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It is very hard (or ineffective) to take an old system and add to it security features like plug-ins. Therefore, a computer system is much more reliable designed with the approach of security-by-design. Nowadays, there are several tools, middlewares, and platforms designed with this concept in mind, but they must be appropriately used to guarantee a suitable level of reliability and safety. A security-by-design approach is fundamental when creating a distributed application in the IoT field, composed of sensors, actuators, and cloud services. The IoT usually requires handling different programming languages and technologies in which a developer might not be very expert. Through a use case, we analyzed the security of some IoT components of Amazon Web Services (AWS) from a novice programmer’s point of view. Even if such a platform could be secure by itself, a novice programmer could do something wrong and leave some possible attack points to a malicious user. To this end, we also surveyed a small pool of novice IoT programmers from a consulting engineering company. Even if we discovered that AWS seems quite robust, we noticed that some common security concepts are often not clear or applied, leaving the door open to possible issues.

In the Internet of Things era, users are willing to personalize the joint behavior of their connected entities, i.e., smart devices and online service, by means of trigger-action rules such as “IF the entrance Nest security camera detects a movement, THEN blink the Philips Hue lamp in the kitchen.” Unfortunately, the spread of new supported technologies makes the number of possible combinations between triggers and actions continuously growing, thus motivating the need of assisting users in discovering new rules and functionality, e.g., through recommendation techniques. To this end, we present , a semantic Conversational Search and Recommendation (CSR) system able to suggest pertinent IF-THEN rules that can be easily deployed in different contexts starting from an abstract user’s need. By exploiting a conversational agent, the user can communicate her current personalization intention by specifying a set of functionality at a high level, e.g., to decrease the temperature of a room when she left it. Stemming from this input, implements a semantic recommendation process that takes into account ( a ) the current user’s intention , ( b ) the connected entities owned by the user, and ( c ) the user’s long-term preferences revealed by her profile. If not satisfied with the suggestions, then the user can converse with the system to provide further feedback, i.e., a short-term preference , thus allowing to provide refined recommendations that better align with the original intention. We evaluate by running different offline experiments with simulated users and real-world data. First, we test the recommendation process in different configurations, and we show that recommendation accuracy and similarity with target items increase as the interaction between the algorithm and the user proceeds. Then, we compare with other similar baseline recommender systems. Results are promising and demonstrate the effectiveness of in recommending IF-THEN rules that satisfy the current personalization intention of the user.

In the Internet of Things era, users are willing to personalize the joint behavior of their connected entities, i.e., smart devices and online service, by means of IF-THEN rules. Unfortunately, how to make such a personalization effective and appreciated is still largely unknown. On the one hand, contemporary platforms to compose IF-THEN rules adopt representation models that strongly depend on the exploited technologies, thus making end-user personalization a complex task. On the other hand, the usage of technology-independent rules envisioned by recent studies opens up new questions, and the identification of available connected entities able to execute abstract users' needs become crucial. To this end, we present HeyTAP, a conversational and semantic-powered trigger-action programming platform able to map abstract users' needs to executable IF-THEN rules. By interacting with a conversational agent, the user communicates her personalization intentions and preferences. User's inputs, along with contextual and semantic information related to the available connected entities, are then used to recommend a set of IF-THEN rules that satisfies the user's needs. An exploratory study on 8 end users preliminary confirms the effectiveness and the appreciation of the approach, and shows that HeyTAP can successfully guide users from their needs to specific rules.

Video games are not just played for fun; they have become a handy instrument for the cognitive, emotional, and social development of children. However, several barriers prevent many children with disabilities from playing action-oriented video games, alone or with their peers. In particular, children with severe motor disabilities, who rely on one-switch interaction for accessing electronic devices, find fast-paced games that require rapid decision-making and timely responses, completely unplayable. This article contributes to lowering such barriers by presenting GNomon ( G aming NOMON ), a software framework based on the NOMON mode of interaction that allows the creation of action-oriented single-switch video games. The article reports the results of two studies that evaluate the playability and rehabilitation suitability of GNomon-based video games. The playability of GNomon-based games is evaluated by assessing their learnability, effectiveness, errors, satisfaction, memorability, and enjoyability with a group of eight children with severe motor disabilities. The suitability for pediatric rehabilitation is determined by means of a focus group with a team of speech therapists, physiotherapists, and psychologists from a Local Health Agency in Turin, Italy. The results of the playability study are positive: All children had fun playing GNomon-based video games, and seven of eight were able to interact and play autonomously. The results of the rehabilitation-suitability study also entail that GNomon-based games can be exploited in training hand-eye coordination and maintenance of selective attention over time. The article finally offers critical hindsight and reflections and shows possible new future game concepts.

The advent of the 5G network is a key enabler to the growth of IoT, with the promise to innovate and revolutionize contemporary architectures by enabling new IoT-optimized services. Far from being just a bandwidth and latency improvement, the real potential of 5G lies in the intelligent management of network resources, and in the possibility of offering new services at the network level. Developers of IoT applications will no longer be forced to adopt a cloud-centric approach, where all storage and computation is centralized, but will be able to exploit network-provided resources, adopting Edge or Fog computing approaches, with numerous advantages such as higher locality, increased computation power and reliability, reduced latency and power consumption. Network operators, on the other hand, need to offer a compelling set of services while designing the intelligent components of their 5G networks, which would drive IoT developers to prefer their network-hosted services to cloud-based ones managed by over-the-top players. This paper aims at identifying which sets of services may be offered by a 5G network, by analyzing the computing, storage, and communication services that are currently offered by 11 major IoT platform providers, as well as those that are currently not being provided due to limitations of the cloud computing paradigm.

With the increase of connected devices and online services, and recently IoT devices, the number of notifications received by every person is growing. The importance of notifications, as they become part of people’s lives, often depends on various factors that can differently influence the reaction and the disruption of recipients. For this reason, the generation and the distribution of notifications has been gaining importance in the design of new applications, services, and smart devices. Nowadays, developers have not yet fully exploited all the advantages that the availability of multiple devices could bring in the customization and the distribution of notifications, e.g., exploiting a cross-device approach. This paper presents XDN (cross device notification), a framework to assist developers in creating cross-device notifications by scripting. The XDN architecture includes: a library to enable developers to design personalized notifications to be distributed among ad-hoc networks of IoT/mobile devices; a GUI to assist developers in implementing and testing (through a simulator) notification strategies; a server-side runtime environment; and an XDN IoT/mobile library for device support. We discuss the requirements for cross device notification systems, and we present the features of the XDN framework, in particular from the point of view of developer advantages, validated through different scenarios.

The increasing complexity of the new breed of distributed intelligent systems, such as the Internet of Things (IoT), which require a diversity of languages and protocols, can only be tamed with design and programming best practices. Interest is also growing for including the human factor, as advocated by the Ambient Intelligence (AmI) research field, whose focus is on transparently and intelligently supporting people. These new design methodologies are increasingly needed in the toolbox of new electronic and computer engineers, and teaching strategies should be devised that allow students to acquire a systems-level view instead of getting lost in technology-oriented approaches. This paper describes a study carried out over two academic years, in a course in ambient intelligence at Politecnico di Torino, Italy. In the course, a project-based learning approach was adopted, in which students design and prototype an AmI system, and their progress is closely monitored throughout the semester. The paper presents the learning goals and teaching strategies, analyzes the learning outcomes from the qualitative and quantitative points of view, and highlights the lessons learned in the process

In recent years, the Smart City concept is emerging as a way to increase efficiency, reduce costs, and improve the overall quality of citizen life. The rise of Smart City solutions is encouraged by the increasing availability of Internet of Things (IoT) devices and crowd sensing technologies. This paper presents an IoT Crowd Sensing platform that offers a set of services to citizens by exploiting a network of bicycles as IoT probes. Based on a survey conducted to identify the most interesting bike-enabled services, the SmartBike platform provides: real time remote geo-location of users’ bikes, anti-theft service, information about traveled route, and air pollution monitoring. The proposed SmartBike platform is composed of three main components: the SmartBike mobile sensors for data collection installed on the bicycle; the end-user devices implementing the user interface for geo-location and anti-theft; and the SmartBike central servers for storing and processing detected data and providing a web interface for data visualization. The suitability of the platform was evaluated through the implementation of an initial prototype. Results demonstrate that the proposed SmartBike platform is able to provide the stated services, and, in addition, that the accuracy of the acquired air quality measurements is compatible with the one provided by the official environmental monitoring system of the city of Turin. The described platform will be adopted within a project promoted by the city of Turin, that aims at helping people making their mobility behavior more sustainable.

Smart environments SmE are a growing combination of various computing frameworks ubiquitous, pervasive etc., devices, control algorithms, and a complex web of interactions. It is at the core of user facilitation in a number of industrial, domestic, and public areas. On the basis of their application areas, SmE may be critical in terms of correctness, reliability, safety, security, etc. To achieve error-free and requirement-compliant implementation, these systems are designed by resorting to various modeling approaches including ontology and statecharts. This paper attempts to consider correctness, reliability, safety, and security in the design process of SmE and their related components by proposing a design time modeling and formal verification methodology. The proposed methodology covers various design features related to modeling and formal verification of SmE focusing on users, devices, environment, control algorithms, and their interaction against the set of the requirements through model checking. A realistic case study of a Bank Door Security Booth System BDSB is tested. The results show the successful verification of the properties related to the safety, security, and desired reliable behavior of BDSB. Copyright © 2013 John Wiley & Sons, Ltd.

Due to the increasing spread of location-aware applications, developers interest in user location estimation has grown in recent years. As users spend the majority of their time in few meaningful places (i.e., groups of near locations that can be considered as a unique place, such as home, school or the workplace), this paper presents a new energy efficient method to estimate user presence in a meaningful place. Specifically, instead of using commonly used but energy hungry methods such as GPS and network positioning techniques, the proposed method applies a Machine Learning algorithm based on Decision Trees, to predict the user presence in a meaningful place by collecting and analyzing: a) user activity, b) information from received notifications (receipt time, generating service, sender-receiver relationship), and c) device status (battery level and ringtone mode). The results demonstrate that, using 20 days of training data and testing the system with data coming from 14 persons, the accuracy (percentage of correct predictions) is 89.40% (standard deviation: 8.27%) with a precision of 89.04% and a recall of 89.40%. Furthermore, the paper analyzes the importance of each considered feature, by comparing the prediction accuracy obtained with different combinations of features.

In the field of Ambient Assisted Living a limited amount of research aims at supporting caregivers that work with people with disabilities in assisted living facilities (ALFs). In fact, research activities on healthcare support systems in AAL mainly focus on improving the quality of life for people in their own homes or supporting nurses and doctors in hospitals. This paper explores and applies the Internet of Things paradigm in the ALFs context. In particular, we present the design, the implementation, and the experimental evaluation of a system capable of supporting the daily activities of healthcare assistants that operate in ALFs for people with physical or cognitive disabilities. The solution combines wearable and mobile technologies to improve assistance requests and anomaly detection. With this healthcare support system, caregivers can be automatically alerted of potentially hazardous situations that happen to the inhabitants while these are out of sight. Furthermore, inhabitants can require assistance instantly and from any point of the facility. We evaluated the system in two ways. We performed a functional test with two professional caregivers, and we deployed the system in an ALF in Italy for 36 hours, collecting the opinions of the involved caregivers and inhabitants.

Children that rely on the use of single switches to access electronic devices, due to physical impairments, can play dynamic video games as their normally developing peers thanks to GNomon: one of the first framework for creating dynamic one-switch video games. However, children with physical impairments that also have cognitive disabilities find additional difficulties in using the current GNomon-based interface. To compensate these difficulties, the "classical" GNomon widget must be rethought and simplified. This paper proposes a parallel between three different widgets specifically designed for GNomon-based interfaces. Each widget employs metaphor in a different way and it has been evaluated with six children with motor and cognitive disabilities. The results are encouraging: a widget, based on the "big and small" concept, is preferred by children and is a candidate to make single-switch games easier to approach.

The increasing popularity and complexity of the Internet of Things (IoT) requires engineers to master various languages and protocols. Interest is also growing in the human factors for transparently and intelligently supporting people with these new IoT systems. New design methodologies are increasingly needed in the toolbox of new electronic and computer engineers, and teaching strategies must allow students to acquire a systems-level view. This article describes the experience of a university undergraduate course in ambient intelligence (AmI) at Politecnico di Torino, Italy. In the course, through a project-based learning approach, students design and prototype an AmI system, and their progress is closely monitored throughout the semester. The article presents the course organization and its main results, including the relationship with industry stakeholders that collaborated in the course delivery.

Nowadays, notifications are increasingly gaining momentum in our society. New smart devices and appliances are developed everyday with the ability to generate, send and show messages about their status, acquired data and/or information received from other devices and users. Consequently, the number of notifications received by a user is growing and the tolerance to them could decrease in a short time. This paper presents a smart notification system that uses machine learning algorithms to adequately manage incoming notifications. According to context awareness and user habits, the system decides: a) who should receive an incoming notification; b) what is the best moment to show the notification to the chosen user(s); c) on which device(s) the chosen user(s) should receive the notification; d) which is the best way to notify the incoming notification. After the design of a general architecture, as a first step in building such a system, three different machine learning algorithms were compared in the task of establishing the best device on which the incoming notification should be delivered. The algorithms were applied to a dataset derived from real data provided by the MIT Media Laboratory Reality Mining project, enriched with additional synthetic information.

Being able to play games in early years is very important for the development of children. Even though, children with physical disabilities encounter several obstacles that exclude them from engaging in many popular games. In particular, children with severe motor disabilities that rely on one-switch interfaces for accessing electronic devices find dynamic video games completely unplayable. In this paper we present the development and evaluation of GNomon: a framework, based on the NOMON interaction modality, that enables the creation of dynamic one-switch games for children with severe motor disabilities. The framework was designed following a series of guidelines elicited in close collaboration with a team of speech therapists, physiotherapists and psychologists from one of the Local Health Agencies in Turin, Italy. Likewise, three mini games were developed for testing the playability of GNomon-based games. Finally, we conducted a series of trials with 8 children with severe motor disabilities assisted by the health agency, in which we found that all of them enjoyed playing the GNomon- based mini games and that 7 of them were able to interact and play autonomously.

The impact of electricity consumption in buildings on the overall energy budget of European and North-American states is steadily growing and requires solutions for achieving a more sustainable development. Smart metering and energy management system can be hardly afforded by residential homes, for their cost and the required granularity. Empowering smart homes with suitable power consumption models allows to estimate, in real-time, the current home consumption on the basis of currently active devices. In this paper we introduce an ontology based power consumption model (PowerOnt) for smart homes, and we discuss some application use cases where the proposed approach may successfully be exploited. Results show that the modeling approach has the potential to support energy efficiency measures into residential homes with few or no metering devices.

Nowadays, video games represent one of the most popular forms of structured play. They allow children to enjoy very entertaining game mechanics, a variety of game genres and novel modes of interaction. However, this is not always the case for children with severe motor disabilities that rely on one-switch interfaces to access electronic devices. This work in progress presents GNomon, a framework based on the NOMON interaction modality which enables the creation of dynamic, entertaining and complex one-switch video games for children with severe motor disabilities. The framework was designed in close collaboration with a team of speech therapists, physiotherapists and psychologists from one of the Local Health Agencies in Turin, Italy. We also report the design and implementation of two GNomon-based games, which have already been accepted by the health agency experts to be tested with a group of their assisted children.

This paper presents two one-switch games designed for children with severe motor disabilities, based on the GNomon framework. These mini games demonstrate that it is possible to make dynamic video games with time-dependent game mechanics and flexible layout configurations while being accessible and playable with a single switch. The games were designed in close collaboration with a team of speech therapists, physiotherapists, and psychologists from one of the Local Health Agencies in Turin, Italy. Moreover, the games have been already evaluated with a group of children with different motor impairments through a series of trials with encouraging results.

This paper describes evolutionary simulation-based validation, a new point in the spectrum of design validation techniques, besides pseudo-random simulation, designer-generated patterns and formal verification. The proposed approach is based on coupling an evolutionary algorithm with a hardware simulator, and it is able to fit painlessly in an existing industrial flow. Prototypical tools were used to validate gate-level designs, comparing them against both their RT-level specifications and different gate-level implementations. Experimental results show that the proposed method is effectively able to deal with realistic designs, discovering potential problems, and, although approximate in nature, it is able to provide a high degree of confidence in the results and it exhibits a natural robustness even when used starting from incomplete information.

Design and development of increasingly complex intelligent environments require rich design flows that include strong validation and verification methodologies. Formal verification techniques are often advocated, and they require formally described models of the smart home devices, their interconnections, and their controlling algorithms. Complete verification can only be achieved if all used models are verified, including individual device models. This paper proposes an approach to formally verify the correctness of device models described as UML State Charts, by checking their consistency with respect to the properties, declared in an Ontology, for the categories to which each device belongs. The paper describes the verification methodology and presents some first verification results.

In the 21st century, all the major countries around the world are coming together to reduce the impact of energy generation and consumption on the global environment. Energy conservation and its efficient usage has become a top agenda on the desks of many governments. In the last decade, the drive to make homes automated and to deliver a better assisted living picked pace and the research into home automation systems accelerated, usually based on a centralized residential gateway. However most devised solutions fail to provide users with information about power consumption of different house appliances. The ability to collect power consumption information can lead us to have a more energy efficient society. The goal addressed in this paper is to enable residential gateways to provide the energy consumption information, in a machine understandable format, to support third party applications and services. To reach this goal, we propose a Semantic Energy Information Publishing Framework. The proposed framework publishes, for different appliances in the house, their power consumption information and other properties, in a machine understandable format. Appliance properties are exposed according to the existing semantic modeling supported by residential gateways, while instantaneous power consumption is modeled through a new modular Energy Profile ontology.

Ambient Intelligence (AmI) research is giving birth to a multitude of futuristic home scenarios and applications; however a clear discrepancy between current installations and research-level designs can be easily noticed. Whether this gap is due to the natural distance between research and engineered applications or to mismatching of needs and solutions remains to be understood. This paper discusses the results of a survey about user expectations with respect to intelligent homes. Starting from a very simple and open question about what users would ask to their intelligent homes, we derived user perceptions about what intelligent homes can do, and we analyzed to what extent current research solutions, as well as commercially available systems, address these emerging needs. Interestingly, most user concerns about smart homes involve comfort and household tasks and most of them can be currently addressed by existing commercial systems, or by suitable combinations of them. A clear trend emerges from the poll findings: the technical gap between user expectations and current solutions is actually narrower and easier to bridge than it may appear, but users perceive this gap as wide and limiting, thus requiring the AmI community to establish a more effective communication with final users, with an increased attention to real-world deployment.

Nowadays domotic systems have the potential of providing low cost, readily available services and intelligence comparable to futuristic Smart Homes. However they suffer from serious interoperation problems, which are currently preventing their wide spread adoption in real-world intelligent homes. This paper proposes a rule-based approach to interoperation between different domotic networks, with a device-to-device granularity. The approach builds upon the DogOnt ontology and the DOG home gateway and provides the ability to interoperate a virtually infinite number of different domotic devices. Experimental results involving two real word domotic plants confirm the approach feasibility.

Faceted classification is a technique originated and refined in the library science field, that recently gained a lot of attention for creating efficient search interfaces for web databases. Faceted search requires the definition of a formal representation model, a search algorithm and a responsive user interface. This paper proposes FaSet, a representation model and search algorithm supporting the implementation of faceted search engines. FaSet relies on set theory, and strikes a good balance between expressive power and ease of implementation on web architectures. The paper presents the formal definition of the model, search and ranking algorithms, and a relational mapping of data structures and algorithms that enables its efficient implementation.

Current domotic systems manufacturers develop their systems nearly in isolation, responding to different marketing policies and to different technological choices. While there are many available approaches to enable interoperation with domotic systems as a whole, few solutions tackle interoperation between single domotic devices belonging to different technology networks. This paper introduces an automatic device-to-device interoperation solution exploiting ontology- based semantic device modeling. By explicitly modeling interoperation through ontology concepts and relations, the devised solution allows to automatically generate devicelevel interoperation rules. These rules, executed by a suitable rule-execution layer integrated in an intelligent domotic gateway (DOG), allow interconnecting arbitrary devices in a general and scalable manner. A case study is shown, where the automatic-generation of interoperation rules is applied to two real-world demo cases, with either KNX or BTicino devices.

This paper introduces an ontology-based model for domotic device inter-operation. Starting from a previously published ontology (DogOnt) a refactoring and extension is described allowing to explicitly represent device capabilities, states and commands, and supporting abstract modeling of device inter-operation.

One of the critical issues in search engines is the size of search indexes: as the number of documents handled by an engine increases, the search must preserve its efficiency, despite the growth of indexing structures. A widely agreed solution to this problem is the adoption of smaller, or pruned, indexes that allow increasing the retrieval speed while keeping the search quality as high as possible. This paper extends the notion of pruned index to semantic search systems based on conceptual vector space models and proposes a new self-similarity metric for index pruning. A conceptual vector space model represents documents as vectors in a n-dimensional space where each dimension corresponds to an ontology concept. The pruning algorithm proposed in this paper acts on the basis of document self-similarity, preserving only the most significant components of a document conceptual vector. Unlike many already proposed algorithms, the self-similarity metric is only based on local information and does not require to recompute the whole pruned index when new documents are added, i.e., it can be used on-line, possibly combined with other off-line pruning policies. The proposed metric is tested against two benchmark sets respectively related to Siderurgy (250 documents annotated with respect to the e-Class ontology) and Disability (2500 documents annotated with respect to the Passepartout ontology). Results show that the compression ratio achieved by this technique is satisfying (50%), while ranking similarity with results coming from non-pruned indexes remains sufficiently high (80%), thus preserving the quality of provided results.

The integration of semantic representation and retrieval technologies into mainstream Web applications depends on the ease of adoption and re-use of existing information and meta-data. While for textual information, analysis of the text content is quite standardized, for multimedia resources many archives and repositories resorted do define different metadata representations, creating obstacles to interoperability. The flexibility of RDF allows representing and managing all kind of meta-data and of semantic information about multimedia resources, and this paper proposes a possible strategy, based on a structure called RDF Descriptor, that allows representing, reconciling and semantically tagging multimedia resources of different media formats, and possibly coming from different sources with different representations. Experimental results show the feasibility of the approach by reporting result on cross-media and cross-archive semantic searches.

Ontology visualization and exploration is not a trivial task as many issues can affect the effectiveness of interactions. As ontologies are, in the general case, quite connected graphs where concepts are the nodes and semantic relationships the edges, the problems include space allocation, edge superposition, scene over-crowding, etc. In this paper we propose a solution for the visualization and the exploration of ontologies using a 3-dimensional space, where information is represented on a 3D view-port enriched by visual cues. Our visualization tool aims at tackling representation issues of ontology models (as space allocation or the completeness and read-ability of displayed information) by adopting different views, at different granularities, in order to grant a constant navigability of the rendered model. Each provided view represents semantic information according to a different, task-based visualization paradigm, at a suitable level of detail. Besides being primarily implemented as a Protégé plug-in, the proposed solution (named OntoSphere3D) is designed to be a reusable visualization component within Semantic Web applications; in fact, every scene can be exploited as a standalone facility that provides access to ontological data through an intuitive and appealing 3D interface. A case-study, is presented, where re-usability is demonstrated by integrating the OntoSphere3D visualization inside an Eclipse-based tool for Web Service design (called Web Services Design Tool) developed by some of the authors in the context of another research project. © 2007 ACM.

The introduction of Semantic Web techniques in Service-oriented Architectures enables explicit representation and reasoning about semantically rich descriptions of service operations. Those techniques hold promise for the automated discovery, selection, composition and binding of services. This paper describes an approach to derive formal specifications of Web Service compositions on the basis of the interpretation of informal user requests expressed in restricted Natural Language. Our approach leverages the semantic and ontological description of a portfolio of known service operations (called the Semantic Service Catalog). Each user request is processed against a Natural Language vocabulary that includes lexical constructs designed to convey the operations' semantics, in order to recognize and extract fundamental functional requirements implied by the request, and associate them to entries in the Catalog. In addition, the request interpreter extracts from the request the overall service logic, expressed in terms of a set of modular templates describing control and data flow among the selected operations. The result is a composition specification that associates on demand each user request to a new composed service. That specification is formal and can thus be transformed in an executable flow document for a target service composition engine.

Working at system level is attracting increasing interest. New issues must be taken into account, such as validation and verification at all steps. This paper presents a system-level design methodology that supports verification. Starting from a description in a proper subset of VHDL, a Petri Net description is obtained and used for validation purposes and for building the corresponding automaton. An efficient BDD-based tool for Process Algebra manipulation supports formal equivalence proofs. Experimental results show that the approach is feasible also for real-size industrial cases.

A great jump towards the advent of the Semantic Web will take place when a critical mass of web resources is available for use in a semantic way. This goal can be reached by the creation of semantic meta-data in the publication workflow, or by the development of systems and applications able to associate semantics to resources (i.e., annotating them) automatically. Those applications should analyze the content of a web page and should be able to associate some ontology classes to it. One particular issue in this context is to define a suitable relationship between each concept of the ontology and some words (or, more in general, strings) which are expected to appear in resources dealing with that concept, playing the role of "triggers" suggesting the relevance of a given text fragment to a concept.We hereby propose an approach that, starting from a set of textual representations created by experts (synsets), is able to automatically widen their lexical coverage by computing new, larger synsets, increasing the capability of a semantic application to correctly recognize the ontology classes a document is related to. In such approach, the initial textual representations are integrated and augmented by exploiting lexical networks like WordNet, which contain syntactic information connected through semantic relationships. Some algorithms are proposed to avoid misleading terms and consequently to perform sense disambiguation in WordNet.

The introduction of the Semantic Web techniques in Service-oriented Architectures enables explicit representation and reasoning about semantically rich descriptions of service operations. Those techniques hold promise for the automated discovery, selection, composition and binding of services. This paper describes an approach to derive formal specifications of Web Service compositions on the basis of the interpretation of informal user requests expressed in (controlled) Natural Language. Our approach leverages the semantic and ontological description of a portfolio of known service operations (called Semantic Service Catalog).

The introduction of the semantic Web paradigm in service-oriented architectures enables explicit representation and reasoning about services, via a semantically rich description of their operations. We propose an approach towards service selection and composition based upon the interpretation of user requests expressed through an informal human-computer interaction interface that employs (restricted) natural language.

As an increasing number of users access information on the World Wide Web, there is a opportunity to improve well known strategies for Web prefetching, dynamic user modeling and dynamic site customization in order to obtain better subjective performance and satisfaction in Web surfing. We propose a new method to exploit user navigational path behavior to predict, in real-time, future requests. Real-time user adaptation avoids the use of statistical techniques on Web logs by adopting a predictive user model. We designed a new model derived from the finite state machine (FSM) formalism together with an evolutionary algorithm that evolves a population of FSMs for achieving a good prediction rate, and we evaluated the performance of the prediction system using the concepts of precision and applicability.

As an increasing number of users access information on the World Wide Web, there is a opportunity to improve well known strategies for Web caching, prefetching, dynamic user modeling and dynamic site customization in order to obtain better subjective performance and satisfaction in Web surfing. We propose a new method to exploit user navigational path behavior to predict, in real-time, future requests. Predicting user next requests is useful not only for document caching/prefetching, it is also suitable for quick dynamic portal adaptation to user behavior. Real-time user adaptation prevents the use of statistical techniques on Web logs, and we propose the adoption of a predictive user model based on finite state machines together with an evolutionary algorithm that evolves a population of FSMs for achieving a good prediction rate.

The problem of assisting people with special needs is assuming a central role in our society, and information and communication technologies are asked to have a key role in aiding people with both physical and cognitive disabilities. This paper describes an eye tracking system, whose strong points are the simplicity and the consequent affordability of costs, designed and implemented to allow people with severe motor disabilities to use gaze as an input device for selecting areas on a computer screen. The motivation for this kind of input device, together with the communication impairments that it may help to solve are reported in the paper, that then describes the adopted technical solution, compared to existing approaches, and reports the results obtained by its experimentation.

The cost for testing integrated circuits represents a growing percentage of the total cost for their production. The former strictly depends on the length of the test session, and its reduction has been the target of many efforts in the past. This paper proposes a new method for reducing the test length by adopting a new architecture and exploiting an evolutionary optimization algorithm. A prototype of the proposed approach was tested on ISCAS standard benchmarks and the experimental results show its effectiveness.

In present days, most of the design activity is performed at a high level of abstraction, thus designers need to be sure that their designs are syntactically and semantically correct before starting the automatic synthesis process. The goal of this paper is to propose an automatic input pattern generation tool able to assist designers in the generation of a test bench for difficult parts of small-or medium-sized digital protocol interfaces. The proposed approach exploit a Genetic Algorithm connected to a commercial simulator for cultivating a set of input sequence able to execute given statements in the interface description. The proposed approach has been evaluated on the new ITC'99 benchmark set, a collection of circuits offering a wide spectrum of complexity. Experimental results show that some portions of the circuits remained uncovered, and the subsequent manual analysis allowed identifying design redundancies.

This paper presents a novel approach to equivalence verification of RT-level descriptions. The proposed approach sacrifices exactness in favor of applicability: it is not always able to produce an answer, but it is able to check sequential equivalence of large systems. Furthermore, being based on commercial VHDL tools, it does not have arbitrary limitations in the syntax of the descriptions.

The generation of test patterns for sequential circuits is one of the most challenging problems arising in the field of Computer-Aided Design for VLSI circuits. In the past decade, Genetic Algorithms have been deeply investigated as a possible approach: several algorithms have been described, and significant improvements have been proposed with respect to their original versions. As a result, GA-based test pattern generators can now effectively compete with other methods, such as topological or symbolic ones. This paper discusses the advantages and disadvantages of GA-based approaches and describes GATTO, a state-of-the-art GA-based test pattern generator. Other algorithms belonging to the same category are outlined as well. The paper puts GATTO and other GA-based tools in perspective, and shows that Evolutionary computation techniques can successfully compete with more traditional approaches, or be integrated with them.© (1998) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

The use of parallel architectures for the solution of CPU and memory critical problems in the Electronic CAD area has been limited up to now by several factors, like the lack of efficient algorithms, the reduced portability of the code, and the cost of hardware. However, portable message-passing libraries are now available, and the same code runs on high-cost supercomputers, as well as on common workstation networks. The paper presents an effective ATPG system for large sequential circuits developed using the PVM library and based on a Genetic Algorithm. The tool, named GATTO*, runs on a DEC Alpha AXP farm and a CM-5. Experimental results are provided.

The paper proposes a distributed framework that provides a systematic way to publish environment data which is being updated continuously; such updates might be issued at specific time intervals or bound to some environment specific event. The framework targets smart environments having networks of devices and sensors which are interacting with each other and with their respective environments to gather and generate data and willing to publish this data. This paper addresses the issues of supporting the data publishers to maintain up-to-date and machine understandable representations, separation of views (static or dynamic data) and delivering up-to-date information to data consumers in real time, helping data consumers to keep track of changes triggered from diverse environments and keeping track of evolution of the smart environment. The paper also describes a prototype implementation of the proposed architecture. A preliminary use case implementation over a real energy metering infrastructure is also provided in the paper to prove the feasibility of the architecture.

This paper analyzes corewar, a very peculiar computer game popular in mid 80's where different programs fight in the memory of a virtual computer. The /spl mu/GP, an evolutionary assembly-program generator, is used to evolve efficient programs, and the game is exploited to evaluate new evolutionary techniques. The paper introduces a new migration model that exploits the polarization effect and a new hierarchical coarse-grained approach applicable whenever the final goal can be seen as a combination of semi-independent sub goals. Additionally, two very general enhancements are proposed. Analyzed techniques are orthogonal and broadly applicable to different real-life contexts. Experimental results show that all these techniques are able to outperform a previous approach.

The constantly increasing circuit size makes the sequential ATPG problem a challenging area even when simulation-based algorithms are exploited. Several techniques have been proposed which mainly resort to logic simulation, reverting to fault simulation only when strictly required. In this paper we present a new Genetic Algorithm-based test generation method which exploits information coming from a logic simulator (e.g., the circuit activity and the reached states) to guide the search process, in particular in the fault excitation phase. Experimental results show the effectiveness of the proposed method when compared with other Genetic Algorithm-based test generators.

The introduction of semantics in the next generation of the Web, the semantic Web, is strongly based on conceptual description of resources by means of semantic annotations. Effective technologies are therefore required lo correctly map the available syntactic information onto a set of relevant conceptual entities able to model the knowledge domain to which a resource belongs. In attempting to address such issue, we propose an evolutionary optimization of semantic annotation relevance which can improve text-to-concept mapping using information from both the syntactic and the semantic domains. The proposed algorithm leverages relevance information on resource contents, with respect to a subset of a given ontology, and performs several ontology navigation steps for extracting the set of most relevant annotations, in terms of semantic expressiveness. The fitness function of the algorithm is strongly time dependent since the set of annotation to be refined may vary according to user requests, to changes in the domain ontology and is related to the granularity of the annotation set.