We describe a simple, fast and easy to implement recursive algorithm with four alternate intuitive heuristics for inferring Probabilistic Finite State Automata. The algorithm is an extension for stochastic machines of the k-tails method introduced in 1972 by Biermann and Feldman for non-stochastic machines. Experiments comparing the two are done and benchmark results are also presented. It is a...

We present a novel approach for automatically inferring models of multiobject events. Objects are first detected and tracked, their motion is then segmented into a set of primitive events. These primitive events then form the nodes in aMarkov network that encodes the entire event space. A bottomup/top-down search algorithm is developed to detect typical event structures that are used for classi...

WePIGE illustrates a new approach for extracting fine-grained provenance information from XML artefact-based workflow executions. The extraction framework relies on the usage of XPath mapping rules for inferring data and service dependency links [2]. This demonstration illustrates the usage of the WePIGE graphical user interface for exploring the provenance graph generated by a predefined set o...

There is extensive literature on inferring loop invariants statically (i.e. without explicitly executing the program under analysis). We report on a new dynamic technique for inferring loop invariants based on the invariant detector Daikon [2]. Unlike InvGen [4], this new technique follows a counter example guided approach for refining candidate loop invariants. Let us consider the following an...

Text compression by inferring a phrase hierarchy from the input is a recent technique that shows promise both as a compression scheme and as a machine learning method that extracts some comprehensible account of the structure of the input text. Its performance as a data compression scheme outstrips other dictionary schemes, and the structures that it learns from sequences have been put to such ...

The commercial contractual relationship among Autonomous Systems (ASs) is important for understanding the reachability and traffic flow in the Internet. Several heuristic algorithms have been proposed for inferring AS relationships by extracting information from Border Gateway Protocol (BGP) routing tables. However, there is no evaluation of the AS relationships inferred from existing algorithm...

Inferring user’s location has emerged to be a critical and interesting issue in social media field. It is a challenging problem due to the sparse geo-enabled features in social media, for example, only less than 1% of tweets are geo-tagged. This paper proposes a location inferring model for microblog users who have not geo-tagged based on their tweets content and bilateral follow friends. An ap...

Automating Malware Detection by Inferring Intent

In this paper, a technique for inferring the configuration of a clapper’s hands from a hand clapping sound is described. The method was developed based on analysis of synthetic and recorded hand clap sounds, labeled with the corresponding hand configurations. A naïve Bayes classifier was constructed to automatically classify the data using two different feature sets. The results indicate that t...

Inferring Good Maximally Redundant Classification Tests (GMRTs) as Formal Concepts is considered. Two kinds of classification subcontexts are defined: attributive and object ones. The rules of forming and reducing subcontexts based on the notion of essential attributes and objects are given. They lead to the possibility of the inferring control. In particular, an improved Algorithm for Searchin...