dataesr/scientific-paragraphs-categorization huggingface dataset:
Dataset Construction After watching the ewe activity monitoring video, the breeding staff selected 300 video clips with estrus climbing behavior from the video, with each clip ranging from 4 to 20 s in length. We used Python script to decompose video key frames of video clips, and take one frame every three frames to obtain 3640 original images. In these original images, we found that each image contains not only the crawling behavior of ewes in estrus, but also other behaviors of ewes not in estrus, such as feeding, standing, walking, lying down, etc. In order to expand the sample size of the dataset and improve the generalization ability of the model, we performed data enhancement processing on the dataset. Firstly, in view of the uncertainty of the location of ewe's climbing behavior in the shaded area, we performed horizontal flipping, horizontal moving, and vertical moving of the samples in the dataset.
Then, according to the complex and variable illumination characteristics of the shaded area, brightness enhancement and brightness reduction were carried out. Data enhancement processing is shown in Figure  3. After the abovementioned data enhancement processing, we finally obtained 5600 sample images of the dataset. We used the open-source labeling tool LabelImg to label the dataset and generate the corresponding xml file manually. After randomly dividing the obtained 5600 images according to the ratio of training, validation, and testing of 7:2:1  [38] , there were 3920 images in the training set, 1120 images in the validation set, and 560 images in the test set. The dataset labels were created as shown in Figure  4. We used the open-source labeling tool LabelImg to label the dataset and generate the corresponding xml file manually.
The prediction target mechanism of multi-scale feature fusion was added in the target detection layer. Three feature fusion techniques were employed to gather the target's location and semantic information; the detection is then performed on three distinct object detection layers. The picture was divided into grids of various sizes. Each grid was allocated three anchor boxes of varying sizes for bounding box prediction. The bounding box of the retrieved object has five predictions: x, y, w, h, and c. Where x, y is the target detection frame's center coordinates, c is the confidence level, w, h is the detection frame's width and height. Finally, the output tensors of the three target detection layers were merged, and the output is as shown in Equation (1): N × (3 × (13 × 13 + 26 × 26 + 52 × 52)) × (5 + M) = N × 10,647 × (5 + M) (1) According to YOLO v3, target detection is performed on the COCO dataset, and the number of categories in the dataset is 80.
The improved model has four target detection layers. Finally, the output tensors of the four target detection layers should be merged. For the input image, the output of the model is as shown in Equation (2): N × (3 × (13 × 13 + 26 × 26 + 52 × 52 + 104 × 104)) × (5 + M) = N × 43,095 × (5 + M) (2) In Equations (  1 ) and (  2 ), N is the number of input images and M denotes the total number of categories in the dataset. The number of dataset categories in this paper is 1, so M is 1. The improved model output is shown in Figure  6b.
Picture Resolution Maintains Aspect Ratio The resolution size of the dataset image we created is 2560 pixels × 1440 pixels, and the data-loading module of the model will adjust the resolution of the input image to 416 pixels × 416 pixels. Therefore, the resolution of the image will be adjusted during the training, verification, and testing of the model. Therefore, we add two residual unit to the last residual structure in the model, which is used to help the model extract more information about the deeper features of ewe estrus climbing behavior, thus increasing the model's detection precision. The number of residual units in the residual structure of our improved model is 1, 2, 8, 8, and 6, respectively. The optimized location of the residual structure unit is shown in Figure  6a  in Improvement 2.
Picture Resolution Maintains Aspect Ratio The resolution size of the dataset image we created is 2560 pixels × 1440 pixels, and the data-loading module of the model will adjust the resolution of the input image to 416 pixels × 416 pixels. Therefore, the resolution of the image will be adjusted during the training, verification, and testing of the model. If the resolution of the image is directly adjusted, it will not only cause serious distortion due to the stretching of the image, but also lead the model to extract the wrong features. In order to reduce the adverse effect of picture distortion on the model and enable the model to extract effective features, we redefine the adjusted picture resolution function in the data-loading module and adopt the method of maintaining the horizontal and vertical ratio of the picture, as shown in Equations (  3 )-(  6 ).
w 3 = w 2 × w 1 w 2 , h 1 h 2 min (3) h 3 = h 2 × w 1 w 2 , h 1 h 2 min (4) d w = (w 1 -w 3 )//2 (5) d h = (h 1 -h 3 )//2 (6) In Equations (  3 )-(  6 ), w 1 is the width of the image resolution required by the model, h 1 is the height of the image resolution required by the model, w 2 is the width of the image resolution of the dataset, h 2 is the height of the image resolution of the dataset, w 3 is the width of the picture adjusted to maintain the aspect ratio, h 3 is the height of the picture adjusted to maintain the aspect ratio, d w is the size of the pixels that need to be filled on the periphery of the image's width, d h is the size of the pixels that need to be filled on the periphery of the image's height. When we carry out the picture fill, the fill pixel size is gray RGB (128, 128, 128).
Re-Clustering of Anchor Boxes The YOLO v3 model obtains nine anchor boxes in the COCO dataset using the Kmeans algorithm. Anchor boxes are a set of values with fixed width and height values. Among the anchor boxes, the three sizes of 10 × 13, 16 × 30 and 33 × 23 correspond to the 52 × 52 target detection layer and are responsible for detecting small targets. Among them, the three sizes of anchor boxes, 30 × 61, 62 × 45 and 59 × 119, correspond to the 26 × 26 target detection layer and are responsible for detecting medium-sized targets. Among them, the three sizes of anchor boxes, 116 × 90, 156 × 198 and 373 × 326, correspond to the 13 × 13 target detection layer and are responsible for detecting large targets. width of the picture adjusted to maintain the aspect ratio, h3 is the height of the picture adjusted to maintain the aspect ratio, dw is the size of the pixels that need to be filled on the periphery of the image's width, dh is the size of the pixels that need to be filled on the periphery of the image's height.
Re-clustering of Anchor Boxes The YOLO v3 model obtains nine anchor boxes in the COCO dataset using the Kmeans algorithm. Anchor boxes are a set of values with fixed width and height values. Among the anchor boxes, the three sizes of 10×13, 16×30 and 33×23 correspond to the 52×52 target detection layer and are responsible for detecting small targets. Among them, The K-means algorithm is an unsupervised learning method that uses distance as a similarity indicator. First, the width and height of the anchor boxes are selected as the clustering objects. Then, the distance between each sample box and the clustering center is calculated to assign the sample boxes. Finally, constant iterations are performed to update the clustering centers. Since the K-means algorithm has a large randomness in the selection of the initial clustering centroids, this randomness will have a certain impact on the clustering results, leading to different results for each clustering, meaning the algorithm cannot determine the global optimal solution.
Therefore, we used the K-means++ algorithm instead of Kmeans algorithm, and the selection of initial clustering centroids is improved to make the clustering better. The goal of the clustering algorithm is to make the intersection ratio (IOU) between the anchor box and the labeled box as large as possible, and the objective function uses IOU as the measure. The objective function is shown in Equation  (7). f = min k ∑ p=1 n ∑ q=1 [1 -IOU] (7) In Equation  (7) , p is the cluster center, k is the number of cluster centroids, q is the target frame of the sample labels, and n is the number of samples in the dataset. The relationship between the number of clustering centers and the average intersection ratio of the K-means++ algorithm on the dataset of this paper is shown in Figure  8a. The horizontal coordinate is the number of anchor boxes K, and the vertical coordinate is the value of the average intersection ratio IOU.
The distribution of the dataset label boxes is shown in Figure  8b. The values of the cluster center coordinates we obtained are the normalized data. Therefore, the cluster center coordinate values need to be multiplied by the size of the model input image to get the final anchor box. The input image size of the model in this paper is 416 × 416 pixels. Therefore, the clustering effect is shown in Figure  8c. We rounded the obtained anchor box sizes and arranged them by size. Among them, the three sizes of anchor point boxes   1.
Test Platform and Parameter Setting Our model is on the same computer during training, validation, and testing. The computer was configured with an operating system of Windows 10, a GPU of GeForceRTX2080, a processor of Inter(R) Core(TM) i7-9700k CPU@3. 2 GHz, and a running memory of 32 G. Our model was built using the PyTorch deep learning framework (Facebook AI Research Institute, Menlo Park, California, USA), PyCharm development tool (JetBrains, Prague, Czech Republic), and implemented through the Python language. The computational framework uses CUDA version 10. 0 (NVIDIA Corporation, Santa Clara, CA, USA). The relevant parameters of the model are set as follows: the training sample batch size is 10, the initial learning rate is set to 0. 001, the weight decay is set to 0. 0005, the momentum decay is set to 0.
From Figure  11c , we can see that F1 has a high value when the confidence threshold of the model is 0. 5. In summary, we determined the optimal confidence threshold of the model to be 0. 5. At this point, the P of the model was 98. 56%, the R was 98. 04%, and the F1 value was 98%. Figure  11d  shows the model AP value of 99. 78% at a confidence threshold of 0. 5, and the mAP value of 99. 78% since the dataset category was 1.
2023a; Curtis-Lake et al. 2023). Here, we present new JWST observations of GN-z11 in the GOODS-N field. These observations have been conducted as part of the JWST Advanced Deep Extragalactic Survey (JADES). In this paper, we focus on the nine-band JWST/ NIRCam imaging, providing constraints on the SED, morphology, and large-scale environment of GN-z11. The JWST/NIRSpec observations are presented in a companion paper  (Bunker et al. 2023). The NIRSpec spectrum definitively measures a redshift, z spec = 10. 60, and provides a rich set of diagnostics of the physical properties of the galaxy. In the listing of JADES confirmed redshifts, GN-z11 is given the designation JADES-GN-z10-0. We present the details of the observations, data reduction, and photometry measurement in Section 2. We present a detailed analysis of the morphology of GN-z11 in Section 3, while we perform the SED analysis and present key results on the stellar populations in Section 4.
Because ForcePho runs on the individual exposures, it avoids issues of how mosaicking smooths the PSF or introduces covariance between pixels. We also analyze the mosaics. For this, we need an estimate of the PSF after image combination. We build the empirical PSF (ePSF) from bright stars in the NIRCam mosaic with the Photutils package  (Bradley et al. 2022). We first select bright stars from the public CANDELS catalog in the GOODS-N field  (Barro et al. 2019) , restricting to those with CLASS_STAR > 0. 75 and FLAGS = 0. Through visual inspection, we end up with 10 and 9 unsaturated stars that have the highest S/N in the individual SW (F090W, F115W, F150W, and F200W) and LW (F277W, F335M, F356W, F410M, and F444W) mosaics, respectively. The HST H-band magnitudes for these stars range from 19.
2 to 22. 4 AB mag. We use the EPSFBuilder module in Photutils to model ePSF from the stars, which follows the prescription of  Anderson & King (2000)  and  Anderson (2016). As expected, the resulting ePSF is mildly broader (see Figure  2 ) than the WebbPSF, which is oversampled and un-mosaicked. We then fit both single Sérsic and PS models to the combined mosaicked data using ProFit  (Robotham et al. 2017 ) and Lenstronomy  (Birrer & Amara 2018). Both ProFit and Lenstronomy are configurable codes that produce consistent results, similar to the commonly used GALFIT fitting software (e. g. ,  Kawinwanichakij et al. 2021; Robertson et al. 2023b ).
Acknowledgments We are grateful to the many people who worked for decades to turn JWST into a reality and in particular to our other colleagues on the NIRCam and NIRSpec instrument teams. We thank the referee for a constructive report that has helped to improve multiple aspects of the paper. All of the data presented in this paper were obtained from the  Mikulski Archive for Space Telescopes (MAST)  at the  Space Telescope Science Institute. The specific observations analyzed can be accessed via 10. 17909/247y-bk69.
Literature review This section offers a perspective of AI-based articles published in TF&SC and its earlier title named, Technology Forecasting. A deep dive into the articles published in TF&SC illustrates that AI has touched different aspects of human life, society, and business organizations. Broadly speaking, AI has equally disrupted organizations and individuals. At the organizational level, AI has transformed traditional manufacturing into intelligent and sustainable manufacturing  (Bag et al. , 2021; Chatterjee et al. , 2021b; Zeba et al. , 2021). The transformative implications of AI in manufacturing emphasize the role of intelligent agents, expert systems, big data analytics, blockchain, and IoT in the context of Industry 4. 0  (Culot et al. , 2020; Dwivedi et al. , 2022). AI supports organizations at all four stages of innovation, i.
e. , idea generation, screening of idea, experimentation, and lastly, development & commercialization of an idea  (Fredström et al. , 2022; Trocin et al. , 2021; Truong and Papagiannidis, 2022). Organizations have also started delegating their crucial functions, including decision-making, recruitment, and customer relationship management, to AI  (Allal-Chérif, 2022; Ashaari et al. , 2021; Chatterjee et al. , 2021a; Dwivedi et al. , 2023; Pietronudo et al. , 2022; Zeba et al. , 2021). Pietronudo et al. (2022)  mentioned that in the context of medical services, AI could anticipate problems and find ways to cope with unexpected situations. Likewise,  Chatterjee et al. (2021a)  documented that due to AI, organizations detect patterns in the voluminous data of consumers and provide them with the best possible solution to their queries.
(2016)  for a complete theoretical discussion and  Roberts et al. (2019)  for practical implementation. STM has been efficiently applied to uncover hidden topics within the information management literature  (Sharma et al. , 2021b)  and a corpus of articles assessing investors' attention  (Goodell et al. , 2022). The current study adapts a social exchange model proposed by  Nelson et al. (2017)  that explores knowledge diffusion among research disciplines. This model is adapted to assess the magnitude of knowledge exchange between published research concerning AI and other scholarly disciplines classified by research databases. As depicted in Fig. 3 , there is a reciprocity of knowledge flow between the other disciplines and research on AI published in TF&SC. This reciprocity of knowledge flow is assessable by analyzing the references and citations of articles  (Sharma et al.
, 2022). The overall knowledge inflow is estimated from the references used in all AI articles reviewed in this study. The knowledge inflows from various individual disciplines are used to measure the disciplinary impact on published research concerning AI. The overall knowledge outflow is estimated from the citations generated by the articles. Hence, the knowledge outflows to various disciplines represent the disciplinary impact of the published AI research. Past studies suggest that Scimago, Scopus, and Web of Science databases provide a disciplinary classification of research journals that may be used to assess knowledge inflows and outflows  (Sharma et al. , 2021a). This study has used the Scopus database's disciplinary classification to assess AI research's disciplinary influence.
Data and text preprocessing This study analyzes the overall intellectual evolution of AI in TF&SC from all the published research documents, including research articles and reviews. Fig. 4  depicts the data collection method to be used in the current study. All the keywords to extract the relevant papers on AI are carefully selected by reviewing the seminal papers that have contributed significantly to AI literature in different research contexts. Table  1  provides all the keywords with studies from which these are adapted. The keywords are grouped by logical operators (OR) to develop the search protocol for this study. The wildcard character (*) is used to select all combinations of the keywords, such as "*neural network*" will cover both "artificial neural network" as well as "neural networks".
This study has used the Scopus database as it provides more coverage of articles, as discovered in a previous study by  Singh et al. (2020)  that provided a bibliometric overview of TF&SC since its inception to 2020. The data collection steps of the current study are depicted in Fig. 4. The search protocol developed by the authors retrieved a total of 799 articles from the entire corpus of TF&SC. These articles were independently screened by two different co-authors, and non-relevant articles were filtered out, which resulted in the final document set of 608 articles exploring different aspects of AI. The text corpus for the STM-based topic modeling was organized by concatenating the title, abstract, and keywords of each research paper, which is a standard method for similar work  (Sharma et al.
, 2021b). The text preprocessing for topic modeling involved the removal of stop words, non-English characters, numbers, publisher information, and punctuation. The most frequent bi-grams and trigrams were discovered using an n-gram tokenizer developed by the authors using the ngram package in the R programming environment, which were concatenated to preserve their semantic association. This study used the STM package in R to perform the topic modeling  (Roberts et al. , 2019). The STM package supports evaluating multiple topic models with a varying number of topics and finding the optimal number of topics based upon a tradeoff between held-out likelihood and semantic coherence  (Roberts et al. , 2016; Sharma et al. , 2021b). Fig. 5  shows that a model of eight topics can be used for the corpus prepared from the final document set of 608 articles.
Practical implications for expanding AI scholarship From a practical perspective, this study provides a roadmap to practitioners, managers, and policy-makers regarding significant focus areas in AI. For example, this study reports that big-data analytics has become a key enabler for sustainable supply chains and circular economy. However, this study predicts that we may still observe a consistent rise in practical applications of AI-driven big-data analytics technologies in the future. Hence, we might reasonably anticipate practitioners and managers making data-driven decisions impacting the entire organization and other stakeholders. Considering the fact that AI has become a key driving force for the circular economy, practitioners and managers must develop an in-depth understanding of contemporary focus areas in AI from this timely research.
With a more advanced and comprehensive data analytics approach, this study demonstrates the extraction of latent topics from AI research literature corpus in a data-driven way by automatically digging deeper into the text literature without any subjective bias. The comprehensive review and synthesis of all the critical topics from the AI research published in TF&SC will serve as a gateway to the ongoing AI and big data analytics research streams as well as emerging streams of intellectual interest, such as the adoption of AI by consumers and AI-ML for healthcare. In this way, this study makes a significant contribution to the AI research community. Moreover, the editorial team and the policymakers may gain a significant overview of the key themes, which will enable them to design proactive interventions to advance AI knowledge through TF&SC special issues in the future.
Limitations and future research directions This study has certain inevitable limitations. First, this study only considers AI articles published in TF&SC that mostly focus on the societal implications of technological advancements. Hence, the findings of the current study may not be generalized in a broader sense. The results confine comprehensiveness as the paper only focuses on the conceptual and intellectual structure. A future study may provide a complete knowledge integration using scientometric analysis methods such as citation and co-citation analysis. Lastly, the study uses only the Scopus database for citation information and disciplinary classification for interdisciplinary assessments. Future researchers may use other disciplinary classification methods, such as Scimago and Web of Science, as suggested by other authors in the past  (Sarin et al.
The topics such as Topic 3-Machine Learning for Healthcare, Topic 4-Big Data Analytics for Circular Economy and Sustainable Supply Chain, Topic 5-Adoption of AI by Consumers, and Topic 8-AI for Decision Making and Risk Management are showing a rising trend over the years. These scholarly hotspots may be explored more by potential researchers in the future. Further, this study discovered that AI research contributes knowledge to multiple disciplines, such as Social Sciences, Engineering, Computer Science, and Mathematics, other than the main focus area, Business, Management, and Accounting. Moreover, AI research draws knowledge from various disciplines, enabling interdisciplinary knowledge exchange. The interdisciplinarity in knowledge exchange and key conceptual topics contribute to the profound impact on the theoretical advancement and practical accomplishment of AI-based systems for human amelioration and social betterment.
However, benchmark datasets can be developed and made publicly available so that the accuracy of various forecasting approaches can be compared (Xu et al. , 2021) Topic 3 -Machine Learning for • To enhance user acceptance of AI-based Healthcare healthcare devices, more studies are needed to assess the most significant predictors of the adoption of wearable medical devices with considering the technical differences across devices and cultural differences among users (Huarng et al. , 2022) • Future research work may explore further the effectiveness and efficiency of big data analytics in clinical decision- making, hospital data management, medical resource optimization, and pre- cision recommender systems for medi- cines (Weerasinghe et al. , 2022) Topic 4 -Big Data Analytics for • The potential applications of Circular Economy and Sustainable advancements in IoT, cloud computing, Supply Chain AI-ML, and blockchain may be explored in implementing digital supply chain twins across sectors (Kamble et al.