dataesr/scientific-paragraphs-categorization huggingface dataset:
Comparison with other fire detection methods To verified the generality of our proposed algorithm for the environment, we compared it with current state-of-the-art fire detection algorithms on publicly available datasets. We compared the results of  Muhammad et al. (2018a,b) ,  Chaoxia et al. (2020) ,  Pan et al. (2020) , and Our algorithm under the BoWFire  (Chino et al. , 2015)  dataset. The BoWFire dataset was derived from real fire and urban fire scenarios. The specific data of this experiment were shown in Table  5  below. In the analysis in this section we focus on the A-D algorithm in Table  5  with the four evaluation metrics of FireYOLO (E) proposed in this paper. In the above we have experimentally concluded that FireYOLO has very few missed images but many false positives, in other words a low FN but extremely high FP values, and by analysing Equations 14, 15, 19 we conclude that FireYOLO will have a high FAR and low AR and DR.
However, this does not mean that FireYOLO is very inaccurate, as the experimental data in Table  5E  show that FireYOLO's accuracy can be at the top of all these algorithms, but it is really not good enough. We then move on to analyze the reasons for the change in these four evaluation metrics when upgrading from the E to the F algorithm: the final detection results for the FIRE 1 type dataset are divided into three types: correctly detected flames, missed flames and misdetected flames (there will also be images that are both misdetected and missed), then when the algorithm is upgraded from E to F, in addition to the number of misdetected images being Comparison of FireYOLO with other advanced target detection algorithms. The shade of color indicates how much attention the algorithm is paying to the image, with a darker color in a region indicating that the algorithm is paying more attention to that region.
c SSIM is called structural similarity, and it ranges from (0,1), and the larger the value, the better the quality of the image. When two images are exactly the same, SSIM = 1 at this time. d PSNR is called Peak Signal to Noise Ratio, which is the ratio of the energy of the peak signal to the average energy of the noise, the larger its value, the higher the picture quality. e Entropy is mainly a measure of how much information an image contains, and a higher value means more information and better image quality. reduced, while the number of images with undetected flames will be greatly increased. The final combination of these changes will result in a dramatic increase in AR, DR and NFAR as well as a dramatic decrease in FAR. When we compared the differences between the four evaluation criteria of the A-D and F algorithms, we found that when these algorithms were applied to the BoWFire dataset, the algorithm in this paper achieved the highest AR (95.
Development workflow Gammapy uses all the standard tools of modern community-driven open-source software development. The code repository is hosted on GitHub 5  , which allows for convenient and public interaction of developers, contributors and users via the GitHub interface. This includes, for instance, feature requests for missing functionality, issues for bugs or questions and pull requests for code contributions. The code base is continuously built and tested in different virtual environments using the continuous integration service Travis CI. This helps to maintain high coding standards and compatibility with different versions of Numpy and Astropy. The documentation of Gammapy is generated using Sphinx. An online version of the latest documentation is built automatically and is available on Read the Docs  6.
The Gammapy toolbox 4. 1 Sub-packages The Gammapy code base is structured into several sub-packages where each of the packages bundle corresponding functionality in a namespace. This follows the concept of other Python packages such as Astropy and Scipy. The following list gives a rough overview of the different sub-packages with a short description: • gammapy. astro -Galactic population and emission models of TeV sources • gammapy. background -Background estimation and modeling There are some cases (e. g. gammapy. spectrum. models and several sub-modules of gammapy. utils) where the end-user functionality is exposed one level further down in the hierarchy. This is because putting everything into the top-level gammapy. spectrum or gammapy. utils namespace would lead to an unstructured collection of functions and classes.
Morphology fitting command-line tool Gammapy includes a simple to use command-line script gammapy-sherpa-like to perform a Poisson maximum likelihood morphology fitting using FITS files as input. The input counts, exposure and background maps can be specified by the corresponding parameters --counts, --exposure and --background. The source model is defined in a JSON file and should be passed using the --sources option. The model parameters for a multi-Gaussian point spread function (PSF) can be specified in JSON format using the --psf option. Fit results and additional information are stored in result. json.
Planned functionality Gammapy already includes key analysis features like morphology and spectrum fitting but both are currently limited to either 2D image-based data or 1D spectral data. As a major next step we plan to support joint likelihood fitting of datasets, as illustrated in Figure  5. Events are binned into longitude, latitude and energy cubes and fitted simultaneously with spectral and spatial models taking energy-dependent background, exposure and point spread function (PSF) into account. This approach will allow joint likelihood analysis across different experiments. For instance simultaneous likelihood fitting of Fermi and H. E. S. S. data. Support for un-binned analyses is not planned.
• gammapy. catalogγ-ray source catalog access and processing • gammapy. datasets -Easy access to bundled and remote datasets • gammapy. detect -Source detection tools and algorithms • gammapy. hspec -Interface to spectral fitting with Sherpa • gammapy. image -Image processing and analysis tools • gammapy. irf -Instrument response function (IRF) access and handling • gammapy. morphology -Morphology models and tools • gammapy. obs -Observation handling • gammapy. spectrum -Spectrum models and tools • gammapy. stats -Statistical functions • gammapy. time -Handling of time series and γ-ray lightcurves • gammapy. utils -Utility functions and classes (in sub-modules).
Each participant had to complete 120 tasks (4 techniqes × 30 tasks). The order of technique was counterbalanced by the 4 × 4 Latin square. For dependent variables, we measured task time, the physical path length of the used wrist, and the virtual path length of the pointer in VR for each task. The path length data was divided by the target distance of each task as a ratio for comparison. The interaction volume was calculated by recorded physical fingertip trajectory. In the post-test questionnaire, we used NASA-TLX  [16]   for the subjective fatigue, the avatar embodiment questionnaire  [12]  with three subscales (ownership, agency, and location) for VBO, and Igroup Presence Questionnaire (IPQ)  [37, 39]  for presence. Participants responded with a 7-point Likert scale inside VR. Finally, the tracking quality of the Oculus Quest was recorded through every frame during each task for filtering out the data of tracking loss.
Also, we analyzed the interaction volume data by participants and conditions. By performing a logarithmic transformation, we found a statistically significant difference between techniqe ( 3,36 = 32. 8,  <. 01, 2  = 0. 73). In a post-hoc analysis, hand tracking ( = 0. 44,  = 0. 14) had a statistically significant higher volume than Attach ( = 0. 21,  = 0. 1), Direct ( = 0. 22,  = 0. 12), and ray-casting ( = 0. 11,  = 0. 05) ( <. 01 for three comparisons). There was no difference between Attach and Direct, but both had a statistically significant higher volume compared to ray-casting (Ray-Attach:  =. 02, Ray-Direct  =. 04). Fatigue and NASA-TLX. Figure  13a  shows the results of NASA-TLX. We were particularly interested in the mental and physical demand, and our analysis revealed significant differences (Mental:   Finally, Figure  13c  shows the General Presence in IPQ.
Progress in space weather research and awareness needs community-wide strategies and procedures to evaluate our modeling assets. Here we present the activities of the Ambient Solar Wind Validation Team embedded in the COSPAR ISWAT initiative. We aim to bridge the gap between model developers and end-users to provide the community with an assessment of the state-of-the-art in solar wind forecasting. To this end, we develop an open online platform for validating solar wind models by comparing their solutions with in situ spacecraft measurements. The online platform will allow the space weather community to test the quality of state-of-the-art solar wind models with unified metrics providing an unbiased assessment of progress over time. In this study, we propose a metadata architecture and recommend community-wide forecasting goals and validation metrics.
Metadata Architecture A comprehensive metadata architecture is needed to support a sustainable validation of solar wind models. Here we propose seven types of metadata, all of which are required to register new community models in the online platform. These metadata components include information on the observational input data, data preprocessing, model description, model setting, model output, model chain, and model solution. We will store the metadata components in an associated metadata template file according to the Space Physics Archive Search and Extract (SPASE) standards. More details on SPASE metadata can be found in  Fung et al. , 2022. In addition, as the backbone of the online platform, the Comprehensive Assessment of Models and Events Using Library Tools (CAMEL; Rasta ¨tter et al.
Model Description Metadata The individual models are the building blocks of the numerical framework. This type of metadata describes the individual models in the model chain used to model the solar wind in different domains. This metadata compo-Fig. 2. Overview of metadata components for registering an ambient solar wind model in the database. This is an example of a standard model framework consisting of a coronal and a heliospheric model. We note that the model chain, which consists of both the coronal and heliospheric domains, is surrounded by the orange boundaries. A more detailed description of the individual components is provided in the boxes below. nent describes each model and stores the model domain, simulation type, and version information.
Summary The Ambient Solar Wind Validation Team embedded in the Committee on Space Research (COSPAR) -International Space Weather Action Teams (ISWAT) initiative was formed to support the space weather community to maintain up-to-date solar wind model validation and track progress over time. This study presents our first steps towards developing an open platform for validating solar wind forecasting models. We propose a metadata architecture consisting of seven components to support a continuous, transparent, and reproducible validation of solar wind models. Next, we recommend community-wide forecasting goals, including the forecast of the temporal evolution of the solar wind and abrupt changes from slow to fast solar wind, as well as the forecast of the magnetic polarity and magnetic boundary crossings.
Acknowledgments The authors thank all contributors to the  COSPAR ISWAT initiative , which supported this research effort. The authors acknowledge the following organizations and programs: M. A. R. , and C. M. , acknowledge the  Austrian Science Fund (FWF) :  P34437 ,  J4160-N27 ,  P31659 ,  P31521 ; K. M. acknowledges support by the  NASA   HGI program  (#  80HQTR19T0028 ), the  NASA  cooperative agreement  NNG11PL10A  and  80NSSC21M0180 ; J. J. G. A. acknowledges to Investigadores por Mexico- CONACYT  ( CONACYT Fellow ),  CONACYT LN 315829 (2021 ) and  CONACYT-AEM 2017-01-292684  grants. S. D. acknowledges support from the argentianean grants  PICT-2019-02754  ( FONCyT-ANPCyT ) and UBACyT-20020190100247BA (UBA). The authors thank two anonymous reviewers and the editor for their constructive criticism that improved the presentation of our results and made the content more appealing to a broader audience.
Finally, the proposed approach actually defines a family of MRA-derived methods with the multilinear regression algorithm as a free parameter. While LSE_CI for instance selects edges based on a direct exploitation of residual variance CIs, other algorithms perform model selection such as LASSO or STEP-xx. There are many other regression methods that we have not tested, which might provide specific advantages depending on the dataset, or the specific interest or requirements of the researchers. as a function of the perturbation intensity in the absence of noise. We note that points A and C are not symmetrical with respect to B although symmetrical perturbations were applied. (b) The impact of perturbations at different intensities in the presence of Gaussian noise at 0. 1%, 0. 5%, and 1%. Black vertical lines indicate the error standard deviation.
The proposed method avail a feature selection technique, Mutual information (MI), to acquire the most relevant feature set regarding the output class (i. e. severity). Moreover, to differentiate each class in the multi-class data, OvRCL iteratively runs ML algorithms as binary classifiers in an ensemble framework to significantly ameliorate classification performance. In our experiments, we use four classifiers, namely, the k-Nearest Neighbors (k-NN), Support Vector Machines (SVM), Random Forest (RF), and Bagging classifier, to get the consensus. Analysis was done using a real crash dataset obtained from an open data source of Leeds city council. A four-year crash data (2015-2018) is used for training and the OvRCL is tested on the 2019 data. Moreover, to validate the performance of OvRCL, this study also utilizes two more datasets with high-class imbalance.
Recently,  (Iranitalab & Khattak, 2017)  employ clustering in conjunction with classification to further improve accuracy, while  (Katanalp & Eren, 2020)  examine features that are well suited for severity prediction. However, these studies have relatively low performance in predicting the severity on an imbalanced dataset. Furthermore, to the best of our knowledge, there exists no literature where the combination of multiple techniques, particularly those suited for identifying hard-to-detect multiple classes has been studied for crash severity prediction. Motivated by the above discussion, this paper presents a new approach for predicting the crash severity level by combining multiple ideas from feature selection, multi-class classification, and ensemble learning. These concepts are merged uniquely in the proposed model to elevate the classification performance on an unbalanced dataset.
These models were selected as they have been widely used in the literature on crash severity prediction and show good results on a variety of datasets  (Haleem et al. , 2015; Iranitalab & Khattak, 2017; Kabeer, 2016; Kannojiya et al. , 2020; Yan et al. , 2021; Z. Yang et al. , 2022). However, OvRCL is a framework in which the chosen classifiers are not determinant of the results. OvRCL allows for the unique and iterative identification of classes while each classifier employs an OvR method for every individual class. Fig. 1  illustrates the abstract view of the main contribution of this study. The rest of the paper is organized as follows: Section 2 presents the literature review and gives a summary of the related work. In Section 3, we briefly describe the various classifiers used in our ensemble approach and detail the proposed OvR consensus learning model.
They also suggested the characteristics that are influential in making any difference in the severity level. Moreover,  (Xie et al. , 2009)  compared the performance of traditional Ordered Probit and Bayesian inference Ordered Probit (BOP) models in driver's injury severity, and it was shown that the BOP model works better when the size of the dataset is small. However, when the dataset size increases, BOP's performance decreases. To model and categorize the data, k-NN and C-means clustering were respectively adopted  (Lv et al. , 2009). This work shows that using a clustering technique in combination with classifiers can help increase performancean idea also used by later authors. The study analyzes traffic data and suggests crash-causing conditions. (Kim et al. , 2010)  presented the probabilistic analysis of the severity of pedestrians in pedestrian-vehicle crashes by using the Mixed Logit model.
For instance,  (Ijaz et al. , 2021)  presented a comparative study of DT, Decision jungle, and RF for severity prediction involving crashes of three-wheeled motorized rickshaws. According to the results from a stratified 10-fold cross-validation, the decision jungle surpassed the DT and RF with an overall accuracy of 83. 7%. The authors also used Spearman correlation analysis to determine that lightning conditions, crashes involving young drivers (between 20 and 30 years), crashes on highways with high-speed limits, and shiny weather conditions result in more severe crashes. In addition to this, a comparative study of tree-based and non-parametric models for the prediction of severity in single-vehicle crashes was given by  (Yan et al. , 2021). This study uses five crash severity sub-datasets and found that in each dataset, urban freeways are a decisive factor that causes crashes, whereas rural freeways are more closely associated with more serious crashes.
Furthermore, DT, RF, and Gradient Boosted Trees were utilized by  (Amini et al. , 2022)  for the development of a hybrid framework incorporating explainable AI, predictive analytics, and heuristic optimization methods. The proposed methodology aims to examine and explain the risk variables for injury severity in automobile accidents. Apart from tree-based approaches,  (Hou et al. , 2022)  investigates random parameters logit models for out-of-sample prediction, quantifying marginal effects, and analyzing temporal instability for crash severity. Besides classical ML models,  (Rahim & Hassan, 2021)  applied Convolutional Neural Network (CNN) with customized loss function for the prediction of crash severity. In this study, the dataset is first transformed into images using t-SNE and the convex hull algorithm.
The proposed model also shows higher precision compared to other state-of-the-art models. Moreover, the consensus learning technique has also been used by  (Liu et al. , 2021; Tang et al. , 2018)  for clustering problems, and shows good results. Additionally,  (Hussain & Qaisar, 2022)  have demonstrated that using OvR ensemble learning can significantly enhance the results. The authors employed the scheme on epileptic seizure classification and showed boosted classification accuracy, particularly for the multi-class classification problem. This forms the basis of our work to implement feature selection, and consensus learning for individual-class identification from difficult-to-detect classes in an imbalanced dataset for crash severity prediction.
Methodology In this study, an ensemble of four classical classifiers is used to find a consensus of classification labels. Moreover, we also compute the discriminatory power of each feature in the crash prediction dataset. This enables the classifiers to use a subset of the feature that helps in the classification process with the added advantage of reducing the size of the data, thereby improving its efficiency. This section describes the feature selection strategy, the classifiers used in our approach, and the proposed approach of using OvRCL for achieving consensus-based class prediction.
Notations Let X denote the data matrix which is divided into X train and X test to represent the train and test data, respectively. N is the number of instances in X and F is the set of features. Let f i be a single feature from F, and S be the set of selected features whereas S ′ be the set of non-selected features, (i. e. , S ′ = F -S). We denote X S train to represent the training dataset with only the selected features while the corresponding test dataset is represented by X S test. C is the set of labels containing all the  Predicts the severity of crashes along with the determination of factors responsible for severe crashes. classes {c 1 , ⋯, c K }, where c j denotes the single class and K is the total number of classes (not to be confused with k used for nearest neighbors in k-NN).
(  6 ) end for returnŷ multi-class classification by repeatedly exploiting binary classification as it divides the multi-class data into multiple binary classification models. A binary classifier (CL) is then trained on each model and the final prediction is made based on the highest score. Instead of executing OvR for complete classification and then obtaining the majority vote, OvRCL performs consensus voting individually at the class level. The classifiers are better able to perform for difficult-to-detect classes since the objective is to distinguish only one class from the others in an OvR scenario. For a dataset with K classes, an algorithm is iterated for j = 1⋯ K times, such that c j = 1 if CL(x i ) = j, and 2 otherwise ∀ i = 1⋯N, where x i is an instance of X having N number of instances.
The label for this instance will only be updated if the number of classifiers predicting the label to be k is greater than the number of classifiers predicting the label to be j. Let the total number of classifiers used in the ensemble learning be P and the number of classifiers predicting the label as j be denoted as p j , then the label of an instances x i may only be updated if p k P > pj P. The overall algorithm is shown in Fig. 2. The MI score for feature selection is computed on the training dataset and the same selected feature set is then used for the test data. Thereafter, each classifier is trained on the training dataset using the one-vsrest strategy and the OvRCL algorithm is used for the final prediction. The following shows the algorithm of the proposed OvRCL algorithm.
Prediction methods A variety of classification techniques can be used to predict the label  (Ashraf et al. , 2022; Bano & Hussain, 2021; Hameed et al. , 2020; Si et al. , 2022; Zelenkov & Volodarskiy, 2021). These models are trained on the training data and predict the labels on the test data. This research implements consensus-based learning by employing k-NN, SVM, RF, and Bagging classifiers because they have been extensively employed in the literature on crash severity prediction on a variety of datasets and show comparatively good results  (Haleem et al. , 2015; Iranitalab & Khattak, 2017; Kabeer, 2016; Kannojiya et al. , 2020; Yan et al. , 2021; Yang et al. , 2022). We briefly describe these classifiers below.
Dataset description The experimental setup used in this paper is divided into two parts: 1. The first part is a detailed case study used to evaluate the important features that impact crash severity prediction and well as a qualitative analysis for policy decision making. Preprocessing is the important step to remove features that may not be helpful in determining the information of interest. Therefore, all the datasets are cleaned by removing the spatial and temporal features as they are not relevant for crash severity prediction. Summary of the Leeds accidents, which is used for a detailed case study, is given below. We remove features such as Reference Number, Grid Ref: Easting, Grid Ref: Northing, crash Date, Time, and Age of Casualty. The remaining data contains 8 features in Leeds accidents, 9 features in bicycle crashes, and 15 features in Manchester accidents.