Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Paving the way for autonomic clouds: State-of-the-art and future directions(Springer Verlag service@springer.de, 2018) Joseph, C.; Chandrasekaran, K.Cloud Computing is the core technology that helps in catering to the computing needs of the current generation. As the customers increase, data center providers are looking for efficient mechanisms to handle the management of the large reservoir of resources involved in the Cloud environment. In order to support efficient managing, it is the need of the day to adopt the concept of Autonomic Computing into Cloud. Several researchers have been attempted to improve the managing capability of the Cloud, by encorporating autonomic capabilities for resources in the Cloud. Most of the researches attempt to automate some aspects while the remaining portion of the Cloud does not have autonomic functionalities. An autonomic Cloud is one where all the operations can be handled without human intervention. There is a long way to go to achieve this vision. In our study, we first categorize the various existing approaches on the basis of the methodology employed and analyze the different self-*properties considered by the different approaches. It is observed that in each approach, researchers focus on one or at most two self-*properties. Based on our analysis, we suggest some of the future directions that can be paved on by researchers working in this domain. © 2018, Springer Nature Singapore Pte Ltd.Item Sentiment extraction from naturalistic video(Elsevier B.V., 2018) Radhakrishnan, V.; Joseph, C.; Chandrasekaran, K.Sentiment analysis on video is quite an unexplored field of research wherein the emotion and sentiment of the speaker are extracted by processing the frames, audio and text obtained from the video. In recent times, sentiment analysis from naturalistic audio has been an upcoming field of research. This is typically done by performing automatic speech recognition on audio, followed by extracting the sentiment exhibited by the speaker. On the other hand, techniques for extracting sentiments from text are quite developed and tech giants have already optimized these methods to process large amounts of customer review, feedback and reactions. In this paper, a new model for sentiment analysis from audio is proposed which is a hybrid of Keyword Spotting System (KWS) and Maximum Entropy (ME) Classifier System. This model is developed with the aim to outperform other conventional classifiers and to provide a single integrated system for audio and text processing. In addition, a web application for dynamic processing of YouTube videos is described. The WebApp provides an index-based result for each phrase that is detected in the video. Often, the emotion of the viewer of a video corresponds to its content. In this regard, it is useful to map these emotions to the text transcript of the video and assign a suitable weight to it while predicting the sentiment that the speaker exhibits. This paper describes such an application that was developed to analyze facial expressions using Affdex API. Thus, using the combined statistics from all the three aforementioned components, a robust and portable system for emotion detection is obtained that provides accurate predictions and can be deployed on any modern systems with minimal configuration changes. © 2018 The Authors. Published by Elsevier B.V.