dataesr/scientific-paragraphs-categorization huggingface dataset:
WISC-V-defined strength (or weakness) was retained for a child when the difference between one of its subtest-scaled scores and the average of all its subtest-scaled scores was more than the 90th centile (or < 10th) of the distribution of this difference in the standardization population. We finally selected 36 non-redundant quantitative age-standardized neuropsychological variables from our routine set. All but two variables (NEPSY-II route finding and visuomotor precision accuracy were converted to ordinal scale variables) were collected with standard deviation (population-based age-related calibration data provided with each neuropsychological test). A stratification based on functional domains is provided in Table  1. Following DCD recommendations,  13  we considered the psychometrics from the WISC-V associated with visuospatial and fine motor variables (a + b) to be the reference set of variables for the study (27 of the 36 variables).
The number of beneficiary parents interviewed differed for each pilot (see Annex B3), reflecting the different scales of the pilot. For the first pilot, for instance, all 240 beneficiary parents were targeted to be interviewed at baseline (before the start of the pilot), at midline (during program implementation) and at endline (after the program ended). The baseline survey aimed at gathering beneficiaries' characteristics at the beginning of the pilot, including levels of need. The midline collected data on beneficiaries' feedback about the implementation of the program and rates of non-attendance. The endline checked beneficiaries' learning and collected overall feedback from the participants. In addition, non-beneficiaries were also interviewed at baseline to verify the program reached the farmers who needed the intervention the most.
Methods : Two reviewers searched for the effect of stress in BMS and published on five main databases and three from the grey literature. Various questionnaires and biomarkers were analysed. Of the 2489 selected articles, 30 met the inclusion criteria. Studies englobed questionnaires, such as Perceived Stress Questionnaire, Lipp Stress Symptoms Inventory, Holmes-Rahe scale, Depression, Anxiety, and Stress Scale (DASS-21), Recent Experience Test; and various biomarkers, such as cortisol, opiorphin, IgA, α-amylase and interleukins. Results: In all studies with questionnaires, stress was significantly increased in the BMS group vs. control. Patients with BMS presented 25. 73% higher cortisol levels, 28. 17% higher IgA levels and 40. 62% higher α-amylase levels than controls. Metaanalysis found that BMS subjects presented 3.
| Information sources and search Detailed individual search strategies were developed, in English, for each bibliographic electronic database: EMBASE, LIVIVO, PubMed (Medline), Scopus and Web of Science. A grey literature search was performed on Google Scholar, Open Grey, ProQuest and Clinical trial. All database searches were conducted from the starting coverage date through 12 January 2022. More information on the search strategies is provided in Appendix 1. (which can be found online). Furthermore, the authors hand-searched the reference lists of the selected articles for any additional references that might have been missed in the database searches. All references were managed, and the duplicated hits were removed by a reference manager software (EndNote X7® Basic-Thomson Reuters).
| Study selection and data collection process This part followed a two-phase process. In phase one, two authors (A. L. P. and A. G. D. S) independently evaluated the titles and abstracts of all identified electronic database citations. In phase two, the same authors evaluated full-text data. They independently screened papers on phases 1 and 2, applied the eligibility criteria, collected key information from the selected studies and crosschecked the information. The final selection was based solely on full-text assessment of the studies. When disagreement appeared, a third author (Y. B. ) was involved to make a final decision about the inclusion or exclusion of studies. For each of the included studies, the following items were recorded: author(s), year of publication, country, sample size, demographic features of the sample (n, mean age and standard deviation, percentage of women), BMS diagnostic criteria, BMS subtype, Stress Questionnaire or biomarker, results, and main conclusions.
| Synthesis of results Statistical pooling of data using meta-analysis was planned whenever trials were considered combinable and relatively homogeneous in relation to design, interventions and outcomes. Heterogeneity within studies was evaluated either by considering clinical (differences about participants, type of interventions and results), methodological (design, and risk of bias) and statistical characteristics (effect of studies) or by using inconsistency indexes (I 2 ). 66  quantitative synthesis was appropriate, a method of mean difference effect meta-analysis was performed using RevMan 5. 3 and Heterogeneity assessed using the Cochran Q test and I 2 statistics. Regarding the analysis model, choice of fixed or random effect was based on heterogeneity values. Heterogeneity was calculated by I 2 , and a value greater than 50% was considered an indicator of substantial heterogeneity between studies, and the preference given to a random effect.
| Study Selection The initial database search identified 2489 studies. After eliminating duplicated hits, 1182 studies remained; 1105 of them were excluded after title and abstract revision, resulting in a final number of 77 articles. Furthermore, 213 studies were found with Google Scholar, 00 with OpenGrey and 03 with ProQuest. None of them were selected for full-text reading. One additional study was selected from handsearch of the references lists of the included studies, and 02 studies were considered based on experts' suggestions. Thus, 80 studies became part of phase 2. During phase 2, a total of 50 studies were excluded (reasons for exclusion may be found in Appendix 2). Thirty studies were included for qualitative and quantitative synthesis. A flowchart of the process of identification, inclusion and exclusion of studies is shown in Figure  1.
| Heterogeneity of methods An important limitation of the included studies was the lack of universal measure units for the biomarkers, and the lack of highly validated questionnaires. For instance, different units for biomarkers were used as μg/dL, nmoL/L, pg/mL, ng/mL and mg/mL. In this meta-analysis, the results were transformed in nmoL/L for cortisol, in ng/mL for opiorphin, mg/mL for IgA, in kU/L for alpha-amylase and pg/mL for interleukins. For the questionnaires, seven different types of questionnaires were used to evaluate stress, which may be difficult to validate the results in a sizable population by means of a meta-analysis. Additionally, some authors refused to send us the exact values of mean and standard deviation for the biomarkers used, which complicates data extrapolation and conduct of a low heterogeneity meta-analysis.
Author Year Country Study design Sample groups (n), Mean age (SD), % fem BMS diagnostic criteria BMS type Stress questionnaire or biomarker Extra data Results Main conclusion n o i t a EMBASE n=75 BMS group showed higher levels of cortisol compared to the control group with statistically significant differences (p <. 05) PUBMED n=694 WEB OF SCIENCE n=485 Records identified through database searching SCOPUS n=1132 (n=2489) BMS group showed significant higher salivary cortisol levels at each time of day sampled IL-8 pro-inflammatory chemokine is increased in the plasma of BMS patients LIVIVO n=103 c i f i t n e d I 0. 361 (±0. 47) for BMS and 0. 152 (±0. 23) μg/dL for controls Records after duplicates removed (n=1182) Records screened from databases OpenGrey (n=0) In the diurnal analysis, BMS cortisol levels were 1.
Is burning mouth syndrome associated with stress? A meta-analysis. J Oral Rehabil. 2023;00:1-37. doi:10. 1111/ joor. 13536 ("Burning mouth syndrome" OR "Burning mouth syndromes" OR glossalgia OR stomatodynia) AND (stress OR opiorphin OR IN TITLE: Google Scholar tive association between BMS and stress, where more stress factors opiorfhin OR opiorphine OR opiorfhine OR sialorphin OR SMR pentapeptide OR oxytocin OR oxytocine OR BDNF OR A p p e Database OpenGrey brain-derived neurotrophic factor OR brain derived neurotrophic factor OR cortisol OR proinflammatory cytokines OR alfha-were evidenced in questionnaire-based studies, and higher cortisol, amylases OR α-amylase) filetype:pdf α-amylase, IgA and IL-8 biomarker levels were found in BMS subjects as compared to healthy controls. No differences were found for opi-orphin, IL-1β, IL-2, IL-4, IL-6, IL-8, IL-10 and TNF-α.
These defects can sometimes be found in aeronautical parts like turbine disks and blades, and has received much less attention in mechanical domain. Analysing all the features that might affect fatigue life requires a large number of samples to be tested which can be extremely costly. Therefore, a plausible approach is to generate synthetic microstructures that are very close to reality which can be simulated numerically to create a large database of mechanical response to the presence of defects, their morphology and spatial distribution. Present work focuses on analysing the effects of defect population in a naturally isotropic material inconel 100 under cyclic loads where the granular characteristics of all tested samples are similar. In such a case, synthetic microstructures can be generated by distributing the defects in a homogeneous material space according to a pattern similar to real defects.
With tools like Ripley's K-function, second order properties of point pattern can be measured: the points in our context are centroids of defect volumes  [21]. Neither many researchers have considered SPP analysis to estimate the 3D spatial characterization of defects nor to generate numerical microstructures using the same  [14, 22]. In the case of clustered defects, regular shapes cannot be assumed for defects since contributions of various features in degrading material's performance is merely unknown. Hence, a deep learning strategy called Generative Adversarial Networks (GANs) and Convolutional Neural Networks (CNNs) are integrated together to recreate realistic synthetic defects that can be placed via stochastic process defined by SPP in material space. GANs are a very recent development in the field of deep learning that can learn to create data that doesn't exist in the database  [23, 24].
Data acquisition For the entire process, 4 reference samples of IN100 were usedwhich were tomographed using Nikon XT H 450. XCT images of these specimens were processed and the defects were segmented to create binary masks. Each connected defect volumes were labelled separately such that they are identified and accessible in the segmented volumes. The resolution of each voxel was (25 µm) 3. For the training of GAN and CNN, the defect volumes were cropped and rigorous augmentation techniques were applied to increase the size of database via random rotation, flip etc. Finally around 1200 pores and 1200 shrinkages were resized to a shape of 32 × 32 × 32 pixels for pores and 64 × 64 × 32 pixels for shrinkages. Pixel values of the dataset were normalized from [0,255] to [0,1].
Data were acquired using a National Instruments PXIe-6358 acquisition card. The sampling frequency was f s = 150 kHz and the acquisition time was set to 30 s, which is five orders of magnitude larger than the long- est convective time, thus ensuring statistical convergence of the quantities presented herein. A sketch and a picture of the experimental setup with the near-field microphone array is shown in Fig. 1 , a picture of the HW installation is shown in Fig. 1. The reference system adopted for the present study is shown as well.
Paper organization The remainder of the paper is organized as follows. Section 2 explains the methodology when conducting this review. Section 3 presents the research background. Section 4 displays the proposed works based on traditional machine learning while Section 5 browses deep learning-based solutions. Section 6 emphasizes the class imbalance problem and summarizes state of the art techniques used for credit card fraud detection. Section 7 details the testing metrics with regard to their relevance to the credit card fraud problem. It also presents the different datasets included in the literature, and the best results achieved in the reviewed works. Section 8 highlights open issues and sets out some research directions. Finally Section 9 concludes the paper. Table  1  shows the abbreviations used in this paper.
It mainly discusses works based on data mining techniques and classifies the literature based on range of factors, including publication year, publisher, method used, and research area (credit fraud, cryp-tocurrency fraud, insurance, financial). It is a comprehensive review of research focusing on the detection of financial fraud, credit card fraud, insurance fraud and other types of fraud. The data mining techniques used to detect financial frauds are described. Datasets and validation metrics were also specified. Finally, the pros and cons of each data mining technique are specified in the paper. However, the review is restricted to 'classification' techniques only, and does not describe the complete detection chain, which is important in order to gain an idea of the features used for detection.
Furthermore, the authors did not focus on credit card fraud and did not cover the class imbalance issue or feature engineering problems. There was also a lack of consideration for new trends such as big data. In  Lucas and Jurgovsky (2020) , the authors considered the challenges of data-driven credit card fraud detection. Specifically, they focused on the data imbalance problem and how to address the evolving behaviour issue (dataset drift) in state-of-the-art solutions. However, some papers were too old and no comparative analysis was provided. Popat and Chaudhary (  2018 ) is a short survey in which only seven related works were discussed based on their machine learning techniques. However, the authors did not discuss the details of each reviewed work. In  Kanika and Singla (2020) , the authors analysed deep learning based fraud detection techniques for online transactions.
The authors also provided information about the main datasets used and the results achieved. However, the scope of the study was limited to deep learning techniques. In  Mittal et al. (2020) , the authors classified credit card fraud and summarized the main features described in the reviewed studies. They also discussed credit card fraud detection. A shortlist of research directions was suggested but lacks the detail needed to better guide researchers. Table  2  shows a comparison of this study with the most recent surveys. After identifying the limitations of existing surveys, the following research objectives were formulated: To review recent research papers; To survey papers using deep learning techniques; To analyse the usefulness of disruptive technologies and new methods for the credit card fraud detection; To study the security concerns related to credit card fraud detection.
This step consists of checking the If-Then-Else rules for detecting defrauding patterns that have been already found by human investigators. These rules are kept confidential by the industry for security reasons  (Dal Pozzolo et al. , 2018)   1. The next step is Scoring Rules, followed the DDM. This model includes Investigators which requires human intervention to double-check transactions. The DDM uses historical transaction data that have been previously trained to build a classifier, or relies on another statistical model to detect fraudulent transactions. The scores of legal transactions are estimated. If the score overreaches a well-established threshold, the transaction is rejected and then forwarded to experts for further analysis. The DDM is usually trained from a labelled dataset and is fully automated.
Credit Card FDS Challenges Many challenges are faced when dealing with credit card fraud detection as depicted in Fig. 9. These issues are mainly classified into three categories: Data-related challenges: these challenges are related to the data required for building a robust credit card fraud detection system. They include: -Class imbalance; -Lack of real data; -Data drift/shift; -Data overlapping. Security-related challenges, which mainly involve privacy concerns. Deployment/Implementation challenges which are related to the efficiency of the final FDS and covers: -Distributed implementation. -Time complexity. Data-related challenges. FDS relies on the DDSM module which mainly requires a high-quality dataset to build effective detection models. However, many issues are faced by researchers when dealing with credit card datasets.
Class imbalance: An unbalanced or skewed dataset is a dataset in which the distribution of samples in the known categories is skewed (or biased). The distribution can range from a slight skew to a severe imbalance where the amount of raw data in the minority category is less than in the majority category. Unbalanced classifications present a challenge to predictive modelling. In fact, most machine learning algorithms used for classification assume an equal number of samples per class  (Sun et al. , 2009). Data imbalance results in predictive models with poor predictive performance, specifically for the minority class. The model will favor the majority class while the minority class is generally more relevant. Thus, the model is more exposed to classification mistakes for the minority class than the majority class.
Traditional machine learning algorithms require a balanced dataset  (Zheng, 2020). However, in practice, there are far fewer samples in the abnormal class compared to normal samples. In fraud detection specifically, the number of fraudulent transactions is very small compared to the number of legitimate transactions. An unbalanced dataset degrades the performance of most machine learning algorithms, such as Support Vector Machine (SVM) and random forests  (Dablain et al. , 2022). The techniques proposed to solve this problem go in two directions, either adapting the algorithm to make it powerful enough to handle unbalanced data sets, or solving the unbalanced data set using pre-process sampling methods  (Lucas and Jurgovsky, 2020). Lack of real data: This challenge is also referred to as the unavailability of sufficient labelled data.
In the context of credit card fraud detec-tion, fraudulent and non-fraudulent transactions usually overlap because fraudsters mimic the behaviour of real cardholders in order to fool the FDS. Most research dealing with the data overlap problem considers it to be similar to the class imbalance problem  (Li et al. , 2021; Vuttipittayamongkol and Elyan, 2020). It was shown in  Denil and Trappenberg (2010)  that class imbalance and data overlap have interdependent effects on classifier performance. Moreover, the same study showed that, unlike the class imbalance problem, the data overlap causes a linear drop in the classifier performance. The most common solution reported in the literature to overcome the class imbalance and overlap problems consists of three main steps. The first step involves dividing the original dataset into an overlapping subset and a non-overlapping subset.
In  Yang et al. (2019) , the authors proposed a federated learning framework based on behavioral features. This model allows banks to train a fraud detection model while distributing training data over their local storage. Afterward, a common detection system is constructed by combining the updates that were computed locally by the local detection models. Therefore, involved banks can benefit from the shared model without disseminating their local dataset thus protecting sensitive cardholder data. Furthermore, it is important to devise protocols that prevent fraudsters from compromising the identity of users. For instance,  Yang et al. (2021)  discusses the interesting problem of the detection of identity fraud in cyber-physical systems (CPS) with clients having limited resources. The authentication between two clients is delegated to and proxied by a trusted authentication server.
Bayesian approach The Bayesian approach emphasizes the use of statistics to build models and uses probabilistic inference to predict outcomes. Bayesian ML algorithms include Hidden Markov chains, Naive Bayes (NB), Linear Regression (linR), Logistic Regression (logR), Gradient Boosting (GBT), Adaboost, etc. A Naive Bayesian model is simple and easy to construct and is well-suited for massive datasets. It is well established that NB outperforms several highly sophisti-cated classification models. It has been widely used in predicting credit card fraud, for example,  (Mohammed et al. , 2018; Thennakoon et al. , 2019; Baabdullah et al. , 2020). GBT and AdaBoosting Algorithm are also useful when dealing with massive data. Boosting is an ensemble learning technique that integrates the predictive power of many base estimators to improve the robustness of the prediction models.
Unsupervised learning models the regularities in raw data, and could be used for anomaly detection by assuming that genuine transactions are more frequent than fraudulent ones in the test data. Though largely tested for credit card fraud detection, both KNN and SVM are computationally expensive and may show reduced performance in detecting credit card fraud for large datasets. One of the unsupervised analogy-based solutions suggested in the literature to detect fraudulent transactions is the use of recommender systems. A recommender system, also known as a recommendation system or engine, is an information filtering system aiming at predicting the preference/rating that an end user may assign to an entity  (Shapira, 2015). This type of system was originally designed to help recommend items to users and is thus useful in several contexts, such as social media platforms and open web content recommenders.
Connectionist approach Models in this category are mainly Artificial Neural Network (ANNs) (also known as NNs). An ANN is a model inspired by the biological neural networks of the human brain. This approach includes models such as ANN, reinforcement learning, and DL. In Connectionism, an input may have an output; but the path taken to get to that output is hidden. ANN comprises an interconnected set of artificial neurons. Generally, an ANN is adaptive, i. e. it changes its internal structure based on the external or internal information flowing through the network structure during the learning phase. Modern neural networks are nonlinear statistical data modelling tools. They are used to model complex associations between inputs and outputs or to discover hidden patterns in datasets  (Daniel, 2013).
, 2014). They demonstrated that periodic features increase savings (see Section 7) by 13% and increase performance by more than 200%. Wang et al. (  2018 ) presents a detection system based on Back Propagation Neural Network (BPNN). The model relies on swarm intelligence namely the whale population optimal solution  (Mirjalili and Lewis, 2016) , to find the initial weights and threshold of the neural network training model. The used neural network contains 2 layers of input and 2 layers of output with 20 hidden layers and it has been tested on 500 samples of a European dataset. The whale algorithm is used to find the initial optimal values of the model while the BPNN is used to correct and adjust these values in order to output the optimal model parameters. This technique solves some problems related to BPNN, such as network defects, slow convergence rate, and poor system stability.