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129 results on '"Kaur, Rishemjit"'

1. A Bi-level assessment of Twitter in predicting the results of an election: Delhi Assembly Elections 2020

Author

Singh, Maneet, Iyengar, S. R. S., Saxena, Akrati, and Kaur, Rishemjit

Subjects
Computer Science - Social and Information Networks
Abstract

Elections are the backbone of any democratic country, where voters elect the candidates as their representatives. The emergence of social networking sites has provided a platform for political parties and their candidates to connect with voters in order to spread their political ideas. Our study aims to use Twitter in assessing the outcome of Delhi Assembly elections held in 2020, using a bi-level approach, i.e., concerning political parties and their candidates. We analyze the correlation of election results with the activities of different candidates and parties on Twitter, and the response of voters on them, especially the mentions and sentiment of voters towards a party. The Twitter profiles of the candidates are compared both at the party level as well as the candidate level to evaluate their association with the outcome of the election. We observe that the number of followers and the replies to the tweets of candidates are good indicators for predicting actual election outcome. However, we observe that the number of tweets mentioning a party and the sentiment of voters towards the party shown in tweets are not aligned with the election result. We also use machine learning models on various features such as linguistic, word embeddings and moral dimensions for predicting the election result (win or lose). The random forest model using tweet features provides promising results for predicting if the tweet belongs to a winning or losing candidate., Comment: 15 pages, 11 figures and 2 tables

Published
2022

2. A Multi-Opinion Based Method for Quantifying Polarization on Social Networks

Author

Singh, Maneet, Iyengar, S. R. S., and Kaur, Rishemjit

Subjects
Computer Science - Social and Information Networks
Abstract

Social media platforms have emerged as a hub for political and social interactions, and analyzing the polarization of opinions has been gaining attention. In this study, we have proposed a measure to quantify polarization on social networks. The proposed metric, unlike state-of-the-art methods, does not assume a two-opinion case and applies to multiple opinions. We tested our metric on different networks with a multi-opinion scenario and varying degrees of polarization. The scores obtained from the proposed metric were comparable to state-of-the-art methods on binary opinion-based benchmark networks. The technique also differentiated among networks with different levels of polarization in a multi-opinion scenario. We also quantified polarization in a retweet network obtained from Twitter regarding the usage of drugs like hydroxychloroquine or chloroquine in treating COVID-19. Our metric indicated a high level of polarized opinions among the users. These findings suggest uncertainty among users in the benefits of using hydroxychloroquine and chloroquine drugs to treat COVID-19 patients., Comment: 14 pages, 4 figures and 1 table

Published
2022

3. Morality-based Assertion and Homophily on Social Media: A Cultural Comparison between English and Japanese Languages

Author

Singh, Maneet, Kaur, Rishemjit, Matsuo, Akiko, Iyengar, S. R. S., and Sasahara, Kazutoshi

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, J.4, I.2.7
Abstract

Moral psychology is a domain that deals with moral identity, appraisals and emotions. Previous work has primarily focused on moral development and the associated role of culture. Knowing that language is an inherent element of a culture, we used the social media platform Twitter to compare moral behaviors of Japanese tweets with English tweets. The five basic moral foundations, i.e., Care, Fairness, Ingroup, Authority and Purity, along with the associated emotional valence were compared between English and Japanese tweets. The tweets from Japanese users depicted relatively higher Fairness, Ingroup, and Purity, whereas English tweets expressed more positive emotions for all moral dimensions. Considering moral similarities in connecting users on social media, we quantified homophily concerning different moral dimensions using our proposed method. The moral dimensions Care, Authority and Purity for English and Ingroup, Authority and Purity for Japanese depicted homophily on Twitter. Overall, our study uncovers the underlying cultural differences with respect to moral behavior in English- and Japanese-speaking users., Comment: 21 pages, 7 figures, 1 Table, 6 supplementary figures, Accepted in Frontiers in Psychology

Published
2021

4. What a million Indian farmers say?: A crowdsourcing-based method for pest surveillance

Author

Adhikari, Poonam, Kumar, Ritesh, Iyengar, S. R. S, and Kaur, Rishemjit

Subjects
Computer Science - Artificial Intelligence, Computer Science - Information Retrieval, I.2.7
Abstract

Many different technologies are used to detect pests in the crops, such as manual sampling, sensors, and radar. However, these methods have scalability issues as they fail to cover large areas, are uneconomical and complex. This paper proposes a crowdsourced based method utilising the real-time farmer queries gathered over telephones for pest surveillance. We developed data-driven strategies by aggregating and analyzing historical data to find patterns and get future insights into pest occurrence. We showed that it can be an accurate and economical method for pest surveillance capable of enveloping a large area with high spatio-temporal granularity. Forecasting the pest population will help farmers in making informed decisions at the right time. This will also help the government and policymakers to make the necessary preparations as and when required and may also ensure food security.

Published
2021

5. Neural Machine Translation for Low-Resource Languages: A Survey

Author

Ranathunga, Surangika, Lee, En-Shiun Annie, Skenduli, Marjana Prifti, Shekhar, Ravi, Alam, Mehreen, and Kaur, Rishemjit

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence, I.2.7
Abstract

Neural Machine Translation (NMT) has seen a tremendous spurt of growth in less than ten years, and has already entered a mature phase. While considered as the most widely used solution for Machine Translation, its performance on low-resource language pairs still remains sub-optimal compared to the high-resource counterparts, due to the unavailability of large parallel corpora. Therefore, the implementation of NMT techniques for low-resource language pairs has been receiving the spotlight in the recent NMT research arena, thus leading to a substantial amount of research reported on this topic. This paper presents a detailed survey of research advancements in low-resource language NMT (LRL-NMT), along with a quantitative analysis aimed at identifying the most popular solutions. Based on our findings from reviewing previous work, this survey paper provides a set of guidelines to select the possible NMT technique for a given LRL data setting. It also presents a holistic view of the LRL-NMT research landscape and provides a list of recommendations to further enhance the research efforts on LRL-NMT., Comment: 35 pages, 8 figures

Published
2021

6. Can Self Reported Symptoms Predict Daily COVID-19 Cases?

Author

Patwa, Parth, Reddy, Viswanatha, Sukumaran, Rohan, TV, Sethuraman, Nashnoush, Eptehal, Shankar, Sheshank, Kaur, Rishemjit, Singh, Abhishek, and Raskar, Ramesh

Subjects
Computer Science - Machine Learning, Computer Science - Computers and Society
Abstract

The COVID-19 pandemic has impacted lives and economies across the globe, leading to many deaths. While vaccination is an important intervention, its roll-out is slow and unequal across the globe. Therefore, extensive testing still remains one of the key methods to monitor and contain the virus. Testing on a large scale is expensive and arduous. Hence, we need alternate methods to estimate the number of cases. Online surveys have been shown to be an effective method for data collection amidst the pandemic. In this work, we develop machine learning models to estimate the prevalence of COVID-19 using self-reported symptoms. Our best model predicts the daily cases with a mean absolute error (MAE) of 226.30 (normalized MAE of 27.09%) per state, which demonstrates the possibility of predicting the actual number of confirmed cases by utilizing self-reported symptoms. The models are developed at two levels of data granularity - local models, which are trained at the state level, and a single global model which is trained on the combined data aggregated across all states. Our results indicate a lower error on the local models as opposed to the global model. In addition, we also show that the most important symptoms (features) vary considerably from state to state. This work demonstrates that the models developed on crowd-sourced data, curated via online platforms, can complement the existing epidemiological surveillance infrastructure in a cost-effective manner. The code is publicly available at https://github.com/parthpatwa/Can-Self-Reported-Symptoms-Predict-Daily-COVID-19-Cases., Comment: Accepted as a full-length oral presentation at the International Workshop on Artificial Intelligence for Social Good (AI4SG), IJCAI-21

Published
2021

7. More than smell – COVID-19 is associated with severe impairment of smell, taste, and chemesthesis

Author

Parma, Valentina, Ohla, Kathrin, Veldhuizen, Maria G, Niv, Masha Y, Kelly, Christine E, Bakke, Alyssa J, Cooper, Keiland W, Bouysset, Cédric, Pirastu, Nicola, Dibattista, Michele, Kaur, Rishemjit, Liuzza, Marco Tullio, Pepino, Marta Y, Schöpf, Veronika, Pereda-Loth, Veronica, Olsson, Shannon B, Gerkin, Richard C, Domínguez, Paloma Rohlfs, Albayay, Javier, Farruggia, Michael C, Bhutani, Surabhi, Fjaeldstad, Alexander W, Kumar, Ritesh, Menini, Anna, Bensafi, Moustafa, Sandell, Mari, Konstantinidis, Iordanis, Di Pizio, Antonella, Genovese, Federica, Öztürk, Lina, Thomas-Danguin, Thierry, Frasnelli, Johannes, Boesveldt, Sanne, Saatci, Özlem, Saraiva, Luis R, Lin, Cailu, Golebiowski, Jérôme, Hwang, Liang-Dar, Ozdener, Mehmet Hakan, Guàrdia, Maria Dolors, Laudamiel, Christophe, Ritchie, Marina, Havlícek, Jan, Pierron, Denis, Roura, Eugeni, Navarro, Marta, Nolden, Alissa A, Lim, Juyun, Whitcroft, KL, Colquitt, Lauren R, Ferdenzi, Camille, Brindha, Evelyn V, Altundag, Aytug, Macchi, Alberto, Nunez-Parra, Alexia, Patel, Zara M, Fiorucci, Sébastien, Philpott, Carl M, Smith, Barry C, Lundström, Johan N, Mucignat, Carla, Parker, Jane K, van den Brink, Mirjam, Schmuker, Michael, Fischmeister, Florian Ph S, Heinbockel, Thomas, Shields, Vonnie DC, Faraji, Farhoud, Santamaría, Enrique, Fredborg, William EA, Morini, Gabriella, Olofsson, Jonas K, Jalessi, Maryam, Karni, Noam, D’Errico, Anna, Alizadeh, Rafieh, Pellegrino, Robert, Meyer, Pablo, Huart, Caroline, Chen, Ben, Soler, Graciela M, Alwashahi, Mohammed K, Welge-Lüssen, Antje, Freiherr, Jessica, de Groot, Jasper HB, Klein, Hadar, Okamoto, Masako, Singh, Preet Bano, Hsieh, Julien W, Reed, Danielle R, Hummel, Thomas, Munger, Steven D, Hayes, John E, Abdulrahman, Olagunju, Dalton, Pamela, Yan, Carol H, Voznessenskaya, Vera V, Chen, Jingguo, Sell, Elizabeth A, and Walsh-Messinger, Julie

Subjects
Neurosciences, Dental/Oral and Craniofacial Disease, Clinical Research, Adult, Aged, Betacoronavirus, COVID-19, Coronavirus Infections, Female, Humans, Male, Middle Aged, Olfaction Disorders, Pandemics, Pneumonia, Viral, SARS-CoV-2, Self Report, Smell, Somatosensory Disorders, Surveys and Questionnaires, Taste, Taste Disorders, Young Adult, head and neck surgery, olfaction, somatosensation, GCCR Group Author, Biological Sciences, Neurology & Neurosurgery
Abstract

Recent anecdotal and scientific reports have provided evidence of a link between COVID-19 and chemosensory impairments, such as anosmia. However, these reports have downplayed or failed to distinguish potential effects on taste, ignored chemesthesis, and generally lacked quantitative measurements. Here, we report the development, implementation, and initial results of a multilingual, international questionnaire to assess self-reported quantity and quality of perception in 3 distinct chemosensory modalities (smell, taste, and chemesthesis) before and during COVID-19. In the first 11 days after questionnaire launch, 4039 participants (2913 women, 1118 men, and 8 others, aged 19-79) reported a COVID-19 diagnosis either via laboratory tests or clinical assessment. Importantly, smell, taste, and chemesthetic function were each significantly reduced compared to their status before the disease. Difference scores (maximum possible change ±100) revealed a mean reduction of smell (-79.7 ± 28.7, mean ± standard deviation), taste (-69.0 ± 32.6), and chemesthetic (-37.3 ± 36.2) function during COVID-19. Qualitative changes in olfactory ability (parosmia and phantosmia) were relatively rare and correlated with smell loss. Importantly, perceived nasal obstruction did not account for smell loss. Furthermore, chemosensory impairments were similar between participants in the laboratory test and clinical assessment groups. These results show that COVID-19-associated chemosensory impairment is not limited to smell but also affects taste and chemesthesis. The multimodal impact of COVID-19 and the lack of perceived nasal obstruction suggest that severe acute respiratory syndrome coronavirus strain 2 (SARS-CoV-2) infection may disrupt sensory-neural mechanisms.

Published
2020

9. Mining Social Networks for Dissemination of Fake News Using Continuous Opinion-Based Hybrid Model

Author

Singh, Maneet, Iyengar, S. R. S., Kaur, Rishemjit, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Li, Bohan, editor, Yue, Lin, editor, Jiang, Jing, editor, Chen, Weitong, editor, Li, Xue, editor, Long, Guodong, editor, Fang, Fei, editor, and Yu, Han, editor

Published
2022
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10. Cooperative control of multi-agent systems to locate source of an odor

Author

Sinha, Abhinav, Kaur, Rishemjit, Kumar, Ritesh, and Bhondekar, Amol P.

Subjects
Computer Science - Systems and Control, Computer Science - Multiagent Systems, Computer Science - Robotics
Abstract

This work targets the problem of odor source localization by multi-agent systems. A hierarchical cooperative control has been put forward to solve the problem of locating source of an odor by driving the agents in consensus when at least one agent obtains information about location of the source. Synthesis of the proposed controller has been carried out in a hierarchical manner of group decision making, path planning and control. Decision making utilizes information of the agents using conventional Particle Swarm Algorithm and information of the movement of filaments to predict the location of the odor source. The predicted source location in the decision level is then utilized to map a trajectory and pass that information to the control level. The distributed control layer uses sliding mode controllers known for their inherent robustness and the ability to reject matched disturbances completely. Two cases of movement of agents towards the source, i.e., under consensus and formation have been discussed herein. Finally, numerical simulations demonstrate the efficacy of the proposed hierarchical distributed control., Comment: 8 pages, initial results on our work

Published
2017

11. Possible roles of phytochemicals with bioactive properties in the prevention of and recovery from COVID-19.

Author

Sachiko Koyama, Joseph, Paule V., Shields, Vonnie D. C., Heinbockel, Thomas, Adhikari, Poonam, Kaur, Rishemjit, Kumar, Ritesh, Alizadeh, Rafieh, Bhutani, Surabhi, Calcinoni, Orietta, Mucignat-Caretta, Carla, Jingguo Chen, Cooper, Keiland W., Das, Subha R., Rohlfs Domínguez, Paloma, Guàrdia, Maria Dolors, Klyuchnikova, Maria A., Laktionova, Tatiana K., Eri Mori, and Namjoo, Zeinab

Published
2024
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12. Quantifying moral foundations from various topics on Twitter conversations

Author

Kaur, Rishemjit and Sasahara, Kazutoshi

Subjects
Computer Science - Computers and Society, Computer Science - Social and Information Networks
Abstract

Moral foundations theory explains variations in moral behavior using innate moral foundations: Care, Fairness, Ingroup, Authority, and Purity, along with experimental supports. However, little is known about the roles of and relationships between those foundations in everyday moral situations. To address these, we quantify moral foundations from a large amount of online conversations (tweets) about moral topics on the social media site Twitter. We measure moral loadings using latent semantic analysis of tweets related to topics on abortion, homosexuality, immigration, religion, and immorality in general, showing how the five moral foundations function in spontaneous conversations about moral violating situations. The results indicate that although the five foundations are mutually related, Purity is the most distinctive foundation and Care is the most dominant foundation in everyday conversations on immorality. Our study shows a new possibility of natural language processing and social big data for moral psychology., Comment: 16 pages, 5 figures, 4 tables, The Proceedings of the 2016 IEEE International Conference on Big Data

Published
2016
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14. Possible roles of phytochemicals with bioactive properties in the prevention of and recovery from COVID-19

Author

Koyama, Sachiko, primary, Shields, Vonnie D.C., additional, Heinbockel, Thomas, additional, Joseph, Paule V., additional, Adhikari, Poonam, additional, Kaur, Rishemjit, additional, Kumar, Ritesh, additional, Alizadeh, Rafieh, additional, Bhutani, Surabhi, additional, Calcinoni, Orietta, additional, Mucignat-Caretta, Carla, additional, Chen, Jingguo, additional, Cooper, Keiland W., additional, Das, Subha R., additional, Domínguez, Paloma Rohlfs, additional, Guàrdia, Maria Dolors, additional, Klyuchnikova, Maria A., additional, Laktionova, Tatiana K., additional, Mori, Eri, additional, Namjoo, Zeinab, additional, Nguyen, Ha, additional, Özdener, Mehmet Hakan, additional, Parsa, Shima, additional, Poyraz, Elif Özdener, additional, Strub, Daniel Jan, additional, Taghizadeh-Hesary, Farzad, additional, Ueha, Rumi, additional, and Voznessenskaya, Vera V., additional

Published
2024
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15. Multidimensional Analysis of Fake News Spreaders on Twitter

Author

Singh, Maneet, Kaur, Rishemjit, Iyengar, S. R. S., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Chellappan, Sriram, editor, Choo, Kim-Kwang Raymond, editor, and Phan, NhatHai, editor

Published
2020
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17. Human Opinion Inspired Feature Selection Strategy for Predicting the Pleasantness of a Molecule

Author

Kumar, Ritesh, Kaur, Rishemjit, Bhondekar, Amol P., Raghava, Gajendra P. S., Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Bhattacharyya, Siddhartha, editor, Chaki, Nabendu, editor, Konar, Debanjan, editor, Chakraborty, Udit Kr., editor, and Singh, Chingtham Tejbanta, editor

Published
2018
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19. Twitter sentiment analysis for COVID-19 associated mucormycosis

Author

Singh, Maneet, Dhillon, Hennaav, Ichhpujani, Parul, Iyengar, Sudarshan, and Kaur, Rishemjit

Subjects
User groups -- Beliefs, opinions and attitudes, Mucormycosis -- Public opinion, Online social networks -- Usage, User group, Health, Twitter (Online social network) -- Usage
Abstract

Byline: Maneet. Singh, Hennaav. Dhillon, Parul. Ichhpujani, Sudarshan. Iyengar, Rishemjit. Kaur Purpose: COVID-19-associated mucormycosis (CAM) was a serious public health problem during the second wave of COVID-19 in India. We [...]

Published
2022

20. Neural Machine Translation for Low-resource Languages: A Survey.

Author

RANATHUNGA, SURANGIKA, LEE, EN-SHIUN ANNIE, SKENDULI, MARJANA PRIFTI, SHEKHAR, RAVI, ALAM, MEHREEN, and KAUR, RISHEMJIT

Subjects
MACHINE translating, LANGUAGE & languages, DATA augmentation
Abstract

Neural Machine Translation (NMT) has seen tremendous growth in the last ten years since the early 2000s and has already entered amature phase.While considered the most widely used solution for Machine Translation, its performance on low-resource language pairs remains sub-optimal compared to the high-resource counterparts due to the unavailability of large parallel corpora. Therefore, the implementation of NMT techniques for low-resource language pairs has been receiving the spotlight recently, thus leading to substantial research on this topic. This article presents a detailed survey of research advancements in low-resource language NMT (LRL-NMT) and quantitative analysis to identify the most popular techniques.We provide guidelines to select the possible NMT technique for a given LRL data setting based on our findings.We also present a holistic view of the LRL-NMT research landscape and provide recommendations to enhance the research efforts further. [ABSTRACT FROM AUTHOR]

Published
2023
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21. The Smell Network

Author

Kumar, Ritesh, Kaur, Rishemjit, Bhondekar, Amol P., Wang, Leon, editor, Uesugi, Shiro, editor, Ting, I-Hsien, editor, Okuhara, Koji, editor, and Wang, Kai, editor

Published
2015
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22. Effects of Topological Variations on Opinion Dynamics Optimizer

Author

Kaur, Rishemjit, Kumar, Ritesh, Bhondekar, Amol P, Suzuki, Reiji, Arita, Takaya, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Tan, Ying, editor, Shi, Yuhui, editor, Buarque, Fernando, editor, Gelbukh, Alexander, editor, Das, Swagatam, editor, and Engelbrecht, Andries, editor

Published
2015
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27. COVID-19 and ophthalmology: A scientometric analysis

Author

Kalra, Gagan, Kaur, Rishemjit, Ichhpujani, Parul, Chahal, Rutvi, and Kumar, Suresh

Subjects
Epidemics -- Influence -- India, Ophthalmology -- Forecasts and trends, Medical journals -- Management, Company business management, Market trend/market analysis, Health
Abstract

Byline: Gagan. Kalra, Rishemjit. Kaur, Parul. Ichhpujani, Rutvi. Chahal, Suresh. Kumar Purpose: Coronavirus disease pandemic has impacted global healthcare tremendously and ophthalmology is one of the high-hit specialties. An increasing [...]

Published
2021

32. Mere end lugtesans - COVID-19 er associeret med svær påvirkning af lugtesansen, smagssansen og mundfølelsen

Author

Parma, Valentina, Ohla, Kathrin, Veldhuizen, Maria G, Niv, Masha Y, Kelly, Christine E, Bakke, Alyssa J, Cooper, Keiland W, Bouysset, Cédric, Pirastu, Nicola, Dibattista, Michele, Kaur, Rishemjit, Liuzza, Marco Tullio, Pepino, Marta Y, Schöpf, Veronika, Pereda-Loth, Veronica, Olsson, Shannon B, Gerkin, Richard C, Rohlfs Domínguez, Paloma, Albayay, Javier, Farruggia, Michael C, Bhutani, Surabhi, Fjaeldstad, Alexander W, Kumar, Ritesh, Menini, Anna, Bensafi, Moustafa, Sandell, Mari, Konstantinidis, Iordanis, Di Pizio, Antonella, Genovese, Federica, Öztürk, Lina, Thomas-Danguin, Thierry, Frasnelli, Johannes, Boesveldt, Sanne, Saatci, Özlem, Saraiva, Luis R, Lin, Cailu, Golebiowski, Jérôme, Hwang, Liang-Dar, Ozdener, Mehmet Hakan, Guàrdia, Maria Dolors, Laudamiel, Christophe, Ritchie, Marina, Havlícek, Jan, Pierron, Denis, Roura, Eugeni, Navarro, Marta, Nolden, Alissa A, Lim, Juyun, Whitcroft, Katherine L, Colquitt, Lauren R, Ferdenzi, Camille, Brindha, Evelyn V, Altundag, Aytug, Macchi, Alberto, Nunez-Parra, Alexia, Patel, Zara M, Fiorucci, Sébastien, Philpott, Carl M, Smith, Barry C, Lundström, Johan N, Mucignat, Carla, Parker, Jane K, van den Brink, Mirjam, Schmuker, Michael, Fischmeister, Florian Ph S, Heinbockel, Thomas, Shields, Vonnie D C, Faraji, Farhoud, Santamaría, Enrique, Fredborg, William E A, Morini, Gabriella, Olofsson, Jonas K, Jalessi, Maryam, Karni, Noam, D’Errico, Anna, Alizadeh, Rafieh, Pellegrino, Robert, Meyer, Pablo, Huart, Caroline, Chen, Ben, Soler, Graciela M, Alwashahi, Mohammed K, Welge-Lüssen, Antje, Freiherr, Jessica, de Groot, Jasper H B, Klein, Hadar, Okamoto, Masako, Singh, Preet Bano, Hsieh, Julien W, Abdulrahman, Olagunju, Dalton, Pamela, Yan, Carol H, Voznessenskaya, Vera V, Chen, Jingguo, Sell, Elizabeth A, Walsh-Messinger, Julie, Archer, Nicholas S, Koyama, Sachiko, Deary, Vincent, Roberts, S Craig, Yanık, Hüseyin, Albayrak, Samet, Nováková, Lenka Martinec, Croijmans, Ilja, Mazal, Patricia Portillo, Moein, Shima T, Margulis, Eitan, Mignot, Coralie, Mariño, Sajidxa, Georgiev, Dejan, Kaushik, Pavan K, Malnic, Bettina, Wang, Hong, Seyed-Allaei, Shima, Yoluk, Nur, Razzaghi-Asl, Sara, Justice, Jeb M, Restrepo, Diego, Reed, Danielle R, Hummel, Thomas, Munger, Steven D, Hayes, John E, Indústries Alimentàries, Qualitat i Tecnologia Alimentària, Tecnologia Alimentària, Temple University [Philadelphia], Pennsylvania Commonwealth System of Higher Education (PCSHE), Forschungszentrum Jülich GmbH | Centre de recherche de Juliers, Helmholtz-Gemeinschaft = Helmholtz Association, Mersin University, The Hebrew University of Jerusalem (HUJ), AbScent, Pennsylvania State University (Penn State), Penn State System, University of California [Irvine] (UC Irvine), University of California (UC), Université Côte d'Azur (UCA), University of Edinburgh, Università degli studi di Bari Aldo Moro = University of Bari Aldo Moro (UNIBA), Central Scientific Instruments Organisation (CSIR), Università degli Studi 'Magna Graecia' di Catanzaro = University of Catanzaro (UMG), University of Illinois at Urbana-Champaign [Urbana], University of Illinois System, Medizinische Universität Wien = Medical University of Vienna, Groupement scientifique de Biologie et de Medecine Spatiale (GSBMS), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National d'Études Spatiales [Toulouse] (CNES), Tata Institute for Fundamental Research (TIFR), Arizona State University [Tempe] (ASU), Universidad de Extremadura - University of Extremadura (UEX), Università degli Studi di Padova = University of Padua (Unipd), Yale School of Medicine [New Haven, Connecticut] (YSM), San Diego State University (SDSU), Aarhus University [Aarhus], University of Hertfordshire [Hatfield] (UH), Scuola Internazionale Superiore di Studi Avanzati / International School for Advanced Studies (SISSA / ISAS), Neurosciences Sensorielles Comportement Cognition, Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), Helsingin yliopisto = Helsingfors universitet = University of Helsinki, University of Turku, Aristotle University of Thessaloniki, Leibniz-Institute for Food Systems Biology at the Technical University of Munich, Monell Chemical Senses Center, Centre des Sciences du Goût et de l'Alimentation [Dijon] (CSGA), Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Université Bourgogne Franche-Comté [COMUE] (UBFC), Université de Montréal (UdeM), Wageningen University and Research Centre (WUR), Medical Science University, Sidra Medicine [Doha, Qatar], Institut de Chimie de Nice (ICN), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), University of Southern Queensland (USQ), Institut de Recerca i Tecnologia Agroalimentàries = Institute of Agrifood Research and Technology (IRTA), DreamAir Llc, Charles University [Prague] (CU), Anthropologie Moléculaire et Imagerie de Synthèse (AMIS), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS), University of Massachusetts System (UMASS), Oregon State University (OSU), Ear Institute, UCL, Lyon Neuroscience Research center, Karunya University, Biruni University, Assi Sette Llaghi Varese, Stanford School of Medicine [Stanford], Stanford Medicine, Stanford University-Stanford University, University of East Anglia [Norwich] (UEA), California Department of Food and Agriculture (CDFA), Unité mixte de recherche interactions plantes-microorganismes, Institut National de la Recherche Agronomique (INRA)-Université Toulouse III - Paul Sabatier (UT3), Maastricht University [Maastricht], Institute for Biology - Neurobiology, Freie Universität Berlin, Karl-Franzens-Universität Graz, Howard University College of Medicine, Towson University, University of California [San Diego] (UC San Diego), Proteomics, Center for Applied Medical Research (CIMA), Stockholm University, University of Gastronomic Sciences, Iran University of Medical Sciences, Goethe Universität Frankfurt, University of Tennessee, IBM T.J. Watson Research Center, Université libre de Bruxelles (ULB), Guangzhou Medical University, Buenos Aires University and GEOG (Grupo de Estudio de Olfato y Gusto), Sultan Qaboos University (SQU), Federal University of Technology of Akure (FUTA), A.N. Severtsov Institute of Ecology and Evolution, Russian Academy of Sciences [Moscow] (RAS), Hospital of Xi'an Jiaotong University, University of Pennsylvania, University of Dayton, CSIRO Agriculture and Food (CSIRO), Indiana University [Bloomington], Indiana University System, University of Northumbria at Newcastle [United Kingdom], University of Stirling, Middle East Technical University [Ankara] (METU), Utrecht University [Utrecht], Instituto Universitario del Hospital Italiano [Buenos Aires, Argentina], Institute for Research in Fundamental Sciences [Tehran] (IPM), Hebrew University of Jerusalem, Technische Universität Dresden = Dresden University of Technology (TU Dresden), Terrazas del Club Hipico, University Medical Centre Ljubljana [Ljubljana, Slovenia] (UMCL), Tata Institute of Fundamental Research [Bangalore], Universidade de São Paulo = University of São Paulo (USP), University of Florida [Gainesville] (UF), University of Colorado Anschutz [Aurora], Center for Smell and Taste, Department of Food Science, Pennsylvania State University., Julien, Sabine, Tıp Fakültesi, UCL - SSS/IONS/NEUR - Clinical Neuroscience, UCL - (SLuc) Service d'oto-rhino-laryngologie, Department of Food and Nutrition, Senses and Food, Research Center Jülich, University of California [Irvine] (UCI), University of California, Università degli studi di Bari Aldo Moro (UNIBA), Università degli Studi 'Magna Graecia' di Catanzaro [Catanzaro, Italie] (UMG), University of Extremadura, University of Padova, Yale University School of Medicine, Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon, University of Helsinki, Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA), Institute of Agrifood Research and Technology (IRTA), Universita degli Studi di Padova, Centre National de la Recherche Scientifique (CNRS)-Institut National de la Recherche Agronomique (INRA)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Karl-Franzens-Universität [Graz, Autriche], University of California San Diego Health, University of Brussels, University of Pennsylvania [Philadelphia], Tata Institute of Fundamental Research, University of São Paulo (USP), UCL - SSS/IONS - Institute of NeuroScience, FSE Campus Venlo, and RS: FSE UCV

Subjects
Male, Taste, Physiology, Smagstab, Audiology, AcademicSubjects/SCI01180, Settore BIO/09 - Fisiologia, Behavioral Neuroscience, chemistry.chemical_compound, Olfaction Disorders, Taste Disorders, 0302 clinical medicine, RATINGS, Hyposmia, Surveys and Questionnaires, CHEMOSENSITIVITY, [SDV.IDA]Life Sciences [q-bio]/Food engineering, Viral, PALADAR, 030223 otorhinolaryngology, Sensory Science and Eating Behaviour, media_common, TASTE, US NATIONAL-HEALTH, [SDV.IDA] Life Sciences [q-bio]/Food engineering, Middle Aged, Biological Sciences, 16. Peace & justice, Sensory Systems, 3. Good health, Smell, GCCR Group Author, ddc:540, Smell loss, Female, Original Article, medicine.symptom, Corrigendum, Coronavirus Infections, olfaction, Adult, somatosensation, medicine.medical_specialty, 663/664, Coronavirus disease 2019 (COVID-19), OLFACTORY DISORDERS, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), media_common.quotation_subject, Pneumonia, Viral, head and neck surgery, Aged, Betacoronavirus, COVID-19, Humans, Pandemics, SARS-CoV-2, Self Report, Somatosensory Disorders, Young Adult, Anosmia, Sensory system, Olfaction, 03 medical and health sciences, Chemesthesis, Physiology (medical), Perception, medicine, Neurology & Neurosurgery, Behaviour Change and Well-being, business.industry, R-PACKAGE, 3112 Neurosciences, Pneumonia, Parosmia, COMPONENT, Smagssans, [SDV.AEN] Life Sciences [q-bio]/Food and Nutrition, Sensoriek en eetgedrag, chemistry, Lugtetab, business, [SDV.AEN]Life Sciences [q-bio]/Food and Nutrition, 030217 neurology & neurosurgery, Lugtesans
Abstract

Correction: Chemical Senses, Volume 46, 2021, bjab050, https://doi.org/10.1093/chemse/bjab050 Published: 08 December 2021 Recent anecdotal and scientific reports have provided evidence of a link between COVID-19 and chemosensory impairments, such as anosmia. However, these reports have downplayed or failed to distinguish potential effects on taste, ignored chemesthesis, and generally lacked quantitative measurements. Here, we report the development, implementation, and initial results of a multilingual, international questionnaire to assess self-reported quantity and quality of perception in 3 distinct chemosensory modalities (smell, taste, and chemesthesis) before and during COVID-19. In the first 11 days after questionnaire launch, 4039 participants (2913 women, 1118 men, and 8 others, aged 19-79) reported a COVID-19 diagnosis either via laboratory tests or clinical assessment. Importantly, smell, taste, and chemesthetic function were each significantly reduced compared to their status before the disease. Difference scores (maximum possible change +/- 100) revealed a mean reduction of smell (-79.7 +/- 28.7, mean +/- standard deviation), taste (-69.0 +/- 32.6), and chemesthetic (-37.3 +/- 36.2) function during COVID-19. Qualitative changes in olfactory ability (parosmia and phantosmia) were relatively rare and correlated with smell loss. Importantly, perceived nasal obstruction did not account for smell loss. Furthermore, chemosensory impairments were similar between participants in the laboratory test and clinical assessment groups. These results show that COVID-19-associated chemosensory impairment is not limited to smell but also affects taste and chemesthesis.The multimodal impact of COVID-19 and the lack of perceived nasal obstruction suggest that severe acute respiratory syndrome coronavirus strain 2 (SARS-CoV-2) infection may disrupt sensory-neural mechanisms.

Published
2020

35. Leveraging machine learning and self-administered tests to predict COVID-19: An olfactory and gustatory dysfunction assessment through crowd-sourced data in India

Author

Kumar, Ritesh, primary, Singh, Maneet, additional, Singh, Prateek, additional, Parma, Valentina, additional, Ohla, Kathrin, additional, Olsson, Shannon B, additional, Saini, Varun, additional, Rani, Jyotsna, additional, Kishore, Kunal, additional, Kumari, Priyanka, additional, Ichhpujani, Parul, additional, Sharma, Anupma, additional, Kumar, Saurav, additional, Sharma, Manu, additional, Bhondekar, Amol P, additional, Kothari, Anamika, additional, Sardana, Viren, additional, Iyengar, Sudarshan, additional, Dash, Debasis, additional, and Kaur, Rishemjit, additional

Published
2021
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37. Corrigendum to: More than smell: COVID-19 is associated with severe impairment of smell, taste, and chemesthesis

Author

Parma, Valentina, Ohla, Kathrin, Veldhuizen, Maria G., Niv, Masha Y., Kelly, Christine E., Bakke, Alyssa J., Cooper, Keiland W., Bouysset, Cédric, Pirastu, Nicola, Dibattista, Michele, Kaur, Rishemjit, Liuzza, Marco Tullio, Pepino, Marta Y., Schöpf, Veronika, Pereda-Loth, Veronica, Olsson, Shannon B., Gerkin, Richard C., Rohlfs Domínguez, Paloma, Albayay, Javier, Farruggia, Michael C., Bhutani, Surabhi, Fjaeldstad, Alexander W., Kumar, Ritesh, Menini, Anna, Bensafi, Moustafa, Sandell, Mari, Konstantinidis, Iordanis, Di Pizio, Antonella, Genovese, Federica, Öztürk, Lina, Thomas-Danguin, Thierry, Frasnelli, Johannes, Boesveldt, Sanne, Saatci, Özlem, Saraiva, Luis R., Lin, Cailu, Golebiowski, Jérôme, Hwang, Liang Dar, Ozdener, Mehmet Hakan, Guàrdia, Maria Dolors, Laudamiel, Christophe, Ritchie, Marina, Havlícek, Jan, Pierron, Denis, Roura, Eugeni, Navarro, Marta, Nolden, Alissa A., Lim, Juyun, Whitcroft, Katherine L., Colquitt, Lauren R., Ferdenzi, Camille, Brindha, Evelyn V., Altundag, Aytug, Macchi, Alberto, Nunez-Parra, Alexia, Patel, Zara M., Fiorucci, Sébastien, Philpott, Carl M., Smith, Barry C., Lundström, Johan N., Mucignat, Carla, Parker, Jane K., Van Den Brink, Mirjam, Schmuker, Michael, Fischmeister, Florian Ph S., Heinbockel, Thomas, Shields, Vonnie D.C., Faraji, Farhoud, Santamaría, Enrique, Fredborg, William E.A., Morini, Gabriella, Olofsson, Jonas K., Jalessi, Maryam, Karni, Noam, D'Errico, Anna, Alizadeh, Rafieh, Pellegrino, Robert, Meyer, Pablo, Huart, Caroline, Chen, Ben, Soler, Graciela M., Alwashahi, Mohammed K., Welge-Lüssen, Antje, Freiherr, Jessica, De Groot, Jasper H.B., Klein, Hadar, Okamoto, Masako, Singh, Preet Bano, Hsieh, Julien W., Abdulrahman, Olagunju, Dalton, Pamela, Yan, Carol H., Voznessenskaya, Vera V., Chen, Jingguo, Sell, Elizabeth A., Walsh-Messinger, Julie, Archer, Nicholas S., Koyama, Sachiko, Deary, Vincent, Roberts, S.C., Yanlk, Hüseyin, Albayrak, Samet, Nováková, Lenka Martinec, Croijmans, Ilja, Mazal, Patricia Portillo, Moein, Shima T., Margulis, Eitan, Mignot, Coralie, Mariño, Sajidxa, Georgiev, Dejan, Kaushik, Pavan K., Malnic, Bettina, Wang, Hong, Seyed-Allaei, Shima, Yoluk, Nur, Razzaghi-Asl, Sara, Justice, Jeb M., Restrepo, Diego, Reed, Danielle R., Hummel, Thomas, Munger, Steven D., Hayes, John E., UCL - SSS/IONS - Institute of NeuroScience, UCL - SSS/IONS/NEUR - Clinical Neuroscience, and UCL - (SLuc) Service d'oto-rhino-laryngologie

Subjects
Behavioral Neuroscience, Sensoriek en eetgedrag, Behaviour Change and Well-being, Physiology, Physiology (medical), ddc:540, Life Science, Sensory Systems, Sensory Science and Eating Behaviour, VLAG
Abstract

This is a correction notice for article bjaa041 (DOI: https:// doi.org/10.1093/chemse/bjaa041), published 20 June 2020. An incorrect version of the caption to Figure 5 was mistakenly included in the published paper. An updated version is given below. Neither the data nor the paper's conclusions were affected by this correction. The authors sincerely apologize for the error. (A) Correlations between the 3 principal components with respect to changes in 3 chemosensory modalities (i.e., taste, smell, and chemesthesis). Shades of gray indicate positive correlation, whereas shades of red indicate negative correlations. White denotes no correlation. (B) Clusters of participants identified by k-means clustering. The scatterplot shows each participant's loading on dimension 1 (degree of smell and taste loss, PC1 on x-Axis) and dimension 2 (degree of chemesthesis loss, PC2 on y-Axis). Based on the centroid of each cluster, participants in cluster 1 (blue, N = 1767; top left) are generally characterized by significant smell, taste and chemesthesis loss. Participants in cluster 2 (orange, N = 1724; bottom center) are generally characterized by ratings that reflect smell/taste loss with preserved chemesthesis. Loadings for participants in cluster 3 (green, N = 548; right side) are generally characterized by reduced smell and taste loss, and preserved chemesthesis.

Published
2021

46. Consensus based odour source localisation by multi-agent systems

Author

Sinha, Abhinav, Kaur, Rishemjit, Kumar, Ritesh, Bhondekar, Amol, and Sinha, Abhinav

Subjects
[INFO.INFO-SY] Computer Science [cs]/Systems and Control [cs.SY], [INFO.INFO-MA] Computer Science [cs]/Multiagent Systems [cs.MA], [INFO.INFO-AU] Computer Science [cs]/Automatic Control Engineering
Abstract

This paper presents an investigation of the task of localising unknown source of an odour by heterogeneous multi-agent systems. A hierarchical cooperative control strategy has been proposed as a potential candidate to solve the problem. The agents are driven into consensus as soon as the information about the location of source is acquired. The controller has been designed in a hierarchical manner of group decision making, agent path planning, and robust control. In group decision making, a Particle Swarm Algorithm has been used along with the information of the movement of odour molecules to predict the odour source location. Next, a trajectory has been mapped using this predicted location of source, and the information is passed to the control layer. A variable structure control has been used in the control layer due to its inherent robustness and disturbance rejection capabilities. Cases of movement of agents towards the source under consensus, and parallel formation have been discussed. The efficacy of the proposed scheme has been confirmed by simulations.

Published
2017

47. A cooperative control framework for odor source localization by multi-agent systems

Author

Sinha, Abhinav, Kaur, Rishemjit, Kumar, Ritesh, Bhondekar, Amol, and Sinha, Abhinav

Subjects
[INFO.INFO-SY] Computer Science [cs]/Systems and Control [cs.SY], [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], sliding mode control, particle swarm, homogeneous multi-agent systems (MAS), consensus, Odor source localization, formation keeping, cooperative, [INFO.INFO-MA] Computer Science [cs]/Multiagent Systems [cs.MA], inverse sine hyperbolic reaching law, optimization, control, olfaction
Abstract

This work is aimed at solving the problem of odor source localization by multi-agent systems. A hierarchical cooperative control has been put forward to solve the problem of locating source of an odor by driving the agents in consensus when at least one agent obtains information about location of the source. Synthesis of the proposed controller has been carried out in a hierarchical manner of group decision making, path planning and control. Decision making utilizes information of the agents using conventional Particle Swarm Algorithm and information of the movement of filaments to predict the location of the odor source. The predicted source location in the decision level is then utilized to map a trajectory and pass that information to the control level. The distributed control layer uses sliding mode controllers known for their inherent robustness and the ability to reject matched disturbances completely. Two cases of movement of agents towards the source, i.e., under consensus and formation have been discussed herein. Finally, numerical simulations demonstrate the efficacy of the proposed hierarchical distributed control in both one and two dimensions.

Published
2017

49. SMELL AND LANGUAGE : DATACENTRIC APPROACH TO PREDICTING SMELL OF A MOLECULE

Author

Kumar, Ritesh, Kaur, Rishemjit, Bhondekar, Amol Purushottam, and Raghava, Gajendra Pal Singh

Abstract

Predicting the smell of a molecule is an open question till date. We have been chemocentric in approach to understanding it, with the advent of data analysis technologies, it is possible to look it from a datacentric point of view. We have accumulated 3026 odoriferous molecules and their perceived smells from the large publically available dataset online. We developed a graphical method to analyze the smell or perceptual descriptors of the molecules using normalized co-occurrence value i.e. how many times the perceptual descriptors have occurred together. We developed a simple method to annotate the smell of a molecules in two groups using the community detection technique generally used in social network analysis. We then developed a machine learning method using physico-chemical properties of the molecules and predicted the smell of a molecule. We report MCC, accuracy, precision, recall and ROC values obtained from this procedure. The resulting ROC value of 0.7 indicate that such an analysis can be undertaken and are effective in predicting the smell of a molecule. The graphical method to analyze the perceptual descriptors space presents interesting insights regarding language and olfaction, e.g. clustering of typical words together and their unrelatedness to semantics., Journal of Digital Olfaction Society, Vol 4, No 1 (2016): Journal of Digital Olfaction Volume 4

Published
2016
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