The research area of ambient assisted living has led to the development of activity recognition systems (ARS) based on human activity recognition (HAR). These systems improve the quality of life and the health care of the elderly and dependent people. However, before making them available to end users, it is necessary to evaluate their performance in recognizing activities of daily living, using data set benchmarks in experimental scenarios. For that reason, the scientific community has developed and provided a huge amount of data sets for HAR. Therefore, identifying which ones to use in the evaluation process and which techniques are the most appropriate for prediction of HAR in a specific context is not a trivial task and is key to further progress in this area of research. This work presents a systematic review of the literature of the sensor-based data sets used to evaluate ARS. On the one hand, an analysis of different variables taken from indexed publications related to this field was performed. The sources of information are journals, proceedings, and books located in specialized databases. The analyzed variables characterize publications by year, database, type, quartile, country of origin, and destination, using scientometrics, which allowed identification of the data set most used by researchers. On the other hand, the descriptive and functional variables were analyzed for each of the identified data sets: occupation, annotation, approach, segmentation, representation, feature selection, balancing and addition of instances, and classifier used for recognition. This paper provides an analysis of the sensor-based data sets used in HAR to date, identifying the most appropriate dataset to evaluate ARS and the classification techniques that generate better results.

Feature selection is an important and active issue in clustering and classification problems. By choosing an adequate feature subset, a dataset dimensionality reduction is allowed, thus contributing to decreasing the classification computational complexity, and to improving the classifier performance by avoiding redundant or irrelevant features. Although feature selection can be formally defined as an optimisation problem with only one objective, that is, the classification accuracy obtained by using the selected feature subset, in recent years, some multi-objective approaches to this problem have been proposed. These either select features that not only improve the classification accuracy, but also the generalisation capability in case of supervised classifiers, or counterbalance the bias toward lower or higher numbers of features that present some methods used to validate the clustering/classification in case of unsupervised classifiers. The main contribution of this paper is a multi-objective approach for feature selection and its application to an unsupervised clustering procedure based on Growing Hierarchical Self-Organising Maps (GHSOMs) that includes a new method for unit labelling and efficient determination of the winning unit. In the network anomaly detection problem here considered, this multi-objective approach makes it possible not only to differentiate between normal and anomalous traffic but also among different anomalies. The efficiency of our proposals has been evaluated by using the well-known DARPA/NSL-KDD datasets that contain extracted features and labelled attacks from around 2 million connections. The selected feature sets computed in our experiments provide detection rates up to 99.8% with normal traffic and up to 99.6% with anomalous traffic, as well as accuracy values up to 99.12%.This work has been funded by FEDER funds and the Ministerio de Ciencia e Innovación of the Spanish Government under Project No. TIN2012-32039

This work establishes the technological elements that have enabled the preservation, promotion, and dissemination of tangible and intangible cultural heritage in the period from 2018 to 2022. For this, a Systematic Literature Review (SLR) was conducted in the scientific databases Scopus, Science Direct, IEEE and Web of Science, which facilitated the identification of 146 articles related to the topic. A quantitative and qualitative analysis of the journals, authors and topics was carried out, detailing the important variables required to establish the sought-out elements; for this purpose, the following were quantified in the papers: type, topic, categorization, country, and language; in the publications, the type of heritage chosen, the place of the heritage and the type of intervention were investigated. The number of publications reporting the use of some type of technology was also identified, finding that 70% of them show a technological approach to preserve cultural heritage, while 30% refer to other types of interventions. The technologies reported to be used the most are 3D digital technologies (44% of those showing technological applications), augmented reality or virtual reality, henceforth AR/VR (15%).

Pluvial flash floods in urban areas are becoming increasingly frequent due to climate change and human actions, negatively impacting the life, work, production and infrastructure of a population. Pluvial flooding occurs when intense rainfall overflows the limits of urban drainage and water accumulation causes hazardous flash floods. Although flash floods are hard to predict given their rapid formation, Early Warning Systems (EWS) are used to minimize casualties. We performed a systematic review to define the basic structure of an EWS for rain flash floods. The structure of the review is as follows: first, Section 2 describes the most important factors that affect the intensity of pluvial flash floods during rainfall events. Section 3 defines the key elements and actors involved in an effective EWS. Section 4 reviews different EWS architectures for pluvial flash floods implemented worldwide. It was identified that the reviewed projects did not follow guidelines to design early warning systems, neglecting important aspects that must be taken into account in their implementation. Therefore, this manuscript proposes a basic structure for an effective EWS for pluvial flash floods that guarantees the forecasting process and alerts dissemination during rainfall events.

The Internet of things (IoT) is an active area in the current research community due to the improvement in mobile computing and wireless networks. Currently, the IoT is involved in many fields like smart cities, smart health monitoring, smart tracking, and smart factory; therefore, it is introducing new research opportunities and industrial revolutions. Smart health, in particular, is very important and trendy domain for researchers and practitioners due to its continuous monitoring of health of patients. The objective of smart health is to provide medical facilities to patients at anytime and anywhere. The smart health monitoring systems are mostly connected with the wireless network medium that is extremely vulnerable for threats. However various attacks are observed that can endanger these health monitoring applications and systems. These attacks include Denial of Service (DoS) Attack, Fingerprint and Timing-based Snooping, Router Attack, Select and Forwarding attack, Sensor attack and Replay Attack. In this paper, we discuss these attacks with their impact on health monitoring systems with some suggestive measures from our research findings.