Over recent decades, neuroimaging tools, partic-ularly electroencephalography (EEG), have revolutionized our understanding of the brain and its functions. EEG is extensively used in traditional brain-computer interface (BCI) systems due to its low cost, non-invasiveness, and high temporal resolution. This makes it invaluable for identifying different brain states relevant to both medical and non-medical applications. Although this practice is widely recognized, current methods are mainly confined to lab or clinical environments because they rely on data from multiple EEG electrodes covering the entire head. Nonethe-less, a significant advancement for these applications would be their adaptation for “real-world” use, using portable devices with a single-channel. In this study, we tackle this challenge through two distinct strategies: the first approach involves training models with data from multiple channels and then testing new trials on data from a single channel individually. The second method focuses on training with data from a single channel and then testing the performances of the models on data from all the other channels individually. To efficiently classify cognitive tasks from EEG data, we propose Convolutional Neural Networks (CNNs) with only a few parameters and fast learnable spectral-temporal features. We demonstrated the feasibility of these approaches on EEG data recorded during mental arithmetic and motor imagery tasks from three datasets. We achieved the highest accuracies of 100%, 91.55% and 73.45% in binary and 3-class classification on specific channels across three datasets. This study can contribute to the development of single-channel BCI and provides a robust EEG biomarker for brain states classification.

In this paper we propose methods that can help the decision-makers to find a compromise between willingness to do and ability to do by introducing the difficulty considerations in the multiple criteria decision analysis problems. Two problems are considered: ranking alternatives and improving existing solution. Usually, in the classical approaches of multiple criteria decision analysis, only the degree of satisfaction is considered to compare alternatives. However, sometimes a good alternative is difficult to implement by a decision-maker even if he spends necessary cost for it. First we give the definition of the concept of difficulty function, then we show how to introduce it in decision problems using operators based on fuzzy measures. This allows us to consider interactions between criteria under two aspects: 1) the overall satisfaction resulting from the simultaneous satisfaction or not of certain criteria; 2) the overall difficulty resulting from the difficulty or not of satisfying certain criteria simultaneously. After that, we present two examples of the difficulty function assessment in the case of a non-linear model. Finally, we propose an illustration concerning the problem of managing the students effort when improving their scores on a set of subjects. This illustration focus on the extension of the concept of worth index which quantifies the gain of improvement related to a subset of objectives when it is difficult to improve all the objectives simultaneously.

Belief functions are quite generic models when it comes to represent uncertain data, as it extends a wide range of uncertainty models (possiblity and probability distributions, among others). Usually, belief functions are defined over finite spaces, however many real word problems require to deal with beliefs over a continuous space while maintaining computational efficiency. This paper discusses the case of focal sets on the unit simplex, and proposes efficient inference tools to manipulate them. Such sets can be used to represent unknown proportions that one may face in various fields like soil contamination managing, plastic sorting or image reconstruction. In this paper, we illustrate their use on an industrial problem of plastic sorting, where the proportion of material impurities must not go over a limit while minimizing the rejection of sorted materials, whose nature is uncertain.

Document Indexing is but not limited to summarizing document contents with a small set of keywords or concepts of a knowledge base. Such a compact representation of document contents eases their use in numerous processes such as content-based information retrieval, corpus-mining and classification. An important effort has been devoted in recent years to (partly) automate semantic indexing, i.e. associating concepts to documents, leading to the availability of large corpora of semantically indexed documents. In this paper we introduce a method that hierarchically clusters documents based on their semantic indices while providing the proposed clusters with semantic labels . Our approach follows a neighbor joining strategy. Starting from a distance matrix reflecting the semantic similarity of documents, it iteratively selects the two closest clusters to merge them in a larger one. The similarity matrix is then updated. This is usually done by combining similarity of the two merged clusters, e.g. using the average similarity. We propose in this paper an alternative approach where the new cluster is first semantically annotated and the similarity matrix is then updated using the semantic similarity of this new annotation with those of the remaining clusters. The hierarchical clustering so obtained is a binary tree with branch lengths that convey semantic distances of clusters. It is then post-processed by using the branch lengths to keep only the most relevant clusters. Such a tool has numerous practical applications as it automates the organization of documents in meaningful clusters (e.g. papers indexed by MeSH terms, bookmarks or pictures indexed by WordNet) which is a tedious everyday task for many people. We assess the quality of the proposed methods using a specific benchmark of annotated clusters of bookmarks that were built manually. Each dataset of this benchmark has been clustered independently by several users. Remarkably, the clusters automatically built by our method are congruent with the clusters proposed by experts. All resources of this work, including source code, jar file, benchmark files and results are available at this address: http://sc.nicolasfiorini.info .

Ontologies are core elements of numerous applications that are based on computer-processable expert knowledge. They can be used to estimate the Information Content (IC) of the key concepts of a domain: a central notion on which depend various ontology-driven analyses, e.g. semantic measures. This paper proposes new IC models based on the belief functions theoretical framework. These models overcome limitations of existing ICs that do not consider the inductive inference assumption intuitively assumed by human operators, i.e. that occurrences of a concept (e.g. Maths) not only impact the IC of more general concepts (e.g. Sciences), as considered by traditional IC models, but also the one of more specific concepts (e.g. Algebra). Interestingly, empirical evaluations show that, in addition to modelling the aforementioned assumption, proposed IC models compete with best state-of-the-art models in several evaluation settings.

BACKGROUND: Semantic approaches such as concept-based information retrieval rely on a corpus in which resources are indexed by concepts belonging to a domain ontology. In order to keep such applications up-to-date, new entities need to be frequently annotated to enrich the corpus. However, this task is time-consuming and requires a high-level of expertise in both the domain and the related ontology. Different strategies have thus been proposed to ease this indexing process, each one taking advantage from the features of the document. RESULTS: In this paper we present USI (User-oriented Semantic Indexer), a fast and intuitive method for indexing tasks. We introduce a solution to suggest a conceptual annotation for new entities based on related already indexed documents. Our results, compared to those obtained by previous authors using the MeSH thesaurus and a dataset of biomedical papers, show that the method surpasses text-specific methods in terms of both quality and speed. Evaluations are done via usual metrics and semantic similarity. CONCLUSIONS: By only relying on neighbor documents, the User-oriented Semantic Indexer does not need a representative learning set. Yet, it provides better results than the other approaches by giving a consistent annotation scored with a global criterion - instead of one score per concept.

International audience

Reducing calibration time while maintaining good classification accuracy has been one of the most challenging problems in electroencephalography (EEG) -based brain-computer interfaces (BCIs) research during the last years. Most of machine learning approaches that have been attempted to address this issue are based on knowledge transfer between different BCIs users. Assuming that there is a common underlying data generating process, they try to learn a subject-independent classification model from multiple users in order to classify data of future users. In this paper, we propose a novel approach that allows inter-subjects classification of EEG signals without relying on the strong assumptions considered in previous work. It consists of learning a prediction model of a new BCI user through an ensemble of classifiers where base classifiers are trained on data from other users separately and weighted according to the performance of the ensemble on few labeled data of the new user. Evaluation on real EEG data showed that our approach allows achieving good classification accuracy when the size of calibration set is small.

UNLABELLED: The semantic measures library and toolkit are robust open-source and easy to use software solutions dedicated to semantic measures. They can be used for large-scale computations and analyses of semantic similarities between terms/concepts defined in terminologies and ontologies. The comparison of entities (e.g. genes) annotated by concepts is also supported. A large collection of measures is available. Not limited to a specific application context, the library and the toolkit can be used with various controlled vocabularies and ontology specifications (e.g. Open Biomedical Ontology, Resource Description Framework). The project targets both designers and practitioners of semantic measures providing a JAVA library, as well as a command-line tool that can be used on personal computers or computer clusters. AVAILABILITY AND IMPLEMENTATION: Downloads, documentation, tutorials, evaluation and support are available at http://www.semantic-measures-library.org.

Within an emergency unit, the head manager is required to make difficult decisions based on experts’ assessments of many criteria, including personal injuries, environmental impacts, and economic and media consequences. Uncertainty in this collective assessment is related to the multiplicity of experts’ points of view and imprecise assessments. We are proposing a decision support system derived from a situation-awareness model, generalized herein to the case of multiple actors. It is able of representing, merging, and aggregating expert assessments. Imprecise criteria assessments are first represented by intervals and then merged in the form of a possibility distribution that keeps track of all the information provided, that is, without any loss of information. Next, a Choquet integral based aggregation is carried out to consider the relative importance of criteria and interactions between criteria in the overall assessment of the foreseeable alternatives to get out of the crisis. Finally, a determination of the contributions of each criterion assessment uncertainty to the overall assessment uncertainty provides useful information to the head manager in controlling the decision deliberation by reducing the inconsistent points in the experts’ assessments. The proposals are applied to the emergency issues resulting from a traffic accident occurring at a grade crossing.

Abstract Background Because of the increasing number of electronic resources, designing efficient tools to retrieve and exploit them is a major challenge. Some improvements have been offered by semantic Web technologies and applications based on domain ontologies. In life science, for instance, the Gene Ontology is widely exploited in genomic applications and the Medical Subject Headings is the basis of biomedical publications indexation and information retrieval process proposed by PubMed. However current search engines suffer from two main drawbacks: there is limited user interaction with the list of retrieved resources and no explanation for their adequacy to the query is provided. Users may thus be confused by the selection and have no idea on how to adapt their queries so that the results match their expectations. Results This paper describes an information retrieval system that relies on domain ontology to widen the set of relevant documents that is retrieved and that uses a graphical rendering of query results to favor user interactions. Semantic proximities between ontology concepts and aggregating models are used to assess documents adequacy with respect to a query. The selection of documents is displayed in a semantic map to provide graphical indications that make explicit to what extent they match the user's query; this man/machine interface favors a more interactive and iterative exploration of data corpus, by facilitating query concepts weighting and visual explanation. We illustrate the benefit of using this information retrieval system on two case studies one of which aiming at collecting human genes related to transcription factors involved in hemopoiesis pathway. Conclusions The ontology based information retrieval system described in this paper (OBIRS) is freely available at: http://www.ontotoolkit.mines-ales.fr/ObirsClient/ . This environment is a first step towards a user centred application in which the system enlightens relevant information to provide decision help.

The design and use of Performance Measurement Systems (PMS's) for industrial improvement and control have received considerable attention in recent years. Indeed, industrial performances are now defined in terms of numerous and multi-level criteria to be synthesized for overall improvement purposes. This article is a contribution to the decision-maker's information needs for optimizing the improvement of an overall performance versus the allocated resources and for choosing the right actions in order to achieve the required overall performance. The latter is decomposed into elementary performances according to decision-makers' preferences represented by a fuzzy integral aggregation. The causes-effects links between possible actions and performances are represented by a fuzzy ordinal influence model. The proposed fuzzy models are both applied for improvement actions selection on a case study submitted by a company manufacturing kitchens and bathrooms.

The design and use of performance measurement systems (PMS's) for industrial improvement and control have received considerable attention in recent years. Indeed, industrial performances are now defined in terms of numerous and multi-level criteria to be synthesized for overall improvement purposes. Only a few quantitative models for PMS's have been proposed in order to better monitor the continuous improvement cycle. Among them is the one proposed by the authors, based on a fuzzy Choquet integral aggregation operator. It allows expressing an overall performance according to the relative importance of criteria and interactions between elementary expressions. This article is a contribution to the decision-maker's requirements for optimizing the improvement of an overall performance versus the allocated resources. It is proved that with a Choquet Integral aggregation, the resulting profile of elementary performances for optimal improvement can only take particular forms, which greatly aid the diagnosis and control tasks.

Two distinct and parallel research communities have been working along the lines of the model-based diagnosis approach: the fault detection and isolation (FDI) community and the diagnostic (DX) community that have evolved in the fields of automatic control and artificial intelligence, respectively. This paper clarifies and links the concepts and assumptions that underlie the FDI analytical redundancy approach and the DX consistency-based logical approach. A formal framework is proposed in order to compare the two approaches and the theoretical proof of their equivalence together with the necessary and sufficient conditions is provided.