Fine-grained access control and data confidentiality in public cloud

We initially propose an adaptable structure where user can utilize his property estimations and a hunt search to locally determine pursuit ability, and a record can be recovered just when its keywords coordinate the query and the user's trait esteems can pass the approach check. Utilizing this structure, we propose a novel plan called Keyword Search with Access Control (KSAC), which empowers keyword seek with access power over encoded information. Keyword Search with Access Control (KSAC) uses ongoing cryptographic crude called various leveled predicate encryption to implement fine-grained access control and perform multi-field query search. In the interim, it likewise bolsters the hunt capacity deviation, and accomplishes productive access arrangement update just as catchphrase update without bargaining information security. To improve the protection, Keyword Search With Access Control (KSAC) likewise plants commotions in the search to conceal users' entrance benefits. Serious assessments on genuine world dataset are directed to approve the appropriateness of the proposed plan and exhibit its insurance for user's entrance benefit

A Temporal Usage Pattern-based Tag Recommendation Approach

While social tagging can benefit Internet users managing their resources, it suffers the problems such as diverse and/or unchecked vocabulary and unwillingness to tag. Use of freely new tags and/or reuse of frequent tags have degraded coherence of corresponding resources of each tag that further frustrates people in retrieving information due to cognitive dissonance. Tag recommender systems can recommend users the most relevant tags to the resource they intend to annotate, and drastically transfer the tagging process from generation to recognition to reduce user’s cognitive effort and time. Prior research on tag recommendation has addressed the time-dependence issues of tags by applying a time decaying measure to determine the recurrence probability of a tag according to its recency instead of its usage pattern. In response, this study intends to propose the temporal usage pattern-based tag recommendation technique to consider the usage patterns and temporal characteristic of tags for making recommendations

System Oriented Social Scrutinizer: Centered Upon Mutual Profile Erudition

Social recommender systems are getting up more attention for product advertisement and social connectivity. A good recommender               should think about the system and the user. The user will have a preference list of some items and these preferences can be useful in suggesting the things which can help the endorsing system to identify better items. In this paper, the idea of social recommender systems as a pattern matching and regular expression making is used for unification of similarities. The concept of mutual profile pattern expression can be applied on various networking platforms. In these type of shared platforms, people all around the globe share resources and interact with each other. In order to manage or scrutinize users according to their interests and likeness, the mutual profile pattern of users can be used. Further predicting of membership function is performed to show how much extent does the profile matches

Study on a fractional-order controllers based on best rational approximation of fractional calculus operators

This paper presents a rational approximation method for fractional calculus operators s±αin the given frequency range and the error, which is based on the best rational approximation definition. The fractional integral operator is selected as an example to describe the construction of the rational approximation functions. An application case (s0.3) is used to illustrate the effectiveness of the proposed method. The obtained approximation function in the frequency domain is a best rational approximation function, which can further improve the accuracy of the approximation without increasing the orders. On the basis of the presented rational approximation method, a rational approximation equation of a fractional-order PID controller is obtained. Finally, the method for analyzing the optimization and frequency characteristics of the fractional-order controller is implemented to demonstrate the good frequency characteristic and best structure. The results from theoretical analysis and experimental verification show that the proposed method provides a new design idea for the effective application of the fractional-order PID controller in engineering

A Stable-Matching-Based User Linking Method with User Preference Order

With the development of social networks, more and more users choose to use multiple accounts from different networks to meet their needs. Linking a particular user’s multiple accounts not only can improve user’s experience of the net-services such as recommender system, but also plays a significant role in network security. However, multiple accounts of the same user are often not directly linked to each other, and further, the privacy policy provided by the service provider makes it harder to find accounts for a particular user. In this paper, we propose a stable-matching-based method with user preference order for the problem of low accuracy of user linking in cross-media sparse data. Different from the traditional way which just calculates the similarity of accounts, we take full account of the mutual influence among multiple accounts by regarding different networks as bilateral (multilateral) market and user linking as a stable matching problem in such a market. Based on the combination of Game-Theoretic Machine Learning and Pairwise, a novel user linking method has been proposed. The experiment shows that our method has a 21.6% improvement in accuracy compared with the traditional linking method and a further increase of about 7.8% after adding the prior knowledge

Non-intrusive load monitoring and its challenges in a NILM system framework

With the increasing of energy demand and electricity price, researchers gain more and more interest among the residential load monitoring. In order to feed back the individual appliance’s energy consumption instead of the whole-house energy consumption, non-intrusive load monitoring (NILM) is a good choice for residents to respond the time-of-use price and achieve electricity saving. In this paper, we discuss the system framework of NILM and analyse the challenges in every module. Besides, we study and compare the public datasets and accuracy metrics of non-intrusive load monitoring techniques