Miracle-dz/UniFAR-FTUs huggingface dataset:
Revisiting Parity of Human vs. Machine Conversational Speech Transcription

A number of studies have compared human and machine transcription, showing that automatic speech recognition (ASR) is approaching human performance in some contexts. Most studies look at differences as measured by the standard speech recognition scoring criterion: word error rate (WER). This study looks at more ﬁne-grained analysis of differences for conversational speech data where systems have reached human parity in terms of average WER, speciﬁcally insertions vs. deletions, word category, and word context characterized by linguistic surprisal. In contrast to ASR systems, humans are more likely to miss words than to misrecognize them, and they are much more likely to make errors in transcribing words associated primarily with conversational contexts (ﬁllers, backchan-nels and discourse cue words). The differences are more pronounced for more informal contexts, i.e. conversations between family members. Although human transcribers may miss these words, conversational partners seem to use them in turntaking and processing disﬂuencies. Thus, ASR systems may need su-perhuman transcription performance for spoken language technology to achieve human-level conversation skills.
A Perspective on Muscle Synergies and Different Theories Related to Their Adaptation

The human motor system is a complex neuro-musculo sensory system that needs further investigations of neuro-muscular commands and sensory-motor coupling to decode movement execution. Some researchers suggest that the central nervous system (CNS) activates a small set of modules termed muscle synergies to simplify motor control. Further, these modules form functional building blocks of movement as they can explain the neurophysiological characteristics of movements. We can identify and extract these muscle synergies from electromyographic signals (EMG) recorded in the laboratory by using linear decomposition algorithms, such as principal component analysis (PCA) and non-Negative Matrix Factorization Algorithm (NNMF). For the past three decades, the hypothesis of muscle synergies has received considerable attention as we attempt to understand and apply the concept of muscle synergies in clinical settings and rehabilitation. In this article, we first explore the concept of muscle synergies. We then present different strategies of adaptation in these synergies that the CNS employs to accomplish a movement goal.
Using DFT to Calculate the Parameters of the Crystal Field in Mn2+ Doped Hydroxyapatite Crystals

Crystal field parameters for two nonequivalent positions Ca (I) and Ca (II) for hydroxyapatite (HAp) crystals from the density functional theory (DFT) are calculated. Calculations are compared with the experimental electron paramagnetic resonance (EPR) spectra (registered at two microwave frequencies) for the synthesized Mn-HAp powders Ca9.995Mn0. 005(PO4)6(OH)2. It is found that in the investigated species, the manganese is redistributed between both calcium sites with prevalence in Ca (I). Agreement between the calculated and experimental data proves that crystal field parameters in HAp can be calculated in the classical DFT model using the distributed electron density.
Clarity-2021 Challenges: Machine Learning Challenges for Advancing Hearing Aid Processing

In recent years, rapid advances in speech technology have been made possible by machine learning challenges such as CHiME, REVERB, Blizzard, and Hurricane. In the Clarity project, the machine learning approach is applied to the problem of hearing aid processing of speech-in-noise, where current technology in enhancing the speech signal for the hearing aid wearer is often ineffective. The scenario is a (simulated) cuboid-shaped living room in which there is a single listener, a single target speaker and a single interferer, which is either a competing talker or domestic noise. All sources are static, the target is always within ± 30 ◦ azimuth of the listener and at the same elevation, and the interferer is an omnidirectional point source at the same elevation. The target speech comes from an open source 40-speaker British English speech database collected for this purpose. This paper provides a baseline description of the round one Clarity challenges for both enhancement (CEC1) and prediction (CPC1). To the authors’ knowledge, these are the ﬁrst machine learning challenges to consider the problem of hearing aid speech signal processing.
Comparison of Real- and Complex-Valued NN Equalizers for Photonics-Aided 90-Gbps D-band PAM-4 Coherent Detection

5G defines below 100 GHz as the millimeter-wave bands, whereas 100 GHz - 3 THz is categorized as THz band in 6G. Deep leraning (DL) is expected to enable a significant paradigm shift in 6G wireless networks. In this paper, D-band 90-Gbps single channel PAM-4 signal generation and transmission over 10-km SMF and 3-m wireless link at 140-GHz can be achieved. A novel complex-valued neural network (CVNN) equalizer using ‘ℂReLU’ activation function to directly recover PAM-4 signals from received noised signals is demonstrated. There are three DSP options in the experiment. In Opt.1, firstly conducted cascaded multi-modulus algorithm (CMMA) pre-equalization (pre-EQ) at transmitter, then processed via frequency offset estimation (FOE), carrier phase recovery (CPR) and finally real-valued neural network (RVNN) equalization at receiver. Here, the RVNN equalizers include DNN with a softmax output layer, two-step joint-DNN equalizer and LSTM. The experimental results show that LSTM-based equalizer outperforms the other real NN-based equalizers by average 0.5 to 1.5 dB at BER of 10−3 magnitude. Differently from Opt. 1, real-valued CMMA pre-EQ at transmitter is unexpected for CVNN in the other two options. In Opt. 2, we only combine down-conversion and CVNN regarded as ‘pure data-driven’ training at receiver. This pure data-driven CVNN equalizer improves BER a lot and also has a larger computation burden, especially BER is as low as 1 × 10−4 with n0 = 571 and n1 = 200 training cells and the time complexity reaches 350000 in one iteration. Thanks to the aid of traditional mathematical-oriented models including FOE and CPR, the computation burden of CVNN in Opt. 3 is released significantly. Furthermore, we compare the performance of CVNN and RVNN in terms of BER decision accuracy, time complexity and receiver sensitivity. Followed by the same DSP with the same complexity, the comparison result between DNN and CVNN in the same structure of [371-260-1] with 11000 samples and 300 epochs shows that CVNN performs better due to its reservation of phase information. Therefore, we believe that the joint use of model-based, e.g., FOE, CPR steps and complex DL-based techniques has a potential for the future 6G wireless physical layer algorithms.
Lowermost Mantle Shear‐Velocity Structure From Hierarchical Trans‐Dimensional Bayesian Tomography

The core‐mantle boundary (CMB) is the most extreme boundary within the Earth where the liquid, iron‐rich outer core interacts with the rocky, silicate mantle. The nature of the lowermost mantle atop the CMB, and its role in mantle dynamics, is not completely understood. Various regional studies have documented significant heterogeneities at different spatial scales. While there is a consensus on the long scale length structure of the inferred S‐wave speed tomograms, there are also notable differences stemming from different imaging methods and datasets. Here we aim to overcome over‐smoothing and avoid over‐fitting data for the case where the spatial coverage is sparse and the inverse problem ill‐posed. We present an S‐wave tomography model at a global scale for the Lowermost Mantle (LM) using the Hierarchical Trans‐Dimensional Bayesian Inversion (HTDBI) framework, LM‐HTDBI. Our LM‐HTDBI analysis of ScS‐S travel times includes uncertainty, and the complexity of the model is deduced from the data itself through an implicit parameterization of the model space. Our comprehensive resolution estimates indicate that short‐scale anomalies are significant and resolvable features of the lowermost mantle regardless of the chosen mantle‐model reference to correct the travel times above the D’’ layer. The recovered morphology of the Large‐Low‐Shear‐wave Velocity Provinces (LLSVPs) is complex, featuring small high‐velocity patches among low‐velocity domains. Instead of two large, unified, and smooth LLSVPs, the newly obtained images suggest that their margins are not uniformly flat.
Women’s Language Features in Emily in Paris: A Sociolinguistics Study

This undergraduate thesis discusses the usage of women’s language features in the television series Emily in Paris. The study analyzes the features of women’s language and its functions in the television series. This study uses observation method in collecting the data. The data were taken from Emily in Paris television series and the movie scripts. Descriptive qualitative method is used to analyze the data by applying women’s language features theory by Lakoff. Based on the analysis and data taken, there are 480 utterance of women’s language features found and ten features of women’s language used by the female characters in Emily in Paris. Lexical hedges or fillers is the most used feature and precise color terms is the least used feature.
METHODOLOGICAL APPROACH TO CONGRUENCE OF QUADRILATERALS IN HYPERBOLIC GEOMETRY

In this paper we will prove new criteria for the congruence of convex quadrilaterals in Hyperbolic geometry and consequently, display the appropriate methodological approach in teaching the same. There are seven criteria for the congruence of hyperbolic quadrilaterals, while there are five for the congruence of Euclidean quadrilaterals. Using a comparative geometric analysis of quadrilateral congruence criteria in Euclidean and Hyperbolic geometry we described all possible cases and made a methodological approach to the problem. The obtained results can influence the approaches to the study of these contents with students in the hyperbolic geometry teaching.
Factors Affecting Biodiesel Production from Non-edible Vegetable Oil Via Base-catalyzed Transesterification Process: Synthesis

Biodiesel (fatty acid alkyl ester) has received attention as greener, renewable, alternative source of energy; and its utilization as diesel fuel is becoming an auspicious source of energy that can substitute the non-renewable petroleum products. It can make significant contributions in diminishing the emission of harmful gaseous due to combustion of conventional fuel sources and hence, it is helpful to overcome challenges associated with energy crisis coupled with environmental pollution, climate change and global warming. Biodiesel is a carbon-neutral fuel source, biodegradable, non-toxic and can be synthesized from locally available non-edible vegetable oils via base-catalyzed transesterification process. This study presents the insight of operational variables or factors that influence the processing and yield of biodiesel (fatty acid alkyl ester) through transesterification of non-edible vegetable oils. These operational variables include, the amount of free fatty acid (FFA) in the non-edible vegetable oils, moisture in the oils, the amount and types of alcohol used, the non-edible vegetable oil to alcohol molar ratio, time allowed for the reaction, intensity of mixing, temperature, types of catalyzed used, concentration of catalyst and purity of reactants. The considered variables for preparation of biodiesel should be optimized to achieve the maximum possible conversion of reactants and thereby provide the highest possible yield of biodiesel product at the minimum possible operational cost.
How Moral Expectancy Violations Influence Audiences’ Affective Dispositions Toward Characters

Affective disposition theory (ADT) explains that the moral judgments of character behavior inform dispositions toward characters. These dispositions bias moral judgments of characters’ subsequent behaviors and establish behavioral expectations. We used expectancy violations theory to help specify people’s dispositions toward characters. In study 1, we modified the footbridge dilemma to develop experimental stimuli and predictions. Studies 2 and 3 observed the disposition formation process longitudinally and validated our stimulus: a custom-built visual novel. Study 4 tested our predictions. Studies 2 through 4 used pre-registered hypotheses, sampling, and data analyses. Results demonstrated that the current disposition (positive vs. negative) changes how a novel (im)moral behavior affects that disposition. Schema-violating behaviors provoked larger mean differences in participants’ dispositions toward protagonists compared to antagonists. Specifically, people were hyper-scrutinous of moral paragons and entrenched despised characters in moral skepticism. Additionally, we observed differences in dispositions toward characters who did not act when they could (inaction).
Auxiliary Loss Function for Target Speech Extraction and Recognition with Weak Supervision Based on Speaker Characteristics

Automatic speech recognition systems deteriorate in presence of overlapped speech. A popular approach to alleviate this is target speech extraction. The extraction system is usually trained with a loss function measuring the discrepancy between the estimated and the reference target speech. This often leads to distortions to the target signal which is detrimental to the recognition accuracy. Additionally, it is necessary to have the strong supervision provided by parallel data consisting of speech mixtures and single-speaker signals. We propose an auxiliary loss function for retraining the target speech extraction. It is composed of two parts: ﬁrst, a speaker identity loss, forcing the estimated speech to have correct speaker characteristics, and second, a mixture consistency loss, making the extracted sources sum back to the original mixture. The only supervision required for the proposed loss is speaker characteristics obtained from several segments spoken by the target speaker. Such weak supervision makes the loss suitable for adapting the system directly on real recordings. We show that the proposed loss yields signals more suitable for speech recognition and further, we can gain additional improvements by adaptation to target data. Overall, we can reduce the word error rate on LibriCSS dataset from 27.4% to 24.0%.
Causal Confusion Reduction for Robust Multi-Domain Dialogue Policy

In the multi-domain dialogue system, dialog policy plays an important role since it determines the suitable actions based on the user’s goals. However, in many recent works, most of the dialogue optimizations, especially that use reinforcement learning (RL) methods, do not perform well. The main problem is that the initial step of optimization that involves the behavior cloning (BC) methods suffer from the causal confusion problem, which means that the agent misidentiﬁes true cause of an expert action in current state. This paper proposes a novel method to improve the performance of BC method in dialogue system. Instead of only predicting correct action given a state from dataset, we introduce the auxiliary tasks to predict both of current belief state and recent user utterance in order to reduce causal confusion of the expert action in the dataset since those features are important in every dialog turn. Experiments on ConvLab-2 shows that, by using this method, all of RL based optimizations are improved. Furthermore, the agent based on the proximal policy optimization shows very signiﬁcant improvement with the help of the proposed BC agent weights both in policy evaluation as well as in end-to-end system evaluation.
Estimation of the Values of Wooden Materials in Urban Regeneration: The Case of Seoullo in Korea

The importance of wooden buildings has been highlighted in recent years due to the energy-efficient and carbon storage effect of wood. This study investigates the willingness to pay for one-time tax payments of the citizens currently living in Seoul when Seoullo uses wood as a material for covering the surface of the facilities. The study examines the economic feasibility of Seoullo, which is a part of the government urban regeneration project that built a park on the desolate bridge in 2017. The result indicates that Seoul citizens who participated in the survey feel a greater preference for wood material rather than existing concrete used in Seoullo despite the tax burden. As a result, it is found that the respondents with children under the age of 8, respondents who are aware of the urban regeneration project, and respondents with respiratory disease have a lower marginal willingness to pay than those in the opposite groups. Moreover, the result shows that the respondents who consider farming or returning to rural areas, respondents in need of city improvement, respondents who chose wood as a preferred building material, and respondents with the experience of visiting Seoullo have a greater marginal willingness to pay for the wooden material than those in the opposite groups.
Mindfulness- and Acceptance-Based Interventions for Stroke Survivors: A Systematic Review and Meta-Analysis

Mindfulness- and acceptance-based intervention (MABI) is an emerging evidence-based practice, but no systematic review incorporating meta-analyses for MABIs in stroke survivors has been conducted. The objective of this systematic review was to measure the effectiveness of MABIs on outcomes for people with stroke. Three electronic databases, PubMed, CINAHL, and PsycINFO, were searched to identify relevant studies published in peer-reviewed journals. The methodological quality of the included studies was assessed. Data were extracted and combined in a meta-analysis with a random-effect model to compute the size of the intervention effect. A total of 11 studies met the eligibility criteria. Meta-analyses found a small-to-moderate effect of MABIs on depressive symptoms (standardized mean difference [SMD] = 0.39, 95% confidence interval [CI] = [0.12, 0.66]) and a large effect on mental fatigue (SMD = 1.22, 95% CI = [0.57, 1.87]). No statistically significant effect of MABIs on anxiety, quality of life, and mindfulness was found, but there was a trend in favor of MABIs overall. This meta-analysis found positive effects of MABIs on depressive symptoms and mental fatigue in stroke survivors, but future high-quality studies are needed to guarantee treatment effects of MABIs on varied outcomes in stroke survivors.
Streaming End-to-End Speech Recognition for Hybrid RNN-T/Attention Architecture

We present a novel architecture with its decoding approach for improving recurrent neural network-transducer (RNN-T) performance. RNN-T is promising for building time-synchronous automatic speech recognition (ASR) systems and thus enhancing streaming ASR applications. We note that encoder-decoder-based sequence-to-sequence models (S2S) have been also used successfully by the ASR community. In this paper, we integrate these popular models in the RNN-T+S2S approach; higher recognition performance than either is achieved due to their integration. However, it is generally deemed to be complicated to use S2S in streaming systems, because the attention mechanism can use arbitrarily long past and future contexts during decoding. Our RNN-T+S2S is composed of the shared encoder, an RNN-T decoder and a triggered attention-based decoder which uses time restricted encoder outputs for attention weight computation. By using the trigger points generated from RNN-T outputs, the S2S branch of RNN-T+S2S activates only when the triggers are detected, which makes streaming ASR practical. Experiments on public and private datasets created to research various tasks demonstrate that our proposal can yield superior recognition performance.
Continuing to Meet the Needs of Middle School Students in Southwest Florida Despite COVID-19 Restrictions

We describe two established STEM camps that went virtual following the outbreak of COVID-19, during the summer of 2020. Our Summer Research Opportunity (SRO) camp engages middle school science fair winners in a forensic research opportunity, while The Immokalee Foundation (TIF) camp introduces middle school students from historically underrepresented groups to STEM topics. Home kits were carefully prepared and delivered to all participants to enhance their engagement with our faculty facilitators. What was modified to accommodate the virtual experience is discussed. Our program assessment used a retrospective pretest-posttest design delivering a modified STEM Semantics survey to measure interest in STEM topics and STEM careers. While the programming and the population was different for each camp, both programs achieved positive outcomes in the virtual environment enhancing participant interest in, excitement for, and importance of STEM topics and STEM careers. The local TV news even broadcasted a short video of one of the camps.
RoadVecNet: a new approach for simultaneous road network segmentation and vectorization from aerial and google earth imagery in a complex urban set-up

ABSTRACT In this study, we present a new automatic deep learning-based network named Road Vectorization Network (RoadVecNet), which comprises interlinked UNet networks to simultaneously perform road segmentation and road vectorization. Particularly, RoadVecNet contains two UNet networks. The first network with powerful representation capability can obtain more coherent and satisfactory road segmentation maps even under a complex urban set-up. The second network is linked to the first network to vectorize road networks by utilizing all of the previously generated feature maps. We utilize a loss function called focal loss weighted by median frequency balancing (MFB_FL) to focus on the hard samples, fix the training data imbalance problem, and improve the road extraction and vectorization performance. A new module named dense dilated spatial pyramid pooling, which combines the benefit of cascaded modules with atrous convolution and atrous spatial pyramid pooling, is designed to produce more scale features over a broader range. Two types of high-resolution remote sensing datasets, namely, aerial and Google Earth imagery, were used for road segmentation and road vectorization tasks. Classification results indicate that the RoadVecNet outperforms the state-of-the-art deep learning-based networks with 92.51% and 93.40% F1 score for road surface segmentation and 89.24% and 92.41% F1 score for road vectorization from the aerial and Google Earth road datasets, respectively. In addition, the proposed method outperforms the other comparative methods in terms of qualitative results and produces high-resolution road segmentation and vectorization maps. As a conclusion, the presented method demonstrates that considering topological quality may result in improvement of the final road network, which is essential in various applications, such as GIS database updating. GRAPHICAL ABSTRACT