dataesr/scientific-paragraphs-categorization huggingface dataset:
Adsorption of benzene and phenol at the Pt(111)/water interface The adsorption free energy of benzene and phenol are studied to quantitatively evaluate the accuracy level of our QM/MM hybrid scheme compared to the experimental estimates from Campbell et. al. 71  This provides us also the opportunity to compare MMSolv solvation energies to the one obtained with the widely used PCM, implemented in VASPsol. 21  a start, we compare the solvation energies of molecules in solution to their experimental counterparts, taken from the Minnesota database of solvation free energies. 72 The experimental standard solvation free energies of water, phenol and benzene in water are reported to be -6. 31, -6. 62 and -0. 87 kcal•mol -1 , respectively. The implicit solvent, which is fitted on this kind of reference data, is in qualitative agreement, yielding -7.
Acknowledgement This work benefited from the support of the project MuSiC  ANR-14-CE06-0030  of the  French National Research Agency (ANR). The authors thank the  SYSPROD  project and  AXELERA Pôle de Compétitivité  for financial support (PSMN Data Center). AWG would like to acknowledge support by the  National Science Foundation  (grant  CHE-1416571 ).
Introduction One of the most common reasons of death in Algeria or other Maghreb countries is chronic disease. Nevertheless, chronic disease is a vital issue to be fixed for a healthy human life. More recently, Cardiovascular Disease (CVD) is the leading cause of death for both men and women globally. Though real-life consultants can be able to predict the disease with an enormous number of tests and requiring a huge processing time, sometimes, their prediction may be incorrect because of lack of skilled knowledge  [1]. Meanwhile, the introduction of artificial intelligence and machine learning has helped to extract relevant data from large databases which are available in hospitals to make a good decision. It involves data mining techniques to analyze medical data  [2]. For this reason, data mining has gained popularity due to its tools with the potential to identify trends within data and turn them into knowledge that could serve as the strong basis for the analysis  [3].
An "Optimal Multi-Nominal Logistic Regression (OMLR) algorithm has been proposed in  [5]  and is used to train the data set for heart disease. Experiments are conducted on the dataset of UCI heart disease and the results show 92% accuracy in the detection of heart severity. The Fast Correlation-Based Feature Selection (FCBF) method has been exploited in  [6] , to filter redundant features in order to improve the quality of heart disease classification. Then, the authors performed a classification based on different algorithms such as K-Nearest Neighbour, Support Vector Machine, Random Forest and a Multilayer Perception optimized by Particle Swarm Optimization (PSO) combined with Ant Colony Optimization (ACO) approaches. A predictive model for heart disease diagnosis using a fuzzy rulebased approach with decision tree has been proposed in  [7].
Description of the Dataset and Attributes The dataset used in this article is taken from the UCI Repository Of Machine Learning Databases  1. Formally, it is named Heart Disease Dataset. The Cleveland (Cleveland Clinic Foundation) database was selected for this research because it is a commonly used database for machine learning researchers with comprehensive and complete records. In this field, the dataset is a collection of medical analytical reports with a total of 303 records with 14 medical features. The various features and their description are shown in Table  1. Besides, the categorical feature "Class" contains whether a patient has a presence or absence of heart disease. Its original values 1, 2, 3 and 4 were transformed in one that is the presence (1) of heart disease. The predicted attribute Discrete 0 = no presence; 1 = presence.
Feature Selection Feature selection is a process of selecting a relevant feature of original features according to definite condition. Further, feature collection algorithms intended with different evaluation criteria mostly fall into three categories: the filter, wrapper, and hybrid models  [10]. In our work, we used only the wrapper method under Keel tool. As per our objective, from among the 14 attributes of the dataset, two attributes pertaining to age and sex are used to identify the personal information of the patient. The remaining 12 attributes are considered important as they contain vital clinical records.
Classification Algorithms Under Weka tool, different predictive algorithms were chosen to build the first model, namely: Multi-Objective Evolutionary Fuzzy Classifier (MOEFC), Logistic Regression (LR), Adaptive Boosting (AdaBoostM1), while Genetic Fuzzy System-LogitBoost (GFS-LB), Fuzzy Unordered Rule Induction Algorithm (FURIA) and Fuzzy Hybrid Genetic Based Machine Learning (FH-GBML) were used under Keel tool to build the second model. Therefore, we selected the best model in order to achieve the highest possible performance on medical datasets and allow effective data classification.
Evaluation of Results Setting up the Experiment under WEKA Software. In our experiment, the problem has been transformed into binary classification with 0 presents absence and 1 presence of heart disease. For this, Table  2  shows the results obtained by binary classification and 10-fold cross-validation. The highest accuracy 80. 20 is gained by majority voting, while LR obtained lowest accuracy and AdaBoostM1 has the highest accuracy when applied without ensemble. Setting Up the Experiment under KEEL Software. Our purpose is to make a comparison of three methods that belong to different ML techniques. In this step, we have used a GFS-LogitBoost-C classifier with a previous pre-processing stage of prototype selection guided by a Generational Genetic Algorithm for Feature Selection (GGA-FS) model.
We have also used a FURIA classifier with a previous preprocessing stage of replacing missing values guided by a KNN-MV (K-Nearest Neighbor Imputation) algorithm as well as prototype feature selection guided by SSGA-Integer-knn-FS (Steady-state GA with integer coding scheme for wrapper feature selection with K-NN) and an FH-GBML that uses a Generational Genetic Algorithm for Feature Selection (GGA-FS). After the models are trained, the instances of the dataset are classified according to the training and test files. These results are the inputs for the visualization and test modules. The module Vis-Clas-Tabular receives these results as inputs and generates output files with several performance metrics computed from them, such as confusion matrices for each method. There is also another type of results flow which interconnects each possible pair of methods with a test module.
Conclusion Efficient classification of healthcare dataset is a major machine learning problem then and now. Diagnosis, Prediction of cardiovascular diseases and the precision of results can be improved if relationships and patterns from these complex healthcare datasets are extracted efficiently. This paper analyses some of the different classification algorithms like Logistic Regression (LR), Adaptive Boosting (AdaBoostM1), Multi-Objective Evolutionary Fuzzy Classifier (MOEFC), Fuzzy Unordered Rule Induction (FURIA), Genetic Fuzzy System-LogitBoost (GFS-LB) and Fuzzy Hybrid Genetic Based Machine Learning (FH-GBML). The performance evaluation of these algorithms is done based on Accuracy, Sensitivity, Specificity and Error rate using WEKA and KEEL tools.
For complex engineering and scientific applications, Computational Fluid Dynamics (CFD) simulations require a huge amount of computational power. As such, it is of paramount importance to carefully assess the performance of CFD codes and to study them in depth for enabling optimisation and portability. In this paper, we study three complex CFD codes, OpenFOAM, Alya and CHORUS representing two numerical methods, namely the finite volume and finite-element methods, on both structured and unstructured meshes. To all codes, we apply a generic performance analysis method based on a set of metrics helping the code developer in spotting the critical points that can potentially limit the scalability of a parallel application. We show the root cause of the performance bottlenecks studying the three applications on the MareNostrum4 supercomputer.
Hardware Environment The MareNostrum4 supercomputer is a Tier-0 supercomputer ranked 30th in the Top500 (November 2019) with a total of 3456 × 86 compute nodes. MareNos-trum4 is based on Intel Xeon Platinum processors from the Skylake generation. The nodes of the system are interconnected using Intel Omni-Path high performance network and run SuSE Linux Enterprise Server as operating system. Each node is equipped with: CPU -2 sockets Intel Xeon Platinum 8160 CPU with 24 cores each running at 2. 10 GHz for a total of 48 cores per node. Figure  1  shows a schematic view of a node of MareNostrum4.
Context OpenFOAM (for 'Open-source Field Operation And Manipulation ') is the free, open source CFD software developed primarily by OpenCFD Ltd since 2004  (Chen et al. 2014). Currently, there are three main variants of OpenFOAM software that are released as free and open-source software under the GNU General Public License Version 3: CFD Direct , 1 the open source CFD toolbox 2 and foam-extend. 3 The development model follows a cathedral style  (Morgan 2000). OpenFOAM constitutes a C++ CFD toolbox for customised numerical solvers (over 60 of them) that can perform simulations of basic CFD, combustion, turbulence modelling, electromagnetics, heat transfer, multiphase flow, stress analysis and even financial mathematics. It has a large user base across most areas of engineering and science, from both commercial and academic organisations.
The discretisation is based on low-order (typically secondorder) gradient schemes. It is possible to select different choices of iterative solvers for the solution of the linear solver algebra based on SpMV (Sparse Matrix-Vector) multiplication, ranging from Conjugate Gradient up to Multi-Grid Methods. The temporal schemes can be selected among first-and second-order implicit Crank-Nicolson and Euler schemes. Algorithm 1 OpenFOAM pseudo-code for Navier-Stokes equations Solve ( fvm::ddt(rho,U) +fvm::div(U,U) -fvm::laplacian(mu,U) == -fvc::grad(p) +f );.
Parallelisation The method of parallel computing used by Open-FOAM is based on domain decomposition, in which the geometry and associated fields are broken into subdomains and allocated to separate processors for solution. The process of parallel computation involves: decomposition of mesh and fields; running the application in parallel; and, post-processing the decomposed case. 6 The method of parallel computing used by OpenFOAM is based on the standard MPI. A convenient interface, Pstream, that is a stream-based parallel communication library, is used to plug in any MPI library into OpenFOAM. It is a light wrapper around the selected MPI interface  (Jasak 2012). Traditionally, FVM parallelisation uses the halo layer approach: data for cells next to a processor boundary is duplicated. Halo layer covers all processor boundaries and is explicitly updated through parallel communication cells.
Context Alya is a high performance computational mechanics code to solve complex coupled multi-physics/multiscale/multi-domain problems, which are mostly coming from the engineering realm. Among the different physics solved by Alya we can mention: incompressible/compressible flows, non-linear solid mechanics, chemistry, particle transport, heat transfer, turbulence modelling, electrical propagation, etc. Alya  (Vázquez et al. 2016 ) was specially designed for massively parallel supercomputers and is part of the Unified European Application Benchmark Suite (UEABS) a set of 13 codes highly scalable, relevant and publicly available.
Dimemas accepts as input an Extrae trace gathered from an execution on a real HPC cluster. The output of Dimemas is a new trace reconstructing the time behaviour of the parallel application as if it was ran on a machine modelled by a set of performance parameters (also provided as input). For this paper, we employed Dimemas to simulate the behaviour of the CFD codes under study when running on a parallel system with an ideal network, i. e. zero latency and infinite bandwidth. Clustering is a tool that, based in a Extrae trace, can identify regions of the trace with the same computational behaviour. This classification is done based on hardware counters defined by the user. Clustering is useful for detecting iterative patterns and regions of code that appear several times during the execution.
e. the total number of L3 misses per microseconds executed by all process. In Figure  5 , we show the number of misses per microsecond for the L3 cache (x-axis) when increasing the number of MPI processes (y-axis). Since the L3 is shared among all cores of a socket, this corresponds to the number of petitions to main memory that are issued per microsecond by each socket. We can see how the pressure to memory increases as the number of MPI processes increases. We conclude that the main factor limiting the scalability of OpenFOAM within a computational node is the amount of L3 cache misses. This limitation could be mitigated with a data layout that ensures a better data locality for the different levels of caches.
Figures  7  and 8  show the number of bytes exchanged, in total and per process. It is clear that the total amount of data being exchanged decreases. Therefore, the problem does not seem to be related to network bandwidth. In Figure  9 , we can see the number of MPI calls of each type executed per process. We observe that the number of asynchronous point to point MPI calls performed per process increases with the number of MPI processes. This observation together with the previous charts demonstrate that the transfer efficiency lost by OpenFOAM is due to the overhead introduced by calling the asynchronous point to point MPI calls a high number of times to exchange a low number of bytes. The suggestion for the developers to address this issue would be to try grouping several point to point MPI calls into a single one to reduce the number of calls.
This is the effect of fixing the number of MPI processes while increasing the number of nodes to study the scalability within a node. As L3 is a shared resource per socket, when we run 2 MPI processes per node we are using 24 nodes (48 sockets) but when we run 4 MPI processes per node we are using 12 nodes (24 sockets). Therefore, the total L3 capacity is reduced. The increase in L3 misses is due to the replacement of data done by other processes in the same node. Although the number of L3 misses increases with the number of MPI process per node, the IPC (right most column of Figure  19 ) is not affected significantly: we observe in fact similar values of IPC from 2 to 12 MPI processes. Nevertheless, the IPC decreases from 12 to 24 MPI processes and from 24 to 48 MPI processes. As with OpenFOAM, this seems to be a problem of memory bandwidth and we studied this phenomenon measuring the L3 miss density.
About CFD Applications The analysis of the scalability within a computational node of the three applications lead to the same conclusion: when using a high number of MPI processes per node (i. e. above 12 or 24) the IPC drops because of the amount of data petitions that are issued to memory. Our suggestion to application developers is to improve the pattern of data access and data layout to maximise the reuse of data in the different caches levels. When analysing the multi-node scalability of Open-FOAM and Alya we concluded for both codes that their main limitation when scaling to a high number of cores will be the number of MPI asynchronous point to point calls. These calls seem innocuous to the programmer because they are asynchronous and allow to overlap communication and computation, but the reality is the overhead of calling MPI cannot be overlapped and is the bottleneck that prevents these applications to scale.
The evaporation of a drop deposited on a heated substrate is a complex process, which combines several phenomena such as the Marangoni effect, mass and thermal transfers, etc. We developed, in this paper, a mathematical model and a numerical simulation code used to carry out an in-depth study about the evaporation of a drop deposited on a heated substrate surrounded by air. This numerical study was supported by experimental work. The numerical findings obtained showed the existence of a condensation phenomenon for certain configurations. At the beginning of the experiment, the evaporation started at the triple point. However, a local region of the interface remained relatively cold. In this region, the concentration gradient (C v , C sat ) directed from the liquid-gas interface to the air resulted in condensation of water steam.
Numerical procedure The simulation of the problem was achieved by using the calculating code COMSOL Multiphysics Version 4. 2b. This code offers the ability to track, the gas-liquid interface of the droplet over time when the liquid domain deforms and narrows following the process of evaporation. Figure  2  shows the typical mesh adopted in this study. The mesh is narrowed mainly at the borders especially along the liquid interfaces. The numerical difficulty of the problem lies in having to simulating a physical problem characterized by a variable interface in time and space. The deformed mesh method is implemented with the Comsol code. It is convenient for cases where boundaries move as a function of time. The idea of the method is not to generate a new mesh for each border configuration, but just to disrupt nods mesh to adopt to the displaced mesh.
A forum or social media post can express multiple emotions, such as love, joy or anger. Emotion classification has been proven useful for measuring aspects such as user satisfaction. Despite its usefulness, research in emotion classification is limited, because the task is multi-label and publicly available data sets and lexica are very limited. A number of emotion classifiers for general-domain text have been proposed recently, but only a few for text in the domain of Open Source Software (OSS), such as EmoTxt. In this paper, we explore different lexica and two multi-label algorithms for classifying emotions in text related to OSS. We trained various multi-label classifiers using HOMER and RAkEL on a data set of Stack Overflow posts and a data set of JIRA Issue Tracker comments. The classifiers have been enriched with features derived from different state-of-the-art lexica.
Related work Recently, researchers have shown interest in automatic classification of emotions in text. In this section, we review the state-of-the-art. The Affect Analysis Model  [35]  is an unsupervised emotion classification method. It relies on rules and a manually annotated database. Among others, it contains affective strength for emoticons, affect words, common abbreviations and acronyms. Feeler  [36]  is also unsupervised and based on the cosine similarity of high-dimensional vectors. Its features encode TF-IDF-weighted unigrams and are enriched using lexica, such as the WordNet Affect Lexicon  [37]. The unsupervised method presented in  [38]  uses this last lexicon too and reduction tools, such as Latent Semantic Analysis and Non-negative Matrix Factorisation. In contrast to these methods, supervised learning has been combined with a psychological approach in  [39].
Recently, there is research interest in the classification of emotions in texts related to software engineering and development. For instance, in Murgia et al. [47] , the authors performed a qualitative and quantitative analysis regarding the feasibility of applying automatic classification techniques, such as Naïve-Bayes, SVM or k-Nearest Neighbours, for classifying emotions in issue comments. In  [10] , emotions, such as anger, love, sadness and joy, conveyed in posts from the JIRA Issue Tracker 4 were detected by multiple Linear SVMs. It is not indicated if the multi-label task was addressed and how. Apart from text, features encode information from the WordNet Affect Lexicon, SentiStrength  [49]  and a politeness detection tool  [50]. EmoTxt 5  [11]  is an emotion classifier based on the principles in  [10] , separately trained on two corpora: JIRA Issue Tracker posts  [48]  and Stack Overflow comments  [51].
EmoTxt uses six binary SVMs for classifying joy, love, sadness, anger, surprise, and fear following a one-vs. -all approach with Binary Relevance. 6 Each SVM can assign a specific emotion, only. Apart from the features in  [10] , the authors added TF-IDF. Although EmoTxt can be seen as multi-label if the output of all classifiers is merged, it was evaluated using uniquely single-label metrics, i. e. precision, recall and F-score. EmoTxt has been implemented in EMTk  [52]  and in EmoD  [53] , two toolkits that analyse emotions and sentiment in software engineering documents and data sources related to repositories. 4  The data set contains 4000 entries and is not described in detail. Most probably, same data set presented in  [48]. 5 EmoTxt is freely available: github. com/collab-uniba/Emotion_and_Polarity_ SO.
It implies detecting which words are truly elongated, e. g. ''looove'' vs. ''issue''. Thus, rules about words that contain repeated letters, should be applied. Then, the rules need to be validated against a dictionary. However, elongated words can represent out-of-dictionary words, e. g. superlatives, software names and acronyms, making validation harder. Moreover, disambiguation is needed to determine which word matches the L OMG I hope it isn't all in one file! Surprise, fear Joy, anger (  8 ) Surprise (  15 ) Joy  (18)  context, in cases such as ''os'' (operating system) and ''oss'' (open source software). EmoTxt performed significantly different than in  [11]  for some emotions. For models trained and tested on JIRA, the maximum F-score difference is for joy, 57. 3% vs. 86% in  [11]. The reasons remain unclear.
This consideration, along with the moderate inter-annotator agreement, may explain the F-score differences. The lack of statistical difference between models that used or excluded lemmatisation may suggest that minor feature variations do not affect emotion classification. This may also hold for the number of n-grams and skip-bigrams. Similarly, the lack of statistical difference between using SenticNet 5 or NRC lexica may indicate that, despite the variation of theories or annotations, emotions are represented equally. Moreover, model parameters are tuned using Bayesian Optimisation, maximising performance, despite using varying features. Two statistically indifferent models may not perform exactly the same. Maybe the test data was not large enough to reveal a difference. However, testing on larger data does not guarantee that the difference will be observable.
Conclusions and future work Emotion classification is the task of determining which emotions are expressed in non-neutral text. The task is complex because text can express multiple emotions. The state-of-theart lacks freely available annotated data and external resources and only offers few classification tools, especially in the domain of Open Source Software (OSS). We explored two multi-label classifiers, i. e. HOMER and RAkEL, and lexica, i. e. SenticNet 5, the NRC Word-Emotion Association Lexicon and the NRC Affect Intensity Lexicon, to develop an emotion classifier for text related to OSS. The classifiers were evaluated on collections of JIRA Issue Tracker comments and Stack Overflow posts. We evaluated against EmoTxt, a state-of-the-art emotion classifier for OSSrelated text, a random baseline and the most frequent class one.