Abstract

Conversational Agents (CAs) powered with deep language models (DLMs) have shown tremendous promise in the domain of mental health. Prominently, the CAs have been used to provide informational or therapeutic services (e.g., cognitive behavioral therapy) to patients. However, the utility of CAs to assist in mental health triaging has not been explored in the existing work as it requires a controlled generation of follow-up questions (FQs), which are often initiated and guided by the mental health professionals (MHPs) in clinical settings. In the context of ‘depression’, our experiments show that DLMs coupled with process knowledge in a mental health questionnaire generate 12.54% and 9.37% better FQs based on similarity and longest common subsequence matches to questions in the PHQ-9 dataset respectively, when compared with DLMs without process knowledge support. Despite coupling with process knowledge, we find that DLMs are still prone to hallucination, i.e., generating redundant, irrelevant, and unsafe FQs. We demonstrate the challenge of using existing datasets to train a DLM for generating FQs that adhere to clinical process knowledge. To address this limitation, we prepared an extended PHQ-9 based dataset, PRIMATE, in collaboration with MHPs. PRIMATE contains annotations regarding whether a particular question in the PHQ-9 dataset has already been answered in the user’s initial description of the mental health condition. We used PRIMATE to train a DLM in a supervised setting to identify which of the PHQ-9 questions can be answered directly from the user’s

Abstract

Non-financial incentives in the form of awards often act as a driver of positive reinforcement and elevation of social status in the offline world. The elevated social status results in people becoming more active, aligning to a change in the communities' expectations. However, the impact in terms of longevity of social influence and community acceptance of leaders of these incentives in the form of awards are not well-understood in the online world. Our work aims to shed light on the impact of these awards on the awardee and the community. We focus on three large subreddits with a snapshot of 219K posts and 5.8 million comments contributed by 88K Reddit users who received 14,146 awards. Our work establishes that the behaviour of awardees change statistically significantly for a short time after getting an award; however, the change is ephemeral since the awardees return to their pre-award behaviour within days. Additionally, via a user survey, we identified a long-lasting impact of awards-we found that the community's stance softened towards awardees.

Abstract

Protests and mass mobilization are scarce; however, they may lead to dramatic outcomes when they occur. Social media such as Twitter has become a center point for the organization and development of online protests worldwide. It becomes crucial to decipher various narratives shared during an online protest to understand people’s perceptions. In this work, we propose an unsupervised clustering-based framework to understand the narratives present in a given online protest. Through a comparative analysis of tweet clusters in 3 protests around government policy bills, we contribute novel insights about narratives shared during an online protest. Across case studies of government policy-induced online protests in India and the United Kingdom, we found familiar mass mo- bilization narratives across protests. We found reports of on-ground activities and call-to-action for people’s participation narrative clusters in all three protests under study. We also found protest-centric narratives in different protests, such as skepticism around the topic. The results from our analysis can be used to understand and compare people’s perceptions of future mass mobilizations. Index Terms—Social Media Protest, Unsupervised clustering, Protests, Narratives, Twitter

Abstract

Codes with locality allow for efficient recovery from single node failures by minimizing the number of nodes accessed to repair a failed node. These codes can also be extended to handle multiple erasures. Codes with hierarchical locality are another extension of codes with locality, which offer multiple levels of locality as the number of erasures increase. Maximally recoverable codes (MRC) are a class of codes, which satisfy the locality property and in addition also recover from all information theoretically recoverable erasure patterns. In this work, we construct MRC with hierarchical locality based on generator matrices of linearized Reed-Solomon codes and the field size is better than the earlier known construction. We also give a random construction of MRC with hierarchical locality and characterize the field size required. Finally, we present sparse generator matrices for MRC with locality and also sparse and balanced generator matrices for MRC with locality parameter 2 for large set of parameters.

Abstract

The plants of nano aerial vehicles (NAVs) are inherently unstable. Hence, a NAV needs a flight controller to accomplish a mission. Furthermore, the sensing and computational capabilities of NAV's autopilot hardware are limited. Hence, the implementation of the full state feedback controllers with gain scheduling is difficult. This paper proposes a flight controller scheme that consists of two parts: a Simultaneously Stabilizing Output Feedback Linear (SSOFL) controller and a Proximal Policy Optimization (PPO) deep reinforcement learning agent, which is connected in parallel to the SSOFL controller. In this scheme, the single SSOFL controller provides stabilization and nominal tracking performance to the NAV throughout its flight envelope by accomplishing simultaneous stabilization (SS). Additionally, the PPO agent is trained using the closed-loop (CL) nonlinear plant with this SSOFL controller to enhance the tracking performance. The effectiveness of the proposed flight controller scheme is verified using the six-degree-of-freedom nonlinear simulations of the fixed-wing nano aerial vehicle.

Abstract

This paper proposes a Reinforcement Learning (RL) based technique to develop a simple neural network controller for the task of waypoint navigation in quadrotors. In this paper, the application of Twin Delayed Deep Deterministic (TD3) Policy Gradient algorithm for high and low-level control implementation for quadrotors is discussed. The proposed methods are tested on high fidelity Gym-Pybullet-Drones simulator. The effectiveness of the methods developed is validated through numerical simulations. The simulation results indicate that both control policies are successful in navigating through the assigned waypoint, with the low-level controller being accurate in the nominal flight conditions. In the presence of disturbance inputs, the high-level controller performs better when compared to the low-level controller.

Abstract

Deep learning has shown remarkable performances in image classification, including those of plants and leaves. However, high-performing networks in terms of accuracy may not be using the salient regions for making the prediction and could be prone to biases. This work proposes a neural network architecture incorporating handcrafted features and fusing them with the learned features. Using hybrid features provides better control and understanding of the feature space while leveraging deep learning capabilities. Furthermore, a new IoU-based metric is introduced to assess the CNN-based classifier’s performance based on the regions focused on making predictions. Experiments over multiple leaf disease datasets demonstrate the performance improvement with the model using hybrid features. Classification using hybrid features performed better than the baseline models in terms of P@1 and also on the IoU …

Abstract

Note-taking methods and devices have improved tremendously over the past few decades, and people are finding new ways to write notes and take photos. Automatic extraction, recognition, and retrieval are necessary to process the huge chunk of digitized document data. However, an important step in all of these pipelines is the pre-processing step, mainly image enhancement or clean-up, which enhances the text regions and suppresses the non-text regions. In this article, we look at the problem of image enhancement or clean-up on one such important class of images (i.e., mobile captured glass board images). We present a simple yet efficient algorithm using the concepts of classical image processing techniques to solve the problem, and the obtained results are promising in comparison to the Office Lens.

Abstract

Wall-Frames, the recommended structural system for lifeline buildings is expected to have satisfactory and desirable seismic performance, especially if located in high seismic regions. In this work, fiber modeling approach is used to model inelasticity in walls and moment frames in a 6 storey-3-bay 2D wall-frame system. Displacement-controlled nonlinear static pushover analyses results from commercial software PERFORM3D suggest, the fiber type inelasticity in numerical models help predict nonlinear static behavior of study wall-frames reasonably well, compared to behavior of wall-frames with lumped inelasticity. In particular, increasing wall plan-aspect ratio enhances earthquake resistant virtues of wallframes, namely stiffness, strength, and ductility. Further, early yielding is observed in stiffer wall-frames and reasonable ductility achieved in all study wall-frames. Alongside, limit states of structural damages are also monitored to grade the damages and in turn the seismic performance of wall-frames. In addition, results obtained from nonlinear static analyses are confirmed by performing nonlinear time history analyses of study wall-frames, towards quantifying the critical earthquake resistant virtues—results and investigations from the study is a precursor towards recommending design measures to ensure postearthquake functionality of lifeline buildings.

Abstract

Wall-Frames, the recommended structural system for lifeline buildings is expected to have satisfactory and desirable seismic performance, especially if located in high seismic regions. In this work, fiber modeling approach is used to model inelasticity in walls and moment frames in a 6 storey-3-bay 2D wall-frame system. Displacement-controlled nonlinear static pushover analyses results from commercial software PERFORM3D suggest, the fiber type inelasticity in numerical models help predict nonlinear static behavior of study wall-frames reasonably well, compared to behavior of wall-frames with lumped inelasticity. In particular, increasing wall plan-aspect ratio enhances earthquake resistant virtues of wall-frames, namely stiffness, strength, and ductility. Further, early yielding is observed in stiffer wall-frames and reasonable ductility achieved in all study wall-frames. Alongside, limit states of structural damages are also monitored to grade the damages and in turn the seismic performance of wall-frames. In addition, results obtained from nonlinear static analyses are confirmed by performing nonlinear time history analyses of study wall-frames, towards quantifying the critical earthquake resistant virtues — results and investigations from the study is a precursor towards recommending design measures to ensure post-earthquake functionality of lifeline buildings. Keywords: Fiber modeling, Nonlinear static analysis, Limit states, Structural damages