A Bloom filter is a space efficient structure for storing static sets, where the space efficiency is gained at the expense of a small probability of false-positives. A Bloomier filter generalizes a Bloom filter to compactly store a function with a static support. In this article we give a simple construction of a Bloomier filter. The construction is linear in space and requires constant time to...

A Bloom filter represents a set S of elements approximately, by using fewer bits than a precise representation. The price for succinctness is allowing some errors: for any x ∈ S it should always answer ‘Yes’, and for any x / ∈ S it should answer ‘Yes’ only with small probability. The latter are called false positive errors. In this work we study the possibility of Bloom filters in a stronger se...

Bloom Filters provide space-efficient storage of sets at the cost of a probability of false positives on membership queries. The size of the filter must be defined a priori based on the number of elements to store and the desired false positive probability, being impossible to store extra elements without increasing the false positive probability. This leads typically to a conservative assumpti...

A bloom filter is a probabilistic data structure used to test whether an element is a member of a set. The bloom filter shares some similarities to a standard hash table but has a higher storage efficiency. As a drawback, bloom filters allow the existence of false positives. These properties make bloom filters a suitable candidate for storing topological information in large-scale mobile ad hoc...

Bloom Filters are widely used in many applications including database management systems. With a certain allowable error rate, this data structure provides an efficient solution for membership queries. The error rate is inversely proportional to the size of the Bloom filter. Currently, Bloom filters are stored in main memory because the low locality of operations makes them impractical on secon...

Bloom filters are probabilistic data structures commonly used for approximate membership problems in many areas of Computer Science (networking, distributed systems, databases, etc.). With the increase in data size and distribution of data, problems arise where a large number of Bloom filters are available, and all them need to be searched for potential matches. As an example, in a federated cl...

Building upon the finding that people can assess the logicality of conjunctive probability statements intuitively (Vallee-Tourangeau & Faure-Bloom, 2015), we examine whether increasing the salience of people’s implicit knowledge about the logic of sets can impact the influence of logical considerations on probability judgments. We compared the rate of heuristic responding in a control group wit...

Ubiquitous sensing devices frequently disseminate their data between them. The use of a distributed event-based system that decouples publishers of subscribers arises as an ideal candidate to implement the dissemination process. In this paper, we present a network architecture which merges the network and overlay layers of typical structured event-based systems. Directional Random Walks (DRWs) ...

This paper aims at exploring the characteristics and skills required of mental Health human services workers. An analysis of the vital skills will be presented and the fundamental characteristics such as having a caring heart, patience, compassionate, and driven with the desire to see all people gain the best out of their lives. This is because the success of our lives is driven with having a s...

The tremendous growth in the field of modern communication and network systems places demands on the security. As the network complexity grows, the need for the automated detection and timely alert is required to detect the abnormal activities in the network. To diagnose the system against the malicious signatures, a high speed Network Intrusion Detection System is required against the attacks....