On various forms of bipolarity in flexible querying

International audienceThe paper discusses the modeling of “if possible" in requirements of the form “A and if possible B". We distinguish between two types of understanding: either i) A and B are requirements of the same nature and are viewed as constraints with different levels of priority, or ii) they are of different nature (only A induces constraint(s) and B is only used for breaking ties among items that are equally satisfying A). We indicate that the two views are related to different types of bipolarity, and discuss them in relation with possibilistic logic. The disjunctive dual of the first view (“A or at least B") is then presented in this logical setting. We also briefly mention the idea of an extension of the second view where B may refer both to bonus conditions or malus conditions that may increase or decrease respectively the interest in an item satisfying A

Constraint-wish and satisfied-dissatisfied: an overview of two approaches for dealing with bipolar querying

In recent years, there has been an increasing interest in dealing with user preferences in flexible database querying, expressing both positive and negative information in a heterogeneous way. This is what is usually referred to as bipolar database querying. Different frameworks have been introduced to deal with such bipolarity. In this chapter, an overview of two approaches is given. The first approach is based on mandatory and desired requirements. Hereby the complement of a mandatory requirement can be considered as a specification of what is not desired at all. So, mandatory requirements indirectly contribute to negative information (expressing what the user does not want to retrieve), whereas desired requirements can be seen as positive information (expressing what the user prefers to retrieve). The second approach is directly based on positive requirements (expressing what the user wants to retrieve), and negative requirements (expressing what the user does not want to retrieve). Both approaches use pairs of satisfaction degrees as the underlying framework but have different semantics, and thus also different operators for criteria evaluation, ranking, aggregation, etc

Bipolarity and multipolarity in aggregation structures

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Fuzzy Bipolar Conditions of Type "or else"

International audiencePreviously studied fuzzy bipolar conditions of type "and if possible" are made of a mandatory condition c and an optional condition w. They allow expressing complex preferences of a conjunctive nature. We define in this paper, a new kind of fuzzy bipolar conditions of the form "or else" which express complex preferences of a disjunctive nature. We show that the "or else" form can be used as a negation operator of the "and if possible" form and vice versa. We also show that these both forms are compatible and, therefore, fuzzy bipolar conditions of both types can be used together in the same bipolar query

Music information retrieval: conceptuel framework, annotation and user behaviour

Understanding music is a process both based on and influenced by the knowledge and experience of the listener. Although content-based music retrieval has been given increasing attention in recent years, much of the research still focuses on bottom-up retrieval techniques. In order to make a music information retrieval system appealing and useful to the user, more effort should be spent on constructing systems that both operate directly on the encoding of the physical energy of music and are flexible with respect to users’ experiences. This thesis is based on a user-centred approach, taking into account the mutual relationship between music as an acoustic phenomenon and as an expressive phenomenon. The issues it addresses are: the lack of a conceptual framework, the shortage of annotated musical audio databases, the lack of understanding of the behaviour of system users and shortage of user-dependent knowledge with respect to high-level features of music. In the theoretical part of this thesis, a conceptual framework for content-based music information retrieval is defined. The proposed conceptual framework - the first of its kind - is conceived as a coordinating structure between the automatic description of low-level music content, and the description of high-level content by the system users. A general framework for the manual annotation of musical audio is outlined as well. A new methodology for the manual annotation of musical audio is introduced and tested in case studies. The results from these studies show that manually annotated music files can be of great help in the development of accurate analysis tools for music information retrieval. Empirical investigation is the foundation on which the aforementioned theoretical framework is built. Two elaborate studies involving different experimental issues are presented. In the first study, elements of signification related to spontaneous user behaviour are clarified. In the second study, a global profile of music information retrieval system users is given and their description of high-level content is discussed. This study has uncovered relationships between the users’ demographical background and their perception of expressive and structural features of music. Such a multi-level approach is exceptional as it included a large sample of the population of real users of interactive music systems. Tests have shown that the findings of this study are representative of the targeted population. Finally, the multi-purpose material provided by the theoretical background and the results from empirical investigations are put into practice in three music information retrieval applications: a prototype of a user interface based on a taxonomy, an annotated database of experimental findings and a prototype semantic user recommender system. Results are presented and discussed for all methods used. They show that, if reliably generated, the use of knowledge on users can significantly improve the quality of music content analysis. This thesis demonstrates that an informed knowledge of human approaches to music information retrieval provides valuable insights, which may be of particular assistance in the development of user-friendly, content-based access to digital music collections

Mobile Health in Remote Patient Monitoring for Chronic Diseases: Principles, Trends, and Challenges

Chronic diseases are becoming more widespread. Treatment and monitoring of these diseases require going to hospitals frequently, which increases the burdens of hospitals and patients. Presently, advancements in wearable sensors and communication protocol contribute to enriching the healthcare system in a way that will reshape healthcare services shortly. Remote patient monitoring (RPM) is the foremost of these advancements. RPM systems are based on the collection of patient vital signs extracted using invasive and noninvasive techniques, then sending them in real-time to physicians. These data may help physicians in taking the right decision at the right time. The main objective of this paper is to outline research directions on remote patient monitoring, explain the role of AI in building RPM systems, make an overview of the state of the art of RPM, its advantages, its challenges, and its probable future directions. For studying the literature, five databases have been chosen (i.e., science direct, IEEE-Explore, Springer, PubMed, and science.gov). We followed the (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) PRISMA, which is a standard methodology for systematic reviews and meta-analyses. A total of 56 articles are reviewed based on the combination of a set of selected search terms including RPM, data mining, clinical decision support system, electronic health record, cloud computing, internet of things, and wireless body area network. The result of this study approved the effectiveness of RPM in improving healthcare delivery, increase diagnosis speed, and reduce costs. To this end, we also present the chronic disease monitoring system as a case study to provide enhanced solutions for RPMsThis research work was partially supported by the Sejong University Research Faculty Program (20212023)S

Exploiting the Volatile Nature of Data and Information in Evolving Repositories and Systems with User Generated Content

Modern technological advances have created a plethora of an extremely large, highly heterogeneous and distributed collection of datasets that are highly volatile. This volatile nature makes their understanding, integration and management a challenging task. One of the first challenging issues is to create the right models that will capture not only the changes that have taken place on the values of the data but also the semantic evolution of the concepts that the data structures represent. Once this information has been captured, the right mechanisms should be put in place to enable the exploitation of the evolution information in query formulation, reasoning, answering and representation. Additionally, the continuously evolving nature of the data hinders the ability of determining the quality of the data that is observed at a specific moment, since there is a great deal of uncertainty on whether this information will remain as is. Finally, an important task in this context, known as information filtering, is to match a specific piece of information which is recently updated (or added) in a repository to a user or query at hand. In this dissertation, we propose a novel framework to model and query data which have the explicit evolution relationships among concepts. As a query language we present an expressive evolution graph traversal query language which is tested on a number of real case scenarios: the history of Biotechnology, the corporate history of US companies and others. In turn, to support query evaluation we introduce an algorithm using the idea of finding Steiner trees on graphs which is capable of computing answers on-the-fly taking into account the evolution connections among concepts. To address the problem of data quality in user generated repositories (e.g. Wikipedia) we present a novel algorithm which detects individual controversies by using the substitutions in the revision history of a content. The algorithm groups the disagreements between users by means of a context, i.e. the surrounding content, and by applying custom filters. In the extensive experimental evaluation we showed that the proposed ideas lead to high effectiveness on a various sources of controversies. Finally, we exploit the problem of producing recommendations in evolving repositories by focusing on the cold start problem, i.e. when no or little past information about the users and/or items is given. In the dissertation we present a number of novel algorithms which cope with the cold-start by leveraging the item features using the k-neighbor classifier, Naive Bayes classifier and maximum entropy principle. The obtained results enable recommender systems to operate in rapidly updated domains such that news, university courses and social data