A Review on Resemblance of User Profiles in Social Networks using Similarity Measures

Online Social Networking is increasing at a fast rate. There are lots of profiles of the users and there is too much resemblance between the user profiles which can help recruiter’s to select the best candidates for the Job Profile. Now, each similarity measure has its own applicability and best suited to a particular type of attribute values and if these measures are collectively combined then it can help us to find the best resemblance among the user profile ,the result of which matches to the actual result. In this paper, the discussion of the past studies is done and how our research is proposing a framework for finding the resemblance is being discussed.

Automatic Definition and Application of Similarity Measures for Self-operation of Network

Self-operation concept is proposed to learn the past experiences of network operations and apply the learned operation experiences to solve new but similar problems. It works based upon the observation that actions appropriate for achieving an objective resemble each other in similar network contexts. Plenty of such similarities exist at the level of network elements, functions, and their relations. Similarity measure definition and application are essential components for the self-operation to apply the learned operation experiences. This paper provides a solution for self-operation to define and apply two types of similarity measures for two self-operation use cases. The first use case answers how to select a best suitable function to achieve any given objective. The second use case tells how the selected function should be configured with the most optimal parameter values so that the given objective could be achieved. This solution is realized on a demonstrator implementing the self-operation concept. Corresponding experiments are made with the demonstrator. The experimental results show that the self-operation solution works well.Peer reviewe

Geographies of online social interaction: a big data analytics approach to social media platform Sina Weibo

Social media has revolutionized many aspects of people’s social life. However, few studies have utilized massive individual-level data from social media to examine the effects of geography. In this study a program was developed to collect and analyze data from Sina Weibo in ten selected Chinese cities. Four geographic concepts, i.e., borders, distance, places, and urban system hierarchy were chosen to measure the geographic effects by investigating geographical distribution of people’s connections and comparing tweets similarity between different cities. The results show that these geographic concepts are playing an important role in the formation of new online connections and shaping people’s interests. Social media users still tend to establish connections and share more common interests with people who live in the same city or close to them. People who live in the first-tier cities have more opportunities to establish connections across the country and their interests cover a broader range

A framework for an adaptable and personalised e-learning system based on free web resources

An adaptable and personalised E-learning system (APELS) architecture is developed to provide a framework for the development of comprehensive learning environments for learners who cannot follow a conventional programme of study. The system extracts information from freely available resources on the Web taking into consideration the learners' background and requirements to design modules and a planner system to organise the extracted learning material to facilitate the learning process. The process is supported by the development of an ontology to optimise and support the information extraction process. Additionally, natural language processing techniques are utilised to evaluate a topic's content against a set of learning outcomes as defined by standard curricula. An application in the computer science field is used to illustrate the working mechanisms of the proposed framework and its evaluation based on the ACM/IEEE Computing Curriculum.A variety of models are developed and techniques used to support the adaptability and personalisation features of APELS. First, a learner’s model was designed by incorporating students’ details, students’ requirements and the domain they wish to study into the system. In addition, learning style theories were adopted as a way of identifying and categorising the individuals, to improve their on-line learning experience and applying it to the learner’s model. Secondly, the knowledge extraction model is responsible for the extraction of the learning resources from the Web that would satisfy the learners’ needs and learning outcomes. To support this process, an ontology was developed to retrieve the relevant information as per users’ needs. In addition, it transforms HTML documents to XHTML to provide the information in an accessible format and easier for extraction and comparison purposes. Moreover, a matching process was implemented to compute the similarity measure between the ontology concepts that are used in the ACM/IEEE Computer Science Curriculum and those extracted from the websites. The website with the highest similarity score is selected as the best matching website that satisfies the learners’ request. A further step is required to evaluate whether the content extracted by the system is the appropriate learning material of the subject. For this purpose, the learning outcome validation process is added to ensure that the content of the selected websites will enable the appropriate learning based to the learning outcomes set by standard curricula. Finally, the information extracted by the system will be passed to a Planner model that will structure the content into lectures, tutorials and workshops based on some predefined learning constraints. The APELS system provides a novel addition to the field of adaptive E-learning systems by providing more personalized learning material to each user in a time-efficient way saving his/her time looking for the right course from the hugely available resources on the Web or going through the large number of websites and links returned by traditional search engines. The APELS system will adapt better to the learner’s style based on feedback and assessment once the learning process is initiated by the learner. The APELS system is expected to develop over time with more users