A web-based pervasive recommendation system for mobile tourist guides

Mobile tourist guides have attracted considerable research interest during the past decade, resulting in numerous standalone and web-based mobile applications. Particular emphasis has been given to personalization of services, typically based on travel recommender systems used to assist tourists in choosing places to visit; these systems address an important aspect of personalization and hence reduce the information burden for the user. However, existing systems fail to exploit information, behaviours, evaluations or ratings of other tourists with similar interests, which would potentially provide ground for the cooperative production of improved tourist content and travel recommendations. In this paper, we extend this notion of travel recommender systems utilizing collaborative filtering techniques while also taking into account contextual information (such as the current user’s location, time, weather conditions and places already visited by the user) for deriving improved recommendations in pervasive environments. We also propose the use of wireless sensor network (WSN) installations around tourist sites for enabling precise localization and also providing mobile users convenient and inexpensive means for uploading tourist information and ratings about points of interest (POI) via their mobile devices. We also introduce the concept of ‘context-aware rating’, whereby user ratings uploaded through WSN infrastructures are weighted higher to differentiate among users that rate POIs using the mobile tourist guide application while onsite and others using the Internet away from the POI.     

Mobile information access in the real world: A story of three wireless devices

The importance of the user perspective to the wireless information access experience cannot be understated: simply put, users will not indulge in devices that are perceived to be difficult to use and in technologies that do not offer quality infotainment – combined information and entertainment – content. In this paper, we investigate the impact that mobile devices have on the user wireless infotainment access experience in practice. To this end, we have undertaken an empirical study placed in a ‘real-world’ setting, in which participants undertook typical infotainment access tasks on three different wireless-enabled mobile devices: a laptop, a personal digital assistant and a head mounted display device. Results show that, with the exception of participants’ level of self-consciousness when using such devices in public environments, the user wireless information access experience is generally unaffected by device type. Location was shown, though, to be a significant factor when users engage in tasks such as listening to online music or navigation. Whilst the interaction between device and environment was found to influence entertainment-related tasks in our experiments, the informational ones were not affected. However, the interaction effects between device and user type was found to affect both types of tasks. Lastly, a user’s particular computing experience was shown to influence the perceived ease of wireless information access only in the case of online searching, irrespective of whether this is done for primarily informational purposes or entertainment ones.     

物联网情景感知技术研究

与互联网不同,物联网(Internet of Things, IoT)通过各类通信技术将具有标识、感知或者执行能力的物理实体互联,形成了“物物互连”的虚拟网络。随着计算机及通信技术的迅速发展,计算资源将通布人们周围的环境,情景感知技术应运而生。情景感知获得传感器采集的情景信息以后,对信息进行智能处理,自主地为用户提供服务。物联网具有海量信息的特性,传统的情景信息处理方法已不再适用。对物联网情景感知技术进行了详细的介绍,首先给出情景和情景感知的概念及其研究发展和应用。然后,结合物联网特性,以情景感知流程为主线,探讨了信息获取、建模和智能处理等内容。最后,系统结构是情景感知的关键,因此对现有的系统结构进行了分析和对比,结合物联网环境论述了当前情景感知系统的不足之处,并给出了情景感知系统的参考结构。     

Graph convolution machine for context-aware recommender system

The latest advance in recommendation shows that better user and item representations can be learned via performing graph convolutions on the user-item interaction graph. However, such finding is mostly restricted to the collaborative filtering (CF) scenario, where the interaction contexts are not available. In this work, we extend the advantages of graph convolutions to context-aware recommender system (CARS, which represents a generic type of models that can handle various side information). We propose Graph Convolution Machine (GCM), an end-to-end framework that consists of three components: an encoder, graph convolution (GC) layers, and a decoder. The encoder projects users, items, and contexts into embedding vectors, which are passed to the GC layers that refine user and item embeddings with context-aware graph convolutions on the user-item graph. The decoder digests the refined embeddings to output the prediction score by considering the interactions among user, item, and context embeddings. We conduct experiments on three real-world datasets from Yelp and Amazon, validating the effectiveness of GCM and the benefits of performing graph convolutions for CARS.     

Personalization and Context Management

Supporting the individual user in his working, learning, or information access is one of the main goals of user modeling. Personal or group user models make it possible to represent and use information about preferences, knowledge, abilities, emotional states, and many other characteristics of a user to adapt the user experience and support. Nowadays, the disappearing computer enables the user to access her information from a variety of personal and public displays and devices. To support a new generation of contextualized and personalized information and services, this paper addresses the problem of context management. Context management is a new approach to the design of context-aware systems in ubiquitous computing that combines personalization and contextualization. The presented framework for context management integrates user modeling and context modeling, which can benefit from each other and give rise to more valid models for personalized and contextualized information delivery. The paper will introduce a base framework and tools for designing context-aware applications and decompose the underlying framework into its foundational components. As two illustrative application cases, the paper discusses implementations of an intelligent advertisement board and an audio-augmented museum environment.     

Context-aware middleware for resource management in the wireless Internet

The provisioning of Web services over the wireless Internet introduces novel challenging issues for service design and implementation: from user/terminal mobility during service execution, to wide heterogeneity of portable access devices and unpredictable modifications in accessible resources. In this scenario, there are frequent provision-time changes in the context, defined as the logical set of accessible resources depending on client location, access terminal capabilities, and system/service management policies. The development of context-dependent services requires novel middlewares with full context visibility. We propose a middleware for context-aware resource management, called CARMEN, capable of supporting the automatic reconfiguration of wireless Internet services in response to context changes without any intervention on the service logic. CARMEN determines the context on the basis of metadata, which include declarative management policies and profiles for user preferences, terminal capabilities, and resource characteristics. In addition, CARMEN exploits the mobile agent technology to implement mobile middleware components that follow the provision-time movement of clients to support locally their customized service access. The proposed middleware shows how metadata and mobile agents can favor component reusability and automatic service reconfiguration, by reducing the development/ deployment complexity.     

Context data management: an architectural framework for context-aware services

Mobile devices are equipped with increasing processing power and sensing capabilities, and mobile services can benefit from these features to provide a more personalized and context-aware experience to final users. To efficiently collect and deliver context information, a proper architecture is required, where heterogeneous context information can be processed to provide higher-level context information, context data are represented uniformly, and applications can process context data with high-level queries. This paper fulfils this goal of interoperability and domain independence by defining a framework for context data management relying on open standards (XMPP and REST), acting as an enabler for third-party context-aware applications; other main novelties of our work are the definition of a ContextML for standard context data representation, and a Context Query Language (CQL) to access context information based on high-level data filtering.     

Towards a journalist-based news recommendation system: The Wesomender approach

The present paper introduces a context-aware recommendation system for journalists to enable the identification of similar topics across different sources. More specifically a journalist-based recommendation system that can be automatically configured is presented to exploit news according to expert preferences. News contextual features are also taken into account due to the their special nature: time, current user interests, location or existing trends are combined with traditional recommendation techniques to provide an adaptive framework that deals with heterogeneous data providing an enhanced collaborative filtering system. Since the Wesomender approach is able to generate context-aware recommendations in the journalism field, a quantitative evaluation with the aim of comparing Wesomender results with the expectations of a team of experts is also performed to show that a context-aware adaptive recommendation engine can fulfil the needs of journalists daily work when retrieving timely and primary information is required.     

Supporting pervasive computing applications with active context fusion and semantic context delivery

Future pervasive computing applications are envisioned to adapt the applications’ behaviors by utilizing various contexts of an environment and its users. Such context information may often be ambiguous and also heterogeneous, which make the delivery of unambiguous context information to real applications extremely challenging. Thus, a significant challenge facing the development of realistic and deployable context-aware services for pervasive computing applications is the ability to deal with these ambiguous contexts. In this paper, we propose a resource optimized quality assured context mediation framework based on efficient context-aware data fusion and semantic-based context delivery. In this framework, contexts are first fused by an active fusion technique based on Dynamic Bayesian Networks and ontology, and further mediated using a composable ontological rule-based model with the involvement of users or application developers. The fused context data are then organized into an ontology-based semantic network together with the associated ontologies in order to facilitate efficient context delivery. Experimental results using SunSPOT and other sensors demonstrate the promise of this approach.     

Non-dominated differential context modeling for context-aware recommendations

Context plays an important role in the process of decision making. A user’s preferences on the items may vary from contexts to contexts, e.g., a user may prefer to watch a different type of the movies, if he or she is going to enjoy the movie with partner rather than with children. Context-aware recommender systems, therefore, were developed to adapt the recommendations to different contextual situations, such as time, location, companion, etc. Differential context modeling is a series of recommendation models which incorporate contextual hybrid filtering into the neighborhood based collaborative filtering approaches. In this paper, we propose to enhance differential context modeling by utilizing a non-dominated user neighborhood. The notion of dominance relation was originally proposed in multi-objective optimization, and it was reused to definite non-dominated user neighborhood in collaborative filtering recently. These non-dominated user neighbors refer to the neighbors that dominate others from different perspectives of the user similarities, such as the user-user similarities based on ratings, demographic information, social relationships, and so forth. In this paper, we propose to identify the non-dominated user neighborhood by exploiting user-user similarities over multiple contextual preferences. Our experimental results can demonstrate the effectiveness of the proposed approaches in comparison with popular context-aware collaborative filtering models over five real-world contextual rating data sets.

     

An intelligent infotainment dissemination scheme for heterogeneous vehicular networks

In a vehicular network, applications are expected to be aware of network conditions and be adaptable to the varying environment in order to achieve the acceptable and predictable performance. Moreover, the establishment of vehicle infotainment system in a heterogeneous vehicular network needs to pay special attention to the distinctive network characteristic in wired/wireless networks, such as dynamic topology and less available bandwidth, and the exploitation of the underutilized peers’ resources to support large-scale services. This work thus proposes a service-oriented information dissemination scheme for vehicle infotainment system to ensure the possibility of heterogeneous vehicular communication and a framework for delivering real-time services over an IP-based network is provided to assure interoperability, roaming, and end-to-end session management. The main focus of this work is to implement an adaptive vehicle infotainment system, in which cache relay nodes ((Xiao, Zhuang, & Liu, 2005)) can be intelligently determined. Fuzzy logic inference system is employed to determine appropriate cache relay nodes to cache infotainment data streams and distribute them to different peers through service-oriented architecture. The experimental results demonstrate that effectiveness and practicability of the proposed work.     

基于物联网技术的货车载荷实时监测系统设计

针对目前货车载荷监测主要依靠固定式称重的现状,设计了一种基于物联网技术的货车载荷实时监测系统。系统以嵌入式系统和多传感器融合算法为核心,由信号采集,信号处理与传输,云服务器等单元组成。采用主控芯片STM32F105RCT6和电阻式应变片传感器,实现数据采集与数据处理;处理后的信息通过无线通信模块发送至云端,云端服务器利用神经网络技术对所得到的数据进行拟合处理,实现了用户在远程情况下对货车的载重量进行实时的监测与管控。实验测试证明,本系统能够准确,高效地采集到货车载荷量,并且所测货车载荷量的精度在2.75%以内,无线传输效果稳定,并能将载荷数据实时上传至云平台,反馈于用户。本系统综合运用了智能传感器、物联网和云计算等技术,具有实时强、精度高、装配便捷等特点。     

Exploiting users’ social relations to forward data in opportunistic networks: The HiBOp solution

Opportunistic networks, in which nodes opportunistically exploit any pair-wise contact to identify next hops towards the destination, are one of the most interesting technologies to support the pervasive networking vision. Opportunistic networks allow content sharing between mobile users without requiring any pre-existing Internet infrastructure, and tolerate partitions, long disconnections, and topology instability in general. In this paper we propose a context-aware framework for routing and forwarding in opportunistic networks. The framework is general, and able to host various flavors of context-aware routing. In this work we also present a particular protocol, HiBOp, which, by exploiting the framework, learns and represents through context information, the users’ behavior and their social relations, and uses this knowledge to drive the forwarding process. The comparison of HiBOp with reference to alternative solutions shows that a context-aware approach based on users’ social relations turns out to be a very efficient solution for forwarding in opportunistic networks. We show performance improvements over the reference solutions both in terms of resource utilization and in terms of user perceived QoS.     

Usability study of ME2.0

Mobile context-aware applications execute in the background of hosts mobile devices. The applications source process and aggregate hosts’ contextual and personal information. This information is disclosed to ubiquitously pervasive services that adapt their offerings to individual preferences. Unfortunately, many developers continue to ignore the user perspective in context-aware application designs as they complicate their overall task and generate exponential requirements. The additional incorporation of privacy mechanisms in context-aware applications to safeguard context and personal information disclosures also complicates users’ tasks resulting to misconfigured or completely abandoned applications. Misconfigured applications give end-users a false assurance of privacy exposing them to comprising services. We present a usability study on Mobile Electronic Personality Version 2 a privacy enhanced context-aware mobile application for personalising ubiquitous services and adapting pervasive smart-spaces. We draw conclusions on key issues related to user needs, based on user interviews, surveys, prototypes and field evaluations. Users’ needs are evaluated against five themes, learn-ability, efficiency, memorability, errors, satisfaction and privacy contention. In addition, design layout preferences, privacy manageability and consensus design comprehension are also evaluated. Clarity of priorities in context-aware mobile applications shaped by usability studies effectively increases the acceptance of levels of potential users.     

A Graphical Model for Context-Aware Visual Content Recommendation

Existing recommender systems provide an elegant solution to the information overload in current digital libraries such as the Internet archive. Nowadays, the sensors that capture the user's contextual information such as the location and time are become available and have raised a need to personalize recommendations for each user according to his/her changing needs in different contexts. In addition, visual documents have richer textual and visual information that was not exploited by existing recommender systems. In this paper, we propose a new framework for context-aware recommendation of visual documents by modeling the user needs, the context and also the visual document collection together in a unified model. We address also the user's need for diversified recommendations. Our pilot study showed the merits of our approach in content based image retrieval.     

Secure lightweight password authenticated key exchange for heterogeneous wireless sensor networks

Several three-party password authenticated key exchange (3-PAKE) protocols have recently been proposed for heterogeneous wireless sensor networks (HWSN). These are efficient and designed to address security concerns in ad-hoc sensor network applications for a global Internet of Things framework, where a user may request access to sensitive information collected by resource-constrained sensors in clusters managed by gateway nodes. In this paper we first analyze three recently proposed 3-PAKE protocols and discuss their vulnerabilities. Then, based on Radio Frequency Identification technologies we propose a novel 3-PAKE protocol for HWSN applications, with two extensions for additional security features, that is provably secure, efficient and flexible.     

A resource elasticity framework for QoS-aware execution of cloud applications

Cloud infrastructures consisting of heterogeneous resources are increasingly being utilized for hosting large-scale distributed applications from diverse users with discrete needs. The multifarious cloud applications impose varied demands for computational resources along with multitude of performance implications. Successful hosting of cloud applications necessitates service providers to take into account the heterogeneity existing in the behavior of users, applications and system resources while respecting the user’s agreed Quality of Service (QoS) criteria. In this work, we propose a QoS-Aware Resource Elasticity (QRE) framework that allows service providers to make an assessment of the application behavior and develop mechanisms that enable dynamic scalability of cloud resources hosting the application components. Experimental results conducted on the Amazon EC2 cloud clearly demonstrate the effectiveness of our approach while complying with the agreed QoS attributes of users.     

iJADE Web-miner: an intelligent agent framework for Internet shopping

There is growing interest in using intelligent software agents for a variety of tasks, including navigating and retrieving information from the Internet and from databases, online shopping activities, user authentication, negotiation for resources, and decision making. We propose an integrated framework for information retrieval and information filtering in the context of Internet shopping. We focus on applying agent technology, together with Web mining technology, to automate a series of product search and selection activities. It is based on a multiagent development platform, namely, iJADE (intelligent Java agent development environment), which supports various e-commerce applications. The framework comprises an automatic facial authentication utility and six other modules, namely, customer requirements definition, a requirement-fuzzification scheme, a fuzzy agents-negotiation scheme, a fuzzy product-selection scheme, a product-defuzzification scheme, and a product-evaluation scheme. A series of experiments were carried out and favorable results were produced in executing the framework. From an experimental point of view, we used a database of 1,020 facial images that were obtained under various conditions of facial expression, viewing perspective and size. An overall correct recognition rate of over 85 percent was attained. For the product selection test of our fuzzy shopper system, an average matching rate of more than 81 percent was achieved.     

车联网系统关键技术剖析

车联网技术是物联网技术在交通领域的延伸及应用,是中国"十二五"规划的重点应用领域之一。文章首先介绍了车联网的基本概念,接着对车联网中的RFID、传感器、无线传输、信息安全、标准化、数据融合、异构网络融合、大数据处理、云计算、移动计算等关键技术进行了剖析。与此同时,也对部分关键技术的发展趋势进行了总结和展望。     

一种基于用户情境聚类的个性化推荐算法

面向基于情境感知的推荐问题,提出一种基于用户情境聚类的个性化推荐算法。该算法利用情境预过滤的思想,首先运用模糊聚类的方法对历史数据集中用户的情境进行聚类,构造与当前用户情境相似度较高的用户集合,再与传统的基于用户的协同过滤算法相结合进行个性化推荐。实验采用公开数据集,结果表明该算法在多维情境信息条件下可用,并且推荐准确度要高于传统协同过滤算法,在聚类粒度不同的情况下对推荐结果也会产生不同的影响。