scholarly journals Subspace Learning and Joint Distribution Adaptation for Unsupervised Cross-Database Microexpression Recognition

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Yanliang Zhang ◽  
Ying Liu ◽  
Geng Li ◽  
Hongxing Peng
Keyword(s):  
Conditional Probability ◽  
Joint Distribution ◽  
Probability Distributions ◽  
Lie Detection ◽  
Subspace Learning ◽  
Experimental Results ◽  
Target Domain ◽  
Business Negotiation ◽  
Source Domain ◽  
Potential Applications

Microexpression recognition has been widely favored by researchers due to its many potential applications, such as business negotiation and lie detection. Cross-database microexpression recognition is more challenging and attractive than normal microexpression recognition because the training and testing samples come from different databases. The ensuing challenge is that the feature distributions between training and testing samples differ too much. As a result, the performance of current well-performing microexpression recognition methods often fails to achieve the desired effect. In this paper, we overcome this problem by introducing Subspace Learning and Joint Distribution Adaptation (SLJDA) by projecting the source and target domains into the subspace and later reducing the distance between them and then minimizing the distance between the marginal and conditional probability distributions of the data between the source domain and the target domain. To evaluate its performance, a large number of cross-database experiments are performed in the SMIC database and CASMEII database. The experimental results show the superiority of the method compared with existing microexpression recognition methods.

scholarly journals Domain Adaptation for Pedestrian Detection Based on Prediction Consistency

2014 ◽  
Vol 2014 ◽  
pp. 1-7
Author(s):  
Yu Li-ping ◽  
Tang Huan-ling ◽  
An Zhi-yong
Keyword(s):  
Computer Vision ◽  
Detection Rate ◽  
Domain Adaptation ◽  
Learning Algorithm ◽  
Pedestrian Detection ◽  
Experimental Results ◽  
Challenging Problem ◽  
Target Domain ◽  
Source Domain ◽  
Adaptation Model

Pedestrian detection is an active area of research in computer vision. It remains a quite challenging problem in many applications where many factors cause a mismatch between source dataset used to train the pedestrian detector and samples in the target scene. In this paper, we propose a novel domain adaptation model for merging plentiful source domain samples with scared target domain samples to create a scene-specific pedestrian detector that performs as well as rich target domain simples are present. Our approach combines the boosting-based learning algorithm with an entropy-based transferability, which is derived from the prediction consistency with the source classifications, to selectively choose the samples showing positive transferability in source domains to the target domain. Experimental results show that our approach can improve the detection rate, especially with the insufficient labeled data in target scene.

scholarly journals Transfer Learning for Collaborative Filtering Using a Psychometrics Model

2016 ◽  
Vol 2016 ◽  
pp. 1-9
Author(s):  
Haijun Zhang ◽  
Bo Zhang ◽  
Zhoujun Li ◽  
Guicheng Shen ◽  
Liping Tian
Keyword(s):  
Collaborative Filtering ◽  
Transfer Learning ◽  
Learning Algorithm ◽  
Experimental Results ◽  
Extended Model ◽  
Target Domain ◽  
Rating Data ◽  
Source Domain ◽  
One Dimensional ◽  
Recommendation Algorithms

In a real e-commerce website, usually only a small number of users will give ratings to the items they purchased, and this can lead to the very sparse user-item rating data. The data sparsity issue will greatly limit the recommendation performance of most recommendation algorithms. However, a user may register accounts in many e-commerce websites. If such users’ historical purchasing data on these websites can be integrated, the recommendation performance could be improved. But it is difficult to align the users and items between these websites, and thus how to effectively borrow the users’ rating data of one website (source domain) to help improve the recommendation performance of another website (target domain) is very challenging. To this end, this paper extended the traditional one-dimensional psychometrics model to multidimension. The extended model can effectively capture users’ multiple interests. Based on this multidimensional psychometrics model, we further propose a novel transfer learning algorithm. It can effectively transfer users’ rating preferences from the source domain to the target domain. Experimental results show that the proposed method can significantly improve the recommendation performance.

scholarly journals Adversarial Hard Attention Adaptation

Information ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 224
Author(s):  
Hui Tao ◽  
Jun He ◽  
Quanjie Cao ◽  
Lei Zhang
Keyword(s):  
Visual Attention ◽  
Domain Knowledge ◽  
Domain Adaptation ◽  
Experimental Results ◽  
Target Domain ◽  
Source Domain ◽  
Visual Attention Model ◽  
Attention Model ◽  
Cross Domain ◽  
Adversarial Training

Domain adaptation is critical to transfer the invaluable source domain knowledge to the target domain. In this paper, for a particular visual attention model, saying hard attention, we consider to adapt the learned hard attention to the unlabeled target domain. To tackle this kind of hard attention adaptation, a novel adversarial reward strategy is proposed to train the policy of the target domain agent. In this adversarial training framework, the target domain agent competes with the discriminator which takes the attention features generated from the both domain agents as input and tries its best to distinguish them, and thus the target domain policy is learned to align the local attention feature to its source domain counterpart. We evaluated our model on the benchmarks of the cross-domain tasks, such as the centered digits datasets and the enlarged non-centered digits datasets. The experimental results show that our model outperforms the ADDA and other existing methods.

The Concept of Animals in Balinese Proverbs

2017 ◽  
Vol 3 (1) ◽  
pp. 87
Author(s):  
I Wayan Budiarta ◽  
Ni Wayan Kasni
Keyword(s):  
Short Story ◽  
Cognitive Processes ◽  
Qualitative Data ◽  
Syntactic Structure ◽  
The Body ◽  
Biological Characteristic ◽  
Speech Community ◽  
Target Domain ◽  
Source Domain ◽  
Mapping Process

This research is aimed to figure out the syntactic structure of Balinese proverbs, the relation of meaning between the name of the animals and the meaning of the proverbs, and how the meanings are constructed in logical dimension. This research belongs to a qualitative as the data of this research are qualitative data which taken from a book entitled Basita Paribahasa written by Simpen (1993) and a book of Balinese short story written by Sewamara (1977). The analysis shows that the use of concept of animals in Balinese proverbs reveal similar characteristics, whether their form, their nature, and their condition. Moreover, the cognitive processes which happen in resulting the proverb is by conceptualizing the experience which is felt by the body, the nature, and the characteristic which owned by the target with the purpose of describing event or experience by the speech community of Balinese. Analogically, the similarity of characteristic in the form of shape of source domain can be proved visually, while the characteristic of the nature and the condition can be proved through bodily and empirical experiences. Ecolinguistics parameters are used to construct of Balinese proverbs which happen due to cross mapping process. It is caused by the presence of close characteristic or biological characteristic which is owned by the source domain and target domain, especially between Balinese with animal which then are verbally recorded and further patterned in ideological, biological, and sociological dimensions.

Correlation alignment with attention mechanism for unsupervised domain adaptation

2021 ◽  
pp. 1-7
Author(s):  
Rong Chen ◽  
Chongguang Ren
Keyword(s):  
Natural Language Processing ◽  
Language Processing ◽  
Transfer Process ◽  
Negative Transfer ◽  
Domain Adaptation ◽  
Attention Mechanism ◽  
Target Domain ◽  
Source Domain ◽  
Second Order Statistics ◽  
Unsupervised Domain Adaptation

Domain adaptation aims to solve the problems of lacking labels. Most existing works of domain adaptation mainly focus on aligning the feature distributions between the source and target domain. However, in the field of Natural Language Processing, some of the words in different domains convey different sentiment. Thus not all features of the source domain should be transferred, and it would cause negative transfer when aligning the untransferable features. To address this issue, we propose a Correlation Alignment with Attention mechanism for unsupervised Domain Adaptation (CAADA) model. In the model, an attention mechanism is introduced into the transfer process for domain adaptation, which can capture the positively transferable features in source and target domain. Moreover, the CORrelation ALignment (CORAL) loss is utilized to minimize the domain discrepancy by aligning the second-order statistics of the positively transferable features extracted by the attention mechanism. Extensive experiments on the Amazon review dataset demonstrate the effectiveness of CAADA method.

scholarly journals 20,000 In League Under the Sea: Anonymous Communication, Trust, MLATs, and Undersea Cables

2015 ◽  
Vol 2015 (1) ◽  
pp. 4-24 ◽  
Author(s):  
Aaron D. Jaggard ◽  
Aaron Johnson ◽  
Sarah Cortes ◽  
Paul Syverson ◽  
Joan Feigenbaum
Keyword(s):  
Autonomous System ◽  
Probability Distributions ◽  
Arms Race ◽  
Experimental Results ◽  
Modular System ◽  
Anonymous Communication ◽  
Legal Assistance ◽  
Correlation Attacks ◽  
Submarine Cables ◽  
Undersea Cables

Abstract Motivated by the effectiveness of correlation attacks against Tor, the censorship arms race, and observations of malicious relays in Tor, we propose that Tor users capture their trust in network elements using probability distributions over the sets of elements observed by network adversaries. We present a modular system that allows users to efficiently and conveniently create such distributions and use them to improve their security. To illustrate this system, we present two novel types of adversaries. First, we study a powerful, pervasive adversary that can compromise an unknown number of Autonomous System organizations, Internet Exchange Point organizations, and Tor relay families. Second, we initiate the study of how an adversary might use Mutual Legal Assistance Treaties (MLATs) to enact surveillance. As part of this, we identify submarine cables as a potential subject of trust and incorporate data about these into our MLAT analysis by using them as a proxy for adversary power. Finally, we present preliminary experimental results that show the potential for our trust framework to be used by Tor clients and services to improve security.

Red-hot faces and burnt hearts

2021 ◽  
Vol 19 (2) ◽  
pp. 482-516
Author(s):  
Sérgio N. Menete ◽  
Guiying Jiang
Keyword(s):  
Heat Source ◽  
Conceptual Structure ◽  
Two Dimensions ◽  
Human Beings ◽  
Target Domain ◽  
Bodily Experience ◽  
Source Domain ◽  
African Languages

Abstract People from different languages draw from the knowledge they have from the domain of heat (source domain) and apply it to the domain of anger (target domain) through metaphor. This was also found to be the case with Amharic and Changana. Our study investigates how anger is metaphorically conceptualized in these two languages. Many similarities were found even though variations do exist cross-linguistically. It is suggested that the similarities between these languages in conceptualizing anger lie in the fact that human beings share the same bodily experience: (physiology) embodiment, even though variations may arise due to the differences in cultural embodiment (race, values and geographical localization, etc). The study seeks to demonstrate how these two dimensions contribute to the overall conceptual structure of anger is heat metaphor in these two (unrelated) African languages.

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