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 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 A Co-Embedding Model with Variational Auto-Encoder for Knowledge Graphs

2022 ◽  
Vol 12 (2) ◽  
pp. 715
Author(s):  
Luodi Xie ◽  
Huimin Huang ◽  
Qing Du
Keyword(s):  
State Of The Art ◽  
Relation Extraction ◽  
Semantic Space ◽  
Knowledge Graph ◽  
High Quality ◽  
Gaussian Distributions ◽  
Benchmark Datasets ◽  
Semantic Spaces ◽  
Knowledge Graphs ◽  
Low Dimensional

Knowledge graph (KG) embedding has been widely studied to obtain low-dimensional representations for entities and relations. It serves as the basis for downstream tasks, such as KG completion and relation extraction. Traditional KG embedding techniques usually represent entities/relations as vectors or tensors, mapping them in different semantic spaces and ignoring the uncertainties. The affinities between entities and relations are ambiguous when they are not embedded in the same latent spaces. In this paper, we incorporate a co-embedding model for KG embedding, which learns low-dimensional representations of both entities and relations in the same semantic space. To address the issue of neglecting uncertainty for KG components, we propose a variational auto-encoder that represents KG components as Gaussian distributions. In addition, compared with previous methods, our method has the advantages of high quality and interpretability. Our experimental results on several benchmark datasets demonstrate our model’s superiority over the state-of-the-art baselines.

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 Classification of Imbalanced Big Data using SMOTE with Rough Random Forest

2019 ◽  
Vol 9 (2) ◽  
pp. 5174-5184
Keyword(s):  
Big Data ◽  
Random Forest ◽  
Hybrid Approach ◽  
Imbalanced Data ◽  
Important Research ◽  
Important Research Topic ◽  
Benchmark Datasets ◽  
Data Environment ◽  
Volume Characteristics

Learning from datasets is an important research topic today. Amongst the various data mining tools available for the purpose, none works satisfactorily in the case of imbalanced data mainly because this type of data gives rise to various minority classes, which may affect the learning process. In addition to the large volume, characteristics of Big Data also include velocity and variety. The Synthetic Minority Oversampling Technique (SMOTE) is a widely used technique to balance imbalanced data. Here, we have focussed on extending this concept to conform to the Big Data environment by combining it with the concepts of rough random forest (RRF). This hybrid approach comprising SMOTE and RRF algorithms for learning from imbalanced datasets has been applied on various benchmark datasets from the KEEL Dataset Repository. The results obtained are satisfactory. The velocity aspect of Big Data has been handled by this method on the dynamic dataset of the stock market. The results obtained have been verified using popular online websites related to stock markets

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.

scholarly journals Partial Adversarial Behavior Deception in Security Games

2020 ◽  
Author(s):  
Thanh H. Nguyen ◽  
Arunesh Sinha ◽  
He He
Keyword(s):  
Gradient Descent ◽  
Important Research ◽  
Behavioral Models ◽  
Scalable Algorithm ◽  
Defense Strategies ◽  
Important Research Topic ◽  
Adversarial Behavior ◽  
Security Games ◽  
Attack Data ◽  
Projected Gradient Descent

Learning attacker behavior is an important research topic in security games as security agencies are often uncertain about attackers' decision making. Previous work has focused on developing various behavioral models of attackers based on historical attack data. However, a clever attacker can manipulate its attacks to fail such attack-driven learning, leading to ineffective defense strategies. We study attacker behavior deception with three main contributions. First, we propose a new model, named partial behavior deception model, in which there is a deceptive attacker (among multiple attackers) who controls a portion of attacks. Our model captures real-world security scenarios such as wildlife protection in which multiple poachers are present. Second, we introduce a new scalable algorithm, GAMBO, to compute an optimal deception strategy of the deceptive attacker. Our algorithm employs the projected gradient descent and uses the implicit function theorem for the computation of gradient. Third, we conduct a comprehensive set of experiments, showing a significant benefit for the attacker and loss for the defender due to attacker deception.

UBAR

2021 ◽  
Vol 20 (3) ◽  
pp. 1-25
Author(s):  
Elham Shamsa ◽  
Alma Pröbstl ◽  
Nima TaheriNejad ◽  
Anil Kanduri ◽  
Samarjit Chakraborty ◽  
...  
Keyword(s):  
Resource Management ◽  
Quality Of Experience ◽  
State Of The Art ◽  
State Of Charge ◽  
User Preference ◽  
Management Approach ◽  
High Quality ◽  
Trade Off ◽  
Run Time

Smartphone users require high Battery Cycle Life (BCL) and high Quality of Experience (QoE) during their usage. These two objectives can be conflicting based on the user preference at run-time. Finding the best trade-off between QoE and BCL requires an intelligent resource management approach that considers and learns user preference at run-time. Current approaches focus on one of these two objectives and neglect the other, limiting their efficiency in meeting users’ needs. In this article, we present UBAR, User- and Battery-aware Resource management, which considers dynamic workload, user preference, and user plug-in/out pattern at run-time to provide a suitable trade-off between BCL and QoE. UBAR personalizes this trade-off by learning the user’s habits and using that to satisfy QoE, while considering battery temperature and State of Charge (SOC) pattern to maximize BCL. The evaluation results show that UBAR achieves 10% to 40% improvement compared to the existing state-of-the-art approaches.

scholarly journals Global optimization of an encapsulated Si/SiO$$_2$$ L3 cavity with a 43 million quality factor

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
J. P. Vasco ◽  
V. Savona
Keyword(s):  
Global Optimization ◽  
Photonic Crystal ◽  
Quality Factor ◽  
State Of The Art ◽  
Optimization Strategy ◽  
High Quality ◽  
Optimal Value ◽  
Out Of Plane ◽  
Fabrication Tolerances ◽  
Structural Imperfections

AbstractWe optimize a silica-encapsulated silicon L3 photonic crystal cavity for ultra-high quality factor by means of a global optimization strategy, where the closest holes surrounding the cavity are varied to minimize out-of-plane losses. We find an optimal value of $$Q_c=4.33\times 10^7$$ Q c = 4.33 × 10 7 , which is predicted to be in the 2 million regime in presence of structural imperfections compatible with state-of-the-art silicon fabrication tolerances.

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