MODELLING CRISP AND FUZZY QUALITATIVE TEMPORAL RELATIONS

Building a model for temporal knowledge representation and reasoning assumes choosing basic notions - primitives, the time instant or/and the time interval. This paper considers primitives for modelling crisp and fuzzy qualitative temporal relations. Based on fuzzified Allen s temporal relations between intervals, new relations between the fuzzy time point and the fuzzy time interval are proposed

Analysing imperfect temporal information in GIS using the Triangular Model

Rough set and fuzzy set are two frequently used approaches for modelling and reasoning about imperfect time intervals. In this paper, we focus on imperfect time intervals that can be modelled by rough sets and use an innovative graphic model [i.e. the triangular model (TM)] to represent this kind of imperfect time intervals. This work shows that TM is potentially advantageous in visualizing and querying imperfect time intervals, and its analytical power can be better exploited when it is implemented in a computer application with graphical user interfaces and interactive functions. Moreover, a probabilistic framework is proposed to handle the uncertainty issues in temporal queries. We use a case study to illustrate how the unique insights gained by TM can assist a geographical information system for exploratory spatio-temporal analysis

A Human-Centric Approach to Group-Based Context-Awareness

The emerging need for qualitative approaches in context-aware information processing calls for proper modeling of context information and efficient handling of its inherent uncertainty resulted from human interpretation and usage. Many of the current approaches to context-awareness either lack a solid theoretical basis for modeling or ignore important requirements such as modularity, high-order uncertainty management and group-based context-awareness. Therefore, their real-world application and extendability remains limited. In this paper, we present f-Context as a service-based context-awareness framework, based on language-action perspective (LAP) theory for modeling. Then we identify some of the complex, informational parts of context which contain high-order uncertainties due to differences between members of the group in defining them. An agent-based perceptual computer architecture is proposed for implementing f-Context that uses computing with words (CWW) for handling uncertainty. The feasibility of f-Context is analyzed using a realistic scenario involving a group of mobile users. We believe that the proposed approach can open the door to future research on context-awareness by offering a theoretical foundation based on human communication, and a service-based layered architecture which exploits CWW for context-aware, group-based and platform-independent access to information systems

A fuzzy temporal data-mining model for activity recognition in smart homes

At present time, aging of the population is one of the main challenges of the 21st century. The current situation is leading to an increased number of people afflicted with cognitive disorders such as Alzheimer's disease. This group of people suffers from a progressive decline in their abilities to perform what are called the activities of the daily living (ADLs).The consequence of this reality is the urgent need for more home assistance services, as these people desire to continue living independently at home. To address this important issue, Smart Home laboratories such as LIARA, DOMUS and MavHome perform research in order to propose technological solutions for assistance provision to residents of the Smart Home. Assisting people in carrying out their ADLs, increasing quality of life and optimizing spent energy are some of the goals in Smart Home design. Technically speaking, a Smart Home is an ambient environment which, through its embedded sensors, captures data resulting from the observation of activities carried out in this environment. This data is then analyzed by artificial intelligence techniques in order to provide information about home state normality and needed assistance. In the end, the system aims to intervene by providing guidance through its actuators. In this context, activity recognition becomes a key element in order to be able to provide adequate information services at the right moment. This thesis aims to contribute to this important challenge relating to activity recognition in the Smart Home designed for cognitive assistance. This contribution follows in the footsteps of temporal data mining and activity recognition approaches, and proposes a new way to automatically recognize and memorize ADLs from low-level sensors. From a formal point of view, the originality of the thesis relies on the proposition of a new unsupervised temporal datamining model for activity recognition addressing the problem of current temporal approaches based on Allen's framework. This new model incorporates some applications of fuzzy logic in order to take into account the uncertainty present in the realization of daily living activities by the resident. More specifically, we propose an extension of the fuzzy clustering technique in order to cluster the observations based on the degree of similarity between observations, so that activities are modeled and recognized. Moreover, anomaly recognition, decision making for assistance provision and judgment for simultaneous activities are some of the applicative contributions of this thesis. From a practical and experimental standpoint, the contribution of this research has been validated in order to evaluate how it would perform in a realistic context. To achieve this, we used MATLAB software as a simulation platform to test the proposed model. We then performed a series of tests which took the form of several case studies relating to common activities of daily living, in order to show the functionality and efficiency of the proposed temporal data-mining approach for real-life cases. This was especially relevant to the activity recognition application. We obtained very promising results which have been analyzed and compared to existing approaches. Finally, most parts of the contribution presented in this thesis have been published in documents ensuing from reputed international conferences (Springer LNCS proceedings [7], AAAI symposium and workshops [8, 9], MAICS [10], IEEE [11]) and a recognized journal (Springer Journal of Ambient Intelligence and Humanized Computing [12, 13]). This clearly constitutes recognition showing the potential of the proposed contribution

Effective information integration and reutilization : solutions to technological deficiency and legal uncertainty

Thesis (Ph. D.)--Massachusetts Institute of Technology, Engineering Systems Division, Technology, Management, and Policy Program, February 2006."September 2005."Includes bibliographical references (p. 141-148).The amount of electronically accessible information has been growing exponentially. How to effectively use this information has become a significant challenge. A post 9/11 study indicated that the deficiency of semantic interoperability technology hindered the ability to integrate information from disparate sources in a meaningful and timely fashion to allow for preventive precautions. Meanwhile, organizations that provided useful services by combining and reusing information from publicly accessible sources have been legally challenged. The Database Directive has been introduced in the European Union and six legislative proposals have been made in the U.S. to provide legal protection for non-copyrightable database contents, but the Directive and the proposals have differing and sometimes conflicting scope and strength, which creates legal uncertainty for valued-added data reuse practices. The need for clearer data reuse policy will become more acute as information integration technology improves to make integration much easier. This Thesis takes an interdisciplinary approach to addressing both the technology and the policy challenges, identified above, in the effective use and reuse of information from disparate sources.(cont.) The technology component builds upon the existing Context Interchange (COIN) framework for large-scale semantic interoperability. We focus on the problem of temporal semantic heterogeneity where data sources and receivers make time-varying assumptions about data semantics. A collection of time-varying assumptions are called a temporal context. We extend the existing COIN representation formalism to explicitly represent temporal contexts, and the COIN reasoning mechanism to reconcile temporal semantic heterogeneity in the presence of semantic heterogeneity of time. We also perform a systematic and analytic evaluation of the flexibility and scalability of the COIN approach. Compared with several traditional approaches, the COIN approach has much greater flexibility and scalability. For the policy component, we develop an economic model that formalizes the policy instruments in one of the latest legislative proposals in the U.S. The model allows us to identify the circumstances under which legal protection for non-copyrightable content is needed, the different conditions, and the corresponding policy choices.(cont.) Our analysis indicates that depending on the cost level of database creation, the degree of differentiation of the reuser database, and the efficiency of policy administration, the optimal policy choice can be protecting a legal monopoly, encouraging competition via compulsory licensing, discouraging voluntary licensing, or even allowing free riding. The results provide useful insights for the formulation of a socially beneficial database protection policy.by Hongwei Zhu.Ph.D

Rethinking the risk matrix

So far risk has been mostly defined as the expected value of a loss, mathematically PL (being P the probability of an adverse event and L the loss incurred as a consequence of the adverse event). The so called risk matrix follows from such definition. This definition of risk is justified in a long term “managerial” perspective, in which it is conceivable to distribute the effects of an adverse event on a large number of subjects or a large number of recurrences. In other words, this definition is mostly justified on frequentist terms. Moreover, according to this definition, in two extreme situations (high-probability/low-consequence and low-probability/high-consequence), the estimated risk is low. This logic is against the principles of sustainability and continuous improvement, which should impose instead both a continuous search for lower probabilities of adverse events (higher and higher reliability) and a continuous search for lower impact of adverse events (in accordance with the fail-safe principle). In this work a different definition of risk is proposed, which stems from the idea of safeguard: (1Risk)=(1P)(1L). According to this definition, the risk levels can be considered low only when both the probability of the adverse event and the loss are small. Such perspective, in which the calculation of safeguard is privileged to the calculation of risk, would possibly avoid exposing the Society to catastrophic consequences, sometimes due to wrong or oversimplified use of probabilistic models. Therefore, it can be seen as the citizen’s perspective to the definition of risk