Modern storage systems orchestrate a group of disks to achieve their performance and reliability goals. Even though such are designed withstand the failure individual disks, multiple poses unique set challenges. We empirically investigate disk data from large number production systems, specifically focusing on impact failures RAID systems. Our covers about one million SATA six models for periods up 5 years. show how observed weaken protection provided by RAID. The count reallocated sectors...

Bridge crack detection is essential to ensure bridge safety. The introduction of deep learning technology has made it possible detect cracks automatically and accurately. In this study, the Inception-Resnet-v2 algorithm was systematically improved applied real-time cracks. We propose an end-to-end model based on a convolutional neural network. This combines advantages Inception convolution residual networks, broadening network width alleviating training problem calculation speed while still...

A predominant portion of Internet services, like content delivery networks, news broadcasting, blogs sharing and social etc., is data centric. significant amount new generated by these services each day. To efficiently store maintain backups for such a challenging task current storage systems. Chunking based deduplication (dedup) methods are widely used to eliminate redundant hence reduce the required total space. In this paper, we propose novel Frequency Based (FBC) algorithm. Unlike most...

Due to its better scalability, Key-Value (KV) store has superseded traditional relational databases for many applications, such as data deduplication, on-line multi-player gaming, and Internet services like Amazon Facebook. The KV efficiently supports two operations (key lookup pair insertion) through an index structure that maps keys their associated values. is also commonly used implement the chunk in where a ID (SHA1 value computed based on chunk's content) key associative metadata (e.g.,...

Modern storage systems orchestrate a group of disks to achieve their performance and reliability goals. Even though such are designed withstand the failure individual disks, multiple poses unique set challenges. We empirically investigate disk data from large number production systems, specifically focusing on impact failures RAID systems. Our covers about one million SATA six models for periods up 5 years. show how observed weaken protection provided by RAID. The count reallocated sectors...

There is a huge amount of duplicated or redundant data in current storage systems. So de-duplication, which uses lossless compression schemes to minimize the at inter-file level, has been receiving broad attention recent years. But there are still research challenges approaches and systems, such as: how chunking files more efficiently better leverage potential similarity identity among dedicated applications; store chunks effectively reliably into secondary devices. In this paper, we propose...

Data deduplication has recently become commonplace in most secondary storage and even some primary for the capacity optimization purpose. Aside from its write performance, read performance of been gaining significance with a wide range deployments. In this paper, we emphasize importance reconstituting data stream unique shared chunks physically dispersed over storage. We newly introduce indicator called Chunk Fragmentation Level (CFL). also validate that CFL is very effective to indicate...

We propose Migratory Compression (MC), a coarse-grained data transformation, to improve the effectiveness of traditional compressors in modern storage systems. In MC, similar chunks are re-located together, compression factors. After decompression, migrated return their previous locations. evaluate and overhead explore reorganization approaches on variety datasets, present prototype implementation MC commercial deduplicating file system. also compare more established technique delta...

Owing to the development of computerized vision technology, object detection based on convolutional neural networks is being widely used in field bridge crack detection. However, these have limited utility because low precision and poor real-time performance. In this study, an improved single-shot multi-box detector (SSD) called ISSD proposed, which seamlessly combines depth separable deformation convolution module (DSDCM), inception (IM), feature recalibration (FRM) a tightly coupled manner...

Multispectral pedestrian detection based on deep learning can provide a robust and accurate under different illumination conditions, which has important research significance in safety. In order to reduce the log-average miss rate of object new one-stage detector suitable for multispectral is proposed. First, realize complementarity between information flows two modalities feature extraction stage loss, low-cost cross-modality complementary module (CFCM) Second, suppress background noise...

With the exploration and development of marine resources, deep learning is more widely used in underwater image processing. However, quality original images so low that traditional semantic segmentation methods obtain poor results, such as blurred target edges, insufficient accuracy, regional boundary effects. To solve these problems, this paper proposes a method for images. Firstly, enhancement based on multi-spatial transformation performed to improve images, which not common other...

To solve the problem of low detection accuracy small objects in UAV optical remote sensing images due to contrast, dense distribution, and weak features, this paper proposes a object method based on feature alignment candidate regions is proposed for images. Firstly, AFA-FPN (Attention-based Feature Alignment FPN) defines corresponding relationship between mappings, solves misregistration features adjacent levels, improves recognition ability by aligning fusing shallow spatial deep semantic...

Bridge crack is one of the critical optical and visual information to judge health state bridges. The bridge detection methods based on artificial intelligence are essential in this field, but current approaches not satisfactory terms speed accuracy. This study proposes a novel multi-scale network, called MSCNet, comprising texture enhancement mechanism feature aggregation enhance saliency objects background for detection. We use Res2Net as backbone network improve depth expression ability...

A Bloom Filter (BF) is a data structure based on probability to compactly represent/record set of elements (keys). It has wide applications efficiently identifying key that been seen before with minimum amount recording space used. BF heavily used in chunking de-duplication. Traditionally, implemented as in-RAM structure; hence its size limited by the available RAM machine. For certain like de-duplication require big beyond space, it becomes necessary store into secondary storage device....

The remote sensing images in large scenes have a complex background, and the types, sizes, postures of targets are different, making object detection difficult. To solve this problem, an end-to-end multi-size method based on dual attention mechanism is proposed paper. First, MobileNets backbone network used to extract multi-layer features as input MFCA, feature concentration module. MFCA employs suppress noise, enhance effective reuse, improve adaptability target through convolution...

Underwater images have low quality, and underwater targets different sizes. The mainstream target detection networks cannot achieve good results in detecting objects from images. In this study, a lightweight multiscale model with an attention mechanism is designed to solve the above problems. model, MobileNetv3 used as backbone network for preliminary feature extraction. extraction module (LFEM) pays map at channel space levels. features large weights are promoted, while small suppressed....

Reliability in deduplication storage has not attracted much research attention yet. To provide a demanded reliability for an incoming data stream, most systems first carry out process by eliminating duplicates from the stream and then apply erasure coding remaining (unique) chunks. A unique chunk may be shared (i.e., duplicated) at many places of other streams. That is why can reduce required capacity. However, this occasionally becomes problematic to assure certain levels different We...

Deep learning has promoted the research of object detection in aerial scenes. However, most existing networks are limited by large-scale variation objects and confusion category features. To overcome these limitations, this paper proposes a novel framework called DFCformer. DFCformer is mainly composed three parts: backbone network DMViT, which introduces deformation patch embedding multi-scale adaptive self-attention to capture sufficient features objects; FRGC guides feature interaction...

This article proposes a weakly supervised multi-feature fusion pedestrian re-identification method, which introduces mechanism to extract feature information from different layers into the same space and fuse them deep shallow joint features. The goal is fully utilize rich in image improve performance robustness of model. Secondly, by matching target character with unprocessed surveillance videos, one only needs know that identity person appears video, without annotating any frames video...

The complexity of distributed storage systems makes it difficult to evaluate fundamentally new data placement policies for energy efficiency in real-world systems. Simulation studies allow us quickly prototype and test energy-efficient management policies. Unfortunately, there is a lack tools the public domain that support such studies. We introduce EEffSim, highly configurable simulator general-purpose multi-server EEffSim supports migration, locking protocols, write offloading,...

Detecting objects in aerial images is a challenging task due to the large-scale variations and arbitrary orientations with tiny instances. A new multi-scale transformer-based detector called MStrans proposed this paper deal challenges detection. To detect remote instances, adopts patch embedding transformer (MViT) extract global features of image effectively. Furthermore, capture different discriminant for classification regression branch tasks, partial interactive fusion module (PIFM)...