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Volume 11 (2021): Edizione 3 (July 2021)

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Volume 10 (2020): Edizione 4 (October 2020)

Volume 10 (2020): Edizione 3 (July 2020)

Volume 10 (2020): Edizione 2 (April 2020)

Volume 10 (2020): Edizione 1 (January 2020)

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Volume 8 (2018): Edizione 3 (July 2018)

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Dettagli della rivista
Formato
Rivista
eISSN
2449-6499
Pubblicato per la prima volta
30 Dec 2014
Periodo di pubblicazione
4 volte all'anno
Lingue
Inglese

Cerca

Volume 10 (2020): Edizione 3 (July 2020)

Dettagli della rivista
Formato
Rivista
eISSN
2449-6499
Pubblicato per la prima volta
30 Dec 2014
Periodo di pubblicazione
4 volte all'anno
Lingue
Inglese

Cerca

5 Articoli
Accesso libero

Evolutionary Algorithm with a Configurable Search Mechanism

Pubblicato online: 23 May 2020
Pagine: 151 - 171

Astratto

Abstract

In this paper, we propose a new population-based evolutionary algorithm that automatically configures the used search mechanism during its operation, which consists in choosing for each individual of the population a single evolutionary operator from the pool. The pool of operators comes from various evolutionary algorithms. With this idea, a flexible balance between exploration and exploitation of the problem domain can be achieved. The approach proposed in this paper might offer an inspirational alternative in creating evolutionary algorithms and their modifications. Moreover, different strategies for mutating those parts of individuals that encode the used search operators are also taken into account. The effectiveness of the proposed algorithm has been tested using typical benchmarks used to test evolutionary algorithms.

Parole chiave

  • evolutionary algorithm
  • population-based algorithm
  • optimization
  • operator pool
  • operator selection
  • individual selection
Accesso libero

An Algorithm for the Evolutionary-Fuzzy Generation of on-Line Signature Hybrid Descriptors

Pubblicato online: 23 May 2020
Pagine: 173 - 187

Astratto

Abstract

In biometrics, methods which are able to precisely adapt to the biometric features of users are much sought after. They use various methods of artificial intelligence, in particular methods from the group of soft computing. In this paper, we focus on on-line signature verification. Such signatures are complex objects described not only by the shape but also by the dynamics of the signing process. In standard devices used for signature acquisition (with an LCD touch screen) this dynamics may include pen velocity, but sometimes other types of signals are also available, e.g. pen pressure on the screen surface (e.g. in graphic tablets), the angle between the pen and the screen surface, etc. The precision of the on-line signature dynamics processing has been a motivational springboard for developing methods that use signature partitioning. Partitioning uses a well-known principle of decomposing the problem into smaller ones. In this paper, we propose a new partitioning algorithm that uses capabilities of the algorithms based on populations and fuzzy systems. Evolutionary-fuzzy partitioning eliminates the need to average dynamic waveforms in created partitions because it replaces them. Evolutionary separation of partitions results in a better matching of partitions with reference signatures, eliminates dispro-portions between the number of points describing dynamics in partitions, eliminates the impact of random values, separates partitions related to the signing stage and its dynamics (e.g. high and low velocity of signing, where high and low are imprecise-fuzzy concepts). The operation of the presented algorithm has been tested using the well-known BioSecure DS2 database of real dynamic signatures.

Parole chiave

  • biometrics
  • on-line signature
  • dynamic signature
  • dynamic signature verification
  • evolutionary-fuzzy signature partitioning
  • horizontal and vertical partitioning
Accesso libero

Multi Agent Deep Learning with Cooperative Communication

Pubblicato online: 23 May 2020
Pagine: 189 - 207

Astratto

Abstract

We consider the problem of multi agents cooperating in a partially-observable environment. Agents must learn to coordinate and share relevant information to solve the tasks successfully. This article describes Asynchronous Advantage Actor-Critic with Communication (A3C2), an end-to-end differentiable approach where agents learn policies and communication protocols simultaneously. A3C2 uses a centralized learning, distributed execution paradigm, supports independent agents, dynamic team sizes, partially-observable environments, and noisy communications. We compare and show that A3C2 outperforms other state-of-the-art proposals in multiple environments.

Parole chiave

  • multi-agent systems
  • deep reinforcement learning
  • centralized learning
Accesso libero

A New Method for Automatic Determining of the DBSCAN Parameters

Pubblicato online: 23 May 2020
Pagine: 209 - 221

Astratto

Abstract

Clustering is an attractive technique used in many fields in order to deal with large scale data. Many clustering algorithms have been proposed so far. The most popular algorithms include density-based approaches. These kinds of algorithms can identify clusters of arbitrary shapes in datasets. The most common of them is the Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The original DBSCAN algorithm has been widely applied in various applications and has many different modifications. However, there is a fundamental issue of the right choice of its two input parameters, i.e the eps radius and the MinPts density threshold. The choice of these parameters is especially difficult when the density variation within clusters is significant. In this paper, a new method that determines the right values of the parameters for different kinds of clusters is proposed. This method uses detection of sharp distance increases generated by a function which computes a distance between each element of a dataset and its k-th nearest neighbor. Experimental results have been obtained for several different datasets and they confirm a very good performance of the newly proposed method.

Parole chiave

  • clustering algorithms
  • DBSCAN
  • data mining
Accesso libero

Detecting Visual Objects by Edge Crawling

Pubblicato online: 23 May 2020
Pagine: 223 - 237

Astratto

Abstract

Content-based image retrieval methods develop rapidly with a growing scale of image repositories. They are usually based on comparing and indexing some image features. We developed a new algorithm for finding objects in images by traversing their edges. Moreover, we describe the objects by histograms of local features and angles. We use such a description to retrieve similar images fast. We performed extensive experiments on three established image datasets proving the effectiveness of the proposed method.

Parole chiave

  • content-based image retrieval
  • crawler
  • edge detection
  • image descriptor
  • object extraction
5 Articoli
Accesso libero

Evolutionary Algorithm with a Configurable Search Mechanism

Pubblicato online: 23 May 2020
Pagine: 151 - 171

Astratto

Abstract

In this paper, we propose a new population-based evolutionary algorithm that automatically configures the used search mechanism during its operation, which consists in choosing for each individual of the population a single evolutionary operator from the pool. The pool of operators comes from various evolutionary algorithms. With this idea, a flexible balance between exploration and exploitation of the problem domain can be achieved. The approach proposed in this paper might offer an inspirational alternative in creating evolutionary algorithms and their modifications. Moreover, different strategies for mutating those parts of individuals that encode the used search operators are also taken into account. The effectiveness of the proposed algorithm has been tested using typical benchmarks used to test evolutionary algorithms.

Parole chiave

  • evolutionary algorithm
  • population-based algorithm
  • optimization
  • operator pool
  • operator selection
  • individual selection
Accesso libero

An Algorithm for the Evolutionary-Fuzzy Generation of on-Line Signature Hybrid Descriptors

Pubblicato online: 23 May 2020
Pagine: 173 - 187

Astratto

Abstract

In biometrics, methods which are able to precisely adapt to the biometric features of users are much sought after. They use various methods of artificial intelligence, in particular methods from the group of soft computing. In this paper, we focus on on-line signature verification. Such signatures are complex objects described not only by the shape but also by the dynamics of the signing process. In standard devices used for signature acquisition (with an LCD touch screen) this dynamics may include pen velocity, but sometimes other types of signals are also available, e.g. pen pressure on the screen surface (e.g. in graphic tablets), the angle between the pen and the screen surface, etc. The precision of the on-line signature dynamics processing has been a motivational springboard for developing methods that use signature partitioning. Partitioning uses a well-known principle of decomposing the problem into smaller ones. In this paper, we propose a new partitioning algorithm that uses capabilities of the algorithms based on populations and fuzzy systems. Evolutionary-fuzzy partitioning eliminates the need to average dynamic waveforms in created partitions because it replaces them. Evolutionary separation of partitions results in a better matching of partitions with reference signatures, eliminates dispro-portions between the number of points describing dynamics in partitions, eliminates the impact of random values, separates partitions related to the signing stage and its dynamics (e.g. high and low velocity of signing, where high and low are imprecise-fuzzy concepts). The operation of the presented algorithm has been tested using the well-known BioSecure DS2 database of real dynamic signatures.

Parole chiave

  • biometrics
  • on-line signature
  • dynamic signature
  • dynamic signature verification
  • evolutionary-fuzzy signature partitioning
  • horizontal and vertical partitioning
Accesso libero

Multi Agent Deep Learning with Cooperative Communication

Pubblicato online: 23 May 2020
Pagine: 189 - 207

Astratto

Abstract

We consider the problem of multi agents cooperating in a partially-observable environment. Agents must learn to coordinate and share relevant information to solve the tasks successfully. This article describes Asynchronous Advantage Actor-Critic with Communication (A3C2), an end-to-end differentiable approach where agents learn policies and communication protocols simultaneously. A3C2 uses a centralized learning, distributed execution paradigm, supports independent agents, dynamic team sizes, partially-observable environments, and noisy communications. We compare and show that A3C2 outperforms other state-of-the-art proposals in multiple environments.

Parole chiave

  • multi-agent systems
  • deep reinforcement learning
  • centralized learning
Accesso libero

A New Method for Automatic Determining of the DBSCAN Parameters

Pubblicato online: 23 May 2020
Pagine: 209 - 221

Astratto

Abstract

Clustering is an attractive technique used in many fields in order to deal with large scale data. Many clustering algorithms have been proposed so far. The most popular algorithms include density-based approaches. These kinds of algorithms can identify clusters of arbitrary shapes in datasets. The most common of them is the Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The original DBSCAN algorithm has been widely applied in various applications and has many different modifications. However, there is a fundamental issue of the right choice of its two input parameters, i.e the eps radius and the MinPts density threshold. The choice of these parameters is especially difficult when the density variation within clusters is significant. In this paper, a new method that determines the right values of the parameters for different kinds of clusters is proposed. This method uses detection of sharp distance increases generated by a function which computes a distance between each element of a dataset and its k-th nearest neighbor. Experimental results have been obtained for several different datasets and they confirm a very good performance of the newly proposed method.

Parole chiave

  • clustering algorithms
  • DBSCAN
  • data mining
Accesso libero

Detecting Visual Objects by Edge Crawling

Pubblicato online: 23 May 2020
Pagine: 223 - 237

Astratto

Abstract

Content-based image retrieval methods develop rapidly with a growing scale of image repositories. They are usually based on comparing and indexing some image features. We developed a new algorithm for finding objects in images by traversing their edges. Moreover, we describe the objects by histograms of local features and angles. We use such a description to retrieve similar images fast. We performed extensive experiments on three established image datasets proving the effectiveness of the proposed method.

Parole chiave

  • content-based image retrieval
  • crawler
  • edge detection
  • image descriptor
  • object extraction

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