dataesr/scientific-paragraphs-categorization huggingface dataset:
Results and Discussion In this section, we present the results obtained for the detection of extreme sentiments by using BERT. There are five datasets in total and one extra dataset combined using the five datasets referred to as comb_all to view the overall performance of our model. Hence, six experimental results and findings are outlined for each dataset. The datasets used in this experiment are acquired from our previous work, which we refer to as the extended lexicon  [1]. The comprehensive lexicon data is highly unbalanced, as shown in Table  2. The number of negative extremes is very low compared to inconclusive and positive extremes. The split of this data into train, validation, and test sets cause further isolation, which results in higher accuracy of dominant classes while ignoring the minor class, which is negative extreme.
To tackle this issue, we keep the same number from each class and use sampling techniques to increase the number of records to feed the model. RT-polarity: this is a dataset of classified movies, containing the polarity of tweets calculated in the first phase (Section 4). It is highly skewed towards the Inconclusive and the Extreme classes, with a tiny percentage of Negative Extreme. In order to train the model, we use the undersampling technique and take equal numbers from each class of inconclusive, positive extreme and negative. After running three epochs of our model, the validation accuracy of our model becomes flat, around 67%, and our test accuracy is the same, which shows the model is performing well. The model gives the following classification report for RT-polarity. The overall accuracy of the model is 67%.
We chose sen-timent140_test for the experiment and discard the sentiment140_train file because of the absence of the "Extreme Negative" class in polarity. The text, which is the training variable, is the message that needs preprocessing. We clean the text and feed it to our classification model. We run six epochs of our training data and obtain 92% training accuracy and 88% of validation accuracy. After the third epoch, the training accuracy becomes flat at 92% while the validation accuracy remains between 86% and 88%. The accuracy we get on the test set is 88% which is the same as the validation. The classification report for Sentiment140_test given in Table  5  and Figure  4  shows the confusion matrix for Sentiment140_test dataset.
TurntoIslam: The TurntoIslam dataset contains two extra classes of our target variable, "polarity," similar to Ansar1. We regard the Negative Non-Extreme and Positive Non-Extreme as inconclusive. The main goal of our work is to find Extreme positive and Extreme negative terms. Therefore we regard other terms as inconclusive or neutral. The text we will use to train the model is long, so we make chunks of 500 words if the text is more significant than 450 characters, and we maintain the polarity values for each chunk. We then use this data for training our model. Although our training accuracy increases by training epochs, the validation accuracy remains somewhat consistent, around 77%. The accuracy we get on the test set is 79% which exhibit little difference from the validation accuracy. The classification report of the dataset TurntoIslam shows the overall accuracy we obtained is 79% as given in The confusion matrix of TurntoIslam given in Figure  5  indicates that the model works well for the Negative Extreme, which is the most important class of our dataset.
Ansar1: Ansar1 is a dark web forum which contains mixed language discussions on forum. The dataset consist of these discussions from the forum. Ansar1 dataset yields lower accuracy compared to other datasets. This can be attributed to the presence of foreign languages other than the English language. While the central part consists of English, The mixture of another language, especially Arabic, can influence the model, since we are using the BERT pre-trained model in English. Although multilingual pre-trained models are available for BERT, they do not provide better results with the data we are using. The text data it contains is gathered from forum posts. We split the more extensive texts into smaller chunks to feed the BERT model. Although the training accuracy exceeds 80%, the validation accuracy remains between 56% to 58% while running epochs of our model.
The accuracy we get on the test set is 57% which is an approximate value of our validation accuracy. The classification report for Ansar1 data set is shown in Table  7. Figure  6  gives the comparison of results for each class in the form of confusion matrix. comb_all: Our last dataset for the experiment is made by combining all the previous five datasets we tested on. We take data from all datasets and create a new dataset to try with our model. We call this dataset comb_all. To preserve the data consistency, we keep the main classes of polarity, which are "Inconclusive, Positive Extreme and Negative Extreme". This combination of data into a new dataset provides a good insight about the efficacy of using BERT, which seems to perform well. It proves the reliability of BERT for our task and related ones.
Also, it verifies our approach to classify into extreme positive and extreme negative classes. Our training accuracy reaches around 98%, while the validation accuracy of our model is around 95%. The accuracy we get on the test set is also 95%, which confirms the validation accuracy. The classification report of comb_all is shown in Table  8. Figure  7  shows the confusion matrix of our model which achieves good results for extreme values especially for Negative Extreme. For a comprehensive analysis and visualization of the difference between the original lexicon and the expanded one, using BERT, we can look at the results presented in Table  9 , for each dataset. We further plotted graphically these results in Figures  8, 9 , and 10. In terms of overall accuracy, BERT shows promising results for the datasets RT polarity, Sentiment140 and T4SA.
Conclusion and Future Work In this paper, we demonstrate an unsupervised and language-independent approach for detecting people's extreme sentiments on social media platforms. Our approach is based on defining extreme polarity for terms and generating extreme sentiment lexicon by relying upon two standard lexical resources, i. e. , SentiWordNet 3. 0 and SenticNet 5. With this work, we provide a standard lexicon consisting of extreme positive and negative terms polarity. We implemented a prototype system with two different components ESG and ESC. We experimented with our system on five social networks and media data lexicons to analyze its accuracy, effectiveness, and efficiency. We have also used word embeddings to extend the lexicon to analyze the improvement in systems' performance. The obtained results are promising and encouraging, as the system shows excellent improvement using the extended lexicon.
Acknowledgments. This work was supported by  National Founding  from the  FCT-Fundacão  para a Ciência e a Tecnologia, through the  MOVES  Project -PTD/EEI-AUT/28918/2017  and by operation  Centro-01-0145-FEDER-000019-C4  - Centro de Competências em Cloud Computing , co-financed by the  European Regional Development Fund (ERDF)  through the  Programa Operacional Regional do Centro  ( Centro 2020 ), in the scope of the  Sistema de Apoio à Investigação Científica e Tecnológica.
2019 ; De Giovanni 2020). Thus, from procurement to design and from processing to control, all t h ec r i t i c a le l e m e n t si n v o l v e di ns u p p l yc h a i n sn e e dt o be closely monitored before deploying and testing the latest digital technologies relying on a data-driven and collaborative model  (Xu and Dang 2020). This cooperative mode allows sharing the datasets from a centralised data repository, often referred to as a federated learning system, which handles product movement, energy consumption, and other real-time data through embedded systems  (Treleaven, Brown, and Yang 2017; Zhengetal. 2020). However, the deliberation on its (federated system) relevance to practice is still at the nascent stage though it leaves scope for diversified objectives. Against this background, this study recognises a few research gaps to explore the integration of manufacturing processes with smart systems at process and module levels.
Federated learning The collaborative learning mode of machine learning helps iteratively train vast amounts of data from each of the embedded sensors and devices installed inside every manufacturing equipment. Data from the vendors, suppliers, and stockholders are collected and processed before transferring to the following sections for further operations. IoT plays a significant role in the instant delivery of accumulated data from the devices. Such data have critically been used in training to obtain functional insights on different goods and commodities in realtime. One of the key factors that assist in designing and modelling accumulated sensory data is standard console built-up, where data can be stored as packets with timestamps (O'Leary 2013). Thus, centralised federated learning is performed whenever the data is obtained using IoT-enabled smart sensors that tend to get accumulated on an arbitrary basis and logged based on a specific customised AI algorithm.
Depending on the data obtained from vendors, suppliers, and stocks, values are modelled and trained. If datasets are generated from a single period, sampling rate needs are assigned for training and processing to a sepa ra teF AImodel. I nsuchcases,tra iningval uesm ust be correlated and normalised to avoid sampling errors affecting the overall outcome for a particular component in the system model  (Gupta et al. 2020; Treleaven,Brown, and Yang 2017; Z h e n ge ta  l. 2020 ). Thus, to avoid such discrepancies at the output, further modelling is done using batch processing. Each batch consists of components of the same type obtained from multiple vendors a tt h es a m ef r e q u e n c yf o rp r o c e s s i n g. E a c ho ft h et o o l s andfeaturesisidentifiedandgroupedwithspecificidentification (ID) numbers.
Model design background and key parameters (1) Total number of components from particular vendor = X = X1+X2+. + E W(Rn, Bn) + S ′ (1) Where S ′ = n t=1 S B + N + D S (2) Samples tested correction and modelling using FAI at time't'isobtainedusingtheequation(1). Similarly ,the dataset for all the components is modelled and stored in a shared data repository. This data repository can be revoked using their unique packet identifier (PID). Thus, using suitable PID and sampling test results, the correct proportion of tools and components from processing to production can be revoked from the storage. These capabilities are provided with every section until the final product is delivered to the clients  (Parunak 1996; Allam and Dhunny 2019; ). Apart from the information handling and data processing, FAI helps to handle the stock comparison and pricing values.
AI and Smart contract Algorithms and learning methodologies involved in smart manufacturing entities vary with time. Thus, this basic featurization endorses the smart contract (SC) usage that extends the production process visibility to engineer and train the functions and standard operating procedures. SC is framed between the suppliers and the manufacturers (mutual agreement) on the risk of raw materials, processing, testing, and validation of goods  (Baryannis et al. 2019; Magazzeni,McBurney ,and Nash 2017; Min2010). SC has provisions to include the insurance schemes available for the assembly, inventory, and product delivery. Since the entire operation is handled using FAI and SC, the arbitration of data obtained from all the market shareholders is modelled for effective management in real-time.
Data with the least possible error values help fetch the desired benefits of utilising AI and SC in the workplace. A new normalisation phenomenon is introduced in the manufacturing process to cut off accumulated errors, a n dd a t am i s m a t c hf r o mo b t a i n e dd a t a s h e e t st op r ocessed data  (Nofer et al. 2017; Sayeed, Marco-Gisbert, and Caira 2020). This method is data-centric, and normalisation helps mitigate the system's accumulated data errors without affecting the policy to a greater extent. This novel error limiting factor is called Trust Threshold Limit (TTL).
Trust Threshold Limit (TTL) TTL refers to the maximum limit that any goods and components involved in assembly, manufacturing, and delivery can be experimentally utilised with minimum wastage or losses. Thus, as the name indicates, TTL sets the threshold limit for all the tools and devices actively participating in product delivery. SC depends on the permitted TTL limit for each entry in the data socket, exceeding which the chance of loss in the product market is high. With TTL value, the responsibility of the policy-makerandF AIfunctionistooptimisethescalingfactor forthecomponentwithinthatparticularlimit  (Mohanta et al. 2020; Sayeed, Marco-Gisbert, and Caira 2020; Y u Zhang and Wen 2017 ;Y u a n y uZ h a n ge ta l. 2019). The no veldecisionaidmodelandthethresholdlimitvalues helps the operations, production and logistics in their purchase, procurement, distribution, and delivery.
Methods The model framework is structured under four stages of sequential processes, as shown in Figure  1. T h ep r ocess gets started with data collection, followed by data normalisation. The processed data is tested and analyzed using suitable testing and validation methods (support vector machine learning). All these segmented and normalised datasets are then modelled and trained using FAI for decision-making and validation in real-time. SC is provided with the market risk knowledge and resource utilisation metrics during all the stages of data accumulation, processing, and control, thereby ensuring the market standards for better product delivery. Policymakers and standard decision-making units critically evaluate the outcomes at every stage. Finally, the end product is delivered with the same features to the consumer market  (Gupta et al.
Stage 1: Data collection and classification The entire process of product design and delivery relies solely on the accuracy of data gathered from each of the available components in the system. Discrete datasets from each supplier, vendor, and stockholder are actively collected along with their market risk policy to frame their suitable SC. As these data are entirely obtained from the IoT-enabled smart sensors, these data require a lot of pre-and post-processing functions to be performed. Information about manufacturing, warranty, composition, maker's policy, structural details are logged and modleled during the collection process. Sensors, actuators, and other embedded electronic equipment collect the data and efficiently hand them to the central data repository for further processing and analysis.
After the data collection, the accumulated data are modelled and mapped with their unique ID. The equipment data are stored in their respective local data repositories for longterm referencing and policy formulation. These data are then retrieved for the 1st stage of processing, called Trust validation. The data obtained and stored from the suppliers and stockholders are cross-referenced and verified to prove confidence and originality regarding measurements, validity, defects, or other deformities. If any flaws are identified during this stage of trust validation, SC will immediately help manufacturers raise a replacement or r e f u n dt h es t o c k s. T h i sb l o c k c h a i nf e a t u r en e e d st ob e mentioned at the time of policy formation. This can be m a t e r i a l i s e da n df o l l o w e da sas t a n d a r dd e f e c ti d e n t i fication and originality check of goods and commodities covered under the same risk assessment policy(Khan and Byun 2020).
In addition to the existing literature  (Ivanov, Sokolov, and Dolgui 2014, 2019) , the impact of engaging smart technologies are explained in detail with the help of AI and blockchain. This sheds lights for new research and design perspectives of altering the industrial design for performance enhancement and monetary benefits. The datasets are modelled using the support vector machine learning model (SVML) in the post-trustvalidation phase. D u r i n gt h i su n s u p e r v i s e dM Lt r a i ning, the dataset is provided with classifiers where each classifier offers the information on the source of goods, timestamp, and purchase details. All these data are mode l l e da n dm a p p e du n d e rS Cg r o u pp o l i c yi n t e n d e dt o avoid the market risk practice. Depending on the supplier's datasheets and procedures, the manufacturer has the freedom to plan additional policies if the system's performance predicts a better ratio than expected during the policy formation  (Mohanta et al.
2020 ;YuZhangandWen 2017). Each support vector is identified and mapped with theirclassifiersandloggedinthesamedatabaseforeach reference and identification. All the mapped dataset is logged in a customised database for easy and smooth identification and utilisation. During the end-product delivery, these classifiers and the support vectors are invoked from the system database for final checking and clearance. In case of any contradiction to the proposed details, S and FAI values, will be cross-checked and modified as per the risk assessment SC in the system (Magazzeni, McBurney, and Nash 2017;Y u a n y uZ h a n ge ta l. 2019).
Stage 2: Grouping and normalisation All the logged data and support vectors are moved to the next section of data normalisation. The data with errors are identified and removed/correlated with the next least possible error values required for processing the tools. Thus, error rectification is completely done during this process of data normalisation. In some worst-case scenarios, if the schematic of a particular component consecutively fails to meet the expectations, then the financial blockchain is invoked using the SC. One of the most prominent examples of today's automotive world for such financial SC and their smart policy is recalling Honda Model cars due to their faulty airbags. Under such circumstances, group policies are shared between all the involved parties, from suppliers to manufacturers.
Due to these unexpected circumstances, the loss incurred is equally shared by all the commodity vendors and companies  (Sayeed, Marco-Gisbert, and Caira 2020; Zhengetal. 2020). If the defect is identified in the product (Airbag asinH ondacars),thenthefa ultyproductidisrevoked from the database for vendor identification. If the ven-dorSCisnotassignedundersuchfinancialblockchain, then the company is entirely liable for the incidents. If SC is derived in favour of the manufacturer, then the v e n d o r sw i l lt a k et h ew h o l er e s p o n s i b i l i t ya n da d d r e s s the incidents with compensation or replacement of the entire automobile itself. Thus, data error normalisation and schematic verification play a major role in training and SC formation.
Stage 3: Control, verification, and analysis Two of the critical stages in the manufacturing and assembly in the fully automated smart manufacturing industry are verification and analysis. In this stage, the component datasets are evaluated for fixing their TTL range. This range plays a significant role in energy usage, supply-demand management, cost, and flawless product delivery. Normalised data obtained after verifying the schematics is shared with the assembly and verification section. TTL of the component is set before starting the process  (Magazzeni, McBurney, and Nash 2017; Yu Zhang and Wen 2017). Once the process is initia t e d ,T T Lf o rt h a tt o o li sp i c k e df r o mt h ed a t am o d e l for fitting and assembly. During the assembly and fitting schemes, the dataset is verified for TTL; thereby, the reference limit for the whole processing mechanism solely depends on the limit set by TTL.
In this system driven control and processing, all the accumulated datasets are going through series of processes as follows: (1) Fitting (2) Assembly (3) Verification (4) Analysis (  5 ) Testing (6) Polishing under the same TTL value from the SVML training used for deriving SC. All the SC under this stage is assigned under the insurance and market risk blockchain category. SC associated with insurance blockchain helps the retail business venturestotakepartinthein vestmentprocedures,thereby helps in boosting their market share and business development processes (Yuanyu  Zhang et al. 2019 ). Market liability is insured, and the IoT devices closely monitor the actions performed at each section. As the data generated from embedded devices are a continuous process, classifiers and the support vectors are assigned instantly irrespective of the functionality and operations performed at different stages of the assembly and delivery process.
If any mismatch occurs during the production and delivery stages, the dataset is retrieved from the local database, and their TTL is critically examined with their support vectors. Thus, errors are eliminated. Compari n gw i t ht h ee x i s t i n gi n d u s t r i a ls e t u p ,e n g a g i n gs m a r t technologies will give maximum insights about the crucial designs and process automation. Joining up with the available resources, the operations and control schemes can be modelled for maximum benefits. For every stage of product design and fitting, the SC can be provided with suitable regulatory primitives, thereby bringing all the datasets under a single database  (Zheng et al. 2020 ). Insurance companies offer higher flexibility in share exchanges for policymaking and production strategy analysissubjectedtomarketriskandinvestmentreturns.
Stage 4: Decision making, FAI, and product dispatch The next critical and most crucial stage of AAM design is the decision-making by which the real-time testing is evaluated. Data obtained from this stage is used as a reference for all SC forms considered a baseline scheme for identifying the TTL values. The convolutional data processing method offers more error functions than the FAI scheme  (Giancaspro 2017; Macrinici, Cartofeanu, and Gao 2018). Apart from the analysis and training, the SC and training datasets are arbitrarily cross-referenced for smooth data interpretation and model evaluation. From the study, key findings include market valuation for each product, cost and distribution quantity to individual shareholders, profit-loss margin and transportation and insurance coverage, quantity, and quality of goods delivery, etc.
The trucks assembled from this factory vary from 10-18 wheels, and each truck goes through the same assembling stages using AI and Blockchain  (Nofer et al. 2017 ). Stage 1 consists of component classification and analysis. Data are modelled and associated with the Raw Material Blockchain. S t a g e 2 comprises embedded equipment and machinery data that helps in the investigation, grouping, and classification of data. This section is closely associated with financial and insurance blockchain. The final stage in the design of AAM for XZY company is the final assembly of theproduct,whereallthesectionsandstagesareinvolved in achieving the desired outcome. In this stage, 3, TTL, and SC help finalise the FAI values by which the company stakeholders will plan and execute their business plans in real-time.
Data collection and vector classification In this design stage, goods, software, tools, and components from all the parties are unloaded and verified for their originality and standards in real-time. All stock values, component lists and specifications are labelled, tagged, and received at the unloading section. Stocklist, storage requirements, processing features, marking detailsliketimeandvalidityofthecontractaremapped andstoredwiththeirrespectivesuppliers(GuoandRyan 2021). This stage uses a unique identifier for easy recovery in case of loss, damage, or faulty components. Data about the suppliers are kept confidential throughout the process as the SC policy will give additional safety and security details from the manufacturer. Cross functional embedded equipment can be combined together for data collection and processing.