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Author

Günther Hölbling

Bio: Günther Hölbling is an academic researcher from University of Passau. The author has contributed to research in topics: Digital television & Interactivity. The author has an hindex of 5, co-authored 15 publications receiving 89 citations.

Papers
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Proceedings ArticleDOI
25 Oct 2010
TL;DR: An intuitive authoring system and player for interactive non-linear video called SIVA Suite is presented for demonstration and additional forms of interactivity are realized as clickable objects in the video and a table of contents for the video.
Abstract: In this paper, an intuitive authoring system and player for interactive non-linear video called SIVA Suite is presented for demonstration. Such videos are enriched by additional content. Possible forms of additional content are plaintext, richtext, images and videos. Interactivity is implemented based upon selection buttons which allow the user to follow different plotlines. Additional forms of interactivity are realized as clickable objects in the video and a table of contents for the video. The software provides a tool for manually cutting videos and an automated shot detection. The non-linear flow of the video can be designed using a scene graph with fork nodes. Editors for text and images support the user in adding information to the video. A finished video project is exported to an XML file with a specific schema and Flash video (flv) files. The player processes the XML file, plays the interactive video and shows additional contents. It can be customized to the requirements of the presentation of the video and the corporate design of the homepage the video is embedded in.

31 citations

Journal ArticleDOI
TL;DR: A prototype platform that uses mobile devices to support multiuser and personalized access for iTV services and connects to the set-top box with ad hoc mechanisms over an existing home network, enabling inexperienced users to access and use the services without having to worry about configuration.
Abstract: The recent digitalization of television creates new opportunities for enhancing the viewer's experience with interactivity. Interactive TV (iTV) is often solely understood as the ability to change a program's storyline. Besides this interpretation, iTV in general means providing some kind of interactive add-ons or TV-related content and services. For example, the viewer might participate in a game show, gather additional information on news topics, or buy a product presented in a commercial. The combination of digital TV and modern set-top boxes facilitates the deployment of such innovative services. In this context, we developed a prototype platform that uses mobile devices to support multiuser and personalized access for iTV services. The mobile devices connect to the set-top box with ad hoc mechanisms over an existing home network, enabling inexperienced users to access and use the services without having to worry about configuration.

13 citations

Journal ArticleDOI
TL;DR: This paper presents an approach to build a TV recommendation system called PersonalTV that enables the use of multiple classifiers, each one specialized on selected attributes of detailed program information, to generate adequate recommendations.
Abstract: This paper presents an approach to build a TV recommendation system called PersonalTV that enables the use of multiple classifiers, each one specialized on selected attributes of detailed program information. For generating adequate recommendations, the system makes use of content filtering and the preferences directly specified by the user within an MPEG-7 profile. By tracking user actions and interpreting their semantics, the system is able to individually weight these actions and dynamically adjusts the process to the user’s evolving preferences. We show how specialized spam fighting methods can successfully be transferred to the area of recommendation systems and adapted accordingly. Being lightweight, these methods are especially applicable in resource-constrained environments such as TV set-top boxes or mobile devices. Moreover, the use of the variance of the beta-distribution as a confidence value for each recommendation is presented.

11 citations

Proceedings ArticleDOI
09 Jun 2010
TL;DR: This paper presents an individualized and flexible tag generation process based on generated and user added tags, and program recommendations are derived in a collaborative filtering step.
Abstract: With the application of the Web 2.0 philosophy to more and more online services and platforms, tagging has become a well established collaboration method. It is often used to simplify organization, navigation and discovery of information and resources in huge archives. In parallel, due to recent developments in digital television, audiences are confronted with a rising amount of available content and demand for better ways to discover programs of interest. In this paper, we propose a tagging-based solution to this problem. Using a content-based filtering approach, we present an individualized and flexible tag generation process. User specific as well as collaborative tag generation is enabled. Based on generated and user added tags, program recommendations are derived in a collaborative filtering step.

9 citations


Cited by
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Journal Article
TL;DR: Der DES basiert auf einer von Horst Feistel bei IBM entwickelten Blockchiffre („Lucipher“) with einer Schlüssellänge von 128 bit zum Sicherheitsrisiko, und zuletzt konnte 1998 mit einem von der „Electronic Frontier Foundation“ (EFF) entwickkelten Spezialmaschine mit 1.800 parallel arbeit
Abstract: Im Jahre 1977 wurde der „Data Encryption Algorithm“ (DEA) vom „National Bureau of Standards“ (NBS, später „National Institute of Standards and Technology“ – NIST) zum amerikanischen Verschlüsselungsstandard für Bundesbehörden erklärt [NBS_77]. 1981 folgte die Verabschiedung der DEA-Spezifikation als ANSI-Standard „DES“ [ANSI_81]. Die Empfehlung des DES als StandardVerschlüsselungsverfahren wurde auf fünf Jahre befristet und 1983, 1988 und 1993 um jeweils weitere fünf Jahre verlängert. Derzeit liegt eine Neufassung des NISTStandards vor [NIST_99], in dem der DES für weitere fünf Jahre übergangsweise zugelassen sein soll, aber die Verwendung von Triple-DES empfohlen wird: eine dreifache Anwendung des DES mit drei verschiedenen Schlüsseln (effektive Schlüssellänge: 168 bit) [NIST_99]. Der DES basiert auf einer von Horst Feistel bei IBM entwickelten Blockchiffre („Lucipher“) mit einer Schlüssellänge von 128 bit. Da die amerikanische „National Security Agency“ (NSA) dafür gesorgt hatte, daß der DES eine Schlüssellänge von lediglich 64 bit besitzt, von denen nur 56 bit relevant sind, und spezielle Substitutionsboxen (den „kryptographischen Kern“ des Verfahrens) erhielt, deren Konstruktionskriterien von der NSA nicht veröffentlicht wurden, war das Verfahren von Beginn an umstritten. Kritiker nahmen an, daß es eine geheime „Trapdoor“ in dem Verfahren gäbe, die der NSA eine OnlineEntschlüsselung auch ohne Kenntnis des Schlüssels erlauben würde. Zwar ließ sich dieser Verdacht nicht erhärten, aber sowohl die Zunahme von Rechenleistung als auch die Parallelisierung von Suchalgorithmen machen heute eine Schlüssellänge von 56 bit zum Sicherheitsrisiko. Zuletzt konnte 1998 mit einer von der „Electronic Frontier Foundation“ (EFF) entwickelten Spezialmaschine mit 1.800 parallel arbeitenden, eigens entwickelten Krypto-Prozessoren ein DES-Schlüssel in einer Rekordzeit von 2,5 Tagen gefunden werden. Um einen Nachfolger für den DES zu finden, kündigte das NIST am 2. Januar 1997 die Suche nach einem „Advanced Encryption Standard“ (AES) an. Ziel dieser Initiative ist, in enger Kooperation mit Forschung und Industrie ein symmetrisches Verschlüsselungsverfahren zu finden, das geeignet ist, bis weit ins 21. Jahrhundert hinein amerikanische Behördendaten wirkungsvoll zu verschlüsseln. Dazu wurde am 12. September 1997 ein offizieller „Call for Algorithm“ ausgeschrieben. An die vorzuschlagenden symmetrischen Verschlüsselungsalgorithmen wurden die folgenden Anforderungen gestellt: nicht-klassifiziert und veröffentlicht, weltweit lizenzfrei verfügbar, effizient implementierbar in Hardund Software, Blockchiffren mit einer Blocklänge von 128 bit sowie Schlüssellängen von 128, 192 und 256 bit unterstützt. Auf der ersten „AES Candidate Conference“ (AES1) veröffentlichte das NIST am 20. August 1998 eine Liste von 15 vorgeschlagenen Algorithmen und forderte die Fachöffentlichkeit zu deren Analyse auf. Die Ergebnisse wurden auf der zweiten „AES Candidate Conference“ (22.-23. März 1999 in Rom, AES2) vorgestellt und unter internationalen Kryptologen diskutiert. Die Kommentierungsphase endete am 15. April 1999. Auf der Basis der eingegangenen Kommentare und Analysen wählte das NIST fünf Kandidaten aus, die es am 9. August 1999 öffentlich bekanntmachte: MARS (IBM) RC6 (RSA Lab.) Rijndael (Daemen, Rijmen) Serpent (Anderson, Biham, Knudsen) Twofish (Schneier, Kelsey, Whiting, Wagner, Hall, Ferguson).

624 citations

Journal ArticleDOI
TL;DR: A literature review about recommender systems in the television domain was performed andRecommender systems were categorized according to seven research questions (RQs) according to different research and development perspectives.
Abstract: A literature review about recommender systems in the television domain was performed.Recommender systems were categorized according to seven research questions (RQs).282 relevant research papers were collected between 2003 and 2015 (until May) using a research methodology.Preliminary findings about the research papers were presented.We presented and discussed the results of this literature review according to RQs. Recommender Systems (RSs) are software tools and techniques providing suggestions of relevant items to users. These systems have received increasing attention from both academy and industry since the 1990s, due to a variety of practical applications as well as complex problems to solve. Since then, the number of research papers published has increased significantly in many application domains (books, documents, images, movies, music, shopping, TV programs, and others). One of these domains has our attention in this paper due to the massive proliferation of televisions (TVs) with computational and network capabilities and due to the large amount of TV content and TV-related content available on the Web. With the evolution of TVs and RSs, the diversity of recommender systems for TV has increased substantially. In this direction, it is worth mentioning that we consider "recommender systems for TV" as those that make recommendations of both TV-content and any content related to TV. Due to this diversity, more investigation is necessary because research on recommender systems for TV domain is still broader and less mature than in other research areas. Thus, this literature review (LR) seeks to classify, synthesize, and present studies according to different perspectives of RSs in the television domain. For that, we initially identified, from the scientific literature, 282 relevant papers published from 2003 to May, 2015. The papers were then categorized and discussed according to different research and development perspectives: recommended item types, approaches, algorithms, architectural models, output devices, user profiling and evaluation. The obtained results can be useful to reveal trends and opportunities for both researchers and practitioners in the area.

70 citations

Patent
01 Jun 2012
TL;DR: In this paper, personalized content is generated from different media items using a content index and user interactions with the media items are analyzed and metadata of segments of media items that are determined to be of particular interest to the users is recorded.
Abstract: Personalized content is generated from different media items using a content index. The content index is generated or updated by identifying segments of media items that are of particular interest to users. User interactions with the media items are analyzed and metadata of segments of media items that are determined to be of particular interest to the users is recorded. The parameters associated with a request for personalized content for a user are matched with the recorded metadata to identify relevant media items or segments of media items which are transmitted to the user as the personalized content.

54 citations

Journal ArticleDOI
TL;DR: This work introduces an authoring tool called SIVA Producer, a specialization to one main medium, in this case video, that allows creating efficient authoring tools using well known paradigms and a distinction of the terms “interactive video”, ‘annotated video’, “non-linear video“ and “hypervideo” is given.
Abstract: With growing bandwidths in the Internet and seemingly unlimited storage capacities on web servers, media became more and more important in the daily use of the World Wide Web. While about ten years ago only text and images with small file sizes (and as a result small resolutions) could be used, it is possible to watch high quality multimedia presentations nowadays. But those rarely exist because of tedious to learn authoring tools. A specialization to one main medium, in our case video, allows creating efficient authoring tools using well known paradigms. This work introduces an authoring tool called SIVA Producer. An iterative process for improving the usability of the authoring tool is described. Furthermore, a distinction of the terms "interactive video", "annotated video", "non-linear video" and "hypervideo" is given.

51 citations