dataesr/scientific-paragraphs-categorization huggingface dataset:
Content analysis of decision makers' perceptions In combination with the historical analysis that we conducted, we analyzed the key stakeholders' narratives by using the qualitative data analysis program ATLAS. ti (version 1. 0. 50). The narratives were coded into categories compliant with the Result Based Monitoring model  (Daniel and Turner 2015; Xue et al. 2013) , and the Benefits Realization Management model  (Serra and Kunc 2015) , distinguishing the expected outcomes and the real outputs of the project. This process facilitated the distinction between the 'project success' perspective on performance and the 'project efficiency' perspective on performance. We then considered all pieces of narratives that referred to goals, objectives, future results, challenges and expectations as the 'project outcomes'.
Hence, this explains the low computational requirements of Algorithms 2-4 in comparison with Algorithm 1 as summarised in Figure  9b. The e(R)SVD scheme was invoked in each LATIN iteration. However, due to the small number of generated modes, the required time to update the temporal functions is drastically decreased in comparison with the other schemes; see Figure  9a  for a profiler summary. It is worth noting that the timing for each algorithm depends on the available computational resources. However, we expect their relative performance not to change. The RSVD algorithm is implemented in MATLAB ® and uses its built-in SVD routine.
Efficient ontology reuse is a key factor in the Semantic Web to enable and enhance the interoperability of computing systems. One important aspect of ontology reuse is concerned with ranking most relevant ontologies based on a keyword query. Apart from the semantic match of query and ontology, the state-of-the-art often relies on ontologies' occurrences in the Linked Open Data (LOD) cloud to determine relevance. We observe that ontologies of some application domains, in particular those related to Web of Things (WoT), often do not appear in the underlying LOD datasets used to define ontologies' popularity, resulting in ineffective ranking scores. This motivated us to investigate -based on the problematic WoT case -whether the scope of ranking models can be extended by relying on qualitative attributes instead of an explicit popularity feature.
We propose a novel approach to ontology ranking by (i) selecting a range of relevant qualitative features, (ii) proposing a popularity measure for ontologies based on scholarly data, (iii) training a ranking model that uses ontologies' popularity as prediction target for the relevance degree, and (iv) confirming its validity by testing it on independent datasets derived from the state-of-the-art. We find that qualitative features help to improve the prediction of the relevance degree in terms of popularity. We further discuss the influence of these features on the ranking model.
Problem statement. We find that the approach to derive popularity from LOD datasets, as computed in many state-of-the-art tools, can be problematic for ontologies of some domains. We illustrate this problem in Fig. 1 , which shows the number of ontologies contained in the well-known LOV platform that have never been reused in LOD datasets  3. In total, only ∼ 35% of the ontologies in the repository have been reused. We identify particular critical domains with no reuse in any LOD dataset for any ontology in the collection, namely: Services, Industry, IoT, Transport, and Health. We consider all these domains highly relevant to WoT application domains (e. g. , smart mobility, smart health care, industry 4. 0), which thus forms our motivating case to investigate qualitative ontology ranking from this perspective.
From a more general viewpoint, this case highlights the problem that the likeliness of missing relevant information to explicitly determine popularity for all ontologies in a collection is high, leading to the computation of ineffective popularity scores in the ranking model. Contributions. This research contributes to the extension of scope and effectiveness of popularity-driven ontology ranking models, aiming to make these models less dependent on the underlying popularity measure, such as the se- lection of LOD datasets (and the way these datasets are assembled). In this respect, we investigate whether the relevance degree in terms of popularity can be predicted with qualitative properties of the ontology instead of relying on an explicit popularity feature as it is common in the state-of-the-art.
Such a ground truth is obtained by (i) selecting a set of queries with a set of relevant ontologies per query, and by (ii) assigning relevance judgments l to each query-ontology pair. Obtaining a ground truth is a difficult task and annotating data with human assessors is costly  [17]. Thus, several approaches are employed to automatically mine a ground truth by deriving labels from sources such as user click logs of existing search engines and exploiting usage patterns in LOD datasets. However, such approaches also have their limitations, e. g. , using user click logs requires access to back-ends of existing search engines with a large user base, which are usually closed systems.
Related Work Learning-to-rank techniques have been previously applied to build ontology ranking models. The CBRBench ground truth  [6]  was gathered through human labeling based on how well ontology terms meet their definition in a dictionary, comprising ten queries with a total of 819 relevance judgments. CBRBench was used to learn a ranking model in DWRank  [8]. Termpicker  [28]  proposes a ground truth derived from LOD datasets and a ranking model that relies on popularity features, offering ontology term recommendations upon a query in form of triple patterns. In CARRank  [33] , a ground truth was obtained through human labeling for evaluation purposes, resulting in ∼ 400 query-term relevance judgments. Our work differs from these efforts, as we aim to rank ontologies instead of terms.
Φ8 PageRank The importance derived through owl:imports statements. Φ9 Consistency Whether a reasoner does not detect inconsistencies. Φ10 Richness (Width) The size of the ontology in terms of width. Φ11 Richness (Depth) The size of the ontology in terms of depth. ontology collections for WoT application domains, to the best of our knowledge, no ranking mechanism that effectively considers these ontologies' popularity exists. We base our experiments on the collection of the LOV4IoT catalog as it contains the largest number of ontologies and more extensive metadata about the collection.
Word2Vec  [20]  trains a neural network to predict the surroundings of a word. We employ this approach to find closely related words of the input search terms and compute a score based on the cosine distance and the lucene match. The respective matching score is given in Eq. 2. Word2VecMatch(q, o, R) = w i ∈cosineDistance(q,M w2v ) cosineDistance(q, wi, Mw2v) • Lucene(wi, o, R) (2) WordNet (Φ 3 ). WordNet  [21]  is a lexical database in English. We use this source to find senses and synonyms of the keyword input and compute a score for these words based on the Lucene search, as given in Eq. 3. WordNetMatch(q, o, R) = w i ∈sense(q,D WordNet ) ∪ w i ∈synonym(q,D WordNet ) Lucene(wi, o, R) (3).
e. , citations of the current year and of the year of publication). Although the citation history is often used to measure a study's impact, the associated reason for the citation remains unknown, which is a potential threat to the validity of our popularity scores. We assume that the proposed measure reflects the overall ontologies' relevance for the scientific community (e. g. , we assume that for outdated ontologies the citation count will decline and the score is penalized accordingly through the linear trend). In the following experiments, the proposed ranking model is tested on completely independent datasets to evaluate whether our training data is accurate and the assumptions hold.
While MAP is only a binary measure (i. e. , 0: considered not relevant, 1-4: considered equally relevant), the NDCG@k and ERR@k scores do consider the multi-valued relevance labels (i. e. , these metrics consider how well the ranking model matches the relevance degree 0-4). Whereas NDCG@k only depends on the position in the ranking, ERR@k discounts the results appearing after relevant ones, which supposedly better reflects user behavior of search engines  [9]. The ranking model is trained by optimizing the ERR@10 score using 10-fold cross validation, meaning that the training data is randomly partitioned into ten equal sized subsamples. Iteratively, nine of these folds are used for training and the remaining one for validation. Feature sets: the training dataset is prepared by extracting the feature vectors for each query-ontology pair as introduced in Section 3.
Ranking Model Training and Validation In the first experiment we train and validate the ranking model, as presented in the following. Data collection: the data for training and validation is collected from the LOV4IoT catalog  13. 455 ontology files related to WoT applications could be downloaded through the catalog (each file being treated as a separate ontology). Only 433 files were syntactically correct and stored as named graphs in a local triple store. We derive training examples by using the available classification labels from the LOV4IoT catalog as queries (i. e. , ontologies' domain 14  and described sensor devices  15  ), and consider the correspondingly tagged ontologies as relevant. As previously motivated, we rely on scholarly data to derive degrees of relevance. From the initial collection, 395 ontologies could be assigned to 125 different scientific publications based on the LOV4IoT metadata.
NDCG@10 and ERR@10 both reflect this degree, as they take into account multi-valued relevance labels. We thus conclude that qualitative features helped to improve the ranking with regard to the popularitybased relevance degree captured in the ground truth. Subsequently, this implies that the proposed approach can extend the scope of state-of-the-art rankings, by improving the performance for domains in which ontologies were never reused in LOD datasets. In such cases, the explicit popularity feature always results in the same score for all ontologies (i. e. , zero) and effective ranking is only based on relevance (i. e. , corresponding to model (a)). The presented approach in contrast predicts the popularity based on the qualitative features (i. e. , corresponding to model (b)), even when no explicit information of popularity or reuse is present.
Ranking Model Evaluation and Comparison The second experiment aims at evaluating and comparing the model with independent datasets derived from state-of-the-art rankings. We do this in order to ensure that our assumptions for the ground truth, as introduced in Section 3, hold and to confirm whether the findings from the first experiments are valid. Due to the lack of existing benchmarks and implementations of ranking models proposed in the literature, we derive test sets from state-of-the-art tools which must: (i) provide an open API that returns the computed ranking score of the top-ranked ontologies for a query; (ii) make the underlying ontology collection available for download; and (iii) incorporate a popularity measure in their ranking model. We choose to compare the proposed ranking model to approaches from two different domains that fulfill these requirements: the LOV repository  [32] , which measures popularity based on LOD occurrences (by excluding the problematic domains without any reuse in LOD for the test sets); and the NCBO recommender 2.
For the LOV test set we only consider domains with at least five ontologies that have been reused in LOD datasets, in order to ensure that the derived ground truth sufficiently reflects the ontologies' popularity (see Fig. 1 ). This process resulted in test datasets with 2998 (LOV) and 4313 (BioPortal) query-ontology relevance scores. Experiment and results: in the second experiment we test both, the validated relevance model (a) and the full model (b), from the first experiment on the newly derived datasets. The results are illustrated in Fig. 4 , showing the comparison of the performance for the LOV and BioPortal test set, as well as the mean performance of the full ranking model from the first experiment (indicated by the dashed lines). The experiment results lead to two important conclusions.
First, it shows that the learned models behave reasonably well on these completely independent datasets, evidenced by the similar performance compared to the first experiment. This confirms that the underlying ground truth to train our model is valid and, subsequently, implies that the citation history of ontologies in WoT domains is a fair approximation of their popularity. Secondly, we observe a similar behavior of the relevance and the full ranking model as in the first experiment, for which the full model improves the ranking in terms of relevance degree. Albeit the improvement on test sets is lower as in the previous experiment, it shows the same trend and thus validates our previous conclusion that the selected qualitative features help to predict the popularity-driven relevance degree of ontologies.
However, such kind of database that considers both hazard time and space of the degradation are not available in the literature. In order to validate the proposed inference framework, we construct a synthetic discrete degradation model through Monte Carlo simulations. Therefore, numerical experiments will be conducted and compared for identifying preliminary properties and advantages of our model in terms of statistical inference and computation of quantities of interest for reliability and maintenance. The article is organized as follows: Section 2 introduces the classical Gamma process for temporal variability, the Gaussian random field with its simulation method and the construction of degradation model is detailed. Section 3 develops quantities of interest which are useful in the reliability analysis, namely the distribution of the failure time and the distribution of the remaining lifetime of the unit.
Numerical simulations The deterioration model is assumed to be an observable process in space and time with limited information. However, such kinds of database which vary in space and time are not available in the literature. Therefore, a synthetic discrete degradation model is constructed through MC simulations. The purpose of this section is to validate the inference methodology and the approximation of the quantities of interest which depend on the estimated parameters. We consider M = 1, 10, 100 sample paths of the field (G j ) M j=1 , each trajectory G j being simulated at N t equidistant periods on the interval time [0, 30] (in years) and N z equidistant locations in one-and two-dimensional space. The model in time (its temporal increments are time-independent), i. e the shape parameter is linear α(t) = at.
Data Pre-Processing The global solar radiation measurements were collected at Petit-canal Gros-Cap (16 • 23 N latitude and 61 • 24 W longitude), along the cliffs of Guadeloupe island. The dataset includes measurements at 1 Hz with a CM22 from Kipp and Zonen pyranometer (type SP Lite) whose response time is less than a second. For this study, a sampled database was used at 5, 10, 15, 30 min and 1 h of the solar global radiation measured for a period starting in January 2012 and ending in December 2012. In this dataset, there are no missing values. However, if some of them were missing, it would be possible to proceed by regression method. Since the objective of our work focused on the modeling process of the forecast model, the year of data, whether more recent or older, would have no influence on the obtained results due to the short time scales considered here, i.
Validation Metrics The Hybrid forecast model performance can be evaluated using the following classical statistical performance indicators: relative MBE (rMBE) (Equation (  3 )), relative MAE (rMAE) (Equation (  4 )), relative RMSE (rRMSE) (Equation (  5 )), and skills scores (Equation (  6 )). These metric errors are typically used to evaluate the predictive performance of a solar forecast model. Relative error metrics are normalized to the mean observed irradiance data for the considered period  [21] , i. e. , here, one year: • Relative Mean Biais Error (rMBE) rMBE = 1 N ∑ N i=1 ( pi -o i ) 1 N ∑ N i=1 o i (3) • Relative Mean Absolute Error (rMAE) rMAE = 1 N ∑ N i=1 | pi -o i | 1 N ∑ N i=1 o i (4) • Relative Root Mean Square Error (rRMSE) rRMSE = 1 N ∑ N i=1 ( pi -o i ) 2 1 N ∑ N i=1 o i (5) where o i is the observed value of GHI, pi is the forecast value of GHI and N the number of point in the dataset for the considered period.
Results: Strategy 1: Sampling Data = Forecast Horizon Note that, in Strategy 1, the sampling time is equal to the forecast horizon. For example, for a horizon of 5 min, input data or learning dataset are sampled at 5 min and, for a horizon of 2 h, input data are sampled at 2 h. The initial dataset is sampled at 1 s. In the EMD case, the number N of intrinsic mode functions (IMF) is proportional to the length of the dataset  [18] : N = T n∆t (14) where T represents the total data length, ∆t represents the digitizing rate and n represents the minimum number of ∆t needed to define the frequency accurately. Consequently, the longer is the horizon time. the smaller is the number of decomposition components. Table  1  shows the Hybrid model forecasting performances using the first strategy. It can be noted that, whatever the multiscale decomposition-hybrid model chosen, the rRMSE error increased with the forecast horizon, as illustrated in Figure  6.
Variability Influence on Hybrid Forecast Model Performances Six months of data were used for the training stage and the six following months for test data (forecasting stage). The forecast errors were calculated daily, then the days of the same class were grouped and an average of the errors was obtained. The results according to each class and each hybrid models are summarized in Table  5  for a forecast horizon of 5 min. It is noted that the best performance was obtained with the WD-Hybrid model. The clear sky days showed the lowest error metrics (rRMSE = 2. 91%). On most days, forecasts matched observations almost perfectly due to their low daily variability demonstrated in Section 3. 2. 1. As previously mentioned in Section 3. 2. 1, the most variable days are the intermittent cloudy sky days.
Two comments can be given. We then established a dataset of four classes of typical days in accordance with previous studies from literature: clear sky day, intermittent clear sky day, cloudy sky and intermittent cloudy sky. We used other statistical parameters to characterize the variability of each class such as the variability score and magnitude of ramp rate. Firstly, the results reveal that the variability of insolation conditions was not the only factor that decreased forecast accuracy. Indeed, the daily profile of irradiance was an influence factor. These analysis were supported by the study of cumulative distribution of ramp rates and VS scores. In other works, we may develop a parameter which may give accurate information on daily irradiance profile. Secondly, the results are particularly relevant for WD-hybrid model.
At first, the numerical global and local model are developed to simulate the thermomechanical behavior of the solder joints  [4] , and the Ansys TM finite element software is a tool used to modelize and compute the inelastic train due to the thermal variation. The viscoplastic behavior of the solder joints is taken into account using Anand model. Secondly, the KA-CMA-ES algorithm is applied to the global optimization of the MSs, in order to optimize the thermomechanical performance of solder joints by maximizing the number of life cycles. In the 6th and the last section, the paper closes with a short conclusion and perspective on future work.
4). min De in ðfdgÞ s:t: ffdg Lb fdg fdg Ub ð18Þ The design optimization problem is carried out with the KA-CMA-ES algorithm, implemented with Matlab ® (Fig. 10 ). Table  5  shows the optimal and initial design calculated by a simple CMA-ES algorithm and KA-CMA-ES, also the results show that the objective function (inelastic strain range De in ) is minimized. Figure  11  shows the new design and the new inelastic strain distribution after the application of the design optimization procedure. The maximum inelastic strain (SMX = 0. 08193) is more smaller than the inelastic strain in the initial configuration (SMX = 0. 13557). The typical convergence histories of plastic deformation De in are plotted in Figure  12. This plots clearly indicate that the KA-CMA-ES converges more quickly than standard CMA-ES and give same results.
The development of data management services capable to cope with very large data resources is a key challenge to allow the future einfrastructures to address the needs of the next generation extreme scale scientific experiments. To face this challenge, in November 2017 the H2020 eXtreme DataCloud -XDC project has been launched. Lasting for 27 months and combining the expertise of eight large European research organisations, the project aims at developing scalable technologies for federating storage resources and managing data in highly distributed computing environments. The targeted platforms are the current and next generation e-Infrastructures deployed in Europe, such as the European Open Science Cloud (EOSC), the European Grid Infrastructure (EGI), and the Worldwide LHC Computing Grid (WLCG).
The project is use-case driven with a multidisciplinary approach, addressing requirements from research communities belonging to a wide range of scientific domains: High Energy Physics, Astronomy, Photon and Life Science, Medical research. XDC is aimed at implementing data management scalable services, combining already established data management and orchestration tools, to address the following high level topics: policy driven data management based on Quality-of-Service, Data Life-cycle management, smart placement of data with caching mechanisms to reduce access latency, meta-data with no predefined schema handling, execution of pre-processing applications during ingestion, data management and protection of sensitive data in distributed e-infrastructures, intelligent data placement based on access patterns.
Introduction The eXtreme DataCloud (XDC) project  [1]  develops scalable technologies for federating storage resources and managing data in highly distributed computing environments. The provided services are capable of operating at the unprecedented scale required by the most demanding, data intensive, research experiments in Europe and Worldwide. The targeted platforms for the released products are the already existing and the next generation e-Infrastructures deployed in Europe, such as the European Open Science Cloud (EOSC)  [2] , the European Grid Infrastructure (EGI)  [3] , the Worldwide LHC Computing Grid (WLCG)  [4]  and the computing infrastructures that will be funded by the upcoming H2020 EINFRA-12 call. XDC is funded by the H2020 EINFRA-21-2017 Research and Innovation action under the topic Platform-driven e-Infrastructure innovation  [5].
The project continues the effort invested by the now ended INDIGO-DataCloud project  [6]  in the direction of providing friendly, web-based user interfaces and mobile access to the infrastructure data management services. The project builds on the INDIGO-DataCloud achievements in the field of Quality of Services and data lifecycle management, developing smart orchestration tools to realize easily an effective policy driven data management. One of the main objectives of the project is to provide data management solutions for the following use cases:  Dynamic extension of a computing center to a remote site providing transparent bidirectional access to the data stored in both locations.  Dynamic inclusion of sites with limited storage capacity in a distributed infrastructure, proving transparent access to the data stored remotely.