Jorge Proença

Is pulse transit time a good indicator of blood pressure changes during short physical exercise in a young population

The Pulse Transit Time (PTT) is generally assumed to be a good surrogate measure to comfortably track blood pressure (BP) and blood pressure changes. This paper investigates PTT variations for healthy young subjects during a sequence of short-term physical exercises. PTT was measured by two different methodologies having different measurement accuracies as well as underlying assumptions: the total PTT from heart to fingertip and the difference of fingertip and earlobe PTTs. Small non consistent changes and very low correlation of both PTTs with systolic blood pressure (SBP) have been observed for the study population (−0.19 ± 0.45 and 0.22 ± 0.46). In conclusion, there might be a need for an improved measurement accuracy of the sensors and data processing techniques in use. The applicability of the Moens-Korteweg equation is also questionable for young people having flexible arteries. In this case, significant radius changes do occur in the large arteries during exercise, which might counteract a PTT decrease with the BP elevation. These radius effects are excluded from the Moens-Korteweg model.

A Cybersecurity Detection Framework for Supervisory Control and Data Acquisition Systems

This paper presents a distributed intrusion detection system (DIDS) for supervisory control and data acquisition (SCADA) industrial control systems, which was developed for the CockpitCI project. Its architecture was designed to address the specific characteristics and requirements for SCADA cybersecurity that cannot be adequately fulfilled by techniques from the information technology world, thus requiring a domain-specific approach. DIDS components are described in terms of their functionality, operation, integration, and management. Moreover, system evaluation and validation are undertaken within an especially designed hybrid testbed emulating the SCADA system for an electrical distribution grid.

Improving network security monitoring for industrial control systems

Programmable Logic Controller (PLC) technology plays an important role in the automation architectures of several critical infrastructures such as Industrial Control Systems (ICS), controlling equipment in contexts such as chemical processes, factory lines, power production plants or power distribution grids, just to mention a few examples. Despite their importance, PLCs constitute one of the weakest links in ICS security, frequently due to reasons such as the absence of secure communication mechanisms, authenticated access or system integrity checks. While events such as the Stuxnet worm have raised awareness for this problem, industry has slowly reacted, either due to reliability or cost concerns. This paper introduces the Shadow Security Unit, a low-cost device deployed in parallel with a PLC or Remote Terminal Unit (RTU), being capable of transparently intercepting its communications control channels and physical process I/O lines to continuously assess its security and operational status. The proposed device does not require significant changes to the existing control network, being able to work in standalone or integrated within an ICS protection framework.

Prosodic and Phonetic Features for Speaking Styles Classification and Detection

This study presents an approach to the task of automatically classifying and detecting speaking styles. The detection of speaking styles is useful for the segmentation of multimedia data into consistent parts and has important applications, such as identifying speech segments to train acoustic models for speech recognition. In this work the database consists of daily news broadcasts in Portuguese television, on which two main speaking styles are evident: read speech from voice-over and anchors, and spontaneous speech from interviews and commentaries. Using a combination of phonetic and prosodic features we can separate these two speaking styles with a good accuracy (93.7% read, 69.5% spontaneous). This is performed in two steps. The first step separates the speech segments from the non-speech audio segments and the second step classifies read versus spontaneous speaking style. The use of phonetic and prosodic features provides alternative information that leads to an improvement of the classification and detection task.

Segmented Dynamic Time Warping for Spoken Query-by-Example Search.

This paper describes a low-resource approach to a Query-byExample task, where spoken queries must be matched in a large dataset of spoken documents sometimes in complex or nonexact ways. Our approach tackles these complex match cases by using Dynamic Time Warping to obtain alternative paths that account for reordering of words, small extra content and small lexical variations. We also report certain advances on calibration and fusion of sub-systems that improve overall results, such as manipulating the score distribution per query and using an average posteriorgram distance matrix as an extra sub-system. Results are evaluated on the MediaEval task of Query-by-Example Search on Speech (QUESST). For this task, the language of the audio being searched is almost irrelevant, approaching the use case scenario to a language of very low resources. For that, we use as features the posterior probabilities obtained from five phonetic recognizers trained with five different languages.

Characterizing Parkinson’s Disease Speech by Acoustic and Phonetic Features

This study intends to identify acoustic and phonetic characteristics of the speech of Parkinson’s Disease (PD) patients, usually manifesting hypokinetic dysarthria. A speech database has been collected from a control group and from a group of patients with similar PD severity, but with different degrees of hypokinetic dysarthria. First and second formant frequencies of vowels in continuous speech were analyzed. Several classifiers were built using phonetic features and a range of acoustic features based on cepstral coefficients with the objective of identifying hypokinetic dysarthria. Results show a centralization of vowel formant frequencies for PD speech, as expected. However, some of the features highlighted in literature for discriminating PD speech were not always found to be statistically significant. The automatic classification tasks to identify the most problematic speakers resulted in high precision and sensitivity by using two formant metrics simultaneously and in even higher performance by using acoustic dynamic parameters.

A Distributed IDS for Industrial Control Systems

Cyber-threats are one of the most significant problems faced by modern Industrial Control Systems ICS, such as SCADA Supervisory Control and Data Acquisition systems, as the vulnerabilities of ICS technology become serious threats that can ultimately compromise human lives. This situation demands a domain-specific approach to cyber threat detection within ICS, which is one of the most important contributions of the CockpitCI FP7 project http://CockpitCI.eu. Specifically, this paper will present the CockpitCI distributed Intrusion Detection System IDS for ICS, which provides its core cyber-detection and analysis capabilities, also including a description of its components, in terms of role, operation, integration, and remote management. Moreover, it will also introduce and describe new domain-specific solutions for ICS security such as the SCADA Honeypot and the Shadow Security Unit, which are part of the CockcpitCI IDS framework.

Automatic Annotation of Disfluent Speech in Children’s Reading Tasks

The automatic evaluation of reading performance of children is an important alternative to any manual or 1-on-1 evaluation by teachers or tutors. To do this, it is necessary to detect several types of reading miscues. This work presents an approach to annotate reading speech while detecting false-starts, repetitions and mispronunciations, three of the most common disfluencies. Using speech data of 6–10 year old children reading sentences and pseudowords, we apply a two-step process: first, an automatic alignment is performed to get the best possible word-level segmentation and detect syllable based false-starts and word repetitions by using a strict FST (Finite State Transducer); then, words are classified as being mispronounced or not through a likelihood measure of pronunciation by using phone posterior probabilities estimated by a neural network. This work advances towards getting the amount and severity of disfluencies to provide a reading ability score computed from several sentence reading tasks.

Specialized Honeypots for SCADA Systems

In this chapter we examine the role of specialized honeypots for detecting and profiling cyber attacks on SCADA-based Industrial Control Systems, debate how to implement such honeypots and provide a complete example of such an appliance. The honeypot concept has been used in general-purpose intrusion detection systems for a long time, with well-recognized contributions in revealing and analysing cyber attacks. However, a number of specialized requirements associated with SCADA systems within Industrial Control Systems in general are not addressed by typical honeypots. In this paper we discuss how the different approaches to security of typical information systems and industrial control systems lead to the need of specialized SCADA honeypots for process control networks. Based on that discussion, we propose a reference architecture for a SCADA network honeypot, discuss possible implementation strategies—based on the lessons learned from the development of a proof-of-concept Modbus honeypot—and propose two alternative deployment strategies, one based on low cost hardware appliances physically and logically located in the automation or field networks and the other based on virtualized field network honeypots physically located in the datacentre and logically located in the field or automation network.

Rule-Based Algorithms for Automatic Pronunciation of Portuguese Verbal Inflections

The correct automatic pronunciation of words is a nontrivial problem, even for inflexions of Portuguese verbs, and has not been systematically solved yet, if verbal irregularity is taken into account. The purpose of this work is to enhance a grapheme-to-phoneme system with a verb pronunciation system for both varieties of Portuguese, Brazilian (BP) and European (EP), given only its infinitive form. The most common verbs for BP and EP (1000 and 2600 respectively) constituted our database to test the pronunciation system. A detailed and systematic analysis of regular and non-regular pronunciation forms of the inflected verbs was performed, and an index of irregularity for verb pronunciation is proposed. A rule-based algorithm to pronounce all inflexions according to verb paradigms is also described. The defined paradigms are, with a high level of certainty, representative of all the verbs for Portuguese.