| 1. |
- Bae, Juhee, et al.
(author)
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Towards a methodological framework to address data challenges in lake water quality predictions
- 2024
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In: 3rd International Conference on Water Management in Changing Conditions. - : European Water Association; IFAT. ; , s. 5-8
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Conference paper (peer-reviewed)abstract
- Climate change has impacted global temperatures, triggering extreme weather and adverse environmental effects. In Sweden, these changes have caused shifts in weather patterns, leading to disruptions in infrastructure. This, in turn, has influenced water turbidity levels, negatively impacting water quality. To tackle these issues, a study was conducted using machine learning to predict turbidity with six meteorological variables collected for two years. Our preliminary research showed a substantial influence of seasonal changes on water turbidity, especially air temperature. Identifying supporting indicators such as lagged features is crucial and considerably improved the turbidity prediction performance for two of the machine learning models used. However, the study also identified challenges like data collection and uncertainty issues. We recommend improving data collection quality with higher frequency, minimizing geographical gaps between data collection points, sharing calibration assumptions, checking the sensors regularly, and accounting for data anomalies. Understanding these challenges and their potential implications could lead to more methodological enhancements.
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| 2. |
- Darwish, Amena, et al.
(author)
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Learning Individual Driver’s Mental Models Using POMDPs and BToM
- 2020
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In: DHM2020. - Amsterdam : IOS Press. - 9781643681047 - 9781643681054 ; , s. 51-60
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Conference paper (peer-reviewed)abstract
- Advanced driver assistant systems are supposed to assist the driver and ensure their safety while at the same time providing a fulfilling driving experience that suits their individual driving styles. What a driver will do in any given traffic situation depends on the driver’s mental model which describes how the driver perceives the observable aspects of the environment, interprets these aspects, and on the driver’s goals and beliefs of applicable actions for the current situation. Understanding the driver’s mental model has hence received great attention from researchers, where defining the driver’s beliefs and goals is one of the greatest challenges. In this paper we present an approach to establish individual drivers’ temporal-spatial mental models by considering driving to be a continuous Partially Observable Markov Decision Process (POMDP) wherein the driver’s mental model can be represented as a graph structure following the Bayesian Theory of Mind (BToM). The individual’s mental model can then be automatically obtained through deep reinforcement learning. Using the driving simulator CARLA and deep Q-learning, we demonstrate our approach through the scenario of keeping the optimal time gap between the own vehicle and the vehicle in front.
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| 3. |
- Hamed, Omar, et al.
(author)
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Pedestrian Intention Recognition and Action Prediction Using a Feature Fusion Deep Learning Approach
- 2021
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In: USB Proceedings The 18th International Conference on Modeling Decisions for Artificial Intelligence. - 9789152710272 ; , s. 89-100
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Conference paper (peer-reviewed)abstract
- Recognizing Pedestrians intention to cross a street and predicting their imminent crossing action are major challenges for advanced driver assistance systems (ADAS) and Autonomous Vehicles (AV). In this paper we address these problems by proposing a new neural network architecture that uses feature fusion. The approach is used to recog[1]nise/predict the pedestrians intention/action 1.5 sec (45 frames) ahead. We evaluate our approach on the recently suggested benchmark by Rasouli et al. and show that our approach outperforms current state of the art models. We observe further improved results when the model is trained and tested on a stronger balanced subset of the PIE dataset.
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| 4. |
- Hamed, Omar, et al.
(author)
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Pedestrian’s Intention Recognition, Fusion of Handcrafted Features in a Deep Learning Approach
- 2021
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In: AAAI-21 / IAAI-21 / EAAI-21 Proceedings: A virtual conference February 2-9, 2021. - Palo Alto : AAAI Press. - 9781577358664 ; , s. 15795-15796
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Conference paper (peer-reviewed)abstract
- The safety of vulnerable road users (VRU) is a major concernfor both advanced driver assistance systems (ADAS) and autonomousvehicle manufacturers. To guarantee people safetyon roads, autonomous vehicles must be able to detect thepresence of pedestrians, track them, and predict their intentionto cross the road. Most of the earlier work on pedestrianintention recognition focused on using either handcraftedfeatures or an end-to-end deep learning approach. In thisproject, we investigate the impact of fusing handcrafted featureswith auto learned features by using a two-stream neuralnetwork architecture. Our results show that the combined approachimproves the performance. Furthermore, the proposedmethod achieved very good results on the JAAD dataset. Dependingon whether we considered the immediate frames beforethe crossing or only half a second before that point, wereceived prediction accuracy of 91%, and 84%, respectively.
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| 5. |
- Helldin, Tove, et al.
(author)
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Situation Awareness in Telecommunication Networks Using Topic Modeling
- 2018
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In: 2018 21st International Conference on Information Fusion, FUSION 2018. - : IEEE. - 9780996452762 - 9780996452779 - 9781538643303 ; , s. 549-556
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Conference paper (peer-reviewed)abstract
- For an operator of wireless telecommunication networks to make timely interventions in the network before minor faults escalate into issues that can lead to substandard system performance, good situation awareness is of high importance. Due to the increasing complexity of such networks, as well as the explosion of traffic load, it has become necessary to aid human operators to reach a good level of situation awareness through the use of exploratory data analysis and information fusion techniques. However, to understand the results of such techniques is often cognitively challenging and time consuming. In this paper, we present how telecommunication operators can be aided in their data analysis and sense-making processes through the usage and visualization of topic modeling results. We present how topic modeling can be used to extract knowledge from base station counter readings and make design suggestions for how to visualize the analysis results to a telecommunication operator.
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| 6. |
- Huhnstock, Nikolas Alexander, 1988-, et al.
(author)
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An Infinite Replicated Softmax Model for Topic Modeling
- 2019
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In: Modeling Decisions for Artificial Intelligence. - Cham : Springer. - 9783030267728 - 9783030267735 ; , s. 307-318
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Conference paper (peer-reviewed)abstract
- In this paper, we describe the infinite replicated Softmax model (iRSM) as an adaptive topic model, utilizing the combination of the infinite restricted Boltzmann machine (iRBM) and the replicated Softmax model (RSM). In our approach, the iRBM extends the RBM by enabling its hidden layer to adapt to the data at hand, while the RSM allows for modeling low-dimensional latent semantic representation from a corpus. The combination of the two results is a method that is able to self-adapt to the number of topics within the document corpus and hence, renders manual identification of the correct number of topics superfluous. We propose a hybrid training approach to effectively improve the performance of the iRSM. An empirical evaluation is performed on a standard data set and the results are compared to the results of a baseline topic model. The results show that the iRSM adapts its hidden layer size to the data and when trained in the proposed hybrid manner outperforms the base RSM model.
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| 7. |
- Huhnstock, Nikolas Alexander, 1988-, et al.
(author)
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On the behavior of the infinite restricted boltzmann machine for clustering
- 2018
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In: SAC '18 Proceedings of the 33rd Annual ACM Symposium on Applied Computing. - New York, NY, USA : Association for Computing Machinery (ACM). - 9781450351911 ; , s. 461-470
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Conference paper (peer-reviewed)abstract
- Clustering is a core problem within a wide range of research disciplines ranging from machine learning and data mining to classical statistics. A group of clustering approaches so-called nonparametric methods, aims to cluster a set of entities into a beforehand unspecified and unknown number of clusters, making potentially expensive pre-analysis of data obsolete. In this paper, the recently, by Cote and Larochelle introduced infinite Restricted Boltzmann Machine that has the ability to self-regulate its number of hidden parameters is adapted to the problem of clustering by the introduction of two basic cluster membership assumptions. A descriptive study of the influence of several regularization and sparsity settings on the clustering behavior is presented and results are discussed. The results show that sparsity is a key adaption when using the iRBM for clustering that improves both the clustering performances as well as the number of identified clusters.
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| 8. |
- Karlsson, Alexander, et al.
(author)
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Evaluation of Evidential Combination Operators
- 2013
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Conference paper (peer-reviewed)abstract
- We present an experiment for evaluating precise and imprecise evidential combination operators. The experiment design is based on the assumption that only limited statistical information is available in the form of multinomial observations. We evaluate three different evidential combination operators; one precise, the Bayesian combination operator, and two imprecise, the credal and Dempster’s combination operator, for combining independent pieces of evidence regarding some discrete state space of interest. The evaluation is performed by using a score function that takes imprecision into account. The results show that the precise framework seems to perform equally well as the imprecise frameworks.
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| 9. |
- Karlsson, Alexander, et al.
(author)
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Modeling uncertainty in bibliometrics and information retrieval : an information fusion approach
- 2015
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In: Scientometrics. - : Akademiai Kiado. - 0138-9130 .- 1588-2861. ; 102:3, s. 2255-2274
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Journal article (peer-reviewed)abstract
- We describe ongoing research where the aim is to apply recent results from the research field of information fusion to bibliometric analysis and information retrieval. We highlight the importance of ‘uncertainty’ within information fusion and argue that this concept is crucial also for bibliometrics and information retrieval. More specifically, we elaborate on three research strategies related to uncertainty: uncertainty management methods, explanation of uncertainty and visualization of uncertainty. We exemplify our strategies to the classical problem of author name disambiguation where we show how uncertainty can be modeled explained and visualized using information fusion. We show how an information seeker can benefit from tracing increases/decreases of uncertainty in the reasoning process. We also present how such changes can be explained for the information seeker through visualization techniques, which are employed to highlight the complexity involved in the process of modeling and managing uncertainty in bibliometric analysis. Finally we argue that a further integration of information fusion approaches in the research area of bibliometrics and information retrieval may results in new and fruitful venues of research.
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| 10. |
- Olson, Nasrine, et al.
(author)
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Little Scientist, Big Data : Information fusion towards meeting the information needs of scholars
- 2014
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In: Libraries in the Digital Age (LIDA) Proceedings: Assessing Libraries and Library Users and Use. - : University of Zadar, Department of Information Sciences. ; , s. 14-
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Conference paper (other academic/artistic)abstract
- With increasing numbers of scholarly publications, and multiplicity of publication-types and outlets, overviews of research fields have become a challenge. We bring together bibliometric methods, information retrieval, information fusion, and data visualization within a new project, INCITE - Information Fusion as an E-service in Scholarly Information Use, with the aim to develop improved methods and tools addressing emerging user-needs. In this paper we report on ongoing research within that project. (a) We elaborate on a qualitative user-study in which the emerging needs of researchers in the age of big data are explored. The study is based on interviews and dialogue with seven scholars at different academic levels. Data analysis was informed by adaptive theory, in accordance to which iterative pre-coding, provisional codes, and memo-writing were used to reach a more abstract level of analysis. A number of challenges related to the multiplicity of information sources and extent of data were identified including difficulties in keeping track of all the relevant sources; the inability to utilize extensive sets of data being taken for granted; and using data reduction strategies that at times go against the scholar’s own ideals of scholarly rigor. In analysing these difficulties, we have identified potential solutions that could facilitate the process of forming overviews of different research areas. (b) An example of such a solution is presented, which is builds on the Dempster-Shafer Theory and is designed to allow for interactive individual ranking of information sources in the process of a coordinated search across different information sources.
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