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The security of web browsers, being fundamental to Internet access infrastructure, has garnered significant attention. Current approaches to identify browser vulnerabilities predominantly rely on code auditing and componentized unit testing. ...

• A coverage-guided fuzzing framework for the browser rendering engine is proposed.
• Incorporating the ideas of templates and context-awareness to design sample generators.
• Runtime path coverage collection is achieved through compile-...

This work tackles the cross-domain object detection problem which aims to generalize a pre-trained object detector to different domains (driving scenes) without labels. An uncertainty-aware and class-balanced domain adaptation method is proposed based on ...

The neighborhood threshold in the neighborhood rough set has a significant impact on the neighborhood relation. When the neighborhood threshold of an object exceeds the critical value, the labels of objects in the neighborhood are not completely ...

• The neighborhood margin accurately represents the local state of the neighborhood, taking into account decision information.
• The margin neighborhood with a self-tuning the neighborhood threshold.
• The margin neighborhood rough set ...

Spectral feature selection, an excellent dimensionality reduction method, is extensively used in knowledge mining and protein sequence analysis. However, the graph representation derived from data with potential noises significantly impacts ...

• The proposed method obtains a purer neighborhood to generate a precise graph representation.
• The global and local structures are preserved to learn the projection matrix.
• The proposed method can effectively preprocess data for ...

System call sequences representing the runtime behavior of an application is particularly useful for anomaly detection in mobile applications. However, one of the main obstacles in this area is the lack of publicly available high-quality ...

• We propose the first maliciousness-aware system call-based anomaly detection dataset for mobile APPs.
• We present a distributed data collection framework for efficient extraction of runtime system calls.
• We establish a comprehensive ...

In this article, we propose a novel center-based clustering algorithm based on bidirectional conical information granularity. The main purpose is to fully absorb the semantic information of the ordinal relationship between objects to improve the ...

In this paper, a graph-based density clustering framework is proposed that detects the boundary points of clusters rather than cluster exemplars in high density regions. The framework introduces the connection density to measure the density ...

• Introducing the connection density to measure the density relationship between points.
• Utilizing the connectivity of the graph to achieve clustering by cutting off edges with low connection density.
• Automatically identifying ...

Rules play a crucial role in classification tasks, driving the advancement of artificial intelligence. However, how to improve the interpretability of extracted rules while ensuring the performance of classification tasks is always a challenge, ...

Neighborhood relations are established by employing a distance metric and parameters, where the distance metric is utilized to measure the dissimilarities between objects. The information about adjacent objects tends to vary, even though these ...

• The similarity measure of shared neighbors is superior to the distance metric in quantifying differences.
• The shared neighbors rough set model is regarded as a framework of fusing neighborhood information.
• The attribute reduction ...

Multi-label feature selection based on fuzzy rough sets, as a key step of multi-label data preprocessing, has been widely concerned by scholars in recent years. Most of the existing multi-label feature selection algorithms directly treat labels ...

Fuzzy c-means (FCM) clustering is a clustering method based on fuzzy theory. This method shows good adaptability by assigning membership values to each sample to represent the degree of membership of the sample to each cluster. However, when ...

Ensemble clustering has attracted much attention for its robustness and effectiveness compared to single clustering. As one of the representative methods, most co-association matrix-based ensemble clustering typically only take into account a ...

As an important part of machine learning, clustering methods have been continuously paid attention to. Current clustering methods divide data objects usually based on Euclidean metric, which is a basic and effective metric method. However, with ...

• We design a non-Euclidean metric.
• We construct a multi-granularity staged clustering method.
• We provide a clustering methods on complex spatial structure data.

Density-based clustering algorithms are known for their ability to detect irregular clusters, but they have limitations when it comes to dealing with clusters of varying densities. In this paper, we propose a new clustering algorithm called ...

Three-way decision (3WD) is a powerful tool for granular computing to deal with uncertain data, commonly used in information systems, decision-making, and medical care. Three-way decision gets much research in traditional rough set models. ...

• Combine three-way decision with SVM to solve the imbalanced classification problem.
• A new three-way fuzzy membership function to measure the significance of imbalanced samples.
• Consider the characteristics of imbalanced data in the ...

We introduce the notions of augmented formal contexts and generalized approximable concepts and show that all the generalized approximable concepts of an augmented formal context generates a continuous lattice under inclusion order, on the ...

Federated learning (FL) supports multiple participants to collaborate to train a global model in computing power networks, while the sensitive training data of all participants is kept locally for privacy protection. Although it is widely used in ...

Host-based intrusion detection systems (HIDS) can automatically identify adversarial applications by learning models from system events that represent normal system behaviors. The system call is the only way for applications to ...

• Present the first system call-based dataset for anomaly detection of Linux kernels.

Many extensions of rough sets have been trying to seek appropriate granular structures, such as neighborhood systems, disjoint intervals and coverings. However, few of them consider data-driven approaches to generating posets-structured coverings ...

Recent years have witnessed a tremendous increase of first-person videos captured by wearable devices. Such videos record information from different perspectives than the traditional third-person view, and thus show a wide range of potential usages. ...