Single-user interactive computer applications are pervasive in our daily lives and work. Leveraging single-user applications for supporting multi-user collaboration has the potential to significantly increase the availability and improve the usability of collaborative applications. In this article, we report an innovative Transparent Adaptation (TA) approach and associated supporting techniques that can be used to convert existing and new single-user applications into collaborative ones, without changing the source code of the original application. The cornerstone of the TA approach is the operational transformation (OT) technique and the method of adapting the single-user application programming interface to the data and operation models of OT. This approach and supporting techniques were developed and tested in the process of transparently converting two commercial off-the-shelf single-user applications (Microsoft Word and PowerPoint) into real-time collaborative applications, called CoWord and CoPowerPoint, respectively. CoWord and CoPowerPoint not only retain the functionalities and “look-and-feel” of their single-user counterparts, but also provide advanced multi-user collaboration capabilities for supporting multiple interaction paradigms, ranging from concurrent and free interaction to sequential and synchronized interaction, and for supporting detailed workspace awareness, including multi-user telepointers and radar views. The TA approach and generic collaboration engine software component developed from this work are potentially applicable and reusable in adapting a wide range of single-user applications.

RightsACM allow an authors' version of their own ACMcopyrighted work on their personal server or on servers belonging to their employers. As a collective and highly dynamic social group, human crowd is a fascinating phenomenon which has been constantly concerned by experts from various areas. Recently, computer-based modeling and simulation technologies have emerged to support investigation of the dynamics of crowds, such as a crowd's behaviors under normal and emergent situations. This paper assesses the major existing technologies for crowd modeling and simulation. We first propose a two-dimensional categorization mechanism to classify existing work depending on the size of crowds and the timescale of the crowd phenomena of interest. Four evaluation criteria have also been introduced to evaluate existing crowd simulation systems from the point of view of both a modeler and an end-user. We have discussed some influential existing work in crowd modeling and simulation regarding their major features, performance as well as the technologies used in these work. We have also discussed some open problems in the area. This paper will provide the researchers with useful information and insights on the state-of-the-art of the technologies in crowd modeling and simulation as well as future research directions.

Maintaining a consistent view of the simulated world among different simulation nodes is a fundamental problem in large-scale distributed virtual environments (DVEs). In this paper, we characterize this problem by quantifying the time-space inconsistency in a DVE. To this end, a metric is defined to measure the time-space inconsistency in a DVE. One major advantage of the metric is that it may be estimated based on some characteristic parameters of a DVE, such as clock asynchrony, message transmission delay, the accuracy of the dead reckoning algorithm, the kinetics of the moving entity, and human factors. Thus the metric can be used to evaluate the time-space consistency property of a DVE without the actual execution of the DVE application, which is especially useful in the design stage of a DVE. Our work also clearly shows how the characteristic parameters of a DVE are interrelated in deciding the time-space inconsistency, so that we may fine-tune the DVE to make it as consistent as possible. To verify the effectiveness of the metric, a Ping-Pong game is developed. Experimental results show that the metric is effective in evaluating the time-space consistency property of the game.

Genetic programming (GP) is a powerful evolutionary algorithm that has been widely used for solving many real-world optimization problems. However, traditional GP can only solve a single task in one independent run, which is inefficient in cases where multiple tasks need to be solved at the same time. Recently, multifactorial optimization (MFO) has been proposed as a new evolutionary paradigm toward evolutionary multitasking. It intends to conduct evolutionary search on multiple tasks in one independent run. To enable multitasking GP, in this paper, we propose a novel multifactorial GP (MFGP) algorithm. To the best of our knowledge, this is the first attempt in the literature to conduct multitasking GP using a single population. The proposed MFGP consists of a novel scalable chromosome encoding scheme which is capable of representing multiple solutions simultaneously, and new evolutionary mechanisms for MFO based on self-learning gene expression programming. Further, comprehensive experimental studies are conducted on multitask scenarios consisting of commonly used GP benchmark problems and real world applications. The obtained empirical results confirmed the efficacy of the proposed MFGP. Index Terms-Genetic programming (GP), multifactorial evolutionary algorithm (MFEA), multifactorial optimization (MFO), multitask learning (MTL), symbolic regression problem (SRP). I. INTRODUCTION G ENETIC programming (GP), which was first proposed by Cramer [1], is a powerful population-based evolutionary algorithm for solving user-defined tasks by automatic generation of computer programs [2]. In the past few years,

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.