1,379 research outputs found

    "When and Where?": Behavior Dominant Location Forecasting with Micro-blog Streams

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    The proliferation of smartphones and wearable devices has increased the availability of large amounts of geospatial streams to provide significant automated discovery of knowledge in pervasive environments, but most prominent information related to altering interests have not yet adequately capitalized. In this paper, we provide a novel algorithm to exploit the dynamic fluctuations in user's point-of-interest while forecasting the future place of visit with fine granularity. Our proposed algorithm is based on the dynamic formation of collective personality communities using different languages, opinions, geographical and temporal distributions for finding out optimized equivalent content. We performed extensive empirical experiments involving, real-time streams derived from 0.6 million stream tuples of micro-blog comprising 1945 social person fusion with graph algorithm and feed-forward neural network model as a predictive classification model. Lastly, The framework achieves 62.10% mean average precision on 1,20,000 embeddings on unlabeled users and surprisingly 85.92% increment on the state-of-the-art approach.Comment: Accepted as a full paper in the 2nd International Workshop on Social Computing co-located with ICDM, 2018 Singapor

    Searching Spontaneous Conversational Speech:Proceedings of ACM SIGIR Workshop (SSCS2008)

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    Entity Linking to Wikipedia : Grounding entity mentions in natural language text using thematic context distance and collective search

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    This thesis proposes new methods for entity linking in natural language text that assigns entity mentions in unstructured natural language text to the semi-structured encyclopedia Wikipedia. Doing so, entity linking grounds a mention to an encyclopedic entry in Wikipedia and embeds it into this Linked-Open-Data hub. This enables a higher level view on single documents, provides hints for further reading and may be used to add details from other sources. Furthermore, enriching text documents with such links simultaneously resolves the ambiguity of entity names. This ambiguity is an unsolved challenge for many text mining applications: one entity may be designated by a multitude of names and every mention may denote a multitude of entities. Resolving the ambiguity of entity names is thus a crucial step for entity based retrieval, an open problem for most information retrieval and extraction tasks. For instance, search engines relying on heuristic string matches often retrieve irrelevant results as they can not satisfyingly resolve ambiguity. Moreover, there is a huge number of entity mentions that can not be linked to Wikipedia since albeit of its size, Wikipedia has a restricted coverage. Earlier and current work often ignored this and consequently all mentions of uncovered entities. Other approaches handle only entity mentions of specific types or are focussed on English as target language. Apart from such restrictions, no method achieves perfect linking performance. These are the tasks approached in this thesis. We introduce new methods for candidate entity retrieval and candidate entity consolidation, the key components to recall and precision, exploiting both the vast amount of structured and unstructured information stored in Wikipedia. First, we propose a new contextual similarity measure based on latent topic distributions inferred from unstructured natural language text. We show that this thematic distance between mention and candidate entity contexts yields a lower linking error rate than purely word based distances. Being language independent, this method enables high performance entity linking in previously neglected languages such as German and French. This approach is especially suitable, albeit not restricted to link person names, the class of mentions with highest ambiguity. We next propose a new candidate retrieval method to enable successful entity linking also for other entities that are not referenced canonically or exhibit the thematic coherence of persons. We introduce collective search that uses the structured information encoded in Wikipedia’s hyperlink graph to arrive at sets of strongly related candidate entities. This enables us to better handle synonymy, one of the hardest problems in entity linking and not thoroughly treated in previous work. We emphasize on general applicability and evaluate this method on a broad collection of benchmark corpora both in a supervised as well as in an unsupervised setting. We show that candidate enhancement through collective search increases linking performance on nearly all of these corpora and that our method is the most stable compared to other state-of-the-art approaches. Presenting the first unification of diverse performance measures, we also make a step forward to the comparability of entity linking methods. In conclusion, we provide state-of-the-art entity linking methods for nearly all of the current use cases. When it comes to fine-tuning, we note that entity linking has subjective aspects and adaptions may be necessary depending on the task at hand

    Towards Population of Knowledge Bases from Conversational Sources

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    With an increasing amount of data created daily, it is challenging for users to organize and discover information from massive collections of digital content (e.g., text and speech). The population of knowledge bases requires linking information from unstructured sources (e.g., news articles and web pages) to structured external knowledge bases (e.g., Wikipedia), which has the potential to advance information archiving and access, and to support knowledge discovery and reasoning. Because of the complexity of this task, knowledge base population is composed of multiple sub-tasks, including the entity linking task, defined as linking the mention of entities (e.g., persons, organizations, and locations) found in documents to their referents in external knowledge bases and the event task, defined as extracting related information for events that should be entered in the knowledge base. Most prior work on tasks related to knowledge base population has focused on dissemination-oriented sources written in the third person (e.g., new articles) that benefit from two characteristics: the content is written in formal language and is to some degree self-contextualized, and the entities mentioned (e.g., persons) are likely to be widely known to the public so that rich information can be found from existing general knowledge bases (e.g., Wikipedia and DBpedia). The work proposed in this thesis focuses on tasks related to knowledge base population for conversational sources written in the first person (e.g., emails and phone recordings), which offers new challenges. One challenge is that most conversations (e.g., 68% of the person names and 53% of the organization names in Enron emails) refer to entities that are known to the conversational participants but not widely known. Thus, existing entity linking techniques relying on general knowledge bases are not appropriate. Another challenge is that some of the shared context between participants in first-person conversations may be implicit and thus challenging to model, increasing the difficulty, even for human annotators, of identifying the true referents. This thesis focuses on several tasks relating to the population of knowledge bases for conversational content: the population of collection-specific knowledge bases for organization entities and meetings from email collections; the entity linking task that resolves the mention of three types of entities (person, organization, and location) found in both conversational text (emails) and speech (phone recordings) sources to multiple knowledge bases, including a general knowledge base built from Wikipedia and collection-specific knowledge bases; the meeting linking task that links meeting-related email messages to the referenced meeting entries in the collection-specific meeting knowledge base; and speaker identification techniques to improve the entity linking task for phone recordings without known speakers. Following the model-based evaluation paradigm, three collections (namely, Enron emails, Avocado emails, and Enron phone recordings) are used as the representations of conversational sources, new test collections are created for each task, and experiments are conducted for each task to evaluate the efficacy of the proposed methods and to provide a comparison to existing state-of-the-art systems. This work has implications in the research fields of e-discovery, scientific collaboration, speaker identification, speech retrieval, and privacy protection

    FSD50K: an Open Dataset of Human-Labeled Sound Events

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    Most existing datasets for sound event recognition (SER) are relatively small and/or domain-specific, with the exception of AudioSet, based on a massive amount of audio tracks from YouTube videos and encompassing over 500 classes of everyday sounds. However, AudioSet is not an open dataset---its release consists of pre-computed audio features (instead of waveforms), which limits the adoption of some SER methods. Downloading the original audio tracks is also problematic due to constituent YouTube videos gradually disappearing and usage rights issues, which casts doubts over the suitability of this resource for systems' benchmarking. To provide an alternative benchmark dataset and thus foster SER research, we introduce FSD50K, an open dataset containing over 51k audio clips totalling over 100h of audio manually labeled using 200 classes drawn from the AudioSet Ontology. The audio clips are licensed under Creative Commons licenses, making the dataset freely distributable (including waveforms). We provide a detailed description of the FSD50K creation process, tailored to the particularities of Freesound data, including challenges encountered and solutions adopted. We include a comprehensive dataset characterization along with discussion of limitations and key factors to allow its audio-informed usage. Finally, we conduct sound event classification experiments to provide baseline systems as well as insight on the main factors to consider when splitting Freesound audio data for SER. Our goal is to develop a dataset to be widely adopted by the community as a new open benchmark for SER research

    Using the Web Infrastructure for Real Time Recovery of Missing Web Pages

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    Given the dynamic nature of the World Wide Web, missing web pages, or 404 Page not Found responses, are part of our web browsing experience. It is our intuition that information on the web is rarely completely lost, it is just missing. In whole or in part, content often moves from one URI to another and hence it just needs to be (re-)discovered. We evaluate several methods for a \justin- time approach to web page preservation. We investigate the suitability of lexical signatures and web page titles to rediscover missing content. It is understood that web pages change over time which implies that the performance of these two methods depends on the age of the content. We therefore conduct a temporal study of the decay of lexical signatures and titles and estimate their half-life. We further propose the use of tags that users have created to annotate pages as well as the most salient terms derived from a page\u27s link neighborhood. We utilize the Memento framework to discover previous versions of web pages and to execute the above methods. We provide a work ow including a set of parameters that is most promising for the (re-)discovery of missing web pages. We introduce Synchronicity, a web browser add-on that implements this work ow. It works while the user is browsing and detects the occurrence of 404 errors automatically. When activated by the user Synchronicity offers a total of six methods to either rediscover the missing page at its new URI or discover an alternative page that satisfies the user\u27s information need. Synchronicity depends on user interaction which enables it to provide results in real time

    Proceedings of the EACL Hackashop on News Media Content Analysis and Automated Report Generation

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    HANDLING CHANGE IN A PRODUCTION TASKBOT. EFFICIENTLY MANAGING THE GROWTH OF TWIZ, AN ALEXA ASSISTANT

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    A Conversational Agent aims to converse with users, with a focus on natural behaviour and responses. They can be extremely complex as there are several parts which constitute it, several courses of action and infinite possible inputs. As so, behaviour checking is essential, especially if used in a production context, as wrong behaviour can have big consequences. Nevertheless, developing a robust and correctly behaving Task Bot, should not hinder research and must allow for continuous improvement of vanguard solutions. Hence, manual testing of such a complex system is bound to encounter several limits, either on the extension of the testing or on the time consumption of developers’ work. As so, we propose the development of a tool to automatically test, with a much broader test surface, these highly sophisticated systems. We introduce a solution, which leverages past conversation replay and mimicking to generate synthetic conversations. This allows for time-savings on quality assurance and better change handling. A key part of a Conversational Agent is the retrieval component. This is responsible for the correct retrieval of information, that is useful to the user. In task-guiding assistants, the retrieval element should not narrow the user’s behaviour, by omitting tasks that could be relevant. However, achieving perfect information matching to a user’s query is arduous, since there could be a plethora of words the user could say in order to attempt to accomplish an objective. To tackle this, we make use of a semantic retrieval algorithm adapting it to this domain by generating a synthetic dataset.Um Agente Conversacional visa ter conversas com utilizadores, focando-se no comportamento e nas respostas naturais. Estes podem ser, no entanto, extremamente complexos. São várias as partes que os constituem, os fluxos possíveis e os pedidos que o utilizador pode fazer. Assim, a verificação de comportamento é essencial, especialmente se usada em um contexto de produção, pois o comportamento errado pode ter grandes consequências. No entanto, o desenvolvimento de um Task Bot robusto e de comportamento correto não deve prejudicar a pesquisa e deve permitir a melhoria contínua das soluções. Portanto, testagem manual de um sistema tão complexo depara-se com vários limites, seja na extensão do teste ou no consumo de tempo do trabalho dos developers. Assim, propomos também o desenvolvimento de uma ferramenta para testes automáticos, com uma frente de teste muito mais ampla, para estes sistemas sofisticados. Apresentamos uma solução que aproveita a repetição e a simulação de conversas anteriores para gerar conversas sintéticas. Isso permite reduzir o tempo gasto na verificação de qualidade e permite melhor adaptação a mudanças. Uma parte fundamental de um agente conversacional é o retriever. Esta é a componente responsável pela obtenção de informação relevante. Nos assistentes que têm como objetivo a orientação de tarefas, o retriever não deve restringir o comportamento do utilizador, ao omitir tarefas que possam ser relevantes. No entanto, obter uma correspondência perfeita de informações com o pedido do utilizador é árduo, pois pode haver uma infinidade de formas que o utilizador pode formular o seu pedido pretendendo o mesmo objetivo. Para ultrupassar este problema, utilizamos um algoritmo de retrieval semântico, adaptando-o ao domínio em questão através da geração de um conjunto de dados sintético

    Optimising a defence-aware threat modelling diagram incorporating a defence-in-depth approach for the internet-of-things

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    Modern technology has proliferated into just about every aspect of life while improving the quality of life. For instance, IoT technology has significantly improved over traditional systems, providing easy life, time-saving, financial saving, and security aspects. However, security weaknesses associated with IoT technology can pose a significant threat to the human factor. For instance, smart doorbells can make household life easier, save time, save money, and provide surveillance security. Nevertheless, the security weaknesses in smart doorbells could be exposed to a criminal and pose a danger to the life and money of the household. In addition, IoT technology is constantly advancing and expanding and rapidly becoming ubiquitous in modern society. In that case, increased usage and technological advancement create security weaknesses that attract cybercriminals looking to satisfy their agendas. Perfect security solutions do not exist in the real world because modern systems are continuously improving, and intruders frequently attempt various techniques to discover security flaws and bypass existing security control in modern systems. In that case, threat modelling is a great starting point in understanding the threat landscape of the system and its weaknesses. Therefore, the threat modelling field in computer science was significantly improved by implementing various frameworks to identify threats and address them to mitigate them. However, most mature threat modelling frameworks are implemented for traditional IT systems that only consider software-related weaknesses and do not address the physical attributes. This approach may not be practical for IoT technology because it inherits software and physical security weaknesses. However, scholars employed mature threat modelling frameworks such as STRIDE on IoT technology because mature frameworks still include security concepts that are significant for modern technology. Therefore, mature frameworks cannot be ignored but are not efficient in addressing the threat associated with modern systems. As a solution, this research study aims to extract the significant security concept of matured threat modelling frameworks and utilise them to implement robust IoT threat modelling frameworks. This study selected fifteen threat modelling frameworks from among researchers and the defence-in-depth security concept to extract threat modelling techniques. Subsequently, this research study conducted three independent reviews to discover valuable threat modelling concepts and their usefulness for IoT technology. The first study deduced that integration of threat modelling approach software-centric, asset-centric, attacker-centric and data-centric with defence-in-depth is valuable and delivers distinct benefits. As a result, PASTA and TRIKE demonstrated four threat modelling approaches based on a classification scheme. The second study deduced the features of a threat modelling framework that achieves a high satisfaction level toward defence-in-depth security architecture. Under evaluation criteria, the PASTA framework scored the highest satisfaction value. Finally, the third study deduced IoT systematic threat modelling techniques based on recent research studies. As a result, the STRIDE framework was identified as the most popular framework, and other frameworks demonstrated effective capabilities valuable to IoT technology. Respectively, this study introduced Defence-aware Threat Modelling (DATM), an IoT threat modelling framework based on the findings of threat modelling and defence-in-depth security concepts. The steps involved with the DATM framework are further described with figures for better understatement. Subsequently, a smart doorbell case study is considered for threat modelling using the DATM framework for validation. Furthermore, the outcome of the case study was further assessed with the findings of three research studies and validated the DATM framework. Moreover, the outcome of this thesis is helpful for researchers who want to conduct threat modelling in IoT environments and design a novel threat modelling framework suitable for IoT technology

    Automatic summarization of narrative video

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    The amount of digital video content available to users is rapidly increasing. Developments in computer, digital network, and storage technologies all contribute to broaden the offer of digital video. Only users’ attention and time remain scarce resources. Users face the problem of choosing the right content to watch among hundreds of potentially interesting offers. Video and audio have a dynamic nature: they cannot be properly perceived without considering their temporal dimension. This property makes it difficult to get a good idea of what a video item is about without watching it. Video previews aim at solving this issue by providing compact representations of video items that can help users making choices in massive content collections. This thesis is concerned with solving the problem of automatic creation of video previews. To allow fast and convenient content selection, a video preview should take into consideration more than thirty requirements that we have collected by analyzing related literature on video summarization and film production. The list has been completed with additional requirements elicited by interviewing end-users, experts and practitioners in the field of video editing and multimedia. This list represents our collection of user needs with respect to video previews. The requirements, presented from the point of view of the end-users, can be divided into seven categories: duration, continuity, priority, uniqueness, exclusion, structural, and temporal order. Duration requirements deal with the durations of the preview and its subparts. Continuity requirements request video previews to be as continuous as possible. Priority requirements indicate which content should be included in the preview to convey as much information as possible in the shortest time. Uniqueness requirements aim at maximizing the efficiency of the preview by minimizing redundancy. Exclusion requirements indicate which content should not be included in the preview. Structural requirements are concerned with the structural properties of video, while temporal order requirements set the order of the sequences included in the preview. Based on these requirements, we have introduced a formal model of video summarization specialized for the generation of video previews. The basic idea is to translate the requirements into score functions. Each score function is defined to have a non-positive value if a requirement is not met, and to increase depending on the degree of fulfillment of the requirement. A global objective function is then defined that combines all the score functions and the problem of generating a preview is translated into the problem of finding the parts of the initial content that maximize the objective function. Our solution approach is based on two main steps: preparation and selection. In the preparation step, the raw audiovisual data is analyzed and segmented into basic elements that are suitable for being included in a preview. The segmentation of the raw data is based on a shot-cut detection algorithm. In the selection step various content analysis algorithms are used to perform scene segmentation, advertisements detection and to extract numerical descriptors of the content that, introduced in the objective function, allow to estimate the quality of a video preview. The core part of the selection step is the optimization step that consists in searching the set of segments that maximizes the objective function in the space of all possible previews. Instead of solving the optimization problem exactly, an approximate solution is found by means of a local search algorithm using simulated annealing. We have performed a numerical evaluation of the quality of the solutions generated by our algorithm with respect to previews generated randomly or by selecting segments uniformly in time. The results on thirty content items have shown that the local search approach outperforms the other methods. However, based on this evaluation, we cannot conclude that the degree of fulfillment of the requirements achieved by our method satisfies the end-user needs completely. To validate our approach and assess end-user satisfaction, we conducted a user evaluation study in which we compared six aspects of previews generated using our algorithm to human-made previews and to previews generated by subsampling. The results have shown that previews generated using our optimization-based approach are not as good as manually made previews, but have higher quality than previews created using subsample. The differences between the previews are statistically significant
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