The Joint Method of Triple Attention and Novel Loss Function for Entity Relation Extraction in Small Data-Driven Computational Social Systems

Gao, Honghao; Huang, Jiadong; Yuan, Tao; Hussain, Walayat; Huang, Yuzhe

doi:10.1109/tcss.2022.3178416

Cited by 32 publications

(6 citation statements)

References 25 publications

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“…With the surging pace of IoT development, system security and attack protection have attracted much research interest [33,34]. Among the related works, Malware detection and identification, using machine learning techniques, are mostly discussed.…”

Section: Malware Identification On Opcode or Api Namesmentioning

confidence: 99%

Robust Malware identification via deep temporal convolutional network with symmetric cross entropy learning

et al. 2023

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Recent developments in the field of Internet of things (IoT) have aroused growing attention to the security of smart devices. Specifically, there is an increasing number of malicious software (Malware) on IoT systems. Nowadays, researchers have made many efforts concerning supervised machine learning methods to identify malicious attacks. High-quality labels are of great importance for supervised machine learning, but noises widely exist due to the non-deterministic production environment. Therefore, learning from noisy labels is significant for machine learning-enabled Malware identification. In this study, motivated by the symmetric cross entropy with satisfactory noise robustness, the authors propose a robust Malware identification method using temporal convolutional network (TCN). Moreover, word embedding techniques are generally utilised to understand the contextual relationship between the input operation code (opcode) and application programming interface function names. Here, considering the numerous unlabelled samples in real-world intelligent environments, the authors pre-train the TCN model on an unlabelled set using a word embedding method, that is, Word2Vec. In the experiments, the proposed method is compared with several traditional statistical methods and more recent neural networks on a synthetic Malware dataset and a realworld dataset. The performance comparisons demonstrate the better performance and noise robustness of their proposed method, especially that the proposed method can yield the best identification accuracy of 98.75% in real-world scenarios.

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Section: Malware Identification On Opcode or Api Namesmentioning

confidence: 99%

Robust Malware identification via deep temporal convolutional network with symmetric cross entropy learning

et al. 2023

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“…The results of the traditional sorting are implemented by analyzing the keywords in the database rather than on the semantic level. Then, the work on semantic retrieval and related issues has gradually attracted the attention of researchers, 33 and the accumulation of relevant research provides experiences and inspirations for semantic retrieval and matching of Chinese information retrieval systems.…”

Section: Related Workmentioning

confidence: 99%

An intelligent recommendation method for coal mine accident case via ontology knowledge service

Luo

Zhang²,

Chen

2023

Engineering Reports

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Coal mine accidents, for example, water leakage and collapse, can damage the circuit system, and they in turn can affect the stable operation of the coal mine. Therefore, it is necessary to identify the causes of coal mine accidents and reduce the number of accidents in coal mines. Government and enterprises have already accumulated numerous coal mine accident cases. To summarize the characteristics and rules of accidents, survey the deep cause of the accident, avoid the recurrence of similar accidents, and early pre‐control of risk source, there is a need to analyze the correlation in the accident characteristics. Then, with the help of ontology knowledge service model, we address this issue in this article. For guaranteeing the inference efficiency of ontology knowledge, we propose a semiautomatic construction method for coal mine accident cases to construct ontology. Here, the reliability of ontology construction and the professionalism of domain knowledge provide a feasible approach to ontology learning using structured and semi‐structured data sources. Furthermore, the weighted Word2vec and spectral clustering method are combined, an intelligent recommendation algorithm with accident ontology is accordingly presented, while presenting a local optimal distance calculation similarity strategy. This method can serve as an assistant to help users mine similar coal mine accident cases. Finally, the experimental results show that after comparing with some other popular methods, the proposed approach can achieve a satisfactory performance on the coal mine dataset with an accuracy of 99.47%, the precision of 98.92%, and the F1‐score of 99.35.

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“…Knowledge graph (KG) is a technique that uses graph models to describe knowledge and model the association relationships between things [ 20 , 21 , 22 ]. KGs are composed of triples,

, and entities that have attribute–value pairs, which are connected by relationships to form a web-like structure [ 23 , 24 ].…”

Section: Related Workmentioning

confidence: 99%

Enhancing Cross-Lingual Entity Alignment in Knowledge Graphs through Structure Similarity Rearrangement

Liu

Jin

Shi

et al. 2023

Sensors

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Cross-lingual entity alignment in knowledge graphs is a crucial task in knowledge fusion. This task involves learning low-dimensional embeddings for nodes in different knowledge graphs and identifying equivalent entities across them by measuring the distances between their representation vectors. Existing alignment models use neural network modules and the nearest neighbors algorithm to find suitable entity pairs. However, these models often ignore the importance of local structural features of entities during the alignment stage, which may lead to reduced matching accuracy. Specifically, nodes that are poorly represented may not benefit from their surrounding context. In this article, we propose a novel alignment model called SSR, which leverages the node embedding algorithm in graphs to select candidate entities and then rearranges them by local structural similarity in the source and target knowledge graphs. Our approach improves the performance of existing approaches and is compatible with them. We demonstrate the effectiveness of our approach on the DBP15k dataset, showing that it outperforms existing methods while requiring less time.

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The Joint Method of Triple Attention and Novel Loss Function for Entity Relation Extraction in Small Data-Driven Computational Social Systems

Cited by 32 publications

References 25 publications

Robust Malware identification via deep temporal convolutional network with symmetric cross entropy learning

Robust Malware identification via deep temporal convolutional network with symmetric cross entropy learning

An intelligent recommendation method for coal mine accident case via ontology knowledge service

Enhancing Cross-Lingual Entity Alignment in Knowledge Graphs through Structure Similarity Rearrangement

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