• 2018-07
  • 2018-10
  • 2018-11
  • 2019-04
  • 2019-05
  • 2019-06
  • 2019-07
  • 2019-08
  • 2019-09
  • 2019-10
  • 2019-11
  • 2019-12
  • 2020-01
  • 2020-02
  • 2020-03
  • 2020-04
  • 2020-05
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  • 2020-07
  • 2020-08
  • 2020-09
  • 2020-10
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  • 2021-01
  • 2021-02
  • br User knowledge representation Various


    User knowledge representation Various representation approaches of user interests and preferences are proposed. In this work, we advocate a multidimensional semantic approach based on Bouzghoub et al. meta-model (Bouzeghoub and Kostadinov, 2005), depicted in Fig. 6, to model the user profile. The instantiation of this meta-model for COTS components search personalization has resulted in the user model we presented in Yanes et al. (2015). We first briefly introduce the user model and then we present how it guanylyl cyclase is constructed and updated.
    The recommendation process The recommendation process consists of four phases as illustrated in Fig. 7, namely query expansion, COTS components repository filtering, COTS components retrieval, and results ranking and display. The user triggers the process by submitting a query composed of a set of keywords expressing his/her functional requirements together with his/her preferences for functional attributes. The query is first expanded with new terms in order to enhance the recommendation relevance. Second, the COTS components repository is filtered based on a domain of interest list grouping those deduced from the user query and the ones stored in his/her profile, in order to exclude COTS components that have no relation with user needs. Third, a formal query is generated from the expanded one and then executed against the screened repository using a full-text-search, what returns a list of results satisfying the user query. Finally, recommendation results are ranked using a three staged algorithm to take into account the satisfaction degree of returned COTS components with respect to functional attributes and quality ones. The final list is displayed according to the customization sub-dimension of the user profile.
    Experimental evaluation and results We implement a prototype named IUSECOTS of the ontology-based recommender system we propose. Fig. 12 illustrates the system interface. This prototype is used to evaluate the recommendation relevance by conducting a set of experimentations. Indeed, recommender systems, like information retrieval systems, are used to be experimentally evaluated i.e. by conducting a lab-based evaluation. For this purpose, Cranfield paradigm is largely adopted (Cleverdon, 1960). The evaluation process requires a collection test data, a set of queries and relevance judgments of the whole collection against each query. Test collection data may be a standard one or specifically be built otherwise. Obtained evaluation measures are combined in order to score the system. When the system to evaluate is a personalized information retrieval one, it is question to involve in the study a number of users over a period of time, and to compare results to a baseline. We detail in the sequel how we applied this evaluation approach.
    Rationale and contribution The key building blocks of IT infrastructures responsible for the management, control, and regulation of industrial operations are generally referred to as Industrial Control Systems (ICS). Among these, a wide variety of critical systems exists, notably Safety-Critical Systems (SCS) and Safety-Related Systems (SRS). A SCS has full responsibility for controlling hazards and consequently its failure or malfunction may result in catastrophic outcomes, such as death or serious injury to people, loss or severe damage to equipment/property, or environmental harm. SRS support SCS, since they include the hardware and software that carries out one or more safety functions. Thus, failure of an SRS increases the risk for the safety of people and/or of the environment (EN50129 says: “SRS carries responsibility for safety”). The focus of this paper is on SRS. Due to their importance, SRS must be proven to be reliable, through rigorous and internationally accepted methodologies. Standards (e.g. EN50129) exist, classifying quantitatively the likelihood of a failure through the concept of Safety Integrity Level (SIL). Specifications include four SIL levels, where SIL0 indicates that there are no safety requirements and SIL4 is typically reserved to SCS. Table 1 shows the classification of the different SIL levels. For each of them, we reported the associated Tolerable Hazard Rate (THR) bounds, the failure mode, the consequent hazard, and the related system typology (i.e., SCS or SRS). Making a system compliant to a specific SIL means providing evidence of the achievement of THR thresholds, which – for a complex system – is by no means a trivial task.