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In the context of Industry 4.0, a growing use is being made of simulation-based decision-support tools commonly named Digital Twins. Digital Twins are replicas of the physical manufacturing assets, providing means for the monitoring and control of individual assets. Although extensive research on Digital Twins and their applications has been carried out, the majority of existing approaches are asset specific. Little consideration is made of human factors and interdependencies between different production assets are commonly ignored. In this paper, we address those limitations and propose innovations for cognitive modeling and co-simulation which may unleash novel uses of Digital Twins in Factories of the Future. We introduce a holistic Digital Twin approach, in which the factory is not represented by a set of separated Digital Twins but by a comprehensive modeling and simulation capacity embracing the full manufacturing process including external network dependencies. Furthermore, we introduce novel approaches for integrating models of human behavior and capacities for security testing with Digital Twins and show how the holistic Digital Twin can enable new services for the optimization and resilience of Factories of the Future. To illustrate this approach, we introduce a specific use-case implemented in field of Aerospace System Manufacturing.

This paper addresses the background and current state of the field of agent-based modelling and simulation (ABMS). It revisits the issue of ABMS represents as a new development, considering the extremes of being an overhyped fad, doomed to disappear, or a revolutionary development, shifting fundamental paradigms of how research is conducted. This paper identifies key ABMS resources, publications, and communities. It also proposes several complementary definitions for ABMS, based on practice, intended to establish a common vocabulary for understanding ABMS, which seems to be lacking. It concludes by suggesting research challenges for ABMS to advance and realize its potential in the coming years.

This research focuses on sensing context, modeling human behavior and developing a new architecture for a cognitive phone platform. We combine the latest positioning technologies and phone sensors to capture human movements in natural environments and use the movements to study human behavior. Contexts in this research are abstracted as a Context Pyramid which includes six levels: Raw Sensor Data, Physical Parameter, Features/Patterns, Simple Contextual Descriptors, Activity-Level Descriptors, and Rich Context. To achieve implementation of the Context Pyramid on a cognitive phone, three key technologies are utilized: ubiquitous positioning, motion recognition, and human behavior modeling. Preliminary tests indicate that we have successfully achieved the Activity-Level Descriptors level with our LoMoCo (Location-Motion-Context) model. Location accuracy of the proposed solution is up to 1.9 meters in corridor environments and 3.5 meters in open spaces. Test results also indicate that the motion states are recognized with an accuracy rate up to 92.9% using a Least Square-Support Vector Machine (LS-SVM) classifier.

Phasic firing changes of midbrain dopamine neurons have been widely characterized as reflecting a reward prediction error (RPE). Major personality traits (e.g., extraversion) have been linked to inter-individual variations in dopaminergic neurotransmission. Consistent with these two claims, recent research (Smillie et al., 2011; Cooper et al., 2014) found that extraverts exhibited larger RPEs than introverts, as reflected in feedback related negativity (FRN) effects in EEG recordings. Using an established, biologically-localized RPE computational model, we successfully simulated dopaminergic cell firing changes which are thought to modulate the FRN. We introduced simulated individual differences into the model: parameters were systematically varied, with stable values for each simulated individual. We explored whether a model parameter might be responsible for the observed covariance between extraversion and the FRN changes in real data, and argued that a parameter is a plausible source of such covariance if parameter variance, across simulated individuals, correlated almost perfectly with the size of the simulated dopaminergic FRN modulation, and created as much variance as possible in this simulated output. Several model parameters met these criteria, while others did not. In particular, variations in the strength of connections carrying excitatory reward drive inputs to midbrain dopaminergic cells were considered plausible candidates, along with variations in a parameter which scales the effects of dopamine cell firing bursts on synaptic modification in ventral striatum. We suggest possible neurotransmitter mechanisms underpinning these model parameters. Finally, the limitations and possible extensions of our general approach are discussed.

The pervasiveness of online social networks has reshaped the way people access information. Online social networks make it common for users to inform themselves online and share news among their peers, but also favor the spreading of both reliable and fake news alike. Because fake news may have a profound impact on the society at large, realistically simulating their spreading process helps evaluate the most effective countermeasures to adopt. It is customary to model the spreading of fake news via the same epidemic models used for common diseases; however, these models often miss concepts and dynamics that are peculiar to fake news spreading. In this paper, we fill this gap by enriching typical epidemic models for fake news spreading with network topologies and dynamics that are typical of realistic social networks. Specifically, we introduce agents with the role of influencers and bots in the model and consider the effects of dynamical network access patterns, time-varying engagement, and different degrees of trust in the sources of circulating information. These factors concur with making the simulations more realistic. Among other results, we show that influencers that share fake news help the spreading process reach nodes that would otherwise remain unaffected. Moreover, we emphasize that bots dramatically speed up the spreading process and that time-varying engagement and network access change the effectiveness of fake news spreading.

An examination of our many modes of online identity and how we live on the continuum between the virtual and the real.Hello Avatar!Or, {llSay(0, \"Hello, Avatar!\"); is a tiny piece of user-friendly code that allows us to program our virtual selves.In Hello Avatar , B.

Ever since the worldwide demand for gambling services started to spread, its expansion has continued steadily. To wit, online gambling is a major industry in every European country, generating billions of Euros in revenue for commercial actors and governments alike. Despite such evidently beneficial effects, online gambling is ultimately a vast social experiment with potentially disastrous social and personal consequences that could result in an overall deterioration of social and familial relationships. Despite the relevance of this problem in society, there is a lack of tools for characterizing the behavior of online gamblers based on the data that are collected daily by betting platforms. This paper uses a time series clustering algorithm that can help decision-makers in identifying behaviors associated with potential pathological gamblers. In particular, experimental results obtained by analyzing sports event bets and black jack data demonstrate the suitability of the proposed method in detecting critical (i.e., pathological) players. This algorithm is the first component of a system developed in collaboration with the Portuguese authority for the control of betting activities.

We present the design, implementation and evaluation of MobiSens, a versatile mobile sensing platform for a variety of real-life mobile sensing applications. MobiSens addresses common requirements of mobile sensing applications on power optimization, activity segmentation, recognition and annotation, interaction between mobile client and server, motivating users to provide activity labels with convenience and privacy concerns. After releasing three versions of MobiSens to the Android Market with evolving UI and increased functionalities, we have collected 13,993 h of data from 310 users over five months. We evaluate and compare the user experience and the sensing efficiency in each release. We show that the average number of activities annotated by a user increases from 0.6 to 6. This result indicates the activity auto-segmentation/recognition feature and certain UI design changes significantly improve the user experience and motivate users to use MobiSens more actively. Based on the MobiSens platform, we have developed a range of mobile sensing applications including Mobile Lifelogger, SensCare for assisted living, Ground Reporting for soldiers to share their positions and actions horizontally and vertically, and CMU SenSec, a behavior-driven mobile Security system.

This article discusses the possibilities of obtaining new information related to human behavior, namely the changes or progressive development of pilots’ abilities during training. The main assumption is that a pilot’s ability can be evaluated based on a corresponding behavioral model whose parameters are estimated using mathematical identification procedures. The mean values of the identified parameters are obtained via statistical methods. These parameters are then monitored and their changes evaluated. In this context, the paper introduces and examines relevant mathematical models of human (pilot) behavior, the pilot-aircraft interaction, and an example of the mathematical analysis.

Models of eye movements of an observer searching for human targets are helpful in developing accurate models of target acquisition times and false positive detections. We develop a new model describing the distribution of gaze positions for an observer which includes both bottom-up (salience) and top-down (task dependent) factors. We validate the combined model against a bottom-up model from the literature and against the bottom up and top down parts alone using human performance data on stationary targets. The new model is shown to be significantly better. The new model requires a large amount of data about the terrain and the target that is obtained directly from the 3D simulation through an automated process.