Abstract

Three decades ago, Pease et al. introduced the problem of Byzantine Agreement [PSL 80] where nodes need to maintain a consistent view of the world in spite of the challenge posed by Byzantine faults. Subsequently, it is well known that Byzantine agreement over a completely connected synchronous network of n nodes tolerating up to t faults is (efficiently) possible if and only if t < n/3. Pease et al. further empowered the nodes with the ability to authenticate themselves and their messages and proved that agreement in this new model (popularly known as authenticated Byzantine agreement (ABA)) is possible if and only if t < n. (which is a huge improvement over the bound of t < n/3 in the absence of authentication for the same functionality). To understand the utility, potential and limitations of using authentication in distributed protocols for agreement, Gupta et al. [GGBS10] studied ABA in new light. They generalize the existing models and thus, attempt to give a unified theory of agreements over the authenticated and non-authenticated domains. In this paper we extend their results to synchronous (undirected) networks and give a complete characterization of agreement protocols. As a corollary, we show that agreement can be strictly easier than all-pair point-to-point communication. It is well known that in a synchronous network over n nodes of which up to any t are corrupted by a Byzantine adversary, BA is possible only if all pair point-to-point reliable communication is possible [Dol82, DDWY93]. Thus, a folklore in the area is that maintaining global consistency (agreement) is at least as hard as the problem of all pair point-to-point communication. Equivalently, it is widely believed that protocols for BA over incomplete networks exist only if it is possible to simulate an overlay-ed complete network. Surprisingly, we show that the folklore is not always true. Thus, it seems that agreement protocols may be more fundamental to distributed computing than reliable communication.

Abstract

IGIPT is a web-based integrated platform for the identification of genomic islands (GIs). It incorporates thirteen parametric measures based on anomalous nucleotide composition on a single platform, thus improving the predictive power of a horizontally acquired region, since it is known that no single measure can absolutely predict a horizontally transferred region. The tool filters putative GIs based on standard deviation from genomic average and also provide raw output in MS excel format for further analysis. To facilitate the identification of various structural features, viz., tRNA integration sites, repeats, etc. in the vicinity of GIs, the tool provides option to extract the predicted regions and its flanking regions.

Abstract

Online advertising offers significantly finer granularity, which has been leveraged in state-of-the-art targeting methods, like Behavioral Targeting (BT). Such methods have been fur-ther complemented by recent work in Look-alike Modeling(LAM) which helps in creating models which are customized according to each advertiser’s requirements and each cam-paign’s characteristics, and which show ads to users who are most likely to convert on them, not just click them. In Look-a like Modeling given data about converters and non-converters, obtained from advertisers, we would like to train models automatically for each ad campaign. Such custom models would help target more users who are similar to the set of converters the advertiser provides. The advertisers get more freedom to define their preferred sets of users which should be used as a basis to build custom targeting models.In behavioral data, the number of conversions (positive class) per campaign is very small (conversions per impression for the advertisers in our data set are much less than 10−4),giving rise to a highly skewed training dataset, which has most records pertaining to the negative class. Campaigns with very few conversions are called as tail campaigns, and those with many conversions are called head campaigns.Creation of Look-alike Models for tail campaigns is very challenging and tricky using popular classifiers like Linear SVM and GBDT, because of the very few number of posi-tive class examples such campaigns contain. In this paper,we present an Associative Classification (AC) approach to LAM for tail campaigns. Pairs of features are used to deriverules to build a Rule-based Associative Classifier, with the rules being sorted by frequency-weighted log-likelihood ratio(F-LLR). The top krules, sorted by F-LLR, are then applied to any test record to score it. Individual features can also form rules by themselves, though the number of such rules in the top krules and the whole rule-set is very small. Our algorithm is based on Hadoop, and is thus very efficient in terms of speed.

Abstract

The thermostat control variable in conventional HVAC System is air temprature Ta

Abstract

The study, conducted in composite climate of India, exhibits improvement in energy efficiency of a conditioned building block with the use of energy conservation measures (ECMs) recommended by National Energy Conservation Building Code (ECBC). In first part of it, subject building model was individually applied with five ECMs and two combinations of these ECMs by using constant thermostat settings. Similarly in second part of the study the same was done with adaptive thermostat settings. Sensitivity analysis showed that the effect of ECMs get reduced with increase in building footprint and internal loads. Analysis of thermal comfort in an unconditioned building using same set of ECMs was also carried out to see improvement in comfort hours.

Abstract

This article presents an approach for speeding EnergyPlus simulations. The computing run time of an energy simulation depends on several variables and is directly proportional to the simulation RunPeriod. In the proposed approach, data parallelization is achieved by breaking an annual simulation into several segments of smaller RunPeriod, each handled by a separate computer/processor. The speed gain achieved by running 12 one-month RunPeriod segments in parallel as compared to single simulation of 12 months is between three and six times. Segmentation of simulation has resulted in minor deviations between the results obtained through segmented simulations and annual simulations. Methods for reducing these deviations on annual and monthly basis are presented in this article using 12 benchmark models each simulated for five cities. On annual basis, a maximum deviation of 0.06% was observed …

Abstract

In this paper, we have made an effort to propose approachesto find similar legal judgements by extending the popu-lar techniques used in information retrieval and search en-gines. Legal judgements are complex in nature and re-fer other judgements. We have analyzed all-term, legal-term, co-citation and bibliographic coupling-based similaritymethods to find similar judgements. The experimental re-sults show that the legal-term cosine similarity method per-forms better than all-term cosine similarity method. Also,the results show that bibliographic coupling similarity methodimproves the performance over co-citation approach.

Abstract

MapReduce framework has received a wide acclaim over the past few years for large scale computing. It has become a standard paradigm for batch oriented workloads. As the adoption of this paradigm has increased rapidly, scheduling of these MapReduce jobs has become a problem of great interest in research community. We propose an approach which tries to maintain harmony among the jobs running on the cluster, and in turn decrease their runtime. In our model, the scheduler is made aware of different types of jobs running on the cluster. The scheduler tries to allocate a task on a node if the incoming task does not affect the tasks already running on that node. From the list of available pending tasks, our algorithm selects the one that is most compatible with the tasks already running on that node. We bring up heuristic and machine learning based solutions to our approach and try to maintain a resource balance on the cluster by not overloading any of the nodes, thereby reducing the overall runtime of the jobs. The results show a saving of runtime of around 21% in the case of heuristic based approach and around 27% in the case of machine learning based approach when compared to Yahoo's Capacity scheduler.

Abstract

A synergistic approach is described combining computer simulation with experiment in the interpretation of small angle neutron scattering (SANS) and inelastic scattering experiments on the structure and dynamics of proteins and other biopolymers. Simulation models can be tested by calculating neutron scattering structure factors and comparing the results directly with experimental scattering profiles. If the scattering profiles agree the simulations can be used to provide a detailed decomposition and interpretation of the experiments, and if not, the models can be rationally adjusted. Comparison with neutron experiment can be made at the level of the scattering functions or, less directly, of structural and dynamical quantities derived from them. This methodology is discussed in the context of the protein glass transition, protein-solvent dynamical coupling, the density of the hydration shell of proteins and the structural analysis of lignocellulosic biomass.

Abstract

The world of E-commerce is expanding, posing a large arena of products, their descriptions, customer and professional reviews that are pertinent to them. Most of the product attribute extraction techniques in literature work on structured descriptions using several text analysis tools. However, attributes in these descriptions are limited compared to those in customer reviews of a product, where users discuss deeper and more specific attributes. In this paper, we propose a novel supervised domain independent model for product attribute extraction from user reviews. The user generated content contains unstructured and semi-structured text where conventional language grammar dependent tools like parts-of-speech taggers, named entity recognizers, parsers do not perform at expected levels. We used Wikipedia and Web to identify product attributes from customer reviews and achieved F1score of 0.73.