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University of Insubria, Italy

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22 Papers

Semi-automatic Segmentation of MRI Brain Metastases Combining Support Vector Machine and Morphological Operators

Topics: Support Vector Machines and Kernel Methods; Applications: Image Processing and Artificial Vision, Pattern Recognition, Decision Making, Industrial and Real World Applications, Financial Applications, Neural Prostheses and Medical Applications, Neural Based Data Mining and Complex Information Process

Authors: Gloria Gonella ¹ , Elisabetta Binaghi ¹ , Paola Nocera ² and Cinzia Mordacchini ³

Affiliations: ¹ Department of Theorical and Applied Science, Insubria University, Varese and Italy , ² C. S. Health Physics, ASST dei Sette Laghi, Varese, Italy, Department of Physics, University of Milan, Milan and Italy , ³ C. S. Health Physics, ASST dei Sette Laghi, Varese and Italy

Digital Object Identifier: 10.5220/0008019304570463

Proceedings of NCTA 2019/IJCCI, ISBN: 978-989-758-384-1

Quick Abstract

The objective of this study is to develop a semi-automatic, interactive segmentation strategy for efficient and accurate brain metastases delineation on Post Gadolinium T1-weighted brain MRI images. Salient aspects of the proposed solutions are the combined use of machine learning and image processing techniques, based on Support Vector Machine and Morphological Operators respectively, to delineate pathological and healthy tissues. The overall segmentation procedure is designed to operate on a clinical setting to reduce the workload of health-care professionals but leaving to them full control of the process. The segmentation process was validated for in-house collected image data obtained from radiation therapy studies. The results prove that the allied use of SVM and Morphological Operators produces accurate segmentations, useful for their insertion in clinical practice.

Query and Product Suggestion for Price Comparison Search Engines based on Query-product Click-through Bipartite Graphs

Topics: Searching and Browsing; Personalized Web Sites and Services; Recommendation Systems; Web Interface, Web Intelligence, Big data; Data Web Mining

Authors: Lucia Noce , Ignazio Gallo and Alessandro Zamberletti

Affiliation: University of Insubria, Italy

Digital Object Identifier: 10.5220/0005753400170024

Proceedings of WEBIST 2016, ISBN: 978-989-758-186-1

Quick Abstract

Query suggestion is a technique for generating alternative queries to facilitate information seeking, and has become a needful feature that commercial search engines provide to web users. In this paper, we focus on query suggestion for price comparison search engines. In this specific domain, suggestions provided to web users need to be properly generated taking into account whether both the searched and the suggested products are still available for sale. To this end, we propose a novel approach based on a slightly variant of classical query-URL graphs: the query-product click through bipartite graph. Such graph is built using information extracted both from search engine logs and specific domain features such as categories and products popularities. Information collected from the query-product graph can be used to suggest not only related queries but also related products. The proposed model was tested on several challenging datasets, and also compared with a recent competing query suggestion approach specifically designed for price comparison engines. Our solution outperforms the competing approach, achieving higher results both in terms of relevance of the provided suggestions and coverage rates on top-8 suggestions.

Related Ontology Subjects/Areas/Topics (7)

Enterprise Information Systems	Searching and Browsing	Web Information Systems and Technologies
Personalized Web Sites and Services	Software Agents and Internet Computing	Web Interfaces and Applications
Recommendation Systems

Defining and Evaluating Software Project Success Indicators - A GQM-based Case Study

Topics: Software Metrics; Project Planning, Monitoring and Control; Quality Control and Assurance

Authors: Luigi Lavazza ¹ , Enrico Frumento ² and Riccardo Mazza ³

Affiliations: ¹ CEFRIEL and Università degli Studi dell’Insubria, Italy , ² CEFRIEL, Italy , ³ WIND, Italy

Digital Object Identifier: 10.5220/0005517301050116

Proceedings of ICSOFT-EA 2015/ICSOFT, ISBN: 978-989-758-114-4

Quick Abstract

KPI (Key Process Indicators) and success indicators are often defined in a rather generic and imprecise manner. This happens because they are defined very early in the project’s life, when little details about the project are known, or simply because the definition does not follow a systematic and effective methodology. We need to precisely define KPI and project success indicators, guarantee that the data upon which they are based can be effectively and efficiently measured, and assure that the computed indicators are adequate with respect to project objectives, and represent the viewpoints of all the involved stakeholders. A complete and coherent process for managing KPI and success indicators lifecycle –instrumented with specific techniques and tools, including the Goal/Question/Metrics (GQM) method for the definition of measures and the R statistic language and environment for analyzing data and computing indicators– was applied in the evaluation of the European research project MUSES. The MUSES case study shows that the proposed process provides an easy and well supported path to the definition and implementation of effective KPI and project success indicators.

Related Ontology Subjects/Areas/Topics (4)

Project Planning, Monitoring and Control		Software Metrics		Software Project Management
Software Engineering

An Operational Semantics for XML Fuzzy Queries

Topics: Fuzzy Information Retrieval and Data Mining; Fuzzy Information Processing, Fusion, Text Mining

Authors: Alessandro Campi ¹ , Sam Guinea ¹ and Paola Spoletini ²

Affiliations: ¹ Politecnico di Milano, Italy , ² Università dell'Insubria, Italy

Digital Object Identifier: 10.5220/0005155502050210

Proceedings of FCTA 2014/IJCCI, ISBN: 978-989-758-053-6

Quick Abstract

XML has become a widespread format for data exchange over the Internet. The current state of the art in querying XML data is represented by XPath and XQuery, both of which define binary predicates. In this paper, we advocate that binary selection can at times be restrictive due to very nature of XML, and to the uses that are made of it. We therefore suggest a querying framework, called FXPath, based on fuzzy logics. In particular, we propose the use of fuzzy predicates for the definition of more ``vague'' and softer queries. We also introduce a function called ``deep-similar'', which aims at substituting XPath's typical ``deep-equal'' function. Its goal is to provide a degree of similarity between two XML trees, assessing whether they are similar both structure-wise and content-wise. In this paper we present the formal syntax and semantics of Fuzzy XPath, and discuss implementation issues

Related Ontology Subjects/Areas/Topics (6)

Artificial Intelligence		Fuzzy Information Processing, Fusion, Text Mining		Fuzzy Systems
Computational Intelligence		Fuzzy Information Retrieval and Data Mining		Soft Computing

Automatic Segmentation of MR Brain Tumor Images using Support Vector Machine in Combination with Graph Cut

Topics: Pattern Recognition; Industrial, Financial and Medical Applications; Support Vector Machines and Applications; Image Processing and Artificial Vision Applications

Authors: Elisabetta Binaghi , Massimo Omodei , Valentina Pedoia , Sergio Balbi , Desiree Lattanzi and Emanuele Monti

Affiliation: Insubria University, Italy

Digital Object Identifier: 10.5220/0005068501520157

Proceedings of NCTA 2014/IJCCI, ISBN: 978-989-758-054-3

Quick Abstract

This work focuses the attention on the automatic segmentation of meningioma from multispectral brain Magnetic Resonance imagery. The Authors address the segmentation task by proposing a fully automatic method hierarchically structured in two phases. The preliminary unsupervised phase is based on Graph Cut framework. In the second phase, preliminary segmentation results are refined using a supervised classification based on Support Vector Machine. The overall segmentation procedure is conceived fully automatic and tailored to non-volumetric data characterized by poor inter-slice spacing, in an attempt to facilitate the insertion in clinical practice. The results obtained in this preliminary study are encouraging and prove that the segmentation benefits from the allied use of Graph Cut and Support Vector Machine frameworks.

Related Ontology Subjects/Areas/Topics (23)

Artificial Intelligence	Health Engineering and Technology Applications	Pattern Recognition
Biomedical Engineering	Human-Computer Interaction	Physiological Computing Systems
Biomedical Signal Processing	Image Processing and Artificial Vision Applications	Sensor Networks
Computational Intelligence	Industrial, Financial and Medical Applications	Signal Processing
Computer-Supported Education	Methodologies and Methods	Soft Computing
Data Manipulation	Neural Networks	Support Vector Machines and Applications
Domain Applications and Case Studies	Neurocomputing	Theory and Methods
Fuzzy Systems	Neurotechnology, Electronics and Informatics

Data Quality Evaluation of Scientific Datasets - A Case Study in a Policy Support Context

Topic: Information Quality

Authors: Antonella Zanzi ¹ and Alberto Trombetta ²

Affiliations: ¹ Università degli Studi dell’Insubria and European Commission, Italy , ² Università degli Studi dell’Insubria, Italy

Digital Object Identifier: 10.5220/0004476401670174

Proceedings of DATA 2013, ISBN: 978-989-8565-67-9

Quick Abstract

In this work we present the rule-based approach used to evaluate the quality of scientific datasets in a policy support context. The used case study refers to real datasets in a context where low data quality limits the accuracy of the analysis results and, consequently, the significance of the provided policy advice. The applied solution consists in the identification of types of constraints that can be useful as data quality rules and in the development of a software tool to evaluate a dataset on the basis of a set of rules expressed in the XML markup language. As rule types we selected some types of data constraints and dependencies already proposed in data quality works, but we experimented also the use of order dependencies and existence constraints. The case study was used to develop and test the adopted solution, which is anyway generally applicable to other contexts.

Related Ontology Subjects/Areas/Topics (3)

Data Engineering

Data Management and Quality

Information Quality

Unsupervised Feature Learning using Self-organizing Maps

Topics: Machine Learning Technologies for Vision; Features Extraction; Object and Face Recognition; Categorization and Scene Understanding

Authors: Marco Vanetti , Ignazio Gallo and Angelo Nodari

Affiliation: Università degli Studi dell’Insubria, Italy

Digital Object Identifier: 10.5220/0004210305960601

Proceedings of VISAPP 2013/VISIGRAPP, ISBN: 978-989-8565-47-1

Quick Abstract

In recent years a great amount of research has focused on algorithms that learn features from unlabeled data. In this work we propose a model based on the Self-Organizing Map (SOM) neural network to learn features useful for the problem of automatic natural images classification. In particular we use the SOM model to learn single-layer features from the extremely challenging CIFAR-10 dataset, containing 60.000 tiny labeled natural images, and subsequently use these features with a pyramidal histogram encoding to train a linear SVM classifier. Despite the large number of images, the proposed feature learning method requires only few minutes on an entry-level system, however we show that a supervised classifier trained with learned features provides significantly better results than using raw pixels values or other handcrafted features designed specifically for image classification. Moreover, exploiting the topological property of the SOM neural network, it is possible to reduce the number of features and speed up the supervised training process combining topologically close neurons, without repeating the feature learning process.

Related Ontology Subjects/Areas/Topics (3)

Computer Vision, Visualization and Computer Graphics

Features Extraction

Image and Video Analysis

A New Query Suggestion Algorithm for Taxonomy-based Search Engines

Topic: Data Mining in Electronic Commerce

Authors: Roberto Zanon ¹ , Simone Albertini ² , Moreno Carullo ¹ and Ignazio Gallo ²

Affiliations: ¹ 7Pixel S.r.l., Italy , ² University of Insubria, Italy

Digital Object Identifier: 10.5220/0004108001510156

Proceedings of KDIR 2012/IC3K, ISBN: 978-989-8565-29-7

Quick Abstract

The objective of this work is the realization of an algorithm to provide a query suggestion feature in order to support the search engine of a commercial web site. Starting from web server logs, our solution creates a model analyzing the queries submitted by the users. Given a submitted query, the system searches the most adequate queries to suggest. Our method implements an already known session based proposal enriching it by exploiting specific information available in the current context: the category the user is browsing on the web site and a solution to overcome the limits of a pure session based approach considering also similarity between queries. Quantitative and qualitative experiments show that the proposed model is suitable in terms of resources employed and user’s satisfaction degree.

Related Ontology Subjects/Areas/Topics (5)

Artificial Intelligence		Knowledge Discovery and Information Retrieval		Symbolic Systems
Data Mining in Electronic Commerce		Knowledge-Based Systems

CROSS LAYER DATA ASSESSMENT IN WIRELESS SENSOR NETWORKS

Topics: Data Quality and Integrity; Security: Vulnerability and Privacy

Authors: Alberto Coen Porisini and Sabrina Sicari

Affiliation: Università degli Studi dell’Insubria, Italy

Digital Object Identifier: 10.5220/0003901102030208

Proceedings of SENSORNETS 2012, ISBN: 978-989-8565-01-3

Quick Abstract

Wireless sensor networks (WSN) are the target of different kinds of security attacks. The network nodes, which sense, aggregate, encrypt and transmit data, play a key role for assuring data quality. In this paper we present a way in which the network sink can evaluate the nodes reputation in order to determine whether one or more nodes are behaving maliciously. The approach combines different techniques such secure localization and privacy aware transmission in order to assess both nodes reputation and data quality.

Related Ontology Subjects/Areas/Topics (5)

Data Manipulation		Obstacles		Sensor Networks
Data Quality and Integrity		Security: Vulnerability and Privacy

TOWARDS THE DEFINITION OF A FRAMEWORK FOR SERVICE DEVELOPMENT IN THE AGROFOOD DOMAIN - A Conceptual Model

Topics: Web Services and Web Engineering; Web Security and Privacy; Context-Awareness

Authors: Donato Barbagallo ¹ , Cinzia Cappiello ¹ , Alberto Coen Porisini ² , Pietro Colombo ² , Marco Comerio ³ , Flavio De Paoli ³ , Chiara Francalanci ¹ and Sabrina Sicari ²

Affiliations: ¹ Politecnico di Milano, Italy , ² Università degli Studi dell'Insubria, Italy , ³ Università degli Studi di Milano-Bicocca, Italy

Digital Object Identifier: 10.5220/0003935102640267

Proceedings of WEBIST 2012, ISBN: 978-989-8565-08-2

Quick Abstract

This paper describes the work done in the project MoseForAgrofood (Ms4A) whose goal is to take advantage of mobile technologies to develop augmented Web services for the agrofood domain. The use of personal devices, such as smart phones and tablets, brings in specific issues such as the need to consider quality of protection, quality of services, quality of data at any stage of the service life cycle. In this paper we propose a conceptual model that aims to support the service development and provisioning addressing the above issues by managing suitable non-functional properties.

Related Ontology Subjects/Areas/Topics (6)

Context-Awareness		Mobile Information Systems		Web Security and Privacy
Internet Technology		Web Information Systems and Technologies		Web Services and Web Engineering

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