Abstract

With the growth of web 2.0, people are using it as a medium to express their opinion and thoughts. With the explosion of blogs, journal like user-generated content on the web, companies, celebrities and politicians are concerned about mining and analyzing the discussions about them or their products. In this paper, we present a method to perform opinion mining and summarize opinions at entity level for English blogs. We first identify various objects (named entities) which are talked about by the blogger, then we identify the modifiers which modify the orientation towards these objects. Finally, we generate object centric opinionated summary from blogs. We perform experiments like named entity identification, entity-modifier relationship extraction and modifier orientation estimation. Experiments and Results presented in this paper are cross verified with the judgment of human annotators.

Abstract

Microblogs such as Facebook, Twitter, Google+ present a nice opportunity for advertising on these blogs that are contextually related to the microblog content. The challenge with advertising on such microblog platforms is that there is very little content to classify the tweet as suitable for advertising, which makes it a very hard classification problem. Despite the importance of this area, little formal published research exists. In this work, we try to address the problem of identifying the microblogs that could be targeted for advertisements. We describe a system that learns to classify which microblogs are suitable for targeted advertising. The experiments are conducted on Twitter data. The system user features derived from the user data, the tweet content and uses external resources such as query logs and term frequency from previous twitter data.

Abstract

Summarization has emerged as an increasingly useful approach to tackle the problem of information overload. Extracting information from online conversations can be of very good commercial and educational value. But majority of this information is present as noisy unstructured text making traditional document summarization techniques difficult to apply. In this paper, we propose a novel approach to address the problem of conversation summarization. We develop an automatic text summarizer which extracts sentences from the conversation to form a summary. Our approach consists of three phases. In the first phase, we prepare the dataset for usage by correcting spellings and segmenting the text. In the second phase, we represent each sentence by a set of predefined features. These features capture the statistical, linguistic and sentimental aspects along with the dialogue structure of the conversation. Finally, in the third phase we use a machine learning algorithm to train the summarizer on the set of feature vectors. Experiments performed on conversations taken from the technical domain show that our system significantly outperforms the baselines on ROUGE F-scores.

Abstract

We propose a model of coverage patterns and a methodology to ex-tract coverage patterns from transactional databases. We have discussed how thecoverage patterns are useful by considering the problem of banner advertisementsplacement in e-commerce web sites. Normally, advertiser expects that the banneradvertisement should be displayed to a certain percentage of web site visitors. Onthe other hand, to generate more revenue for a given web site, the publisher has tomeet the coverage demands of several advertisers by providing appropriate setsof web pages. Given web pages of a web site, a coverage pattern is a set of pagesvisited by a certain percentage of visitors. The coverage patterns discovered fromclick-stream data could help the publisher in meeting the demands of several ad-vertisers. The efficiency and advantages of the proposed approach is shown byconducting experiments on real world click-stream data sets.

Abstract

A model of crop specific virtual labs is proposed to improve practical agricultural education by considering the agricultural educa-tion system in India. In agricultural education, the theoretical concepts are being imparted through class room lectures and laboratory skills are imparted in the dedicated laboratories. Further, practical agricultural education is being imparted by exposing the students to the field prob-lems through Rural Agricultural Work Experience Program (RAWEP),experiential learning and internships. In spite of these efforts, there isa feeling that the level of practical skills exposed to the students is not up to the desired level. So we have to devise the new ways and means to enhance the practical knowledge and skills of agricultural students to understand the real-time crop problems and provide the corrective stepsat the field level. Recent developments in ICTs, thus, provide an oppor-tunity to improve practical education by developing virtual crop labs.The virtual crop labs contain a well organized, indexed and summarized digital data (text, photograph, and video). The digital data corresponds to farm situations reflecting life cycles of several farms of different crops cultivated under diverse farming conditions. The practical knowledge of the students could be improved, if we systematically expose them to virtual crop labs along with course teaching. We can employ cloud com-puting platform to store huge amounts of data and render to students and other stakeholders in an online manner

Abstract

Cool roofs, cool pavements, and urban vegetation reduce cooling energy use in buildings, lower local air pollution, and decrease greenhouse gas (GHG) emissions from urban areas. To promote widespread and large-scale implementation of cool roofs to moderate urban heat-island phenomenon, more awareness and understanding of cool roof benefits at the local level is needed. As part of an investigation of strategies to mitigate urban heat-island effects, field data gathered from a monitoring project on cool roofs in India were reviewed. An innovative field-based analytical method was developed to quantify cooling energy savings resulting from the installation of cool roofs on commercial buildings. For buildings monitored in the Metropolitan Hyderabad region, the measured annual energy savings from roof-whitening of previously black roofs ranged from 20 to 22 kWh/m2 of roof area, corresponding to a cooling …

Abstract

We evaluate differences in clear-sky upwelling shortwave radiation reaching the top of the atmosphere in response to increasing the albedo of roof surfaces in an area of India with moderately high aerosol loading. Treated (painted white) and untreated (unpainted) roofs on two buildings in northeast India were analyzed on five cloudless days using radiometric imagery from the IKONOS satellite. Comparison of a radiative transfer model (RRTMG) and radiometric satellite observations shows good agreement (R 2= 0.927). Results show a mean increase of~ 50 W m− 2 outgoing at the top of the atmosphere for each 0.1 increase of the albedo at the time of the observations and a strong dependence on atmospheric transmissivity.

Abstract

This study evaluates improvement in energy efficiency of an air conditioned building block employing energy conservation measures (ECMs) recommended by Indian Energy Conservation Building Code 2007 (ECBC) through a prescriptive route. First part evaluates energy savings by implementing five ECMs of envelope independently and two combinations of ECMs keeping constant thermostat setting throughout the year. In the second part of the study same ECMs are considered to the subject building model allowing thermostat settings as per thermal adaptation resulting from change in outdoor temperature. Actual measurements were taken and simulation model was fine-tuned. Annual energy consumption of building is used to evaluate the effect of individual ECMs and their combinations on both part of the study, i.e. fixed thermostat and adaptive thermostat settings. The simulation result shows that together …

Abstract

Regression is the study of functional dependency of one variable with respect to other variables. In this paperwe propose a novel regression algorithm, BINGR, for predicting dependent variable, having the advantage oflow computational complexity. The algorithm is interesting because instead of directly predicting the valueof the response variable, it recursively narrows down the range in which response variable lies. BINGRreduces the computation order to logarithmic which is much better than that of existing standard algorithms.As BINGR is parameterless, it can be employed by any naive user. Our experimental study shows that ourtechnique is as accurate as the state of the art, and faster by an order of magnitude

Abstract

In this paper we are proposing neural network based feature transformation framework for developing emotion independent speaker identification system. Most of the present speaker recognition systems may not perform well during emotional environments. In real life, humans extensively express emotions during conversations for effectively conveying the messages. Therefore, in this work we propose the speaker recognition system, robust to variations in emotional moods of speakers. Neural network models are explored to transform the speaker specific spectral features from any specific emotion to neutral. In this work, we have considered eight emotions namely, Anger, Sad, Disgust, Fear, Happy, Neutral, Sarcastic and Surprise. The emotional databases developed in Hindi, Telugu and German are used in this work for analyzing the effect of proposed feature transformation on the performance of speaker identification system. In this work, spectral features are represented by mel-frequency cepstral coefficients, and speaker models are developed using Gaussian mixture models. Performance of the speaker identification system is analyzed with various feature mapping techniques. Results have demonstrated that the proposed neural network based feature transformation has improved the speaker identification performance by 20 %. Feature transformation at the syllable level has shown the better performance, compared to sentence level.