Investigating the impact of emotion on temporal orientation in a deep multitask setting

AbstractTemporal orientation is an important aspect of human cognition which shows how an individual emphasizes past, present, and future. Theoretical research in psychology shows that one’s emotional state can influence his/her temporal orientation. We hypothesize that measuring human temporal orientation can benefit from concurrent learning of emotion. To test this hypothesis, we propose a deep learning-based multi-task framework where we concurrently learn a unified model for temporal orientation (our primary task) and emotion analysis (secondary task) using tweets. Our multi-task framework takes users’ tweets as input and produces three temporal orientation labels (past, present or future) and four emotion labels (joy, sadness, anger, or fear) with intensity values as outputs. The classified tweets are then grouped for each user to obtain the user-level temporal orientation and emotion. Finally, we investigate the associations between the users’ temporal orientation and their emotional state. Our analysis reveals that joy and anger are correlated to future orientation while sadness and fear are correlated to the past orientation.

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ANXIETY OF THE PERSONEMOTIONAL STATUS

BULLETIN Series Psychology ◽

10.51889/2020-3.1728-7847.04 ◽

2020 ◽

pp. 24-30

Author(s):

N.M. Irgebaeva ◽

◽

M.I. Akmusaeva ◽

Keyword(s):

Mental State ◽

Emotional State ◽

Psychological Symptoms ◽

Adverse Outcome ◽

Negative Impact ◽

Theoretical Research ◽

State Of Mind ◽

Long Time ◽

The Impact

The article considers the impact of anxiety on the qualitative characteristics of man and the problems of his theoretical research in the field of psychology.The concept of anxiety in psychology is the ability of a person to feel different discomforts depending on the experience in different situations. Everyone has a level of anxiety called temporary and normal. If a person wants, he can overcome it. And if anxiety persists for a long time, it will not be able to cope on its own, and this will have a negative impact on human health and disrupt normalcy. Increased levels of anxiety, along with the emergence of various diseases, lead to a significant decline in quality of life. There are psychological and physiological manifestations of anxiety, which are determined by certain methods in science. The most obvious of the psychological symptoms - obscure problems - unjustified tense experiences. In psychology, anxiety is an emotional state with a negative connotation. Anxiety is characterized by the expectation of something negative, such as an adverse outcome or adverse outcome. Anxiety is a person's experience in different situations (joy, sorrow, shame, anger, fear, etc.). Anxiety is associated with a mental state, which is a person's emotional state and various feelings that indicate anxiety. Anxiety is associated with a mental state, which is a person's emotional state and various feelings that indicate anxiety. For example: confusion, anxiety, excitement, indifference, impatience, delay, etc. There are other meanings of the mental state of anxiety, which are: depression - a shameful mental state accompanied by anxiety; Propaganda is a phenomenon that takes place not only in a state of anxiety, but also in a state of anger and rage; grief is a state of mind accompanied by anxiety, worry, grief, mental state is a state of mind, violence is a state of mind, grief accompanied by long-term anxiety, etc., so anxiety is a double state of mind.

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The impact of the new media on public relations in the public administration in Kurdistan Region

Journal of Raparin University ◽

10.26750/vol(7).no(1).paper16 ◽

2019 ◽

Vol 7 (1) ◽

pp. 268-288

Author(s):

Dlan Ismail Mawlud ◽

Hoshyar Mozafar Ali

Keyword(s):

Information Technology ◽

New Media ◽

Public Relations ◽

Public Administration ◽

Management System ◽

Theoretical Research ◽

Kurdistan Region ◽

The Public ◽

Government Institutions ◽

The Impact

The development of technology, information technology and various means of communication have a significant impact on public relations activity; especially in government institutions. Many government institutions have invested these means in their management system, in order to facilitate the goals of the institution, and ultimately the interaction between the internal and external public. In this theoretical research, I tried to explain the impact of the new media on public relations in the public administration, based on the views of specialists. The aim of the research is to know the use of the new media of public relations and how in the system of public administration, as well as, Explaining the role it plays in public relations activities of government institutions. Add to this, analyzing the way of how new media and public relations participate in the birth of e-government. In the results, it is clear that the new media has facilitated public relations between the public and other institutions, as it strengthened relations between them

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Predictive Analysis of the Impact of Corporate R&D Support Using Deep Learning

Journal of Korea Technology Innovation Society ◽

10.35978/jktis.2020.2.23.1.20 ◽

2020 ◽

Vol 23 (1) ◽

pp. 20-41

Author(s):

Pilseong Jang ◽

Jaeyoun You ◽

Seung Hwan Oh

Keyword(s):

Deep Learning ◽

Predictive Analysis ◽

The Impact

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An Ensemble Deep Learning Approach to Explore the Impact of Enticement, Engagement and Experience in Reward Based Crowdfunding

SSRN Electronic Journal ◽

10.2139/ssrn.3615176 ◽

2020 ◽

Author(s):

Arvind Srinivasan ◽

Akilandeswari P

Keyword(s):

Deep Learning ◽

Learning Approach ◽

The Impact

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The impact of deep learning on document classification using semantically rich representations

Information Processing & Management ◽

10.1016/j.ipm.2019.05.003 ◽

2019 ◽

Vol 56 (5) ◽

pp. 1618-1632 ◽

Cited By ~ 19

Author(s):

Zenun Kastrati ◽

Ali Shariq Imran ◽

Sule Yildirim Yayilgan

Keyword(s):

Deep Learning ◽

Document Classification ◽

The Impact

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Investigating the impact of pre-processing techniques and pre-trained word embeddings in detecting Arabic health information on social media

Journal Of Big Data ◽

10.1186/s40537-021-00488-w ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Yahya Albalawi ◽

Jim Buckley ◽

Nikola S. Nikolov

Keyword(s):

Social Media ◽

Deep Learning ◽

Comprehensive Evaluation ◽

Classification Problem ◽

Data Sets ◽

Word Embeddings ◽

Data Set ◽

Lower Accuracy ◽

Health Related ◽

The Impact

AbstractThis paper presents a comprehensive evaluation of data pre-processing and word embedding techniques in the context of Arabic document classification in the domain of health-related communication on social media. We evaluate 26 text pre-processings applied to Arabic tweets within the process of training a classifier to identify health-related tweets. For this task we use the (traditional) machine learning classifiers KNN, SVM, Multinomial NB and Logistic Regression. Furthermore, we report experimental results with the deep learning architectures BLSTM and CNN for the same text classification problem. Since word embeddings are more typically used as the input layer in deep networks, in the deep learning experiments we evaluate several state-of-the-art pre-trained word embeddings with the same text pre-processing applied. To achieve these goals, we use two data sets: one for both training and testing, and another for testing the generality of our models only. Our results point to the conclusion that only four out of the 26 pre-processings improve the classification accuracy significantly. For the first data set of Arabic tweets, we found that Mazajak CBOW pre-trained word embeddings as the input to a BLSTM deep network led to the most accurate classifier with F1 score of 89.7%. For the second data set, Mazajak Skip-Gram pre-trained word embeddings as the input to BLSTM led to the most accurate model with F1 score of 75.2% and accuracy of 90.7% compared to F1 score of 90.8% achieved by Mazajak CBOW for the same architecture but with lower accuracy of 70.89%. Our results also show that the performance of the best of the traditional classifier we trained is comparable to the deep learning methods on the first dataset, but significantly worse on the second dataset.

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Improving Drought Tolerance in Mungbean (Vigna radiata L. Wilczek): Morpho-Physiological, Biochemical and Molecular Perspectives

Agronomy ◽

10.3390/agronomy11081534 ◽

2021 ◽

Vol 11 (8) ◽

pp. 1534

Author(s):

Chandra Mohan Singh ◽

Poornima Singh ◽

Chandrakant Tiwari ◽

Shalini Purwar ◽

Mukul Kumar ◽

...

Keyword(s):

Drought Stress ◽

Drought Tolerance ◽

Crop Production ◽

Mitigation Strategies ◽

Theoretical Research ◽

Whole Genome Sequence ◽

Complex Nature ◽

Drought Tolerant ◽

Breeding Programmes ◽

The Impact

Drought stress is considered a severe threat to crop production. It adversely affects the morpho-physiological, biochemical and molecular functions of the plants, especially in short duration crops like mungbean. In the past few decades, significant progress has been made towards enhancing climate resilience in legumes through classical and next-generation breeding coupled with omics approaches. Various defence mechanisms have been reported as key players in crop adaptation to drought stress. Many researchers have identified potential donors, QTLs/genes and candidate genes associated to drought tolerance-related traits. However, cloning and exploitation of these loci/gene(s) in breeding programmes are still limited. To bridge the gap between theoretical research and practical breeding, we need to reveal the omics-assisted genetic variations associated with drought tolerance in mungbean to tackle this stress. Furthermore, the use of wild relatives in breeding programmes for drought tolerance is also limited and needs to be focused. Even after six years of decoding the whole genome sequence of mungbean, the genome-wide characterization and expression of various gene families and transcriptional factors are still lacking. Due to the complex nature of drought tolerance, it also requires integrating high throughput multi-omics approaches to increase breeding efficiency and genomic selection for rapid genetic gains to develop drought-tolerant mungbean cultivars. This review highlights the impact of drought stress on mungbean and mitigation strategies for breeding high-yielding drought-tolerant mungbean varieties through classical and modern omics technologies.

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Data augmentation for computed tomography angiography via synthetic image generation and neural domain adaptation

Current Directions in Biomedical Engineering ◽

10.1515/cdbme-2020-0015 ◽

2020 ◽

Vol 6 (1) ◽

Author(s):

Malte Seemann ◽

Lennart Bargsten ◽

Alexander Schlaefer

Keyword(s):

Computed Tomography ◽

Neural Networks ◽

Deep Learning ◽

Medical Imaging ◽

Computed Tomography Angiography ◽

Data Augmentation ◽

Domain Adaptation ◽

Synthetic Image ◽

Wide Range ◽

The Impact

AbstractDeep learning methods produce promising results when applied to a wide range of medical imaging tasks, including segmentation of artery lumen in computed tomography angiography (CTA) data. However, to perform sufficiently, neural networks have to be trained on large amounts of high quality annotated data. In the realm of medical imaging, annotations are not only quite scarce but also often not entirely reliable. To tackle both challenges, we developed a two-step approach for generating realistic synthetic CTA data for the purpose of data augmentation. In the first step moderately realistic images are generated in a purely numerical fashion. In the second step these images are improved by applying neural domain adaptation. We evaluated the impact of synthetic data on lumen segmentation via convolutional neural networks (CNNs) by comparing resulting performances. Improvements of up to 5% in terms of Dice coefficient and 20% for Hausdorff distance represent a proof of concept that the proposed augmentation procedure can be used to enhance deep learning-based segmentation for artery lumen in CTA images.

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Are e-learning Webinars the future of medical education? An exploratory study of a disruptive innovation in the COVID-19 era

Cardiology in the Young ◽

10.1017/s1047951120004503 ◽

2020 ◽

pp. 1-10

Author(s):

Colin J. McMahon ◽

Justin T. Tretter ◽

Theresa Faulkner ◽

R. Krishna Kumar ◽

Andrew N. Redington ◽

...

Keyword(s):

Deep Learning ◽

Carbon Footprint ◽

Quantitative Research ◽

Survey Design ◽

Hybrid Approach ◽

Disruptive Innovation ◽

Cross Sectional Survey ◽

Cross Sectional ◽

E Learning ◽

The Impact

Abstract Objective: This study investigated the impact of the Webinar on deep human learning of CHD. Materials and methods: This cross-sectional survey design study used an open and closed-ended questionnaire to assess the impact of the Webinar on deep learning of topical areas within the management of the post-operative tetralogy of Fallot patients. This was a quantitative research methodology using descriptive statistical analyses with a sequential explanatory design. Results: One thousand-three-hundred and seventy-four participants from 100 countries on 6 continents joined the Webinar, 557 (40%) of whom completed the questionnaire. Over 70% of participants reported that they “agreed” or “strongly agreed” that the Webinar format promoted deep learning for each of the topics compared to other standard learning methods (textbook and journal learning). Two-thirds expressed a preference for attending a Webinar rather than an international conference. Over 80% of participants highlighted significant barriers to attending conferences including cost (79%), distance to travel (49%), time commitment (51%), and family commitments (35%). Strengths of the Webinar included expertise, concise high-quality presentations often discussing contentious issues, and the platform quality. The main weakness was a limited time for questions. Just over 53% expressed a concern for the carbon footprint involved in attending conferences and preferred to attend a Webinar. Conclusion: E-learning Webinars represent a disruptive innovation, which promotes deep learning, greater multidisciplinary participation, and greater attendee satisfaction with fewer barriers to participation. Although Webinars will never fully replace conferences, a hybrid approach may reduce the need for conferencing, reduce carbon footprint. and promote a “sustainable academia”.

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