We propose a video retrieval system for everyday scenes with common objects. Our system exploits the predictions made by deep neural networks for image understanding tasks using natural language processing (NLP). It aims to capture the relationships between objects in a video scene as well as the ordering of the matching scenes. For each video in the database, it identifies and generates a sequence of key scene images. For each such scene, it generates most probable captions using state-of-the-art models for image captioning. The captions are parsed and represented by tree structures using NLP techniques. These are then stored and indexed in a database system. When a user poses a query video, a sequence of key scenes are generated. For each scene, its caption is generated using deep learning and parsed into its corresponding tree structure. After that, optimized tree-pattern queries are constructed and executed on the database to retrieve a set of candidate videos. Finally, these candidate videos are ranked using a combination of longest common subsequence of scene matches and tree-edit distance between parse trees. We evaluated the performance of our system using the MSR-VTT dataset, which contained everyday scenes. We observed that our system achieved higher mean average precision (mAP) compared to two recent techniques, namely, CSQ and DnS.

Variant calling is a fundamental task that is performed to identify variants in an individual's genome compared to a reference human genome. This task can enable better understanding of an individual's risk to diseases and eventually lead to new innovations in precision medicine and drug discovery. However, variant calling on a large number of human genome sequences requires significant computing and storage resources. While access to such resources is possible today (e.g., through cloud computing), reducing the cost of analyzing genomes has become a major challenge. Motivated by these reasons, we address the problem of accelerating the variant calling pipeline on a large number of human genome sequences using a commodity cluster. We propose a novel approach that synergistically combines data and task parallelism for different stages of the variant calling pipeline across different sequences with minimal synchronization. Our approach employs futures to enable asynchronous computations in order to improve the overall cluster utilization and thereby, accelerate the variant calling pipeline. On a 16-node cluster, we observed that our approach was 3X-4.7X faster than the state-of-the-art Big Data Genomics software.

The various data and privacy regulations introduced around the globe, require data to be stored in a secure and privacy-preserving fashion. Non-compliance with these regulations come with major consequences. This has led to the formation of huge data silos within organizations leading to difficult data analysis along with an increased risk of a data breach. Isolating data also prevents collaborative research. To address this, we present Private-Share, a framework that would enable secure sharing of large scale data. In order to achieve this goal, Private-Share leverages the recent advances in blockchain technology specifically the InterPlanetary File System and Ethereum.

In this paper, we propose a system for large-scale image retrieval on everyday scenes with common objects by leveraging advances in deep learning and natural language processing (NLP). Unlike recent state-of-the-art approaches that extract image features from a convolutional neural network (CNN), our system exploits the predictions made by deep neural networks for image understanding tasks. Our system aims to capture the relationships between objects in an everyday scene rather than just the individual objects in the scene. It works as follows: For each image in the database, it generates most probable captions and detects objects in the image using state-of-the-art deep learning models. The captions are parsed and represented by tree structures using NLP techniques. These are stored and indexed in a database system. When a user poses a query image, its caption is generated using deep learning and parsed into its corresponding tree structures. Then an optimized tree-pattern query is constructed and executed on the database to retrieve a set of candidate images. Finally, these candidate images are ranked using the tree-edit distance metric computed on the tree structures. A query based on only objects detected in the query image can also be formulated and executed. In this case, the ranking scheme uses the probabilities of the detected objects. We evaluated the performance of our system on the Microsoft COCO dataset containing everyday scenes (with common objects) and observed that our system can outperform state-of-the-art techniques in terms of mean average precision for large-scale image retrieval.

We present a system for large-scale image retrieval on everyday scenes with common objects. Our system leverages advances in deep learning and natural language processing (NLP) for improved understanding of images by capturing the relationships between the objects within an image. As a result, a user can retrieve highly relevant images and obtain suggestions for similar image queries to further explore the repository. Each image in the repository is processed (using deep learning) to obtain the most probable captions and objects in it. The captions are parsed into tree structures using NLP techniques, and stored and indexed in a database system. When a query image is posed, an optimized tree-pattern query is executed by the database system to obtain candidate matches, which are then ranked using tree-edit distance of the tree structures to output the top-k matches. Word embeddings and Bloom filters are used to obtain similar image queries. By clicking the suggested similar image queries, a user can intuitively explore the repository.

In this paper, we present a system for fast approximate score computation, a fundamental task for score-based structure learning of multinomial Bayesian networks. Our work is motivated by the fact that exact score computation on large datasets is very time consuming. Our system enables approximate score computation on large datasets in an efficient and scalable manner with probabilistic error bounds on the statistics required for score computation. Our system has several novel features including gossip-based decentralized computation of statistics, lower resource consumption via a probabilistic approach of maintaining statistics, and effective distribution of tasks for score computation using hashing techniques. The demo will provide a real-time and interactive experience to a user on how our system employs the principle of gossiping and hashing techniques in a novel way for fast approximate score computation. The user will be able to control different aspects of our system's execution on a cluster with up to 32 nodes. The approximate scores output by our system can be then used by existing score-based structure learning algorithms.