scholarly journals A Deep Audiovisual Approach for Human Confidence Classification

2021 ◽  
Vol 3 ◽  
Author(s):  
Sushovan Chanda ◽  
Kedar Fitwe ◽  
Gauri Deshpande ◽  
Björn W. Schuller ◽  
Sachin Patel
Keyword(s):  
Deep Learning ◽  
Communication Skills ◽  
Self Efficacy ◽  
Video Data ◽  
Data Set ◽  
Confidence Levels ◽  
Average Recall ◽  
Interview Session ◽  
Audio Data

Research on self-efficacy and confidence has spread across several subfields of psychology and neuroscience. The role of one’s confidence is very crucial in the formation of attitude and communication skills. The importance of differentiating the levels of confidence is quite visible in this domain. With the recent advances in extracting behavioral insight from a signal in multiple applications, detecting confidence is found to have great importance. One such prominent application is detecting confidence in interview conversations. We have collected an audiovisual data set of interview conversations with 34 candidates. Every response (from each of the candidate) of this data set is labeled with three levels of confidence: high, medium, and low. Furthermore, we have also developed algorithms to efficiently compute such behavioral confidence from speech and video. A deep learning architecture is proposed for detecting confidence levels (high, medium, and low) from an audiovisual clip recorded during an interview. The achieved unweighted average recall (UAR) reaches 85.9% on audio data and 73.6% on video data captured from an interview session.

scholarly journals Entrepreneurial self-efficacy and entrepreneurial intention: The mediating role of the need for independence

2021 ◽  
Vol 17 (4) ◽  
pp. 91-119
Author(s):  
Victor Osadolor ◽  
◽  
Kalu Emmanuel Agbaeze ◽  
Ejikeme Emmanuel Isichei ◽  
Samuel Taiwo Olabosinde ◽  
...  
Keyword(s):  
Self Efficacy ◽  
Structural Equation ◽  
Entrepreneurial Intention ◽  
Partial Least Square ◽  
Least Square ◽  
Data Set ◽  
Behavioral Reasoning Theory ◽  
The Relationship ◽  
Behavioral Reasoning

PURPOSE: The paper focuses on assessing the direct effect of entrepreneurial self-efficacy and entrepreneurial intention and the indirect effect of the need for independence on the relationship between the constructs. Despite increased efforts towards steering the interest of young graduates towards entrepreneurial venture, the response rate has been rather unimpressive and discouraging, thus demanding the need to account for what factors could drive intention towards venture ownership among graduates in Nigeria. METHODOLOGY: A quantitative approach was adopted and a data set from 235 graduates was used for the study. The data was analyzed using the partial least square structural equation model (PLS-SEM). FINDINGS: It was found that self-efficacy does not significantly affect intention. It was also found that the need for independence affects entrepreneurial intention. The study found that the need for independence fully mediates the relationship between entrepreneurial self-efficacy and entrepreneurial intention. PRACTICAL IMPLICATIONS: This paper provides new insight into the behavioral reasoning theory, through its application in explaining the cognitive role of the need for independence in decision-making, using samples from a developing economy. ORIGINALITY AND VALUE: The study advances a new perspective on the underlining factors that account for an entrepreneur’s intent to start a business venture, most especially among young graduates in Nigeria, through the lens of the behavioral reasoning theory. We further support the application of the theory in entrepreneurship literature, given the paucity of studies that have adopted the theory despite its relevance.

TOPOLOGICAL MAPPINGS OF VIDEO AND AUDIO DATA

2008 ◽  
Vol 18 (06) ◽  
pp. 481-489 ◽  
Author(s):  
COLIN FYFE ◽  
WESAM BARBAKH ◽  
WEI CHUAN OOI ◽  
HANSEOK KO
Keyword(s):  
Mean Squared Error ◽  
Video Data ◽  
Self Organizing Map ◽  
Generative Topographic Mapping ◽  
Data Set ◽  
Squared Error ◽  
Nonlinear Projection ◽  
Audio Data ◽  
Video And Audio ◽  
Self Organizing

We review a new form of self-organizing map which is based on a nonlinear projection of latent points into data space, identical to that performed in the Generative Topographic Mapping (GTM).1 But whereas the GTM is an extension of a mixture of experts, this model is an extension of a product of experts.2 We show visualisation and clustering results on a data set composed of video data of lips uttering 5 Korean vowels. Finally we note that we may dispense with the probabilistic underpinnings of the product of experts and derive the same algorithm as a minimisation of mean squared error between the prototypes and the data. This leads us to suggest a new algorithm which incorporates local and global information in the clustering. Both ot the new algorithms achieve better results than the standard Self-Organizing Map.

scholarly journals Convolutional Networks Used to Classify Video and Audio Data

2019 ◽  
Vol 27 (45) ◽  
pp. 113-120
Author(s):  
Marcel Nikmon ◽  
Roman Budjač ◽  
Daniel Kuchár ◽  
Peter Schreiber ◽  
Dagmar Janáčová
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Classification ◽  
Current Data ◽  
Video Data ◽  
Batch Size ◽  
Network Architectures ◽  
Convolutional Networks ◽  
Audio Data ◽  
Video And Audio

Abstract Deep learning is a kind of machine learning, and machine learning is a kind of artificial intelligence. Machine learning depicts groups of various technologies, and deep learning is one of them. The use of deep learning is an integral part of the current data classification practice in today’s world. This paper introduces the possibilities of classification using convolutional networks. Experiments focused on audio and video data show different approaches to data classification. Most experiments use the well-known pre-trained AlexNet network with various pre-processing types of input data. However, there are also comparisons of other neural network architectures, and we also show the results of training on small and larger datasets. The paper comprises description of eight different kinds of experiments. Several training sessions were conducted in each experiment with different aspects that were monitored. The focus was put on the effect of batch size on the accuracy of deep learning, including many other parameters that affect deep learning [1].

scholarly journals The role of convolutional neural networks in scanning probe microscopy: a review

2021 ◽  
Vol 12 ◽  
pp. 878-901
Author(s):  
Ido Azuri ◽  
Irit Rosenhek-Goldian ◽  
Neta Regev-Rudzki ◽  
Georg Fantner ◽  
Sidney R Cohen
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Scanning Probe Microscopy ◽  
Scanning Probe ◽  
Data Set ◽  
Probe Microscopy ◽  
Instrumental Control

Progress in computing capabilities has enhanced science in many ways. In recent years, various branches of machine learning have been the key facilitators in forging new paths, ranging from categorizing big data to instrumental control, from materials design through image analysis. Deep learning has the ability to identify abstract characteristics embedded within a data set, subsequently using that association to categorize, identify, and isolate subsets of the data. Scanning probe microscopy measures multimodal surface properties, combining morphology with electronic, mechanical, and other characteristics. In this review, we focus on a subset of deep learning algorithms, that is, convolutional neural networks, and how it is transforming the acquisition and analysis of scanning probe data.

scholarly journals Rapid Disaster Data Dissemination and Vulnerability Assessment through Synthesis of a Web-Based Extreme Event Viewer and Deep Learning

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
P. Shane Crawford ◽  
Mohammad A. Al-Zarrad ◽  
Andrew J. Graettinger ◽  
Alexander M. Hainen ◽  
Edward Back ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Data Dissemination ◽  
Extreme Event ◽  
Video Data ◽  
Data Set ◽  
Disaster Data ◽  
Vulnerability Measurement ◽  
Infrastructure Vulnerability ◽  
Deep Learning Neural Network

Infrastructure vulnerability has drawn significant attention in recent years, partly because of the occurrence of low-probability and high-consequence disruptive events such as 2017 hurricanes Harvey, Irma, and Maria, 2011 Tuscaloosa and Joplin tornadoes, and 2015 Gorkha, Nepal, and 2017 Central Mexico earthquakes. Civil infrastructure systems support social welfare, thus viability and sustained operation is critical. A variety of frameworks, models, and tools exist for advancing infrastructure vulnerability research. Nevertheless, providing accurate vulnerability measurement remains challenging. This paper presents a state-of-the-art data collection and information extraction methodology to document infrastructure at high granularity to assess preevent vulnerability and postevent damage in the face of disasters. The methods establish a baseline of preevent infrastructure functionality that can be used to measure impacts and temporal recovery following a disaster. The Extreme Events Web Viewer (EEWV) presented as part of the methodology is a GIS-based web repository storing spatial and temporal data describing communities before and after disasters and facilitating data analysis techniques. This web platform can store multiple geolocated data formats including photographs and 360° videos. A tool for automated extraction of photography from 360° video data at locations of interest specified in the EEWV was created to streamline data utility. The extracted imagery provides a manageable data set to efficiently document characteristics of the built and natural environment. The methodology was tested to locate buildings vulnerable to flood and storm surge on Dauphin Island, Alabama. Approximately 1,950 buildings were passively documented with vehicle-mounted 360° video. Extracted building images were used to train a deep learning neural network to predict whether a building was elevated or nonelevated. The model was validated, and methods for iterative neural network training are described. The methodology, from rapidly collecting large passive datasets, storing the data in an open repository, extracting manageable datasets, and obtaining information from data through deep learning, will facilitate vulnerability and postdisaster analyses as well as longitudinal recovery measurement.

scholarly journals The impact of entrepreneurial education and competitiveness on business performance of Indonesian SMEs

2020 ◽  
Vol 15 (6) ◽  
pp. 1732-1743
Author(s):  
Hairudinor Hairudinor ◽  
Adjat Daradjat ◽  
Nasir Asman
Keyword(s):  
Self Efficacy ◽  
Business Performance ◽  
Entrepreneurial Education ◽  
Empirical Examination ◽  
Future Studies ◽  
Interview Session ◽  
Prime Concern ◽  
Entrepreneurial Activities ◽  
The Impact

The prime concern of the present study is the investigation of the link between effective entrepreneurial education and competitiveness that influence the business performance of Indonesian SMEs. The study was conducted to determine the empirical examination between constructs through utilization of PLS. The results of the study found that entrepreneurial education and competitiveness significantly and positively influence the business performance, but moderation role of self-efficacy was not observed between constructs. Therefore, H1 and H2, which were the direct hypotheses were accepted statistically but moderating hypotheses H3 and H4 were rejected on statistical grounds. The study provides guidelines for SMEs to initiate entrepreneurial activities for business performance. However, it is observed that various limitations are also attached with the present study. For example, this study is limited in terms of some qualitative analysis where the interview session with the respondents would be quite meaningful. In addition, this study missed the implication on some bigger business firms or large industries as it is only addressing the SMEs firms that are working in the region of Indonesia. Future studies are highly recommended to focus on these limitations.   Keyword: Entrepreneurial Education; Competitiveness; Self-Efficacy; Business Performance

scholarly journals Perceived Teacher Autonomy Support and Students' Deep Learning: The Mediating Role of Self-Efficacy and the Moderating Role of Perceived Peer Support

2021 ◽  
Vol 12 ◽  
Author(s):  
Jingxian Zhao ◽  
Yue Qin
Keyword(s):  
College Students ◽  
Factor Analysis ◽  
Deep Learning ◽  
Peer Support ◽  
Self Efficacy ◽  
Autonomy Support ◽  
Teacher Autonomy ◽  
Mediation Effect ◽  
The Relationship

The purpose of this research is to test the mediation effect of self-efficacy on college student's perception of teacher autonomy support and students' deep learning, and whether the peer support perceived by students can moderate the relationship between perceived teacher autonomy support and deep learning. A survey of 1,800 college students from a provincial undergraduate normal university in Guizhou Province in China was conducted through the revised Perceived Teacher Autonomy Support Scale, Deep Learning Scale, Self-Efficacy Scale, and Perceived Peer Support Scale (Mean age = 21 years old, SD = 1.34). Data use SPSS23.0, AMOS22.0 for descriptive analysis and correlation analysis, exploratory factor analysis (EFA) and confirmatory factor analysis (CFA), moderation effect, and mediation effect analysis. The research results show that after controlling for gender, major, and grade, self-efficacy partially moderates the connection between perceived teacher autonomy support and deep learning of college students. Moreover, perceived peer support mediates the relationship between perceived teacher autonomy support and students' self-efficacy.

Female entrepreneurs’ cognitive attributes and venture growth in Japan: the moderating role of perceived social legitimacy

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Norifumi Kawai ◽  
Tomoyo Kazumi
Keyword(s):  
Self Efficacy ◽  
Safety Net ◽  
Performance Outcomes ◽  
Growth Equation ◽  
Unified Framework ◽  
Female Entrepreneurs ◽  
Data Set ◽  
Content Type ◽  
Social Legitimacy

Purpose By drawing upon social cognitive and legitimacy perspectives, this study aims to explore the role of perceived social legitimacy as an informal institutional force that moderates the effects of female entrepreneurs’ self-efficacy and entrepreneurial tenacity on venture growth. Design/methodology/approach This study uses a data set of 308 Japanese female entrepreneurs, who are a subject of limited extant scholarly attention, to test the hypothesised relationships empirically. Findings Consistent with the unified framework, the study was able to identify that the acquisition of social legitimacy required by female entrepreneurs serves as a crucial safety net under which entrepreneurial self-efficacy and tenacity can significantly affect venture growth. Research limitations/implications The study highlights that high levels of entrepreneurial traits alone are not necessarily sufficient to guarantee women’s venture growth. In doing so, this study stimulates the development of theory on the complementary role of the social legitimacy of entrepreneurship in fueling and mobilising the female entrepreneurs’ cognitive resources as the key to venture growth in the Japanese context. Practical implications Policymakers should be dedicated to implementing more gender-specific policies designed to continually cultivate women’s cognitive attributes in tandem with the promotion of social awareness to embrace entrepreneurship as a promising career option. Originality/value The originality of this study lies in stimulating a debate on the underlying heterogeneity of female entrepreneurs in the performance outcomes of two entrepreneurial cognitive attributes. By integrating the concept of perceived social legitimacy, the study can respond to Miao et al. (2017), who sought further examination of untested boundary conditions in the cognitive characteristics-venture growth equation.

Sign in / Sign up

Export Citation Format

Share Document