Abstract

Over the last decade, the volume of unannotated user-generated web content has skyrocketed but manually annotating data is costly in terms of time and resources. We leverage the advancements in Machine Learning to reduce these costs. We create a semantically searchable dance database with automatic annotation and retrieval. We use a pose estimation module to retrieve body pose and generate Labanotation over recorded videos. Though generic, it provides an essential application due to large amount of videos available online. Labanotation can be further exploited to generate ontology and is also very relevant for preservation and digitization of such resources. We also propose a semi-automatic annotation model which generates semantic annotations over any video archive using only 2-4 manually annotated clips. We experiment on two publicly available ballet datasets. High-level concepts such as ballet pose and steps are used to make the semantic library. These also act as descriptive meta-tags making the videos retrievable using a semantic text or video query.

Abstract

Edge computing has emerged as an effective solution for delay sensitive IoT applications. In the edge-cloud hierarchy, reliability and fault tolerance are important issues. This paper proposes a novel agent-based reliable and fault-tolerant hierarchical IoT-cloud architecture which can survive the failures of the edge servers. In the proposed architecture, the cloud is distributed over four levels (cloud-fog-mist-dew) based on the processing power and distance from the end IoT device. It makes the whole system reliable by replicating the data on the edge of the network. The proposed system is fault tolerant as it redirects the application on an alternate server. In case of a server’s failure, redirection is done at the best possible level in the hierarchy, based on delay-tolerance of the IoT application. The application keeps on working even if the system fails on any level, on cloud, fog or mist. The solution is proposed using mobile agents (MAs) on servers to share systems’ state and other important information with other agents in the hierarchy, to be used for application redirection in case of server’s failure. The proposed system is simulated using Matlab and results prove its efficiency. Index Terms— Edge Computing, Real-time IoT, Reliability, Fault Tolerance, Fault Localization, Cloud, Mobile Agent.

Abstract

A river water quality management model under average climatic conditions may not be able to account for the extreme risk of low water quality which is more prominent under an increase in river water temperature and altered river flows. A modeling framework is developed to assess the risk of river low water quality extremes by integrating a statistical downscaling model based on Canonical Correlation Analysis, risk quantification model based on Frank Archimedean Copula function and multiple logistic regression model integrated with a river water quality simulation model, QUAL2 K. The results reveal that the combination of predicted decrease in low flows of approximately 57% and increase in maximum river water temperatures of approximately 1.2
C has shown an increase of about 46% in risk of low water quality conditions for the future scenarios along Tunga-Bhadra River, India. The extreme risk of low water quality is observed to increase by 50.6% for the period 2020– 2040 when compared with the current extreme conditions of 4.5% and average risk conditions of about 3% for the period 1988–2005. The study captured the occurrence of extremes of low water quality with evidence of a strong link between climate and water quality impairment events.

Abstract

The popularity and applicability of mobile crowdsensing applications are continuously increasing due to the widespread of mobile devices and their sensing and processing capabilities. However, we need to offer appropriate incentives to the mobile users who contribute their resources and preserve their privacy. Blockchain technologies enable semi-anonymous multi-party interactions and can be utilized in crowdsensing applications to maintain the privacy of the mobile users while ensuring first-rate crowdsensed data. In this work, we propose to use blockchain technologies and smart contracts to orchestrate the interactions between mobile crowdsensing providers and mobile users for the case of spatial crowdsensing, where mobile users need to be at specific locations to perform the tasks. Smart contracts, by operating as processes that are executed on the blockchain, are used to preserve users’ privacy and make payments. Furthermore, for the assignment of the crowdsensing tasks to the mobile users, we design a truthful, cost-optimal auction that minimizes the payments from the crowdsensing providers to the mobile users. Extensive experimental results show that the proposed privacy preserving auction outperforms state-of-the-art proposals regarding cost by ten times for high numbers of mobile users and tasks.

Abstract

Nations have set-up Critical Information Infrastruc-ture (CII) protection centers to provide safety of organizations which are deemed to be critical for the nation. In this context,this paper proposes a framework to identify Critical Information Infrastructures (CIIs). This framework has been adopted to identify the banks which are critical for Indian economy. Previously,Reserve Bank of India, the central bank of the country, has announced three banks as systemically important banks. The proposed approach, in this paper, considers a publicly available data set publishing the number (volume) and the amount (value)of financial transactions in the Indian domestic market involving all the banks. The proposed approach finds the individual ranks of each participating bank based on these two parameters.Finally, rank aggregation method combines these two rank lists(based on the value and the volume) to generate a final rank of the banks. Several rank aggregation methods have been compared in this context.

Abstract

Internationally, the cooling degree-days (CDD) and heating degree-days (HDD) have been commonly used as indicators of cooling and heating load occurring due to outdoor temperature. These indicators have also applied for classification of climate zones as referred in ASHRAE Standard 169-2013. As of now various base temperatures has been used in the different part of the world. Additionally, the analysis for base temperature had been done in 1980 by ASHRAE. This study aims to determine the base temperature for cities in India. The simulation approach has been used to identify base temperature using Energy Signature and Performance line method for 60 cities of India. The US-DOE developed 16 reference buildings which have been used for eight climate zones of India. These eight climate zones have been classified using the methodology of ASHRAE Standard 169–2013 on weather data of Indian cities. It was observed that the cooling base temperature is varying from 6.8 °C to 28.6 °C for the large hotel and 8.7–28.1 °C for office buildings while the heating base temperature is ranging from 13.8 °C to 21.4 °C for the large hotel and 10.4–18.7 °C respectively for different cities. The estimated base temperature for cooling and heating is 18 °C for India.

Abstract

This contribution presents an efficient techniquefor the speech enhancement of a signal using Kepstrum basedapproach. In any speech enhancement system, the noise is beingsuppressed in spectral domain assuming the degraded componentare stationary or slowly varying for few frames of the signal.As conventional methods for noise elimination fails due to non-stationary properties of the speech signal, in this context, adistortion filter and speech remover filter are being introduced.The main focus of this paper is involved in developing a newspeech enhancement system based on speech characteristics andit’s modelling provide high quality and intelligibility on mobilesor embedded platforms that have two or more microphones.The proposed method was evaluated for machinery, babble, andcar noise types, using both objective and subjective measuresfor both quality and intelligibility of the enhanced speech.The results show significant improvements in comparison with single microphone speech enhancement, kepstrum and Distortion Correlation Filter methods

Abstract

In this work, we explore the combination of MFCCand RCC features for Indian Language identification(LID) task.MFCC represents the vocal tract information while RCCrepresents the excitation source information, which arecomplementary to each other. We use Deep neural network(DNN) framework for end to end learning. To combine these twoinformation we uses different fusion techniques such as Min,Max, Average and Min-Max for decision level fusion. We performexperiments on 13 Indian languages data set. We compare ourresults with state of art I-vector based approach. Min-max basedfusion approach gives 9.64% ERR compare to 10.18% of 39D I-vector based approach.

Abstract

Formants (peaks in the spectrum) are typically found in vowel regions.However, resonances of vocal tract are present in both voiced and unvoiced regions.It is important to focus on extracting vocal tract resonances from speech signal toprovide compact and effective representation. This work applies multilayered neuralnetworks for the estimation of vocal tract resonances from linear prediction cepstralcoefficients. To train such a neural network, the voiced regions of speech signal are firstprobed forreliable spectral prominent regions. A sequence of spectral peaks detectedby imposing continuity constraints in voiced regions is referred to as reliable spectralprominent regions. A multilayer feedforward neural network is then trained to model amapping function from linear prediction cepstral coefficients to vocal tract resonancesestimated in these reliable spectral prominent regions. This modeled function is usedto estimate vocal tract resonances in all speech regions including unvoiced sections

Abstract

Intensity and duration are both pertinent aspects of an emotional experience, yet how they are related is unclear. Though stronger emotions usually last longer, sometimes they abate faster than the weaker ones. We present a quantitative model of affective adaptation, the process by which emotional responses to unchanging affective stimuli weaken with time, that addresses this intensity-duration problem. The model, described by three simple linear algebraic equations, assumes that the relationship between an affective stimulus and its experiencer can be broken down into three parameters. Self-relevance and explanation level combine multiplicatively to determine emotion intensity whereas the interaction of these with explanatory ease determines its duration. The model makes predictions, consistent with available empirical data, about emotion intensity, its duration, and adaptation speed for different scenarios. It predicts when the intensity-duration correlation is positive, negative or even absent, thus offering a solution to the intensity-duration problem. The model also addresses the shortcomings of past models of affective adaptation with its enhanced predictive power and by offering a more complete explanation to empirical observations that earlier models explain inadequately or fail to explain altogether. The model has potential applications in areas such as virtual reality training, games, human-computer interactions, and robotics.