A Web Knowledge-Driven Multimodal Retrieval Method in Computational Social Systems: Unsupervised and Robust Graph Convolutional Hashing

Duan, Youxiang; Chen, Ning; Alshehri, Mohammad Dahman; Liu, Lei; Zhang, Peiying; Yu, Keping

doi:10.1109/tcss.2022.3216621

Cited by 11 publications

(7 citation statements)

References 42 publications

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“…The human brain affects neural networks, which are made up of numerous neurons and form amazing networks. Deep learning networks can be used to teach both supervised and unsupervised categories [ 17 , 18 , 19 , 20 ]. CNN, RNN, and many other networks with more than three layers are considered deep learning approaches.…”

Section: Literature Reviewmentioning

confidence: 99%

A BERT Framework to Sentiment Analysis of Tweets

Bello

Leung

2023

Sensors

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Sentiment analysis has been widely used in microblogging sites such as Twitter in recent decades, where millions of users express their opinions and thoughts because of its short and simple manner of expression. Several studies reveal the state of sentiment which does not express sentiment based on the user context because of different lengths and ambiguous emotional information. Hence, this study proposes text classification with the use of bidirectional encoder representations from transformers (BERT) for natural language processing with other variants. The experimental findings demonstrate that the combination of BERT with CNN, BERT with RNN, and BERT with BiLSTM performs well in terms of accuracy rate, precision rate, recall rate, and F1-score compared to when it was used with Word2vec and when it was used with no variant.

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Section: Literature Reviewmentioning

confidence: 99%

A BERT Framework to Sentiment Analysis of Tweets

Bello

Leung

2023

Sensors

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“…I N recent years, with the development of technologies such as the Internet of Things (IoT), cloud computing, and big data, the application of artificial intelligence (AI) in the electrical industry has become a new research hotspot [1]- [3]. The traditional grid is a rigid system with disadvantages such as operation and maintenance costs, poor coordination, poor reliability, and poor interaction, etc.…”

Section: A Background Presentationmentioning

confidence: 99%

Multi-Target-Aware Energy Orchestration Modeling for Grid 2.0: A Network Virtualization Approach

et al. 2023

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Smart Grid -Grid 2.0, is a promising grid scenario due to its low cost, high reliability, and high security. However, the high-intensity complex task interaction and the differentiated quality of service (QoS) requirements pose challenges to the energy orchestration of the dynamic and time-varying smart grid. In addition, Network Virtualization (NV) technology is widely utilized to decouple physical network resources. In this work, based on Virtual Network Embedding (VNE), we propose an energy orchestration strategy to better support the operation and maintenance of Grid 2.0. Specifically, a series of constraint indicators for energy orchestration is first formulated. Second, based on the advantages of deep reinforcement learning (DRL), a five-layer architecture energy orchestration model is proposed. It frequently extracts the grid energy matrix as the state input, adopts two-stage actions to reasonably orchestrate the grid energy, and combines the reward mechanism of DRL to effectively interact with the environment. Finally, we conduct experiments in a simulation environment and demonstrate the advantages of the proposed strategy through analysis and comparison.

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“…TDSRDH is compared with eight state-of-the-art methods, including the methods based on shallow structure(CCA [4], SCM [15], SePH [16]) and the methods based on deep structure(DCMH [22], AADAH [23], RDCMH [39], MGAH [42], CPAH [43], URGCH [7]). For a fair comparison, the deep features extracted by the pre-trained ResNet-152 are used as the image input for all baselines based on shallow-structure.…”

Section: ) Baselinementioning

confidence: 99%

“…However, the different structures and feature distributions of different modal data make it impossible to compare multimodal data directly. For this reason, how to effectively mine the semantic consistency and correlation between different modalities is still a challenging problem [7].…”

Section: Introductionmentioning

confidence: 99%

Transformer-Based Discriminative and Strong Representation Deep Hashing for Cross-Modal Retrieval

Zhou,

Han,

Chen

et al. 2023

IEEE Access

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Cross-modal hashing retrieval has attracted extensive attention due to its low storage requirements as well as high retrieval efficiency. In particular, how to more fully exploit the correlation of different modality data and generate a more distinguished representation is the key to improving the performance of this method. Moreover, Transformer-based models have been widely used in various fields, including natural language processing, due to their powerful contextual information processing capabilities. Based on these motivations, we propose a Transformer-based Distinguishing Strong Representation Deep Hashing (TDSRDH). For text modality, since the sequential relations between words imply semantic relations that are not independent relations, we thoughtfully encode them using a transformer-based encoder to obtain a strong representation. In addition, we propose a triple-supervised loss based on the commonly used pairwise loss and quantization loss. The latter two ensure the learned features and hash-codes can preserve the similarity of the original data during the learning process. The former ensures that the distance between similar instances is closer and the distance between dissimilar instances is farther. So that TDSRDH can generate more discriminative representations while preserving the similarity between modalities. Finally, experiments on the three datasets MIRFLICKR-25K, IAPR TC-12, and NUS-WIDE demonstrated the superiority of TDSRDH over the other baselines. Moreover, the effectiveness of the proposed idea was demonstrated by ablation experiments.

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A Web Knowledge-Driven Multimodal Retrieval Method in Computational Social Systems: Unsupervised and Robust Graph Convolutional Hashing

Cited by 11 publications

References 42 publications

A BERT Framework to Sentiment Analysis of Tweets

A BERT Framework to Sentiment Analysis of Tweets

Multi-Target-Aware Energy Orchestration Modeling for Grid 2.0: A Network Virtualization Approach

Transformer-Based Discriminative and Strong Representation Deep Hashing for Cross-Modal Retrieval

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