Abstract

To determine the significance of urine albumin/creatinine ratio (UACR) as to predict proteinuria in new-onset hypertension and uric acid in women with preeclampsia and its comparison with healthy normotensive pregnant women. Methods. Healthy normotensive pregnant women (n= 45), women with preeclampsia (n= 36) were included in the study. The preeclamptic group was further divided into two subgroups: mild (n= 25) and severe preeclampsia (n= 11). Results. Higher mean values of urine albumin/creatinine ratio (UACR) and uric acid were found in preeclamptic pregnant women (319.05±247.56 mg/g, 5.98±2.1 mg/dl) as compared to healthy normotensive pregnant women (22.15±8.1 mg/g, 4.07±0.91 mg/dl) in the third trimester with highly significant difference (p< 0.001) statistically. When comparison of the mean values of urine albumin/creatinine ratio and uric acid was done between mild (199.9±46.78 mg/g, 5.24±1.89 mg/dl) and severe preeclamptic pregnant women (589.67±305.38 mg/g, 7.64±1.4 mg/dl), the values were found to be elevated in the latter group with highly significant difference (p< 0.001) statistically. The positive and highly significant correlation was found in UACR/blood pressure, uric acid/blood pressure and UACR/uric acid. Conclusion. It was concluded that UACR and serum uric acid levels increased as disease progressed from mild to severe conditions.

Abstract

Bilingual dictionaries are the key component of the cross-lingual similarity estimation methods. Usually such dictionary generation is accomplished by manual or automatic means. Automatic generation approaches include to exploit parallel or comparable data to derive dictionary entries. Such approaches require large amount of bilingual data in order to produce good quality dictionary. Many time the language pair does not have large bilingual comparable corpora and in such cases the best automatic dictionary is upper bounded by the quality and coverage of such corpora. In this work we propose a method which exploits continuous quasi-comparable corpora to derive term level associations for enrichment of such limited dictionary. Though we propose our experiments for English and Hindi, our approach can be easily extendable to other languages. We evaluated dictionary by manually computing the precision. In experiments we show our approach is able to derive interesting term level associations across languages.

Abstract

For privacy reasons, personally identifiable information like age and gender of people is not available publicly. However accurate prediction of such information has important applications in the fields of advertising, forensics and business intelligence. Existing methods for this problem have focused on classifier learning using content based features like word n-grams and style based features like Part of Speech (POS) n-grams. Two major drawbacks of previous approaches are:(1) they do not consider the semantic relation between words, and (2) they do not handle polysemy. We propose a novel method to address these drawbacks by representing the document using Wikipedia concepts and category information. Experimental results show that classifiers learned using such features along with previously used features help us achieve significantly better accuracy compared to the state-of-the-art methods. Indeed, feature selection shows that our novel features are more effective than previously used content based features.

Abstract

The systems for weather observation and forecast are being operated to deal with adverse weather in general to mankind. Weatherbased decision support systems (DSSs) are being build to improve the efficiency of the production systems in the domains of health, agriculture, livestock, transport, business, planing, governance and so on. The weather-based DSS provides appropriate suggestions based on the weather condition of the given period for the selected domain. In the literature, the notion of reuse is being employed in improving the efficiency of DSSs. In this paper, we have proposed a framework to identify similar weather conditions, which could help in improving the performance of weather-based DSSs with better reuse. In the proposed framework, the range of weather variable is divided into categories based on its influence on that domain. We form a weather condition for a period which is the combination of category values of weather variables. By comparing the daily/weekly weather conditions of a given year to weather conditions of subsequent years, the proposed framework identifies the extent of reuse. We have conducted the experiment by applying the proposed framework on 30 years of weather data of Rajendranagar, Hyderabad and using the categories employed by India Meteorological Department in Meteorology0 domain. The results show that there is a significant degree of similarity among daily and weekly weather conditions over the years. The results provide an opportunity to improve the efficiency of weather-based DSSs by improving the degree of reuse of the developed suggestions/knowledge for the corresponding weather conditions.

Abstract

A large number of web queries are related to product entities. Studying evolution of product entities can help analysts understand the change in particular attribute values for these products. However, studying the evolution of a product requires us to be able to link various versions of a product together in a temporal order. While it is easy to temporally link recent versions of products in a few domains manually, solving the problem in general is challenging. The ability to temporally order and link various versions of a single product can also improve product search engines. In this paper, we tackle the problem of finding the previous version (predecessor) of a product entity. Given a repository of product entities, we first parse the product names using a CRF model. After identifying entities corresponding to a single product, we solve the problem of finding the previous version of any given particular version of the product. For the second task, we leverage innovative features with a Naïve Bayes classifier. Our methods achieve a precision of 88% in identifying the product version from product entity names, and a precision of 53% in identifying the predecessor.

Abstract

Entities are centric to a large number of real world applications. Wikipedia shows entity infoboxes for a large number of entities. However, not much structured information is available about character entities in books. Automatic discovery of characters from books can help in effective summarization. Such a structured summary which not just introduces characters in the book but also provides a high level relationship between them can be of critical importance for buyers. This task involves the following challenging novel problems: 1. automatic discovery of important characters given a book; 2. automatic social graph construction relating the discovered characters; 3. automatic summarization of text most related to each of the characters; and 4. automatic infobox extraction from such summarized text for each character. As part of this demo, we design mechanisms to address these challenges and experiment with publicly available books.

Abstract

Given a knowledge base, annotating any text with entities in the knowledge base enhances automated understanding of the text. Entities provide extra contextual information for the automated system to understand and interpret the text better. In the special case when the text is in the form of short text queries, automated understanding can be critical in improving the quality of search results and recommendations. Annotation of queries helps semantic retrieval, ensuring diversity of search results including retrieval of relevant news stories. In this paper, we present SIEL@ERD, a system for automated stamping of entity information in short query text. Our system builds from the state-of-the-art TAGME system and is optimized for time and performance efficiency. Our system achieved an F1 measure of 0.53 and the latency of 0.31 seconds on a dataset of 500 queries and a Freebase snapshot provided for the short track in the Entity Recognition and Disambiguation Challenge at SIGIR 2014.

Abstract

Our computing demands have grown so much that we need a robust distributed computing platform to process data. To feed such data hungry systems, we need an equally robust distributed file systems that span across multiple geographically separate locations. Most distributed file systems break any file into a set of blocks or chunks which are spread across the cluster. The major bottleneck is to identify where to place the blocks and where to place the replicas so that cluster is optimized on certain parameters like disk utilization, minimizing network congestion, maximizing throughput, optimal power utilization, etc. This paper proposes assigning a distance measure for each one of the data sources with respect to the other and placing the blocks on the specified disk on the node that minimizes the total distance of the last few requests made for that block. As the request pattern and parameters change, the distances are updated and the blocks are moved dynamically to minimized the distance, in effect optimizing on the required parameters. The distance function is to be modeled based on the cluster and the parameters you wish to optimize on. It can be a function of just bandwidth or bandwidth and latency or other miscellaneous features like disk utilization, processing power, disk speed, power utilization, cooling requirements, temperature, etc. A detailed performance analysis was carried out with disk bandwidth, network bandwidth and disk utilization as the parameters and the performance is far better in comparison to the reference system (10% or more depending on specification differences) which has no understanding of the different type of disks present and the nature of the cluster. Due to the performance aware nature, the system was inherently able to utilize memory for speeding up performance through the use of in-memory partitions.

Abstract

As soon as natural disaster events happen, users are eager to know more about them. However, search engines currently provide a ten blue links interface for queries related to such events. Relevance of results for such queries can be significantly improved if users are shown a structured summary of the fresh events related to such queries. This would not just reduce the number of user clicks to get the relevant information but would also help users get updated with more fine grained attribute-level information. Twitter is a great source that can be exploited for obtaining such fine-grained structured information for fresh natural disaster events. Such events are often reported on Twitter much earlier than on other news media. However, extracting such structured information from tweets is challenging because: 1. tweets are noisy and ambiguous; 2. there is no well defined schema for various types of natural disaster events; 3. it is not trivial to extract attribute-value pairs and facts from unstructured text; and 4. it is difficult to find good mappings between extracted attributes and attributes in the event schema. We propose algorithms to extract attribute-value pairs, and also devise novel mechanisms to map such pairs to manually generated schemas for natural disaster events. Besides the tweet text, we also leverage text from URL links in the tweets to fill such schemas. Our schemas are temporal in nature and the values are updated whenever fresh information flows in from human sensors on Twitter. Evaluation on ~58000 tweets for 20 events shows that our system can fill such event schemas with an F1 of ~0.6.

Abstract

Increasing roof reflectance reduces absorption of solar radiation, roof surface temperature and heat ingress into the building. This leads to an increase in indoor comfort and reduction in room air temperature that in turn results in energy saving by reducing AC demand. To assess this impact of cool roofs on buildings in rural and peri-urban areas of India, a study was performed on two un-conditioned school buildings in Hyderabad (ZPH School) and Nagpur (VNHM School). Two classrooms of same size, function, and occupancy from both the school buildings were monitored for a period of ten weeks starting from March 2014 till May 2014. The data from the experiments and their analyses showed promising results. The average reduction and peak reduction in indoor air, roof underdeck and roof overdeck surface temperatures are 2.1 ◦C, 5.0 ◦C and 12.3 ◦C and 4.3 ◦C, 10.0 ◦C and 26.3 ◦C respectively for the room with white roof as opposed to grey roof in ZPH School and 1.5 ◦C, 4.0 ◦C and 9.5 ◦C, and 3.3 ◦C, 4.2 ◦C, and 25.2 ◦C respectively in VNHM School. The results from the experiment prove that white roof has a significant effect in reducing the indoor air temperatures in buildings due to reduction in outdoor and indoor roof surface temperature.