Computer Science Engineering

variations in crystallinity are exhibited by unsupported MoSa catalysts when they are prepared with various amounts of Fe, Co or Ni. This effect has been investigate by scanning electron microscopy, transmission electron microscopy and electron probe microanalysis (EPMA). The systems appeared to be essentially biphasic with no observable contamination of the MO& phase by Group VIII metal atoms down to the detection limit of the EPMA.

We compare fully sulfided unsupported systems active in hydrodesulfurization and related reactions, namely Fe-MO, CO-MO, Ni-Mo, Co-W and Ni-W. The results concerning Fe-MO and Co-W are new; for the other systems, previously published data are used. The addition of a small amount of Group VIII metal (atomic proportion r = VIII/(VIB + VIII) = 0.01 -0.02) brings about a marked decrease in the specific surface area of Group VIB metal sulfides; an increase is observed for higher values. At r = 0.01 -0.02, MO!& and WSs have a better crystallinity.

Background Several tools are available to identify miRNAs from deep-sequencing data, however, only a few of them, like miRDeep, can identify novel miRNAs and are also available as a standalone application. Given the difference between plant and animal miRNAs, particularly in terms of distribution of hairpin length and the nature of complementarity with its duplex partner (or miRNA star), the underlying (statistical) features of miRDeep and other tools, using similar features, are likely to get affected. Results The potential effects on features, such as minimum free energy, stability of secondary structures, excision length, etc., were examined, and the parameters of those displaying sizable changes were estimated for plant specific miRNAs. We found most of these features acquired a new set of values or distributions for plant specific miRNAs. While the length of conserved positions (nucleus) in mature miRNAs were relatively longer in plants, the difference in distribution of minimum free energy, between real and background hairpins, was marginal. However, the choice of source (species) of background sequences was found to affect both the minimum free energy and miRNA hairpin stability. The new parameters were tested on an Illumina dataset from maize seedlings, and the results were compared with those obtained using default parameters. The newly parameterized model was found to have much improved specificity and sensitivity over its default counterpart. Conclusions In summary, the present study reports behavior of few general and tool-specific statistical features for improving the prediction accuracy of plant miRNAs from deep-sequencing data.

The process of jumbling up the contents of a secret message so as to secure it, is known as cryptography, whereas the process of hiding the very existence of a secret message is known as steganography. The term "steganography" describes the method by which the contents of a secret message are concealed inside some other medium, so as to avoid any kind of detection by an intruder. In this paper, we have proposed two new approaches wherein both cryptography and steganography are used to encrypt the data and also to conceal these encrypted contents in some other medium. In the first method proposed, we have secured an image by converting it into an encrypted text using S-DES algorithm and a secret key and then concealing this encrypted text in some other image, whereas in the second method, we have secured an image directly by encrypting it using S-DES algorithm and an image key. The contents thus obtained are concealed inside another image so as to hide its very existence. Both these techniques have been tested and it has been observed that they prevent the possibilities of steganalysis also.

Language is a trademark for exchange of information and interface for communication between two entities namely sender and receiver. We need some processing technique to make machines interpret the meaning of input sentences. The formal process of making intelligent machine interpret the meaning and generate their answer is called Natural Language Processing. It is important to pay attention to the processing of the sentences called parsing. Parsing in natural language is defined as to analyze the input sentence in terms of grammatical constituents, identifying the parts of speech, syntactic relations and generate the possible parse trees to interpret its meaning. Automaton (plural automata) means a machine which performs the range of functions according to a predetermined set of coded instructions. This paper presents a technique of parsing sentences in English language with the application of Modular Finite State Automata.

The growth of social media over the years has given a platform to internet users to share their experiences and views about products, services and processes. This increasing use of web applications and social networks has resulted into tremendous amount of data rich in volume and variety. Social media frameworks like Facebook, Instagram have become one of the main platform for communication and information sharing. Twitter is a social network that allows public to express their sentiments in real time. Techniques to manage and extract value from this huge amount data are collectively called big data. Sentiment analysis uses this data to predict trends and attitudes of people about products and services. This study is focused on sentiment analysis over Twitter. The proposed framework is implemented with Machine learning over Hadoop and evaluations have shown better accuracy, precision and recall as compared to existing techniques.

The recent developments in the field of Human-Computer Interaction have emphasized the focus of design more towards user-centered rather than computercentered approach. This led to design of interfaces which are highly effective, intelligent and adaptive, and can adjust themselves with respect to the user's behavioral changes. In this study, interaction challenges for disabled people are listed and the possible alternatives via intelligent and affective devices are proposed for such users.

The recent developments in the field of Human-Computer Interaction have emphasized the focus of design more towards user-centered rather than computer-centered approach. This led to design of interfaces which are highly effective, intelligent and adaptive, and can adjust themselves with respect to the user's behavioral changes. Such designs are called intelligent and affective interfaces. The proposed framework is designed for affective computing machines, which can sense and adapt according to the moods and emotions of the user, using Fuzzy and Image processing techniques.

Parsing is the process of analyzing the sentence for its structure, content and meaning, i.e. to uncover the structure, articulate the constituents and the relation between the constituents of the input sentence. This paper briefly describes the parsing techniques in natural language processing. Parsing is the prime task in processing of natural language, as it forms the basis for all the natural language applications, like machine translation, question answering and information retrieval. We have discussed the top-down, bottomup and the basic top-down parsing along with their issues and a brief review of the statistical and dependency parsing.