scholarly journals SAEP: A Surrounding-Aware Individual Emotion Prediction Model Combined with T-LSTM and Memory Attention Mechanism

2021 ◽  
Vol 11 (23) ◽  
pp. 11111
Author(s):  
Yakun Wang ◽  
Yajun Du ◽  
Jinrong Hu ◽  
Xianyong Li ◽  
Xiaoliang Chen
Keyword(s):  
Prediction Model ◽  
Alternative Methods ◽  
Attention Network ◽  
Attention Networks ◽  
Proposed Model ◽  
Decoder Architecture ◽  
Novel Variant ◽  
Emotional Changes ◽  
Evolving Context

The future emotion prediction of users on social media has been attracting increasing attention from academics. Previous studies on predicting future emotion have focused on the characteristics of individuals’ emotion changes; however, the role of the individual’s neighbors has not yet been thoroughly researched. To fill this gap, a surrounding-aware individual emotion prediction model (SAEP) based on a deep encoder–decoder architecture is proposed to predict individuals’ future emotions. In particular, two memory-based attention networks are constructed: The time-evolving attention network and the surrounding attention network to extract the features of the emotional changes of users and neighbors, respectively. Then, these features are incorporated into the emotion prediction task. In addition, a novel variant LSTM is introduced as the encoder of the proposed model, which can effectively extract complex patterns of users’ emotional changes from irregular time series. Extensive experimental results show that the proposed approach outperforms five alternative methods. The SAEP approach has improved by approximately 4.21–14.84% micro F1 on a dataset built from Twitter and 7.30–13.41% on a dataset built from Microblog. Further analyses validate the effectiveness of the proposed time-evolving context and surrounding context, as well as the factors that may affect the prediction results.

scholarly journals Temporal circuit of macroscale dynamic brain activity supports human consciousness

2020 ◽  
Vol 6 (11) ◽  
pp. eaaz0087 ◽  
Author(s):  
Zirui Huang ◽  
Jun Zhang ◽  
Jinsong Wu ◽  
George A. Mashour ◽  
Anthony G. Hudetz
Keyword(s):  
Default Mode Network ◽  
Functional Role ◽  
Brain Activity ◽  
Human Consciousness ◽  
Attention Network ◽  
Attention Networks ◽  
Default Mode ◽  
Dorsal Attention Network ◽  
Brain States

The ongoing stream of human consciousness relies on two distinct cortical systems, the default mode network and the dorsal attention network, which alternate their activity in an anticorrelated manner. We examined how the two systems are regulated in the conscious brain and how they are disrupted when consciousness is diminished. We provide evidence for a “temporal circuit” characterized by a set of trajectories along which dynamic brain activity occurs. We demonstrate that the transitions between default mode and dorsal attention networks are embedded in this temporal circuit, in which a balanced reciprocal accessibility of brain states is characteristic of consciousness. Conversely, isolation of the default mode and dorsal attention networks from the temporal circuit is associated with unresponsiveness of diverse etiologies. These findings advance the foundational understanding of the functional role of anticorrelated systems in consciousness.

scholarly journals Multi-Agent Actor-Critic with Hierarchical Graph Attention Network

2020 ◽  
Vol 34 (05) ◽  
pp. 7236-7243
Author(s):  
Heechang Ryu ◽  
Hayong Shin ◽  
Jinkyoo Park
Keyword(s):  
Representation Learning ◽  
Policy Learning ◽  
Group Level ◽  
Multiple Agents ◽  
Attention Network ◽  
Attention Networks ◽  
Hierarchical Graph ◽  
Proposed Model ◽  
Multi Agent ◽  
Strategic Policies

Most previous studies on multi-agent reinforcement learning focus on deriving decentralized and cooperative policies to maximize a common reward and rarely consider the transferability of trained policies to new tasks. This prevents such policies from being applied to more complex multi-agent tasks. To resolve these limitations, we propose a model that conducts both representation learning for multiple agents using hierarchical graph attention network and policy learning using multi-agent actor-critic. The hierarchical graph attention network is specially designed to model the hierarchical relationships among multiple agents that either cooperate or compete with each other to derive more advanced strategic policies. Two attention networks, the inter-agent and inter-group attention layers, are used to effectively model individual and group level interactions, respectively. The two attention networks have been proven to facilitate the transfer of learned policies to new tasks with different agent compositions and allow one to interpret the learned strategies. Empirically, we demonstrate that the proposed model outperforms existing methods in several mixed cooperative and competitive tasks.

The Importance of the Prediction Model in the Validation of Alternative Tests

2001 ◽  
Vol 29 (2) ◽  
pp. 135-143 ◽  
Author(s):  
Andrew P. Worth ◽  
Michael Balls
Keyword(s):  
Prediction Model ◽  
Predictive Ability ◽  
Test System ◽  
Alternative Methods ◽  
In Vivo Test ◽  
Testing Strategies ◽  
Adequate Test ◽  
Alternative Test

An overview is presented of the validation process adopted by the European Centre for the Validation of Alternative Methods, with particular emphasis on the central role of the prediction model (PM). The development of an adequate PM is considered to be just as important as the development of an adequate test system, since the validity of an alternative test can only be established when both components (the test system and the PM) have successfully undergone validation. It is argued, however, that alternative tests and their associated PMs do not necessarily need to undergo validation at the same time, and that retrospective validation may be appropriate when a test system is found to be reliable, but the case for its relevance remains to be demonstrated. For an alternative test to be considered “scientifically valid”, it is necessary for three conditions to be fulfilled, referred to here as the criteria for scientific relevance, predictive relevance, and reliability. A minimal set of criteria for the acceptance of any PM is defined, but it should be noted that required levels of predictive ability need to be established on a case-by-case basis, taking into account the inherent variability of the alternative and in vivo test data. Finally, in view of the growing shift in emphasis from the use of standalone alternative tests to alternative testing strategies, the importance of making the PM an integral part of the testing strategy is discussed.

scholarly journals A Multi-Attention Network for Aspect-Level Sentiment Analysis

2019 ◽  
Vol 11 (7) ◽  
pp. 157 ◽  
Author(s):  
Qiuyue Zhang ◽  
Ran Lu
Keyword(s):  
Neural Networks ◽  
Sentiment Analysis ◽  
Specific Aspect ◽  
Experimental Results ◽  
Sequence Information ◽  
Attention Network ◽  
Attention Networks ◽  
Recent Advances ◽  
Proposed Model ◽  
The Impact

Aspect-level sentiment analysis (ASA) aims at determining the sentiment polarity of specific aspect term with a given sentence. Recent advances in attention mechanisms suggest that attention models are useful in ASA tasks and can help identify focus words. Or combining attention mechanisms with neural networks are also common methods. However, according to the latest research, they often fail to extract text representations efficiently and to achieve interaction between aspect terms and contexts. In order to solve the complete task of ASA, this paper proposes a Multi-Attention Network (MAN) model which adopts several attention networks. This model not only preprocesses data by Bidirectional Encoder Representations from Transformers (BERT), but a number of measures have been taken. First, the MAN model utilizes the partial Transformer after transformation to obtain hidden sequence information. Second, because words in different location have different effects on aspect terms, we introduce location encoding to analyze the impact on distance from ASA tasks, then we obtain the influence of different words with aspect terms through the bidirectional attention network. From the experimental results of three datasets, we could find that the proposed model could achieve consistently superior results.

Social Networking Sites Continuance: An Application of Extended Theory of Planned Behaviour

2015 ◽  
Vol 8 (1) ◽  
Author(s):  
Himanshu Rajput
Keyword(s):  
Social Networking ◽  
Social Networking Sites ◽  
Theory Of Planned Behaviour ◽  
Standard Theory ◽  
Planned Behaviour ◽  
Privacy Concerns ◽  
Proposed Model ◽  
Continued Use ◽  
Moderating Role

Social networking sites (SNSs) have become popular in India with the proliferation of Internet. SNSs have gained the interests of academicians and researchers. The current study is an endeavor to understand the continuance of social networking sites in India. The study applies an extended version of theory of planned behavior. Additional factors privacy concerns and habits were incorporated into the standard theory of planned behaviour. A survey was conducted in a Central University in India. Overall, data was collected from 150 respondents. PLS-SEM was used to test the proposed model. All the hypotheses except the moderating role of habits between intentions and continued use of social networking sites, were supported by the results. Habits were found to affect continued use of social networking sites indirectly through continued intentions.

scholarly journals Social exclusion increases the executive function of attention networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Huoyin Zhang ◽  
Shiyunmeng Zhang ◽  
Jiachen Lu ◽  
Yi Lei ◽  
Hong Li
Keyword(s):  
Social Exclusion ◽  
Executive Control ◽  
Brain Regions ◽  
Negative Influence ◽  
Future Research ◽  
Research Attention ◽  
Attention Network ◽  
Attention Networks ◽  
The Social ◽  
And Control

AbstractPrevious studies in humans have shown that brain regions activating social exclusion overlap with those related to attention. However, in the context of social exclusion, how does behavioral monitoring affect individual behavior? In this study, we used the Cyberball game to induce the social exclusion effect in a group of participants. To explore the influence of social exclusion on the attention network, we administered the Attention Network Test (ANT) and compared results for the three subsystems of the attention network (orienting, alerting, and executive control) between exclusion (N = 60) and inclusion (N = 60) groups. Compared with the inclusion group, the exclusion group showed shorter overall response time and better executive control performance, but no significant differences in orienting or alerting. The excluded individuals showed a stronger ability to detect and control conflicts. It appears that social exclusion does not always exert a negative influence on individuals. In future research, attention to network can be used as indicators of social exclusion. This may further reveal how social exclusion affects individuals' psychosomatic mechanisms.

scholarly journals The Role of Board Independence and Ownership Structure in Improving the Efficacy of Corporate Financial Distress Prediction Model Evidence from India

2021 ◽  
Vol 14 (7) ◽  
pp. 333
Author(s):  
Shilpa H. Shetty ◽  
Theresa Nithila Vincent
Keyword(s):  
Prediction Model ◽  
Ownership Structure ◽  
Financial Distress ◽  
Prediction Models ◽  
Receiver Operating Curve ◽  
Financial Measures ◽  
Financial Variables ◽  
Financial Distress Prediction ◽  
Distress Prediction

The study aimed to investigate the role of non-financial measures in predicting corporate financial distress in the Indian industrial sector. The proportion of independent directors on the board and the proportion of the promoters’ share in the ownership structure of the business were the non-financial measures that were analysed, along with ten financial measures. For this, sample data consisted of 82 companies that had filed for bankruptcy under the Insolvency and Bankruptcy Code (IBC). An equal number of matching financially sound companies also constituted the sample. Therefore, the total sample size was 164 companies. Data for five years immediately preceding the bankruptcy filing was collected for the sample companies. The data of 120 companies evenly drawn from the two groups of companies were used for developing the model and the remaining data were used for validating the developed model. Two binary logistic regression models were developed, M1 and M2, where M1 was formulated with both financial and non-financial variables, and M2 only had financial variables as predictors. The diagnostic ability of the model was tested with the aid of the receiver operating curve (ROC), area under the curve (AUC), sensitivity, specificity and annual accuracy. The results of the study show that inclusion of the two non-financial variables improved the efficacy of the financial distress prediction model. This study made a unique attempt to provide empirical evidence on the role played by non-financial variables in improving the efficiency of corporate distress prediction models.

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