一种基于上下文的用户兴趣模型

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,而且对在手持设备上的个性化上下文觉察服务的用户模型研究也不多见。依托用在PC上的个性化用户模型试探性的研究手持设备上加入上下文信息的用户兴趣模型,并对设计过程给出详细论述,通过模拟实验证明模型的有效性,并对未来手持设备上用户模型进行展望。     

基于上下文感知的普适服务框架

为了更好地适应用户的个性化需求和普适计算环境的特征,提出一种基于上下文感知的普适服务框架。该框架包括服务呈现层、服务管理层、服务提供层和上下文感知层。通过基于社区的服务管理方式来屏蔽普适计算环境中服务的异构性、分布性,动态地感知与当前计算环境和用户活动相关的上下文信息,采用基于上下文的服务推荐机制为用户提供个性化的服务,从而更好地适应人的意图和环境 因素。     

觉察上下文的Web服务研究与应用

现有的Web服务框架不能适应普适计算的特点,即无法为移动用户提供自定义的个性化的服务.通过对现有Web服务的扩展,提出了一个觉察上下文的自适应的Web服务开发与部署框架.在该框架中,客户端的上下文信息作为SOAP消息的头块被分布式地传递和处理,Web服务可为客户端提供自定义的个性化的服务.同时,上下文是可扩展的,通过自动的上下文处理,Web服务可自适应新的上下文而不需要改变Web服务本身.     

基于模板和上下文的语义Web服务动态组合

为了适应环境和上下文信息的动态变化并提供适时适地的服务,Web服务组合要能满足处于动态变化环境中的个性化用户需求。文中提出了一个基于模板和上下文的语义Web服务组合框架,该框架使用抽象服务流程进行Web服务组合建模,利用本体来进行上下文信息建模并支持基于JESS的上下文信息推理,在原有基于语义的Web服务匹配的基础上,实时地感知上下文信息来进行Web服务动态绑定。该方法提高了服务组合的成功率和动态适应性,并且满足了用户的个性化需求。     

一种CBR与RBR相结合的智能家庭推理系统*

介绍了一种CBR与RBR相结合的智能家庭推理系统。将CBR与RBR两种人工智能技术相结合,运用于普适计算的典型应用智能家庭中,首先通过RBR推理出当前用户的活动以及心情等较高级上下文;然后再用CBR进行上下文的再处理,融合多类型或历史的上下文,自动预测相似度最大的上下文,并基于该上下文为用户提供个性化服务。     

上下文感知的高铁信息服务推荐方法研究

针对如何为高铁站旅客即时提供高效的个性化服务问题,将上下文信息引入到服务推荐过程中,对用户上下文信息按照时间、空间、用户、应用四个方面进行数据分析处理,采用改进FP-Growth算法,使之支持对二维数据项的挖掘,获取上下文与服务功能之间的关联规则。通过与当前上下文信息的相似度计算,构造出当前应用场景下可用的个性化服务集合。实验证明该方法可提高服务匹配的准确率和命中率,有助于提升旅客服务满意度。     

一种用户态活动感知计算模型的设计与实现

活动感知计算通过提取分析出用户某种状态的复合信息减少用户交互,更好的无缝融合用户当前的上下文信息和周围的情景,从而逐渐成为上下文感知计算中一个新的研究热点。本文提出了一种能较为准确表达用户当前整体状态的复合信息概念——用户态,和一套感知、分析、推理其的系统模型。根据模型过滤后的个性化推荐服务内容更加准确,服务质量和使用效率得到进一步提高。     

融合多种上下文的室内应急疏散导航位置模型

针对室内应急响应中如何科学、高效地对人员进行个性化实时疏散导航问题进行了研究,构建了一种面向复杂室内环境下融合多种上下文的应急疏散导航位置模型。利用改进的Dijkstra算法,结合实际环境动态上下文计算出实时疏散导航路径,为不同类型用户提供地图导航、语音导航、短信导航等个性化服务,同时为应急指挥者提供辅助决策支持。并以医院为例进行了实验,实验表明基于该模型及算法能够高效计算出满足不同类型用户需求的实时疏散导航路径,可应用于医院、商场等环境的应急疏散导航与救援中。     

基于上下文感知的普适计算辅助教学设计与实现

随着计算机技术的发展,普适计算作为一种新的计算模式孕育而生。基于普适计算的应用技术能为使用人群提供主动的,个性化的服务,这一特点可以极大地改善现有计算机辅助教学过程中一些固有缺陷。文中分析了普适计算在教学中的意义,研究了近年来国内外在上下文感知领域的探索,指出了普适计算技术在实验教学中的优越性,设计了一个基于上下文感知的普适计算辅助教学实验。     

Web服务组合上下文模型

基于上下文感知计算,提出一种新颖的Web服务组合上下文模型.一方面,服务组合中的信息被定义为服务和用户上下文,前者又被区分为概要、会话和填充上下文;另一方面,采用多策略机制管理上下文.为此,定义了一种基于ECA规则的语言-服务扩展策略描述语言(PDL-S).采用以上语言描述上下文管理策略和组合服务.该模型不但充分利用了上下文感知计算,而且具有良好的协作性、扩展性和灵活性.     

智能环境基于用户交互模态的情境感知服务

智能环境系统构成的复杂性和大量的情境信息增加了为用户提供和布局服务的难度。情境感知服务系统能感知系统运行和人机交互情境,根据相应的决策机制选择适当的设备为用户提供服务,从而简化环境设施的功能布局。在决策过程中引入用户交互模态信息有利于系统根据用户的交互能力和对人机界面的偏好提供服务。把情境细分为用户信息、设备信息、软件信息和环境拓扑信息,针对智能环境提出了一种基于用户交互模态的情境感知服务系统,同时详细说明了利用该技术选择服务的推理过程及其应用方法。通过智慧家庭环境中的场景案例验证了该系统的有效性。     

Hybrid approach for context-aware service discovery in healthcare domain

Context-awareness computing is a research field which often refers to healthcare as an interesting and rich area of application. Context aware computing attains environments monitoring by means of sensors to provide relevant information or services according to the identified context. In particular, wireless ad hoc sensor networks for medical purposes are playing an increasing role within healthcare. Body Sensor Networks (BSN) are being designed for prophylactic and follow-up monitoring of patients in e.g. their homes, during hospitalization, and in emergencies. This work presents an integrated environment aimed at providing personalized healthcare services which appropriately meet the user?s context. Deploying the semantics embedded in web services and context models is a mandatory step in the automation of service discovery, invocation and composition. Nevertheless, in a context aware domain purely logic-based reasoning on respectively context and services may not be enough. The main idea of this work is related to enrich with qualitative representation of context underling data by means of Fuzzy Logic in order to automatically recognize the context and to consequently find the right set of healthcare services among the available ones. Semantic formalisms (e.g., OWL, OWL-S, etc.) enable the context and services modeling in terms of domain ontology concepts. On the other hand, soft computing techniques support activity of unsupervised context analysis and healthcare semantic service discovery. Goal is to define context-aware system whose quality of retrieved services relies on the acquisition of user context by means of a robust theoretical approach. Moreover, this work defines hybrid architecture which attains a synergy between the agent-based paradigm and the fuzzy modeling. Specifically, the system exploits some task oriented agents in order to achieve context recognition, services matchmaking and brokerage activities.     

On the Design and Prototype Implementation of a Multimodal Situation Aware System

In this paper we describe the design concepts and prototype implementation of a situation aware ubiquitous computing system using multiple modalities such as National Marine Electronics Association (NMEA) data from global positioning system (GPS) receivers, text, speech, environmental audio, and handwriting inputs. While most mobile and communication devices know where and who they are, by accessing context information primarily in the form of location, time stamps, and user identity, the concept of sharing of this information in a reliable and intelligent fashion is crucial in many scenarios. A framework which takes the concept of context aware computing to the level of situation aware computing by intelligent information exchange between context aware devices is designed and implemented in this work. Four sensual modes of contextual information like text, speech, environmental audio, and handwriting are augmented to conventional contextual information sources like location from GPS, user identity based on IP addresses (IPA), and time stamps. Each device derives its context not necessarily using the same criteria or parameters but by employing selective fusion and fission of multiple modalities. The processing of each individual modality takes place at the client device followed by the summarization of context as a text file. Exchange of dynamic context information between devices is enabled in real time to create multimodal situation aware devices. A central repository of all user context profiles is also created to enable self-learning devices in the future. Based on the results of simulated situations and real field deployments it is shown that the use of multiple modalities like speech, environmental audio, and handwriting inputs along with conventional modalities can create devices with enhanced situational awareness.      

基于凸函数证据理论的关联感知云服务信任模型

针对大规模分布式云计算系统中的服务可信度辨别问题,提出一种基于凸函数证据理论的关联感知云服务信任模型。对云计算系统中云服务提供商、服务和用户之间的信任关系进行形式化描述,充分挖掘了同一服务商中的不同云服务之间的关联性,利用凸函数证据理论对有序命题的处理能力,构建了云计算系统中的可信服务推荐方法,根据用户需求为其提供合理可靠的云服务。与经典证据理论方法的对比结果表明,基于凸函数证据理论的关联感知云服务信任模型在保证有效性和健壮性的同时,充分利用了云计算系统中云服务之间的关联信息,能够根据用户的请求提供合理的云服务。     

一种基于上下文协商的动态服务组合方法

普适计算的计算环境和交互信息动态变化,为了提供适时适地的服务,服务组合除了满足用户的需求之外,还要适应环境的变化.以面向普适计算的分布式文档管理系统为例,提出一种基于上下文协商的动态服务组合方法,适应普适计算环境下资源动态变化的特点,同时满足用户对服务的需求.首先定义上下文和带有上下文信息的服务模型;然后给出服务和设备以及服务和用户之间的上下文协商约束条件,根据约束条件提出基本算法实现服务动态组合,并对基本算法进行优化;最后通过原型系统和实验验证算法的性能和有效性,并通过实验数据分析上下文对于服务组合的影响.实验数据说明:提出的方法能够应用在普适计算环境中上下文敏感的服务组合问题上,提高服务组合的动态适应性和网络资源利用率.     

普适计算环境下基于中间件的上下文质量管理框架研究

随着信息技术的快速发展,分布式计算技术逐渐向普适计算技术演化,从而达到信息空间和物理空间融合的最终目标,为用户提供普适的智能化服务。为了达到这个目标,一个主要的困难就是如何有效地连续监测、才甫获与解释环境相关的上下文信息来确保精确的上下文感知性。很多研究者已先后投身于上下文感知的普适应用的研究工作中,但大多数往往直接针对原始上下文进行处理,没有考虑上下文质量(QoC)的影响。因此,提出了一种基于中间件的上下文质量管理框架,即通过上下文的质量门阂管理、重复与不一致的上下文丢弃等不同层次的控制机制,为上下文感知服务和应用用户提供有效而可靠的上下文服务。     

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.     

A hybrid positioning system for technology‐independent location‐aware computing

Location‐aware computing is a form of context‐aware mobile computing that refers to the ability of providing users with services that depend on their position. Locating the user terminal, often called positioning, is essential in this form of computing. Towards this aim, several technologies exist, ranging from personal area networking, to indoor, outdoor, and up to geographic area systems. Developers of location‐aware software applications have to face with a number of design choices, that typically depend on the chosen technology. This work addresses the problem of easing the development of pull location‐aware applications, by allowing uniform access to multiple heterogeneous positioning systems. Towards this aim, the paper proposes an approach to structure location‐aware mobile computing systems in a way independent of positioning technologies. The approach consists in structuring the system into a layered architecture, that provides application developers with a standard Java Application Programming Interface (JSR‐179 API), and encapsulates location data management and technology‐specific positioning subsystems into lower layers with clear interfaces. In order to demonstrate the proposed approach we present the development of HyLocSys. It is an open hybrid software architecture designed to support indoor/outdoor applications, which allows the uniform (combined or separate) use of several positioning technologies. HyLocSys uses a hybrid data model, which allows the integration of different location information representations (using symbolic and geometric coordinates). Moreover, it allows support to handset‐ and infrastructure‐based positioning approaches while respecting the privacy of the user. The paper presents a prototypal implementation of HyLocSys for heterogeneous scenarios. It has been implemented and tested on several platforms and mobile devices. Copyright © 2009 John Wiley & Sons, Ltd.     

Web user profiles with time-decay and prototyping

User profiling represents an important initial step in personalizing web services and in building recommendation systems. Non-invasive profiling methods monitor users’ behavior and infer interest profiles from their past actions. Most existing profiling methods, which relate the users’ interests to a given ontology, consider only the user’s past actions when calculating his/her profile. The profiling algorithms use a time-decay function for users’ past actions to adapt the profile to shifts in the user’s interests over time. In our work, we propose a hybrid method that combines time-decay and profile correction using prototype profiles. The additional profile correction step considers the interests of similar users and expands the interest scores beyond the concepts detected in the user’s past actions, which facilitates faster profile adaptation to the user’s new interests. In our experimental work, we experimented extensively with two real data sets: data of an online advertising network and student data in an online e-learning environment. We measured the quality of the computed user profiles by correlating them to users’ future actions. Experiments revealed that it is crucial to build the user’s profile using a large number of events from his/her past and to update the profile regularly. When we are unable to do so, the profile correction can be used to keep the quality of the profile from dropping too low. The results show that our method significantly outperforms existing ontological profiling methods.