Hewlett-Packard (India)

Data replication is a key technology in distributed systems that enables higher availability and performance. This article surveys optimistic replication algorithms. They allow replica contents to diverge in the short term to support concurrent work practices and tolerate failures in low-quality communication links. The importance of such techniques is increasing as collaboration through wide-area and mobile networks becomes popular.Optimistic replication deploys algorithms not seen in traditional “pessimistic” systems. Instead of synchronous replica coordination, an optimistic algorithm propagates changes in the background, discovers conflicts after they happen, and reaches agreement on the final contents incrementally.We explore the solution space for optimistic replication algorithms. This article identifies key challenges facing optimistic replication systems---ordering operations, detecting and resolving conflicts, propagating changes efficiently, and bounding replica divergence---and provides a comprehensive survey of techniques developed for addressing these challenges.

Subscribers to the popular news or blog feeds (RSS/Atom) often face the problem of information overload as these feed sources usually deliver large number of items periodically. One solution to this problem could be clustering similar items in the feed reader to make the information more manageable for a user. Clustering items at the feed reader end is a challenging task as usually only a small part of the actual article is received through the feed. In this paper, we propose a method of improving the accuracy of clustering short texts by enriching their representation with additional features from Wikipedia. Empirical results indicate that this enriched representation of text items can substantially improve the clustering accuracy when compared to the conventional bag of words representation.

Uncertainties in design variables and problem parameters are often inevitable and must be considered in an optimization task if reliable optimal solutions are sought. Besides a number of sampling techniques, there exist several mathematical approximations of a solution's reliability. These techniques are coupled in various ways with optimization in the classical reliability-based optimization field. This paper demonstrates how classical reliability-based concepts can be borrowed and modified and, with integrated single and multiobjective evolutionary algorithms, used to enhance their scope in handling uncertainties involved among decision variables and problem parameters. Three different optimization tasks are discussed in which classical reliability-based optimization procedures usually have difficulties, namely (1) reliability-based optimization problems having multiple local optima, (2) finding and revealing reliable solutions for different reliability indices simultaneously by means of a bi-criterion optimization approach, and (3) multiobjective optimization with uncertainty and specified system or component reliability values. Each of these optimization tasks is illustrated by solving a number of test problems and a well-studied automobile design problem. Results are also compared with a classical reliability-based methodology.

Recent advances in smart devices have sustained them as a better alternative for the design of human-machine interaction (HMI), because they are equipped with accelerometer sensor, gyroscope sensor, and an advanced operating system. This paper presents a continuous hand gestures recognition technique that is capable of continuous recognition of hand gestures using three-axis accelerometer and gyroscope sensors in a smart device. To reduce the influence of unstableness of a hand making the gesture and compress the data, a gesture coding algorithm is developed. An automatic gesture spotting algorithm is developed to detect the start and end points of meaningful gesture segments. Finally, a gesture is recognized by comparing the gesture code with gesture database using dynamic time warping algorithm. In addition, a prototype system is developed to recognize the continuous hand gestures-based HMI. With the smartphone, the user is able to perform the predefined gestures and control smart appliances using the Samsung AllShare protocol.

Due to the various reasons, such as atmospheric effects and differences in acquisition, it is often the case that there exists a large difference between the spectral bands of satellite images collected from different geographic locations. The large shift between the spectral distributions of training and test data causes the current state-of-the-art supervised learning approaches to output unsatisfactory maps. We present a novel semantic segmentation framework that is robust to such a shift. The key component of the proposed framework is color mapping generative adversarial networks (ColorMapGANs) that can generate fake training images that are semantically exactly the same as training images, but whose spectral distribution is similar to the distribution of the test images. We then use the fake images and the ground truth for the training images to fine-tune the already trained classifier. Contrary to the existing generative adversarial networks (GANs), the generator in ColorMapGAN does not have any convolutional or pooling layers. It learns to transform the colors of the training data to the colors of the test data by performing only one elementwise matrix multiplication and one matrix-addition operation. Due to the architecturally simple but powerful design of ColorMapGAN, the proposed framework outperforms the existing approaches with a large margin in terms of both accuracy and computational complexity.

Research for recognizing online handwritten words in Indic scripts is at its early stages when compared to Latin and Oriental scripts. In this paper, we address this problem specifically for two major Indic scripts--Devanagari and Tamil. In contrast to previous approaches, the techniques we propose are largely data driven and script independent. We propose two different techniques for word recognition based on Hidden Markov Models (HMM): lexicon driven and lexicon free. The lexicon-driven technique models each word in the lexicon as a sequence of symbol HMMs according to a standard symbol writing order derived from the phonetic representation. The lexicon-free technique uses a novel Bag-of-Symbols representation of the handwritten word that is independent of symbol order and allows rapid pruning of the lexicon. On handwritten Devanagari word samples featuring both standard and nonstandard symbol writing orders, a combination of lexicon-driven and lexicon-free recognizers significantly outperforms either of them used in isolation. In contrast, most Tamil word samples feature the standard symbol order, and the lexicon-driven recognizer outperforms the lexicon free one as well as their combination. The best recognition accuracies obtained for 20,000 word lexicons are 87.13 percent for Devanagari when the two recognizers are combined, and 91.8 percent for Tamil using the lexicon-driven technique.

Hand pose recognition has been a problem of great interest to the Computer Vision and Human Computer Interaction community for many years and the current solutions either require additional accessories at the user end or enormous computation time. These limitations arise mainly due to the high dexterity of human hand and occlusions created in the limited view of the camera. This work utilizes the depth information and a novel algorithm to recognize scale and rotation invariant hand poses dynamically. We have designed a volumetric shape descriptor enfolding the hand to generate a 3D cylindrical histogram and achieved robust pose recognition in real time.

In this paper, principal component analysis (PCA) is applied to the problem of online handwritten character recognition in the Tamil script. The input is a temporally ordered sequence of (x,y) pen coordinates corresponding to an isolated character obtained from a digitizer. The input is converted into a feature vector of constant dimensions following smoothing and normalization. PCA is used to find the basis vectors of each class subspace and the orthogonal distance to the subspaces used for classification. Pre-clustering of the training data and modification of distance measure are explored to overcome some common problems in the traditional subspace method, in empirical evaluation, these PCA -based classification schemes are found to compare favorably with nearest neighbour classification.

A system for online recognition of handwritten Tamil characters is presented. A handwritten character is constructed by executing a sequence of strokes. A structure- or shape-based representation of a stroke is used in which a stroke is represented as a string of shape features. Using this string representation, an unknown stroke is identified by comparing it with a database of strokes using a flexible string matching procedure. A full character is recognized by identifying all the component strokes. Character termination, is determined using a finite state automaton. Development of similar systems for other Indian scripts is outlined.

In this paper, we describe a system for the automatic recognition of isolated handwritten Devanagari characters obtained by linearizing consonant conjuncts. Owing to the large number of characters and resulting demands on data acquisition, we use structural recognition techniques to reduce some characters to others. The residual characters are then classified using the subspace method. Finally the results of structural recognition and feature-based matching are mapped to give final output. The proposed system is evaluated for the writer dependent scenario.

RC4 is the most popular stream cipher in the domain of cryptology. In this paper, we present a systematic study of the hardware implementation of RC4, and propose the fastest known architecture for the cipher. We combine the ideas of hardware pipeline and loop unrolling to design an architecture that produces 2 RC4 keystream bytes per clock cycle. We have optimized and implemented our proposed design using VHDL description, synthesized with 130, 90, and 65 nm fabrication technologies at clock frequencies 625 MHz, 1.37 GHz, and 1.92 GHz, respectively, to obtain a final RC4 keystream throughput of 10, 21.92, and 30.72 Gbps in the respective technologies.

We present a comparison of elastic matching schemes for writer dependent on-line handwriting recognition of isolated Tamil characters. Three different features are considered namely, preprocessed x-y coordinates, quantized slope values, and dominant point coordinates. Seven schemes based on these three features and dynamic time warping distance measure are compared with respect to recognition accuracy, recognition speed, and number of training templates. Along with these results, possible grouping strategies and error analysis is also presented in brief.

In this paper we present the results of parallelizing two life sciences applications, Markov random fields-based (MRF) liver segmentation and HMMER's Viterbi algorithm, using GPUs. We relate our experiences in porting both applications to the GPU as well as the techniques and optimizations that are most beneficial. The unique characteristics of both algorithms are demonstrated by implementations on an NVIDIA 8800 GTX Ultra using the CUDA programming environment. We test multiple enhancements in our GPU kernels in order to demonstrate the effectiveness of each strategy. Our optimized MRF kernel achieves over 130times speedup, and our hmmsearch implementation achieves up to 38times speedup. We show that the differences in speedup between MRF and hmmsearch is due primarily to the frequency at which the hmmsearch must read from the GPU's DRAM.

Hidden Markov Models (HMM) have long been a popu- lar choice for Western cursive handwriting recognition fol- lowing their success in speech recognition. Even for the recognition of Oriental scripts such as Chinese, Japanese and Korean, Hidden Markov Models are increasingly being used to model substrokes of characters. However, when it comes to Indic script recognition, the published work em- ploying HMMs is limited, and generally focussed on iso- lated character recognition. In this effort, a data-driven HMM-based online handwritten word recognition system for Tamil, an Indic script, is proposed. The accuracies obtained ranged from 98% to 92.2% with different lexicon sizes (1K to 20K words). These initial results are promising and warrant further research in this direction. The results are also encouraging to explore possibilities for adopting the approach to other Indic scripts as well.

In this paper we present an online handwritten symbol recognition system for Telugu, a widely spoken language in India. The system is based on hidden Markov models (HMM) and uses a combination of time-domain and frequency-domain features. The system gives top-1 accuracy of 91.6% and top-5 accuracy of 98.7% on a dataset containing 29,158 train samples and 9,235 test samples. We also introduce a cost-effective and natural data collection procedure based on ACECADreg Digimemoreg and describe its usage in building a Telugu handwriting dataset.

In this paper we propose an approach to recognize human actions using depth images. Here, we capture the motion dynamics of the object from the depth difference image and average depth image. The features from the space-time depth difference images are obtained from hierarchical division of the silhouette bounding box. We also make use of motion history images to represent the temporal information about the action. We make use of the translation, scale and orientation invariant Hu moments to represent the features of the motion history image and the average depth image. We then classify human actions using support vector machines. We analyze the representation efficiency of Hu moments and the hierarchical division of bounding boxes separately in order to evaluate the contribution of each of the features. The results show superior performance of over 90% when both features are combined.

This paper explains the architecture and design of Arduino based car parking system. Authorization of driver or user is the basic rule used to park a vehicle in a parking place. Authorization card will be given to each user, which carries the vehicle number or other details. If the user is authorized and space is available in the parking, then the parking gate will open and the user is allowed to park the vehicle in parking place else the user is not allowed even the user is authorized person. If car is allowed to park, then mobile notification will be send to user about parking. It solves the parking issue in urban areas, also provides security to a vehicle and an unauthorized user is not allowed to enter into a parking place. It helps to park vehicle in multifloored parking also as it will display which floor has free space.

This paper describes character based elastic matching using local features for recognizing online handwritten data. Dynamic time warping (DTW) has been used with four different feature sets: x-y features, shape context (SC) and tangent angle (TA) features, generalized shape context feature (GSC) and the fourth set containing x-y, normalized first and second derivatives and curvature features. Nearest neighborhood classifier with DTW distance was used as the classifier. In comparison, the SC and TA feature set was found to be the slowest and the fourth set was best among all in the recognition rate. The results have been compiled for the online handwritten Tamil and Telugu data. On Telugu data we obtained an accuracy of 90.6% with a speed of 0.166 symbols/sec. To increase the speed we have proposed a 2-stage recognition scheme using which we obtained accuracy of 89.77% but with a speed of 3.977 symbols/sec.

Online handwriting recognition of Indian scripts has been drawing increasing attention in recent years. Related research has gained further momentum due to recent planned funding by the Govt. of India towards technology development of Indian languages and scripts. Standard databases of handwritten characters of a few Indian scripts have already become available. These include online handwritten character databases of Bangla, Devanagari, Tamil and Telugu and these are available free of cost on request. In the present paper, we present benchmark recognition results of the above databases of four most popular scripts of the Indian subcontinent based on two existing feature extraction methods viz. point-float and direction code histogram features and three classifiers viz. Nearest Neighbour (NN), Multilayer Perceptron (MLP) and Hidden Markov Model (HMM) to test the effectiveness of the existing classification methods and provide benchmark results for future online handwriting recognition research of these Indic scripts.

The pace of development in the world has increased over the years and with it, the use of hi-tech gadgets, consumer durables, automobiles, etc. has also gone up. In this context, as resources become more and more scarce, there are multiple challenges that emerge both from a sustainable development perspective, and from the perspective of meeting profitability objectives of a firm. Remanufacturing has come up in a big way as an answer to these challenges, but firms are struggling with respect to revenue management of this nascent area. We assess the current literature and distil the key factors that firms need to consider as they assimilate remanufacturing in their operations and revenue management strategy. We provide an assessment of white spaces in research in this area and also outline the directions for future research.