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Since financial assets on stock exchanges were created, investors have sought to predict their future values. Currently, cryptocurrencies are also seen as assets. Machine learning is increasingly adopted to assist and automate investments. The main objective of this paper is to make daily predictions about the movement direction of financial time series through classification models, financial time series preprocessing methods, and feature selection with genetic algorithms. The target time series are Bitcoin, Ibovespa, and Vale. The methodology of this paper includes the following steps: collecting time series of financial assets; data preprocessing; feature selection with genetic algorithms; and the training and testing of machine learning models. The results were obtained by evaluating the models with the area under the ROC curve metric. For the best prediction models for Bitcoin, Ibovespa, and Vale, values of 0.61, 0.62, and 0.58 were obtained, respectively. In conclusion, the feature selection allowed the improvement of performance in most models, and the input series in the form of percentage variation obtained a good performance, although it was composed of fewer attributes in relation to the other sets tested. [ABSTRACT FROM AUTHOR]

Claimed identities of speakers can be verified by means of automatic speaker verification (ASV) systems, also known as voice biometric systems. Focusing on security and robustness against spoofing attacks on ASV systems, and observing that the investigation of attacker's perspectives is capable of leading the way to prevent known and unknown threats to ASV systems, several countermeasures (CMs) have been proposed during ASVspoof 2015, 2017, 2019, and 2021 challenge campaigns that were organized during INTERSPEECH conferences. Furthermore, there is a recent initiative to organize the ASVSpoof 5 challenge with the objective of collecting the massive spoofing/deepfake attack data (i.e., phase 1), and the design of a spoofing-aware ASV system using a single classifier for both ASV and CM, to design integrated CM-ASV solutions (phase 2). To that effect, this paper presents a survey on a diversity of possible strategies and vulnerabilities explored to successfully attack an ASV system, such as target selection, unavailability of global countermeasures to reduce the attacker's chance to explore the weaknesses, state-of-the-art adversarial attacks based on machine learning, and deepfake generation. This paper also covers the possibility of attacks, such as hardware attacks on ASV systems. Finally, we also discuss the several technological challenges from the attacker's perspective, which can be exploited to come up with better defence mechanisms for the security of ASV systems. [ABSTRACT FROM AUTHOR]

Biometrics-based authentication has become the most well-established form of user recognition in systems that demand a certain level of security. For example, the most commonplace social activities stand out, such as access to the work environment or to one's own bank account. Among all biometrics, voice receives special attention due to factors such as ease of collection, the low cost of reading devices, and the high quantity of literature and software packages available for use. However, these biometrics may have the ability to represent the individual impaired by the phenomenon known as dysphonia, which consists of a change in the sound signal due to some disease that acts on the vocal apparatus. As a consequence, for example, a user with the flu may not be properly authenticated by the recognition system. Therefore, it is important that automatic voice dysphonia detection techniques be developed. In this work, we propose a new framework based on the representation of the voice signal by the multiple projection of cepstral coefficients to promote the detection of dysphonic alterations in the voice through machine learning techniques. Most of the best-known cepstral coefficient extraction techniques in the literature are mapped and analyzed separately and together with measures related to the fundamental frequency of the voice signal, and its representation capacity is evaluated on three classifiers. Finally, the experiments on a subset of the Saarbruecken Voice Database prove the effectiveness of the proposed material in detecting the presence of dysphonia in the voice. [ABSTRACT FROM AUTHOR]

The index derived from Scheimpflug tomography demonstrated good accuracy to discriminate keratoconus and normal cornea and aided in early detection of ectasia in healthy corneas. Purpose: To assess the efficiency of an index derived from multiple logistic regression analysis (MLRA) to measure differences in corneal tomography findings between subclinical keratoconus (KC) in 1 eye, corneal ectasia, and healthy corneas. Setting: 2 private Brazilian ophthalmological centers. Design: Multicenter case–control study. Methods: This study included 187 eyes with very asymmetric ectasia and with normal corneal topography and tomography (VAE-NTT) in the VAE-NTT group, 2296 eyes with healthy corneas in the control group (CG), and 410 eyes with ectasia in the ectasia group. An index, termed as Boosted Ectasia Susceptibility Tomography Index (BESTi), was derived using MLRA to identify a cutoff point to distinguish patients in the 3 groups. The groups were divided into 2 subgroups with an equal number of patients: validation set and external validation (EV) set. Results: 2893 patients with 2893 eyes were included. BESTi had an area under the curve (AUC) of 0.91 with 86.02% sensitivity (Se) and 83.97% specificity (Sp) between CG and the VAE-NTT group in the EV set, which was significantly greater than those of the Belin-Ambrósio Deviation Index (BAD-D) (AUC: 0.81; Se: 66.67%; Sp: 82.67%; P <.0001) and Pentacam random forest index (PRFI) (AUC: 0.87; Se: 78.49%; Sp: 79.88%; P =.021). Conclusions: BESTi facilitated early detection of ectasia in subclinical KC and demonstrated higher Se and Sp than PRFI and BAD-D for detecting subclinical KC. [ABSTRACT FROM AUTHOR]

The accumulation and integration of electronic health records have opened up opportunities for their new uses over the course of a patient’s care. In artificial intelligence (AI), natural language processing (NLP) methods that can extract important information from clinical documents are gaining success. For clinicians to consider using automatically extracted information in their decision making, the information should be explainable to support a reasoning process that is traceable and understandable. We review the state of the art in several types of information extraction methods designed for use in health care. Our focus is on comparing the approaches based on the explainability of the extracted results. We highlight the advantages and challenges faced by the adopted methods in conveying support for the extracted information. [ABSTRACT FROM AUTHOR]

This paper deals with Chebyshev wavelets. We analyze their properties computing their Fourier transform. Moreover, we discuss the differential properties of Chebyshev wavelets due to the connection coefficients. Uniform convergence of Chebyshev wavelets and their approximation error allow us to provide rigorous proofs. In particular, we expand the mother wavelet in Taylor series with an application both in fractional calculus and fractal geometry. Finally, we give two examples concerning the main properties proved. [ABSTRACT FROM AUTHOR]

Swallowing dynamics involves the coordination and interaction of several muscles and nerves which allow correct food transport from mouth to stomach without laryngotracheal penetration or aspiration. Clinical swallowing assessment depends on the evaluator's knowledge of anatomic structures and of neurophysiological processes involved in swallowing. Any alteration in those steps is denominated oropharyngeal dysphagia, which may have many causes, such as neurological or mechanical disorders. Videofluoroscopy of swallowing is presently considered to be the best exam to objectively assess the dynamics of swallowing, but the exam needs to be conducted under certain restrictions, due to patient's exposure to radiation, which limits periodical repetition for monitoring swallowing therapy. Another method, called cervical auscultation, is a promising new diagnostic tool for the assessment of swallowing disorders. The potential to diagnose dysphagia in a noninvasive manner by assessing the sounds of swallowing is a highly attractive option for the dysphagia clinician. Even so, the captured sound has an amount of noise, which can hamper the evaluator's decision. In that way, the present paper proposes the use of a filter to improve the quality of audible sound and facilitate the perception of examination. The wavelet denoising approach is used to decompose the noisy signal. The signal to noise ratio was evaluated to demonstrate the quantitative results of the proposed methodology. Keywords: Swallowing sound; Cervical auscultation; Wavelet denoising

Most of the work we do in signal processing these days is data driven. The shift from the more traditional and model-driven approaches to those that are data driven has also underlined the importance of explainability of our solutions. Because most traditional signal processing approaches start with a number of modeling assumptions, they are comprehensible by the very nature of their construction. However, this is not necessarily the case when we choose to rely more heavily on the data and minimize modeling assumptions. [ABSTRACT FROM AUTHOR]

Wavelets have been placed at the forefront of scientific researches involving signal processing, applied mathematics, pattern recognition and related fields. Nevertheless, as we have observed, students and young researchers still make mistakes when referring to one of the most relevant tools for time–frequency signal analysis. Thus, this correspondence clarifies the terminologies and specific roles of four types of wavelet transforms: the continuous wavelet transform (CWT), the discrete wavelet transform (DWT), the discrete-time wavelet transform (DTWT) and the stationary discrete-time wavelet transform (SDTWT). We believe that, after reading this correspondence, readers will be able to correctly refer to, and identify, the most appropriate type of wavelet transform for a certain application, selecting relevant and accurate material for subsequent investigation. [ABSTRACT FROM AUTHOR]

An abstract interpretation is usually required to analyze acoustic compositions. Nevertheless, there is much signal processing-related research focusing on music processing and similar topics. In that context, the semantic information contained in the melody involving major and minor chords, sharps and flats associated with semibreve, minim, crotchet, quaver, semiquaver and demisemiquaver notes can help in the study of musical sounds. Thus, multiresolution analysis based on discrete wavelet-packet transform (DWPT) associated with a support vector machine (SVM) is used in this paper to inspect and classify those signals, correlating them with a respective acoustic pattern. Results over hundreds of inputs provided almost full accuracy, reassuring the efficacy of the proposed approach for both off-line and real-time usage. [ABSTRACT FROM AUTHOR]

Today's modern world is filled with uncertainties and contradictions. As artificial intelligence (AI) advances, machines are frequently expected to mimic the human brain and, consequently, face the conflicts associated with this task. To overcome them, feature engineering has emerged as the field of science responsible for turning raw data into relevant input information, setting up classifiers in the fused digital signal processing (DSP) and pattern recognition (PR) domain. Despite the ongoing efforts to improve feature learning, handcrafted extraction still plays a very important role. In this context, a careful choice of features is extremely relevant for creating an accurate classification. This article sheds light on the problem of feature quality by using a nonclassical logical system capable of handling conflictive situations. It is known as paraconsistent logic (PL). [ABSTRACT FROM AUTHOR]

Objective: To test the capacity of the Logistic CASUS Score on the second postoperative day, the total serum bilirubin dosage on the second postoperative day and the extracorporeal circulation time, as possible predictive factors of long-term stay in Intensive Care Unit after cardiac surgery. Methods: Eight-two patients submitted to cardiac surgery with extracorporeal circulation were selected. The Logistic CASUS Score on the second postoperative day was calculated and bilirubin dosage on the second postoperative day was measured. The extracorporeal circulation time was also registered. Patients were divided into two groups: Group A, those who were discharged up to the second day of postoperative care; Group B, those who were discharged after the second day of postoperative care. Results: In this study, 40 cases were listed in Group A and 42 cases in Group B. The mean extracorporeal circulation time was 83.9±29.4 min in Group A and 95.8±29.31 min in Group B. Extracorporeal circulation time was not significant in this study (P=0.0735). The level of P significance of bilirubin dosage on the second postoperative day was 0.0003 and an area under the ROC curve of 0.708 with a cut-off point at 0.51 mg/dl was registered. The level of P significance of Logistic CASUS Score on the second postoperative day was 0.0001 and an area under the ROC curve of 0.723 with a cut-off point at 0.40% was registered. Conclusion: The Logistic CASUS Score on the second postoperative day has shown to be better than the bilirubin dosage on the second postoperative day as a predictive tool for calculating the length of stay in intensive care unit during the postoperative care period of patients. Notwithstanding, extracorporeal circulation time has failed to prove itself as an efficient tool to predict an extended length of stay in intensive care unit. [ABSTRACT FROM AUTHOR]

Following two decades of research focusing on the discrete wavelet transform (DWT) and driven by students' high level of questioning, I decided to write this essay on one of the most significant tools for time-frequency signal analysis. As it is widely applicable in a variety of fields, I invite readers to follow this lecture note, which is specially dedicated to show a practical strategy for the interpretation of DWT-based transformed signals while extracting useful information from them. The particular focus resides on the procedure used to find the time support of frequencies and how it is influenced by the wavelet family and the support size of corresponding filters. [ABSTRACT FROM PUBLISHER]

Biometric authentication based on fingerprints, voice, hand shape, facial measurements and iris analysis, among others, are quite common nowadays. In a similar manner, the analysis of acoustic patterns generated during the friction between pen and paper at the time a person subscribes has been shown to be a feasible, adequate, and non-invasive alternative to those techniques. An interesting implementation for such an approach, which is described in this paper, is based on the association of the time-frequency analysis supported by the discrete wavelet-packet transform with one of two pattern-matching classifiers, namely Euclidian norma and an original scoring equation derived from correlation, acting semantically. Valuable results were obtained during the tests, motivating further research. The proposed technique is novel on literature, offering a contribution to the state-of-the-art. [ABSTRACT FROM AUTHOR]

This paper covers a particular area of interest in pattern recognition and knowledge-based systems (PRKbS), being intended for both young researchers and academic professionals who are looking for a polished and refined material. Its aim, playing the role of a tutorial that introduces three feature extraction (FE) approaches based on zero-crossing rates (ZCRs), is to offer cutting-edge algorithms in which clarity and creativity are predominant. The theory, smoothly shown and accompanied by numerical examples, innovatively characterises ZCRs as being neurocomputing agents. Source-codes in C/C++ programming language and interesting applications on speech segmentation, image border extraction and biomedical signal analysis complement the text. [ABSTRACT FROM AUTHOR]

This tutorial, dedicated both to young professionals and students working with digital signal processing and pattern recognition, introduces three feature extraction approaches based on signal energy, characterising alternative and innovative ways for its use. The proposed theory, smoothly presented, is complemented with numerical examples, source-codes in C/C++ programming language, and applications in a diversity of computational problems, namely, neurophysiological signal processing, speech processing, and image processing. The lack of novelty in current energy-based approaches and the feasibility of a balance among creativity , simplicity , and accuracy constitutes the motivation for this text, which reveals how relevant the concept of signal energy may be, if properly employed. [ABSTRACT FROM AUTHOR]

The discrete wavelet transform (DWT) [1] is one of the most powerful tools for time-frequency signal analysis. Its applicability is extremely relevant in various areas of science, as exemplified in [2], with digital signal processing (DSP) as the most notable one. After teaching this topic for many years, I have noted that neither young DSP students nor experienced researchers have perceived several interesting aspects of the DWT from a practical point of view. Thus, the objective of this article is to construe such relevant aspects, providing useful tips to calculate the transform in one (1-D) and two (2-D) dimensions. The entire discussion is also valid to the discrete wavelet-packet transform (DWPT), which extends the decomposition carried out by the DWT so that a finer time-frequency analysis takes place, and also to the discrete shapelet transform (DST) [3], which extends the properties of the DWT and DWPT so that a joint time-frequency-shape signal analysis becomes possible. [ABSTRACT FROM PUBLISHER]