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Modeling and simulation of large-scale systems: A systematic comparison of modeling paradigms (2019)
Journal Article
Schweiger, G., Nilsson, H., Schoeggl, J., Birk, W., & Posch, A. (2020). Modeling and simulation of large-scale systems: A systematic comparison of modeling paradigms. Applied Mathematics and Computation, 365, Article 124713. https://doi.org/10.1016/j.amc.2019.124713

A trend across most areas where simulation-driven development is used is the ever increasing size and complexity of the systems under consideration, pushing established methods of modeling and simulation towards their limits. This paper complements e... Read More about Modeling and simulation of large-scale systems: A systematic comparison of modeling paradigms.

A variable neighborhood search algorithm with reinforcement learning for a real-life periodic vehicle routing problem with time windows and open routes (2019)
Journal Article
Chen, B., Qu, R., Bai, R., & Laesanklang, W. (2020). A variable neighborhood search algorithm with reinforcement learning for a real-life periodic vehicle routing problem with time windows and open routes. RAIRO: Operations Research, 54(5), 1467-1494. https://doi.org/10.1051/ro/2019080

Based on a real-life container transport problem, a model of Open Periodic Vehicle Routing Problem with Time Windows (OPVRPTW) is proposed in this paper. In a wide planning horizon, which is divided into a number of shifts, a fixed number of trucks a... Read More about A variable neighborhood search algorithm with reinforcement learning for a real-life periodic vehicle routing problem with time windows and open routes.

Deep Reinforcement Learning based Patch Selection for Illuminant Estimation (2019)
Journal Article
Xu, B., Liu, J., Hou, X., Liu, B., & Qiu, G. (2019). Deep Reinforcement Learning based Patch Selection for Illuminant Estimation. Image and Vision Computing, 91, https://doi.org/10.1016/j.imavis.2019.08.002

Previous deep learning based approaches to illuminant estimation either resized the raw image to lower resolution or randomly cropped image patches for the deep learning model. However, such practices would inevitably lead to information loss or the... Read More about Deep Reinforcement Learning based Patch Selection for Illuminant Estimation.

Multi-head CNN–RNN for multi-time series anomaly detection: An industrial case study (2019)
Journal Article
Canizo, M., Triguero, I., Conde, A., & Onieva, E. (2019). Multi-head CNN–RNN for multi-time series anomaly detection: An industrial case study. Neurocomputing, 363, 246-260. https://doi.org/10.1016/j.neucom.2019.07.034

Detecting anomalies in time series data is becoming mainstream in a wide variety of industrial applications in which sensors monitor expensive machinery. The complexity of this task increases when multiple heterogeneous sensors provide information of... Read More about Multi-head CNN–RNN for multi-time series anomaly detection: An industrial case study.

A comparison of presentation methods for conducting youth juries (2019)
Journal Article
Dowthwaite, L., Perez Vallejos, E., Koene, A., Cano, M., & Portillo, V. (2019). A comparison of presentation methods for conducting youth juries. PLoS ONE, 14(6), Article e0218770. https://doi.org/10.1371/journal.pone.0218770

The 5Rights Youth Juries are an educational intervention to promote digital literacy by engaging participants (i.e. jurors) in a deliberative discussion around their digital rights. The main objective of these jury-styled focus groups is to encourage... Read More about A comparison of presentation methods for conducting youth juries.

A Taxonomy of Traffic Forecasting Regression Problems From a Supervised Learning Perspective (2019)
Journal Article
Angarita-Zapata, J. S., Masegosa, A. D., & Triguero, I. (2019). A Taxonomy of Traffic Forecasting Regression Problems From a Supervised Learning Perspective. IEEE Access, 7, 68185 -68205. https://doi.org/10.1109/ACCESS.2019.2917228

One contemporary policy to deal with traffic congestion is the design and implementation of forecasting methods that allow users to plan ahead of time and decision makers to improve traffic management. Current data availability and growing computatio... Read More about A Taxonomy of Traffic Forecasting Regression Problems From a Supervised Learning Perspective.

Designing and evaluating mobile self-reporting techniques: crowdsourcing for citizen science (2019)
Journal Article
Younis, E. M. G., Kanjo, E., & Chamberlain, A. (2019). Designing and evaluating mobile self-reporting techniques: crowdsourcing for citizen science. Personal and Ubiquitous Computing, 23(2), 329-338. https://doi.org/10.1007/s00779-019-01207-2

In recent years, mobile phone technology has taken tremendous leaps and bounds to enable all types of sensing applications and interaction methods, including mobile journaling and self-reporting to add metadata and to label sensor data streams. Mobil... Read More about Designing and evaluating mobile self-reporting techniques: crowdsourcing for citizen science.

The Classification of Minor Gait Alterations Using Wearable Sensors and Deep Learning (2019)
Journal Article
Turner, A., & Hayes, S. (2019). The Classification of Minor Gait Alterations Using Wearable Sensors and Deep Learning. IEEE Transactions on Biomedical Engineering, 66(11), 3136-3145. https://doi.org/10.1109/tbme.2019.2900863

Objective: This paper describes how non-invasive wearable sensors can be used in combination with deep learning to classify artificially induced gait alterations without the requirement for a medical professional or gait analyst to be present. This a... Read More about The Classification of Minor Gait Alterations Using Wearable Sensors and Deep Learning.

Citizen science and the professional-amateur divide: lessons from differing online practices (2019)
Journal Article
Dowthwaite, L., & Sprinks, J. (2019). Citizen science and the professional-amateur divide: lessons from differing online practices. JCOM: Journal of Science Communication, 18(01), Article A06. https://doi.org/10.22323/2.18010206

Online citizen science platforms increasingly provide types of infrastructural support previously only available to organisationally-based professional scientists. Other practices, such as creative arts, also exploit the freedom and accessibility aff... Read More about Citizen science and the professional-amateur divide: lessons from differing online practices.

Recognition of Haptic Interaction Patterns in Dyadic Joint Object Manipulation (2014)
Journal Article
Madan, C. E., Kucukyilmaz, A., Sezgin, T. M., & Basdogan, C. (2015). Recognition of Haptic Interaction Patterns in Dyadic Joint Object Manipulation. IEEE Transactions on Haptics, 8(1), 54-66. https://doi.org/10.1109/toh.2014.2384049

The development of robots that can physically cooperate with humans has attained interest in the last decades. Obviously, this effort requires a deep understanding of the intrinsic properties of interaction. Up to now, many researchers have focused o... Read More about Recognition of Haptic Interaction Patterns in Dyadic Joint Object Manipulation.