Indian Institute of Technology Hyderabad (IITH) is a premier institute of science and technology established in 2008. IITH has been consistently ranked in the&nbsp;<a href="https://iith.ac.in/reports/" rel="noopener noreferrer">top 10 institutes</a>&nbsp;in India for Engineering&nbsp;<a href="https://www.nirfindia.org/2021/EngineeringRanking.html" rel="noopener noreferrer" target="_blank">according to NIRF</a>&nbsp;making it one of the most coveted schools for science and technology in the country.

IITH offers&nbsp;<a href="https://iith.ac.in/academics/under-graduate/">undergraduate programs</a>&nbsp;in all the classical engineering disciplines, applied sciences, design, as well as several modern interdisciplinary areas. Students are given a flexibility to explore a broad set of areas, and potentially pursue a minor or double major in a discipline that is not their own. Students who wish to seek a deeper understanding of their own discipline are strongly encouraged to get involved in cutting-edge research with the help of a faculty to mentor them, and earn an Honors in their own field.

The very foundation of IIT Hyderabad is based on&nbsp;<a href="https://iith.ac.in/research/">research and innovation</a>. The vibrant research culture is evident from the number of patents and publications that IITH produces consistently every year. IITH offers&nbsp;<a href="https://iith.ac.in/academics/programmes-offered/">graduate programs</a>&nbsp;at both a masters, and a doctoral level, in several diverse areas. There are separate programs for technology, design, science, and liberal arts. The&nbsp;<a href="https://iith.ac.in/academics/post-graduate/">MTech program</a>&nbsp;is offered in various modes and durations to cater to the ever-growing need of postgraduate level professionals in the country.

IITH encourages and supports&nbsp;<a href="https://iic-iith.netlify.app/index.html" rel="noopener noreferrer" target="_blank">innovation and entrepreneurship</a>&nbsp;at every stage. The minor program in entrepreneurship is open to all students of IITH, and many of the courses are offered by industrialists who are entrepreneurs themselves. There are various&nbsp;<a href="https://iith.ac.in/research/centres-incubators/">innovation centers and incubators</a>&nbsp;that promote entrepreneurship and help mentor young innovators. IITH has been very successful in building tie-ups with leading academic institutions around the globe.

IITH enjoys a very special relationship with Japanese universities and industries that goes beyond academic and research collaborations. In fact, some of the iconic buildings in IITH campus will carry the signature of Japanese architecture.

IITH creates a unique holistic ecosystem for education that offers interactive learning, a very flexible academic structure, cutting-edge research, strong industry collaboration, and entrepreneurship. This is an environment which enables students and faculty to translate their dreams into realities.

IIT Hyderabad

Retrieving relevant tweets corresponding to cultural events can be used in various applications like event reporting, event recommendation, etc. This type of retrieval is challenging due to short length of the tweet, noise, out of vocabulary words, abbreviations in the tweet. In this paper, we focus on the problem of retrieving relevant tweets related to given cultural event of a festival. We consider several factors like BM25, DFR, presence of artist name, relevant hashtag, festival name for finding the relevance of tweets to the event. We apply BM25 + DFR model to retrieve candidate set of tweets related to each event of a festival. We find the top hashtags for each event by exploring meta-attributes of an event. We re-rank the initial rank list from BM25 + DFR based on two strategies, namely, presence of the event meta-attributes (artist name, festival name, title, etc.) and the identified top hashtags in the tweet, and based on the timestamp of the event. We experimented on a subset of CLEF 2017 cultural microblog contextualization dataset. The experimental results show that the proposed method is able to put relevant tweets at the top of the retrieval list.

World University root node

Department of Computer Science and Engineering

CEUR Workshop Proceedings

IITH at CLEF 2017: Finding relevant tweets for cultural events

Users in social media often participate in discussions regarding different events happening in the physical world (e.g., concerts, conferences, festivals) by posting messages, replying to or forwarding messages related to such events. In various applications like event recommendation, event reporting, etc. it might be useful to find user discussions related to such events from social media. Finding event related hashtags can be useful for this purpose. In this paper, we focus on the problem of finding relevant hashtags for a given event. Features are defined to identify the event related hashtags. We specifically look for features that use similarities of the hashtags with the event metadata attributes. A learning to rank algorithm is applied to learn the importance weights of the features towards the task of predicting the relevance of a hashtag to the given event. We experimented on events from four different categories (namely, Award ceremonies, E-commerce events, Festivals, and Product launches). Experimental results show that our method significantly outperforms the baseline methods. © 2017 by SCITEPRESS - Science and Technology Publications.

IC3K 2017 - Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management

Exploiting meta attributes for identifying event related hashtags

The phenomenal growth in the healthcare data has inspired us in investigating robust and scalable models for data mining. For classification problems Information Gain(IG) based Decision Tree is one of the popular choices. However, depending upon the nature of the dataset, IG based Decision Tree may not always perform well as it prefers the attribute with more number of distinct values as the splitting attribute. Healthcare datasets generally have many attributes and each attribute generally has many distinct values. In this paper, we have tried to focus on this characteristics of the datasets while analysing the performance of our proposed approach which is a variant of Decision Tree model and uses the concept of Correlation Ratio(CR). Unlike IG based approach, this CR based approach has no biasness towards the attribute with more number of distinct values. We have applied our model on some benchmark healthcare datasets to show the effectiveness of the proposed technique. © 2016 IEEE.

Proceedings - 2016 IEEE 16th International Conference on Bioinformatics and Bioengineering, BIBE 2016

CRDT: Correlation Ratio Based Decision Tree Model for Healthcare Data Mining

Rank aggregation mechanisms have been used in solving problems from various domains such as bioinformatics, natural language processing, information retrieval, etc. Metasearch is one such application where a user gives a query to the metasearch engine, and the metasearch engine forwards the query to multiple individual search engines. Results or rankings returned by these individual search engines are combined using rank aggregation algorithms to produce the final result to be displayed to the user. We identify few aspects that should be kept in mind for designing any rank aggregation algorithm for metasearch. For example, generally equal importance is given to the input rankings while performing the aggregation. However, depending on the indexed set of web pages, features considered for ranking, ranking functions used etc. by the individual search engines, the individual rankings may be of different qualities. So, the aggregation algorithm should give more weight to the better rankings while giving less weight to others. Also, since the aggregation is performed when the user is waiting for response, the operations performed in the algorithm need to be light weight. Moreover, getting supervised data for rank aggregation problem is often difficult. In this paper, we present an unsupervised rank aggregation algorithm that is suitable for metasearch and addresses the aspects mentioned above. We also perform detailed experimental evaluation of the proposed algorithm on four different benchmark datasets having ground truth information. Apart from the unsupervised Kendall-Tau distance measure, several supervised evaluation measures are used for performance comparison. Experimental results demonstrate the efficacy of the proposed algorithm over baseline methods in terms of supervised evaluation metrics. Through these experiments we also show that Kendall-Tau distance metric may not be suitable for evaluating rank aggregation algorithms for metasearch. © 2015 Elsevier Ltd. All rights reserved.

Expert Systems with Applications

Preference relations based unsupervised rank aggregation for metasearch

Physical rehabilitation supported by the computer-assisted-interface is gaining popularity among health-care fraternity. In this paper, we have proposed a computer-vision-assisted contactless methodology to facilitate palm and finger rehabilitation. Leap motion controller has been interfaced with a computing device to record parameters describing 3-D movements of the palm of a user undergoing rehabilitation. We have proposed an interface using Unity3D development platform. Our interface is capable of analyzing intermediate steps of rehabilitation without the help of an expert, and it can provide online feedback to the user. Isolated gestures are classified using linear discriminant analysis (DA) and support vector machines (SVM). Finally, a set of discrete hidden Markov models (HMM) have been used to classify gesture sequence performed during rehabilitation. Experimental validation using a large number of samples collected from healthy volunteers reveals that DA and SVM perform similarly while applied on isolated gesture recognition. We have compared the results of HMM-based sequence classification with CRF-based techniques. Our results confirm that both HMM and CRF perform quite similarly when tested on gesture sequences. The proposed system can be used for home-based palm or finger rehabilitation in the absence of experts. © 2015 IEEE.

IEEE Transactions on Biomedical Engineering

Computer-Vision-Assisted Palm Rehabilitation With Supervised Learning

A large amount of work has been done on syntactic analysis of English texts. But, for analyzing the short phrases without any structured contexts like capitalization, subject-object-verb order, etc. these techniques are not yet proved to be appropriate. In this paper we have attempted the syntactic analysis of the phrases where contextual information is not available. We have developed stemmer, POS tagger, chunker and Named Entity tagger for English short phrases like chats, messages, and queries, using root dictionary and language specific rules. We have evaluated the technique on English queries and observed that our system outperforms some commonly used NLP tools. © Springer International Publishing Switzerland 2014.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

An efficient tool for syntactic processing of english query text

With more and more news sources publishing news articles online, news recommendation systems are becoming increasingly popular in recent days. However, if a news recommender shows to the user many news articles that cover same story or talk about same persons or events, then the recommendations might become monotonous for the user. A possible way to handle this scenario is to diversify the recommended news articles so that the articles in the recommendation list are not too similar to each other. Existing research works in literature discuss how to use user-specific relevance of news articles and diversification to produce the final recommendations. To be able to compute user-specific relevance of articles, several recommender systems suggest that users have a login, or they set cookies to track page visits of the users. Often users are unsure whether the information collected via these means will be used for the sole purpose of news recommendation. Privacy of the identity and page access patterns is a concern for many users. In this work, we consider the problem where diverse recommendations are to be given for users, but no past data about users is collected or stored by the system. We use only article popularity to estimate the relevance. We identify few aspects of news articles on which diversification can be performed. These aspects are incorporated in an optimization framework. An approximation algorithm is used to generate the recommendations. Experimental analysis shows that in scenarios where no individual user data is stored or accessible by the recommender system, diversification results in higher click rates and increase in the number of satisfied users. Performances of the multiple diversification aspects and approaches are also analyzed. © 2014 IEEE.

DSAA 2014 - Proceedings of the 2014 IEEE International Conference on Data Science and Advanced Analytics

Diversification in news recommendation for privacy concerned users

The field of context and intelligence, as a topic of pervasive computing, has been gaining considerable momentum. Typically, context-aware intelligence is applied to understand the situation of the users and their behavior with the objective of providing adaptive services that are closely associated with that context. In this work, we have taken an orthogonal approach wherein we attempt to aggregate knowledge and cognition, of the user, on a given topic to build models out of them. The model thus created is analyzed to derive inferences about the user, where the analysis is performed on a graph model comprising topics based information obtained by mining domain specific personal data sources and from certain facts on which the user has expressed fair level of belief. We have explored the possibility of deriving beneficial information by provisioning an appropriate representation of knowledge as belief-graph with specific orientation in healthcare and call this model as Med-Tree. Subject to privacy conditions, we open up the belief-graph model to establish objective based social connections that gets contextually bound. As a next step, such contextually bound ad-hoc networks are subjected to advanced querying process resulting in useful information extraction and inferences. Leveraging on user's knowledge or the belief-graph, the proposed Med-Tree could help derive benefits towards better personal healthcare and disease management. © 2013 IEEE.

13th IEEE International Conference on BioInformatics and BioEngineering, IEEE BIBE 2013

Med-Tree: A user knowledge graph framework for medical applications

Rating prediction is an important problem for rating based recommender systems. In Rating Prediction, the task is to predict the rating that a user would give to an item that he/she has not rated in the past. Most of the existing algorithms for the task concentrate on the absolute ratings given to different items by different users in the past. However, there are few recent research work that point out some drawbacks of absolute rating based systems and algorithms, and suggest the use of preference relations between pairs of items to capture the users' interests about the items. In this paper, we propose a rating prediction algorithm that considers the relative ratings given by the users for different pairs of items. The algorithm models the users and items using a matrix factorization framework. The learned model of users and items are first used to predict the personalized utility of an item for a user. This utility is then converted to a valid rating value in a predefined rating scale by employing a personalized scaling. Experimental evaluation on a benchmark dataset reveals that better prediction accuracies may be achieved by modeling the users and items using relative rating information.

Rating prediction using preference relations based matrix factorization

In the Recommendation Problem, it is often important to find a set of items similar to a particular item or a group of items. This problem of finding similar items for the recommendation task may also be viewed as a link prediction problem in a network, where the items can be treated as the nodes. The strength of the edge connecting two items rep- resents the similarity between the items. In this context, a central challenge is to suitably define an appropriate dissimilarity function between the items. For content based recommender systems, the dissimilarity function should take into account the individual attributes of the items. The same at- tribute may have different importances in different parts of the underlying network. We focus on the problem of learning a suitable dissimilarity function between items and address it by formulating it as a constrained optimization problem which captures the local weightages of the attributes in different regions of the graph. The constraints are imposed in such a way that the non-connected nodes show higher value of dissimilarity than the connected nodes. The local tuning of the weights learns the optimal value of weights in various parts of the network: from the portions having rich graph information to the portions having only content informtion. Detailed experimentation shows the superiority of the proposed algorithm over the Adamic Adar metric as well as logistic regression methodology. Copyright © 2012 by the Association for Computing Machinery, Inc. (ACM).

RecSys'12 - Proceedings of the 6th ACM Conference on Recommender Systems

Local learning of item dissimilarity using content and link structure

Users in recommender systems often express their opinions about different items by rating the items on a fixed rating scale. The rating information provided by the users is used by the recommender systems to generate personalized recommendations for them. Few recent research work on rating based recommender systems advocate the use of preference relations instead of absolute ratings in order to produce better recommendations. Use of preference relations for neighborhood based collaborative recommendation has been looked upon in recent literature. On the other hand, Matrix Factorization algorithms have been shown to perform well for recommender systems, specially when the data is sparse. In this work, we propose a matrix factorization based collaborative recommendation algorithm that considers preference relations. Experimental results show that the proposed method is able to achieve better recommendation accuracy over the compared baseline methods. © 2012 Springer-Verlag.

Preference relation based matrix factorization for recommender systems

User based collaborative filtering algorithms are widely used for generating recommendations for users. Standard user based collaborative filtering algorithms do not consider time as a factor while measuring the user similarities and building the recommendation list. However, users' interests often shift with time. Recommender systems should therefore rely on recent purchases of the users to address this user dynamics. Items also have their own dynamics. Most of the items in a recommender system are widely popular just after their releases but do not sell that well afterwards. Giving more importance to the recent purchases of the experts may capture the item dynamics and hence result in better recommendation accuracy. We study the performances of different time-aware user based collaborative filtering algorithms on several benchmark datasets. The proposed algorithms use the time-of-purchase information for calculating user similarities. The time information is also used while combining the purchase behaviors of the experts and generating the final recommendation. Copyright © 2012 SciTePress - Science and Technology Publications.

KDIR 2012 - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval

User based collaborative filtering with temporal information for purchase data

Rank Aggregation is the problem of aggregating ranks given by various experts to a set of entities. In context of web, it has applications like building metasearch engines, combining user preferences etc. For many of these applications, it is difficult to get labeled data and the aggregation algorithms need to be evaluated against unsupervised evaluation metrics. We consider the Kendall-Tau unsupervised metric which is widely used for evaluating rank aggregation task. Kendall Tau distance between two permutations is defined as the number of pairwise inversions among the permutations. The original Kendall Tau distance treats each inversion equally, irrespective of the differences in rank positions of the inverted items. In this work, we propose a variant of Kendall-Tau distance that takes into consideration this difference in rank positions. We study, examine and compare various available supervised as well as unsupervised metrics with the proposed metric. We experimentally demonstrate that our modification in Kendall Tau Distance makes it potentially better than other available unsupervised metrics for evaluating aggregated ranking. © 2011 Springer-Verlag Berlin Heidelberg.

Displacement based unsupervised metric for evaluating rank aggregation

Collaborative filtering is a widely used technique for rating prediction in recommender systems. Memory based collaborative filtering algorithms assign weights to the users to capture similarities between them. The weighted aver- age of similar users' ratings for the test item is output as prediction. We propose a memory based algorithm that is markedly different from the existing approaches. We use preference relations instead of absolute ratings for similarity calculations, as preference relations between items are generally more consistent than ratings across like-minded users. Each user's ratings are viewed as a preference graph. Similarity weights are learned using an iterative method motivated by online learning. These weights are used to create an aggregate preference graph. Ratings are inferred to max- imally agree with this aggregate graph. The use of preference relations allows the rating of an item to be influenced by other items, which is not the case in the weighted-average approaches of the existing techniques. This is very effective when the data is sparse, specially for the items rated by few users. Our experiments show that the our method outperforms other methods in the sparse regions. However, for dense regions, sometimes our results are comparable to the competing approaches, and sometimes worse. Copyright 2010 ACM.