A smartphone-based multi-sensor wireless platform for cycling performance monitoring

In recent years there has been a significant evolution regarding applications for mobile devices that provide location-based services. The mobile devices available on the market already provide a set of integrated sensors and it is also possible to acquire data from external sensors. This chapter presents the development and results concerning a mobile sensing platform applied to cycling which performs data collection using both sensors integrated in the smartphone and multiple wireless sensor nodes, which are used to acquire relevant performance parameters. The data collected by the developed mobile app is stored in a local database and also uploaded to a remote database, where it can be accessed later using the mobile app or a web browser. This mobile app allows users to share data with friends, join or create events, locate friends, consult graphs and access past routes in a map. Based on these functionalities, this system aims to provide detailed feedback regarding the user performance and enhance the enjoyment of the cyclists.This work has been supported by FCT (Fundação para a Ciência e Tecnologia) in the scope of the project: UID/EEA/04436/2013.info:eu-repo/semantics/publishedVersio

A Machine Learning Approach for Prediction of Signaling SIP Dialogs

POCI-01-0145-FEDER-030433 LISBOA-01-0145-FEDER-0307095 UIDB/EEA/50008/2020In this paper, we propose a machine learning methodology for prediction of signaling sessions established with the Session Initiation Protocol (SIP). Given the increasing importance of predicting and detecting abnormal sequences of SIP messages to avoid SIP signaling-based attacks, we first propose a Bayesian inference method capable of representing the statistical relation between a SIP message, observed by a SIP user agent or a SIP server, and prior trustworthy SIP dialogs. The Bayesian inference method, a Hidden Markov Model (HMM) enriched with $n-$ gram Markov observations, is updated over time, so the inference can be used in real-time. The HMM is then used for predicting and detecting SIP dialogs through a lightweight implementation of Viterbi algorithm for sparse state spaces. Experimental results are also reported, where a SIP dataset representing prior information collected by a SIP user agent and/or a SIP server is used to predict or detect if a received sequence of SIP messages is legitimate according to similar SIP dialogs already observed. Finally, we discuss the results obtained for a dataset of abnormal SIP sequences, not observed during the inference stage, showing the effective utility of the proposed methodology to detect abnormal SIP sequences in a short period of time.publishersversionpublishe

Abnormal Signaling SIP Dialogs Detection based on Deep Learning

Funding Information: V. CONCLUSIONS This work proposed four classification models based on LSTM RNNs to classify SIP dialogs. The detection probability was evaluated based on experimental data. To detect abnormal SIP dialogs, we have adopted classification features computed from the output of the LSTM RNN model and two different classification schemes were proposed. A semi-supervised scheme is shown to reach higher performance, achieving a detection probability of 99.45%, thus confirming the effective utility of the proposed methodology to detect abnormal SIP sequences in a short period of time. ACKNOWLEDGEMENTS This work was funded by Fundac¸ão para a Ciência e Tecnologia, under the projects InfoCent-IoT (PTDC/EEI-TEL/30433/2017), CoSHARE (PTDC/EEI-TEL/30709/2017), and RFSense (UIDB/50008/2020).The detection of abnormal sequences of SIP messages in real-time is crucial to avoid SIP signaling-based attacks. In this paper, we propose a deep learning approach to detect signaling patterns of multimedia sessions established with the Session Initiation Protocol (SIP). The approach is based on a recurrent neural network (RNN). We study the performance of different Long Short-term Memory (LSTM) RNN architectures, which are trained using a SIP signaling dataset of trustworthy SIP dialogs captured by a SIP server. The trained RNNs are then used to detect the SIP dialogs in real-time. After characterizing the dataset adopted for the training, validation, and testing, we present the experimental results obtained for the different RNN architectures, showing that the classification probability of trustworthy SIP dialogs exceeds 93% in the test stage. Finally, we present two methodologies to detect abnormal SIP dialogs, i.e., not contained in the trustworthy training dataset. After a detailed analysis of the skewness and kurtosis computed with the numerical RNN outputs, we show that they can be used as classification features. The first method is based on a K-means unsupervised classifier, while the second one is based on a semi-supervised threshold-based classifier. Experimental results show that the threshold-based classifier achieves 99.45% of detection probability, showing the effective utility of the proposed methodology to detect abnormal SIP sequences in a short period of time.authorsversionpublishe

Classification of Abnormal Signaling SIP Dialogs through Deep Learning

POCI-01-0145-FEDER-030433 UIDB/50008/2020 PRT/BD/152200/2021Due to the high utilization of the Session Initiation Protocol (SIP) in the signaling of cellular networks and voice over IP multimedia systems, the avoidance of security vulnerabilities in SIP systems is a major aspect to assure that the operators can reach satisfactory readiness levels of service. This work is focused on the detection and prediction of abnormal signaling SIP dialogs as they evolve. Abnormal dialogs include two classes: the ones observed so far and thus labeled as abnormal and already known, but also the unknown ones, i.e., specific sequences of SIP messages never observed before. Taking advantage of recent advances in deep learning, we use Long Short-Term Memory (LSTM) recurrent neural networks (RNNs) to detect and predict dialogs already observed. Additionally, and based on the outputs of the LSTM neural network, we propose two different classifiers capable of identifying unknown SIP dialogs, given the high level of vulnerability they may represent for the SIP operation. The proposed approaches achieve higher SIP dialogs detection scores in a shorter time when compared to a reference probabilistic-based approach. Moreover, the proposed detectors of unknown SIP dialogs achieve a detection probability above 94%, indicating its capability to detect a significant number of unknown SIP dialogs in a short amount of time.publishersversionpublishe

Ocorrência de xenótimo em amostras aluvionares da região centro-leste de Portugal, Zona Centro Ibérica-Zona de Ossa Morena

Foi identificado, possivelmente pela primeira vez em Portugal, xenótimo aluvionar em concentrados de bateia colhidos numa campanha de prospecção de terras raras desenvolvida pelo ex-IGM no centro-leste deste país. O xenótimo ocorre em grãos sub-rolados de dimensão média =250um, em concentrações mais significativas em Nisa, Stº António das Areias e Marvão. A geologia regional e o cortejo mineral das amostras sugerem proveniência do xenótimo dos maciços graníticos de Penamacor e Nisa e ainda das Arcoses da Beira Baixa e níveis de cascalheiras plio-plistocénicas com intercalações argilo-arenosas

Hierarchical reactivation of transcription during mitosis-to-G1 transition by Brn2 and Ascl1 in neural stem cells

During mitosis, chromatin condensation is accompanied by a global arrest of transcription. Recent studies suggest transcriptional reactivation upon mitotic exit occurs in temporally coordinated waves, but the underlying regulatory principles have yet to be elucidated. In particular, the contribution of sequence-specific transcription factors (TFs) remains poorly understood. Here we report that Brn2, an important regulator of neural stem cell identity, associates with condensed chromatin throughout cell division, as assessed by live-cell imaging of proliferating neural stem cells. In contrast, the neuronal fate determinant Ascl1 dissociates from mitotic chromosomes. ChIP-seq analysis reveals that Brn2 mitotic chromosome binding does not result in sequence-specific interactions prior to mitotic exit, relying mostly on electrostatic forces. Nevertheless, surveying active transcription using single-molecule RNA-FISH against immature transcripts reveals differential reactivation kinetics for key targets of Brn2 and Ascl1, with transcription onset detected in early (anaphase) versus late (early G1) phases, respectively. Moreover, by using a mitotic-specific dominant-negative approach, we show that competing with Brn2 binding during mitotic exit reduces the transcription of its target gene Nestin. Our study shows an important role for differential binding of TFs to mitotic chromosomes, governed by their electrostatic properties, in defining the temporal order of transcriptional reactivation during mitosis-to-G1 transition