Multichart Schemes for Detecting Changes in Disease Incidence

Data mining for detecting Bitcoin Ponzi schemes

Soon after its introduction in 2009, Bitcoin has been adopted by cyber-criminals, which rely on its pseudonymity to implement virtually untraceable scams. One of the typical scams that operate on Bitcoin are the so-called Ponzi schemes. These are fraudulent investments which repay users with the funds invested by new users that join the scheme, and implode when it is no longer possible to find ...

Detecting semantic changes in Alzheimer’s disease with vector space models

Numerous studies have shown that language impairments, particularly semantic deficits, are evident in the narrative speech of people with Alzheimer’s disease from the earliest stages of the disease. Here, we present a novel technique for capturing those changes, by comparing distributed word representations constructed from healthy controls and Alzheimer’s patients. We investigate examples of w...

Detecting Changes in Scenes

Comparing Reputation Schemes for Detecting Malicious Nodes in Sensor Networks

Remotely deployed sensor networks are vulnerable to both physical and electronic security breaches. The sensor nodes, once compromised, can send erroneous data to the base station, thereby possibly compromising network effectiveness. We assume that sensor nodes are organized in a hierarchy and use offline neural network based learning technique to predict the data sensed at any node given the d...

DETECTING AND PREDICTING CHANGES -1- Detecting and Predicting Changes

When required to predict sequential events, such as random coin tosses or basketball free throws, people reliably use inappropriate strategies, such as inferring temporal structure when none is present. . We investigate the ability of human observers predict sequential events in dynamically changing environments, where there is an opportunity to detect true temporal structure. In two experiment...