Department of Data Science

Indian Institute of Technology Palakkad&nbsp;(IIT Palakkad or IIT PKD) is a public autonomous&nbsp;engineering&nbsp;and&nbsp;research&nbsp;institute located in&nbsp;Palakkad,&nbsp;Kerala. It was&nbsp;one of the five new&nbsp;IITs&nbsp;proposed in the&nbsp;2014 Union budget of India. The campus was inaugurated on 3 August 2015. The institute has 94 faculty, 978 students, and 63 non-teaching staffs.

IT Palakkad currently offers four-year Bachelor of Technology (B.Tech) and two years MS programmes in 8 different engineering disciplines including&nbsp;Computer Science, Electrical, Mechanical, Civil among others.

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IIT Palakkad

We address the problem of index pruning without compromising the quality of ad-hoc similarity search among sets and ranked lists. We discuss three different ways to prune the index structure and, by linking the index structure with the concept of Locality Sensitive Hashing (LSH), we introduce two solutions to query processing over the pruned index. Through a probabilistic analysis we ensure that a user-defined recall goal is still guaranteed. We are able to formulate an optimization problem that can determine the optimal pruning factor for all three pruning methods. The experimental evaluations over real-world data validate that the optimal pruning factor indeed ensures the recall goal without any significant effect on the quality of similarity search on a much smaller index.

World University root node

20th International Workshop on the Web and Databases, WebDB 2017 - Web of Opinions: Truths, Beliefs and Conficts

LSH-based probabilistic pruning of inverted indices for sets and ranked lists

In this paper, we introduce the first method that (1) can complete kernel matrices with completely missing rows and columns as opposed to individual missing kernel values, with help of information from other incomplete kernel matrices. Moreover, (2) the method does not require any of the kernels to be complete a priori, and (3) can tackle non-linear kernels. The kernel completion is done by finding, from the set of available incomplete kernels, an appropriate set of related kernels for each missing entry. These aspects are necessary in practical applications such as integrating legacy data sets, learning under sensor failures and learning when measurements are costly for some of the views. The proposed approach predicts missing rows by modelling both within-view and between-view relationships among kernel values. For within-view learning, we propose a new kernel approximation that generalizes and improves Nyström approximation. We show, both on simulated data and real case studies, that the proposed method outperforms existing techniques in the settings where they are available, and extends applicability to new settings. © 2016, The Author(s).

Fulltext

Machine Learning

Multi-view kernel completion

Caring for individuals with dementia is frequently associated with extreme physical and emotional stress, which often leads to depression. Smart home technology and advances in machine learning techniques can provide innovative solutions to reduce caregiver burden. One key service that caregivers provide is prompting individuals with memory limitations to initiate and complete daily activities. We hypothesize that sensor technologies combined with machine learning techniques can automate the process of providing reminder-based interventions. The first step toward automated interventions is to detect when an individual faces difficulty with activities. We propose machine learning approaches based on one-class classification that learn normal activity patterns. When we apply these classifiers to activity patterns that were not seen before, the classifiers are able to detect activity errors, which represent potential prompt situations. We validate our approaches on smart home sensor data obtained from older adult participants, some of whom faced difficulties performing routine activities and thus committed errors.

IEEE Journal on Selected Topics in Signal Processing

One-Class Classification-Based Real-Time Activity Error Detection in Smart Homes

Recent past has seen an inexorable shift towards the use of deep learning techniques to solve a myriad of problems in the field of medical imaging. In this paper, a novel segmentation method involving a fully-connected deep neural network called Deep Segmentation Network (DSN) is proposed to perform supervised regression for brain extraction from T1-weighted magnetic resonance (MR) images. In contrast to the existing patch-based feature learning techniques, DSN works on full 3D volumes, simplifying pre- and postprocessing operations, to efficiently provide a voxel-wise binary mask delineating the brain region. The model is evaluated using three publicly available datasets and is observed to either outdo or perform comparably to the state-of-the-art methods. DSN is able to achieve a maximum and minimum Dice Similarity Coefficient (DSC) of 97:57 and 92:82 respectively across all the datasets. Experiments conducted in this paper highlight the ability of the DSN model to automatically learn feature representations; making it a simple yet highly effective approach for brain segmentation. Preliminary experiments also suggest that the proposed model has the potential to segment sub-cortical structures accurately.

ACM International Conference Proceeding Series

Supervised deep segmentation network for brain extraction

Maintaining a clean and hygienic civic environment is an indispensable yet formidable task, especially in developing countries. With the aim of engaging citizens to track and report on their neighborhoods, this paper presents a novel smartphone app, called SpotGarbage, which detects and coarsely segments garbage regions in a user-clicked geo-tagged image. The app utilizes the proposed deep architecture of fully convolutional networks for detecting garbage in images. The model has been trained on a newly introduced Garbage In Images (GINI) dataset, achieving a mean accuracy of 87:69%. The paper also proposes optimizations in the network architecture resulting in a reduction of 87:9% in memory usage and 96:8% in prediction time with no loss in accuracy, facilitating its usage in resource constrained smartphones.

UbiComp 2016 - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing

SpotGarbage: Smartphone app to detect garbage using deep learning

Datenbank-Spektrum

Mining Entity Rankings

We consider the problem of similarity search in a set of topk lists under the generalized Kendall's Tau distance. This distance describes how related two rankings are in terms of discordantly ordered items. We consider pair and tripletsbased indices to counter the shortcomings of naive inverted indices and derive efficient query schemes by relating the proposed index structures to the concept of locality sensitive hashing (LSH). Specifically, we devise four different LSH schemes for Kendall's Tau using two generic hash families over individual elements or pairs of them. We show that each of these functions has the desired property of being locality sensitive. Further, we discuss the selection of hash functions for the proposed LSH schemes for a given query ranking, called query-driven LSH and derive bounds for the required number of hash functions to use in order to achieve a predefined recall goal. Experimental results, using two realworld datasets, show that the devised methods outperform the SimJoin method the state of the art method to query for similar sets and are far superior to a plain inverted index-based approach.

Efficient similarity search across Top-k Lists under the Kendall's Tau distance

Heterogeneity of features and lack of correspondence between data points of different domains are the two primary challenges while performing feature transfer. In this paper, we present a novel supervised domain adaptation algorithm (SHDA-RF) that learns the mapping between heterogeneous features of different dimensions. Our algorithm uses the shared label distributions present across the domains as pivots for learning a sparse feature transformation. The shared label distributions and the relationship between the feature spaces and the label distributions are estimated in a supervised manner using random forests. We conduct extensive experiments on three diverse datasets of varying dimensions and sparsity to verify the superiority of the proposed approach over other baseline and state of the art transfer approaches.

International Joint Conference on Artificial Intelligence

Supervised Heterogeneous Domain Adaptation via Random Forests

An early paper by Rashmi et al. presented the construction of an (n, k, d = n - 1) MBR regenerating code featuring the inherent double replication of all code symbols and repair-by-transfer (RBT), both of which are important in practice. We first show that no MBR code can contain even a single code symbol that is replicated more than twice. We then go on to present two new families of MBR codes which feature double replication of all systematic message symbols. The codes also possess a set of d nodes whose contents include the message symbols and which can be repaired through help-by-transfer (HBT). As a corollary, we obtain systematic RBT codes for the case d = (n - 1) that possess inherent double replication of all code symbols and having a field size of O(n) in comparison with the general, O(n2) field size requirement of the earlier construction by Rashmi et al. For the cases (k = d = n - 2) or (k + 1 = d = n - 2), the field size can be reduced to q = 2 and hence the codes can be binary. We also give a necessary and sufficient condition for the existence of MBR codes having double replication of all code symbols and also suggest techniques which will enable an arbitrary MBR code to be converted to one with double replication of all code symbols. © 2016 IEEE.

IEEE International Symposium on Information Theory - Proceedings

On MBR codes with replication

This work proposes and evaluates a novel approach to determine interesting category for ranked lists using $\nu$-SVM. We identify three characteristics (features), entropy, unlikability and peculiarity and show how to train a classifier on these features using a set of Wikipedia tables. The learned model is evaluated by relevance assessments obtained through a user study, reflecting the correctness of our approach.