Generally, Medical Imaging Modalities (MIM) have a distinctive nature of low contrast, complex background, and low resolution, containing burned-in textual data of patients. The conventional OCRs hardly recognise these burned-in textual data under these conditions, as they are designed for mainly bi-level text with a minimum resolution of 300 dpi. With a focus on solving these challenges, an enhanced CNN model for medical image character recognition (MICR) is proposed in this paper. The Lenet-5 architecture inspires this proposed Model. To further enhance this new technique to recognise visually similar characters, this paper proposes an ensemble classifier of CNN base learners. Intensive experiments are done using an open-source medical imaging dataset. The problem of low resolution at 96dpi and background interference is targeted by using small 3 X 3 CNN filters to extract local features and changing the pooling layer to a learning layer by replacing it with 5 X 5 filters with a stride of 2 and training on a low-resolution character dataset. The final prediction is based on a majority voting algorithm. The consensus of the base learners improves the model’s stability in recognising visually similar characters. Finally, our proposed models and the Lenet-5 are compared using the Medpix medical image collection. Further investigation shows that our proposed model shows a 10% increase in accuracy compared with the base model and other past algorithms in recognising burned-in textual data on medical imaging modalities.

Ensemble clustering is a technique which combines multiple clustering results, and instance weighting is a technique which highlights important instances in a dataset.Both techniques are known to enhance clustering performance and robustness.In this research, ensembles and instance weighting are integrated with the spectral clustering algorithm.We believe this is the first attempt at creating diversity in the generative mechanism using density based instance weighting for a spectral ensemble.The proposed approach is empirically validated using synthetic datasets comparing against spectral and a spectral ensemble with random instance weighting.Results show that using the instance weighted sub-sampling approach as the generative mechanism for an ensemble of spectral clustering leads to improved clustering performance on datasets with imbalanced clusters.

DBSCAN is arguably the most popular density-based clustering algorithm, and it is capable of recovering non-spherical clusters. One of its main weaknesses is that it treats all features equally. In this paper, we propose a density-based clustering algorithm capable of calculating feature weights representing the degree of relevance of each feature, which takes the density structure of the data into account. First, we improve DBSCAN and introduce a new algorithm called DBSCANR. DBSCANR reduces the number of parameters of DBSCAN to one. Then, a new step is introduced to the clustering process of DBSCANR to iteratively update feature weights based on the current partition of data. The feature weights produced by the weighted version of the new clustering algorithm, W-DBSCANR, measure the relevance of variables in a clustering and can be used in feature selection in data mining applications where large and complex real-world data are often involved. Experimental results on both artificial and real-world data have shown that the new algorithms outperformed various DBSCAN type algorithms in recovering clusters in data.

Sentiment Analysis is a popular classification problem where the degree of emotion is analysed based on text. Convolution Neural Network (CNN) are part of various sentiment classification models, but their use has been limited to one-dimensional (1D) convolution or shallow two-dimensional (2D) convolution. The primary focus of this research is to use deep 2D-CNN architectures, inspired by the popular computer vision model ResNet, to replace a shallow 2D-CNN in an existing BLSTM (Bidirectional Long Short Term Memory)-2DCNN model. This research investigates a new method for sentiment analysis which is an amalgam of the practices popular in Natural Language Processing (NLP) as well as Computer Vision, striving to extract opinions from a text by transforming text into images. The text images are formed by transforming the word embeddings matrix into a greyscale image with the help of BLSTM cell. Intensive experiments have been conducted on the Sentiment140 dataset with our novel BLSTM-ResNet model, which contains residual blocks with skip connections. Several BLSTM-ResNet variants were tested to investigate the impact of network depth and dataset size on sentiment detection. Moreover, two sets of residual blocks are designed to form our shallow and deep BLSTM-ResNet models. Our best shallow BLSTM-ResNet models have achieved 4.06% and 3.43% increases in accuracy for dataset sizes 80,000 and 200,000 respectively, compared with the baseline BLSTM-2DCNN model. In addition, an overall improvement is observed on accuracy with every additional residual block in our shallow BLSTM-ResNet model until accuracy saturates, and the same trend has been seen on the impact of dataset size on the performance. Our deep BLSTM-ResNet models show the same positive impact of network depth impact and dataset size on sentiment analysis. Further investigation on the shallow and deep BLSTM-ResNet models shows that deep BLSTM-ResNet outperforms shallow BLSTM-ResNet, in general.

Adoption of Cloud Computing is on the rise[1] and many datacenter operators adhere to strict energy efficiency guidelines[2]. In this paper a novel approach to scheduling in a Cloud Computing context is proposed. The algorithm Maxmin Fast Track (MXFT) revises the Max-min algorithm to better support smaller tasks with stricter Service Level Agreements (SLAs), which makes it more relevant to Cloud Computing. MXFT is inspired by queuing in supermarkets, where there is a fast lane for customers with a smaller number of items. The algorithm outperforms Max-min in task execution times and outperforms Min-min in overall makespan. A by-product of investigating this algorithm was the development of simulator called “ScheduleSim”[3] which makes it simpler to prove a scheduling algorithm before committing to a specific scheduling problem in Cloud Computing and therefore might be a useful precursor to experiments using the established simulator CloudSim[4].

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