Bricks and urbanism in the Indus Valley rise and decline

The Indus Civilization, often denoted by its major city Harappa, spanned almost two millennia from 3200 to 1300 BC. Its tradition reaches back to 7000 BC: a 5000 year long expansion of villages and towns, of trading activity, and of technological advancements culminates between 2600 and 1900 BC in the build-up of large cities, writing, and political authority; it emerges as one of the first great civilizations in history. During the ensuing 600 years, however, key technologies fall out of use, urban centers are depopulated, and people emigrate from former core settlement areas. Although many different hypotheses have been put forward to explain this deurbanization, a conclusive causal chain has not yet been established. We here combine literature estimates on brick typology, and on urban area for individual cities. In the context of the existing extensive data on Harappan artifact find sites and put in their chronological context, the combined narratives told by bricks, cities, and spatial extent can provide a new point of departure for discussing the possible reasons for the mysterious "decline".Comment: 11 pages, 3 figures, Supplementary Material. Submitted to PLOS On

Sentiment Classification of Online Customer Reviews and Blogs Using Sentence-level Lexical Based Semantic Orientation Method

ABSTRACT Sentiment analysis is the process of extracting knowledge from the peoples‟ opinions, appraisals and emotions toward entities, events and their attributes. These opinions greatly impact on customers to ease their choices regarding online shopping, choosing events, products and entities. With the rapid growth of online resources, a vast amount of new data in the form of customer reviews and opinions are being generated progressively. Hence, sentiment analysis methods are desirable for developing efficient and effective analyses and classification of customer reviews, blogs and comments. The main inspiration for this thesis is to develop high performance domain independent sentiment classification method. This study focuses on sentiment analysis at the sentence level using lexical based method for different type data such as reviews and blogs. The proposed method is based on general lexicons i.e. WordNet, SentiWordNet and user defined lexical dictionaries for sentiment orientation. The relations and glosses of these dictionaries provide solution to the domain portability problem. The experiments are performed on various data sets such as customer reviews and blogs comments. The results show that the proposed method with sentence contextual information is effective for sentiment classification. The proposed method performs better than word and text level corpus based machine learning methods for semantic orientation. The results highlight that the proposed method achieves an average accuracy of 86% at sentence-level and 97% at feedback level for customer reviews. Similarly, it achieves an average accuracy of 83% at sentence level and 86% at feedback level for blog comment

A Mosque Among the Stars

A Mosque Among The Stars was the first anthology that dealt with the subject of Muslim characters and/or Islamic themes and Science Fiction

GGM classifier with multi-scale line detectors for retinal vessel segmentation

Persistent changes in the diameter of retinal blood vessels may indicate some chronic eye diseases. Computer-assisted change observation attempts may become challenging due to the emergence of interfering pathologies around blood vessels in retinal fundus images. The end result is lower sensitivity to thin vessels for certain computerized detection methods. Quite recently, multi-scale line detection method proved to be worthy for improved sensitivity toward lower-caliber vessels detection. This happens largely due to its adaptive property that responds more to the longevity patterns than width of a given vessel. However, the method suffers from the lack of a better aggregation process for individual line detectors. This paper investigates a scenario that introduces a supervised generalized Gaussian mixture classifier as a robust solution for the aggregate process. The classifier is built with class-conditional probability density functions as a logistic function of linear mixtures. To boost the classifier’s performance, the weighted scale images are modeled as Gaussian mixtures. The classifier is trained with weighted images modeled on a Gaussian mixture. The net effect is increased sensitivity for small vessels. The classifier’s performance has been tested with three commonly available data sets: DRIVE, SATRE, and CHASE_DB1. The results of the proposed method (with an accuracy of 96%, 96.1% and 95% on DRIVE, STARE, and CHASE_DB1, respectively) demonstrate its competitiveness against the state-of-the-art methods and its reliability for vessel segmentation

SENTIMENT CLASSIFICATION OF ONLINE CUSTOMER REVIEWS AND BLOGS USING SENTENCE-LEVEL LEXICAL BASED SEMANTIC ORIENTATION METHOD

Sentiment analysis is the process of extracting knowledge from the peoples’ opinions, appraisals and emotions toward entities, events and their attributes. These opinions greatly impact on customers to ease their choices regarding online shopping, choosing events, products and entities. With the rapid growth of online resources, a vast amount of new data in the form of customer reviews and opinions are being generated progressively. Hence, sentiment analysis methods are desirable for developing efficient and effective analyses and classification of customer reviews, blogs and comments. The main inspiration for this thesis is to develop high performance domain independent sentiment classification method. This study focuses on sentiment analysis at the sentence level using lexical based method for different type data such as reviews and blogs. The proposed method is based on general lexicons i.e. WordNet, SentiWordNet and user defined lexical dictionaries for sentiment orientation. The relations and glosses of these dictionaries provide solution to the domain portability problem. The experiments are performed on various datasets such as customer reviews and blogs comments. The results show that the proposed method with sentence contextual information is effective for sentiment classification. The proposed method performs better than word and text level corpus based machine learning methods for semantic orientation. The results highlight that the proposed method achieves an average accuracy of 86% at sentence-level and 97% at feedback level for customer reviews. Similarly, it achieves an average accuracy of 83% at sentence level and 86% at feedback level for blog comments

Sentiment Classification of Online Customer Reviews and Blogs Using Sentence-level Lexical Based Semantic Orientation Method

ABSTRACT Sentiment analysis is the process of extracting knowledge from the peoples‟ opinions, appraisals and emotions toward entities, events and their attributes. These opinions greatly impact on customers to ease their choices regarding online shopping, choosing events, products and entities. With the rapid growth of online resources, a vast amount of new data in the form of customer reviews and opinions are being generated progressively. Hence, sentiment analysis methods are desirable for developing efficient and effective analyses and classification of customer reviews, blogs and comments. The main inspiration for this thesis is to develop high performance domain independent sentiment classification method. This study focuses on sentiment analysis at the sentence level using lexical based method for different type data such as reviews and blogs. The proposed method is based on general lexicons i.e. WordNet, SentiWordNet and user defined lexical dictionaries for sentiment orientation. The relations and glosses of these dictionaries provide solution to the domain portability problem. The experiments are performed on various data sets such as customer reviews and blogs comments. The results show that the proposed method with sentence contextual information is effective for sentiment classification. The proposed method performs better than word and text level corpus based machine learning methods for semantic orientation. The results highlight that the proposed method achieves an average accuracy of 86% at sentence-level and 97% at feedback level for customer reviews. Similarly, it achieves an average accuracy of 83% at sentence level and 86% at feedback level for blog comment

Processing and Structure of Carbon Nanofiber Paper

A unique concept of making nanocomposites from carbon nanofiber paper was explored in this study. The essential element of this method was to design and manufacture carbon nanofiber paper with well-controlled and optimized network structure of carbon nanofibers. In this study, carbon nanofiber paper was prepared under various processing conditions, including different types of carbon nanofibers, solvents, dispersants, and acid treatment. The morphologies of carbon nanofibers within the nanofiber paper were characterized with scanning electron microscopy (SEM). In addition, the bulk densities of carbon nanofiber papers were measured. It was found that the densities and network structures of carbon nanofiber paper correlated to the dispersion quality of carbon nanofibers within the paper, which was significantly affected by papermaking process conditions. Copyright (c) 2009 Zhongfu Zhao et al