Abstract

Preventing traffic congestion by forecasting near time traffic flows is an important problem as it leads to effective use of transport resources. Social network provides information about activities of humans and social events. Thus, with the help of social network, we can extract which humans will attend a particular event (in near time) and can estimate flow of traffic based on it. This opens up a wide area of research which poses need to have a framework for traffic management that can capture essential parameters of real-life behaviour and provide a way to iterate upon and evaluate new ideas. In this paper, we present building blocks of a framework and a system to simulate a city with its transport system, humans and their social network. We emphasize on relevant parameters selected and modular design of the framework. Our framework defines metrics to evaluate congestion avoidance strategies. To show utility of the framework, we present experimental studies of few strategies on a public transport system.

Abstract

A queue is required when a service provider is not able to handle jobs arriving over the time. In a highly flexible and dynamic environment, some jobs might demand for faster execution at run-time especially when the resources are limited and the jobs are competing for acquiring resources. A user might demand for speed up (reduced wait time) for some of the jobs present in the queue at run time. In such cases, it is required to accelerate (directly sending the job to the server) urgent jobs (requesting for speed up) ahead of other jobs present in the queue for an earlier completion of urgent jobs. Under the assumption of no additional resources, such acceleration of jobs would result in slowing down of other jobs present in the queue. In this paper, we formulate the problem of Speed Up Scheduling without acquiring any additional resources for the scheduling of on-line speed up requests posed by a user at run-time and present algorithms for the same. We apply the idea of Speed Up Scheduling to two different domains-Web Scheduling and CPU Scheduling. We demonstrate our results with a simulation based model using trace driven workload and synthetic datasets to show the usefulness of Speed Up scheduling. Speed Up provides a new way of addressing urgent jobs, provides a different evaluation criteria for comparing scheduling algorithms and has practical applications.

Abstract

Preventing traffic congestion by forecasting near time traffic flows is an important problem as it leads to effective use of transport resources. Social network provides information about activities of humans and social events. Thus, with the help of social network, we can extract which humans will attend a particular event (in near time) and can estimate flow of traffic based on it. This opens research area to build a framework for traffic management that can capture essential parameters of real-life behavior and provide a way to iterate upon and evaluate new ideas. In this paper, we present building blocks of such framework and a system to simulate a city with its transport system, humans and their social network. We emphasize on relevant parameters selected and modular design of the framework. To show the utility of the framework, we present experimental studies of few strategies on a public transport system.

Abstract

Crowd management is a complex, challenging and crucial task. Lack of appropriate management of crowd has, in past, led to many unfortunate stampedes with significant loss of life. To increase the crowd management efficiency, we deploy automated real time detection of stampede prone areas. Further, we use robotic agents for aiding the crowd management police in controlling the crowd in these stampede prone areas. Lastly, we simulate a multi agent system based on our model and use it to illustrate the utility and viability of robotic agents for detecting and reducing congestion.

Abstract

We present an automated disease term classification model using machine learning techniques that classifies a medical term to a specific disease class. We work on five particular diseases: Cancer, AIDS, Arthritis, Diabetes and heart related ailments. We identify and classify medical terms like drug names, symptoms, abbreviations, disease names, tests, etc., into their specific diseases classes. The results illustrate that our model for disease term classification finds all disease term classes with an average F-score of 0.966.

Abstract

Sonorant segmentation of speech signals is critical in developing Automatic Speech Recognition (ASR) systems, audio search systems and for automatic segmentation of speech corpora. In this work, acoustic features based on excitation source and vocal tract system characteristics of sonorant sounds are proposed for segmentation of sonorant regions in continuous speech. The features are based on zero frequency resonator signal energy, strength of excitation and dominant resonance frequency around epochs. An algorithm is developed to relate these features in hierarchical manner using knowledge-based approach. The performance of the proposed algorithm is studied on three different datasets, at varying levels of degradation. TIMIT database is used to test the validity and AMI meeting corpus and Telugu (an Indian language) dataset are considered to test the utility of the proposed features.

Abstract

In this paper, we investigate two different recurrent neural network (RNN) architectures: Elman RNN and recently proposed clockwork RNN [1] for statistical parametric speech synthesis (SPSS). Of late, deep neural networks are being used for SPSS which involve predicting every frame independent of the previous predictions, and hence requires post-processing for ensuring smooth evolution of speech parameters. RNNs, on the other hand, are intuitively better suited for the task as they inherently model temporal dependencies, but were restricted in use because of the difficulty in training. Lately, techniques such as sparse initialization, Nesterov’s accelerated gradient, gradient clipping and leaky integration (LI) have been shown to overcome this difficulty. We study the utility of these techniques for SPSS task. In addition, we show that clockwork RNN is equivalent to an Elman RNN with a particular form of LI. This perspective enables us to understand the reason why a simple Elman RNN with LI units performs well on sequential tasks.

Abstract

During production of emotional speech there are deviations in the components of speech production mechanism when compared to normal speech. The objective of this study is to capture the deviations in features related to the excitation source component of speech, and to develop a system for automatic recognition of emotions based on these deviations. The emotions considered for this study are: anger, happy, neutral and sad. The study shows that there are useful features in the deviations of the excitation source features at subsegmental level, and they can be exploited to develop an emotion recognition system. A hierarchical binary decision tree approach is used for classification.

Abstract

Plug loads accounts for 20% to 30% of building energy consumption and has an increasing trend. Automatic plug load identification is one of the technique for effectively managing the plug load consumption.There are several studies on Non Intrusive load monitoring (NILM) but limited studies on Intrusive load monitoring (ILM). ILM is a technique that uses a low end power meter on every plug load to monitor it’s power consumption. In this paper, machine-l earning techniques are applied on low-frequency ILM data for identification of plug load and it’s state (ON/Sleep).The results show the identification accuracies are close to 98%

Abstract

Measuring “similarity” has been established as a fundamental problem and has been widely studied. In this paper we propose a novel approach for establishing similarity in context of citation network. With the rapidly growing size of academic literature, the problem of finding similar research papers has become a challenging task. Research papers in a citation network often form communities based on an under lying concept. Our research shows that dispersion based similarity measure can be used as a strong measure for finding similar papers based on similar connectivity in those communities and structural relevance of the citation network. Our results show that our approach works better than other conventional link-based similarity measures both quantitatively and qualitatively.One of the direct benefits of this research is to support the highly specialised information needs of a scholarly researcher working in a specialised field of research.