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2. Deep Fake Analyser: A Review Based on Detecting the Deepfakes

Author

Srivastava, Santosh Kumar, Srivastava, Durgesh, Kantha, Praveen, Mahto, Manoj Kumar, Kumar, Sheo, Singh, Hare Ram, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, Santosh, K. C., editor, Sood, Sandeep Kumar, editor, Pandey, Hari Mohan, editor, and Virmani, Charu, editor

Published
2024
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3. Optimizing Radio Resource Allocation for 6G Wireless Networks via Machine Learning

Author

Srivastava, Santosh Kumar, Mahto, Manoj Kumar, Verma, Deepak Kumar, Kantha, Praveen, Pisello, Anna Laura, Editorial Board Member, Bibri, Simon Elias, Editorial Board Member, Ahmed Salih, Gasim Hayder, Editorial Board Member, Battisti, Alessandra, Editorial Board Member, Piselli, Cristina, Editorial Board Member, Strauss, Eric J., Editorial Board Member, Matamanda, Abraham, Editorial Board Member, Gallo, Paola, Editorial Board Member, Marçal Dias Castanho, Rui Alexandre, Editorial Board Member, Chica Olmo, Jorge, Editorial Board Member, Bruno, Silvana, Editorial Board Member, He, Baojie, Editorial Board Member, Niglio, Olimpia, Editorial Board Member, Pivac, Tatjana, Editorial Board Member, Olanrewaju, AbdulLateef, Editorial Board Member, Pigliautile, Ilaria, Editorial Board Member, Karunathilake, Hirushie, Editorial Board Member, Fabiani, Claudia, Editorial Board Member, Vujičić, Miroslav, Editorial Board Member, Stankov, Uglješa, Editorial Board Member, Sánchez, Angeles, Editorial Board Member, Jupesta, Joni, Editorial Board Member, Pignatta, Gloria, Editorial Board Member, Shtylla, Saimir, Editorial Board Member, Alberti, Francesco, Editorial Board Member, Buckley, Ayşe Özcan, Editorial Board Member, Mandic, Ante, Editorial Board Member, Ahmed Ibrahim, Sherif, Editorial Board Member, Teba, Tarek, Editorial Board Member, Al-Kassimi, Khaled, Editorial Board Member, Rosso, Federica, Editorial Board Member, Abdalla, Hassan, Editorial Board Member, Trapani, Ferdinando, Editorial Board Member, Magnaye, Dina Cartagena, Editorial Board Member, Chehimi, Mohamed Mehdi, Editorial Board Member, van Hullebusch, Eric, Editorial Board Member, Chaminé, Helder, Editorial Board Member, Della Spina, Lucia, Editorial Board Member, Aelenei, Laura, Editorial Board Member, Parra-López, Eduardo, Editorial Board Member, Ašonja, Aleksandar N., Editorial Board Member, Amer, Mourad, Series Editor, and Al-Turjman, Fadi, editor

Published
2024
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4. Analyzing the Utility of Fuzzy Logic in Calculating the Probability of Pulmonary Effusions

Author

Srivastava, Santosh Kumar, Mahto, Manoj Kumar, Verma, Deepak Kumar, Kantha, Praveen, Pisello, Anna Laura, Editorial Board Member, Bibri, Simon Elias, Editorial Board Member, Ahmed Salih, Gasim Hayder, Editorial Board Member, Battisti, Alessandra, Editorial Board Member, Piselli, Cristina, Editorial Board Member, Strauss, Eric J., Editorial Board Member, Matamanda, Abraham, Editorial Board Member, Gallo, Paola, Editorial Board Member, Marçal Dias Castanho, Rui Alexandre, Editorial Board Member, Chica Olmo, Jorge, Editorial Board Member, Bruno, Silvana, Editorial Board Member, He, Baojie, Editorial Board Member, Niglio, Olimpia, Editorial Board Member, Pivac, Tatjana, Editorial Board Member, Olanrewaju, AbdulLateef, Editorial Board Member, Pigliautile, Ilaria, Editorial Board Member, Karunathilake, Hirushie, Editorial Board Member, Fabiani, Claudia, Editorial Board Member, Vujičić, Miroslav, Editorial Board Member, Stankov, Uglješa, Editorial Board Member, Sánchez, Angeles, Editorial Board Member, Jupesta, Joni, Editorial Board Member, Pignatta, Gloria, Editorial Board Member, Shtylla, Saimir, Editorial Board Member, Alberti, Francesco, Editorial Board Member, Buckley, Ayşe Özcan, Editorial Board Member, Mandic, Ante, Editorial Board Member, Ahmed Ibrahim, Sherif, Editorial Board Member, Teba, Tarek, Editorial Board Member, Al-Kassimi, Khaled, Editorial Board Member, Rosso, Federica, Editorial Board Member, Abdalla, Hassan, Editorial Board Member, Trapani, Ferdinando, Editorial Board Member, Magnaye, Dina Cartagena, Editorial Board Member, Chehimi, Mohamed Mehdi, Editorial Board Member, van Hullebusch, Eric, Editorial Board Member, Chaminé, Helder, Editorial Board Member, Della Spina, Lucia, Editorial Board Member, Aelenei, Laura, Editorial Board Member, Parra-López, Eduardo, Editorial Board Member, Ašonja, Aleksandar N., Editorial Board Member, Amer, Mourad, Series Editor, and Al-Turjman, Fadi, editor

Published
2024
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5. Cloud-Integrated Big Data Algorithms for Deep Learning in Healthcare System

Author

Srivastava, Santosh Kumar, Mahto, Manoj Kumar, Verma, Deepak Kumar, Kantha, Praveen, Pisello, Anna Laura, Editorial Board Member, Bibri, Simon Elias, Editorial Board Member, Ahmed Salih, Gasim Hayder, Editorial Board Member, Battisti, Alessandra, Editorial Board Member, Piselli, Cristina, Editorial Board Member, Strauss, Eric J., Editorial Board Member, Matamanda, Abraham, Editorial Board Member, Gallo, Paola, Editorial Board Member, Marçal Dias Castanho, Rui Alexandre, Editorial Board Member, Chica Olmo, Jorge, Editorial Board Member, Bruno, Silvana, Editorial Board Member, He, Baojie, Editorial Board Member, Niglio, Olimpia, Editorial Board Member, Pivac, Tatjana, Editorial Board Member, Olanrewaju, AbdulLateef, Editorial Board Member, Pigliautile, Ilaria, Editorial Board Member, Karunathilake, Hirushie, Editorial Board Member, Fabiani, Claudia, Editorial Board Member, Vujičić, Miroslav, Editorial Board Member, Stankov, Uglješa, Editorial Board Member, Sánchez, Angeles, Editorial Board Member, Jupesta, Joni, Editorial Board Member, Pignatta, Gloria, Editorial Board Member, Shtylla, Saimir, Editorial Board Member, Alberti, Francesco, Editorial Board Member, Buckley, Ayşe Özcan, Editorial Board Member, Mandic, Ante, Editorial Board Member, Ahmed Ibrahim, Sherif, Editorial Board Member, Teba, Tarek, Editorial Board Member, Al-Kassimi, Khaled, Editorial Board Member, Rosso, Federica, Editorial Board Member, Abdalla, Hassan, Editorial Board Member, Trapani, Ferdinando, Editorial Board Member, Magnaye, Dina Cartagena, Editorial Board Member, Chehimi, Mohamed Mehdi, Editorial Board Member, van Hullebusch, Eric, Editorial Board Member, Chaminé, Helder, Editorial Board Member, Della Spina, Lucia, Editorial Board Member, Aelenei, Laura, Editorial Board Member, Parra-López, Eduardo, Editorial Board Member, Ašonja, Aleksandar N., Editorial Board Member, Amer, Mourad, Series Editor, and Al-Turjman, Fadi, editor

Published
2024
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6. List of contributors

Author

Aatiaoui, Abdelmalik El, primary, Abdelhameed, Reda M., additional, Ahmad, Rabia, additional, Arora, Charu, additional, Bano, Karina, additional, Bano, Sarvaree, additional, Benjamin, Stephen Rathinaraj, additional, Chandrawanshi, Swati, additional, Chaurasia, Himani, additional, Dagdag, Omar, additional, Danao, Kishor, additional, Daoudi, Walid, additional, Dewangan, Ranjana, additional, Dumore, Monali, additional, El-Shahat, Mahmoud, additional, Fule, Ritesh, additional, Gollavelli, Ganesh, additional, Haldhar, Rajesh, additional, Higgins, Prerna, additional, Iqbal, Naseem, additional, Jain, Bhawana, additional, Júnior, Eli José Miranda Ribeiro, additional, Kashyap, Arun Kumar, additional, Kaushal, Sandeep, additional, Khan, Usman Ali, additional, Kumar, Ranvijay, additional, Kumar, Sandeep, additional, Lamghafri, Selma, additional, Mahajan, Ujwala, additional, Mehra, Swati, additional, Mittal, Jyoti, additional, Mohamad Noh, Mohamad Firdaus, additional, Mukhrjee, Arpita, additional, Nandurkar, Deweshri, additional, Naz, Sabiha, additional, Noor, Tayyaba, additional, Pandey, Archana, additional, Pandey, Ashutosh, additional, Pandey, Avinash, additional, Patel, Jyoti, additional, Rai, Neena, additional, Rai, Nidhi, additional, Rawat, Reena, additional, Raza, Maryam, additional, Rokde, Vijayshri, additional, Sahu, Barsa, additional, Sahu, Rakesh Kumar, additional, Sahu, Reema, additional, Sahu, Sushama, additional, Sanwaria, Sunita, additional, Shaukat, Iqra, additional, Shivhare, Ruchi, additional, Shrivastava, A.K, additional, Shrivastava, Shilpi, additional, Siddiqui, Shaziya H., additional, Singh, Ajaya Kumar, additional, Soni, Sanju, additional, Srivastava, Nivedita, additional, Srivastava, Santosh Kumar, additional, Tiwari, Alka, additional, Vasudeva Rao, Bakuru, additional, Verma, Anjali, additional, Verma, Dakeshwar Kumar, additional, Vijayasri, K., additional, Yifu, Chen, additional, and Yong-Chien, Ling, additional

Published
2024
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8. A Benchmark for Generalizable and Interpretable Temporal Question Answering over Knowledge Bases

Author

Neelam, Sumit, Sharma, Udit, Karanam, Hima, Ikbal, Shajith, Kapanipathi, Pavan, Abdelaziz, Ibrahim, Mihindukulasooriya, Nandana, Lee, Young-Suk, Srivastava, Santosh, Pendus, Cezar, Dana, Saswati, Garg, Dinesh, Fokoue, Achille, Bhargav, G P Shrivatsa, Khandelwal, Dinesh, Ravishankar, Srinivas, Gurajada, Sairam, Chang, Maria, Uceda-Sosa, Rosario, Roukos, Salim, Gray, Alexander, Lima, Guilherme, Riegel, Ryan, Luus, Francois, and Subramaniam, L Venkata

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

Knowledge Base Question Answering (KBQA) tasks that involve complex reasoning are emerging as an important research direction. However, most existing KBQA datasets focus primarily on generic multi-hop reasoning over explicit facts, largely ignoring other reasoning types such as temporal, spatial, and taxonomic reasoning. In this paper, we present a benchmark dataset for temporal reasoning, TempQA-WD, to encourage research in extending the present approaches to target a more challenging set of complex reasoning tasks. Specifically, our benchmark is a temporal question answering dataset with the following advantages: (a) it is based on Wikidata, which is the most frequently curated, openly available knowledge base, (b) it includes intermediate sparql queries to facilitate the evaluation of semantic parsing based approaches for KBQA, and (c) it generalizes to multiple knowledge bases: Freebase and Wikidata. The TempQA-WD dataset is available at https://github.com/IBM/tempqa-wd., Comment: 7 pages, 2 figures, 7 tables. arXiv admin note: substantial text overlap with arXiv:2109.13430

Published
2022

10. SYGMA: System for Generalizable Modular Question Answering OverKnowledge Bases

Author

Neelam, Sumit, Sharma, Udit, Karanam, Hima, Ikbal, Shajith, Kapanipathi, Pavan, Abdelaziz, Ibrahim, Mihindukulasooriya, Nandana, Lee, Young-Suk, Srivastava, Santosh, Pendus, Cezar, Dana, Saswati, Garg, Dinesh, Fokoue, Achille, Bhargav, G P Shrivatsa, Khandelwal, Dinesh, Ravishankar, Srinivas, Gurajada, Sairam, Chang, Maria, Uceda-Sosa, Rosario, Roukos, Salim, Gray, Alexander, Riegel, Guilherme LimaRyan, Luus, Francois, and Subramaniam, L Venkata

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

Knowledge Base Question Answering (KBQA) tasks that in-volve complex reasoning are emerging as an important re-search direction. However, most KBQA systems struggle withgeneralizability, particularly on two dimensions: (a) acrossmultiple reasoning types where both datasets and systems haveprimarily focused on multi-hop reasoning, and (b) across mul-tiple knowledge bases, where KBQA approaches are specif-ically tuned to a single knowledge base. In this paper, wepresent SYGMA, a modular approach facilitating general-izability across multiple knowledge bases and multiple rea-soning types. Specifically, SYGMA contains three high levelmodules: 1) KB-agnostic question understanding module thatis common across KBs 2) Rules to support additional reason-ing types and 3) KB-specific question mapping and answeringmodule to address the KB-specific aspects of the answer ex-traction. We demonstrate effectiveness of our system by evalu-ating on datasets belonging to two distinct knowledge bases,DBpedia and Wikidata. In addition, to demonstrate extensi-bility to additional reasoning types we evaluate on multi-hopreasoning datasets and a new Temporal KBQA benchmarkdataset on Wikidata, namedTempQA-WD1, introduced in thispaper. We show that our generalizable approach has bettercompetetive performance on multiple datasets on DBpediaand Wikidata that requires both multi-hop and temporal rea-soning

Published
2021

13. Logical Neural Networks

Author

Riegel, Ryan, Gray, Alexander, Luus, Francois, Khan, Naweed, Makondo, Ndivhuwo, Akhalwaya, Ismail Yunus, Qian, Haifeng, Fagin, Ronald, Barahona, Francisco, Sharma, Udit, Ikbal, Shajith, Karanam, Hima, Neelam, Sumit, Likhyani, Ankita, and Srivastava, Santosh

Subjects
Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Computer Science - Logic in Computer Science
Abstract

We propose a novel framework seamlessly providing key properties of both neural nets (learning) and symbolic logic (knowledge and reasoning). Every neuron has a meaning as a component of a formula in a weighted real-valued logic, yielding a highly intepretable disentangled representation. Inference is omnidirectional rather than focused on predefined target variables, and corresponds to logical reasoning, including classical first-order logic theorem proving as a special case. The model is end-to-end differentiable, and learning minimizes a novel loss function capturing logical contradiction, yielding resilience to inconsistent knowledge. It also enables the open-world assumption by maintaining bounds on truth values which can have probabilistic semantics, yielding resilience to incomplete knowledge., Comment: 10 pages (incl. references), 38 pages supplementary, 7 figures, 9 tables, 6 algorithms. In submission to NeurIPS 2020

Published
2020

18. Exploring the Potential of Large Language Models in Supply Chain Management: A Study Using Big Data

Author

Zhang, Justin, Gupta, Shivam, Srivastava, Santosh, Routray, Susmi, and Bag, Surajit

Abstract

This study aims to identify emerging topics, themes, and potential areas for applying large language models (LLMs) in supply chain management through data triangulation. This study involved the synthesis of 33 published articles and a total of 3421 social media documents, including tweets, posts, expert opinions, and industry reports on utilizing LLMs in supply chain management. By employing BERT models, four core themes were derived: Supply chain optimization, supply chain risk and security management, supply chain knowledge management, and automated contract intelligence, which provides the present status of LLM in the supply chain. The results of this study will empower managers to identify prospective applications and areas for improvement, affording them a comprehensive understanding of the antecedents, decisions, and outcomes detailed in the framework. The insights garnered from this study are highly valuable to both researchers and managers, equipping them to harness the latest advancements in LLM technology and its role within supply chain management.

Published
2024
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23. ENHANCING THIRD-GENERATION SOLAR CELL EFFICIENCY AND STABILITY THROUGH P-TYPE SILICON INTEGRATION: PROCESS ANALYSIS AND PERFORMANCE EVALUATION.

Author

Srivastava, Santosh Kumar and Singh, Jitendra

Subjects
*SOLAR cells, *SILICON, *KESTERITE, *THIN film deposition, *SURFACE morphology
Abstract

Third-generation solar cells have emerged as a potential solution to the effectiveness and stability issues encountered in conventional solar technology. This study focuses on the characteristics of copper-zinc-tin-sulfide (CZTS) thin films inside this innovative architectural framework, which is an important step toward improving third-generation solar cells by incorporating a p-type silicon layer. This integrated method provides a versatile and manageable setting for film deposition, underscoring the effort put into creating high-quality CZTS thin films. Using X-ray diffraction (XRD), the study assessed the structural change of CZTS films after annealing, finding that kesterite phases were dominant. Images captured by a scanning electron microscope (SEM) reveal the microstructure and surface morphology of CZTS-coated Silicon nanowires (Si-NWs). A detailed analysis of the current-voltage characteristics provides evidence of the operational potential of the Si-NWs-CZTS coated solar cell. Significant performance parameters observed include a Voc value of 0.45 ± 0.02V, Isc value of 8.25 ± 0.30 mA/cm², FF value of 24 ± 2%, and η value of 1.0 ± 0.1%. The encouraging results indicate the capacity of using P-type silicon to enhance the performance of third-generation solar cells. [ABSTRACT FROM AUTHOR]

Published
2024
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29. Early Detection of Lung Nodules Using a Revolutionized Deep Learning Model.

Author

Srivastava, Durgesh, Srivastava, Santosh Kumar, Khan, Surbhi Bhatia, Singh, Hare Ram, Maakar, Sunil K., Agarwal, Ambuj Kumar, Malibari, Areej A., and Albalawi, Eid

Subjects
*CONVOLUTIONAL neural networks, *PULMONARY nodules, *DEEP learning, *LUNG cancer, *CANCER-related mortality
Abstract

According to the WHO (World Health Organization), lung cancer is the leading cause of cancer deaths globally. In the future, more than 2.2 million people will be diagnosed with lung cancer worldwide, making up 11.4% of every primary cause of cancer. Furthermore, lung cancer is expected to be the biggest driver of cancer-related mortality worldwide in 2020, with an estimated 1.8 million fatalities. Statistics on lung cancer rates are not uniform among geographic areas, demographic subgroups, or age groups. The chance of an effective treatment outcome and the likelihood of patient survival can be greatly improved with the early identification of lung cancer. Lung cancer identification in medical pictures like CT scans and MRIs is an area where deep learning (DL) algorithms have shown a lot of potential. This study uses the Hybridized Faster R-CNN (HFRCNN) to identify lung cancer at an early stage. Among the numerous uses for which faster R-CNN has been put to good use is identifying critical entities in medical imagery, such as MRIs and CT scans. Many research investigations in recent years have examined the use of various techniques to detect lung nodules (possible indicators of lung cancer) in scanned images, which may help in the early identification of lung cancer. One such model is HFRCNN, a two-stage, region-based entity detector. It begins by generating a collection of proposed regions, which are subsequently classified and refined with the aid of a convolutional neural network (CNN). A distinct dataset is used in the model's training process, producing valuable outcomes. More than a 97% detection accuracy was achieved with the suggested model, making it far more accurate than several previously announced methods. [ABSTRACT FROM AUTHOR]

Published
2023
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36. A clerodane diterpene from Polyalthia longifolia as a modifying agent of the resistance of methicillin resistant Staphylococcus aureus

Author

Gupta, Vivek Kumar, Tiwari, Nimisha, Gupta, Priyanka, Verma, Surjeet, Pal, Anirban, Srivastava, Santosh Kumar, and Darokar, Mahendra Pandurang

Subjects
Drug resistance in microorganisms -- Care and treatment -- Research, Materia medica, Vegetable -- Physiological aspects -- Properties -- Research, Staphylococcus aureus infections -- Care and treatment -- Research, Plant extracts -- Physiological aspects -- Properties -- Research, Biological sciences, Health, Science and technology
Abstract

ABSTRACT Background: Staphylococcus aureus infections are raising serious concern across the world. The effectiveness of conventional drugs is continuously decreasing due to global emergence of multidrug resistance (MDR) and therefore, [...]

Published
2016
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42. Synthesis of 2-oxo-azetidine derivatives of 2-amino thiazole and their biological activity

Author

Samadhiya Pushkal, Sharma Ritu, Srivastava Santosh K., and Srivastava Savitri D.

Subjects
synthesis, 2-aminothiazole, azetidinone, antimicrobial, antitubercular, antiinflammatory, Chemistry, QD1-999
Abstract

A new series of N-[2-(2-aminothiazolyl)ethyl]-4-(substitutedphenyl)- 3-chloro-2-oxo-1-iminoazetidine, compounds 4(a-m) have been synthesized from 2-aminothiazole as a starting material. The structure of all the synthesized compounds were confirmed by chemical and spectral analyses such as IR, 1H NMR, 13C NMR and FAB-Mass. All the final synthesized compounds 4(a-m) were screened for their antibacterial and antifungal activities against some selected bacteria and fungi and antitubercular activity screened against M. tuberculosis with their MIC value. Antiinflammatory activity screened against albino rats (either sex) and gave acceptable results.

Published
2012
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43. Synthesis and biological activity of 4-thiazolidinone derivatives of phenothiazine

Author

Sharma Ritu, Samadhiya Pushkal, Srivastava Savitri D., and Srivastava Santosh K.

Subjects
synthesis, phenothiazine, 4-oxo-thiazolidine, antimicrobial, antitubercular, Chemistry, QD1-999
Abstract

A new series of N-[3-(10H-phenothiazinyl)-propyl]-2-(substituted phenyl)-4-oxo-5-( substituted benzylidene)-1,3-thiazolidine-carboxamide, 5(as) have been synthesized. The cycloaddition reaction of thioglycolic acid with N-[3-(10H-phenothiazinyl)-propyl]-N’-[(substituted phenyl)-methylidene]- urea, 3(a-s) in the presence of anhydrous ZnCl2 afforded new heterocyclic compounds N-[3-(10H-phenothiazinyl)-propyl]-2-(substituted phenyl)-4-oxo- 1,3-thiazolidine-carboxamide, 4(a-s). The later product on treatment with several selected substituted aromatic aldehydes in the presence of C2H5ONa undergoes Knoevenagel reaction to yield 5(a-s). The structure of compounds 1, 2, 3(a-s), 4(a-s) and 5(a-s) were confirmed by IR, 1H NMR, 13C NMR, Fmass and chemical analysis. All above compounds were screened for their antimicrobial activity against some selected bacteria and fungi and for antituberculosis activity compounds have been screened against the bacterium M. tuberculosis.

Published
2012
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46. SYGMA: A System for Generalizable and Modular Question Answering Over Knowledge Bases

Author

Neelam, Sumit, primary, Sharma, Udit, additional, Karanam, Hima, additional, Ikbal, Shajith, additional, Kapanipathi, Pavan, additional, Abdelaziz, Ibrahim, additional, Mihindukulasooriya, Nandana, additional, Lee, Young-Suk, additional, Srivastava, Santosh, additional, Pendus, Cezar, additional, Dana, Saswati, additional, Garg, Dinesh, additional, Fokoue, Achille, additional, Bhargav, G P Shrivatsa, additional, Khandelwal, Dinesh, additional, Ravishankar, Srinivas, additional, Gurajada, Sairam, additional, Chang, Maria, additional, Uceda-Sosa, Rosario, additional, Roukos, Salim, additional, Gray, Alexander, additional, Lima, Guilherme, additional, Riegel, Ryan, additional, Luus, Francois, additional, and Subramaniam, L V, additional

Published
2022
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