This article presents case studies using two wave function collapse (WFC) methods, graph-based WFC and simple tiled WFC, to create playable levels for two logic puzzle games:Strimko(Latin Squares) andFlow(connecting dots with pipes). We then evaluate the quality of the generated levels through extensive experiments. Our results indicate that WFC-generated levels are high quality, follow the graph structures' constraints, and are generated faster than levels generated by depth-first search and genetic algorithms. WFC methods can also adapt to new system specifications, common in puzzle games, by changing only the data instead of the code. This increases the stability of content production based on procedural content generation since it relies on data rather than procedures. Furthermore, WFC methods increase the efficiency of the manual process of creating in-game puzzle levels, allowing game designers to complete more tasks in the same amount of time and create a wider variety of assets.

Due to the ubiquity of sensor-equipped smartphones, it has become increasingly feasible for users to capture videos together with associated geographic metadata, for example the location and the orientation of the camera. Such contextual information creates new opportunities for the organization and retrieval of geo-referenced videos. In this study we explore the task of landmark retrieval through the analysis of two types of state-of-the-art techniques, namely media-content-based and geocontext-based retrievals. For the content-based method, we choose the Spatial Pyramid Matching (SPM) approach combined with two advanced coding methods: Sparse Coding (SC) and Locality-Constrained Linear Coding (LLC). For the geo-based method, we present the Geo Landmark Visibility Determination (GeoLVD) approach which computes the visibility of a landmark based on intersections of a camera's field-of-view (FOV) and the landmark's geometric information available from Geographic Information Systems (GIS) and services. We first compare the retrieval results of the two methods, and discuss the strengths and weaknesses of each approach in terms of precision, recall and execution time. Next we analyze the factors that affect the effectiveness for the content-based and the geo-based methods, respectively. Finally we propose a hybrid retrieval method based on the integration of the visual (content) and geographic (context) information, which is shown to achieve significant improvements in our experiments. We believe that the results and observations in this work will enlighten the design of future geo-referenced video retrieval systems, improve our understanding of selecting the most appropriate visual features for indexing and searching, and help in selecting between the most suitable methods for retrieval based on different conditions.

The Dynamic Adaptive Streaming over HTTP (referred as MPEG DASH) standard is designed to provide high quality of media content over the Internet delivered from conventional HTTP web servers. The visual content, divided into a sequence of segments, is made available at a number of different bitrates so that an MPEG DASH client can automatically select the next segment to download and play back based on current network conditions. The task of transcoding media content to different qualities and bitrates is computationally expensive, especially in the context of large-scale video hosting systems. Therefore, it is preferably executed in a powerful cloud environment, rather than on the source computer (which may be a mobile device with limited memory, CPU speed and battery life). In order to support the live distribution of media events and to provide a satisfactory user experience, the overall processing delay of videos should be kept to a minimum. In this paper, we propose a novel dynamic scheduling methodology on video transcoding for MPEG DASH in a cloud environment, which can be adapted to different applications. The designed scheduler monitors the workload on each processor in the cloud environment and selects the fastest processors to run high-priority jobs. It also adjusts the video transcoding mode (VTM) according to the system load. Experimental results show that the proposed scheduler performs well in terms of the video completion time, system load balance, and video playback smoothness.

Due to technological advances and the popularity of camera sensors, it is now straightforward for users to capture and share videos. A large number of geo-tagged photos and videos have been accumulating continuously on the web, posing a challenging problem for mining this type of media data. In one application scenario, users might desire to know what the Points of Interest (POI) are which contain important objects or places in a video. Existing solutions attempt to examine the content of the videos and recognize objects and events. This is typically time-consuming and computationally expensive and the results can be uneven. Therefore these methods face challenges when applied to large video repositories. We propose a novel technique that leverages sensor-generated meta-data (camera locations and viewing directions) which are automatically acquired as continuous streams together with the video frames. Existing smartphones can easily accommodate such integrated recording tasks. By considering a collective set of videos and leveraging the acquired auxiliary meta-data, our approach is able to detect interesting regions and objects (POIs) and their distances from the camera positions in a fully automated way. Because of its computational efficiency, the proposed method scales well and our experiments show very promising results.

Similar to positioning data, camera orientation information has become a powerful contextual feature utilized by a number of GIS and social media applications. Such auxiliary information facilitates higher-level semantic analysis and management of video assets in such applications, e.g., video summarization and video indexing systems. However, it is problematic that raw sensor data collected from current mobile devices is often not accurate enough for subsequent geospatial analysis. To date, an effective orientation data correction system for mobile video content has been lacking. Here we present a content-based approach that improves the accuracy of noisy orientation sensor measurements generated by mobile devices in conjunction with video acquisition. Our preliminary experimental results demonstrate significant accuracy enhancements which benefit upstream sensor-aided GIS applications to access video content more precisely.

In addition to positioning data, other sensor information -- such as orientation data, have become a useful and powerful contextual feature. Such auxiliary information can facilitate higher-level semantic description inferences in many multimedia applications, e.g., video tagging and video summarization. However, sensor data collected from current mobile devices is often not accurate enough for upstream multimedia analysis. An effective orientation data correction system for mobile multimedia content has been an elusive goal so far. Here we present a system, termed Oscor, which aims to improve the accuracy of noisy orientation sensor measurements generated by mobile devices during image and video recording. We provide a user-friendly camera interface to facilitate the gathering of additional information, which enables the correction process on the server-side. Geographic field-of-view (FOV) visualizations based on the original and corrected sensor data help users understand the corrected contextual information and how the erroneous data possibly may affect further processes.

One type of Peer-to-Peer (P2P) live streaming has not yet been significantly investigated, namely topologies that provide many-to-many, interactive connectivity. Exemplar applications of such P2P systems include spatial audio services for networked virtual environments (NVEs) and distributed online games. Numerous challenging problems have to be overcome-among them providing low delay, resilience to churn, effective load balancing, and rapid convergence-in such dynamic environments. We propose a novel P2P overlay dissemination mechanism, termed IDM, that can satisfy such demanding real-time requirements. Our target application is to provide spatialized voice support in multiplayer NVEs, where each bandwidth constrained peer potentially communicates with all other peers within its area-of-interest (AoI). With IDM each peer maintains a set of partners, termed helpers, which may act as stream forwarders. We prove analytically that the system reachability is maximized when the loads of helpers are balanced proportionally to their network capacities. We then propose a game-theoretic algorithm that balances the loads of the peers in a fully distributed manner. Of practical importance in dynamic systems, we prove that our algorithm converges to an approximately balanced state from any prior state in rapid O(\log \log n) time, where n is the number of users. We further evaluate our technique with simulations and show that it can achieve near optimal system reachability and satisfy the tight latency constraints of interactive audio under conditions of churn, avatar mobility, and heterogeneous user access network bandwidth.

Video associated positioning data has become a useful contextual feature to facilitate analysis and management of media assets in GIS and social media applications. Moreover, with today's sensor-equipped mobile devices, the location of a camera can be continuously acquired in conjunction with the captured video stream without much difficulty. However, most sensor information collected from mobile devices is not highly accurate due to two main reasons: (a) the varying surrounding environmental conditions during data acquisition, and (b) the use of low-cost, consumer-grade sensors in current mobile devices. In this paper, we enhance the noisy positioning data generated by smartphones during video recording by analyzing typical error patterns for real collected data and introducing two robust algorithms, based on Kalman filtering and weighted linear least square regression, respectively. Our experimental results demonstrate significant benefits of our methods, which help upstream sensor-aided applications to access media content precisely.

Camera motion information facilitates higher-level semantic description inference in many video applications, e.g., video retrieval. However, an efficient and accurate methodology for annotating videos with camera motion information is still an elusive goal. In our recent work we have investigated the fusion of captured video with a continuous stream of sensor meta-data. For these so-called sensor-rich videos we present a system, called Motch, which precisely partitions a video document into subshots, automatically characterizes the camera motions and provides video subshot browsing based on an interactive, map-based interface. Moreover, the system computes and presents motion type statistics for each video in real time and renders different subshots distinctively on the map synchronously with the video playback.

Mobile video traffic is growing rapidly in networks due to the continuing user adoption of smartphones and tablet computers. While video viewing is now prevalent on such devices, they also easily enable the recording and uploading of videos for quick publishing on popular video sharing websites. Dynamic Adaptive Streaming over HTTP, or DASH, is a media streaming standard that has recently been developed by the Motion Pictures Experts Group (MPEG) and which has gained attention for its ability to enable media players to render videos with high quality under various network conditions. MPEG-DASH has been ratified at the end of 2011 and is now also known as ISO/IEC 23009--1. It is noteworthy that the focus of the initial standard is limited to the efficient server-to-client distribution of videos.

User-generated videos have become increasingly popular in recent years. Due to advances in camera technology it is now very easy and convenient to record videos with mobile devices, such as smartphones. Here we consider an application where users collect and share a large set of videos that are related to a geographic area, say a city. Such a repository can be a great source of information for prospective tourists when they plan to visit a city and would like to get a preview of its main areas. The challenge that we address is how to automatically create a preview video summary from a large set of source videos.

Camera motion information is one aspect that helps to infer higher-level semantic descriptions in many video applications, e.g., in video retrieval. However, an efficient methodology for annotating camera motion information is still an elusive goal. Here we propose and present a novel and efficient approach for the task of partitioning a video document into sub-shots and characterizing their camera motion. By leveraging location (GPS) and digital compass data, which are available from most current smartphone handsets, we exploit the geographical sensor information to detect transitions between two sub-shots based on the variations of both the camera location and the shooting direction. The advantage of our method lies in its considerable accuracy. Additionally, the computational efficiency of our scheme enables it to be deployed on mobile devices and to process videos while recording. We utilize this capability to show how the HEX motion estimation algorithm in the H.264/AVC encoder can be simplified with the aid of our camera motion information. Our experimental results show that we can reduce the computation of the HEX algorithm by up to 50% while achieving comparable video quality.

To present user-generated videos that relate to geographic areas for easy access and browsing it is often natural to use maps as interfaces. A common approach is to place thumbnail images of video keyframes in appropriate locations. Here we consider the challenge of determining which keyframes to select and where to place them on the map. Our proposed technique leverages sensor-collected meta-data which are automatically acquired as a continuous stream together with the video. Our approach is able to detect interesting regions and objects (hotspots) and their distances from the camera in a fully automated way. Meaningful keyframes are adaptively selected based on the popularity of the hotspots. Our experiments show very promising results and demonstrate excellent utility for the users.

Result presentations from searches into video repositories is still a challenging problem. Current systems usually display a ranked list that shows the first frame of each video. Users then explore the videos one-by-one. In our recent work we have investigated the fusion of captured video with a continuous stream of sensor meta-data. These so-called sensor-rich videos can conveniently be captured with today's smartphones. Importantly, the recorded sensor-data streams enable processing and result resentation in novel and useful ways.

Networked virtual environments are increasingly used for collaboration tasks and other interactive applications. While the graphics in such virtual worlds are usually three-dimensional, interactive 3D voice support is still in its infancy. Here we describe our demonstration system that supports P2P-based positional audio and interactive voice communication with the SecondLife platform.

Newly emerging game--based application systems such as Second Life1 provide 3D virtual environments where multiple users interact with each other in real--time. They are filled with autonomous, mutable virtual content which is continuously augmented by the users. To make the systems highly scalable and dynamically extensible, they are usually built on a client--server based grid subspace division where the virtual worlds are partitioned into manageable sub--worlds. In each sub--world, the user continuously receives relevant geometry updates of moving objects from remotely connected servers and renders them according to her viewpoint, rather than retrieving them from a local storage medium. In such systems, the determination of the set of objects that are visible from a user's viewpoint is one of the primary factors that affect server throughput and scalability. Specifically, performing real--time visibility tests in extremely dynamic virtual environments is a very challenging task as millions of objects and sub-millions of active users are moving and interacting. We recognize that the described challenges are closely related to a spatial database problem, and hence we map the moving geometry objects in the virtual space to a set of multi-dimensional objects in a spatial database while modeling each avatar both as a spatial object and a moving query. Unfortunately, existing spatial indexing methods are unsuitable for this kind of new environments.The main goal of this paper is to present an efficient spatial index structure that minimizes unexpected object popping and supports highly scalable real--time visibility determination. We then uncover many useful properties of this structure and compare the index structure with various spatial indexing methods in terms of query quality, system throughput, and resource utilization. We expect our approach to lay the groundwork for next--generation virtual frameworks that may merge into existing web--based services in the near future.

Random data placement, which is efficient and scalable for large-scale storage systems, has recently emerged as an alternative to traditional data striping. In this report, we study the disk replacement problem (DRP) to find a sequence of disk additions and removals for a storage system, while migrating the data and respecting the following constraints: (1) the data is initially balanced across the existing distributed disk configuration, (2) the data must again be balanced across the new configuration, and (3) the data migration cost must be minimized. In practice, migrating data from old disks to new devices is complicated by the fact that the total number of disks connected to the storage system is often limited by a fixed number of available slots and not all the old and new disks can be connected at the same time. This article presents solutions for both cases where the number of disk slots is either unconstrained or constrained.