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In last years, the introduction of new wireless communication systems has stimulated the technical and scientific community to investigate future evolution scenarios for 3G networks, generically referred to as Beyond-3G or 4G. In order to guarantee high end-to-end quality of service and security, Beyond-3G networks will cope with some issues such as session security in wireless environments and seamless mobility among different coverage domains and, possibly, access technologies. In this paper we analyze the security threats emerging from some mechanisms for mobility management, and we propose a solution to improve the security level under the assumption that both communicating terminals are attached to a 3GPP IP Multimedia Subsystem that supports both the Session Initiation Protocol and the Mobile IP version 6 protocol. Keywords: Beyond-3G networks, IP Multimedia Subsystem, Mobility management, Mobile IP version 6, Binding authentication, Session initiation protocol. [ABSTRACT FROM AUTHOR]

The development of video technology and video-related applications demands strong support in semantic data models. To meet such a requirement, many video semantic data models have been proposed. The semantic model plays a key role in providing query capability and other features for a video database. However, to our knowledge, the criteria for a good semantic model remain open at present. As a result, people lack the rules for evaluating an existing model and the guidelines for the design of a new data model when necessary. To address this issue, this paper proposes twenty one properties as the criteria for video semantic models, and gives the evaluation result of eleven existing rich semantic models according to these properties. It shows that these models mostly concentrate on basic expressive power and query capability, and fulfill users' primary requirements. But in some advanced features such as expressive power, acquisition and analysis of semantic information, and query capability etc., there are rooms for further enhancement. The paper concludes by indicating some research directions for video semantic models. [ABSTRACT FROM AUTHOR]

In Harmonic windows scheduling (HWS), a data file is divided into N pages and the pages are scheduled in c channels in such a way that each page i appears at least once in some channel in every window of size i. The optimal HWS problem aims to maximize N. Let κ be the largest integer satisfying Hκ ≤ c, where Hn is the nth harmonic number. Then κ is an upper bound on N, if the HWS framework is used. Thus an optimal HWS schedule wastes "bandwidth" at least c-Hκ. Harmonic block windows scheduling (HBWS) generalizes HWS by grouping b consecutive pages into a superpage. Let N be the total number of pages scheduled. The ratio N/b is directly proportional to the maximum initial waiting time in Media-on-Demand applications. We propose a method that starts with a HWS schedule and modifies it to generate a HBWS schedule that achieves a higher ratio N/b. For up to five channels, we demonstrate that we can always achieve N/b >κ. We also prove that as we increase b, N/b approaches the theoretical upper bound. [ABSTRACT FROM AUTHOR]

This paper presents an effective overlay H.263+ video multicast system over the Internet. The proposed system consists of overlay multicast tree construction, target bandwidth determining process, and H.263+ rate control. Overlay multicast tree construction algorithm and target bandwidth determining process work alternatively to satisfy the average delay constraint, and H.263+ rate control is implemented to enhance the human visual perceptual quality over the multicast tree. Finally, experimental results are provided to show the performance of the proposed system. [ABSTRACT FROM AUTHOR]

Automated visual surveillance has emerged as a trendy application domain in recent years. Many approaches have been developed on video processing and understanding. Content-based access to surveillance video has become a challenging research area. The results of a considerable amount of work dealing with automated access to visual surveillance have appeared in the literature. However, the event models and the content-based querying and retrieval components have significant gaps remaining unfilled. To narrow these gaps, we propose a database model for querying surveillance videos by integrating semantic and low-level features. In this paper, the initial design of the database model, the query types, and the specifications of its query language are presented. [ABSTRACT FROM AUTHOR]

This work proposes an approach for managing multimedia information associated to 3D environments navigated by the user. The influence of context (location, user profile, user history, time, device and network) on such management is considered in different stages of the multimedia information lifecycle, with a particular reference to the authoring and presentation phases. The different types of contexts will be modeled (i.e., with a particular emphasis on location and user history) and analyzed in relation to content creation, leading both to the definition of a hierarchy of contexts and to a data structure modeled on authoring needs. Such formalizations will be then used as key components of an implementation architecture for controlling and proactively managing multimedia information in the presentation phase. Application examples related to cultural heritage are presented, showing how such architecture can manage complex user experiences on the basis of changes of context. [ABSTRACT FROM AUTHOR]

This paper introduces a Generic Language for Interface DEvelopment (GLIDE) that supports the rapid prototyping of Graphical User Interfaces for the Agents Channeling ContExt Sensitive Services (ACCESS) architecture. ACCESS is an open agent-based framework that enables the fabrication and deployment of context aware applications. GUIs developed for ACCESS are contained within dynamic composite objects that are composed of a number of interacting components. Scripts written in GLIDE define the structure of the GUI's composite object and abstract out inter-component dependencies, thus ensuring that the GUI constituents remain modular, extensible and reusable. The GLIDE framework acts as a semantic model of the interface and enables the system to dynamically reconfigure and reorganize itself at run-time so as to adapt to user interaction. Additionally, GLIDE provides a mechanism to ensure that the abstract composition of the interface remains consistent across a range of ubiquitous devices. [ABSTRACT FROM AUTHOR]

The development of technology generates huge amounts of non-textual information, such as images. An efficient image annotation and retrieval system is highly desired. Clustering algorithms make it possible to represent visual features of images with finite symbols. Based on this, many statistical models, which analyze correspondence between visual features and words and discover hidden semantics, have been published. These models improve the annotation and retrieval of large image databases. However, current state of the art including our previous work produces too many irrelevant keywords for images during annotation. In this paper, we propose a novel approach that augments the classical model with generic knowledge-based, WordNet. Our novel approach strives to prune irrelevant keywords by the usage of WordNet. To identify irrelevant keywords, we investigate various semantic similarity measures between keywords and finally fuse outcomes of all these measures together to make a final decision. We have implemented various models to link visual tokens with keywords based on knowledge-based, WordNet and evaluated performance using precision, and recall using benchmark dataset. The results show that by augmenting knowledge-based with classical model we can improve annotation accuracy by removing irrelevant keywords. [ABSTRACT FROM AUTHOR]

Haptic perception refers to the human ability to perceive spatial properties through tactile and haptic sensations. Humans have an uncanny ability to analyze objects based only on sparse information from haptic stimuli. Contextual clues about material of an object, its overall shape, size and weight configurations perceived by individuals, lead to recognition of an object and its spatial features. In this paper, we present strategies and algorithms to model context in haptic applications that allow user to explore objects in virtual reality/augmented reality, haptically. Our methodology is based on modeling user's cognitive and motor strategy of haptic exploration. Additionally we also model physiological arrangement of tactile sensors in the human hand. These models provide the context to adapt haptic displays to a user's style of haptic perception and exploration and the present state of the user's exploration. We designed a tactile cueing paradigm to test the validity of the contextual models. Initial results show improvement in accuracy and efficiency of haptic perception when compared to the conventional approaches that do not model context in haptic rendering. [ABSTRACT FROM AUTHOR]

The icon language interface is designed to provide the people with language disabilities with more smooth and convenient communication environment than the traditional keyboard-based input system. In order for that, vocabulary commanding tendencies and characteristics of proto-corpus built upon the frequently-used conversation sections will be analyzed, and the Korean language's vocabulary and their meanings applied to the icons will be retrieved by the use of morpheme, phrase and semantic analysis techniques. The icon types to be intuitively recognized and communicable by users are selected, and they are matched with the retrieved Korean vocabulary and meanings. In order to create a relevant situation to communicate from the relations between neighboring icons, the icon language is formed through the each definition of icon language's vocabulary, grammar rules, parts of speech and semantic system. [ABSTRACT FROM AUTHOR]

Music description is nowadays considered an important matter in Information and Communication Technology. The encoding formats commonly accepted and employed are often characterized by a partial view of the whole problem: they describe music data or metadata for score, audio tracks, computer performances of music pieces, but they seldom encode all these aspects together. In this paper, we present the MX formalism that aims to address this limitation of the existing formats, by providing a Semantic Web compatible representation of music information in terms of structural and semantic features, by means of XML and OWL. [ABSTRACT FROM AUTHOR]

In this paper we introduce a new indexing approach to representing multimedia object classes generated by the Expectation Maximization clustering algorithm in a balanced and dynamic tree structure. To this aim the EM algorithm has been modified in order to obtain at each step of its recursive application balanced clusters. In this manner our tree provides a simple and practical solution to index clustered data and support efficient retrieval of the nearest neighbors in high dimensional object spaces. [ABSTRACT FROM AUTHOR]

The traditional multidimensional models have a static structure where members of dimensions are computed in a unique way. However, data (particularly multimedia data) is often characterized by descriptors that can be obtained by various computation modes. We define these computation modes as "functional versions" of the descriptors. We propose a Functional Multiversion Multidimensional Model ("F2M model") by integrating the concept of "version of dimension". This concept defines dimensions with members computed according to various functional versions. In order to allow the user to choose the best representation of data, this new approach integrates a choice of computation modes of these members into the model. We implement a multimedia data warehouse in the medical field by integrating the multimedia data of a therapeutic study into a multidimensional model. We formally define a conceptual model and we present a prototype for this study. [ABSTRACT FROM AUTHOR]

The importance of web discussion boards is growing with the interest of sharing knowledge and doubts with colleagues in a working/studying environment. The challenge is to organize the structure of discussion boards, to make the navigation easier, and to effectively extract relevant information. Message hierarchies in web discussion boards, manually organised by users participating into the discussion, might grow uncontrolled, thus making navigation more and more difficult for users. The goal of this paper is to develop a technique to organise messages in a message board, by automatically classifying and annotating pairs of postings to guide users through discussion segments relevant to their navigational goals. [ABSTRACT FROM AUTHOR]

Information retrieval is moving beyond the stage where users simply type one or more keywords and retrieve a ranked list of documents. In such a scenario users have to go through the returned documents in order to find what they are actually looking for. More often they would like to get targeted answers to their queries without extraneous information, even if their requirements are not well specified. In this paper we propose an approach for designing a web retrieval system able to find the desired information through several interactions with the users. The proposed approach allows to overcome the problems deriving from ambiguous or too vague queries, using semantic search and topic detection techniques. The results of the very first experiments on a prototype system are also reported. [ABSTRACT FROM AUTHOR]

Most face based biometric systems and the underlying recognition algorithms are often more suited for verification (one-to-one comparison) instead of identification (one-to-many comparison) purposes. This is even more true in case of large face database, as the computational cost of an accurate comparison between the query and a gallery of many thousands of individuals could be too high for practical applications. In this paper we present a 3D based face recognition method which relies on normal image to represent and compare face geometry. It features fast comparison time and good robustness to a wide range of expressive variations thanks to an expression weighting mask, automatically generated for each enrolled subject. To better address one-to-many recognition applications, the proposed approach is improved via DFT based indexing of face descriptors and k-d-tree based spatial access to clusters of similar faces. We include experimental results showing the effectiveness of the presented method in terms of recognition accuracy and the improvements in one-to-many recognition time achieved thanks to indexing and retrieval techniques applied to a large parametric 3D face database. [ABSTRACT FROM AUTHOR]

Nowadays content and services are available at different sources and places, thus a user can be seen as an integral part of numerous applications in which he/she interacts with service providers, product sellers, governmental organisations, friends and colleagues. Information access personalization can be defined as any set of actions that can tailor information to a particular user or set of users. To achieve effective personalization, single users and organizations must rely on all available information, including: user profile, channel peculiarities, users current interests and typical behaviour, source content, source structure, as well as domain knowledge. In addition, efficient and intelligent techniques are needed to effectively use the discovered knowledge to enhance the users' experience. These techniques must address important challenges emanating from the size and the heterogeneous nature of the data itself, as well as the dynamic nature of user interactions with data sources. These challenges include scalability of the personalization solutions in the process of data integration, and successful integration of techniques from knowledge representation, semantic web, information retrieval and filtering, databases, user modelling and human-computer interaction. Our approach addresses these issues with particular attention to the process of data tailoring, which consists of the exploitation of knowledge about the user, the adopted channel and the environment, altogether called context, to the end of reducing the amount of information imported on the mobile device. Tailoring is needed because of two main reasons: one is the need to keep information manageable, in order for the user not to be confused by too much noise; the second reason is the frequent case that the mobile device be a small one, like a palm computer or a cellular phone, in which condition only the most precious information must be kept on board. We consider open, networked, peer-based systems, according to paradigms where there is no previous knowledge and relationship among the parties, which may be mobile as well as fixed devices. The interaction among such devices is transient, since it is subject to network and device availability: indeed the nature of these devices and the variety of ambient strongly affect the cooperation methods and techniques. Semantic based caching techniques are exploited to cope with the above mentioned network availability problems, always allowing the single party to retain the appropriate portion of needed data, while other fragments, stored at different peers, can be queried only when a connection is available. The mobile and dynamic context where the devices cooperate determines the fraction of data located on board, which, due to the limited amount of memory available, must be refreshed according to semantic context-based criteria. On the other hand, power aware data access techniques must be employed to manage the problem of limited battery life. Consider the example of a semantic community formed to enable scientific collaboration in the medicine context; here, different resource structures and meanings are provided by the community peers. Besides selecting and semantically integrating the most appropriate resources provided by the various peers, it is the special goal of such techniques to obey the constraints imposed by the device context. For instance, during a home visit, a doctor in need of information about the symptoms of a rare disease can search, through his/her PDA or cell phone, clinical databases and research reports on the web looking for assistance in his/her diagnosis.… [ABSTRACT FROM AUTHOR]

The development of technology generates huge amounts of non-textual information, such as images. An efficient image annotation and retrieval system is highly desired. Clustering algorithms make it possible to represent visual features of images with finite symbols. Based on this, many statistical models, which analyze correspondence between visual features and words and discover hidden semantics, have been published. These models improve the annotation and retrieval of large image databases. However, image data usually have a large number of dimensions. Traditional clustering algorithms assign equal weights to these dimensions, and become confounded in the process of dealing with these dimensions. In this tutorial, first, we will present current state of the art and its shortcomings. We will present some classical models (e.g., translation model (TM), cross-media relevance model etc.). Second, we will present weighted feature selection algorithm as a solution to the existing problem. For a given cluster, we determine relevant features based on histogram analysis and assign greater weight to relevant features as compared to less relevant features. Third, we will exploit spatial correlation to disambiguate visual features, and spatial relationship will be constructed by spatial association rule mining. Fourth, we will present the continuous relevance model and multiple Bernoulli model for avoiding clustering. We will present mechanisms to link visual tokens with keywords based on these models. Fifth, we will present mechanisms to improve accuracy of classical model, TM by exploiting the WordNet knowledge-base. Sixth, we will present a framework to model semantic visual concept in video/images by fusing multiple evidence with the usage of an ontology. Seventh, we will show that weighted feature selection is better than traditional ones (TM) for automatic image annotation and retrieval. Finally, we will discuss open problems and future directions in the domain of image and video. [ABSTRACT FROM AUTHOR]

Much of modern scientific research depends on databases, but do we need anything more than conventional database technology to support scientific data? One of the reasons for the development of the Grid is the sheer size of the datasets involved. This has introduced new problems for distributed data, distributed scientific programming, and the combination of the two. However there are other, equally important issues which demand new database technology. In this talk I want raise some of them. Annotation of existing data now provides a new form of communication between scientists, but conventional database technology provides little support for attaching annotations. I shall show why new models of both data and query languages are needed. Closely related to annotation is provenance — knowing where your data has come from. This is now a real problem in bioinformatics with literally hundreds of databases, most of which are derived from others by a process of transformation, correction and annotation. Preserving past states of a database — archiving — is also important for verifying the basis of scientific research, yet few published scientific databases do a good job of archiving. Past "editions" of the database get lost. I shall describe a system that allows frequent archiving and efficient retrieval with remarkably little space overhead. Finally, what do scientific databases have to do with multimedia information systems? Ostensibly nothing. However presentation of data has given us some clues about how to approach some of the problems above. [ABSTRACT FROM AUTHOR]