Context Aware Computing for The Internet of Things: A Survey

As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201

Exploiting Social Networks for Recommendation in Online Image Sharing Systems

This thesis aims to demonstrate the distinct and so far little explored value of knowledge derived from social interaction data within large web-scale image sharing systems like Flickr, Picasa Web, Facebook and others for image recommendation. I have shown how such systems can be significantly improved through personalisation that takes into account the social context of users by modelling their interactions by mining data, building and evaluating systems that incorporate this information. These improvements allow users to search and browse large online image collections more quickly and to find results that more accurately match their personal information needs when compared to existing methods. Traditional information retrieval and recommendation datasets are contrived to provide stable baselines for researchers to compare against but they rarely accurately reflect the media systems users tend to encounter online. The online photo sharing site Flickr provides rich and varied data that can be used by researchers to analyse and understand users’ interactions with images and with each other. I analyse such data by modelling the connections between users as multigraphs and exploiting the resultant topologies to produce features that can be used to train recommender systems based on machine learnt classifiers. The core contributions of this work include insight into the nature of very large-scale on- line photo collections and the communities that form around them, as well as the dynamic nature of the interactions users have with their media. I do this through the rigorous evaluation of both a probabilistic tag recommendation system and a machine learnt classifier trained to mimic user decisions regarding image preference. These implementations focus on treating the user as both a unique individual and as a member of potentially many explicit and implicit communities. I also explore the validity of the Flickr ‘Favourite’ feedback label as proxy for user preference, which is particularly important when considering other analogous media systems to which my findings transfer. My conclusions highlight how vital both social context information and the understanding of user behaviour are for online image sharing systems. In the field of information retrieval the diverse nature of users is often forgotten in the hunt for increases in esoteric performance metrics. This thesis places them back at the centre of the problem of multimedia information retrieval and shows how their variety and uniqueness are valuable traits that can be exploited to augment and improve the experience of browsing and searching shared online image collections

Model driven design and data integration in semantic web information systems

The Web is quickly evolving in many ways. It has evolved from a Web of documents into a Web of applications in which a growing number of designers offer new and interactive Web applications with people all over the world. However, application design and implementation remain complex, error-prone and laborious. In parallel there is also an evolution from a Web of documents into a Web of `knowledge' as a growing number of data owners are sharing their data sources with a growing audience. This brings the potential new applications for these data sources, including scenarios in which these datasets are reused and integrated with other existing and new data sources. However, the heterogeneity of these data sources in syntax, semantics and structure represents a great challenge for application designers. The Semantic Web is a collection of standards and technologies that offer solutions for at least the syntactic and some structural issues. If offers semantic freedom and flexibility, but this leaves the issue of semantic interoperability. In this thesis we present Hera-S, an evolution of the Model Driven Web Engineering (MDWE) method Hera. MDWEs allow designers to create data centric applications using models instead of programming. Hera-S especially targets Semantic Web sources and provides a flexible method for designing personalized adaptive Web applications. Hera-S defines several models that together define the target Web application. Moreover we implemented a framework called Hydragen, which is able to execute the Hera-S models to run the desired Web application. Hera-S' core is the Application Model (AM) in which the main logic of the application is defined, i.e. defining the groups of data elements that form logical units or subunits, the personalization conditions, and the relationships between the units. Hera-S also uses a so-called Domain Model (DM) that describes the content and its structure. However, this DM is not Hera-S specific, but instead allows any Semantic Web source representation as its DM, as long as its content can be queried by the standardized Semantic Web query language SPARQL. The same holds for the User Model (UM). The UM can be used for personalization conditions, but also as a source of user-related content if necessary. In fact, the difference between DM and UM is conceptual as their implementation within Hydragen is the same. Hera-S also defines a presentation model (PM) which defines presentation details of elements like order and style. In order to help designers with building their Web applications we have introduced a toolset, Hera Studio, which allows to build the different models graphically. Hera Studio also provides some additional functionality like model checking and deployment of the models in Hydragen. Both Hera-S and its implementation Hydragen are designed to be flexible regarding the user of models. In order to achieve this Hydragen is a stateless engine that queries for relevant information from the models at every page request. This allows the models and data to be changed in the datastore during runtime. We show that one way to exploit this flexibility is by applying aspect-orientation to the AM. Aspect-orientation allows us to dynamically inject functionality that pervades the entire application. Another way to exploit Hera-S' flexibility is in reusing specialized components, e.g. for presentation generation. We present a configuration of Hydragen in which we replace our native presentation generation functionality by the AMACONT engine. AMACONT provides more extensive multi-level presentation generation and adaptation capabilities as well aspect-orientation and a form of semantic based adaptation. Hera-S was designed to allow the (re-)use of any (Semantic) Web datasource. It even opens up the possibility for data integration at the back end, by using an extendible storage layer in our database of choice Sesame. However, even though theoretically possible it still leaves much of the actual data integration issue. As this is a recurring issue in many domains, a broader challenge than for Hera-S design only, we decided to look at this issue in isolation. We present a framework called Relco which provides a language to express data transformation operations as well as a collection of techniques that can be used to (semi-)automatically find relationships between concepts in different ontologies. This is done with a combination of syntactic, semantic and collaboration techniques, which together provide strong clues for which concepts are most likely related. In order to prove the applicability of Relco we explore five application scenarios in different domains for which data integration is a central aspect. This includes a cultural heritage portal, Explorer, for which data from several datasources was integrated and was made available by a mapview, a timeline and a graph view. Explorer also allows users to provide metadata for objects via a tagging mechanism. Another application is SenSee: an electronic TV-guide and recommender. TV-guide data was integrated and enriched with semantically structured data from several sources. Recommendations are computed by exploiting the underlying semantic structure. ViTa was a project in which several techniques for tagging and searching educational videos were evaluated. This includes scenarios in which user tags are related with an ontology, or other tags, using the Relco framework. The MobiLife project targeted the facilitation of a new generation of mobile applications that would use context-based personalization. This can be done using a context-based user profiling platform that can also be used for user model data exchange between mobile applications using technologies like Relco. The final application scenario that is shown is from the GRAPPLE project which targeted the integration of adaptive technology into current learning management systems. A large part of this integration is achieved by using a user modeling component framework in which any application can store user model information, but which can also be used for the exchange of user model data

Automatic recognition of multiparty human interactions using dynamic Bayesian networks

Relating statistical machine learning approaches to the automatic analysis of multiparty communicative events, such as meetings, is an ambitious research area. We have investigated automatic meeting segmentation both in terms of “Meeting Actions” and “Dialogue Acts”. Dialogue acts model the discourse structure at a fine grained level highlighting individual speaker intentions. Group meeting actions describe the same process at a coarse level, highlighting interactions between different meeting participants and showing overall group intentions. A framework based on probabilistic graphical models such as dynamic Bayesian networks (DBNs) has been investigated for both tasks. Our first set of experiments is concerned with the segmentation and structuring of meetings (recorded using multiple cameras and microphones) into sequences of group meeting actions such as monologue, discussion and presentation. We outline four families of multimodal features based on speaker turns, lexical transcription, prosody, and visual motion that are extracted from the raw audio and video recordings. We relate these lowlevel multimodal features to complex group behaviours proposing a multistreammodelling framework based on dynamic Bayesian networks. Later experiments are concerned with the automatic recognition of Dialogue Acts (DAs) in multiparty conversational speech. We present a joint generative approach based on a switching DBN for DA recognition in which segmentation and classification of DAs are carried out in parallel. This approach models a set of features, related to lexical content and prosody, and incorporates a weighted interpolated factored language model. In conjunction with this joint generative model, we have also investigated the use of a discriminative approach, based on conditional random fields, to perform a reclassification of the segmented DAs. The DBN based approach yielded significant improvements when applied both to the meeting action and the dialogue act recognition task. On both tasks, the DBN framework provided an effective factorisation of the state-space and a flexible infrastructure able to integrate a heterogeneous set of resources such as continuous and discrete multimodal features, and statistical language models. Although our experiments have been principally targeted on multiparty meetings; features, models, and methodologies developed in this thesis can be employed for a wide range of applications. Moreover both group meeting actions and DAs offer valuable insights about the current conversational context providing valuable cues and features for several related research areas such as speaker addressing and focus of attention modelling, automatic speech recognition and understanding, topic and decision detection

Web information search and sharing :

制度:新 ; 報告番号:甲2735号 ; 学位の種類:博士(人間科学) ; 授与年月日:2009/3/15 ; 早大学位記番号:新493

Generic adaptation framework for unifying adaptive web-based systems

The Generic Adaptation Framework (GAF) research project first and foremost creates a common formal framework for describing current and future adaptive hypermedia (AHS) and adaptive webbased systems in general. It provides a commonly agreed upon taxonomy and a reference model that encompasses the most general architectures of the present and future, including conventional AHS, and different types of personalization-enabling systems and applications such as recommender systems (RS) personalized web search, semantic web enabled applications used in personalized information delivery, adaptive e-Learning applications and many more. At the same time GAF is trying to bring together two (seemingly not intersecting) views on the adaptation: a classical pre-authored type, with conventional domain and overlay user models and data-driven adaptation which includes a set of data mining, machine learning and information retrieval tools. To bring these research fields together we conducted a number GAF compliance studies including RS, AHS, and other applications combining adaptation, recommendation and search. We also performed a number of real systems’ case-studies to prove the point and perform a detailed analysis and evaluation of the framework. Secondly it introduces a number of new ideas in the field of AH, such as the Generic Adaptation Process (GAP) which aligns with a layered (data-oriented) architecture and serves as a reference adaptation process. This also helps to understand the compliance features mentioned earlier. Besides that GAF deals with important and novel aspects of adaptation enabling and leveraging technologies such as provenance and versioning. The existence of such a reference basis should stimulate AHS research and enable researchers to demonstrate ideas for new adaptation methods much more quickly than if they had to start from scratch. GAF will thus help bootstrap any adaptive web-based system research, design, analysis and evaluation

A review on Video Classification with Methods, Findings, Performance, Challenges, Limitations and Future Work

In recent years, there has been a rapid development in web users and sufficient bandwidth. Internet connectivity, which is so low cost, makes the sharing of information (text, audio, and videos) more common and faster. This video content needs to be analyzed for prediction it classes in different purpose for the users. Many machines learning approach has been developed for the classification of video to save people time and energy. There are a lot of existing review papers on video classification, but they have some limitations such as limitation of the analysis, badly structured, not mention research gaps or findings, not clearly describe advantages, disadvantages, and future work. But our review paper almost overcomes these limitations. This study attempts to review existing video-classification procedures and to examine the existing methods of video-classification comparatively and critically and to recommend the most effective and productive process. First of all, our analysis examines the classification of videos with taxonomical details, the latest application, process, and datasets information. Secondly, overall inconvenience, difficulties, shortcomings and potential work, data, performance measurements with the related recent relation in science, deep learning, and the model of machine learning. Study on video classification systems using their tools, benefits, drawbacks, as well as other features to compare the techniques they have used also constitutes a key task of this review. Lastly, we also present a quick summary table based on selected features. In terms of precision and independence extraction functions, the RNN (Recurrent Neural Network), CNN (Convolutional Neural Network) and combination approach performs better than the CNN dependent method

Neuromorphic Learning Systems for Supervised and Unsupervised Applications

The advancements in high performance computing (HPC) have enabled the large-scale implementation of neuromorphic learning models and pushed the research on computational intelligence into a new era. Those bio-inspired models are constructed on top of unified building blocks, i.e. neurons, and have revealed potentials for learning of complex information. Two major challenges remain in neuromorphic computing. Firstly, sophisticated structuring methods are needed to determine the connectivity of the neurons in order to model various problems accurately. Secondly, the models need to adapt to non-traditional architectures for improved computation speed and energy efficiency. In this thesis, we address these two problems and apply our techniques to different cognitive applications. This thesis first presents the self-structured confabulation network for anomaly detection. Among the machine learning applications, unsupervised detection of the anomalous streams is especially challenging because it requires both detection accuracy and real-time performance. Designing a computing framework that harnesses the growing computing power of the multicore systems while maintaining high sensitivity and specificity to the anomalies is an urgent research need. We present AnRAD (Anomaly Recognition And Detection), a bio-inspired detection framework that performs probabilistic inferences. We leverage the mutual information between the features and develop a self-structuring procedure that learns a succinct confabulation network from the unlabeled data. This network is capable of fast incremental learning, which continuously refines the knowledge base from the data streams. Compared to several existing anomaly detection methods, the proposed approach provides competitive detection accuracy as well as the insight to reason the decision making. Furthermore, we exploit the massive parallel structure of the AnRAD framework. Our implementation of the recall algorithms on the graphic processing unit (GPU) and the Xeon Phi co-processor both obtain substantial speedups over the sequential implementation on general-purpose microprocessor (GPP). The implementation enables real-time service to concurrent data streams with diversified contexts, and can be applied to large problems with multiple local patterns. Experimental results demonstrate high computing performance and memory efficiency. For vehicle abnormal behavior detection, the framework is able to monitor up to 16000 vehicles and their interactions in real-time with a single commodity co-processor, and uses less than 0.2ms for each testing subject. While adapting our streaming anomaly detection model to mobile devices or unmanned systems, the key challenge is to deliver required performance under the stringent power constraint. To address the paradox between performance and power consumption, brain-inspired hardware, such as the IBM Neurosynaptic System, has been developed to enable low power implementation of neural models. As a follow-up to the AnRAD framework, we proposed to port the detection network to the TrueNorth architecture. Implementing inference based anomaly detection on a neurosynaptic processor is not straightforward due to hardware limitations. A design flow and the supporting component library are developed to flexibly map the learned detection networks to the neurosynaptic cores. Instead of the popular rate code, burst code is adopted in the design, which represents numerical value using the phase of a burst of spike trains. This does not only reduce the hardware complexity, but also increases the result\u27s accuracy. A Corelet library, NeoInfer-TN, is implemented for basic operations in burst code and two-phase pipelines are constructed based on the library components. The design can be configured for different tradeoffs between detection accuracy, hardware resource consumptions, throughput and energy. We evaluate the system using network intrusion detection data streams. The results show higher detection rate than some conventional approaches and real-time performance, with only 50mW power consumption. Overall, it achieves 10^8 operations per Joule. In addition to the modeling and implementation of unsupervised anomaly detection, we also investigate a supervised learning model based on neural networks and deep fragment embedding and apply it to text-image retrieval. The study aims at bridging the gap between image and natural language. It continues to improve the bidirectional retrieval performance across the modalities. Unlike existing works that target at single sentence densely describing the image objects, we elevate the topic to associating deep image representations with noisy texts that are only loosely correlated. Based on text-image fragment embedding, our model employs a sequential configuration, connects two embedding stages together. The first stage learns the relevancy of the text fragments, and the second stage uses the filtered output from the first one to improve the matching results. The model also integrates multiple convolutional neural networks (CNN) to construct the image fragments, in which rich context information such as human faces can be extracted to increase the alignment accuracy. The proposed method is evaluated with both synthetic dataset and real-world dataset collected from picture news website. The results show up to 50% ranking performance improvement over the comparison models