Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of acoustic source localization with adaptive neural network using distance mating‐based red deer algorithm

Multichannel, audio processing approaches are widely examined in human–computer interaction, autonomous robots, audio surveillance, and teleconferencing systems. The numerous applications are linked to the speech technology and acoustic analysis area. Much attention is received to the active speakers and spatial localization of acoustic sources on the acoustic sensor arrays. Baseline approaches provide negotiable performance in a real‐world comprised of far‐field/near‐field monitoring, reverberant and noisy environments, and also the outdoor/indoor scenarios. A practical system to detect defects in complex structures is the time difference mapping (TDM) technique. The significant scope of the research is to search the location using the minimum distance point in the time difference database to be apart from the verification point. In the case of the improved “time difference mapping (I‐TDM)” technique and traditional “time difference mapping (T‐TDM)” technique, the denser grids and vast database permit increased accuracy. In the database, if the location points are not present, then the accurate localization of the I‐TDM and T‐TDM techniques is damaged. Hence, to handle these problems, this article plans to develop acoustic source localization according to the deep learning strategy. The audio dataset is gathered from the benchmark source called the SSLR dataset and is initially subjected to preprocessing, which involves artifact removal and smoothing for effective processing. Further, the adaptive convolutional neural network (CNN)‐based feature set creation is performed. Here, the adaptive CNN is accomplished by the improved optimization algorithm called distance mating‐based red deer algorithm (DM‐RDA). With this trained feature set, the acoustic source localization is done by the weight updated deep neural network, in which the same DM‐RDA is used for optimizing the training weight. The simulation outcome proves that the designed model produced enhanced performance compared to other traditional source localization estimators.
Statistical inference under imputation for proportional hazard model with missing covariates

ABSTRACT Missing covariate data are common in biomedical studies. In this article, by using the non parametric kernel regression technique, a new imputation approach is developed for the Cox-proportional hazard regression model with missing covariates. This method achieves the same efficiency as the fully augmented weighted estimators (Qi et al. 2005. Journal of the American Statistical Association, 100:1250) and has a simpler form. The asymptotic properties of the proposed estimator are derived and analyzed. The comparisons between the proposed imputation method and several other existing methods are conducted via a number of simulation studies and a mouse leukemia data.
Dilated Convolutional Neural Network for Skin Cancer Classification Based on Image Data

Skin cancer is a disorder of cell growth in the skin. Skin cancer has a big impact, causing physical disabilities that can be seen directly and high treatment costs. In addition, skin cancer also causes death if nor treated properly. Generally, dermatologists diagnose the presence of skin cancer in the human body by using the Biopsy process. In this study, the Dilated Convolutional Neural Network method was used to classify skin cancer image data. Dilated Convolutional Neural Network method is a development method of the Convolutional Neural Network method by modifying the dilation factors. The Dilated Convolutional Neural Network method is divided into two stages, including feature extraction and fully connected layer. The data used in this study is HAM1000 dataset. The data are dermoscopic image datasets which consists of 10015 images data from 7 types of skin cancer. This study conducted several experimental scenarios of changes in the value of d, which are 2,4,6, and 8 to get the optimal results. The parameters used in this study are epoch = 100, minibatch size = 8, learning rate = 0.1, and dropout = 0.5. The best results in this study were obtained with value of d=2 with the value of accuracy is 85.67% and the sensitivity is 65.48%.
Ergodic Theory and Number Theory

Elon Lindenstrauss was awarded the 2010 Fields Medal for his results on measure rigidity in ergodic theory, and their applications to number theory. The web page of the ICM 2010 contains the following brief description of Elon Lindenstrauss’ achievements: Lindenstrauss has made far-reaching advances in ergodic theory, the study of measure preserving transformations. His work on a conjecture of Furstenberg and Margulis concerning the measure rigidity of higher rank diagonal actions in homogeneous spaces has led to striking applications. Specifically, jointly with Einsiedler and Katok, he established the conjecture under a further hypothesis of positive entropy. It has impressive applications to the classical Littlewood Conjecture in the theory of diophantine approximation. Developing these as well other powerful ergodic theoretic and arithmetical ideas, Lindenstrauss resolved the arithmetic quantum unique ergodicity conjecture of Rudnick and Sarnak in the theory of modular forms. He and his collaborators have found many other unexpected applications of these ergodic theoretic techniques in problems in classical number theory. His work is exceptionally deep and its impact goes far beyond ergodic theory.
Ipma's Analysis on Factors Affecting Indrive Indonesia's Customer Loyalty

Purpose: The purpose of this study was to identify the factors that influence customer loyalty to indrive application users in the city of Bandung, Indonesia using IPMA analysis on SmartPLS Theoretical framework: This research is a development of theoretical aspects of the online transportation industry in Indonesia which consists of app design and trust variables as moderator variables between e-service quality and customer satisfaction. Then there is price, value for money and perceived quality which affect customer satisfaction, and customer satisfaction will affect customer loyalty. Design/methodology/approach: This study uses a quantitative method with data sources derived from surveys through the distribution of online questionnaires to 160 inDrive application users. The data analysis technique used is SEM-PLS and IPMA Analysis using SmartPLS software. Findings: Customer satisfaction is the variable that most influences customer loyalty of inDrive application users in Bandung based on IPMA Analysis, because it has the highest performance value when compared to other variables Research, Practical & Social implications: InDrive management must pay attention to customer satisfaction for each inDrive application user in the city of Bandung in order to achieve, maintain the consistency and sustainability of the inDrive company in the city of Bandung to maintain customer loyalty. Originality/value: The study is the first study conducted to analyze the factors that influence customer loyalty in indrive application users in the Bandung City, Indonesia, so that it can be a reference and additional reference on academic knowledge and managerial aspects.
Quantum ratchet effect in a time non-uniform double-kicked model

The quantum ratchet effect means that the directed transport emerges in a quantum system without a net force. The delta-kicked model is a quantum Hamiltonian model for the quantum ratchet effect. This paper investigates the quantum ratchet effect based on a time non-uniform double-kicked model, in which two flashing potentials alternately act on a particle with a homogeneous initial state of zero momentum, while the intervals between adjacent actions are not equal. The evolution equation of the state of the particle is derived from its Schrodinger equation, and the numerical method to solve the evolution equation is pointed out. The results show that quantum resonances can induce the ratchet effect in this time non-uniform double-kicked model under certain conditions; some quantum resonances, which cannot induce the ratchet effect in previous models, can induce the ratchet effect in this model, and the strengths of the ratchet effect in this model are stronger than those in previous models under certain conditions. These results enrich people’s understanding of the delta-kicked model, and provides a new optional scheme to control the quantum transport of cold atoms in experiment.
Design considerations for robust noise rejection in otoacoustic emissions measured in-field using adaptive filtering

Otoacoustic emission (OAE) measurement is a sensitive and effective technique to monitor changes in the inner ear potentially induced by noise exposure. However, outside a controlled testing environment, measurements are challenging since the level of ambient noise might be higher than the low-level OAE response. Therefore, an OAE system was designed, suitable to measure OAEs repeatedly on an individual worker in noisy test conditions. This system features a left and right earpiece, each equipped with a pair of miniature loudspeakers, an external and an internal microphone. In addition to the passive attenuation of the earpiece, adaptive filtering on the distortion product OAE (DPOAE) signals is used to further enhance the ratio between the measured OAE signal and interfering noise. The adaptive filtering technique uses the sound captured by the ipsilateral external and internal microphones as well as from the contralateral internal and external microphones. In this paper, the accuracy of DPOAE signals are studied using different combinations of the four microphones in single and dual adaptive filter topologies, as well as the benefits of adding a fixed transfer function in the adaptive filtering algorithm topology to estimate the acoustic path. Side-by-side comparison shows that a dual-stage adaptive filtering algorithm, using a combination of the contralateral internal microphone with the ipsilateral external microphone, is the most promising approach to denoise the DPOAE signal.
Rank based Least-squares Independent Component Analysis

In this paper, we propose a nonparametric rank-based alternative to the least-squares independent component analysis algorithm developed. The basic idea is to estimate the squared-loss mutual information, which used as the objective function of the algorithm, based on its copula density version. Therefore, no marginal densities have to be estimated. We provide empirical evaluation of the proposed algorithm through simulation and real data analysis. Since the proposed algorithm uses rank values rather than the actual values of the observations, it is extremely robust to the outliers and suffers less from the presence of noise than the other algorithms.
Linear Regression Problem Relaxations Solved by Nonconvex ADMM With Convergence Analysis

In this work, we focus on studying the differentiable relaxations of several linear regression problems, where the original formulations are usually both nonsmooth with one nonconvex term. Unfortunately, in most cases, the standard alternating direction method of multipliers (ADMM) cannot guarantee global convergence when addressing these kinds of problems. To address this issue, by smoothing the convex term and applying a linearization technique before designing the iteration procedures, we employ nonconvex ADMM to optimize challenging nonconvex-convex composite problems. In our theoretical analysis, we prove the boundedness of the generated variable sequence and then guarantee that it converges to a stationary point. Meanwhile, a potential function is derived from the augmented Lagrange function, and we further verify that the objective function is monotonically nonincreasing. Under the Kurdyka-Łojasiewicz (KŁ) property, the global convergence is analyzed step by step. Finally, experiments on face reconstruction, image classification, and subspace clustering tasks are conducted to show the superiority of our algorithms over several state-of-the-art ones.
Barriers and facilitators of brown rice consumption among staff at Durban University of Technology, South Africa

South Africa is experiencing an epidemiological and nutrition transition that is characterised by a surge in the prevalence of overweight and obesity, with an increase in the consumption of energy-dense, low nutritive value foods and beverages and a marked decrease in physical activity. Numerous studies have documented the healthful benefits of making the transitional shift from white rice to brown rice. The aim of this study was to explore the barriers and facilitators of brown rice consumption among staff at Durban University of Technology in South Africa. In this exploratory qualitative study, focus group discussions (FGDs) were conducted to gain an understanding of brown rice consumption among staff at the university. The study sample comprised of non-diabetic, permanent and contract staff at the University. Three focus group discussions were conducted until meaning saturation was reached; two were face to face and one online. Data were analysed using the thematic analysis method. Participants perceived healthy foods as safe and nutrient-dense, with consumption leading to positive health outcomes whereas unhealthy foods were perceived as energy-dense, nutrient-poor and hyper-palatable. The main determinants of food choice included peer and family influence, psychological state, education, availability, affordability, state of health and taste. Factors that influenced rice choice included sensory acceptability, availability, affordability, cultural acceptability, and convenience. Major barriers of brown rice consumption included family preferences and influence, sensory acceptability, time-intensive preparation, affordability, knowledge and skills. Two facilitators of brown rice consumption emerged: natural sensory appeal and healthful benefits. The sensory attributes of brown rice have had limited consumer acceptance, compelling the activation of consumer education and visually enticing cues in preparation methods and extended application uses in food products. This study found that the inclusion and acceptance of brown rice into the participants’ diet is limited. A multi-stakeholder approach towards consumer education and practical preparation solutions is likely to positively influence consumer acceptance of brown rice. Innovative and creative food behaviour modifications should be promoted to channel change towards healthier food choices for a positive impact on health and wellness. Key words: Brown rice, focus group discussions, perception, barriers, facilitators, consumption
Students’ spatial thinking toward the school environment in Indonesia

This study describes the level of cognitive mapping among students in one of the senior high schools in Indonesia. This study aimed to determine the cognitive map of secondary students using a sketch map. The cognitive map elements studied were paths, edges, landmarks, districts, and nodes. This research uses an interpretive paradigm to reveal how students make meaning of their school environment through map sketches. The research approach used was descriptive qualitative. Data collection techniques included a map sketching test and direct interviews with six randomly selected key informants. Cognitive map analysis was used to analyse the cognitive map elements in students' map sketches. The results showed that second-grade students have diverse cognitive maps of their school environment. Thus, this study recommends that attention and substantial efforts from various parties, especially geography subject teachers, be more intense in utilising the surrounding environment as a geography learning laboratory at SMUK ST. Fransiskus Xaverius Ruteng.
Phosphorus Doping Strategy-Induced Synergistic Modification of Interlayer Structure and Chemical State in Ti3C2Tx toward Enhancing Capacitance

Heteroatom doping is considered an effective method to substantially improve the electrochemical performance of Ti3C2Tx MXene for supercapacitors. Herein, a facile and controllable strategy, which combines heat treatment with phosphorous (P) doping by using sodium phosphinate (NaH2PO2) as a phosphorus source, is used to modify Ti3C2Tx. The intercalated ions from NaH2PO2 act as “pillars” to expand the interlayer space of MXene, which is conducive to electrolyte ion diffusion. On the other hand, P doping tailors the surface electronic state of MXene, optimizing electronic conductivity and reducing the free energy of H+ diffusion on the MXene surface. Meanwhile, P sites with lower electronegativity owning good electron donor characteristics are easy to share electrons with H+, which is beneficial to charge storage. Moreover, the adopted heat treatment replaces –F terminations with O-containing groups, which enhances the hydrophilicity and provides sufficient active sites. The change in surface functional groups increases the content of high valence-stated Ti with a high electrochemical activity that can accommodate more electrons during discharge. Synergistic modification of interlayer structure and chemical state improves the possibility of Ti3C2Tx for accommodating more H+ ions. Consequently, the modified electrode delivers a specific capacitance of 510 F g−1 at 2 mV s−1, and a capacitance retention of 90.2% at 20 A g−1 after 10,000 cycles. The work provides a coordinated strategy for the rational design of high-capacitance Ti3C2Tx MXene electrodes.
To what extent does share ownership affect informative earnings management? Evidence from Indonesian manufacturing sector

Inconsistent findings regarding the relationship between share ownership and earnings management, specifically in terms of informative and opportunistic practices, have been observed in previous studies. To address this gap, the present research aims to investigate the impact of institutional, governmental, and family ownership on informative earnings management. The sample comprises 615 manufacturing firm-year observations listed on the Indonesian Stock Exchange. Earnings management is assessed by examining discretionary accruals, which are further categorized into informative and opportunistic acts based on earnings growth. Logistic regression analysis is employed to analyze the data. The results indicate that both institutional and family ownership have a positive effect on informative earnings management. This suggests that institutional and family shareholders play influential roles in monitoring managerial behavior, particularly in encouraging informative earnings management practices rather than opportunistic ones. Conversely, governmental ownership does not have a significant effect on earnings management. This finding suggests that government shareholders may have lesser interest in evaluating managerial performance based on earnings and instead prioritize political and social considerations. Overall, this study contributes to the existing literature by shedding light on the distinct influences of different types of share ownership on earnings management practices, particularly in terms of their impact on informative earnings management.
Tau magnetic moment

The τ lepton magnetic moment theoretical predictions and measurements are reviewed. While it is believed that such a high mass particle is a good candidate to show up new physics, this is not the case up to now. The magnetic moment of elementary fermions, and in particular the anomalous magnetic moment of the electron, had an historical impact both in relativistic quantum mechanics and in quantum field theories. Besides, many new physics models were discarded when confronted with these magnitudes. More recently, the discrepancy of the experiments and the theoretical predictions for the muon anomalous magnetic moment is still an open issue. For the τ lepton, instead, while the theoretical prediction is well known for the standard model and some new physics models, the data are very far of determining even its sign or the first figure. We will discuss the most important theoretical aspects of the τ magnetic moment, and also the current accepted measurements and future perspectives, in particular related to B-factories.
Modelling Facial Features for Emotion Recognition and Synthesis

ABSTRACT Emotion recognition and synthesis is one of the most important challenges for effective human–computer interaction. In this paper, a novel approach for emotion recognition is presented by modelling facial feature deformations. The presented approach is based on the fact that facial features such as lip, nose, eyes, and eyebrows get deformed due to variation in emotions. To measure the change in shapes of different facial features, landmark points are extracted around the facial features. Thin plate spline (TPS) is used to model the deformation of these landmark points. The basic property of TPS mapping function is that it is capable of computing rigid as well as non-rigid transformations between neutral and emotion image frames. The rigid transformation parameters represent affine parameters caused by head movement and non-rigid transformation parameters are used as representatives of facial feature deformation caused by emotion. To prove the modelling ability of TPS, non-rigid parameters are fed to support vector machine for emotion recognition. Moreover, an attempt is made to synthesize emotion by using TPS warping function. The mean of non-rigid transformation for an emotion is used as a template to warp the neutral image to emotion image. To evaluate the proposed approach, extended Cohn-Kanade database and JAFFE database are used and experimental results show 95% and 70% accuracy for them, respectively.
A Model to Prevent Gray Hole Attack in Mobile Ad-Hoc Networks

—Over the past few years, Mobile Ad-hoc Networks (MANET) has been playing an important role in ubiquitous networks based on its ability to support mobility without depending on infrastructure-based design, dynamic topology, and thus, are known as decentralized environment. One of the advantages of MANET is that its nodes can act both as routers and hosts. This, therefore, implies that its nodes can transmit packets between source to destination nodes. As a result of such and many more advantages, these networks are more vulnerable to different types of network attacks. In the recent past, several secured routing protocols were proposed and implemented for MANET. However, those protocols cannot fully guarantee security within these networks in terms of Denial of Services (DoS) attacks such as black hole and gray hole attacks. The review of the literature showed that existing solutions cannot always ensure true node classification. This is because MANET’s cooperative existence sometimes leads to the false exclusion of innocent nodes and/or proper classification of malicious nodes. A new Gray Hole Prevention (GRAY-HP) algorithm for the detection of malicious nodes with the actual high accuracy ratio of node classification is proposed in this paper. The proposed algorithm employs and modifies the gray-attack prevention technique known as Secure Detection Prevention and Elimination Gray Hole (SDPEGH), and the proactive scheme. It has been confirmed by Network Simulator 2 (NS2) computer simulation that the proposed algorithm outperforms the Genetic Algorithm to Bacterial Foraging Optimization (GA-BFO) and Rough Set Theory (RSetTheory) algorithms in terms of throughput, routing overhead and delivery ratio. The proposed GRAY-HP algorithm guarantees the successful elimination of Gray hole nodes, while it also ensures that no legitimate nodes are excluded