Temporal Convolutional Neural Networks for Radar Micro-Doppler Based Gait Recognition

The capability of sensors to identify individuals in a specific scenario is a topic of high relevance for sensitive sectors such as public security. A traditional approach involves cameras; however, camera-based surveillance systems lack discretion and have high computational and storing requirements in order to perform human identification. Moreover, they are strongly influenced by external factors (e.g., light and weather). This paper proposes an approach based on a temporal convolutional deep neural networks classifier applied to radar micro-Doppler signatures in order to identify individuals. Both sensor and processing requirements ensure a low size weight and power profile, enabling large scale deployment of discrete human identification systems. The proposed approach is assessed on real data concerning 106 individuals. The results show good accuracy of the classifier (the best obtained accuracy is 0.89 with an F1-score of 0.885) and improved performance when compared to other standard approaches

Thyroid disease treatment prediction with machine learning approaches

The thyroid is an endocrine gland located in the anterior region of the neck: its main task is to produce thyroid hormones, which are functional to our entire body. Its possible dysfunction can lead to the production of an insufficient or excessive amount of thyroid hormone. Therefore, the thyroid can become inflamed or swollen due to one or more swellings forming inside it. Some of these nodules can be the site of malignant tumors. One of the most used treatments is sodium levothyroxine, also known as LT4, a synthetic thyroid hormone used in the treatment of thyroid disorders and diseases. Predictions about the treatment can be important for supporting endocrinologists' activities and improve the quality of the patients' life. To date, there are numerous studies in the literature that focus on the prediction of thyroid diseases on the trend of the hormonal parameters of people. This work, differently, aims to predict the LT4 treatment trend for patients suffering from hypothyroidism. To this end, a dedicated dataset was built that includes medical information related to patients being treated in the”AOU Federico II” hospital of Naples. For each patient, the clinical history is available over time, and therefore on the basis of the trend of the hormonal parameters and other attributes considered it was possible to predict the course of each patient's treatment in order to understand if this should be increased or decreased. To conduct this study, we used different machine learning algorithms. In particular, we compared the results of 10 different classifiers. The performances of the different algorithms show good results, especially in the case of the Extra-Tree Classifier, where the accuracy reaches 84%

Model Checking to Improve Precision of Design Pattern Instances Identification in OO Systems

In the last two decades some methods and tools have been proposed to identify the Design Pattern (DP) instances implemented in an existing Object Oriented (OO) software system. This allows to know which OO components are involved in each DP instance. Such a knowledge is useful to better understand the system thus reducing the effort to modify and evolve it. The results obtained by the existing methods and tools can suffer a lack of completeness or precision due to the presence of false positive/negative. Model Checking (MC) algorithms can be used to improve the precision of DP's instances detected by a tool by automatically refining the results it produces. In this paper a MC based technique is defined and applied to the results of an existing DPs mining tool, called Design Pattern Finder (DPF), to improve the precision by verifying automatically the DPs instances it detects. To verify and assess the feasibility and the effectiveness of the proposed technique, we carried out a case study where it was applied on some open source OO systems. The results showed that the proposed technique allowed to improve the precision of the DPs instances detected by the DPF tool

Knowledge Management Integrated with E-Learning in Open Innovation

This paper presents a framework aiming to support an «innovation chain» in an Open Innovation (OI) perspective. In order to transfer research results from producers to users, it is necessary to develop a Knowledge Manage-ment System supporting formalization, packaging and characterization to be able to select, understand and collect research results and/or innovations deriving from them. Suitable skills are required to transfer and collect innovation. Since in OI the knowledge producer and fi nal users are by defi nition geographically distant, the required specialist skills have to be acquired through an e-learning system. This system must offer Learning Objects that can be combined within a course that also takes into account the user’s past experiences. This work proposes an approach based on the integration of these two systems, and presents PROMETHEUS, a tool supporting this approach. The results of preliminary experimentation highlighted the strengths and weaknesses of the approach. They will be used to plan further experimentation and initiatives serving to facilitate the transfer of research results from state of the art to state of practice