Mining a medieval social network by kernel SOM and related methods

This paper briefly presents several ways to understand the organization of a large social network (several hundreds of persons). We compare approaches coming from data mining for clustering the vertices of a graph (spectral clustering, self-organizing algorithms. . .) and provide methods for representing the graph from these analysis. All these methods are illustrated on a medieval social network and the way they can help to understand its organization is underlined

The perception of crowding, quality and well-being:a study of Vietnamese public health services

Purpose: The purpose of this paper is to examine the extent to which the perception of crowding by medical staff and patients impacts patients’ perceived service quality (SQ), overall satisfaction and emotional well-being. Design/methodology/approach: Data were collected from 258 matched pairs of medical staff members and their patients at six public hospitals. Findings: Medical staff-perceived crowding negatively influences patients’ perceived SQ. The perceived SQ then impacts patients’ overall satisfaction and emotional well-being. Patients’ perceived crowding does not significantly impact their perceived SQ but increases the positive emotional well-being of patients. Originality/value: Scant research has investigated a matched pair of service providers and their customers. This study concentrates on how individuals’ perceived human crowding and medical staff SQ affect consumers’ emotional well-being. This research leads to the formulation of theoretical and public policy suggestions to improve the quality of interactive services with minimal cost and disruption.</p

Recherche et représentation de communautés dans des grands graphes

15 pagesNational audienceThis paper deals with the analysis and the visualization of large graphs. Our interest in such a subject-matter is related to the fact that graphs are convenient widespread data structures. Indeed, this type of data can be encountered in a growing number of concrete problems: Web, information retrieval, social networks, biological interaction networks... Furthermore, the size of these graphs becomes increasingly large as the progression of the means for data gathering and storage steadily strengthens. This calls for new methods in graph analysis and visualization which are now important and dynamic research fields at the interface of many disciplines such as mathematics, statistics, computer science and sociology. In this paper, we propose a method for graphs representation and visualization based on a prior clustering of the vertices. Newman and Girvan (2004) points out that “reducing [the] level of complexity [of a network] to one that can be interpreted readily by the human eye, will be invaluable in helping us to understand the large-scale structure of these new network data”: we rely on this assumption to use a priori a clustering of the vertices as a preliminary step for simplifying the representation of the graphs - as a whole. The clustering phase consists in optimizing a quality measure specifically suitable for the research of dense groups in graphs. This quality measure is the modularity and expresses the “distance” to a null model in which the graph edges do not depend on the clustering. The modularity has shown its relevance in solving the problem of uncovering dense groups in a graph. Optimization of the modularity is done through a stochastic simulated annealing algorithm. The visualization/representation phase, as such, is based on a force-directed algorithm described in Truong et al. (2007). After giving a short introduction to the problem and detailing the vertices clustering and representation algorithms, the paper will introduce and discuss two applications from the social network field

On some multiplicity and mixed multiplicity formulas (Forum Math. 26(2014), 413-442)

This paper gives the additivity and reduction formulas for mixed multiplicities of multi-graded modules $M$ and mixed multiplicities of arbitrary ideals, and establishes the recursion formulas for the sum of all the mixed multiplicities of $M.$ As an application of these formulas we get the recursion formulas for the multiplicity of multi-graded Rees modules

A novel ontology framework supporting model-based tourism recommender

In this paper, we present a tourism recommender framework based on the cooperation of ontological knowledge base and supervised learning models. Specifically, a new tourism ontology, which not only captures domain knowledge but also specifies knowledge entities in numerical vector space, is presented. The recommendation making process enables machine learning models to work directly with the ontological knowledge base from training step to deployment step. This knowledge base can work well with classification models (e.g., k-nearest neighbours, support vector machines, or naıve bayes). A prototype of the framework is developed and experimental results confirm the feasibility of the proposed framework. © 2021, Institute of Advanced Engineering and Science. All rights reserved

Efficient and adaptive incentive selection for crowdsourcing contests

The success of crowdsourcing projects relies critically on motivating a crowd to contribute. One particularly effective method for incentivising participants to perform tasks is to run contests where participants compete against each other for rewards. However, there are numerous ways to implement such contests in specific projects, that vary in how performance is evaluated, how participants are rewarded, and the sizes of the prizes. Also, the best way to implement contests in a particular project is still an open challenge, as the effectiveness of each contest implementation (henceforth, incentive) is unknown in advance. Hence, in a crowdsourcing project, a practical approach to maximise the overall utility of the requester (which can be measured by the total number of completed tasks or the quality of the task submissions) is to choose a set of incentives suggested by previous studies from the literature or from the requester’s experience. Then, an effective mechanism can be applied to automatically select appropriate incentives from this set over different time intervals so as to maximise the cumulative utility within a given financial budget and a time limit. To this end, we present a novel approach to this incentive selection problem. Specifically, we formalise it as an online decision making problem, where each action corresponds to offering a specific incentive. After that, we detail and evaluate a novel algorithm, HAIS, to solve the incentive selection problem efficiently and adaptively. In theory, in the case that all the estimates in HAIS (except the estimates of the effectiveness of each incentive) are correct, we show that the algorithm achieves the regret bound o

STUDY ON TREATMENT OF THE LEACHATE FROM LANDFILL SITE AT NAMSON, SOCSON, HANOI

Joint Research on Environmental Science and Technology for the Eart