Digital Twins and Enabling Technologies in Museums and Cultural Heritage: An Overview.

This paper presents an overview of various types of virtual museums (ViM) as native artifacts or as digital twins (DT) of physical museums (PM). Depending on their mission and features, we discuss various enabling technologies and sensor equipment with their specific requirements and complexities, advantages and drawbacks in relation to each other at all stages of a DT's life cycle. A DT is a virtual construct and embodies innovative concepts based on emerging technologies (ET) using adequate sensor configurations for (meta-)data import and exchange. Our keyword-based search for articles, conference papers, (chapters from) books and reviews yielded 43 contributions and 43 further important references from Industry 4.0, Tourism and Heritage 4.0. After closer examination, a reference corpus of 40 contributions was evaluated in detail and classified along with their variants of DT-content-, communication-, and collaboration-centric and risk-informed ViMs. Their system features correlate with different application areas (AA), new or improved technologies-mostly still under development-and sensors used. Our proposal suggests a template-based, generative approach to DTs using standardized metadata formats, expert/curator software and customers'/visitors' engagement. It advocates for stakeholders' collaboration as part of a comprehensive validation and verification assessment (V&VA) throughout the DT's entire life cycle.

Forecasting Key Retail Performance Indicators Using Interpretable Regression.

Foot traffic, conversion rate, and total sales during a period of time may be considered to be important indicators of store performance. Forecasting them may allow for business managers plan stores operation in the near future in an efficient way. This work presents a regression method that is able to predict these three indicators based on previous data. The previous data includes values for the indicators in the recent past; therefore, it is a requirement to have gathered them in a suitable manner. The previous data also considers other values that are easily obtained, such as the day of the week and hour of the day of the indicators. The novelty of the approach that is presented here is that it provides a confidence interval for the predicted information and the importance of each parameter for the predicted output values, without additional processing or analysis. Real data gathered by Follow Up, a customer experience company, was used to test the proposed method. The method was tried for making predictions for up to one month in the future. The results of the experiments show that the proposed method has a comparable performance to the best methods proposed in the past that do not provide confidence intervals or parameter rankings. The method obtains RMSE of 0.0713 for foot traffic prediction, 0.0795 for conversion rate forecasting, and 0.0757 for sales prediction.

Feature Selection for Health Care Costs Prediction Using Weighted Evidential Regression.

Although many authors have highlighted the importance of predicting people's health costs to improve healthcare budget management, most of them do not address the frequent need to know the reasons behind this prediction, i.e., knowing the factors that influence this prediction. This knowledge allows avoiding arbitrariness or people's discrimination. However, many times the black box methods (that is, those that do not allow this analysis, e.g., methods based on deep learning techniques) are more accurate than those that allow an interpretation of the results. For this reason, in this work, we intend to develop a method that can achieve similar returns as those obtained with black box methods for the problem of predicting health costs, but at the same time it allows the interpretation of the results. This interpretable regression method is based on the Dempster-Shafer theory using Evidential Regression (EVREG) and a discount function based on the contribution of each dimension. The method "learns" the optimal weights for each feature using a gradient descent technique. The method also uses the nearest k-neighbor algorithm to accelerate calculations. It is possible to select the most relevant features for predicting a patient's health care costs using this approach and the transparency of the Evidential Regression model. We can obtain a reason for a prediction with a k-NN approach. We used the Japanese health records at Tsuyama Chuo Hospital to test our method, which included medical examinations, test results, and billing information from 2013 to 2018. We compared our model to methods based on an Artificial Neural Network, Gradient Boosting, Regression Tree and Weighted k-Nearest Neighbors. Our results showed that our transparent model performed like the Artificial Neural Network and Gradient Boosting with an R2 of 0.44.

Technology Support for Collaborative Preparation of Emergency Plans.

Preparing a plan for reaction to a grave emergency is a significant first stage in disaster management. A group of experts can do such preparation. Best results are obtained with group members having diverse backgrounds and access to different relevant data. The output of this stage should be a plan as comprehensive as possible, taking into account various perspectives. The group can organize itself as a collaborative decision-making team with a process cycle involving modeling the process, defining the objectives of the decision outcome, gathering data, generating options and evaluating them according to the defined objectives. The meeting participants may have their own evidences concerning people's location at the beginning of the emergency and assumptions about people's reactions once it occurs. Geographical information is typically crucial for the plan, because the plan must be based on the location of the safe areas, the distances to move people, the connecting roads or other evacuation links, the ease of movement of the rescue personnel, and other geography-based considerations. The paper deals with this scenario and it introduces a computer tool intended to support the experts to prepare the plan by incorporating the various viewpoints and data. The group participants should be able to generate, visualize and compare the outcomes of their contributions. The proposal is complemented with an example of use: it is a real case simulation in the event of a tsunami following an earthquake at a certain urban location.

Ubiquitous mobile knowledge construction in collaborative learning environments.

Knowledge management is a critical activity for any organization. It has been said to be a differentiating factor and an important source of competitiveness if this knowledge is constructed and shared among its members, thus creating a learning organization. Knowledge construction is critical for any collaborative organizational learning environment. Nowadays workers must perform knowledge creation tasks while in motion, not just in static physical locations; therefore it is also required that knowledge construction activities be performed in ubiquitous scenarios, and supported by mobile and pervasive computational systems. These knowledge creation systems should help people in or outside organizations convert their tacit knowledge into explicit knowledge, thus supporting the knowledge construction process. Therefore in our understanding, we consider highly relevant that undergraduate university students learn about the knowledge construction process supported by mobile and ubiquitous computing. This has been a little explored issue in this field. This paper presents the design, implementation, and an evaluation of a system called MCKC for Mobile Collaborative Knowledge Construction, supporting collaborative face-to-face tacit knowledge construction and sharing in ubiquitous scenarios. The MCKC system can be used by undergraduate students to learn how to construct knowledge, allowing them anytime and anywhere to create, make explicit and share their knowledge with their co-learners, using visual metaphors, gestures and sketches to implement the human-computer interface of mobile devices (PDAs).

Mobile, collaborative situated knowledge creation for urban planning.

Geo-collaboration is an emerging research area in computer sciences studying the way spatial, geographically referenced information and communication technologies can support collaborative activities. Scenarios in which information associated to its physical location are of paramount importance are often referred as Situated Knowledge Creation scenarios. To date there are few computer systems supporting knowledge creation that explicitly incorporate physical context as part of the knowledge being managed in mobile face-to-face scenarios. This work presents a collaborative software application supporting visually-geo-referenced knowledge creation in mobile working scenarios while the users are interacting face-to-face. The system allows to manage data information associated to specific physical locations for knowledge creation processes in the field, such as urban planning, identifying specific physical locations, territorial management, etc.; using Tablet-PCs and GPS in order to geo-reference data and information. It presents a model for developing mobile applications supporting situated knowledge creation in the field, introducing the requirements for such an application and the functionalities it should have in order to fulfill them. The paper also presents the results of utility and usability evaluations.