Pushing the Quality Level in Networked News Business: Semantic-Based Content Retrieval and Composition in International News Publishing

Electronic publishing exploits numerous possibilities to present or exchange information and to communicate via most current media like the Internet. By utilizing modern Web technologies like Web Services, loosely coupled services, and peer-to-peer networks we describe the integration of an intelligent business news presentation and distribution network. Employing semantics technologies enables the coupling of multinational and multilingual business news data on a scalable international level and thus introduce a service quality that is not achieved by alternative technologies in the news distribution area so far. Architecturally, we identified the loose coupling of existing services as the most feasible way to address multinational and multilingual news presentation and distribution networks. Furthermore we semantically enrich multinational news contents by relating them using AI techniques like the Vector Space Model. Summarizing our experiences we describe the technical integration of semantics and communication technologies in order to create a modern international news network.

MPEG-7: The Multimedia Content Description Interface

In this paper a review of the most used MPEG-7 descriptors are presented. Some considerations for choosing the most proper descriptor for a particular image or video data set are outlined.

ImageSpace: An Environment for Image Ontology Management

More and more researchers have realized that ontologies will play a critical role in the development of the Semantic Web, the next generation Web in which content is not only consumable by humans, but also by software agents. The development of tools to support ontology management including creation, visualization, annotation, database storage, and retrieval is thus extremely important. We have developed ImageSpace, an image ontology creation and annotation tool that features (1) full support for the standard web ontology language DAML+OIL; (2) image ontology creation, visualization, image annotation and display in one integrated framework; (3) ontology consistency assurance; and (4) storing ontologies and annotations in relational databases. It is expected that the availability of such a tool will greatly facilitate the creation of image repositories as islands of the Semantic Web.

Services for Content Creation and Presentation in an Iconographical Digital Library

Content creation and presentation are key activities in a multimedia digital library (MDL). The proper design and intelligent implementation of these services provide a stable base for overall MDL functionality. This paper presents the framework and the implementation of these services in the latest version of the “Virtual Encyclopaedia of Bulgarian Iconography” multimedia digital library. For the semantic description of the iconographical objects a tree-based annotation template is implemented. It provides options for autocompletion, reuse of values, bilingual entering of data, automated media watermarking, resizing and conversing. The paper describes in detail the algorithm for automated appearance of dependent values for different characteristics of an iconographical object. An algorithm for avoiding duplicate image objects is also included. The service for automated appearance of new objects in a collection after their entering is included as an important part of the content presentation. The paper also presents the overall service-based architecture of the library, covering its main service panels, repositories and their relationships. The presented vision is based on a long-term observation of the users’ preferences, cognitive goals, and needs, aiming to find an optimal functionality solution for the end users.

Digital revision of USGS 1:500,000-scale New Jersey state base map /

Shows stages and operations undertaken in revising the New Jersey state base map.

Efficient Image and Video Representations for Retrieval

Image (Video) retrieval is an interesting problem of retrieving images (videos) similar to the query. Images (Videos) are represented in an input (feature) space and similar images (videos) are obtained by finding nearest neighbors in the input representation space. Numerous input representations both in real valued and binary space have been proposed for conducting faster retrieval. In this thesis, we present techniques that obtain improved input representations for retrieval in both supervised and unsupervised settings for images and videos. Supervised retrieval is a well known problem of retrieving same class images of the query. We address the practical aspects of achieving faster retrieval with binary codes as input representations for the supervised setting in the first part, where binary codes are used as addresses into hash tables. In practice, using binary codes as addresses does not guarantee fast retrieval, as similar images are not mapped to the same binary code (address). We address this problem by presenting an efficient supervised hashing (binary encoding) method that aims to explicitly map all the images of the same class ideally to a unique binary code. We refer to the binary codes of the images as `Semantic Binary Codes' and the unique code for all same class images as `Class Binary Code'. We also propose a new class­ based Hamming metric that dramatically reduces the retrieval times for larger databases, where only hamming distance is computed to the class binary codes. We also propose a Deep semantic binary code model, by replacing the output layer of a popular convolutional Neural Network (AlexNet) with the class binary codes and show that the hashing functions learned in this way outperforms the state­ of ­the art, and at the same time provide fast retrieval times. In the second part, we also address the problem of supervised retrieval by taking into account the relationship between classes. For a given query image, we want to retrieve images that preserve the relative order i.e. we want to retrieve all same class images first and then, the related classes images before different class images. We learn such relationship aware binary codes by minimizing the similarity between inner product of the binary codes and the similarity between the classes. We calculate the similarity between classes using output embedding vectors, which are vector representations of classes. Our method deviates from the other supervised binary encoding schemes as it is the first to use output embeddings for learning hashing functions. We also introduce new performance metrics that take into account the related class retrieval results and show significant gains over the state­ of­ the art. High Dimensional descriptors like Fisher Vectors or Vector of Locally Aggregated Descriptors have shown to improve the performance of many computer vision applications including retrieval. In the third part, we will discuss an unsupervised technique for compressing high dimensional vectors into high dimensional binary codes, to reduce storage complexity. In this approach, we deviate from adopting traditional hyperplane hashing functions and instead learn hyperspherical hashing functions. The proposed method overcomes the computational challenges of directly applying the spherical hashing algorithm that is intractable for compressing high dimensional vectors. A practical hierarchical model that utilizes divide and conquer techniques using the Random Select and Adjust (RSA) procedure to compress such high dimensional vectors is presented. We show that our proposed high dimensional binary codes outperform the binary codes obtained using traditional hyperplane methods for higher compression ratios. In the last part of the thesis, we propose a retrieval based solution to the Zero shot event classification problem - a setting where no training videos are available for the event. To do this, we learn a generic set of concept detectors and represent both videos and query events in the concept space. We then compute similarity between the query event and the video in the concept space and videos similar to the query event are classified as the videos belonging to the event. We show that we significantly boost the performance using concept features from other modalities.

GPX - Gardens Point XML Information Retrieval at INEX 2004

Traditional information retrieval (IR) systems respond to user queries with ranked lists of relevant documents. The separation of content and structure in XML documents allows individual XML elements to be selected in isolation. Thus, users expect XML-IR systems to return highly relevant results that are more precise than entire documents. In this paper we describe the implementation of a search engine for XML document collections. The system is keyword based and is built upon an XML inverted file system. We describe the approach that was adopted to meet the requirements of Content Only (CO) and Vague Content and Structure (VCAS) queries in INEX 2004.

From the consciousness industry to the creative industries : consumer-created content, social network markets, & the growth of knowledge

In studies of media industries, too much attention has been paid to providers and firms, too little to consumers and markets. But with user-created content, the question first posed more than a generation ago by the uses & gratifications method and taken up by semiotics and the active audience tradition (‘what do audiences do with media?’), has resurfaced with renewed force. What’s new is that where this question (of what the media industries and audiences did with each other) used to be individualist and functionalist, now, with the advent of social networks using Web 2.0 affordances, it can be re-posed at the level of systems and populations as well.

Search engine content analysis

Search engines have forever changed the way people access and discover knowledge, allowing information about almost any subject to be quickly and easily retrieved within seconds. As increasingly more material becomes available electronically the influence of search engines on our lives will continue to grow. This presents the problem of how to find what information is contained in each search engine, what bias a search engine may have, and how to select the best search engine for a particular information need. This research introduces a new method, search engine content analysis, in order to solve the above problem. Search engine content analysis is a new development of traditional information retrieval field called collection selection, which deals with general information repositories. Current research in collection selection relies on full access to the collection or estimations of the size of the collections. Also collection descriptions are often represented as term occurrence statistics. An automatic ontology learning method is developed for the search engine content analysis, which trains an ontology with world knowledge of hundreds of different subjects in a multilevel taxonomy. This ontology is then mined to find important classification rules, and these rules are used to perform an extensive analysis of the content of the largest general purpose Internet search engines in use today. Instead of representing collections as a set of terms, which commonly occurs in collection selection, they are represented as a set of subjects, leading to a more robust representation of information and a decrease of synonymy. The ontology based method was compared with ReDDE (Relevant Document Distribution Estimation method for resource selection) using the standard R-value metric, with encouraging results. ReDDE is the current state of the art collection selection method which relies on collection size estimation. The method was also used to analyse the content of the most popular search engines in use today, including Google and Yahoo. In addition several specialist search engines such as Pubmed and the U.S. Department of Agriculture were analysed. In conclusion, this research shows that the ontology based method mitigates the need for collection size estimation.

Primary students’ spatial visualization and spatial orientation : an evidence base for instruction

This paper reports on the performance of 58 11 to 12-year-olds on a spatial visualization task and a spatial orientation task. The students completed these tasks and explained their thinking during individual interviews. The qualitative data were analysed to inform pedagogical content knowledge for spatial activities. The study revealed that “matching” or “matching and eliminating” were the typical strategies that students employed on these spatial tasks. However, errors in making associations between parts of the same or different shapes were noted. Students also experienced general difficulties with visual memory and language use to explain their thinking. The students’ specific difficulties in spatial visualization related to obscured items, the perspective used, and the placement and orientation of shapes.

PhD forum : multiple camera management using wide base-line matching

Manual calibration of large and dynamic networks of cameras is labour intensive and time consuming. This is a strong motivator for the development of automatic calibration methods. Automatic calibration relies on the ability to find correspondences between multiple views of the same scene. If the cameras are sparsely placed, this can be a very difficult task. This PhD project focuses on the further development of uncalibrated wide baseline matching techniques.

From Prosumer to Produser: Understanding User-Led Content Creation

Alvin Toffler’s image of the prosumer (1970, 1980, 1990) continues to influence in a significant way our understanding of the user-led, collaborative processes of content creation which are today labelled “social media” or “Web 2.0”. A closer look at Toffler’s own description of his prosumer model reveals, however, that it remains firmly grounded in the mass media age: the prosumer is clearly not the self-motivated creative originator and developer of new content which can today be observed in projects ranging from open source software through Wikipedia to Second Life, but simply a particularly well-informed, and therefore both particularly critical and particularly active, consumer. The highly specialised, high end consumers which exist in areas such as hi-fi or car culture are far more representative of the ideal prosumer than the participants in non-commercial (or as yet non-commercial) collaborative projects. And to expect Toffler’s 1970s model of the prosumer to describe these 21st-century phenomena was always an unrealistic expectation, of course. To describe the creative and collaborative participation which today characterises user-led projects such as Wikipedia, terms such as ‘production’ and ‘consumption’ are no longer particularly useful – even in laboured constructions such as ‘commons-based peer-production’ (Benkler 2006) or ‘p2p production’ (Bauwens 2005). In the user communities participating in such forms of content creation, roles as consumers and users have long begun to be inextricably interwoven with those as producer and creator: users are always already also able to be producers of the shared information collection, regardless of whether they are aware of that fact – they have taken on a new, hybrid role which may be best described as that of a produser (Bruns 2008). Projects which build on such produsage can be found in areas from open source software development through citizen journalism to Wikipedia, and beyond this also in multi-user online computer games, filesharing, and even in communities collaborating on the design of material goods. While addressing a range of different challenges, they nonetheless build on a small number of universal key principles. This paper documents these principles and indicates the possible implications of this transition from production and prosumption to produsage.

XCFS - a novel approach for clustering XML documents using both the structure and the content

XML document clustering is essential for many document handling applications such as information storage, retrieval, integration and transformation. An XML clustering algorithm should process both the structural and the content information of XML documents in order to improve the accuracy and meaning of the clustering solution. However, the inclusion of both kinds of information in the clustering process results in a huge overhead for the underlying clustering algorithm because of the high dimensionality of the data. This paper introduces a novel approach that first determines the structural similarity in the form of frequent subtrees and then uses these frequent subtrees to represent the constrained content of the XML documents in order to determine the content similarity. The proposed method reduces the high dimensionality of input data by using only the structure-constrained content. The empirical analysis reveals that the proposed method can effectively cluster even very large XML datasets and outperform other existing methods.

Will they know enough? Pre-service primary teachers' knowledge base for teaching intergrated social sciences

A significant issue in primary teacher education is developing a knowledge base which prepares teachers to teach in a range of subject areas. In Australia, the problem in primary social science education is compounded by the integrated nature of the key learning area of Studies of Society and Environment (SOSE). Recent debates on teaching integrated social sciences omit discussions on the knowledge base for teaching. In this paper, a case study approach is used to investigate primary pre-service teachers’ approaches to developing a knowledge base in designing a SOSE curriculum unit. Data from five teacher-educators who taught primary SOSE curriculum indicates that novice teachers’ subject content knowledge, as revealed through their curriculum planning, lacked a disciplinary basis. However, understanding of inquiry learning, which is fundamental to social science education, was much stronger. This paper identifies a gap in the scholarship on teaching integrated social science and illustrates the need to support and develop primary teachers’ disciplinary knowledge in teacher education.