Using classifiers to predict linear feedback shift registers

Proceeding of: IEEE 35th International Carnahan Conference on Security Technology. October 16-19, 2001, LondonPreviously (J.C. Hernandez et al., 2000), some new ideas that justify the use of artificial intelligence techniques in cryptanalysis are presented. The main objective of that paper was to show that the theoretical next bit prediction problem can be transformed into a classification problem, and this classification problem could be solved with the aid of some AI algorithms. In particular, they showed how a well-known classifier called c4.5 could predict the next bit generated by a linear feedback shift register (LFSR, a widely used model of pseudorandom number generator) very efficiently and, most importantly, without any previous knowledge over the model used. The authors look for other classifiers, apart from c4.5, that could be useful in the prediction of LFSRs. We conclude that the selection of c4.5 by Hernandez et al. was adequate, because it shows the best accuracy of all the classifiers tested. However, we have found other classifiers that produce interesting results, and we suggest that these algorithms must be taken into account in the future when trying to predict more complex LFSR-based models. Finally, we show some other properties that make the c4.5 algorithm the best choice for this particular cryptanalytic problem.Publicad

FPGA Implementation of an Adaptive Noise Canceller for Robust Speech Enhancement Interfaces

This paper describes the design and implementation results of an adaptive Noise Canceller useful for the construction of Robust Speech Enhancement Interfaces. The algorithm being used has very good performance for real time applications. Its main disadvantage is the requirement of calculating several operations of division, having a high computational cost. Besides that, the accuracy of the algorithm is critical in fixed-point representation due to the wide range of the upper and lower bounds of the variables implied in the algorithm. To solve this problem, the accuracy is studied and according to the results obtained a specific word-length has been adopted for each variable. The algorithm has been implemented for Altera and Xilinx FPGAs using high level synthesis tools. The results for a fixed format of 40 bits for all the variables and for a specific word-length for each variable are analyzed and discussed

Case study as an innovative teaching methodology for environmental engineering learning

Este trabajo recoge la experiencia de incorporar el estudio mediante un caso como método docente dentro la asignatura Ingeniería Ambiental (Grado en Ingeniería Química, UAM). Se pretende profundizar en el desarrollo de varias competencias de la asignatura que no se adquieren de manera adecuada con los métodos docentes tradicionales. Dichas competencias incluyen pensamiento crítico, toma y comunicación de decisiones. El caso de estudio está basado en una situación real y resulta más complejo que los problemas resueltos en clase por el profesor. El caso se desarrolla junto a diversas cuestiones que guían a los estudiantes hacia la solución del problema. Los estudiantes resuelven el caso en clase trabajando en pequeños grupos siempre con la ayuda y guía del profesor. Posteriormente, los estudiantes exponen sus principales resultados y conclusiones generando un debate con sus compañeros y con el profesor. La implementación de esta metodología docente ha dado lugar a un aprendizaje más consistente favoreciendo la asimilación de conceptos teóricos complejos, la discusión de ideas y el razonamiento crítico. Además, se ha potenciado la capacidad de transmitir los conocimientos adquiridos por parte de los estudiantesIn this work, the application of a case study has been implemented as a new teaching methodology in the subject of Environmental Engineering (B.Sc. in Chemical Engineering at UAM University). The main goals are focused on improving important competences of the subject which are not correctly acquired by the students with the traditional theoretical lessons. These competences include critical thinking, decisions making and communication skills. The case has been inspired in a real situation and it is more complex than the problems usually solved by the professor in regular classes. The proposed case study has been developed together with questions which will guide the students to solve the problem. The students have solved the case in the classroom working in small groups, always with the support of the professor. Afterwards, the students showed their main results and conclusions generating a debate with their colleagues and the professor. The case study approach has proved to improve the assimilation of complex theoretical concepts, to favor the discussion and the ability to communicate the acquired knowledge as well as to develop critical thinkin

Bio-inspired broad-class phonetic labelling

Recent studies have shown that the correct labeling of phonetic classes may help current Automatic Speech Recognition (ASR) when combined with classical parsing automata based on Hidden Markov Models (HMM).Through the present paper a method for Phonetic Class Labeling (PCL) based on bio-inspired speech processing is described. The methodology is based in the automatic detection of formants and formant trajectories after a careful separation of the vocal and glottal components of speech and in the operation of CF (Characteristic Frequency) neurons in the cochlear nucleus and cortical complex of the human auditory apparatus. Examples of phonetic class labeling are given and the applicability of the method to Speech Processing is discussed

Predicting COVID-19 progression from diagnosis to recovery or death linking primary care and hospital records in Castilla y León (Spain).

This paper analyses COVID-19 patients' dynamics during the first wave in the region of Castilla y León (Spain) with around 2.4 million inhabitants using multi-state competing risk survival models. From the date registered as the start of the clinical process, it is assumed that a patient can progress through three intermediate states until reaching an absorbing state of recovery or death. Demographic characteristics, epidemiological factors such as the time of infection and previous vaccinations, clinical history, complications during the course of the disease and drug therapy for hospitalised patients are considered as candidate predictors. Regarding risk factors associated with mortality and severity, consistent results with many other studies have been found, such as older age, being male, and chronic diseases. Specifically, the hospitalisation (death) rate for those over 69 is 27.2% (19.8%) versus 5.3% (0.7%) for those under 70, and for males is 14.5%(7%) versus 8.3%(4.6%)for females. Among patients with chronic diseases the highest rates of hospitalisation are 26.1% for diabetes and 26.3% for kidney disease, while the highest death rate is 21.9% for cerebrovascular disease. Moreover, specific predictors for different transitions are given, and estimates of the probability of recovery and death for each patient are provided by the model. Some interesting results obtained are that for patients infected at the end of the period the hazard of transition from hospitalisation to ICU is significatively lower (p < 0.001) and the hazard of transition from hospitalisation to recovery is higher (p < 0.001). For patients previously vaccinated against pneumococcus the hazard of transition to recovery is higher (p < 0.001). Finally, internal validation and calibration of the model are also performed

Glottal-Source Spectral Biometry for Voice Characterization

The biometric signature derived from the estimation of the power spectral density singularities of a speaker’s glottal source is described in the present work. This consists in the collection of peak-trough profiles found in the spectral density, as related to the biomechanics of the vocal folds. Samples of parameter estimations from a set of 100 normophonic (pathology-free) speakers are produced. Mapping the set of speaker’s samples to a manifold defined by Principal Component Analysis and clustering them by k-means in terms of the most relevant principal components shows the separation of speakers by gender. This means that the proposed signature conveys relevant speaker’s metainformation, which may be useful in security and forensic applications for which contextual side information is considered relevant

Bio-inspired Dynamic Formant Tracking for Phonetic Labelling

It is a known fact that phonetic labeling may be relevant in helping current Automatic Speech Recognition (ASR) when combined with classical parsing systems as HMM's by reducing the search space. Through the present paper a method for Phonetic Broad-Class Labeling (PCL) based on speech perception in the high auditory centers is described. The methodology is based in the operation of CF (Characteristic Frequency) and FM (Frequency Modulation) neurons in the cochlear nucleus and cortical complex of the human auditory apparatus in the automatic detection of formants and formant dynamics on speech. Results obtained informant detection and dynamic formant tracking are given and the applicability of the method to Speech Processing is discussed

A Hybrid Parameterization Technique for Speaker Identification

Classical parameterization techniques for Speaker Identification use the codification of the power spectral density of raw speech, not discriminating between articulatory features produced by vocal tract dynamics (acoustic-phonetics) from glottal source biometry. Through the present paper a study is conducted to separate voicing fragments of speech into vocal and glottal components, dominated respectively by the vocal tract transfer function estimated adaptively to track the acoustic-phonetic sequence of the message, and by the glottal characteristics of the speaker and the phonation gesture. The separation methodology is based in Joint Process Estimation under the un-correlation hypothesis between vocal and glottal spectral distributions. Its application on voiced speech is presented in the time and frequency domains. The parameterization methodology is also described. Speaker Identification experiments conducted on 245 speakers are shown comparing different parameterization strategies. The results confirm the better performance of decoupled parameterization compared against approaches based on plain speech parameterization

El consumo moderado y continuado de vino tinto promueve el metabolismo fenólico intestinal

Los polifenoles presentes en el vino son ampliamente metabolizados por la microbiota a lo largo del tracto gastrointestinal. Estos metabolitos fenólicos de origen microbiano parecen tener un papel relevante en los efectos beneficiosos para la salud derivados del consumo moderado de vino. Entre otros efectos, los polifenoles del vino y/o sus metabolitos pueden modificar o modular selectivamente la microbiota oral y del intestino. Con el objetivo de dilucidar como el consumo de vino afecta al metabolismo fenólico intestinal y conocer la relevancia fisiológica de estos efectos, se ha realizado un estudio de intervención en humanos que incluye a 41 voluntarios sanos (33 casos y 8 controles), basado en el consumo moderado (250mL/día) de vino tinto, durante 4 semanas. Antes y después de la intervención, se recolectaron muestras de heces procedentes de los voluntarios. El análisis de metabolitos fenólicos mediante UPLC-ESI-MS/MS ha revelado un aumento significativo en el contenido total de metabolitos de origen microbiano, principalmente de ácidos benzoicos y 4-hidroxivaléricos, en las heces de los voluntarios tras la ingesta de vino, lo que demuestra que el perfil metabólico microbiano de las heces se modifica significativamente por la ingesta moderada de polifenoles del vino.Los autores agradecen al MINECO y al CSlC la financiación obtenida para este estudio.Peer reviewe