Abstract

Mobile devices are gaining prominence for a variety of activities. From casual browsing to complex tasks like online shopping and banking, people have started using mobiles for almost everything previously done on a desktop. As more and more tasks are shifting towards mobiles, it has become crucial to redesign desktop elements to fit and function consistently on mobile devices as well. One such element is data visualization. The process of designing visualizations for desktops is fairly matured; however, when it comes to mobile devices there are still a number of challenges faced. Most of these challenges revolve around the limited screen space and fewer conventional mediums of interactions offered by these devices. This paper presents a set of design patterns that help overcome some of the challenges faced when designing data visualizations. These patterns are designed for line charts on mobile devices. However, some of them might apply to other kinds of charts as well. There are four patterns presented in this paper, namely details slider, legend filter, selection brush, and responsive data grid. These patterns can be used either individually or together to help make visualizations more convenient to use on mobile devices.

Abstract

News reporting is increasingly becoming an exercise in attention catching headlines1, and raising expectations which trigger sharing on social media. The proposed paper examines how conventional journalism can stay viable despite the transformations brought by rapidly changing media forms. We examine the role of human curiosity as a major contributing factor in online content navigating mechanisms such as clickbaits, listicles, and sharing behaviour on social media. As news reporting shifts online, the navigation on the internet resembles the exploration of primitive humans beyond their immediate needs. The clickbaity headlines are becoming popular with editors and readers putting pressure on news reports to ’dumbdown’. This paper will explore how evolutionary anthropology, psychology, neuroscience and sociology provide insights into the working of clickbaits: ‘how exactly do they attract viewers and why?’ It is based on a study of news reports in three mainstream Indian newspapers over one year (2014) during which an important general election was held. It studies the headlines that garner attention and analyse their morphology. We hope to use our learnings on clickbaits to see how journalism which stays true to its mandate of informing citizens and providing news can attract views and readership and thus remain viable.

Abstract

Observational evidence in clinical practice is critical in healthcare and policy making. Researchers spend a lot of time searching for relevant published articles to write a systematic review of a topic. In this paper, we present our participation as the team of IIIT Hyderabad at Task2 Technologically Assisted Reviews in Empirical Medicine as an effort to automate this task and deliver relevant information in medical literature. We base our approach on query expansion according to relevance feedback. Query expansion is a standard technique in information retrieval tasks with growing use in medical literature [1, 2]. Articles returned from pubmed query performed during a systematic review are first indexed using lucene’s inverted index. The query is porcessed for term boosting, fuzzy search and used for scoring documents according to TF-IDF similarity. Relevance feedback is used to update the query and become more pragmatic.

Abstract

This paper analyzes, compares and looks at tradeoffs in different load distribution schemes for consistent hashing in DHTs(distributed hash tables). Different traffic patterns, including an adversarial pattern, were constructed to test the load distribution. A simulator was made for each load distribution scheme and the load on each node was recorded for each traffic pattern. It was shown that increasing the number of consistent hash rings doesn't change the load distribution characteristics of the DHT in simple traffic scenarios, but in the case of adversarial traffic, increasing the number of hash rings leads to an improvement of around 15% in the load distribution statistics

Abstract

Millimeter wave (mmWave) communication systems employ hybrid RF/baseband transceivers that divide the spatial signal processing into radio frequency and baseband domains in order to reduce the hardware complexity resulting from the use of large number of antennas elements. In this paper, we consider two designs for hybrid multi-user MIMO downlink that minimizes the sum mean square error (SMSE), based on perfect channel knowledge (non-robust design) and transceiver design considering the channel imperfections (robust design). In the robust design, the channel state information (CSI) is assumed to be perturbed by estimation error and follows Gaussian distribution with known error variance. In both the designs, reduced hardware complexity is achieved by analog-digital hybrid processing by using orthogonal matching pursuit (OMP)-based sparse signal processing. We evaluate the performance of both the proposed schemes based on various parameters and also compare it with conventional fully-digital system. We present numerical results over various dictionaries. The comparison results show that robust design is resilient to the presence of CSI errors. Furthermore, we also demonstrate the convergence of both the proposed algorithms to a limit even though global convergence is hard to prove due to non convex nature of overall optimization problem.

Abstract

—Many socio-environmental aspects manifest themselves over space and time, interacting at varying scales of these dimensions. Satellite imagery, available repetitively over a region, provide important clues of these observations across these dimensions. But, also pose enormous challenges in terms of data processing, extracting significant patterns (indicating the underlying processes) and be able to further model them as scientific knowledge of the environmental process. In this paper, an effort has been made to propose a time-variant analysis method based on the shape characteristics of the vegetation response over time to help identify regions of significant changes. The study covers four agricultural-year periods between 2008 and 2012 over the district of West Godavari, in south of India. This approach shows that the effect of 2009 drought year on the agricultural practices vary spatially depending on the access to resources and the time-lag that manifests itself in such processes. In this study, we also find that nearly 80% of the region is well endowed and hence resilient to the climatic vagaries.

Abstract

Predicting the outcome of a match has always been at the center of sports analytics. Indian Premier League (IPL), a professional Twenty20 (T20) cricket league in India, has established itself as one of the biggest tournaments in cricket history. In this paper, we propose a model to predict the winner at the end of each over in the second innings of an IPL cricket match. Our methodology not only incorporates the dynamically updating game context as the game progresses, but also includes the relative strength between the two teams playing the match. Estimating the relative strength between two teams involves modeling the individual participating players’ potentials. To model a player, we use his career as well as recent performance statistics. Using the various dynamic features, we evaluate several supervised learning algorithms to predict the winner of the match. Finally, using the Random Forest Classifier (RFC), we have achieved an accuracy of 65.79% - 84.15% over the course of second innings, with an overall accuracy of 75.68%.

Abstract

Search queries follow a long tail distribution which results in harder management of ad space for sponsored search. During keyword auctions, advertisers also tend to target head query keywords, thereby creating an imbalance in demand for head and tail keywords. This leads to under-utilization of ad space of tail query keywords. In this paper, we have explored a mechanism that allows the advertisers to bid on concepts rather than keywords. The tail query keywords are utilized by allocating a mix of head and tail keywords related to the concept. In the literature, an effort has been made to improve sponsored search by extracting the knowledge of coverage patterns among the keywords of transactional query logs. In this paper, we propose an improved approach to allow advertisers to bid on high level concepts instead of keywords in sponsored search. The proposed approach utilizes the knowledge of levelwise coverage patterns to allocate incoming search queries to advertisers in an efficient manner by utilizing the long tail. Experimental results on AOL search query data set show improvement in ad space utilization and reach of advertisers.

Abstract

Query recommendation (QR) support search engine to provide alternative queries as a recommendation using similarity-based approaches. In the literature, orthogonal query recommendation (OQR) has been proposed to compute the diversity of QR when the user does not formulate proper queries. The OQR uses dissimilarity measure in QR to recommend completely different queries. In this paper, we propose an approach in QR by extending association rules, diverse patterns, and unbalanced concept hierarchy of search terms. We conceptualize association rules based QR, and order the rules based on confidence and diversity. Subsequently, the high ranked rules based on confidence and diversity are provided in QRs. The experimental results on real world AOL click-through dataset show that the diverse QRs improve the performance significantly

Abstract

This paper describes the International Institute of Information Technology of Hyderabad’s submission to the task Classification Of Spanish Election Tweets (COSET) as a part of IBEREVAL-2017[1]. The task is to classify Spanish election tweets into political, policy, personal, campaign and other issues. Our system uses Support Vector Machines with radial basis function kernel to classify tweets. We dwell upon the character and word level features along with the word embeddings and train the classification model with them and present the results. Our best run achieves a F1-macro score of 0.6054 on the test corpus for first phase and 0.8509 for the second phase.