Player behavior and traffic characterization for MMORPGs: a survey

Massively Multiplayer Online Role-Playing Games (MMORPGs) are one of the most exciting and most rapidly expanding genres of online games. This survey presents state-of-the-art and current research efforts in measuring, characterizing, and modeling the player behavior and the network traffic, as well as the relationships between them. We also tabulate the measurement, modeling and analysis results regarding network traffic and player behavior found in literature, with key information regarding each dataset. Finally, we list currently confirmed claims and open research questions in the area of MMORPGs.     

How Cancer Cells Invade Bladder Epithelium and Form Tumors: The Mouse Bladder Tumor Model as a Model of Tumor Recurrence in Patients

Non-muscle-invasive bladder cancer is the most common form of bladder cancer. The main problem in managing bladder tumors is the high recurrence after the transurethral resection of bladder tumors (TURBT). Our study aimed to examine the fate of intravesically applied cancer cells as the implantation of cancer cells after TURBT is thought to be a cause of tumor recurrence. We established an orthotopic mouse bladder tumor model with MB49-GFP cancer cells and traced them during the first three days to define their location and contacts with normal urothelial cells. Data were obtained by Western blot, immunolabeling, and light and electron microscopy. We showed that within the first two hours, applied cancer cells adhered to the traumatized epithelium by cell projections containing α3β1 integrin on their tips. Cancer cells then migrated through the epithelium and on day 3, they reached the basal lamina or even penetrated it. In established bladder tumors, E-cadherin and desmoplakin 1/2 were shown as feasible immunohistochemical markers of tumor margins based on the immunolabeling of various junctional proteins. Altogether, these results for the first time illustrate cancer cell implantation in vivo mimicking cellular events of tumor recurrence in bladder cancer patients.     

Stationary and Progressive Phenotypes Caused by the p.G90D Mutation in Rhodopsin Gene

Mutations in rhodopsin gene (RHO) are a frequent cause of retinitis pigmentosa (RP) and less often, congenital stationary night blindness (CSNB). Mutation p.G90D has previously been associated with CSNB based on the examination of one family. This study screened 60 patients. Out of these 60 patients, 32 were affected and a full characterization was conducted in 15 patients. We described the clinical characteristics of these 15 patients (12 male, median age 42 years, range 8–71) from three families including visual field (Campus Goldmann), fundus autofluorescence (FAF), optical coherence tomography (OCT) and electrophysiology. Phenotypes were classified into four categories: CSNB (N = 3, 20%) sector RP (N = 3, 20%), pericentral RP (N = 1, 6.7%) and classic RP (N = 8, 53.3% (8/15)). The phenotypes were not associated with family, sex or age (Kruskal–Wallis, p > 0.05), however, cystoid macular edema (CME) was observed only in one family. Among the subjects reporting nyctalopia, 69% (22/32) were male. The clinical characteristics of the largest p.G90D cohort so far showed a large frequency of progressive retinal degeneration with 53.3% developing RP, contrary to the previous report.     

Online learning and fusion of orientation appearance models for robust rigid object tracking

We introduce a robust framework for learning and fusing of orientation appearance models based on both texture and depth information for rigid object tracking. Our framework fuses data obtained from a standard visual camera and dense depth maps obtained by low-cost consumer depth cameras such as the Kinect. To combine these two completely different modalities, we propose to use features that do not depend on the data representation: angles. More specifically, our framework combines image gradient orientations as extracted from intensity images with the directions of surface normals computed from dense depth fields. We propose to capture the correlations between the obtained orientation appearance models using a fusion approach motivated by the original Active Appearance Models (AAMs). To incorporate these features in a learning framework, we use a robust kernel based on the Euler representation of angles which does not require off-line training, and can be efficiently implemented online. The robustness of learning from orientation appearance models is presented both theoretically and experimentally in this work. This kernel enables us to cope with gross measurement errors, missing data as well as other typical problems such as illumination changes and occlusions. By combining the proposed models with a particle filter, the proposed framework was used for performing 2D plus 3D rigid object tracking, achieving robust performance in very difficult tracking scenarios including extreme pose variations.     

MMORPG Player actions: Network performance, session patterns and latency requirements analysis

Providing Massively Multiplayer Online Role-Playing Games (MMORPGs) is a big challenge for future mobile, IP-based networks. Understanding how the players’ actions affect the network parameters, the game platform, and the overall perceived quality is highly relevant for the purposes of game design, as well as for the networking infrastructure and network support for games. We break player actions down into discrete categories, and show that each category is distinct in terms of several key metrics. We discuss which categories of actions could be supported on current mobile devices, and present evidence in form of a user survey demonstrating the demand for such services. The starting points into the discussion include the networking, session and latency requirements for particular player actions on one side, and the players’ interest on the other. The Blizzard Entertainment’s World of Warcraft (WoW) is used as a case study.     

Doing a Good Job—the Effect of Primary Task Quality on Well‐Being and Job Satisfaction

This paper investigates whether employees’ assessment of their primary task quality has a significant impact on their well‐being and job satisfaction, respectively. Furthermore, the paper hypothesizes that professional values and norms affect employees’ quality expectations on their work tasks and thus their assessment of primary task quality. The paper proposes a measure for primary task quality and uses it in the analyses of responses from 1,247 preschool teachers and teaching assistants in 94 public daycare centers in Denmark. The results indicate that an important factor for employees is their experience of quality in the job they perform. Moreover, quality expectations can differ between employees performing the same task due to, for example, difference in professional training. This leads us to propose a new research direction for job design theory that addresses employees’ assessment of the quality of their primary task job performance.     

Maximizing Reward in a Non-Stationary Mobile Robot Environment

The ability of a robot to improve its performance on a task can be critical, especially in poorly known and non-stationary environments where the best action or strategy is dependent upon the current state of the environment. In such systems, a good estimate of the current state of the environment is key to establishing high performance, however quantified. In this paper, we present an approach to state estimation in poorly known and non-stationary mobile robot environments, focusing on its application to a mine collection scenario, where performance is quantified using reward maximization. The approach is based on the use of augmented Markov models (AMMs), a sub-class of semi-Markov processes. We have developed an algorithm for incrementally constructing arbitrary-order AMMs on-line. It is used to capture the interaction dynamics between a robot and its environment in terms of behavior sequences executed during the performance of a task. For the purposes of reward maximization in a non-stationary environment, multiple AMMs monitor events at different timescales and provide statistics used to select the AMM likely to have a good estimate of the environmental state. AMMs with redundant or outdated information are discarded, while attempting to maintain sufficient data to reduce conformation to noise. This approach has been successfully implemented on a mobile robot performing a mine collection task. In the context of this task, we first present experimental results validating our reward maximization performance criterion. We then incorporate our algorithm for state estimation using multiple AMMs, allowing the robot to select appropriate actions based on the estimated state of the environment. The approach is tested first with a physical robot, in a non-stationary environment with an abrupt change, then with a simulation, in a gradually shifting environment.     

WebFlow – a visual programming paradigm for Web/Java based coarse grain distributed computing

We present here recent work at NPAC aimed at developing WebFlow – a general purpose Web-based visual interactive programming environment for coarse grain distributed computing. We follow the 3-tier architecture with the central control and integration WebVM layer in tier-2, interacting with the visual graph editor applets in tier-1 (front-end) and the legacy systems in tier-3. WebVM is given by a mesh of Java Web servers such as Jeeves from JavaSoft or Jigsaw from MIT/W3C. All system control structures are implemented as URL-addressable servlets which enable Web browser-based authoring, monitoring, publication, documentation and software distribution tools for distributed computing. We view WebFlow/WEbVM as a promising programming paradigm and co-ordination model for the exploding volume of Web/Java software, and we illustrate it in a set of ongoing application development activities. © 1997 John Wiley & Sons, Ltd.     

Distributed Computation Multi-agent System

This article addresses a formal model of a distributed computation multi-agent system. This model has evolved from the experimental research on using multi-agent systems as a ground for developing fuzzy cognitive maps. The main paper contribution is a distributed computation multi-agent system definition and mathematical formalization based on automata theory. This mathematical formalization is tested by developing distributed computation multi-agent systems for fuzzy cognitive maps and artificial neural networks – two typical distributed computation systems. Fuzzy cognitive maps are distributed computation systems used for qualitative modeling and behavior simulation, while artificial neural networks are used for modeling and simulating complex systems by creating a non-linear statistical data model. An artificial neural network encapsulates in its structure data patterns that are hidden in the data used to create the network. Both of these systems are well suited for formal model testing. We have used evolutionary incremental development as an agent design method which has shown to be a good approach to develop multi-agent systems according to the formal model of a distributed computation multi-agent system.