ObjectWith social and economic development, more and more areas are evolving toward modern cities [1]. Road information is critical for urban planning, traffic navigation and GIS data updates [2]. Traditional manual data collection methods are inefficient. The advancement of remote sensing technology allows for the automatic extraction of road information from high resolution remote sensing images [3]-[7].MethodThis paper proposes a road extraction model for high-resolution remote sensing images based on multi-headed self-retaining mechanism and U-Net (MSAU-Net) to achieve accurate extraction of roads in high resolution remote sensing images. MSAU-Net is made up of three components: the Canny edge detection operator, the U-Net base network architecture, and the multi-head self-attention module, as shown in Fig. 1. The road edge feature information is extracted by the Canny edge detection operator [8] and inputted to the model for training and learning via convolutional operation to make the road edges smoother and more accurate.Fig. 1.The architecture of MSAU-Net.The MSAU-Net network includes an encoder, a decoder, and an intermediate multi-headed self-attention module M-SABlock. The image is first passed through the Canny edge operator to detect edges, then through the encoding convolution module to extract the image's underlying features, and finally through two downsampling blocks in sequence to reduce space size and obtain high-level features. The number of channels is doubled after each downsampling block. The result of the downsampling is then fed to the self-attention module, which summarizes the global information and produces the encoder output. Convolution is used to extract local information in the encoding module. To recover its corresponding feature images, the decoder employs three upsampling blocks. By implementing downsampling to upsampling skip architecture through the multi-headed self-attention module, the features are copied from the encoder to the decoder. Because lower-level features already contain more global information, they can proceed directly to the decoder side; however, because higher-level features have relatively less global information, M-SABlock can extract global information more effectively. This can improve the road extraction accuracy of the model.Result and DiscussionOn the CHN6-CUG dataset [9] and Massachusetts roads dataset [10], the road extraction performance of this paper's network was compared to that of FCN [11], U-Net [12], ResU-Net [13], and SAU-Net [14] networks using the same experimental settings. As can be seen in Fig. 2(a,c), when compared to other algorithms, the algorithm in this paper can effectively combine global contextual information to provide a global information-guided flow for the decoder. Simultaneously, it can dynamically select suitable sensory fields for remote sensing images of different sizes and better integrate multi-scale feature information through self-learning. As a result, it can better extract the edge contour of blurred complex roads and significantly reduce the phenomenon of over- and under-extraction. Meanwhile, the proposed algorithm outperforms other algorithms in both overall and detail performance.To realize the quantitative analysis of the network for road extraction, this paper uses Hausdorff distance (HD) [15], Precision (PRE), Dice similarity coefficient (DSC) [16], Sensitivity (SEN) and Mean intersection over union (MIOU) as evaluation metrics. As shown in Fig. 2(b), the road extraction effect of the algorithm in this paper is significantly better than FCN, U-Net, ResU-Net and SAU-Net. The average MIOU and DSC of the proposed algorithm are improved by 6.96% and 7.16%, respectively, when compared to ResU-Net. The PRE and SEN of the proposed algorithm are improved by 9.08% and 6.56%, respectively, when compared to SAU-Net.As shown in Fig. 2(d), the proposed algorithm outperforms other algorithms in these metrics and achieves the best results with an average HD, PRE, DSC, SEN and MIOU of 2.209 cm, 90.12%, 91.26%, 89.38%, and 88.74% on the test set, respectively. The average PRE and DSC of the proposed algorithm are improved by 7.78% and 8.14%, respectively, when campared to FCN.The MIOU and SEN of the proposed algorithm are improved by 5.58% and 7.41%, respectively, when compared to U-Net. It indicates that our algorithm is more accurate and efficient.Fig. 2.Experiments result. (a) Visual comparisons of road extraction results with different comparing algorithms on the CHN6-CUG dataset, (b) Road extraction metrics for CHN6-CUG dataset. (c) Visual comparisons of road extraction results with different comparing algorithms on the Massachusetts roads dataset, (d) Road extraction metrics for Massachusetts roads dataset.ConclusionThe experimental results show that the proposed algorithm can achieve accurate and effective road extraction on CHN6-CUG dataset and Massachusetts roads dataset. In the future, we will reduce the number of parameters of the model and devote to the research of lightweight model.

Target detection based on outdoor and remote sensing (RS) images are used in many fields [1] , such as civil and military [2] , traffic surveillance [3] and disaster forecast [4] . However, in harsh environmental conditions, as air contaminants increase, light is scattered in the air and the image becomes hazy [5] . Therefore, image dehazing is an important issue in target detection for outdoor images and RS images. Therefore, image dehazing is an important issue in target detection for outdoor images and RS images. It has recently been proposed a variety of methods to dehaze images, which can be broadly categorized into three types: image enhancement-based, image restoration-based, and deep learning-based methods, respectively. Image enhancement-based dehazing methods highlight useful information without considering the cause of haze. Image restoration-based dehazing methods analyze the effect of atmospheric scattering model (ASM) on the image. Deep learning-based dehazing methods learn the intrinsic relationships between images and obtain clear images by training.

Most SAR ship detection methods based on deep learning merely pursing high detection accuracy while ignoring the model’s complexity. And the factor of serious interfere caused by speckle noise is not considered, thus leading to the detection performance decline under complex imagery backgrounds. To address these problems, a lightweight network for multiscale SAR ship detection is proposed. The backbone of YOLOX is replaced by improved attention ShuffleNetV2 (IAS), which has fewer parameters and better feature extraction ability. Then, a lightweight attention enhanced path aggregation feature pyramid network (LAE-PAFPN) is proposed. Three parallel ECA attention modules are embedded into LAE-PAFPN to refine the feature of ships while suppressing the interfere of the speckle noise. The experiments are conducted on SSDD dataset, show that the mAP of our method have achieved to 97.93% while the FLOPs and parameters are 11.05 G and 2.84 M, respectively.

Ship detection is of great importance in territorial security and marine environmental protection. However, the accurate detection of small ships is a challenging task in complex environments mainly due to small ships having few features on remote sensing images. In this letter, we propose a small ship detection model based on YOLOv5s. The major features of the proposed model include: (1) a detection layer is added with a shallow feature map in the Head and skip connections are employed in the Neck to improve the detection accuracy of small ships; (2) a novel and effective Hybrid Spatial Pyramid Pooling (HSPP) is proposed to fuse the local and global information of feature maps; (3) a Coordinate Attention mechanism is employed in the Backbone to augment the representations of small ships, and EIOU is used as the loss function for bounding box regression to enhance the localization accuracy of the proposed model; (4) K-means++ algorithm is used to obtain more reasonable anchors for small ship detection. We introduce the multi-scale ship dataset OSSD, which contains 10133 images. Experiments on LEVIR-Ship and OSSD validate the effectiveness of our proposed model. The code and OSSD will be available at https://github.com/wenjieo/Improved-YOLOv5s-for-small-ship-detection.

Abstract How to realistically model an agent's steering behaviour is a critical issue in agent‐based crowd simulation. In this work, we investigate some proactive steering strategies for agents to minimize potential collisions. To this end, a behaviour‐based modelling framework is first introduced to model the process of how humans select and execute a proactive steering strategy in crowded situations and execute the corresponding behaviour accordingly. We then propose behaviour models for two inter‐related proactive steering behaviours, namely gap seeking and following. These behaviours can be frequently observed in real‐life scenarios, and they can easily affect overall crowd dynamics. We validate our work by evaluating the simulation results of our model with the real‐world data and comparing the performance of our model with that of two state‐of‐the‐art crowd models. The results show that the performance of our model is better or at least comparable to the compared models in terms of the realism at both individual and crowd levels.

Software systems for data-centric smart city applicationsThe smart city is the key technology for efficient management, sustainable development, and efficient governance in the current worldwide urbanization process.It incorporates the latest information technologies particularly data-driven trends to improve support for the everyday life of people, especially in security, transportation, and social services.The smart city paradigm can be specified as a large-scale distributed system in which the massive data generated by smart electronic devices, smart environments, and Internet of Things (IoT) can be organized, managed, and analyzed.The design of scalable software applications, frameworks, and packages is important in forming approaches which integrate smart city infrastructures (environment, physical ICT infrastructure), public processes, and services.Recently, software system development for data-centric smart city applications has become a very active area of research in academia and has attracted significant interest from industry.Since computing issues for smart cities are highly interdisciplinary and cover various topics, a special issue of Software: Practice and Experience provides the ideal forum for presenting and discussing the latest research.The goal of this special issue is to present outstanding research results in regard to software systems for data-centric smart city applications.We received 23 manuscript submissions in total; of these, 7 papers were accepted after several rounds of very constructive and deep reviews.Large-scale wireless communication is the fundamental infrastructure needed to ensure the operation of smart city, cloud computing, the IoT, etc.A relay network can provide an efficient solution to reliable transmission of large amounts of data.However, when base stations are densely populated, energy consumption becomes a critical problem.To solve this problem, Lam et al propose a software system for robust power management taking uncertain channel gains into consideration.The system relies on a distributed power allocation algorithm to reduce the overhead of extra information exchange while guaranteeing performance with respect to energy savings and robustness in a dynamic communications environment.Smart city uses many data sources.To manage the massive volume of data, data compression is essential, especially for surveillance videos.Since lossy compression methods cause information loss, a high compression ratio negatively affects data analysis.Xiao et al tackled this problem by proposing a sensitive-object-oriented compression method for surveillance videos.Regions with sensitive objects critical to the analysis of surveillance videos are detected prior to compression.Higher bit rates are assigned to these regions to enable a high compression ratio for the whole video stream without sacrificing the performance of data analysis centered on the sensitive objects.Data analysis is at the very core of smart city applications.Four interesting topics are included in this special issue, covering cloud-based mining for traffic data, low-cost computing for face tracking, fast processing of forensic data, and accurate analysis of human emotions: 1. Yu et al propose a SPARK-based software framework for trajectory pattern mining and trajectory clustering for taxi journeys.The proposed algorithms adopt in-memory computation and load the trajectory sequences into resilient distribution datasets to overcome high I/O overhead and communication overhead.High efficiency and scalability were achieved for the whole distributed framework.2. Fast processing of forensic data in heterogeneous distributed system is demonstrated by Quick et al.Their work gives digital forensic specialists a method and framework to review and analyze media in a timely manner through digital forensic data reduction.

Research on agent-based crowd simulation has gained tremendous momentum in recent years due to the increase of computing power. One key issue in this research area is to develop various behavioral models to capture the microscopic behaviors of individuals (i.e., agents) in a crowd. In this paper, we propose a novel behavior model for modeling the gap seeking behavior which can be frequently observed in real world scenarios where an individual in a crowd proactively seek for gaps in the crowd flow so as to minimize potential collision with other people. We propose a two-level modeling framework and introduce a gap seeking behavior model as a proactive conflict minimization maneuver at global navigation level. The model is integrated with the reactive collision avoidance model at local steering level. We evaluate our model by simulating a real world scenario. The results show that our model can generate more realistic crowd behaviors compared to the classical social-force model in the given scenario.

We investigate a vascular deformation modeling method that can be used for percutaneous vascular interventional virtual surgery simulation. Triangle meshes are used to construct the virtual vascular tissues. The physical parameters of the meshes are obtained through biomechanics experiments with real porcine vascular under the small deformation. A new method is then proposed to establish the relation between the vascular elasticity modulus E and spring coefficient k for improving modeling accuracy. In addition, the influence scope analysis method for mass particle force optimization is used to improve simulation speed of tissue deformation. The propose method is compared with the finite element simulation, which shows that the proposed method is able to achieve the real time simulation for the virtual operations with a higher simulation accuracy.

Design space exploration refers to the evaluation of implementation alternatives for many engineering and design problems. A popular exploration approach is to run a large number of simulations of the actual system with varying sets of configuration parameters to search for the optimal ones. Due to the potentially huge resource requirements, cloud-based simulation execution strategies should be considered in many cases. In this paper, we look at the issue of running large-scale simulation-based design space exploration problems on commercial Infrastructure-as-a-Service clouds, namely Amazon EC2, Microsoft Azure and Google Compute Engine. To efficiently manage cloud resources used for execution, the key problem would be to accurately predict the running time for each simulation instance in advance. This is not trivial due to the currently wide range of cloud resource types which offer varying levels of performance. In addition, the widespread use of virtualization techniques in most cloud providers often introduces unpredictable performance interference. In this paper, we propose a resource and application-aware (RA2) prediction approach to combat performance variability on clouds. In particular, we employ neural network based techniques coupled with non-intrusive monitoring of resource availability to obtain more accurate predictions. We conducted extensive experiments on commercial cloud platforms using an evacuation planning design problem over a month-long period. The results demonstrate that it is possible to predict simulation execution times in most cases with high accuracy. The experiments also provide some interesting insights on how we should run similar simulation problems on various commercially available clouds.

ABSTRACT Serious games have recently shown great potential to be adopted in many applications, such as training and education. However, one critical challenge in developing serious games is the authoring of a large set of scenarios for different training objectives. In this paper, we propose a data‐driven approach to automatically generate scenarios for serious games. Compared with other scenario generation methods, our approach leverages on the simulated player performance data to construct the scenario evaluation function for scenario generation. To collect the player performance data, an artificial intelligence (AI) player model is designed to imitate how a human player behaves when playing scenarios. The AI players are used to replace human players for data collection. The experiment results show that our data‐driven approach provides good prediction accuracy on scenario's training intensities. It also outperforms our previous heuristic‐based approach in its capability of generating scenarios that match closer to specified target player performance.Copyright © 2014 John Wiley & Sons, Ltd.

Multi-Player Online Games (MPOGs) have attracted enormous users in recent years. In contrast to the large number of low interactive MPOGs, the choices of highly interactive MPOGs are limited and most of them do not accept players whose network connections are slow due to the complexity involved in consistency maintenance. Most MPOGs adopt a data-centric consistency maintenance mechanism that only ensures the consistency of the final states, which however does not work well for highly interactive MPOGs because consistent views of incremental state changes are also important in those games. Instead of striving for a general solution for all highly interactive MPOGs, we advocate tailored solutions for different kinds of games by exploiting their semantics. To showcase this idea, we present the critical consistency model for highly interactive two-player online ball games, which only requires consistent views to be reached at critical states. We further contribute the progressive slowdown latency compensation technique to achieve critical consistency, which is demonstrated by an online table tennis game P2T2 (Peer-to- Peer Table Tennis).

An Interest Management (IM) mechanism eliminates irrelevant status updates transmitted in Distributed Virtual Environments (DVE). This paper proposes a new hierarchical IM mechanism for DVEs. The hierarchical mechanism divides the virtual world into multiple levels of cells and keeps the relationship between an entity and an Area-Of-Interest (AOI) at a particular cell level according to their relative position. As their relative position changes, the relationship level is updated accordingly. Compared with the traditional area-based and cell-based mechanisms, the proposed hierarchical mechanism significantly reduces the communication bandwidth consumption of IM and thus considerably improves the scalability of DVEs. In addition, the proposed mechanism also has much lower computation cost than the traditional mechanisms and very acceptable storage requirement for its data structures.

In this paper, we propose a new approach to modeling natural steering behaviors of virtual humans. We suspect that a small number of steering strategies are sufficient for generating typical pedestrian behaviors observed in daily-life situations. Through these limited strategies we show that complex steering behaviors are generated by executing appropriate steering strategies at the appropriate time. In our model, decisions on the selection, scheduling and execution of steering strategies in a given situation are based on the matching results between the currently perceived spatial-temporal patterns and the prototypical cases in an agent's experience base. From a modeler's point of view, our approach is intuitive to use. Our model is carefully evaluated through a three-stage validation process, using experimental studies on basic test scenarios, model comparisons under standard but more complex test scenarios, and sensitivity analysis on key model parameters. Experimental results show that our model is able to generate results that reflect the collective efficiency of crowd dynamics and is in agreement with existing literature on pedestrian studies.

ABSTRACT For a virtual training system, how to effectively and quickly generate training scenarios has become a challenging issue. A scenario generation system is needed to produce scenarios that can meet different objectives and at the same time be customized for individuals. In this paper, we introduce a scenario generation framework for mission‐based virtual training, which aims to generate scenarios from both trainer and trainee's perspective. The framework allows a trainer to direct the scenario generation process, so that the generated scenarios reflect the trainer's preferences over different mission objectives. It also considers how the scenarios could adapt to different trainees’ skill levels. The representation of scenario beat is proposed, and the scenario generation process adopts a combinatorial optimization approach generating the sequence of scenario beats. The efficacy of the proposed framework is demonstrated through an empirical study of human players in a simple food distribution mission game. The results show that a trainee can achieve better performance improvement when playing the customized scenarios tailored to the trainee's skill level as compared with the uncustomized scenarios. Copyright © 2013 John Wiley & Sons, Ltd.

Maintaining interactivity is one of the key challenges in distributed virtual environments (DVEs). In this paper, we consider a new problem, termed the interactivity-constrained server provisioning problem, whose goal is to minimize the number of distributed servers needed to achieve a prespecified level of interactivity. We identify and formulate two variants of this new problem and show that they are both NP-hard via reductions to the set covering problem. We then propose several computationally efficient approximation algorithms for solving the problem. The main algorithms exploit dependencies among distributed servers to make provisioning decisions. We conduct extensive experiments to evaluate the performance of the proposed algorithms. Specifically, we use both static Internet latency data available from prior measurements and topology generators, as well as the most recent, dynamic latency data collected via our own large-scale deployment of a DVE performance monitoring system over PlanetLab. The results show that the newly proposed algorithms that take into account interserver dependencies significantly outperform the well-established set covering algorithm for both problem variants.

In objective-based data farming, decision variables of the Red Team are evolved using evolutionary algorithms such that a series of rigorous Red Team strategies can be generated to assess the Blue Team's operational tactics. Typically, less than 10 decision variables (out of 1000+) are selected by subject matter experts (SMEs) based on their past experience and intuition. While this approach can significantly improve the computing efficiency of the data farming process, it limits the chance of discovering “surprises” and moreover, data farming may be used only to verify SMEs' assumptions. A straightforward solution is simply to evolve all Red Team parameters without any SME involvement. This modification significantly increases the search space and therefore we refer to it as high-dimensional objective-based data farming (HD-OBDF). The potential benefits of HD-OBDF include: possible better performance and information about more important decision variables. In this paper, several state-of-the-art multi-objective evolutionary algorithms are applied in HD-OBDF to assess their suitability in terms of convergence speed and Pareto efficiency. Following that, we propose two approaches to identify dominant/key evolvable parameters in HD-OBDF - decision variable coverage and diversity spread.

An Autonomous Bee Colony Optimization (A-BCO) algorithm for solving multi-objective numerical problems is proposed. In contrast with previous Bee Colony algorithms, A-BCO utilizes a diversity-based performance metric to dynamically assess the archive set. This assessment is employed to adapt the bee colony structures and flying patterns. This self-adaptation feature is introduced to optimize the balance between exploration and exploitation during the search process. Moreover, the total number of search iterations is also determined/optimized by A-BCO, according to user pre-specified conditions, during the search process. We evaluate A-BCO upon numerical benchmark problems and the experimental results demonstrate the effectiveness and robustness of the proposed algorithm when compared with the Non-dominated Sorting Genetic Algorithm II and the latest Multi-objective Bee Colony Algorithm proposed to date.

Generating human-like behaviors for virtual agents has become increasingly important in many applications, such as crowd simulation, virtual training, digital entertainment, and safety planning. One of challenging issues in behavior modeling is how virtual agents make decisions given some time-critical and uncertain situations. In this paper, we present HumDPM, a decision process model for virtual agents, which incorporates two important factors of human decision making in time-critical situations: experience and emotion. In HumDPM, rather than relying on deliberate rational analysis, an agent makes its decisions by matching past experience cases to the current situation. We propose the detailed representation of experience case and investigate the mechanisms of situation assessment, experience matching and experience execution. To incorporate emotion into HumDPM, we introduce an emotion appraisal process in situation assessment for emotion elicitation. In HumDPM, the decision making process of an agent may be affected by its emotional states when: 1) deciding whether it is necessary to do a re-match of experience cases, 2) determining the situational context, and 3) selecting experience cases. We illustrate the effectiveness of HumDPM in crowd simulation. A case study for emergency evacuation in a subway station scenario is conducted, which shows how a varied crowd composition leads to different evacuation behaviors, due to the retrieval of different experiences and the variation of agents' emotional states.

Situation awareness is the perception of environmental elements within a volume of time and space, the comprehension of their meaning, and the projection of their status in the near future. The quality of situation awareness directly affects the decision making process for human soldiers in Military Operations on Urban Terrain (MOUT). It is therefore important to accurately model situation awareness in order to generate realistic tactical behaviors for the non-player characters (also known as bots) in MOUT simulations. This is a very challenging problem due to the time constraints in decision-making process and the heterogeneous cue types involved in MOUT. Although there are some theoretical models on situation awareness, they generally do not provide computational mechanisms suitable for MOUT simulations. In this paper, we propose a computational model of situation awareness for the bots in MOUT simulations. The computational model aims to form up situation awareness quickly with some key cues of the tactical situation. It is also designed to work together with some novel features that help to produce realistic tactical behaviors. These features include case-based reasoning, qualitative spatial representation and expectations. The effectiveness of the computational model is assessed with Twilight City, a virtual environment that we have built for MOUT simulations.

We present a modular evolutionary framework, coined CASE for "complex adaptive system evolver", to automate the modeling and analysis of agent-based simulations (ABSs). The field of agent-based modeling is rapidly growing due to its capabilities to expose the emerging complex phenomena occurring in a wide range of natural and artificial systems such as biological cells, societies, battlefields, stock markets, etc. Nevertheless, studying agent-based simulations is a complicated, interdisciplinary and time-consuming process. Indeed, a large number of simulation parameters has to be considered to identify and fully understand the conditions leading to the emerging phenomena of interest. To tackle this difficulty, the study of ABSs is thus typically conducted in an iterative manner, where each iteration includes the successive and manual modeling of ABSs and analysis of simulation outcomes. To automate this iterative and time-consuming process, we propose CASE, a platform-independent framework capable of evolving ABSs to exhibit the desired emerging behaviors. Through this evolutionary approach, the examination, i.e., the modeling, execution and analysis, of ABSs is automated. This process automation significantly facilitates the examination of complex systems using ABSs. In this paper, we present in detail this modular evolutionary framework which is illustrated with an example experiment. In this experiment, CASE is utilized for Automated Red Teaming, a simulation-based vulnerability assessment technique commonly employed by defense analysts. The aim of this paper is to introduce this flexible computational framework which may potentially benefit related fields involving agent-based simulations such as the gaming or financial industries.

We present an interactive framework for rapid 3D reconstruction of objects from sets of photographs. Our framework implements advanced 2D image-based modeling techniques that accurately compute geometries of digital photorealistic 3D models in a rapid and robust method. It is implemented in an application tool to assist users to perform feature detection, matching and meshing. Our system demonstrates that with a few user's intervention through our application tool, efficient generation of 3D object models can be generated from a set of photos.

An Interest Management (IM) mechanism eliminates irrelevant status updates transmitted in Networked Virtual Environments (NVE). However, IM itself involves both computation and communication overhead, of which the latter is the focus of this paper. Traditionally, there are area-based and cell-based IM mechanisms. This paper proposes a hybrid IM mechanism for peer-to-peer NVEs, that utilizes the cell-based mechanism to reduce Area-Of-Interest (AOI) updates in the area-based mechanism so as to reduce its communication overhead. To compare the new mechanism with the two traditional approaches, a multiplayer game scenario is simulated. The performance results show that, compared to the traditional mechanisms, the hybrid mechanism reduces the upload bandwidth consumption by more than 25.28 percent, reduces the overhead ratio from more than 67.54 percent to only 25.17 percent, and allows more than 5000 players in the Internet to join the same game with today's network upload bandwidth.

Abstract In this paper, we propose a framework for modeling lower‐level pedestrian navigational behaviors. We aim not only to generate realistic simulation results but also to make our framework flexible and extendible, and easy to use for model developers. A divide‐and‐conquer methodology is first adopted to divide the complex navigational behaviors into three levels, which allows us to focus on the intermediate level. We then propose a pattern‐based framework for modeling pedestrian navigational behavior at this level. In our framework, spatial‐temporal patterns are used to represent the situational perception, and a pattern‐matching mechanism is proposed to model the navigational choices of individual pedestrians. To demonstrate the effectiveness of our framework, a computational model is constructed to simulate pedestrian behaviors in a corridor with medium to relatively high density of pedestrians. Simulation results with this model are summarized in this paper. Copyright © 2010 John Wiley & Sons, Ltd.

The fundamental goal of distributed virtual environments (DVEs) is to create a common and consistent presentation of the virtual world among a set of computers interconnected by a network. This paper investigates update scheduling algorithms to make efficient use of network capacity and improve consistency in DVEs. Our approach is to schedule state updates according to their potential impacts on consistency. In DVEs, the perceptions of participants are affected by both the spatial magnitude and temporal duration of inconsistency in the virtual world. Using the metric of time-space inconsistency, we analytically derive the optimal update schedules for minimizing the impact of inconsistency. Based on the analysis, we propose a number of scheduling algorithms that integrate spatial and temporal factors. These algorithms also take into consideration the effect of network delays. The algorithms can be used on top of many existing mechanisms such as dead reckoning. Experimental results show that our proposed algorithms significantly outperform the intuitive algorithms that are based on spatial or temporal factors only.

Inspired by the philosophy of ancient Chinese Taoism, Xu's Bayesian ying-yang (BYY) learning technique performs clustering by harmonizing the training data (yang) with the solution (ying). In our previous work, the BYY learning technique was applied to a fuzzy cerebellar model articulation controller (FCMAC) to find the optimal fuzzy sets; however, this is not suitable for time series data analysis. To address this problem, we propose an incremental BYY learning technique in this paper, with the idea of sliding window and rule structure dynamic algorithms. Three contributions are made as a result of this research. First, an online expectation-maximization algorithm incorporated with the sliding window is proposed for the fuzzification phase. Second, the memory requirement is greatly reduced since the entire data set no longer needs to be obtained during the prediction process. Third, the rule structure dynamic algorithm with dynamically initializing, recruiting, and pruning rules relieves the "curse of dimensionality" problem that is inherent in the FCMAC. Because of these features, the experimental results of the benchmark data sets of currency exchange rates and Mackey-Glass show that the proposed model is more suitable for real-time streaming data analysis.

Abstract Generating realistic behaviors for the non‐player characters (also known as bots) is an important task for Military Operations on Urban Terrain (MOUT) simulations. Most of the current bots reasoning models are based on complete rational analysis of all alternatives. However, such models may not be adequate in time critical and uncertain situations. In such situations, due to time constraints and incomplete information, humans rely mainly on experience rather than some structured analysis of the given situation to make decisions. In our previous work, we have developed SNAP, a time critical decision‐making framework which aims to imitate human decision‐making processes for MOUT simulations. A major limitation that we found about SNAP is its difficulty in dealing with dynamic changes in MOUT simulations. In this paper, we propose to address this problem by incorporating a novel feature of expectations into SNAP. The effectiveness of this new feature is assessed with Twilight City, a virtual environment for MOUT simulations. Experimental results show that our expectation model works well in dealing with dynamic changes in MOUT. Copyright © 2009 John Wiley & Sons, Ltd.

In distributed virtual environments, e.g., online gaming, collaborative designs and distributed military simulations, interactivity is one of the most important requirements. The users may notice serious degradations in quality of service when interacting in the virtual world if the response from the system is much slower than what they have experienced in real life. In this paper, we consider the problem of placing distributed servers in the network to reduce client-server communication latencies, which is termed the server placement problem. We proposed two new network-aware placement algorithms which take into account users' locations in the network and connectivity at the autonomous system level to determine good sites for servers. Extensive experiments with realistic network models showed that these new algorithms significantly outperform existing approaches that require full knowledge of network connectivity at the router-level topologies.

Abstract Human crowd is a fascinating social phenomenon in nature. This paper presents our work on designing behavior model for virtual humans in a crowd simulation under normal‐life and emergency situations. Our model adopts an agent‐based approach and employs a layered framework to reflect the natural pattern of human‐like decision making process, which generally involves a person's awareness of the situation and consequent changes on the internal attributes. The social group and crowd‐related behaviors are modeled according to the findings and theories observed from social psychology (e.g., social attachment theory). By integrating our model into an agent execution process, each individual agent can response differently to the perceived environment and make realistic behavioral decisions based on various physiological, emotional, and social group attributes. To demonstrate the effectiveness of our model, a case study has been conducted, which shows that realistic human behaviors can be generated at both individual and group level. Copyright © 2008 John Wiley & Sons, Ltd.

Abstract Deliberative reasoning based on the rational analysis of various alternatives often requires too much information and may be too slow in time critical situations. In these situations, humans rely mainly on their intuitions rather than some structured decision‐making processes. An important and challenging problem in Military Operations on Urban Terrain (MOUT) simulations is how to generate realistic tactical behaviors for the non‐player characters (also known as bots), as these bots often need to make quick decisions in time‐critical and uncertain situations. In this paper, we describe our work on Snap, a time critical decision‐making framework for the bots in MOUT simulations. The novel features of Snap include case‐based reasoning (CBR) and thin slicing. CBR is used to make quick decisions by comparing the current situation with past experience cases. Thin slicing is used to model human's ability to quickly form up situation awareness under uncertain and complex situations using key cues from partial information. To assess the effectiveness of Snap, we have integrated it into Twilight City, a virtual environment for MOUT simulations. Experimental results show that Snap is very effective in generating quick decisions during time critical situations for MOUT simulations. Copyright © 2008 John Wiley & Sons, Ltd.

We have developed a novel collision avoidance model and shown how the model can be used as a basic building block to generate various behaviors for intelligent agents. The main difference between our model and other path planning models is that our model is concerned with the execution of a planned path in a dynamic environment rather than the planning of the path itself. As a result, our model reflects more closely the decision process of humans in collision avoidance. We argue that collision avoidance is in fact a highly complex social interaction between two or more agents, and that our model has the potential of creating a truly heterogenous population for realistic crowd simulations. We have also conducted some basic experiments on the model to investigate the resultant behaviors. The results show that the proposed model is effective in generating various human-like collision avoidance behaviours.

Abstract Autonomous navigation systems are important to Military Operations on Urbanized Terrain (MOUT) simulations for generating realistic tactical behaviours for the non‐player characters (or bots). In this paper, we describe our work on Quartz, an autonomous navigation system for MOUT simulations. Novel features of Quartz include qualitative spatial representation and hierarchical spatial reasoning which enables fast situation analysis and human‐like path planning in a dynamic environment. To assess the effectiveness of Quartz, we have integrated it into Twilight City, a virtual environment for MOUT simulations. Experimental results show that Quartz is very effective for quick tactical path generation in dynamic MOUT environments. Copyright © 2007 John Wiley & Sons, Ltd.

We investigate the causal order of events in distributed virtual environments (DVEs). We first define the critical causal order relation among the events. Then, we propose some mechanisms to enhance the prevalent RO (receive order delivery) mechanism in DVEs so that the real-time property of DVEs is preserved while the critical causal order violations are reduced. These mechanisms are implemented as a middleware. Experimental results show that the middleware performs well in reducing the critical causality violations in simulation and incurs little processing overhead.

Distributed virtual environments (DVEs) allow many simultaneous human users to interact with each other in shared, 3D virtual worlds. The interactivity of a DVE is of crucial importance, as its success greatly depends on users' perceptions when interacting within the virtual world. In this paper, different from traditional application-centric approaches like dead reckoning, we propose a network-centric approach to enhance the interactivity of DVEs by directly reducing the network latencies in client-server communications. We consider a key problem with this approach, termed the server placement problem. Generally, this problem concerns how to place servers in the network to reduce client-server communication latencies. We then suggest several degree-based server placement approaches. Extensive simulation studies using realistic models have shown that appropriate server placement is very effective in enhancing the interactivity of large-scale DVEs

Considerable efforts are needed to construct a virtual environment for Military Operations on Urbanized Terrain (MOUT). The models of various objects used in one application are often specifically designed for that application, thus are not generally suitable for other applications. We often have to rebuild many object models for a new simulation setup, which is very tedious and time consuming. To reduce the cost and time in building up different simulation environments, it is therefore highly desirable to have a repository of object models that are interoperable and extensible thus can be reused in different applications. To this end, we propose a generic model framework which supports model interoperability and reusability for MOUT simulations. Our experiences with Twilight City, a virtual training environment for MOUT, show that the proposed framework is successful in creating a repository of models for various simulation setups.

Distributed virtual environments (DVEs) are distributed systems that allow multiple geographically distributed clients (users) to interact simultaneously in a computer-generated, shared virtual world. Applications of DVEs can be seen in many areas nowadays, such as online games, military simulations, collaborative designs, etc. To support large-scale DVEs with real-time interactions among thousands or more distributed clients, a geographically distributed server architecture (GDSA) is generally needed, and the virtual world can be partitioned into many distinct zones to distribute the load among the servers. Due to the geographic distributions of clients and servers in such architectures, it is essential to efficiently assign the participating clients to servers to enhance users' experience in interacting within the DVE. This problem is termed the client assignment problem. In this paper, we propose a two-phase approach, consisting of an initial assignment phase and a refined assignment phase to address this problem. Both phases are shown to be NP-hard, and several heuristic assignment algorithms are then devised based on this two-phase approach. Via extensive simulation studies with realistic settings, we evaluate these algorithms in terms of their performances in enhancing interactivity of the DVE.

Collaborative document repository manager (CDRM) is one of the three key components in a collaborative editing system. It provides a repository for storing shared documents and a portal for managing shared documents, initiating collaborative editing sessions, and viewing session information. While most collaborative systems have been using ad hoc and tightly-coupled CDRMs, our work is to provide a generic WebDAV-based CDRM to take advantage of WebDAV's ubiquitous accessibility, directly writable medium, and access transparency. This WebDAV-based CDRM has been deployed into the CoOffice (collaborative office) and the CoStarOffice (collaborative staroffice) systems for evaluation, demonstration, and usability study

A distributed virtual environment (DVE) enables geographically distributed clients to interact with each other in a simulated environment. Due to the distributed architecture of DVEs, it is generally not easy to evaluate the performance of DVEs. In this paper, we propose a consistency model based on a metric called time-space inconsistency. The model relates a human participant's perception to the characteristic parameters of a DVE. Based on the model, the performance of a DVE can be easily evaluated without the actual execution of the DVE application, which is especially useful in the designing stage of a DVE. A ping-pong game is developed to verify the proposed model. Experiment results show that the model is effective in evaluating the performance of the game.