scholarly journals Latent Emotion Memory for Multi-Label Emotion Classification

2020 ◽  
Vol 34 (05) ◽  
pp. 7692-7699
Author(s):  
Hao Fei ◽  
Yue Zhang ◽  
Yafeng Ren ◽  
Donghong Ji
Keyword(s):  
State Of The Art ◽  
Research Topic ◽  
Context Information ◽  
Important Research ◽  
Emotion Classification ◽  
Proposed Model ◽  
Important Research Topic ◽  
Benchmark Datasets ◽  
Memory Network ◽  
Emotion Memory

Identifying multiple emotions in a sentence is an important research topic. Existing methods usually model the problem as multi-label classification task. However, previous methods have two issues, limiting the performance of the task. First, these models do not consider prior emotion distribution in a sentence. Second, they fail to effectively capture the context information closely related to the corresponding emotion. In this paper, we propose a Latent Emotion Memory network (LEM) for multi-label emotion classification. The proposed model can learn the latent emotion distribution without external knowledge, and can effectively leverage it into the classification network. Experimental results on two benchmark datasets show that the proposed model outperforms strong baselines, achieving the state-of-the-art performance.

scholarly journals An All-Batch Loss for Constructing Prediction Intervals

2021 ◽  
Vol 11 (4) ◽  
pp. 1728
Author(s):  
Hua Zhong ◽  
Li Xu
Keyword(s):  
Gradient Descent ◽  
State Of The Art ◽  
Prediction Interval ◽  
Feedforward Neural Networks ◽  
Important Research ◽  
Likelihood Principle ◽  
High Quality ◽  
Construction Methods ◽  
Important Research Topic ◽  
Benchmark Datasets

The prediction interval (PI) is an important research topic in reliability analyses and decision support systems. Data size and computation costs are two of the issues which may hamper the construction of PIs. This paper proposes an all-batch (AB) loss function for constructing high quality PIs. Taking the full advantage of the likelihood principle, the proposed loss makes it possible to train PI generation models using the gradient descent (GD) method for both small and large batches of samples. With the structure of dual feedforward neural networks (FNNs), a high-quality PI generation framework is introduced, which can be adapted to a variety of problems including regression analysis. Numerical experiments were conducted on the benchmark datasets; the results show that higher-quality PIs were achieved using the proposed scheme. Its reliability and stability were also verified in comparison with various state-of-the-art PI construction methods.

scholarly journals Hidden Trigger Backdoor Attacks

2020 ◽  
Vol 34 (07) ◽  
pp. 11957-11965 ◽  
Author(s):  
Aniruddha Saha ◽  
Akshayvarun Subramanya ◽  
Hamed Pirsiavash
Keyword(s):  
Deep Learning ◽  
Visual Inspection ◽  
State Of The Art ◽  
Extensive Study ◽  
Research Topic ◽  
Test Time ◽  
Important Research ◽  
Important Research Topic ◽  
Deep Networks ◽  
Real World Applications

With the success of deep learning algorithms in various domains, studying adversarial attacks to secure deep models in real world applications has become an important research topic. Backdoor attacks are a form of adversarial attacks on deep networks where the attacker provides poisoned data to the victim to train the model with, and then activates the attack by showing a specific small trigger pattern at the test time. Most state-of-the-art backdoor attacks either provide mislabeled poisoning data that is possible to identify by visual inspection, reveal the trigger in the poisoned data, or use noise to hide the trigger. We propose a novel form of backdoor attack where poisoned data look natural with correct labels and also more importantly, the attacker hides the trigger in the poisoned data and keeps the trigger secret until the test time. We perform an extensive study on various image classification settings and show that our attack can fool the model by pasting the trigger at random locations on unseen images although the model performs well on clean data. We also show that our proposed attack cannot be easily defended using a state-of-the-art defense algorithm for backdoor attacks.

scholarly journals User-Personalized Review Rating Prediction Method Based on Review Text Content and User-Item Rating Matrix

Information ◽  
2018 ◽  
Vol 10 (1) ◽  
pp. 1 ◽  
Author(s):  
Bingkun Wang ◽  
Bing Chen ◽  
Li Ma ◽  
Gaiyun Zhou
Keyword(s):  
State Of The Art ◽  
Prediction Method ◽  
Research Topic ◽  
Prediction Methods ◽  
Product Reviews ◽  
Important Research ◽  
Important Research Topic ◽  
Wide Range ◽  
Rating Prediction ◽  
Text Content

With the explosive growth of product reviews, review rating prediction has become an important research topic which has a wide range of applications. The existing review rating prediction methods use a unified model to perform rating prediction on reviews published by different users, ignoring the differences of users within these reviews. Constructing a separate personalized model for each user to capture the user’s personalized sentiment expression is an effective attempt to improve the performance of the review rating prediction. The user-personalized sentiment information can be obtained not only by the review text but also by the user-item rating matrix. Therefore, we propose a user-personalized review rating prediction method by integrating the review text and user-item rating matrix information. In our approach, each user has a personalized review rating prediction model, which is decomposed into two components, one part is based on review text and the other is based on user-item rating matrix. Through extensive experiments on Yelp and Douban datasets, we validate that our methods can significantly outperform the state-of-the-art methods.

scholarly journals A deep learning-based method for vehicle licenseplate recognition in natural scene

2019 ◽  
Vol 8 ◽  
Author(s):  
Jianzong Wang ◽  
Xinhui Liu ◽  
Aozhi Liu ◽  
Jing Xiao
Keyword(s):  
State Of The Art ◽  
Research Topic ◽  
Character Segmentation ◽  
License Plate ◽  
License Plate Recognition ◽  
Important Research ◽  
Natural Scene ◽  
Important Research Topic ◽  
Car Accident ◽  
Accident Scene

AbstractVehicle license platerecognition in natural scene is an important research topic in computer vision. The license plate recognition approach in the specific scene has become a relatively mature technology. However, license plate recognition in the natural scene is still a challenge since the image parameters are highly affected by the complicated environment. For the purpose of improving the performance of license plate recognition in natural scene, we proposed a solution to recognize real-world Chinese license plate photographs using the DCNN-RNN model. With the implementation of DCNN, the license plate is located and the features of the license plate are extracted after the correction process. Finally, an RNN model is performed to decode the deep features to characters without character segmentation. Our state-of-the-art system results in the accuracy and recall of 92.32 and 91.89% on the car accident scene dataset collected in the natural scene, and 92.88 and 92.09% on Caltech Cars 1999 dataset.

Conformal Optimization Algorithm for Undevelopable Surfaces and its Application

2014 ◽  
Vol 602-605 ◽  
pp. 3570-3574
Author(s):  
Zhen Hua Luo ◽  
Fen Jiang
Keyword(s):  
Approximation Algorithms ◽  
Optimization Algorithm ◽  
Manufacturing Process ◽  
Research Topic ◽  
Important Research ◽  
Computer Aided Geometric Design ◽  
Industrial Manufacturing ◽  
Important Research Topic ◽  
Computer Aided ◽  
Planar Materials

In the industrial manufacturing process, most kinds of surfaces are processed by planar materials, but undevelopable surfaces are difficult develop to the plane. The approximation algorithms to develop a undevelopable surface is an important research topic in Computer Aided Geometric Design (CAGD). In this paper, we propose a new approximation algorithms based optimization algorithm. We guarantee the deformation vector make the minimum changes during the developing process. In the paper, some numerical example are given and the can illustrate the our method is effective.

scholarly journals The happy victimizer phenomenon: Not found here

2014 ◽  
Vol 46 (1) ◽  
pp. 145-161
Author(s):  
Ana Jevtic ◽  
Jovan Miric
Keyword(s):  
Emotional Development ◽  
Positive Emotions ◽  
Negative Emotions ◽  
Age Groups ◽  
Research Topic ◽  
Important Research ◽  
Important Research Topic ◽  
Significant Difference ◽  
Moral Explanations

Children?s attribution of emotions to a moral transgressor is an important research topic in the psychology of moral and emotional development. This is especially because of the so-called Happy Victimizer Phenomenon (HVP) where younger children attribute positive emotions to a moral transgressor described in a story. In the two studies that we have conducted (children aged 5, 7 and 9, 20 of each age; 10 of each age in the second study) we have tested the possible influence of the fear of sanctions and the type of transgression (stealing and inflicting body injuries) on the attribution of emotions. Children were presented with stories that described transgressions and they were asked to answer how the transgressor felt. The fear of sanctions did not make a significant difference in attribution but the type of transgression did - more negative emotions were attributed for inflicting body injuries than for stealing. Positive emotions were explained with situational-instrumental explanations in 84% of cases while negative emotions were explained with moral explanations in 63,5%. Girls attributed more positive emotions (61%) than boys (39%). However, our main finding was that, for the aforementioned age groups, we did not find the HVP effect although it has regularly been registered in foreign studies. This finding denies the generalizability of the phenomenon and points to the significance of disciplining styles and, even more so, culture for children?s attribution of emotions to moral transgressors.

scholarly journals Facial Landmark Annotation and Fuzzy Model to Detect Players’ Focus

2021 ◽  
Author(s):  
Alisson Steffens Henrique ◽  
Esteban Walter Gonzalez Clua ◽  
Rodrigo Lyra ◽  
Anita Maria da Rocha Fernandes ◽  
Rudimar Luis Scaranto Dazzi
Keyword(s):  
Fuzzy Model ◽  
Research Topic ◽  
Important Research ◽  
Facial Landmark ◽  
Important Research Topic ◽  
Focus Detection ◽  
Data Log ◽  
Game Analytics

Game Analytics is an important research topic in digitalentertainment. Data log is usually the key to understand players’behavior in a game. However, alpha and beta builds may need aspecial attention to player focus and immersion. In this paper, wepropose t he us e of player’s focus detection, through theclassification of pictures. Results show that pictures can be usedas a new source of data for Game Analytics, feeding developerswith a better understanding of players enjoyment while in testingphases .

scholarly journals Two-Level Transformer and Auxiliary Coherence Modeling for Improved Text Segmentation

2020 ◽  
Vol 34 (05) ◽  
pp. 7797-7804
Author(s):  
Goran Glavašš ◽  
Swapna Somasundaran
Keyword(s):  
State Of The Art ◽  
Language Transfer ◽  
Text Segmentation ◽  
Word Embeddings ◽  
Neural Architecture ◽  
Text Coherence ◽  
Sentence Level ◽  
Proposed Model ◽  
Benchmark Datasets ◽  
Cross Lingual

Breaking down the structure of long texts into semantically coherent segments makes the texts more readable and supports downstream applications like summarization and retrieval. Starting from an apparent link between text coherence and segmentation, we introduce a novel supervised model for text segmentation with simple but explicit coherence modeling. Our model – a neural architecture consisting of two hierarchically connected Transformer networks – is a multi-task learning model that couples the sentence-level segmentation objective with the coherence objective that differentiates correct sequences of sentences from corrupt ones. The proposed model, dubbed Coherence-Aware Text Segmentation (CATS), yields state-of-the-art segmentation performance on a collection of benchmark datasets. Furthermore, by coupling CATS with cross-lingual word embeddings, we demonstrate its effectiveness in zero-shot language transfer: it can successfully segment texts in languages unseen in training.

Trends and Transformations in European Agricultural Economy, Rural Communities and Food Sustainability in Context of New Common Agricultural Policy (CAP) Reforms

2016 ◽  
pp. 1-24
Author(s):  
Andrei Jean-Vasile ◽  
Alexandra Alecu
Keyword(s):  
Rural Communities ◽  
Agricultural Policy ◽  
Common Agricultural Policy ◽  
Research Topic ◽  
Economic Sector ◽  
Important Research ◽  
European Level ◽  
Agricultural Economy ◽  
Security Assurance ◽  
Important Research Topic

Agriculture continues to be quite a debate for the last two and a half decades at least at the European level and for Romania Common Agricultural Policy (CAP) reforms has a big impact in developing the convergence to the European agricultural model. Agriculture becomes nowadays a multirole economic sector, with major implications on rural community's sustainability and on food security assurance. In this context, the transformations in European agricultural economy, rural communities and food sustainability in context of Common Agricultural Policy (CAP) reforms represent an important research topic in the context of EU-28 policy diversification from the larger context of Romanian approach.

Deriving Information System Security and Privacy From Value Cocreation Theory

2019 ◽  
Vol 10 (4) ◽  
pp. 1-25
Author(s):  
Christophe Feltus
Keyword(s):  
Information System ◽  
Value Chain ◽  
System Architecture ◽  
Enterprise Architecture ◽  
Research Topic ◽  
Security And Privacy ◽  
Important Research ◽  
Important Research Topic ◽  
Value Cocreation ◽  
The Relationship

Traditionally, the relationship between the company and its providers have for objective to generate value at the company side in exchange of money. This relationship is largely investigated through the vector of value chain. In this article, security and privacy cocreation (SPCC) is investigated as a specialization of value cocreation. Although it is an important research topic, and despite a plethora of research aiming at depicting the fundamental of SPCC, few contributions have been appeared until now in the area of a language to support SPCC design and deployment. However, such a language is necessary to describe elements of the information system, as well as their underlying dependencies. As a result, this article proposes extending an existing enterprise architecture language to support the process of decision-making and to allow understanding and analysis of the impacts associated to a change of the system architecture as a whole.

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