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Any behaviour that deviates from the social norms of a wider society and is viewed negatively by society as a whole is considered to be a behavioural problem. In office environments, there is a significant prevalence of behaviour problems, and they have a long-lasting impact on work productivity. Intervention provision is a current component of behavioural problem-solving strategies from which people may benefit through smart environments. While many interventions are presently employed in both smart and non-smart environments, it is unknown how well they work in relation to user preferences. Additionally, change levels are not the foundation of the present intervention methods. The study looked into how providing interventions can help smart environment users to promote good behaviour. The study used the Design Science Research (DSR) Methodology's rigour cycle to deploy the Generic User Model and the Positive Behaviour Change (PBC) model. The PBC Model was examined through an artificial-summative evaluation. The results of the artificial-summative evaluation showed that change-based interventions can be effective for reducing inappropriate behaviour and increasing positive behaviour, and that input from user models can give intervention users personalised effects. The implication of the results from this study is that intervention providers do not only need to focus on modifying negative behaviours, but also strengthening and sustaining good behaviours to motivate continuous future occurrence. [ABSTRACT FROM AUTHOR]

Electric Vehicles (EVs) are considered the best alternative vehicular technology having the potential to cater climatic change and energy security issues of the nations. Despite various promotional policy measures, the penetration of EVs is also observed very low, especially in the developing economies in the Indian subcontinent. This demands a comprehensive analysis of individuals' EV adoption characteristics and the review of recent literature indicated that most of the research on EV adoption solely focuses on either the psychological aspects leading to its purchase, ignoring the available alternatives or on the conventional choice modelling techniques which do not explicitly consider the psychological motivation to choose the innovative technology. Therefore, this study integrates both behaviour modelling and conventional choice modelling in order to unfold the important determinants of EV adoption in developing economies. This study was conducted in India and the results of Hybrid Choice Modelling (HCM) highlighted that latent attitudes such as marketing perceptions, EV performance, policy perceptions, and EV usage constraints are significant factors influencing the EV adoption intention of individuals. In addition, the study also revealed that on-road price, EV adoption intention, willingness to pay extra for EVs, monthly household income, and age are also important predictors for choice between EVs and conventional vehicles. The study also pinpoints that pricing the electric SUV cars below INR 17 lakhs would make people consider choosing it over the conventional one. • Integrated the latent attitudes of individuals with conventional choice attributes. • Pricing electric cars (SUVs) below INR 17 lakhs enhance their faster diffusion. • A wide network of dealership showrooms will improve the perception of EV markets. • Performance of EV in terms of top speed, power, and acceleration affects the choice. • Lower income groups and elders can be the early adopters of EVs. [ABSTRACT FROM AUTHOR]

In today's online market, recommendation systems have become universal and are an aspect of any online shopping portal. The traditional approach uses the subscriber's historical knowledge, and this technique is not adequate for resolving problems with a cold start. These issues include recommendations for non-registered users or newly added customers and new items added. Session-based recommendations based on recurrent neural networks are gaining popularity for product recommendations. This is due to recurrent neural networks' ability to record sequential feature data more effectively throughout the current session, which results in more similarity between consumer behaviour sequences. Nevertheless, most state-of-the-art recurring neural networking systems completely ignore the long-term details of multiple sessions and concentrate solely on short-term communication in a single session. This paper presents a hybrid time-centric prediction model to address research issues that learn the customers' short and long-term behaviours. Experiments on the recsys challenge data set are carried out to assess the efficiency of the hybrid time-centric prediction models over the existing hybrid models in terms of HitRate and Mean-Reciprocal Rate. [ABSTRACT FROM AUTHOR]

Understanding users' individual differences may provide clues to help identify computer users who are prone to act insecurely. We examine factors that impact home users' reported computer security behaviour. We conducted two online surveys with a total of 650 participants to investigate the relationship between self-reported security behaviour and users' knowledge, motivation, confidence, risk propensity and sex-typed characteristics. We found that all of these factors impacted security behaviour, with knowledge as the most important predictor. We further show that a user's affinity to feminine or masculine characteristics is a better determinant of security behaviour than using binary male/female descriptors. Our study enabled us to confirm earlier results in the literature in a non-organisational setting, and to extend the literature by studying additional factors and by comparing the relative importance of each factor as a predictor of security behaviour. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This article showcases the adaptability of existing mobility devices for the Algerian disabled population. It aims to develop a behavior model of disabled Algerian persons through (1) development of a theoretical model based on literature review and (2) improvement of this model by using local collected data from our developed questionnaire. The majority of existing mobility devices for disabled persons are adapted developed countries context. The Algerian context (people and environment) has its own specifications and needs. Researchers does not address the disabled people behavior modelling in Algeria, due to the unavailability of data and the difficulty of their collection. In order to overcome this problem and develop adapted mobility devices for this particular population, the deployment of a questionnaire to collect local data, becomes necessary. First, a behavior model is deduced from the literature review then a questionnaire is developed and deployed to complete the missing field of this model. The deployment questionnaire panel is composed of 260 persons including users, their families and staff working in disabled person's aid organization. The main result of this paper consist in a specific behavior model of Algerian disabled persons. This population are looking for reliable and smart wheelchair (97%) to be able to overcome their daily mobility difficulties due to the infrastructure and the global economic context. The proposed model can generate adapted wheelchair for Algerian context. The results of the questionnaire are an important step to understand disabilities in Algeria and developing countries. Development of a behavior model for the disabled persons displacement. Development and deployment of questionnaire for disabled persons in Algeria. Review of disabled person situation and existing wheelchair in Algeria. Identification of Algerian disabled persons requirement and demonstrate that are different from European one. Definition of recommendation set for the design of wheelchair adapted to Algerian context. [ABSTRACT FROM AUTHOR]

• In comparison, WPB is less likely to be engaged by individuals than other PEB. • Contextual factors can be considered as pre-conditions to engage in WPB. • Individuals' engagement levels can be increased by structural interventions. Despite being widely recognised as a sustainable way to reduce waste generation, the implementation of prevention policies has been far from successful. Unlike recycling, waste prevention depends on the interplay of different behaviours, motivations, and structural conditions. Increasing people's engagement in waste prevention depends on identifying the direct and indirect impacts on behavioural costs and promoting long-term behavioural change. This study analyses the individuals' behaviour in Campinas to compare, through an integrated method, their engagement level on pro-environmental and waste prevention actions, considering sociodemographic factors and the local context. A questionnaire survey was conducted for 14 months with a final sample of 888 respondents. Among the 20 most difficult actions, 25% were waste-related, and 20% were linked to consumption behaviours. Results also show a significant interference of age and contextual factors in facilitating or hindering pro-environmental and waste prevention actions. House type, for example, can be considered a pre-condition to implement home composting, but it is not decisive. Waste prevention behaviour is composed of mostly private actions hidden from monitoring processes. These findings show that expensive structural improvements can be outperformed by implementing specific educational programs that address social aspects. [ABSTRACT FROM AUTHOR]

Massive open online courses (MOOCs) show highly irregular participation behaviour among users. In this study, using data from Computer Science 50x of HarvardX, we investigated one extreme, yet common strategy to foresee the endgame: taking the final problem set at the beginning of the course. We found such a strategy to be the only dominant trajectory alternative to following the sequence prescribed by the syllabus. Whereas all students who took and passed the final problem set at the beginning of the course subsequently completed the course, those who took and failed the final problem set at the beginning of the course finished the fewest number of milestones, even fewer than those who never attempted the final problem set. Moreover, students with a lower prior programming proficiency were more likely than better prepared students both to take the final problem set early and to fail it. This study revealed the disconcerting phenomenon that many students dropped out of a MOOC because, apparently, their confidence was crushed even before they learned any course content. The study suggests that future MOOC practices and policies should offer informative and constructive syllabi to accommodate students' need for previewing the endgame. [ABSTRACT FROM AUTHOR]

The aim of this paper was to analyse and model the behaviour decision-making of rescue crews in multi-helicopter collaborative search and rescue mission. Firstly, based on decision field theory, a dynamic behavioural decision-making model was put forward considering personal behaviour decision-making preference. Besides, considering physical characteristics, safety requirements and rescue crews' behaviour decision-making, a multi-helicopter collaborative search and rescue behaviour model was established. Then, based on the survey of four general aviation helicopter search and rescue companies, the search and rescue efficiency by teams composed of different decision-making preferences was simulated based on distributed ant colony algorithm in experiments. Results showed that rescue crews with different personal preferences have different behaviour characteristics. Besides, teams composed of mixed preferences are more efficient than teams composed of single preferences, and the most optimal composition way is when the positive type is slightly more than the conservative type and balanced type. [ABSTRACT FROM AUTHOR]

Purpose: Mobile phones have become one of the most favored devices to maintain social connections as well as logging digital information about personal lives. The privacy of the metadata being generated in this process has been a topic of intense debate over the last few years, but most of the debate has been focused on stonewalling such data. At the same time, such metadata is already being used to automatically infer a user's preferences for commercial products, media, or political agencies. The purpose of this paper is to understand the predictive power of phone usage features on individual privacy attitudes. Design/methodology/approach: The present study uses a mixed-method approach, involving analysis of mobile phone metadata, self-reported survey on privacy attitudes and semi-structured interviews. This paper analyzes the interconnections between user's social and behavioral data as obtained via their phone with their self-reported privacy attitudes and interprets them based on the semi-structured interviews. Findings: The findings from the study suggest that an analysis of mobile phone metadata reveals vital clues to a person's privacy attitudes. This study finds that multiple phone signals have significant predictive power on an individual's privacy attitudes. The results motivate a newer direction of automatically inferring a user's privacy attitudes by leveraging their phone usage information. Practical implications: An ability to automatically infer a user's privacy attitudes could allow users to utilize their own phone metadata to get automatic recommendations for privacy settings appropriate for them. This study offers information scientists, government agencies and mobile app developers, an understanding of user privacy needs, helping them create apps that take these traits into account. Originality/value: The primary value of this paper lies in providing a better understanding of the predictive power of phone usage features on individual privacy attitudes. [ABSTRACT FROM AUTHOR]

The great number of social network users and the expansion of this kind of tool in the last years demand the storage of a great volume of information regarding user behaviour. In this article, we utilise interaction records from Facebook users and metrics from complex networks study, to identify different user behaviours using clustering techniques. We found three different user profiles regarding interactions performed in the social network: viewer, participant and content producer. Moreover, the groups we found were characterised by the C4.5 decision-tree algorithm. The 'viewer' mainly observes what happens in the network. The 'participant' interacts more often with the content, getting a higher value of closeness centrality. Therefore, users with a participant profile are responsible, for example, for the faster transmission of information in the virtual environment, a crucial function for the Facebook social network. We noted too that 'content producer' users had a greater quantity of publications in their pages, leading to a superior degree of input interactions than the other two profiles. Finally, we also verify that the profiles are not mutually exclusive, that is, the user of a profile can at determined moment perform the behaviour of another profile. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

A decision-support system for combating abalone poaching is proposed. A dynamic Bayesian network (DBN) is used to model context-based behaviour of vessels in a maritime abalone poaching situation. The context and behaviour is informed by Expert knowledge. The model is utilised for both data generation and behaviour classification. Data generation is performed by sampling in the DBN. The result is that a set of vessels are simulated in an abalone poaching situation. Several vessel classes including poaching, patrol, fishing, tourist, and recreational vessels are modelled. The generated data is intended to model surveillance data that may have been produced by sensors such as optical, infrared or radar sensors. Classification is performed using the filtering and smoothing inference methods on the DBN. A vessel class is inferred given tracked vessel data and contextual information. The purpose is to identify vessels that exhibit poaching behaviour. The novelty of this work includes a derivation of the generalised pseudo Bayes smoothing algorithm for the classification model. This smoothing algorithm is demonstrated to provide more accurate classification results than the previously proposed filtering method. [ABSTRACT FROM AUTHOR]

The role of occupants is important as building energy consumption is significantly attributed to their behaviour. Given the diverse activities within, and high levels of personal control over the indoor environment, occupants’ behaviour is one of the key uncertainties in predicting energy use in the residential sector. With an aim to better understand residential adaptive thermal comfort behaviours, longitudinal field observations were conducted with smartphone surveys and simultaneous temperature measurements in a sample of Australian homes (n = 42). This paper derives statistical models to enable predicting of the percentage of adaptive strategies in use (e.g. operation of air-conditioners, heaters, fans and windows), as a function of temperature. The analysis on our Sydney sample indicated that an outdoor temperature of 25 °C was the most favourable condition, inclusive of all seasons investigated throughout the 2-year monitoring period, maximising the use of natural ventilation and simultaneously minimising the householders’ dependence on their home air-conditioning system. This study also revealed personal and demographic characteristics that can have a significant impact on the householder’s decision to use their air-conditioning system. [ABSTRACT FROM AUTHOR]

Most research on the topic conducted in the past did not elaborate in depth on the attributes of games that correspond to specific learning results. In this study, the means-end chain theory has been adopted as the theoretical basis to focus on the players of SimCity and The Sims (two of the most representative titles of the simulation game genre) as the subjects of the research. Through in-depth interviews conducted using the Soft Laddering approach, the study aims to examine the key attributes that would trigger learning results for players when playing games and the terminal values that the players would experience internally. The study also featured group comparison of subjects based on their gender. Results of the study showed that gamers still emphasise on the key aspects of Simulated reality and Self-created game content for simulation games. These two attributes have been found to lead to the learning results of Gain authentic experience and Cultivate imagination and creativity, ultimately leading to the terminal value of Sense of accomplishment. [ABSTRACT FROM AUTHOR]

Integrating culture into the behavioural models of virtual characters requires knowledge from very different disciplines such as cross-cultural psychology and computer science. If culture-related behavioural differences are simulated with a virtual character system, users might not necessarily understand the intent of the designer. This is, in part, due to the influence of culture on not only users, but also designers. To gain a greater understanding of the instantiation of culture in the behaviour of virtual characters, and on this potential mismatch between designer and user, we have conducted two experiments. In these experiments, we tried to simulate one dimension of culture (Masculinity vs. Femininity) in the behaviour of virtual characters. We created four scenarios in the first experiment and six in the second. In each of these scenarios, the same two characters interact with each other. The verbal and non-verbal behaviour of these characters differs depending on their cultural scripts. In two user perception studies, we investigated how these differences are judged by human participants with different cultural backgrounds. Besides expected differences between participants from Masculine and Feminine countries, we found significant differences in perception between participants from Individualistic and Collectivistic countries. We also found that the user's interpretation of the character's motivation had a significant influence on the perception of the scenarios. Based on our findings, we give recommendations for researchers that aim to design culture-specific behaviours for virtual characters. [ABSTRACT FROM AUTHOR]

Intentional dissemination of information is a key role of information systems. Yet methods for controlling access to information – as opposed to data – are still in their infancy, especially in relation to the key ‘decision points' that need to be made regarding what information users can access when, and under what conditions. This paper presents the case for access control to be explicitly integrated into models of information behaviour, especially as they relate to information access on the relatively unregulated internet. An observational case study relating to information access in Indonesia – which is a highly regulated society – is presented, through advertising on rogue websites – in this case, behaviour which is strictly regulated in the physical world (such as the promotion of gambling) is relatively unchecked online; the absence of effective controls, as suggested by situational crime prevention theory [Clarke, R. V. G., ed. 1997.Situational Crime Prevention. Criminal Justice Press] is reflected in the high levels of offending seen online. The extent to which information systems can manage information-seeking behaviour in a way that is consistent with regulatory or policy requirements in the online environment is discussed, with a view to establishing a foundation and terminology to modify information behaviour theory for the online environment. [ABSTRACT FROM PUBLISHER]

The paper deals with the problem of verifying behaviour-based control systems. Although failures in sensor hardware and software can have strong influences on the robot’s operation, they are often neglected in the verification process. Instead, perfect sensing is assumed. Therefore, this paper provides an approach for modelling the sensor chain in a formal way and connecting it to the formal model of the control system. The resulting model can be verified using model checking techniques, which is shown on the examples of the control systems of an autonomous indoor robot and an autonomous off-road robot. [ABSTRACT FROM AUTHOR]

A unified Bayesian model that simultaneously performs behavioural modelling, information fusion and classification is presented. The model is expressed in the form of a dynamic Bayesian network (DBN). Behavioural modelling is performed by tracking the continuous dynamics of a entity and incorporating various contextual elements that influence behaviour. The entity is classified according to its behaviour. Classification is expressed as a conditional probability of the entity class given its tracked trajectory and the contextual elements. Inference in the DBN is performed using a derived Gaussian sum filter. The model is applied to classify vessels, according to their behaviour, in a maritime piracy situation. The novel aspects of this work include the unified approach to behaviour modelling and classification, the way in which contextual information is fused, the unique approach to classification according to behaviour and the associated derived Gaussian sum filter inference algorithm. [ABSTRACT FROM AUTHOR]

Indices of association are used to quantify and evaluate social affiliation among animals living in groups. Association models assume that physical proximity is an indication of social affiliation; however, individuals seen associating might simply be together by chance. This problem is particularly pronounced in studies of captive animals, whose movements are sometimes severely spatially restricted relative to the wild. Few attempts have been made to estimate – and thus control for – chance encounters based on enclosure size and shape. Using geometric probability and Geographic Information Systems, we investigated the likely effect of chance encounters on association indices within dyads (pairs of animals), when different distance criteria for defining associations are used in shapes of a given area. We developed a simple R script, which can be used to provide a robust estimate of the probability of a chance encounter in a square of any area. We used Monte Carlo methods to determine that this provided acceptable estimates of the probability of chance encounters in rectangular shapes and the shapes of six actual zoo enclosures, and we present an example of its use to correct observed indices of association. Applying this correction controls for differences in enclosure size and shape, and allows association indices between dyads housed in different enclosures to be compared. [ABSTRACT FROM AUTHOR]

A generative model for modelling maritime vessel behaviour is proposed. The model is a novel variant of the dynamic Bayesian network (DBN). The proposed DBN is in the form of a switching linear dynamic system (SLDS) that has been extended into a larger DBN. The application of synthetic data fabrication of maritime vessel behaviour is considered. Behaviour of various vessels in a maritime piracy situation is simulated. A means to integrate information from context based external factors that influence behaviour is provided. Simulated observations of the vessels kinematic states are generated. The generated data may be used for the purpose of developing and evaluating counter-piracy methods and algorithms. A novel methodology for evaluating and optimising behavioural models such as the proposed model is presented. The log-likelihood, cross entropy, Bayes factor and the Bhattacharyya distance measures are applied for evaluation. The results demonstrate that the generative model is able to model both spatial and temporal datasets. [ABSTRACT FROM AUTHOR]

The present investigation aims at evaluating and understanding the formability of Ti-6Al-4V alloy under hot forming conditions (650 °C ≤ T ≤ 750 °C). To fulfil these objectives, it was necessary to establish accurate material models and predict microscopic material evolution depending on temperature and strain rate. Two kinds of microstructure were investigated, the first one considers a conventional grain size of the α-phase (3 µm) whereas a combined forging-rolling process is used to elaborate the second one and allows to obtain a very fine grain of the α-phase (0.5 µm). In this research work, we propose to investigate the capabilities of the titanium alloy under forming conditions quite different from those usually considered in superplastic forming process (lower temperatures and higher strain rates). [ABSTRACT FROM AUTHOR]

The impact of culture on consumer behaviour has been an important research area for decades. The rise of e-commerce prompted the importance of culturally bound differences between websites. Web designers are compelled to adjust website development to the cultural characteristics of the target audience and to reflect local perspectives. The actual target users are often invited to provide valuable feedback on e-commerce applications. However, user tests are extremely costly and time consuming. Tools for automated web design assessment have only recently been introduced and have provoked debate regarding their ability to simulate human interaction. In this article, 40 leading beer-brand-related websites from four different groups (countries) are analysed. The aim of this study is to discover if automated tools predicting user eye activity are able to distinguish between websites from different cultures. The findings indicate that automated tools provide quick and inexpensive results for initial assessment of the website interface, clearly differentiating between websites from different cultural backgrounds, resonating with the current literature. [ABSTRACT FROM AUTHOR]

Abstract: Energy behaviours represent a significant untapped potential for the increase of end-use energy efficiency in buildings. Although energy behaviours are a major determinant of energy use in buildings, energy savings potential due to behaviour are usually neglected, albeit being referred to be as high as those from technological solutions. This paper presents a review of recent literature on energy behaviours in order to recognise recent trends, quantify energy behaviours potential savings, characterise energy behaviour modelling strategies and identify potential research gaps. Energy behaviour research is vast and has been essentially focused on the residential sector, striving to establish behaviour determinants and the best strategies and instruments to promote more efficient energy behaviours. Potential savings of energy behaviours are referred to reach 20%, but values differ up to 100% between experiences and additional studies to quantify behavioural savings are needed, in particular by using standard quantification techniques. Different modelling techniques have been used to model energy behaviours: qualitative approaches from the social sciences trying to interpret behaviour, here named energy behaviour frameworks; quantitative approaches from the engineering and economics that quantify energy consumption, here designated by energy models; and hybrid approaches that are considered the most relevant since they integrate multiple dimensions of energy behaviours, here referred as energy behaviour modelling. Energy behaviours have a crucial role in promoting energy efficiency, but energy behaviours characteristics and complexity create several research challenges that must be overcome so energy behaviours may be properly valorised and integrated in the energy policy context. [Copyright &y& Elsevier]

The system environment provides on-line data on the actions undertaken which, properly analyzed and correlated, will further generate solutions in order to develop said system and make it competitive. The paper aims, in the field of manufacturing technologies, at approaching issues of manufacturing systems, in order to develop a new concept of management, which is in line with the current market dynamics: the concept of competitive management. The concept of competitive management can offer solutions even to make competitive and develop enterprises as a whole. [ABSTRACT FROM AUTHOR]

Behaviour modelling has been associated with higher learning outcomes compared to other training approaches. These cumulative research findings create imperative to examine underlying causal mechanisms or contingency factors that may promote behaviour modelling's advantages even further. We propose group-based learning as one contingency factor because there exists greater opportunity for observation, imitation and feedback. We use a two-by-three experimental laboratory design involving 84 subjects to test for an interaction effect between training context (group-based, individual-based) and training approach (behaviour modelling, exploration, instruction) on several learning outcomes. We use hierarchical regression to show that, while the interaction effect was not significant, the training approach main effect was significant. Consistent with expectations, behaviour modelling outperformed exploration on task performance. There were no significant differences between behaviour modelling and instruction. We conclude that behaviour modelling is associated with higher task performance levels on a complex word-processing task when compared to exploration. [ABSTRACT FROM AUTHOR]

The study described in this paper developed a model of animal movement, which explicitly recognized each individual as the central unit of measure. The model was developed by learning from a real dataset that measured and calculated, for individual cows in a herd, their linear and angular positions and directional and angular speeds. Two learning algorithms were implemented: a Hidden Markov model (HMM) and a long-term prediction algorithm. It is shown that a HMM can be used to describe the animal's movement and state transition behavior within several “stay” areas where cows remained for long periods. Model parameters were estimated for hidden behavior states such as relocating, foraging and bedding. For cows’ movement between the “stay” areas a long-term prediction algorithm was implemented. By combining these two algorithms it was possible to develop a successful model, which achieved similar results to the animal behavior data collected. This modeling methodology could easily be applied to interactions of other animal species. [Copyright &y& Elsevier]

Abstract: We propose a new method that treats visible human behaviour at the level of navigational strategies. By inferring intentions in terms of known goals, it is possible to explain the behaviour of people moving around within the field of view of a video camera. The approach presented here incorporates models of navigation from within psychology which are both simple and conceptually plausible, whilst providing good results in an event-detection application. The output is in the form of statements involving goals, such as “Agent 25 went to exit 8 via sub-goals 34 and 21” for a given navigational strategy, an image representing the path through the scene, and an overall score for each trajectory. The central algorithm generates all plausible paths through the scene to known goal sites and then compares each path to the agent''s actual trajectory thus finding the most likely explanation for their behaviour. Two navigational strategies are examined, shortest path and simplest path. Experimental results are presented for an outdoor car-park and an indoor foyer scene, and our method is found to produce psychologically plausible explanations in the majority of cases. We propose a novel approach to determining the effectiveness of event detection systems, and evaluate the method presented here against this new ground truth. This evaluation method uses human observers to judge the behaviour shown in various video clips, then uses these judgements in correlation with those of the software. We compare the method with a standard machine learning approach based on nearest neighbour. Finally we consider the application of such a system in a binary event-detection or behaviour filtering system. [Copyright &y& Elsevier]

Abstract: During the friction between rotating gas turbine blades and internal body envelope on which is sprayed by the “Atmospheric Plasma Spraying” process a protective porous sacrificial material, there is a blades wear or material displacement. These phenomena which appear during the thermal dilation of the turbine rotor cause a gap between the body casing and the blades top. This clearance lets the combustion gases which do not take part in the power production to pass. There are then reduction in the efficiency and loss of turbine power. Four factors influence the behaviour of the turbine blades and the porous material filled in certain places by boron nitride, which behaves as a lubricant solid. The aim of this work is to investigate the behaviour of a particular sacrificial porous material, the BN–AlCrNi–polyester during inter-reaction with the blades, and the blades’ behaviour under experimental conditions of operating machine. It is then necessary to determine which of these four parameters is dominating and to describe by model the behaviour of each one of them, and the interaction between them. Tests are done with different blade incursion velocities within the coating, as well as with different linear blade speeds and incursion depths. The obtained results are shown in the form of graphs that describe how do the material transfer or blades’ wear occurs, and are the different phenomena observed on the coating. These results help us to appoint the parameters’ values for which we do not have these phenomena. [Copyright &y& Elsevier]

Numerical calculations become a more and more efficient way to estimate the lifetime of structures under thermomechanical loading. But these life estimations cannot be reliable if the needed parameters have not been correctly identified and measured and if not all the damage causes have been considered. The disk testing under gas pressure is an experimental technique used for the mechanical characterization of thin sheets with various conditions: monotonous loading (biaxial rupture tests at strain rates from 10-6 to 10° s-1), constant loading under high stresses (sustained load) at elevated temperature (creep tests), cyclic loading (mechanical fatigue tests); the temperature may be chosen between 20 and 900 °C. The disk testing quickly reveals the damage when crossing cracks through the membrane create leakages detected by a mass spectrometer. Besides, we have developed a calculation method in order to measure the true mechanical properties of the pressurized disk. We evidence the quality of the results obtained with this method in the thermomechanical characterization of thin sheets and its sensitivity to secondary parameters. The analytical method of calculation will be optimized in order to obtain the best modelling of thermomechanical behaviour of materials loaded at temperature and strain rates within very large ranges and subject to different environments as gaseous hydrogen. [ABSTRACT FROM AUTHOR]

The purpose of this study was to develop a new type of guyed mast that incorporates a complex guy cable system with a particular focus on the effect of static loading on the response mast behaviour. The intension of such solution is to increase a number of elastic supports for the mast shaft that will impact on its stability under loading. The static analysis of a one-level guyed mast has been undertaken. The effects of geometrical and physical design parameters on the displacements of the mast were determined. The analysis is illustrated with two-guyed masts: the bending moments and lateral displacements were determined for a typical guyed mast and guyed mast with combined guys. [ABSTRACT FROM AUTHOR]

Abstract: This paper describes a means of unsupervised learning of recurring patterns in user activity through patterns in system level events generated by a graphical user interface. Earlier work has shown that using this distillation of the more complex behavioural interaction between the user and the application provides a symbolic representation of knowledge and goals that could be used to imply preference. Although prior research has explored the possibilities of removing this information acquisition bottleneck in such an expert system using ambient monitoring approaches, some have experienced difficulty in dealing with the varying length training sequences and segmentation of the continuous event stream. Unlike previous work the approach documented here handles interactions of varying sizes and is able to recall recurrent patterns in real time irrespective of the number of interactions learned. In addition to describing the proposed approach we also describe the shortcomings of various previously applied machine learning techniques on the same type of data. We also demonstrate a practical implementation of our approach applied to web browser usage. [Copyright &y& Elsevier]

This paper describes a behavioural model used to simulate realistic eye-gaze behaviour and body animations for avatars representing participants in a shared immersive virtual environment (IVE). The model was used in a study designed to explore the impact of avatar realism on the perceived quality of communication within a negotiation scenario. Our eye-gaze model was based on data and studies carried out on the behaviour of eye-gaze during face-to-face communication. The technical features of the model are reported here. Information about the motivation behind the study, experimental procedures and a full analysis of the results obtained are given in [ ]. [ABSTRACT FROM AUTHOR]

We demonstrate a novel method for producing a synthetic talking-head. The method is based on earlier work in which the behaviour of a synthetic individual is generated by reference to a probabilistic model of interactive behaviour within the visual domain—such models are learnt automatically from typical interactions. We extend this work into a combined visual and auditory domain and employ a state-of-the-art facial appearance model. The result is a real-time synthetic talking-head that responds appropriately and with realistic timing to simple forms of greeting. [Copyright &y& Elsevier]

We propose a statistical framework for the representation and synthesis of stochastic behaviour. Highly non-linear behaviours are modelled by a Gaussian mixture over system state change and observed history. Parameters of the model are estimated during learning from a training set. Behaviour synthesis is achieved via an iterative process of model conditioning, state change selection, and state update. Experimental results demonstrate the use of the framework for the modelling and synthesis of pedestrian trajectories. [Copyright &y& Elsevier]

As human drivers, we instinctively employ our understanding of other road users' behaviour for enhanced efficiency of our drive and safety of the traffic. In recent years, different aspects of assisted and autonomous driving have gotten a lot of attention from the research and industrial community, including the aspects of behaviour modelling and prediction of future state. In this paper, we address the problem of modelling and predicting agent behaviour and state in a roundabout traffic scenario. We present three ways of modelling traffic in a roundabout based on: (i) the roundabout geometry; (ii) mean path taken by vehicles inside the roundabout; and (iii) a set of reference trajectories traversed by vehicles inside the roundabout. The roundabout models are compared in terms of exit-direction classification and state (i.e., position inside the roundabout) prediction of query vehicles inside the roundabout. The exit-direction classification and state prediction are based on a particle-filter classifier algorithm. The results show that the roundabout model based on set of reference trajectories is better suited for both the exit-direction and state prediction. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]