Pub Date : 2022-05-24DOI: 10.1109/NCC55593.2022.9806750
Sudhakar Kumawat, Gagan Kanojia, S. Raman
Deep neural networks have enormous representational power which has lead them to overfit on most datasets. Thus, regularizing them is important in order to reduce overfitting and to enhance their generalization capability. This paper studies the operation of channel patch shuffle as a regularization technique in deep convolutional networks. We propose a novel regularization technique called ShuffieBlock where we show that randomly shuffling small patches or blocks between channels significantly improves their performance. The patches to be shuffled are picked from the same spatial locations in the feature maps such that a patch, when transferred from one channel to another, acts as a structured noise for the later channel. The ShuffieBlock module is easy to implement and improves the performance of several baseline networks for the task of image classification on CIFAR and ImageNet datasets.
{"title":"ShuffleBlock: Shuffle to Regularize Convolutional Neural Networks","authors":"Sudhakar Kumawat, Gagan Kanojia, S. Raman","doi":"10.1109/NCC55593.2022.9806750","DOIUrl":"https://doi.org/10.1109/NCC55593.2022.9806750","url":null,"abstract":"Deep neural networks have enormous representational power which has lead them to overfit on most datasets. Thus, regularizing them is important in order to reduce overfitting and to enhance their generalization capability. This paper studies the operation of channel patch shuffle as a regularization technique in deep convolutional networks. We propose a novel regularization technique called ShuffieBlock where we show that randomly shuffling small patches or blocks between channels significantly improves their performance. The patches to be shuffled are picked from the same spatial locations in the feature maps such that a patch, when transferred from one channel to another, acts as a structured noise for the later channel. The ShuffieBlock module is easy to implement and improves the performance of several baseline networks for the task of image classification on CIFAR and ImageNet datasets.","PeriodicalId":403870,"journal":{"name":"2022 National Conference on Communications (NCC)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125671417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-24DOI: 10.1109/NCC55593.2022.9806781
A. Parihar, Pragya Swami, V. Bhatia
This work proposes a energy harvesting based non-orthogonal multiple access (NOMA) scheme in a heterogeneous cellular network (HCN) to support a ultra-dense network of devices. HCN comprises of macro-cell (MC) tier under-laid by small-cell (SC) tier. The distribution MCs follow the independent Poisson point process model, while the SC tier employs carrier sensing. Carrier sensing reduces interference by allowing only one base station to transmit. The analysis is carried out at SC which pairs two Internet-of- Things (IoT) devices for downlink NOMA transmission. IoT devices have diversified energy profiles and channel conditions which makes them suitable for NOMA pairing. Wireless energy harvesting and cooperative communication is employed at the devices to compensate for the energy and increasing coverage to IoT devices. Unlike previous works, the proposed method employs EH employing interference from SC and M C tiers rather than just the superimposed signal for EH. Expression of outage probability and system throughput are derived for the proposed NOMA transmission and comparisons are drawn with the HCN that do not employ carrier sensing.
{"title":"On Performance of IoT Devices Utilizing Energy Harvesting and Carrier Sensing in NOMA-HCN","authors":"A. Parihar, Pragya Swami, V. Bhatia","doi":"10.1109/NCC55593.2022.9806781","DOIUrl":"https://doi.org/10.1109/NCC55593.2022.9806781","url":null,"abstract":"This work proposes a energy harvesting based non-orthogonal multiple access (NOMA) scheme in a heterogeneous cellular network (HCN) to support a ultra-dense network of devices. HCN comprises of macro-cell (MC) tier under-laid by small-cell (SC) tier. The distribution MCs follow the independent Poisson point process model, while the SC tier employs carrier sensing. Carrier sensing reduces interference by allowing only one base station to transmit. The analysis is carried out at SC which pairs two Internet-of- Things (IoT) devices for downlink NOMA transmission. IoT devices have diversified energy profiles and channel conditions which makes them suitable for NOMA pairing. Wireless energy harvesting and cooperative communication is employed at the devices to compensate for the energy and increasing coverage to IoT devices. Unlike previous works, the proposed method employs EH employing interference from SC and M C tiers rather than just the superimposed signal for EH. Expression of outage probability and system throughput are derived for the proposed NOMA transmission and comparisons are drawn with the HCN that do not employ carrier sensing.","PeriodicalId":403870,"journal":{"name":"2022 National Conference on Communications (NCC)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125472697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-24DOI: 10.1109/NCC55593.2022.9806738
Prakruthi Pradeep, Venkata Sai Chelagamsetty, Avhishek Chatterjee
Motivated by the growing interest in machine learning on nanoscale edge devices, we study the effect of hardware noise and quantization errors on the accuracy of inference by linear classifiers. Our experiments on synthetic and real data sets using well accepted models for hardware noise and errors show that they have a significant impact on the accuracy. For mitigating those effects, we propose an easily implementable strategy by combining insights from linear classification, convex analysis and concentration of measure. Evaluations on synthetic and real data sets show that our simple strategy improves the performance significantly. We end with a brief discussion on a few avenues for further explorations.
{"title":"Linear Classification on Noisy Hardware","authors":"Prakruthi Pradeep, Venkata Sai Chelagamsetty, Avhishek Chatterjee","doi":"10.1109/NCC55593.2022.9806738","DOIUrl":"https://doi.org/10.1109/NCC55593.2022.9806738","url":null,"abstract":"Motivated by the growing interest in machine learning on nanoscale edge devices, we study the effect of hardware noise and quantization errors on the accuracy of inference by linear classifiers. Our experiments on synthetic and real data sets using well accepted models for hardware noise and errors show that they have a significant impact on the accuracy. For mitigating those effects, we propose an easily implementable strategy by combining insights from linear classification, convex analysis and concentration of measure. Evaluations on synthetic and real data sets show that our simple strategy improves the performance significantly. We end with a brief discussion on a few avenues for further explorations.","PeriodicalId":403870,"journal":{"name":"2022 National Conference on Communications (NCC)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134367386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-24DOI: 10.1109/NCC55593.2022.9806804
Shivam Thakker, Dhaval K. Patel, Kathan S. Joshi, M. López-Benítez
Motivated by various health care applications and many other novel fields of Molecular Communication (MC), it has become an important field of research since the last decade. This paper proposes a molecular communication-based model for the spread of the SARS-CoV2 virus in the human body. The virus uses the ACE2 receptor as a gateway to enter the blood vessels, organs and then replicate itself. In response to the infection, the immune system synthesizes pro-inflammatory cytokines such as IL6, IL2, and TNFa. This active bodily response may be further compromised by the generation of anti-inflammatory cytokines such as IL4 and IL10. We also propose a mathematical model using a Markov state transition for a flow-based molecular communication system which contributes to the detection of these pro-inflammatory cytokines level and gives a further inference about the infection in the body by taking multiple cytokines into account.
{"title":"Modelling the Impact of Multiple Pro-inflammatory Cytokines Using Molecular Communication","authors":"Shivam Thakker, Dhaval K. Patel, Kathan S. Joshi, M. López-Benítez","doi":"10.1109/NCC55593.2022.9806804","DOIUrl":"https://doi.org/10.1109/NCC55593.2022.9806804","url":null,"abstract":"Motivated by various health care applications and many other novel fields of Molecular Communication (MC), it has become an important field of research since the last decade. This paper proposes a molecular communication-based model for the spread of the SARS-CoV2 virus in the human body. The virus uses the ACE2 receptor as a gateway to enter the blood vessels, organs and then replicate itself. In response to the infection, the immune system synthesizes pro-inflammatory cytokines such as IL6, IL2, and TNFa. This active bodily response may be further compromised by the generation of anti-inflammatory cytokines such as IL4 and IL10. We also propose a mathematical model using a Markov state transition for a flow-based molecular communication system which contributes to the detection of these pro-inflammatory cytokines level and gives a further inference about the infection in the body by taking multiple cytokines into account.","PeriodicalId":403870,"journal":{"name":"2022 National Conference on Communications (NCC)","volume":"159 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114115710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-24DOI: 10.1109/NCC55593.2022.9806810
Hemlata G. Biradar, Jayanand Gawande
In this paper, a non-contact heart rate measurement method is proposed, which gives a comfortable physiological examination of cardiac pulse without the use of electrodes. This method is based on automated face tracking and blind source separation of the colour channels into separate components and used on colour video recordings of the human face. Here by optimizing non-Gaussianity and negentropy for the recovered signals, an a FastICA (Fast Independent component Analysis) algorithm is employed to extract independent components. For experimentation, COHFACE dataset is used consisting of 160 video's of 40 different people (28 males and 12 females). Heart rate estimated with FastICA is compared with heart rate measured using Finger blood volume pulse (BVP) sensor. This comparison is performed using Bland-Altman and correlation analysis. With proposed method low error rate is observed when compared with Independent Component Analysis (ICA) and other methods with same database.
{"title":"Heart Rate Estimation from Facial Video Sequences using Fast Independent Component Analysis","authors":"Hemlata G. Biradar, Jayanand Gawande","doi":"10.1109/NCC55593.2022.9806810","DOIUrl":"https://doi.org/10.1109/NCC55593.2022.9806810","url":null,"abstract":"In this paper, a non-contact heart rate measurement method is proposed, which gives a comfortable physiological examination of cardiac pulse without the use of electrodes. This method is based on automated face tracking and blind source separation of the colour channels into separate components and used on colour video recordings of the human face. Here by optimizing non-Gaussianity and negentropy for the recovered signals, an a FastICA (Fast Independent component Analysis) algorithm is employed to extract independent components. For experimentation, COHFACE dataset is used consisting of 160 video's of 40 different people (28 males and 12 females). Heart rate estimated with FastICA is compared with heart rate measured using Finger blood volume pulse (BVP) sensor. This comparison is performed using Bland-Altman and correlation analysis. With proposed method low error rate is observed when compared with Independent Component Analysis (ICA) and other methods with same database.","PeriodicalId":403870,"journal":{"name":"2022 National Conference on Communications (NCC)","volume":"159 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116236102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-24DOI: 10.1109/ncc55593.2022.9806784
{"title":"Welcome message from the General Co-chair","authors":"","doi":"10.1109/ncc55593.2022.9806784","DOIUrl":"https://doi.org/10.1109/ncc55593.2022.9806784","url":null,"abstract":"","PeriodicalId":403870,"journal":{"name":"2022 National Conference on Communications (NCC)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123794440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-24DOI: 10.1109/NCC55593.2022.9806815
Vartika Gupta, Tushar P. Kendre, T. Reddy, Vipul Arora
Brain-Computer Interfaces (BCI) provide the users to communicate with computers via brain signals. Significant research within the BCI is devoted to ElectroEncephaloGraphy (EEG), which picks, on the scalp, immensely frail electrical currents delineating brain activity. This paper presents a new ambulatory classification method for EEG Event Related Poten-tials (ERP) for a Practical Brain Computer Interface (BCI). To be more specific, this paper focuses on enhancing the performance of the ERP classification using Ear EEG along with scalp EEG during walking at 1.6m/s. We demonstrate the signal quality of Ear EEG for targets and non-targets. Through a novel estimation of Covariance matrices, this work extends the use of Riemannian Geometry (RG). In addition, the utility of Ear EEG has been justified by the 5% improvement in ERP detection performance after a novel fusion of Riemannian Geometry attributes from Ear EEG and scalp EEG. Further, we also proposed a fusion of feature attributes of both scalp and Ear EEG obtained from the fully connected layer of trained EEGNet CNN model with autoencoders passed through XGBoost. This method improved the state of the art by 10%. The proposed methods serve as novel adaptations of RG and CNN methods for mobile EEG in a practical BCI setup. The proposed method was also validated on the track 5 of the International BCI competition and achieved third position in the challenge.
{"title":"Comparative Performance Analysis of Scalp EEG and Ear EEG based P300 Ambulatory Brain-Computer Interfaces using Riemannian Geometry and Convolutional Neural Networks","authors":"Vartika Gupta, Tushar P. Kendre, T. Reddy, Vipul Arora","doi":"10.1109/NCC55593.2022.9806815","DOIUrl":"https://doi.org/10.1109/NCC55593.2022.9806815","url":null,"abstract":"Brain-Computer Interfaces (BCI) provide the users to communicate with computers via brain signals. Significant research within the BCI is devoted to ElectroEncephaloGraphy (EEG), which picks, on the scalp, immensely frail electrical currents delineating brain activity. This paper presents a new ambulatory classification method for EEG Event Related Poten-tials (ERP) for a Practical Brain Computer Interface (BCI). To be more specific, this paper focuses on enhancing the performance of the ERP classification using Ear EEG along with scalp EEG during walking at 1.6m/s. We demonstrate the signal quality of Ear EEG for targets and non-targets. Through a novel estimation of Covariance matrices, this work extends the use of Riemannian Geometry (RG). In addition, the utility of Ear EEG has been justified by the 5% improvement in ERP detection performance after a novel fusion of Riemannian Geometry attributes from Ear EEG and scalp EEG. Further, we also proposed a fusion of feature attributes of both scalp and Ear EEG obtained from the fully connected layer of trained EEGNet CNN model with autoencoders passed through XGBoost. This method improved the state of the art by 10%. The proposed methods serve as novel adaptations of RG and CNN methods for mobile EEG in a practical BCI setup. The proposed method was also validated on the track 5 of the International BCI competition and achieved third position in the challenge.","PeriodicalId":403870,"journal":{"name":"2022 National Conference on Communications (NCC)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133737507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-24DOI: 10.1109/NCC55593.2022.9806779
V. Rohit, V. K. Velidi
In this paper, we first consider two multi-server M/M/K/FCFS queuing systems, processing two classes of jobs one-on-one respectively. We then operate the two multi-server systems as preemptive-resume priority queues (M/M/K/Prio) and route the two classes of jobs to both systems by splitting the Poisson arrivals of each class probabilistically. In each M/M/K/Prio system, own class of jobs are serviced with high priority while class of jobs routed from other system are serviced with low priority. Through numerical computations, we show that for a Pareto set of split probabilities, the probability of wait for service in queues experienced by both classes of jobs in the joint M/M/K/Prio systems, can be less than what they experienced in the original one-on-one M/M/K/FCFS systems. Through Bargaining theory, we capture the agreement of arrival load split between the two systems.
{"title":"A scheduling approach to harness synergy from two server farms","authors":"V. Rohit, V. K. Velidi","doi":"10.1109/NCC55593.2022.9806779","DOIUrl":"https://doi.org/10.1109/NCC55593.2022.9806779","url":null,"abstract":"In this paper, we first consider two multi-server M/M/K/FCFS queuing systems, processing two classes of jobs one-on-one respectively. We then operate the two multi-server systems as preemptive-resume priority queues (M/M/K/Prio) and route the two classes of jobs to both systems by splitting the Poisson arrivals of each class probabilistically. In each M/M/K/Prio system, own class of jobs are serviced with high priority while class of jobs routed from other system are serviced with low priority. Through numerical computations, we show that for a Pareto set of split probabilities, the probability of wait for service in queues experienced by both classes of jobs in the joint M/M/K/Prio systems, can be less than what they experienced in the original one-on-one M/M/K/FCFS systems. Through Bargaining theory, we capture the agreement of arrival load split between the two systems.","PeriodicalId":403870,"journal":{"name":"2022 National Conference on Communications (NCC)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129237582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-24DOI: 10.1109/NCC55593.2022.9806767
R. Gowriprasad., V. Venkatesh, Sri Rama Murty K
Tabla is a percussion instrument in North Indian music tradition. Teaching practices and performances of tabla are based on stylistic schools called gharana-s. Gharana-s are characterized by their unique playing technique, finger posture, improvisations, and compositional patterns (signature patterns). Recognizing the gharana information from a tabla performance is hence helpful to characterize the performance. In this paper, we explore an approach for gharana recognition from solo tabla recordings by searching for the characteristic tabla phrases in these recordings. The tabla phrases are modeled as sequences of strokes, and characteristic phrases from the gharana compositions are chosen as query patterns. The recording is automatically transcribed into a syllable sequence using Hidden Markov Models (HMM). The Rough Longest Common Subsequence (RLCS) approach is used to search for the query pattern instances. A decision rule is proposed to recognize the gharana from the patterns.
{"title":"Tabla Gharānā Recognition from Tabla Solo Recordings","authors":"R. Gowriprasad., V. Venkatesh, Sri Rama Murty K","doi":"10.1109/NCC55593.2022.9806767","DOIUrl":"https://doi.org/10.1109/NCC55593.2022.9806767","url":null,"abstract":"Tabla is a percussion instrument in North Indian music tradition. Teaching practices and performances of tabla are based on stylistic schools called gharana-s. Gharana-s are characterized by their unique playing technique, finger posture, improvisations, and compositional patterns (signature patterns). Recognizing the gharana information from a tabla performance is hence helpful to characterize the performance. In this paper, we explore an approach for gharana recognition from solo tabla recordings by searching for the characteristic tabla phrases in these recordings. The tabla phrases are modeled as sequences of strokes, and characteristic phrases from the gharana compositions are chosen as query patterns. The recording is automatically transcribed into a syllable sequence using Hidden Markov Models (HMM). The Rough Longest Common Subsequence (RLCS) approach is used to search for the query pattern instances. A decision rule is proposed to recognize the gharana from the patterns.","PeriodicalId":403870,"journal":{"name":"2022 National Conference on Communications (NCC)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131778813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-05-24DOI: 10.1109/NCC55593.2022.9806798
Bibin Baby John, Y. Vasavada
We analyze a low-complexity matched-filter (MF) based approach for detecting the transmit antenna index in the spatial modulation (SM) or space shift keying (SSK) schemes. The detection metric of the scheme resembles the metric calculated by the Single Input Multiple Output (SIMO) Matched Filter (MF) detector. In the uncorrelated Rayleigh fading MIMO (Multiple Input Multiple Output) channels, this metric is the ratio of the squared magnitudes of the sum of the products of complex normal variates, and the numerator and the denominator of the ratio are correlated. The contribution of this paper is to analyze this detection metric. We validate our analysis by simulation, and provide insights on the performance of the SM MF detection.
{"title":"Analysis of the Matched Filter Detector of the Antenna Index in the Spatial Modulation Systems","authors":"Bibin Baby John, Y. Vasavada","doi":"10.1109/NCC55593.2022.9806798","DOIUrl":"https://doi.org/10.1109/NCC55593.2022.9806798","url":null,"abstract":"We analyze a low-complexity matched-filter (MF) based approach for detecting the transmit antenna index in the spatial modulation (SM) or space shift keying (SSK) schemes. The detection metric of the scheme resembles the metric calculated by the Single Input Multiple Output (SIMO) Matched Filter (MF) detector. In the uncorrelated Rayleigh fading MIMO (Multiple Input Multiple Output) channels, this metric is the ratio of the squared magnitudes of the sum of the products of complex normal variates, and the numerator and the denominator of the ratio are correlated. The contribution of this paper is to analyze this detection metric. We validate our analysis by simulation, and provide insights on the performance of the SM MF detection.","PeriodicalId":403870,"journal":{"name":"2022 National Conference on Communications (NCC)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131962556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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