Information Technology

Enterprises are migrating towards cloud computing for their ability to provide agility, robustness and feasibility in operations. To increase the reliability and availability of services, clouds have grown into federated clouds i.e., union of clouds. There are still major issues in federated clouds, which when solved could lead to increased satisfaction to both service providers and clients alike. One such issue is to select the optimal foreign cloud amongst the federation, which provides services according to the client requirements. In this paper, we propose a model to select the optimal cloud service provider based on the capability and performance of the available clouds in the federation. We use two matrix models to obtain the capability and performance parametric values. They are matched with the client requirements and the optimal foreign cloud service provider is selected.

Node localization is very important in Wireless Sensor Network (WSN) and distance estimation between pairs of nodes is the prerequisite for localization and thus the applicability of the reported events. The paper proposes a novel distance estimation algorithm to estimate distances of each node to every other node in the network. The main contribution of the paper is the definition of a dissimilarity matrix representing the distance of each node to every other node in the network. MDS based localization algorithm is used to determine coordinates of the node in a local coordinate system and Helmert Transformation is used to convert the local coordinates of the node into a global coordinate system. The effect of various parameters affecting the performance of proposed algorithm is also presented in the paper. Finally, the efficiency of the proposed algorithm is established through the simulation results.

When designing and using distributed storage systems with cloud technology, the security issues become crucial. One of the promising mechanisms is the computationally secure threshold secret sharing scheme. We propose a computationally secure secret sharing scheme based on the minimally redundant modular code. It reduces the computational complexity of data encoding and decoding and reduce data redundancy. We show that it is computationally secure and provides data redundancy equivalent to the redundancy of the Rabin system. We demonstrate that the minimally redundant modular code does not satisfy the criterion of compactness of a sequence, but it can be used as an asymptotically ideal secret sharing scheme.

The present study has been conducted with a specific aim of assessing depression in elite athletes as compared to non-sporting population. To conduct the study, 50 elite sportspersons were selected as sample. The criteria for selection of elite sportspersons was participation in national level sports tournament with achievement of finishing in first three position of that event. To fulfill the objectives of the present study, 50 non-sportsperson students from colleges and Universities operational in Chhattisgarh were also selected. The age range of selected subjects from both the group were within 18 to 28 years. Depressive symptoms in selected subjects were assessed by Jodhpur Multiphasic Personality Inventory prepared by Joshi and Malik (1981). It was found that non-sportspersons showed significantly more magnitude of low stress tolerance, rigid conscience development and more magnitude of proneness to guilty as compared elite sportspersons. It was concluded that elite sportsperso...

The function of the governor is to maintain the speed of an engine within specified limits whenever there is a variation of load.. This device can be used in almost all vehicles. The objective our investigation to identify the stress concentration areas, areas which are most susceptible to failure when governor is rotating about its axis, also the value of these stresses is measured. This analysis is carried out with the help of PRO E. The displacement of the various elements of the SPINDLE from the base is also calculated and the graphs are plotted. Effect of the "WEIGHT OF THE ARMS" is the major area of concern for our study and all the calculations are done considering the weight of the arms. Weight of the arms acts on the centroid of the arms and when the governor assembly rotates, centrifugal force starts acting on the centroid of the arms and tends to deflect the arms, this deflection or bending is to be minimized. In our work, we have done the Stress analysis on a p...

This paper presents a reinforcement learning (RL)-based energy management strategy for a hybrid electric tracked vehicle. A control-oriented model of the powertrain and vehicle dynamics is first established. According to the sample information of the experimental driving schedule, statistical characteristics at various velocities are determined by extracting the transition probability matrix of the power request. Two RL-based algorithms, namely Q-learning and Dyna algorithms, are applied to generate optimal control solutions. The two algorithms are simulated on the same driving schedule, and the simulation results are compared to clarify the merits and demerits of these algorithms. Although the Q-learning algorithm is faster (3 h) than the Dyna algorithm (7 h), its fuel consumption is 1.7% higher than that of the Dyna algorithm. Furthermore, the Dyna algorithm registers approximately the same fuel consumption as the dynamic programming-based global optimal solution. The computational cost of the Dyna algorithm is substantially lower than that of the stochastic dynamic programming.

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hybrid technique for extraction of bigram and trigram multiword expressions is presented. This technique works in two phases as first statistical technique is applied to filter the extracted bigrams and trigrams from English text, and after it multiword expressions are extracted from this list using some linguistic rules. Two methods for threshold decision in statistical technique are also presented, first is by minimizing the error in classification, and the second is based on maximizing the recall value.

This paper describes an efficient algorithm for language independent generic extractive summarization for single document. The algorithm is based on structural and statistical (rather than semantic) factors. Through evaluations performed on a single-document summarization for English, Hindi, Gujarati and Urdu documents, we show that the method performs equally well regardless of the language. The algorithm has been applied on DUC data for English documents and various newspaper articles for other languages with corresponding stop words list and modified stemmer. The results of summarization have been compared with DUC 2002 data using degree of representativeness. For other languages, the degree of representativeness we get is highly encouraging.

Multimodal assistants leverage vision as an additional input signal along with other modalities. However, the identification of user intent becomes a challenging task as the visual input might influence the outcome. Current digital assistants rely on spoken input and try to determine the user intent from conversational or device context. However, a dataset which includes visual input (i.e., images or videos) corresponding to questions targeted for multimodal assistant use cases is not readily available. While work in visual question answering (VQA) and visual question generation (VQG) is an important step forward, this research does not capture questions that a visually-abled person would ask multimodal assistants. Moreover, several questions do not seek information from external knowledge (Jain et al., 2017; Mostafazadeh et al., 2016). Recently, the OK-VQA dataset (Marino et al., 2019) tries to address this shortcoming by including questions that need to reason over unstructured kn...

Named Entity Recognition (NER) and Entity Linking (EL) play an essential role in voice assistant interaction, but are challenging due to the special difficulties associated with spoken user queries. In this paper, we propose a novel architecture that jointly solves the NER and EL tasks by combining them in a joint reranking module. We show that our proposed framework improves NER accuracy by up to 3.13% and EL accuracy by up to 3.6% in F1 score. The features used also lead to better accuracies in other natural language understanding tasks, such as domain classification and semantic parsing.

Different software systems for automatic question answering have been developed in recent years. Some systems perform well on specific domains, but may not be appropriate for other domains. As the complexity and scaling of such information systems become ever greater, it is much more challenging to effectively and efficiently determine which toolkits, algorithms, knowledge bases or other resources should be integrated into a system so that one can achieve a desired or optimal level of performance on a given task. In this working notepaper, we present a generic framework that can be used for any machinereading task and automatically find the best configuration of algorithmic components as well as values of their corresponding parameters. Although we have designed the framework for all QA4MRE-2013 tasks (i.e. Main task, Biomedical about Alzheimer’s and Entrance Exam), our analysis will mostly focus on Biomedical about Alzheimer’s task. We introduce the Configuration Space Exploration ...

Generating natural, diverse, and meaningful questions from images is an essential task for multimodal assistants as it confirms whether they have understood the object and scene in the images properly. The research in visual question answering (VQA) and visual question generation (VQG) is a great step. However, this research does not capture questions that a visually-abled person would ask multimodal assistants. Recently published datasets such as KB-VQA, FVQA, and OK-VQA try to collect questions that look for external knowledge which makes them appropriate for multimodal assistants. However, they still contain many obvious and common-sense questions that humans would not usually ask a digital assistant. In this paper, we provide a new benchmark dataset that contains questions generated by human annotators keeping in mind what they would ask multimodal digital assistants. Large scale annotations for several hundred thousand images are expensive and time-consuming, so we also present an effective way of automatically generating questions from unseen images. In this paper, we present an approach for generating diverse and meaningful questions that consider image content and metadata of image (e.g., location, associated keyword). We evaluate our approach using standard evaluation metrics such as BLEU, METEOR, ROUGE, and CIDEr to show the relevance of generated questions with human-provided questions. We also measure the diversity of generated questions using generative strength and inventiveness metrics. We report new state-of-the-art results on the public and our datasets.

This paper describes an efficient algorithm for language independent generic extractive summarization for single document. The algorithm is based on structural and statistical (rather than semantic) factors. Through evaluations performed on a single-document summarization for English, Hindi, Gujarati and Urdu documents, we show that the method performs equally well regardless of the language. The algorithm has been applied on DUC data for English documents and various newspaper articles for other languages with corresponding stop words list and modified stemmer. The results of summarization have been compared with DUC 2002 data using degree of representativeness. For other languages, the degree of representativeness we get is highly encouraging.

We describe CMU’s UIMA-based modular automatic question answering (QA) system. This system answers multiplechoice English questions for the world history entrance exam. Questions are preceded by short descriptions providing a historical context. Given the context and question-specic instructions, we generate veriable assertions for each answer choice. These assertions are evaluated using several evidencing modules, which assign a plausibility score to each assertion. These scores are then aggregated to produce the most plausible answer choice. In the NTCIR-11 QALab evaluations, our system achieved 51.6% accuracy on the training set, 47.2% on Phase 1 testing set, and 34.1% on Phase 2 testing set.

Different software systems for automatic question answering have been developed in recent years. Some systems perform well on specific domains, but may not be appropriate for other domains. As the complexity and scaling of such information systems become ever greater, it is much more challenging to effectively and efficiently determine which toolkits, algorithms, knowledge bases or other resources should be integrated into a system so that one can achieve a desired or optimal level of performance on a given task. In this working notepaper, we present a generic framework that can be used for any machine-reading task and automatically find the best configuration of algorithmic components as well as values of their corresponding parameters. Although we have designed the framework for all QA4MRE-2013 tasks (i.e. Main task, Biomedical about Alzheimer’s and Entrance Exam), our analysis will mostly focus on Biomedical about Alzheimer’s task. We introduce the Configuration Space Exploration...

Medical images generally have low contrast and they get complex type of noise due to the use of various devices and applications of various algorithms. However, most of the denoising methods consider only additive noise or some special noise model dependent on their systems and conditions only. Such methods when applied to real medical images yield poor results.