Abstract Data hallucination generates additional training examples for novel classes to alleviate the data scarcity problem in few‐shot learning (FSL). Existing hallucination‐based FSL methods normally train a general embedding model first by applying information extracted from base classes that have abundant data. In those methods, hallucinators are then built upon the trained embedding model to generate data for novel classes. However, these hallucination methods usually rely on general‐purpose embeddings, limiting their ability to generate task‐oriented samples for novel classes. Recent studies have shown that task‐specific embedding models, which are adapted to novel tasks, can achieve better classification performance. To improve the performance of example hallucination for tasks, a task‐oriented embedding model is used in the proposed method to perform task‐oriented generation. After the initialization, the hallucinator is finetuned by applying a task‐oriented embedding model with the guidance of a teacher–student mechanism. The proposed task‐oriented hallucination method contains two steps. An initial embedding network and an initial hallucinator are trained with a base dataset in the first step. The second step contains a pseudo‐labelling process where the base dataset is pseudo‐labelled using support data of the few‐shot task and a task‐oriented fine‐tuning process where the embedding network and hallucinator are adjusted simultaneously. Both the embedding network and the hallucinator are updated with the support set and the pseudo‐labelled base dataset using knowledge distillation. The experiments are conducted on four popular few‐shot datasets. The results demonstrate that the proposed approach outperforms state‐of‐the‐art methods with 0.8% to 4.08% increases in classification accuracy for 5‐way 5‐shot tasks. It also achieves comparable accuracy to state‐of‐the‐art methods for 5‐way 1‐shot tasks.

SAGE (Percipient StorAGe for Exascale Data Centric Computing) is a European Commission funded project towards the era of Exascale computing. Its goal is to design and implement a Big Data/Extreme Computing (BDEC) capable infrastructure with associated software stack. The SAGE system follows a storage centric approach as it is capable of storing and processing large data volumes at the Exascale regime.

Variety in Big Data means we have a wide range of data types and sources: e.g. File systems and database systems co-exist for decades as two popular data-accessing interfaces. This work is to unify these two interfaces by presenting a Data Interface All-iN-A-place (DIANA). The first challenge lies in distinguishing structured and un-structured data and diverting them to different underlying platforms. It is demonstrated that a speedup of 5000 in indexing has been achieved at the expense of a slowdown of 100 in extracting attributes. A DIANA-based cloud storage system is constructed for versatile, long distance and large volume big data accessing operations to address "Volume" and "Velocity" in Big Data. It encapsulates a dynamic multi-stream/multi-path engine at the socket level, which conforms to Portable Operating System Interface (POSIX).

A grid-powered Web Geographical Information Science (GIS)/Web Processing Service (WPS) system has been developed for archiving and distributing large volumes of geospatial data. However, users, WPS servers, and data resources are always distributed across different locations, attempting to access and archive geospatial data from a GIS survey via conventional Hypertext Transport Protocol, Network File System Protocol, and File Transfer Protocol, which often encounters long waits and frustration in wide area network (WAN) environments. To provide a “local-like” performance, a WAN/grid-optimized protocol known as “GridJet” developed at our lab was used as the underlying engine between WPS servers and clients, which utilizes a wide range of technologies including the one of paralleling the remote file access. No change in the way of using software is required since the multistreamed GridJet protocol remains fully compatible with the existing IP infrastructures. Our recent progress includes a real-world test that PyWPS and Google Earth over the GridJet protocol beat those over the classic ones by a factor of two to eight, where the distribution/archiving distance is over 10 000 km.

In the above-named work the name of one of the authors is incorrectly given. Included here also is the biography with the missing IEEE membership information.

This paper describes the grid-oriented storage (GOS) architecture and its implementations. A GOS-specific file system (GOS-FS), the single-purpose intent of a GOS OS, and secure interfaces via grid security infrastructure (GSI) motivate and enable this new architecture. As an FTP server, GOS with a slimmed OS, with a total volume of around 150 MB, outperforms the standard GridFTP by 20-40 percent. As a file server, GOS-FS acts as a network/grid interface, enabling a user to perform searches and access resources without downloading them locally. In the real-world tests between Cambridge and Beijing, where the transfer distance is 10,000 km, the multistreamed GOS-FS file opening/saving resulted in a remarkable performance increase of about 2-25 times, compared to the single-streamed network file system (NFSv4). GOS is expected to be a variant of or successor to the well-used network-attached storage (NAS) and/or storage area network (SAN) products in the grid era

How to distribute the items in the file system hierarchy across a group of metadata servers is an important issue that determines the holistic metadata processing performance (HMPP) of a cluster file system which manages its metadata by a group of metadata servers. The HMPP is affected by two factors: balance degree of metadata distribution and number of branch points. Two types of well-used metadata distribution policies are the dynamic subtree policy and the random policy. Both of them emphasize one factor and neglect the other factor. As a result, their HMPP is low. In order to make good use of processing capacity of all metadata servers, we present a novel metadata distribution policy, called dynamic dir-grain (DDG) policy, which takes both factors into account. Our performance results show that this policy is potentially more efficient than the other two types of policies under real environments, as well as the conditions of creation or removal of a large hierarchy

Extendible hashing is a kind of fast indexing technology; it provides with a way of storing structural data records so that each of them can be gotten very quickly. In this paper, we present our own specially optimized implementation of using extendible hashing in cluster file system is order to support large directory.

Cluster file system is a key component of system software of clusters. It attracts more and more attention in recent years. In this paper, we introduce the design and implementation of DCFS (the Dawning Cluster File System) - a cluster file system developed for Dawning4000-L. DCFS is a global file system sharing among all cluster nodes. Applications see a single uniform name space, and can use system calls to access DCFS files. The features of DCFS include its scalable architecture, metadata policy, server-side optimization, flexible communication mechanism and easy management. Performance tests of DCFS on Dawning4000-L show that DCFS can provide high aggregate bandwidth and throughput.