Wei Tsang Ooi

Analysis and implications of student contact patterns derived from campus schedules

Characterizing mobility or contact patterns in a campus environment is of interest for a variety of reasons. Existing studies of these patterns can be classified into two basic approaches - model based and measurement based. The model based approach involves constructing a mathematical model to generate movement patterns while the measurement based approach measures locations and proximity of wireless devices to infer mobility patterns. In this paper, we take a completely different approach. First we obtain the class schedules and class rosters from a university-wide Intranet learning portal, and use this information to infer contacts made between students. The value of our approach is in the population size involved in the study, where contact patterns among 22341 students are analyzed. This paper presents the characteristics of these contact patterns, and explores how these patterns affect three scenarios. We first look at the characteristics from the DTN perspective, where we study inter-contact time and time distance between pairs of students. Next, we present how these characteristics impact the spread of mobile computer viruses, and show that viruses can spread to virtually the entire student population within a day. Finally, we consider aggregation of information from a large number of mobile, distributed sources, and demonstrate that the contact patterns can be exploited to design efficient aggregation algorithms, in which only a small number of nodes (less than 0.5%) is needed to aggregate a large fraction (over 90%) of the data.

A stochastic dynamic traveling salesman problem with hard time windows

Just-in-time (JIT) trucking service, i.e., arriving at customers within specified time windows, has become the norm for freight carriers in all stages of supply chains. In this paper, a JIT pickup/delivery problem is formulated as a stochastic dynamic traveling salesman problem with time windows (SDTSPTW). At a customer location, the vehicle either picks up goods for or delivers goods from the depot, but does not provide moving service to transfer goods from one location to another. Such routing problems are NP-hard in deterministic settings, and in our context, complicated further by the stochastic, dynamic nature of the problem. This paper develops an efficient heuristic for the SDTSPTW with hard time windows. The heuristic is shown to be useful both in controlled numerical experiments and in applying to a real-life trucking problem.

MoViMash: online mobile video mashup

With the proliferation of mobile video cameras, it is becoming easier for users to capture videos of live performances and socially share them with friends and public. As an attendee of such live performances typically has limited mobility, each video camera is able to capture only from a range of restricted viewing angles and distance, producing a rather monotonous video clip. At such performances, however, multiple video clips can be captured by different users, likely from different angles and distances. These videos can be combined to produce a more interesting and representative mashup of the live performances for broadcasting and sharing. The earlier works select video shots merely based on the quality of currently available videos. In real video editing process, however, recent selection history plays an important role in choosing future shots. In this work, we present MoViMash, a framework for automatic online video mashup that makes smooth shot transitions to cover the performance from diverse perspectives. Shot transition and shot length distributions are learned from professionally edited videos. Further, we introduce view quality assessment in the framework to filter out shaky, occluded, and tilted videos. To the best of our knowledge, this is the first attempt to incorporate history-based diversity measurement, state-based video editing rules, and view quality in automated video mashup generations. Experimental results have been provided to demonstrate the effectiveness of MoViMash framework.

Supporting zoomable video streams with dynamic region-of-interest cropping

Streaming of an arbitrary region of interest (RoI) from a high resolution video is essential to supporting zooming and panning within a video stream. This paper explores two methods for RoI-based streaming, referring to them as tiled streaming and monolithic streaming. Tiled streaming partitions video frames into grid of tiles and encodes each tile as an independently decodable stream. Monolithic streaming applies to video encoded using off-the-shelf encoder, and relies on a pre-computed dependency information to send the necessary bits for the RoI. We evaluated these two methods in terms of bandwidth efficiency, storage requirement, and computational costs under different video encoding parameters. Experimental results show that bandwidth efficiency of tiled streams for RoI-based streaming reduces when tile size increases, despite improvement in compression efficiency. In the case of monolithic streams, use of a larger motion vector range coupled with careful run-time optimization can still improve the bandwidth efficiency, despite an increase in motion vector dependency.

Avatar mobility in user-created networked virtual worlds: measurements, analysis, and implications

We collected mobility traces of avatars spanning multiple regions in Second Life, a popular user-created virtual world. We analyzed the traces to characterize the dynamics of the avatars’ mobility and behavior, both temporally and spatially. We discuss the implications of our findings on the design of peer-to-peer architecture, interest management, mobility modeling of avatars, server load balancing and zone partitioning, caching, and prefetching for user-created virtual worlds.

Critical video quality for distributed automated video surveillance

Large-scale distributed video surveillance systems pose new scalability challenges. Due to the large number of video sources in such systems, the amount of bandwidth required to transmit video streams for monitoring often strains the capability of the network. On the other hand, large-scale surveillance systems often rely on computer vision algorithms to automate surveillance tasks. We observe that these surveillance tasks present an opportunity for trade-off between the accuracy of the tasks and the bit rate of the video being sent. This paper shows that there exists a sweet spot, which we term critical video quality that can be used to reduce video bit rate without significantly affecting the accuracy of the surveillance tasks. We demonstrate this point by running extensive experiments on standard face detection and face tracking algorithms. Our experiments show that face detection works equally well even if the quality of compression is significantly reduced, and face tracking still works even if the frame rate is reduced to 6 frames per second. We further develop a prototype video surveillance system to demonstrate this idea. Our evaluation shows that we can achieve up to 29 times reduction in video bit rate when detecting faces and 16 times reduction when tracking faces. This paper also proposes a formal rate-accuracy optimization framework which can be used to determine appropriate encoding parameters in distributed video surveillance systems that are subjected to either bandwidth constraints or accuracy constraints.

Controlling Gossip Protocol Infection Pattern Using Adaptive Fanout

We propose and evaluate a model for controlling infection patterns defined over rounds or real time in a gossip-based protocol using adaptive fanout. We model three versions of gossip-based protocols: the synchronous protocol, the pseudosynchronous protocol and the asynchronous protocol. Our objective is to ensure that the members of a group receive a desired message within a bounded latency with very high probability. We argue that the most important parameter that controls the latency of message delivery is the fanout used during gossiping, i.e., the number of gossip targets chosen in a particular instance of gossip. We formally analyze the three protocols and provide expressions for fanout. We introduce the idea of using variable fanouts in different rounds in the synchronous protocol. We define fanout as a function of time for the asynchronous protocol such that an expected infection pattern is observed with high probability. For a better understanding of the theoretical model, we develop a pseudosynchronous protocol to highlight the modelling done in order to derive time dependent fanout. We show that our protocols generate Theta(n log n) messages, which is optimal for gossip protocols. We aim to use the gossiping mechanism for large-scale group communication with soft real time constraints. This would alleviate the dependence on tree-based deterministic protocols which usually lack scalability

Games are up for DVFS

Graphics-intensive computer games are no longer restricted to high-performance desktops, but are also available on a variety of portable devices ranging from notebooks to PDAs and mobile phones. Battery life has been a major concern in the design of both the hardware and the software for such devices. Towards this, dynamic voltage and frequency scaling (DVFS) has emerged as a powerful technique. However, the showcase application for DVFS algorithms so far has largely been video decoding, primarily because it is computationally expensive and its workload exhibits a high degree of variability. This paper investigates the possibility of applying DVFS to interactive computer games, which to the best of our knowledge has not been studied before. We show that the variability in the workload associated with a popular first person shooter game like Quake II is significantly higher than video decoding. Although this variability makes game applications an attractive candidate for DVFS, it is unclear if DVFS algorithms can be applied to games due to their interactive (and hence highly unpredictable) nature. In this paper, we show using detailed experiments that (surprisingly) interactive computer games are highly amenable to DVFS. Towards this we present a novel workload characterization of computer games, based on the game engine for Quake II. We believe that our findings might potentially lead to a number of innovative DVFS algorithms targeted towards game applications, exactly as video decoding has motivated a variety of schemes for DVFS

Video quality for face detection, recognition, and tracking

Many distributed multimedia applications rely on video analysis algorithms for automated video and image processing. Little is known, however, about the minimum video quality required to ensure an accurate performance of these algorithms. In an attempt to understand these requirements, we focus on a set of commonly used face analysis algorithms. Using standard datasets and live videos, we conducted experiments demonstrating that the algorithms show almost no decrease in accuracy until the input video is reduced to a certain critical quality, which amounts to significantly lower bitrate compared to the quality commonly acceptable for human vision. Since computer vision percepts video differently than human vision, existing video quality metrics, designed for human perception, cannot be used to reason about the effects of video quality reduction on accuracy of video analysis algorithms. We therefore investigate two alternate video quality metrics, blockiness and mutual information, and show how they can be used to estimate the critical video qualities for face analysis algorithms.

Crowdsourced automatic zoom and scroll for video retargeting

Screen size and display resolution limit the experience of watching videos on mobile devices. The viewing experience can be improved by determining important or interesting regions within the video (called regions of interest, or ROIs) and displaying only the ROIs to the viewer. Previous work focuses on analyzing the video content using visual attention model to infer the ROIs. Such content-based technique, however, has limitations. In this paper, we propose an alternative paradigm to infer ROIs from a video. We crowdsource from a large number of users through their implicit viewing behavior using a zoom and pan interface, and infer the ROIs from their collective wisdom. A retargeted video, consisting of relevant shots determined from historical users behavior, can be automatically generated and replayed to subsequent users who would prefer a less interactive viewing experience. This paper presents how we collect the user traces, infer the ROIs and their dynamics, group the ROIs into shots, and automatically reframe those shots to improve the aesthetics of the video. A user study with 48 participants shows that our automatically retargeted video is of comparable quality to one handcrafted by an expert user