Abstract
Licensing technologies are one of the main ways to produce and bring academic research to society. Despite previous studies' dedicated efforts to identify licensing probabilities, the question of how the expertise and prestige that a university has in a given technological field influences the licensing probabilities is still little addressed. This article aims to identify information in patent documents to estimate the probabilities of licensing technologies produced at the university. For that, we performed a data mining of licensed and unlicensed patents from an important Brazilian University (n = 1,578). We estimated the licensing probabilities using the Logistic Regression technique, based on the Maximum Likelihood Estimation. The results suggest that the variables of know-how in the main field and Technological strength in the main field are the most important/influential variables in estimating the probabilities of licensing a given patent. The main conclusion obtained from the results is that: universities, to obtain more licenses, must increase their know-how (expertise) in some technological fields, maintaining a reasonable level between specialization and diversification. Additionally, the higher the citations received (prestige/recognition) by a university in a given technological field, the greater the probability of patent licensing in that technological field. In terms of practical contributions, this study suggests that: investments in specific technological fields generate more competitive advantages for the university and, thus, more technological successes.

Abstract
Physiological signals are often corrupted by noisy sources. Usually, artificial intelligence algorithms analyze the whole signal, regardless of its varying quality. Instead, experienced cardiologists search for a high-quality signal segment, where more accurate conclusions can be draw. We propose a methodology that simultaneously selects the optimal processing region of a physiological signal and determines its decoding into a state sequence of physiologically meaningful events. Our approach comprises two phases. First, the training of a neural network that then enables the estimation of the state probability distribution of a signal sample. Second, the use of the neural network output within an integer program. The latter models the problem of finding a time window by maximizing a likelihood function defined by the user. Our method was tested and validated in two types of signals, the phonocardiogram and the electrocardiogram. In phonocardiogram and electrocardiogram segmentation tasks, the system's sensitivity increased on average from 95.1% to 97.5% and from 78.9% to 83.8%, respectively, when compared to standard approaches found in the literature.

Abstract
The application of the Internet of Things concept in domains such as industrial control, building automation, human health, and environmental monitoring, introduces new privacy and security challenges. Consequently, traditional implementation of monitoring and security mechanisms cannot always be presently feasible and adequate due to the number of IoT devices, their heterogeneity and the typical limitations of their technical specifications. In this paper, we propose an IP flow-based Intrusion Detection System (IDS) framework to monitor and protect IoT networks from external and internal threats in real-time. The proposed framework collects IP flows from an IoT network and analyses them in order to monitor and detect attacks, intrusions, and other types of anomalies at different IoT architecture layers based on some flow features instead of using packet headers fields and their payload. The proposed framework was designed to consider both the IoT network architecture and other IoT contextual characteristics such as scalability, heterogeneity, interoperability, and the minimization of the use of IoT networks resources. The proposed IDS framework is network-based and relies on a hybrid architecture, as it involves both centralized analysis and distributed data collection components. In terms of detection method, the framework uses a specification-based approach drawn on normal traffic specifications. The experimental results show that this framework can achieve approximate to 100% success and 0% of false positives in detection of intrusions and anomalies. In terms of performance and scalability in the operation of the IDS components, we study and compare it with three different conventional IDS (Snort, Suricata, and Zeek) and the results demonstrate that the proposed solution can consume fewer computational resources (CPU, RAM, and persistent memory) when compared to those conventional IDS.

Abstract
Many current AutoML platforms include a very large space of alternatives (the configuration space) that make it difficult to identify the best alternative for a given dataset. In this paper we explore a method that can reduce a large configuration space to a significantly smaller one and so help to reduce the search time for the potentially best workflow. We empirically validate the method on a set of workflows that include four ML algorithms (SVM, RF, LogR and LD) with different sets of hyperparameters. Our results show that it is possible to reduce the given space by more than one order of magnitude, from a few thousands to tens of workflows, while the risk that the best workflow is eliminated is nearly zero. The system after reduction is about one order of magnitude faster than the original one, but still maintains the same predictive accuracy and loss. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract

Abstract
The use of social media in health is emerging as a means of bringing the various actors together with several benefits. In the specific case of cancer disease, these tools can help patients to improve their psychological well-being and their outcomes. As cancer is the cause of a quarter of deaths in Portugal, it is a pressing issue to understand which tools and information both patients and health professionals find most useful to build effective health social media. It was observed that there is a latent need for an oncology social environment, allowing greater well-being for patients and strengthening their relationship with health professionals and institutions, constituting an asset to the services provided. This chapter fills a gap in the bibliography by bringing together the views of both patients and health professionals from several areas, in close collaboration with the Francisco Gentil Portuguese Oncology Institute of Porto, E.P.E. © 2024, IGI Global. All rights reserved.