SciTePress - Affiliation Profile

Affiliation

Select a Subject
Refine Ontology Subject

University of Palermo, Italy

Context (please use complete words only)

Field

Sort by

24 Papers

ABBIE: Attention-Based BI-Encoders for Predicting Where to Split Compound Sanskrit Words

Topics: Neural Networks; Natural Language Processing; Industrial Applications of AI; Deep Learning

Authors: Irfan Ali ¹ , Liliana Lo Presti ¹ , Igor Spano ² and Marco La Cascia ¹

Affiliations: ¹ Department of Engineering, University of Palermo, Palermo, Italy , ² Department of Cultures and Society, University of Palermo, Palermo, Italy

Digital Object Identifier: 10.5220/0013155300003890

Proceedings of ICAART 2025, ISBN: 978-989-758-737-5

Quick Abstract

Sanskrit is a highly composite language, morphologically and phonetically complex. One of the major challenges in processing Sanskrit is the splitting of compound words that are merged phonetically. Recognizing the exact location of splits in a compound word is difficult since several possible splits can be found, but only a few of them are semantically meaningful. This paper proposes a novel deep learning method that uses two bi-encoders and a multi-head attention module to predict the valid split location in Sanskrit compound words. The two bi-encoders process the input sequence in direct and reverse order respectively. The model learns the character-level context in which the splitting occurs by exploiting the correlation between the direct and reverse dynamics of the characters sequence. The results of the proposed model are compared with a state-of-the-art technique that adopts a bidirectional recurrent network to solve the same task. Experimental results show that the proposed model correctly identifies where the compound word should be split into its components in 89.27% of cases, outperforming the state-of-the-art technique. The paper also proposes a dataset developed from the repository of the Digital Corpus of Sanskrit (DCS) and the University of Hyderabad (UoH) corpus.

Population Protocols for Adaptive Event Dissemination with Autonomous Agents in Vehicular Networks

Topics: Multi-Agent Systems; Mobile Agents; Agent Communication and Languages

Authors: Vincenzo Agate ¹ , Farwa Batool ² , Antonio Bordonaro ¹ , Alessandra De Paola ¹ , Pierluca Ferraro ¹ , Giuseppe Lo Re ¹ , Marco Morana ¹ and Antonio Virga ¹

Affiliations: ¹ University of Palermo, Palermo, Italy , ² Scuola IMT Alti Studi Lucca, Lucca, Italy

Digital Object Identifier: 10.5220/0013341100003890

Proceedings of ICAART 2025, ISBN: 978-989-758-737-5

Quick Abstract

Recent advances in distributed vehicle-to-vehicle communication promise to transform the user’s driving experience, providing new services capable of improving safety, efficiency and quality of travelling. Due to the large amount of information exchanged, a major challenge of Vehicular Networks is the adoption of appropriate data dissemination protocols that ensure good performance in real-time event detection, while guaranteeing low communication overhead. To this aim, this paper proposes an adaptive event dissemination algorithm which exploits Population Protocols (PPs) for modelling vehicle interactions as coordinated behaviors of autonomous agents in a distributed system. The experimental evaluation performed on realistic vehicle tracks over real-world maps demonstrates the system’s ability to efficiently disseminate information in the network in order to support reliable and distributed event detection services.

A Low Cost Solution for NOAA Remote Sensing

Topics: Connectivity and Communication; Remote Sensing and Telemetry; Modeling and Simulation

Authors: Edoardo Ardizzone , Alessandro Bruno , Francesco Gugliuzza and Roberto Pirrone

Affiliation: Università degli Studi di Palermo, Italy

Digital Object Identifier: 10.5220/0006639101280134

Proceedings of SENSORNETS 2018, ISBN: 978-989-758-284-4

Quick Abstract

United States National Oceanic and Atmospheric Administration (NOAA) weather satellites adopt Advanced Very High Resolution Radiometer (AVHRR) sensors to acquire remote sensing data and broadcast Automatic Picture Transmission (APT) images. The orientation of the scan lines is perpendicular to the orbit of the satellite. In this paper we propose a new low cost solution for NOAA remote sensing. More in detail, our method focuses on the possibility of directly sampling the modulated signal and processing it entirely in software enabled by recent breakthroughs on Software Defined Radios (SDR) and CPU computational speed, while keeping the costs extremely low. We aim to achieve good results with inexpensive SDR hardware, like the RTL-SDR (a repurposed DVB-T USB dongle). Nevertheless, we faced some problems caused by hardware limits such as high receiver noise figure and low ADC resolution. Furthermore, we detected several inherent drawbacks of frequent tuner saturations. For this purpose we developed a software-hardware integrated system able to perform the following steps: satellite pass prediction, time scheduling, signal demodulation, image cropping and filtering. Although we employed low cost components, we obtained good results in terms of signal demodulation, synchronization and image reconstruction.

Related Ontology Subjects/Areas/Topics (8)

Connectivity and Communication	Obstacles	Software and Architectures
Multimedia	Remote Sensing	Telecommunications
Multimedia Signal Processing	Sensor Networks

Multi-modal Medical Image Registration by Local Affine Transformations

Topics: Stochastic Methods; Medical Imaging

Authors: Liliana Lo Presti and Marco La Cascia

Affiliation: University of Palermo, Italy

Digital Object Identifier: 10.5220/0006656405340540

Proceedings of ICPRAM 2018, ISBN: 978-989-758-276-9

Quick Abstract

Image registration is the process of finding the geometric transformation that, applied to the floating image, gives the registered image with the highest similarity to the reference image. Registering a pair of images involves the definition of a similarity function in terms of the parameters of the geometric transformation that allows the registration. This paper proposes to register a pair of images by iteratively maximizing the empirical mutual information through coordinate gradient descent. Hence, the registered image is obtained by applying a sequence of local affine transformations. Rather than adopting a uniformly spaced grid to select image blocks to locally register, as done by state-of-the-art techniques, this paper proposes a method which is similar in spirit to boosting strategies used in classification. In this work, a probability distribution over the pixels of the registered image is maintained. At each pixel, this distribution represents the probability that a local affine transformation of a block centered on this pixel should be computed to improve the similarity between the registered and the reference images. The distribution is updated iteratively during the registration process to move probability mass towards pixels unaffected by the estimated local transformation. The paper presents preliminary results by a qualitative evaluation on several pairs of medical images acquired by different sources.

Related Ontology Subjects/Areas/Topics (6)

Applications		Pattern Recognition		Stochastic Methods
Medical Imaging		Software Engineering		Theory and Methods

Agents Displacement in Arbitrary Geometrical Spaces - An Evolutionary Computation based Approach

Topics: Evolutionary Computing; Agent Models and Architectures; Simulation

Authors: Francesco D'Aleo ¹ , Fabio D'Asaro ¹ , Valerio Perticone ¹ , Giovanni Rizzo ¹ and Marco Elio Tabacchi ²

Affiliations: ¹ Università degli Studi di Palermo, Italy , ² Università degli Studi di Palermo and Ed Istituto Nazionale di Ricerche Demopolis, Italy

Digital Object Identifier: 10.5220/0005230601980202

Proceedings of ICAART 2015, ISBN: 978-989-758-073-4

Quick Abstract

In many different social contexts, communication allows a collective intelligence to emerge. However, a correct way of exchanging information usually requires determined topological configurations of the agents involved in the process. Such a configuration should take into account several parameters, e.g. agents positioning, their proximity and time efficiency of communication. Our aim is to present an algorithm, based on evolutionary programming, which optimizes agents placement on arbitrarily shaped areas. In order to show its ability to deal with arbitrary bi-dimensional topologies, this algorithm has been tested on a set of differently shaped areas that present concavities, convexities and obstacles. This approach can be extended to deal with concrete cases, such as object localization in a delimited area.

Related Ontology Subjects/Areas/Topics (13)

Agent Models and Architectures	Computational Intelligence	Methodologies and Technologies
Agents	Enterprise Information Systems	Operational Research
Artificial Intelligence	Evolutionary Computing	Simulation
Bioinformatics	Information Systems Analysis and Specification	Soft Computing
Biomedical Engineering

An Automatic System for Helping Health Consumers to Understand Medical Texts

Topics: Knowledge Management; Design and Development Methodologies for Healthcare IT; e-Health; Electronic Health Records and Standards; e-Health for Public Health

Authors: Marco Alfano ¹ , Biagio Lenzitti ² , Giosuè Lo Bosco ³ and Valerio Perticone ²

Affiliations: ¹ University of Palermo and Anghelos Centre on Communication Studies, Italy , ² University of Palermo, Italy , ³ University of Palermo and Euro-Mediterranean Institute of Science and Technology, Italy

Digital Object Identifier: 10.5220/0005283606220627

Proceedings of HEALTHINF 2015/BIOSTEC, ISBN: 978-989-758-068-0

Quick Abstract

Medical texts (reports, articles, etc.) are usually written by professionals (physicians, medical researchers, etc.) who use their own language and communication style. On the other hand, these texts are often read by health consumers (as in the case of medical reports) who do not have the same skills and vocabularies of the experts and can have difficulties in text comprehension. To help a health consumer in understanding a medical text, it would be desirable to have an automatic system that, given a text written with medical (technical) terms, translates them in simple or plain language and provides additional information with the same kind of language. We have designed such a system. It processes online medical documents and provides health consumers with the needed information for their understanding. To this end, we use a medical vocabulary for finding the technical terms in the medical texts, a consumer health vocabulary (CHV) for translating the technical terms into their consumer equivalents and a health-consumer dictionary for finding supplementary information on the terms. We have built a prototype that processes Italian medical reports and uses infobuttons next to the technical terms for allowing easy retrieval of the desired information.

Related Ontology Subjects/Areas/Topics (15)

Biomedical Engineering	e-Health for Public Health	Knowledge Management
Cloud Computing	Electronic Health Records and Standards	Ontologies and the Semantic Web
Data Engineering	Enterprise Information Systems	Platforms and Applications
Design and Development Methodologies for Healthcare IT	Health Information Systems	Society, e-Business and e-Government
e-Health	Information Systems Analysis and Specification	Web Information Systems and Technologies

An Unsupervised Method for Suspicious Regions Detection in Mammogram Images

Topics: Feature Selection and Extraction; Medical Imaging

Authors: Marco Insalaco , Alessandro Bruno , Alfonso Farruggia , Salvatore Vitabile and Edoardo Ardizzone

Affiliation: University of Palermo, Italy

Digital Object Identifier: 10.5220/0005277103020308

Proceedings of ICPRAM 2015, ISBN: 978-989-758-077-2

Quick Abstract

Over the past years many researchers proposed biomedical imaging methods for computer-aided detection and classification of suspicious regions in mammograms. Mammogram interpretation is performed by radiologists by visual inspection. The large volume of mammograms to be analyzed makes such readings labour intensive and often inaccurate. For this purpose, in this paper we propose a new unsupervised method to automatically detect suspicious regions in mammogram images. The method consists mainly of two steps: preprocessing; feature extraction and selection. Preprocessing steps allow to separate background region from the breast profile region. In greater detail, gray levels mapping transform and histogram specifications are used to enhance the visual representation of mammogram details. Then, local keypoints and descriptors such as SURF have been extracted in breast profile region. The extracted keypoints are filtered by proper parameters tuning to detect suspicious regions. The results, in terms of sensitivity and confidence interval are very encouraging.

Related Ontology Subjects/Areas/Topics (6)

Applications		Medical Imaging		Software Engineering
Feature Selection and Extraction		Pattern Recognition		Theory and Methods

Automatic Illustration of Short Texts via Web Images

Topic: Text and Document Visualization

Authors: Sandro Aldo Aramini , Edoardo Ardizzone and Giuseppe Mazzola

Affiliation: Università degli Studi di Palermo, Italy

Digital Object Identifier: 10.5220/0005307301390148

Proceedings of IVAPP 2015/VISIGRAPP, ISBN: 978-989-758-088-8

Quick Abstract

In this paper we propose a totally unsupervised and automatic illustration method, which aims to find onto the Web a set of images to illustrate the content of an input short text. The text is modelled as a semantic space and a set of relevant keywords is extracted. We compare and discuss different methods to create semantic representations by keyword extraction. Keywords are used to query Google Image Search engine for a list of relevant images. We also extract information from the Web pages that include the retrieved images, to create an Image Semantic Space, which is compared to the Text Semantic Space in order to rank the list of retrieved images. Tests showed that our method achieves very good results, which overcome those obtained by using a state-of-the-art application. Furthermore we developed a Web tool to test our system and evaluate results within the Internet community.

Related Ontology Subjects/Areas/Topics (3)

Abstract Data Visualization

Computer Vision, Visualization and Computer Graphics

Text and Document Visualization

Keyword based Keyframe Extraction in Online Video Collections

Topics: Video Analysis; Information Retrieval; Web Applications; Image Understanding; Object Recognition

Authors: Edoardo Ardizzone ¹ , Marco La Cascia ² and Giuseppe Mazzola ¹

Affiliations: ¹ Università degli Studi di Palermo, Italy , ² Università degli studi di palermo, Italy

Digital Object Identifier: 10.5220/0005190001700177

Proceedings of ICPRAM 2015, ISBN: 978-989-758-077-2

Quick Abstract

Keyframe extraction methods aim to find in a video sequence the most significant frames, according to specific criteria. In this paper we propose a new method to search, in a video database, for frames that are related to a given keyword, and to extract the best ones, according to a proposed quality factor. We first exploit a speech to text algorithm to extract automatic captions from all the video in a specific domain database. Then we select only those sequences (clips), whose captions include a given keyword, thus discarding a lot of information that is useless for our purposes. Each retrieved clip is then divided into shots, using a video segmentation method, that is based on the SURF descriptors and keypoints. The sentence of the caption is projected onto the segmented clip, and we select the shot that includes the input keyword. The selected shot is further inspected to find good quality and stable parts, and the frame which maximizes a quality metric is selected as the best and the most significant frame. We compare the proposed algorithm with another keyframe extraction method based on local features, in terms of Significance and Quality.

Related Ontology Subjects/Areas/Topics (12)

Applications	Image Understanding	Pattern Recognition
Computer Vision, Visualization and Computer Graphics	Information Retrieval	Software Engineering
Data Engineering	Object Recognition	Video Analysis
Image and Video Analysis	Ontologies and the Semantic Web	Web Applications

Combining Top-down and Bottom-up Visual Saliency for Firearms Localization

Topics: Multimedia Databases, Indexing, Recognition and Retrieval; Image and Video Processing, Compression and Segmentation; Multimedia Security and Cryptography

Authors: Edoardo Ardizzone , Roberto Gallea , Marco La Cascia and Giuseppe Mazzola

Affiliation: Università degli Studi di Palermo, Italy

Digital Object Identifier: 10.5220/0005054300250032

Proceedings of SIGMAP 2014/ICETE, ISBN: 978-989-758-046-8

Quick Abstract

Object detection is one of the most challenging issues for computer vision researchers. The analysis of the human visual attention mechanisms can help automatic inspection systems, in order to discard useless infor- mation and improving performances and efficiency. In this paper we proposed our attention based method to estimate firearms position in images of people holding firearms. Both top-down and bottom-up mechanisms are involved in our system. The bottom-up analysis is based on a state-of-the-art approach. The top-down analysis is based on the construction of a probabilistic model of the firearms position with respect to the peo- ple’s face position. This model has been created by analyzing information from of a public available database of movie frames representing actors holding firearms.

Related Ontology Subjects/Areas/Topics (8)

Image and Video Processing, Compression and Segmentation	Multimedia Databases, Indexing, Recognition and Retrieval	Multimedia Systems and Applications
Multimedia	Multimedia Security and Cryptography	Telecommunications
Multimedia and Communications	Multimedia Signal Processing

Topic(s):

Filter listed results, by

Publication date

From:

To:

Author

Locate Author in list below

Data pager
Data pager
1 2 3 4 5 6 Page 1 of 6, items 1 to 10 of 52.

Add Author(s) to filter:

Alessandro Ardizzone (10)

Marco La Cascia (6)

Roberto Pirrone (5)

Giuseppe Mazzola (3)

Vincenzo Cannella (3)

Salvatore Gaglio (3)

Giuseppe Lo Re (3)

Marco Morana (2)

Giuseppe Russo (2)

Liliana Lo Presti (2)

Conference

Locate Conference in list below

Data pager
Data pager
1 2 Page 1 of 2, items 1 to 10 of 12.

Add Conference(s) to filter:

ICAART(4)

ICPRAM(4)

VISAPP(4)

WEBIST(3)

SIGMAP(2)

BIOSIGNALS(1)

HEALTHINF(1)

ICSOFT(1)

IVAPP(1)

PECCS(1)

Title:
ISBN:
Year:
Acronym:
Subject:

Title:
Author:
Affiliation:
Subject:

Name:
Affiliation:
Country:
Conference:
Subject:

Name:
Country:
Subject: