Miracle-dz/UniFAR-FTUs huggingface dataset:
Mispronunciation Detection and Diagnosis in L2 English Speech Using Multidistribution Deep Neural Networks

This paper investigates the use of multidistribution deep neural networks (DNNs) for mispronunciation detection and diagnosis (MDD), to circumvent the difficulties encountered in an existing approach based on extended recognition networks (ERNs). The ERNs leverage existing automatic speech recognition technology by constraining the search space via including the likely phonetic error patterns of the target words in addition to the canonical transcriptions. MDDs are achieved by comparing the recognized transcriptions with the canonical ones. Although this approach performs reasonably well, it has the following issues: 1) Learning the error patterns of the target words to generate the ERNs remains a challenging task. Phones or phone errors missing from the ERNs cannot be recognized even if we have well-trained acoustic models; and 2) acoustic models and phonological rules are trained independently, and hence, contextual information is lost. To address these issues, we propose an acoustic-graphemic-phonemic model (AGPM) using a multidistribution DNN, whose input features include acoustic features, as well as corresponding graphemes and canonical transcriptions (encoded as binary vectors). The AGPM can implicitly model both grapheme-to-likely-pronunciation and phoneme-to-likely-pronunciation conversions, which are integrated into acoustic modeling. With the AGPM, we develop a unified MDD framework, which works much like free-phone recognition. Experiments show that our method achieves a phone error rate (PER) of 11.1%. The false rejection rate (FRR), false acceptance rate (FAR), and diagnostic error rate (DER) for MDD are 4.6%, 30.5%, and 13.5%, respectively. It outperforms the ERN approach using DNNs as acoustic models, whose PER, FRR, FAR, and DER are 16.8%, 11.0%, 43.6%, and 32.3%, respectively.
Joint Allocation on 3C Resources for Three-Tier Cooperation Mobile Computing Networks

The three-layer cellular network integrating mobile cloud computing (MCC) and mobile edge computing (MEC) provides an effective example for emerging services with intensive computing and low latency requirements. Computing intensive and delay sensitive tasks can be processed by local, edge nodes or cloud services. In the paper, we consider a three-tier collaborative computing network composed of mobile devices, edge nodes and cloud servers. In this network, we jointly optimize the offloading decision, the computation, communication and caching (3C) resource to minimize task delay. However, the formulated problem is a large-scale mixed integer nonlinear optimization problem with the increasing number of base stations and devices, which is difficult to solve. To deal with the problem, we propose a parallel processing scheme based on alternating direction method of multipliers (ADMM) and relaxation method. The proposed scheme first reconstructs the original problem through relaxation and auxiliary variables, then decomposes the large-scale problem into several sub problems and improves the task processing efficiency through parallel processing. Simulation results show that the proposed scheme can achieve near optimal performance under low complexity, and can reduce the task delay about 26% compared with other schemes.
The Role of Grain Boundaries in Low‐Temperature Plasticity of Olivine Revealed by Nanoindentation

The rheological properties of olivine influence large‐scale, long‐term deformation processes on rocky planets. Studies of the deformation of olivine at low temperatures and high stresses have emphasized the importance of a grain‐size effect impacting yield stress. Laboratory studies indicate that aggregates with finer grains are stronger than those with coarser grains. However, the specific interactions between intracrystalline defects and grain boundaries leading to this effect in olivine remain unresolved. In this study, to directly observe and quantify the mechanical properties of olivine grain boundaries, we conduct nanoindentation tests on well characterized bicrystals. Specifically, we perform room‐temperature spherical and Berkovich nanoindentation tests on a subgrain boundary (13°, [100]/(016)) and a high‐angle grain boundary (60°, [100]/(011)). These tests reveal that plasticity is easier to initiate if the high‐angle grain boundary is within the deformation volume, whereas the subgrain boundary does not impact the initiation of plasticity. Additionally, the high‐angle grain boundary acts as a barrier to slip transmission, whereas the subgrain boundary does not interact with dislocations in a measurable manner. We suggest that the distribution of grain‐boundary types in olivine‐rich rocks might play a role in generating local differences in mechanical behavior during deformation.
Towards Improving Realism of Perception in Artery

Simulation tools are frequently used to analyze vehicular networks. However, these tools are not ready yet to model the world in the necessary level of detail for Collective Perception. The network load generated by the Collective Perception Service highly depends on the sender's sensors, which are modeled insufficiently by current vehicular network simulators. This paper presents an Environment Model extension to Artery, a popular simulation framework for the ETSI C-ITS communication stack. It enables sensor-equipped vehicles to perceive objects from their perspective individually to improve the realism of perception. Furthermore, it adds new sensors that produce synthetic object data for detected objects based on the characteristics of various automotive sensors. A performance evaluation shows that the computational overhead for the extension is limited, so it is suitable even for large-scale simulation studies using Artery.
Ferric Carboxymaltose in Patients with Acute Decompensated Heart Failure and Iron Deficiency: A Real-Life Study

Background: The correction of iron deficiency (ID) with ferric carboxymaltose (FCM) is a recommended intervention in heart failure (HF) with reduced ejection fraction. Our aim is to evaluate, in a real-life setting, the clinical significance of ID screening and FCM treatment in acute decompensated HF (ADHF). Methods: In a cohort of ADHF patients, the prevalence of ID and FCM administration were investigated. Among the 104 patients admitted for ADHF, in n = 90 (median age 84, 53.5% with preserved left ventricular ejection fraction—LVEF), a complete iron status evaluation was obtained. ID was detected in n = 73 (81.1%), 55 of whom were treated with in-hospital FCM. The target dose was reached in n = 13. Results: No significant differences were detected in terms of age, sex, comorbidities, or LVEF between the FCM-supplemented and -unsupplemented patients. During a median follow-up of 427 days (IQR 405–466) among the FCM-supplemented patients, only 14.5% received FCM after discharge; the mortality and rehospitalizations among FCM-supplemented and -unsupplemented patients were similar (p = ns). In a follow-up evaluation, ID was still present in 75.0% of the FCM-supplemented patients and in 69.2% of the unsupplemented patients (p = ns). Conclusions: In this real-life ADHF cohort, FCM was administered at lower-than-prescribed doses, thus having no impact on ID correction. The significance of our findings is that only achieving the target dose of FCM and pursuing outpatient treatment can correct ID and produce long-term clinical benefits.
The Deep Ocean Density Structure at the Last Glacial Maximum: What Was It and Why?

The search for reliable proxies of past deep ocean temperature and salinity has proved difficult, thereby limiting our ability to understand the coupling of ocean circulation and climate over glacial-interglacial timescales. Previous inferences of deep ocean temperature and salinity from sediment pore fluid oxygen isotopes and chlorinity indicate that the deep ocean density structure at the Last Glacial Maximum (LGM, approximately 20,000 years BP) was set by salinity, and that the density contrast between northern and southern sourced deep waters was markedly greater than in the modern ocean. High density stratification could help explain the marked contrast in carbon isotope distribution recorded in the LGM ocean relative to that we observe today, but what made the ocean's density structure so different at the LGM? How did it evolve from one state to another? Further, given the sparsity of the LGM temperature and salinity data set, what else can we learn by increasing the spatial density of proxy records? We investigate the cause and feasibility of a highly and salinity stratified deep ocean at the LGM and we work to increase the amount of information we can glean about the past ocean from pore fluid profiles of oxygen isotopes and chloride. Using a coupled ocean--sea ice--ice shelf cavity model we test whether the deep ocean density structure at the LGM can be explained by ice--ocean interactions over the Antarctic continental shelves, and show that a large contribution of the LGM salinity stratification can be explained through lower ocean temperature. In order to extract the maximum information from pore fluid profiles of oxygen isotopes and chloride we evaluate several inverse methods for ill-posed problems and their ability to recover bottom water histories from sediment pore fluid profiles. We demonstrate that Bayesian Markov Chain Monte Carlo parameter estimation techniques enable us to robustly recover the full solution space of bottom water histories, not only at the LGM, but through the most recent deglaciation and the Holocene up to the present. Finally, we evaluate a non-destructive pore fluid sampling technique, Rhizon samplers, in comparison to traditional squeezing methods and show that despite their promise, Rhizons are unlikely to be a good sampling tool for pore fluid measurements of oxygen isotopes and chloride.
Super-Resolution of SAR Images With Speckle Noise Based on Combination of Cubature Kalman Filter and Low-Rank Approximation

In this article, two novel methods for synthetic aperture radar (SAR) image super-resolution (SR) are proposed. The main challenge for SAR image SR reconstruction is speckle noise. For this reason, a novel algorithm termed the importance sampling cubature Kalman filter (ISCKF) is proposed to reconstruct a high-resolution (HR) image from a series of low-resolution (LR) images. However, as the reconstructed image usually embraces residual noise visually, we establish a nonlinear low-rank optimization model in order to further reduce the speckle noise drastically. Correspondingly, an alternating direction method of multipliers based on the low-rank model (ADMM-LR) algorithm is proposed to solve it, which yields the other novel method termed ISCKF + ADMM-LR for SAR image SR. In addition, we establish the computational complexity of the proposed algorithms. The experimental results of both the simulated images and real SAR images demonstrate that the performance of the proposed methods is superior to some state-of-the-art methods in both SR and despeckle.
COMPARATIVE ANALYSIS OF NEURAL NETWORK MODELS FOR THE PROBLEM OF SPEAKER RECOGNITION

The subject matter of the article are the neural network models designed or adapted for the problem of voice analysis in the context of the speaker identification and verification tasks. The goal of this work is to perform a comparative analysis of relevant neural network models in order to determine the model(s) that best meet the chosen formulated criteria, – model type, programming language of model’s implementation, parallelizing potential, binary or multiclass, accuracy and computing complexity. Some of these criteria were chosen because of universal importance, regardless of particular application, such as accuracy and computational complexity. Others were chosen due to the architecture and challenges of the scientific communication system mentioned in the work that performs tasks of the speaker identification and verification. The relevance of the paper lies in the prevalence of audio as a communication medium, which results in a wide range of practical applications of audio intelligence in various fields of human activity (business, law, military), as well as in the necessity of enabling and encouraging efficient environment for inward-facing audio-based scientific communication among young scientists in order for them to accelerate their research and to acquire scientific communication skills. To achieve the goal, the following tasks were solved: criteria for models to be judged upon were formulated based on the needs and challenges of the proposed model; the models, designed for the problems of speaker identification and verification, according to formulated criteria were reviewed with the results compiled into a comprehensive table; optimal models were determined in accordance with the formulated criteria. The following neural network based models have been reviewed: SincNet, VGGVox, Jasper, TitaNet, SpeakerNet, ECAPA_TDNN. Conclusions. For the future research and practical solution of the problem of speaker authentication it will be reasonable to use a convolutional neural network implemented in the Python programming language, as it offers a wide variety of development tools and libraries to utilize.
Unlocking the secrets of Miri country music festival in Malaysia: a moderated-mediation model examining the power of FOMO, flow and festival satisfaction in driving revisiting intentions

ABSTRACT This study explores how the fear of missing out (FOMO) affects the flow experience, festival satisfaction, and revisitation intention of attendees at a country music festival. Based on the cognitive appraisal theory, the study surveyed 317 participants and found that several factors contribute to the flow experience, which in turn impacts festival satisfaction and revisitation intention. The results reveal that participants with higher FOMO levels have a higher intention to revisit, with festival satisfaction mediating the relationship. This research repositions FOMO as the fear of missing out on the same event that individuals are attending. By highlighting this alternative understanding, the study suggests that FOMO can be leveraged as a resource for event organizers, potentially providing opportunities for engagement and enhancing attendee experiences. The proposed conceptual model and the insights gained from this research offer valuable contributions to the field and provide a foundation for further investigation and theoretical development.
Associations between Neuropsychiatric Symptoms and Alzheimer’s Disease Biomarkers in People with Mild Cognitive Impairment

Background: Neuropsychiatric symptoms (NPS) are associated with faster decline in mild cognitive impairment (MCI). This study aimed to investigate the association between NPS severity and Alzheimer’s disease (AD) biomarkers, i.e., amyloid-β (Aβ), phosphorylated tau protein (p-tau) and hippocampal volume ratio (HR), to characterise in more detail MCI patients with a poor prognosis. Methods: A total of 506 individuals with MCI and 99 cognitively unimpaired older adults were selected from the ADNI dataset. The patients were divided into three different groups based on their NPI-Q total scores: no NPS (n = 198), mild NPS (n = 160) and severe NPS (n = 148). Regression models were used to assess the association between the severity of NPS and each biomarker level and positivity status. Results: Cerebrospinal fluid Aβ levels were positively associated with older age and lower MMSE scores, while higher p-tau levels were associated with female sex and lower MMSE scores. Only patients with severe NPS had a lower HR (β = −0.18, p = 0.050), i.e., more pronounced medio-temporal atrophy, than those without NPS. Discussion: Only HR was associated with the presence of NPS, partially in line with previous evidence showing that severe NPS may be explained primarily by greater grey matter loss. Future longitudinal studies will be needed to ascertain the relevance of this finding.
IDF-Sign: Addressing Inconsistent Depth Features for Dynamic Sign Word Recognition

Inconsistent hand and body features pose barriers to sign language recognition and translation leading to unsatisfactory models. Existing recognition models are built up on the spatial-temporal depth <inline-formula> <tex-math notation="LaTeX">$S_{p}$ </tex-math></inline-formula> features. Finding suitable expert features for the <inline-formula> <tex-math notation="LaTeX">$S_{p}$ </tex-math></inline-formula> model is challenging especially for dynamic sign words because many inconsistent features exist across hand motions and shapes. In this article, we propose IDF-Sign: an efficient and consistent <inline-formula> <tex-math notation="LaTeX">$S_{p}$ </tex-math></inline-formula> model from a spatial-temporal multivariate pairwise consistency feature ranking (PairCFR) approach. The temporal features are obtained by computing the 3D position vector of skeletal hand joint coordinates, while the spatial features were obtained by taking every ten spatial coordinates in the 3D video frames and averaging it and doing so until the end of the frames. The PairCFR was used to rank and select the best <inline-formula> <tex-math notation="LaTeX">$S_{p}$ </tex-math></inline-formula> model features at different feature thresholds. We employed a threshold selection to compute a mid-point value of each ranked feature according to its weight. The receiver operating characteristics (ROC) scheme was employed to identify the relationship between the sensitive parameters and the <inline-formula> <tex-math notation="LaTeX">$S_{p}$ </tex-math></inline-formula> features, and the obtained values were utilized as modeling inputs. To verify the IDF-Sign, we design a real-life experiment with a leap motion sensor (LMS) consisting of ten signers with a total of ninety dynamic sign words. LMS provides the depth videos, since depth videos are too dense for the <inline-formula> <tex-math notation="LaTeX">$S_{p}$ </tex-math></inline-formula> model to treat directly, we read the depth videos in comma-separated files in real time. Extensive IDF-Sign evaluations using machine learning on ASL, GSL, DSG, and ASL-similar datasets prove the Optimized Forest achieved an average recognition performance of 95%, 78%, 65.07%, and 95% of the top-1, respectively.
On the Detection and Solution of Coverage Holes in 5G Networks through Relay User Equipment: a combined DBSCAN and Deep-Q Network Approach

This paper proposes a model to detect and solve coverage holes in 5G Radio Access Network (RAN) deployments operating with millimeter waves. The proposed model utilizes a DBSCAN-based detector to identify coverage constrained areas and then proposes the use of Relay User Equipment (RUE) capabilities to extend the RAN coverage. To optimize the activation and deactivation of RUEs, a Deep-Q-Network-based algorithm is proposed, aiming to improve spectral efficiency and decrease the outage probability experienced by network users. The obtained results demonstrate the effectiveness of the model in accurately detecting coverage constrained areas and efficiently solving these issues by means of an effective RUE activation, leading to significant improvements in network performance while minimizing the time that RUEs remain in active mode, which implies potential benefits for MNOs and UE holders and significant energy savings.
Trigger Point Injections Versus Medical Management for Acute Myofascial Pain: A Systematic Review and Meta-Analysis

Myofascial pain is a common problem resulting in musculoskeletal pain characterized by myofascial trigger points. These trigger points can cause substantial discomfort and functional limitations. This meta-analysis aims to assess the effectiveness and safety of trigger point injections versus medical management alone in treating acute onset myofascial pain. A thorough search was conducted across four databases, namely, PubMed, SCOPUS, Web of Science (WOS), and Cochrane Library, to identify randomized controlled trials that compared the effectiveness of trigger point injections versus medical management for the treatment of acute myofascial pain. The search encompassed articles published from the databases’ inception until June 2023. The relevant data were extracted and analyzed using the standardized mean difference (SMD) and 95% confidence interval (CI). Of the 1151 records identified, four met the inclusion criteria for the systematic review, and all were included in the meta-analysis. The analysis of four randomized controlled trials (RCTs) showed that trigger point injections were effective in reducing pain scores compared to medical treatment (SMD = -2.09 (95% CI: -3.34 to -0.85, P = 0.001)). The data revealed a negative standardized mean difference, which was significant and consistent in favor of trigger point injections. Overall, these findings highlight the beneficial impact of trigger point injections in reducing acute myofascial pain when compared to isolated medical management.
Data augmentation for children ASR and child-adult speaker classification using voice conversion methods

Many young children prefer speech based interfaces over text, as they are relatively slow and error-prone with text input. However, children ASR can be challenging due to the lack of transcribed children speech corpora. In this paper, we investigate a voice conversion method based on WORLD vocoder to generate childlike speech for data augmentation. Since noise may lead to severe artifacts in converted speech, we also investigate using speech enhancement to improve the quality of converted speech. On a publicly available children speech corpus, we evaluated the performance of the proposed data augmentation method against existing data augmentation methods based on linear prediction coefficients. Our proposed data augmentation method substantially outperformed the prior work on children ASR. Additionally, on a task to classify the speaker, adult or child, data generated using our proposed method was shown to mimic real children better compared to the reference methods.
Comparing Self-Supervised Pre-Training and Semi-Supervised Training for Speech Recognition in Languages with Weak Language Models

This paper investigates the potential of improving a hybrid automatic speech recognition model trained on 10 hours of transcribed data with 200 hours of untranscribed data in low-resource languages. First, we compare baseline methods of cross-lingual transfer with MFCC features and features extracted with the multilingual self-supervised model XLSR-53. Subsequently, we compare two approaches that can leverage the untranscribed data: semi-supervised training with LF-MMI and continued self-supervised pre-training of XLSR-53. Our results on well-resourced English broadcast data derived from MGB show that both methods achieve 18% and 27% relative improvements compared to the baseline, respectively. On the low-resource South African Soap Opera dataset, the relative improvement with semi-supervised training is only 3% due to the inherently weak language model. However, continued pre-training achieves 8.6% relative improvement because it does not rely on any external information.