A Survey on Encryption and Improved Virtualization Security Techniques for Cloud Infrastructure

Cloud Computing is one of the latest developments in the IT industry which offers on-demand services without requiring to create an IT infrastructure. It provides scalability, high performance and relatively low cost feasible solution for organizations. Despite of all its advantages, security is still a critical challenge in cloud computing paradigm. This paper presents a survey on some possible techniques used for encrypting user data and also providing techniques used in improving virtualization security for the cloud infrastructure

An Optimistic Approach for Clustering Multi-version XML Documents Using Compressed Delta

Today with Standardization of XML as an information exchange over web, huge amount of information is formatted in the XML document. XML documents are huge in size. The amount of information that has to be transmitted, processed, stored, and queried is often larger than that of other data formats. Also in real world applications XML documents are dynamic in nature. The versatile applicability of XML documents in different fields of information maintenance and management is increasing the demand to store different versions of XML documents with time. However, storage of all versions of an XML document may introduce the redundancy. Self describing nature of XML creates the problem of verbosity,in result documents are in huge size. This paper proposes optimistic approach to Re-cluster multi-version XML documents which change in time by reassessing distance between them by using knowledge from initial clustering solution and changes stored in compressed delta. Evolving size of XML document is reduced by applying homomorphic compression before clustering them which retains its original structure. Compressed delta stores the changes responsible for document versions, without decompressing them. Test results shows that our approach performs much better than using full pair-wise document comparison

Deep Learning for User Behaviour Prediction Using Streaming Analytics

Streams of web user interactions reflect behaviour of customers or users of a web application through which a company is being operated online. The interactions may be in the form of visits to web components and even purchases made by users in case of e-Commerce applications. Modelling user behaviour can help the organizations to ascertain patterns of user behaviours and improve their products and services to meet their needs besides making promotional schemes. There are many existing methods for modelling user behaviour. However, of late, deep learning models are found to be more accurate and useful. In this paper a deep learning based framework is proposed for predicting web user behaviour from streams of user interactions. The framework is based on the mechanisms that exploit Recurrent Neural Network (RNN), one of the deep learning approaches, to learn from low-level features of sequential and streaming data. The mechanisms are used to model user interactions and predict the user behaviour with respect to purchasing items in future. In presence of plenty of items, item embeddings is explored for better results. In addition to this, attention mechanisms are employed to achieve RNN model interoperability. The empirical study revealed that the proposed framework is useful besides helping to evaluate different variants of attention mechanisms and item embeddings

AUTOMATIC TEXT-INDEPENDENT SPEAKER TRACKING SYSTEM USING FEED-FORWARD NEURAL NETWORKS (FFNN)

ABSTRACT Speaker tracking is the process of following who says something in a given speech signal. In this paper, we propose a new set of robust source features for Automatic Text-Independent speaker tracking system using Feed-forward neural networks (FFNN). LP analysis is used to extract the source information from the speech signal. This source information is speaker specific. In this approach, instead of capturing the distribution of feature vectors correspond to vocal tract system of the speakers, the time varying speaker-specific source characteristics are captured using Linear Prediction (LP) residual signal of the given speech signal. MFCC features are extracted from the source speech signal, which contains prosody and speaker specific information. These source features which are extracted are proven to be robust and insensitive to channel characteristics and noise. In this paper, finally it is proved that speaker tracking system using source features with FFNN outperformed other existing methods. Keywords: LPC, MFCC, Source feature, Speaker tracking. INTRODUCTION Speech is produced from a time varying vocal tract system excited by a time varying excitation sourc

Essential oil composition of petiole of Cinnamomum verum Bercht. & Presl.

Essential oil isolated from the petiole of  Cinnamomum verum was analysed by gaschromatography and gas chromatography-mass spectrometry. Twenty five compoundsaccounting for 87.31% of the total essential oil were identified. (E)-Cinnamaldehyde (33.04%)followed by eugenol (17.32%), linalool (16.85%) and (E)-cinnamyl acetate (11.78%) were themain components of the essential oil. This is the first report on the composition of essentialoil of petiole of C. verum. &nbsp

Effect of short and long-term storage on essential oil content and composition of cinnamon (Cinnamomum verum Bercht. & Presl.) leaves

The effect of duration of storage of cinnamon (Cinnamomum verum) leaves on the content andchemical composition of essential oil was studied. The results revealed that neither the es-sential oil content (1.9%-2.2%), nor the chemical composition of essential oil (eugenol 87.1%-90.7%; eugenyl acetate 2.9%-5.5%; linalool 0.8%-1.2%; benzyl benzoate 0.3%-0.6%) wasaffected during the storage of leaves for up to 15 months. &nbsp

Synthesis of a mesoscale ordered 2D-conjugated polymer with semiconducting properties

2D materials with high charge carrier mobility and tunable electronic band gaps have attracted intense research effort for their potential use as active components in nanoelectronics. 2D-conjugated polymers (2DCP) constitute a promising sub-class due to the fact that the electronic band structure can be manipulated by varying the molecular building blocks, while at the same time preserving the key features of 2D materials such as Dirac cones and high charge mobility. The major challenge for their use in technological applications is to fabricate mesoscale ordered 2DCP networks since current synthetic routes yield only small domains with a high density of defects. Here we demonstrate the synthesis of a mesoscale ordered 2DCP with semiconducting properties and Dirac cone structures via Ullmann coupling on Au(111). This material has been obtained by combining rigid azatriangulene precursors and a hot dosing approach which favours molecular diffusion and reduces the formation of voids in the network. These results open opportunities for the synthesis of 2DCP Dirac cone materials and their integration into devices.Comment: 21 pages, 3 figure