Journal of King Saud University-Computer and Information Sciences最新文献

Anomalous behaviors in social networks can lead to privacy leaks and the spread of false information. In this paper, we propose an anomalous behavior detection method based on optimized graph embedding representation. Specifically, the user behavior logs are first extracted into a social network user behavior temporal knowledge graph, based on which the graph embedding representation method is used to transform the network topology and temporal information in the user behavior knowledge graph into structural embedding vectors and temporal information embedding vectors, and the hybrid attention mechanism is used to merge the two types of vectors to obtain the final entity embedding to complete the prediction and complementation of the temporal knowledge graph of user behavior. We use graph neural networks, which use the temporal information of user behaviors as a time constraint and capture both user behavioral and semantic information. It converts the two parts of information into vectors for concatenation and linear transformation to obtain a comprehensive representation vector of the whole subgraph, as well as joint deep learning models to evaluate abnormal behavior. Finally, we perform experiments on the Yelp dataset to validate that our method achieves a 9.56% improvement in the F1-score.

Emerging Zoned Namespace (ZNS) is a new-style Solid State Drive (SSD) that manages data in a zoned manner, which can achieve higher performance by strictly obeying the sequential write mode in each zone and alleviating the redundant overhead of garbage collections. Unfortunately, flash memory usually has a serious problem with limited program/erase cycles. Meanwhile, inappropriate data placement strategy of storage systems can lead to imbalanced wear among zones, severely reducing the lifespan of ZNS SSDs. In this paper, we propose a Wear-Leveling-Aware Data Placement (WADP) to solve this problem with negligible performance cost. First, WADP employs a wear-aware empty zone allocation algorithm to quantify the resets of zones and choose the less-worn zone for each allocation. Second, to prevent long-term zone occupation of infrequently written data (namely cold data), we propose a wear-leveling cold zone monitoring mechanism to identify cold zones dynamically. Finally, WADP adopts a real-time I/O pressure-aware data migration mechanism to adaptively migrate cold data for achieving wear-leveling among zones. We implement the proposed WADP in ZenFS and evaluate it with widely used workloads. Compared with state-of-the-art solutions, i.e., LIZA and FAR, the experimental results show that WADP can significantly reduce the standard deviation of zone resets while maintaining decent performance.

Recovering structures from images with 180${}^{\circ }$ or 360${}^{\circ }$ FoV is pivotal in computer vision and computational photography, particularly for VR/AR/MR and autonomous robotics applications. Due to varying distortions and the complexity of indoor scenes, recovering flexible structures from a single image is challenging. We introduce OmniSRNet, a comprehensive deep learning framework that merges distortion-aware learning with bidirectional LSTM. Utilizing a curated dataset with optimized panorama and expanded fisheye images, our framework features a distortion-aware module (DAM) for extracting features and a horizontal and vertical step module (HVSM) of LSTM for contextual predictions. OmniSRNet excels in applications such as VR-based house viewing and MR-based video surveillance, achieving leading results on cuboid and non-cuboid datasets. The code and dataset can be accessed at https://github.com/mmlph/OmniSRNet/.

The recent rapid expansion of cloud computing has led to the prominence of Cloud Data Center (CDC) emerging. However, user requests’ waiting time might be greatly increased for a single physical machine (PM) in the CDC. We provide a cloud resource allocation scheme with virtual machine (VM) inter-group asynchronous failure. This method improves requests’ throughput and reduces wait time of requests. In particular, two PMs with different service rates for mapping multiple VMs are deployed in order to equally distribute cloud users’ requests, and we assume that the two PMs will fail and repair at different probabilities. A finite cache is also introduced to reduce the requests’ blocking rate. We model the VMs and user requests and create a 3-dimensional Markov chain (3DMC) to gauge the requests’ performance metrics. Numerical experiments are performed to obtain multiple performance metrics graphs for the requests. By comparing our scheme with the traditional cloud resource allocation scheme that involves synchronization failure in VM, we find that our scheme has an improvement in throughput, and each scheme has advantages and disadvantages in blocking rate of requests.

Fish counting is crucial in fish farming. Density map-based fish counting methods hold promise for fish counting in high-density scenarios; however, they suffer from ineffective ground truth density map generation. High labeling complexities and disturbance to fish growth during data collection are also challenging to mitigate. To address these issues, LDNet, a versatile network with attention implemented is introduced in this study. An imbalanced Optimal Transport (OT)-based loss function was used to effectively supervise density map generation. Additionally, an Image Manipulation-Based Data Augmentation (IMBDA) strategy was applied to simulate training data from diverse scenarios in fixed viewpoints in order to build a model that is robust to different environmental changes. Leveraging a limited number of training samples, our approach achieved notable performances with an 8.27 MAE, 9.97 RMSE, and 99.01% Accuracy on our self-curated Fish Count-824 dataset. Impressively, our method also demonstrated superior counting performances on both vehicle count datasets CARPK and PURPK+, and Penaeus_1k Penaeus Larvae dataset when only 5%–10% of the training data was used. These outcomes compellingly showcased our proposed approach with a wide applicability potential across various cases. This innovative approach can potentially contribute to aquaculture management and ecological preservation through counting fish accurately.

Recently, Nominal Compound Chain Extraction (NCCE) has been proposed to detect related mentions in a document to improve understanding of the document’s topic. NCCE involves longer span detection and more complicated rules for relation decisions, making it more difficult than previous chain extraction tasks, such as coreference resolution. Current methods achieve certain progress on the NCCE task, but they suffer from insufficient syntax information utilization and incomplete mention relation mining, which are helpful for NCCE. To fill these gaps, we propose a syntax-guided model using a triaffine interaction to improve the performance of the NCCE task. Instead of solely relying on the text information to detect compound mentions, we also utilize the noun-phrase (NP) boundary information in constituency trees to incorporate prior boundary knowledge. In addition, we use biaffine and triaffine operations to mine the mention interactions in the local and global context of a document. To show the effectiveness of our methods, we conduct a series of experiments on a human-annotated NCCE dataset. Experimental results show that our model significantly outperforms the baseline systems. Moreover, in-depth analyses reveal the effect of utilizing syntactic information and mention interactions in the local and global contexts.

Heterogeneous network link prediction is a hot topic in the analysis of networks. It aims to predict missing links in the network by utilizing the rich semantic information present in the heterogeneous network, thereby enhancing the effectiveness of relevant data mining tasks. Existing heterogeneous network link prediction methods utilize meta-paths or meta-graphs to extract semantic information, heavily relying on the priori knowledge. This paper proposes a heterogeneous network link prediction based on network schema and cross-neighborhood attention method (HNLP-NSCA). The heterogeneous node features are projected into a shared latent vector space using fully connected layers. To resolve the issue of prior knowledge dependence on meta-path, the semantic information is extracted by using network schema structures uniquely in heterogeneous networks. Node features are extracted based on the relevant network schema instances, avoiding the problem of meta-path selection. The neighborhood interaction information of input node pairs is sensed via cross-neighborhood attention, strengthening the nonlinear mapping capability of the link prediction. The resulting cross-neighborhood interaction vectors are combined with the node feature vectors and fed into a multilayer perceptron for link prediction. Experimental results on four real-world datasets demonstrate that the proposed HNLP-NSCA mothed outperforms the baseline models.

Transformer-based approaches have demonstrated remarkable performance in image processing tasks due to their ability to model long-range dependencies. Current mainstream Transformer-based methods typically confine self-attention computation within windows to reduce computational burden. However, this constraint may lead to grid artifacts in the reconstructed images due to insufficient cross-window information exchange, particularly in image super-resolution tasks. To address this issue, we propose the Multi-Scale Texture Complementation Block based on Spatial Relaxation Transformer (MSRT), which leverages features at multiple scales and augments information exchange through cross windows attention computation. In addition, we introduce a loss function based on the prior of texture smoothness transformation, which utilizes the continuity of textures between patches to constrain the generation of more coherent texture information in the reconstructed images. Specifically, we employ learnable compressive sensing technology to extract shallow features from images, preserving image features while reducing feature dimensions and improving computational efficiency. Extensive experiments conducted on multiple benchmark datasets demonstrate that our method outperforms previous state-of-the-art approaches in both qualitative and quantitative evaluations.

The rapidly-exploring random tree (RRT) algorithm has a wide range of applications in the field of path planning. However, conventional RRT algorithm suffers from low planning efficiency and long path length, making it unable to handle complex environments. In response to the above problems, this paper proposes an improved RRT algorithm based on restricted sampling area (RSA-RRT). Firstly, to address the problem of low efficiency, a restricted sampling area strategy is proposed. By dynamically restricting the sampling area, the number of invalid sampling points is reduced, thus improving planning efficiency. Then, for the path planning problem in narrow areas, a fixed-angle sampling strategy is proposed, which improves the planning efficiency in narrow areas by conducting larger step size sampling with a fixed angle. Finally, a multi-triangle optimization strategy is proposed to address the problem of longer and more tortuous paths. The effectiveness of RSA-RRT algorithm is verified through improved strategy performance verification and ablation experiments. Comparing with other algorithms in different environments, the results show that RSA-RRT algorithm can obtain shorter paths while taking less time, effectively balancing the path quality and planning speed, and it can be applied in complex real-world environments.