dataesr/scientific-paragraphs-categorization huggingface dataset:
It offers a strong isolation for guest operating systems by means of the KVM hypervisor, Arm TrustZone, SELinux and a virtualization enabled implementation of the GlobalPlatform TEE API. Part of it is based on open-source components like KVM and libvirt  [30]  which eases a potential integration with OpenStack. As expected from the definition of the problem itself, all of the presented works are tightly coupled with hardware technologies. [10]  and  [27]  are adding additional value through the combination with an orchestration and management software, basing their work on Intel chips which are dominating the cloud data center. Meanwhile,  [29]  focus on Arm and opensource components but leave the potential integration with OpenStack as future efforts.
e. , representing the inherent randomness of a phenomenon or system (a roulette, for instance). In addition, the border between these two types of uncertainty is somehow blurred and arbitrary, in that it depends on our point of view and on the level of abstraction of knowledge representation. Multiple uncertainty theories are currently applied in Artificial Intelligence in order to reconcile the data, correct them, extrapolate or predict new values or simply assess the degree of uncertainty of information or error comparing to a dataset  [14]. Probability theory is but one example, if not the best known and most timehonored, of such theories, which focuses on the representation and manipulation of the ontic uncertainty. Other theories are interested in the quantization (or "granulation") of vague data.
In this paper, we focus on a generic representation for uncertainty information, one that can be publishable and reusable. We summarize the mUnc vocabulary enabling both the representation of uncertainty theories with their calculi and the annotation of data with uncertainty information. We present an extension to RDF Semantics by giving a contextual meaning to Named Graphs. We offer the possibility to use mUnc alongside with our framework built on top of the Corese Semantic Web engine  [10]  and using the LDScript function scripting language  [9]  based on the SPARQL filter language, to map between triples and their uncertainty metadata, using multiple mapping modes. We discuss the translatability of uncertainty theories and propose to extend mUnc to fill that gap, and then discuss the negotiation of theories over HTTP as a special case of conneg (Content Negotiation) where clients can state their preferences in terms of uncertainty theories and servers can select or translate among theories to serve the best possible answer.
The rest of the article is organized as follows. Section 2 first recalls the uncertainty in information theory and discusses a model for the existing uncertainty theories in the literature. Then, Section 3 discusses the integration of uncertainty in the semantic web, offering a presentation of the mUnc vocabulary, discussing the publishing of uncertainty approaches and their calculi on the Semantic Web, and giving an overview of metadata mapping modes and how to query over uncertain Linked Data. Section 4 is dedicated to the translatability of uncertainty theories and the negotiation details. Related works are discussed in Section 5 while the last section summarizes our work and offers a glimpse over the perspectives.
Schema and ontology languages offered a backbone to the existing information by providing the vocabulary of a defined topic, the relationships ruling over the different terms and the semantics allowing to logically establish links in between. Several standards (RDF, RDFS, OWL, SPARQL, etc. ) of the Semantic Web technology stack enable the representation and querying of RDF data. To integrate uncertainty in the Semantic Web, this dimension must comply with the standards and, like any other data on the web, must be reusable and publishable. In this section, we offer an ontology covering uncertainty representation, and we show the possibility of replicating the semantics of uncertainty using calculi represented in the LDScript function scripting language.
mUnc: a vocabulary for uncertainty theories To enable uncertainty representation on the Semantic Web, we need to opt for an interchangeable format to write uncertainty theories. We propose mUnc 1 , an RDFS ontology for uncertainty theories. mUnc (for meta-Uncertainty) enables publishing uncertainty information based on uncertainty theories. Figure  1  gives an overview of the core concepts and properties of mUnc. We have adopted the definition of sentence and world proposed in the URW3-XG ontology. A sentence is an expression evaluating a truth value, while the world represents the context in which a sentence is stated. Still, unlike the previous definition, both sentences and worlds can be annotated with meta information. For instance, the sentence ex:S1 representing the triple ⟨ex:StefanoTacconi, dbo:height, 188⟩ referring to the height of the football player is stated in the context of the French language chapter of DBpedia  [4] , assuming that the latter is consistent  [5].
Uncertainty is considered a specialization of the general concept of meta. This simplifies the task if any future extensions for other types of metadata such as provenance or trust are proposed. We do not include the concept of Agent, as it can be included using other vocabularies like the W3C PROV 2 Ontology. An uncertainty theory (Uncertainty Approach) is linked to a set of features and operators. The features are the metrics on which uncertainty theory is based to indicate the degree of truth, credibility, or likelihood of a sentence. Each feature links a value to the uncertainty information. The operators represent the logic to apply to the previous values, while the operations are the implemented calculus for such logic. Other concepts in the URW3-XG ontology like the type or the derivation of uncertainty can be represented as features of an uncertainty approach.
Uncertainty calculi Semantic Web ontology languages focus on classification ontological knowledge and do not support the provision of procedural attachments or functions inside ontologies. Our model allows linking the features of uncertainty approaches to their proper calculi (arithmetic, logical, comparison, etc). To represent the calculi, we rely on the LDScript function definition language  [9] , a programming language whose objects are RDF entities. It is built on top of SPARQL and relies on the SPARQL filter expression language. LDScript as a language permits variable declaration, assignment, function call, return, etc. Using LDScript, we can define functions named with an IRI and one or several arguments that are variables in the SPARQL syntax. This enables defining uncertainty operations and linking them to uncertainty features.
To continue with the previous example, considering the fact that the sentence ex:S1 is true with a probability of 0. 7, and is stated in a context ex:C1 where all contained facts are considered to be true with a probability of 0. 9. The probability of a conjunction of two supposed independent events A and B is given by Equation (1): P(A ∧ B) = P(A) × P(B). (1) Such value can be calculated for the user using the function referenced by ex:multProbability and defined in LDScript as shown in the following example: function ex:multProbability(?pA, ?pB){?pA * ?pB} Therefore, binding the function ex:multProbability(0. 7, 0. 9) during a SPARQL query execution will return 0. 63. The former definition of the probabilistic approach using mUnc can be enriched by linking the IRI of the function to the declared feature, simply by adding the triple: ex:ProbabilityValue ex:and ex:multProbability.
As stated before, each function is considered as a resource, due to the IRI defining its name. We can store such functions in SPARQL files all over the Web, and access their code using their reference. As mentioned before, uncertainty depends on a context. A single world may accept different interpretations using different uncertainty measurements. Dubois  [13]  explains that beliefs are the different views upon a single world. We rely on the fact that data is issued by querying n uncertain data sources s 1 , s 2 ,. , s n , each possibly containing several contexts C i j , each representing consistent information. This means that each context contains a set of triples that do not lead to contradictory reasoning. mUnc does not provide an extension of RDF Semantics. Instead, we rely on the SPARQL query language to provide a mapping between sentences and the uncertainty information presented to the user.
The former keyword allows the previous code to be accessed globally in the engine through its reference, and without the need to rewrite the function with each query. The listing 2 translates the metaList algorithm into LDScript. The result of binding this function in a SPARQL query is a string that groups all uncertainty features and their corresponding values from the Universal Uncertainty Information set of the corresponding sentence. Corese also implements Linked Functions enabling storing LDScript  [9]  functions in external SPARQL query files on the Web. Such functions, referenced by IRIs, may be called at the moment of query C i j = U S C i j if U S C i j ∅ U C i j otherwise Lowest granularity first Override if Exists ÛS C i j = U S C i j ∪ {(F , V ) | (F , V ) ∈ U C i j , ∄V ′ (F , V ′ ) ∈ U S C i j } Multi-level Uncertainty Calculus ÛS C i j = {(F , V ) | (F , V ) ∈ U C i j , ∄V ′ (F , V ′ ) ∈ U S C i j } ∪{(F , V ) | (F , V ) ∈ U S C i j , ∄V ′′ (F , V ′′ ) ∈ U C i j } ∪{(F , V ) | ∃V C i j , V S C i j (F , V C i j ) ∈ U C i j , (F , V S C i j ) ∈ U S C i j , V = eval(Calculus(F ), V C i j , V S C i j )} execution.
The former feature permits publishing and executing the calculi of uncertainty approaches. Additionally, this feature may be extended to allow the capitalization of existing software libraries from other programming languages like C++ or Java. The Semantic Web engine also allows defining specific routines preceding the query execution. One can integrate query transformation or precalculations of some variables. We implemented the previous meta-mapping mode in extension to the visitor "@metadata" and enabled rewriting SPARQL queries to simplify querying for uncertainty information. Using "@metadata" and with munc:metaList publicly defined, querying for the height of the football player Stefano Tacconi in a data source is as follows.
RELATED WORKS mUnc can extend the work done by Cabrio et al. [6]  by enriching the proposed fuzzy labeling algorithms with definitions of other uncertainty theories that can be more suitable to the data. The Linked Data sources can adopt this approach to enrich federated queries with uncertainty information and, progressively, build a consensus-based Linked Data source. A set of other applications such as fake news detection (definition of a theory and logic for fake news), argumentation-based systems and even community-based data sources such as DBpedia can use mUnc to enrich their future content with uncertainty information. Furthermore, mUnc shares the same objectives as the W3C Credible Web community group  [20]  as for exchanging data which bears directly on credibility assessment while keeping standardization in data interchange.
We obtained retrievals with residuals less than 8% for about 90 samples. Samples were alternately dried or humidified, and size distributions were limited to diameters of less than 1. 0 or 2. 5 µm by using a cyclone. The single-scatter albedo at 532 nm for these samples ranged from 0. 59 to 1. 00 when computed with CAPS extinction and Particle Soot Absorption Photometer (PSAP) absorption measurements. The GRASP retrieval provided single-scatter albedos that are highly correlated with the in situ single-scatter albedos, and the correlation coefficients ranged from 0. 916 to 0. 976, depending upon the simulated solar zenith angle. The GRASP single-scatter albedos exhibited an average absolute bias of +0. 023-0. 026 with respect to the extinction and absorption measurements for the entire dataset.
These upward-pointing instruments have narrow fields of view that measure sky radiances at up to 28 scattering angles; AERONET's upward-pointing geometry is advantageous over satellite viewing geometries because the measurements are uncomplicated by direct surface reflectance. These instruments have another significant advantage over satellite instruments, though-they directly observe the sun's extinction after it has been attenuated through the atmosphere. Thus, the AERONET instruments measure AOD by application of the extinction law (e. g. ,  [15] , Equation (2. 23)) instead of inferring it from the scattering field, and this offers a tremendous advantage over downward-pointing satellite viewing geometries for determining aerosol absorption. Routine AERONET products include AOD, aerosol absorption optical depth (AAOD), single-scatter albedo (SSA), complex refractive index, size distribution, asymmetry parameter and phase function (data is available at aeronet.
gsfc. nasa. gov). The relative robustness of the AERONET retrievals (compared to satellite retrievals) as well as the uniformity of data processing at hundreds of locations with decades of measurements have rendered AERONET as the de facto standard for validation of satellite instruments and global aerosol models. Unfortunately, the spatial representation of any AERONET site is much smaller than a global model grid (a 1 • × 1 • global model grid is approximately 112 × 112 km 2 at the equator). For instance, the backbone of the AERONET absorption and size distribution retrievals are the almucantar radiance scans (a measurement sequence that scans the sky at a constant viewing zenith angle that is equal to the solar zenith angle). Thus, if all aerosols are located within 5 km of the surface and the solar zenith angle is θ • = 77 • , then the spatial representation of an almucantar scan is at best 43 km.
Traditionally, AERONET validation exercises involve heavily instrumented aircraft flying over an appropriate ground site at a variety of altitudes; the flight data is averaged over the altitude range of the aircraft and compared to the column retrievals. However, it is difficult to obtain many quality comparisons with this approach. Research flight hours are expensive, so flight missions tend to have multiple agendas that may not prioritize spirals over AERONET sites at the low sun elevations required for quality AERONET retrievals (i. e. , solar zenith angles greater than 50 degrees). Of course, long flight legs in clear skies may be used instead of spiral descents, but that inevitably results in much larger horizontal sample sizes for the aircraft instrumentation than is observed by the AERONET radiometer (thus adding noise to the comparison).
The validation sampling problem for these narrow field of view satellite instruments cannot be solved with large networks of surface radiometers or LiDARs, either. For instance, we compiled statistics for 1. 6 years of CALIPSO overpasses within 40 km and 30 min of Level 2. 0 AERONET almucantar retrievals (at ∼170 AERONET sites), and found only 40 coincident retrievals; this amounts to an average of 25 coincident retrievals per year. AERONET requires approximately one year of lag time after data acquisition to achieve Level 2. 0 status (because of post-field calibration), so it would take approximately 5 years to obtain 100 satellite/AERONET retrieval comparisons. That is too much time for too few comparisons, even if AERONET were robustly validated. Nonetheless, the AERONET retrieval products have been used extensively as validation tools for global and transport models  [21] [22] [23] [24] [25] [26] , computation of the direct radiative effect at various AERONET sites  [27] , as a possible surrogate for surface concentrations  [28, 29] , and the development of aerosol climatologies  [30].
g. ,  [38] [39] [40] [41] [42] [43] ), ground-based AERONET photometers and LiDARs  [44] , sky cameras [41], polar-nephelometer data  [45] , and surface measurements of AOD  [46]. In addition, GRASP has already been prepared to evaluate Sentinel-4 and 3MI/Metop data and adapted for producing many user-oriented products such as direct radiative forcing  [47]. Finally, we recognize that this laboratory approach has limitations as well, and we discuss some of them in Section 5. 3 (first and foremost, we do not actually use AERONET instruments for this experiment). Nonetheless, the approach described here provides a valuable "necessary but not sufficient" component of an algorithm assessment process, and should be included in NASA's program as a supplement to conventional validation techniques.
Method Although this work emphasizes evaluation of the AERONET retrieval algorithm, we are not actually using the AERONET product (or data from any of the AERONET radiometers, for that matter). Rather, we are working with a newer version of the algorithm called the GRASP  [36, 37]. The GRASP retrieval algorithm builds upon the legacy code that was first developed for the AERONET surface sun photometers. The algorithm uses kernels that are an expansion of the AERONET kernels, but the input module has enhanced flexibility so that the GRASP algorithm can be applied to a wide variety of airborne, satellite, and surface-based instruments. The GRASP algorithm version 0. 7. 4 is publicly available at www. grasp-open. com at the time of this writing.
The aerosol size and refractive index are then used to compute the SSA, extinction-to-backscatter ratio (lidar ratio), asymmetry parameter, and the phase function at all scattering angles. The SSAs provided by GRASP are verified with multiple independent measurements of aerosol absorption (α), extinction (τ), and scattering (κ). The size distributions provided by GRASP are verified with aerodynamic particle size measurements. The CAPS Monitor is manufactured by Aerodyne Research Inc. It uses very highly reflective mirrors in a cavity that is less than 30 cm in length to obtain very long optical paths (up to 2 km)  [54, 55]. We incorporated three CAPS instruments in our experiment to obtain particle extinctions at three wavelengths (0. 450, 0. 530, and 0. 630 µm). The CAPS instruments have a 1-s response time and a detection level of less than 2 Mm -1.
AERONET and GRASP Retrieval Algorithms AERONET is a network of sun and sky-scanning radiometers located at surface sites throughout the world (details may be found in reference  [11] ). The number of instruments in the network changes constantly, but 342 radiometers transmitted data to NASA GSFC on 9 August 2017. The radiometers have a narrow field of view (1. 2 degrees) and are mounted on programmable trackers that enable sky-radiance measurements as well as direct sun measurements. One particular set of sky-radiance measurements is called the almucantar scan, whereby the instrument scans the sky at a constant elevation angle that is equal to the solar elevation angle, and pauses at specific azimuth angles to measure radiance (nominally at four wavelengths: 440, 675, 870, and 1020 nm). These direct sun and almucantar sky-radiance measurements form the basis of the size distribution and complex refractive index retrievals in the AERONET database  [12].
Once particle size and complex refractive index are determined, the AERONET processing algorithm uses this information in a forward model to compute aerosol absorption optical depths (AAODs), SSAs, asymmetry parameters, and phase functions. An early accuracy assessment of the AERONET retrievals can be found in reference  [13] , but AERONET processing has been subsequently improved by incorporating a model of randomly oriented spheroids  [67]  into the AERONET retrieval  [68, 69]. Additionally, the AERONET sky-radiance measurements have been combined with satellite radiance measurements to enhance the information content of the retrievals  [70]. The GRASP code is based upon the same concept as the operational AERONET retrieval algorithm, so we can use GRASP to mimic the AERONET retrievals. We configured the GRASP code to mimic the routine AERONET processing by: 1.
subsampling the PI-Neph at the same scattering angles routinely sampled by the AERONET instruments, 2. constraining the code to infer real refractive indices between 1. 3-1. 6 and imaginary refractive indices between 0. 0005-0. 5, and disallowing refractive index variability with respect to size, 3. using 22 radii of equal lognormal spacing between 0. 05 and 15 µm to infer the volume size distributions, and 4. allowing sphere and spheroid shapes. Like AERONET, the GRASP code independently iterates the volume of particles at each radius, the complex refractive index of the particles, and the percentage of spherically shaped particles until the computed extinctions and radiances converge with the measurements. This results in particle populations with spectrally dependent complex refractive indices that are not constrained with lognormal-shaped size distributions.
Our configuration differs from the AERONET measurements in some important ways, though. For instance, the PI-Neph provides scattering phase functions at the 473, 532, and 671 nm wavelengths, whereas the AERONET instruments use the 440, 675, 870, and 1020 nm wavelengths. The multiple scattering that occurs in the atmosphere must be properly modeled in the AERONET retrievals, but it is not an issue with the STEAR experiment. Finally, the STEAR instruments and the AERONET instruments have different responsivities and sensitivities. All these discrepancies could cause the GRASP code to produce different results than the AERONET code for a given aerosol system. Nonetheless, this experiment provides important "necessary but not sufficient" evidence. That is, if GRASP is unable to provide robust retrievals with the STEAR apparatus, then it is likely that the AERONET is not providing robust retrievals as well.
Simulating AERONET Scans at Different Solar Zenith Angles The PI-Neph can obtain high angular resolution measurements at essentially all scattering angles between 4. 5 and 175 degrees. The AERONET field instruments, on the other hand, have a different geometry with less angular resolution and range. The almuncantar scans used for the AERONET inversion products maintain a fixed elevation angle (equal to the solar elevation angle) and pause at 28 azimuth angles ranging from 3-180 degrees (  [71] , and https://aeronet. gsfc. nasa. gov/new_web/Documents/ AERONETcriteria_final1_excerpt. pdf, Table  1 ). Consequently, the angular sampling varies with solar zenith angle and the maximum sampled scattering angle is equal to twice the solar zenith angle. High-quality Level 2 AERONET retrievals require 50 • < θ • < 77 • in the Version 2 dataset.
Additionally, the PI-Neph data was subsampled with a simulated solar zenith angle of 50 • for the GRASP input (e. g. , see the top panel of Figure  3 ). The blue circles of Figure  4  indicate retrievals with residuals of 8% or less, which is consistent with AERONET Level 2 quality control at high AOD. The white diamonds indicate retrievals with residuals greater than 8%. Blue circles in the left and right panels are identical, but the right panel has zoomed axes. Both panels indicate that the two datasets are highly correlated, with a correlation coefficient of cc = 0. 971 for data that comply with the 8% residual restriction. The GRASP algorithm has a bias of +0. 024 with respect to the in situ measurements when subject to the 8% residual restriction. Additional statistics for the blue circles are provided in Table  2.