Qiuyu Lai^1,2, Wang Kang³, Lei Yang^1,2, Chun Yang^1,2,*, Delin Zhang^2,*

Intelligent Automation & Soft Computing, Vol.39, No.4, pp. 649-664, 2024, DOI:10.32604/iasc.2024.051724 - 06 September 2024

Abstract Mathematical named entity recognition (MNER) is one of the fundamental tasks in the analysis of mathematical texts. To solve the existing problems of the current neural network that has local instability, fuzzy entity boundary, and long-distance dependence between entities in Chinese mathematical entity recognition task, we propose a series of optimization processing methods and constructed an Adversarial Training and Bidirectional long short-term memory-Selfattention Conditional random field (AT-BSAC) model. In our model, the mathematical text was vectorized by the word embedding technique, and small perturbations were added to the word vector to generate adversarial samples, while More >

Ruijin Xue^1,2, Shuang Feng^1,2,*, Qi Wang^1,2

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3107-3122, 2024, DOI:10.32604/cmc.2024.052600 - 15 August 2024

Abstract Users’ interests are often diverse and multi-grained, with their underlying intents even more so. Effectively capturing users’ interests and uncovering the relationships between diverse interests are key to news recommendation. Meanwhile, diversity is an important metric for evaluating news recommendation algorithms, as users tend to reject excessive homogeneous information in their recommendation lists. However, recommendation models themselves lack diversity awareness, making it challenging to achieve a good balance between the accuracy and diversity of news recommendations. In this paper, we propose a news recommendation algorithm that achieves good performance in both accuracy and diversity. Unlike… More >

Shuyi Li, Hongchao Hu^*, Xiaohan Yang, Guozhen Cheng, Wenyan Liu, Wei Guo

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2331-2359, 2024, DOI:10.32604/cmc.2024.047275 - 15 May 2024

Abstract Adversarial distillation (AD) has emerged as a potential solution to tackle the challenging optimization problem of loss with hard labels in adversarial training. However, fixed sample-agnostic and student-egocentric attack strategies are unsuitable for distillation. Additionally, the reliability of guidance from static teachers diminishes as target models become more robust. This paper proposes an AD method called Learnable Distillation Attack Strategies and Evolvable Teachers Adversarial Distillation (LDAS&ET-AD). Firstly, a learnable distillation attack strategies generating mechanism is developed to automatically generate sample-dependent attack strategies tailored for distillation. A strategy model is introduced to produce attack strategies that… More >

Kai Chen^1,2, Jinwei Wang³, James Msughter Adeke^1,2, Guangjie Liu^1,2,*, Yuewei Dai^1,4

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3247-3265, 2024, DOI:10.32604/cmc.2024.046082 - 26 March 2024

Abstract In recent years, various adversarial defense methods have been proposed to improve the robustness of deep neural networks. Adversarial training is one of the most potent methods to defend against adversarial attacks. However, the difference in the feature space between natural and adversarial examples hinders the accuracy and robustness of the model in adversarial training. This paper proposes a learnable distribution adversarial training method, aiming to construct the same distribution for training data utilizing the Gaussian mixture model. The distribution centroid is built to classify samples and constrain the distribution of the sample features. The… More >

Zhicong Qiu^1,2, Xianmin Wang^1,*, Huawei Ma¹, Songcao Hou¹, Jing Li^1,2,*, Zuoyong Li²

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1243-1256, 2023, DOI:10.32604/iasc.2023.038241 - 21 June 2023

Abstract Reweighting adversarial examples during training plays an essential role in improving the robustness of neural networks, which lies in the fact that examples closer to the decision boundaries are much more vulnerable to being attacked and should be given larger weights. The probability margin (PM) method is a promising approach to continuously and path-independently measuring such closeness between the example and decision boundary. However, the performance of PM is limited due to the fact that PM fails to effectively distinguish the examples having only one misclassified category and the ones with multiple misclassified categories, where… More >

Tianzi Zhao^1,2,3,4, Liang Jin^1,2,3,*, Xiaofeng Zhou^1,2,3, Shuai Li^1,2,3, Shurui Liu^1,2,3,4, Jiang Zhu^1,2,3

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 329-346, 2023, DOI:10.32604/cmc.2023.038595 - 08 June 2023

Abstract The widespread usage of Cyber Physical Systems (CPSs) generates a vast volume of time series data, and precisely determining anomalies in the data is critical for practical production. Autoencoder is the mainstream method for time series anomaly detection, and the anomaly is judged by reconstruction error. However, due to the strong generalization ability of neural networks, some abnormal samples close to normal samples may be judged as normal, which fails to detect the abnormality. In addition, the dataset rarely provides sufficient anomaly labels. This research proposes an unsupervised anomaly detection approach based on adversarial memory… More >

Muhammad Shahzad Haroon^*, Husnain Mansoor Ali

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3513-3527, 2022, DOI:10.32604/cmc.2022.029858 - 16 June 2022

Abstract Intrusion detection system plays an important role in defending networks from security breaches. End-to-end machine learning-based intrusion detection systems are being used to achieve high detection accuracy. However, in case of adversarial attacks, that cause misclassification by introducing imperceptible perturbation on input samples, performance of machine learning-based intrusion detection systems is greatly affected. Though such problems have widely been discussed in image processing domain, very few studies have investigated network intrusion detection systems and proposed corresponding defence. In this paper, we attempt to fill this gap by using adversarial attacks on standard intrusion detection datasets… More >

Sudan Prasad Uprety, Seung Ryul Jeong^*

Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 1-11, 2022, DOI:10.32604/iasc.2022.018757 - 03 September 2021

Abstract Natural Language Understanding and Speech Understanding systems are now a global trend, and with the advancement of artificial intelligence and machine learning techniques, have drawn attention from both the academic and business communities. Domain prediction, intent detection and entity extraction or slot fillings are the most important parts for such intelligent systems. Various traditional machine learning algorithms such as Bayesian algorithm, Support Vector Machine, and Artificial Neural Network, along with recent Deep Neural Network techniques, are used to predict domain, intent, and entity. Most language understanding systems process user input in a sequential order: domain… More >

Jinyuan Li¹, Hao Li¹, Guorong Cui¹, Yan Kang^{1, *}, Yang Hu¹, Yingnan Zhou²

CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 925-940, 2020, DOI:10.32604/cmc.2020.09903 - 10 June 2020

Abstract With continuous urbanization, cities are undergoing a sharp expansion within the regional space. Due to the high cost, the prediction of regional traffic flow is more difficult to extend to entire urban areas. To address this challenging problem, we present a new deep learning architecture for regional epitaxial traffic flow prediction called GACNet, which predicts traffic flow of surrounding areas based on inflow and outflow information in central area. The method is data-driven, and the spatial relationship of traffic flow is characterized by dynamically transforming traffic information into images through a two-dimensional matrix. We introduce… More >