1,190 research outputs found

    Sharing Human-Generated Observations by Integrating HMI and the Semantic Sensor Web

    Get PDF
    Current “Internet of Things” concepts point to a future where connected objects gather meaningful information about their environment and share it with other objects and people. In particular, objects embedding Human Machine Interaction (HMI), such as mobile devices and, increasingly, connected vehicles, home appliances, urban interactive infrastructures, etc., may not only be conceived as sources of sensor information, but, through interaction with their users, they can also produce highly valuable context-aware human-generated observations. We believe that the great promise offered by combining and sharing all of the different sources of information available can be realized through the integration of HMI and Semantic Sensor Web technologies. This paper presents a technological framework that harmonizes two of the most influential HMI and Sensor Web initiatives: the W3C’s Multimodal Architecture and Interfaces (MMI) and the Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE) with its semantic extension, respectively. Although the proposed framework is general enough to be applied in a variety of connected objects integrating HMI, a particular development is presented for a connected car scenario where drivers’ observations about the traffic or their environment are shared across the Semantic Sensor Web. For implementation and evaluation purposes an on-board OSGi (Open Services Gateway Initiative) architecture was built, integrating several available HMI, Sensor Web and Semantic Web technologies. A technical performance test and a conceptual validation of the scenario with potential users are reported, with results suggesting the approach is soun

    Analysis and Forecasting of Trending Topics in Online Media Streams

    Full text link
    Among the vast information available on the web, social media streams capture what people currently pay attention to and how they feel about certain topics. Awareness of such trending topics plays a crucial role in multimedia systems such as trend aware recommendation and automatic vocabulary selection for video concept detection systems. Correctly utilizing trending topics requires a better understanding of their various characteristics in different social media streams. To this end, we present the first comprehensive study across three major online and social media streams, Twitter, Google, and Wikipedia, covering thousands of trending topics during an observation period of an entire year. Our results indicate that depending on one's requirements one does not necessarily have to turn to Twitter for information about current events and that some media streams strongly emphasize content of specific categories. As our second key contribution, we further present a novel approach for the challenging task of forecasting the life cycle of trending topics in the very moment they emerge. Our fully automated approach is based on a nearest neighbor forecasting technique exploiting our assumption that semantically similar topics exhibit similar behavior. We demonstrate on a large-scale dataset of Wikipedia page view statistics that forecasts by the proposed approach are about 9-48k views closer to the actual viewing statistics compared to baseline methods and achieve a mean average percentage error of 45-19% for time periods of up to 14 days.Comment: ACM Multimedia 201

    Mining Threat Intelligence about Open-Source Projects and Libraries from Code Repository Issues and Bug Reports

    Full text link
    Open-Source Projects and Libraries are being used in software development while also bearing multiple security vulnerabilities. This use of third party ecosystem creates a new kind of attack surface for a product in development. An intelligent attacker can attack a product by exploiting one of the vulnerabilities present in linked projects and libraries. In this paper, we mine threat intelligence about open source projects and libraries from bugs and issues reported on public code repositories. We also track library and project dependencies for installed software on a client machine. We represent and store this threat intelligence, along with the software dependencies in a security knowledge graph. Security analysts and developers can then query and receive alerts from the knowledge graph if any threat intelligence is found about linked libraries and projects, utilized in their products

    Relationship based Entity Recommendation System

    Get PDF
    With the increase in usage of the internet as a place to search for information, the importance of the level of relevance of the results returned by search engines have increased by many folds in recent years. In this paper, we propose techniques to improve the relevance of results shown by a search engine, by using the kinds of relationships between entities a user is interested in. We propose a technique that uses relationships between entities to recommend related entities from a knowledge base which is a collection of entities and the relationships with which they are connected to other entities. These relationships depict more real world relationships between entities, rather than just simple “is-a” or “has-a” relationships. The system keeps track of relationships on which user is clicking and uses this click count as a preference indicator to recommend future entities. This approach is very useful in modern day semantic web searches for recommending entities of user’s interests

    The Semantic Web MIDI Tape: An Interface for Interlinking MIDI and Context Metadata

    Get PDF
    The Linked Data paradigm has been used to publish a large number of musical datasets and ontologies on the Semantic Web, such as MusicBrainz, AcousticBrainz, and the Music Ontology. Recently, the MIDI Linked Data Cloud has been added to these datasets, representing more than 300,000 pieces in MIDI format as Linked Data, opening up the possibility for linking fine-grained symbolic music representations to existing music metadata databases. Despite the dataset making MIDI resources available in Web data standard formats such as RDF and SPARQL, the important issue of finding meaningful links between these MIDI resources and relevant contextual metadata in other datasets remains. A fundamental barrier for the provision and generation of such links is the difficulty that users have at adding new MIDI performance data and metadata to the platform. In this paper, we propose the Semantic Web MIDI Tape, a set of tools and associated interface for interacting with the MIDI Linked Data Cloud by enabling users to record, enrich, and retrieve MIDI performance data and related metadata in native Web data standards. The goal of such interactions is to find meaningful links between published MIDI resources and their relevant contextual metadata. We evaluate the Semantic Web MIDI Tape in various use cases involving user-contributed content, MIDI similarity querying, and entity recognition methods, and discuss their potential for finding links between MIDI resources and metadata

    Classifying Crises-Information Relevancy with Semantics

    Get PDF
    Social media platforms have become key portals for sharing and consuming information during crisis situations. However, humanitarian organisations and affected communities often struggle to sieve through the large volumes of data that are typically shared on such platforms during crises to determine which posts are truly relevant to the crisis, and which are not. Previous work on automatically classifying crisis information was mostly focused on using statistical features. However, such approaches tend to be inappropriate when processing data on a type of crisis that the model was not trained on, such as processing information about a train crash, whereas the classifier was trained on floods, earthquakes, and typhoons. In such cases, the model will need to be retrained, which is costly and time-consuming. In this paper, we explore the impact of semantics in classifying Twitter posts across same, and different, types of crises. We experiment with 26 crisis events, using a hybrid system that combines statistical features with various semantic features extracted from external knowledge bases. We show that adding semantic features has no noticeable benefit over statistical features when classifying same-type crises, whereas it enhances the classifier performance by up to 7.2% when classifying information about a new type of crisis
    • 

    corecore