publications

2022

1. Fast Adaptation of Manipulator Trajectories to Task Perturbation by Differentiating through the Optimal Solution

   Shashank Srikanth, Mithun Babu, Houman Masnavi, and 3 more authors

   Sensors, 2022

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   Joint space trajectory optimization under end-effector task constraints leads to a challenging non-convex problem. Thus, a real-time adaptation of prior computed trajectories to perturbation in task constraints often becomes intractable. Existing works use the so-called warm-starting of trajectory optimization to improve computational performance. We present a fundamentally different approach that relies on deriving analytical gradients of the optimal solution with respect to the task constraint parameters. This gradient map characterizes the direction in which the prior computed joint trajectories need to be deformed to comply with the new task constraints. Subsequently, we develop an iterative line-search algorithm for computing the scale of deformation. Our algorithm provides near real-time adaptation of joint trajectories for a diverse class of task perturbations, such as (i) changes in initial and final joint configurations of end-effector orientation-constrained trajectories and (ii) changes in end-effector goal or way-points under end-effector orientation constraints. We relate each of these examples to real-world applications ranging from learning from demonstration to obstacle avoidance. We also show that our algorithm produces trajectories with quality similar to what one would obtain by solving the trajectory optimization from scratch with warm-start initialization. Most importantly, however, our algorithm achieves a worst-case speed-up of 160x over the latter approach.

2020

1. Driving the last mile: Characterizing and understanding distracted driving posts on social networks

   Hemank Lamba, Shashank Srikanth, Dheeraj Reddy Pailla, and 3 more authors

   In Proceedings of the International AAAI Conference on Web and Social Media, 2020

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   In 2015, 391,000 people were injured due to distracted driving in the US. One of the major reasons behind distracted driving is the use of cell-phones, accounting for 14% of fatal crashes. Social media applications have enabled users to stay connected, however, the use of such applications while driving could have serious repercussions - often leading the user to be distracted from the road and ending up in an accident. In the context of impression management, it has been discovered that individuals often take a risk (such as teens smoking cigarettes, indulging in narcotics, and participating in unsafe sex) to improve their social standing. Therefore, viewing the phenomena of posting distracted driving posts under the lens of self-presentation, it can be hypothesized that users often indulge in risk-taking behavior on social media to improve their impression among their peers. In this paper, we first try to understand the severity of such social-media-based distractions by analyzing the content posted on a popular social media site where the user is driving and is also simultaneously creating content. To this end, we build a deep learning classifier to identify publicly posted content on social media that involves the user driving. Furthermore, a framework proposed to understand factors behind voluntary risk-taking activity observes that younger individuals are more willing to perform such activities, and men (as opposed to women) are more inclined to take risks. Grounding our observations in this framework, we test these hypotheses on 173 cities across the world. We conduct spatial and temporal analysis on a city-level and understand how distracted driving content posting behavior changes due to varied demographics. We discover that the factors put forth by the framework are significant in estimating the extent of such behavior.

2. Sunpy: A python package for solar physics

   Stuart Mumford, Nabil Freij, Steven Christe, and 8 more authors

   Journal of Open Source Software, 2020

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   The Sun, our nearest star, is a local laboratory for studying universal physical processes. Solar physics as a discipline includes studying the Sun both as a star and as the primary driver of space weather throughout the heliosphere. Due to the Sun’s proximity, the temporal and spatial resolution of solar observations are orders of magnitude larger than those of other stars. This leads to significant differences in the data-analysis software needs of solar physicists compared with astrophysicists. The sunpy Python package is a community-developed, free, and open-source solar data analysis environment for Python. It is managed by the SunPy Project, an organization that facilitates and promotes the use of open development and open source packages like sunpy through community engagement and tools such as GitHub, mailing lists, and matrix.

3. Are Bots Humans? Analysis of Bot Accounts in 2019 Indian Lok Sabha Elections (Workshop Paper)

   Hitkul Hitkul, Omkar Gurjar, Aanshul Sadaria, and 4 more authors

   In 2020 IEEE Sixth International Conference on Multimedia Big Data (BigMM), 2020

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   Social media platforms have taken political and cultural conversations to an online platform making them more accessible. Ability to anonymously post has allowed more people to participate fearlessly. However, this has also led to an opportunity to spread miss information and manipulative content. Political groups around the globe have used Bot accounts to help spread their preferred narrative online during elections. In the midst of 2019 Indian Lok Sabha Elections speculations were made about the presence of cyber-troops/IT Cells which operate fake accounts and push propaganda. Our finding suggests that a portion of Bot accounts seems to be operated by humans in the background. These accounts have a very distinct usage pattern on Twitter compared to legitimate human users. Our experiments also point out that only 1.3% of total interactions are directed from Humans to Bots, showing Bot accounts inability to gel well in the online social network.

4. Analyzing traffic violations through e-challan system in metropolitan cities (workshop paper)

   Ritwik Mishra, Ponnurangam Kumaraguru, Rajiv Ratn Shah, and 5 more authors

   In 2020 IEEE Sixth International Conference on Multimedia Big Data (BigMM), 2020

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   Given that India is now moving towards automated solutions to curb traffic violations and road accidents, we focus our efforts on characterizing these violations in Indian cities. In this work, we present our characterization of the traffic violations via an Automated e-challan (electronic trafficviolation receipt) issuance system of Ahmedabad and New Delhi. To explore this, we collected an exhaustive dataset of over 6 million e-challans. Characterizing the fine payment behavior, we find that 57% of unique vehicles in Ahmedabad are involved in repeat offenses. The temporal analysis shows a significant difference in e-challans issued during the festivals. Spatially, different violation types are distributed differently with the existence of certain unique hotspots. Finally, we also demonstrate how e-challans can act as a proxy measure to analyze the efficacy of the Motor Vehicles (Amendment) Act 2019. Our work suggests that high penalties may not have a long term impact on decreasing traffic violations. Keywords-big data; traffic violations; e-challan

2019

1. INFER: INtermediate representations for FuturE pRediction

   Shashank Srikanth, Junaid Ahmed Ansari, R. Karnik Ram, and 3 more authors

   In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019

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   In urban driving scenarios, forecasting future trajectories of surrounding vehicles is of paramount importance. While several approaches for the problem have been proposed, the best-performing ones tend to require extremely detailed input representations (eg. image sequences). But, such methods do not generalize to datasets they have not been trained on. We propose intermediate representations that are particularly well-suited for future prediction. As opposed to using texture (color) information, we rely on semantics and train an autoregressive model to accurately predict future trajectories of traffic participants (vehicles) (see fig. above). We demonstrate that using semantics provides a significant boost over techniques that operate over raw pixel intensities/disparities. Uncharacteristic of state-of-the-art approaches, our representations and models generalize to completely different datasets, collected across several cities, and also across countries where people drive on opposite sides of the road (left-handed vs right-handed driving). Additionally, we demonstrate an application of our approach in multi-object tracking (data association). To foster further research in transferrable representations and ensure reproducibility, we release all our code and data.

2. Is change the only constant? Profile change perspective on# LokSabhaElections2019

   Kumari Neha, Shashank Srikanth, Sonali Singhal, and 3 more authors

   arXiv preprint arXiv:1909.10012, 2019

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   Users on Twitter are identified with the help of their profile attributes that consists of username, display name, profile image, to name a few. The profile attributes that users adopt can reflect their interests, belief, or thematic inclinations. Literature has proposed the implications and significance of profile attribute change for a random population of users. However, the use of profile attribute for endorsements and to start a movement have been under-explored. In this work, we consider #LokSabhaElections2019 as a movement and perform a large-scale study of the profile of users who actively made changes to profile attributes centered around #LokSabhaElections2019. We collect the profile metadata for 49.4M users for a period of 2 months from April 5, 2019 to June 5, 2019 amid #LokSabhaElections2019. We investigate how the profile changes vary for the influential leaders and their followers over the social movement. We further differentiate the organic and inorganic ways to show the political inclination from the prism of profile changes. We report how the addition of election campaign related keywords lead to spread of behavior contagion and further investigate it with respect to "Chowkidar Movement" in detail.