The computer-aided diagnostic method based on deep learning can automatically identify whether X-ray images exhibit symptoms of osteoarthritis, thereby improving the quality and speed of early osteoarthritis diagnosis. However, osteoarthritis involves changes in structures such as soft tissue and joint spaces, and X-ray images of osteoarthritis have low contrast and high noise. These factors can blur the boundaries of osteoarthritic lesion areas, making feature extraction from X-ray images of osteoarthritis relatively difficult. In addition, the max-pooling operation in previous models tends to lose details and positional information in X-ray images, reducing the model's sensitivity to osteoarthritic structure features. To address the two issues in the diagnosis model mentioned above, we propose a deep neural network based on a masked strategy and fuzzy pooling (DNNMSFP) to automatically diagnose bone joint X-ray images to observe whether the image exhibits arthritis symptoms. The DNNMSFP model integrates the advantages of masked autoencoders and fuzzy pooling to address the aforementioned issues. First, the DNNMSFP model uses a self-supervised feature learning module designed using a masked strategy to learn effective features from noisy X-ray images. Furthermore, the DNNMSFP model replaces the max-pooling operation of the original feature learner with fuzzy pooling. Fuzzy pooling is a variant of traditional pooling operations that performs Gaussian blurring on pixel values in feature maps to extract features and reduce dimensionality. Comprehensive experiments on a real bone joint X-ray image dataset validate the superiority of the DNNMSFP model. Specifically, the DNNMSFP model achieves an excellent prediction performance on the real dataset, with precision, recall, and accuracy of 0.974, 0.948, and 0.965, respectively.

Abstract Multi‐view subspace clustering always performs well in high‐dimensional data analysis, but is sensitive to the quality of data representation. To this end, a two‐stage fusion strategy is proposed to embed representation learning into the process of multi‐view subspace clustering. This article first proposes a novel matrix factorization method that can separate the coupling consistent and complementary information from observations of multiple views. Based on the obtained latent representations, we further propose two subspace clustering strategies: feature‐level fusion and subspace‐level hierarchical strategy. The feature‐level method concatenates all kinds of latent representations from multiple views, and the original problem therefore degenerates to a single‐view subspace clustering process. The subspace‐level hierarchical method performs different self‐expressive reconstruction processes on the corresponding complementary and consistent latent representations coming from each view, that is, the prior constraints imposed on different types of subspace representations are related to the relevant input factors. Finally, extensive experimental results on real‐world datasets demonstrate the superiority of our proposed methods by comparing them against some state‐of‐the‐art subspace clustering algorithms.

The ever-increasing demand of storage capacity and system performance leads to the scaling requirement in redundant arrays of independent disks (RAID)-structured storage systems. Existing approaches mainly focus on minimizing data migration in offline scenario, but not consider the user accesses issued by applications. However, in online scenario, the scaling I/Os and user I/Os usually interfere with each other, and result in significant performance degradation. Thus, it is of big significance to develop an efficient online scaling scheme to mitigate the impact of I/O interference. In this paper, we propose popularity-based online scaling (POS), which exploits workload locality by scaling storage zones with high popularity in a higher priority. POS can efficiently alleviate the interference between scaling I/Os and user I/Os, and it is also general enough to scale various RAID systems like RAID-0, RAID-5, and RAID-6. It can also be readily deployed atop various conventional RAID scaling approaches to improve their performance. To demonstrate the effectiveness of POS, we implement various conventional RAID scaling approaches, and also implement POS on top of these approaches for comparison. Extensive simulations with real-world workloads show that POS can efficiently reduce the response time of user requests and scaling I/Os, and also improves the sequentiality of data accesses.

In cloud computing, how to use limited hardware resources to meet the increasing demands has become a major issue. KSM (Kernel Same-page Merging) is a content-based page sharing mechanism used in Linux that merges equal memory pages, thereby significantly reducing memory usage and increasing the density of virtual machines or containers. However, KSM introduces a large overhead in CPU and memory bandwidth usage due to the use of red-black trees and content-based page comparison. To reduce the deduplication overhead, in this paper, we propose a new design called AMT-KSM, which leverages array mapped tries to realize lightweight memory deduplication. The basic idea is to divide each memory page into multiple segments and use the concatenated strings of the hash values of segments as indexed keys in the tries. By doing this, we can significantly reduce the time required for searching duplicate pages as well as the number of page comparisons. We conduct experiments to evaluate the performance of our design, and results show that compared with the conventional KSM, AMT-KSM can reduce up to 44.9% CPU usage and 31.6% memory bandwidth usage.