Classification of Conversational Sentences Using an Ensemble Pre-Trained Language Model with the Fine-Tuned Parameter

Sentence classification is the process of categorizing a sentence based on the context of the sentence.Sentence categorization requires more semantic highlights than other tasks,such as dependence parsing,which requires more syntactic elements.Most existing strategies focus on the general semantics of a conversation without involving the context of the sentence,recognizing the progress and comparing impacts.An ensemble pre-trained language model was taken up here to classify the conversation sentences from the conversation corpus.The conversational sentences are classified into four categories:information,question,directive,and commission.These classification label sequences are for analyzing the conversation progress and predicting the pecking order of the conversation.Ensemble of Bidirectional Encoder for Representation of Transformer(BERT),Robustly Optimized BERT pretraining Approach(RoBERTa),Generative Pre-Trained Transformer(GPT),DistilBERT and Generalized Autoregressive Pretraining for Language Understanding(XLNet)models are trained on conversation corpus with hyperparameters.Hyperparameter tuning approach is carried out for better performance on sentence classification.This Ensemble of Pre-trained Language Models with a Hyperparameter Tuning(EPLM-HT)system is trained on an annotated conversation dataset.The proposed approach outperformed compared to the base BERT,GPT,DistilBERT and XLNet transformer models.The proposed ensemble model with the fine-tuned parameters achieved an F1_score of 0.88.

A More Efficient Approach for Remote Sensing Image Classification

Over the past decade,the significant growth of the convolutional neural network(CNN)based on deep learning(DL)approaches has greatly improved the machine learning(ML)algorithm’s performance on the semantic scene classification(SSC)of remote sensing images(RSI).However,the unbalanced attention to classification accuracy and efficiency has made the superiority of DL-based algorithms,e.g.,automation and simplicity,partially lost.Traditional ML strategies(e.g.,the handcrafted features or indicators)and accuracy-aimed strategies with a high trade-off(e.g.,the multi-stage CNNs and ensemble of multi-CNNs)are widely used without any training efficiency optimization involved,which may result in suboptimal performance.To address this problem,we propose a fast and simple training CNN framework(named FST-EfficientNet)for RSI-SSC based on an EfficientNetversion2 small(EfficientNetV2-S)CNN model.The whole algorithm flow is completely one-stage and end-to-end without any handcrafted features or discriminators introduced.In the implementation of training efficiency optimization,only several routine data augmentation tricks coupled with a fixed ratio of resolution or a gradually increasing resolution strategy are employed,so that the algorithm’s trade-off is very cheap.The performance evaluation shows that our FST-EfficientNet achieves new state-of-the-art(SOTA)records in the overall accuracy(OA)with about 0.8%to 2.7%ahead of all earlier methods on the Aerial Image Dataset(AID)and Northwestern Poly-technical University Remote Sensing Image Scene Classification 45 Dataset(NWPU-RESISC45D).Meanwhile,the results also demonstrate the importance and indispensability of training efficiency optimization strategies for RSI-SSC by DL.In fact,it is not necessary to gain better classification accuracy by completely relying on an excessive trade-off without efficiency.Ultimately,these findings are expected to contribute to the development of more efficient CNN-based approaches in RSI-SSC.

Review of Remotely Sensed Imagery Classification Patterns Based on Object-oriented Image Analysis

With the wide use of high-resolution remotely sensed imagery, the object-oriented remotely sensed informa- tion classification pattern has been intensively studied. Starting with the definition of object-oriented remotely sensed information classification pattern and a literature review of related research progress, this paper sums up 4 developing phases of object-oriented classification pattern during the past 20 years. Then, we discuss the three aspects of method- ology in detail, namely remotely sensed imagery segmentation, feature analysis and feature selection, and classification rule generation, through comparing them with remotely sensed information classification method based on per-pixel. At last, this paper presents several points that need to be paid attention to in the future studies on object-oriented RS in- formation classification pattern: 1) developing robust and highly effective image segmentation algorithm for multi-spectral RS imagery; 2) improving the feature-set including edge, spatial-adjacent and temporal characteristics; 3) discussing the classification rule generation classifier based on the decision tree; 4) presenting evaluation methods for classification result by object-oriented classification pattern.

Pine wilt disease detection in high-resolution UAV images using object-oriented classification

Pine wilt disease(PWD)is currently one of the main causes of large-scale forest destruction.To control the spread of PWD,it is essential to detect affected pine trees quickly.This study investigated the feasibility of using the object-oriented multi-scale segmentation algorithm to identify trees discolored by PWD.We used an unmanned aerial vehicle(UAV)platform equipped with an RGB digital camera to obtain high spatial resolution images,and multiscale segmentation was applied to delineate the tree crown,coupling the use of object-oriented classification to classify trees discolored by PWD.Then,the optimal segmentation scale was implemented using the estimation of scale parameter(ESP2)plug-in.The feature space of the segmentation results was optimized,and appropriate features were selected for classification.The results showed that the optimal scale,shape,and compactness values of the tree crown segmentation algorithm were 56,0.5,and 0.8,respectively.The producer’s accuracy(PA),user’s accuracy(UA),and F1 score were 0.722,0.605,and 0.658,respectively.There were no significant classification errors in the final classification results,and the low accuracy was attributed to the low number of objects count caused by incorrect segmentation.The multi-scale segmentation and object-oriented classification method could accurately identify trees discolored by PWD with a straightforward and rapid processing.This study provides a technical method for monitoring the occurrence of PWD and identifying the discolored trees of disease using UAV-based high-resolution images.

Double Polarization SAR Image Classification based on Object-Oriented Technology

This paper proposed to use double polarization synthetic aperture radar (SAR) image to classify surface feature, based on DEM. It takes fully use of the polarization information and external information. This pa-per utilizes ENVISAT ASAR APP double-polarization data of Poyang lake area in Jiangxi Province. Com-pared with traditional pixel-based classification, this paper fully uses object features (color, shape, hierarchy) and accessorial DEM information. The classification accuracy improves from the original 73.7% to 91.84%. The result shows that object-oriented classification technology is suitable for double polarization SAR’s high precision classification.

Object-oriented crop classification based on UAV remote sensing imagery

UAV remote sensing images have the advantages of high spatial resolution,fast speed,strong real-time performance,and convenient operation,etc.,and have become a recently developed,vital means of acquiring surface information.It is an important research task for precision agriculture to make full use of the spectrum,texture,color and other characteristic information of crops,especially the spatial arrangement and structure information of features,to explore effective methods for the classification of multiple varieties of crops.In order to explore the applicability of the object-oriented method to achieve accurate classification of UAV high-resolution images,the paper used the object-oriented classification method in ENVI to classify the UAV high-resolution remote sensing image obtained from the orderly structured 28 species of crops in the test field,which mainly includes image segmentation and object classification.The results showed that the plots obtained after classification were continuous and complete,basically in line with the actual situation,and the overall accuracy of crop classification was 91.73%,with Kappa coefficient of 0.87.Compared with the crop planting area based on remote sensing interpretation and field survey,the area error of 17 species of crops in this study was controlled within 15%,which provides a basis for object-oriented crop classification of UAV remote sensing images.

A statistical approach to classification of keratoconus

INTRODUCTIONThe front portion of the eye consists of a transparent layer called the cornea.The cornea is an important optical component for vision and plays a role in the specific refraction of the eye.The cornea normally has convexity but the amount of protrusion progressively increases in patients with keratoconus.In other words,the cornea prolapses forward.Keratoconus is a bilateral,typically asymmetric and non-inflammatory degeneration of the cornea caused by corneal protrusion as a result of progressive thinning of the corneal stroma.Corneal thinning generally occurs in the inferior,inferotemporal or central regions of the cornea.

ORDERED-OBJECT-ORIENTED METHOD:A NEW APPROACH OF SAMPLE PART CALCULATION AND DESIGN

This paper proposed a new approach of sample part classification and design, a so called Or-dered-object-oriented method (O-O-O method). Based on the theory of neural networks, fuzzy clustering algorithm and adaptive pattern recognition, O-O-O method can be used to classify and design the sample parts automatically. The basic theory, the main step as well as the characteristics of the method are analysed. The construction of the ordered object in application is also presented in this paper.

Quadrilateral plate fractures of the acetabulum:Classification,approach,implant therapy and related research progress

The quadrilateral plate(QP)is an essential structure of the inner wall of the acetabulum,an important weight-bearing joint of the human body,which is often involved in acetabular fractures.The operative exposure,reduction and fixation of QP fractures have always been the difficulties in orthopedics due to the special morphological structure and anatomical features of the QP.Fortunately,there have been many effective methods and instruments developed for QP exposure,reduction and fixation by virtue of the combined efforts of numerous orthopedists.At the same time,each method presents with its own advantages and disadvantages,resulting in different prognoses.It is necessary to have a thorough understanding of the anatomy,radiology and fixation techniques of the QP in terms of patient prognosis optimization.In this paper,the anatomical features,definition and classification of QP,operative approach selection,implant internal fixation methods and efficacy were reviewed.

A Scheme of Vegetation Classification of Taiwan, China

The complexity of natural conditions leads to the complexity of vegetation types of Taiwan of China, which has both tropical and cold-temperate vegetation types, and could be depicted as the vegetation miniature of China or even for the world. The physiognomic-floristic principle was adopted for the vegetation classification of Taiwan. The units of rank from top to bottom are: class of vegetation-type, order of vegetation-type, vegetation-type, alliance group, alliance and association. The high-rank units (class, order and vegetation-type) are classified by ecological physiognomy, while the median and lower units by the species composition of community. At the same time the role of dominant species and character species will also be considered. The dominant species are the major factor concerned with the median ranks (alliance group, and alliance) because they are the chief components of community, additionally their remarkable appearance is easy to identify; the character species (or diagnostic species) are for relatively low ranks (association) because they will clearly show the interspecies relation-ship and the characteristics of community. According to this principle, vegetation of Taiwan is classi-fied into five classes of vegetation-types (forests, thickets, herbaceous vegetation, rock fields vegetation, swamps and aquatic vegetation), 29 orders of vegetation-types (cold-temperate needle-leaved forests, cool-temperate needle-leaved forests, warm-temperate needle-leaved forests, warm needle-leaved forests, deciduous broad-leaved forests, mixed evergreen and deciduous broad-leaved forests, evergreen mossy forests, evergreen sclerophyllous forests, evergreen broad-leaved forests, tropical rain forests, tropical monsoon forests, coastal forests, warm bamboo forests, evergreen needle-leaved thickets, sclerophyllous thickets, deciduous broad-leaved thickets, evergreen broad-leaved thickets, xerothermic thorn-succulent thickets, bamboo thickets, meadows, sparse shrub grasslands, savannahic grasslands, sparse scree communities, chasmophytic vegetation, woody swamps, herbaceous swamps, moss bogs, fresh water aquatic vegetation, salt water aquatic vegetation) and 53 vegetation-types. The main alliances of each vegetation-type are described.

Combined hepatocellular-cholangiocarcinoma:An update on epidemiology,classification,diagnosis and management

Background:Combined hepatocellular-cholangiocarcinoma(CHC)is a rare subtype of primary hepatic malignancies,with variably reported incidence between 0.4%–14.2%of primary liver cancer cases.This study aimed to systematically review the epidemiological,clinicopathological,diagnostic and therapeutic data for this rare entity.Data sources:We reviewed the literature of diagnostic approach of CHC with special reference to its clinical,molecular and histopathological characteristics.Additional analysis of the recent literature in order to evaluate the results of surgical and systemic treatment of this entity has been accomplished.Results:The median age at CHC’s diagnosis appears to be between 50 and 75 years.Evaluation of tumor markers[alpha fetoprotein(AFP),carbohydrate antigen 19–9(CA19–9)and carcinoembryonic antigen(CEA)]along with imaging patterns provides better opportunities for CHC’s preoperative diagnosis.Reported clinicopathologic prognostic parameters possibly correlated with increased tumor recurrence and grimmer survival odds include advanced age,tumor size,nodal and distal metastases,vascular and regional organ invasion,multifocality,decreased capsule formation,stem-cell features verification and increased GGT as well as CA19–9 and CEA levels.In case of inoperable or recurrent disease,combinations of cholangiocarcinoma-directed systemic agents display superior results over sorafenib.Liver-directed methods,such as transarterial chemoembolization(TACE),percutaneous ethanol injection(PEI),hepatic arterial infusion chemotherapy(HAIC),radioembolization and ablative therapies,demonstrate inferior efficacy than in cases of hepatocellular carcinoma(HCC)due to CHC’s common hypovascularity.Conclusions:CHC demonstrates an overlapping clinical and biological pattern between its malignant ingredients.Natural history of the disease seems to be determined by the predominant tumor element.Gold standard for diagnosis is histology of surgical specimens.Regarding therapeutic interventions,major hepatectomy is acknowledged as the cornerstone of treatment whereas minor hepatectomy and liver transplantation may be applied in patients with advanced cirrhosis.Despite all therapeutic attempts,prognosis of CHC remains dismal.

Classification and Extraction of Urban Land-Use Information from High-Resolution Image Based on Object Multi-features

Urban land provides a suitable location for various economic activities which affect the development of surrounding areas. With rapid industrialization and urbanization, the contradictions in land-use become more noticeable. Urban administrators and decision-makers seek modern methods and technology to provide information support for urban growth. Recently, with the fast development of high-resolution sensor technology, more relevant data can be obtained, which is an advantage in studying the sustainable development of urban land-use. However, these data are only information sources and are a mixture of ＂information＂ and ＂noise＂. Processing, analysis and information extraction from remote sensing data is necessary to provide useful information. This paper extracts urban land-use information from a high-resolution image by using the multi-feature information of the image objects, and adopts an object-oriented image analysis approach and multi-scale image segmentation technology. A classification and extraction model is set up based on the multi-features of the image objects, in order to contribute to information for reasonable planning and effective management. This new image analysis approach offers a satisfactory solution for extracting information quickly and efficiently.

Machining Process Classification Based on Carbon Footprint Analysis

Despite spending considerable effort on the development of manufacturing technology during the production process,manufacturing companies experience resources waste and worse ecological influences. To overcome the inconsistencies between energy-saving and environmental conservation,a uniform way of reporting the information and classification was presented. Based on the establishment of carbon footprint( CFP) for machine tools operation,carbon footprint per kilogram( CFK) was proposed as the normalized index to evaluate the machining process.Furthermore,a classification approach was developed as a tracking and analyzing system for the machining process. In addition,a case study was also used to illustrate the validity of the methodology. The results show that the approach is reasonable and feasible for machining process evaluation,which provides a reliable reference to the optimization measures for low carbon manufacturing.

A new empirical chart for rockburst analysis in tunnelling:Tunnel rockburst classification(TRC)

Rockburst is defined as a phenomenon with immediate dynamic instability under excavation unloading conditions of deep or high geostress areas.Inadequate knowledge and lack of characterizing information prevent engineers and experts from achieving appropriate prediction results related to the rockburst behaviour.In this study,a data set including 220 rockburst instances was collected for rockburst classification via the geostatistical method.An update of the 2D graph,the tunnel rockburst classification(TRC)chart,was introduced based on analysing three indicators,namely,elastic energy index(Wet),tangential stress in rock mass(σ_(0)),and uniaxial compressive strength(σ_(c)).Distribution and correlation of data were drawn on 2D plot,and the boundaries of rockburst were distinguished according to the achieved interpolate points by kriging method.Hierarchically,the validation phase was performed using an additional set of 28 case histories obtained from several projects around the world.The results showed that the TRC chart with an average error percentage of 3.6%in the prediction of rockburst had a significant and effective implementation in comparison to the exiting heuristic systems.Despite the initial character of the prediction,the described chart may be a helpful tool in the first steps of design and construction.

A classification method of building structures based on multi-feature fusion of UAV remote sensing images

In order to improve the accuracy of building structure identification using remote sensing images,a building structure classification method based on multi-feature fusion of UAV remote sensing image is proposed in this paper.Three identification approaches of remote sensing images are integrated in this method:object-oriented,texture feature,and digital elevation based on DSM and DEM.So RGB threshold classification method is used to classify the identification results.The accuracy of building structure classification based on each feature and the multi-feature fusion are compared and analyzed.The results show that the building structure classification method is feasible and can accurately identify the structures in large-area remote sensing images.

Bootstrap inference of the skew-normal two-way classification random effects model with interaction

In this paper,we consider the statistical inference problems for the fixed effect and variance component functions in the two-way classification random effects model with skewnormal errors.Firstly,the exact test statistic for the fixed effect is constructed.Secondly,using the Bootstrap approach and generalized approach,the one-sided hypothesis testing and interval estimation problems for the single variance component,the sum and ratio of variance components are discussed respectively.Further,the Monte Carlo simulation results indicate that the exact test statistic performs well in the one-sided hypothesis testing problem for the fixed effect.And the Bootstrap approach is better than the generalized approach in the one-sided hypothesis testing problems for variance component functions in most cases.Finally,the above approaches are applied to the real data examples of the consumer price index and value-added index of three industries to verify their rationality and effectiveness.

Object-Based Classification of Urban Distinct Sub-Elements Using High Spatial Resolution Orthoimages and DSM Layers

This paper aims to assess the ways in which multi-resolution object-based classification methods can be used to group urban environments made up of a mixture of buildings, sub-elements such as car parks, roads, shades and pavements and foliage such as grass and trees. This involves using both unmanned aerial vehicles (UAVs) which provide high-resolution mosaic Orthoimages and generate a Digital Surface Model (DSM). For the study area chosen for this paper, 400 Orthoimages with a spatial resolution of 7 cm each were used to build the Orthoimages and DSM, which were georeferenced using well distributed network of ground control points (GCPs) of 12 reference points (RMSE = 8 cm). As these were combined with onboard RTK-GNSS-enabled 2-frequency receivers, they were able to provide absolute block orientation which had a similar accuracy range if the data had been collected by traditional indirect sensor orientation. Traditional indirect sensor orientation involves the GNSS receiver in the UAV receiving a differential signal from the base station through a communication link. This allows for the precise position of the UAV to be established, as the RTK uses correction, allowing position, velocity, altitude and heading to tracked, as well as the measurement of raw sensor data. By assessing the results of the confusion matrices, it can be seen that the overall accuracy of the object-oriented classification was 84.37%. This has an overall Kappa of 0.74 and the data that had poor classification accuracy included shade, parking lots and concrete pavements. These had a producer accuracy (precision) of 81%, 74% and 74% respectively, while lakes and solar panels each scored 100% in comparison, meaning that they had good classification accuracy.

Object-Oriented Modeling of Virtual Assembly Process

Virtual assembly is a Virtual Reality (VR) based engineering application which allows engineers to evaluate, analyze, and plan the assembly of mechanical systems. To model the virtual assembly process, new methodology must be applied. Based on the idea that the virtual assembly system is an event driven system, the interactive behavior and information model is proposed to describe the dynamic process of virtual assembly. Definition of the object-oriented model of virtual assembly is put forward.

Study on Customer Demand Forecasting Models,Stock Management,Classification and Policies for Automobile Parts Manufacturing Company N.A.C.C.(An Advance on Classical Models)

The primary intent of the current research is to provide insights regarding the management of spare parts within the supply chain,in conjunction with offering some methods for enhancing forecasting and inventory management.In particular,to use classical forecasting methods,the use of weak and unstable demand is not recommended.Furthermore,statistical performance measures are not involved in this particular context.Furthermore,it is expected that maintenance contracts will be aligned with different levels.In addition to the examination of some literature reviews,some tools will guide us through this process.The article proposes new performance analysis methods that will help integrate inventory management and statistical performance while considering decision maker priorities through the use of different methodologies and parts age segmentation.The study will also identify critical level policies by comparing different types of spenders according to the inventory management model,also with separate and common inventory policies.Each process of the study is combined with a comparative analysis of different forecasting methods and inventory management models based on N.A.C.C.parts supply chain data,allowing us to identify a set of methodologies and parameter recommendations based on parts segmentation and supply chain prioritization.