Abstract

Cognitive impairment, alterations in mood, emotion dysregulation are just a few of the consequences of depression. Despite depression being reported as the most common mental disorder worldwide, examining depression or risks of depression is still challenging. Emotional reactivity has been observed to predict the risk of depression, but the results have been mixed for negative emotional reactivity (NER). To better understand the emotional response conflict, we asked our participants to describe their feeling in meaningful sentences alongside reporting their reactions to the emotionally evocative words. We presented a word on the screen and asked participants to perform two tasks, rate their feeling after reading the word using the self-assessment manikin (SAM) scale, and describe their feeling using the property generation task. The emotional content was analyzed using a novel machine-learning algorithm approach. We performed these two tasks in blocks and

Abstract

Hashtag segmentation is the task of breaking a hashtag into its constituent tokens. Hashtags often encode the essence of usergenerated posts, along with information like topic and sentiment, which are useful in downstream tasks. Hashtags prioritize brevity and are written in unique ways - transliterating and mixing languages, spelling variations, creative named entities. Benchmark datasets used for the hashtag segmentation task - STAN, BOUN - are small in size and extracted from a single set of tweets. However, datasets should reflect the variations in writing styles of hashtags and also account for domain and language specificity, failing which the results will misrepresent model performance. We argue that model performance should be assessed on a wider variety of hashtags, and datasets should be carefully curated. To this end, we propose HashSet, a dataset comprising of: a) 1.9k manually annotated dataset; b) 3.3M loosely supervised dataset. HashSet dataset is sampled from a different set of tweets when compared to existing datasets and provides an alternate distribution of hashtags to build and validate hashtag segmentation models. We show that the performance of SOTA models for Hashtag Segmentation drops substantially on proposed dataset, indicating that the proposed dataset provides an alternate set of hashtags to train and assess models. Datasets and results are released publicly and can be accessed from https://github.com/prashantkodali/HashSet

Abstract

Code mixing is the linguistic phenomenon where bilingual speakers tend to switch between two or more languages in conversations. Recent work on code-mixing in computational settings has leveraged social media code mixed texts to train NLP models. For capturing the variety of code mixing in, and across corpus, Language ID (LID) tags based measures (CMI) have been proposed. Syntactical variety/patterns of code-mixing and their relationship vis-a-vis computational model’s performance is under explored. In this work, we investigate a collection of English(en)-Hindi(hi) code-mixed datasets from a syntactic lens to propose, SyMCoM, an indicator of syntactic variety in code-mixed text, with intuitive theoretical bounds. We train SoTA en-hi PoS tagger, accuracy of 93.4%, to reliably compute PoS tags on a corpus, and demonstrate the utility of SyMCoM by applying it on various syntactical categories on a collection of datasets, and compare datasets using the measure.

Abstract

In the last two decades, Information and Communication Technologies (ICTs) have played a pivotal role in empowering rural populations in India by making knowledge more accessible. Digital Green is one such ICT that employs a participatory approach with smallholder farmers to produce instructional agricultural videos that encompass content specific to them. With the help of human mediators, they disseminate these videos to farmers using projectors to improve the adoption of agricultural practices. Digital Green’s web-based data tracker (CoCo) stores the attendance and adoption logs of millions of farmers, the videos screened to them and their demographic information. In our work, we leverage this data for a period of ten years between 2010-2020 across five states in India where Digital Green is most active and use it to conduct a holistic evaluation of the ICT. First, we find disparities in the adoption rates of farmers, following which we use statistical tests to identify the different factors that lead to these disparities as well as gender-based inequalities. We find that farmers with higher adoption rates adopt videos of shorter duration and belong to smaller villages. Second, to provide assistance to farmers facing challenges, we model the adoption of practices from a video as a prediction problem and experiment with different model architectures. Our classifier achieves accuracies ranging from 79% to 90% across the five states, demonstrating its potential for assisting future ethnographic investigations. Third, we use SHAP values in conjunction with our model for explaining the impact of various network, content and demographic features on adoption. Our research finds that farmers greatly benefit from past adopters of a video from their group and village. We also discover that videos with a low content-specificity benefit some farmers more than others. Next, we highlight the implications of our findings by translating them into recommendations for providing focused assistance, community building, video screening, revisiting participatory approach and mitigating inequalities. Lastly, we conclude with a discussion on how our work can assist future investigations into the lived experiences of farmers. CCS Concepts: • Human-centered computing → Empirical studies in collaborative and social computing. Additional Key Words and Phrases: Diagnosis, ICT4D, Agriculture, Social Networks

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

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

An FFT is an essential algorithm for radar signal processing in a radar system. Due to increase in computational power of FPGAs, it is possible to perform FFT operation onboard in an airborne vehicle. However, the FPGA resources have become a limitation for processing real-time signals using conventional methods. To address this issue, we have proposed a parallel pipelined FFT architecture that can achieve very high throughput with very low latency, making it capable of processing real-time continuous data. This architecture is implemented in a radar system, which works from L band to Ku band. In this radar system, the received RF signal is downconverted into an IF signal of 1 GHz frequency with a 500 MHz bandwidth and converted to digital data using a 10-bit ADC. On the converted digital data, a 512-point FFT is implemented on a Xilinx Virtex-7 XC7VX485T FPGA using 8 parallel channels with 64 data frames and is compared with the conventional IP core-based architecture. The proposed architecture takes 1.307µs to implement FFT, which is 5.15 times faster than the IP core-based architecture and requires fewer arithmetic computations. The overall total number of complex multiplications, complex additions, multipliers & adders were reduced by 10.42%, 30.64%, 10.42% & 23.90% respectively. Apart from very low latency and fewer arithmetic operations, the proposed parallel FFT architecture achieved a throughput of 1.350 Giga Samples per second (Gsps). Index Terms—Fast Fourier Transform (FFT), DSP, Radar Systems, Parallel Architecture, FPGA.