Semantic Approach for Discovery and Visualization of Academic Information Structured with OAI-PMH

There are different channels to communicate the results of a scientific research; however, several research communities state that the Open Access (OA) is the future of acad emic publishing. These Open Ac cess Platforms have adopted OAI - PMH (Open Archives Initiative - the Protocol for Metadata Harvesting) as a standard for communication and interoperability. Nevertheless, it is significant to highlight that the open source know ledge discovery services based on an index of OA have not been developed. Therefore, it is necessary to address Knowledge Discovery (KD) within these platforms aiming at studen ts, teachers and/ or researchers , to recover both , the resources requested and th e resources that are not explicitly requested – which are also appropriate . This objective represents an important issue fo r structured resources under OAI - PMH. This fact is caused because interoperability with other developments carried out outside their implementation environment is generally not a priority (Level 1 "Shared term definitions"). It is here , where the Semantic Web (SW) beco mes a cornerstone of this work. Consequently, we propose OntoOAIV, a semantic approach for the selective knowledge disco very an d visu alization into structured information with OAI - PMH, focused on supporting the activities of scientific or academic research for a specific user. Because of the academic nature of the structured resources with OAI - PMH, the field of application chosen is the context information of a student. Finally, in order to validate the proposed approach, we use the RUDAR (Roskilde University Digital Archive) and REDALYC (Red de Revistas Científicas de América Latina y el Caribe, España y Portugal) repositor ies, which imple ment the OAI - PMH protocol , as well as one s tudent profile for carrying out KD

Estimating occupancy levels in enclosed spaces using environmental variables: A fitness gym and living room as evaluation scenarios

The understanding of occupancy patterns has been identified as a key contributor to achieve improvements in energy efficiency in buildings since occupancy information can benefit different systems, such as HVAC (Heating, Ventilation, and Air Conditioners), lighting, security, and emergency. This has meant that in the past decade, researchers have focused on improving the precision of occupancy estimation in enclosed spaces. Although several works have been done, one of the less addressed issues, regarding occupancy research, has been the availability of data for contrasting experimental results. Therefore, the main contributions of this work are: (1) the generation of two robust datasets gathered in enclosed spaces (a fitness gym and a living room) labeled with occupancy levels, and (2) the evaluation of three Machine Learning algorithms using different temporal resolutions. The results show that the prediction of 3-4 occupancy levels using the temperature, humidity, and pressure values provides an accuracy of at least 97%

HyRA: A Hybrid Recommendation Algorithm Focused on Smart POI. Ceutí as a Study Scenario

Nowadays, Physical Web together with the increase in the use of mobile devices, Global Positioning System (GPS), and Social Networking Sites (SNS) have caused users to share enriched information on theWeb such as their tourist experiences. Therefore, an area that has been significantly improved by using the contextual information provided by these technologies is tourism. In this way, the main goals of this work are to propose and develop an algorithm focused on the recommendation of Smart Point of Interaction (Smart POI) for a specific user according to his/her preferences and the Smart POIs’ context. Hence, a novel Hybrid Recommendation Algorithm (HyRA) is presented by incorporating an aggregation operator into the user-based Collaborative Filtering (CF) algorithm as well as including the Smart POIs’ categories and geographical information. For the experimental phase, two real-world datasets have been collected and preprocessed. In addition, one Smart POIs’ categories dataset was built. As a result, a dataset composed of 16 Smart POIs, another constituted by the explicit preferences of 200 respondents, and the last dataset integrated by 13 Smart POIs’ categories are provided. The experimental results show that the recommendations suggested by HyRA are promising.Project (the SmartSDK project is co-funded by the EU’s Horizon2020 programme under agreement number 723174 - c 2016 EC and the CONACYT’s agreement number 737373) Doctorado IndustrialAdministración y Dirección de EmpresasIngeniería, Industria y ConstrucciónTurism

Framework for consistent generation of linked data: the case of the user's academic profile on the web

Decision management is relevant for high-value decisions that involve multiple types of input data. Since the Web allows users to keep in touch with other users and likewise, share their data (such as features, interests, and preferences) with applications and devices to customize a provided service, the online data related to these users can be collected as input data for a decision-making process. However, these data are usually provided to the application or device used in a given time, causing three major issues: data are isolated when are provided to a specific entity, data are scattered in the network, and data are found in different formats (structured, semi-structured, and unstructured). Therefore, with the aim of supporting decision makers to make better decisions, in a certain scenario, the proposal to automatically unify, align, and integrate the user data concerning this scope into a centralized and standardized structure that allows, at the same time, to model the user's profile on the Web in a consistent and updated manner as well as to generate linked data from the integrated information is addressed. This is where Decision Support Systems, Semantic Web, and context-enriched services become the cornerstones of the computational approach proposed as a solution to these issues. Firstly, given the generality of fields that can constitute a user profile, the definition of a scope that allows validating the proposed approach is emphasized for this research work. Secondly, the proposal, development, and evaluation of the computational solutions that allow dealing with the data modeling, integration, generation, and updating consistently are highlighted in this research. Therefore, a study focused on the academic area is proposed for this work in order to support researchers and data managers at the institutional level in processes and activities concerning this area, specifically at Tecnologico de Monterrey. To achieve this goal, the design of an interdisciplinary, justified, and interoperable meta-schema (called Academic SUP) that allows to model the user's academic profile on the Web, as well as the development of a computational framework (named as AkCeL) that allows to integrate, generate, and update data into such a meta-schema consistently are proposed in this research work. In addition, in order to support researchers in their decision-making processes, the development of a recommendation algorithm (called C-HyRA) that allows providing a research areas list interesting for researchers, as well as the adoption of a visualization platform related to the academic area to present the information generated by AkCeL are put forward in this proposal. As a result, unified, consistent, reliable, and updated information of the researcher' academic profile is provided on the Web from this approach, in both text and graphics, through the VIVO platform to be consumed primarily by researchers and educational institutions to support their networks and statistics of collaboration/publication and research.Doctor en Ciencias Computacionale

Indoor Environment Dataset to Estimate Room Occupancy

The estimation of occupancy is a crucial contribution to achieve improvements in energy efficiency. The drawback of data or incomplete data related to occupancy in enclosed spaces makes it challenging to develop new models focused on estimating occupancy with high accuracy. Furthermore, considerable variation in the monitored spaces also makes it difficult to compare the results of different approaches. This dataset comprises the indoor environmental information (pressure, altitude, humidity, and temperature) and the corresponding occupancy level for two different rooms: (1) a fitness gym and (2) a living room. The fitness gym data were collected for six days between 18 September and 2 October 2019, obtaining 10,125 objects with a 1 s resolution according to the following occupancy levels: low (2442 objects), medium (5325 objects), and high (2358 objects). The living room data were collected for 11 days between 14 May and 4 June 2020, obtaining 295,823 objects with a 1 s resolution, according to the following occupancy levels: empty (50,978 objects), low (202,613 objects), medium (35,410 objects), and high (6822 objects). Additionally, the number of fans turned on is provided for the living room data. The data are publicly available in the Mendeley Data repository. This dataset can be used to train and compare different machine learning, deep learning, and physical models for estimating occupancy at enclosed spaces

Measuring Indoor Occupancy through Environmental Sensors: A Systematic Review on Sensor Deployment

The COVID-19 pandemic has changed our common habits and lifestyle. Occupancy information is valued more now due to the restrictions put in place to reduce the spread of the virus. Over the years, several authors have developed methods and algorithms to detect/estimate occupancy in enclosed spaces. Similarly, different types of sensors have been installed in the places to allow this measurement. However, new researchers and practitioners often find it difficult to estimate the number of sensors to collect the data, the time needed to sense, and technical information related to sensor deployment. Therefore, this systematic review provides an overview of the type of environmental sensors used to detect/estimate occupancy, the places that have been selected to carry out experiments, details about the placement of the sensors, characteristics of datasets, and models/algorithms developed. Furthermore, with the information extracted from three selected studies, a technique to calculate the number of environmental sensors to be deployed is proposed

Transforming communication channels to the co-creation and diffusion of intangible heritage in smart tourism destination ::creation and testing in Ceutí (Spain)

Creating smart tourism destinations requires innovative solutions which cover the main pillars of sustainability as sociocultural, environmental, and economic aspects, in order to spread the cultural heritage of these tourist destinations to their visitors. One of the most demanded approaches by the new hyper-connected visitors is the expectation of plunging and becoming a short-term resident to receive a real experience during their visit. Therefore, the scope of this research covers the objective of designing an innovative communication channel between a visitor and a point of interest (POI), which in turn allows agile experiences to be built and provided and increases the dissemination of cultural heritage through new technologies, considering the real needs of the territories and the new digital visitors. In order to address these topics, this paper proposes an innovative and co-created progressive Web-App for visitors called Be Memories in order to spread the intangible heritage of a tourist destination, where the content is co-created by residents of the destination. The tool has been tested in Ceutí, a Spanish village with a high cultural value, which needs to be disseminated through new innovative tools. The trial was launched during local festivities of the village using an Internet of Things device, called a Smart Spot, to establish a communication channel between the visitor and POI. The results of the test were measured using Google Analytics, the reactions of Be Memories in social networks, and the acceptance of other cities and European committees. The results have concluded that Be Memories is able to enable a local experience via agile, fresh, and crowd-sourced content that people enjoy. This channel presents a complementary level of information with respect to official sources, documentaries, and local guide tours, at the same time enabling a mechanism to promote physical visits, walking tours, and cultural heritage via low-cost and sustainable infrastructure

Semantic Approach for Discovery and Visualization of Academic Information Structured with OAI-PMH

There are different channels to communicate the results of a scientific research; however, several research communities state that the Open Access (OA) is the future of acad emic publishing. These Open Ac cess Platforms have adopted OAI - PMH (Open Archives Initiative - the Protocol for Metadata Harvesting) as a standard for communication and interoperability. Nevertheless, it is significant to highlight that the open source know ledge discovery services based on an index of OA have not been developed. Therefore, it is necessary to address Knowledge Discovery (KD) within these platforms aiming at studen ts, teachers and/ or researchers , to recover both , the resources requested and th e resources that are not explicitly requested – which are also appropriate . This objective represents an important issue fo r structured resources under OAI - PMH. This fact is caused because interoperability with other developments carried out outside their implementation environment is generally not a priority (Level 1 "Shared term definitions"). It is here , where the Semantic Web (SW) beco mes a cornerstone of this work. Consequently, we propose OntoOAIV, a semantic approach for the selective knowledge disco very an d visu alization into structured information with OAI - PMH, focused on supporting the activities of scientific or academic research for a specific user. Because of the academic nature of the structured resources with OAI - PMH, the field of application chosen is the context information of a student. Finally, in order to validate the proposed approach, we use the RUDAR (Roskilde University Digital Archive) and REDALYC (Red de Revistas Científicas de América Latina y el Caribe, España y Portugal) repositor ies, which imple ment the OAI - PMH protocol , as well as one s tudent profile for carrying out KD

Student Dataset from Tecnologico de Monterrey in Mexico to Predict Dropout in Higher Education

High dropout rates and delayed completion in higher education are associated with considerable personal and social costs. In Latin America, 50% of students drop out, and only 50% of the remaining ones graduate on time. Therefore, there is an urgent need to identify students at risk and understand the main factors of dropping out. Together with the emergence of efficient computational methods, the rich data accumulated in educational administrative systems have opened novel approaches to promote student persistence. In order to support research related to preventing student dropout, a dataset has been gathered and curated from Tecnologico de Monterrey students, consisting of 50 variables and 143,326 records. The dataset contains non-identifiable information of 121,584 High School and Undergraduate students belonging to the seven admission cohorts from August–December 2014 to 2020, covering two educational models. The variables included in this dataset consider factors mentioned in the literature, such as sociodemographic and academic information related to the student, as well as institution-specific variables, such as student life. This dataset provides researchers with the opportunity to test different types of models for dropout prediction, so as to inform timely interventions to support at-risk students