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Artificial ants are "small" units, moving autonomously on a shared, dynamically changing "space", directly or indirectly exchanging some kind of information. Artificial ants are frequently conceived as a paradigm for collective adaptive systems. In this paper, we discuss means to represent continuous moves of "ants" in discrete models. More generally, we challenge the role of the notion of "time" in artificial ant systems and models. We suggest a modeling framework that structures behavior along causal dependencies rather than temporal relations. We present all arguments with the help of a simple example. As a modeling framework we employ Heraklit; an emerging framework that has already proven its worth in many contexts. Different concrete collective systems share similar features, despite differences in the size of basic sets, concrete values of functions, etc., and can therefore be conceived as instantiations of a single schema. Hence, we need a representation of systems on the schematic level. [ABSTRACT FROM AUTHOR]

Frequency-selective (FS) digital predistortion (DPD), which focuses on suppressing the nonlinear distortion at a specific band, has gained increased interest in recent years. However, there is a lack of a general FS DPD approach that is applicable to most scenarios and is suitable for practical implementation. This paper proposes an FS DPD method based on a generalized indirect learning architecture (GILA). Our work on this GILA-based FS DPD method is composed of two contributions. First, an FS Volterra series model structure, which consists of a basic linear part and a partial-band pre-compensation part, is proposed to construct an FS predistorter. Second, a GILA-based identification method is proposed to extract coefficients of the FS predistorter with the FS Volterra series model structure. The proposed GILA-based FS DPD is a general method with low complexity for implementation. Both simulations and experiments verify that the target band of the DPD linearization is arbitrary for the proposed GILA-based FS DPD method. As a special case, when using the proposed method to focus on suppressing the out-of-band distortion, the adjacent channel power ratio performance of the proposed method can be 7.1 dB better than that of the conventional full-band DPD. [ABSTRACT FROM AUTHOR]

In this paper, a signal-based digital predistortion (DPD) scheme is proposed for the linearization of power amplifiers (PAs) in broadcasting transmitters. The proposed DPD scheme is called the signal-based DPD to distinguish it from the conventional model-based DPD, where the motivation of the former is to synthesize the predistorted signal and that of the latter is to construct the preinverse function of the PA. Our work on this signal-based DPD scheme mainly consists of two parts: a signal-based predistorter and its identification. The signal-based predistorter is derived according to the typical fixed point approach associated with the contraction mapping theorem (CMT), and it has an online cascade structure that is constructed by reproducing offline iterations of the CMT. Identification methods are proposed by tackling the divergent output problem of the signal-based predistorter with a cascade structure. In particular, three coefficient extraction strategies, which have different complexities and different linearization results, are proposed to identify the signal-based predistorter. The experimental results verify that the proposed signal-based DPD scheme can achieve the best linearization performance compared with all the typical model-based DPD methods, including the indirect learning architecture-based DPD, direct learning architecture-based DPD, and iterative learning control-based DPD. [ABSTRACT FROM AUTHOR]

Recently, there has been an increasing interest towards understanding human movement behaviors. In this regard, one of the approaches is to retrieve the unknown underlying objective function that the human has to optimize while achieving a certain movement behavior. Existing research of behavioral understanding merely depends on predefined optimality criteria, where the minimum time, minimum variance or/and minimum effort are mainly adopted. These criteria are assumed to be constant, where the human is assumed to have the same preferences during the movement duration. However, in this paper, the optimality criteria underlying the kinematic characteristics of a certain human behavior are assumed to be exponentially discounted to account for the change in the human preferences that could happen while achieving this behavior. A new Inverse Discounted-based Linear Quadratic Regulator (ID-LQR) algorithm is developed in the light of Inverse Optimal Control (IOC) framework to find out the discounted cost function that could reproduce the measured human behavior perfectly. Meanwhile, an Incremental version of the ID-LQR algorithm is proposed to continuously refine the so far learned cost function in the case of sequentially presented demonstrations. The saccadic eye gaze movement is studied as an example to quantify both the proposed ID-LQR and Inverse ID-LQR approaches. Simulation results are encouraging and show that the saccadic trajectories generated by ID-LQR approach match the experimental data in many aspects, including position and velocity profiles of saccades. Moreover, when it is assessed by a subsequent set of scenarios, the incremental ID-LQR algorithm confirms its capability to generalize the so far retrieved cost function for the unseen saccadic demonstrations. [ABSTRACT FROM AUTHOR]

Behavior modeling has multiple applications in the intelligent environment domain. It has been used in different tasks, such as the stratification of different pathologies, prediction of the user actions and activities, or modeling the energy usage. Specifically, behavior prediction can be used to forecast the future evolution of the users and to identify those behaviors that deviate from the expected conduct. In this paper, we propose the use of embeddings to represent the user actions, and study and compare several behavior prediction approaches. We test multiple model (LSTM, CNNs, GCNs, and transformers) architectures to ascertain the best approach to using embeddings for behavior modeling and also evaluate multiple embedding retrofitting approaches. To do so, we use the Kasteren dataset for intelligent environments, which is one of the most widely used datasets in the areas of activity recognition and behavior modeling. [ABSTRACT FROM AUTHOR]

Featured Application: The proposed approach can learn transparent behavior models represented as Behavior Trees, which could be used to alleviate the heaven endeavor of manual agent programming in game and simulation. In modern training, entertainment and education applications, behavior trees (BTs) have already become a fantastic alternative to finite state machines (FSMs) in modeling and controlling autonomous agents. However, it is expensive and inefficient to create BTs for various task scenarios manually. Thus, the genetic programming (GP) approach has been devised to evolve BTs automatically but only received limited success. The standard GP approaches to evolve BTs fail to scale up and to provide good solutions, while GP approaches with domain-specific constraints can accelerate learning but need significant knowledge engineering effort. In this paper, we propose a modified approach, named evolving BTs with hybrid constraints (EBT-HC), to improve the evolution of BTs for autonomous agents. We first propose a novel idea of dynamic constraint based on frequent sub-trees mining, which can accelerate evolution by protecting preponderant behavior sub-trees from undesired crossover. Then we introduce the existing ‘static’ structural constraint into our dynamic constraint to form the evolving BTs with hybrid constraints. The static structure can constrain expected BT form to reduce the size of the search space, thus the hybrid constraints would lead more efficient learning and find better solutions without the loss of the domain-independence. Preliminary experiments, carried out on the Pac-Man game environment, show that the hybrid EBT-HC outperforms other approaches in facilitating the BT design by achieving better behavior performance within fewer generations. Moreover, the generated behavior models by EBT-HC are human readable and easy to be fine-tuned by domain experts. [ABSTRACT FROM AUTHOR]

In traditional traffic simulation studies, vehicle behavior has typically been modeled using complex analytical frameworks, which often struggle to encompass the full range of variables affecting vehicle operations. Addressing this gap, our research introduces an innovative data-driven framework for traffic simulation that incorporates human driving data into its decision-making processes. This enables the modeling of diverse vehicle behaviors by taking into account both vehicle-specific characteristics and environmental factors. At the core of this framework are two advanced deep neural networks, convolutional long short-term memory and convolutional gated recurrent unit, which underpin our vehicle traffic simulation model. Utilizing datasets from the Next Generation Simulation project, specifically the I-80 and US-101 road sections, our study further evaluates the framework's performance through single-step continuous prediction, as well as transferability tests, employing the TransMSEloss function to optimize prediction accuracy. Our findings reveal that the proposed data-driven model significantly outperforms traditional models, achieving an exceptional accuracy of 97.22% in training and 95.76% in testing. Notably, in continuous prediction, our model maintains an 89.57% accuracy up to the fifth step, exceeding the traditional framework's 82.82% by 5% to 10% at each step. Time cost analysis indicates that while the data-driven framework's advantages are more pronounced in large-scale simulations, it also demonstrates strong transferability, with a 93.48% accuracy on diverse datasets, showcasing its applicability across different traffic scenarios. This study not only highlights the potential of deep learning in traffic simulation, but also sets a new benchmark for accuracy and scalability in the field. [ABSTRACT FROM AUTHOR]

In this paper, culture-related behaviors are investigated on several channels of communication for virtual characters. Prototypical behaviors were formalized in computational models based on a literature review as well as a corpus analysis, exemplifying the German and Japanese cultures. Therefore, aspects of verbal behavior, communication management and nonverbal behavior were taken into account. In evaluation studies conducted in the targeted cultures, each aspect's impact on human observers was tested. With it, we investigated for which of the aspects, observers prefer agent behavior that was designed to resemble their own cultural background. [ABSTRACT FROM AUTHOR]

This paper presents a fast analog circuit analysis algorithm, fundamental circuit-based circuit analysis, for circuits being repeatedly modified and verified in product development. The algorithm reuses previous circuit simulation result on successive changed circuit analysis to achieve simulation operation reduction. The algorithm is implemented with SPICE simulator on linear and nonlinear circuit applications with the proposed device delta models. The experiments show that the algorithm increases the speed of the circuit simulation five to ten times over directly simulations under the same simulation accuracy. [ABSTRACT FROM AUTHOR]

The development of mobile internet has led to a massive amount of data being generated from mobile devices daily, which has become a source for analyzing human behavior and trends in public sentiment. In this paper, we build a system called MoSa (Mobile Sentiment analysis) to analyze this data. In this system, sentiment analysis is used to analyze news comments on the THAAD (Terminal High Altitude Area Defense) event from Toutiao by employing algorithms to calculate the sentiment value of the comment. This paper is based on HowNet; after the comparison of different sentiment dictionaries, we discover that the method proposed in this paper, which use a mixed sentiment dictionary, has a higher accuracy rate in its analysis of comment sentiment tendency. We then statistically analyze the relevant attributes of the comments and their sentiment values and discover that the standard deviation of the comments' sentiment value can quickly reflect sentiment changes among the public. Besides that, we also derive some special models from the data that can reflect some specific characteristics. We find that the intrinsic characteristics of situational awareness have implicit symmetry. By using our system, people can obtain some practical results to guide interaction design in applications including mobile Internet, social networks, and blockchain based crowdsourcing. [ABSTRACT FROM AUTHOR]

Lead-free solder is a new material that has been utilized in manufacturing electronic components and packages; therefore, the material behavior had not been analyzed completely. This paper summarizes our effort to model the change in lead-free solder hardness behavior with respect to aging time and temperature as a measure of the components' reliability. ANFIS is a modeling technique that had been used in analyzing the current trend and predicting future progression. The ANFIS model was developed based on the BPN-ANN structure with two inputs and one output using Matlab®. The developed model was compared to different regression models that are being used frequently in the literature. The well-trained ANFIS model gave very accurate results for predicting the hardness (output) with a small Root Mean Square Error (RMSE) compared to the Minitab® regression models. ANFIS is one of the best techniques in modeling non-linear data, and it can give better and more accurate data representation and future prediction. [ABSTRACT FROM AUTHOR]

Human performance is a key factor in a manufacturing system. Behavior modeling is a very important but difficult problem when describing human activities. Performance modeling based on data mining is an effective way to help managers predict personnel's capabilities and to recruit appropriate new staff with relevant skills, which can be significant to ensure an enterprise's competitiveness. The K-nearest neighbor (KNN) algorithm is the most common classification algorithm in cases of no prior knowledge of data distribution. In the paper, an improved KNN algorithm was proposed to cope with the human performance prediction problem with the improvements in three aspects, which are the neighboring distance calculation based on entropy, the classification determination strategy, and the quantitative description method of human performance. The improved KNN algorithm was proved to have better classification ability in comparison with other seven typical KNN algorithms and five classic classification algorithms using data sets from the University of California, Irvine (UCI) machine learning repository. The improved KNN algorithm was further tested with a real-world case of performance prediction, and the effectiveness of the algorithm was confirmed. [ABSTRACT FROM AUTHOR]

Infrared thermography is a useful imaging technique for analyzing the thermomechanical behaviour of materials. It allows, under certain conditions, surface temperature monitoring and, via a diffusion model, estimation of heat sources induced by dissipative and/or thermally coupled deformation mechanisms. However, the noisy and discrete character of thermal data, the regularizing effect of heat diffusion and heat exchanges with the surroundings complicate the passage from temperature to heat source. The aim of this paper is to show that the prior use of reduced-basis projection of thermal data improves the signal-to-noise ratio before estimating the heat source distributions. The reduced basis is generated by proper orthogonal decomposition (POD) of physically-admissible thermal fields. These fields are solutions of ideal diffusion problems related to a set of putative heat sources. preprocessing is applied to different direct methods (finite differences, spectral solution, local least-squares fitting) already used in the past. The gain of this preprocessing is determined using a numerical penalizing benchmark test. The methods are finally compared using data extracted from a dynamic cyclic test on a pure copper specimen. [ABSTRACT FROM AUTHOR]

As system designs grow ever more complex, our ability to assimilate, process, and then make equally complex decisions is challenged to keep pace. Intricate relationships within each system, among interoperating systems, and between each system and the external elements of its environment are themselves challenged by the sheer number of moving pieces. The actual number of permutations of configurations and possible behaviors for our systems now far exceeds that which a human is capable of predicting without automated assistance. This paper demonstrates how the Monterey Phoenix (MP) approach can be used to decompose a complex problem into smaller, more manageable models. When taken separately (using human cognition), these models are easier to read and write, and when taken together (using automation), they increase awareness of the possible behaviors that are latent in a design, so that many more use cases can be exposed. Additionally, this paper utilizes a commercial flight scenario to provide an example of how a manually crafted, moderately complex activity model can be restructured into smaller, separate models that are simpler to work with, and that expose additional behavior in simulation, which is not present in the original activity model. [ABSTRACT FROM AUTHOR]

In the context of the global economic downturn caused by COVID-19, e-commerce has become the first choice for people to shop. Many merchants choose to launch promotion activities through some social media platforms. Price discounts can dramatically increase sales volume in social e-commerce due to the interaction of online consumers. It is urgent for e-commerce merchants to learn rules about discount information dissemination in social media, so as to formulate reasonable discount strategies and achieve sustainable business. This paper constructs an evolutionary game model for e-commerce platforms and merchants when they implement promotion strategies through social media, investigates discount information dissemination among consumers under multiple situations by introducing price discount parameters, and further discusses the influence mechanism of discount size and platform reputation on consumers' purchase behavior. Results show that in low-reputation e-commerce platforms, the price discount is the main motivation to purchase. Consumers' preference for the high discount is weakened by the increase in platforms' reputation. Discounts should be set according to the different reputations. Businesses in a high-reputation environment are relatively more profitable. E-commerce businesses should work together, apply reasonable pricing, and improve their quality to create a green and healthy shopping environment, in order to get benefits and sustainable development. [ABSTRACT FROM AUTHOR]

A high-temperature equation of state for tungsten was constructed in this study using experimental data on its thermodynamic properties, thermal expansion, compressibility, and bulk compression modulus. The totality of experimental data were optimized by the temperature-dependent Tait equation over a pressure range from 0 up to 1000 kbar and over a temperature range from 20 K to the melting point. An extended Einstein model was used to describe the temperature dependence of thermodynamic and thermophysical parameters. A linear temperature dependence was embraced for the derivative of the isothermal bulk modulus. The resultant equation of state provides a good fit to the whole set of experimental data within measurement uncertainties associated with individual quantities. [ABSTRACT FROM AUTHOR]

This research aimed at building an intelligent system that can detect abnormal behavior for the elderly at home. Active RFID tags can be deployed at home to help collect daily movement data of the elderly who carries an RFID reader. When the reader detects the signals from the tags, RSSI values that represent signal strength are obtained. The RSSI values are reversely related to the distance between the tags and the reader and they are recorded following the movement of the user. The movement patterns, not the exact locations, of the user are the major concern. With the movement data (RSSI values), the clustering technique is then used to build a personalized model of normal behavior. After the model is built, any incoming datum outside the model can be viewed as abnormal and an alarm can be raised by the system. In this paper, we present the system architecture for RFID data collection and preprocessing, clustering for anomaly detection, and experimental results. The results show that this novel approach is promising. [ABSTRACT FROM AUTHOR]

A heterogeneous material with a certain periodic microstructure may have specific properties, such as negative Poisson’s ratio and zero thermal expansion coefficients, which can satisfy some requirement of high technology. However, currently research about how to fabricate such 3-D periodic microstructures is rarely reported in the published literature and only some 2D fabrication procedures were developed. To fabricate such components, a hybrid-layered manufacturing process has been developed and applies spraying, engraving, and refinishing technologies, among which the engraving is the key technology for generating the voids of periodic microstructures with their required dimensions and accuracies. To implement such an accurate engraving, it is important to study its engraving behavior. This paper establishes a behavior model of the engraving operation and performs its behavior simulation, thus providing a reliable basis for future practical manufacturing. [ABSTRACT FROM AUTHOR]

The advance of Internet and Web technologies has boosted the development of electronic commerce. More and more people have changed their traditional trading behaviors and started to conduct Internet shopping. However, the exponentially increasing product information provided by Internet enterprises causes the problem of information overload, and this inevitably reduces the customer's satisfaction and loyalty. To overcome this problem, in this paper we propose a multi-agent system that is capable of eliciting expert knowledge and of recommending optimal products for individual consumers. The recommendations are based on both product knowledge from domain experts and the customer's preferences from system–consumer interactions. In addition, the system also uses behavior patterns collected from previous consumers to predict what the current consumer may expect. Experiments have been conducted and the results show that our system can give sensible recommendations, and it is able to adapt to the most up-to-date preferences for the customers. [ABSTRACT FROM AUTHOR]

In this paper, we present a methodology for generating guaranteed passive time-domain models of subsystems described by tabulated frequency-domain data obtained through measurement or through physical simulation. Such descriptions are commonly used to represent on- and off-chip interconnect effects, package parasitics, and passive devices common in high-frequency integrated circuit applications. The approach, which incorporates passivity constraints via convex optimization algorithms, is guaranteed to produce a passive-system model that is optimal in the sense of having minimum error in the frequency band of interest over all models with a prescribed set of system poles. We demonstrate that this algorithm is computationally practical for generating accurate high-order models of data sets representing realistic, complicated multiinput, multioutput systems. [ABSTRACT FROM AUTHOR]

Defining information required by automatic test systems frequently involves a description of system behavior. To facilitate capturing the required behavior information in the context of testing, a formal model of behavior was developed for use by test systems. The approach taken in defining the behavior model was based on information modeling and was derived from recent work in formal methods by the hardware and software design communities. Specifically, an information model was developed in EXPRESS capturing the relationships between essential entities characterizing behavior. In this paper, we provide a high level description of the behavior information model and several examples applying the model in a test environment. [ABSTRACT FROM AUTHOR]

With the increasing number of surveillance cameras in both indoor and outdoor locations, there is a grown demand for an intelligent system that detects abnormal events. Although human action recognition is a highly reached topic in computer vision, abnormal behavior detection is lately attracting more research attention. Indeed, several systems are proposed in order to ensure human safety. In this paper, we are interested in the study of the two main steps composing a video surveillance system which are the behavior representation and the behavior modeling. Techniques related to feature extraction and description for behavior representation are reviewed. Classification methods and frameworks for behavior modeling are also provided. Moreover, available datasets and metrics for performance evaluation are presented. Finally, examples of existing video surveillance systems used in real world are described. [ABSTRACT FROM AUTHOR]

Cyber-Physical Systems (CPS) form the new backbone of digital ecosystems. Upcoming CPS will be operated on a unifying basis, the Internet of Behaviors (IoB). It features autonomous while federated CPS architectures and requires corresponding behavior modeling for design and control. CPS design and control involves stakeholders in different roles with different expertise accessing behavior models, termed Digital twins. They mirror the physical CPS part and integrate it with the digital part. Representing role-specific behaviors and provided with automated execution capabilities Digital twins facilitate dynamic adaptation and (re-)configuration. This paper proposes to conceptualize agent-based design for behavior-based Digital twins through subject-oriented models. These models can be executed and, thus, increase the transparency at design and runtime. Patterns recognizing environmental factors and operation details facilitate the configuration of CPS. Subject-oriented runtime support enables dynamic adaptation and the federated use of CPS components. [ABSTRACT FROM AUTHOR]

The development of mobile internet has led to a massive amount of data being generated from mobile devices daily, which has become a source for analyzing human behavior and trends in public sentiment. In this paper, we build a system called MoSa (Mobile Sentiment analysis) to analyze this data. In this system, sentiment analysis is used to analyze news comments on the THAAD (Terminal High Altitude Area Defense) event from Toutiao by employing algorithms to calculate the sentiment value of the comment. This paper is based on HowNet; after the comparison of different sentiment dictionaries, we discover that the method proposed in this paper, which use a mixed sentiment dictionary, has a higher accuracy rate in its analysis of comment sentiment tendency. We then statistically analyze the relevant attributes of the comments and their sentiment values and discover that the standard deviation of the comments’ sentiment value can quickly reflect sentiment changes among the public. Besides that, we also derive some special models from the data that can reflect some specific characteristics. We find that the intrinsic characteristics of situational awareness have implicit symmetry. By using our system, people can obtain some practical results to guide interaction design in applications including mobile Internet, social networks, and blockchain based crowdsourcing.

In the field of near-infrared weak light detection, an InP/InGaAs single-photon avalanche diode (SPAD) is preferred due to the advantages of high sensitivity, low cost and room-temperature operation. To properly simulate and optimize the SPAD's front-end circuit, a comprehensive and compact behavior model of the InP/InGaAs SPAD is normally required to accurately describe the statistical behavior of the detectors. In this paper, an InP/InGaAs SPAD analytical model is constructed, which not only includes the direct current (DC) and alternating current (AC) behavior simulating the avalanche and quenching processes, but also describes the dark count, after-pulsing and photon detection efficiency. For dark count noise, three important generation mechanisms are considered, including thermal generation, trap-assisted tunneling and band-to-band tunneling. The model described by the Verilog-A hardware description language (HDL) can be directly implemented in the commercial circuit simulator. A gated mode, passive quenching and recharging circuit is used to simulate and verify the developed model. The simulation results are in good agreement with the reported test data, demonstrating the accuracy of the developed InP/InGaAs SPAD model. [ABSTRACT FROM AUTHOR]

The objective of this paper is to review the topics related to the optimized operation of intelligent buildings with respect to occupant comfort and energy consumption. To simultaneously optimize energy costs and indoor environmental quality, intelligent buildings should consider several continuously changing inputs including energy exchange processes across the building, sets of indoor and outdoor environmental parameters, energy prices, occupants' presence, preferences, and behavior inside the building. Therefore, a well-structured framework supported by computational intelligence and optimization methods, environmental monitoring, and behavior modeling techniques, as well as comfort, productivity, and behavioral studies, are required to make optimal decisions for the indoor environment. In this paper, the main concepts, challenges, the latest studies, findings, and developments related to the six topics of (1) Occupant comfort conditions; (2) Occupant productivity; (3) Building control; (4) Computational optimization; (5) Occupant behavior modeling; (6) Environmental monitoring and analysis, in offices, commercial and residential buildings are reviewed. Moreover, future directions and challenges related to the optimized operation of intelligent buildings are discussed. [ABSTRACT FROM AUTHOR]

• This paper presents a technique for detect usability problems effectively via usability testing in mobile application. • Users interact with mobile applications in dissimilar ways when encountering usability problems. • Dissimilarities of user behaviors helps find usability problems. • The methods for tracing, modeling and comparing user behaviors is proposed to measure the similarity of user behaviors. Usability is one of the critical success factors for mobile applications. However, usability is not easy to improve. Many usability testing studies have been conducted to detect usability problems easily, however they usually carry a substantial cost and require specific expertise. In this paper, we present methods and tools to detect usability problems effectively via usability testing in mobile application. We expect users to interact with mobile applications in dissimilar ways when encountering usability problems. Based on this approach, we propose tracing, modeling and comparing methods for measuring the similarity of user behaviors. We evaluate the similarity of user behavior and use it in detecting problems. In addition, we developed tools for our methods to run in automatic way to save usability testing costs. An experiment was conducted using two mobile applications to confirm that our proposed method is useful. We tested eight tasks and found lower similarity when users encountered a usability problem. The experimental results show that our methods and tools can help find usability problems in mobile applications. [ABSTRACT FROM AUTHOR]

The behavior of an agent may be simple or complex depending on its role. Behavioral simulation using agents can have multiple approaches that have different advantages and disadvantages. By combining different behaviors in a hierarchical model, situational inefficiencies can be compensated. This paper proposes a behavioral hierarchy model that combines different mechanisms in behavior plans. The study simulates the social behavior in an office environment during an emergency using collision avoidance, negotiation, conflict solution, and path-planning mechanisms in the same multi-agent model to find their effects and the efficiency of the combinational setups. Independent agents were designed to have memory expansion, pathfinding, and searching capabilities, and the ability to exchange information among themselves and perform evasive actions to find a way out of congestion and conflict. The designed model allows us to modify the behavioral hierarchy and action order of agents during evacuation scenarios. Moreover, each agent behavior can be enabled or disabled separately. The effects of these capabilities on escape performance were measured in terms of time required for evacuation and evacuation ratio. Test results prove that all mechanisms in the proposed model have characteristics that fit each other well in situations where different hierarchies are needed. Dynamic memory management (DMM), together with a hierarchical behavior plan, achieved a performance improvement of 23.14% in escape time without providing agents with any initial environmental information. [ABSTRACT FROM AUTHOR]

Background: Stepfamilies are a prevalent family form. However, less stable than nuclear, first marriage families due to the presence of risk factors such as the absence of social norms and the presence of stepchildren. Stepfamilies have unique educational needs regarding stepparenting and co-parenting issues. The development and documentation of psychoeducational intervention strategies can facilitate dissemination of ongoing studies and promote transparency. This article describes the background, design and protocol of a randomized controlled trial (RCT) evaluating the eficacy and feasibility of a web-based Psychoeducational Simulation Game (GSteps). Behavior-modeling video training (BMT) is used to demonstrate and promote relational skills, stepparenting and co-parenting effective strategies for adults in stepfamilies. A mental health professional will be available within the GSteps platform for clarification or emotional support. Methods/design: A RCT design is presented to evaluate the outcomes of a self-administered, interactive and web-based psychoeducational Game targeting dyadic marital adjustment and interpersonal skills as the primary outcomes and remarriage beliefs, family function and stepparenting and co-parenting attitudes as the secondary outcomes. Other outcome measures include satisfaction with GSteps, participants’ knowledge learned after the intervention and a purposive sampling method will be used to access feasibility. The minimum required sample size is 112 participants (56 per condition) randomly allocated either to an experimental group (EG), receiving GSteps intervention, or to a wait-list control group (CG). A survey is conducted electronically. Assessments take place at baseline (T0), after the intervention (T1) and 1-month follow-up (T2). Discussion: This protocol presents a RCT aimed at evaluating the efficacy of a web-based psychoeducational intervention (GSteps) designed for improving marital, stepparenting and co-parenting skills in adults who live in stepfamilies. The use of the protocol and results of intervention studies may guide the use and refinement of web-based psychoeducational intervention for stepfamilies. Additionally, GSteps may become a tool for health professionals to enhance stepfamily functioning, stepparenting skills, and marital adjustment of remarried adults. [ABSTRACT FROM AUTHOR]

Traditionally, in building energy modeling (BEM) programs, occupant behavior (OB) inputs are deterministic and less indicative of real world scenarios, contributing to discrepancies between simulated and actual energy use in buildings. This paper presents a new OB modeling tool, with an occupant behavior functional mock-up unit (obFMU) that enables co-simulation with BEM programs implementing functional mock-up interface (FMI). The components detailed in the development of the obFMU include an overview of the DNAS (drivers-needs-actions-systems) ontology and the occupant behavior eXtensible Markup Language (obXML) schema, in addition to details on the creation of the obFMU that contains the co-simulation interface, the data model and solvers. To demonstrate functionality of the tool, three examples of occupant behaviors were simulated, including: (1) turning on and off lights, (2) opening and closing windows, and (3) turning on and off the air conditioners. The obFMU can be used via co-simulation with all building simulation programs that implement the FMI, thus users are not limited to a particular tool. Another advantage is the use of obXML schema to represent occupant behavior, standardize the description of occupant behavior enabling information exchange. [ABSTRACT FROM AUTHOR]

Understanding human behaviors has received considerable attention in neuroscience literature. Existing research addressed the main question: given measurements of a certain human movement, what is the underlying optimality criteria that human has optimized to fulfill this movement? Inverse Optimal Control (IOC) is a well-established approach to understand the biological movements in terms of the optimal control theory. IOC learns the criterion that best describes the demonstrated human behavior. Thus far, gradient-based techniques have been used to obtain the unknown behavior cost. However, these techniques are limited by locating only local optimum parameters. In this paper, behavior learning is modeled as an Inverse Linear Quadratic Regulator (ILQR) problem, where linear behavior dynamics and a quadratic cost are assumed. An efficient meta-heuristic technique, Particle Swarm Optimization (PSO), is used to retrieve the unknown cost in the proposed ILQR problem. Moreover, an evolving-ILQR algorithm is proposed to refine the learned cost once new unseen demonstrations exist to overcome the over-fitting problem. The reach-to-grasp behavior is studied to quantify the proposed approaches. Results are encouraging and show consistency with that in neuroscience literature. Meanwhile, the evolving-ILQR algorithm has quantified in successive scenarios, where the so far retrieved behavior cost has incrementally refined once new unseen demonstrations are available. [ABSTRACT FROM AUTHOR]

Occupant behavior is one of the major factors influencing building energy consumption and contributing to uncertainty in building energy use prediction and simulation. Currently the understanding of occupant behavior is insufficient both in building design, operation and retrofit, leading to incorrect simplifications in modeling and analysis. This paper introduced the most recent advances and current obstacles in modeling occupant behavior and quantifying its impact on building energy use. The major themes include advancements in data collection techniques, analytical and modeling methods, and simulation applications which provide insights into behavior energy savings potential and impact. There has been growing research and applications in this field, but significant challenges and opportunities still lie ahead. [ABSTRACT FROM AUTHOR]

Socio-cyberphysical networks tightly integrate humans, real-world objects and IT infrastructure. In some cases of cyberphysical networks, their configuration can be done in a centralized way (e.g., automated production line configuration). However, for socio-cyberphysical networks the centralized control is not possible due to the independence and autonomy of the network members (e.g., employees of an enterprise). The paper proposes an approach to model such network via a set of interacting socio-inspired services. Such services are capable to model behavior of the network participants taking into account their preferences, strategies and social norms. The interoperability of the services is achieved due to the usage of common standards (such as WSDL and SOAP) at the technological level, and common ontology at the semantic level. The developed conceptual model of socio-inspired service is presented together with behavior patterns and methods for their processing. [ABSTRACT FROM AUTHOR]