Abstract

High dimensional data is often challenging to cluster due to the curse of dimensionality leading to challenges in identifying clusters. The key challenge in high dimensional clustering is to develop a solution that identifies clusters which are as complete as they can be, while not merging well-separated clusters. We propose core points which represent local compact regions. The strongly connected component from the k-nearest neighbor graph of core points provides for a group of points that are strongly mutually connected. These mutually connected regions represent the core structure of the clusters. Our empirical analysis and experimental results present the rationale behind our solution and validate the goodness of the clusters against the state of the art high dimensional clustering algorithms.The novelty of our solution is to use the concept of reverse nearest neighbors to generate natural clusters in high dimensions.

Abstract

The disclosed embodiments illustrate methods and systems to recommend medical intervention activities for caregivers and/or human subjects. The method comprises receipt of medical data of a human subject and contextual data of a plurality of caregivers of the human subjects. The medical data includes periodic physiological data of the human subject received in real-time. A customized ontology is generated, from a pre-determined ontology related to a medical condition, based on the received medical data and the contextual data. Thereafter, a recommendation of medical intervention activities is generated for at least one of the plurality of caregivers and/or the human subject, based on analysis of the generated customized ontology, caregiver profiles, the received contextual data, and the periodic physiological data. The generated recommendation is transmitted to computing devices of the at least one …

Abstract

India is home to the largest under-25 demographic profile in the world, but lacks a job-ready educational system. It requires a wide-spread, skill-oriented educational model, equipping youth to thrive in highly dynamic job markets. As a response to the huge demand for technical education, a large private skill-tutoring ecosystem has sprung up in In-dia but remains geographically limited. This paper, drawn from a three-month ethnographic research conducted in Ameerpet (arguably India's largest IT skilling hub), probes the pedagogic style and characteristics of tutoring, and of-fers reasons why learners prefer to enroll into a physical model of classroom teaching over online courses. We make design suggestions for online learning platforms to attract students who are marginalized in the more formal and com-petitive education system, and opt for Ameerpet-like skill-hubs. Our primary offering is to suggest a shift in …

Abstract

The disclosed embodiments illustrate method and system for auto-generation of the sketch notes-based visual summary of the multimedia content. The method includes determining one or more segments, based on one or more transitions in the multimedia content. The method further includes generating a transcript based on audio content associated with each determined segment. The method further includes retrieving a set of images from an image repository based on each of the identified one or more keywords. The method further includes generating a sketch image of each of one or more of the retrieved set of images associated with each of the identified one or more keywords. The method further includes rendering the sketch notes-based visual summary of the multimedia content, generated based on at least generated one or more sketch images, on a user interface displayed on a display screen of the user …

Abstract

The Interactions website (interactions.acm.org) hosts a stable of bloggers who share insights and observations on HCI, often challenging current practices. Each issue we'll publish selected posts from some of the leading and emerging voices in the field.

Abstract

Recommender systems are a key component of music sharing platforms, which suggest musical recordings a user might like. People often have implicit preferences while listening to music, though these preferences might not always be the same while they listen to music at different times. For example, a user might be interested in listening to songs of only a particular artist at some time, and the same user might be interested in the top-rated songs of a genre at another time. In this paper we try to explicitly model the short term preferences of the user with the help of Last.fm tags of the songs the user has listened to. With a session defined as a period of activity surrounded by periods of inactivity, we introduce the concept of a subsession, which is that part of the session wherein the preference of the user does not change much. We assume the user preference might change within a session and a session might have multiple subsessions. We use our modelling of the user preferences to generate recommendations for the next song the user might listen to. Experiments on the user listening histories taken from Last.fm indicate that this approach beats the present methodologies in predicting the next recording a user might listen to.

Abstract

Recommender Systems are an integral part of music sharing platforms. Often the aim of these systems is to increase the time, the user spends on the platform and hence having a high commercial value. The systems which aim at increasing the average time a user spends on the platform often need to recommend songs which the user might want to listen to next at each point in time. This is different from recommendation systems which try to predict the item which might be of interest to the user at some point in the user lifetime but not necessarily in the very near future. Prediction of next song the user might like requires some kind of modeling of the user interests at the given point of time. Attentive neural networks have been exploiting the sequence in which the items were selected by the user to model the implicit short-term interests of the user for the task of next item prediction, however we feel that features of the songs occurring in the sequence could also convey some important information about the short-term user interest which only the items cannot. In this direction we propose a novel attentive neural architecture which in addition to the sequence of items selected by the user, uses the features of these items to better learn the user short-term preferences and recommend next song to the user.

Abstract

The purpose of this study is to examine the differences between different types of news stories. Given the huge impact of social networks, online content plays an important role in forming or changing the opinions of people. Unlike traditional journalism where only certain news organizations can publish content, online journalism has given chance even for individuals to publish. This has its own advantages like individual empowerment but has given a chance to a lot of malicious entities to spread misinformation for their own benefit. As reported by many organizations in recent history, this even has influence on major events like the outcome of elections. Therefore, it is of great importance now, to have some sort of automated classification of news stories. In this work, we propose a deep hierarchical attention neural architecture combining sentiment and semantic embeddings for more accurate fine grained classification of news stories. Experimental results show that the sentiment embedding along with semantic information outperform several state-of-the art methods in this task.

Abstract

Variational autoencoders were proven successful in domains such as computer vision and speech processing. Their adoption for modeling user preferences is still unexplored, although recently it is starting to gain attention in the current literature. In this work, we propose a model which extends variational autoencoders by exploiting the rich information present in the past preference history. We introduce a recurrent version of the VAE, where instead of passing a subset of the whole history regardless of temporal dependencies, we rather pass the consumption sequence subset through a recurrent neural network. At each time-step of the RNN, the sequence is fed through a series of fully-connected layers, the output of which models the probability distribution of the most likely future preferences. We show that handling temporal information is crucial for improving the accuracy of the VAE: In fact, our model beats the current state-of-the-art by valuable margins because of its ability to capture temporal dependencies among the user-consumption sequence using the recurrent encoder still keeping the fundamentals of variational autoencoders intact.

Abstract

Ensemble POS taggers are a good choice to integrate and leverage benefits of various types of POS taggers. This can help the large number (6500+) of resource-poor languages which do not have much annotated training data by providing ways to integrate semi-supervised/unsupervised taggers with supervised taggers. In this paper we present our experiments of developing ensemble POS taggers using a decision tree. We integrate a semi-supervised data mining approach that uses context based lists (CBLs) for POS tagging with supervised (1) Support Vector Machine based POS tagger, called SVMTool and (2) Conditional Random Field based POS tagger. The results are enhanced semi-supervised ensemble POS taggers which outperform the base methods. In these POS taggers, we use a decision tree to decide when to rely on the output of supervised tagger, and when to rely on the semi-supervised CBL method. The CBL based tagger uses rich contextual information which helps in tagging both existing and unseen words and uses no domain knowledge while supervised taggers give good performance for words present in the training model and can include domain based features. Hence, these algorithms have complementary strengths and in our ensemble we are able to combine these strengths. Enhanced performance of our new POS taggers over the base methods suggests that integrating these methods combines the qualities of these in the new tagger which enhances the performance. Therefore, these new semi-supervised ensemble taggers are more suitable for resource-poor languages.