An Analytical Study of Puzzle Selection Strategies for the ESP Game
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“Human Computation” represents a new paradigm of applications that take advantage of people’s desire to be entertained and produce useful metadata as a by-product. By creating games with a purpose, human computation has shown promise in solving a variety of problems that computer computation cannot currently resolve completely. Using the ESP game as an example, we propose a metric, called system gain, for evaluating the performance of human computation systems, and also use analysis to study the properties of the ESP game. We argue that human computation systems should be played with a strategy. To this end, we implement an Optimal Puzzle Selection Strategy (OPSA) based on our analysis to improve human computation. Using a comprehensive set of simulations, we demonstrate that the proposed OPSA approach can effectively improve the system gain of the ESP game, as long as the number of puzzles in the system is sufficiently large.
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We conduct real experiments to quantify user satisfaction in mobile cloud games using a real cloud gaming system built on the open-sourced GamingAnywhere. We share our experiences in porting GamingAnywhere client to Android OS and perform extensive experiments on both the mobile and desktop clients. The experiment results reveal several new insights: (1) gamers are more satisfied with the graphics quality on mobile devices, while they are more satisfied with the control quality on desktops, (2) the bitrate, frame rate, and network delay significantly affect the graphics and smoothness quality, and (3) the control quality only depends on the client type (mobile versus desktop). To the best of our knowledge, such user studies have never been done in the literature.
On The Battle between Online Gamers and Lags
Although online games have been an important Internet activity today, players inevitably suffer from lag from time to time due to the Internet’s non-QoS-guaranteed architecture. Here by lag we refer to the phenomena when a game fails to respond to user commands or update the screen in a timely fashion due to long system processing or network delays. Currently, little is known about how game players feel about lag and how they react when encountering lag during game play.
In this paper, we present an Internet survey that is designed to understand the following questions: 1) How do players perceive lag, 2) what do players think of the causes of lag, and 3) how do players react to lag. Our results show that game players often struggle with lag, because they are unable to identify the root cause. Therefore, they have to try any combination of possible solutions found on the Internet, blame game companies, or learn to cope. These findings manifest a strong demand for an automatic diagnostic tool that can identify the root cause of lag for gamers.
Understanding The Performance of Thin-Client Gaming
The thin-client model is considered a perfect fit for online gaming. As modern games normally require tremendous computing and rendering power at the game client, deploying games with such models can transfer the burden of hardware upgrades from players to game operators. As a result, there are a variety of solutions proposed for thin-client gaming today. However, little is known about the performance of such thinclient systems in different scenarios, and there is no systematic means yet to conduct such analysis.
In this paper, we propose a methodology for quantifying the performance of thin-clients on gaming, even for thin-clients which are close-sourced. Taking a classic game, Ms. Pac-Man, and three popular thin-clients, LogMeIn, TeamViewer, and UltraVNC, as examples, we perform a demonstration study and determine that 1) display frame rate and frame distortion are both critical to gaming; and 2) different thin-client implementations may have very different levels of robustness against network impairments. Generally, LogMeIn performs best when network conditions are reasonably good, while TeamViewer and UltraVNC are the better choices under certain network conditions.
Quantifying QoS Requirements of Network Services: A Cheat-Proof Framework
Despite all the efforts devoted to improving the QoS of networked multimedia services, the baseline for such improvements has yet to be defined. In other words, although it is well recognized that better network conditions generally yield better service quality, the exact minimum level of network QoS required to ensure satisfactory user experience remains an open question.
In this paper, we propose a general, cheat-proof framework that enables researchers to systematically quantify the minimum QoS needs for real-time networked multimedia services. Our framework has two major features: 1) it measures the quality of a service that users find intolerable by intuitive responses and therefore reduces the burden on experiment participants; and 2) it is cheat-proof because it supports systematic verification of the participants' inputs. Via a pilot study involving 38 participants, we verify the efficacy of our framework by proving that even inexperienced participants can easily produce consistent judgments. In addition, by cross-application and cross-service comparative analysis, we demonstrate the usefulness of the derived QoS thresholds. Such knowledge will serve important reference in the evaluation of competitive applications, application recommendation, network planning, and resource arbitration.
Online Game QoE Evaluation using Paired Comparisons
To satisfy players' gaming experience, there is a strong need for a technique that can measure a game's quality systemically, efficiently, and reliably. In this paper, we propose to use paired comparisons and probabilistic choice models to quantify online games's QoE under various network situations. The advantages of our methodology over the traditional MOS ratings are 1) the rating procedure is simpler thus less burden is on experiment participants, 2) it derives ratio-scale scores, and 3) it enables systematic verification of participants' inputs.
As a demonstration, we apply our methodology to evaluate three popular FPS (first-person-shooter) games, namely, Alien Arena (Alien), Halo, and Unreal Tournament (UT), and investigate their network robustness. The results indicate that Halo performs the best in terms of their network robustness against packet delay and loss. However, if we take the degree of the games' sophistication into account, we consider that the robustness of UT against downlink delays should able be improved. We also show that our methodology can be a helpful tool for making decisions about design alternatives, such how dead reckoning algorithms and time synchronization mechanisms should be implemented.
GamingAnywhere: An Open Cloud Gaming System
Cloud gaming is a promising application of the rapidly expanding cloud computing infrastructure. Existing cloud gaming systems, however, are closed-source with proprietary protocols, which raises the bars to setting up testbeds for experiencing cloud games. In this paper, we present a complete cloud gaming system, called GamingAnywhere, which is to the best of our knowledge the first open cloud gaming system. In addition to its openness, we design GamingAnywhere for high extensibility, portability, and reconfigurability. We implement GamingAnywhere on Windows, Linux, and OS X, while its client can be readily ported to other OS's, including iOS and Android. We conduct extensive experiments to evaluate the performance of GamingAnywhere, and compare it against two well-known cloud gaming systems: OnLive and StreamMyGame. Our experimental results indicate that GamingAnywhere is efficient and provides high responsiveness and video quality. For example, GamingAnywhere yields a per-frame processing delay of 34 ms, which is 3+ and 10+ times shorter than OnLive and StreamMyGame, respectively. Our experiments also reveal that all these performance gains are achieved without the expense of higher network loads; in fact, GamingAnywhere incurs less network traffic. The proposed GamingAnywhere can be employed by the researchers, game developers, service providers, and end users for setting up cloud gaming testbeds, which, we believe, will stimulate more research innovations on cloud gaming systems.
GamingAnywhere is now publicly available at http://gaminganywhere.org.
Are All Games Equally Cloud-Gaming-Friendly? An Electromyographic Approach
Cloud gaming makes any computer game playable on a thin client without worrying the hardware requirements as before. It frees players from the need to constantly upgrade their computers as they can now play games that host on remote servers with a broadband Internet connection and a thin client. However, cloud games are intrinsically more susceptible to latency than online games because game graphics are rendered on server nodes and thin clients do not possess game state information that is required by delay compensation techniques.
In this paper, we investigate how the response latency would affect users' experience when playing games on clouds and how the impact of latency on players' experience varies across different games. We show that not all games are equally friendly to cloud gaming. The same degree of latency may have very different impact on a game's quality of experience depending on the game's real-time strictness. We thus develop a model that can predict a game's real-time strictness based on the rate of players' inputs and game screen dynamics. The model can be used to enhance players' experience in cloud gaming and optimize data center operation cost simultaneously.
Forecasting Online Game Addictiveness
1) The document discusses predicting the addictiveness and lifetime of online games using player behavior data and facial electromyography measurements of emotions.
2) An addictiveness index is defined based on the decline rate of the ratio of player presence over time, and facial muscle activity is measured to quantify emotional responses to different games.
3) A model is created relating emotional strength measurements to addictiveness indexes with 94% accuracy, and is validated with 11% average error rate at predicting addictiveness of unseen games. This could help optimize game development and investment decisions.
Identifying MMORPG Bots: A Traffic Analysis Approach
The document proposes four strategies to detect bots in MMORPG games by analyzing network traffic patterns without intruding on players' experiences. It describes collecting network traces from both human players and bots in Ragnarok Online. The strategies analyze timing of commands, burstiness of traffic over time, and compare client and server traffic burstiness to identify patterns characteristic of bot behavior, such as highly regular response times and smoother traffic patterns than humans. A decision framework determines if a traffic stream indicates bot control based on these strategies.
Toward an Understanding of the Processing Delay of Peer-to-Peer Relay Nodes
Peer-to-peer relaying is commonly used in realtime applications to cope with NAT and firewall restrictions and provide better quality network paths. As relaying is not natively supported by the Internet, it is usually implemented at the application layer. Also, in a modern operating system, the processor is shared, so the receive-process-forward process for each relay packet may take a considerable amount of time if the host is busy handling some other tasks. Thus, if we happen to select a loaded relay node, the relaying may introduce significant delays to the packet transmission time and even degrade the application performance.
In this work, based on an extensive set of Internet traces, we pursue an understanding of the processing delays incurred at relay nodes and their impact on the application performance. Our contribution is three-fold: 1) we propose a methodology for measuring the processing delays at any relay node on the Internet; 2) we characterize the workload patterns of a variety of Internet relay nodes; and 3) we show that, serious VoIP quality degradation may occur due to relay processing, thus we have to monitor the processing delays of a relay node continuously to prevent the application performance from being degraded.
Inferring Speech Activity from Encrypted Skype Traffic
Normally, voice activity detection (VAD) refers to speech processing algorithms for detecting the presence or absence of human speech in segments of audio signals. In this paper, however, we focus on speech detection algorithms that take VoIP traffic instead of audio signals as input. We call this category of algorithms network-level VAD.
Traditional VAD usually plays a fundamental role in speech processing systems because of its ability to delimit speech segments. Network-level VAD, on the other hand, can be quite helpful in network management, which is the motivation for our study. We propose the first real-time network-level VAD algorithm that can extract voice activity from encrypted and non-silence-suppressed Skype traffic. We evaluate the speech detection accuracy of the proposed algorithm with extensive reallife traces. The results show that our scheme achieve reasonably good performance even high degree of randomness has been injected into the network traffic.
Game Bot Detection Based on Avatar Trajectory
In recent years, online gaming has become one of the most popular Internet activities, but cheating activity, such as the use of game bots, has increased as a consequence. Generally, the gaming community disagrees with the use of game bots, as bot users obtain unreasonable rewards without corresponding efforts. However, bots are hard to detect because they are designed to simulate human game playing behavior and they follow game rules exactly. Existing detection approaches either interrupt the players’gaming experience, or they assume game bots are run as standalone clients or assigned a specific goal, such as aim bots in FPS games.
In this paper, we propose a trajectory-based approach to detect game bots. It is a general technique that can be applied to any game in which the avatar’s movement is controlled directly by the players. Through real-life data traces, we show that the trajectories of human players and those of game bots are very different. In addition, although game bots may endeavor to simulate players’ decisions, certain human behavior patterns are difficult to mimic because they are AI-hard. Taking Quake 2 as a case study, we evaluate our scheme’s performance based on reallife traces. The results show that the scheme can achieve a detection accuracy of 95% or higher given a trace of 200 seconds or longer.
A Collusion-Resistant Automation Scheme for Social Moderation Systems
This document summarizes a research paper on developing a collusion-resistant automation scheme for social moderation systems. The researchers propose a community-based scheme that analyzes accusation relations between users to distinguish genuine accusations from malicious ones made by colluders. It clusters users based on incoming and outgoing accusation features to label misbehaving users. Simulation results show the scheme achieves over 90% accuracy in identifying misbehaving users and prevents 90% of victims from false accusations due to collusion. The community-based approach improves over simple count-based schemes that are vulnerable to collusion attacks.
Tuning Skype’s Redundancy Control Algorithm for User Satisfaction
Determining how to transport delay-sensitive voice data has long been a problem in multimedia networking. The difficulty arises because voice and best-effort data are different by nature. It would not be fair to give priority to voice traffic and starve its best-effort counterpart; however, the voice data delivered might not be perceptible if each voice call is limited to the rate of an average TCP flow. To address the problem, we approach it from a user-centric perspective by tuning the voice data rate based on user satisfaction.
Our contribution in this work is threefold. First, we investigate how Skype, the largest and fastest growing VoIP service on the Internet, adapts its voice data rate (i.e., the redundancy ratio) to network conditions. Second, by exploiting implementations of public domain codecs, we discover that Skype’s mechanism is not really geared to user satisfaction. Third, based on a set of systematic experiments that quantify user satisfaction under different levels of packet loss and burstiness, we derive a concise model that allows user-centric redundancy control. The model can be easily incorporated into general VoIP services (not only Skype) to ensure consistent user satisfaction.
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An Analytical Study of Puzzle Selection Strategies for the ESP Game
- 1. An Analytical Study of Puzzle Selection Strategies for the ESP Game Ling-Jyh Chen, Bo-Chun Wang, Kuan-Ta Chen Academia Sinica Irwin King, and Jimmy Lee The Chinese University of Hong Kong
- 2. How the ESP Game Works? PLAYER 1 PLAYER 2 GUESSING: CAR GUESSING: BOY GUESSING: HAT GUESSING: CAR GUESSING: KID SUCCESS! SUCCESS! YOU AGREE ON CAR YOU AGREE ON CAR
- 3. Why is it Important? • Some statistics (July 2008) – 200,000+ players have contributed 50+ million labels. – Each player plays for a total of 91 minutes. – The throughput is about 233 labels/player/hour (i.e., one label every 15 seconds)
- 4. The Ideas behind the ESP Game • “Human Computation” represents a new paradigm of applications. – solve some problems which are difficult to be solved by computers • “Games With A Purpose” (GWAP) – by Dr. Luis von Ahn (CMU) – take advantage of people's desire to be entertained – produce useful metadata as a by-product
- 5. Our Contributions • We propose an evaluation metric for human computation systems. • We study the inner properties of the ESP game using analysis. • We propose an “Optimal Puzzle Selection Algorithm” (OPSA). • We present a comprehensive set of simulation results. • Our model is easy and applicable to other ESP-like systems.
- 6. The Main Ideas • There are two goals : – the system prefers to maximize the number of puzzles which have been played – the system prefers to take as many labels as possible for each puzzle • Unfortunately, there is a trade-off between the two goals.
- 7. The Proposed Metric System Gain ( G ) = ln ( N ) × ln ( S N ) the number of the puzzles thatsum ofbeen played labels the score average scores per puzzle the have all produced T T S r= ⇒N= = E[S] × r N N r r : each puzzle should be played T : total played rounds how many times in average r : each puzzle should be played E[S] : the expected score value of how many times in average each label
- 8. The Proposed Metric System Gain ( G ) = ln ( N ) × ln ( S N ) = l n (T r ) × l n ( E [ S ] × r ) l n ( T ) − l n ( E [ S ]) ⎞ 2 ⎛ = − ⎜ ln ( r ) − ⎟ +C ⎝ 2 ⎠ ln (T ) − ln ( E [ S ]) Maximum G = C , when r = e 2
- 9. The Proposed Metric Maximum G ln (T ) − ln ( E [ S ]) r=e 2
- 10. Puzzle Selection Algorithms • Optimal Puzzle Selection Algorithm (OPSA) – select a puzzle based on our analysis • Random Puzzle Selection Algorithm (RPSA) – select a puzzle by random • Fresh-first Puzzle Selection Algorithm (FPSA) – select a puzzle that has been played least frequently
- 11. Evaluation • Using Monte Carlo Simulations • # of rounds: T = 10,000 • # of puzzles: 100 <= M <= 5,000
- 12. Conclusion • We have proposed a metric to evaluate the performance of GWAP. • We argue that GWAP needs to be “played with a strategy”. • We propose the Optimal Puzzle Selection Algorithm (OPSA) for ESP-like games.