single pass over data without having to do prohibitively expensive similarity calculations. We demonstrate that this approach is scalable, converges rapidly, behaves robustly under parameter changes, and that it captures word similarities on par with those given by state-of-the-art word embeddings.
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| 7. |
- Hess, Andrea, et al.
(författare)
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Exploring communication and mobility behaviour of 3G network users and its temporal consistency
- 2015
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Konferensbidrag (refereegranskat)abstract
- Over the past decade, telecommunication network operators have more and more realized the added value of data analytics for their network deployment efficiency. Early studies targeted the global network perspective by localizing peak loads, both in terms of area and time period. Due to their higher granularity and information richness, current telecommunication datasets allow increasingly deeper insights into the network activities of the users. Existing network traffic classification studies tend to divide users into groups without considering the transitions between different groups caused by individual behavioral traits, which we expect to show observable regularities. Our approach defines a profiling model that characterizes the user behavior as well as its temporal dynamics from two perspectives: w.r.t. (i) the network load the users generate, and (ii) their mobility patterns. The model is evaluated with two unsupervised clustering algorithms of different complexity (namely, XMeans and EM) by means of a 3G trace dataset from a European operator.
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| 8. |
- Kreuger, Per, et al.
(författare)
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Autonomous load balancing of heterogeneous networks
- 2015
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Ingår i: IEEE Vehicular Technology Conference. - : Institute of Electrical and Electronics Engineers Inc.. - 9781479980888
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Konferensbidrag (refereegranskat)abstract
- This paper presents a method for load balancing heterogeneous networks by dynamically assigning values to the LTE cell range expansion (CRE) parameter. The method records hand-over events online and adapts flexibly to changes in terminal traffic and mobility by maintaining statistical estimators that are used to support autonomous assignment decisions. The proposed approach has low overhead and is highly scalable due to a modularised and completely distributed design that exploits self-organisation based on local inter-cell interactions. An advanced simulator that incorporates terminal traffic patterns and mobility models with a radio access network simulator has been developed to validate and evaluate the method.
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| 9. |
- Kreuger, Per, et al.
(författare)
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Distributed dynamic load balancing with applications in radio access networks
- 2018
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Ingår i: International Journal of Network Management. - : John Wiley & Sons. - 1055-7148 .- 1099-1190. ; 28:2
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Tidskriftsartikel (refereegranskat)abstract
- Managing and balancing load in distributed systems remains a challenging problem in resource management, especially in networked systems where scalability concerns favour distributed and dynamic approaches. Distributed methods can also integrate well with centralised control paradigms if they provide high-level usage statistics and control interfaces for supporting and deploying centralised policy decisions. We present a general method to compute target values for an arbitrary metric on the local system state and show that autonomous rebalancing actions based on the target values can be used to reliably and robustly improve the balance for metrics based on probabilistic risk estimates. To balance the trade-off between balancing efficiency and cost, we introduce 2 methods of deriving rebalancing actuations from the computed targets that depend on parameters that directly affects the trade-off. This enables policy level control of the distributed mechanism based on collected metric statistics from network elements. Evaluation results based on cellular radio access network simulations indicate that load balancing based on probabilistic overload risk metrics provides more robust balancing solutions with fewer handovers compared to a baseline setting based on average load.
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| 10. |
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