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Comet assay provides an easy solution to estimate DNA damage in single cells through microscopy assessment. It is widely used in the analysis of genotoxic damages induced by radiotherapy or chemotherapeutic agents. DNA damage is quantified at the single-cell level by computing the displacement between the genetic material within the nucleus, typically called “comet head”, and the genetic material in the surrounding part of the cell, considered as the “comet tail”. Today, the number of works based on Comet Assay analyses is really impressive. In this work, besides revising the solutions available to obtain reproducible and reliable quantitative data, we developed an easy-to-use tool named CometAnalyser. It is designed for the analysis of both fluorescent and silver-stained wide-field microscopy images and allows to automatically segment and classify the comets, besides extracting Tail Moment and several other intensity/morphological features for performing statistical analysis. CometAnalyser is an open-source deep-learning tool. It works with Windows, Macintosh, and UNIX-based systems. Source code, standalone versions, user manual, sample images, video tutorial and further documentation are freely available at: https://sourceforge.net/p/cometanalyser.HIGHLIGHTSComet assay provides an easy solution to estimate DNA damage in single cells.Today, an impressive number of works are based on Comet Assay analyses, especially in the field of cancer research.Comet assay was originally performed as a qualitative analysis.None of the free tools today available work on both fluorescent- and silver-stained images.We developed CometAnalyser, an open-source deep-learning tool designed for easy segmentation and classification of comets in fluorescent- and silver-stained images.

Knowledge modelling techniques based on knowledge graphs can be used in the context of building customized and flexible e-learning systems to provide learning resources that are accessible to anyone, including learners with special needs and accessibility preferences. The paper describes how a personalized educational resource system that considers the needs of learners, especially when it comes to learners with accessibility needs, can benefit from knowledge graph-based technologies used as resource representation formalism. Knowledge graphs can be used to provide learners with suitable learning objects, learning activities, and teaching methods based on their different preferences and needs and to perform domain knowledge reasoning through modelled domain knowledge.

Leading in higher education is becoming more and more complex, leaving many to wonder why they choose to work in the higher education environment. With the increasing reliance upon information and communication technology (ICT) within the higher education institutions (HEI), leadership becomes even more difficult. Despite the multiple benefits afforded to learners using ICT, its innovative use creates unchartered and confusing territory for most HEI leaders. Utilizing the talents of three disparate but intertwined groups including faculty, staff and administrators, HEI seeking to use ICT to reach a broader student pool need to be adept at adaptation. Usually, administrators are charged with formal leadership, faculty are primarily responsible for teaching, scholarly work and service while staff hold diverse supportive roles within most institutions. Despite streamlined job descriptions and roles, research and practice suggest that leadership in HEI is increasingly contested, powers fragmented, and respective groups of agents conflicted. This affects the performance and sustainability of HEI institutions undermining directly research output and teaching excellence. This volume examines how HEI might adapt more quickly to ICT, whether embracing ICT diminishes or enhances learning, and how to lead most effectively within our emerging volatile, uncertain, complex and ambiguous (VUCA) learning environments. Higher education institutions have long been called upon to manage large amounts of information about their students, programs and structures, embedded in complex digital technology networks and expert technical practices and imagined as "smarter universities". These complex projects must be thought of in a new architecture of technologies, experts, standards and practices to enable new technologies to be linked to institutions and processes, so that they can significantly transform HE itself. Technological skills in artificial intelligence, expert systems, big data, knowledge representation, distributed and pervasive technologies, open data, smart cities, privacy, security, etc. can contribute. Against this backdrop, this volume asks the question “How can we effectively lead in higher education if and when we embrace ICT”? (The ‘we’ in this question includes administrators, faculty and staff). The argument that this volume suggests is that effective leadership in HEI requires that stakeholders at all levels and in all roles, be it formal or informal leadership, learn to adapt more quickly and to consider the potential features and benefits of ICT. It is the premise of this book that we cannot succeed unless we work together across all roles in higher education. Only in this way HEI can thrive and serve its faculty, students, and, ultimately, the society at large.

The design and construction of personalized and flexible e-learning systems can benefit from the use of sophisticated knowledge modeling and data-analysis techniques to provide learning resources that are accessible by anyone, including learners with special accessibility needs and preferences. The paper describes a personalized educational resources system that considers learner's needs, especially when it comes to learners with accessibility requirements. We use Semantic Web technologies as resource representation formalism to provide learners with suitable learning objects, learning activities, and teaching methods based on their different preferences and needs. The system supports the creation of interactive semantic queries to bring the result sets closer to user research requirements.

Graphical abstract, Today, we are fully immersed into the era of 3D biology. It has been extensively demonstrated that 3D models: (a) better mimic the physiology of human tissues; (b) can effectively replace animal models; (c) often provide more reliable results than 2D ones. Accordingly, anti-cancer drug screenings and toxicology studies based on multicellular 3D biological models, the so-called “-oids” (e.g. spheroids, tumoroids, organoids), are blooming in the literature. However, the complex nature of these systems limit the manual quantitative analyses of single cells’ behaviour in the culture. Accordingly, the demand for advanced software tools that are able to perform phenotypic analysis is fundamental. In this work, we describe the freely accessible tools that are currently available for biologists and researchers interested in analysing the effects of drugs/treatments on 3D multicellular -oids at a single-cell resolution level. In addition, using publicly available nuclear stained datasets we quantitatively compare the segmentation performance of 9 specific tools.

Mobile platforms have matured to a point where they can provide the infrastructure required to support sophisticated optimization codes. This opens the possibility to envisage new interest for distributed application codes and the opportunity to intensify research on optimization algorithms requiring limited computational resources, as provided by mobile platforms. In this article, we report on some exploratory experience in this area. We illustrate some practical, real-world cases where running optimization programs on mobile or embedded devices can be useful, with particular emphasis on matheuristics approaches. Then, we discuss a practical use case involving the feasibility version of the generalized assignment problem (GAP). We present a JavaScript implementation of a GAP solver that can be executed inside an ordinary browser supporting ECMAScript. We tested the code on different smartphones of varying age and power, as well as on desktop PCs and other embedded devices. Our experiments confirm the viability of mobile devices for computational intensive tasks.

none 2 si This volume has shared both problems and solutions to address today’s chaotic higher education environment. We have heard from authors across the world who have all agreed that there is much work to do to effectively utilize ICT to empower HEI to not only educate learners, but also to equip society for tomorrow’s challenges. The thoughts offered by our authors resonate with us as most folks today have become frustrated with higher education. The value/cost ratio does not always add up for students and their families. But what if higher education and its leaders were able to move their institutions beyond chaos and into clarity? What if we could fully lead HEI organizations in such a way that pedagogy and technology were effectively paired and could create unique learning opportunities for students across the world? none Antonella Carbonaro; Jennifer Moss Breen Antonella Carbonaro; Jennifer Moss Breen

The automatic extraction of biomedical events from the scientific literature has drawn keen interest in the last several years, recognizing complex and semantically rich graphical interactions otherwise buried in texts. However, very few works revolve around learning embeddings or similarity metrics for event graphs. This gap leaves biological relations unlinked and prevents the application of machine learning techniques to promote discoveries. Taking advantage of recent deep graph kernel solutions and pre-trained language models, we propose Deep Divergence Event Graph Kernels (DDEGK), an unsupervised inductive method to map events into low-dimensional vectors, preserving their structural and semantic similarities. Unlike most other systems, DDEGK operates at a graph level and does not require task-specific labels, feature engineering, or known correspondences between nodes. To this end, our solution compares events against a small set of anchor ones, trains cross-graph attention networks for drawing pairwise alignments (bolstering interpretability), and employs transformer-based models to encode continuous attributes. Extensive experiments have been done on nine biomedical datasets. We show that our learned event representations can be effectively employed in tasks such as graph classification, clustering, and visualization, also facilitating downstream semantic textual similarity. Empirical results demonstrate that DDEGK significantly outperforms other state-of-the-art methods.

In the last few years, several initiatives based on extracurricular activities have been organized in many countries around the world, with the aim to reduce the digital gender gap in STEM (Science, Technology, Engineering, Math) fields. Among them, the Digital Girls summer camp, organized every year since 2014 by two Italian universities with the aim to attract female students to ICT (Information and Communication Technologies) disciplines, represents quite a unique initiative for its characteristics of long-duration (3–4 entire weeks) and complete gratuitousness for the participants. The COVID-19 emergency imposed severe changes to such activities, that had to be modified and carried out in the online mode as a consequence of social distancing. However, on one hand, the general lack of high-quality evaluations of these initiatives hinders the possibility to understand the actual impact of extracurricular activities on the future academic choices of the participants. On the other hand, the availability of data collected over different editions of Digital Girls has allowed us to analyze the summer camp impact and to evaluate the pros and cons of in-presence and online activities. The main contribution of this paper is twofold. First, we present an overview of existing experiences, at the national (Italian) and international levels, to increase female participation in integrated STEM and ICT fields. Second, we analyze how summer camp participation can influence girls’ future academic choices, with specific attention to ICT-related disciplines. In particular, the collection of a significant amount of data through anonymous surveys conducted before and after the camp activities over the two editions allowed us to evidence the different impacts of in-presence and online extracurricular activities.

Fitness sensors and health systems are paving the way toward improving the quality of medical care by exploiting the benefits of new technology. For example, the great amount of patient-generated health data available today gives new opportunities to measure life parameters in real time and create a revolution in communication for professionals and patients. In this work, we concentrated on the basic parameter typically measured by fitness applications and devices&mdash, the number of steps taken daily. In particular, the main goal of this study was to compare the accuracy and precision of smartphone applications versus those of wearable devices to give users an idea about what can be expected regarding the relative difference in measurements achieved using different system typologies. In particular, the data obtained showed a difference of approximately 30%, proving that smartphone applications provide inaccurate measurements in long-term analysis, while wearable devices are precise and accurate. Accordingly, we challenge the reliability of previous studies reporting data collected with phone-based applications, and besides discussing the current limitations, we support the use of wearable devices for mHealth.

The huge amounts of self-tracked health data collected by Internet of Things (IoT) fitness devices offer important opportunities to the research community. If properly exploited, IoT health and fitness datasets can help to gain valuable insights into the human health in order to provide better healthcare.However, IoT health data come from a variety of different heterogeneous sources and in proprietary formats, which means that they require an integration process, normally manually done by domain experts, in order to be analysed. This task is not only significantly time consuming but in many cases, error prone.In this study, we designed and developed a web platform for collecting and publishing IoT health and fitness datasets according to Linked Data principles. We leveraged the IFO ontology and the Semantic Web technologies to make the IoT health and fitness datasets freely available to the community in a shared, semantically meaningful, easily discoverable, and reusable manner.The system introduced in this article shows that Semantic Web technologies can be a viable and comprehensive solution for describing, integrating and sharing heterogeneous IoT datasets, thus overcoming the issues of data silos that nowadays dominate the IoT landscape.

A variety of technology enhanced teaching strategies and learning activities have been applied in education, including assessment mechanisms. In this paper, we aim to examine the extent to which peer assessment promotes deep learning and favours the incremental learning of the concepts presented throughout a traditional introductory programming course. In particular, we want to examine how the enactment of the peer-assessor and peer-assessee roles is associated with students’ learning improvements, after enacting recip-rocal peer assessment. We illustrate a case study based on a novel web-based peer assessment tool to improve engagement and learning outcomes in a pro-gramming course. We want to assess whether programming skills are growing through peer review and whether exposure to different programming tech-niques is helpful to students. One of the most remarkable findings of our ex-perience was that students reported that assessing others’ work was an ex-tremely valuable learning activity.

Nowadays, the enormous volume of health and fitness data gathered from IoT wearable devices offers favourable opportunities to the research community. For instance, it can be exploited using sophisticated data analysis techniques, such as automatic reasoning, to find patterns and, extract information and new knowledge in order to enhance decision-making and deliver better healthcare. However, due to the high heterogeneity of data representation formats, the IoT healthcare landscape is characterised by an ubiquitous presence of data silos which prevents users and clinicians from obtaining a consistent representation of the whole knowledge. Semantic web technologies, such as ontologies and inference rules, have been shown as a promising way for the integration and exploitation of data from heterogeneous sources. In this paper, we present a semantic data model useful to: (1) consistently represent health and fitness data from heterogeneous IoT sources; (2) integrate and exchange them; and (3) enable automatic reasoning by inference engines.

World Conference on Educational Technology Researches, WCETR-2011 -- 5 July 2011 through 9 July 2011 -- Nicosia/Kyrenia, [No abstract available]

Route following based on visual landmark matching may require many models to cover all different situations. This paper describes a system that is able to adapt template's modelling parameters to environmental conditions (lighting, shadows, etc.) by a genetic learning technique. 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 problem in the 3D position estimation. The experimental results show that the tracking robustness is improved, while using a small set of templates. © 1998 Elsevier Science B.V. All rights reserved.

The paper presents a web based system that aims to improve Web exploration by enabling users to discover knowledge associated to his need. Indeed, in exploratory search, the user is willing not only to find documents relevant with respect to his query but he is also interested in learning, discovering and understanding knowledge on complex and sometimes unknown topics. We rely on DBpedia to explore the semantics of keywords thus suggesting potentially interesting related topics or keywords to the user and enabling user-friendly and intelligent content discovery.

The Semantic Web offers a generic infrastructure for interchange, integration and creative reuse of structured data, which can help to cross some of the boundaries that Web 2.0 is facing. Currently, Web 2.0 offers poor query possibilities apart from searching by keywords or tags. There has been a great deal of interest in the development of semantic-based systems to facilitate knowledge representation and extraction and content integration [1], [2]. Semantic-based approach to retrieving relevant material can be useful to address issues like trying to determine the type or the quality of the information suggested from a personalized environment. In this context, standard keyword search has a very limited effectiveness. For example, it cannot filter for the type of information, the level of information or the quality of information. Potentially, one of the biggest application areas of content-based exploration might be personalized searching framework (e.g., [3],[4]). Whereas search engines provide nowadays largely anonymous information, new framework might highlight or recommend web pages related to key concepts. We can consider semantic information representation as an important step towards a wide efficient manipulation and retrieval of information [5], [6], [7]. In the digital library community a flat list of attribute/value pairs is often assumed to be available. In the Semantic Web community, annotations are often assumed to be an instance of an ontology. Through the ontologies the system will express key entities and relationships describing resources in a formal machine-processable representation. An ontology-based knowledge representation could be used for content analysis and object recognition, for reasoning processes and for enabling user-friendly and intelligent multimedia content search and retrieval. Text summarization has been an interesting and active research area since the 60’s. The definition and assumption are that a small portion or several keywords of the original long document can represent the whole informatively and/or indicatively. Reading or processing this shorter version of the document would save time and other resources [8]. This property is especially true and urgently needed at present due to the vast availability of information. Concept-based approach to represent dynamic and unstructured information can be useful to address issues like trying to determine the key concepts and to summarize the information exchanged within a personalized environment. In this context, a concept is represented with a Wikipedia article. With millions of articles and thousands of contributors, this online repository of knowledge is the largest and fastest growing encyclopedia in existence. The problem described above can then be divided into three steps: • Mapping of a series of terms with the most appropriate Wikipedia article (disambiguation). • Assigning a score for each item identified on the basis of its importance in the given context. • Extraction of n items with the highest score. Text summarization can be applied to many fields: from information retrieval to text mining processes and text display. Also in personalized searching framework text summarization could be very useful. The chapter is organized as follows: the next Section introduces personalized searching framework as one of the possible application areas of automatic concept extraction systems. Section three describes the summarization process, providing details on system architecture, used methodology and tools. Section four provides an overview about document summarization approaches that have been recently developed. Section five summarizes a number of real-world applications which might benefit from WSD. Section six introduces Wikipedia and WordNet as used in our project. Section seven describes the logical structure of the project, describing software components and databases. Finally, Section eight provides some consideration...

The process of summarizing information is becoming increasingly important in the light of recent advances in resource creation and distribution and the resulting influx of large numbers of information in everyday life. These advances are also challenging educational institutions to adopt the opportunities of distributed knowledge sharing and communication. The chapter presents a summarization system to support tutor in managing student communication and interaction within a learning framework. Results show the adequacy of the system in identifying a good content summarization and then in improving the efficiency and effectiveness of the context in which summarization can be integrated.

The paper presents an ontological approach for enabling personalized searching framework facilitating the user access to desired contents. Through the ontologies the system will express key entities and relationships describing resources 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 content retrieval.

In this chapter, we introduce how to use a Web-based hybrid recommender system developed with a collaborative bookmark management system approach. The system combines content analysis and the development of virtual clusters of students and educational sources. It provides facilitation in the use of a huge amount of digital information stored in a distributed learning environment on the basis of the student’s personal requirements and interests. By adopting a hybrid approach, the system is able to effectively filter relevant resources from a wide heterogeneous environment like the Web, taking advantage of the common interests of the users and also maintaining the benefits provided by content analysis. The basic idea is to appropriately help students classifying domain-specific information found on the Web and saved as bookmarks, to recommend these documents to other students with similar interests, and to notify users periodically about new, potentially interesting documents. Documents are represented using metadata model.

The paper presents the guidelines of a project of three Italian Universities (Bologna, Siena, Trento) which aim is to investigate the use of mobile computing technologies to support the learning processes in a University context. The project covers three main areas. The first area is concerned with finding effective models for mobile learning. The second regards the evaluation of learning processes in mobile learning environments. The third focuses on the technological aspects of mobile learning, and on their integration with e-Learning systems, and more generally, with the information systems of the academic institutions. The project has its foundations in the availability of significant experience on e-learning real processes, and on the availability of the source code of an e-learning system developed in previous projects and currently used by different faculties, and of the newer platform that gathers the experience obtained in the past.

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.

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.

The materialization of fragmented views in data warehouses has the objective of improving the system response time for a given workload. It represents a combinatorial optimization problem arising in the logical design of data warehouses which has so far received little attention from the optimization community. This paper describes the application of a metaheuristic approach, namely the ANTS approach, to this problem. In particular, we propose an integer programming formulation of the problem, derive an efficient lower bound and embed it in an ANTS algorithm. Preliminary computational results, obtained on the well-known TPC-D benchmark, are presented.

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.

We describe an approach to image segmentation based on a two-layer module that is executed until a good segmentation is achieved, providing an evolution of previous segmentation results at each execution. The first layer performs a global segmentation of an image of decreasing area at each evolution by adopting a genetic algorithm learning technique to select segmentation parameters that give better results. The second layer provides the input to the next evolution by selecting the segmented regions that need further optimisation. A main goal of our system is to perform the segmentation without using neither ground-truth information nor human judgement. Thus, edge detection is performed to assess the performance of region segmentation and to guide the evolution of segmentation. Experimental results are consistent with what is observed visually.

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.