Abstract

The roles of trait anxiety, psychological flexibility and coping strategies on the mental health impacts of COVID-19 on college students in India INTRODUCTION The lives of many students had a tremendous impact during the COVID-19 pandemic. Moreover, pandemic woes got extended due to the second wave when everyone was expecting an end to a year long-suffering in India. Schools, universities and institutes were shut down, resulting in the online culture again. Further, uncertainty over the exam dates apart from the competition itself loomed the distress and added misery for those transitioning to college. Recent studies suggest that COVID-19 increases psychological stress. More specifically, quarantine is associated with emotional stress, depression, irritation, adverse changes in sleep patterns and emotional tiredness. Despite the devastating psychosocial and cognitive impact of COVID-19 on individuals suffering from COVID-19 or not, some individuals did not get affected as much as others. In this study, we evaluate the individual coping strategies and psychological flexibility, and their association with trait anxiety to explain these individual differences in psychological health (generalized anxiety, depression, and stress) and cognitive health using cognitive tasks in the context of the COVID-19 pandemic in India. Such analysis would not only help to shed more light on the underlying nature of the effects of pandemic stressors but would also enable healthcare professionals to design particular interventions at the individual level. METHOD Total 254 college students (75 women, 176 men, 1 trans woman, 2 did not specify; mean age=20.57, median age=20) were recruited for this study and were provided monetary incentives. The data collection lasted for 3.5 weeks, starting from 22 September 2021, while the second wave was declining. The study consisted of a general demographics survey, COVID-19 specific impact variables (such as living conditions, unhealthy behaviours, impact on academic life), and delirium questions. Patient Health Questionnaire (PHQ-9) for measuring the severity of depressive symptoms, Generalized Anxiety Disorder Questionnaire (GAD-7) to assess anxiety disorder, Perceived Stress Scale (PSS-10) for evaluating the degree to which the student perceives life as unpredictable and State-Trait Anxiety Inventory (STAI-10) for measuring potentiality as well as the actuality of anxiety. To assess the coping style and psychological flexibility, Brief COPE-28 and Multidimensional Psychological Flexibility Inventory (MPFI-24) were employed, respectively. Further, Stroop Task and Sustained Attention to Response Task (SART) were used as objective measures to assess the individual's ability to inhibit cognitive interference and attention, respectively. RESULTS Among 254 college students who participated in this study, 73.6% reported having a close one infected with the virus, among which 19.7% passed away, showing the significant prevalence of COVID-19 in India during the time of the study. Further, 26.7% of the participants reported being self infected by the virus, among which 2.9% were put on a ventilator. Among these covid infected participants, 17.6% reported experiencing strange things that they could not explain, 16.2% felt people were talking about them behind their back, and 2.9% heard or saw things that others could not, which corroborates recent findings on delirium in COVID-19 patients. On a scale of 1-5, among the sample, the mean academic stress was found to be 4.07 (median=4) and stress due to social isolation 3.93 (median=4). Three major analyses: correlational, regression and mediation, were performed to evaluate the relation of COVID-19 impact variables and psychological health. We observed a strong negative correlation (p<0.01) between the measures such as perception of mental space, proper sleep, managing academics, social isolation during the pandemic with at least three out of the four mental health variables: PHQ, GAD, PSS and STAI-S (state) suggesting that poorer the student's ability to manage his/her/their life effectively the worse is the overall anxiety levels and depressive symptoms. For further analysis, these measures were collated into a single index called COVID-19 Impact Index, as they describe the overall impact on the lives of college students during the pandemic. The COVID-19 Impact Index was significantly positively correlated (p<0.01) with the measures such as PHQ [ρ=0.54], GAD [ρ=0.51] and PSS [ρ=0.48], suggesting a high level of distress and depressive symptoms among students most impacted by COVID-19. For investigating the relationship between dependent variables (PSS, PHQ, GAD and STAI-S) with the variables such as COVID-19 Impact Index, coping strategies, psychological flexibility and STAI-T, a multilinear regression model was trained for each dependent variable individually. The individual models trained for the outcome as PSS, PHQ, GAD, and STAI-S accounted for a significant portion of the variance by predicting 62.8%, 60.3%, 58.8%, and 47.5% of the variance, respectively. Significant contributors in the variance of all the models were found to be trait anxiety (STAI-T) and COVID-19 Impact Index, which suggests that the student's predisposition to experience negative emotions such as worries, fears, and anxiety is among other factors a relatively significant predictor of the negative experience during the pandemic time. Avoidant coping strategy and psychological inflexibility were also significant predictors in most of the models, suggesting that the students who chose to avoid rather than deal with the stressors posed by the COVID-19 situation were more likely to develop depressive symptoms, stress and anxiety. The central role of STAI-T is further confirmed with the mediational analysis of the direct effect of the COVID-19 Impact Index on psychological health variables (PSS, PHQ, GAD and STAI-S). STAI-T is found to be most significantly positively mediating the direct effect in all the models compared to other parallel mediators: psychological flexibility and coping strategies, suggesting a higher increase in overall worsening of psychological health due to COVID-19 impact when a student has a predisposition of anxiety as compared to other students. DISCUSSION AND CONCLUSION This study showed the severe impact of COVID-19 amongst the students after the second wave ended, corroborating recent worldwide studies. Further, it helped to identify specific factors that result in individual differences while dealing with the pandemic's stressors due to its uncertain nature. Predisposition to anxiety (STAI-T), among other factors, was crucial when predicting the propensity to depression, stress and anxiety; thus, such individuals could be identified early on. Health professionals can further employ approaches that promote psychological flexibility, such as Acceptance Commitment Therapy and educate about different coping styles promoting active coping.

Abstract

The impact of the second wave of COVID-19 on Indian students and the mediating roles of psychological flexibility and coping strategies on this impact INTRODUCTION India was hit severely by the second wave of COVID-19 from April to June 2021, with daily cases rising exponentially and imposed mandatory lockdown. With schools and colleges closed, students were forced to deal with online schooling, social isolation, close ones getting infected, and uncertainty regarding personal goals since major exams and job offers were cancelled. Previous studies have found detrimental effects of the pandemic imposed environment, but not many studies have been done in India, especially with a focus on students. We aimed to detect the specific environmental factors created by the pandemic that impact these students' mental health and attention capacity and understand the psychological mechanisms that exacerbate or mitigate this impact. In this study, we looked at two mechanisms, the first being psychological flexibility: recognizing and adapting to various situational demands. In the context of COVID-19, it is one of the key resilience variables in the fights against mental health issues. The second was coping strategies, which can be categorized into two styles: approach coping, where individual attempts to handle a problem by actively taking steps towards a solution, and avoidant coping, where an individual attempts to retreat from or deny the problem. METHOD 290 students were recruited for the study via mail and snowball sampling. The study took thirty minutes, and participants were provided monetary incentives. The experiment consisted of Demographics: age, gender and intermediate or college student; COVID-19 environment variables such as length of lockdown, perceived personal space, verbal/physical fights at home and infection among family; General Health Questionnaire-12 (GHQ-12) to measure psychological distress; Patient Health Questionnaire-2 (PHQ-2) to screen for depression; COVID-19 Student Stress Questionnaire-7 (CSSQ-7) to measure stress; CompACT-8 to measure psychological flexibility; Brief COPE-28 to assess coping styles; Sustained Attention to Response Task (SART) to measure sustained attention and Stroop task to measure selective attention. The data collection started on 30 May 2021 with the last data collected on 22 June 2021. RESULTS Participants' profile 71% of the participants were male [average age=18.8], and 48% were college students. Coming to the effects of COVID-19, 30.7% were infected with the virus. Moreover, almost 90% of the participants were either themselves infected or had a close one infected with COVID-19, and 32.4% had a death of a close one, which shows how far-reaching and severe the virus was in India during the second wave. Participants gave an average rating of 3.5 out of 5 when asked how much they worried about the effect of COVID-19 on their lives. 65.5% of the participants were distressed, 37.9% were screened for depression, and 68.6% were moderate to highly stressed. These results reflect the severity of the pandemic's mental health and personal life impacts. Our sample had significantly higher GHQ scores than those seen in general population samples in India during the same period, indicating that students were impacted more within the context of the second wave than the general population. Environmental Factors and psychological health variables When looking at the correlational analysis, we detected six environmental factors (length of lockdown, self getting infected, close ones getting infected, lack of personal space, increase in fights at home and COVID-19 worry) that were significantly associated with the outcome health variables (distress, depression and stress) and were used to form the COVID-19 Impact Index. This index had moderate positive correlations (p<0.01) with GHQ [ρ=0.36], CSSQ [ρ=0.37] and avoidant coping [ρ=0.3] and negative correlations (p<0.01) with flexibility [ρ=-0.31]. Psychological Flexibility had significant negative correlations (p<0.01) with GHQ-12 [ρ=-0.51], PHQ-2 [ρ=-0.25] and CSSQ-7 [ρ=-0.32] whereas avoidant coping had significant positive correlations (p<0.01) with GHQ-12 [ρ=0.54], PHQ-2 [ρ=0.27] and CSSQ-7 [ρ=0.39]. This suggests that flexibility correlated significantly with better overall psychological health while COVID-19 Impact Index and avoidant coping correlated with worse overall health. Cognitive health and psychological health variables Unlike previous studies, none of the predictor variables (COVID-19 Impact Index, psychological flexibility, coping strategies) correlated with either attention measure. Within the outcome variables, sustained attention correlated negatively with stress and selective attention correlated negatively with distress and depression. Regression analysis was done to see how well the predictor variables could predict the outcome variables. We only ran models for the outcome health variables since the attention measures did not correlate with the predictors. The final model for distress accounted for 42.5% of the variance, the final model for depression accounted for 14.4% of the variance, and the final model for stress accounted for 28% of the variance. Results show that the predictors account for a moderate portion of the variance among distress and stress. The prediction for depression was low since we used a screener questionnaire instead of the entire questionnaire. Since GHQ-12 and CSSQ-7 correlated with COVID-19 Impact Index, we conducted mediation analysis for both relations with three parallel mediators (flexibility, avoidant and approach coping). Results showed that flexibility mediated a decrease in the effects of the index on distress while avoidant coping mediated an increase in the effects of the index on distress and stress. CONCLUSION To summarize, this study showed the severe impact of COVID-19 on students in India. It detected factors like length of lockdown, lack of personal space, increased fights at home, infection of self and close ones, and general worry about COVID-19 as detrimental to mental health. It also showed psychological flexibility as a resilience mechanism that mitigates this detrimental impact, while avoidant coping strategies exacerbate the impact. These results support interventions and approaches that promote flexibility, like Acceptance Commitment Therapy. Knowing the specific factors of the environment that affect mental health may also contribute to the development and testing of these approaches.

Abstract

This paper presents a descriptive analysis to understand how researchers play a significant role within emerging innovation clusters. We highlight reasons that enable or inhibit researchers towards productization within scientific institutes in India. The paper focuses on understanding the above from a researcher’s point of view by analysing their motivation and concerns. Most research work from deep-tech computer science domains contain a shelf life when it comes to productization. Therefore, associated activities have a designated time period related to productization to avoid research leading to the product from falling into the Valley of death. As a result, researchers who work on projects resulting in potential productization become a crucial part of a successful research to productization flow of work. Our paper develops an understanding of and evaluates different aspects of a researchers’ journey as they relate to productization of deep-tech Computer Science research work. Index Terms - Innovation Clusters, Productization, Emerging Economy, Deep-tech Computer Science, India

Abstract

The emergence of vehicle connectivity technologies and associated applications have paved the way for increased consumer interest in connected vehicles. These modern day vehicles are now capable of sending/receiving vast amounts of data and offloading computation (which is one possible service) to servers thereby improving safety, comfort, driving experience, etc. In the early stages of connectivity, all the data communication and computation offloading happened between the cloud server and the vehicles. However, this is not feasible in scenarios having strict timing requirements and bandwidth cost constraints. Vehicular Edge Computing (VEC) demonstrated an efficient way to tackle the above problem. In order to optimally utilize the resources of the edge servers for data delivery, an efficient edge resource allocation framework needs to be developed. In a recent work, data/service delivery to connected vehicles assumed a worst-case scenario that all vehicles with routes passing through an edge appear in the edge coverage region simultaneously. However, this worst-case scenario is very pessimistic, which results in overestimation of edge resources. We address this by precisely computing the set of vehicles which simultaneously appear in the coverage region of an edge (which we call vehicle overlaps). In this work, we first propose an optimization framework for edge resource allocation that minimizes the bandwidth cost of data delivery to connected vehicles while considering the traffic flow and vehicle overlaps. Then, we propose an efficient heuristic to deliver data based on minimizing global edge bandwidth cost gradient under vehicle overlaps. We demonstrate the improvement in resource allocation considering vehicle overlaps. Using real world traffic data, we also demonstrate reduction in data delivery times using the proposed heuristic.

Abstract

A processor-implemented method for generating a lip-sync for a face to a target speech of a live session to a speech in one or more languages in-sync with improved visual quality using a machine learning model and a pre-trained lip-sync model is provided. The method includes (i) determining a visual representation of the face and an audio representation, the visual representation includes crops of the face; (ii) modifying the crops of the face to obtain masked crops; (iii) obtaining a reference frame from the visual representation at a second timestamp; (iv) combining the masked crops at the first timestamp with the reference to obtain lower half crops; (v) training the machine learning model by providing historical lower half crops and historical audio representations as training data; (vi) generating lip-synced frames for the face to the target speech, and (vii) generating an in-sync lip-synced frames by the pre-trained lip-sync model.

Abstract

Kathakali is one of the major forms of Classical Indian Dance. The dance form is distinguished by the elaborately colourful makeup, costumes and face masks. In this work, we present (a) a framework to analyze the facial expressions of the actors and (b) novel visualization techniques for the same. Due to extensive makeup, costumes and masks, the general face analysis techniques fail on Kathakali videos. We present a dataset with manually annotated Kathakali sequences for four downstream tasks, i.e. face detection, background subtraction, landmark detection and face segmentation. We rely on transfer learning and fine-tune deep learning models and present qualitative and quantitative results for these tasks. Finally, we present a novel application of style-transfer of Kathakali video onto a cartoonized face. The comprehensive framework presented in the paper paves the way for better understanding, analysis, pedagogy and visualization of Kathakali videos. CCS Concepts: Applied computing --> Performing arts; Human-centered computing --> Visualization

Abstract

In this work, we describe an end-to-end deep learning architecture for Heart Murmur Detection from Phonocardiogram(PCG) recordings as part of The George B. Moody PhysioNet Challenge 2022. Our team, “Team IIITH” received a weighted accuracy score of 0.708 (ranked 19th out of 40 teams) and Challenge cost of 13264 (ranked 22nd out of 39 teams) on the official hidden test set. In our approach, the PCG recordings are first downsampled to 1000 Hz before being passed through a Butterworth’s low and high pass filter to remove baseline wanders and high-frequency noise present in the recordings. The PCG recordings are then broken down into 10-second segments and normalized to bring all trainable samples to the same size. To extract embeddings more efficiently, we built a custom 1-dimensional Residual Network (ResNet) where the 10-second inputs are passed through variable-sized kernel ResNets in parallel, before being concatenated and passed through the next ResNet layer to account for different length dependencies across the PCG signal. The output of this custom ResNet is then fed to a 2-layer feed-forward network for final classification. Cross-Entropy Loss with class weights was employed to account for class imbalance. Our approach obtained a 5-fold Cross-Validation weighted accuracy score of 0.71 and challenge cost score of 12067 on the training set

Abstract

Standard Model Predictive Control (MPC) or trajectory optimization approaches perform only a local search to solve a complex non-convex optimization problem. As a result, they cannot capture the multi-modal characteristic of human driving. A global optimizer can be a potential solution but is computationally intractable in a real-time setting. In this paper, we present a real-time MPC capable of searching over different driving modalities. Our basic idea is simple: we run several goaldirected parallel trajectory optimizations and score the resulting trajectories based on user-defined meta cost functions. This allows us to perform a search over several locally optimal motion plans. Although conceptually straightforward, realizing this idea in real-time with existing optimizers is highly challenging from technical and computational standpoints. With this motivation, we present a novel batch non-holonomic trajectory optimization whose underlying matrix algebra is easily parallelizable across problem instances and reduces to computing large batch matrix-vector products. This structure, in turn, is achieved by deriving a linearization-free multi-convex reformulation of the non-holonomic kinematics and collision avoidance constraints. We extensively validate our approach using both synthetic and real data sets (NGSIM) of traffic scenarios. We highlight how our algorithm automatically takes lane-change and overtaking decisions based on the defined meta cost function. Our batch optimizer achieves trajectories with lower meta cost, up to 6x faster than competing baselines.

Abstract

Causality detection and identification is centered on identifying semantic and cognitive connections in a sentence. In this paper, we describe the effort of team LTRC for Causal News Corpus - Event Causality Shared Task 2022 at the 5th Workshop on Challenges and Applications of Automated Extraction of Sociopolitical Events from Text (CASE 2022) (Tan et al., 2022a). The shared task consisted of two subtasks: 1) identifying if a sentence contains a causality relation, and 2) identifying spans of text that correspond to cause, effect and signals. We fine-tuned transformer-based models with adapters for both subtasks. Our best-performing models obtained a binary F1 score of 0.853 on held-out data for subtask 1 and a macro F1 score of 0.032 on held-out data for subtask 2. Our approach is ranked third in subtask 1 and fourth in subtask 2. The paper describes our experiments, solutions, and analysis in detail.

Abstract

Automatic extraction of “significant” components of a legal contract, has the potential to simplify the end user’s comprehension. In essence, “significant” pieces of information have 1) information pertaining to material/practical details about a specific contract and 2) information that is novel or comes as a “surprise” for a specific type of contract. It indicates that the significance of a component may be defined at an individual contract level and at a contract-type level. A component, sentence, or paragraph, may be considered significant at a contract level if it contains contract-specific information (CSI), like names, dates, or currency terms. At a contract-type level, components that deviate significantly from the norm for the type may be considered significant (type-specific information (TSI)). In this paper, we present approaches to extract “significant” components from a contract at both these levels. We attempt to do this by identifying patterns in a pool of documents of the same kind. Consequently, in our approach, the solution is formulated in two parts: identifying CSI using a BERT-based contract-specific information extractor and identifying TSI by scoring sentences in a contract for their likelihood. In this paper, we even describe the annotated corpus of contract documents that we created as a first step toward the development of such a language-processing system. We also release a dataset of contract samples containing sentences belonging to CSI and TSI.