基于主动推理的情境感知系统框架

情境感知计算是普及计算引发的新的研究领域。在分析情境感知计算系统模型的基础上，研究并改进了系统开发模型CTK，提出基于主动推理的系统框架ACTK，以智能家庭原型的实现证明了它对情境感知应用的开发和研究更好的支持。     

Optimizing social media message dissemination problem for emergency communication

Social media is increasingly being used as a communication bridge between government, emergency responders and managers, and the general public in extreme events. Passing information through social media channels enables individuals to send and receive content in real-time and without limitation of location and geography. While the use of social media in extreme event situations has become prevalent, there is often little strategy involved in message dissemination and too little understanding of the effects that underlying online social networks have on message distribution. In this study, we introduce a formal model for social media message dissemination in social networks through time. Our proposed model includes emphasis on single and multiple message scenarios and examines key communication characteristics in the development of more intentional and targeted social messaging strategies. We present a detailed experimental design on randomly generated networks and real-world sub-networks of the Twitter social graph and discuss our findings. We also include a Tabu Search procedure for solving single-message problem and discuss its potential value for large-scale problems in real-world applications.     

Actionable Social Media Competitive Analytics For Understanding Customer Experiences

A large amount of user-generated content is now freely available on social media sites. To increase their competitive advantage, companies need to monitor and analyze not only the customer-generated content on their own social media sites, but also the content on their competitors’ social media sites. In this article, we describe a framework to integrate several techniques including quantitative analysis, text mining, and sentiment analysis for analyzing and comparing social media content from business competitors. Specifically, we conducted an in-depth case study which applies our developed framework to the analysis and comparison of social media content on the Facebook sites of the three largest drugstore chains in the United States: Walgreens, CVS, and Rite Aid. We found similarities and differences in the social media use among the three drugstore chains. We discuss the implications of our findings and provide recommendations to help companies develop their social media competitive analysis strategies.     

Automatic adaptation of mobile applications to different user devices using modular mobile agents

Wearable, handheld, and embedded or standalone intelligent devices are becoming quite common and can support a diverse range of applications. In order to simplify development of applications which can adapt to a variety of mobile devices, we propose an adaptation framework which includes three techniques: follow‐me, context‐aware adaptation, and remote control scheme. For the first, we construct a personal agent capable of carrying its owner's applications. Second, we design a personal agent capable of carrying applications with an adaptable hierarchical structure. Then, applications can be adapted approximately to the context of evices by using an attribute‐based component decision algorithm. Finally, to achieve a remote control scheme, we distribute the computational load of applications on the resource‐restricted mobile devices. An application is divided into two parts that can be executed on a user device and a server separately. In short, this framework facilitates the development of widespread applications for ubiquitous computing environments. Furthermore, it enables the applications to follow their owners and automatically adapt to different devices. Copyright © 2005 John Wiley & Sons, Ltd.     

A framework for tag-aware recommender systems

In social tagging system, a user annotates a tag to an item. The tagging information is utilized in recommendation process. In this paper, we propose a hybrid item recommendation method to mitigate limitations of existing approaches and propose a recommendation framework for social tagging systems. The proposed framework consists of tag and item recommendations. Tag recommendation helps users annotate tags and enriches the dataset of a social tagging system. Item recommendation utilizes tags to recommend relevant items to users. We investigate association rule, bigram, tag expansion, and implicit trust relationship for providing tag and item recommendations on the framework. The experimental results show that the proposed hybrid item recommendation method generates more appropriate items than existing research studies on a real-world social tagging dataset.     

Design for emergence: experiments with a mixed reality urban playground game

In this paper we present our work in the design of ubiquitous social experiences, aiming to foster group participation and spontaneous playful behaviours in a city environment. We outline our approach of design for emergence: to provide just enough of a game context and challenge for people to be creative, to extend and enrich the experience of play through their interaction in the real world. CitiTag is our mixed reality testbed, a wireless location-based multiplayer game based on the concept of playground ‘tag’. We describe the design and implementation of CitiTag and discuss results from two user studies.     

Understanding urban structures and crowd dynamics leveraging large-scale vehicle mobility data

A comprehensive understanding of city structures and urban dynamics can greatly improve the efficiency and quality of urban planning and management, while the traditional approaches of which, such as manual surveys, usually incur substantial labor and time. In this paper, we propose a data-driven framework to sense urban structures and dynamics from large-scale vehicle mobility data. First, we divide the city into fine-grained grids, and cluster the grids with similar mobility features into structured urban areas with a proposed distance-constrained clustering algorithm (DCCA). Second, we detect irregular mobility traffic patterns in each area leveraging an ARIMA-based anomaly detection algorithm (ADAM), and correlate them to the urban social and emergency events. Finally, we build a visualization system to demonstrate the urban structures and crowd dynamics.We evaluate our framework using real-world datasets collected from Xiamen city, China, and the results show that the proposed framework can sense urban structures and crowd comprehensively and effectively.     

A cloudification methodology for multidimensional analysis: Implementation and application to a railway power simulator

Many scientific areas make extensive use of computer simulations to study complex real-world processes. These computations are typically very resource-intensive and present scalability issues as experiments get larger even in dedicated clusters, since these are limited by their own hardware resources. Cloud computing raises as an option to move forward into the ideal unlimited scalability by providing virtually infinite resources, yet applications must be adapted to this new paradigm. This process of converting and/or migrating an application and its data in order to make use of cloud computing is sometimes known as cloudifying the application. We propose a generalist cloudification method based in the MapReduce paradigm to migrate scientific simulations into the cloud to provide greater scalability. We analysed its viability by applying it to a real-world railway power consumption simulatior and running the resulting implementation on Hadoop YARN over Amazon EC2. Our tests show that the cloudified application is highly scalable and there is still a large margin to improve the theoretical model and its implementations, and also to extend it to a wider range of simulations. We also propose and evaluate a multidimensional analysis tool based on the cloudified application. It generates, executes and evaluates several experiments in parallel, for the same simulation kernel. The results we obtained indicate that out methodology is suitable for resource intensive simulations and multidimensional analysis, as it improves infrastructure’s utilization, efficiency and scalability when running many complex experiments.     

Seamless User-Level Handoff in Ubiquitous Multimedia Service Delivery

Advancing mobile computing technologies are enabling ubiquitous personal computing environment. In this paper, we focus on an important problem in such environment: user mobility. In the case of user mobility, a user is free to access his/her personalized service at anytime, anywhere, through any possible mobile/fixed devices. Providing mobility support in this scenario poses a series of challenges. The most essential problem is to preserve the user's access to the same service despite changes of the accessing host or service provider. Existing system-level mobility solutions are insufficient to address this issue since it is not aware of the application semantics. On the other hand, making each application to be mobility-aware will greatly increase the development overhead. We argue that the middleware layer is the best place to address this problem. On one hand, it is aware of application semantics. On the other hand, by building application-neutral mobility functions in the middleware layer, we eliminate the need to make each application mobility-aware. In this paper, we design a middleware framework to support user mobility in the ubiquitous computing environment. Its major mobility functions include user-level handoff management and service instantiation across heterogeneous computing platforms. We validate the major mobility functions using our prototype middleware system, and test them on two multimedia applications (Mobile Video Player and Mobile Audio Player). To maximally approximate the real-world user-mobility scenario, we have conducted experiments on a variety of computing platforms and communication paradigms, ranging from T1-connected high-end PC to handheld devices with wireless networks. The results show that our middleware framework is able to provide efficient user mobility support in the heterogeneous computing environment.     

Learning disentangled user representation with multi-view information fusion on social networks

User representation learning is one prominent and critical task of user analysis on social networks, which derives conceptual user representations to improve the inference of user intentions and behaviors. Previous efforts have shown its substantial value in multifarious real-world applications, including product recommendation, textual content modeling, link prediction, and many more. However, existing studies either underutilize multi-view information, or neglect the stringent entanglement among underlying factors that govern user intentions, thus deriving deteriorated representations. To overcome these shortages, this paper proposes an adversarial fusion framework to fully exploit substantial multi-view information for user representation, consisting of a generator and a discriminator. The generator learns representations with a variational autoencoder, and is forced by the adversarial fusion framework to pay specific attention to substantial informative signs, thus integrating multi-view information. Furthermore, the variational autoencoder used in the generator is novelly designed to capture and disentangle the latent factors behind user intentions. By fully utilizing multi-view information and achieving disentanglement, our model learns robust and interpretable user representations. Extensive experiments on both synthetic and real-world datasets demonstrate the superiority of our proposed model.     

Bayanihan: building and studying web-based volunteer computing systems using Java

Project Bayanihan is developing the idea of volunteer computing, which seeks to enable people to form very large parallel computing networks very quickly by using ubiquitous and easy-to-use technologies such as web browsers and Java. By utilizing Java’s object-oriented features, we have built a flexible software framework that makes it easy for programmers to write different volunteer computing applications, while allowing researchers to study and develop the underlying mechanisms behind them. In this paper, we show how we have used this framework to write master-worker style applications, and to develop approaches to the problems of programming interface, adaptive parallelism, fault-tolerance, computational security, scalability, and user interface design.     

A rapid prototyping software infrastructure for user interfaces in ubiquitous augmented reality

Recent user interface concepts, such as multimedia, multimodal, wearable, ubiquitous, tangible, or augmented-reality-based (AR) interfaces, each cover different approaches that are all needed to support complex human–computer interaction. Increasingly, an overarching approach towards building what we call ubiquitous augmented reality (UAR) user interfaces that include all of the just mentioned concepts will be required. To this end, we present a user interface architecture that can form a sound basis for combining several of these concepts into complex systems. We explain in this paper the fundamentals of DWARFs user interface framework (DWARF standing for distributed wearable augmented reality framework) and an implementation of this architecture. Finally, we present several examples that show how the framework can form the basis of prototypical applications.     

Clairvoyance: A framework to integrate shared displays and mobile computing devices

Supporting formal and informal meetings with digital information and ubiquitous software systems every day becomes increasingly mandatory. These meetings require that the integration of devices participating in the meeting and the information flow among them should be done as seamless as possible to avoid jeopardizing the natural interactions among participants. Trying to contribute to address such a challenge, this article presents a framework that allows devices integration and smooth information flow. This framework, named Clairvoyance, particularly integrates mobile computing devices and large-screen TVs through a mobile ad hoc network, and thus it eases the implementation of shared displays intended to be used in formal and informal meetings. Clairvoyance provides a set of services through an API, which can be used to develop ubiquitous applications that support meetings in particular scenarios. The preliminary evaluation of this framework considered its usage to implement a ubiquitous system that supports social meetings among friends or relatives. According to developers, the framework is easy to use and it provided all required services for such an application. The solution obtained was then utilized by end-users in simulated meetings. The evaluation results indicate that the Clairvoyance services were suitable to support the informal meetings, and that the devices integration and information flow were transparent for the end-users.     

Touch-Space: Mixed Reality Game Space Based on Ubiquitous, Tangible, and Social Computing

This paper presents a novel computer entertainment system which recaptures human touch and physical interaction with the real-world environment as essential elements of the game play, whilst also maintaining the exciting fantasy features of traditional computer entertainment. Our system called ‘Touch-Space’ is an embodied (ubiquitous, tangible, and social) computing based Mixed Reality (MR) game space which regains the physical and social aspects of traditional game play. In this novel game space, the real-world environment is an essential and intrinsic game element, and the human’s physical context influences the game play. It also provides the full spectrum of game interaction experience ranging from the real physical environment (human to human and human to physical world interaction), to augmented reality, to the virtual environment. It allows tangible interactions between players and virtual objects, and collaborations between players in different levels of reality. Thus, the system re-invigorates computer entertainment systems with social human-to-human and human-to-physical touch interactions. Correspondence to: Professor A. Cheok, National University of Singapore, 10 Kent Ridge Crescent, Singapore 119260. Email: adriancheok@nus.edu.sg     

The State of the Art in Visualizing Dynamic Multivariate Networks

Most real-world networks are both dynamic and multivariate in nature, meaning that the network is associated with various attributes and both the network structure and attributes evolve over time. Visualizing dynamic multivariate networks is of great significance to the visualization community because of their wide applications across multiple domains. However, it remains challenging because the techniques should focus on representing the network structure, attributes and their evolution concurrently. Many real-world network analysis tasks require the concurrent usage of the three aspects of the dynamic multivariate networks. In this paper, we analyze current techniques and present a taxonomy to classify the existing visualization techniques based on three aspects: temporal encoding, topology encoding, and attribute encoding. Finally, we survey application areas and evaluation methods; and discuss challenges for future research.     

Dynamics of opinion formation,social power evolution,and naïve learning in social networks

The past few decades have witnessed a prevalence of applying dynamical models to the study of social networks. This paper reviews recent advances in the investigation of social networks with a predominant focus on agent-based models. Starting from classical models of opinion dynamics, we survey several recently developed models on opinion formation and social power evolution. These models extend the classical models’ cognitive assumption that individuals’ opinions evolve on a single issue by incorporating various sociological or psychological hypotheses to account for the evolution of opinions over multiple or a sequence of interdependent issues. We summarize basic results on the asymptotic behaviors of these models and discuss their sociological interpretations. In addition, we show how these models play a role in the emergence of collective intelligence by applying them to a naïve learning setting. Novel results that reveal how individuals successfully learn an unknown truth over issue sequences are presented. Finally, we conclude the paper and discuss potential directions for future research.     

Non-Gaussian Data Clustering via Expectation Propagation Learning of Finite Dirichlet Mixture Models and Applications

Learning appropriate statistical models is a fundamental data analysis task which has been the topic of continuing interest. Recently, finite Dirichlet mixture models have proved to be an effective and flexible model learning technique in several machine learning and data mining applications. In this article, the problem of learning and selecting finite Dirichlet mixture models is addressed using an expectation propagation (EP) inference framework. Within the proposed EP learning method, for finite mixture models, all the involved parameters and the model complexity (i.e. the number of mixture components), can be evaluated simultaneously in a single optimization framework. Extensive simulations using synthetic data along with two challenging real-world applications involving automatic image annotation and human action videos categorization demonstrate that our approach is able to achieve better results than comparable techniques.     

A fog-assisted system to defend against Sybils in vehicular crowdsourcing

Technological advancements in vehicular transportation systems have led to the growth of novel paradigms, in which vehicles and infrastructures collaborate to infer high-level knowledge about phenomena of interest. Vehicular Social Network (VSN) is one such paradigm in which vehicular network entities are considered as part of an Online Social Network (OSN), paving the way for new services derived from social context. Although vehicular crowdsourcing has tremendous benefits, its deployment in real systems requires to solve important challenges including defense against Sybil attacks. This paper proposes a novel fog-assisted system that uses SybilDriver to minimize the presence of Sybil entities in VSN-based crowdsourcing applications. The proposed system exploits the characteristics of Vehicular Ad-hoc NETworks (VANETs) and OSNs to effectively recognize Sybils, and the adoption of fog computing helps reduce the overall network overhead by processing data closer to the vehicles. We perform detailed experiments on real-world publicly available datasets primarily to assess the effectiveness of SybilDriver against different Sybil attack strategies. Our experimental results show that SybilDriver detects Sybils with higher performance than state-of-the-art techniques under different settings. Furthermore, an evaluation of the fog architecture in terms of message complexity demonstrates low impact on the network overhead.     

Unsupervised and supervised learning to evaluate event relatedness based on content mining from social-media streams

Due to the explosive growth of social-media applications, enhancing event-awareness by social mining has become extremely important. The contents of microblogs preserve valuable information associated with past disastrous events and stories. To learn the experiences from past events for tackling emerging real-world events, in this work we utilize the social-media messages to characterize real-world events through mining their contents and extracting essential features for relatedness analysis. On one hand, we established an online clustering approach on Twitter microblogs for detecting emerging events, and meanwhile we performed event relatedness evaluation using an unsupervised clustering approach. On the other hand, we developed a supervised learning model to create extensible measure metrics for offline evaluation of event relatedness. By means of supervised learning, our developed measure metrics are able to compute relatedness of various historical events, allowing the event impacts on specified domains to be quantitatively measured for event comparison. By combining the strengths of both methods, the experimental results showed that the combined framework in our system is sensible for discovering more unknown knowledge about event impacts and enhancing event awareness.     

Computing in social networks with relationship algebra

Communities are the latest phenomena on the Internet. At the heart of each community lies a social network. In this paper, we show a generalized framework to understand and reason in social networks. Previously, researchers have attempted to use inference-specific type of relationships. We propose a framework to represent and reason with general case of social relationship network in a formal way. We call it relationship algebra. In the paper, we first present this algebra then show how this algebra can be used for various interesting computing on a social network weaved in the virtual communities. We show applications such as determining reviewers in a semi-professional network maintained by conference management systems, finding conflict of interest in a publication system, or to infer various trust relationships in a community of close associates, etc. We also show how future community networks can be used to determine who should be immunized in the case of a contagious disease outbreak and how these networks could be used in crime prevention, etc.