Modeling interactive memex-like applications based on self-modifiable petri nets

This paper introduces an interactive Memex-like application using a self-modifiable Petri Net model – Self-modifiable Color Petri Net (SCPN). The Memex (“memory extender”) device proposed by Vannevar Bush in 1945 focused on the problems of “locating relevant information in the published records and recording how that information is intellectually connected.” The important features of Memex include associative indexing and retrieval. In this paper, the self-modifiable functions of SCPN are used to achieve trail recording and retrieval. A place in SCPN represents a website and an arc indicates the trail direction. Each time when a new website is visited, a place corresponding to this website will be added. After a trail is built, users can use it to retrieve the websites they have visited. Besides, useful user interactions are supported by SCPN to achieve Memex functions. The types of user interactions include: forward, backward, history, search, etc. A simulator has been built to demonstrate that the SCPN model can realize Memex functions. Petri net instances can be designed to model trail record, back, and forward operations using this simulator. Furthermore, a client-server based application system has been built. Using this system, a user can surf online and record his surfing history on the server according to different topics and share them with other users

Using Personal Environmental Comfort Systems to Mitigate the Impact of Occupancy Prediction Errors on HVAC Performance

Heating, Ventilation and Air Conditioning (HVAC) consumes a significant fraction of energy in commercial buildings. Hence, the use of optimization techniques to reduce HVAC energy consumption has been widely studied. Model predictive control (MPC) is one state of the art optimization technique for HVAC control which converts the control problem to a sequence of optimization problems, each over a finite time horizon. In a typical MPC, future system state is estimated from a model using predictions of model inputs, such as building occupancy and outside air temperature. Consequently, as prediction accuracy deteriorates, MPC performance--in terms of occupant comfort and building energy use--degrades. In this work, we use a custom-built building thermal simulator to systematically investigate the impact of occupancy prediction errors on occupant comfort and energy consumption. Our analysis shows that in our test building, as occupancy prediction error increases from 5\% to 20\% the performance of an MPC-based HVAC controller becomes worse than that of even a simple static schedule. However, when combined with a personal environmental control (PEC) system, HVAC controllers are considerably more robust to prediction errors. Thus, we quantify the effectiveness of PECs in mitigating the impact of forecast errors on MPC control for HVAC systems.Comment: 21 pages, 13 figure

Engineering simulations for cancer systems biology

Computer simulation can be used to inform in vivo and in vitro experimentation, enabling rapid, low-cost hypothesis generation and directing experimental design in order to test those hypotheses. In this way, in silico models become a scientific instrument for investigation, and so should be developed to high standards, be carefully calibrated and their findings presented in such that they may be reproduced. Here, we outline a framework that supports developing simulations as scientific instruments, and we select cancer systems biology as an exemplar domain, with a particular focus on cellular signalling models. We consider the challenges of lack of data, incomplete knowledge and modelling in the context of a rapidly changing knowledge base. Our framework comprises a process to clearly separate scientific and engineering concerns in model and simulation development, and an argumentation approach to documenting models for rigorous way of recording assumptions and knowledge gaps. We propose interactive, dynamic visualisation tools to enable the biological community to interact with cellular signalling models directly for experimental design. There is a mismatch in scale between these cellular models and tissue structures that are affected by tumours, and bridging this gap requires substantial computational resource. We present concurrent programming as a technology to link scales without losing important details through model simplification. We discuss the value of combining this technology, interactive visualisation, argumentation and model separation to support development of multi-scale models that represent biologically plausible cells arranged in biologically plausible structures that model cell behaviour, interactions and response to therapeutic interventions

Impact of Personalized Interactive Storytelling on Suspension of Disbelief in Clinical Simulation

The literature review found suspension of disbelief (SOD) in clinical simulation heavily weighted on educators alone within high-fidelity environments. The project examined a co-created narrative background story applied to a simulated patient’s clinical profile to determine achieving an improved connectedness toward the simulated patient leading to enhanced SOD and enhanced levels of learning and reaction. The studied population was third-semester associate degree nursing students over 18 years of age with prior clinical simulation experience who were not repeating the semester. The research methodology used a quantitative experimental design with cluster sampling, randomization, and post-Likert-scored questionnaires. The intervention group co-created personalized storytelling narratives for the simulated patient’s clinical profile. After the clinical simulation activity, both intervention and control groups completed questionnaires examining their ability to achieve SOD during the activity and their levels and reaction and learning. Results using two-tailed t tests indicated the intervention revealed an enhanced level of presence during the participation. The improved presence revealed a positive, engaging experience applicable to future nursing roles and enhanced knowledge, skills, and confidence. Conclusions were drawn that applying co-created storytelling to a simulated patient’s clinical profile improves presence, suggesting an enhanced ability to achieve SOD during the activity. Recommendations for future research projects include studying storytelling in clinical simulation with a larger sample size and having participants create an entire clinical profile, analyzing the influence of emotional position toward simulation on SOD, and maintaining usage of intervention once learned

Reinforcement learning for personalized dialogue management

Language systems have been of great interest to the research community and have recently reached the mass market through various assistant platforms on the web. Reinforcement Learning methods that optimize dialogue policies have seen successes in past years and have recently been extended into methods that personalize the dialogue, e.g. take the personal context of users into account. These works, however, are limited to personalization to a single user with whom they require multiple interactions and do not generalize the usage of context across users. This work introduces a problem where a generalized usage of context is relevant and proposes two Reinforcement Learning (RL)-based approaches to this problem. The first approach uses a single learner and extends the traditional POMDP formulation of dialogue state with features that describe the user context. The second approach segments users by context and then employs a learner per context. We compare these approaches in a benchmark of existing non-RL and RL-based methods in three established and one novel application domain of financial product recommendation. We compare the influence of context and training experiences on performance and find that learning approaches generally outperform a handcrafted gold standard

Building BROOK: A multi-modal and facial video database for Human-Vehicle Interaction research

With the growing popularity of Autonomous Vehicles, more opportunities have bloomed in the context of Human-Vehicle Interactions. However, the lack of comprehensive and concrete database support for such specific use case limits relevant studies in the whole design spaces. In this paper, we present our work-in-progress BROOK, a public multi-modal database with facial video records, which could be used to characterise drivers' affective states and driving styles. We first explain how we over-engineer such database in details, and what we have gained through a ten-month study. Then we showcase a Neural Network-based predictor, leveraging BROOK, which supports multi-modal prediction (including physiological data of heart rate and skin conductance and driving status data of speed) through facial videos. Finally we discuss related issues when building such a database and our future directions in the context of BROOK. We believe BROOK is an essential building block for future Human-Vehicle Interaction Research. More details and updates about the project BROOK is online at https: //unnc-idl-ucc.github.io/BROOK/