Abstract Modern data architectures increasingly rely on decentralized data products described through semi-structured artifacts, such as YAML descriptors, to support scalability, interoperability, and governance. Ensuring that these descriptors comply with organizational and regulatory policies remains a challenging and labor-intensive task, largely due to the natural-language nature of policies and the heterogeneity of descriptor schemas. In this paper, we investigate a model-based approach to evaluating natural-language policies over semi-structured data product descriptors expressed in YAML, leveraging Large Language Models (LLMs) as reasoning components. We formalize the policy-descriptor compliance task as a binary decision problem and propose an LLM-based microservice architecture that combines structured prompt engineering with deterministic output constraints to support automated policy verification. To evaluate the approach, we construct an augmentation-based benchmark comprising 4,000 policy-descriptor pairs derived from 40 manually annotated seed combinations. The benchmark is designed to assess model robustness to policy paraphrases and semantically equivalent descriptor rewrites. We evaluate four open-source instruction-tuned LLMs under identical conditions and report results in terms of accuracy, precision, recall, F1-score, and execution time. Experimental results show that LLMs can effectively support policy verification in semi-structured environments, achieving up to 72% accuracy. However, we observe a consistent conservative bias toward non-compliance, as well as systematic failure modes involving conjunctions, bidirectional constraints, and conditionally inapplicable rules. Our analysis highlights both the potential and the current limitations of LLM-driven governance, and suggests that expressing policies as explicit procedural checks substantially improves validation reliability.

Abstract In a global society where collaboration increasingly involves speakers of different languages, communication barriers remain a significant challenge. The problem becomes particularly relevant in urgent scenarios, where critical information must be delivered rapidly and understood correctly across speakers of several languages. To address this challenging issue, we introduce a multilingual framework for near real-time speech-to-speech translation that explicitly detects and preserves urgency. The system extends the cascade paradigm by integrating audio- and text-based urgency recognition with urgency-aware speech synthesis, and has been implemented as an open-source prototype accessible through WebRTC. As an additional contribution, we created the first publicly available multilingual dataset explicitly annotated for urgency, comprising 200 recordings in English, French, German, Dutch, and Italian. The framework has been evaluated through both quantitative and qualitative analyses. The experimental results indicate that our system achieves real-time performance while maintaining translation accuracy and effectively preserving perceived urgency. While most current solutions are offered by commercial providers and deployed on the cloud, raising significant privacy concerns, our system is released open-source and can be executed on premises using a mid-range server. The dataset is publicly available at https://zenodo.org/records/17502378 .

In an increasingly connected world, where people from different countries work closely together to pursue common goals, language barriers remain a significant obstacle to seamless communication. This issue becomes even more critical in emergency situations, where urgent messages must be conveyed immediately to multiple recipients, minimizing the risk of misunderstanding due to limited knowledge of the local language. To address this challenge, we propose a novel system for near real-time voice translation that preserves the tone of urgency. Our system incorporates advanced artificial intelligence components for speech recognition, urgency detection, machine translation, and speech synthesis. Being specifically tailored for urgency detection and urgency-aware translation, it enhances state-of-the-art translation technologies by preserving the urgency tone, which is often lost in conventional systems. We developed a working prototype of our system and conducted experiments that show the efficiency and low latency of our solution.

Artificial Intelligence is playing an increasingly prominent role in healthcare. For this integration to continue evolving effectively, professionals in the field, including doctors, psychologists, and nurses, must trust the technology. In the domain of Large Language Models, this trust requires transparency in the sources of information, ensuring they are verifiable and reviewed. This paper presents an intelligent diagnostic assistant designed to support differential diagnosis and disease comparison, leveraging an LLM enriched with the Retrieval-Augmented Generation technique to address trust-related challenges. The knowledge base for our system is PubMed, a biomedical article aggregator, which supplies relevant articles on the considered disorders, as well as evidence supporting or refuting potential links between them. Retrieval-Augmented Generation empowers the model to incorporate external knowledge tailored to the specific question while providing citations for its statements. These citations not only enhance the trustworthiness of the answers but also enable practitioners to explore the referenced articles in greater detail. We developed a prototype of our system, and conducted an evaluation through questionnaires administered to 12 professional psychologists. Results show that our system compares favorably with state of the art Large Language Models in providing the relevant information to distinguish between two diseases, and that the provided details are useful for supporting differential diagnosis.

This survey article explores graph-based approaches to multimodal human activity recognition in indoor environments, emphasizing their relevance to advancing multimodal representation and reasoning. With the growing importance of integrating diverse data sources such as sensor events, contextual information, and spatial data, effective human activity recognition methods are essential for applications in smart homes, digital health, and more. We review various graph-based techniques, highlighting their strengths in encoding complex relationships and improving activity recognition performance. Furthermore, we discuss the computational efficiencies and generalization capabilities of these methods across different environments. By providing a comprehensive overview of the state-of-the-art in graph-based human activity recognition, this article aims to contribute to the development of more accurate, interpretable, and robust multimodal systems for understanding human activities in indoor settings.

Zixiu Wu, Simone Balloccu, Ehud Reiter, Rim Helaoui, Diego Reforgiato Recupero, Daniele Riboni. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.

Abstract In the last years, scientific and industrial research has experienced a growing interest in acquiring large annotated data sets to train artificial intelligence algorithms for tackling problems in different domains. In this context, we have observed that even the market for football data has substantially grown. The analysis of football matches relies on the annotation of both individual players’ and team actions, as well as the athletic performance of players. Consequently, annotating football events at a fine-grained level is a very expensive and error-prone task. Most existing semi-automatic tools for football match annotation rely on cameras and computer vision. However, those tools fall short in capturing team dynamics and in extracting data of players who are not visible in the camera frame. To address these issues, in this manuscript we present FootApp, an AI-based system for football match annotation. First, our system relies on an advanced and mixed user interface that exploits both vocal and touch interaction. Second, the motor performance of players is captured and processed by applying machine learning algorithms to data collected from inertial sensors worn by players. Artificial intelligence techniques are then used to check the consistency of generated labels, including those regarding the physical activity of players, to automatically recognize annotation errors. Notably, we implemented a full prototype of the proposed system, performing experiments to show its effectiveness in a real-world adoption scenario.

Research on natural language processing for counselling dialogue analysis has seen substantial development in recent years, but access to this area remains extremely limited due to the lack of publicly available expert-annotated therapy conversations. In this work, we introduce AnnoMI, the first publicly and freely accessible dataset of professionally transcribed and expert-annotated therapy dialogues. It consists of 133 conversations that demonstrate high- and low-quality motivational interviewing (MI), an effective counselling technique, and the annotations by domain experts cover key MI attributes. We detail the data collection process including dialogue selection, transcription and annotation. We also present analyses of AnnoMI and discuss its potential applications.

The rapid increase of the elderly population and new advances in pervasive computing technologies allow innovative tools and applications to support independent living for frail people and identify early symptoms of health problems, including neurodegenerative disorders. Among several studies reported in the literature, monitoring locomotion traces to detect symp-toms of cognitive impairment has gained increasing attention. Therefore, in this work, we propose a novel technique for the recognition of locomotion patterns related to cognitive decline based on sensor data acquired in smart homes. In particular, we introduce a vision-based method to graphically represent indoor trajectories with random rotation, using different handcrafted features designed for image analysis tasks and combined with features extracted directly from spatio-temporal sequences of movements. Experiments on a real-world dataset acquired in a smart-home test-bed show that the proposed approach achieves promising results.

Thanks to the seamless integration of sensing, networking, and artificial intelligence, cyber-physical systems promise to improve healthcare by increasing efficiency and reducing costs. Specifically, cyber-physical systems are being increasingly applied in smart-homes to support independent and healthy aging. Due to the growing prevalence of noncommunicable diseases in the senior population, a key application in this domain is the detection of cognitive issues based on sensor data. In this article, we propose a novel cyber-physical system for cognitive assessment in smart-homes. Cognitive evaluation relies on clinical indicators characterizing symptoms of dementia based on the individual’s movement patterns. However, recognizing these patterns in smart-homes is challenging, because movement is constrained by the home layout and obstacles. Since different abnormal patterns are characterized by undulatory-like trajectories, we conjecture that frequency-based locomotion features may more effectively capture these patterns with respect to traditional features in the spatio-temporal domain. Based on this intuition, we introduce novel feature extraction techniques and adopt state-of-the-art machine learning algorithms for short- and long-term cognitive evaluation. Our system includes a user-friendly interface that enables clinicians to inspect the data and predictions. Extensive experiments carried out with a real-world dataset acquired from both cognitively healthy seniors and people with dementia show the superiority of our frequency-based features. Moreover, further experiments with an ensemble method show that prediction accuracy can be enhanced by combining features in the frequency and time domains.

Mobile sensor-based activity recognition is a growing research field with important applications areas, such as healthcare and well-being. Data collected from multiple sensors, including smartphone sensors that are now ubiquitous, can be exploited to build predictive models capable of recognizing actions and activities performed by humans in their daily life. This involves several processing steps, from the cleansing of raw data to the extraction of suitable features and the induction of proper classifiers. Dimensionality reduction techniques may also be important for the efficiency and the exploitability of the induced models, especially when dealing with multi-sensor data leading to high-dimensional feature vectors. In such a scenario, feature selection algorithms can be very useful to identify and retain only the most informative and predictive features. However, little research has so far investigated which selection approaches may be most appropriate in sensor-based activity recognition tasks. To give a contribution in this direction, our paper compares the performance of different feature selection methods, both univariate and multivariate, on a public domain benchmark containing smartphone sensor data. We performed a comprehensive evaluation considering the extent to which each method effectively identifies the most predictive features and the overall stability of the selection process, i.e., its robustness to changes in the input data. Our results give interesting insight on which methods may be most suited in this domain, showing that it is possible to significantly reduce the data dimensionality without compromising the activity recognition performance.

We present RePlay, an interaction substitution method designed to support people with upper extremity motor impairments while interacting with mobile apps. RePlay overcomes key limitations of existing approaches, allowing users to quickly access all app interface elements, even those not visible to the OS (a common situation especially with games). This is achieved through personalized mapping of interface elements to alternative inputs, such as external switches or non-verbal vocal sounds, hence adapting to users with diverse abilities. RePlay was implemented as an Android Accessibility Service, running without any changes at OS level.

Recognition of activities of daily living (ADLs) is an enabling technology for several ubiquitous computing applications. Most activity recognition systems rely on supervised learning to extract activity models from labeled datasets. A problem with that approach is the acquisition of comprehensive activity datasets, which is an expensive task. The problem is particularly challenging when focusing on complex ADLs characterized by large variability of execution. Moreover, several activity recognition systems are limited to offline recognition, while many applications claim for online activity recognition. In this paper, we propose POLARIS, a framework for unsupervised activity recognition. POLARIS can recognize complex ADLs exploiting the semantics of activities, context data, and sensors. Through ontological reasoning, our algorithm derives semantic correlations among activities and sensor events. By matching observed events with semantic correlations, a statistical reasoner formulates initial hypotheses about the occurred activities. Those hypotheses are refined through probabilistic reasoning, exploiting semantic constraints derived from the ontology. Our system supports online recognition, thanks to a novel segmentation algorithm. Extensive experiments with real-world datasets show that the accuracy of our unsupervised method is comparable to the one of supervised approaches. Moreover, the online version of our system achieves essentially the same accuracy of the offline version.

Manual event tagging may be a very long and stressful activity, due the monotonous operations involved. This is particularly true when dealing with online video tagging, as for football matches, in which the burden of events to tag can consist of many thousands of actions, according to the desired level of granularity. In this work we describe an actual solution, developed for an existing football match tagging application, in which the GUI has been enhanced and integrated with a Voice User Interface, aiming at reducing tagging time and error rate. Empirical tests have revealed the efficiency and the benefits brought by the developed solution.

The rapid increase of the senior population in our societies calls for innovative tools to early detect symptoms of cognitive decline. To this aim, several methods have been recently proposed that exploit Internet of Things data and artificial intelligence techniques to recognize abnormal behaviors. In particular, the analysis of position traces may enable early detection of cognitive decline. However, indoor movement analysis introduces several challenges. Indeed, indoor movements are constrained by the ambient shape and by the presence of obstacles, and are affected by variability of activity execution. In this paper, we propose a novel method to identify abnormal indoor movement patterns that may indicate cognitive decline according to well known clinical models. Our method relies on trajectory segmentation, visual feature extraction from trajectory segments, and vision-based deep learning on the edge. In order to avoid privacy issues, we rely on indoor localization technologies without the use of cameras. Preliminary experimental results with a real-world dataset gathered from cognitively healthy persons and people with dementia show that this research direction is promising.

Due to the emerging popularity of pervasive healthcare applications, tools for monitoring activities in smart homes are gaining momentum. Existing methods mainly rely on supervised learning algorithms for recognizing activities based on sensor data. A key issue with those approaches is the acquisition of comprehensive training sets of activities. Indeed, that task incurs significant costs in terms of manual labeling effort; moreover, labeling by external observers violates the individual's privacy. For these reasons, there is an increasing interest in unsupervised activity recognition methods. A popular approach relies on knowledge-based models expressed by ontologies of activities, environment and sensors. Unfortunately, those models require significant knowledge engineering efforts, and are often limited to a specific application. In this paper, we address the issues of existing methods by proposing a novel hybrid approach. Our intuition is that a generic knowledge-based model of activities can be refined to target specific individuals and environments by collaboratively acquiring feedback from inhabitants. Specifically, we propose a collaborative active learning method to refine correlations among sensor events and activity types that are initially extracted from a high-level ontology. Generic correlations are personalized to each target smart-home considering the similarity between the feedback target and the feedback provider in terms of environment and inhabitant's profiles. Moreover, thanks to this method, new sensors installed in the home are seamlessly integrated in the recognition framework. In order to reduce the burden of providing feedback, we also propose a technique to carefully select the conditions that trigger a feedback request. We conducted experiments with a real-world dataset and a generic ontology of activities. Results show that our hybrid method outperforms state-of-the-art supervised and unsupervised activity recognition techniques while triggering an acceptable number of feedback queries.

In several domains, including healthcare and home automation, it is important to unobtrusively monitor the activities of daily living (ADLs) executed by people at home. A popular approach consists in the use of sensors attached to everyday objects to capture user interaction, and ADL models to recognize the current activity based on the temporal sequence of used objects. However, both knowledge-based and data-driven approaches to object-based ADL recognition have different issues that limit their applicability in real-world deployments. Hence, in this paper, we pursue an alternative approach, which consists in mining ADL models from the Web. Existing attempts in this sense are mainly based on Web page mining and lexical analysis. One issue with those attempts relies on the high level of noise found in the textual content of Web pages. In order to overcome that issue, our intuition is that pictures illustrating the execution of a given activity offer much more compact and expressive information than the textual content of a Web page regarding the same activity. Hence, we present a novel method to couple Web mining and computer vision for automatically extracting ADL models from visual items. Our method relies on Web image search engines to select the most relevant pictures for each considered activity. We use off-the-shelf computer vision APIs and a lexical database to extract the key objects appearing in those pictures. We introduce a probabilistic technique to measure the relevance among activities and objects. Through experiments with a large dataset of real-world ADLs, we show that our method significantly improves the existing approach.

Recognition of activities of daily living (ADLs) is an enabling technology for several ubiquitous computing applications. In this field, most activity recognition systems rely on supervised learning methods to extract activity models from labeled datasets. An inherent problem of that approach consists in the acquisition of comprehensive activity datasets, which is expensive and may violate individuals' privacy. The problem is particularly challenging when focusing on complex ADLs, which are characterized by large intra- and inter-personal variability of execution. In this paper, we propose an unsupervised method to recognize complex ADLs exploiting the semantics of activities, context data, and sensing devices. Through ontological reasoning, we derive semantic correlations among activities and sensor events. By matching observed sensor events with semantic correlations, a statistical reasoner formulates initial hypotheses about the occurred activities. Those hypotheses are refined through probabilistic reasoning, exploiting semantic constraints derived from the ontology. Extensive experiments with real-world datasets show that the accuracy of our unsupervised method is comparable to the one of state of the art supervised approaches.

According to the World Health Organization, the rate of people aged 60 or more is growing faster than any other age group in almost every country, and this trend is not going to change in a near future. Since senior citizens are at high risk of non communicable diseases requiring long-term care, this trend will challenge the sustainability of the entire health system. Pervasive computing can provide innovative methods and tools for early detecting the onset of health issues. In this paper we propose a novel method relying on medical models, provided by cognitive neuroscience researchers, describing abnormal activity routines that may indicate the onset of early symptoms of mild cognitive impairment. A non-intrusive sensor-based infrastructure acquires low-level data about the interaction of the individual with home appliances and furniture, as well as data from environmental sensors. Based on those data, a novel hybrid statistical-symbolical technique is used to detect first the activities being performed and then the abnormal aspects in carrying out those activities, which are communicated to the medical center. Differently from related works, our method can detect abnormal behaviors at a fine-grained level, thus providing an important tool to support the medical diagnosis. In order to evaluate our method we have developed a prototype of the system and acquired a large dataset of abnormal behaviors carried out in an instrumented smart home. Experimental results show that our technique has a high precision while generating a small number of false positives.

Large datasets of real network flows acquired from the Internet are an invaluable resource for the research community. Applications include network modeling and simulation, identification of security attacks, and validation of research results. Unfortunately, network flows carry extremely sensitive information, and this discourages the publication of those datasets. Indeed, existing techniques for network flow sanitization are vulnerable to different kinds of attacks, and solutions proposed for microdata anonymity cannot be directly applied to network traces. In our previous research, we proposed an obfuscation technique for network flows, providing formal confidentiality guarantees under realistic assumptions about the adversary's knowledge. In this paper, we identify the threats posed by the incremental release of network flows, we propose a novel defense algorithm, and we formally prove the achieved confidentiality guarantees. An extensive experimental evaluation of the algorithm for incremental obfuscation, carried out with billions of real Internet flows, shows that our obfuscation technique preserves the utility of flows for network traffic analysis.

Recommender systems suggesting venues offer very useful services to people on the move and a great business opportunity for advertisers. These systems suggest venues by matching the current context of the user with the venue features, and consider the popularity of venues, based on the number of visits (“check-ins”) that they received. Check-ins may be explicitly communicated by users to geo-social networks, or implicitly derived by analysing location data collected by mobile services. In general, the visibility of explicit check-ins is limited to friends in the social network, while the visibility of implicit check-ins limited to the service provider. Exposing check-ins to unauthorized users is a privacy threat since recurring presence in given locations may reveal political opinions, religious beliefs, or sexual orientation, as well as absence from other locations where the user is supposed to be. Hence, on one side mobile app providers host valuable information that recommender system providers would like to buy and use to improve their systems, and on the other we recognize serious privacy issues in releasing that information. In this paper, we solve this dilemma by providing formal privacy guarantees to users and trusted mobile providers while preserving the utility of check-in information for recommendation purposes. Our technique is based on the use of differential privacy methods integrated with a pre-filtering process, and protects against both an untrusted recommender system and its users, willing to infer the venues and sensitive locations visited by other users. Extensive experiments with a large dataset of real users' check-ins show the effectiveness of our methods.

A major challenge of ubiquitous computing resides in the acquisition and modelling of rich and heterogeneous context data, among which, ongoing human activities at different degrees of granularity. In a previous work, we advocated the use of probabilistic description logics (DLs) in a multilevel activity recognition framework. In this paper, we present an in-depth study of activity modeling and reasoning within that framework, as well as an experimental evaluation with a large real-world dataset. Our solution allows us to cope with the uncertain nature of ontological descriptions of activities, while exploiting the expressive power and inference tools of the OWL 2 language. Targeting a large dataset of real human activities, we developed a probabilistic ontology modeling nearly 150 activities and actions of daily living. Experiments with a prototype implementation of our framework confirm the viability of our solution.

Through this software the authors aim to promote the sharing of network logs within the research community. The (k, j)obfuscation technique opens sundry interesting future directions. In fact, many networking and security tasks can be re-thought based on obfuscated datasets, for instance, quality of service (QoS), traffic classification, anomaly detection and more.

Different recommender systems suggest points of interest (POIs) based on data shared through geo-social networks (GSN). These systems are a very useful resource for mobile users, and an important business opportunity for advertisers. However, GSN data (e.g., the check-in of a person in a particular place) may be private information that a user may not want to release outside her social network. Even if the GSN service is trusted, and users' data is not directly released, an adversary may be able to reconstruct the data of a GSN user by mining the received recommendations. In this demo we will illustrate an implementation of the POI-Ti-Dico platform for privacy-conscious geo-social recommendation of POIs. The platform includes a server-side private recommender system and a mobile application for the Android framework. Recommendations are computed using a very large dataset of real check-ins.

In the last decade, the release of network flows has gained significant popularity among researchers and networking communities. Indeed, network flows are a fundamental tool for modeling the network behavior, identifying security attacks, and validating research results. Unfortunately, due to the sensitive nature of network flows, security and privacy concerns discourage the publication of such datasets. On the one hand, existing techniques proposed to sanitize network flows do not provide any formal guarantees. On the other hand, microdata anonymization techniques are not directly applicable to network flows. In this paper, we propose a novel obfuscation technique for network flows that provides formal guarantees under realistic assumptions about the adversary's knowledge. Our work is supported by extensive experiments with a large set of real network flows collected at an important Italian Tier II Autonomous System, hosting sensitive government and corporate sites. Experimental results show that our obfuscation technique preserves the utility of network flows for network traffic analysis.

Web queries, credit card transactions, and medical records are examples of transaction data flowing in corporate data stores, and often revealing associations between individuals and sensitive information. The serial release of these data to partner institutions or data analysis centers in a nonaggregated form is a common situation. In this paper, we show that correlations among sensitive values associated to the same individuals in different releases can be easily used to violate users' privacy by adversaries observing multiple data releases, even if state-of-the-art privacy protection techniques are applied. We show how the above sequential background knowledge can be actually obtained by an adversary, and used to identify with high confidence the sensitive values of an individual. Our proposed defense algorithm is based on Jensen-Shannon divergence; experiments show its superiority with respect to other applicable solutions. To the best of our knowledge, this is the first work that systematically investigates the role of sequential background knowledge in serial release of transaction data.

The retrieval of close-by points of interest (POIs) is becoming a popular location-based service (LBS), often integrated with navigational services and geo-social networks. However, the access to POI services is prone to potentially serious privacy issues, since requests for POIs often include sensitive information like the user's location and her personal interests. Many techniques to enforce privacy in LBS have been proposed in the literature, in some cases focusing on anonymizing the requests and in others on obfuscating information in order to decrease its sensitivity. In many cases privacy protection comes at some cost in terms of service precision and performance. In this paper we propose a novel technique that combines the above cited approaches, overcomes some of their limitations in terms of assumptions on adversary knowledge, while still guaranteeing service precision. Our privacy solution has been integrated in an existing distributed system to share and retrieve POIs based not only on the user's current location but also on other (possibly sensitive) context data.

The anonymization of location based queries through the generalization of spatio-temporal information has been proposed as a privacy preserving technique. We show that the presence of multiple concurrent requests, the repetition of similar requests by the same issuers, and the distribution of different service parameters in the requests can significantly affect the level of privacy obtained by current anonymity-based techniques. We provide a formal model of the privacy threat, and we propose an incremental defense technique based on a combination of anonymity and obfuscation. We show the effectiveness of this technique by means of an extensive experimental evaluation.

This paper presents preliminary results on anonymization and obfuscation techniques to preserve users' privacy in context-aware service provisioning. The techniques are based on generalizing request parameters as well as the context data provided to the application. Local context semantic aggregation is used to improve the quality of service that can be achieved while preserving privacy. The paper also shows how the software architecture of the CARE middleware can be extended to implement the proposed techniques.

The large scale adoption of adaptive services in pervasive and mobile computing is likely to be conditioned to the availability of reliable privacy-preserving technologies. Unfortunately, the research in this field can still be considered in its infancy. This paper considers a specific pervasive computing scenario, and shows that the application of state-of-the-art techniques for the anonymization of service requests is insufficient to protect the privacy of users. A specific class of attacks, called shadow attacks, is formally defined and a set of defense techniques is proposed. These techniques are validated through the use of a simulator and an extensive set of experiments.

Due to the widespread availability of accurate localization technologies, navigation systems are more and more present on mobile devices. These applications usually provide facilities for managing and searching points of interest. However, currently available navigation software does not support sharing of points of interest, and the search facilities are quite primitive, being exclusively based on location and categories. In this paper we present novel algorithms for context-aware retrieval of distributed resources. These algorithms can be executed on any unstructured peer-to-peer network, and are based on the distributed evaluation of a scoring function that takes into account a wide set of context data. The algorithms preserve the correctness of the result set until a certain time-to-live, while reducing the exchange of data in the network. The proposed algorithms have been integrated into a Web service-based, peer-to-peer system for management and sharing of an extended form of points of interest.

Context-awareness in mobile and ubiquitous computing requires the acquisition, representation and processing of context information which is not limited to the device features, network status, or user location but includes semantically rich data like user preferences and user current activity.

Context-awareness in mobile and ubiquitous computing requires the acquisition, representation and processing of information which goes beyond the device features, network status, and user location, to include semantically rich data, like user interests and user current activity. On the other hand, when services have to be provided on-the-fly to many mobile users, the efficiency of reasoning with these data becomes a relevant issue. Experimental evidence has lead us to consider currently impractical a tight integration of ontological reasoning with rule based reasoning at the time of request. This paper illustrates a hybrid approach where ontological reasoning is loosely coupled with the efficient rule-based reasoning of a middleware architecture for service adaptation. While rule-based reasoning is performed at the time of service request to evaluate adaptation policies and reconcile possibly conflicting context information, ontological reasoning is mostly performed asynchronously by local context providers to derive non-shallow context information. A limited form of ontological reasoning is activated at the time of request only when essential for service provisioning.

Mobile and ubiquitous computing emphasize the need for highly adaptive delivery of Internet services. While several systems and even products exist that guarantee shallow or deep adaptation, they are usually based on profile information and customer relationship management modules stored and operating at the service provider. On the contrary, We assume that profile information, including user personal data and preferences, device capabilities, network bandwidth, location and other contextual information, as well as policy rules that can dynamically change this data, are provided by different sources. We describe a formal framework for aggregating this information and for solving conflicts between policy rules. We provide both a theoretical study of the properties of our techniques and a practical evaluation study obtained through a prototype implementation.