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This research paper focuses on the accuracy and limitations of user values elicited through a textual interface with questions asked in isolation. The primary objective was to conduct a user study using a textual interface that uses questions in isolation to assess the effectiveness and accuracy of this interface type and questioning style. The study involved exploring various scenarios and associated user values, as well as comparing the textual interface with graphical and audio interfaces tested in four related studies. The user study consisted of 15 participants who interacted with the textual interface. Afterwards, they were tasked with judging and adapting their behaviour models while also evaluating the interface's usability. The findings indicate that while the textual interface demonstrates decent usability, participants did not perceive a strong need for the current system and that compared to other interface types, the textual interface does not yield the most accurate results. This research provides insights into the usability and limitations of a textual interface for eliciting user values. It emphasizes the need for further exploration and development of alternative interface types to enhance accuracy and user engagement., CSE3000 Research Project, Computer Science and Engineering

Psychological frameworks are rarely used to understand irrigation adoption behaviour in developing countries. A Bayesian belief network (BBN) model was developed that integrated socio-economic characteristics and psychological factors to understand farmer behaviours with respect to irrigation practices in four districts of Maharashtra, India. Strong norms, risk perceptions of water scarcity, and attitude play roles in the adoption of irrigation technology and practices. Critically, it was found that no one factor can explain adoption behaviour; rather, an ensemble of factors is needed to understand farmer behaviour. A farmer who is highly educated, middle-aged, and moderately wealthy with a significant level of family help and an open well as their main water source, while receiving low promotional information related to water scarcity and irrigation adoption, is most likely to adopt irrigation technology. The application of the BBN in this study enables stakeholders and policymakers to better understand the linkages between different factors and behaviour., Sanitary Engineering, Water Resources

Mobile applications became the main interaction channel in several domains, such as banking. Consequently, understanding user behaviour on those apps has drawn attention in order to extract business-oriented outcomes. By combining Markov Chain and graph theory techniques, we successfully developed a process to model the app, to extract the click high utility events, to score the interest on those events and cluster the groups of interest. We tested our approach on an European bank dataset with over 3.5 millions of user's session. By implementing our approach, analysts can gain knowledge of user behaviour in terms of events that are important to the domain.

Mobile applications became the main interaction channel in several domains, such as banking. Consequently, understanding user behaviour on those apps has drawn attention in order to extract business-oriented outcomes. By combining Markov Chain and graph theory techniques, we successfully developed a process to model the app, to extract the click high utility events, to score the interest on those events and cluster the groups of interest. We tested our approach on an European bank dataset with over 3.5 millions of user's session. By implementing our approach, analysts can gain knowledge of user behaviour in terms of events that are important to the domain.

Psychological frameworks are rarely used to understand irrigation adoption behaviour in developing countries. A Bayesian belief network (BBN) model was developed that integrated socio-economic characteristics and psychological factors to understand farmer behaviours with respect to irrigation practices in four districts of Maharashtra, India. Strong norms, risk perceptions of water scarcity, and attitude play roles in the adoption of irrigation technology and practices. Critically, it was found that no one factor can explain adoption behaviour; rather, an ensemble of factors is needed to understand farmer behaviour. A farmer who is highly educated, middle-aged, and moderately wealthy with a significant level of family help and an open well as their main water source, while receiving low promotional information related to water scarcity and irrigation adoption, is most likely to adopt irrigation technology. The application of the BBN in this study enables stakeholders and policymakers to better understand the linkages between different factors and behaviour., Sanitary Engineering, Water Resources

In this ongoing study, we propose a higher order data mining approach for modelling district heating (DH) substations’ behaviour and linking operational behaviour representative profiles with different performance indicators. We initially create substation’s operational behaviour models by extracting weekly patterns and clustering them into groups of similar patterns. The built models are further analyzed and integrated into an overall substation model by applying consensus clustering. The different operational behaviour profiles represented by the exemplars of the consensus clustering model are then linked to performance indicators. The labelled behaviour profiles are deployed over the whole heating season to derive diverse insights about the substation’s performance. The results show that the proposed method can be used for modelling, analyzing and understanding the deviating and sub-optimal DH substation’s behaviours. © 2020, Springer Nature Switzerland AG.

The monitoring of small houses and rooms has become possible due to the advances in IoT sensors, actuators and low power communication protocols in the last few years. As buildings are one of the biggest energy consuming entities, monitoring them has great interest for trying to avoid non-necessary energy waste. Moreover, human behaviour has been reported as being the main discrepancy source between energy usage simulations and real usage, so the ability to monitor and predict actions as opening windows, using rooms, etc. is gaining attention to develop stronger models which may lead to reduce the overall energy consumption of buildings, considering buildings thermal inertia and additional capabilities. In this paper, a case study is described in which four meeting rooms have been monitored to obtain information about the usage of the rooms and later use it to predict their future usage. The results show the possibility to deploy a simple and non-intrusive sensing system whose output could be used to develop advanced control strategies.

This research addresses the problem of predicting future agent behaviour in both single and multi agent settings where multiple agents can enter and exit an environment, and the environment can change dynamically. Both short-term and long-term context was captured in the given domain and utilised neural memory networks to use the derived knowledge for the prediction task. The efficacy of the techniques was demonstrated by applying it to aircraft path prediction, passenger movement prediction in crowded railway stations, driverless car steering, predicting next shot location in tennis and for predicting soccer match outcomes.

The monitoring of small houses and rooms has become possible due to the advances in IoT sensors, actuators and low power communication protocols in the last few years. As buildings are one of the biggest energy consuming entities, monitoring them has great interest for trying to avoid non-necessary energy waste. Moreover, human behaviour has been reported as being the main discrepancy source between energy usage simulations and real usage, so the ability to monitor and predict actions as opening windows, using rooms, etc. is gaining attention to develop stronger models which may lead to reduce the overall energy consumption of buildings, considering buildings thermal inertia and additional capabilities. In this paper, a case study is described in which four meeting rooms have been monitored to obtain information about the usage of the rooms and later use it to predict their future usage. The results show the possibility to deploy a simple and non-intrusive sensing system whose output could be used to develop advanced control strategies.

The monitoring of small houses and rooms has become possible due to the advances in IoT sensors, actuators and low power communication protocols in the last few years. As buildings are one of the biggest energy consuming entities, monitoring them has great interest for trying to avoid non-necessary energy waste. Moreover, human behaviour has been reported as being the main discrepancy source between energy usage simulations and real usage, so the ability to monitor and predict actions as opening windows, using rooms, etc. is gaining attention to develop stronger models which may lead to reduce the overall energy consumption of buildings, considering buildings thermal inertia and additional capabilities. In this paper, a case study is described in which four meeting rooms have been monitored to obtain information about the usage of the rooms and later use it to predict their future usage. The results show the possibility to deploy a simple and non-intrusive sensing system whose output could be used to develop advanced control strategies.

The monitoring of small houses and rooms has become possible due to the advances in IoT sensors, actuators and low power communication protocols in the last few years. As buildings are one of the biggest energy consuming entities, monitoring them has great interest for trying to avoid non-necessary energy waste. Moreover, human behaviour has been reported as being the main discrepancy source between energy usage simulations and real usage, so the ability to monitor and predict actions as opening windows, using rooms, etc. is gaining attention to develop stronger models which may lead to reduce the overall energy consumption of buildings, considering buildings thermal inertia and additional capabilities. In this paper, a case study is described in which four meeting rooms have been monitored to obtain information about the usage of the rooms and later use it to predict their future usage. The results show the possibility to deploy a simple and non-intrusive sensing system whose output could be used to develop advanced control strategies.

Autonomous robotic systems operating in the vicinity of other agents, such as humans, manually driven vehicles and other robots, can model the behaviour and estimate intentions of the other agents to enhance efficiency of their operation, while preserving safety. We propose a data-driven approach to model the behaviour of other agents, which is based on a set of trajectories navigated by other agents. Then, to evaluate the proposed behaviour modelling approach, we propose and compare two methods for agent intention estimation based on: (i) particle filtering; and (ii) decision trees. The proposed methods were validated using three datasets that consist of real-world bicycle and car trajectories in two different scenarios, at a roundabout and at a t-junction with a pedestrian crossing. The results validate the utility of the data-driven behaviour model, and show that decision-tree based intention estimation works better on a binary-class problem, whereas the particle-filter based technique performs better on a multi-class problem, such as the roundabout, where the method yielded an average gain of 14.88 m for correct intention estimation locations compared to the decision-tree based method. © 2018 by the authors, CAISR/SAS2

Autonomous robotic systems operating in the vicinity of other agents, such as humans, manually driven vehicles and other robots, can model the behaviour and estimate intentions of the other agents to enhance efficiency of their operation, while preserving safety. We propose a data-driven approach to model the behaviour of other agents, which is based on a set of trajectories navigated by other agents. Then, to evaluate the proposed behaviour modelling approach, we propose and compare two methods for agent intention estimation based on: (i) particle filtering; and (ii) decision trees. The proposed methods were validated using three datasets that consist of real-world bicycle and car trajectories in two different scenarios, at a roundabout and at a t-junction with a pedestrian crossing. The results validate the utility of the data-driven behaviour model, and show that decision-tree based intention estimation works better on a binary-class problem, whereas the particle-filter based technique performs better on a multi-class problem, such as the roundabout, where the method yielded an average gain of 14.88 m for correct intention estimation locations compared to the decision-tree based method. © 2018 by the authors, CAISR/SAS2

Occupants perform various actions to satisfy their physical and non-physical needs in buildings. These actions greatly affect building operations and thus energy use. Clearly understanding and accurately modelling occupant behaviour in buildings are crucial to guide energy-efficient building design and operation, and to reduce the gap between design and actual energy performance of buildings. To study and understand occupant behaviour, a cross-sectional questionnaire survey is one of the most useful tools to gain insights on general behaviour patterns and drivers, and to find connections between human, social and local comfort parameters. In this study, 33 projects were reviewed from the energy-related occupant behaviour research literature that employed cross-sectional surveys or interviews for data collection from the perspective of findings, limitations and methodological challenges. This research shows that future surveys are needed to bridge the gaps in literature but they would need to encompass a multidisciplinary approach to do so as until now only environmental and engineering factors were considered in these studies. Insights from social practice theories and techniques must be acquired to deploy robust and unbiased questionnaire results, which will provide new, more comprehensive knowledge in the field, and therefore occupant behaviour could be better understood and represented in building performance simulation to support design and operation of low or net-zero energy buildings.

Autonomous robotic systems operating in the vicinity of other agents, such as humans, manually driven vehicles and other robots, can model the behaviour and estimate intentions of the other agents to enhance efficiency of their operation, while preserving safety. We propose a data-driven approach to model the behaviour of other agents, which is based on a set of trajectories navigated by other agents. Then, to evaluate the proposed behaviour modelling approach, we propose and compare two methods for agent intention estimation based on: (i) particle filtering; and (ii) decision trees. The proposed methods were validated using three datasets that consist of real-world bicycle and car trajectories in two different scenarios, at a roundabout and at a t-junction with a pedestrian crossing. The results validate the utility of the data-driven behaviour model, and show that decision-tree based intention estimation works better on a binary-class problem, whereas the particle-filter based technique performs better on a multi-class problem, such as the roundabout, where the method yielded an average gain of 14.88 m for correct intention estimation locations compared to the decision-tree based method. © 2018 by the authors, CAISR/SAS2

Occupants perform various actions to satisfy their physical and non-physical needs in buildings. These actions greatly affect building operations and thus energy use. Clearly understanding and accurately modelling occupant behaviour in buildings are crucial to guide energy-efficient building design and operation, and to reduce the gap between design and actual energy performance of buildings. To study and understand occupant behaviour, a cross-sectional questionnaire survey is one of the most useful tools to gain insights on general behaviour patterns and drivers, and to find connections between human, social and local comfort parameters. In this study, 33 projects were reviewed from the energy-related occupant behaviour research literature that employed cross-sectional surveys or interviews for data collection from the perspective of findings, limitations and methodological challenges. This research shows that future surveys are needed to bridge the gaps in literature but they would need to encompass a multidisciplinary approach to do so as until now only environmental and engineering factors were considered in these studies. Insights from social practice theories and techniques must be acquired to deploy robust and unbiased questionnaire results, which will provide new, more comprehensive knowledge in the field, and therefore occupant behaviour could be better understood and represented in building performance simulation to support design and operation of low or net-zero energy buildings.

Autonomous robotic systems operating in the vicinity of other agents, such as humans, manually driven vehicles and other robots, can model the behaviour and estimate intentions of the other agents to enhance efficiency of their operation, while preserving safety. We propose a data-driven approach to model the behaviour of other agents, which is based on a set of trajectories navigated by other agents. Then, to evaluate the proposed behaviour modelling approach, we propose and compare two methods for agent intention estimation based on: (i) particle filtering; and (ii) decision trees. The proposed methods were validated using three datasets that consist of real-world bicycle and car trajectories in two different scenarios, at a roundabout and at a t-junction with a pedestrian crossing. The results validate the utility of the data-driven behaviour model, and show that decision-tree based intention estimation works better on a binary-class problem, whereas the particle-filter based technique performs better on a multi-class problem, such as the roundabout, where the method yielded an average gain of 14.88 m for correct intention estimation locations compared to the decision-tree based method. © 2018 by the authors, CAISR/SAS2

Autonomous robotic systems operating in the vicinity of other agents, such as humans, manually driven vehicles and other robots, can model the behaviour and estimate intentions of the other agents to enhance efficiency of their operation, while preserving safety. We propose a data-driven approach to model the behaviour of other agents, which is based on a set of trajectories navigated by other agents. Then, to evaluate the proposed behaviour modelling approach, we propose and compare two methods for agent intention estimation based on: (i) particle filtering; and (ii) decision trees. The proposed methods were validated using three datasets that consist of real-world bicycle and car trajectories in two different scenarios, at a roundabout and at a t-junction with a pedestrian crossing. The results validate the utility of the data-driven behaviour model, and show that decision-tree based intention estimation works better on a binary-class problem, whereas the particle-filter based technique performs better on a multi-class problem, such as the roundabout, where the method yielded an average gain of 14.88 m for correct intention estimation locations compared to the decision-tree based method. © 2018 by the authors, CAISR/SAS2

Autonomous robotic systems operating in the vicinity of other agents, such as humans, manually driven vehicles and other robots, can model the behaviour and estimate intentions of the other agents to enhance efficiency of their operation, while preserving safety. We propose a data-driven approach to model the behaviour of other agents, which is based on a set of trajectories navigated by other agents. Then, to evaluate the proposed behaviour modelling approach, we propose and compare two methods for agent intention estimation based on: (i) particle filtering; and (ii) decision trees. The proposed methods were validated using three datasets that consist of real-world bicycle and car trajectories in two different scenarios, at a roundabout and at a t-junction with a pedestrian crossing. The results validate the utility of the data-driven behaviour model, and show that decision-tree based intention estimation works better on a binary-class problem, whereas the particle-filter based technique performs better on a multi-class problem, such as the roundabout, where the method yielded an average gain of 14.88 m for correct intention estimation locations compared to the decision-tree based method. © 2018 by the authors, CAISR/SAS2

This paper investigates how boxers play mind games to affect their opponent's confidence during the three pre-bout periods of weigh-in, warm-up and ring entrance. Perspectives from four senior male amateur boxers at three boxing gyms in the Greater Manchester region were examined using semi-structured interviews; transcribed verbatim and data coded thematically. It was found that amateurs imitated professional boxers, with the more experienced amateurs adopting a broader range of mind games. This indicated that a form of behaviour modelling may evolve with growing experience in the sport.

This paper investigates how boxers play mind games to affect their opponent's confidence during the three pre-bout periods of weigh-in, warm-up and ring entrance. Perspectives from four senior male amateur boxers at three boxing gyms in the Greater Manchester region were examined using semi-structured interviews; transcribed verbatim and data coded thematically. It was found that amateurs imitated professional boxers, with the more experienced amateurs adopting a broader range of mind games. This indicated that a form of behaviour modelling may evolve with growing experience in the sport.

In this paper we propose an approach forvdetecting anomalies in data from visual surveillancevsensors. The approach includes creating a structure for representing data, building “normal models” by filling the structure with data for the situation at hand, and finally detecting deviations in the data. The approach allows detections based on the incorporation of a priori knowledge about the situation and on data-driven analysis. The main advantages with the approach compared to earlier work is the low computational requirements, iterative update of normal models and a high explainability of found anomalies. The proposed approach is evaluated off-line using real-world data and the results support that the approach could be used to detect anomalies in real-time applications., 10.1109/ICIF.2008.4632410