scholarly journals A Self-Supervised Model for Language Identification Integrating Phonological Knowledge

Electronics ◽  
2021 ◽  
Vol 10 (18) ◽  
pp. 2259
Author(s):  
Qingran Zhan ◽  
Xiang Xie ◽  
Chenguang Hu ◽  
Haobo Cheng
Keyword(s):  
Language Identification ◽  
Full Length ◽  
The Self ◽  
Fine Tuning ◽  
Language Recognition ◽  
Learning Loss ◽  
Training Strategy ◽  
Task Learning ◽  
Phonological Knowledge ◽  
High Level

In this paper, a self-supervised learning pre-trained model is proposed and successfully applied in language identification task (LID). A Transformer encoder is employed and multi-task strategy is used to train the self-supervised model: the first task is to reconstruct the masking spans of input frames and the second task is a supervision task where the phoneme and phonological labels are used with Connectionist Temporal Classification (CTC) loss. By using this multi-task learning loss, the model is expected to capture high-level speech representation in phonological space. Meanwhile, an adaptive loss is also applied for multi-task learning to balance the weight between different tasks. After the pretraining stage, the self-supervised model is used for xvector systems. Our LID experiments are carried out on the oriental language recognition (OLR) challenge data corpus and 1 s, 3 s, Full-length test sets are selected. Experimental results show that on 1 s test set, feature extraction model approach can get best performance and in 3 s, Full-length test, the fine-tuning approach can reach the best performance. Furthermore, our results prove that the multi-task training strategy is effective and the proposed model can get the best performance.

scholarly journals Genome-Wide Metabolic Reconstruction of the Synthesis of Polyhydroxyalkanoates from Sugars and Fatty Acids by Burkholderia Sensu Lato Species

2021 ◽  
Vol 9 (6) ◽  
pp. 1290
Author(s):  
Natalia Alvarez-Santullano ◽  
Pamela Villegas ◽  
Mario Sepúlveda Mardones ◽  
Roberto E. Durán ◽  
Raúl Donoso ◽  
...  
Keyword(s):  
Fatty Acids ◽  
De Novo ◽  
Reducing Power ◽  
Pentose Phosphate ◽  
Fine Tuning ◽  
Metabolic Reconstruction ◽  
Phylogenetic Groups ◽  
Outlier Group ◽  
Pha Genes ◽  
High Level

Burkholderia sensu lato (s.l.) species have a versatile metabolism. The aims of this review are the genomic reconstruction of the metabolic pathways involved in the synthesis of polyhydroxyalkanoates (PHAs) by Burkholderia s.l. genera, and the characterization of the PHA synthases and the pha genes organization. The reports of the PHA synthesis from different substrates by Burkholderia s.l. strains were reviewed. Genome-guided metabolic reconstruction involving the conversion of sugars and fatty acids into PHAs by 37 Burkholderia s.l. species was performed. Sugars are metabolized via the Entner–Doudoroff (ED), pentose-phosphate (PP), and lower Embden–Meyerhoff–Parnas (EMP) pathways, which produce reducing power through NAD(P)H synthesis and PHA precursors. Fatty acid substrates are metabolized via β-oxidation and de novo synthesis of fatty acids into PHAs. The analysis of 194 Burkholderia s.l. genomes revealed that all strains have the phaC, phaA, and phaB genes for PHA synthesis, wherein the phaC gene is generally present in ≥2 copies. PHA synthases were classified into four phylogenetic groups belonging to class I II and III PHA synthases and one outlier group. The reconstruction of PHAs synthesis revealed a high level of gene redundancy probably reflecting complex regulatory layers that provide fine tuning according to diverse substrates and physiological conditions.

scholarly journals Train Fast While Reducing False Positives: Improving Animal Classification Performance Using Convolutional Neural Networks

Geomatics ◽  
2021 ◽  
Vol 1 (1) ◽  
pp. 34-49
Author(s):  
Mael Moreni ◽  
Jerome Theau ◽  
Samuel Foucher
Keyword(s):  
False Positive ◽  
Classification Performance ◽  
Large Datasets ◽  
False Positives ◽  
Fine Tuning ◽  
Training Time ◽  
In The Wild ◽  
Test Sets ◽  
High Level ◽  
Time Decrease

The combination of unmanned aerial vehicles (UAV) with deep learning models has the capacity to replace manned aircrafts for wildlife surveys. However, the scarcity of animals in the wild often leads to highly unbalanced, large datasets for which even a good detection method can return a large amount of false detections. Our objectives in this paper were to design a training method that would reduce training time, decrease the number of false positives and alleviate the fine-tuning effort of an image classifier in a context of animal surveys. We acquired two highly unbalanced datasets of deer images with a UAV and trained a Resnet-18 classifier using hard-negative mining and a series of recent techniques. Our method achieved sub-decimal false positive rates on two test sets (1 false positive per 19,162 and 213,312 negatives respectively), while training on small but relevant fractions of the data. The resulting training times were therefore significantly shorter than they would have been using the whole datasets. This high level of efficiency was achieved with little tuning effort and using simple techniques. We believe this parsimonious approach to dealing with highly unbalanced, large datasets could be particularly useful to projects with either limited resources or extremely large datasets.

Investigation about self-esteem levels of athletes who have been educated in different sports branches according to some variables: A study on physically handicapped individuals

2021 ◽  
Vol 9 (1) ◽  
pp. 134-140
Author(s):  
Taner Bozkuş ◽  
Keyword(s):  
High School ◽  
Personal Information ◽  
Self Esteem ◽  
The Self ◽  
Study Group ◽  
Physically Disabled ◽  
Purposeful Sampling ◽  
Secondary School Graduate ◽  
High Level ◽  
Physically Handicapped

This study aimed to examine the self-esteem of those who did sports in physically disabled individuals by some variables. Based on this aim, the study was designed quantitatively. In this descriptive research, the general survey model that is coherent with the main purpose was used. The study group of the research consisted of 140 individuals aged 18 and over who had physical disabilities and actively engage in sports. Purposeful sampling approaches and easily accessible sampling methods were used in the selection of the study group. The scale form was used to collect research data. The scale form consisted of two parts. In the first part of this form, there was a personal information form containing information about the participants and in the second part, there was the "Rosenberg Self-Esteem Scale" developed by Rosenberg (1965) and adapted into Turkish by Çuhadaroğlu (1986). This form was applied to the participants on a voluntary basis, on the internet between 13.05.2020 and 03.06.2020. Necessary explanations were made to the participants while filling the form and they were provided to answer correctly. In this study, the self-esteem of physically disabled athletes was examined according to some variables. The research group consisted of 140 participants; 42 (30.0%) of them were female and 98 (70.0%) of them were male and the number of male participants was approximate twice the number of female participants. It was found that 18 (12.9%) participants were graduated from elementary and secondary schools, 59 (42.1%) from high school, and 63 (45%) from college, and the number of the participants belonging to the group consisted of graduates from high school and college were approximately four times more than the participants from the elementary and secondary school graduate group. It was determined that 9 (13.6%) of the participants had low, 105 (75%) had medium and 16 (11.4%) had a high level of income. It was observed that 83 (59.3%) of the participants were congenitally disabled and 57 (40.7%) of the participants disabled after birth and the number of congenitally disabled participants approximately 1.5 times more than the number of participants with disabilities after birth. It was determined that the number of participants who were national athletes was approximately 2.5 times those who were not. Among the variables examined, it was seen that there was only a statistically positive and low-level significant relationship between the sports age variable and the self-esteem mean score of the participants (r = .147; p < 0.05). In this context, as the age of the participants increased, the self-esteem of the participants also increased. As a result, it was determined that there was a positive correlation between the age of starting sports and self-esteem in physically disabled individuals, and individuals who started sports at an early age had a higher rate than other individuals.

Fine-tuning the Relationship between Religion and Intellectual Humility

2018 ◽  
Vol 46 (4) ◽  
pp. 305-314 ◽  
Author(s):  
Everett L. Worthington
Keyword(s):  
Religious Belief ◽  
The Self ◽  
Content Area ◽  
Fine Tuning ◽  
Epistemic Status ◽  
Intellectual Humility ◽  
The Core ◽  
Definition Of ◽  
Meaning System ◽  
The Relationship

I examine religious humility, which is one content area of intellectual humility. Intellectual humility is the subtype of humility that involves taking a humble stance in sharing ideas, especially when one is challenged or when an idea is threatening. I position religious humility within the context of general humility, spiritual humility, and relational humility, and thus arrive at several propositions. People who are intensely spiritually humble can hold dogmatic beliefs and believe themselves to be religiously humble, yet be perceived by others of different persuasions as religiously dogmatic and even arrogant. For such people to be truly religiously humble, they must feel that the religious belief is core to their meaning system. This requires discernment of which of the person’s beliefs are truly at the core. But also the religiously humble person must fulfill the definition of general humility, accurately perceiving the strengths and limitations of the self, being teachable to correct weaknesses, presenting oneself modestly, and being positively other-oriented. Humility thus involves (1) beliefs, values, and attitudes and (2) an interpersonal presentational style. Therefore, intellectually humble people must track the positive epistemic status of their beliefs and also must present with convicted civility.

scholarly journals Fine-Tuning by Curriculum Learning for Non-Autoregressive Neural Machine Translation

2020 ◽  
Vol 34 (05) ◽  
pp. 7839-7846
Author(s):  
Junliang Guo ◽  
Xu Tan ◽  
Linli Xu ◽  
Tao Qin ◽  
Enhong Chen ◽  
...  
Keyword(s):  
Machine Translation ◽  
Fine Tuning ◽  
Inference Process ◽  
Neural Machine Translation ◽  
Training Strategy ◽  
Speed Up ◽  
Good Improvement ◽  
Tuning Process ◽  
Translation Accuracy ◽  
The Cost

Non-autoregressive translation (NAT) models remove the dependence on previous target tokens and generate all target tokens in parallel, resulting in significant inference speedup but at the cost of inferior translation accuracy compared to autoregressive translation (AT) models. Considering that AT models have higher accuracy and are easier to train than NAT models, and both of them share the same model configurations, a natural idea to improve the accuracy of NAT models is to transfer a well-trained AT model to an NAT model through fine-tuning. However, since AT and NAT models differ greatly in training strategy, straightforward fine-tuning does not work well. In this work, we introduce curriculum learning into fine-tuning for NAT. Specifically, we design a curriculum in the fine-tuning process to progressively switch the training from autoregressive generation to non-autoregressive generation. Experiments on four benchmark translation datasets show that the proposed method achieves good improvement (more than 1 BLEU score) over previous NAT baselines in terms of translation accuracy, and greatly speed up (more than 10 times) the inference process over AT baselines.

scholarly journals Dissecting Cellular Heterogeneity Based on Network Denoising of scRNA-seq Using Local Scaling Self-Diffusion

2022 ◽  
Vol 12 ◽  
Author(s):  
Xin Duan ◽  
Wei Wang ◽  
Minghui Tang ◽  
Feng Gao ◽  
Xudong Lin
Keyword(s):  
Metric Learning ◽  
Cell Types ◽  
Primary Objective ◽  
The Self ◽  
Cellular Heterogeneity ◽  
Clustering Methods ◽  
Local Scaling ◽  
Self Diffusion ◽  
Cell Clustering ◽  
High Level

Identifying the phenotypes and interactions of various cells is the primary objective in cellular heterogeneity dissection. A key step of this methodology is to perform unsupervised clustering, which, however, often suffers challenges of the high level of noise, as well as redundant information. To overcome the limitations, we proposed self-diffusion on local scaling affinity (LSSD) to enhance cell similarities’ metric learning for dissecting cellular heterogeneity. Local scaling infers the self-tuning of cell-to-cell distances that are used to construct cell affinity. Our approach implements the self-diffusion process by propagating the affinity matrices to further improve the cell similarities for the downstream clustering analysis. To demonstrate the effectiveness and usefulness, we applied LSSD on two simulated and four real scRNA-seq datasets. Comparing with other single-cell clustering methods, our approach demonstrates much better clustering performance, and cell types identified on colorectal tumors reveal strongly biological interpretability.

scholarly journals The Effectiveness Of Behavioral Concept With Modeling Techniques Through Lesson Study To Improve Self Nurturance Of Class X BDPM A Students State SMK Negeri 1 Singaraja

2019 ◽  
Vol 3 (1) ◽  
pp. 18
Author(s):  
Ngurah Adi Mahendra ◽  
I Ketut Dharsana ◽  
Ni Ketut Suarni
Keyword(s):  
Lesson Study ◽  
Hypothesis Test ◽  
T Test ◽  
The Self ◽  
Control Group ◽  
Validity And Reliability ◽  
Control Group Design ◽  
Behavioral Counseling ◽  
Modeling Techniques ◽  
High Level

The purpose of this study was to determine the effectiveness of Behavioral Counseling with a modeling technique through lesson study to improve Self Nurturance in class X BDPM A SMK Negeri 1 Singaraja. This research includes "quasi experiment". The experimental design used was Pretest Postest Control Group Design. The population of this research is 71 grade X students of SMK Negeri 1 Singaraja. Through random sampling techniques, 34 students were placed in the experimental group and 37 students were placed in the control group. The method of data collection in this study used the method of observation, interviews, diaries and the Self Nurturance questionnaire. The self nurturance questionnaire has been tested for its validity and reliability. Analysis of questionnaire data using the Cronbach Alpha method. The study used the Independent Samples t-test with the help of JASP Version 0.7.5.5 showing the value of the hypothesis test results using Independent Samples t-test, getting t = 9,347 with p <0.05. Effect Size (ES) testing shows a high level of effectiveness (ES = 2.221). These results prove that behavioral counseling with effective modeling techniques to improve Self Nurturance class X students at SMK Negeri 1 Singaraja.

scholarly journals Fast Drivable Areas Estimation with Multi-Task Learning for Real-Time Autonomous Driving Assistant

2021 ◽  
Vol 11 (22) ◽  
pp. 10713
Author(s):  
Dong-Gyu Lee
Keyword(s):  
Real Time ◽  
Computational Efficiency ◽  
Autonomous Driving ◽  
Learning Approaches ◽  
Practical Applications ◽  
Task Learning ◽  
Lane Line ◽  
High Level ◽  
Time Autonomous

Autonomous driving is a safety-critical application that requires a high-level understanding of computer vision with real-time inference. In this study, we focus on the computational efficiency of an important factor by improving the running time and performing multiple tasks simultaneously for practical applications. We propose a fast and accurate multi-task learning-based architecture for joint segmentation of drivable area, lane line, and classification of the scene. An encoder-decoder architecture efficiently handles input frames through shared representation. A comprehensive understanding of the driving environment is improved by generalization and regularization from different tasks. The proposed method learns end-to-end through multi-task learning on a very challenging Berkeley Deep Drive dataset and shows its robustness for three tasks in autonomous driving. Experimental results show that the proposed method outperforms other multi-task learning approaches in both speed and accuracy. The computational efficiency of the method was over 93.81 fps at inference, enabling execution in real-time.

scholarly journals ROLE OF SOCIAL ENTREPRENEURSHIP IN BUSINESS MANAGEMENT

2020 ◽  
Author(s):  
Ariawan ◽  
Titien Agustina
Keyword(s):  
Social Entrepreneurship ◽  
Life Stories ◽  
Business Management ◽  
The Self ◽  
Quality Analysis ◽  
Poor People ◽  
Social Entrepreneurs ◽  
Important Field ◽  
High Level

Nowadays a Social entrepreneurship is most important field in all service and public sectors. In other ways it gives all way ofthinking in a social terms like poverty and hunger. Also it is compelling life stories and it gives a progress against increasingWorld issues of poor living and sickness. This term offers the opportunities of living and money for poor people by representinghigher level of social problems. It also gives a chance of improvement by insights process of social entrepreneur’s analysis. Inthe little business these social things, usually reinvent the fact that they have to struggle for maintaining and managing sm allbusiness with comparatively other variety of business. So usually a management can be done in social entrepreneurs in rarebasis for the self fulfilment of all data, in company these issues is ahead from many years by doing isolation, these are hoppingfor complete impact in these issues for better economic growth. There are lots of challenging changes has to do to performthese management in all over World. The most important thing is done in this paper is to make a high level of quality analysis insocial entrepreneurship as on the demand of sectors. These is made so fast as it affect all others analysis like educationdepartment of tenure and recognition

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