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The advent of online social networks (OSN) has transformed a common passive reader into a content contributor. It has allowed users to share information and exchange opinions, and also express themselves in online virtual communities to interact with other users of similar interests. However, OSN have turned the social sphere of users into the commercial sphere. This should create a privacy and security issue for OSN users. OSN service providers collect the private and sensitive data of their customers that can be misused by data collectors, third parties, or by unauthorized users. In this paper, common security and privacy issues are explained along with recommendations to OSN users to protect themselves from these issues whenever they use social media.

To devise vision of the next generation of the web, deep web technologies have gained larger attention in a last few years. An eminent feature of next generation of web is the automation of tasks. A large part of Deep web comprises of online structured domain specific databases that are accessed using web query interfaces. The information contained in these databases is related to a particular domain. This highly relevant information is more suitable for satisfying the information needs of the users and large scale deep web integration. In order to make this extraction and integration process easier, it is necessary to classify the deep web databases into standard\ non-standard category domains. There are mainly two types of classification techniques i.e. manual and automatic. As the size of deep web is increasing at an exponential rate with the passage of time, it has become nearly impossible to classify these deep web search sources manually into their respective domains. For this purpose, several automatic deep web classification techniques have been proposed in the literature. In this paper apart from the literature survey, we propose a framework for analysis of automatic classification techniques of deep web. The framework provides a baseline for the analysis of rudiments of automatic classification techniques based on the parameters such as structured, unstructured, simple/advance query forms, content representative extraction methodology, level of classification, performance evaluation criteria and its results. Furthermore, we studied a number of automatic deep web classification techniques in the light of proposed framework.

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OBJECTIVE:The main aim of the study was to analyze the outcomes of clavicle fractures in adults treated non-surgically and to evaluate the clinical effects of displacement, fracture patterns, fracture location, fracture comminution, shortening and fracture union on shoulder function.METHODS:Seventy clavicle fractures were non-surgically treated in the Orthopedics Department at the Tuanku Ja'afar General Hospital, a tertiary care hospital in Seremban, Malaysia, an average of six months after injury. The clavicle fractures were treated conservatively with an arm sling and a figure-eight splint for three weeks. No attempt was made to reduce displaced fractures, and the patients were allowed immediate free-shoulder mobilization, as tolerated. They were prospectively evaluated clinically and radiographically. Shoulder function was evaluated using the Constant scoring technique.RESULTS:There were statistically significant functional outcome impairments in non-surgically treated clavicle fractures that correlated with the fracture type (comminution), the fracture displacement (21 mm or more), shortening (15 mm or more) and the fracture union (malunion).CONCLUSION:This article reveals the need for surgical intervention to treat clavicle fractures and improve shoulder functional outcomes.