Indexing a Fuzzy Database Using the Technique of Superimposed Coding - Cost Models and Measurements

Recently, new applications have emerged that require database management systems with uncertainty capabilities. Many of the existing approaches to modelling uncertainty in database management systems are based on the theory of fuzzy sets. High performance is a necessary precondition for the acceptance of such systems by end users. However, performance issues have been quite neglected in research on fuzzy database management systems so far. In this article they are addressed explicitly. We propose new index structures for fuzzy database management systems based on the well known technique of superimposed coding together with detailed cost models. The correctness of the cost models as well as the efficiency of the index structures proposed is validated by a number of measurements on experimental fuzzy databases

Cloud Security using Image based Attribute Encryption Scheme

In the realm of specialized life distributed computing has turned out to be fundamental part and furthermore understanding the method for business is changing and is probably going to keep changing into what's to come. Utilizing distributed storage administrations implies that you and others can get to and share records over a scope of gadgets and position. Records, for example, photographs and recordings can now and then be unmanageable to email on the off chance that they are too enormous or you have designate of information. You can transfer your information to a distributed storage supplier implies you can quickly flow your information with the assistance of cloud administration and you can impart your information documents to anybody you pick. Since distributed computing offers dispersed assets by means of system in the open condition hence it makes less secured. Information security has turned into a noteworthy issue in information sharing on cloud. The primary maxim behind our framework is that it secures the information and creates the key for every exchange so every client can secure our mutual information by the outsider i.e. untrustworthy programmer

Zero Shot Recognition with Unreliable Attributes

In principle, zero-shot learning makes it possible to train a recognition model simply by specifying the category's attributes. For example, with classifiers for generic attributes like \emph{striped} and \emph{four-legged}, one can construct a classifier for the zebra category by enumerating which properties it possesses---even without providing zebra training images. In practice, however, the standard zero-shot paradigm suffers because attribute predictions in novel images are hard to get right. We propose a novel random forest approach to train zero-shot models that explicitly accounts for the unreliability of attribute predictions. By leveraging statistics about each attribute's error tendencies, our method obtains more robust discriminative models for the unseen classes. We further devise extensions to handle the few-shot scenario and unreliable attribute descriptions. On three datasets, we demonstrate the benefit for visual category learning with zero or few training examples, a critical domain for rare categories or categories defined on the fly.Comment: NIPS 201

Integrating OLAP and Ranking: The Ranking-Cube Methodology

Recent years have witnessed an enormous growth of data in business, industry, and Web applications. Database search often returns a large collection of results, which poses challenges to both efficient query processing and effective digest of the query results. To address this problem, ranked search has been introduced to database systems. We study the problem of On-Line Analytical Processing (OLAP) of ranked queries, where ranked queries are conducted in the arbitrary subset of data defined by multi-dimensional selections. While pre-computation and multi-dimensional aggregation is the standard solution for OLAP, materializing dynamic ranking results is unrealistic because the ranking criteria are not known until the query time. To overcome such difficulty, we develop a new ranking cube method that performs semi on-line materialization and semi online computation in this thesis. Its complete life cycle, including cube construction, incremental maintenance, and query processing, is also discussed. We further extend the ranking cube in three dimensions. First, how to answer queries in high-dimensional data. Second, how to answer queries which involves joins over multiple relations. Third, how to answer general preference queries (besides ranked queries, such as skyline queries). Our performance studies show that ranking-cube is orders of magnitude faster than previous approaches

Partout: A Distributed Engine for Efficient RDF Processing

The increasing interest in Semantic Web technologies has led not only to a rapid growth of semantic data on the Web but also to an increasing number of backend applications with already more than a trillion triples in some cases. Confronted with such huge amounts of data and the future growth, existing state-of-the-art systems for storing RDF and processing SPARQL queries are no longer sufficient. In this paper, we introduce Partout, a distributed engine for efficient RDF processing in a cluster of machines. We propose an effective approach for fragmenting RDF data sets based on a query log, allocating the fragments to nodes in a cluster, and finding the optimal configuration. Partout can efficiently handle updates and its query optimizer produces efficient query execution plans for ad-hoc SPARQL queries. Our experiments show the superiority of our approach to state-of-the-art approaches for partitioning and distributed SPARQL query processing

Reusable mesh signature scheme for protecting identity privacy of IoT devices

Peer reviewedPublisher PD

Stronger security notions for decentralized traceable attribute-based signatures and more efficient constructions

We revisit the notion of Decentralized Traceable Attribute-Based Signatures (DTABS) introduced by El Kaafarani et al. (CT-RSA 2014) and improve the state-of-the-art in three dimensions: Firstly, we provide a new stronger security model which circumvents some shortcomings in existing models. Our model minimizes the trust placed in attribute authorities and hence provides, among other things, a stronger definition for non-frameability. In addition, our model captures the notion of tracing soundness which is important for many applications of the primitive. Secondly, we provide a generic construction that is secure w.r.t. our strong security model and show two example instantiations in the standard model which are more efficient than existing constructions (secure under weaker security definitions). Finally, we dispense with the need for the expensive zero-knowledge proofs required for proving tracing correctness by the tracing authority. As a result, tracing a signature in our constructions is significantly more efficient than existing constructions, both in terms of the size of the tracing proof and the computational cost required to generate and verify it. For instance, verifying tracing correctness in our constructions requires only 4 pairings compared to 34 pairings in the most efficient existing construction

From Simple Associations to Systemic Reasoning: A Connectionist Representation of Rules, Variables and Dynamic Bindings

Human agents draw a variety of inferences effortlessly, spontaneously, and with remarkable efficiency - as though these inferences are a reflex response of their cognitive apparatus. The work presented in this paper is a step toward a computational account of this remarkable reasoning ability. We describe how a connectionist system made up of simple and slow neuron-like elements can encode millions of facts and rules involving n-ary predicates and variables, and yet perform a variety of inferences within hundreds of milliseconds. We observe that an efficient reasoning system must represent and propagate, dynamically, a large number of variable bindings. The proposed system does so by propagating rhythmic patterns of activity wherein dynamic bindings are represented as the in-phase, i.e., synchronous, firing of appropriate nodes. The mechanisms for representing and propagating dynamic bindings are biologically plausible. Neurophysiological evidence suggests that similar mechanisms may in fact be used by the brain to represent and process sensorimotor information