SciTePress - Affiliation Profile

Affiliation

Select a Subject
Refine Ontology Subject

University of Naples Federico II, Italy

Context (please use complete words only)

Field

Sort by

20 Papers

An Off-line Evaluation of Users’ Ranking Metrics in Group Recommendation

Topic: Group Decision Making

Authors: Silvia Rossi , Francesco Cervone and Francesco Barile

Affiliation: Università degli Studi di Napoli Federico II, Italy

Digital Object Identifier: 10.5220/0006200702520259

Proceedings of ICAART 2017, ISBN: 978-989-758-219-6

Quick Abstract

One of the major issue in designing group recommendation techniques relates to the difficulty of the evaluation process. Up-today, no freely available dataset exists that contains information about groups, like, for example, the group’s choices or social aspects that may characterize the group’s members. The objective of the paper is to analyze the possibility to make an evaluation of ranking-based groups recommendation techniques by using offline testing. Typically, the evaluation of group recommendations is computed, as in the classical single user case, by comparing the predicted group’s ratings with respect to the single users’ ratings. Since the information contained in the datasets are mainly such user’s ratings, here, ratings are used to define different ranking metrics. Results suggest that such an attempt is hardly feasible. Performance seems not to be affected by the choice of ranking technique, except for some particular cases. This could be due to the averaging effect of the evaluation with respect to the single users’ ratings, so a deeper analysis or specific dataset are necessary.

Related Ontology Subjects/Areas/Topics (3)

Agents

Artificial Intelligence

Group Decision Making

Generating and Instantiating Abstract Workflows with QoS User Requirements

Topics: Multi-Agent Systems; Negotiation and Interaction Protocols; Task Planning and Execution; SOA and Software Agents

Authors: Claudia Di Napoli ¹ , Luca Sabatucci ¹ , Massimo Cossentino ¹ and Silvia Rossi ²

Affiliations: ¹ C.N.R. and Istituto di Calcolo e Reti ad Alte Prestazioni, Italy , ² Università degli Studi di Napoli Federico II, Italy

Digital Object Identifier: 10.5220/0006203902760283

Proceedings of ICAART 2017, ISBN: 978-989-758-219-6

Quick Abstract

The growing availability of services accessible through the network makes it possible to build complex applications resulting from their composition that are usually characterized also by non-functional properties, known as Quality of Service (QoS). To exploit the full potential of service technology, automatic QoS-based composition of services is crucial. In this work a framework for automatic service composition is presented that relies on planning and service negotiation techniques for addressing both functional and non-functional requirements. The proposed approach allows for dynamic service composition and QoS attributes, and it can be applied when services are provided in the contest of a competitive market of service providers without knowledge disclosure.

Related Ontology Subjects/Areas/Topics (13)

Agents	Knowledge Engineering and Ontology Development	SOA and Software Agents
Artificial Intelligence	Knowledge-Based Systems	Software Engineering
Artificial Intelligence and Decision Support Systems	Multi-Agent Systems	Symbolic Systems
Distributed and Mobile Software Systems	Negotiation and Interaction Protocols	Task Planning and Execution
Enterprise Information Systems

An Optimal Trajectory Planner for a Robotic Batting Task: The Table Tennis Example

Topics: Optimization Algorithms; Modeling, Simulation and Architecture; Modeling, Analysis and Control of Hybrid Dynamical Systems

Authors: Diana Serra ¹ , Aykut C. Satici ² , Fabio Ruggiero ¹ , Vincenzo Lippiello ¹ and Bruno Siciliano ¹

Affiliations: ¹ University of Naples Federico II, Italy , ² Massachusetts Institute of Technology, United States

Digital Object Identifier: 10.5220/0005982000900101

Proceedings of ICINCO 2016, ISBN: 978-989-758-198-4

Quick Abstract

This paper presents an optimal trajectory planner for a robotic batting task . The specific case of a table tennis game performed by a robot is considered. Given an estimation of the trajectory of the ball during the free flight, the method addresses the determination of the paddle configuration (pose and velocity) to return the ball at a desired position with a desired spin. The implemented algorithm takes into account the hybrid dynamic model of the ball in free flight as well as the state transition at the impact (the reset map). An optimal trajectory that minimizes the acceleration functional is generated for the paddle to reach the desired impact position, velocity and orientation. Simulations of different case studies further bolster the approach along with a comparison with state-of-the-art methods.

Related Ontology Subjects/Areas/Topics (7)

Informatics in Control, Automation and Robotics	Modeling, Simulation and Architectures	Robotics and Automation
Intelligent Control Systems and Optimization	Optimization Algorithms	Signal Processing, Sensors, Systems Modeling and Control
Modeling, Analysis and Control of Hybrid Dynamical Systems

Design, Implementation and Experiments of a Robust Passivity-based Controller for a Rolling-balancing System

Topics: Control and Supervision Systems; Nonlinear Signals and Systems; Mechatronics Systems

Authors: Martin Crespo ¹ , Alejandro Donaire ² , Fabio Ruggiero ³ , Vincenzo Lippiello ³ and Bruno Siciliano ³

Affiliations: ¹ Universidad Nacional de Rosario and CONICET, Argentina , ² Università degli Studi di Napoli Federico II, The University of Newcastle and University Drive, Italy , ³ Università degli Studi di Napoli Federico II, Italy

Digital Object Identifier: 10.5220/0005981700790089

Proceedings of ICINCO 2016, ISBN: 978-989-758-198-4

Quick Abstract

In this paper, we present the design of a robust interconnection and damping assignment controller for a rolling-balancing system known as the disk-on-disk. The underactuation feature of this system hampers the control design, and since we consider matched disturbances, the problem becomes even more challenging. To overcome this difficulty, we propose to design first a controller to stabilize the desired equilibrium of the case where the disturbance is not present, and then we robustify this controller by adding a nonlinear PID outer loop that compensates the disturbance. Finally, we evaluate the practical applicability of the control design by implementing the controllers on a real hardware for the disk-on-disk system.

Related Ontology Subjects/Areas/Topics (6)

Control and Supervision Systems		Mechatronics Systems		Robotics and Automation
Informatics in Control, Automation and Robotics		Nonlinear Signals and Systems		Signal Processing, Sensors, Systems Modeling and Control

Social Utilities and Personality Traits for Group Recommendation: A Pilot User Study

Topic: Group Decision Making

Authors: Silvia Rossi and Francesco Cervone

Affiliation: Universita’ degli Studi di Napoli “Federico II”, Italy

Digital Object Identifier: 10.5220/0005709600380046

Proceedings of ICAART 2016, ISBN: 978-989-758-172-4

Quick Abstract

Recommendations to a group of users can be provided by the aggregation of individual users’ recommendations using social choice functions. Standard aggregation techniques do not consider the possibility of evaluating social interactions, roles, and influences among group’s members, as well as their personalities, which are, indeed, crucial factors in the group’s decision-making process. Instead of defining a specific social choice function to take into account such features, the proposed solution relies on the definition of a utility function, for each agent, that takes into account other group members’ preferences. Such function models the level of a user’s altruistic behavior starting from his/her agreeableness personality trait. Once such utility values are evaluated, the goal is to recommend items that maximize the social welfare. Performance is evaluated with a pilot user study and compared with respect to Least Misery. Results showed that while for small groups LM performs slightly better, in the other cases the two methods are comparable.

Related Ontology Subjects/Areas/Topics (3)

Agents

Artificial Intelligence

Group Decision Making

Model-Driven Engineering of a Railway Interlocking System

Topics: Model-Driven Architecture; Model-Based Testing and Validation

Authors: Fabio Scippacercola ¹ , Roberto Pietrantuono ¹ , Stefano Russo ² and András Zentai ³

Affiliations: ¹ Università degli Studi di Napoli Federico II, Italy , ² Università Di Napoli Federico Ii, Italy , ³ Prolan Process Control Co., Hungary

Digital Object Identifier: 10.5220/0005244805090519

Proceedings of MODELSWARD 2015, ISBN: 978-989-758-083-3

Quick Abstract

Model-Driven Engineering (MDE) promises to enhance system development by reducing development time, and increasing productivity and quality. MDE is gaining popularity in several industry sectors, and is attractive also for critical systems where they can reduce efforts and costs for verification and validation (V&V), and can ease certification. Incorporating model-driven techniques into a legacy well-proven development cycle is not simply a matter of placing models and transformations in the design and implementation phases. We present the experience in the model-driven design and V&V of a safety-critical system in the railway domain, namely the Prolan Block, a railway interlocking system manufactured by the Hungarian company Prolan Co., required to be CENELEC SIL-4 compliant. The experience has been carried out in an industrial- academic partnership within the EU project CECRIS. We discuss the challenges and the lessons learnt in this pilot project of introducing MD design and testing techniques into the company’s traditional V-model process.

Related Ontology Subjects/Areas/Topics (6)

Applications and Software Development		Model-Based Testing and Validation		Model-Driven Software Development
Languages, Tools and Architectures		Model-Driven Architecture		Software Engineering

User and Group Profiling in Touristic Web Portals Through Social Networks Analysis

Topics: User Modeling; Social Networks and Organizational Culture; Web Information Filtering and Retrieval; Recommendation Systems

Authors: Silvia Rossi , Francesco Barile and Antonio Caso

Affiliation: Universita’ degli Studi di Napoli “Federico II”, Italy

Digital Object Identifier: 10.5220/0005448704550465

Proceedings of WEBIST 2015, ISBN: 978-989-758-106-9

Quick Abstract

Touristic Web Portals can be considered windows on cultural cities. By providing all the necessary information in one single portal, the user is free to decide her/his preferred items/activities without the need of consulting different information sources. However, this kind of interface introduces the typical information overload problem. In this work, we present our framework for profiling both a single user and a group of users that relies on a not intrusive analysis of the users’ behaviors on social networks/media. By using data drawn from social networks, it is possible to obtain useful indirect information to profile occasional users. Moreover, the analysis of the behavior of small close groups on social networks may help an automatic system in the merge of the different preferences the users may have, simulating somehow a decision process similar to a natural interaction. In this direction, our aim is to identify key users taking in account concepts from research on users’ connectivity and on users’ communication activity.

Related Ontology Subjects/Areas/Topics (8)

Enterprise Information Systems	Society, e-Business and e-Government	Web Information Systems and Technologies
Recommendation Systems	Software Agents and Internet Computing	Web Interfaces and Applications
Social Networks and Organizational Culture	User Modeling

A Method of Topic Detection for Great Volume of Data

Authors: Flora Amato , Francesco Gargiulo , Antonino Mazzeo and Carlo Sansone

Affiliation: University of Naples Federico II, Italy

Digital Object Identifier: 10.5220/0005145504340439

Proceedings of KomIS 2014/DATA, ISBN: 978-989-758-035-2

Quick Abstract

Topics extraction has become increasingly important due to its effectiveness in many tasks, including information filtering, information retrieval and organization of document collections in digital libraries. The Topic Detection consists to find the most significant topics within a document corpus. In this paper we explore the adoption of a methodology of feature reduction to underline the most significant topics within a document corpus. We used an approach based on a clustering algorithm (X-means) over the t f −id f matrix calculated starting from the corpus, by which we describe the frequency of terms, represented by the columns, that occur in each document, represented by a row. To extract the topics, we build n binary problems, where n is the numbers of clusters produced by an unsupervised clustering approach and we operate a supervised feature selection over them considering the top features as the topic descriptors. We will show the results obtained on two different corpora. Both collections are expressed in Italian: the first collection consists of documents of the University of Naples Federico II, the second one consists in a collection of medical records.

Combining Syntactic and Semantic Vector Space Models in the Health Domain by using a Clustering Ensemble

Topics: Knowledge Management; e-Health; e-Health for Public Health

Authors: Flora Amato , Francesco Gargiulo , Antonino Mazzeo , Sara Romano and Carlo Sansone

Affiliation: University of Naples Federico II, Italy

Digital Object Identifier: 10.5220/0004250403820385

Proceedings of HEALTHINF 2013/BIOSTEC, ISBN: 978-989-8565-37-2

Quick Abstract

The adoption of services for automatic information management is one of the most interesting open problems in various professional and social fields. We focus on the health domain characterized by the production of huge amount of documents, in which the adoption of innovative systems for information management can significantly improve the tasks performed by the actors involved and the quality of the health services offered. In this work we propose a methodology for automatic documents categorization based on the adoption of unsupervised learning techniques. We extracted both semantic and syntactic features in order to define the vector space models and proposed the use of a clustering ensemble in order to increase the discriminative power of our approach. Results on real medical records, digitalized by means of a state-of-the-art OCR technique, demonstrated the effectiveness of the proposed approach.

Related Ontology Subjects/Areas/Topics (13)

Biomedical Engineering	Enterprise Information Systems	Ontologies and the Semantic Web
Cloud Computing	Health Information Systems	Platforms and Applications
Data Engineering	Information Systems Analysis and Specification	Society, e-Business and e-Government
e-Health	Knowledge Management	Web Information Systems and Technologies
e-Health for Public Health

Personalized Thermal Comfort Forecasting for Smart Buildings via Locally Weighted Regression with Adaptive Bandwidth

Topics: Smart Cities; Smart Homes (Domotics); Algorithms for Reduced Power, Energy and Heat

Authors: Carlo Manna , Nic Wilson and Kenneth N. Brown

Affiliation: University College Cork, Ireland

Digital Object Identifier: 10.5220/0004375100320040

Proceedings of SMARTGREENS 2013, ISBN: 978-989-8565-55-6

Quick Abstract

A personalized thermal comfort prediction method is proposed for use in combination with smart controls for building automation. Occupant thermal comfort is traditionally measured and predicted by the Predicted Mean Vote (PMV) metric, which is based on extensive field trials linking reported comfort levels with the various factors. However, PMV is a statistical measure applying to large populations, and the actual thermal comfort could be significantly different from the predicted value for small groups of people. Moreover it may be hard to use for a real-time controller due to the number of sensor readings needed. In the present paper, we propose Robust Locally Weighted Regression with Adaptive Bandwidth (LRAB), a kernel based method, to learn individual occupant thermal comfort based on historical reports. Using publicly available datasets, we demonstrate that this technique is significantly more accurate in predicting individual comfort than PMV and other kernel methods. Therefore, is a promising technique to be used as input to adpative HVAC control systems.

Related Ontology Subjects/Areas/Topics (6)

Algorithms for Reduced Power, Energy and Heat		Smart Cities		Smart Homes (Domotics)
Energy and Economy		Smart Grids		Sustainable Computing and Communications

Context (please use complete words only)

Field

Sort by

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 Page 1 of 5, items 1 to 10 of 41.

Add Author(s) to filter:

C. Sansone (6)

Silvia Rossi (4)

Walter Balzano (3)

Maria del Sorbo (3)

S. P. Romano (3)

Fabio Ruggiero (3)

Francesco Gargiulo (3)

Carlo Manna (2)

Francesco Barile (2)

Bruno Siciliano (2)

Conference

Locate Conference in list below

Data pager
Data pager
1 2 Page 1 of 2, items 1 to 10 of 13.

Add Conference(s) to filter:

ICAART(3)

ICEIS(3)

PRIS(2)

WEBIST(2)

WINSYS(1)

ICEC(1)

BIOSIGNALS(1)

DATA(1)

HEALTHINF(1)

SMARTGREENS(1)

Title:
ISBN:
Year:
Acronym:
Subject:

Title:
Author:
Affiliation:
Subject:

Name:
Affiliation:
Country:
Conference:
Subject:

Name:
Country:
Subject: