A Hierarchical Spatial-Temporal Cross-Attention Scheme for Video Summarization Using Contrastive Learning.

Video summarization (VS) is a widely used technique for facilitating the effective reading, fast comprehension, and effective retrieval of video content. Certain properties of the new video data, such as a lack of prominent emphasis and a fuzzy theme development border, disturb the original thinking mode based on video feature information. Moreover, it introduces new challenges to the extraction of video depth and breadth features. In addition, the diversity of user requirements creates additional complications for more accurate keyframe screening issues. To overcome these challenges, this paper proposes a hierarchical spatial-temporal cross-attention scheme for video summarization based on comparative learning. Graph attention networks (GAT) and the multi-head convolutional attention cell are used to extract local and depth features, while the GAT-adjusted bidirection ConvLSTM (DB-ConvLSTM) is used to extract global and breadth features. Furthermore, a spatial-temporal cross-attention-based ConvLSTM is developed for merging hierarchical characteristics and achieving more accurate screening in similar keyframes clusters. Verification experiments and comparative analysis demonstrate that our method outperforms state-of-the-art methods.

An Order-Preserving Encryption Scheme Based on Weighted Random Interval Division for Ciphertext Comparison in Wearable Systems.

With the rapid development of wearable devices with various sensors, massive sensing data for health management have been generated. This causes a potential revolution in medical treatments, diagnosis, and prediction. However, due to the privacy risks of health data aggregation, data comparative analysis under privacy protection faces challenges. Order-preserving encryption is an effective scheme to achieve private data retrieval and comparison, but the existing order-preserving encryption algorithms are mainly aimed at either integer data or single characters. It is urgent to build a lightweight order-preserving encryption scheme that supports multiple types of data such as integer, floating number, and string. In view of the above problems, this paper proposes an order-preserving encryption scheme (WRID-OPES) based on weighted random interval division (WRID). WRID-OPES converts all kinds of data into hexadecimal number strings and calculates the frequency and weight of each hexadecimal number. The plaintext digital string is blocked and recombined, and each block is encrypted using WRID algorithm according to the weight of each hexadecimal digit. Our schemes can realize the order-preserving encryption of multiple types of data and achieve indistinguishability under ordered selection plaintext attack (IND-OCPA) security in static data sets. Security analysis and experiments show that our scheme can resist attacks using exhaustive methods and statistical methods and has linear encryption time and small ciphertext expansion rate.

A Survey of techniques for fine-grained web traffic identification and classification.

After decades of rapid development, the scale and complexity of modern networks have far exceed our expectations. In many conditions, traditional traffic identification methods cannot meet the demand of modern networks. Recently, fine-grained network traffic identification has been proved to be an effective solution for managing network resources. There is a massive increase in the use of fine-grained network traffic identification in the communications industry. In this article, we propose a comprehensive overview of fine-grained network traffic identification. Then, we conduct a detailed literature review on fine-grained network traffic identification from three perspectives: wired network, mobile network, and malware traffic identification. Finally, we also draw the conclusion on the challenges of fine-grained network traffic identification and future research prospects.

Death receptor and mitochondria-mediated hepatocyte apoptosis underlies liver dysfunction in rats exposed to organic pollutants from drinking water.

Persistent organic pollutants in drinking water impose a substantial risk to the health of human beings, but the evidence for liver toxic effect and the underlying mechanism is scarce. This study aimed to examine the liver toxicity and elucidate the molecular mechanism of organic pollutants in drinking water in normal human liver cell line L02 cells and rats. The data showed that organic extraction from drinking water remarkably impaired rat liver function, evident from the increase in the serum level of alanine aminotransferase, aspartate aminotransferase, and cholinesterase, and decrease in the serum level of total protein and albumin. Organic extraction dose-dependently induced apoptotic cell death in rat liver and L02 cells. Administration of rats with organic extraction promoted death receptor signaling pathway through the increase in gene and protein expression level of Fas and FasL. Treatment of rats with organic extraction also induced mitochondria-mediated apoptosis via increasing the expression level of proapoptotic protein, Bax, but decreasing the expression level of antiapoptotic protein, Bcl-2, resulting in an upregulation of cytochrome c and activation of caspase cascade at both transcriptional and post-transcriptional levels. Moreover, organic extraction enhanced rat liver glutathione S-transferases activity and reactive oxygen species generation, and upregulated aryl hydrocarbon receptor and glutathione S-transferase A1 at both transcriptional and translational levels. Collectively, the results indicate that organic extraction from drinking water impairs liver function, with the involvement of death receptor and mitochondria-mediated apoptosis in rats. The results provide evidence and molecular mechanisms for organic pollutants in drinking water-induced liver dysfunction, which may help prevent and treat organic extraction-induced liver injury.

Protecting location privacy for outsourced spatial data in cloud storage.

As cloud computing services and location-aware devices are fully developed, a large amount of spatial data needs to be outsourced to the cloud storage provider, so the research on privacy protection for outsourced spatial data gets increasing attention from academia and industry. As a kind of spatial transformation method, Hilbert curve is widely used to protect the location privacy for spatial data. But sufficient security analysis for standard Hilbert curve (SHC) is seldom proceeded. In this paper, we propose an index modification method for SHC (SHC(∗)) and a density-based space filling curve (DSC) to improve the security of SHC; they can partially violate the distance-preserving property of SHC, so as to achieve better security. We formally define the indistinguishability and attack model for measuring the privacy disclosure risk of spatial transformation methods. The evaluation results indicate that SHC(∗) and DSC are more secure than SHC, and DSC achieves the best index generation performance.

A security-awareness virtual machine management scheme based on Chinese wall policy in cloud computing.

Cloud computing gets increasing attention for its capacity to leverage developers from infrastructure management tasks. However, recent works reveal that side channel attacks can lead to privacy leakage in the cloud. Enhancing isolation between users is an effective solution to eliminate the attack. In this paper, to eliminate side channel attacks, we investigate the isolation enhancement scheme from the aspect of virtual machine (VM) management. The security-awareness VMs management scheme (SVMS), a VMs isolation enhancement scheme to defend against side channel attacks, is proposed. First, we use the aggressive conflict of interest relation (ACIR) and aggressive in ally with relation (AIAR) to describe user constraint relations. Second, based on the Chinese wall policy, we put forward four isolation rules. Third, the VMs placement and migration algorithms are designed to enforce VMs isolation between the conflict users. Finally, based on the normal distribution, we conduct a series of experiments to evaluate SVMS. The experimental results show that SVMS is efficient in guaranteeing isolation between VMs owned by conflict users, while the resource utilization rate decreases but not by much.