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

Tables or ranked lists summarize facts about a group of entities in a concise and structured fashion. They are found in all kind of domains and easily comprehensible by humans. Some globally prominent examples of such rankings are the tallest buildings in the World, the {\^{a}}€¦

Mining and Querying Ranked Entities

In a distributed storage system, code symbols are dispersed across space in nodes or storage units as opposed to time. In settings such as that of a large data center, an important consideration is the efficient repair of a failed node. Efficient repair calls for erasure codes that in the face of node failure, are efficient in terms of minimizing the amount of repair data transferred over the network, the amount of data accessed at a helper node as well as the number of helper nodes contacted. Coding theory has evolved to handle these challenges by introducing two new classes of erasure codes, namely regenerating codes and locally recoverable codes as well as by coming up with novel ways to repair the ubiquitous Reed-Solomon code. This survey provides an overview of the efforts in this direction that have taken place over the past decade. © 2018, Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature.

World University root node

Science China Information Sciences

Erasure coding for distributed storage: an overview

In this paper, we study vector codes with all-symbol locality, where the local code is either a Minimum Bandwidth Regenerating (MBR) code or a Minimum Storage Regenerating (MSR) code. In the first part, we present vector codes with all-symbol MBR locality, for all parameters, that have both optimal minimum-distance and optimal rate. These codes combine ideas from two popular codes in the distributed storage literature; Product-Matrix codes and Tamo-Barg codes. In the second part which deals with codes having all-symbol MSR locality, we follow a Pairwise Coupling Transform-based approach to arrive at optimal minimum-distance and optimal rate, for a range of parameters. All the code constructions presented in this paper have a low field-size that grows linearly with the code-length n. © 2018 IEEE.

IEEE International Symposium on Information Theory - Proceedings

Codes with Combined Locality and Regeneration Having Optimal Rate, dmin and Linear Field Size

Recovery of data packets from packet erasures in a timely manner is critical for many streaming applications. An early paper by Martinian and Sundberg introduced a framework for streaming codes and designed rate-optimal codes that permit delay-constrained recovery from an erasure burst of length up to B. A recent work by Badr et al. extended this result and introduced a sliding-window channel model C(N, B, W). Under this model, in a sliding-window of width W, one of the following erasure patterns are possible (i) a burst of length at most B or (ii) at most N (possibly non-contiguous) arbitrary erasures. Badr et al. obtained a rate upper bound for streaming codes that can recover with a time delay T, from any erasure patterns permissible under the C(N, B, W) model. However, constructions matching the bound were absent, except for a few parameter sets. In this paper, we present a family of codes that achieves the rate upper bound for all feasible parameters N, B, W and T. © 2018 IEEE.

Rate-Optimal Streaming Codes for Channels with Burst and Isolated Erasures

We describe the SemEval task of extracting keyphrases and relations between them from scientific documents, which is crucial for understanding which publications describe which processes, tasks and materials. Although this was a new task, we had a total of 26 submissions across 3 evaluation scenarios. We expect the task and the findings reported in this paper to be relevant for researchers working on understanding scientific content, as well as the broader knowledge base population and information extraction communities.

International Workshop on Semantic Evaluation

SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publications

In this paper, we show how the presence of locality within a binary cyclic code can be exploited to improve decoding performance and to reduce decoding complexity. We pursue two approaches. Under the first approach, we show how the ordered statistics decoding (OSD) method can be modified by inserting a simple single round belief-propagation step at the start that involves only the local codes. The resultant locality-aware OSD algorithm yields an appreciable signal-to-noise ratio (SNR) gain for a given level of reliability and essentially the same level of decoder complexity. Under the second, trellis decoding approach, we show that the careful introduction of locality results in the creation of a cyclic subcode that possesses lower maximum state complexity. In addition, we present a simple means of deriving an upper bound to the state complexity profile of any cyclic code that is based only on the zeros of the code. Furthermore, we show how the decoding speed of either locality-aware OSD or trellis decoding can be significantly increased in the presence of locality, in the moderate-to-high SNR regime, by making the use of a quick-look decoder that often returns the maximum likelihood code word. © 1972-2012 IEEE.

IEEE Transactions on Communications

Exploiting Locality for Improved Decoding of Binary Cyclic Codes

IoT end-nodes require high performance and extreme energy efficiency to cope with complex near-sensor data analytics algorithms. Processing on multiple programmable processors operating in near-threshold is emerging as a promising solution to exploit the energy boost given by low-voltage operation, while recovering the related frequency degradation with parallelism. In this work, we present a heterogeneous cluster architecture extending a traditional parallel processor cluster with a reconfigurable Integrated Programmable Array (IPA) accelerator. While programmable processors guarantee programming legacy to easily manage peripherals, radio software stacks as well as the global program flow, offloading data-intensive and control-intensive kernels to the IPA leads to much higher system level performance and energy-efficiency. Experimental results show that the proposed heterogeneous cluster outperforms an 8-core homogeneous architecture by up to 4.8× in performance and 4.5× in energy efficiency when executing a mix of control-intensive and data-intensive kernels typical of near-sensor data analytics applications. © 2018 IEEE.

Proceedings - IEEE International Symposium on Circuits and Systems

A Heterogeneous Cluster with Reconfigurable Accelerator for Energy Efficient Near-Sensor Data Analytics

Learning from different modalities is a challenging task. In this paper, we look at the challenging problem of cross modal face verification and recognition between caricature and visual image modalities. Caricature have exaggerations of facial features of a person. Due to the significant variations in the caricatures, building vision models for recognizing and verifying data from this modality is an extremely challenging task. Visual images with significantly lesser amount of distortions can act as a bridge for the analysis of caricature modality. We introduce a publicly available large Caricature-VIsual dataset [CaVI] with images from both the modalities that captures the rich variations in the caricature of an identity. This paper presents the first cross modal architecture that handles extreme distortions of caricatures using a deep learning network that learns similar representations across the modalities. We use two convolutional networks along with transformations that are subjected to orthogonality constraints to capture the shared and modality specific representations. In contrast to prior research, our approach neither depends on manually extracted facial landmarks for learning the representations, nor on the identities of the person for performing verification. The learned shared representation achieves 91% accuracy for verifying unseen images and 75% accuracy on unseen identities. Further, recognizing the identity in the image by knowledge transfer using a combination of shared and modality specific representations, resulted in an unprecedented performance of 85% rank-1 accuracy for caricatures and 95% rank-1 accuracy for visual images.

MM 2018 - Proceedings of the 2018 ACM Multimedia Conference

Deep cross modal learning for caricature verification and identification(CaViNet)

This work proposes and evaluates a novel approach to determining interesting attributes, in order to categorize entities accordingly. Once identified, such categories are of immense value to allow constraining (filtering) a user's current view to subsets of entities. We show how a classifier is trained that is able to tell whether or not a categorical attribute can act as a constraint, in the sense of human-perceived interestingness. The training data is harnessed from Wikipedia tables, treating the presence or absence of a table as an indication that the attribute used as a filter constraint is reasonable or not. For learning the classification model, we review four well-known statistical measures (features) for categorical attributes—entropy, unalikeability, peculiarity and coverage. We additionally propose three new statistical measures to capture the distribution of data, tailored to our main objective. The learned model is evaluated by relevance assessments obtained through a user study, reflecting the applicability of the approach as a whole and, further, demonstrates the superiority of the proposed diversity measures over existing measures like information entropy.

ACM International Conference Proceeding Series

Learning interesting attributes for automated data categorization∗

In the analysis of metabolism, two distinct and complementary approaches are frequently used: Principal component analysis (PCA) and stoichiometric flux analysis. PCA is able to capture the main modes of variability in a set of experiments and does not make many prior assumptions about the data, but does not inherently take into account the flux mode structure of metabolism. Stoichiometric flux analysis methods, such as Flux Balance Analysis (FBA) and Elementary Mode Analysis, on the other hand, are able to capture the metabolic flux modes, however, they are primarily designed for the analysis of single samples at a time, and assume the stoichiometric steady state of the metabolic network. We will discuss a new methodology for the analysis of metabolism, called Principal Metabolic Flux Mode Analysis (PMFA), which marries the PCA and stoichiometric flux analysis approaches in an elegant regularized optimization framework. In short, the method incorporates a variance maximization objective form PCA coupled with a stoichiometric regularizer, which penalizes projections that are far from any flux modes of the network. For interpretability, we also discuss a sparse variant of PMFA that favors flux modes that contain a small number of reactions. PMFA has several benefits: (1) it can be applied to large metabolic network in efficient way as PMFA does not enumerate elementary modes, (2) the method is more robust to the steady-state violations than competing approaches, and (3) can compactly capture the variation in the data by a few factors. This chapter will describe the detailed steps how to do the above task on experimental data from fluxomic and gene expression measurements. © Springer Science+Business Media, LLC, part of Springer Nature 2018.