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Service matching approaches trade precision for recall, creating the need for users to choose the correct services, which obviously is a major obstacle for automating the service discovery and aggregation processes. Our approach to overcome this problem, is to eliminate the appearance of false positives by returning only the correct services. As different users have different semantics for what is correct, we argue that the correctness of the matching results must be determined according to the achievement of users' goals: that only services achieving users' goals are considered correct. To determine such correctness, we argue that the matching process should be based primarily on the high-level functional specifications (namely goals, achievement contexts, and external behaviors). In this article, we propose models, data structures, algorithms, and theorems required to correctly match such specifications. We propose a model called G + , to capture such specifications, for both services and users, in a machine-understandable format. We propose a data structure, called a Concepts Substitutability Graph (CSG), to capture the substitution semantics of application domain concepts in a context-based manner, in order to determine the semantic-preserving mapping transformations required to match different G + models. We also propose a behavior matching approach that is able to match states in an m-to-n manner, such that behavior models with different numbers of state transitions can be matched. Finally, we show how services are matched and aggregated according to their G + models. Results of supporting experiments demonstrate the advantages of the proposed service matching approaches.

Existing service matching techniques such as keyword-based and ontology-based, do not guarantee the correctness of the matching results (i.e. do not guarantee fulfilling user goals). This paper deals with this problem by capturing the high-level functional aspects (namely goals, contexts, and expected external behaviors) for both web services and users in a machine-processable format, then matching these aspects using the proposed functional substitutability matching scheme (FSMS). Based on FSMS, this paper describes a direct matching technique in which a user request is examined against one service description at a time, such that web services match users requests when they have substitutable goals, contexts and expected external behaviors. The substitutability semantics between the elements of application domains are captured via the proposed substitutability graphs, which are used during the matching process to mediate between users requests and web services descriptions. Simulation results show that the proposed matching approach succeeds in retrieving only the correct answers, while keywordbased and ontology-based retrieval techniques could not eliminate the appearance of false negatives and false positives.

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Recently, many educational institutes are expanding their education delivery methodologies to incorporate online, remote, and flexible learning, which is a strategic response to facilitate and fulfil the increasing demand for access to higher education. Unfortunately, online education requires substantial investments in different online education platforms, technologies, and infrastructure, creating obstacles for realising the online education strategy for many developing countries. In this paper, we argue that we could use social networks as one of the delivery platforms for online education, due to their easy access and popularity among young generations. Therefore, we carried out this study to measure and analyse the acceptance of faculty and educational stakeholders for social networks adoption as an educational delivery platform. Hence, we adapted the Technology Acceptance Model (TAM) to determine and analyse the factors and variants affecting faculty's acceptance. We used the TAM as an internal variable, and we used privacy, infrastructure, institutional support and access devices as external variables to assess the faculty needs for adopting social networks into educational settings. The study examined 14 hypotheses corresponding to these factors using data collected from 382 respondents in six different universities within Libya, performing structural equation modelling, descriptive analysis, and confirmatory factor analysis. Results show that privacy, institutional support, perceived usefulness and perceived ease of use were seen to have a significant effect on behavioural intention. Additionally, perceived ease of use and behavioural intention contributed significantly towards the actual usage of social networks. The results also show that faculty and educational stakeholders have not provided enough for institutions or encouraged the use of social networks within the context of educational institutions across Libya.

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