Cross-Interaction Hierarchical Attention Networks for Urban Anomaly Prediction

Predicting anomalies (e.g., blocked driveway and vehicle collisions) in urban space plays an important role in assisting governments and communities for building smart city applications, ranging from intelligent transportation to public safety. However, predicting urban anomalies is not trivial due to the following two factors: i) The sequential transition regularities of anomaly occurrences is complex, which exhibit with high-order and dynamic correlations. ii) The Interactions between region, time and anomaly category is multi-dimensional in real-world urban anomaly forecasting scenario. How to fuse multiple relations from spatial, temporal and categorical dimensions in the predictive framework remains a significant challenge. To address these two challenges, we propose a Cross-Interaction Hierarchical Attention network model (CHAT) which uncovers the dynamic occurrence patterns of time-stamped urban anomaly data. Our CHAT framework could automatically capture the relevance of past anomaly occurrences across different time steps, and discriminates which types of cross-modal interactions are more important for making future predictions. Experiment results demonstrate the superiority of CHAT framework over state-of-the-art baselines.

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Light Field Image Quality Enhancement by a Lightweight Deformable Deep Learning Framework for Intelligent Transportation Systems

Electronics ◽

10.3390/electronics10101136 ◽

2021 ◽

Vol 10 (10) ◽

pp. 1136

Author(s):

David Augusto Ribeiro ◽

Juan Casavílca Silva ◽

Renata Lopes Rosa ◽

Muhammad Saadi ◽

Shahid Mumtaz ◽

...

Keyword(s):

Deep Learning ◽

Image Quality ◽

Intelligent Transportation Systems ◽

Light Field ◽

State Of The Art ◽

Intelligent Transportation ◽

Transportation Systems ◽

Machine Learning Algorithms ◽

Learning Framework ◽

Reconstruction Methods

Light field (LF) imaging has multi-view properties that help to create many applications that include auto-refocusing, depth estimation and 3D reconstruction of images, which are required particularly for intelligent transportation systems (ITSs). However, cameras can present a limited angular resolution, becoming a bottleneck in vision applications. Thus, there is a challenge to incorporate angular data due to disparities in the LF images. In recent years, different machine learning algorithms have been applied to both image processing and ITS research areas for different purposes. In this work, a Lightweight Deformable Deep Learning Framework is implemented, in which the problem of disparity into LF images is treated. To this end, an angular alignment module and a soft activation function into the Convolutional Neural Network (CNN) are implemented. For performance assessment, the proposed solution is compared with recent state-of-the-art methods using different LF datasets, each one with specific characteristics. Experimental results demonstrated that the proposed solution achieved a better performance than the other methods. The image quality results obtained outperform state-of-the-art LF image reconstruction methods. Furthermore, our model presents a lower computational complexity, decreasing the execution time.

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EDPANs: Enhanced Dual Path Attention Networks for Single Image Super-Resolution

Journal of Circuits System and Computers ◽

10.1142/s021812662150300x ◽

2021 ◽

pp. 2150300

Author(s):

Guoan Cheng ◽

Ai Matsune ◽

Huaijuan Zang ◽

Toru Kurihara ◽

Shu Zhan

Keyword(s):

State Of The Art ◽

Super Resolution ◽

Single Image ◽

Residual Network ◽

Dense Structure ◽

Attention Networks ◽

Residual Structure ◽

Quantitative Results ◽

Image Super Resolution ◽

Single Image Super Resolution

In this paper, we propose an enhanced dual path attention network (EDPAN) for image super-resolution. ResNet is good at implicitly reusing extracted features, DenseNet is good at exploring new features. Dual Path Network (DPN) combines ResNets and DenseNet to create a more accurate architecture than the straightforward one. We experimentally show that the residual network performs best when each block consists of two convolutions, and the dense network performs best when each micro-block consists of one convolution. Following these ideas, our EDPAN exploits the advantages of the residual structure and the dense structure. Besides, to deploy the computations for features more effectively, we introduce the attention mechanism into our EDPAN. Moreover, to relieve the parameters burden, we also utilize recursive learning to propose a lightweight model. In the experiments, we demonstrate the effectiveness and robustness of our proposed EDPAN on different degradation situations. The quantitative results and visualization comparison can sufficiently indicate that our EDPAN achieves favorable performance over the state-of-the-art frameworks.

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Malware Detection Based on Graph Attention Networks for Intelligent Transportation Systems

Electronics ◽

10.3390/electronics10202534 ◽

2021 ◽

Vol 10 (20) ◽

pp. 2534

Author(s):

Cagatay Catal ◽

Hakan Gunduz ◽

Alper Ozcan

Keyword(s):

Intelligent Transportation Systems ◽

Malware Detection ◽

Application Programming Interface ◽

Intelligent Transportation ◽

Transportation Systems ◽

Convolutional Network ◽

Attention Networks ◽

Security Issues ◽

Call Graphs ◽

Network Metrics

Intelligent Transportation Systems (ITS) aim to make transportation smarter, safer, reliable, and environmentally friendly without detrimentally affecting the service quality. ITS can face security issues due to their complex, dynamic, and non-linear properties. One of the most critical security problems is attacks that damage the infrastructure of the entire ITS. Attackers can inject malware code that triggers dangerous actions such as information theft and unwanted system moves. The main objective of this study is to improve the performance of malware detection models using Graph Attention Networks. To detect malware attacks addressing ITS, a Graph Attention Network (GAN)-based framework is proposed in this study. The inputs to this framework are the Application Programming Interface (API)-call graphs obtained from malware and benign Android apk files. During the graph creation, network metrics and the Node2Vec model are utilized to generate the node features. A GAN-based model is combined with different types of node features during the experiments and the performance is compared against Graph Convolutional Network (GCN). Experimental results demonstrated that the integration of the GAN and Node2Vec models provides the best performance in terms of F-measure and accuracy parameters and, also, the use of an attention mechanism in GAN improves the performance. Furthermore, node features generated with Node2Vec resulted in a 3% increase in classification accuracy compared to the features generated with network metrics.

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A recommendations model with multiaspect awareness and hierarchical user-product attention mechanisms

Computer Science and Information Systems ◽

10.2298/csis190925024b ◽

2020 ◽

Vol 17 (3) ◽

pp. 849-865

Author(s):

Zhongqin Bi ◽

Shuming Dou ◽

Zhe Liu ◽

Yongbin Li

Keyword(s):

State Of The Art ◽

Weight Vector ◽

User Preferences ◽

The Other ◽

Attention Networks ◽

Proposed Model ◽

Network Methods ◽

Public Datasets ◽

Novel Model ◽

Attention Weight

Neural network methods have been trained to satisfactorily learn user/product representations from textual reviews. A representation can be considered as a multiaspect attention weight vector. However, in several existing methods, it is assumed that the user representation remains unchanged even when the user interacts with products having diverse characteristics, which leads to inaccurate recommendations. To overcome this limitation, this paper proposes a novel model to capture the varying attention of a user for different products by using a multilayer attention framework. First, two individual hierarchical attention networks are used to encode the users and products to learn the user preferences and product characteristics from review texts. Then, we design an attention network to reflect the adaptive change in the user preferences for each aspect of the targeted product in terms of the rating and review. The results of experiments performed on three public datasets demonstrate that the proposed model notably outperforms the other state-of-the-art baselines, thereby validating the effectiveness of the proposed approach.

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Multi-View Attribute Graph Convolution Networks for Clustering

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2020/411 ◽

2020 ◽

Cited By ~ 1

Author(s):

Jiafeng Cheng ◽

Qianqian Wang ◽

Zhiqiang Tao ◽

Deyan Xie ◽

Quanxue Gao

Keyword(s):

Neural Networks ◽

State Of The Art ◽

Graph Embedding ◽

Structured Data ◽

Attention Networks ◽

Graph Data ◽

Graph Reconstruction ◽

Node Attributes ◽

Graph Neural Networks ◽

Geometric Relationship

Graph neural networks (GNNs) have made considerable achievements in processing graph-structured data. However, existing methods can not allocate learnable weights to different nodes in the neighborhood and lack of robustness on account of neglecting both node attributes and graph reconstruction. Moreover, most of multi-view GNNs mainly focus on the case of multiple graphs, while designing GNNs for solving graph-structured data of multi-view attributes is still under-explored. In this paper, we propose a novel Multi-View Attribute Graph Convolution Networks (MAGCN) model for the clustering task. MAGCN is designed with two-pathway encoders that map graph embedding features and learn the view-consistency information. Specifically, the first pathway develops multi-view attribute graph attention networks to reduce the noise/redundancy and learn the graph embedding features for each multi-view graph data. The second pathway develops consistent embedding encoders to capture the geometric relationship and probability distribution consistency among different views, which adaptively finds a consistent clustering embedding space for multi-view attributes. Experiments on three benchmark graph datasets show the superiority of our method compared with several state-of-the-art algorithms.

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A Novel Stacked Ensemble for Hate Speech Recognition

Applied Sciences ◽

10.3390/app112411684 ◽

2021 ◽

Vol 11 (24) ◽

pp. 11684

Author(s):

Mona Khalifa A. Aljero ◽

Nazife Dimililer

Keyword(s):

Machine Learning ◽

Hate Speech ◽

State Of The Art ◽

Majority Voting ◽

Support Vector ◽

Timely Manner ◽

Machine Learning Classifiers ◽

Significant Challenge ◽

Final Output ◽

Harmful Content

Detecting harmful content or hate speech on social media is a significant challenge due to the high throughput and large volume of content production on these platforms. Identifying hate speech in a timely manner is crucial in preventing its dissemination. We propose a novel stacked ensemble approach for detecting hate speech in English tweets. The proposed architecture employs an ensemble of three classifiers, namely support vector machine (SVM), logistic regression (LR), and XGBoost classifier (XGB), trained using word2vec and universal encoding features. The meta classifier, LR, combines the outputs of the three base classifiers and the features employed by the base classifiers to produce the final output. It is shown that the proposed architecture improves the performance of the widely used single classifiers as well as the standard stacking and classifier ensemble using majority voting. We also present results on the use of various combinations of machine learning classifiers as base classifiers. The experimental results from the proposed architecture indicated an improvement in the performance on all four datasets compared with the standard stacking, base classifiers, and majority voting. Furthermore, on three of these datasets, the proposed architecture outperformed all state-of-the-art systems.

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State-of-the-art review: preventing child and youth pedestrian motor vehicle collisions: critical issues and future directions

Injury Prevention ◽

10.1136/injuryprev-2020-043829 ◽

2020 ◽

pp. injuryprev-2020-043829 ◽

Cited By ~ 2

Author(s):

Marie-Soleil Cloutier ◽

Emilie Beaulieu ◽

Liraz Fridman ◽

Alison K Macpherson ◽

Brent E Hagel ◽

...

Keyword(s):

Risk Factors ◽

English Language ◽

Motor Vehicle ◽

State Of The Art ◽

Intersectoral Collaboration ◽

Future Directions ◽

Vehicle Collisions ◽

The Road ◽

Vehicle Collision ◽

Critical Issues

AimTo undertake a comprehensive review of the best available evidence related to risk factors for child pedestrian motor vehicle collision (PMVC), as well as identification of established and emerging prevention strategies.MethodsArticles on risk factors were identified through a search of English language publications listed in Medline, Embase, Transport, SafetyLit, Web of Science, CINHAL, Scopus and PsycINFO within the last 30 years (~1989 onwards).ResultsThis state-of-the-art review uses the road safety Safe System approach as a new lens to examine three risk factor domains affecting child pedestrian safety (built environment, drivers and vehicles) and four cross-cutting critical issues (reliable collision and exposure data, evaluation of interventions, evidence-based policy and intersectoral collaboration).ConclusionsResearch conducted over the past 30 years has reported extensively on child PMVC risk factors. The challenge facing us now is how to move these findings into action and intervene to reduce the child PMVC injury and fatality rates worldwide.

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Gaussian Transformer: A Lightweight Approach for Natural Language Inference

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.33016489 ◽

2019 ◽

Vol 33 ◽

pp. 6489-6496 ◽

Cited By ~ 2

Author(s):

Maosheng Guo ◽

Yu Zhang ◽

Ting Liu

Keyword(s):

Neural Networks ◽

Natural Language ◽

State Of The Art ◽

Research Area ◽

High Order Interaction ◽

Training Time ◽

Attention Networks ◽

Local Dependency ◽

Active Research ◽

Active Research Area

Natural Language Inference (NLI) is an active research area, where numerous approaches based on recurrent neural networks (RNNs), convolutional neural networks (CNNs), and self-attention networks (SANs) has been proposed. Although obtaining impressive performance, previous recurrent approaches are hard to train in parallel; convolutional models tend to cost more parameters, while self-attention networks are not good at capturing local dependency of texts. To address this problem, we introduce a Gaussian prior to selfattention mechanism, for better modeling the local structure of sentences. Then we propose an efficient RNN/CNN-free architecture named Gaussian Transformer for NLI, which consists of encoding blocks modeling both local and global dependency, high-order interaction blocks collecting the evidence of multi-step inference, and a lightweight comparison block saving lots of parameters. Experiments show that our model achieves new state-of-the-art performance on both SNLI and MultiNLI benchmarks with significantly fewer parameters and considerably less training time. Besides, evaluation using the Hard NLI datasets demonstrates that our approach is less affected by the undesirable annotation artifacts.

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Multi-Term Attention Networks for Skeleton-Based Action Recognition

Applied Sciences ◽

10.3390/app10155326 ◽

2020 ◽

Vol 10 (15) ◽

pp. 5326

Author(s):

Xiaolei Diao ◽

Xiaoqiang Li ◽

Chen Huang

Keyword(s):

Neural Network ◽

Time Scales ◽

Action Recognition ◽

State Of The Art ◽

Attention Networks ◽

Weighted Fusion ◽

Temporal Features ◽

Benchmark Datasets ◽

Spatio Temporal ◽

Different Time Scales

The same action takes different time in different cases. This difference will affect the accuracy of action recognition to a certain extent. We propose an end-to-end deep neural network called “Multi-Term Attention Networks” (MTANs), which solves the above problem by extracting temporal features with different time scales. The network consists of a Multi-Term Attention Recurrent Neural Network (MTA-RNN) and a Spatio-Temporal Convolutional Neural Network (ST-CNN). In MTA-RNN, a method for fusing multi-term temporal features are proposed to extract the temporal dependence of different time scales, and the weighted fusion temporal feature is recalibrated by the attention mechanism. Ablation research proves that this network has powerful spatio-temporal dynamic modeling capabilities for actions with different time scales. We perform extensive experiments on four challenging benchmark datasets, including the NTU RGB+D dataset, UT-Kinect dataset, Northwestern-UCLA dataset, and UWA3DII dataset. Our method achieves better results than the state-of-the-art benchmarks, which demonstrates the effectiveness of MTANs.

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Local Feature-Aware Siamese Matching Model for Vehicle Re-Identification

Applied Sciences ◽

10.3390/app10072474 ◽

2020 ◽

Vol 10 (7) ◽

pp. 2474

Author(s):

Honglie Wang ◽

Shouqian Sun ◽

Lunan Zhou ◽

Lilin Guo ◽

Xin Min ◽

...

Keyword(s):

Feature Extraction ◽

Large Scale ◽

Feature Matching ◽

State Of The Art ◽

Intelligent Transportation ◽

The State ◽

Local Feature ◽

Public Security ◽

Matching Model ◽

Image Deformation

Vehicle re-identification is attracting an increasing amount of attention in intelligent transportation and is widely used in public security. In comparison to person re-identification, vehicle re-identification is more challenging because vehicles with different IDs are generated by a unified pipeline and cannot only be distinguished based on the subtle differences in their features such as lights, ornaments, and decorations. In this paper, we propose a local feature-aware Siamese matching model for vehicle re-identification. A local feature-aware Siamese matching model focuses on the informative parts in an image and these are the parts most likely to differ among vehicles with different IDs. In addition, we utilize Siamese feature matching to better supervise our attention. Furthermore, a perspective transformer network, which can eliminate image deformation, has been designed for feature extraction. We have conducted extensive experiments on three large-scale vehicle re-ID datasets, i.e., VeRi-776, VehicleID, and PKU-VD, and the results show that our method is superior to the state-of-the-art methods.

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