Gautam Shroff

Distributed side-by-side programming

Recent work has proposed a variation of pair programming called side-by-side programming, wherein two programmers, sitting next to each other and using different workstations, work together on the same task. We have defined a distributed approximation of this idea and implemented it in both a compiled and interpretive environment. Our experiments with these implementations provide several new preliminary results regarding different aspects of (distributed) side-by-side programming.

Influencing factors in outsourced software maintenance

Software lifecycle management is a complex phenomenon with each stage posing its unique technical and other challenges. Maintenance and enhancement of software brings in its own share of complexities to this phase. While uncertainties associated with software baseline in themselves pose a huge challenge in planning and estimation of maintenance activities, there are several other factors that contribute to overall success of software maintenance project especially in an outsourcing scenario. This paper brings out the results of an analysis of some such factors, their interrelationship and influence on software maintenance activities and effort.

InstantApps: A WYSIWYG model driven interpreter for web applications

We describe InstantApps, a WYSIWIG, model driven interpreter for developing and running web based database oriented applications. Applications are dynamically rendered through efficient runtime interpretation of meta-data which is manipulated through an intuitive visual designer. We also capture complex (multi-table) forms, workflow as well as business logic (using a variant of Googles MapReduce abstraction), distinguishing our approach from similar platforms in the literature as well as others recently made available on the internet

An AR Inspection Framework: Feasibility Study with Multiple AR Devices

We present an Augmented Reality (AR) based re-configurable framework for inspection that can be utilized in cross-domain applications such as maintenance and repair assistance in industrial inspection, health sector to record vitals, and automotive/avionics domain inspection, amongst others. The novelty of the inspection framework as compared to the existing counterparts are three fold. Firstly, the inspection check-list can be prioritized by detecting the parts viewed in inspectors field using deep learning principles. Second, the backend of the framework is easily configurable for different applications where instructions and assistance manuals can be directly imported and visually integrated with inspection type. Third, we conduct a feasibility study on inspection modes such as Google Glass, Google Cardboard, Paper based and Tablet for inspection turnaround time, ease, and usefulness by taking a 3D printer inspection use-case.

Collaborative development of business applications

Collaborative software development models, inspired by the open source community, are also being considered for development and deployment of business applications. We describe current work and future directions towards a hosted model to support collaboration during software development. We submit that such a platform can enable rapid application development, facilitate globally located virtual teams spanning locations to work as a single unit, and also encourage re-use, especially of technical frameworks. Going forward, we foresee extending such environments to providing an end-to-end development process accessible to globally distributed virtual teams

Searching for Logical Patterns in Multi-sensor Data from the Industrial Internet

Engineers analysing large volumes of multi-sensor data from vehicles, engines etc. often seek to search for events such as “hard-stops”, “lane passing” or “engine overload”. Apart from such visual analysis for engineering purposes, manufactures also need to count occurrences of such events via on-board monitoring sensors that ideally rely on classifiers; searching for patterns in available data is also useful for preparing training sets in this context. In this paper, we propose a method for searching for multi-sensor patterns in large volumes of sensor data using qualitative symbols (QSIM (Say, Functions representable in pure QSIM, 251–255, 1996, [1])) such as “steady”, “increasing”, “decreasing”. Patterns can include symbol-sequences for multiple sensors, as well as approximate duration, level or slope values. Logical symbols are extracted from multi-sensor time-series and registered in a trie-based index structure. We demonstrate the effectiveness of our retrieval and ranking technique on real-life vehicular sensor data in the visual analytics as well as classifier training and detection scenarios.

Multi-sensor event detection using shape histograms

Vehicular sensor data consists of multiple time-series arising from a number of sensors. Using such multi-sensor data we would like to detect occurrences of specific events that vehicles encounter, e.g., corresponding to particular maneuvers that a vehicle makes or conditions that it encounters. Events are characterized by similar waveform patterns reappearing within one or more sensors. Further such patterns can be of variable duration. In this paper, we propose a method for detecting such events in time-series data using a novel feature descriptor motivated by similar ideas in image processing. We define the shape histogram: a constant dimension descriptor that nevertheless captures patterns of variable duration. We demonstrate the efficacy of using shape histograms as features to detect events in an SVM-based, multi-sensor, supervised learning scenario, i.e., multiple time-series are used to detect an event. We present results on real-life vehicular sensor data and show that our technique performs better than available pattern detection implementations on our data, and that it can also be used to combine features from multiple sensors resulting in better accuracy than using any single sensor. Since previous work on pattern detection in time-series has been in the single series context, we also present results using our technique on multiple standard time-series datasets and show that it is the most versatile in terms of how it ranks compared to other published results.

Comparative benchmarking of causal discovery algorithms

In this paper we present a comprehensive view of prominent causal discovery algorithms, categorized into two main categories (1) assuming acyclic and no latent variables, and (2) allowing both cycles and latent variables, along with experimental results comparing them from three perspectives: (a) structural accuracy, (b) standard predictive accuracy, and (c) accuracy of counterfactual inference. For (b) and (c) we train causal Bayesian networks with structures as predicted by each causal discovery technique to carry out counterfactual or standard predictive inference. We compare causal algorithms on two publicly available and one simulated datasets having different sample sizes: small, medium and large. Experiments show that structural accuracy of a technique does not necessarily correlate with higher accuracy of inferencing tasks. Further, surveyed structure learning algorithms do not perform well in terms of structural accuracy in case of datasets having large number of variables.

Online anomaly detection with concept drift adaptation using recurrent neural networks

Anomaly detection in time series is an important task with several practical applications. The common approach of training one model in an offline manner using historical data is likely to fail under dynamically changing and non-stationary environments where the definition of normal behavior changes over time making the model irrelevant and ineffective. In this paper, we describe a temporal model based on Recurrent Neural Networks (RNNs) for time series anomaly detection to address challenges posed by sudden or regular changes in normal behavior. The model is trained incrementally as new data becomes available, and is capable of adapting to the changes in the data distribution. RNN is used to make multi-step predictions of the time series, and the prediction errors are used to update the RNN model as well as detect anomalies and change points. Large prediction error is used to indicate anomalous behavior or a change (drift) in normal behavior. Further, the prediction errors are also used to update the RNN model in such a way that short term anomalies or outliers do not lead to a drastic change in the model parameters whereas high prediction errors over a period of time lead to significant updates in the model parameters such that the model rapidly adapts to the new norm. We demonstrate the efficacy of the proposed approach on a diverse set of synthetic, publicly available and proprietary real-world datasets.

Automated Product-Attribute Mapping

Aggregate analysis, such as comparing country-wise sales versus global market share across product categories, is often complicated by the unavailability of common join attributes, e.g., category, across diverse datasets from different geographies or retail chains. Sometimes this is a missing data issue, while in other cases it may be inherent, e.g., the records in different geographical databases may actually describe different product ‘SKUs’, or follow different norms for categorization. Often a tedious manual mapping process is often employed in practice. We focus on improving such a process using machine-learning driven automation. Record linkage techniques, such as [5] can be used to automatically map products in different data sources to a common set of global attributes, thereby enabling federated aggregation joins to be performed. Traditional record-linkage techniques are typically unsupervised, relying textual similarity features across attributes to estimate matches. In this paper, we present an ensemble model combining minimal supervision using Bayesian network models together with unsupervised textual matching for automating such ‘attribute fusion’. We present results of our approach on a large volume of real-life data from a market-research scenario and compare with a standard record matching algorithm. Our approach is especially suited for practical implementation since we also provide confidence values for matches, enabling routing of items for human intervention where required.