Apart from the driving behavioural change that can be the direct consequence of operating a car phone, phone-use related behaviour may also be a threat to traffic safety. Making notes or looking up telephone numbers while driving are examples of such behaviour. In a driving simulator experiment, 20 drivers drove in two conditions under normal driving conditions and while being distracted because of telephone engagement. In the ''distracted'' condition they had to handle a mobile phone while their attention was drawn off the road for up to several seconds by a telephone number search task. Results showed both a deterioration in driver performance on different vehicle parameters including behavioural (speed) compensation as a result of the demanding telephone task. In an effort to develop an on-board detection system for this type of driver inattention, the data were used to serve as input for a real time diagnosis system based on Statistics, Artificial Neural Networks (ANNs) and Fuzzy Logic (FL). System performance in recognising normal and deteriorated driving behaviour was 89%. On-line detection of driver distraction is considered feasible in the near future.

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%.

Parking block regions host dangerous behaviors that can be detected from a surveillance camera perspective. However, these regions are often occluded, subject to ground bumpiness or steep slopes, and thus they are hard to segment. Firstly, the paper proposes a pyramidal solution that takes advantage of satellite views of the same scene, based on a deep Convolutional Neural Network (CNN). Training a CNN from the surveillance camera perspective is rather impossible due to the combinatory explosion generated by multiple point-of-views. However, CNNs showed great promise on previous works over satellite images. Secondly, even though there are many datasets for occupancy detection in parking lots, none of them were designed to tackle the parking block segmentation problem directly. Given the lack of a suitable dataset, we also propose APKLOT, a dataset of roughly 7000 polygons for segmenting parking blocks from the satellite perspective and from the camera perspective. Moreover, our method achieves more than 50% intersection over union (IoU) in all the testing sets, that is, at both the satellite view and the camera view.

People interact with systems and applications through several devices and are willing to share information about preferences, interests and characteristics. Social networking profiles, data from advanced sensors attached to personal gadgets, and semantic web technologies such as FOAF and microformats are valuable sources of personal information that could provide a fair understanding of the user, but profile information is scattered over different user models. Some researchers in the ubiquitous user modeling community envision the need to share user model’s information from heterogeneous sources. In this paper, we address the syntactic and semantic heterogeneity of user models in order to enable user modeling interoperability. We present a dynamic user profile structure based in Simple Knowledge Organization for the Web (SKOS) to provide knowledge representation for ubiquitous user model. We propose a two-tier matching strategy for concept schemas alignment to enable user modeling interoperability. Our proposal is proved in the application scenario of sharing and reusing data in order to deal with overweight and obesity.

Current research on the career satisfaction of graduates limits educational institutions in devising methods to attain high career satisfaction. Thus, this study aims to use data science models to understand and predict career satisfaction based on information collected from surveys of university alumni. Five machine learning (ML) algorithms were used for data analysis, including the decision tree, random forest, gradient boosting, support vector machine, and neural network models. To achieve optimal prediction performance, we utilized the Bayesian optimization method to fine-tune the parameters of the five ML algorithms. The five ML models were compared with logistic and ordinal regression. Then, to extract the most important features of the best predictive model, we employed the SHapley Additive exPlanations (SHAP), a novel methodology for extracting the significant features in ML. The results indicated that gradient boosting is a marginally superior predictive model, with 2–3% higher accuracy and area under the receiver operating characteristic curve (AUC) compared to logistic and ordinal regression. Interestingly, concerning low career satisfaction, those with the worst scores for the phrase “how frequently applied knowledge, skills, or technological tools from the academic training” were less satisfied with their careers. To summarize, career satisfaction is related to academic training, alumni satisfaction, employment status, published articles or books, and other factors.

This paper presents a methodology for designing a humanitarian supply chain (HSC) for immunization in Mexico. The study analyzes the abilities of HSCs provided in the literature and those that are relevant for the HSC vaccination in Mexico. In addition, the stakeholders involved in HSC for immunization were studied. A sentiment analysis of more than 900 newspaper articles was conducted to determine society’s perception of the government’s strategy for vaccine distribution. Considering these aspects and a review of the state-of-the-art in the design of supply chains, a methodology for HSC is proposed. Some of the findings are that resilience and agility, the most important abilities in HSCs in Mexico, can be achieved through government support and strong collaboration between different stakeholders, which was found to be the weakest point of the strategy. The problems observed in the vaccination strategy in Mexico were as follows: (1) a single physical space per municipality was defined for the application of the vaccine, (2) there is a shortage of vaccines, and (3) as far as we know, no methodology has been applied to carry out immunization. This approach considers the design of HSCs in terms of capabilities and stakeholders, which, to the best of our knowledge, is a novel aspect of the literature. This methodology can be used for various scenarios of HSCs in Mexico.