Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Audio Effects for Snare Drum Recording Transformations

The ability to perceptually modify drum recording parameters in a post-recording process would be of great benefit to engineers limited by time or equipment. In this work, a data-driven approach to post-recording modification of the dampening and microphone positioning parameters commonly associated with snare drum capture is proposed. The system consists of a deep encoder that analyzes audio input and predicts optimal parameters of one or more third-party audio effects, which are then used to process the audio and produce the desired transformed output audio. Furthermore, two novel audio effects are specifically developed to take advantage of the multiple parameter learning abilities of the system. Perceptual quality of transformations is assessed through a subjective listening test, and an object evaluation is used to measure system performance. Results demonstrate a capacity to emulate snare dampening; however, attempts were not successful for emulating microphone position changes.
An Interactive 3D Visualization Tool to Analyze the Functionality of Convolutional Neural Networks

Convolutional Neural Networks have outperformed traditional image processing approaches for many image classification tasks. However, despite achieving high classification accuracy on multiple datasets, these machine learning approaches are not transparent and act more like a black box. For this reason, explaining how they work and how they perform classifications is essential for research and education. This lack of interpretability prevents students and beginners in the field from fully understanding the internal functionality of these networks. Moreover, it makes it hard for an expert to debug the models and enhance their performance. Lastly, not explaining how a model produces an output decreases the user’s trust in such machine learning models, especially in medicine. Visualizing the internal variables of convolutional networks enables us to understand how the model processes the input data and generates the output classifications. It also allows us to show how neural networks work by exploring the sensitivity of the model to small changes in the input and how the internal variables affect the results of the classification. These models usually have multiple convolutional and fully-connected layers with hundreds or even thousands of neurons in each layer. Visualizing this large number of variables intuitively is challenging as it is computationally expensive and hard to display with limited screen resolution. This thesis proposes a novel interactive tool to visualize convolutional neural networks in using 3D graphics. The first part of our method partitions ii the feature maps into clusters instead of showing all feature maps simultaneously. We use a K-Means clustering algorithm to cluster the feature maps and then use principal component analysis to represent each cluster’s parameters. The tool also allows the user to create modified versions of the original input by applying binary masks and helps them to analyze the impact of removing different regions of the initial input data. We evaluate this new tool by surveying eight participants with diverse backgrounds and asking them multiple questions about its effectiveness and user experience. In addition, we ask participants to compare the proposed tool with another baseline visualization method. Our evaluation indicates that clustering the feature maps in each layer is an effective and helpful way for the user to understand how convolutional neural networks work.
Indonesian Journal of Electrical Engineering and Computer Science

Received Jun 11, 2022 Revised Aug 4, 2022 Accepted Aug 29, 2022 The supercapacitor technology is maturing to play an important role in the today’s hybrid electrical energy storage systems. Supercapacitors are dominated by unique properties that can accompany other energy storage strategies due to its functional characteristics-such as quick charge and discharge ability, high power density, very long cycle life. It results in to improved system efficiency of hybrid electric energy systems with reduced environmental pollution levels. In this paper, efforts are made to construct a low-cost model of supercapacitor using synthesised carbon material obtained from the pyrolyzed scrap tyre by using both aqueous and Aloe vera gel as semi-aqueous or semi-organic electrolyte. The test results are obtained in the specially developed laboratory to investigate the characteristics of fabricated supercapacitor in terms of capacitance, internal resistance and peak current. The obtained results are superior as compare to Vulcan XC-72R with tradeoff between peak discharge current and other parameters. New carbon material obtained from scrap tyres is cost effective when used along with Aloe vera gel as an electrolyte. Use of scrap tyre carbon as electrode material and Aloe vera gel as naturally available material for electrolyte is an innovation of the presented work.
Fields of cohomological dimension one versus C_1-fields

Ax gave examples of fields of cohomological dimension 1 which are not C1fields. Kato and Kuzumaki asked whether a weak form of the C1-property holds for all fields of cohomological dimension 1 (existence of solutions in extensions of coprime degree rather than existence of a solution in the ground field). Using work of Merkur’ev and Suslin, and of Rost, D. Madore and I recently produced examples which show that the answer is in the negative. In the present note, I produce examples which require less work than the original ones. In the original paper, some of the examples were given by forms of degree 3 in 4 variables. Here, for an arbitrary prime p ≥ 5, I use forms of degree p in p+ 1 variables.
Two-Step Generalized Least Squares Estimators in Multi-equation

Despite the critical analysis of Pagan (1984) and several subsequent applied studies, empirical models characterized by expectations are often estimated with "generated regressor" proxies that are treated as ordinary nonstochastic regressors. This paper offers a Generalized Least Squares estimator designed to cope with the nonscalar disturbance matrix precipatated by generated regressors. The approach is designed as a natural extension of Pagan's analysis and the author demonstrates how it may be applied to multi-equation models. Experimentation with numerical examples reveals the potential severity of ignoring the problem. These results also suggest an easily calculated indicator of potential inference distortion in models that fail to account for "generated regressors. " Copyright 1987 by MIT Press.
A Kiswahili Dataset for the Development of a Text-to-Speech System

: A Text-to-Speech (TTS) system requires adequate data to efficiently build an intelligent and natural system. Based on open-source, free-access data availability, languages are classified into high-resource and low-resource. Kiswahili language, which has a vast population of speakers, is still classified as a low-resource language. It is a low-resource language since there is a limited dataset for building Natural language processing (NLP) systems. This article presents a Kiswahili dataset that contributes to the required language processing resources to build a TTS system or other NLP tasks. A TTS system based on an Artificial Neural Network (ANN) is the current state of the art, and this dataset has been successfully used to build a TTS system. The dataset consists of 7,108 audio clips from a single speaker, with each audio clip varying from 1s to 12.5s. The total audio length is approximately 16 hrs. The dataset created thus meets all the requirements for developing a Kiswahili ANN-based project.
Virtual private network (VPN) model for AIS real time monitoring

The need for a ship monitoring system based on Automatic Identification System (AIS) has been developed and provides many broad benefits for the maritime world in Indonesia. The system can become an early warning system (EWS) for ship operators, port operators, and even ship owners for tracking a movement of their ships. By internet connection based Virtual Private Network (VPN) can connect several remote base stations (RBS), VPN technology can integrate many RBS from several remote places into one AIS monitoring system. VPN-based internet connection is used to guarantee a secure network, private internet connection, and can access a network remotely. VPN technology encrypts data exchange even before data transmission reaches the public network. Connecting to the internet using a VPN connection is like accessing the internet using a special tunnel, not in the main internet network. Thus the use of a VPN-based internet connection in the AIS monitoring system can ensure the security of data transmission from RBS to the AIS monitoring server.
Study on the Application of Microcontroller-Based GPS Sensors on Floater Gliders to Reach the Specified Destination

The purpose of the study is to determine the performance of the control system of the floater glider to a predetermined location. The method used was a combination between software simulation and an experimental free-running test. The Floater control system is equipped with GPS as input and output sensors to determine coordinates or waypoints. The microcontroller mounted was Pixhawk 2.4. 8 based on ARM cortex M4 – 32 bits. The floater was driven by two Brushless DC motors and servo motors to drive the Z-Peller steering to the desired trajectory. The floater system applies the default application software from Pixhawk, namely mission planner to plan and also monitor the actual trajectory. To have a proper procedure for testing the designed control system, the three stages of the test were conducted, firstly numerical tuning test using step function on MATLAB simulation, then trajectory simulation test of waypoint using mission planner, and finally the field test in the ILH pond. From the three stages of the test, it was found that the selection of PID gain values Kp = 0.2, Ki = 0.3, and Kd = 0.02, indicating the performance of the floater glider control system to the planned location has a good fit between the trajectory simulation results and the waypoint testing in the pond. These results can be used as a reference in conducting further research so that the navigation accuracy of the control system will be improved.
Schaum's outline of theory and problems of fluid dynamics

A revised, updated edition of this solved-problem Outline, it is the most up-to-date supplement on the market in this subject. SI units are stressed, new problems added, and the writing style is modernized. Boundary layers, gas dynamics, and theories of turbulence are covered in relation to the latest theories and practices. New artwork is added, amounting to a 20% revision. This is the follow-up course to fluid mechanics and hydraulics and is a basic college engineering course.
Helicoidal trajectories of a charge in a nonconstant magnetic field

In this note we investigate the existence of helicoidal trajectories for a charged particle in a magnetic field. More precisely, denoting by p = p(t) the position in R 3 at the time t of the particle, we say that it moves along a helicoidal trajectory if there exists a versor n in R 3 such that the component of p(t) in the direction of n describes a uniform right motion, whereas the projection p⊥(t )o fp(t) on a plane orthogonal to n is periodic. In particular, if the closed curve supported by p⊥ is simple; i.e., it has no self-intersections, the helicoidal trajectory p(t) will be called simple. From classical physics, in the presence of an external magnetic field B, the motion of a particle of mass m and charge e is driven by the Lorentz force, namely p(t) is a solution of m¨ = e ˙
Debugging real accelerators

Particle losses and emittance growth in the injection process can result form mismatched injected beams arising from quadrupole errors in the ring and injection line. We describe a method, based on carefully analyzing the BPM-corrector response matrix, which allows the accurate determination of quadrupole errors and, at the same time, determines BPM and corrector calibration errors as well as the BPM resolution. Results from SPEAR, NSLS, ALS, and CELSIUS will be briefly described.
N ov 2 00 5 Berry phase correction to electron density in solids and “ exotic ” dynamics

Recent results on the semiclassical dynamics of an electron in a solid are explained using techniques developed for “exotic” Galilean dynamics. The system is indeed Hamiltonian and Liouville’s theorem holds for the symplectic volume form. Suitably defined quantities satisfy hydrodynamic equations. cond-mat/0506051. to appear in Mod. Phys. Lett. B. mailto: duval@cpt.univ-mrs.fr UMR 6207 du CNRS associée aux Universités d’Aix-Marseille I et II et Université du Sud Toulon-Var; Laboratoire affilié à la FRUMAM-FR2291 e-mail: zalanh@ludens.elte. hu e-mail: horvathy@lmpt.univ-tours.fr e-mail: Luigi.Martina@le.infn. it e-mail: peter@Physik.Uni-Bielefeld.DE
Utility of an Automated Artificial Intelligence Echocardiography Software in Risk Stratification of Hospitalized COVID-19 Patients

Cardiovascular risk factors, biomarkers, and diseases are associated with poor prognosis in COVID-19 infections. Significant progress in artificial intelligence (AI) applied to cardiac imaging has recently been made. We assessed the utility of AI analytic software EchoGo in COVID-19 inpatients. Fifty consecutive COVID-19+ inpatients (age 66 ± 13 years, 22 women) who had echocardiography in 4/17/2020–8/5/2020 were analyzed with EchoGo software, with output correlated against standard echocardiography measurements. After adjustment for the APACHE-4 score, associations with clinical outcomes were assessed. Mean EchoGo outputs were left ventricular end-diastolic volume (LVEDV) 121 ± 42 mL, end-systolic volume (LVESV) 53 ± 30 mL, ejection fraction (LVEF) 58 ± 11%, and global longitudinal strain (GLS) −16.1 ± 5.1%. Pearson correlation coefficients (p-value) with standard measurements were 0.810 (<0.001), 0.873 (<0.001), 0.528 (<0.001), and 0.690 (<0.001). The primary endpoint occurred in 26 (52%) patients. Adjusting for APACHE-4 score, EchoGo LVEF and LVGLS were associated with the primary endpoint, odds ratios (95% confidence intervals) of 0.92 (0.85–0.99) and 1.22 (1.03–1.45) per 1% increase, respectively. Automated AI software is a new clinical tool that may assist with patient care. EchoGo LVEF and LVGLS were associated with adverse outcomes in hospitalized COVID-19 patients and can play a role in their risk stratification.
ATP line splitting in association with reduced intracellular magnesium and pH: a brain 31P MR spectroscopic imaging (MRSI) study of pediatric patients with myelin oligodendrocyte glycoprotein antibody‐associated disorders (MOGADs)

Over the past four decades, ATP, the obligatory energy molecule for keeping all cells alive and functioning, has been thought to contribute only one set of signals in brain 31P MR spectra. Here we report for the first time the observation of two separate β‐ATP peaks in brain spectra acquired from patients with myelin oligodendrocyte glycoprotein antibody‐associated disorders (MOGADs) using 3D MRSI at 7 T. In voxel spectra with β‐ATP line splitting, these two peaks are separated by 0.46 ± 0.18 ppm (n = 6). Spectral lineshape analysis indicates that the upper field β‐ATP peak is smaller in relative intensity (24 ± 11% versus 76 ± 11%), and narrower in linewidth (56.8 ± 10.3 versus 41.2 ± 10.3 Hz) than the downfield one. Data analysis also reveals a similar line splitting for the intracellular inorganic phosphate (Pi) signal, which is characterized by two components with a smaller separation (0.16 ± 0.09 ppm) and an intensity ratio (26 ± 7%:74 ± 7%) comparable to that of β‐ATP. While the major components of Pi and β‐ATP correspond to a neutral intracellular pH (6.99 ± 0.01) and a free Mg2+ level (0.18 ± 0.02 mM, by Iotti's conversion formula) as found in healthy subjects, their minor counterparts relate to a slightly acidic pH (6.86 ± 0.07) and a 50% lower [Mg2+] (0.09 ± 0.02 mM), respectively. Data correlation between β‐ATP and Pi signals appears to suggest an association between an increased [H+] and a reduced [Mg2+] in MOGAD patients.
Estimating ancient biogeographic patterns with statistical model discrimination

The geographic ranges in which species live is a function of many factors underlying ecological and evolutionary contingencies. Observing the geographic range of an individual species provides valuable information about these historical contingencies for a lineage, determining the distribution of many distantly related species in tandem provides information about large‐scale constraints on evolutionary and ecological processes generally. We present a linear regression method that allows for the discrimination of various hypothetical biogeographical models for determining which landscape distributional pattern best matches data from the fossil record. The linear regression models used in the discrimination rely on geodesic distances between sampling sites (typically geologic formations) as the independent variable and three possible dependent variables: Dice/Sorensen similarity; Euclidean distance; and phylogenetic community dissimilarity. Both the similarity and distance measures are useful for full‐community analyses without evolutionary information, whereas the phylogenetic community dissimilarity requires phylogenetic data. Importantly, the discrimination method uses linear regression residual error to provide relative measures of support for each biogeographical model tested, not absolute answers or p‐values. When applied to a recently published dataset of Campanian pollen, we find evidence that supports two plant communities separated by a transitional zone of unknown size. A similar case study of ceratopsid dinosaurs using phylogenetic community dissimilarity provided no evidence of a biogeographical pattern, but this case study suffers from a lack of data to accurately discriminate and/or too much temporal mixing. Future research aiming to reconstruct the distribution of organisms across a landscape has a statistical‐based method for determining what biogeographic distributional model best matches the available data.
Crystal structure of a type III Rubisco in complex with its product 3‐phosphoglycerate

The crystal structure of the complex of ribulose‐1,5‐bisphosphate carboxylase/oxygenase (Rubisco; EC 4.1.1.39) from Archaeoglobus fulgidus (afRubisco) with its products 3PGAs has been determined to a resolution of 1.7 Å and is of the closed form. Type III Rubiscos such as afRubisco have 18 out of the 19 essential amino acid residues of canonical Rubisco; the 19th is Tyr rather than Phe. Superposition with the structure of a complex of the similar tkRubisco with the six‐carbon intermediate analog 2CABP shows the same conformation of the 19 residues except for Glu46 and Thr51. Glu46 adopts a unique conformation different from that in other Rubiscos and makes two H‐bonds with the ligand 3PGA. Similar to other closed state Rubiscos, the backbone of Thr51 is rotated and the side chain makes an H‐bond with the ligand 3PGA. Two product 3PGA molecules are bound at the active site, overlapping well with the 2CABP of tkRubisco/2CABP. The positions of the P1 and P2 phosphate groups differ by 0.4 and 0.53 Å, respectively, between 2CABP and the two 3PGAs. This afRubisco/3PGA complex mimics an intermediate stage of the carboxylation reaction which occurs after the production of the two 3PGA products but before the reopening of the active site. The stability of this complex suggests that the Rubisco active site will not reopen before both 3PGA products are formed.
A New Hybrid Optimization Model Based on Imperialistic Competition and Differential Evolution Meta-Heuristic and clustering Algorithms

Metaheuristic algorithms such as imperialistic competition algorithm and differential evolution are now becoming powerful methods for solving many difficult optimization problems. In this paper, we propose a new meta-heuristic method; this method is based clustering Algorithm that is implemented by the local search in each cluster and global search between the leaders of each clusters. We also intend to combine the advantages of existing algorithms into the new optimization model based clustering Algorithm. In each cluster, we use the behavior of the colony in the ICA and the DE for the leader to survey the search space. After a detailed explanation of this algorithm, we will then compare the proposed model with the other algorithms such as: particle swarm optimization, genetic, imperialistic competition and differential evolution algorithms. The results show the ability of this new algorithm in dealing with different types of optimization problems.
A Multimodal Multilevel Converged Attention Network for Hand Gesture Recognition With Hybrid sEMG and A-Mode Ultrasound Sensing

Gesture recognition based on surface electromyography (sEMG) has been widely used in the field of human–machine interaction (HMI). However, sEMG has limitations, such as low signal-to-noise ratio and insensitivity to fine finger movements, so we consider adding A-mode ultrasound (AUS) to enhance the recognition impact. To explore the influence of multisource sensing data on gesture recognition and better integrate the features of different modules. We proposed a multimodal multilevel converged attention network (MMCANet) model for multisource signals composed of sEMG and AUS. The proposed model extracts the hidden features of the AUS signal with a convolutional neural network (CNN). Meanwhile, a CNN-LSTM (long-short memory network) hybrid structure extracts some spatial-temporal features from the sEMG signal. Then, two types of CNN features from AUS and sEMG are spliced and transmitted to a transformer encoder to fuse the information and interact with sEMG features to produce hybrid features. Finally, the classification results are output employing fully connected layers. Attention mechanisms are used to adjust the weights of feature channels. We compared MMCANet’s feature extraction and classification performance with that of manually extracted sEMG-AUS features using four traditional machine-learning (ML) algorithms. The recognition accuracy increased by at least 5.15%. In addition, we tried deep learning (DL) methods with CNN on single modals. The experimental results showed that the proposed model improved 14.31% and 3.80% over the CNN method with single sEMG and AUS, respectively. Compared with some state-of-the-art fusion techniques, our method also achieved better results.