Search results for " Computer Science"
showing 10 items of 3983 documents
Online Induction of Probabilistic Real Time Automata
2012
Probabilistic real time automata (PRTAs) are a representation of dynamic processes arising in the sciences and industry. Currently, the induction of automata is divided into two steps: the creation of the prefix tree acceptor (PTA) and the merge procedure based on clustering of the states. These two steps can be very time intensive when a PRTA is to be induced for massive or even unbounded data sets. The latter one can be efficiently processed, as there exist scalable online clustering algorithms. However, the creation of the PTA still can be very time consuming. To overcome this problem, we propose a genuine online PRTA induction approach that incorporates new instances by first collapsing…
E-learning approach of the graph coloring problem applied to register allocation in embedded systems
2016
The main aim of this paper consists in developing an effective e-learning tool, focused on evolutionary algorithms, in order to solve the graph coloring problem. Subsidiary, we apply graph coloring for register allocation in embedded systems. From didactic viewpoint, our tool has benefits in the learning process because it helps students to observe the relationship between the graph coloring problem and CPU registers allocation with the help of four developed modules: the genetic algorithm, the graphical viewer, the interference graph for a C program and a web application which collects the simulation results. All these applications are combined by a graphical interface which allows the use…
LeSSS: Learned Shared Semantic Spaces for Relating Multi-Modal Representations of 3D Shapes
2015
In this paper, we propose a new method for structuring multi-modal representations of shapes according to semantic relations. We learn a metric that links semantically similar objects represented in different modalities. First, 3D-shapes are associated with textual labels by learning how textual attributes are related to the observed geometry. Correlations between similar labels are captured by simultaneously embedding labels and shape descriptors into a common latent space in which an inner product corresponds to similarity. The mapping is learned robustly by optimizing a rank-based loss function under a sparseness prior for the spectrum of the matrix of all classifiers. Second, we extend …
Learning small programs with additional information
1997
This paper was inspired by [FBW 94]. An arbitrary upper bound on the size of some program for the target function suffices for the learning of some program for this function. In [FBW 94] it was discovered that if “learning” is understood as “identification in the limit,” then in some programming languages it is possible to learn a program of size not exceeding the bound, while in some other programming languages this is not possible.
On the use of neighbourhood-based non-parametric classifiers
1997
Alternative non-parametric classification schemes, which come from the use of different definitions of neighbourhood, are introduced. In particular, the Nearest Centroid Neighbourhood along with the neighbourhood relation derived from the Gabriel Graph and the Relative Neighbourhood Graph are used to define the corresponding (k-)Nearest Neighbour-like classifiers. Experimental results are reported to compare the performance of the approaches proposed here to the one obtained with the k-Nearest Neighbours rule.
The power of procrastination in inductive inference: How it depends on used ordinal notations
1995
We consider inductive inference with procrastination. Usually it is defined using constructive ordinals. For constructive ordinals there exist many different systems of notations. In this paper we study how the power of inductive inference depends on used system of notations.
A basic analysis toolkit for biological sequences
2007
This paper presents a software library, nicknamed BATS, for some basic sequence analysis tasks. Namely, local alignments, via approximate string matching, and global alignments, via longest common subsequence and alignments with affine and concave gap cost functions. Moreover, it also supports filtering operations to select strings from a set and establish their statistical significance, via z-score computation. None of the algorithms is new, but although they are generally regarded as fundamental for sequence analysis, they have not been implemented in a single and consistent software package, as we do here. Therefore, our main contribution is to fill this gap between algorithmic theory an…
Structure and form. Changes of state in architecture – from modern to contemporary
2016
The relationship between the terms Structure and Form, while expressing a consideration on the tectonic component of composition, registers a cyclical evolution of the archi-tecture of the building. The solidity of the Modern, the liquidity of "post-modern conditions", the evaporation of the contemporary aesthetics of disappearance outline a return to a solid state of the matter outlining the basic assumption of formal research in architecture in the sculptural mass of the building.
Social network analysis: the use of graph distances to compare artificial and criminal networks
2021
Aim: Italian criminal groups become more and more dangerous spreading their activities into new sectors. A criminal group is made up of networks of hundreds of family gangs which extended their influence across the world, raking in billions from drug trafficking, extortion and money laundering. We focus in particular on the analysis of the social structure of two Sicilian crime families and we used a Social Network Analysis approach to study the social phenomena. Starting from a real criminal network extracted from meetings emerging from the police physical surveillance during 2000s, we here aim to create artificial models that present similar properties. Methods: We use specific tools of s…
An ontological-based knowledge organization for bioinformatics workflow management system
2012
Motivation and Objectives In the field of Computer Science, ontologies represent formal structures to define and organize knowledge of a specific application domain (Chandrasekaran et al., 1999). An ontology is composed of entities, called classes, and relationships among them. Classes are characterized by features, called attributes, and they can be arranged into a hierarchical organization. Ontologies are a fundamental instrument in Artificial Intelligence for the development of Knowledge-Based Systems (KBS). With its formal and well defined structure, in fact, an ontology provides a machine-understandable language that allows automatic reasoning for problems resolution. Typical KBS are E…