Bodhisattwa Majumder

Properly extracting attribute qualities from product titles is an import and necessary aspect in delivering eCommerce customers with a rewarding online shopping experience. A rich set of product attributes drastically improves the discoverability of any given product in the product space, which in turn helps the user navigate to their desired product. Our previous applications exposed an absence of syntactic structures, especially in short product titles making the attribute extraction process… Show more

Properly extracting attribute qualities from product titles is an import and necessary aspect in delivering eCommerce customers with a rewarding online shopping experience. A rich set of product attributes drastically improves the discoverability of any given product in the product space, which in turn helps the user navigate to their desired product. Our previous applications exposed an absence of syntactic structures, especially in short product titles making the attribute extraction process more challenging, ultimately leading to a less discoverable interface for customers. Building on our previously shown combinations of Conditional Random Fields and Structured Perceptrons providing efficient systems for attribute extraction, we demonstrate the potential of Deep Recurrent Networks in this domain. Specifically models such as the Bidirectional LSTM-CRF, Bidirectional LSTM, and others improve the model's overall F1 score, as compared to the shallow models by at least 3.91%. Alternatively, we present attribute extraction as a classification problem where the expected product attribute value might not be present in the product title, for example the case with gender. In this situation, deep models have outperformed previous machine learning benchmarks, thus enriching the overall customer experience while shopping on Walmart.com. Show less

Image memes (simply memes) are becoming increasingly popular over social media. In this work, we for the first time, investigate the evolution and life-cycle of an online community that uses image based memes as their primary means of communication. We find that the frequency of various image memes that are reused within the community follows Zipf- Mandelbrot distribution. The community in its initial days borrows image memes popular over the Internet, but gradually emerges with memes local to… Show more

Image memes (simply memes) are becoming increasingly popular over social media. In this work, we for the first time, investigate the evolution and life-cycle of an online community that uses image based memes as their primary means of communication. We find that the frequency of various image memes that are reused within the community follows Zipf- Mandelbrot distribution. The community in its initial days borrows image memes popular over the Internet, but gradually emerges with memes local to the community, finally surpassing the popularity of the former. These memes tend to exhibit temporal variations in their usage patterns. We characterize each day by their temporal activity and popularity. The interaction between memes across days leading to active user engagement within the community is captured using HDP- HMM, a non-parametric Bayesian variation of the infinite HMM. We group the observed days where the states encode possible ‘moods’ on the observed days. With the model, we also establish the significance of familiarity vs. freshness which is key to the growth and evolution of the community. Finally, using the adaptor grammar framework, we identify motifs from the community activity and and use it to predict the ‘mood’ of a day based on the ‘moods’ of the previous days. Show less

With the advancement of huge data generation and data handling capability, Machine Learning and Probabilistic modelling enables an immense opportunity to employ predictive analytics platform in high security critical industries namely data centers, electricity grids, utilities, airport etc. where downtime minimization is one of the primary objectives. This paper proposes a novel, complete architecture of an intelligent predictive analytics platform, Fault Engine, for huge device network… Show more

With the advancement of huge data generation and data handling capability, Machine Learning and Probabilistic modelling enables an immense opportunity to employ predictive analytics platform in high security critical industries namely data centers, electricity grids, utilities, airport etc. where downtime minimization is one of the primary objectives. This paper proposes a novel, complete architecture of an intelligent predictive analytics platform, Fault Engine, for huge device network connected with electrical/information flow. Three unique modules, here proposed, seamlessly integrate with available technology stack of data handling and connect with middleware to produce online intelligent prediction in critical failure scenarios. The Markov Failure module predicts the severity of a failure along with survival probability of a device at any given instances. The Root Cause Analysis model indicates probable devices as potential root cause employing Bayesian probability assignment and topological sort. Finally, a community detection algorithm produces correlated clusters of device in terms of failure probability which will further narrow down the search space of finding route cause. The whole Engine has been tested with different size of network with simulated failure environments and shows its potential to be scalable in real-time implementation.
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This paper proposes a double auction mechanism for energy trade between buying and selling agents. The framework is general enough, requiring neither the agents' preferences nor the energy pricing to be fixed values across the spatially distributed agents. A microgrid controller implements a distributed algorithm to maximize individual participating agents' utilities as well as the social welfare. This is accomplished by the controller in an iterative manner, such that the need to obtain… Show more

This paper proposes a double auction mechanism for energy trade between buying and selling agents. The framework is general enough, requiring neither the agents' preferences nor the energy pricing to be fixed values across the spatially distributed agents. A microgrid controller implements a distributed algorithm to maximize individual participating agents' utilities as well as the social welfare. This is accomplished by the controller in an iterative manner, such that the need to obtain private information pertaining to individual agents' preferences is obviated. Simulation results with a set of seven buyers and an equal number of sellers indicate that the proposed iterative double auction can establish social welfare maximization, requiring only a reasonable amount of computational overhead. Show less

The purpose of a multimodal biometric system is to construct a robust classifier of genuine and imposter candidates by extracting useful information from several biometric sources which fail to perform well in identification or verification as individual biometric systems. Amongst different levels of information fusion, very few approaches exist in literature exploring score level fusion. In this paper, we propose a novel adaptive weight and exponent based function mapping the matching scores… Show more

The purpose of a multimodal biometric system is to construct a robust classifier of genuine and imposter candidates by extracting useful information from several biometric sources which fail to perform well in identification or verification as individual biometric systems. Amongst different levels of information fusion, very few approaches exist in literature exploring score level fusion. In this paper, we propose a novel adaptive weight and exponent based function mapping the matching scores from different biometric sources into a single amalgamated matching score to be used by a classifier for further decision making. Differential Evolution (DE) has been employed to adjust these tunable parameters with the objective being the minimization of the overlapping area of the frequency distributions of genuine and imposter scores in the fused score space, which are estimated by Gaussian kernel density method to achieve higher level of accuracy. Experimental results show that, the proposed method outperforms the conventional score-level fusion rules (sum, product, tanh, exponential) when tested on two databases of 4 modalities (fingerprint, iris, left ear and right ear) of 200 and 516 users and thus confirms the effectiveness of score level fusion. The preliminary results provide adequate motivation towards future research in the line of the application of meta-heuristics in score level fusion. Show less

The paper proposes a novel non-homogeneity measure based kernelized image segmentation algorithm for noisy images. Every 3×3 neighbourhood of every single pixel is considered for generating localized spatial domain non-homogeneity measures for every individual window. Then these spatial domain non-homogeneity measures are converted into fuzzy domain non-homogeneity coefficients by aggregating the localized measures into a single distribution and then deriving fuzzy domain values from a Gaussian… Show more

The paper proposes a novel non-homogeneity measure based kernelized image segmentation algorithm for noisy images. Every 3×3 neighbourhood of every single pixel is considered for generating localized spatial domain non-homogeneity measures for every individual window. Then these spatial domain non-homogeneity measures are converted into fuzzy domain non-homogeneity coefficients by aggregating the localized measures into a single distribution and then deriving fuzzy domain values from a Gaussian membership function. Quantitative analyses have been rendered with respect to state-of-the-art noisy-image segmentation techniques and results show improved performance. Speckle-noise ridden SAR images and Rician noise ridden medical images are finally considered to show real-life applications of our algorithm. Show less

The increasing penetration of distributed renewable generation brings new power producers to the market. Rooftop photovoltaic (PV) panels allow home owners to generate more power than personally needed and this excess production could be voluntarily sold to nearby homes, alleviating additional transmission costs especially in rural areas. Power is sold as a continuous quantity and power markets involve pricing that may change on a minute-to-minute basis. Forward markets assist with scheduling… Show more

The increasing penetration of distributed renewable generation brings new power producers to the market. Rooftop photovoltaic (PV) panels allow home owners to generate more power than personally needed and this excess production could be voluntarily sold to nearby homes, alleviating additional transmission costs especially in rural areas. Power is sold as a continuous quantity and power markets involve pricing that may change on a minute-to-minute basis. Forward markets assist with scheduling power in advance [25]. The speed and complexity of the calculations needed to support online distributed auctions is a good fit for intelligent agents [14]. This paper describes the simulation of a two-tier double auction for short-term forward power exchanges between participants at the outer edges of a power distribution system (PDS). The paper describes the double auction algorithms and demonstrates online auction execution in a simulated distributed system of intelligent agents assisting with voltage/var control near distributed renewable generation. The agents were enhanced to autonomously create local power market organizations and execute the series of online power auctions using Advanced Message Queuing Protocol (AMQP). Show less

This paper presents a novel optimal edge detection scheme based on the concepts of fuzzy Smallest Univalue Assimilating Nucleus (SUSAN) and JADE (an adaptive Differential Evolution variant with Optional External Archive). Initially, the Univalue Assimilating Nucleus (USAN) area is calculated from the intensities of every neighborhood pixel of a pixel of interest in the test image. The USAN area edge map of each of the RGB components is fuzzified subject to the optimization of fuzzy entropy… Show more

This paper presents a novel optimal edge detection scheme based on the concepts of fuzzy Smallest Univalue Assimilating Nucleus (SUSAN) and JADE (an adaptive Differential Evolution variant with Optional External Archive). Initially, the Univalue Assimilating Nucleus (USAN) area is calculated from the intensities of every neighborhood pixel of a pixel of interest in the test image. The USAN area edge map of each of the RGB components is fuzzified subject to the optimization of fuzzy entropy, together with fuzzy edge sharpness factor, using JADE. Adaptive thresholding converts the fuzzy edge map to spatial domain edge map. Finally, the individual RGB edge maps are concatenated to obtain the final image edge map. Qualitative and quantitative comparisons have been rendered with respect to a few promising edge detectors and also optimal fuzzy edge detectors based on metaheuristic algorithms like the classic Differential Evolution (the classic DE/rand/1) and the Particle Swarm Optimization (PSO). Show less