A semantic web environment for context-aware m-commerce

Recent advances in mobile devices and their sensing capabilities have enabled the collection of rich contextual information and mobile device usage records through the device logs. These context-rich logs open a venue for mining the personal preferences of mobile users under varying contexts and thus enabling the development of personalized context-aware recommendation and other related services, such as mobile online advertising. In this article, we illustrate how to extract personal context-aware preferences from the context-rich device logs, or context logs for short, and exploit these identified preferences for building personalized context-aware recommender systems. A critical challenge along this line is that the context log of each individual user may not contain sufficient data for mining his or her context-aware preferences. Therefore, we propose to first learn common context-aware preferences from the context logs of many users. Then, the preference of each user can be represented as a distribution of these common context-aware preferences. Specifically, we develop two approaches for mining common context-aware preferences based on two different assumptions, namely, context-independent and context-dependent assumptions, which can fit into different application scenarios. Finally, extensive experiments on a real-world dataset show that both approaches are effective and outperform baselines with respect to mining personal context-aware preferences for mobile users.

Typical tag recommendation systems for photos shared on social networks such as Flickr, use visual content analysis, collaborative filtering or personalization strategies to produce annotations. However, the dependence on manual intervention and the knowledge of sufficient personal preferences coupled with the folksonomic issues limit the scope of these strategies. In this paper, we present a fully automatic and folksonomically scalable tag recommendation model that can recommend tags for a user's photos without an explicit knowledge of the user's personal tagging preferences. The model is learned using the collective tagging behavior of other users in the user's local interaction network, which we believe approximates the user's preferences, at least partially. The tag recommendation model generates content-based annotations and then uses a Naive Bayes formulation to translate these annotations to a set of folksonomic tags selected from the tags used by the users in the local interaction network. Quantitative and qualitative comparisons with 890 Flickr networks show that this approach is highly useful for tag recommendation in the presence of insufficient information of a user's own preferences.

User preference-based automatic orchestration of web services using a multi-agent

In software technology, over the diversified environment, services can be rendered using an innovative mechanism of a novel paradigm called web services. In a business environment, rapid changes and requirements from various customers can be adapted using this service. For service management and discovery, the classification of Web services having the same functions is an efficient technique. However, there will be short lengthened Web services functional description documents, having less information, and sparse features. This makes difficulties in modelling short text in various topic models and leads to make an effect in the classification of Web services. A Mixed Wide and PSO-Bi-LSTM-CNN model (MW-PSO-Bi-LSTM-CNN) is proposed in this work for solving this issue. In this technique, the Web service category‟s breadth prediction is performed by combining Web services description document‟s discrete features, which exploits the wide learning model. In the next stage, the PSO-Bi-LSTM-CNN model is used for mining Web services description document word‟s context information and word order, for performing the Web service category‟s depth prediction. Here, particle swarm optimization (PSO) is integrated with the Bi-LSTM-CNN network for computing various hyper-parameters in an automatic manner. In third stage, Web service categories, results of depth, and breadth prediction are integrated using a linear regression model as final service classification result. At last, MW-PSO-Bi-LSTM-CNN, Wide&Bi-LSTM, and Wide&Deep web service classification techniques are compared and a better result with respect to web service classification accuracy is obtained using the proposed technique as shown in experimental results.

The aim of personalizing web information systems is to tailor content (media) to the user's personal preferences, goals and context, in turn increasing the reusability of that content. However, most developers are increasingly seeking to apply 'Web as a Platform' based approaches where web-based content is integrated with web services to provide the platform and environment features that were traditionally offered by desktop environments and applications. Therefore, next generation personalized web information systems need to be capable of dynamically personalizing, adapting and integrating web content and web services to create customized experiences for individual users. The goal of the research presented in this paper is to provide innovative techniques and technologies to achieve this. As part of this research, a novel architecture for the generation of Personalized Learning Activities, which highlights both the need for, and the power of combining and personalizing content and services has been developed. The system has been evaluated in the context of a Higher Education use case based on the user's perception of three key elements of the Personalized Learning Activity: the personalization, the usability and the educational benefits.

We present XSearcher, an associative memory based desktop search system, which exploits associations by creating semantic links of personal desktop resources from explicit and implicit user activities. With these links, associations among memory fragments can be built or rebuilt in a user's brain during a search. The personalized ranking scheme uses these links together with a user's personal preferences to rank results by both relevance and importance. XSearcher enhances traditional keyword based search systems since it is closer to the way that human associative memory works.

Summary form only given. The advent of Web services, new network technologies, and new devices gives new opportunities and imposes new requirements for the design and the execution of adaptable information system. Processes can be composed using Web services, but their orchestration has to also consider users needs at runtime, where these needs are expressed as context information. Context information is formed by functional and not functional characteristics and Web services must be selected also considering non functional aspects. In this scenario, new approaches and new frameworks have to be studied in order to overcome the limitations of the current standards, such as WSDL and WS-BPEL, which only consider functional aspects of Web services. Adaptive information systems based on web services allows to select web services based on functional characteristics and QoS, to execute a context aware orchestration of processes composed of Web services. Considering the ever increasing of information technology usage in our everyday life and the huge concentration of computational resources at remote service centers, energy costs become one of the biggest challenging issues for IT managers. Mechanisms to improve energy efficiency in service centers are divided at different levels which range from single components to the whole facility, considering both equipment and application issues.

Web services are very prevalent nowadays. Recommending Web services that users are interested in becomes an interesting and challenging research problem. In this paper, we present AWSR (Active Web Service Recommendation), an effective Web service recommendation system based on users' usage history to actively recommend Web services to users. AWSR extracts user's functional interests and QoS preferences from his/her usage history. Similarity between user's functional interests and a candidate Web service is calculated first. A hybrid new metric of similarity is developed to combine functional similarity measurement and nonfunctional similarity measurement based on comprehensive QoS of Web services. The AWSR ranks publicly available Web services based on values of the hybrid metric of similarity, so that a Top-K Web service recommendation list is created for a user. AWSR has been implemented and deployed on the Web. By conducting large-scale experiments based on a real-world Web services dataset, it is shown that our system effectively recommends Web services based on users functional interests and non-functional requirements with excellent performance.

Early career teachers (ECTs) face great stress arising from the demands of the teaching job which are translated into everyday challenges. This article reports a mixed-methods study which examined teacher buoyancy, focusing on how ECTs harness personal and contextual resources when facing the everyday challenges of teaching. The quantitative data analysis showed six factors which constituted the personal and contextual dimensions of teacher buoyancy. The qualitative findings offered an in-depth understanding of these dimensions. ECTs counted on the emotional, cognitive, and behavioural emphases of personal resources in making immediate to short-term responses to everyday challenges. Striving for professional growth and Taking care of one’s well-being were distal personal resources that enhanced ECTs’ potential to manage these challenges. Accessibility to contextual resources and ECTs’ utilisation of these resources constituted the dual facets of the contextual dimension of teacher buoyancy. Implications for initial teacher education and induction of ECTs are discussed.

Modeling user preferences using neural networks and tensor factorization model

By increasing popularity of SOC, using Web services in applications has increased too. SOC creates a loosely coupled environment in which the actual execution environment might differ significantly from the one with the presupposed conditions during application design. Therefore, although an appropriate Web service might have been selected, by passing time, the Web service may not be efficient enough or may not be applicable under specific conditions. For service-oriented systems to be flexible and self-adaptive, it is necessary to automatically select and use a similar service instead of the one which causes the above mentioned problems. Finding a similar service means specifying the proper services which fulfill the same requirements as those fulfilled by the problematic service. In most of the previous works, a number of the best services (k) are selected and ordered based on functional similarity. The user must select one of these services based on his/her preferences. One important metric in selecting a similar service is considering QoS properties and user preferences about QoS. Because of the importance of this issue, in the present paper, an architecture is proposed in which, in addition to functional similarity, QoS properties and user preferences are also considered in selecting a similar service.

Trust is one of the most critical quality factors for service requestors when they select the services from a large pool of Web services. However, existing web service trust models either do not focus on satisfying user's preferences for different quality of service (QoS) attributes, or do not pay enough attention to the impact of malicious ratings on trust evaluation. To address these gaps, a user-oriented trust model considering user's preferences and false ratings is proposed in this paper. The model introduces an approach to automatically mine user's preferences from their requirements, the preferences are used to determine the weights of each QoS attribute when integrating local trust into the multi-dimensional QoS attributes. The local trust on a service for the user is derived by combining the trust on QoS attributes and the trust on user's ratings. In this model, the users are classified into different groups according to their preferences, the honesty of each group is assessed by filtering out dishonest users using a hybrid approach which combines rating consistency clustering and an average method. To calculate the global trustworthiness of a service for the users group, the weight of ratings is dynamically adjusted according to the results of honesty assessment. The simulation results indicate that the model works well on personalized evaluation of trust, and it can effectively dilute the influence of malicious ratings.

The expansion of web service provider's number over the web has motivated the research community to invest in web service discovery area. However, in order to efficiently identify appropriate services, web services requesters should express their requests in rich, standard and machine-readable manner. In this context, we propose the use of a Service Request Template based on SAWSDL and WS-Policy standards. We use SAWSDL language which constitutes an extension of WSDL to express both functional and semantic needs, while we use WS-Policy framework to specify both non-functional requirements and user's preferences. We think that the proposed template would make it possible to overcome problems due to the heterogeneity between web services requests and published web services when executing discovery process. To perform efficient service discovery, we develop a structure-based matching algorithm that offers three mechanisms: semantic, data types and policy matching.

In this paper we present a Japanese-English transfer- based machine translation system. Our main research contribution is that the transfer rules are not handcrafted but are learnt automatically from a parallel corpus. We learn specific transfer rules from sentence pairs, which are then generalized in a consolidation phase to avoid overtraining. The system has been implemented in Amzi! Prolog, which offers scalability for large rule bases, full Unicode support for Japanese characters, and several APIs for the seamless integration of the translation functionality into common office environments, e.g. Microsoft Word. The dynamic nature of our system allows for an easy customization of the rule base according to the user's personal preferences.

In many of today's software systems, most notably online social networks, users can share personal information. Behind the simple action of sharing is a more complicated thought process regarding privacy: which content to share, with whom to share, and why to share. For a user, it's time-consuming and error-prone to check individual personal content for privacy violations. Hence, it would be ideal if a personal assistant can learn its users' privacy preferences and subsequently help users' decision-making by signaling potentially private content. A personalized privacy assistant can help its user make privacy decisions taking into account the ambiguity and uncertainty of privacy predictions as well as its user's personal preferences. Moreover, an explanation of why an image is considered public or private can aid the user in understanding the assistant's decisions.