Dissertation and Thesis

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    Some Contributions to the Asymptotic Theory of Estimation in Non-regular Case
    (1989-02-28) Samanta, Tapas
    The introduction of the concept of onymptotie efficiency of statistical eatimators in connection with proposing and developing she method of maximu, 1ikelihood by . Fislior (Fisher (1922, 1929)) 1s really the atar ting point of the asymptotie theory of es timation. -istorieally, however, Laplace (1774) and Gausa (1009) had made tuo irferent studieo oarlier then Fisher bo ti connected d th as ymp to tie theory of estination. Fisher considered only consistent a symptotically rewnal untinotoro and meured the asyaptotie performance of an anti- mator by ite aayep totle variaree. Thus, a coreistent aeynp to tieally nomal eetimoator with let possible aayno to tie voriance wes defined to be an erricient estimator. Fisher also clained to have proved that under certain regulari ty condi tiona the naxinum likelhaod estinatar (MLE) 1s offieient in the above sense. In the thirties and forties several authors (Dugue (1936a, 1936b, 1937), Wlks (1938), Neynan (1949) ond ethere) attompted to a btain a Figorous proof of the erricieney of the MLE and there uas a genet al teller that there exinte an afrieient es timator in the general case unich nay be obtained by the method of maximum likelihood. This belief enieted until 3.. Hodges produced in 1951 the " revolutionary exanples of;auper ofricient osti- ma tore (ane ean se, for example, Ghonh (1905) or Le Can (1953) hera It rirot appenred). Hodgen exanple nhowe that in the unual rogular casee there axist neymptotically nomal estinatere uhone aaymptotic variances are alunye lees than or equal to that of tha MLE and are trietly less than that of the MLE at partieuler values of the para- ter and at these particular valuen the asymp to tie variance may even e made equal to zero. Thus, the MLE is net errieient in tha above sense and indeed, within the elass of all symptotically normal esti- natera re entinator ui th minimal symptotic variance exists.However, the ideas of Fiaher and the existence of " super ofricient " otinators greatly influenced the developement of the theory of efficient entimation and a modern approach to the theory of synap totie efrrietent entimation emerged in the fundamantal paper of Le Can (1983). The theory vos further developed in the worke of Le Can (1960, 1964, 1972), Hajek (1970, 1972), unlfouitz (1965), Millar (1903) and othere. Thie approach which renched nore ar lese itarinal form in the papere of Hajek (1972) and Le Can (1972) consi dere all e tina tore in oteed of rontricting to the class of naynptotically nornal en tinn tora only, tut the ofricieney or the performance of these estinn tors is meanur ed in a slightly different uay. Mmiller (1983) prenenta a very clenr expoeitian of thie theory extending sone of the basie results of Le Can (1072). A lucid aneount of this developrent of kajek - La Can theory of afrieient estination is available in Choah (1905).
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    Development of Some Scalable Pattern Recognition Algorithms for Real Life Data Analysis
    (2017-11-20) Garai, Partha
    A huge amount of data is being generated continuously as a result of recent advancement and wide use of high-throughput technologies. With the rapid increase in size of data distributed worldwide, understanding the data has become critical. In this regard, dimensionality reduction and clustering have become the necessary preprocessing steps of multiple research areas and applications. One of the important problems of real life large data sets is uncertainty. Some of the sources of this uncertainty include imprecision in computation and vagueness in class denitions. The uncertainty may also be present in the denition of class membership function. In this background, the thesis addresses the problem of dimensionality reduction and clustering of real life data sets, in the presence of noise and uncertainty. The thesis rst presents the problem of feature selection using both type-1 and interval type-2 fuzzyrough sets, which are eective for dimensionality reduction of real life data sets when uncertainty is present in the data set. The properties of fuzzy-rough sets allow greater exibility in handling noisy and real valued data. While the concept of lower approximation and boundary region of rough sets deals with uncertainty, incompleteness, and vagueness in class denition, the use of either type-1 or interval type-2 fuzzy sets enables ecient handling of overlapping classes in uncertain environment. Moreover, a new concept of \simultaneous attribute selection and feature extraction" is introduced for dimensionality reduction, integrating judiciously the merits of both feature selection and extraction. A scalable rough-fuzzy clustering algorithm is introduced for large real life data sets, where the theory of rough hypercuboid approach, interval type-2 fuzzy sets, and c-means algorithm are integrated judiciously to handle the uncertainty present in a data set. While the concept of rough hypercuboid approach deals with uncertainty, incompleteness, and vagueness in cluster denition, the use of fuzzy membership of interval type-2 fuzzy sets in the boundary region of a cluster enables ecient handling of overlapping partitions in uncertain environment. Finally, the application of both clustering and feature selection algorithms is demonstrated by grouping functionally similar microRNAs from microarray data. The proposed approach can automatically select the optimum set of features while clustering the microRNAs, making the complexity of the algorithm lower.
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    A Study of the SHA-2 Cryptographic Hash Family
    (Indian Statistical Institute, Kolkata, 2009-02-01) Sanadhya Somitra Kumar
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    Adversarial Attack on Neural Machine Translation System
    (Indian Statistical Institute, Kolkata, 2019-06) Abijith, K P
    Nowadays Deep Neural Network based solutions are deployed to solve numerous tasks. Thus, it has become absolutely important to study the robustness of these systems. Machine Translation is one of the popular applications of Deep Neural Networks. This thesis studies the robustness of Neural Machine Translation systems by generating adversarial examples with the objective to fool the model. Whenever there is a change in the source, i.e. when a word in the input sentence is replaced by an unrelated word, the translation system is supposed to re ect the changes while doing translation. These unwanted invariance learned by the model is undesirable. With intention to exploit this undesirable property learned by a Neural Machine Translation system we design an attack called: Invariance-based targeted attack. This attack introduces multiple changes(replacement of words) to the original input sentence, keeping the translation unchanged. In-order to facilitate the explanation of the design of the attack we introduce two methods: (i) Min-Grad method: To identify the position where a replacement of the word makes the least change in the translation, and (ii) Soft-Attn method: To search for a new word to replace, given a list of choices. The initial part of the report explain the preliminary explorations we did in-order to get some insights on how to do the problem formulation. These experiments are run on LSTM based models with single replacement policy. Using the learning from the rst part we extend the experiments to Transformer and BLSTM based models, which are considered as the state-of-the-art systems for machine translation.
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    Contributions to Analysis of Consumer Expenditure
    (Indian Statistical Institute, Kolkata, 1964-04) Iyengar, N Sreenivasa
    Abstract
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    Some Results on Combinatorial Batch Codes and Permutation Binomials over Finite Fields
    (Indian Statistical Institute,Kolkata, 2015) Bhattacharya, Srimanta
    In this thesis,we study combinatorial batch codes (CBCs) and permutation binomials (PBs) over �nite �elds of even characteristic. Our primary motivation for considering these problems comes from their importance in cryptography. CBCs are replication based variants of batch codes, which were introduced in [IKOS04a] as a tool for reducing the computational overhead of private information retrieval protocols (a cryptographic primitive). On the other hand, permutation polynomials, with favourable cryptographic properties, have applications in symmetric key encryption schemes, especially in block ciphers. Moreover, these two objects are interesting in their own right, and they have connections with other important combinatorial objects. CBCs are much similar to unbalanced expanders, a much studied combinatorial object having numerous applications in theoretical computer science. On the other hand, the speci�c class of PBs that we consider in this work, are intimately related to orthomorphisms. Orthomorphisms are relevant in the construction of mutually orthogonal latin squares, a classical combinatorial objects having applications in design of statistical experiments. These aspects motivate us to explore theoretical properties of CBCs and PBs over �nite �elds. However, these two objects are inherently widely di�erent; CBCs are purely combinatorial objects, and PBs are algebraic entities. So, we explore these two objects independently in two di�erent parts, where our entire focus lies in exploring theoretical aspects of these objects. In Part I, we consider CBCs. There, we provide bounds on the parameters of CBCs and obtain explicit constructions of optimal CBCs. In Part II, we consider PBs over �nite �elds. There, we obtain explicit characterization and enumeration of subclasses of PBs under certain restrictions. Next, we describe these two parts in more detail.
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    Discriminative Dictionary Learning by Exploiting Inter-Class Similarity for HEp-2 Cell Classi cation
    (Indian Statistical Institute,Kolkata, 2019-07) Panda, Aditya
    In this literature we present an algorithm for automatic classi cation of IIF images of HEp-2 cells into relevant classes. Our algorithm is majorly based on the \Dictionary Learning" algorithm and we have rede ned it's objective function to suit our purpose. The major di culty in HEp-2 cell image classi cation lies in it's low inter-class variability and substantial intra-class variations. To address these issues, we have modi ed the objective function of \Dictionary Learning" to learn inter-class features. Moreover, we used a local feature extractor based pre-processing stage and also a \spatial decomposition" classi er set-up for better classifying test images. We evaluated our algorithm on three most widely accepted bamechmark data-sets for HEp-2 cell classi cation, ICPR 2012, ICIP 2013 and SNP data-sets. Proposed algorithm has achieved superior results than other popular dictionary learning algorithms for HEp-2 cell classi cation. Moreover, when comparing with other algorithms for HEp-2 cell classi cation, including the winners of ICPR 2012, ICIP 2013 and SNP data-set, we show that proposed algorithm reports very competitive result. Though our proposed algorithm is designed to be application speci c to HEp-2 cell, still we evaluated its performance on another popular benchmark data-set, \Diabetic Retinopathy" data-set. Our algorithm provided higher accuarcy than other state-ofthe- art algorithms on that data-set too.
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    BlockV: A Blockchain Enabled Peer-Peer Ride Sharing Service
    (Indian Statistical Institute,Kolkata, 2019-07) Pal, Panchalika
    Today's ride sharing is a centralized trust based system where users trust the service providers for the ride set up, tracking, cancellation, fare calculation etc. Any malicious activity in the centralized server based system or a malicious driver or a malicious rider destroys the fairness involved in the ride and causes inconvenience to the parties. After the completion of the ride, the drivers are rated by the riders. There are possibilities that, a malicious rider can claim the refund with a fake complain and give the driver poor rating intentionally or a malicious driver follows a longer path unnecessarily and charges the rider more. Current system is not capable of deciding the correctness of the objections raised by either parties regarding the ride and provides a biased outcome of each objections as per the centralized company's marketing strategies. In this context, we present BlockV, a blockchain enabled anonymous permissionless solution to ensure end to end fairness of the ride. The creation, completion, dissatisfaction or abortion of any ride will be written in the blockchain ledger, hence will be available to all participants in the peer to peer network. This simultaneously ensures the fairness in maintenance of the inbuilt reputation system. We have implemented a prototype in Ethereum private network and KOVAN test network and analyzed the security.
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    Recognition of Strokes in Tennis Videos Using Deep Learning
    (Indian Statistical Institute,Kolkata, 2019-07) Singanporia, Kushal
    Prior introduction of neural nets to domain of computer vision, action recognition requires specific domain knowledge. Still domain knowledge is useful in action recognition but with availability of huge data and neural nets, data-driven feature learning methods have emerged as an alternative. Recent trends in action recognition uses LSTM and its various modifications, as LSTM have memory retaining capability which other architectures lake. In this work we performed action recognition on different tennis strokes. Our work relay on architecture proposed By Husain, Dellen, and Torras, 2016. Architecture is comprised of various modified VGG-nets connected in parallel. As it doesn’t include LSTM, which makes it different than other works.
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    Classi cation of Micro-Blog Texts
    (Indian Statistical Institute,Kolkata, 2019-07) Sen, Bihan
    Classi cation of micro-blog texts is a very common task for sentiment analysis, user opinion mining, product review analysis, crisis managements, identifying ofensive and hate speech propagation across social media, restricting unnecessary expansion of fake news and rumors etc. In this dissertation, we consider two problems from this domain: (i) classi cation of tweets during crisis scenarios like natural disasters, terrorist attacks etc and (ii) identifying o ensive tweets. We tried both statistical and deep learning approaches. Datasets from the TREC-IS 2018 and 2019 tasks, and OLID from O enseEval workshop were used for our experiments. The rst task is formulated as a multi-label classi cation task, while the second is a binary classi cation problem. Our results suggest that preprocessing of social media text is very crucial for classi cation. We also conclude that Deep Learning approaches do not always outperform traditional learning. We also took part as an active participant in the TREC-IS 2019A task. Out of all 34 submissions from across the world, one of our submissions achieved the highest macro-averaged F-1 score on this task (0.1969) and outperformed the second highest score (0.1556) by a substantial margin.