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The rising auto usage deriving from growth in jobs and residential population is making traffic congestion less tolerable in urban and suburban areas. This results in air pollution, energy waste and unproductive and unpleasant consumption of people’s time. Public transport cannot be the only answer to this increasing transport demand. Car pooling has emerged to be a viable possibility for reducing private car usage in congested areas. Its actual practice requires a suitable information system support and, most important, the capability of effectively solving the underlying combinatorial optimization problem. This paper presents an application of the ANTS approach, one of the approaches which follow the Ant Colony Optimization (ACO) paradigm, to the car pooling optimization problem. Computational results are presented both on datasets derived from the literature about problems similar to car pooling and on real-world car pooling instances.

With the aim to individualise human-computer interaction, an Intelligent Tutoring System (ITS) has to keep track of what and how the student has learned. Hence, it is necessary to maintain a Student Model (SM) dealing with complex knowledge representation, such as incomplete and inconsistent knowledge and belief revision. With this in view, the main objective of this paper is to present and discuss the student modelling approach we have adopted to implement Pitagora 2.0, an ITS based on a co-operative learning model, and designed to support teaching-learning activities in a Euclidean Geometry context. In particular, this approach has led us to develop two distinct modules that cooperate to implement the SM of Pitagora 2.0. The first module resembles a "classical" student model, in the sense that it maintains a representation of the current student knowledge level, which can be used by the teacher in order to tune its teaching strategies to the specific student needs. In addition, our system contains a second module that implements a virtual partner, called companion. This module consists of a computational model of an "average student" which cooperates with the student during the learning process. The above mentioned module calls for the use of machine learning algorithms that allow the companion to improve in parallel with the real student. Computational results obtained when testing this module in simulation experiments are also presented. © 1995 Kluwer Academic Publishers.

The problem considered in this paper consists in assigning frequencies to radio links between base stations and mobile transmitters in order to minimize the global interference over a given region. This problem is NP-hard and few results have been reported on techniques for solving it to optimality. We have applied to this problem an ANTS metaheuristic, that is, an approach following the ant colony optimization paradigm. Computational results, obtained on a number of standard problem instances, testify the effectiveness of the proposed approach.

The paper describes a novel comprehensive approach to image-contrast enhancement in the spatial domain. Instead of defining another transformation function our strategy consists of adopting a general functional form, able to map different transformation functions, and in using a learning technique to select the parameter values that are optimal for the image being processed. First, local measures of spatial activity are assigned to each pixel of the image. Second, the local contrast value for each pixel is computed according to a function which is based on human visual response. Third, the parameters of a comprehensive contrast-enhancement function are selected by a genetic algorithm on the basis of the spatial activity of the image resulting from the transformation. The validity of the proposed technique is confirmed both perceptually, that is, higher fitness values correspond to the images that have been judged better by human observers, and by comparative evaluations of our algorithm with respect to classical methods. © 1999 IEEE.

Ant Colony Optimization is a paradigm for designing combinatorial optimization metaheuristic algorithms, which construct a solution on the basis of information provided both by some standard constructive heuristic and by previously obtained solutions. In this chapter, we present current results obtained by ACO algorithms on several hard combinatorial optimization problems. Furthermore, we describe in more detail a particular ACO algorithm, the ANTS metaheuristic, presenting its general structure and reporting results obtained on the quadratic and on the frequency assignment problems.

Ant Colony Optimization (ACO) is a class of constructive meta-heuristic algorithms sharing the common approach of constructing a solution on the basis of information provided both by a standard constructive heuristic and by previously constructed solutions. This tutorial is composed of three parts. The first one frames the ACO approach in current trends of research on metaheuristic algorithms for combinatorial optimization; the second outlines current research within the ACO framework, reporting recent results obtained on different problems, while the third part focuses on a particular research line, the ANTS metaheuristic, providing some details on the algorithm and presenting results recently obtained on the quadratic and on the frequency assignment problems.

Unsupervised image segmentation is a complicated task due to the lack of appropriate measures for judging the quality of segmentation results. At present, most segmentation results are evaluated visually and qualitatively. In a previous paper [1], we presented a system that performs image segmentation without using neither ground-truth information nor human judgement. A genetic algorithm is used to efficiently search the set of parameters that maximises a segmentation quality criterion based on the integration of information from region boundaries and edge pixels. The performance of that system is degraded by the need of binarizing the edge maps, resulting in an information loss that may even hinder the convergence of the GA. In this paper we describe the solutions adopted to overcome this problem and to increase the objectiveness of segmentation results. Basically, human visual criteria are firstly embodied in an automatic image-contrast enhancement-process. The connectivity-code intermediate representation [2] and saliency-enhancing operations derived from the psychology field are then used to increase the significance of edge-pixels that have a small intensity but a high perceptual importance. Both computer simulated and real images have been used to test the performance of our adaptive segmentation system. The increased objectiveness of the segmentation results is confirmed both perceptually and by higher fitness values than those obtained by the previous system.

The paper describes a system to control vehicle accesses in restricted areas. The signalling of vehicles whose license- plates do not belong to a specific database is the aim of the system. The adaptation to different environmental conditions, and the identification of a vehicle by processing the license- plate pattern as a whole, without considering the recognition of the characters, are its two main characteristics. The system implements a recognition engine constituted by two modules. First, the system analyzes the video-recorded sequences to select a frame in which the license-plate satisfies pre-defined constraints, and extracts the license- plate template on which the matching with the model templates stored in the database will be performed. Second, vehicle identification is performed by a genetic template matching that, without requiring a high computational complexity, provides adaptation to normal environmental variations by exploiting learning capabilities. The implemented system, forced to distinguish only between authorized and unauthorized vehicles according to a threshold in the genetic fitness function, shows robust performance on Italian cars, but it is adaptable to different license-plate models, and is independent from outdoor conditions.

Genetic Algorithms and Classifier Systems are often used in biologic-like and evolutionary behaviors’ simulations. The basic example is Wood7 Wilson’s world. In this environment it is interesting to study some problems: Can evolve the cooperative behaviors of organisms present in the world? How and when do the behaviors evolve? Some preliminary results show the conditions under that cooperative behavior rules are developing rapidly. Particularly we have pointed out the likely of following observations: a. The cooperative behavior develops more easily if the initial population start from the same point. b. It exists some thresholds under that the cooperative behavior can’t evolve; these thresholds depend to the population size.

The paper presents an ontological approach for enabling semantic-aware video retrieval framework facilitating the user access to desired contents. Through the ontologies, the system will express key entities and relationships describing videos in a formal machine-processable representation. An ontology-based knowledge representation could be used for content analysis and concept recognition, for reasoning processes and for enabling user-friendly and intelligent multimedia content retrieval.

The use of knowledge graphs (KGs) in advanced applications is constantly growing, as a consequence of their ability to model large collections of semantically interconnected data. The extraction of relational facts from plain text is currently one of the main approaches for the construction and expansion of KGs. In this paper, we introduce a novel unsupervised and automatic technique of KG learning from corpora of short unstructured and unlabeled texts. Our approach is unique in that it starts from raw textual data and comes to: i) identify a set of relevant domain-dependent terms; ii) extract aggregate and statistically significant semantic relationships between terms, documents and classes; iii) represent the accurate probabilistic knowledge as a KG; iv) extend and integrate the KG according to the Linked Open Data vision. The proposed solution is easily transferable to many domains and languages as long as the data are available. As a case study, we demonstrate how it is possible to automatically learn a KG representing the knowledge contained within the conversational messages shared on social networks such as Facebook by patients with rare diseases, and the impact this can have on creating resources aimed to capture the “voice of patients”.

This paper proposes an ontological approach for enabling semantic-aware video content annotation and retrieval in order to facilitate content video analysis. Semantic Web ontologies express key entities and relationships which describe videos in a formal machine-processable representation. Ontology-based knowledge representation systems can be used for content analysis, concept recognition, reasoning processes and for enabling user-friendly and intelligent multimedia content retrieval, thus meeting the challenges of video analysis.

A system for landmark tracking by a template matching approach is described. Route following based on landmarks may require many models to cover all different situations, so a genetic algorithm learning technique is used to adapt modelling parameters to environmental conditions (lighting, shadows, reflexes, etc.) during the tracking. In addition, the mobile robot self-localisation is obtained by a stereo approach that uses the centres of matching in the two images to solve in a simple way the correspondence analysis in the 3D position estimation. The experimental results show that the tracking robustness is improved when the adaptive template matching is used for landmark tracking.

The aim of this paper is to evaluate the student learning about Computer Science subjects. A questionnaire based on ordinal scored items has been submitted to the students through a computer automated system. The data collected have been analyzed by using a latent variable model for ordinal data within the Item Response Theory framework. The scores obtained from the model allow to classify the students according to the reached competence.

This paper describes the tracking of an object in a sequence of video-recorded images by a template matching approach based on a genetic algorithm learning technique to generate adaptive templates. The system is intended for real images. Contrary to a controlled environment, where the template may be generated from a noise free image and all successive frames do not show great changes, we hypothesise a set of images acquired at possible different environmental conditions. The varying environmental conditions greatly influence the matching, but they cannot be coded/used because they are not known a priori. In addition, 2D projections of 3D objects may vary during the sequence. Despite these difficulties, the need for robust systems that can locate objects in frame sequences is rapidly increasing due to the explosion in the amount of visual data in the communications systems.

The experience from years of development and use, the advance of technology, and the development of authoring tools for questions and tests has resulted in a sophisticated, computer based assessment system. However, there is still a lot of room for further development. Some of the current ideas for development are discussed in the remainder of this work. A primary aim of assessment, both formative and summative, is provide the necessary information to improve future educational experiences because it provides feedback on whether the course and learning objectives have been achieved to satisfactory level. Yet, it is important that the assessment data be accurate and relevant to effectively make informed decisions about the curriculum. Moreover, formative assessment can also be used to help bridge the gap between assessment and learning. This may be achieved particularly where assessment strategies are combined with useful feedback, and integrated within the learning process. The answers to the described objectives are enhanced if we could integrate adaptive testing techniques; accurate and fitted assessment data may improve both the curriculum and the student ability level. The idea behind a computerized adaptive testing (CAT) is quite forward: to apply to each examinee only those items useful to know his proficiency level. As a consequence of this, CAT is more efficient than conventional (i.e., fixeditem) tests. It provides more precise measurements for same-length tests or shorter tests for same-precision measurements.

Digital technology is increasingly central to our lives, particularly among young people. However, a severe concern remains about the existing digital gender gap: despite the demand for ICT professionals is more and more increasing, women are under-represented in the ICT sectors and their low participation rates can be traced back to school years with internalised gender stereotypes. The pre-sent work explores the summer camp “Digital girls” case study with the aim of involving women from an early age in traditionally male sectors. The summer camp is organized by the engineering and computer science departments of two Italian universities that from 2014 have decided to undertake work to dissemi-nate and raise awareness of ICT issues to girls in high school. The summer camp offers a protected context in which participants learn the principles of computer programming and are exposed to workshops on social networks, free software, cybersecurity, and interact with female ICT experts. The paper focuses on the analysis of data relating to the correlation between previous programming expe-rience and the choice of university studies and social perceptions of the work environment.

Deploying ad-hoc learning environments to use and represent data from multiple sources and networks and to dynamically respond to user demands could be very expensive and ineffective in the long run. Moreover, most of the available data is wasted without extracting potentially useful information and knowledge because of the lack of established mechanisms and standards. It is preferable to focus on data availability to choose and develop interoperability strategies suitable for smart learning systems based on open standards and allowing seamless integration of third-party data and custom applications. This paper highlights the opportunity to take advantage of emerging technologies, like the linked open data platforms and automatic reasoning to effectively handle the vast amount of information and to use data linked queries in the domain of cognitive smart learning systems., Journal of e-Learning and Knowledge Society, Vol 16 No 1 (2020): Je-LKS - Special Issue on "Smart Learning in Smart Cities"

Recommender systems have become an important research area since the emergence of the first research paper on collaborative filtering in the mid-1990s. In general, recommender systems directly help users to select content, products, or services by aggregating and analysing historical data including suggestions from other users, and turning them into predictions of users’ possible future preferences. Recommender systems combine ideas from user profiling, information filtering, data mining, machine learning and social networking to provide personalized and meaningful recommendations. For example, while standard search engines are very likely to generate the same results to the same search queries entering from different users, recommender systems are able to generate results that are personalized taking into account the individual user’s profile.