Smart Innovation, Systems and Technologies

Volume 76

Series editors

Robert James Howlett, Bournemouth University and KES International,Shoreham-by-sea, UKe-mail: rjhowlett@kesinternational.org

Lakhmi C. Jain, University of Canberra, Canberra, Australia;Bournemouth University, UK;KES International, UKe-mails: jainlc2002@yahoo.co.uk; Lakhmi.Jain@canberra.edu.au

About this Series

The Smart Innovation, Systems and Technologies book series encompasses thetopics of knowledge, intelligence, innovation and sustainability. The aim of theseries is to make available a platform for the publication of books on all aspects ofsingle and multi-disciplinary research on these themes in order to make the latestresults available in a readily-accessible form. Volumes on interdisciplinary researchcombining two or more of these areas is particularly sought.

The series covers systems and paradigms that employ knowledge andintelligence in a broad sense. Its scope is systems having embedded knowledgeand intelligence, which may be applied to the solution of world problems inindustry, the environment and the community. It also focusses on theknowledge-transfer methodologies and innovation strategies employed to makethis happen effectively. The combination of intelligent systems tools and a broadrange of applications introduces a need for a synergy of disciplines from science,technology, business and the humanities. The series will include conferenceproceedings, edited collections, monographs, handbooks, reference books, andother relevant types of book in areas of science and technology where smartsystems and technologies can offer innovative solutions.

High quality content is an essential feature for all book proposals accepted for theseries. It is expected that editors of all accepted volumes will ensure thatcontributions are subjected to an appropriate level of reviewing process and adhereto KES quality principles.

More information about this series at http://www.springer.com/series/8767

Giuseppe De Pietro • Luigi GalloRobert J. Howlett • Lakhmi C. JainEditors

Intelligent InteractiveMultimedia Systemsand Services 2017

123

EditorsGiuseppe De PietroNational Research Council of Italy(CNR-ICAR)

Institute for High-Performance Computingand Networking

NaplesItaly

Luigi GalloNational Research Council of Italy(CNR-ICAR)

Institute for High-Performance Computingand Networking

NaplesItaly

Robert J. HowlettBournemouth UniversityPooleUK

and

KES InternationalShoreham-by-SeaUK

Lakhmi C. JainUniversity of CanberraCanberra, ACTAustralia

and

Bournemouth UniversityPooleUK

and

KES InternationalShoreham-by-SeaUK

ISSN 2190-3018 ISSN 2190-3026 (electronic)Smart Innovation, Systems and TechnologiesISBN 978-3-319-59479-8 ISBN 978-3-319-59480-4 (eBook)DOI 10.1007/978-3-319-59480-4

Library of Congress Control Number: 2017941493

© Springer International Publishing AG 2018This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or partof the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations,recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmissionor information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilarmethodology now known or hereafter developed.The use of general descriptive names, registered names, trademarks, service marks, etc. in thispublication does not imply, even in the absence of a specific statement, that such names are exempt fromthe relevant protective laws and regulations and therefore free for general use.The publisher, the authors and the editors are safe to assume that the advice and information in thisbook are believed to be true and accurate at the date of publication. Neither the publisher nor theauthors or the editors give a warranty, express or implied, with respect to the material contained herein orfor any errors or omissions that may have been made. The publisher remains neutral with regard tojurisdictional claims in published maps and institutional affiliations.

Printed on acid-free paper

This Springer imprint is published by Springer NatureThe registered company is Springer International Publishing AGThe registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

Dear Readers,

We introduce to you a series of carefully selected papers presented during the 10thKES International Conference on Intelligent Interactive Multimedia Systems andServices (IIMSS-17).

At a time when computers are more widespread than ever, and computer usersrange from highly qualified scientists to non-computer expert professionals, intel-ligent interactive systems are becoming a necessity in modern computer systems.The solution of “one-fits-all” is no longer applicable to wide ranges of users ofvarious backgrounds and needs. Therefore, one important goal of many intelligentinteractive systems is dynamic personalization and adaptivity to users. Multimediasystems refer to the coordinated storage, processing, transmission, and retrieval ofmultiple forms of information, such as audio, image, video, animation, graphics,and text. The growth rate of multimedia services has become explosive, astechnological progress matches consumer needs for content.

The conference took place as part of the Smart Digital Futures 2017 multi-themeconference, which groups AMSTA, IDT, InHorizons, InMed, SEEL with IIMSS inone venue. It was a forum for researchers and scientists to share work and expe-riences on intelligent interactive systems and multimedia systems and services. Itincluded a general track and eight invited sessions.

The invited session “Processing visual data in intelligent systems: methods andapplications” (Chaps. 1–8) specifically focuses on processing and understandingvisual data in intelligent systems. The invited session “Cognitive Systems andRobotics” (Chaps. 9–20) focused on two main research areas, strictly related amongthem: adaptive and human-like cognitive systems, and artificial intelligence systemsand cognitive robotics. The invited session “Big Data Management & Metadata”(Chaps. 21–24) focuses on models, techniques, and algorithms capable of dealingwith the volume, velocity, variety, veracity, and value of big data. Differently, theinvited session “Intelligent Big Data Analytics: Models, Techniques, Algorithms”(Chapter 25) discusses models, techniques, and algorithms for supporting intelli-gent analytics over big data in critical application contexts. The invited session

v

“Autonomous System” (Chaps. 26–29) considers technical and non-technical issuesfor what concerns intelligent, autonomous systems. The invited session “MobileData Analytics” (Chaps. 30–43) focuses on modeling, processing, and analyzingdata generated by mobile devices, positioning technologies, and mobile users’activities. The invited session “Smart Environments and Information Systems”(Chaps. 44–49) provides insight into the most recent efforts in the field of infor-mation systems operating in dynamic environments. The invited session“Innovative Information Services for Advanced Knowledge Activity” (Chaps. 50–53) focuses on novel functionalities for information services. Finally, the generaltrack (Chaps. 54–57) focuses on topics related to image processing algorithms andimage processing-based rehabilitation and recommender systems.

Our gratitude goes to many people who have greatly contributed to puttingtogether a fine scientific program and exciting social events for IIMSS 2017. Weacknowledge the commitment and hard work of the program chairs and the invitedsession organizers. They have kept the scientific program in focus and made thediscussions interesting and valuable. We recognize the excellent job done by theprogram committee members and the extra reviewers. They evaluated all the paperson a very tight schedule. We are grateful for their dedication and contributions. Wecould not have done it without them. More importantly, we thank the authors forsubmitting and trusting their work to the IIMSS conference.

We hope that readers will find in this book an interesting source of knowledge infundamental and applied facets of intelligent interactive multimedia and, maybe,even some motivation for further research.

The editors

vi Preface

Organization

Honorary Chairs

Toyohide Watanabe Nagoya University, JapanLakhmi C. Jain University of Canberra, Australia and Bournemouth

University, UK

Co-General Chairs

Giuseppe De Pietro National Research Council of Italy, ItalyLuigi Gallo National Research Council of Italy, Italy

Executive Chair

Robert J. Howlett University of Bournemouth, UK

Programme Chair

Antonino Mazzeo University of Naples Federico II, Italy

Publicity Chair

Giuseppe Caggianese National Research Council of Italy, Italy

vii

Invited Session Chairs

Processing Visual Data in Intelligent Systems: Methods and Applications

Francesco Bianconi Università degli Studi di Perugia, ItalyElena González Universidade de Vigo, SpainManuel Ángel Aguilar Universidad de Almería, Spain

Cognitive Systems and Robotics

Ignazio Infantino National Research Council of Italy, ItalyMassimo Esposito National Research Council of Italy, Italy

Big Data Management and Metadata

Flora Amato University of Naples Federico II, ItalyVincenzo Moscato University of Naples Federico II, Italy

Intelligent Big Data Analytics: Models, Techniques, Algorithms

Alfredo Cuzzocrea University of Trieste, and ICAR-CNR, Italy

Autonomous System

Milan Simic RMIT University, Australia

Mobile Data Analytics

Jalel Akaichi University of Tunis, Tunisia, and King KhalidUniversity, Saudi Arabia

Smart Environments and Information Systems

Rafael H. Bordini FACIN-PUCRS, BrazilMassimo Cossentino National Research Council of Italy, ItalyMarie-Pierre Gleizes University Paul Sabatier of Toulouse, FranceLuca Sabatucci National Research Council of Italy, Italy

viii Organization

Innovative Information Services for Advanced Knowledge Activity

Koichi Asakura Daido University, JapanToyohide Watanabe Nagoya Industrial Research Institute, Japan

International Programme Committee

Manuel Ángel Aguilar Universidad de Almería, SpainFlora Amato Università degli Studi di Napoli Federico II, ItalyMarco Anisetti Università degli Studi di Milano, ItalyKoichi Asakura Daido University, JapanJalel Akaichi University of Tunis, Tunisia, and King Khalid

University, Saudi ArabiaVivek Bannore KES UniSA, AustraliaV. Bellandi Università degli Studi di Milano, ItalyMonica Bianchini University of Perugia, ItalyRafael H. Bordini FACIN-PUCRS, BrazilHelder Coelho Mind-Brain College, BioISI, University of Lisbon,

PortugalLuigi Coppolino Università degli Studi di Napoli “Parthenope”, ItalyMassimo Cossentino National Research Council of Italy, ItalyGiovanni Cozzolino Università degli Studi di Napoli Federico II, ItalyAlfredo Cuzzocrea University of Trieste and ICAR-CNR, ItalySalvatore D’Antonio Università degli Studi di Napoli “Parthenope”, ItalyErnesto Damiani Università degli Studi di Milano, ItalyMario Doeller University of Applied Science Kufstein Tirol, AustriaDinu Dragan University of Novi Sad, Faculty of Technical Sciences,

Novi Sad, SerbiaMassimo Esposito National Research Council of Italy, ItalyMargarita Favorskaya Siberian State Aerospace University, RussiaMarie-Pierre Gleizes University Paul Sabatier of Toulouse, FranceChristos Grecos Central Washington University, USAElena González Universidade de Vigo, SpainVincent Hilaire Université de Belfort-Montbeliard, FranceKatsuhiro Honda Osaka Prefecture University, JapanHsiang-Cheh Huang National University of Kaohsiung, TaiwanIgnazio Infantino National Research Council of Italy, ItalyGwanggil Jeon Xidian University, ChinaDimitris Kanellopoulos Department of Mathematics, University of Patras,

GreeceChengjun Liu New Jersey Institute of Technology, USAMarian Cristian

MihaescuUniversity of Craiova, Romania

Organization ix

Lyudmila Mihaylova University of Sheffield, UKVincenzo Moscato Università degli Studi di Napoli Federico II, ItalyFrancesco Moscato Università degli Studi della Campania, ItalyVincent Oria New Jersey Institute of Technology, USARadu-Emil Precup Politehnica University of Timisoara, RomaniaAntonio Maria Rinaldi Università degli Studi di Napoli Federico II, ItalyLuigi Romano Università degli Studi di Napoli “Parthenope”, ItalyLuca Sabatucci National Research Council of Italy, ItalyMohammed Sadgal Cadi Ayyad University, MoroccoMilan Simic RMIT University, School of Engineering, AustraliaMariacarla Staffa Università degli Studi di Napoli “Parthenope”, ItalyClaudio Sterle Università degli Studi di Napoli Federico II, ItalyPorfirio Tramontana Università degli Studi di Napoli Federico II, ItalyTaketoshi Ushiama Kyushu University, JapanRosa Vicari Universidade Federal do Rio Grande do Sul, BrazilToyohide Watanabe Nagoya Industrial Science Research Institute, JapanAlicja Wieczorkowska Polish-Japanese Academy of Information Technology,

Poland

x Organization

Contents

Hand-Designed Local Image Descriptors vs. Off-the-Shelf CNN-BasedFeatures for Texture Classification: An Experimental Comparison . . . .. . . . 1Raquel Bello-Cerezo, Francesco Bianconi, Silvia Cascianelli,Mario Luca Fravolini, Francesco di Maria, and Fabrizio Smeraldi

Images Selection and Best Descriptor Combination for Multi-shotPerson Re-identification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11Yousra Hadj Hassen, Kais Loukil, Tarek Ouni, and Mohamed Jallouli

Dimensionality Reduction Strategies for CNN-Based Classificationof Histopathological Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21Silvia Cascianelli, Raquel Bello-Cerezo, Francesco Bianconi,Mario L. Fravolini, Mehdi Belal, Barbara Palumbo, and Jakob N. Kather

Optimizing Multiresolution Segmentation for Extracting PlasticGreenhouses from WorldView-3 Imagery . . . . . . . . . . . . . . . . . . . . . . . . . 31Manuel A. Aguilar, Antonio Novelli, Abderrahim Nemamoui,Fernando J. Aguilar, Andrés García Lorca, and Óscar González-Yebra

A New Threshold Relative Radiometric Correction Algorithm(TRRCA) of Multiband Satellite Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41Antonio Novelli, Manuel A. Aguilar, and Eufemia Tarantino

Greenhouse Detection Using Aerial Orthophoto and DigitalSurface Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51Salih Celik and Dilek Koc-San

Comparison of Mesh Simplification Tools in a 3D WatermarkingFramework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60Francesca Uccheddu, Michaela Servi, Rocco Furferi, and Lapo Governi

A Smart-CA Architecture for Opencast Matterhorn . . . . . . . . . . . . . . . . 70Vicente Goyanes, Rubén González,Anxo Sánchez, and Domingo Docampo

xi

An Effective Corpus-Based Question Answering Pipelinefor Italian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80Emanuele Damiano, Raffaele Spinelli,Massimo Esposito, and Giuseppe De Pietro

Towards a Cognitive System for the Identificationof Sleep Disorders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91Antonio Coronato and Giovanni Paragliola

An Ensemble Classifiers Approach for Emotion Classification . . . . . . . . 99Mohamed Walid Chaibi

Sign Languages Recognition Based on NeuralNetwork Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109Manuele Palmeri, Filippo Vella,Ignazio Infantino, and Salvatore Gaglio

Medical Entity and Relation Extraction from Narrative ClinicalRecords in Italian Language . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119Crescenzo Diomaiuta, Maria Mercorella,Mario Ciampi, and Giuseppe De Pietro

Detection of Indoor Actions Through Probabilistic InductionModel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129Umberto Maniscalco, Giovanni Pilato, and Filippo Vella

A ROS Driven Platform for Radiomap Management Optimizationin Fingerprinting Based Indoor Positioning . . . . . . . . . . . . . . . . . . . . . . . 139Giovanni Luca Dierna, Alberto Machì, and Sergio Scirè

Improving Spatial Reasoning by Interacting with aHumanoid Robot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151Agnese Augello, Giuseppe Città, Manuel Gentile, Ignazio Infantino,Dario La Guardia, Adriano Manfré, Umberto Maniscalco,Simona Ottaviano, Giovanni Pilato, Filippo Vella, and Mario Allegra

An Artificial Pain Model for a Humanoid Robot . . . . . . . . . . . . . . . . . . . 161Umberto Maniscalco and Ignazio Infantino

Interaction Capabilities of a Robotic Receptionist . . . . . . . . . . . . . . . . . . 171Carlo Nuccio, Agnese Augello, Salvatore Gaglio, and Giovanni Pilato

Artificial Pleasure and Pain Antagonism Mechanismin a Social Robot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181Antonello Galipó, Ignazio Infantino, Umberto Maniscalco,and Salvatore Gaglio

xii Contents

Move Your Mind: Creative Dancing Humanoids as Supportto STEAM Activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 190Giuseppe Città, Sylvester Arnab, Agnese Augello, Manuel Gentile,Sebastian Idelsohn Zielonka, Dirk Ifenthaler, Ignazio Infantino,Dario La Guardia, Adriano Manfrè, and Mario Allegra

A Recommender System for Multimedia Art Collections. . . . . . . . . . . . . 200Flora Amato, Vincenzo Moscato, Antonio Picariello,and Giancarlo Sperlí

Using Multilayer Perceptron in Computer Security to ImproveIntrusion Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 210Flora Amato, Giovanni Cozzolino, Antonino Mazzeo,and Emilio Vivenzio

WiFiNS: A Smart Method to Improve Positioning Systems CombiningWiFi and INS Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220Walter Balzano, Mattia Formisano, and Luca Gaudino

PAM-SAD: Ubiquitous Car Parking Availability Model Basedon V2V and Smartphone Activity Detection . . . . . . . . . . . . . . . . . . . . . . . 232Walter Balzano and Fabio Vitale

A Composite Methodology for Supporting Early-Detectionof Handwriting Dysgraphia via Big Data Analysis Techniques . . . . . . . . 241Pierluigi D’Antrassi, Iolanda Perrone, Alfredo Cuzzocrea,and Agostino Accardo

SADICO: Self-ADaptIve Approach to theWeb Service COmposition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 254Hajer Nabli, Sihem Cherif, Raoudha Ben Djmeaa,and Ikram Amous Ben Amor

Autonomous Systems Research Embedded in Teaching . . . . . . . . . . . . . . 268Maria Spichkova and Milan Simic

Vehicle Flat Ride Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 278Hormoz Marzbani, Dai Voquoc, Reza N. Jazar, and Mohammad Fard

Autonomous Vehicle Design for Predator Proof Fence Monitoring. . . . .. . . . 289Silas Tullah, Heinz de Chelard, and Milan Simic

Sentiment Analysis Method for Tracking Touristics Reviews in SocialMedia Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 299Yasmine Chaabani, Radhia Toujani, and Jalel Akaichi

Mobility Based Machine Learning Modeling for Event Miningin Social Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 311Radhia Toujani, Zeineb Dhouioui, and Jalel Akaichi

Contents xiii

Ant Colony Optimization Approach for Optimizing Irrigation SystemLayout: Case of Gravity and Collective Network . . . . . . . . . . . . . . . . . . . 323Sahar Marouane, Fahad Alahmari, and Jalel Akaichi

Query Recommendation Systems Based on the Exploration of OLAPand SOLAP Data Cubes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333Olfa Layouni, Assawer Zekri, Marwa Massaâbi, and Jalel Akaichi

Regions Trajectories Data: Evolution of Modelingand Construction Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343Marwa Massaâbi, Olfa Layouni, Assawer Zekri,Mohammad Aljeaid, and Jalel Akaichi

Integrating Trajectory Data in the Warehousing Chain:A New Way to Handle the Trajectory ELT Process . . . . . . . . . . . . . . . . 353Noura Azaiez and Jalel Akaichi

Detection of Opinion Leaders in Social Networks: A Survey. . . . . . . . . . 362Seifallah Arrami, Wided Oueslati, and Jalel Akaichi

A Real Time Two-Level Method for Fingertips Tracking and NumberIdentification in a Video . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 371Ouissem Ben Henia

Trajectory ETL Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 380Assawer Zekri, Marwa Massaâbi, Olfa Layouni, and Jalel Akaichi

Computing Semantic Trajectories: Methods and Used Techniques . . . . . 390Thouraya Sakouhi, Jalel Akaichi, and Usman Ahmed

Ambulance Fastest Path Using Ant Colony Optimization Algorithm . . .. . . 400Hazar Hamdi, Nouha Arfaoui, Yasser Al Mashhour, and Jalel Akaichi

Educational Assessment: Pupils’ Experience in Primary School(Arabic Grammar in 7th Year in Tunisia) . . . . . . . . . . . . . . . . . . . . . . . . 410Wiem Ben Khalifa, Sameh Baccari, Dalila Souilem, and Mahmoud Neji

Clustering Social Network Profiles Using PossibilisticC-means Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 419Mohamed Moussaoui, Montaceur Zaghdoud, and Jalel Akaichi

Big Data Classification: A Combined Approach Based on Paralleland Approx SVM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 429Walid Ksiaâ, Fahmi Ben Rejab, and Kaouther Nouira

The Four Types of Self-adaptive Systems: A Metamodel . . . . . . . . . . . . . 440Luca Sabatucci, Valeria Seidita, and Massimo Cossentino

Context Reasoning and Prediction in Smart Environments:The Home Manager Case . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 451Roberta Calegari and Enrico Denti

xiv Contents

Social Activities Recommendation System for Studentsin Smart Campus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 461Sabrine Ben Abdrabbah, Raouia Ayachi, and Nahla Ben Amor

A Deep Learning Approach for Scientific Paper Semantic Ranking . . . .. . . . 471Francesco Gargiulo, Stefano Silvestri, Mariarosaria Fontanella,Mario Ciampi, and Giuseppe De Pietro

neOCampus: A Demonstrator of Connected, Innovative, Intelligentand Sustainable Campus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 482Marie-Pierre Gleizes, Jérémy Boes, Bérangère Lartigue,and François Thiébolt

MUSA 2.0: A Distributed and Scalable Middleware for User-DrivenService Adaptation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 492Luca Sabatucci, Salvatore Lopes, and Massimo Cossentino

Approximate Algorithm for Multi-source Skyline Querieson Decentralized Remote Spatial Databases . . . . . . . . . . . . . . . . . . . . . . . 502Hideki Sato, Shuichi Hirabayashi, and Masaya Takagi

A New Simple Preprocessing Method for MUSIC Suitablefor Non-contact Vital Sensing Using Doppler Sensors . . . . . . . . . . . . . . . 514Yukihiro Kamiya

A Comparative Study of Communication Methods for EvacuationGuidance Systems in Disaster Situations . . . . . . . . . . . . . . . . . . . . . . . . . . 525Koichi Asakura and Toyohide Watanabe

Research View Shift for Supporting Learning Actionfrom Teaching Action . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 534Toyohide Watanabe

Video Saliency Using Supervoxels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 544Rahma Kalboussi, Mehrez Abdellaoui, and Ali Douik

A Rehabilitation System for Post-operative Heart Surgery . . . . . . . . . . . 554Giuseppe Caggianese, Mariaconsiglia Calabrese, Vincenzo De Maio,Giuseppe De Pietro, Armando Faggiano, Luigi Gallo,Giovanna Sannino, and Carmine Vecchione

Evaluation of the Criteria and Indicators that Determine Qualityin Higher Education: A Questionnaire Proposal. . . . . . . . . . . . . . . . . . . . 565Fouzia Kahloun and Sonia Ayachi Ghannouchi

Toward a Personalized Recommender System for Learning Activitiesin the Context of MOOCs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 575Marwa Harrathi, Narjess Touzani, and Rafik Braham

Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 585

Contents xv

The Four Types of Self-Adaptive Systems: a

Metamodel

L. Sabatucci1, V. Seidita2,1, M. Cossentino1

1 ICAR-CNR, Palermo, Italy2 DIID, Universita degli Studi di Palermo, Italy

{luca.sabatucci, massimo.cossentino}@icar.cnr.it, valeria.seidita@unipa.it

Abstract. The basic ideas of self-adaptive systems are not a novelty incomputer science. There are plenty of systems that are able of monitor-ing their operative context to take run-time decisions. However, morerecently a new research discipline is trying to provide a common frame-work for collecting theory, methods, middlewares, algorithms and toolsfor engineering such software systems. The aim is to collect and classifyexisting approaches, coming from many di↵erent research areas. The ob-jective of this work is providing a unified metamodel for describing thevarious categories of adaptation.

1 Introduction

Today’s society always more depends on complex distributed software systemsavailable 24h and with minimal human supervision and maintenance e↵ort dur-ing the operating phase. The more software systems grow in complexity and size,the more management automation, robustness, and reliability become central:it becomes essential to design and implement them in a more versatile, flexible,resilient and robust way.

In [17] authors discuss how an ambient intelligent system (AmI) plungesinto the real world. The authors underline that, in such complex systems, theboundary between software and society blends and often disappears. The so-cial environment is enriched with artificial intelligence to support humans intheir everyday life. The IBM manifesto of autonomic computing [11] suggests apromising direction for facing software complexity through self-adaptation.

A self-adaptive system is a system with the ability to autonomously modifyits behavior at run-time in response to changes in the environment [5, 7, 21]. Thevision of a computing system that can manage itself is fascinating [5, 7]: to modifythe behavior at run-time for maintaining or enhancing its functions [5]. This vi-sion has deep roots in several research fields, as for instance, artificial intelligence,biologically inspired computing, robotics, requirements/knowledge engineering,control theory, fault-tolerant computing, and so on. In the last decade, the vastand heterogeneous number of works concerning self-adaptation investigated sev-eral aspects of the problem, for instance, specific architectures for implementingadaptive control loops [14], self-organizing paradigms [1], adaptive requirements

2 Sabatucci et al.

[6] and so on. However, to date, many of these problems remain significant in-tellectual challenges [5, 7]. For instance, general purpose software engineeringapproaches are still missing for the provision of self-adaptation [5]. The long-term objective is to establish the foundations for the systematic development offuture generations of self-adaptive systems.

This work resembles existing approaches for systematically engineering self-adaptive system and proposes a unified metamodel of the four types of adap-tation. The objective is to provide a framework for identifying and classifyingsmart systems according to their self-adaptive properties. A metamodel supportsthis framework for aiding the designer to choose the most appropriate categoryof systems, depending on the problem statements. The description reports themain components and some illustrative case studies for each type of adaptation.

The paper is structured as follows: Section 2 analyzes definitions of self-adaptive systems in state of the art. Section 3 presents the unified metamodel anddescribes the four categories in details. Finally, Section 4 reports the conclusions.

2 Related Work

In last decade, the definition of adaptation has been deliberately generic togather many sub-fields under a common umbrella and produce interesting syn-ergy. However, this trend has generated some sub-definitions with sometimessignificant di↵erences. For instance, in [5, 7] a self-adaptive system can modifyits behavior in response to changes in the environment. For the models@run.timecommunity [12], a dynamically adaptive system (DAS) can be conceptualized asa dynamic software product line in which variabilities are bound at runtime forimproving the quality of service (QoS).

Unifying di↵erent definitions is required. Salehie and Tahvildari [20] identifytwo categories of self-adaptation based on impact (the scope of system e↵ects)and cost factors (in terms of time, resources and complexity). The weak adapta-tion mainly involves modifying parameters using pre-defined static mechanisms(limited impact/low cost). Conversely, the strong adaptation deals with high-cost/extensive impact actions such as adding, replacing, removing components.

In [16], the authors provide a classification scheme for four categories of self-adaptive systems: Type 1 consists in anticipating both changes and the possiblereactions at design-time: the system follows a behavioral model that containsdecision-points. For each decision point, the solution is immediately obviousgiven the current perceptions and the acquired knowledge about the environ-ment.

Type 2 consists of systems that own many alternative strategies for reactingto changes. Each strategy can satisfy the goal, but it has a di↵erent impact onsome non-functional requirement. Selecting the best strategy is a run-time op-eration based on the awareness of the di↵erent impact towards these externalaspects. Typically the decision is taken by balancing trade-o↵s between alterna-tives, based on the acquired knowledge about the environment.

Self-Adaptation Metamodel 3

Type 3 consists of systems aware of its objectives and operating with uncer-tain knowledge about the environment. It does not own pre-defined strategies butit rather assemblies ad-hoc functionalities according to the execution context.

Type 4 is inspired by biological systems that are able of self-modifying theirspecification when no other possible additions or simple refinements are possible.

In [2], authors face the same point, but from a requirement engineeringperspective. The premise is that requirement engineering task is to determine thekinds of input of a system and the possible responses to these inputs. Thereforethey identify four types of requirement engineering activities concerning a self-adaptive system.

Level 1 activities are done by humans and resemble the traditional RE ac-tivities. The analysts determine all the possible domains to be considered by thesystem (inputs) and all the possibility system functionalities (reactions).

The system executes Level 2 ’s activities. Whereas the analysts have deter-mined a set of possible behaviors (i.e. reactions), the system can identify thefunctionality to execute next, when the environment does not match any of theinput domains.

Conversely, Level 3 activities are done by humans for implementing thedecision-making procedure that allows the system to apply level 2. Level 3 of-ten includes a meta-level reasoning, that exploits determined program-testablecorrespondences to environmental changes that trigger adaptation.

Implementing the adaptation mechanism (i.e. the feedback loop) is a Level 4

activity, for which humans are responsible.

3 The Proposed MetaModel

A smart system is often immersed in a pre-existing world (or environment)populated by objects and persons it interacts with, it influences and is influenced.Boundaries between the software realizing the smart system and the environmentare becoming lighter, more and more, and they are almost disappearing. Allthis significantly a↵ects the design of smart systems. In this section, we presenta framework for identifying and illustrating which may be the minimal set ofelements (Figure 1) a system as to own for being classified self-adaptive of thefour cited types.

The metamodel in Figure 1 is composed of two parts: the first part includesall the generic elements of a smart system, whereas the second part embracesall the elements implementing the di↵erent types of self-adaptation. The firstpart comes from a previous study of some authors of this paper [17], wherea set of abstractions for representing smart systems in an Ambient Intelligentcontext has been explored and experimented. All the elements in the second parthave been identified reviewing the literature and definitions about managing anddesigning self-adaptive systems.

We argue that, in general, abstractions representing a smart system aremainly the environment and all the entities it is composed of. There may be,

4 Sabatucci et al.

passive, dumb and smart entities, all of them present a state that may be per-ceived and changed during the running phase of the system. Smart entities arecognitive, intentional and rational entities, able to perform actions according tothe principle of rationality. Dumb and passive entities are elements of the envi-ronment, the former are resources (software applications, physical devices,. . . )and the latter are simply objects in the environment such as physical or digitalobjects; both of them are part of the environment and influence the actions ofthe smart system.

Type IV Type III Type II Type I

Entity

Passive Entity

Dumb Entity

Smart EntityPhysical Device

Software Application

Physical Object Digital Object

State

AdaptiveSystem

SolutionStrategy

Env Monitor

Environment

Solution Builder

Design Artifact

Evolution Engine

EffectorRun-Time Model

Run-Time Goal Model

Awareness Engine

changes

perceives

builds

knows

changes

World Representation

knows

builds

influences

updates

Repository of Functionalities

Fig. 1. Portion of metamodel that describes the four types of self-adaptive systems.

The second part of the metamodel shows all the elements of a self-adaptivesystem. As it may be deduced by the definitions, the set of elements for eachtype is contained in the higher ones. A smart system belongs to the first type ofself-adaptation if it owns a kind of smart entity, the Adaptive System composedof the elements necessary to know the environment and to act on it. The secondtype principally requires an Awareness Engine qualified to know the model atrun-time of the system and to change or influence the solution strategy builtat design time. The third type involves a more complex element that is theSolution Builder, this is able to build a completely new solution strategy byusing a repository of established functionalities. Finally, the fourth type requiresthe Evolution Engine that is able to set up a Solution Builder.

Self-Adaptation Metamodel 5

In the following, each type is broadly described. It is important to notethat Design Artifact, Run-Time Model, Awareness Engine, Solution Builder,

Evolution Engine are not classic elements of a problem domain you may find ina metamodel but are strictly related to the software solution. This is to stressthe fact that self-adaptive systems cannot be conceived or designed if not as analive part of the domain.

3.1 Type I

Adaptation of Type I is the simplest implementation of smart systems. It arisesfrom a deep analysis of the domain for analyzing all the possible changes thesystem will observe and react to. The design activity includes the study of all thepossible reactions the system will enact. The design leads to the definition of abehavioral model that contains decision-points: a decision point is analogous toa ‘if...then...else’ statement, whereas conditions typically depend on perceptionsof the execution environment.Problem Statement. The Adaptation of Type I is a good choice when:– it is necessary a smart system able to operate in di↵erent ways, according

to the state of the environment, acquired by perceptions;– changes mainly a↵ect observable attributes of the environment;– it is possible to anticipate (at design-time) all the environment changes that

are of interest for the system;– dealing with uncertainty is not central in the implementation.

System Description. An Adaptive System of Type I is a specific class ofartificial system, i.e. a smart entity that operates in an environment. An AdaptiveSystem of Type I owns e↵ectors for changing the state of the environment andperceptors (Env Monitor) for acquiring the current state of the environment.Decisions points are rules that connect knowledge and perception (cause) to ane↵ector (consequence).Known Usage. An illustrative scenario for Type I is the Robotic NavigationSystem presented in [5]. The scenario illustrates autonomous control softwaresystem of unmanned vehicles (UVs). The adaptation system must consider theregular tra�c environment, including the infrastructures and other vehicles. Acause of adaptation can be an unexpected obstacle (people crossing the road). Tothis aim, it is necessary to monitor the environment (perceptors) and to detectpossible obstacles in front of the vehicle. An example of a cause-consequence ruleis: IF an obstacle is in front of the vehicle THEN maneuver around the obstaclefor avoiding a collision.

Another example is a smart information system for airports [15]. A causeof adaptation occurs when a flight delays. According to the importance of thedelay, the system may select di↵erent actions: inform the traveler, rebook theflight as soon as possible, book a hotel and re-plan the flight the next day.Limits. Do not use Type I:– if the system decision must deal with non-functional aspects; for instance,

to rebook a service as soon as possible, or to change the itinerary for main-taining the traveler’s satisfaction.

– if a flexible strategy for changing user’s goals is required.

6 Sabatucci et al.

3.2 Type II

Adaptation of Type II consists of complex systems that o↵er many alternativestrategies for addressing the same goal. Di↵erent strategies encompass di↵erentoperative situations and provide a di↵erent impact on the non-functional require-ments. This situation requires revising the traditional analysis of requirementsphase for including the detailed exploration of a large problem space. Selectingthe contextual strategy to apply is a run-time decision that considers complextrade-o↵s among the state of the environment and the desired quality of service.This decision requires a higher level of knowledge than Type I. It must includethe awareness of functional/non-functional requirements, and the di↵erent wayeach operation impacts on them.

Problem Statement. The Adaptation of Type II is a good choice when:

– it is necessary a smart system that can detect and recover run-time deviationsbetween behavior and requirements;

– it is not possible to anticipate all the environment changes at design-time,and therefore it is preferable to discover them at run-time;

– changes can also a↵ect requirements

– it is necessary to incorporate a degree of uncertainty in requirements.

System Description. An Adaptive System of Type II contains a set of e↵ectorsand perceptors as well as the Type I and extends it with a set of solution strate-gies and an awareness engine. A solution strategy is a plan which actions arethe monitors and e↵ectors. A solution strategy (or its components) is linked tosome functional requirement that motivates its execution. Moreover, the solutionstrategy (or its components) may also be related to one or more non-functionalrequirements, describing the kind of expected impact of its execution. This com-plex model is often a run-time artifact that the Awareness Engine acquires andmanages.

Known Usage. An example of Type II adaptation is contained in the scenario ofLondon Ambulance Service (LAS) [10]. According to o�cial recommendations,such a system must involve a full process of consultation between management,sta↵, trade union representatives and the Service’s information technology ad-visers. Authors have identified many solutions for the main goal (to respondemergency calls within a specific time). A solution is composed of many tasks,each one addressing a specific subgoal. The system follows an adaptation of TypeII because it may enact alternative functions. The system reasons on which taskto select (and therefore which is the best solution) according to quality aspects,for instance, the time, the e�ciency and the reliability. Authors used a goalmodel [9] for depicting the problem requirements together with tasks and qual-ity aspects. This goal model is implemented as a run-time model the systemreceives as an input for taking contextual decisions.

Another example comes from dynamic workflow execution engines. In [4] theauthors provide a workflow execution engine where tasks are related to abstractservices. Each abstract service is the high-level description of many actual ser-vices provided by di↵erent providers. A real service owns some quality of services.

Self-Adaptation Metamodel 7

The execution of the workflow requires selecting among alternative concrete ser-vices. The choice is to optimize one of the global quality of service assets [8]:di↵erent solutions occur if the user decides to optimize the total cost or the totaltime to complete.Limits. Do not use Type II:

– if user’s functional/non-functional requirements are dynamics;– if you desire a flexible way for extending system functionalities.

3.3 Type III

Adaptation of Type III represents an advanced implementation of a smart systemthat is instructed with a set of basic functionalities. The system can use forassembling ad-hoc behaviors not contained in any of the predefined solutionstrategies. This kind of system is particularly suitable for working with uncertainknowledge about the environment and the requirements.Problem Statement The Adaptation of Type III is a good choice when:

– the problem domain is not entirely anticipated at design-time,– requirements evolve frequently due to business rules or societal norms;– it is necessary to assemble ad-hoc functionalities on the fly;– system functionalities are open to third-party providers and dynamics (de-

pending, for instance, from network conditions).

System Description. An Adaptive System of Type III owns the core charac-teristics of Type II, but it supports a new component, called Solution Builder.This element can access to a (dynamic) repository of functionalities and buildone or more new solution strategies for addressing an unanticipated problem.Selecting among many solutions frequently implies an optimization phase thatalso considers non-functional aspects.Known Usage. An example of Type III adaptation is a smart travel assistancesystem [18]. Such a system can compose a trip itinerary according to user’s pref-erences and create travel service as the composition of multiple atomic services(flight booking, hotel reservation, local transportation tickets and so on). Thesystem is also able to monitor either possible problems during the journey, orchanges in user’s preferences, and to optimize the travel itinerary consequently.

The new frontier of service composition over the cloud is called cloud mashup.The adaptive scenario –described in [19]– includes on-demand, on-the-fly appli-cation mashup for addressing a set of designer’s goals. The customer does notprovide goals, so the designer can not incorporate them into the orchestrationmodel. An adaptation system of type III allows injecting goals at run-time.Consequently, the system builds a new mashup by aggregating existing cloudservices, according to availability, cost and reliability [13].Limits. Do not use Type III:

– for real-time systems;– if the system should be able of inspecting itself and autonomously evolving

its functionality.

8 Sabatucci et al.

3.4 Type IV

Adaptation of Type IV is the higher level of a smart system that is able ofinspecting itself, learning from experience and self-modifying its specification.They are designed to a↵ord the worst cases of adaptation: when the system doesnot own suitable actions/strategy to be used, and it is not capable of generatingany one. In this case, the system is able of revising its run-time model, thusto produce a new version of the software. In this category of adaptation, it ismore appropriate to refer to evolution. Indeed these systems are inspired bybiological systems that own the ability to cope with environment variance bygenetic changes.Problem Statement. The Adaptation of Type IV is a good choice:– when developers deal with incomplete information about the highly complex

and dynamic environment, and, consequently, incomplete information aboutthe respective behavior that the system should expose;

– the system must be able of interpreting incomplete run-time models, andapplying –when necessary– changes to those, in order to regulate its behavior;

– the system must be able of generating, at the best of its possibilities, asuitable strategy even when some basic functionality lacks.

System Description. Whereas in classical conception of a self-adaptive system(Type I, II, and III) the system can modify its behavior according to the specifi-cations and to the environment changes, the self-adaptive systems of Type IV arealso able of changing their specification. It may be considered as a strong formof learning. This could include some run-time technique for validating the newspecification, performing, when necessary, possible trade-o↵ analysis betweenseveral potentially conflicting goals.Known Usage. To the best of our knowledge, there are not popular examplesof self-adaptive systems of Type IV.

A case study that could benefit from this kind of adaptation could be asmart firewall [5]. It is a system able to respond to cyber-attacks, but it can notpossibly know all attacks in advance since malicious actors develop new attacktypes all the time. So far, this kind of systems is yet a challenge for artificialintelligence and computer science. It implies a high level of self-awareness andthe ability to learn, to reason with uncertainty and to generate new code forcoping with unexpected scenarios.

4 Conclusions

All the types of self-adaptive systems share a shift of some design decisionstowards run-time in order to improve the control over the behavior.

In practice, the reader has to focus on run-time activities and in particularon the decision-making process. This latter is the algorithm/technique used fordirecting the system behavior.

This paper proposes a framework for classifying systems and their self-adaptiveattributes by means of a set of abstractions grouped into a metamodel. The meta-model o↵ers the designer the possibility to select the right elements related to

Self-Adaptation Metamodel 9

the chosen self-adaptive type. From a designer point of view, the power of thismetamodel is that it considers the smart system and the environment it operatesin strictly tied each other: the environment is part of the software solution andin the same way the system at run-time, with its design artifacts, algorithms andso on, is part of the environment thus realizing the feedback loop regulating allthe activities of a self-adaptive system [3]. The monitor senses the environmentand collects relevant data and events for future reference. The analyzer com-pares data in order to evaluate di↵erences between the actual and the expectedbehavior. The planner uses these data for taking decisions about the behavior tobe executed. Finally, the execute (or act) module applies the planned decisionsthrough its e↵ectors.

Moreover, in order to classify a smart system according to the type of adap-tation, the following guideline focuses on the kind of perception ability anddecision-making process. If the run-time activity is the enactment of a set ofhard-coded actions (selected and/or configured according to the operative con-text), then the adaptation is of Type I. If the system owns a set of pre-definedstrategies (each strategy is an aggregation of actions) and if the strategy isselected and/or configured at run-time, according to quality aspects, then theadaptation is of Type II. If the system is able of assembling a new strategyat run-time, then the adaptation is of Type III. If the system can modify itsrun-time models for generating new functions, then the system is of Type IV.

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