Amit Kumar Mondal

Amit Kumar Mondal, Chanchal K. Roy, Kevin A. Schneider et al.

Software architectural changes involve more than one module or component and are complex to analyze compared to local code changes. Development teams aiming to review architectural aspects (design) of a change commit consider many essential scenarios such as access rules and restrictions on usage of program entities across modules. Moreover, design review is essential when proper architectural formulations are paramount for developing and deploying a system. Untangling architectural changes, recovering semantic design, and producing design notes are the crucial tasks of the design review process. To support these tasks, we construct a lightweight tool [4] that can detect and decompose semantic slices of a commit containing architectural instances. A semantic slice consists of a description of relational information of involved modules, their classes, methods and connected modules in a change instance, which is easy to understand to a reviewer. We extract various directory and naming structures (DANS) properties from the source code for developing our tool. Utilizing the DANS properties, our tool first detects architectural change instances based on our defined metric and then decomposes the slices (based on string processing). Our preliminary investigation with ten open-source projects (developed in Java and Kotlin) reveals that the DANS properties produce highly reliable precision and recall (93-100%) for detecting and generating architectural slices. Our proposed tool will serve as the preliminary approach for the semantic design recovery and design summary generation for the project releases.

Amit Kumar Mondal

Reinforcement learning is one of the core components in designing an artificial intelligent system emphasizing real-time response. Reinforcement learning influences the system to take actions within an arbitrary environment either having previous knowledge about the environment model or not. In this paper, we present a comprehensive study on Reinforcement Learning focusing on various dimensions including challenges, the recent development of different state-of-the-art techniques, and future directions. The fundamental objective of this paper is to provide a framework for the presentation of available methods of reinforcement learning that is informative enough and simple to follow for the new researchers and academics in this domain considering the latest concerns. First, we illustrated the core techniques of reinforcement learning in an easily understandable and comparable way. Finally, we analyzed and depicted the recent developments in reinforcement learning approaches. My analysis pointed out that most of the models focused on tuning policy values rather than tuning other things in a particular state of reasoning.

Amit Kumar Mondal, Banani Roy, Chanchal K. Roy et al.

With the rapid advancement of Big Data platforms such as Hadoop, Spark, and Dataflow, many tools are being developed that are intended to provide end users with an interactive environment for large-scale data analysis (e.g., IQmulus). However, there are challenges using these platforms. For example, developers find it difficult to use these platforms when developing interactive and reusable data analytic tools. One approach to better support interactivity and reusability is the use of microlevel modularisation for computation-intensive tasks, which splits data operations into independent, composable modules. However, modularizing data and computation-intensive tasks into independent components differs from traditional programming, e.g., when accessing large scale data, controlling data-flow among components, and structuring computation logic. In this paper, we present a case study on modularizing real world computationintensive tasks that investigates the impact of modularization on processing large scale image data. To that end, we synthesize image data-processing patterns and propose a unified modular model for the effective implementation of computation-intensive tasks on data-parallel frameworks considering reproducibility, reusability, and customization. We present various insights of using the modularity model based on our experimental results from running image processing tasks on Spark and Hadoop clusters.

N Dinesh Reddy, Iman Abbasnejad, Sheetal Reddy et al.

Real time outdoor navigation in highly dynamic environments is an crucial problem. The recent literature on real time static SLAM don't scale up to dynamic outdoor environments. Most of these methods assume moving objects as outliers or discard the information provided by them. We propose an algorithm to jointly infer the camera trajectory and the moving object trajectory simultaneously. In this paper, we perform a sparse scene flow based motion segmentation using a stereo camera. The segmented objects motion models are used for accurate localization of the camera trajectory as well as the moving objects. We exploit the relationship between moving objects for improving the accuracy of the poses. We formulate the poses as a factor graph incorporating all the constraints. We achieve exact incremental solution by solving a full nonlinear optimization problem in real time. The evaluation is performed on the challenging KITTI dataset with multiple moving cars.Our method outperforms the previous baselines in outdoor navigation.

K. M. Azharul Hasan, Al-Mahmud, Amit Mondal et al.

We describe a Context Free Grammar (CFG) for Bangla language and hence we propose a Bangla parser based on the grammar. Our approach is very much general to apply in Bangla Sentences and the method is well accepted for parsing a language of a grammar. The proposed parser is a predictive parser and we construct the parse table for recognizing Bangla grammar. Using the parse table we recognize syntactical mistakes of Bangla sentences when there is no entry for a terminal in the parse table. If a natural language can be successfully parsed then grammar checking from this language becomes possible. The proposed scheme is based on Top down parsing method and we have avoided the left recursion of the CFG using the idea of left factoring.