Detecting Traffic Information From Social Media Texts With Deep Learning Approaches

Mining traffic-relevant information from social media data has become an emerging topic due to the real-time and ubiquitous features of social media. In this paper, we focus on a specific problem in social media mining which is to extract traffic relevant microblogs from Sina Weibo, a Chinese microblogging platform. It is transformed into a machine learning problem of short text classification. First, we apply the continuous bag-of-word model to learn word embedding representations based on a data set of three billion microblogs. Compared to the traditional one-hot vector representation of words, word embedding can capture semantic similarity between words and has been proved effective in natural language processing tasks. Next, we propose using convolutional neural networks (CNNs), long short-term memory (LSTM) models and their combination LSTM-CNN to extract traffic relevant microblogs with the learned word embeddings as inputs. We compare the proposed methods with competitive approaches, including the support vector machine (SVM) model based on a bag of n-gram features, the SVM model based on word vector features, and the multi-layer perceptron model based on word vector features. Experiments show the effectiveness of the proposed deep learning approaches

GM-TCNet: Gated Multi-scale Temporal Convolutional Network using Emotion Causality for Speech Emotion Recognition

In human-computer interaction, Speech Emotion Recognition (SER) plays an essential role in understanding the user's intent and improving the interactive experience. While similar sentimental speeches own diverse speaker characteristics but share common antecedents and consequences, an essential challenge for SER is how to produce robust and discriminative representations through causality between speech emotions. In this paper, we propose a Gated Multi-scale Temporal Convolutional Network (GM-TCNet) to construct a novel emotional causality representation learning component with a multi-scale receptive field. GM-TCNet deploys a novel emotional causality representation learning component to capture the dynamics of emotion across the time domain, constructed with dilated causal convolution layer and gating mechanism. Besides, it utilizes skip connection fusing high-level features from different gated convolution blocks to capture abundant and subtle emotion changes in human speech. GM-TCNet first uses a single type of feature, mel-frequency cepstral coefficients, as inputs and then passes them through the gated temporal convolutional module to generate the high-level features. Finally, the features are fed to the emotion classifier to accomplish the SER task. The experimental results show that our model maintains the highest performance in most cases compared to state-of-the-art techniques.Comment: The source code is available at: https://github.com/Jiaxin-Ye/GM-TCNe

COOL, a Context Outlooker, and its Application to Question Answering and other Natural Language Processing Tasks

Vision outlookers improve the performance of vision transformers, which implement a self-attention mechanism by adding outlook attention, a form of local attention. In natural language processing, as has been the case in computer vision and other domains, transformer-based models constitute the state-of-the-art for most processing tasks. In this domain, too, many authors have argued and demonstrated the importance of local context. We present and evaluate an outlook attention mechanism, COOL, for natural language processing. COOL adds, on top of the self-attention layers of a transformer-based model, outlook attention layers that encode local syntactic context considering word proximity and consider more pair-wise constraints than dynamic convolution operations used by existing approaches. A comparative empirical performance evaluation of an implementation of COOL with different transformer-based approaches confirms the opportunity of improvement over a baseline using the neural language models alone for various natural language processing tasks, including question answering. The proposed approach is competitive with state-of-the-art methods

Speech emotion recognition via multiple fusion under spatial–temporal parallel network

The authors are grateful to the anonymous reviewers and the editor for their valuable comments and suggestions. This work was supported by the National Natural Science Foundation of China (No. 61702066), the Chongqing Research Program of Basic Research and Frontier Technology, China (No. cstc2021jcyj-msxmX0761) and partially supported by Project PID2020-119478GB-I00 funded by MICINN/AEI/10.13039/501100011033 and by Project A-TIC-434- UGR20 funded by FEDER/Junta de Andalucía Consejería de Transformación Económica, Industria, Conocimiento Universidades.Speech, as a necessary way to express emotions, plays a vital role in human communication. With the continuous deepening of research on emotion recognition in human-computer interaction, speech emotion recognition (SER) has become an essential task to improve the human-computer interaction experience. When performing emotion feature extraction of speech, the method of cutting the speech spectrum will destroy the continuity of speech. Besides, the method of using the cascaded structure without cutting the speech spectrum cannot simultaneously extract speech spectrum information from both temporal and spatial domains. To this end, we propose a spatial-temporal parallel network for speech emotion recognition without cutting the speech spectrum. To further mix the temporal and spatial features, we design a novel fusion method (called multiple fusion) that combines the concatenate fusion and ensemble strategy. Finally, the experimental results on five datasets demonstrate that the proposed method outperforms state-of-the-art methods.National Natural Science Foundation of China 61702066Chongqing Research Program of Basic Research and Frontier Technology, China cstc2021jcyj-msxmX0761MICINN/AEI/10.13039/501100011033: PID2020-119478GB-I00FEDER/Junta de Andalucía A-TIC-434- UGR2

Deep Learning based Densenet Convolution Neural Network for Community Detection in Online Social Networks

Online Social Networks (OSNs) have become increasingly popular, with hundreds of millions of users in recent years. A community in a social network is a virtual group with shared interests and activities that they want to communicate. OSN and the growing number of users have also increased the need for communities. Community structure is an important topological property of OSN and plays an essential role in various dynamic processes, including the diffusion of information within the network. All networks have a community format, and one of the most continually addressed research issues is the finding of communities. However, traditional techniques didn't do a better community of discovering user interests. As a result, these methods cannot detect active communities.  To tackle this issues, in this paper presents Densenet Convolution Neural Network (DnetCNN) approach for community detection. Initially, we gather dataset from Kaggle repository. Then preprocessing the dataset to remove inconsistent and missing values. In addition to User Behavior Impact Rate (UBIR) technique to identify the user URL access, key term and page access. After that, Web Crawling Prone Factor Rate (WCPFR) technique is used find the malicious activity random forest and decision method. Furthermore, Spider Web Cluster Community based Feature Selection (SWC2FS) algorithm is used to choose finest attributes in the dataset. Based on the attributes, to find the community group using Densenet Convolution Neural Network (DnetCNN) approach. Thus, the experimental result produce better performance than other methods