dataesr/scientific-paragraphs-categorization huggingface dataset:
In this paper, we provide an in-depth and comprehensive summary of intelligent ore recognition. We summarize three types of taxonomies. Based on this, we visualize the relevant domain literature and perform a trend analysis using keyword detection in order to better explore the trends in the field. Some suggestions are made for possible future research directions. It is our intention in this paper to provide guidance to researchers in the fields of computing and earth sciences involved in the study of intelligent mineral identification. Due to the limitation of the scope of the study, the way to preprocess the dataset, the different scenarios, scientific questions, data, and applications corresponding to different objectives and others are not fully analyzed and await to be completed in the future.
In this paper, artificial-intelligence-based mineral identification models are classified into three categories. (1) Artificial neural network. Mineral identification models based on artificial neural networks are accurate and have a potential advantage over other methods when, for example, Raman spectroscopy datasets are used for mineral identification, without the need to remove fluorescence. However, artificial neural networks require too much mineral expertise and experience to avoid overtraining and undertraining. (2) Machine learning. In this paper, machine learning is divided into statistical-based machine learning and rule-based machine learning. The model is given the ability to identify minerals in the process of training the model. In the process of training the model, certain rules are adopted to improve efficiency by influencing the training of the model i.
• In this paper, artificial-intelligence-based mineral identification models are classified into three categories. (1) Artificial neural network. Mineral identification models based on artificial neural networks are accurate and have a potential advantage over other methods when, for example, Raman spectroscopy datasets are used for mineral identification, without the need to remove fluorescence. However, artificial neural networks require too much mineral expertise and experience to avoid overtraining and undertraining. (2) Machine learning. In this paper, machine learning is divided into statistical-based machine learning and rule-based machine learning. The model is given the ability to identify minerals in the process of training the model. In the process of training the model, certain rules are adopted to improve efficiency by influencing the training of the model i.
4 Thus, since decades ago, there has been an interest in providing any help developers can use to optimize and accelerate their work (while keeping the same quality standards) is valuable. 5, 6 or instance, software engineers can resort to information available online, including technical knowledge of a variety of languages and algorithms together with source code examples and libraries for reuse. A number of question and answer websites specific to the software development problem (like StackOverflow) or code search platforms are populated and curated in a community effort. 7 hile this is helpful, it can be complemented with a proactive action and an effort to filter, comprehend, adapt and apply all the information available to both prevent the risk of ending up in a situation of information overload and to save time.
At ICSE'06, Boehm predicted a new kind of developer-helping systems for 2020 as "that provide feedback to developers based on domain knowledge, programming knowledge, systems engineering knowledge, or management knowledge. "  14 he description of previous bot systems is almost inline with these expectations but still lacks one essential characteristic that Boehm described as "the use of knowledge. " Storey and Zagalsky  22  identify bots embedding knowledge as one specific type of bots. Thus, knowledge appear as an inflexion point, which opens the way for the study of a specific type of systems-knowledge-empowered DevBots-that we will call software assistants for software engineering.
Data extraction The data we extracted from each article are: 1. The source (journal or conference), the publication year, the title, and authors. This information was extracted directly from the website of the editor. 2. The name of the tool that was eventually either provided in the title of the paper, or introduced in the full content of the paper that. 3. The supported language(s). This information was either directly given by the authors, or inferred from the datasets used for the evaluation, or deduced from the screenshots and/or the solution environment. 4. The execution environment of the assistant that was described by the authors, such as a standalone application, or an Eclipse plugin. 5. A summary of the description and goal of the assistant as provided by the authors. 6. The datasources exploited by the assistant as presented in architecture diagrams, or detailed in the description of the solution.
We distinguished local datasources, for example, the local access to the IDE or the access to local versioning changes, from remote datasources, for example, the access to the StackOverflow database or the access to the continuous integration server of the project. 7. Whether the assistant is a recommender system (RS)-that is, it provides recommendations-for software engineering or not. The concept of recommender system for software engineering is based on the definition adopted F I G U R E 2 Exploration results by Robillard and Walker in their book:  54  [ … ] a software application that provides information items estimated to be valuable for a software engineering task in a given context. 8. If the assistant is a RS: The nature of the output, the explanation system, the confidence indicator, and the feedback system.
RESULTS: ANALYSIS AND CLASSIFICATION OF SOFTWARE ASSISTANTS This section presents the results of the data analysis we conducted on the dataset of primary studies, which are also available online. variation in the number per year. Although there is a maximum in 2012 with 18 papers and a minimum in 2013, 2015, 2016, and 2019 with 5 papers published, the number of submissions over time seems to be stable and there is continuous work. Figure  3B  shows the venues in which our selected papers have been published. It is worth noting that there is no dedicated venue for the topic of software assistants and that these papers have been published in general SE conferences. It is also interesting to see how most of these papers were published in the International Conference on Software Engineering (ICSE) followed by the Automated Software Engineering Conference (ASE), which are both very prestigious conferences.
We also extracted from each paper the environment in which the assistants work and the language (or syntax) that they support. The results can be found in the third and fourth columns of Table  6  and in Figure  5. A star ( * ) means that the assistant supports all languages supported by the IDE. The most popular environment by far is the Eclipse IDE with 52 out of the 112 assistants. In fact, out of all the assistants, 73 of them (65%) are part of an IDE. The second most popular environment is the Web Browser (27 out of 112-24%) and standalone applications (12 out of 112-11%). There is no clear correlation between the environments and the tasks toward which the assistants help. With respect to the languages supported, Java has a strong monopoly with 72 out of the 112 studied assistants (64%).
Nature of the output provided by the assistants We have analyzed what the nature of the output that the assistant provide to the users look like and have identified that these outputs are: textual, graphical, textual and graphical, and by means of annotations or highlights. These categories were proposed by Mens and Lozano  55  to describe the nature of source code recommender systems. This information was extracted manually from each paper included in our study by one author after reading the full content of the paper. All research papers included in our study features screenshots of the recommendation and/or a textual description of the output of the assistant. Thus, the categorization is based on the data provided by the authors in their papers, without personal interpretation required.
Internal validity Internal validity is defined as the extent to which the observed results are reliable and lack methodological errors. The search process relies on an algorithm which performs automated queries, and hence prevents any human error. The exclusion process is the most sensitive part of the exploration protocol. It was conducted manually, and resulted in the rejection of 5925 papers. In order to limit the subjectivity of this treatment, the exclusion was conducted by only one of the authors, following a rigorous protocol defined in Section 3. Additionally, before performing the exclusion, an IRR score was computed with another author to verify compliance with the criteria. The data extraction was also performed manually, and consisted in reading all the considered papers and filling in a table with the criteria defined in the protocol.
This shows that the literature focuses more on supporting software construction tasks rather than software design or maintenance tasks. Hence, we identify assistance systems for software design as an open line for research, to support a broader variety of tasks and participants within the software development life cycle. 64% (72 over 112) of the software assistants support Java, while most of the other identified programming languages are only supported by one assistant over 47. Therefore, 52 over 112 (81%) software assistants are mainly integrated in Eclipse IDE, or are not part of any specific development environment (respectively 46% and 35%). Challenge 1: Java and Eclipse predominance. One reason for this distribution could be the large availability of resources for Eclipse plugin development, Eclipse mainly being used as a Java IDE.
This technical motivation, however, might take the research away from the actual practices and needs of software engineers. Stack Overflow surveys from 2018 to 2020 show that Java is only the 5th most commonly used programming language, behind JavaScript, HTML/CSS or Python. Eclipse is the 8th most commonly used IDE, § behind Visual Studio or IntelliJ. Although these historical technologies remain important, new research work must lead innovation on these popular technologies and their inherent technical and human issues. Action point: In addition to assistants for the Java language and the Eclipse IDE, the community may bring existing or new assistance mechanisms to these increasingly popular environments and languages. Challenge 2: IDE versus standalone application. Most of the software assistants created as standalone application or websites have an alternative integrated to an IDE for the same task (see Table  6 ).
In order to perform analysis and provide recommendations, software assistants exploit data which they obtain locally and/or remotely. Local information represents the artifacts edited by software engineers (e. g. , the source code), the state and history of their IDE, and eventually other client-side information. Remote information describes the data created by co-workers, third-parties or the community (e. g. , source code, the content of company databases or model repositories) as well as answers to questions on social community websites (e. g. , Stack Overflow), or available documentation (e. g. , system requirements). Challenge 6: Data exploitation. Our results first show that, as expected, informer systems mainly use data from local sources (almost 77% of their datasources usage). However, it appears that passive recommender systems tend to exploit much more data from remote data sources than active recommender systems (57% versus 27%).
Based on the nature of this data, one may think that current passive recommender systems are likely to be more accurate and "intelligent" than the existing active recommender systems since they are exploiting external knowledge more heavily. One potential reason to justify this result could be the effort required to build that extra step that makes a passive recommender system into an active recommender system. Action point: Future work could investigate how to facilitate the integration and exploitation of community data, information and knowledge into the analysis algorithms of software assistants with the goal to empower them. Remote datasources rely on the availability of artifacts and knowledge created by the community. Aggregating these elements creates a global knowledge database which references many general concepts, sometimes called background knowledge.
At the same time, we think it is important to pay attention to the evaluation of this new breed of assistants. Rigorous research requires the replicability of the published work in order to compare and evaluate new solutions within the same field. This does not seem to be the case yet in this domain. Only 11 articles out of the 47 primary studies have made a dataset available to provide a benchmark for future comparisons. Finally, another important weakness that we have detected is that that only 43% of these user-centric systems conducted an evaluation with real users. With the results of this systematic mapping study, we would like to encourage researchers to address the challenges that we have identified related to software assistants for helping with software development related activities.
Data source usage by assistant type (y-axis indicates number of assistants) F I G U R E 11 Automation patterns 1097024x, 2023, 3, Downloaded from https://onlinelibrary. wiley. com/doi/10. 1002/spe. 3170 by French Atomic And Alternative Energy Commission, Wiley Online Library on [05/05/2023]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License Number of assistants implementing each automation pattern.
ACKNOWLEDGMENTS This work is partially supported by the  Spanish Government  under projects LOCOSS ( PID2020-114615RB-I00 ) and IPSCA ( PID2021-125527NB-I00 ); and  TRANSACT  (Grant Agreement No. 101007260 ) and  AIDOaRt  (Grant Agreement No. 101007350 ), which have received funding from the  ECSEL Joint Undertaking (JU). The JU receives support from the  European Union 's  Horizon 2020 Research and Innovation Programme and Sweden ,  Czech Republic, France, Netherlands, Finland, Germany, Poland, Austria, Spain, Belgium, Denmark , and  Norway.
10 F 13 1013 Data source usage by assistant type (y-axis indicates number of assistants) F I G U R E 11 Automation patterns 1097024x, 2023, 3, Downloaded from https://onlinelibrary. wiley. com/doi/10. 1002/spe. 3170 by French Atomic And Alternative Energy Commission, Wiley Online Library on [05/05/2023]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License Number of assistants implementing each automation pattern.
Another important trend is the increasing attention paid to the fact that for most university students, mathematics courses are service courses. This induces specific research needs that have been marginalized for a long time  (Hochmuth, 2020). Regarding domains, there is no doubt that, for decades, UME research has mainly focused on Calculus/Analysis for functions of one real variable and linear algebra, both major domains in the first university years. Here too we observe an evolution, even if still modest, with the development of research on more advanced mathematics, and also on the probability and statistics fields, so important today both scientifically and socially, and whose approach is so much transformed by technological advances. More globally, UME research should be deeply impacted by technological evolution, far beyond the attention it has paid for decades to mathematics professional software such as Maple or Mathematica.
A C T Deep Learning is providing new solutions for medical image segmentation problems and becomes a key point for future clinical application. The study of vascular structures is a challenging task due to the extremely small size of the vessel structure, low SNR, and varying contrast in medical image data. In this study, we present an end-to-end deep learning segmentation method relying on the integration of vessel enhancement filters inside a 3-D U-Net based architecture. In particular, the raw data used in the learning process -or used as input-is preprocessed using these filters. The use of vesselness filters can significantly improve the contrast in raw images; this step can also help to improve segmentation decision especially on bifurcations. 3-D U-Net, Dense U-Net and MultiRes U-Net are pitted against each other in the vessel segmentation task with the public IRCAD dataset.
Keshwani et al. [15]  developed a TopNet model, which is a multi-task DL architecture for vessel tree reconstruction. Kitrungrotsakul et al. [16]  proposed an automatic vessel segmentation approach that uses a multipathways DL network. The proposed framework learns a model for binary classification based on extracted training patches in three planes (sagittal, coronal, and transverse planes) of MRI images (Magnetic Resonance Imaging). Huang et al. [17]  used U-Net with variant Dice loss functions for robust liver vessel extraction. Agrawal et al. [18]  proposed a modified U-Net architectures by incorporating squeeze and excitation (SE) blocks and attention gates (AG) to segment vessel from retinal datasets for structures segmentation, the performance of their modified U-Net was promising, with a variation of 1 to 6% in the dice coefficient.
Gite et al. [19]  proposed a comparative study by choosing four benchmark neural network architectures: U-Net, FCN, SegNet, and U-Net++, the performance of these architectures is thoroughly explored and studied. Their results generated by FCN, U-Net, SegNet, and U-Net++ are evaluated on Shenzhen and Montgomery datasets. Comparison between the results shows that the U-Net++ surpasses other architectures by a considerable margin and achieved 98% accuracy. Deshpande et al. [20]  proposed an amproved Otsu and Kapur approach for white blood cells segmentation based on LebTLBO optimization for Leukemia detection, segmentation is done using Multilevel thresholding with Otsu and Kapur methods. To further optimize the segmentation performance, the Learning enthusiasm-based teaching-learning-based optimization (LebTLBO) algorithm is employed, their method shows a significant improvement in the performance measures with optimizing threshold algorithms and the LebTLBO technique.
Data generation The data for this experiment where obtained using the benchmark framework from  [22]. This framework allows to apply vesselness filters over a dataset and compute metrics in user defined regions of interest (ROI). In more details, the vesselness outputs are successively thresholded in the same way we would proceed to compute a Receiver Operating Characteristic (ROC) curve. For each threshold, Matthews Correlation Coefficient (MCC), Dice and usual metrics derived from the confusion matrix are evaluated. This framework enables an automated   Several vesselness filters (Jerman, Sato, Zhang, RORPO) where computed over two optimization schemes. A first version used the results of the benchmark presented in  [22]. In this version the optimization is set up to find the best parameters sets maximizing the mean MCC over the entire dataset.
In practice, for the organ ROI and for each tested parameter set   , the mean MCC for all binary volumes would be computed for each threshold. Then the best parameter   is selected as the parameter set with the highest mean MCC. In the second version, the optimization was done differently. For each parameter set, we chose to optimize the MCC per volume (  ). Then we chose the parameter set maximizing the mean over. In terms of clinical usage, the first strategy correspond to the operator setting thresholding values once for the whole dataset. In the second strategy, the vessels enhancement is fine tuned by the operator for each volume (see Table  3 ). [27]  Our custom 3-D U-Net model is composed of nine convolutional step blocks (see Fig. 2 ). Each convolutional step block contains two 3-D  convolutions of size 3 × 3 × 3 followed by the ReLU activation and a dropout layer with a rate of 25% between them to reduce overfitting.
Evaluation method We evaluated our models by randomly selecting the data (preprocessed or not and coming from the IRCAD database) into the training, validation and testing sets in the ratio of 70% -20% -10% respectively in a cross-validation manner (16 patients for training, 3 patients for validation, and 1 patient for test). For each method, all data are performed to collect experimental results with a different training set. The performance of each model was evaluated using the test set (one CT-scan following our distribution). Dice coefficient was used as an 𝐷𝑖𝑐𝑒 = 2 |𝐴 ∩ 𝐵| |𝐴| + |𝐵| , ( 1 ) where  is the segmented vessel image and  is the ground truth vessel mask. Models are designed for the largest possible input images that can fit the GPU memory. We used an NVIDIA GeForce GTX 1650 (12Gb) with 256 Gb of RAM, available on our local server.
Method Datasets Model Dice Long et al. ,  [31]  Raw data FCN 0. 624 Ronneberger et al. ,  [32]  Raw data 3-D U-Net 0. 723 Milletari et al. ,  [33]  Raw data V-Net 0. 689 Zhou et al. ,  [34]  Raw data U-Net++ 0. 817 Yan et al. ,  [35]  Raw data LVSNet 0. 904 Kitrungrotsakul et al. ,  [36]  Pre-processed DenseNet 0. 903 Kitrungrotsakul et al. ,  [36]  Pre-processed 3-D CNN 0. 879 Our method Pre-processed MultiRes U-Net 0. 913 Our method Pre-processed U-Net 0. 727 Our method Pre-processed Dense U-Net 0. 942 gives the best results compared to other models with or without vessel enhancement filters. Our deep learning with Jerman filter based method significantly outperforms methods without preprocessing in accuracy. We have observed that our approach demonstrates more consistency in terms of bifurcations of liver vessels.
Comparison with state-of-the-art methods In this section, we compare the proposed 3 U-Net models with other vessel segmentation models on processed and raw data, the results and dice are presented in Table  5. For this experiment, we listed most methods, which has previously been tested on IRCAD public datasets. Our proposed models, 3-D Dense U-Net and 3-D MultiRes U-Net, processed using Jerman filter achieved higher Dice coefficients of 0. 942, 0. 913 respectively. Though there is no significant difference between the 3-D U-Net results and the model of the article  [32] , Dice coefficients are 0. 727, 0. 723 respectively. To   We presented a combination of three Deep learning based segmentation with vesselness filters to extract liver vessel from abdominal CT scans. By introducing a vesselness filters into a 3D U-Net architectures, the proposed approach shows significant improvement segmentation results with the reputed IRCAD dataset especially using Jerman filter, which indicates that the proposed model can segment more complete vascular trees.