Profiling Attitudes for Personalized Information Provision

PAROS is a generic system under design whose goal is to offer personalization, recommendation, and other adaptation services to information providing systems. In its heart lies a rich user model able to capture several diverse aspects of user behavior, interests, preferences, and other attitudes. The user model is instantiated with profiles of users, which are obtained by analyzing and appropriately interpreting potentially arbitrary pieces of user-relevant information coming from diverse sources. These profiles are maintained by the system, updated incrementally as additional data on users becomes available, and used by a variety of information systems to adapt the functionality to the users’ characteristics

Minimized Disaster Risk on Multifunction Building through Behavior Analysis Prediction, Density and Usage Movement Inside The Building

The number of disasters that struck the building is influenced by various factors. Important factors are often the cause is such as natural disasters, high accelerated and suddenly activity, the magnitude of the circulation that supports, as well as the availability of other supporting facilities in the building. The density of the building is the focus of major concern in minimizing the number of victims in the building when the disaster occurred. Increasing accidents victims may occur due to tightness when the evacuation or in an attempt to escape. Efforts to minimize such casualties into account the circumstances of users of the building. One alternative in predicting users building density and the flow of circulation are the ESVA (Environmental Socialization Value Analysis) analysis method. This method calculated the number of users and resources to support the building. Mark or grade obtained will illustrate the high risk of accidents can happen. Factors that will be analyzed in this ESVA method related to activity and socialization activities that occur in it. User activity in the building related to its users behavior patterns. Finally, the ESVA development through deepening the analysis of user socialization behavior patterns of the building will increase the accuracy of victim risk prediction disaster in buildings

From academic research on museum galleries to practice-based research for planning shopping malls

This paper explores how findings obtained from case study research on museum gallery layouts provide insights for shopping mall planning and design. The case studies investigated the effects of gallery layouts on visitors' movement patterns in museums, drawing upon the Space Syntax methodology. In the case studies, the local visual cues are considered as important as global spatial structure, and the effects of spatial layout on pedestrian movement are investigated on the basis of both top-down and bottom-up characterizations of space. The case studies analyzed two exemplary museum gallery layouts, the Yale Center for British Art (New Haven, CT) and the Museum of Modern Art (New York, NY). This exploration was able to explain the prediction of movement patterns by different visibility properties that shaped in morphological characteristics of these museums. In this study, understanding the impact of local visual information such as visual cues perceived in space aids understanding possible effects of attractors (i.e. popular displays) on movement. This paper argues that the results obtained from the museum case studies research can provide insights on how pedestrian movement is distributed with the effect of layout and attractors in shopping malls, and aid formulating further research on movement in shopping layouts. The two museum layouts analyzed can be illustrative of two types of shopping mall layouts. Our results suggest that type of shopping layout illustrated by YCBA may be more desirable for visitors as it facilitates encounters and offer clarity in grasping the layout, and the latter may be more advantageous for manipulating visitors with strategically placed attractors

Re-Interpreting Melton’s Study of Gallery Density and Visitor Attention

The works of Edward Robinson and Arthur Melton conducted in the 1920s and 1930s are often cited, but rarely read. The focus of this article is on one of Melton’s (1935) classic visitor studies, re-examined in terms of several explanatory mechanisms including a decision-making model of visitor attention. Melton varied the number of paintings in a gallery from 6 to 36 in increments of 6. As the number of paintings increased, the proportion of paintings actually viewed decreased; however, the average viewing time per painting remained constant. Melton’s findings of decreased attention are discussed in terms of four possible explanations: perceptual distraction, selective choice, object satiation, and fatigue. While fatigue, satiation, and distraction have all been frequently discussed in the visitor literature, selective choice has not. The implications of the attention-value model for selective choice is described in light of Melton’s study

Visiting experiences and behavioural types in cultural audiences: an analysis of two museums in Lisbon

Audiences of cultural events are subject to diverse kinds of experiences in their exercise, which de termine the structure of their to structure their consumption practices and cultural habits. Mapping and analysing visitors’ experiences and their visiting styles is thus fundamental tocenhance museums’ offer appeal. Drawing on a conceptual framework which identifies four main kinds of experiences in cultural practices - (i) intellectual experience (ii) emotional experience; (iii) social experience; and (iv) recreational experience, the audiences of two museums in the city of Lisbon (Fado Museum and Puppets Museum) are analysed in this paper. Considering a typology of diverse audience categories (permanent collection, temporary exhibitions, other events), a detailed study of the assessment of different experiences is pursued, with the aim to confront and identify relevant discriminant categories such as socio-demographic characteristics (e.g. age, gender, qualifications, professional status, nationality, residence, previous artistic practices) and cultural habits (considering their visits to other cultural facilities and events). Considering the conclusions, some policy-oriented recommendations from this analysis are discussed.info:eu-repo/semantics/publishedVersio

Pepper4Museum: Towards a Human-like Museum Guide

With the recent advances in technology, new ways to engage visitors in a museum have been proposed. Relevant examples range from the simple use of mobile apps and interactive displays to virtual and augmented reality settings. Recently social robots have been used as a solution to engage visitors in museum tours, due to their ability to interact with humans naturally and familiarly. In this paper, we present our preliminary work on the use of a social robot, Pepper in this case, as an innovative approach to engaging people during museum visiting tours. To this aim, we endowed Pepper with a vision module that allows it to perceive the visitor and the artwork he is looking at, as well as estimating his age and gender. These data are used to provide the visitor with recommendations about artworks the user might like to see during the visit. We tested the proposed approach in our research lab and preliminary experiments show its feasibility

Human and Artificial Intelligence

Although tremendous advances have been made in recent years, many real-world problems still cannot be solved by machines alone. Hence, the integration between Human Intelligence and Artificial Intelligence is needed. However, several challenges make this integration complex. The aim of this Special Issue was to provide a large and varied collection of high-level contributions presenting novel approaches and solutions to address the above issues. This Special Issue contains 14 papers (13 research papers and 1 review paper) that deal with various topics related to human–machine interactions and cooperation. Most of these works concern different aspects of recommender systems, which are among the most widespread decision support systems. The domains covered range from healthcare to movies and from biometrics to cultural heritage. However, there are also contributions on vocal assistants and smart interactive technologies. In summary, each paper included in this Special Issue represents a step towards a future with human–machine interactions and cooperation. We hope the readers enjoy reading these articles and may find inspiration for their research activities