| 1. |
- Riveiro, Maria, 1978-
(författare)
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Visual analytics for maritime anomaly detection
- 2011
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The surveillance of large sea areas typically involves the analysis of huge quantities of heterogeneous data. In order to support the operator while monitoring maritime traffic, the identification of anomalous behavior or situations that might need further investigation may reduce operators' cognitive load. While it is worth acknowledging that existing mining applications support the identification of anomalies, autonomous anomaly detection systems are rarely used for maritime surveillance. Anomaly detection is normally a complex task that can hardly be solved by using purely visual or purely computational methods. This thesis suggests and investigates the adoption of visual analytics principles to support the detection of anomalous vessel behavior in maritime traffic data. This adoption involves studying the analytical reasoning process that needs to be supported, using combined automatic and visualization approaches to support such process, and evaluating such integration. The analysis of data gathered during interviews and participant observations at various maritime control centers and the inspection of video recordings of real anomalous incidents lead to a characterization of the analytical reasoning process that operators go through when monitoring traffic. These results are complemented with a literature review of anomaly detection techniques applied to sea traffic. A particular statistical-based technique is implemented, tested, and embedded in a proof-of-concept prototype that allows user involvement in the detection process. The quantitative evaluation carried out by employing the prototype reveals that participants who used the visualization of normal behavioral models outperformed the group without aid. The qualitative assessment shows that domain experts are positive towards providing automatic support and the visualization of normal behavioral models, since these aids may reduce reaction time, as well as increase trust and comprehensibility in the system. Based on the lessons learned, this thesis provides recommendations for designers and developers of maritime control and anomaly detection systems, as well as guidelines for carrying out evaluations of visual analytics environments.
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| 2. |
- Johansson, Ronnie, et al.
(författare)
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On the Definition and Scope of Information Fusion as a Field of Research
- 2022
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Ingår i: ISIF Perspectives on Information Fusion. - : International Society of Information Fusion (ISIF). - 2831-4824 .- 2831-4832. ; 5:1, s. 3-12
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Tidskriftsartikel (refereegranskat)abstract
- A definition of information fusion (IF) as a field of research can benefit researchers within the field, who may use such a definition when motivating their own work and evaluating the contributions of others. Moreover, it can enable researchers and practitioners outside the field to more easily relate their own work to the field and more easily understand the scope of IF techniques and methods. Based on strengths and weaknesses of existing definitions, a definition is proposed that is argued to effectively fulfill the requirements that can be put on a definition of IF as a field of research. Although the proposed definition aims to be precise, it does not fully capture the richness and versatility of the IF field. To address that limitation, we highlight some topics to explore the scope of IF, covering the systems perspective of IF and its relation to ma-chine learning, optimization, robot behavior, opinion aggregation, and databases.
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| 3. |
- Nilsson, Maria
(författare)
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Capturing semi-automated decision making : the methodology of CASADEMA
- 2010
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis presents a new methodology named CASADEMA (CApturing Semi-Automated DEcision MAking) which captures the interaction between humans and the technology they use to support their decision-making within the domain of Information Fusion. We are particularly interested in characterising the interaction between human decision makers and artefacts in semi-automated fusion processes. In our investigation we found that the existing approaches are limited in their ability to capture such interactions in sufficient detail. The presented method is built upon a distributed-cognition perspective. The use of this particular theoretical framework from cognitive science enables the method to take into account not only the role of the data captured in the physical and digital artefacts of the fusion system (e.g., radar readings, information from a fax or database, a piece of paper, etc.), but also the cognitive support function of the artefacts themselves (e.g., as an external memory) as part of the fusion process. That is, the interdependencies between the fusion process and decision-making can be captured. This thesis thus contributes to two main fields. Firstly, it enables, through CASADEMA, a distributed-cognition perspective of fusion processes in the, otherwise, rather technology-oriented field of Information Fusion. This has important conceptual implications, since it views fusion processes as extending beyond the boundary of physical/computer systems, to include humans, technology, and tools, as well as the interactions between them. It is argued that a better understanding of these interactions can lead to a better design of fusion processes, making CASADEMA an important contribution to the information fusion field. Secondly, the thesis provides, again in the form of CASADEMA, a practical application of the distributed-cognition theoretical framework. Importantly, the notations and definitions introduced in CASADEMA structure the otherwise currently rather loosely defined concepts and approaches in distributed cognition research. Hence, the work presented here also contributes to the fields of cognitive science and human-computer interaction.
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| 4. |
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| 5. |
- Billing, Erik, 1981-
(författare)
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Cognition reversed : Robot learning from demonstration
- 2009
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The work presented in this thesis investigates techniques for learning from demonstration (LFD). LFD is a well established approach to robot learning, where a teacher demonstrates a behavior to a robot pupil. This thesis focuses on LFD where a human teacher demonstrates a behavior by controlling the robot via teleoperation. The robot should after demonstration be able to execute the demonstrated behavior under varying conditions. Several views on representation, recognition and learning of robot behavior are presented and discussed from a cognitive and computational perspective. LFD-related concepts such as behavior, goal, demonstration, and repetition are defined and analyzed, with focus on how bias is introduced by the use of behavior primitives. This analysis results in a formalism where LFD is described as transitions between information spaces. Assuming that the behavior recognition problem is partly solved, ways to deal with remaining ambiguities in the interpretation of a demonstration are proposed. A total of five algorithms for behavior recognition are proposed and evaluated, including the dynamic temporal difference algorithm Predictive Sequence Learning (PSL). PSL is model-free in the sense that it makes few assumptions of what is to be learned. One strength of PSL is that it can be used for both robot control and recognition of behavior. While many methods for behavior recognition are concerned with identifying invariants within a set of demonstrations, PSL takes a different approach by using purely predictive measures. This may be one way to reduce the need for bias in learning. PSL is, in its current form, subjected to combinatorial explosion as the input space grows, which makes it necessary to introduce some higher level coordination for learning of complex behaviors in real-world robots. The thesis also gives a broad introduction to computational models of the human brain, where a tight coupling between perception and action plays a central role. With the focus on generation of bias, typical features of existing attempts to explain humans' and other animals' ability to learn are presented and analyzed, from both a neurological and an information theoretic perspective. Based on this analysis, four requirements for implementing general learning ability in robots are proposed. These requirements provide guidance to how a coordinating structure around PSL and similar algorithms should be implemented in a model-free way.
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| 6. |
- Ekanayake, Hiran, et al.
(författare)
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Game Interaction State Graphs for Evaluation of User Engagement in Explorative and Experience-based Training Games
- 2010
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Ingår i: 2010 International Conference on Advances in ICT for Emerging Regions (ICTer). - : IEEE conference proceedings. - 9781424490417 ; , s. 40-44
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Konferensbidrag (refereegranskat)abstract
- There is an increasing interest to use computer games for non-traditional education, such as for training purposes. For training education, simulators are considered as offering more realistic learning environments to experience situations that are similar to real world. This type of learning is more beneficial for practicing critical situations which are difficult or impossible in real world training, for instance experience the consequences of unsafe driving. However, the effectiveness of simulation-based learning of this nature is dependent upon the learner's engagement and explorative behaviour. Most current learner evaluation systems are unable to capture this type of learning. Therefore, in this paper we introduce the concept of game interaction state graphs (GISGs) to capture the engagement in explorative and experience-based training tasks. These graphs are constructed based on rules which capture psychologically significant learner behaviours and situations. Simple variables reflecting game state and learner's controller actions provide the ingredients to the rules. This approach eliminates the complexity involved with other similar approaches, such as constructing a full-fledged cognitive model for the learner. GISGs, at minimum, can be used to evaluate the explorative behaviour, the training performance and personal preferences of a learner.
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| 7. |
- Ekanayake, Hiran B., et al.
(författare)
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Comparing expert driving behavior in real world and simulator contexts
- 2013
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Ingår i: International Journal of Computer Games Technology. - : Hindawi Publishing Corporation. - 1687-7047 .- 1687-7055.
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Tidskriftsartikel (refereegranskat)abstract
- Computer games are increasingly used for purposes beyond mere entertainment, and current hi-tech simulators can provide quite, naturalistic contexts for purposes such as traffic education. One of the critical concerns in this area is the validity or transferability of acquired skills from a simulator to the real world context. In this paper, we present our work in which we compared driving in the real world with that in the simulator at two levels, that is, by using performance measures alone, and by combining psychophysiological measures with performance measures. For our study, we gathered data using questionnaires as well as by logging vehicle dynamics, environmental conditions, video data, and users' psychophysiological measurements. For the analysis, we used several novel approaches such as scatter plots to visualize driving tasks of different contexts and to obtain vigilance estimators from electroencephalographic (EEG) data in order to obtain important results about the differences between the driving in the two contexts. Our belief is that both experimental procedures and findings of our experiment are very important to the field of serious games concerning how to evaluate the fitness of driving simulators and measure driving performance. © 2013 Hiran B. Ekanayake et al.
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| 8. |
- Ekanayake, Hiran, et al.
(författare)
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Assessing Performance Competence in Training Games
- 2011
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Ingår i: Affective Computing and Intelligent Interaction. - Berlin, Heidelberg : Springer Berlin/Heidelberg. - 9783642245701 - 9783642245718 ; , s. 518-527
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Konferensbidrag (refereegranskat)abstract
- In-process assessment of trainee learners in game-based simulators is a challenging activity. This typically involves human instructor time and cost, and does not scale to the one tutor per learner vision of computer-based learning. Moreover, evaluation from a human instructor is often subjective and comparisons between learners are not accurate. Therefore, in this paper, we propose an automated, formula-driven quantitative evaluation method for assessing performance competence in serious training games. Our proposed method has been empirically validated in a game-based driving simulator using 7 subjects and 13 sessions, and accuracy up to 90.25% has been achieved when compared to an existing qualitative method. We believe that by incorporating quantitative evaluation methods like these future training games could be enriched with more meaningful feedback and adaptive game-play so as to better monitor and support player motivation, engagement and learning performance.
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| 9. |
- Ekanayake, Hiran B., et al.
(författare)
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Affective Realism of Animated Films in the Development of Simulation-Based Tutoring Systems
- 2013
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Ingår i: International Journal of Distance Education Technologies. - : IGI Global. - 1539-3100 .- 1539-3119. ; 11:2
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents a study focused on comparing real actors based scenarios and animated characters based scenarios with respect to their similarity in evoking psychophysiological activity for certain events by measuring galvanic skin response (GSR). In the experiment, one group (n=11) watched the real actors’ film whereas another group (n=7) watched the animated film, which had the same story and dialogue as the real actors’ film. The results have shown that there is no significant difference in the skin conductance response (SCR) scores between the two groups; however, responses significantly differ when SCR amplitudes are taken into account. Moreover, Pearson’s correlation reported as high as over 80% correlation between the two groups’ SCRs for certain time intervals. The authors believe that this finding is of general importance for the domain of simulation-based tutoring systems in development of and decisions regarding use of animated characters based scenarios.
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| 10. |
- Johansson, Ulf
(författare)
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Obtaining Accurate and Comprehensible Data Mining Models : An Evolutionary Approach
- 2007
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- When performing predictive data mining, the use of ensembles is claimed to virtually guarantee increased accuracy compared to the use of single models. Unfortunately, the problem of how to maximize ensemble accuracy is far from solved. In particular, the relationship between ensemble diversity and accuracy is not completely understood, making it hard to efficiently utilize diversity for ensemble creation. Furthermore, most high-accuracy predictive models are opaque, i.e. it is not possible for a human to follow and understand the logic behind a prediction. For some domains, this is unacceptable, since models need to be comprehensible. To obtain comprehensibility, accuracy is often sacrificed by using simpler but transparent models; a trade-off termed the accuracy vs. comprehensibility trade-off. With this trade-off in mind, several researchers have suggested rule extraction algorithms, where opaque models are transformed into comprehensible models, keeping an acceptable accuracy.In this thesis, two novel algorithms based on Genetic Programming are suggested. The first algorithm (GEMS) is used for ensemble creation, and the second (G-REX) is used for rule extraction from opaque models. The main property of GEMS is the ability to combine smaller ensembles and individual models in an almost arbitrary way. Moreover, GEMS can use base models of any kind and the optimization function is very flexible, easily permitting inclusion of, for instance, diversity measures. In the experimentation, GEMS obtained accuracies higher than both straightforward design choices and published results for Random Forests and AdaBoost. The key quality of G-REX is the inherent ability to explicitly control the accuracy vs. comprehensibility trade-off. Compared to the standard tree inducers C5.0 and CART, and some well-known rule extraction algorithms, rules extracted by G-REX are significantly more accurate and compact. Most importantly, G-REX is thoroughly evaluated and found to meet all relevant evaluation criteria for rule extraction algorithms, thus establishing G-REX as the algorithm to benchmark against.
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