dataesr/scientific-paragraphs-categorization huggingface dataset:
(54) Hybrid LMRPEM Alg. 1 is used to estimate the parameters as it is the most efficient algorithm for all parameter estimation (see section 4. 3). γ ρ and γ ν are the same as given in Table  2. The recursive coefficient and differentiation order estimations are shown in figure 7. The commensurate order convergence is slow but very accurate towards the system true commensurate order ν = 0. 5. The coefficient convergence exhibit fairly slow convergence rate, especially for a 2 , however as it tends to its true value towards the end of the simulation. Therefore, the longer the dataset, the better the convergence to the true parameters. Finally, at the last iteration, the estimated model is expressed as the following: Ĝ(s) = 2. 519s 0. 502 + 1. 013 12. 66s 1. 005 + 2. 601s 0. 502 + 1 , an estimated model that has well converged to the true parameters such as predicted by hybrid LMRPEM Alg.
The LMRPEM algorithm proved to be consistent: parameter estimations are without bias with very low variance. Also, the longer the dataset, the better the convergence to the true parameters as fractional systems have long memory behaviour. Finally, a biological example was proposed: heat transfer in lungs is modeled on a human main bronchus by using thermal two-port network formalism with fractional models. Applying the hybrid LMRPEM method have provided consistent estimates without bias and very low variance. Research perspectives may include further studies regarding a method to tune the forgetting factor γ in a methodical way. The issue regarding computation time with long datasets should also be studied by taking into account the short memory principle as well as the frequency content of the analyzed signals.
Tintarev and Masthoff  [3]  discussed several non-personalized explanation styles such as popularitybased styles, e. g. , ''This movie is recommended to you because it is a popular movie''. The authors of  [13]  proposed another nonpersonalized, post-hoc approach that summarizes positive user reviews on items. The authors used natural language processing (NLP) and sentiment analysis to extract relevant features for explanations. Meanwhile, the users' reviews data needed to implement such an approach are not available for all types of items (e. g. , scientific papers, radio podcasts) and their quantity may also vary greatly between catalog items (e. g. , a US blockbuster as compared to a Swedish art house film). In the case of personalized explanations, the relations between the recommended items and those liked by users (user profile items) are usually explored to draw up the explanations, e.
g. , ''This movie is recommended to you because you have liked comedy movies''. Semantic web resources such as Linked Open Data (LOD) and knowledge graphs (KG) are useful tools for enhancing post-hoc recommendation explanations as they are capable of representing facts and domain knowledge in a formal, machine-readable way. Existing LOD-based explanation approaches rely mainly on the overlap features (i. e. , the KG's entities) between user liked items and the recommended ones. However, the main drawback of existing methods is that neither the KG's hierarchy nor the hierarchical relationships among entities are considered carefully, which may lose the inference power of knowledge graphs and lead to irrelevant or redundant explanations. To address these issues, we proposed in this paper a generic method that allows to exploit efficiently the entire entity hierarchy and to select the most relevant entities for explanation.
score(p, I u , I r ) = ( α n p,Iu |I u | + β n p,Ir |I r | ) * IDF (p) (1) After the ranking step, the top-k properties are used to generate natural language explanations based on a predefined template. As an example, the explanation for the recommended movie Memento to a user who liked Sherlock Holmes and The Prestige, can be (with k = ) is a specificity of the ExpLOD method. Indeed, Ex-pLOD was designed to provide compact explanations for a group of items. Assigning a bonus to properties describing multiple items in I r helps to favor explanations that rely on the same properties for different recommended items. The explanations are then factorized by sentences such as: ''We recommend movies m 1 and m 2 because they are romantic comedy films as most of the ones you liked''. The counterpart is that an item i r ∈ I r is not necessarily as good as it could have been if alone (i.
score(p b , I u , I r ) = ∑ p∈children(p b )∩(Pu∪Pr ) score(p, I u , I r ) * IDF (p b ) (2) Note that the same item may contribute multiple times to p b 's score if it is described by several children of p b. For instance, consider a user profile with two films set in USA; one is described as set in New York while the other is described as both set in New York and set in Washington; then the second film will have more impact on the scoring of the set in USA broader property than the first one as it will contribute twice in the sum of Eq. (  2 ). Note also that the score of the broader property not only takes into account its IDF value but also the IDF values of a subset of its childrenwhich might seem questionable -indeed Eq. (  2 ) can be rewritten as: score(p b , I u , I r ) = ∑ p∈children(p b )∩(Pu∪Pr ) ( α n p,Iu |I u | + β n p,Ir |I r | ) * IDF (p) * IDF (p b ) (3) The authors evaluated ExpLOD through an online user study on three datasets in different recommendation domains (movie, music and book).
Experimental protocol To conduct our user study, we deployed a movie recommendation web application implementing the proposed PEM model and the ExpLOD model. All source code implementing these approaches and the web application can be found at this GitHub repository. 4  For demonstration purpose, we also deployed a demo web application  5  illustrating the proposed PEM method. In a nutshell, each participant involved in the user study took the following steps (a two-stage evaluation): 1. Collection of user demographic data. We first asked the participant to fill a form to collect user's demographic data such as first/last name, gender and email. 2. Generation of recommendations and explanations. The participant was asked to select at least 5 movies from the database in order to build his/her profile (i.
Experimental design 4. 2. 1. Annotated item catalog To implement the web application for our user study, we chose the MovieTweetings dataset 6  as our item catalog, that compiles movie ratings contained in well-structured tweets on Twitter  [23]. It is a daily updated dataset and has been widely used in the recommender system community. In our experimentation we only used MovieTweetings to assemble a broad movie catalog containing 35,944 movies, and did not exploit the user ratings. As both ExpLOD and PEM require item properties from DBpedia for generating explanations, one needs first to map movie items into DBpedia entities. We applied the method described in  [24]  to create mappings, which consists in measuring the Levenshtein distance between movie titles and labels of DBpedia entities (rdfs:label).
g. , Pakistan Tehreek-e-Insaf, ''skos:broader'', Pakistan Tehreek-e-Insaf politicians, ''skos:broader'', Imran Khan, ''skos:broader'', Pakistan Tehreek-e-Insaf). We used a basic approach to get rid of those cycles by removing some of the 4, 084, 504 edges of this graph. The resulting DAG has 4, 080, 682 edges. To simplify the treatment of this graph, we linked its 61,017 distinct root nodes to a single broader node labeled ''Thing''. Note that our goal in this paper is to propose a fast and generic LOD-based approach providing a personalized explanation to users; in our case the movie benchmark is nothing more than a very convenient benchmark to test our approach. For reproducibility purpose, the movie mappings that we generated as well as the used local version of DBpedia category hierarchy are available in this public Zenodo  [26]  repository.
Offline metrics Using a rating dataset one could try to assess automatically the quality of the explanations that would be provided to a user based on the user's profile. Having no user-centric feedbacks, the assessment is limited, however some insights can be gained from such offline metrics. For instance, a method that often fails to provide any explanation or provides very uninformative ones is clearly not ideal. For the capacity of a method to produce explanations, we refer to the explainability precision metric  [28]. It represents typically the proportion of explainable items within the top-n recommendation list relative to the number of recommended items (i. e. , n) and is denoted as EPrec@n. Another aspect that we would like to consider here is the information content conveyed by the properties used in the explanation.
The efficiency of the sub-ontology optimization While the DBpedia knowledge graph contains 1,639,815 properties, only 22,720 (1. 38%) of them are describing one of the 18,983 movies of our benchmark dataset directly. Given this whole ontology and the set of movie annotations that we extracted, we then applied the method proposed in our previous work  [14]  to extract a sub-ontology that relating the movie domain. The resulting sub-ontology contains 28,424 (1. 73%) properties. Excluding from this sub-ontology those without any ancestors (including themselves) that directly annotate a movie allows us to reduce the property hierarchy to be considered, leading to a DAG of 28,016 (1. 70%) properties. For the ExpLOD model, the whole ontology was used when generating the explanations. Using the sub-ontology allowed us to develop a low-latency solution while dealing with the very large DBpedia property hierarchy.
A concrete comparison example In this subsection, we use a toy example to shed light on the differences between ExpLOD and PEM (Fig. 3 ). The recommended movie (Bitter Moon 12 ) has to be explained based on a user profile designed from three movies, The Intouchables, 13 Welcome to the Sticks 14 and Amélie. 15 The top-3 properties according to our proposed PEM method are French comedy films, Films shot in France and French-language films. None of those properties is in the top-3 properties ranked by ExpLOD. The first one is considered by ExpLOD but not ranked in the top-3 properties whereas the last two properties are not even considered since they are not part of the broader property set considered by ExpLOD. The top-3 properties selected by ExpLOD on this example are Film scores by composer, Films by director and Films by French directors.
Conclusions & perspectives The explanation of recommendations is becoming an important feature of recommender systems as it provides users with extra information and helps them make faster and better decisions. In this paper, we propose a generic, personalized post-hoc approach exploiting Linked Open Data. To be more specific, Linked Open Data generally relies on a backbone hierarchy organizing a huge number of concepts so that the broader or is-a relationship among these concepts can be explored. Thanks to algorithmic optimizations relying on the sub-ontology extraction and postorder graph traversal, we demonstrate that this whole hierarchy can be efficiently exploited. We also provide simple solutions to limit explanation redundancy in the proposed approach as well as the use of properties that are helpful to organize the hierarchy but irrelevant for explanation.
Dose-response relationships reflect the effects of a substance on organisms, and are widely used in broad research areas, from medicine and physiology, to vector control and pest management in agronomy. Furthermore, reporting on the response of organisms to stressors is an essential component of many public policies (e. g. public health, environment), and assessment of xenobiotic responses is an integral part of World Health Organization recommendations. Building upon an R script that we previously made available, and considering its popularity, we have now developed a software package in the R environment, BioRssay, to efficiently analyze dose-response relationships. It has more user-friendly functions and more flexibility, and proposes an easy interpretation of the results. The functions in the BioRssay package are built on robust statistical analyses to compare the dose/exposure-response of various bioassays and effectively visualize them in probit-graphs.
Workflow BioRssay is a comprehensive compilation of scripts in R language  [11]  designed to analyze dose-response relationships (or exposure-response: mortality, knockdown effect, etc. ) from bioassays of one or more strains, lines, tissues, cells, etc. [hereafter referred to as "population(s)"]. This package provides a complete analytic workflow, from data quality assessment to statistical analyses and data visualization, as follows (see also Fig. 1 ). a. In the first step, the base mortality in the controls (i. e. a sample of the population not exposed to the tested stimulus) is taken into account to control for the mortality (or effect) associated with the experiment itself, regardless of the exposure (this can be critical when the exposure requires a long period). Data are adjusted following Abbott's formula using a maximum likelihood approach (optim function, method "L-BFGS-B", stats R package  [11] ), to estimate correction factors (Eq.
Benchmarking BioRssay What makes BioRssay stand out for dose-response analyses and how can it complement other similar R packages and functions? We discuss three R packages that provide dose-response analysis: drc  [17] , protti  [18]  and lava  [19]. -The core of the drc package is to provide the user with a comprehensive set of model fitting followed by dose-response analysis  [17] , while BioRssay has been designed primarily to facilitate the analysis of bioassay data in a ready-to-go approach. As such, BioRssay is optimized to compare dose-response between many different populations exposed to the same stimulus. Relevant quantities, as LD and RR, along with their CI, are automatically generated, and a function (model. signif) is dedicated to the comparison of dose-responses. Producing these results would require combining the output of several functions from drc packages (e.
g. drm, ED, ED. comp, comped, comParm, predict. drc, anova. drc). Another main difference stems from the use of a quasi-binomial for model fitting in BioRssay to account for possible data overdispersion, which is not possible in drc, despite the variety of models implemented. Finally, BioRssay also includes data preprocessing by implementing Abbott's correction for mortality in controls before fitting the dose-response. The drm function in drc is much more thorough in terms of error model selection, allowing a dose-response analysis using for example binomial, Poisson, four-and five-parameter log-logistic models, and Weibull models. The mselect function of drc is handy in identifying the correct dose-response model for populations that fail the linearity test. -The function fit_drc_4p from the package protti  [18]  is a wrapper function for the drc package's drm function implementing a four-parameter log-logistic model.
Package accessibility and concluding remarks The package is available freely on GitHub at https:// github. com/ miles ilab/ BioRs say (https:// doi. org/ 10. 5281/ zenodo. 51720 72). We also provide a comprehensive workflow and tutorials, from data preparation to the interpretation of the results, in a dedicated GitHub page https:// miles ilab. github. io/ BioRs say/. Further, the package carries example data sets for self-tests, and more data can be downloaded at https:// github. com/ miles ilab/ DATA. The code is maintained by P. Karunarathne (piyalkarumail@yahoo. com). For suggestions or further development please contact the corresponding authors, P. Labbé and P. Milesi. The BioRssay package can be installed in the R environment using the following code: Probit graphs generated by the BioRssays.
Acknowledgements We want to thank  Jérôme Chopard ,  Haoues Alout ,  Mathieu Tiret ,  Mylène Weill , and  Nicole Pasteur  for their valuable comments on earlier versions of the script and its outputs. We are indebted to the two anonymous reviewers for their suggestions that helped improve the package. This study was funded by the  Agence Nationale de la Recherche ,  ANR-20-CE34-0007.
7 and 3. 6 points for FACIT, and 0. 6 and 1. 1 points for the VAS fatigue scores. Although helpful early data, the cohort of n=32 is small, and the MCIDs need confirming in larger datasets. Healthcare resource for comprehensive rehabilitation is a challenge and as a late-breaking abstract, MCNARRY et al. [34]  presented exciting early data (n=87) from an ongoing randomised controlled trial of Inspiratory Muscle Training (IMT) delivered as an 8-week home-based intervention versus usual care. Participants in the IMT group had significantly reduced breathlessness compared to baseline with a higher maximal inspiratory pressure, fatigue index and fitness assessed by chair step test. The results of the full trial (n=250) are awaited, but IMT as a therapy for post-COVID-19 breathlessness is intriguing.
Advanced analytics One of the key features of a remote monitoring system is the volume of data gathered by the multitude of sensors involved. Extracting clinical insights from such datasets necessitates the application of advanced modelling techniques such as signals processing and machine learning. BUEKERS et al. [62]  modelled the kinetics of heart rate and oxygen uptake (V′ O 2 ) from COPD patients, determined by a wearable data outside of clinical/laboratory settings, during outdoor walks. They demonstrated that estimated kinetics were valid when starting/finishing an outdoor walk and were comparable to the standard 6MWT. SWAMINATHAN et al. [63]  developed a machine learning software called Vironix that provides real-time risk stratification for COVID-19 and provides decision support for return-to-work initiatives.
In a pilot study, 11 participants received daily personalised recommendations on the risk of COVID-19 infection, while their managers received work-from-home recommendations for 4 months. The app released 14 stay-at-home and 10 escalation recommendations with a high user retention rate. Lastly, ROBERTS et al. [64]  described a fully automated machine learning approach to quantify CT airway volumes in IPF patients. When applied to 31 unseen IPF patients with two CT scans each, the difference in median airway volume was observed to be larger in subjects with >10% change in FVC between the two scans ( progressive) than those with <10% change (stable). Overall, during this session, numerous important questions focused on remote monitoring of respiratory patients were raised and discussed. It provided insights into various digital health technologies implemented all across Europe, as well as approaches to make obtained data more valid and improve the engagement of patients in these new technologies.
In this paper, we address fish species identification in underwater video for marine monitoring applications such as the study of marine biodiversity. Video is the least disruptive monitoring method for fish but requires efficient techniques of image processing and analysis to overcome challenging underwater environments. We propose two Deep Convolutional Neural Network (CNN) approaches for fish species classification in unconstrained underwater environment. In the first approach, we use a traditional transfer learning framework and we investigate a new technique based on training/validation loss curves for targeted data augmentation. In the second approach, we propose a hierarchical CNN classification to classify fish first into family levels and then into species categories. To demonstrate the effectiveness of the proposed approaches, experiments are carried out on two benchmark datasets for automatic fish identification in unconstrained underwater environment.
CNNs are able to extract high-level features from nonlinear data by transforming low-level input data through multiple levels of representation. CNNs have also shown high performance for underwater vision enhancement  [20, 21] , and for fish detection  [5, 11, 22, 23]. For fish species classification, the first works based on CNNs applied well-known pretrained networks such as AlexNet  [24] [25] [26] , VGGNet  [27] , GoogleNet  [28, 29]  and ResNet  [30] [31] [32]. Our approaches use ResNeXt 101 network with 101 layers  [33]. This architecture improves accuracy while reducing network complexity and the number of parameters by inheriting characteristics of ResNet, VGG and Inception (more details in Section 2. 1). Other authors proposed their own architectures, but these are made up of only few convolutional layers, and hence are not really deep architectures  [6, 34, 35].
In  [7, 8] , the authors proposed hybrid deep architecture with hand-crafted techniques as principal component analysis (PCA), binary hashing and blockwise histograms to extract features of fish images. Some other works attempted to address fish detection and species classification within the same framework  [5, 22, 36]. An interesting recent survey cites all works on fish species recognition  [37, 38]. In this work, we assume that a fish has already been detected in an underwater image, and we address fish identification by using CNNs. We present deep-learning-based approaches for fish species recognition in unconstrained underwater environment. As the underwater live fish datasets are of limited training data, it is not recommended to learn deep CNNs from scratch because of the huge number of parameters to be trained.
To overcome the problem of limited underwater data, we use transfer learning  [39]  to retrain a pretrained network for coral reef fish classification in the open sea. The pretrained networks are trained on a large dataset as ImageNet  [40]. There are many network topologies that have emerged such as AlexNet  [14] , VGG  [41] , GoogleNet  [42] , ResNet  [43]  and ResNeXt  [33]. Furthermore, in the literature on deep-learning-based fish classification, we find methods that used data augmentation to improve the performances of the model and to avoid overfitting. However, they in general applied data augmentation on all fish training data even though the dataset is imbalanced  [25]. Others have augmented the training data only for species whose sample number is less than a threshold in order to balance the dataset  [7].
On the other hand, in biology, there is a scientific classification of fish species based on taxonomy. This biological and hierarchical classification groups species with similarities and common characteristics in the same taxon. This biological classification inspires us to propose a hierarchical fish species classification based on CNN by first grouping species that have a common taxon in the same subset for an efficient classification. For validation purposes, we carried out experiments on two underwater datasets: the Fish Recognition Ground-Truth (FRGT) (https://homepages. inf. ed. ac. uk/rbf/Fish4Knowledge/ GROUNDTRUTH/RECOG/) and the LifeClef 2015 Fish (LCF-15) (www. imageclef. org/lifeclef/ 2015/fish). The two underwater benchmark datasets are captured by underwater cameras in the open sea.
CNN Transfer Learning and Targeted Data Augmentation Currently, available labeled underwater live fish datasets are not large enough to train a CNN from scratch for fish species recognition. Moreover, immense resources of memories and processors are required. To overcome the difficulties imposed by limited fish training data, we used trained parameters of pretrained ResNeXt model  [33]  for the classification. ResNeXt  [33]  is a simple and highly modularized network architecture for image classification. The network inherits the key ideas of VGGNet  [41] , Inception  [45]  and ResNet  [43]. It is constructed by repeating a building block (as in VGG) that aggregates a set of transforms (like Inception) while considering residual connections as ResNet. The suffix "Next" in ResNeXt means the next dimension (the size of the set of transformations) on top of the ResNet.