基于FDI的用户模型及其在上下文觉察计算中的应用

上下文觉察计算是普适计算的重要内容。当前的上下文觉察计算研究大多没有为用户提供个性化的服务。已有的个性化上下文觉察服务的用户模型,其用户偏好的设置比较简单。本文从心理学的观点出发,提出一个使用基于场依存一独立性（FDI）的用户模型来为用户提供个性化的上下文觉察服务。本文系统分析了这种用户模型对上下文觉察计算的重要性,并给出了使用基于FDI的用户模型在上下文觉察计算中的应用过程。试验表明这种模型提供的个性化服务能够提高上下文觉察计算应用的有效性。     

移动社交网络中矩阵混淆加密交友隐私保护策略

随着移动设备和在线社交网络的快速发展,通过用户的个人属性配置文件匹配,能够帮助用户在邻近的社交网络中迅速找到和自己共同特征的朋友.然而,交友匹配很有可能泄漏用户的敏感信息,因此用户隐私得不到保障.提出一种移动社交网络中交友匹配过程中的隐私保护协议,用户利用混淆矩阵变换算法和内积计算实现交友过程中的隐私安全和高效的匹配;用户可以细粒度定义自己特征属性的特征权重,从而使匹配结果更精确.此外,利用机会分析模型模拟真实交友场景来保证交友的有效性.安全性分析表明,提出的方法更具有隐私性、可用性和更低的通信和计算开销.通过结合真实的社会网络数据进行测试和评估,对比结果显示,比现有解决方案更有效.     

Interest based selection of user generated content for rich communication services

The last few years, we have witnessed an exponential growth in available content, much of which is user generated (e.g. pictures, videos, blogs, reviews, etc.). The downside of this overwhelming amount of content is that it becomes increasingly difficult for users to identify the content they really need, resulting into considerable research efforts concerning personalized search and content retrieval.On the other hand, this enormous amount of content raises new possibilities: existing services can be enriched using this content, provided that the content items used match the user's personal interests. Ideally, these interests should be obtained in an automatic, transparent way for an optimal user experience.In this paper two models representing user profiles are presented, both based on keywords and with the goal to enrich real-time communication services. The first model consists of a light-weight keyword tree which is very fast, while the second approach is based on a keyword ontology containing extra temporal relationships to capture more details of the user's behavior, however exhibiting lower performance. The profile models are supplemented with a set of algorithms, allowing to learn user interests and retrieving content from personal content repositories.In order to evaluate the performance, an enhanced instant messaging communication service was designed. Through simulations the two models are assessed in terms of real-time behavior and extensibility. User evaluations allow to estimate the added value of the approach taken. The experiments conducted indicate that the algorithms succeed in retrieving content matching the user's interests and both models exhibit a linear scaling behavior. The algorithms perform clearly better in finding content matching several user interests when benefiting from the extra temporal information in the ontology based model.     

Deploying personalized mobile services in an agent-based environment

The mobile Internet allows users to obtain digitized contents and services from wired and wireless networks virtually anywhere at any time via different handheld mobile devices. However, due to the distinct features of mobile users, mobile devices and wireless networks, deploying mobile services is not as straightforward as generally expected. To ensure the success of mobile services, this paper presents a multi-agent framework that considers different contexts to support personalized services on wireless networks. In the proposed approach, client users, content providers, and service providers are all considered as software agents. They interoperate on the same platform to request and deliver mobile services. The most important issues related to agent operations and context awareness in an agent world are also discussed and analyzed. To verify our framework, different application services are developed accordingly on a publicly available middleware platform. Experiments are conducted for both services to evaluate their corresponding performance. The preliminary results show that our multi-agent approach to personalization is promising and efficient in the deployment of mobile services.     

Frequency and recency context for the management and retrieval of personal information on mobile devices

As users store increasingly larger amounts of personal information on their mobiles, the task of retrieving such items (e.g., contacts) becomes more difficult. We show that users can be categorized by their communication patterns and that each category benefits differently from supporting contact management applications. By examining mobile user call logs, we show that it is possible to aid retrieval tasks using relatively simple heuristics and algorithms that describe usage context, using solely the dimensions of contact use frequency and recency. We compare and discuss the results of the proposed method applied on two different mobile datasets: a large dataset from NOKIA and a smaller dataset collected by ourselves.     

Adaptive personal agents

Personal agents are an important advance in the management of electronic information. Accurate user profiling is critical to the personalisation of agent-based services. In this paper we describe a framework for personal agents, which provides a range of services using a common user profile. The user profile is automatically learned from observation of the electronic documents a user reads, and tracks user interests over time. We report on a large trial of this framework, which has given us important insights into the effectiveness of personal agent applications. In particular we discuss the benefits obtained from the use of a common profile and the interoperation of several personal agent applications.     

Utilizing contextual ontological user profiles for personalized recommendations

As users may have different needs in different situations and contexts, it is increasingly important to consider user context data when filtering information. In the field of web personalization and recommender systems, most of the studies have focused on the process of modelling user profiles and the personalization process in order to provide personalized services to the user, but not on contextualized services. Rather limited attention has been paid to investigate how to discover, model, exploit and integrate context information in personalization systems in a generic way. In this paper, we aim at providing a novel model to build, exploit and integrate context information with a web personalization system. A context-aware personalization system (CAPS) is developed which is able to model and build contextual and personalized ontological user profiles based on the user’s interests and context information. These profiles are then exploited in order to infer and provide contextual recommendations to users. The methods and system developed are evaluated through a user study which shows that considering context information in web personalization systems can provide more effective personalization services and offer better recommendations to users.     

Search structures and algorithms for personalized ranking

As data of an unprecedented scale are becoming accessible on the Web, personalization, of narrowing down the retrieval to meet the user-specific information needs, is becoming more and more critical. For instance, while web search engines traditionally retrieve the same results for all users, they began to offer beta services to personalize the results to adapt to user-specific contexts such as prior search history or other application contexts. In a clear contrast to search engines dealing with unstructured text data, this paper studies how to enable such personalization in the context of structured data retrieval. In particular, we adopt contextual ranking model to formalize personalization as a cost-based optimization over collected contextual rankings. With this formalism, personalization can be abstracted as a cost-optimal retrieval of contextual ranking, closely matching user-specific retrieval context. With the retrieved matching context, we adopt a machine learning approach, to effectively and efficiently identify the ideal personalized ranked results for this specific user. Our empirical evaluations over synthetic and real-life data validate both the efficiency and effectiveness of our framework.     

Interactive applications of personal situation-aware assistants

Ultraportable mobile computers provide electronic assistance for environments and usage situations, where computer support up to now has not been feasible. For the first time, a true physical and cognitive integration of computer support into the everyday business of the real-world becomes possible, as envisioned in Mark Weiser's concept of “ubiquitous computing” (Weiser, Communication of the ICM 1993, 36 (12): 75–85). However, although Handheld-PC, etc. today; already support a good deal of personal information management and basic access to distributed multimedia information services such as the World-Wide Web, they are still surprisingly difficult to use in “full action”. Specifically, lengthy interaction sequences and the inability to find quickly that important piece of information which is embedded somewhere in the machine, makes using those devices sometimes a very disappointing experience. In this, paper, we outline a new approach to realizing an easy-to-use personal digital assistant systems, based on the concept of Situation Awareness. Using knowledge about task structures, situation dependencies, and task contexts, our concept allows a mobile assistant to proactively provide the right information at the right time and the right place, without intruding upon the users primary task: interacting with reality.     

AIoT-enabled smart surveillance for personal data digitalization: Contextual personalization-privacy paradox in smart home

Artificial intelligence of things technology provides smart surveillance capability for personal data digitalization. It will invade individuals’ information, physical, and social spaces and raise contextual privacy concerns while providing personalized services, which has not been explored in previous research. We theorize three types of smart surveillance and identify three subdimensions of contextual personalization and privacy concerns. Grounded in surveillance theory and personalization-privacy paradox, we examined the different trade-offs of contextual personalization and privacy concerns underlying the three types of smart surveillance on users’ behavioral intention in smart home context. The results also indicated that transparency can lessen the trade-off effects.     

Students' profiles of ICT use: Identification,determinants, and relations to achievement in a computer and information literacy test

Current debates on students' use of information and communication technology (ICT) have brought to attention profiles and purposes of ICT use in either school-related or recreational contexts. Examining these two contexts at the same time, the present study seeks to identify student profiles of ICT use on the basis of the Norwegian International Computer and Information Literacy Study (ICILS) 2013 data (N = 2426). In order to explore profiles of ICT use in schools and at home for different purposes such as recreation, study purposes, exchanging information, and social communication, we take a person-centered approach and apply latent profile analysis. These analyses revealed two independent user profiles and showed that background characteristics (i.e., gender, immigration status) and motivational constructs (i.e., self-efficacy, interest, and enjoyment in ICT) play a significant role in determining profile membership. Significant differences between the user profiles in students' computer and information literacy test performance did not exist. Given that the coverage of ICT at home and in schools has increased substantially over the last decades, the identification of user profiles informs teachers and parents about whether or not students exploit these opportunities to the same extent. Implications for future research and practice are discussed.     

SSW: A Small-World-Based Overlay for Peer-to-Peer Search

Peer-to-peer (P2P) systems have become a popular platform for sharing and exchanging voluminous information among thousands or even millions of users. The massive amount of information shared in such systems mandates efficient semantic-based search instead of key-based search. The majority of existing proposals can only support simple key-based search rather than semantic-based search. This paper presents the design of an overlay network, namely, semantic small world (SSW), that facilitates efficient semantic-based search in P2P systems. SSW achieves the efficiency based on four ideas: 1) semantic clustering, where peers with similar semantics organize into peer clusters, 2) dimension reduction, where to address the high maintenance overhead associated with capturing high-dimensional data semantics in the overlay, peer clusters are adaptively mapped to a one-dimensional naming space, 3) small world network, where peer clusters form into a one-dimensional small world network, which is search efficient with low maintenance overhead, and 4) efficient search algorithms, where peers perform efficient semantic-based search, including approximate point query and range query in the proposed overlay. Extensive experiments using both synthetic data and real data demonstrate that SSW is superior to the state of the art on various aspects, including scalability, maintenance overhead, adaptivity to distribution of data and locality of interest, resilience to peer failures, load balancing, and efficiency in support of various types of queries on data objects with high dimensions.     

Resolving the Topology Mismatch Problem in Unstructured Peer-to-Peer Networks

Prior studies show that more than 70 percent of communication paths in a popular unstructured peer-to-peer (P2P) system (i.e., Gnutella) do not exploit the physical network topology, leading to the topology mismatch problem, and thus, lengthen communication between participating peers. While previous efforts in solving overlay topology matching problems do not guarantee the bounds of performance metrics (e.g., the communication delay between any two overlay peers and the broadcasting scope of any participating peer), in this paper, we present a novel topology matching algorithm that has provable performance qualities. In our proposal, each participating node creates and manages a constant number of overlay connections to other peers in a distributed manner. In rigorous performance analysis, we show that 1) the expected overlay communication delay between any two nodes in our P2P network is a constant; 2) in addition, any joining node has the exponential broadcasting scope in expectation; 3) furthermore, a participating node takes a polylogarithmic overhead to exploit the physical network locality and maintain its flooding scope. Together with extensive simulations, we present our proposal that significantly outperforms two recent solutions, i.e., THANCS and mOverlay, in terms of overlay communication latency and/or broadcasting scope.     

个性化服务技术研究

对个性化服务技术中的用户识别、用户描述文件、个性化推荐技术、个性化服务系统的体系结构及目前的研究方向进行了概述。从实现角度详细讨论了3种个性化推荐技术。个性化服务具有针对性,它的目的就是为了使用户更好地找到需要的信息,通过从用户访问网站的历史记录中得到用户的个人信息,利用个性化推荐的方法将信息推荐给用户。个性化推荐避免用户陷入信息的海洋,提高用户查询效率,使得用户可以得到他们真正想得到的信息,避免繁多的人工搜索。     

iConAwa - An intelligent context-aware system

Context-awareness becomes an increasingly important concept in the development of mobile and ubiquitous systems. Applications and services, which run in these kinds of highly dynamic environments, should be aware of and adapt to their contexts. Context-aware applications improve and enrich people’s interactions with devices, computers and other people.In this paper, design and development of iConAwa, which is an intelligent context-aware multi-agent system proactively providing mobile users with context-aware information and services, is described. In iConAwa, mobile users can get information and services about nearby resources (attraction points) according to their context and also communicate with each other by exchanging messages. Context and point of interest ontologies are developed in OWL. Context and points of interest are modelled in a flexible and extensible way by the developed ontology models. Knowledge sharing and knowledge reuse are also provided by using these ontology models. iConAwa makes use of rule-based context reasoning which provides derivation of high level implicit context from low level explicit context. With this approach context reasoning is decoupled from the source code of the system. JADE agent development framework is used to develop the agents and Jena semantic web framework is used to manipulate ontologies and for rule based reasoning.     

Peer-it: Stick-on solutions for networks of things

An integrated, autonomous stick-on computing platform is proposed, consisting of (i) the Peer-it stick-on, multi-sensor, multi-actuator computer hardware, (ii) the Peer-it component-based software framework, and (iii) the Peer-it profile markup language PeerML, supporting spontaneous interaction among such platforms. The platform implements Peer-to-Peer computing principles in a self-contained, miniaturized, universal and scalable way, giving raise for application scenarios where the real-world artefacts like e.g. machines, tools or appliances–literally every thing–equipped with Peer-it technology can operate in spontaneously interacting, goal-oriented ensembles.Technically, preferences (like capabilities and goals) and context (like time, geo-position, owner, environmental conditions, etc.) of peers are kept as a profile encoded in PeerML in the local memory of Peer-its, and carried along wherever they move in space. Once peers come into spatial proximity of each other, profiles are exchanged via wireless communication, and the “similarity” of preferences is analyzed. In the case of “matching” preferences, an associated application is notified on both peers.Besides a fully functional autonomous hardware platform integrating multiple sensors, actuator arrays and wireless communication technologies, the Peer-it stick-on computer, a low-memory footprint, OSGi compliant Peer-it software framework has been implemented. We demonstrate in a flexible manufacturing systems (FMS) scenario, how the Peer-it technology can improve over centralized FMSs with respect to fault tolerance, scalability, flexibility in reconfiguration, productivity and efficiency.     

Rough–Fuzzy Collaborative Clustering

In this study, we introduce a novel clustering architecture, in which several subsets of patterns can be processed together with an objective of finding a common structure. The structure revealed at the global level is determined by exchanging prototypes of the subsets of data and by moving prototypes of the corresponding clusters toward each other. Thereby, the required communication links are established at the level of cluster prototypes and partition matrices, without hampering the security concerns. A detailed clustering algorithm is developed by integrating the advantages of both fuzzy sets and rough sets, and a measure of quantitative analysis of the experimental results is provided for synthetic and real-world data.     

An augmented reality interface to contextual information

In this paper, we report on a prototype augmented reality (AR) platform for accessing abstract information in real-world pervasive computing environments. Using this platform, objects, people, and the environment serve as contextual channels to more information. The user??s interest with respect to the environment is inferred from eye movement patterns, speech, and other implicit feedback signals, and these data are used for information filtering. The results of proactive context-sensitive information retrieval are augmented onto the view of a handheld or head-mounted display or uttered as synthetic speech. The augmented information becomes part of the user??s context, and if the user shows interest in the AR content, the system detects this and provides progressively more information. In this paper, we describe the first use of the platform to develop a pilot application, Virtual Laboratory Guide, and early evaluation results of this application.     

Rough-fuzzy collaborative clustering.

In this study, we introduce a novel clustering architecture, in which several subsets of patterns can be processed together with an objective of finding a common structure. The structure revealed at the global level is determined by exchanging prototypes of the subsets of data and by moving prototypes of the corresponding clusters toward each other. Thereby, the required communication links are established at the level of cluster prototypes and partition matrices, without hampering the security concerns. A detailed clustering algorithm is developed by integrating the advantages of both fuzzy sets and rough sets, and a measure of quantitative analysis of the experimental results is provided for synthetic and real-world data.