In Contention Resolution Diversity Slotted ALOHA (CRDSA) and Irregular Repetition Slotted ALOHA (IRSA), iterative decoding is typically assumed to be instantaneous, overlooking the decoding latency encountered in practical systems. This paper proposes a Partially Parallel Decoding ($\textsf {PPD}$) framework that offers a tunable trade-off between latency, complexity, and throughput. The proposed framework supports both CRDSA and IRSA with AWGN and Rayleigh fading by incorporating, in each iteration of the decoding process, a scheduling algorithm that selects target packets based on the number of available decoders, along with a slot selection algorithm that identifies a subset of time slots for the equalization step to manage complexity. Simulation results show that in both AWGN and fading environments, the PPD framework achieves performance close to that of sequential decoding while significantly reducing latency—by up to$64\times $for CRDSA and$16\times $for IRSA—with a practical number of decoders.

Few-shot learning (FSL) addresses learning tasks in which only few samples are available for selected object categories. In this paper, we propose a deep learning framework for data hallucination, which overcomes the above limitation and alleviate possible overfitting problems. In particular, our method exploits semantic information into the hallucination process, and thus the augmented data would be able to exhibit semantics-oriented modes of variation for improved FSL performances. Very promising performances on CIFAR-100 and AwA datasets confirm the effectiveness of our proposed method for FSL.

With the growing recognition of both efficiency and security issues in machine learning models, we propose a novel convolutional neural networks (CNNs) training algorithm, called channel prioritization and path ensemble (CPPE), to not only allow dynamically trade-offs between different resource and performance requirements but also enable secure inference without any extra computational cost or memory overhead. Our approach not only prioritizes channels to prune the network in a structured way and ensemble multiple inference paths over different utilization conditions. We demonstrated the effectiveness of channel prioritization by the experiment of the VGG-16 network on various benchmark datasets. The experimental results show that, on the CIFAR-10 dataset, a 10× parameters reduction and a 4× FLOPs reduction can be achieved, with only a 0.2% accuracy drop. Furthermore, we allow CNNs to dynamically trade-offs between resource demand and accuracy with only 4% degradation in accuracy in exchange for 16× FLOPs reduction. By ensembling multiple inference paths, our model can improve robustness against various adversarial attacks without any additional computational cost and memory overhead. Finally, our method is simple and easily applied to any convolutioanl neural networks.

Objective: The aim of this study was to generate synthetic electronic health records (EHRs). The generated EHR data will be more realistic than those generated using the existing medical Generative Adversarial Network (medGAN) method. Materials and Methods: We modified medGAN to obtain two synthetic data generation models-designated as medical Wasserstein GAN with gradient penalty (medWGAN) and medical boundary-seeking GAN (medBGAN)-and compared the results obtained using the three models. We used 2 databases: MIMIC-III and National Health Insurance Research Database (NHIRD), Taiwan. First, we trained the models and generated synthetic EHRs by using these three 3 models. We then analyzed and compared the models' performance by using a few statistical methods (Kolmogorov-Smirnov test, dimension-wise probability for binary data, and dimension-wise average count for count data) and 2 machine learning tasks (association rule mining and prediction). Results: We conducted a comprehensive analysis and found our models were adequately efficient for generating synthetic EHR data. The proposed models outperformed medGAN in all cases, and among the 3 models, boundary-seeking GAN (medBGAN) performed the best. Discussion: To generate realistic synthetic EHR data, the proposed models will be effective in the medical industry and related research from the viewpoint of providing better services. Moreover, they will eliminate barriers including limited access to EHR data and thus accelerate research on medical informatics. Conclusion: The proposed models can adequately learn the data distribution of real EHRs and efficiently generate realistic synthetic EHRs. The results show the superiority of our models over the existing model.

Although live video communication is widely used, it is generally less engaging than face-to-face communication because of limitations on social, emotional, and haptic feedback. Missing eye contact is one such problem caused by the physical deviation between the screen and camera on a device. Manipulating video frames to correct eye gaze is a solution to this problem. In this article, we introduce a system to rotate the eyeball of a local participant before the video frame is sent to the remote side. It adopts a warping-based convolutional neural network to relocate pixels in eye regions. To improve visual quality, we minimize the L2 distance between the ground truths and warped eyes. We also present several newly designed loss functions to help network training. These new loss functions are designed to preserve the shape of eye structures and minimize color changes around the periphery of eye regions. To evaluate the presented network and loss functions, we objectively and subjectively compared results generated by our system and the state-of-the-art, DeepWarp, in relation to two datasets. The experimental results demonstrated the effectiveness of our system. In addition, we showed that our system can perform eye-gaze correction in real time on a consumer-level laptop. Because of the quality and efficiency of the system, gaze correction by postprocessing through this system is a feasible solution to the problem of missing eye contact in video communication.

Retaining eye contact of remote users is a critical issue in video conferencing systems because of parallax caused by the physical distance between a screen and a camera. To achieve this objective, we present a real-time gaze redirection system called Flx-gaze to post-process each video frame before sending it to the remote end. Specifically, we relocate and relight the pixels representing eyes by using a convolutional neural network (CNN). To prevent visual artifacts during manipulation, we minimize not only the L2 loss function but also four novel loss functions when training the network. Two of them retain the rigidity of eyeballs and eyelids; and the other two prevent color discontinuity on the eye peripheries. By leveraging the CPU and the GPU resources, our implementation achieves real-time performance (i.e., 31 frames per second). Experimental results show that the gazes redirected by our system are of high quality under this restrict time constraint. We also conducted an objective evaluation of our system by measuring the peak signal-to-noise ratio (PSNR) between the real and the synthesized images.

In recent years, telecommunication fraud has become more rampant internationally with the development of modern technology and global communication. Because of rapid growth in the volume of call logs, the task of fraudulent phone call detection is confronted with big data issues in real-world implementations. Although our previous work, FrauDetector , addressed this problem and achieved some promising results, it can be further enhanced because it focuses only on fraud detection accuracy, whereas the efficiency and scalability are not top priorities. Other known approaches for fraudulent call number detection suffer from long training times or cannot accurately detect fraudulent phone calls in real time. However, the learning process of FrauDetector is too time-consuming to support real-world application. Although we have attempted to accelerate the the learning process of FrauDetector by parallelization, the parallelized learning process, namely PFrauDetector , still cannot afford the computing cost. In this article, we propose a highly efficient incremental graph-mining-based fraudulent phone call detection approach, namely FrauDetector + , which can automatically label fraudulent phone numbers with a “fraud” tag a crucial prerequisite for distinguishing fraudulent phone call numbers from nonfraudulent ones. FrauDetector + initially generates smaller, more manageable subnetworks from original graph and performs a parallelized weighted HITS algorithm for a significant speed increase in the graph learning module. It adopts a novel aggregation approach to generate a trust (or experience) value for each phone number (or user) based on their respective local values. After the initial procedure, we can incrementally update the trust (or experience) value for each phone number (or user) while a new fraud phone number is identified. An efficient fraud-centric hash structure is constructed to support fast real-time detection of fraudulent phone numbers in the detection module. We conduct a comprehensive experimental study based on real datasets collected through an antifraud mobile application called Whoscall . The results demonstrate a significantly improved efficiency of our approach compared with FrauDetector as well as superior performance against other major classifier-based methods.

Quality of experience (QoE) models predict the subjective quality of multimedia based on the relevant quality of service (QoS) factors. Due to the large space of QoS factors and the high costs of conducting subjective tests, efficient sampling strategies are required to determine which QoS configurations are to be queried, that is, evaluated by subjects. In this study, we extend the IQX model proposed by [M. Fiedler, T. Hoßfeld, and P. Tran-Gia, “A generic quantitative relationship between quality of experience and quality of service,”IEEE Netw., vol. 24, no. 2, pp. 36–41, Mar./Apr.2010.] toward a multidimensional QoS–QoE model (MIQX). To explore the complicated interaction between QoS factors more efficiently, we develop active learning algorithms for the multidimensional QoE model. Then, we conduct comprehensive experiments to compare the effectiveness of applying different sampling methods to crowdsourced video quality assessment tasks. In offline experiments that assume annotators give the same scores after changing the querying order, we demonstrate that active learning performs best and that a space-filling algorithm performs significantly better than random sampling. However, when we analyze the performance of the active sampling approaches more deeply using a novel field experiment, we observe that the active learning algorithms, which have been shown to be effective in the offline setting, can fail due to the habituation effect and individual differences of annotators. The active learning methods can also succeed when these issues are mitigated. These findings suggest that simply simulating the sample acquisition order, which is widely adopted in previous active learning literature[2]–[5], is not sufficient for multimedia quality assessment tasks.

International audience

While cloud servers provide a tremendous amount of resources for networked video applications, most successful stories of cloud-assisted video applications are presentational video services, such as YouTube and NetFlix. This article surveys the recent advances on delay-sensitive video computations in the cloud, which are crucial to cloud-assisted conversational video services, such as cloud gaming, Virtual Reality (VR), Augmented Reality (AR), and telepresence. Supporting conversational video services with cloud resources is challenging because most cloud servers are far away from the end users while these services incur the following stringent requirements: high bandwidth, short delay, and high heterogeneity. In this article, we cover the literature with a top-down approach: from applications and experience, to architecture and management, and to optimization in and outside of the cloud. We also point out major open challenges, hoping to stimulate more research activities in this emerging and exciting direction.

editorial Free Access Share on Guest Editorial: Special Issue on “QoE Management for Multimedia Services” Editors: Lea Skorin-Kapov University of Zagreb, Faculty of Electrical Engineering and Computing, Croatia University of Zagreb, Faculty of Electrical Engineering and Computing, CroatiaView Profile , Martín Varela callstats.io, Finland callstats.io, FinlandView Profile , Tobias Hoßfeld Chair of Communication Networks, Computer Science, University of Würzburg, Germany Chair of Communication Networks, Computer Science, University of Würzburg, GermanyView Profile , Kuan-Ta Chen Academia Sinica, Taiwan Academia Sinica, TaiwanView Profile Authors Info & Claims ACM Transactions on Multimedia Computing, Communications, and ApplicationsVolume 14Issue 2sApril 2018 Article No.: 28pp 1–3https://doi.org/10.1145/3192332Published:25 April 2018Publication History 5citation269DownloadsMetricsTotal Citations5Total Downloads269Last 12 Months29Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

Quality of Experience (QoE) has received much attention over the past years and has become a prominent issue for delivering services and applications. A significant amount of research has been devoted to understanding, measuring, and modelling QoE for a variety of media services. The next logical step is to actively exploit that accumulated knowledge to improve and manage the quality of multimedia services, while at the same time ensuring efficient and cost-effective network operations. Moreover, with many different players involved in the end-to-end service delivery chain, identifying the root causes of QoE impairments and finding effective solutions for meeting the end users’ requirements and expectations in terms of service quality is a challenging and complex problem. In this article, we survey state-of-the-art findings and present emerging concepts and challenges related to managing QoE for networked multimedia services. Going beyond a number of previously published survey articles addressing the topic of QoE management, we address QoE management in the context of ongoing developments, such as the move to softwarized networks, the exploitation of big data analytics and machine learning, and the steady rise of new and immersive services (e.g., augmented and virtual reality). We address the implications of such paradigm shifts in terms of new approaches in QoE modeling and the need for novel QoE monitoring and management infrastructures.

Foveated rendering leverages human visual system to increase video quality under limited computing resources for Virtual Reality (VR). More specifically, it increases the frame rate and the video quality of the foveal vision via lowering the resolution of the peripheral vision. Optimizing foveated rendering systems is, however, not an easy task, because there are numerous parameters that need to be carefully chosen, such as the number of layers, the eccentricity degrees, and the resolution of the peripheral region. Furthermore, there is no standard and efficient way to evaluate the Quality of Experiment (QoE) of foveated rendering systems. In this paper, we propose a framework to compare the performance of different subjective assessment methods on foveated rendering systems. We consider two performance metrics: efficiency and consistency, using the perceptual ratio, which is the probability of the foveated rendering is perceivable by users. A regression model is proposed to model the relationship between the human perceived quality and foveated rendering parameters. Our comprehensive study and analysis reveal several insights: 1) there is no absolute superior subjective assessment method, 2) subjects need to make more observations to confirm the foveated rendering is imperceptible than perceptible, 3) subjects barely notice the foveated rendering with an eccentricity degree of 7.5 degrees+ and peripheral region of a resolution of 540p+, and 4) QoE levels are highly dependent on the individuals and scenes. Our findings are crucial for optimizing the foveated rendering systems for future VR applications.

In this paper, we present DroneFace, an open dataset for testing how well face recognition can work on drones. Because of the high mobility, drones, i.e. unmanned aerial vehicles (UAVs), are appropriate for surveillance, daily patrol or seeking lost people on the streets, and thus need the capability of tracking human targets' faces from the air. Under this context, drones' distances and heights from the targets influence the accuracy of face recognition. In order to test whether a face recognition technique is suitable for drones, we establish DroneFace composed of facial images taken from various combinations of distances and heights for evaluating how a face recognition technique works in recognizing designated faces from the air. Since Face recognition is one of the most successful application in image analysis and understanding, and there exist many face recognition database for various purposes. To the best of our knowledge, DroneFace is the only dataset including facial images taken from controlled distances and heights within unconstrained environment, and can be valuable for future study of integrating face recognition techniques onto drones.

360° videos and Head-Mounted Displays (HMDs) are getting increasingly popular. However, streaming 360° videos to HMDs is challenging. This is because only video content in viewers' Field-of-Views (FoVs) is rendered, and thus sending complete 360° videos wastes resources, including network bandwidth, storage space, and processing power. Optimizing the 360° video streaming to HMDs is, however, highly data and viewer dependent, and thus dictates real datasets. However, to our best knowledge, such datasets are not available in the literature. In this paper, we present our datasets of both content data (such as image saliency maps and motion maps derived from 360° videos) and sensor data (such as viewer head positions and orientations derived from HMD sensors). We put extra efforts to align the content and sensor data using the timestamps in the raw log files. The resulting datasets can be used by researchers, engineers, and hobbyists to either optimize existing 360° video streaming applications (like rate-distortion optimization) and novel applications (like crowd-driven camera movements). We believe that our dataset will stimulate more research activities along this exciting new research direction.

We study the problem of predicting the Field-of-Views (FoVs) of viewers watching 360° videos using commodity Head-Mounted Displays (HMDs). Existing solutions either use the viewer's current orientation to approximate the FoVs in the future, or extrapolate future FoVs using the historical orientations and dead-reckoning algorithms. In this paper, we develop fixation prediction networks that concurrently leverage sensor- and content-related features to predict the viewer fixation in the future, which is quite different from the solutions in the literature. The sensor-related features include HMD orientations, while the content-related features include image saliency maps and motion maps. We build a 360° video streaming testbed to HMDs, and recruit twenty-five viewers to watch ten 360° videos. We then train and validate two design alternatives of our proposed networks, which allows us to identify the better-performing design with the optimal parameter settings. Trace-driven simulation results show the merits of our proposed fixation prediction networks compared to the existing solutions, including: (i) lower consumed bandwidth, (ii) shorter initial buffering time, and (iii) short running time.

With the emergence of social media sites such as Twitter and Facebook, channels have opened for citizens to publish an increasing and varied amount of personalized political information. In addition, social media has enabled users to broaden their political knowledge base through online interactions with individuals or groups. This study examines the ideological score of >0.2 million Facebook pages in Taiwan, where politics has long been polarized between the two major parties the Kuomintang (KMT) and the Democratic Progressive Party (DPP). This paper first measures users ideological scores along a Pro-KMT and Pro-DPP spectrum on the basis of their endorsements of political celebrities’ Facebook pages. Second, due to the one-dimensional polarization in Taiwanese politics, we assume that a user’s ideological position may lead them to endorse other Facebook pages that publish and share similar ideological content. Finally, based on this assumption, we estimate the ideological scores of the collected Facebook pages. Our results do not only present a political ideology estimation of pages with different perspectives (popularity, politicalness and category), they also propose applications of identifying potential political and conflicting/sarcastic pages.

This paper explores a counter-intuitive observation in the global mobile games market: that despite people in East Asian countries currently experiencing a challenging economic environment with lower disposable incomes and less leisure time than people in the West, they still spend much greater amounts of money on mobile gaming on a per-user basis. We link this situation to the tiny yet real happiness (TYRH) phenomenon: a term coined by Haruki Murakami, frequently rumored as a future recipient of the Nobel Prize for Literature, in his 1986 book "Afternoon at Langerhan's Island". The TYRH phenomenon describes that, due to structural inequality problems, people (especially the members of younger generations) may lose their ambition to actively develop their careers, and instead to cherish small, ordinary moments of bliss. More concretely, people implicated in this phenomenon tend to maintain an attitude of "living in the moment" without regard for their current and future lives, and may even retreat into various non-career-related activities, including mobile gaming. In this paper, we investigate the possible role of the TYRH phenomenon in influencing how smartphone users spend money (and time) on mobile games. We find that countries with long work hours, higher scores on the Gini index, lower unemployment rates, and lower life satisfaction are all associated with higher per-user spending on mobile games on both the App Store and Google Play platforms. This suggests that the TYRH phenomenon is indeed positively associated with mobile game-playing and spending behavior, and that countries where the phenomenon is more prominent are likely to contribute disproportionately to the mobile games market, now and in the future.

In recent years, fraud is becoming more rampant internationally with the development of modern technology and global communication. Due to the rapid growth in the volume of call logs, the task of fraudulent phone call detection is confronted with Big Data issues in real-world implementations. While our previous work, FrauDetector, has addressed this problem and achieved some promising results, it can be further enhanced as it focuses on the fraud detection accuracy while the efficiency and scalability are not on the top priority. Meanwhile, other known approaches suffer from long training time and/or cannot accurately detect fraudulent phone calls in real time. In this paper, we propose a highly-efficient parallelized graph-mining-based fraudulent phone call detection framework, namely PFrauDetector, which is able to automatically label fraudulent phone numbers with a "fraud" tag, a crucial prerequisite for distinguishing fraudulent phone call numbers from the normal ones. PFrauDetector generates smaller, more manageable sub-networks from the original graph and performs a parallelized weighted HITS algorithm for significant speed acceleration in the graph learning module. It adopts a novel aggregation approach to generate the trust (or experience) value for each phone number (or user) based on their respective local values. We conduct a comprehensive experimental study based on a real dataset collected through an anti-fraud mobile application, Whoscall. The results demonstrate a significantly improved efficiency of our approach compared to FrauDetector and superior performance against other major classifier-based methods.

We propose the very first non-intrusive measurement methodology for quantifying the performance of commodity Virtual Reality (VR) systems. Our methodology considers the VR system under test as a black-box and works with any VR applications. Multiple performance metrics on timing and positioning accuracy are considered, and detailed testbed setup and measurement steps are presented. We also apply our methodology to several VR systems in the market, and carefully analyze the experiment results. We make several observations: (i) 3D scene complexity affects the timing accuracy the most, (ii) most VR systems implement the dead reckoning algorithm, which incurs a non-trivial correction latency after incorrect predictions, and (iii) there exists an inherent trade-off between two positioning accuracy metrics: precision and sensitivity.

Smart Lenses refer to detachable, orientable and zoomable lenses that stream live videos over wireless networks to heterogeneous computing devices, including tablets and smartphones. Various novel applications are made possible by smart lenses, including mobile photography, smart surveillance cameras, and Unmanned Aerial Vehicle (UAV) cameras. However, to our best knowledge, existing smart lenses are closed and proprietary, and thus we initiate an open-source project called Smart Beholder for end-to-end solutions of smart lenses. The code and documents of Smart Beholder can be found at our website http://www.smartbeholder.org. Our Smart Beholder platform are useful to researchers for fast prototyping, developers for rapid development, and amateurs for hobbies. We have implemented Smart Beholder server (camera) using a popular embedded Linux platform, called Raspberry Pi. We have also realized Smart Beholder client (controller) on various OS's, including Android. Our experimental results show the practicality and efficiency of our proposed Smart Beholder: we outperform commercial products in the market in terms of both objective and subjective metrics. We believe the release of Smart Beholder will stimulate future studies on novel multimedia applications enabled by smart lenses.

Providing high-quality video conferencing experience over the best-effort Internet and wireless networks is challenging, because 2D videos are bulky. In this paper, we exploit the common structure of conferencing videos for an ultra-low-bitrate video conferencing system. In particular, we design, implement, optimize, and evaluate a video conferencing system, which: (i) extracts facial landmarks, (ii) transmits the selected facial landmarks and 2D images, and (iii) warps the untransmitted 2D images at the receiver. Several optimization techniques are adopted for minimizing the running time and maximizing the video quality, e.g., the image and warping frames are optimally determined based on network conditions and video content. The experiment results from real conferencing videos reveal that our proposed system: (i) outperforms the state-of-the-art x265 by up to 11.05 dB in PSNR (Peak Signal-to-Noise Ratio), (ii) adapts to different video content and network conditions, and (iii) runs in real-time at about 12 frame-per-second.

In this article, we have classified cloud gaming platforms into three types based on how games are integrated with platforms. We have also reviewed the history of cloud gaming services, and noted that it is a key moment for cloud gaming services to increase their penetration rates. Last, built upon our extensive research experience in cloud gaming, we share several of our visions into future cloud gaming technologies, business models, and social impacts, in the format of forecasts. While our forecasts may not be an exhausted list, we firmly believe this article will stimulate more discussions among cloud gaming researchers and practitioners, resulting in a sustainable cloud gaming ecosystem.

No abstract available.

The binding between computing devices and displays is becoming dynamic and adaptive, and screencast technologies enable such binding over wireless networks. In this article, we design and conduct the first detailed measurement study on the performance of the state-of-the-art screencast technologies. Several commercial and one open-source screencast technologies are considered in our detailed analysis, which leads to several insights: (1) there is no single winning screencast technology, indicating room to further enhance the screencast technologies; (2) hardware video encoders significantly reduce the CPU usage at the expense of slightly higher GPU usage and end-to-end delay, and should be adopted in future screencast technologies; (3) comprehensive error resilience tools are needed as wireless communication is vulnerable to packet loss; (4) emerging video codecs designed for screen contents lead to a better Quality of Experience (QoE) of screencast; and (5) rate adaptation mechanisms are critical to avoiding degraded QoE due to network dynamics. As a case study, we propose a nonintrusive yet accurate available bandwidth estimation mechanism. Real experiments demonstrate the practicality and efficiency of our proposed solution. Our measurement methodology, open-source screencast platform, and case study allow researchers and developers to quantitatively evaluate other design considerations, which will lead to optimized screencast technologies.

In recent years, fraud is increasing rapidly with the development of modern technology and global communication. Although many literatures have addressed the fraud detection problem, these existing works focus only on formulating the fraud detection problem as a binary classification problem. Due to limitation of information provided by telecommunication records, such classifier-based approaches for fraudulent phone call detection normally do not work well. In this paper, we develop a graph-mining-based fraudulent phone call detection framework for a mobile application to automatically annotate fraudulent phone numbers with a "fraud" tag, which is a crucial prerequisite for distinguishing fraudulent phone calls from normal phone calls. Our detection approach performs a weighted HITS algorithm to learn the trust value of a remote phone number. Based on telecommunication records, we build two kinds of directed bipartite graph: i) CPG and ii) UPG to represent telecommunication behavior of users. To weight the edges of CPG and UPG, we extract features for each pair of user and remote phone number in two different yet complementary aspects: 1) duration relatedness (DR) between user and phone number; and 2) frequency relatedness (FR) between user and phone number. Upon weighted CPG and UPG, we determine a trust value for each remote phone number. Finally, we conduct a comprehensive experimental study based on a dataset collected through an anti-fraud mobile application, Whoscall. The results demonstrate the effectiveness of our weighted HITS-based approach and show the strength of taking both DR and FR into account in feature extraction.

Smart lenses are detachable lenses connected to mobile devices via wireless networks, which are not constrained by the small form factor of mobile devices, and have potential to deliver better photo (video) quality. However, the viewfinder previews of smart lenses on mobile devices are difficult to optimize, due to the strict resource constraints on smart lenses and fluctuating wireless network conditions. In this paper, we design, implement, and evaluate an open-source smart lens, called Smart Beholder. It achieves three design goals: (i) cost effectiveness, (ii) low interaction latency, and (iii) high preview quality by: (i) selecting an embedded system board that is just powerful enough, (ii) minimizing per-component latency, and (iii) dynamically adapting the video coding parameters to maximizing Quality of Experience (QoE), respectively. Several optimization techniques, such as anti-drifting mechanism for video frames and QoE-driven resolution/frame rate adaptation algorithm, are proposed in this paper. Our measurement study shows that Smart Beholder outperforms Altek Cubic and Sony QX100 in terms of lower bitrate, lower latency, slightly higher frame rate, and better preview quality. We also demonstrate that sys adapts to network dynamics. Smart Beholder has been made public at http://www.smartbeholder.org as an experimental platform for researchers and developers to optimize smart lenses and other embedded real-time video streaming systems.

Dynamic and adaptive binding between computing devices and displays is increasingly more popular, and screencast technologies enable such binding over wireless networks. In this paper, we design and conduct the first detailed measurement study on the performance of the state-of-the-art screencast technologies. Several commercial and one open-source screencast technologies are considered in our detailed analysis, which leads to several insights: (i) there is no single winning screencast technology, indicating rooms to further enhance the screencast technologies, (ii) hardware video encoders significantly reduce the CPU usage at the expense of slightly higher GPU usage and end-to-end delay, and should be adopted in future screencast technologies, (iii) comprehensive error resilience tools are needed as wireless communication is vulnerable to packet loss, (iv) emerging video codecs designed for screen contents lead to better Quality of Experience (QoE) of screencast, and (v) rate adaptation mechanisms are critical to avoiding degraded QoE due to network dynamics. Furthermore, our measurement methodology and open-source screencast platform allow researchers and developers to quantitatively evaluate other design considerations, which will lead to optimized screencast technologies.

Optimizing cloud gaming experience is no easy task due to the complex tradeoff between gamer quality of experience (QoE) and provider net profit. We tackle the challenge and study an optimization problem to maximize the cloud gaming provider's total profit while achieving just-good-enough QoE. We conduct measurement studies to derive the QoE and performance models. We formulate and optimally solve the problem. The optimization problem has exponential running time, and we develop an efficient heuristic algorithm. We also present an alternative formulation and algorithms for closed cloud gaming services with dedicated infrastructures, where the profit is not a concern and overall gaming QoE needs to be maximized. We present a prototype system and testbed using off-the-shelf virtualization software, to demonstrate the practicality and efficiency of our algorithms. Our experience on realizing the testbed sheds some lights on how cloud gaming providers may build up their own profitable services. Last, we conduct extensive trace-driven simulations to evaluate our proposed algorithms. The simulation results show that the proposed heuristic algorithms: (i) produce close-to-optimal solutions, (ii) scale to large cloud gaming services with 20,000 servers and 40,000 gamers, and (iii) outperform the state-of-the-art placement heuristic, e.g., by up to 3.5 times in terms of net profits.

We study the problem of optimally adapting ongoing cloud gaming sessions to maximize the gamer experience in dynamic environments. The considered problem is quite challenging because: 1) gamer experience is subjective and hard to quantify; 2) the existing open-source cloud gaming platform does not support dynamic reconfigurations of video codecs; and 3) the resource allocation among concurrent gamers leaves a huge room to optimize. We rigorously address these three challenges by: 1) conducting a crowdsourced user study over the live Internet for an empirical gaming experience model; 2) enhancing the cloud gaming platform to support frame rate and bitrate adaptation on-the-fly; and 3) proposing optimal yet efficient algorithms to maximize the overall gaming experience or ensure the fairness among gamers. We conduct extensive trace-driven simulations to demonstrate the merits of our algorithms and implementation. Our simulation results show that the proposed efficient algorithms: 1) outperform the baseline algorithms by up to 46% and 30%; 2) run fast and scale to large (≤8000 gamers) problems; and 3) achieve the user-specified optimization criteria, such as maximizing average gamer experience or maximizing the minimum gamer experience. The resulting cloud gaming platform can be leveraged by many researchers, developers, and gamers.

Cloud gaming, the newest entry in the online gaming world, leverages the well-known concept of cloud computing to provide real-time gaming services to players. The idea in cloud gaming is to capture the game events from players and transmit them to the cloud, process those events and run the game logic in the cloud, render the game scene as video in the cloud, and stream that video to the players. The advantage is that as long as the client can display video, which pretty much all smartphones, tablets, game consoles, desktops, laptops, and mobile devices today do, the user can play the game without installing it locally, and without needing to have a machine with high-grade 3-D graphics rendering and powerful computing hardware and software. This makes cloud gaming accessible to a huge market of mass consumers. While some variations of cloud gaming systems stream 3-D graphics, in addition to or instead of video, the great majority of cloud gaming implementations are video based. Using the well-known concept of software as a service, cloud gaming is also sometimes referred to as gaming as a service, which is already available as commercial products, such as Sony’s PlayStation Now, Ubitus’s GameNow, G-Cluster, Crytek’s GFACE, PlayGiga, and LiquidSky, to name a few. There are also many efforts concentrating specifically on the underlying technology behind cloud gaming, such as NVIDIA’s Grid, OTOY, CiiNow, Kalydo, and GamingAnywhere [4], the latter being the only open source and free technology. Microsoft is also exploring cloud gaming technologies, with recent successes such as its Kahawai project [1].

No abstract available.

Displays without associated computing devices are increasingly more popular, and the binding between computing devices and displays is no longer one-to-one but more dynamic and adaptive. Screencast technologies enable such dynamic binding over ad hoc one-hop networks or Wi-Fi access points. In this paper, we design and conduct the first detailed measurement study on the performance of state-of-the-art screencast technologies. By varying the user demands and network conditions, we find that Splashtop and Miracast outperform other screencast technologies under typical setups. Our experiments also show that the screencast technologies either: (i) do not dynamically adjust bitrate or (ii) employ a suboptimal adaptation strategy. The developers of future screencast technologies are suggested to pay more attentions on the bitrate adaptation strategy, e.g., by leveraging cross-layer optimization paradigm.

Cloud gaming, i.e., real-time game playing via thin clients, relieves users from being forced to upgrade their computers and resolve the incompatibility issues between games and computers. As a result, cloud gaming is generating a great deal of interests among entrepreneurs, venture capitalists, general publics, and researchers. However, given the large design space, it is not yet known which cloud gaming system delivers the best user-perceived Quality of Service (QoS) and what design elements constitute a good cloud gaming system. This study is motivated by the question: How good is the QoS of current cloud gaming systems? Answering the question is challenging because most cloud gaming systems are proprietary and closed, and thus their internal mechanisms are not accessible for the research community. In this paper, we propose a suite of measurement techniques to evaluate the QoS of cloud gaming systems and prove the effectiveness of our schemes using a case study comprising two well-known cloud gaming systems: OnLive and StreamMyGame. Our results show that OnLive performs better, because it provides adaptable frame rates, better graphic quality, and shorter server processing delays, while consuming less network bandwidth. Our measurement techniques are general and can be applied to any cloud gaming systems, so that researchers, users, and service providers may systematically quantify the QoS of these systems. To the best of our knowledge, the proposed suite of measurement techniques have never been presented in the literature.

We present the first open source cloud gaming system, called GamingAnywhere. In addition to its openness, we have designed, GamingAnywhere for high extensibility, portability, and reconfigurability. We implemented it on Windows, Linux, OS X, and Android. We conducted extensive experiments to evaluate its performance. Our experimental results indicate that GamingAnywhere is efficient, scalable, adaptable to network conditions, and achieves high responsiveness and streaming quality. GamingAnywhere can be employed by researchers, game developers, service providers, and end users for setting up cloud gaming testbeds, which we believe, will stimulate more research into innovations for cloud gaming systems and applications.

The topic "Crowdsourcing for Multimedia" encompasses the full range of techniques that combine human intelligence and a large number of individual contributors to advance the state of the art in multimedia research. The ACM Multimedia 2013 Workshop on Crowdsourcing for Multimedia (CrowdMM 2013) provided a forum for presenting new crowdsourcing techniques, exchanging innovative crowdsourcing ideas, and discussing crowdsourcing best practices for multimedia. The workshop program consisted of presented papers, a keynote speech and a panel discussion. A special feature of this year's workshop was the "Crowdsourcing for Multimedia Ideas Competition", the results of which were presented at the workshop.

There is no doubt that an image's content determines how people assess the image aesthetically. Previous works have shown that image contrast, saliency features, and the composition of objects may jointly determine whether or not an image is perceived as aesthetically pleasing. In addition to an image's content, the way the image is presented may affect how much viewers appreciate it. For example, it may be assumed that a picture will always look better when it is displayed in a larger size. Is this "the-bigger-the-better" rule always valid? If not, in what situations is it invalid?

While cloud gaming opens new business opportunity, it also poses tremendous challenges as the Internet only provides best-effort service and gamers are hard to please. Although researchers have various ideas to improve cloud gaming systems, existing cloud gaming systems are closed and proprietary, and cannot be used to evaluate these ideas. We present GamingAnywhere, the first open-source cloud gaming system, which is extensible, portable, and configurable. GamingAnywhere may be used by: (i) researchers and engineers to implement and test their new ideas, (ii) service providers to develop cloud gaming services, and (iii) gamers to set up private cloud gaming systems. Details on GamingAnywhere are given in this paper. We firmly believe GamingAnywhere will stimulate future studies on cloud gaming and real-time interactive distributed systems.

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. The proposed GamingAnywhere can be employed for setting up cloud gaming testbeds, which, we believe, will stimulate more research innovations on cloud gaming systems.

Modern Massively Multiplayer Online Role-Playing Games (MMORPGs) provide lifelike virtual environments in which players can conduct a variety of activities including combat, trade, and chat with other players. While the game world and the available actions therein are inspired by their offline counterparts, the games' popularity and dedicated fan base are testaments to the allure of novel social interactions granted to people by allowing them an alternative life as a new character and persona. In this paper we investigate the phenomenon of "gender swapping," which refers to players choosing avatars of genders opposite to their natural ones. We report the behavioral patterns observed in players of Fairyland Online, a globally serviced MMORPG, during social interactions when playing as in-game avatars of their own real gender or gender-swapped. We also discuss the effect of gender role and self-image in virtual social situations and the potential of our study for improving MMORPG quality and detecting online identity frauds.

Crowdsourcing has emerged in recent years as a potential strategy to enlist the general public to solve a wide variety of tasks. With the advent of ubiquitous Internet access, it is now feasible to ask an Internet crowd to conduct QoE (Quality of Experience) experiments on their personal computers in their own residences rather than in a laboratory. The considerable size of the Internet crowd allows researchers to crowdsource their experiments to a more diverse set of participant pool at a relatively low economic cost. However, as participants carry out experiments without supervision, the uncertainty of the quality of their experiment results is a challenging problem. In this paper, we propose a crowdsourceable framework to quantify the QoE of multimedia content. To overcome the aforementioned quality problem, we employ a paired comparison method in our framework. The advantages of our framework are: 1) trustworthiness due to the support for cheat detection; 2) a simpler rating procedure than that of the commonly-used but more difficult mean opinion score (MOS), which places less burden on participants; 3) economic feasibility since reliable QoE measures can be acquired with less effort compared with MOS; and 4) generalizability across a variety of multimedia content. We demonstrate the effectiveness and efficiency of the proposed framework by a comparison with MOS. Moreover, the results of four case studies support our assertion that the framework can provide reliable QoE evaluation at a lower cost.

Crowdsourcing for multimedia involves exploiting both human intelligence and the combination of a large number of individual human contributions (i.e., the 'wisdom of the crowd') to develop techniques, systems and data sets that advance the state of the art. The ACM Multimedia 2012 Workshop on Crowdsourcing for Multimedia (CrowdMM 2012) provides a forum presenting crowdsourcing techniques for multimedia, as well as innovative ideas exemplifying how multimedia research can benefit from crowdsourcing. Through presented papers, invited talks and a panel, the workshop will promote interactive discussion on the scope and research potentials of crowdsourcing. The goal is to provide information to the multimedia research community on the principles of crowdsourcing and to inspire researchers to address the limitations of current studies by innovative use of human computation and collective intelligence. The workshop views crowdsourcing in the broad sense: it encompasses both unsolicited human contributions, e.g., tags assigned by users to images, and also solicited contributions, e.g., annotations gathered by making use of crowdsourcing platforms that micro-outsource tasks to a large pool of human workers.

Cloud gaming, i.e., real-time game playing via thin clients, relieves players from the need to constantly upgrade their computers and deal with compatibility issues when playing games. As a result, cloud gaming is generating a great deal of interest among entrepreneurs and the public. However, given the large design space, it is not yet known which platforms deliver the best quality of service and which design elements constitute a good cloud gaming system.

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.

From the perspective of game system designers, players' behavior is one of the most important factors they must consider when designing game systems. To gain a fundamental understanding of the game play behavior of online gamers, exploring users' game play time provides a good starting point. This is because the concept of game play time is applicable to all genres of games and it enables us to model the system workload as well as the impact of system and network QoS on users' behavior. It can even help us predict players' loyalty to specific games. In this paper, we present the World of Warcraft Avatar History (WoWAH) dataset, which comprises the records of 91,065 avatars. The data includes the avatars' game play times and a number of attributes, such as their race, profession, current level, and in-game locations, during a 1,107-day period between Jan. 2006 and Jan. 2009. We believe the WOWAH dataset could be used for various creative purposes, now that it is a public asset of the research community. It is available for free download at http://mmnet.iis.sinica.edu.tw/dl/wowah/.

In recent disasters, the web has served as a medium of communication among disaster response teams, survivors, local citizens, curious onlookers, and zealous people who are willing to assist victims affected by disasters. To encourage and speed up information dissemination, the availability and convenience of use are normally the top concerns in designing disaster response web services, where a design of free-formed inputs without access control is commonly adopted. However, such design may result in personal information disclosure and privacy leakage. In this paper, using a case study of a real-life disaster response service, the MKER (Morakot Event Reporting) forum, we show that the disclosure of personal information and the resulting privacy disclosure is indeed a serious problem that is currently happening. In our case, we have successfully mapped 1,438 unique cell phone numbers and 1,383 unique addresses to individuals using an automated method, not to mention the much greater invasion of privacy that could be effected by manual analysis of the messages posted on the forum. To resolve this issue, we propose several means to mitigate and prevent the mentioned privacy leakage on disaster response services from being happened.

MMORPG is shown to be a killer application of Internet, with a global subscriber number increased to 17 millions in 2010. However, MMORPG servers tend to be overly provisioned because 1)such games do not have standard architectures thus dedicated hardware is assumed; 2) MMORPGs normally adopt a ``sharded design'' to resolve the scalability challenges of content production and workload distribution; and 3) a game is commonly deployed in geographically distributed data centers to protect gamers from excessive network latencies. Therefore, an operator needs to deploy dedicated hardware for each game in each datacenter, even though hardware utilization is low. In this paper, we propose a zone-based server consolidation strategy for MMORPGs, which exploits the unique locality property of players' interactions, to cut down the games' considerable hardware requirement and energy use. We evaluate the effectiveness of our strategy based on a nine-month trace from a popular MMORPG World of War craft. The evaluation results show that, with a per-hour dynamic zone reallocation policy, the server number required can be reduced by 52% and the total energy consumption can be reduced by 62%, while the user-experienced latency remains undegraded.

The vertex-betweenness centrality index is an essential measurement for analyzing social networks, but the computation time is excessive. At present, the fastest algorithm, proposed by Brandes in 2001, requires O(|V| |E|) time, which is computationally intractable for real-world social networks that usually contain millions of nodes and edges. In this paper, we propose a fast and accurate algorithm for estimating vertex-betweenness centrality values for social networks. It only requires O(b2|V|) time, where b is the average degree in the network. Significantly, we demonstrate that the local dynamic information about the vertices is highly relevant to the global betweenness values. The experiment results show that the vertex-betweenness values estimated by the proposed model are close to the real values and their rank is fairly accurate. Furthermore, using data from online role-playing games, we present a new type of dynamic social network constructed from in-game chatting activity. Besides using such online game networks to evaluate our betweenness estimation model, we report several interesting findings derived from conducting static and dynamic social network analysis on game networks.

Abstract TheGames With A Purpose(GWAP) genre is a type ofHuman Computationthat outsources certain steps of the computational process to humans. By taking advantage of people's desire to be entertained, GWAP attracts people to play voluntarily, and also produce useful metadata as a by‐product. The games have shown promise in solving a variety of problems, which computer computation has been unable to resolve completely thus far. In this paper, we propose a metric, calledsystem gain, for evaluating the performance of GWAP systems, and also use analysis to study the properties of GWAP systems. We argue that it is important for GWAP systems to implement properpuzzle selection strategiesin order to collect human intelligence in a more efficient manner. Therefore, based on our analysis, we implement anOptimal Puzzle Selection Strategy(OPSA) to improve GWAP systems. Using a comprehensive set of simulations, we demonstrate that the proposed OPSA approach can effectively improve the system gain of GWAP systems, as long as the number of puzzles in the system is sufficiently large. Copyright © 2010 John Wiley & Sons, Ltd.

The objective of this work is to automatically detect the use of game bots in online games based on the trajectories of account users. Online gaming has become one of the most popular Internet activities in recent years, but cheating activity, such as the use of game bots, has increased as a consequence. Generally, the gaming community disapproves of the use of bots, as users may obtain unreasonable rewards without making corresponding efforts. However, game bots are hard to detect because they are designed to simulate human game playing behavior and they follow game rules exactly. Existing methods cannot solve the problem as the differences between bot and human trajectories are generally hard to describe. In this paper, we propose a method for detecting game bots based on some dissimilarity measurements between the trajectories of either bots or human users. The measurements are combined with manifold learning and classification techniques for detection; and the approach is generalizable to any game in which avatars' movements are controlled by the players directly. Through real-life data traces, we observe that the trajectories of bots and humans are very different. Since certain human behavior patterns are difficult to mimic, the characteristic can be used as a signature for bot detection. To evaluate the proposed scheme's performance, we conduct a case study of a popular online game called Quake 2. The results show that the scheme can achieve a high detection rate or classification accuracy on a short trace of several hundred seconds.

For current Web 2.0 services, manual examination of user uploaded content is normally required to ensure its legitimacy and appropriateness, which is a substantial burden to service providers. To reduce labor costs and the delays caused by content censoring, social moderation has been proposed as a front-line mechanism, whereby user moderators are encouraged to examine content before system moderation is required. Given the immerse amount of new content added to the Web each day, there is a need for automation schemes to facilitate rear system moderation. This kind of mechanism is expected to automatically summarize reports from user moderators and ban misbehaving users or remove inappropriate content whenever possible. However, the accuracy of such schemes may be reduced by collusion attacks, where some work together to mislead the automatic summarization in order to obtain shared benefits. In this paper, we propose a collusion-resistant automation scheme for social moderation systems. Because some user moderators may collude and dishonestly claim that a user misbehaves, our scheme detects whether an accusation from a user moderator is fair or malicious based on the structure of mutual accusations of all users in the system. Through simulations we show that collusion attacks are likely to succeed if an intuitive count- based automation scheme is used. The proposed scheme, which is based on the community structure of the user accusation graph, achieves a decent performance in most scenarios.

Because of the enormous growth in the number of peer-to-peer (P2P) applications, P2P traffic now constitutes a substantial proportion of Internet traffic. The ability to accurately identify different P2P applications from the network traffic is essential for managing a number of network traffic issues, such as service differentiation and capacity planning. However, modern P2P applications often use proprietary protocols, dynamic port numbers, and packet encryptions, which make traditional identification approaches like port-based or signature- based identification less effective. In this paper, we propose an approach for accurately recognizing P2P applications running on monitored hosts based on signaling behavior, which is regulated by the underlying P2P protocol; therefore, each application possesses a distinguishing characteristic. We consider that the signaling behavior of each P2P application can serve as a unique signature for application identification. Our approach is particularly useful for three reasons: 1) it does not need to access the packet payload; 2) it recognizes applications based purely on their signaling behavior; and 3) it can identify particular P2P applications. The performance evaluation shows that 92% of a real-life traffic trace can be correctly recognized within a 5-minute monitoring period.

As the service requirements of network applications shift from high throughput to high media quality, interactivity, and responsiveness, the definition of QoE (Quality of Experience) has become multidimensional. Although it may not be difficult to measure individual dimensions of the QoE, how to capture users' overall perceptions when they are using network applications remains an open question. In this paper, we propose a framework called OneClick to capture users' perceptions when they are using network applications. The framework only requires a subject to click a dedicated key whenever he/she feels dissatisfied with the quality of the application in use. OneClick is particularly effective because it is intuitive, lightweight, efficient, time-aware, and application-independent. We use two objective quality assessment methods, PESQ and VQM, to validate OneClick's ability to evaluate the quality of audio and video clips. To demonstrate the proposed framework's efficiency and effectiveness in assessing user experiences, we implement it on two applications, one for instant messaging applications, and the other for first- person shooter games. A Flash implementation of the proposed framework is also presented.

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.

Until recently, QoE (Quality of Experience) experiments had to be conducted in academic laboratories; however, with the advent of ubiquitous Internet access, it is now possible to ask an Internet crowd to conduct experiments on their personal computers. Since such a crowd can be quite large, crowdsourcing enables researchers to conduct experiments with a more diverse set of participants at a lower economic cost than would be possible under laboratory conditions. However, because participants carry out experiments without supervision, they may give erroneous feedback perfunctorily, carelessly, or dishonestly, even if they receive a reward for each experiment.

VoIP playout buffer dimensioning has long been a challenging optimization problem, as the buffer size must maintain a balance between conversational interactivity and speech quality. The conversational quality may be affected by a number of factors, some of which may change over time. Although a great deal of research effort has been expended in trying to solve the problem, how the research results are applied in practice is unclear.

Understanding the impact of network conditions on player satisfaction, which is one of the major concerns of network game designers, is a popular research topic. Of the various ways to gauge user satisfaction, in this paper, we focus on how network quality affects a player's decision to leave a game prematurely. To answer this question, we analyze a 1,356-million-packet trace from a large commercial massively multiplayer online role-playing game (MMORPG) called ShenZhou Online. We show that both network delay and network loss significantly affect a player's decision to leave a game prematurely. It is feasible to predict whether players will quit prematurely based on the network conditions they experience. The proposed model can determine the relative impact of different types of network impairment. For our traces, the degrees of player intolerance to network delay, delay jitter, client packet loss, and server packet loss are in the proportion of 1:2:4:3 approximately. The model can also be used to make system design decisions. Through simulations, we show that by prioritizing server processing according to the goodness of network conditions, employing dejitter buffers, or replacing TCP with a more lightweight transport protocol, the probability of premature departure can be significantly reduced. In this way, we demonstrate how our model of players' network experience provides feedback for the design of online games.

The demand for effective VoIP and online gaming traffic management methods continues to increase for purposes such as QoS provisioning, usage accounting, and blocking VoIP calls or game connections. However, identifying such flows has become a significant administrative burden because many of the applications use proprietary signaling and transport protocols. The question of how to identify proprietary VoIP traffic has yet to be solved. In this paper, we propose using a deviation-based classifier to identify VoIP and gaming traffic, given that such real-time interactive services normally send out constant-packet-rate (CPR) traffic with a fixed interval, in order to maintain real-timeliness and interactivity. Our contribution is two-fold: 1) We show that scale-free variability measures are more appropriate than scale- dependent ones for quantifying the network variability injected into CPR traffic. 2) Our proposed classifier is particularly lightweight in that it only requires a few inter-packet times to make a decision. The evaluation results show that by only analyzing 10 successive inter-packet times, we can distinguish between CPR and non-CPR traffic with approximately 90% accuracy.

Peer-to-peer relaying is commonly used in real-time 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.

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 real-life traces. The results show that our scheme achieve reasonably good performance even high degree of randomness has been injected into the network traffic.

"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.

In this paper, we propose an analytical model for computing the utility of ESP games, i.e., the throughput rate of appropriate labels for given puzzles. The model targets generalized games, where the number of players, the consensus threshold, and the stopping condition are variable. Via extensive simulations, we show that our model can accurately predict the stopping condition that will yield the optimal utility of an ESP game under a specific setting. A service provider can therefore utilize the model to ensure that the hosted ESP games produce high-quality labels efficiently, given that the number of players willing to invest time and effort in the game is limited.

Opportunistic network is a type of delay tolerant networks (DTN) where network communication opportunities appear opportunistic. In this study, we investigate opportunistic network scenarios based on public network traces, and our contributions are the following: First, we identify the censorship issue in network traces that usually leads to strongly skewed distribution of the measurements. Based on this knowledge, we then apply the Kaplan-Meier Estimator to calculate the survivorship of network measurements, which is used in designing our proposed censorship removal algorithm (CRA) that is used to recover censored data. Second, we perform a rich set of analysis illustrating that UCSD and Dartmouth network traces show strong self-similarity, and can be modeled as such. Third, we pointed out the importance of these newly revealed characteristics in future development and evaluation of opportunistic networks.

A secure multicast framework should only allow authorized members of a group to decrypt received messages; usually one "group key" is shared by all approved members. However, this raises the problem of "one affects all," whereby the actions of one member affect the whole group. Many researchers solve the problem by dividing a group into several subgroups, but most existing solutions require a centralized trusted controller to coordinate cryptographic keys for subgroups. We believe this is a constraint on network scalability. In this paper, we propose a novel framework to solve key management problems in multicast networks. Our contribution is three-fold: 1) We exploit the ElGamal cryptosystem and propose the idea of key composition; 2) A distributed key assignment protocol is proposed to eliminate the need for a centralized trust controller in a secure multicast network that leverages proxy cryptography; and 3) We adopt a hybrid encryption technique that makes our framework more efficient and practical. Comparison with similar frameworks shows the proposed scheme is efficient in both time and space complexity. In addition, costs of most protocol operations are bounded by constants regardless of a group's size and the degree of transit nodes.

With the emergence of active worms, the targets of attacks have been moved from well-known Internet servers to generic Internet hosts, and since the rate at which patches can be applied is always much slower than the spread of a worm, an Internet worm can usually attack or infect millions of hosts in a short time. It is difficult to eliminate Internet attacks globally; thus, protecting client networks from being attacked or infected is a relatively critical issue. In this paper, we propose a method that protects client networks from being attacked by people who try to scan, attack, or infect hosts in local networks via unpatched vulnerabilities. Based on the symmetry of network traffic in both temporal and spatial domains, a bitmap filter is installed at the entry point of a client network to filter out possible attack traffic. Our evaluation shows that with a small amount of memory (less than 1 megabyte), more than 95% of attack traffic can be filtered out in a small- or medium-scale client network.

Abstract — Online gaming is one of the most profitable busi-nesses on the Internet. Among various threats to continuous player subscriptions, network lags are particularly notorious. It is widely known that frequent and long lags frustrate game players, but whether the players actually take action and leave a game is unclear. Motivated to answer this question, we apply survival analysis to a 1, 356-million-packet trace from a sizeable MMORPG, called ShenZhou Online. We find that both network delay and network loss significantly affect a player’s willingness to continue a game. For ShenZhou Online, the degrees of player “intolerance ” of minimum RTT, RTT jitter, client loss rate, and server loss rate are in the proportion of 1:2:11:6. This indicates that 1) while many network games provide “ping time, ” i.e., the RTT, to players to facilitate server selection, it would be more useful to provide information about delay jitters; and 2) players are much less tolerant of network loss than delay. This is due to the game designer’s decision to transfer data in TCP, where packet loss not only results in additional packet delays due to in-order delivery and retransmission, but also a lower sending rate.

The success of Skype has inspired a generation of peer-to-peer-based solutions for satisfactory real-time multimedia services over the Internet. However, fundamental questions, such as whether VoIP services like Skype are good enough in terms of user satisfaction,have not been formally addressed. One of the major challenges lies in the lack of an easily accessible and objective index to quantify the degree of user satisfaction.In this work, we propose a model, geared to Skype, but generalizable to other VoIP services, to quantify VoIP user satisfaction based on a rigorous analysis of the call duration from actual Skype traces. The User Satisfaction Index (USI) derived from the model is unique in that 1) it is composed by objective source-and network-level metrics, such as the bit rate, bit rate jitter, and round-trip time, 2) unlike speech quality measures based on voice signals, such as the PESQ model standardized by ITU-T, the metrics are easily accessible and computable for real-time adaptation, and 3) the model development only requires network measurements, i.e., no user surveys or voice signals are necessary. Our model is validated by an independent set of metrics that quantifies the degree of user interaction from the actual traces.

TCP employs a self-clocking scheme that times the sending of packets. In that, the data packets are sent in a burst when the returning acknowledgement packets are received. This self-clocking scheme (also known as ack-clocking) is deemed a key factor to the the burstiness of TCP traffic and the source of various performance problems-high packet loss, long delay, and high delay jitter. Previous work has suggested contradictively the effectiveness of TCP pacing as a remedy to alleviate the traffic burstiness. In this paper, we analyze systematically, and in more robust experiments the impact of network variabilities on the behavior of TCP clocking schemes. We find that 1) aggregated pacing traffic could be burstier than aggregated ack-clocking traffic. Physical explanation and experimental simulations are provided to support this argument. 2) The round-trip time heterogeneity and flow multiplexing significantly influence the behaviors of both ack-clocking and pacing schemes. Evaluating the performance of clocking schemes without considering these effects is prone to inconsistent results. 3) Pacing outperforms ack-clocking in more realistic settings from the traffic burstiness point of view.

In this paper, we propose a novel framework for secure multicast on overlay networks. Our contributions are three-fold: 1) a technique key composition is proposed to cope with the secure multicast problems, 2) the proposed framework is totally distributed, i.e., no centralized control is required for subgroup configurations, and 3) a comparison of similar frameworks is provided, in which we show the proposed framework is more efficient in that its time and space complexity are bounded by constants, regardless of the number of coexisting groups, the group size, and the degree of transit nodes.

Online gaming is one of the most profitable businesses over the Internet. Among all genres of the online games, the popularity of the MMORPG (Massive Multiplayer Online Role Playing Games) is especially prominent in Asia. Opting for a better understanding of the game traffic and the economic well being of the Internet, we analyze a 1,356-million-packet trace from a sizeable MMORPG, ShenZhou Online. This work is, as far as we know, the first formal analysis on the MMORPG server traces.We find that the MMORPG and FPS (First-Person Shooting) games are similar in that they both generate small packets and require low bandwidths. In particular, the bandwidth requirement of MMORPG is even lower due to the less real-time game play. More distinctive are the strong periodicity, temporal locality, and irregularity observed in the MMORPG traffic. The periodicity is due to a common practice in game implementation, where the game state updates are accumulated within a fixed time window before transmission. The temporal locality in the game traffic is largely due to the game nature where one action leads to another. The irregularity, particular unique in MMORPG traffic, is due to the diversity of game design where the user behavior can be drastically different depending on the quest at hand.