Handbook of Multimedia for Digital Entertainment and Arts- P10 pdf

11 Hack-proof Synchronization Protocol for Multi-player Online Games 261 Fig. 16 The path of the local avatar Q (thicker line) and the path of the non-local avatar P (thinner line) rendered on Q’s local machine which zoomed into the last 60s chronization (AS). Using AS, each host advances in time asynchronously from the other players but enters into the lockstep mode when interaction occurs. When en- tering the lockstep mode, in every timeframe t each involved player must wait for all packets from other players before advancing to timeframe t C 1. Because this is a stop-and-wait protocol, extrapolation cannot be used to smooth out any delay caused by the network latency. In [12], the authors improve the performance of the lockstep protocol by adding pipelines. Extrapolation is still not allowed under the pipelined lockstep protocol. Therefore, if there is an increased network latency and packets are delayed, the game will be stalled. In [10], the authors propose a sliding pipeline protocol that dynamically adjusts the pipeline depth to reflect current network conditions. The authors also introduce a send buffer to hold the commands generated while the size of the pipeline is ad- justed. The sliding pipeline protocol allows extrapolation to smooth out jitters. Although these protocols are designed to defend against the suppress-correct cheat, it can also prevent speed-hacks when entering into the lock-step mode be- cause players are forced to synchronize within a bounded amount of timeframes. 262 Y.S. Fung and J.C.S. Lui However, speed-hack can still be effective when lock-step mode is not activated. And since these protocols do not allow packets to be dropped, any lost packet must be retransmitted until they are finally sent and acknowledged. Therefore, the min- imum timeframe of the game cannot be shorter than the maximum latency of the player with the slowest connection and all clients must run the game at a speed that even the slowest client can support. Furthermore, any sudden increase in the latency will cause jitters to all players. Our protocol does not incur any lock-step requirement to game clients while the advantage of loose synchronization in conventional dead-reckoning protocol is completely preserved. Thus, smooth gameplay can be ensured. As we have proved in Section “Proof of Invulnerability”, a cheater can only cheat by generating malicious timestamps and it can be detected easily and immediately. Therefore, the speed-hack invulnerability of our protocol will be enforced throughout the whole game session so that any action of cheating can be detected immediately. Moreover, the AS protocol requires a game client to enter the lock-step mode when interaction occurs which requires a major modification of the client code to realize it. However, existing games can be modified easily to adapt our proposed protocol. One can simply add a plugin routine to convert a dead-reckoning vector to the synchronization parameters before sending out the update packets, and add another plugin routine to convert back the synchronization parameters to a dead- reckoning vector on receiving the packets. The NEOprotocol [13] is based on[2], the authorsdescribe five forms of cheating and claim that the NEO protocol can prevent these cheating. In [17], the authors show that for the five forms of cheating [13] designed to pre- vent, it prevents only three. They propose another Secure Event Agreement (SEA) protocol that prevents all five forms of cheating which the performance is at worst equal to NEO and in some cases better. In [19], the authors show that both NEO and SEA suffer from the undo cheat. Let P H denote an honest player and P C denote a cheater, and M H ;K H and M C ;K C represent the message and its key from P H and P C respectively. The cheater P C performs the undo cheat as follows: both players send their encrypted game moves (M H and M C ) normally in the commit phase. Then, P H sends key K H in the reveal phase. However, P C delays K C until K H is received and M H is revealed. If P C find that M C is poor against M H ;P C will purposely drop K C and therefore undoing the move M C . The authors then propose another anti-cheat scheme for P2P games called RACS which relies on the existence of a trusted referee. The referee is responsible for T1 - receiving player updates, T2 - simulating game play, T3 - validating and resolving conflicts in the simulation, T4 - disseminating updates to clients and T5 - storing the current game state. The referee used in RACS works very likely to a traditional game server in con- ventional client-server architecture. The security of RACS completely depends on the referee. For example, speed-hack can be prevented with validating every state updates by the referee. Although RACS is more scalable than client-server architec- ture, it suffers from the same problem that the involvement of a trusted third party is required. 11 Hack-proof Synchronization Protocol for Multi-player Online Games 263 Conclusion In this paper, we presented a synchronization protocol for multi-player online games that support dead-reckoning. Meanwhile, it is invulnerable to a very common type of cheat called speed-hack. The general idea is that the server or peer players can use the legal speed of an avatar to compute its position from a set of update param- eters. This eliminates the need to state the avatar’s position directly in the update packets. Even if the cheater is able to modify the data in the update packets, the cheater cannot spoof other players to render a faster moving avatar because the dis- placement an avatar can travel is now bounded by the legal speed of the player that is authorized by the server (in client-server architecture) or among all peers (in P2P architecture). We have used various examples to illustrate our protocol and proved the security feature of our proposal. We have carried out simulations to demonstrate the feasibility of our protocol. References 1. Banavar H, Aggarwal S, Khandelwal A (2004) Accuracy in dead-reckoning based distributed multi-player games. In: Proceedings of NetGames 2004, Portland, August 2004, pp 161–165 2. Baughman NE, Levine BN (2001) Cheat-proof playout for centralized and distributed online games. In: Proceedings of IEEE INFOCOM. IEEE, Piscataway, pp 104–113 3. Counter Hack (2007) Types of Hacks. http://wiki.counter-hack.net/CategoryGeneralInfo 4. DeLap M et al (2004) Is runtime verification applicable to cheat detection. In: Proceedings of NetGames 2004, Portland, August 2004, pp 134–138 5. Diot C, Gautier L (1999) A distributed architecture for multiplayer interactive applications on the internet. In: IEEE Networks magazine, Jul–Aug 1999 6. Diot C, Gautier L, Kurose J (1999) End-to-end transmission control mechanisms for mul- tiparty interactive applications on the internet. In: Proceedings of IEEE INFOCOM, IEEE, Piscataway 7. Even Balance (2007) Official PunkBuster website. http://www.evenbalance.com 8. Feng WC, Feng WC, Chang F, Walpole J (2005) A traffic characterization of popular online games. IEEE/ACM Trans Netw 13(3):488–500 9. Gautier L, Diot C (1998) Design and evaluation of mimaze, a multiplayer game on the Internet. In: Proceedings of IEEE Multimedia (ICMCS’98). IEEE, Piscataway 10. Jamin S, Cronin E, Filstrup B (2003) Cheat-proofing dead reckoned multiplayer games (extended abstract). In: Proc. of 2nd international conference on application and development of computer games, Hong Kong, 6–7 January 2003 11. Lee FW, Li L, Lau R (2006) A trajectory-preserving synchronization method for collaborative visualization. IEEE Trans Vis Comput Graph 12:989–996 (special issue on IEEE Visualiza- tion’06) 12. Lenker S, Lee H, Kozlowski E, Jamin S (2002) Synchronization and cheat-proofing proto- col for real-time multiplayer games. In: International Worshop on Entertainment Computing, Makuhari, May 2002 13. Lo V, GauthierDickey C, Zappala D, Marr J (2004) Low latency and cheatproof event ordering for peer-to-peer games. In: ACM NOSSDAV’04, Kinsale, June 2004 14. Mills DL (1992) Network time protocol (version 3) specification, implmentation and analysis. In: RFC-1305, March 1992 15. MPC Forums (2007) Multi-Player Cheats. http://www.mpcforum.com 16. Pantel L, Wolf L (2002) On the impact of delay on real-time multiplayer games. In: ACM 264 Y.S. Fung and J.C.S. Lui NOSSDAV’02, Miami Beach, May 2002 17. Schachte P, Corman AB, Douglas S, Teague V (2006) A secure event agreement (sea) protocol for peer-to-peer games. In: Proceedings of ARES’06, Vienna, 20–22 April 2006, pp 34–41 18. Simpson ZB (2008) A stream based time synchronization technique for networked computer games. http://www.mine-control.com/zack/timesync/timesync.html 19. Soh S, Webb S, Lau W (2007) Racs: a referee anti-cheat scheme for p2p gaming. In: Proceed- ings of NOSSDAV’07, Urbana-Champaign, 4–5 June 2007, pp 34–42 20. The Z Project (2007) Official HLGuard website. http://www.thezproject.org 21. Wikipedia (2007) Category: Anti-cheat software. http://en.wikipedia.org/wiki/Category:Anti- cheat software Chapter 12 Collaborative Movie Annotation Damon Daylamani Zad and Harry Agius Introduction Web 2.0 has enjoyed great success over the past few years by providing users with a rich application experience through the reuse and amalgamation of different Web services. For example, YouTube integrates video streaming and forum technologies with Ajax to support video-based communities. Online communities and social net- works such as these lie at the heart of Web 2.0. However, while the use of Web 2.0 to support collaboration is becoming common in areas such as online learning [1], operating systems coding [2], e-government [3], and filtering [4], there has been very little research into the use of Web 2.0 to support multimedia-based collab- oration [5], and very little understanding of how users behave when undertaking multimedia content-based activities collaboratively, such as content analysis, se- mantic content classification, annotation, and so forth. At the same time, spurred on by falling resource costs which have reduced limits on how much content users can upload, online communities and social networking sites have grown rapidly in popularity and with this growth has come an increase in the production and sharing of multimedia content between members of the community, particularly users’ self- created content, such as song recordings, home movies, and photos. This makes it even more imperative to understand user behaviour. In this paper, we focus on metadata for self-created movies like those found on YouTube and Google Video, the duration of which are increasing in line with falling upload restrictions. While simple tags may have been sufficient for most purposes for traditionally very short video footage that contains a relatively small amount of semantic content, this is not the case for movies of longer duration which em- body more intricate semantics. Creating metadata is a time-consuming process that takes a great deal of individual effort; however, this effort can be greatly reduced by harnessing the power of Web 2.0 communities to create, update and maintain it. D.D. Zad and H. Agius (  ) School of Information Systems, Computing and Mathematics, Brunel University, Uxbridge, Middlesex, UK e-mail: damon.zad@brunel.ac.uk; harryagius@acm.org B. Furht (ed.), Handbook of Multimedia for Digital Entertainment and Arts, DOI 10.1007/978-0-387-89024-1 12, c Springer Science+Business Media, LLC 2009 265 266 D.D. Zad and H. Agius Consequently, we consider the annotation of movies within Web 2.0 environments, such that users create and share that metadata collaboratively and propose an archi- tecture for collaborative movie annotation. This architecture arises from the results of an empirical experiment where metadata creation tools, YouTube and an MPEG- 7 modelling tool, were used by users to create movie metadata. The next section discusses related work in the areas of collaborative retrieval and tagging. Then, we describe the experiments that were undertaken on a sample of 50 users. Next, the results are presented which provide some insight into how users interact with exist- ing tools and systems for annotating movies. Based on these results, the paper then develops an architecture for collaborative movie annotation. Collaborative Retrieval and Tagging We now consider research in collaborative retrieval and tagging within three areas: research that centres on a community-based approach to data retrieval or data rank- ing, collaborative tagging of non-video files, and collaborative tagging of videos. The research in each of these areas is trying to simplify and reduce the size of a vast problem by using collaboration among members of a community. This idea lies at the heart of the architecture presented in this paper. Collaborative Retrieval Retrieval is a core focus of contemporary systems, particularly Web-based mul- timedia systems. To improve retrieval results, a body of research has focused on adopting the collaborative approach of social networks. One area in which collab- oration has proven beneficial is that of reputation-based retrieval, where retrieval results are weighted according to the reputation of the sources. This approach is employed by Chen et al. [4] who propose adaptive community-based multimedia retrieval using an agent reputation model that is based on social network analy- sis methods. Sub-group analysis is conducted for better support of collaborative ranking and community-based search. In social network analysis, relational data is represented using ‘sociograms’ (directed and weighted graphs), where each partici- pant is represented as a node and each relation is represented as an edge. The value of a node represents an importance factor that forms the corresponding participant’s reputation. Peers who have higher reputations should affect other peers’ reputations to a greater extent, therefore the quality of data retrieval of each peer database can be significantly different. The quality of the data stored in them can also be differ- ent. Therefore, the returned results are weighted according to the reputations of the sources. Communities of peers are created through clustering. Koru [6] is a search engine that exploits Web 2.0 collaboration in order to provide knowledge bases automatically, by replacing professional experts with thousands or 12 Collaborative Movie Annotation 267 even millions of amateur contributors. One example is Wikipedia, which can be directly exploited to provide manually-defined yet inexpensive knowledge bases, specifically tailored to expose the topics, terminology and semantics of individual document collections. Koru is evaluated according to how well it assists real users in performing realistic and practical information retrieval tasks. Collaboration in filtering is common. For example, Chen et al. [7] provide a framework for collaborative filtering that circumvents the problems of traditional memory-based and model-based approaches by applying orthogonal nonnegative matrix tri-factorization (ONMTF). Their algorithm first applies ONMTF to simul- taneously cluster the rows and columns of the user-item matrix, and then adopts the user-based and item-based clustering approaches respectively to attain individual predictions for an unknown test rating. Finally, these ratings are fused with a linear combination. Simultaneously clustering users and items improves on the scalability problem of such systems, while fusing user-based and item-based approaches can improve performancefurther. As another example, Yang and Li[8] propose a collab- orative filtering approach based on heuristic formulated inferences. This is based on the fact that any two users may have some common interest genres as well as differ- ent ones. Their approach introduces a more reasonable similarity measure metric, considers users’ preferences and rating patterns, and promotes rational individual prediction, thus more comprehensively measuring the relevance between user and item. Their results demonstrate that the proposed approach improves the prediction quality significantly over several other popular methods. Collaborative Tagging of Non-Video Media Collaborative tagging has been used to create metadata and semantics for different media. In this section, we review some examples of research concerning collab- orative tagging of non-video media. SweetWiki [9] revisits the design rationale of wikis, taking into account the wealth of new Web standards available, such as for the wiki page format (XHTML), for the macros included in pages (JSPX/XML tags), for the semantic annotations (RDFa, RDF), and for the ontologies it manipulates (OWL Lite). SweetWiki improves access to information with faceted navigation, enhanced search tools and awareness capabilities, and acquaintance networks iden- tification. It also provides a single WYSIWYG editor for both metadata and content editing, with assisted annotation tools (auto-completion and checkers for embedded queries or annotations). SweetWiki allows metadata to be extracted and exploited externally. There is a growing body of research regarding the collaborative tagging of pho- tos. An important impetus for this is the popularity of photo sharing sites such as Flickr. Flickr groups are increasingly used to facilitate the explicit definition of com- munities sharing common interests, which translates into large amounts of content (e.g. pictures and associated tags) about specific subjects [10]. The users of Flickr have created a vast amount of metadata on pictures and photos. This large number 268 D.D. Zad and H. Agius of images has been carefully annotated for the obvious reason they were accessible to all users and therefore the collaboration of these users has resulted in producing an impossible amount of metadata that is not perceivable without such collabo- ration. Zonetag [11] is a prototype mobile application that uploads camera phone photos to Flickr and assist users with context-based tag suggestions derived from multiple sources. A key source of suggestions is the collaborative tagging activ- ity on Flickr, based on the user’s own tagging history and the tags associated with the location of the user. Combining these two sources, a prioritized suggested tag list is generated. They use several heuristics that take into account the tags’ social and temporal context, and other measures that weight the tag frequency to create a final score. These heuristics are spatial, social and temporal characteristics; they gather all tags used in a certain location regardless of the exact location, tags the users themselves applied in a given context are more likely to apply to their cur- rent photo than tags used by others, and finally tags are more likely to apply to a photo if they have been used recently. CONFOTO [12] is a browsing and an- notation service for conference photos which exploits sharing and collaborative tagging through RDF (Resource Description Framework) to gain advantages like unrestricted aggregation and ontology re-use. Finally, Bentley et al. [13] performed two separate experiments: one asking users to socially share and tag their personal photos and one asking users to share and tag their purchased music. They discov- ered multiple similarities between the two in terms of how users interacted and annotated the media, which have implications for the design of future music and photo applications. Collaborative Tagging of Video Media We now review some examples of research concerning collaborative tagging of video media. Yamamoto et al. [14] present an approach for video scene annota- tion based on social activities associated with the content of video clips on the Web. This approach has been demonstrated through assisting users of online forums associate video scenes with user comments and through assisting users of We- blog communications generate entries that quote video scenes. The system extracts deep-content-related information about video contents as annotations automatically, allowing users to view any video, submit and view comments about any scene, and edit a Weblog entry to quote scenes using an ordinary Web browser. These user comments and the links between comments and video scenes are stored in annotation databases. An annotation analysis block produces tags from the accu- mulated annotations, while an application block has a tag-based, scene-retrieval system. IBM’s Efficient Video Annotation (EVA) system [15] is a server-based tool for semantic concept annotation of large video and image collections, optimised for collaborative annotation. It includes features such as workload sharing and support in conducting inter-annotator analysis. Aggregate-level user data may be collected 12 Collaborative Movie Annotation 269 during annotation, such as time spent on each page, number and size of thumbnails, and statistics about the usage of keyboard and mouse. EVA returns visual feedback on the annotation. Annotation progress is displayed for the given concept during annotation and overall progress is displayed on the start page. Ulges et al. [16] present a system that automatically tags videos by detecting high-level semantic concepts, such as objects or actions. They use videos from on- line portals like YouTube as a source of training data, while tags provided by users during upload serve as ground truth annotations. Elliot and Ozsoyoglu [17] present a system that shows how semantic metadata about social networks and family relationships can be used to improve semantic annotation suggestions. This includes up to 82% recall for people annotations as well as recall improvements of 20-26% in tag annotation recall when no anno- tation history is available. In addition, utilising relationships among people while searching can provide at least 28% higher recall and 55% higher precision than keyword search while still being up to 12 times faster. Their approach to speed- ing up the annotation process is to build a real-time suggestion system that uses the available multimedia object metadata such as captions, time, an incomplete set of related concepts, and additional semantic knowledge such as people and their relationships. Finally, Li and Lu [18] suggest that there are five major methods for collaborative tagging and all systems and applications fit into one of these five categories:  Ontology approaches: FolksAnnotation, a system that extracts tags from del.ici.ous and maps them to various ontology concepts, has helped to demon- strate that semantics can be derived from tags. However, before any ontological mapping can occur, the vocabulary usually must be converted to a consistent format for string comparison.  Statistical and pattern approaches: These approaches allow researchers to control and manipulate inconsistency and ambiguity in collaborative tagging. Statistical and pattern methodologies work well in general Internet indexing and searching, such as Google’s PageRank or Amazon’s collaborative filtering system.  Social network approaches: These approaches attempt to incorporate social net- work knowledge into collaborative tagging to improve the understanding of tag behaviours.  Visualization approaches: Some researchers have incorporated the help of visu- alization, such as showing a navigation map or displaying the social network relations of the users.  User consensus formation approaches: These approaches focus on the incon- sistency and ambiguity issues associated with collaborative tagging which stem from a lack of user consensus. Prominent applications, such as those offered by Wikipedia that ask users to contribute more extensive information than tags, have placed more focus on this issue. Given the complexity of the content being con- tributed, collaborative control and consensus formation is vital to the usability of a wiki and is driving extensive research. 270 D.D. Zad and H. Agius Summary This section considered example research related to collaborative retrieval and tagging. There is a great deal of research focused on retrieval that exploits user col- laboration to improve results. Mostly, user activity is utilised rather than information explicitly contributed or annotated; consequently, there tends to be less useful, gen- eral purpose metadata produced that could be exploited by other systems. There is also a rising amount of research being carried out on collaborative annotation of non-video media, especially photos, spurred on by websites such as Flickr and del.icio.us. Such sites provide the means for users to collaborate within a commu- nity to produce extensive and comprehensive annotations. However, the static nature of the media makes it less complicated and time-consuming to annotate than video, where there are a much greater number of semantic elements to consider which can be intricately interconnected due to temporality. There is far less understanding of how users behave collaboratively when annotating video; consequently, a body of research is starting to emerge here, some examples of which were reviewed above, where user comments in blogs and other Web resources, tags in YouTube, sam- ple data sets, and power user annotations have been the source for annotating the videos. Since the majority of systems rely on automatic annotation or manual anno- tation from power users, the power of collaboration from more typical ‘everyday’ users, who are far greater in number, to tackle this enormous amount of data is un- derexplored. As a result, we undertook an experiment with a number of everyday users in order to ascertain their typical behaviour and preferences when annotating video, in particular, when annotating user-created movies (e.g. those found on sites like YouTube). The experiment design and results are described in the following sections. Experiment Design In order to better understand how users collaborate when annotating movies, we undertook an experiment with 50 users. This experiment is now described and the results presented in the subsequent section. Users were asked to undertake a series of tasks using two existing video meta- data tools and their interactions were tracked. The users were chosen from a diverse population in order to produce results from typical users similar to the ZoneTag [11] average user approach. The users were unsupervised, but were communicating with other users via an instant messaging application, e.g. Windows Live Messenger, so that transcripts of all conversations could be recorded for later analysis. These tran- scripts contain important information about the behaviour of users in a collaborative community and contain metadata information if they are considered as comments on the videos. This is similar to the approach of Yamamoto et al. [14] who tried to utilise user comments and blog entries as sources for annotations. Users were also interviewed after they completed all tasks. [...]... Animal Person Properties of Event Properties of Object Properties of Person Time of Event Time of Object Time of Person Relations between events, objects and/ or people Location of Event Person Template Location of Person Location of Object Relations between events, objects and/ or people Relations between events, objects and/ or people Relation Template Relations between noise and other content features... Matavire and I Brown, “Investigating the use of “Grounded Theory” in information systems research,” in Proceedings of the 2008 annual research conference of the South African Institute of Computer Scientists and Information Technologists on IT research in developing countries: riding the wave of technology, 2008, pp 139-147 Part II DIGITAL AUDITORY MEDIA Chapter 13 Content Based Digital Music Management and. .. Ubiquitous information management and communication, 2008, pp 20-24 5 S Boll, “MultiTube–Where Web 2.0 and Multimedia Could Meet,” IEEE MultiMedia, Vol 14, No 1, 2007, pp 9-13 6 D.N Milne, “Exploiting web 2.0 for all knowledge-based information retrieval,” in Proceedings of the ACM first Ph.D workshop in Conference on Information and Knowledge Management (CIKM), 2007, pp 69-76 7 G Chen, F Wang, and C Zhang,... 415-424 17 B Elliott and Z.M Ozsoyoglu, “Annotation suggestion and search for personal multimedia objects on the web,” in Proceedings of the 2008 international conference on Content-based image and video retrieval, 2008, pp 75-84 288 D.D Zad and H Agius 18 Q Li and S.C.Y Lu, “Collaborative Tagging Applications and Approaches,” IEEE MultiMedia, Vol 15, No 3, 2008, pp 14-21 19 H Agius and M Angelides, “MPEG-7... front end systems,” in Proceedings of the 21st Annual ACM Symposium on Applied Computing (SAC ’06), Vol 2, 2006, pp 1348-1355 20 M Angelides and H Agius, “An MPEG-7 scheme for semantic content modelling and filtering of digital video,” Multimedia Systems, Vol 11, No 4, 2006, pp 320-339 21 J Corbin and A Strauss, “Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory,”... cornerstone of the architecture and incorporates the metadata scheme described above It enables the creation and maintenance of content metadata for the movie streams contained within the Resources component and user metadata for the Community Interaction and Profiling component It is related to the movie resources through spatiotemporal decompositions of the content, i.e demarcations of the streams in time and/ or... discovered 272 D.D Zad and H Agius User Groups and Tasks Users were given a series of tasks, requiring them to tag and model the content of the video using the tools above Users were assigned to groups (12-13 per group), one for each of the four different content categories above, but were not informed of this Within these category groups, users worked together in smaller experiment groups of 3-6 users to... shared is an effective means for reducing the sheer effort involved in annotating the movies with metadata, by distributing the effort among a large number of members of the community In support of this, this paper has proposed an architecture for collaborative movie annotation Research in collaborative retrieval, collaborative tagging of non-video media, and collaborative tagging of video media was considered... archive management system realized by us which utilizes the techniques described in this chapter J Zhou ( ) and L Xiao Department of Automation, Tsinghua University, Beijing, China e-mail: jzhou@tsinghua.edu.cn; xiaolx02@mails.tsinghua.edu.cn B Furht (ed.), Handbook of Multimedia for Digital Entertainment and Arts, DOI 10.1007/978-0-387-89024-1 13, c Springer Science+Business Media, LLC 2009 291 ... 3 J.P Zappen, T.M Harrison, and D Watson, “A new paradigm for designing e-government: web 2.0 and experience design,” in Proceedings of the 2008 international conference on Digital government research, 2008, pp 17-26 4 W Chen, J Chen, and Q Li, “Adaptive community-based multimedia data retrieval in a distributed environment,” in Proceedings of the 2nd international . Computing and Mathematics, Brunel University, Uxbridge, Middlesex, UK e-mail: damon.zad@brunel.ac.uk; harryagius@acm.org B. Furht (ed.), Handbook of Multimedia for Digital Entertainment and Arts, DOI. individual effort; however, this effort can be greatly reduced by harnessing the power of Web 2.0 communities to create, update and maintain it. D.D. Zad and H. Agius (  ) School of Information. tagging of non-video media. SweetWiki [9] revisits the design rationale of wikis, taking into account the wealth of new Web standards available, such as for the wiki page format (XHTML), for the