Semantic Role Labeling (SRL) serves as the foundational and pivotal technology for semantic analysis. However, methods relying on pre-trained language models are constrained by issues such as semantic ambiguity and training complexity. Addressing this concern, this paper proposes a Chinese SRL approach that integrates pre-trained language models with Biaffine technology, aiming to enhance the model’s capability in processing semantic information from long sentences while reducing training complexity. By incorporating pooling techniques and part-of-speech features, the model exhibits significant improvements in capturing semantic role boundary relationships. Experimental results demonstrate that the RoBERTa-MPBF model employing maximum pooling achieves an F1 score of 90.89% on the CPB dataset, outperforming models solely based on conditional random fields. Moreover, the introduction of part-of-speech tagging results in an average F1 score improvement of approximately 1.5%. Despite the increased computational burden, considering the performance enhancement, this additional time cost is deemed acceptable. In convolutional kernel size testing, the model maintains F1 scores between 88.6% and 88.8% when the kernel size is 2 or 3. However, as the kernel size increases to 4, the F1 score drops to 80.37%, and further increases to 5 result in an F1 score reduction to 69.51%.

In the dynamic field of network security, the continuous emergence of cyber threats demands advanced technologies that provide adaptive and scalable defense mechanisms. This paper introduces a novel approach to Intrusion Detection Systems (IDS) using Transformer models. Traditional IDS methods struggle with scalability and adapting to new threats, whereas our Transformer-based architecture leverages the model’s scalability and parallel processing capabilities for real-time analysis of large volumes of network traffic. Additionally, the adaptability and generalization abilities of our model enable it to identify new and sophisticated threats, thus enhancing the robustness of network infrastructures against advanced cyber attacks. This study addresses a critical gap in IDS research by demonstrating the efficacy of Transformer models in this domain. Through rigorous empirical evaluations on diverse datasets, we showcase the superior performance and effectiveness of our proposed IDS, underscoring the transformative potential of this technology in network security. Our findings not only contribute to the growing body of literature on Transformer-based IDS but also highlight the need for ongoing research and innovation in this crucial field.

This study presents an innovative approach to combat the rapid spread of fake news on social media. By combining the Salp Swarm Algorithm with a Support Vector Machine, the proposed model achieves improved prediction accuracy. The Popular World Twitter dataset, comprising 10 million tweets, is utilized for training and testing the models. Different metaheuristic algorithms, including MVO, GA, PSO, GOA, and SSA, are used to optimize the SVM and generate five models. The dataset is converted into word representation through various feature extraction techniques, resulting in eight processed datasets. Comparisons with other metaheuristic algorithms demonstrate the superiority of the proposed approach, as it achieves higher accuracy with a reduced set of the most relevant features.

Behavioral attacks on cloud networks target the behaviors and actions of users within cloud computing environments. Attackers can obtain unauthorized access, interfere with services, steal confidential information, or jeopardize system integrity by taking advantage of flaws in the cloud network’s infrastructure, protocols, or apps. Given the growing number of organizations depending on cloud services for data processing and storage, it is imperative to spot behavioral assaults on cloud networks. In order to improve the detection of behavioral assaults in cloud network environments, a unique ensemble technique has been developed and presented in this study. The suggested model combines multiple features with cutting-edge machine-learning approaches to improve detection accuracy. We exhibit the efficacy of our methodology in detecting diverse behavioral attacks in cloud networks through testing and assessment, hence providing improved defense against ever-changing cyber threats. Our work sets a new standard for cloud security and offers a workable way to strengthen defenses against advanced assaults. The paper introduces an advanced ensemble method combining multiple features with cutting-edge machine-learning techniques to enhance the detection of behavioral attacks in cloud networks. By evaluating its effectiveness against various attack types, the model demonstrates significant improvements in detection accuracy, providing a robust solution for evolving cyber threats in cloud security.

This study analyzed the temporal variation of scour hole dimensions caused by a plain wall jet, which is one of the most hazardous issues faced by hydraulic structures. The study employed two recently developed artificial intelligence-based models, Extreme Learning Machine (ELM) and Multi-Gen Genetic Programming (MGGP), to predict scour hole dimensions and identify effective parameters. Both models accurately predicted the scour hole dimensions, with MGGP outperforming ELM for both training and testing data. MGGP presented four equations that can be used by designers to predict the temporal variations of scour hole dimensions with high accuracy. The non-dimensional form of the scouring time was found to be the most effective parameter, while the channel width ratio and standard deviation of sediments had negligible effects on the accuracy of the models. The study found that the effectiveness of the densimetric Froude number should be considered for predicting the temporal variation of scour hole dimensions due to plain wall jets. The proposed equations from both models had higher accuracy than previous empirical models. Overall, this study provides valuable insights into predicting and mitigating jet scour problems in hydraulic structures.

This study presents a framework for detecting and mitigating fake and potentially attacking user communities within 5G social networks. This framework utilizes geo-location information, community trust within the network, and AI community detection algorithms to identify users that can cause harm. The framework incorporates an artificial control model to select appropriate community detection algorithms and employs a trust-based strategy to identify and filter out potential attackers. It adapts its approach by utilizing user and attack requirement data through the artificial conscience control model while considering the dynamics of community trust within the network. What sets this framework apart from other fake user detection mechanisms is its capacity to consider attributes challenging for malicious users to mimic. These attributes include the trust established within the community over time, the geographical location, and the framework's adaptability to different attack scenarios. To validate its efficacy, we apply the framework to synthetic social network data, demonstrating its ability to distinguish potential malicious users from trustworthy ones.

The quality of machine learning (ML) models deployed in dynamic environments tends to decline over time due to disparities between the data used for training and the upcoming data available for prediction, which is commonly known as drift. Therefore, it is important for ML models to be capable of detecting any changes or drift in the data distribution and updating the ML model accordingly. This study presents various drift detection techniques to identify drift in the survival outcomes of patients who underwent cardiac surgery. Additionally, this study proposes several drift adaptation strategies, such as adaptive learning, incremental learning, and ensemble learning. Through a detailed analysis of the results, the study confirms the superior performance of ensemble model, achieving a minimum mean absolute error (MAE) of 10.684 and 2.827 for predicting hospital stay and ICU stay, respectively. Furthermore, the models that incorporate a drift adaptive framework exhibit superior performance compared to the models that do not include such a framework.

Labeling fine-grained objects manually is extremely challenging, as it is not only label-intensive but also requires professional knowledge. Accordingly, robust learning methods for fine-grained recognition with web images collected from Internet of Things have drawn significant attention. However, training deep fine-grained models directly using untrusted web images is confronted by two primary obstacles: 1) label noise in web images and 2) domain variance between the online sources and test datasets. To this end, in this study, we mainly focus on addressing these two pivotal problems associated with untrusted web images. To be specific, we introduce an end-to-end network that collaboratively addresses these concerns in the process of separating trusted data from untrusted web images. To validate the efficacy of our proposed model, untrusted web images are first collected by utilizing the text category labels found within fine-grained datasets. Subsequently, we employ the designed deep model to eliminate label noise and ameliorate domain mismatch. And the chosen trusted web data are utilized for model training. Comprehensive experiments and ablation studies validate that our method consistently surpasses other state-of-the-art approaches for fine-grained recognition task in a real-world scenario. Simultaneously, this introduces a novel pipeline for fine-grained recognition with substantial efficacy in practical applications. The source code and models can be accessed at: https://github.com/NUST-Machine-Intelligence-Laboratory/DDN.

The deep convolutional neural network (DCNN) in computer vision has given promising results. It is widely applied in many areas, from medicine, agriculture, self-driving car, biometric system, and almost all computer vision-based applications. Filters or weights are the critical elements responsible for learning in DCNN. Backpropagation has been the primary learning algorithm for DCNN and provides promising results, but the size and numbers of the filters remain hyper-parameters. Various studies have been done in the last decade on semi-supervised, self-supervised, and unsupervised methods and their properties. The effects of filter initialization, size-shape selection, and the number of filters on learning and optimization have not been investigated in a separate publication to collate all the options. Such attributes are often treated as hyper-parameters and lack mathematical understanding. Computer vision algorithms have many limitations in real-life applications, and understanding the learning process is essential to have some significant improvement. To the best of our knowledge, no separate investigation has been published discussing the filters; this is our primary motivation. This study focuses on arguments for choosing specific physical parameters of filters, initialization, and learning technic over scattered methods. The promising unsupervised approaches have been evaluated. Additionally, the limitations, current challenges, and future scope have been discussed in this paper.