dataesr/scientific-paragraphs-categorization huggingface dataset:
The training algorithm and data selection Data train : is the training labeled dataset mida : is the whole MIDA unlabeled dataset 2D train : is the 2D-representation of train 2D mida : is the 2D-representation of mida Round = 1 : is incrementing each time the steps 2,3,4 are computed * Step1: Train the CVAE → Train the CVAE on train → Use the encoder to convert the data from the feature space to the 2D-latent space: Train 10 classifiers C i on 2D train as follow : for i=1 to 10 do Train the classifier C i on 2D train Save the parameters of C i end * Step3: Identification of the conflict zone → Test the classifier C i on 2D mida → i (k) : is the output class obtained by the classifier C i for the input sample k for each element k of the whole dataset 2D mida do for each two classifiers C i and C j with i = j do if i (k) = j (k) then The element k is considered to be part of the conflict zone end end end * Step4: Evaluation of the diagnosis model if Performance criterion is obtained then End of the training process else → Choose new unlabeled samples from the whole dataset 2D mida in order to reduce the conflict area → Label these new samples → Add these new samples to the training dataset 2D train → Repeat the steps 2, 3 and 4 → Round = Round + 1 end 5446 VOLUME 8, 2020.
Algorithm 3 → 3 The training algorithm and data selection Data train : is the training labeled dataset mida : is the whole MIDA unlabeled dataset 2D train : is the 2D-representation of train 2D mida : is the 2D-representation of mida Round = 1 : is incrementing each time the steps 2,3,4 are computed * Step1: Train the CVAE → Train the CVAE on train → Use the encoder to convert the data from the feature space to the 2D-latent space: Train 10 classifiers C i on 2D train as follow : for i=1 to 10 do Train the classifier C i on 2D train Save the parameters of C i end * Step3: Identification of the conflict zone → Test the classifier C i on 2D mida → i (k) : is the output class obtained by the classifier C i for the input sample k for each element k of the whole dataset 2D mida do for each two classifiers C i and C j with i = j do if i (k) = j (k) then The element k is considered to be part of the conflict zone end end end * Step4: Evaluation of the diagnosis model if Performance criterion is obtained then End of the training process else → Choose new unlabeled samples from the whole dataset 2D mida in order to reduce the conflict area → Label these new samples → Add these new samples to the training dataset 2D train → Repeat the steps 2, 3 and 4 → Round = Round + 1 end 5446 VOLUME 8, 2020.
, 2016; Román et al. , 2017; Popovici et al. , 2018). For instance,  Boichu et al. (2016)  analyzed the volcanic plumes reaching France several times in September 2014, which were emitted by the Holuhraun eruption (a massive eruption in terms of sulfur emissions which has caused repeated episodes of air pollution on a continental scale) of the Icelandic volcano Bárðarbunga. Regarding size distribution properties, the description of these plumes was quite fragmented due to the persistent cloudy conditions that allowed only a few full AERONET aerosol retrieval algorithm in the whole month. The analyses of only direct Sun measurements with the GRASP-AOD application has been revealed quite relevant to complete the aerosol size information data set during the study. Beyond the aforementioned punctual studies, the purpose of the present work is to show that the GRASP-AOD application has the potential to be used for large scale datasets either for climate studies or for near real time modeler needs.
In the study by  Torres et al. (2017) , GRASP-AOD application was deeply described and positioned within the development of the GRASP (Generalized Retrieval of Atmosphere and Surface Properties, see  Dubovik et al. , 2014)  algorithm and software (more information and a free version of the code can be obtained in http://www. grasp-open. com/) in its entirety. To prove the robustness in the retrieval, the work by  Torres et al. (2017)  presented a validation of the application though it is mainly focused on simulated tests (multiple variation of the initial guess, effects of errors in the aerosol optical depth measurements and sensitivity to the refractive index assumptions). A first real data validation was also provided by comparing the aerosol properties obtained from 744 AERONET observations using GRASP-AOD application to those obtained through 165 AERONET aerosol retrieval algorithm from almucantar measurements at 8 different AERONET sites from several pre-selected days.
Data source The only data source used in the analysis belongs to AERONET Level 2. 0 from the recent Version 3  (Giles et al. , 2019; Sinyuk et al. , 2020) , which can be found in the public AERONET database (http://aeronet. gsfc. nasa. gov): 1. The τ measurements used as inputs in the GRASP-AOD application, which have passed a cloud screening criteria to obtain, first, the AERONET Level 1. 5 (based on the study by  Smirnov et al. (2000)  with some modifications introduced in  Giles et al. (2019) ) and automated quality control algorithms  (Giles et al. , 2019)  to achieve the Level 2. 0. 2. The AERONET retrievals used for the comparisons: (a) τ f (500) obtained from the SDA  (O'Neill et al. , 2003) (b)  size distributions standard parameters and τ f (500) computed from the AERONET aerosol retrieval algorithm (general description in  Dubovik and King (2000) ;  Dubovik et al.
The application assumes the complex refractive index as known in the retrieval procedure. Full inversion details of GRASP-AOD and the consequences of the different assumptions can be gained in  Torres et al. (2017)  and references therein. The purpose of this subsection is to describe the particular use made of GRASP-AOD in this validation study. As commented before, the primary input are the almost 2. 8 millions τ measurements belonging to AERONET Level 2. 0 of Version 3 between 1997-2016 in the 30 sites selected for the analysis (see Table  1 ). Regarding the assumption of the refractive index, we have created a database of moving monthly means (2 adjacent months) for all sites using Version 3 AERONET aerosol retrieval algorithm (considering the entire historical database, beyond the period analyzed here).
g. Arica, GSFC or Tomsk). The RMSRE of R Vf comparisons at this group are a bit higher (between 12-19%) than at the previous group. However, correlations coefficients and slopes show similar values (0. 6-0. 8). Three examples of this group (GSFC, Arica and Shirahama) are shown at middle panels of Figure  7. The lowest data percentages (under 20%) are obtained for those sites regularly affected by desert dust episodes: Granada, Ilorin, Lake Argyle, Sede Boker and Solar Village. The only exception is Forth Crete, which should be included in this group even though it shows a higher data percentage (29%). The correlations and slopes values are significantly lower compared to the precedent groups (from 0. 45 to 0. 75). Correlations for Ilorin, Granada and Sede Boker are shown at the bottom panels of Figure  7.
99 and the RMSE is divided by two from 0. 032µm 3 /µm 2 (22. 7%, in relative terms) to 0. 015µm 3 /µm 2 (10. 6%). For R eff , we observe a better correlation when we introduce the aureole measurements, however, the RMSE increases from 0. 159 µm to 0. 191 µm, (or from 32. 1% to 37. 5% in relative terms). This increase is justified by the general overestimation of R Vc , and specially at largest values (3. 5 -4 µm). It should be noted that a comprehensive study on the use of aureole measures to improve the GRASP-AOD application is outside the scope of the present validation study. These first results presented here indicate the direction that may be taken in future analysis, specially to improve the characterization of the coarse mode. Finally, we would like to highlight the versatility of GRASP code that allows easily to integrate the aureole measurements in a predefined inversion scheme such as GRASP-AOD.
Conclusions The work presented here aimed to compliment the study of  Torres et al. (2017)  by the demonstration of the applicability of GRASP-AOD approach to large datasets aerosol optical depth observations. In these regards, the study has proposed a real data validation based on 2. 8 million GRASP-AOD retrievals using AERONET aerosol optical depth observations from 30 AERONET sites during 20 years  (1997) (1998) (1999) (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015) (2016)  in the range between 340-1020 nm. The study has also provided (see subsection 2. 2) several recommendations for applying GRASP-AOD in operational processings. Specifically, we have proposed the assumption of climatological values for the refractive index in the retrieval and the criteria for retrieval quality control.
The detailed consideration of adding aureole data into GRASP-AOD retrieval as well as other innovative retrieval configuration possible with GRASP algorithm, such as the utilization of the multi-pixel approach, are to be explored in future studies. Andrew Clive Banks (Forth Crete), Lucas Alados-Arboledas (Granada), Jack Molinie (Guadelopu), Rachel T. Pinker (Ilorin), Giuseppe Zibordi (Ispra), Sachchida Nand Tripathi (Kanpur), Ian Lau (Lake Argyle), Robert Frouin (Lanai) Valentin Duflot (Reunion -St. Denis), Arnon Karnieli (Sede Boker), Emilio Cuevas-Agullo (St. Cruz de Tenerife), Itaru Sano (Shirahama), Soo-Chin Liew (Singapore), Alkiviadis Bais (Thessaloniki) and Mikhail Panchenko (Tomsk).
Acknowledgements. The authors are grateful to  Oleg Dubovik  for valuable discussions and his help in improving the manuscript. Additional thanks to the rest of GRASP team, specially to  Tatsiana Lapionak , for her constant help concerning the use of GRASP code. We would like to thank the  Cloud Flight GmbH  staff involved in GRASP code activities, and specially  Daniel Marth  for his guiding related to data processing. This research has mainly been performed within the  ESA  funded project  DIVA (Demonstration of an Integrated approach for the Validation and exploitation of Atmospheric missions ). The authors also acknowledge the funding provided by the  European Union  ( H2020-INFRAIA-2014-2015 ) under Grant Agreement No. 654109  ( ACTRIS-2 ). Acknowledgement are addressed to the project Labex CaPPA: the  CaPPA project (Chemical and Physical Properties of the Atmosphere ) is funded by the  French National Research Agency (ANR)  through the PIA We thank the  AERONET, Service National d'Observation PHOTONS/AERONET ,  INSU/CNRS ,  RIMA  and  WRC  staff for their scientific and technical support.
Code and data availability. More detailed information and a free version of GRASP code can be gained at http://www. grasp-open. com/. The website also provides access to all products derived by GRASP activities. This includes all the data processed by GRASP-AOD application which goes beyond the data used in this work. Specifically, the GRASP-AOD data products analyzed in this study (30 AERONET sites in the period 1997-2016) have been saved and stored at https://doi. org/10. 5281/zenodo. 4010385. These data have been processed with version v1. 0. 0 of GRASP algorithm.
A combination of sensors like Passive infrared (PIR), water level sensor, ultrasonic sensor, temperature sensor, pressure and altimeter sensors have been integrated on a single device to investigate the flash floods. The use of most suitable pair of measurement sensors can substantially enhance the advantage of more accuracy and reliability compared to a single sensor. In recent trends Particle swarm optimization is very popular for solving stochastic global optimization problems. The data was trained and processed by modified multi-layer feed forward neural network optimized by particle swarm optimization algorithm. Hybrid Modified Particle swarm optimization has been combined with feed forward neural network for the vigorous investigation of flash floods with less false alarm rate. Simulated results showed that the proposed research algorithm Modified multi-layer feed forward neural network optimized by Particle swarm optimization for multi-modal sensing performed very well in terms of evaluation parameters compared to other existing strategies with minimum false alarm ratio.
Less battery backup can also be known as an acute problem during the surveillance data transmission  [17]. In langrangian sensing approaches, many individual sensors were spread on specific sea surface which has to be monitored. These micro sensors record the various parameters to identify the floods. Micro sensors track the change, observe it and transmit it to the receiver. To receive the data smoothly with higher accuracy, receiver must be in range with these micro sensors. False alarms were found in langrangian sensing therefore some artificial intelligence based algorithm were applied on the system  [18]. Acute flash floods have been recorded in many countries like Pakistan, India and Malaysia etc. Several countries' economy depends on the crops and crops depend on water which is used for irrigation.
Neural system can be viewed as a part of AI that was developed in 1960 based on the biological behavior and structure of brain. Data and information have been accumulated from the Malaysian Meteorological Department located in Petaling Jaya. Past data from the time of (2007 o 2010) that was measured by rain gauges was used. After the data analysis, it was planned that to train the system data of this period (1 Jan 2010 to 31st Jan 2010) would be utilized. Data that was to be used from the departments contained errors, distortion and missed values having N/A and -33. 3 values. Data filtering can be used to retain the missed data or deviations. N/A and -33. 3 were put back by 0 and 0. 1. It's important to improve the insufficiencies of data for the smooth and legitimate authentication and testing.
The model having the least error was chosen to compare the changes with the actual outcomes. Mean Square Error was utilized to evaluate the error, MSE value was 0. 2. A framework was designed utilizing SCADA (supervisory control and data acquisition) technology to stop floods, in first and second model (A and B) forward back propagation (FBP) was adapted. [25]. Artificial Neural Network (ANN) is the most widely used approach for the determination and locating the affected are for floods. After the getting the results of Artificial Neural Network training by Rosenlatt in 1958 ANN was known as a detection model. Rising water level was predicted by utilizing neural system autoregressive model with exogenous information (NNARX) method. This model was created in MATLAB neural network tool box. Precipitation intensity depends on different components (factors) like pressure, wind, temperature, velocity and direction, subsequently the flood estimation system must be exceptionally accurate and intelligent to issue prior warning  [26].
Appropriate sensors were selected to collect the data for the prediction of flash floods. Data set was filtered out and classified using the hybrid algorithm. Proposed algorithm also performed optimization to achieve the better optimal results. Usually collected huge data set is disorganized, redundant and contained missed and repetitive values. To resolve the issue of redundancy the data was organized into the tables for the smooth processing. Redundancy can be defined as the repetitive values that usually increase the size of the data set. The useless and undesired data is generally minimized and eliminated with the normalization. The normalized data was then processed for the forecasting floods. The research has been branched into three phases robust data classification, estimation and prediction.
E. BAROMETRIC PRESSURE AND TEMPERATURE SENSOR BMP-280 The barometric pressure and temperature sensor (BMP-280). BMP-280 is based on piezo-resistive technology. It has been interfaced with controller (Arduino) to estimate the temperature and surroundings pressure. BMP-280 measured the barometric pressure and altitude. The data can be received on personal computers or at cellular phones wirelessly or wired. Each of the sensing parameter was different from each other having different ranges and resolution. Therefore, data collection device was named as multi-modal sensing device or multi-resolution sensing gadget  [30] [31] [32].
I. MULTI-LAYER FEED FORWARD NEURAL NETWORK OPTIMIZED BY PARTICLE SWARM OPTIMIZATION WEIGHTS ESTIMATION (TESTING) Multi-layer feed forward neural network was optimized by particle swarm optimization and their weights were calculated which have been mentioned in the table VI. Table VI showed that data was split, 70% was used for training and 30% data was utilized for the testing of proposed algorithm. Weights for the proposed MFN-PSO were calculated for testing data in Table  10.
A. COLLECTED DATA SET TABLE VIII COLLECTED MULTI-RESOLUTION DATA SET  [30]  Table VIII displayed the collected data set. More than ten thousand numbers of instances were recorded. The data set has seven types of attributes. Column no. 1 represented the number of instances. Column no. 2 is the output of the passive infra-red sensor depicting the presence of water in the vicinity of PIR sensor. Column no. 3 has been acknowledged as water distance in millimeter. Rainfall data values have been mentioned in column no. 4. Column no. 5 demonstrated the carbon dioxide level in the environment. Temperature was presented in the column no. 6. Wind pressure was measured in the column no. 6. Column no. 7 shows the multi-modal sensing device altimeter  [30]. minimum temperature, humidity, wind speed, cloudiness, wind direction and average temperature.
C. MATHEMATICAL EQUATIONS FOR OUTPUT LAYER "o" Oj 1 = logsig(x1w11+x2w21+x3w31+x4w41+x51-ϴj) (14) Desired or target data may be represented by "yd" yd = Targeted data set ( A test vector has been designed to calculate the probability of the flash floods which has been given below: x = [PIR,Distance,Rainfall,CO2,Temperature,Pressure,Altimeter]; Furthermore, to test the data values, seventy-five percent of input data values has been trained, evaluated and the probability result was represented in percentage by using the given function: result = result_net(x'); probability = result*100; if probability < 0 probability = 0; end if probability > 100 probability = 100; The collected data set can be imported in the MATLAB simulation tool. The data file then converted into the variable form so that it may be treated as a variable data file in the MATLAB.
Training of the proposed algorithm has been performed by this collected data. Actually in swarm intelligence algorithms every particle has its own velocity that determines the direction of traveling. x i k+1 = x i k + v i k+1 (16) Eq. 16 determines the computation of the position of the particle that was calculated in the algorithm. % Position update  (17)  for i=1:pop for j=1:kk x(i,j)=x(i,j)+v(i,j); end end "x" represents the updated particle position and "v" represents its corresponding updated particle velocity. "k+1" determines the corresponding time. The velocity was updated in particle swarm optimization using the cost function: v i k+1 = w. v i k+c1. r1(pbest i k -x i k) + c2r2(gbest g k -x i k)); (18) Velocity update in MATLAB; v(i,j)=w*v(i,j)+c1*rand*(pbest(i,j)x(i,j))+c2*rand*(gbest(1,j)-x(i,j)); "k" denotes the recent updated step number, components r1 and r2 are two random series between the range of (0,1), c1 and c2 are the acceleration PSO constants.
9. Output parameters have been computed by the input parameters. The hidden neurons obtained from the input neurons the number of net inputs for a hidden neuron can be estimated using the equation no. 10. 𝐻𝑁 𝑗 ℎ = ∑ 𝑤 𝑖𝑗 ℎ 𝑛 𝑖 𝑖𝑝 𝑖 + 𝑏𝑛 𝑗 ℎ (26) The multi-layer feed forward neural network has been configured and achieved optimal results by applying improved particle swarm optimization on the data. The following pseudo code has been coded in the MATLAB for the development of MFN-PSO. Every neuron comprises of current position and current velocity and they are corresponding to each other. The position can be determined by using the updated velocity. The weights are usually adjusted in particle swarm optimization when neuron was found to be far away from the global best position. Therefore, a pseudo code which has been coded in the MATLAB has been presented here for the updated global best position and computation of fitness.
% updating pbest and fitness for i=1:pop if f(i,1)<f0(i,1) pbest(i,:)=x(i,:); f0(i,1)=f(i,1); end Basically the weight is updated according to the current velocity of the particle and the updated position is determined by the updated velocity also. The algorithm initialized all the weight values to any random values and begins the training. Weight is passed through each data set and the weight fitness is compared. The maximum fitness determines the global best search position. Mean squared error of multi-layer feed forward neural network optimized with PSO was computed using the following equation. They have performed better than the existing approaches but MFN-PSO achieved the best results concretely. The appropriate rate of learning rate was selected and stochastic gradient of particle swarm optimization was also taken into the account foe the development of a competent algorithm.
Processed time and accuracy with less error are the most significant parameter to judge the performance level of the algorithm. MATLAB results have been compared with MFN-PSO and it has been proved that proposed algorithm has performed more accurately and rapidly with 0. 0013707 mean squared errors than the available techniques. The innovation of the proposed research is the bunch of transducers that has been correlated to identify the flash flood robustly. Measurement of CO2 levels and soil magnetic flux were processed using MLP algorithm can be acknowledged as a novel approach that was proposed in earlier research paper. MFN-Cuckoo search performed 100 iterations normalized the data more rapidly with the 0. 030151 error. The proposed algorithm increased the credibility of early warning systems for flash floods.
X. CONCLUSION Simulated results proved that proposed algorithm worked as a better classifier and forecasting tool for the flash floods. Our proposed research can be considered as a cost-effective novel solution. False alarm rate has been reduced by using feed forward propagation with the combination of particle swarm optimization (PSO). It can be concluded on the basis of the results that our proposed research can predict the flash floods in very less time as elapsed time has been mentioned in the output. Moreover, it the proposed algorithms can be adopted to identify the actual event in many real-life applications by collecting their respective data. The real time data can be processed by the proposed algorithms with the slight modification in MATLAB code. The proposed algorithms can be used for the breast cancer classification, prediction of seizures, prediction of syndromes in unborn, heart disease classification and other prediction and classification applications with less false alarm rate.
It was concluded that NGS can be used for the detection of a broad range of viruses, including novel viruses, and therefore can complement, supplement or even replace some of the conventional adventitious virus detection assays. Furthermore, the development of reference viral standards, complete and correctly annotated viral databases, and protocols for the validation and follow-up investigations of NGS signals is necessary to enable broader use of NGS. An international collaborative effort, involving regulatory authorities, industry, academia, and other stakeholders is ongoing toward this goal.