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Intelligence Computing and Systems
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Intelligence Computing and Systems

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Processes".

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 12177

Special Issue Editors

Dr. Yinglin Wang

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Guest Editor
Department of Computer Science and Technology, Shanghai University of Finance and Economics, Shanghai 200433, China
Interests: machine learning; data mining; natural language processing; knowledge graph
Prof. Dr. Xing Wu

E-Mail
Guest Editor
Department of Machine Intelligence, School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
Interests: machine learning with multimodal data; materials informatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue will present extended versions of selected papers presented at the 8th International Conference on Progress in Informatics and Computing (PIC-2021). Initiated in 2010, the PIC conference provides a forum for researchers in academia and industry to exchange ideas and research results in Informatics and Computing fields. In recent years, the conference has put more attention on artificial intelligence, big data analysis, and related applications. For PIC2021, most papers are related to artificial Intelligence or the use of AI methodologies. Hence, this Special Issue will publish papers using intelligent methods and models to solve practical problems in various applications, such as classification of objects, prediction of events, and automation and optimization of decision-making processes. Authors of invited papers should be aware that the final submitted manuscript must provide a minimum of 50% new content and not exceed 30% copy/paste from the proceedings paper.

Dr. Yinglin Wang
Dr. Xing Wu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (6 papers)

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Research

14 pages, 1865 KiB  
Article
Microblog Text Emotion Classification Algorithm Based on TCN-BiGRU and Dual Attention
by Yao Qin, Yiping Shi, Xinze Hao and Jin Liu
Information 2023, 14(2), 90; https://doi.org/10.3390/info14020090 - 3 Feb 2023
Cited by 3 | Viewed by 1809
Abstract
Microblog is an important platform for mining public opinion, and it is of great value to conduct emotional analysis of microblog texts during the current epidemic. Aiming at the problem that most current emotional classification methods cannot effectively extract deep text features, and [...] Read more.
Microblog is an important platform for mining public opinion, and it is of great value to conduct emotional analysis of microblog texts during the current epidemic. Aiming at the problem that most current emotional classification methods cannot effectively extract deep text features, and that traditional word vectors cannot dynamically obtain the semantics of words according to their context, which leads to classification bias, this research put forward a microblog text emotion classification algorithm based on TCN-BiGRU and dual attention (TCN-BiGRU-DATT). First, the vector representation of the text was obtained using ALBERT. Second, the TCN and BiGRU networks were used to extract the emotional information contained in the text through dual pathway feature extraction, to efficiently obtain the deep semantic features of the text. Then, the dual attention mechanism was introduced to allocate the global weight of the key information in the semantic features, and the emotional features were spliced and fused. Finally, the Softmax classifier was applied for emotion classification. The findings of a comparative experiment on a set of microblog text comments collected throughout the pandemic revealed that the accuracy, recall, and F1 value of the emotion classification method proposed in this paper reached 92.33%, 91.78%, and 91.52%, respectively, which was a significant improvement compared with other models. Full article
(This article belongs to the Special Issue Intelligence Computing and Systems)
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13 pages, 350 KiB  
Article
EREC: Enhanced Language Representations with Event Chains
by Huajie Wang and Yinglin Wang
Information 2022, 13(12), 582; https://doi.org/10.3390/info13120582 - 15 Dec 2022
Cited by 1 | Viewed by 1309
Abstract
The natural language model BERT uses a large-scale unsupervised corpus to accumulate rich linguistic knowledge during its pretraining stage, and then, the information is fine-tuned for specific downstream tasks, which greatly improves the understanding capability of various natural language tasks. For some specific [...] Read more.
The natural language model BERT uses a large-scale unsupervised corpus to accumulate rich linguistic knowledge during its pretraining stage, and then, the information is fine-tuned for specific downstream tasks, which greatly improves the understanding capability of various natural language tasks. For some specific tasks, the capability of the model can be enhanced by introducing external knowledge. In fact, these methods, such as ERNIE, have been proposed for integrating knowledge graphs into BERT models, which significantly enhanced its capabilities in related tasks such as entity recognition. However, for two types of tasks, commonsense causal reasoning and predicting the ending of stories, few previous studies have combined model modification and process optimization to integrate external knowledge. Therefore, referring to ERNIE, in this paper, we propose enhanced language representation with event chains (EREC), which focuses on keywords in the text corpus and their implied relations. Event chains are integrated into EREC as external knowledge. Furthermore, various graph networks are used to generate embeddings and to associate keywords in the corpus. Finally, via multi-task training, external knowledge is integrated into the model generated in the pretraining stage so as to enhance the effect of the model in downstream tasks. The experimental process of the EREC model is carried out with a three-stage design, and the experimental results show that EREC has a deeper understanding of the causal relationship and event relationship contained in the text by integrating the event chains, and it achieved significant improvements on two specific tasks. Full article
(This article belongs to the Special Issue Intelligence Computing and Systems)
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14 pages, 1256 KiB  
Article
CA-STD: Scene Text Detection in Arbitrary Shape Based on Conditional Attention
by Xing Wu, Yangyang Qi, Jun Song, Junfeng Yao, Yanzhong Wang, Yang Liu, Yuexing Han and Quan Qian
Information 2022, 13(12), 565; https://doi.org/10.3390/info13120565 - 1 Dec 2022
Cited by 5 | Viewed by 1421
Abstract
Scene Text Detection (STD) is critical for obtaining textual information from natural scenes, serving for automated driving and security surveillance. However, existing text detection methods fall short when dealing with the variation in text curvatures, orientations, and aspect ratios in complex backgrounds. To [...] Read more.
Scene Text Detection (STD) is critical for obtaining textual information from natural scenes, serving for automated driving and security surveillance. However, existing text detection methods fall short when dealing with the variation in text curvatures, orientations, and aspect ratios in complex backgrounds. To meet the challenge, we propose a method called CA-STD to detect arbitrarily shaped text against a complicated background. Firstly, a Feature Refinement Module (FRM) is proposed to enhance feature representation. Additionally, the conditional attention mechanism is proposed not only to decouple the spatial and textual information from scene text images, but also to model the relationship among different feature vectors. Finally, the Contour Information Aggregation (CIA) is presented to enrich the feature representation of text contours by considering circular topology and semantic information simultaneously to obtain the detection curves with arbitrary shapes. The proposed CA-STD method is evaluated on different datasets with extensive experiments. On the one hand, the CA-STD outperforms state-of-the-art methods and achieves 82.9 in precision on the dataset of TotalText. On the other hand, the method has better performance than state-of-the-art methods and achieves the F1 score of 83.8 on the dataset of CTW-1500. The quantitative and qualitative analysis proves that the CA-STD can detect variably shaped scene text effectively. Full article
(This article belongs to the Special Issue Intelligence Computing and Systems)
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12 pages, 1074 KiB  
Article
Entity Linking Method for Chinese Short Text Based on Siamese-Like Network
by Yang Zhang, Jin Liu, Bo Huang and Bei Chen
Information 2022, 13(8), 397; https://doi.org/10.3390/info13080397 - 22 Aug 2022
Cited by 5 | Viewed by 1915
Abstract
Entity linking plays a fundamental role in knowledge engineering and data mining and is the basis of various downstream applications such as content analysis, relationship extraction, question and answer. Most existing entity linking models rely on sufficient context for disambiguation but do not [...] Read more.
Entity linking plays a fundamental role in knowledge engineering and data mining and is the basis of various downstream applications such as content analysis, relationship extraction, question and answer. Most existing entity linking models rely on sufficient context for disambiguation but do not work well for concise and sparse short texts. In addition, most of the methods use pre-training models to directly calculate the similarity between the entity text to be disambiguated and the candidate entity text, and do not dig deeper into the relationship between them. This article proposes an entity linking method for Chinese short texts based on Siamese-like networks to address the above shortcomings. In the entity disambiguation task, the features of the Siamese-like network are used to deeply parse the semantic relationships in the text and make full use of the feature information of the entity text to be disambiguated, capturing the interdependent features within the sentences through an attention mechanism, aiming to find out the most critical elements in the entity text description. The experimental demonstration on the CCKS2019 dataset shows that the F1 value of the method reaches 87.29%, increase of 11.02% compared to the F1 value(that) of the baseline method, fully validating the superiority of the model. Full article
(This article belongs to the Special Issue Intelligence Computing and Systems)
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20 pages, 8976 KiB  
Article
Complex Causal Extraction of Fusion of Entity Location Sensing and Graph Attention Networks
by Yang Chen, Weibing Wan, Jimi Hu, Yuxuan Wang and Bo Huang
Information 2022, 13(8), 364; https://doi.org/10.3390/info13080364 - 31 Jul 2022
Cited by 1 | Viewed by 1498
Abstract
At present, there is no uniform definition of annotation schemes for causal extraction, and existing methods are limited by the dependence of relations on long spans, which makes complex sentences such as multi-causal relations and nested causal relations difficult to extract. To solve [...] Read more.
At present, there is no uniform definition of annotation schemes for causal extraction, and existing methods are limited by the dependence of relations on long spans, which makes complex sentences such as multi-causal relations and nested causal relations difficult to extract. To solve these problems, a head-to-tail entity annotation method is proposed, which can express the complete semantics of complex causal relations and clearly describe the boundaries of entities. Based on this, a causal model, RPA-GCN (relation position and attention-graph convolutional networks), is constructed, incorporating GAT (graph attention network) and entity location perception. The attention layer is combined with a dependency tree to enhance the model’s ability to perceive relational features, and a bi-directional graph convolutional network is constructed to further capture the deep interaction information between entities and relationships. Finally, the classifier iteratively predicts the relationship of each word pair in the sentence and analyzes all causal pairs in the sentence by a scoring function. Experiments on SemEval 2010 task 8 and the Altlex dataset show that our proposed method has significant advantages in solving complex causal extraction compared to state-of-the-art methods. Full article
(This article belongs to the Special Issue Intelligence Computing and Systems)
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17 pages, 27245 KiB  
Article
A Multi-Sensory Guidance System for the Visually Impaired Using YOLO and ORB-SLAM
by Zaipeng Xie, Zhaobin Li, Yida Zhang, Jianan Zhang, Fangming Liu and Wei Chen
Information 2022, 13(7), 343; https://doi.org/10.3390/info13070343 - 15 Jul 2022
Cited by 6 | Viewed by 2985
Abstract
Guidance systems for visually impaired persons have become a popular topic in recent years. Existing guidance systems on the market typically utilize auxiliary tools and methods such as GPS, UWB, or a simple white cane that exploits the user’s single tactile or auditory [...] Read more.
Guidance systems for visually impaired persons have become a popular topic in recent years. Existing guidance systems on the market typically utilize auxiliary tools and methods such as GPS, UWB, or a simple white cane that exploits the user’s single tactile or auditory sense. These guidance methodologies can be inadequate in a complex indoor environment. This paper proposes a multi-sensory guidance system for the visually impaired that can provide tactile and auditory advice using ORB-SLAM and YOLO techniques. Based on an RGB-D camera, the local obstacle avoidance system is realized at the tactile level through point cloud filtering that can inform the user via a vibrating motor. Our proposed method can generate a dense navigation map to implement global obstacle avoidance and path planning for the user through the coordinate transformation. Real-time target detection and a voice-prompt system based on YOLO are also incorporated at the auditory level. We implemented the proposed system as a smart cane. Experiments are performed using four different test scenarios. Experimental results demonstrate that the impediments in the walking path can be reliably located and classified in real-time. Our proposed system can function as a capable auxiliary to help visually impaired people navigate securely by integrating YOLO with ORB-SLAM. Full article
(This article belongs to the Special Issue Intelligence Computing and Systems)
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