dataesr/scientific-paragraphs-categorization huggingface dataset:
The meaning of the context element S P will be explained below. E is the environment, and pc is the program counter label. The concrete parts of the semantics, capturing the computation of the new memory M, are standard. The memory can only be modified by assignment. In the ASSIGNSCALAR rule, the assignment's lefthand side x is evaluated to a location consisting of a memory block b without an offset, i. e. , a scalar location. The right-hand side e is evaluated to a value v. The new memory is obtained by updating the value stored at block b in the memory to be ScalarVal v. The ASSIGNARRAYELEM rule is similar but more involved. The assignment's left-hand side x evaluates to memory block b with an integer index i. The memory M must contain an array arr at block b. The new memory M ′ is obtained by updating arr at position i and storing this new array at block b.
The concrete semantics of If statements uses an unspecified function istrue of type val ⇒bool to select one of the branches to execute. The concrete semantics of While loops evaluates the body once if the condition is true, then re-applies a While inference rule in the new memory configuration. A loop   terminates iff the condition becomes false at some point, in which case the WHILEF rule applies and performs no further changes to the memory. The monitor semantics deserves more detailed explanations. Consider first the expression Γ ′ = Γ(b := s) in the ASSIGNSCALAR rule. This updates the security label of the target block b to the new label s, which is computed from the label sl of the assignment's left-hand side's location, the label sv of the right-hand side value, and the current program counter label pc.
Inspection of memory blocks whose labels are below s in Γ ′ 1 does not yield any information to an attacker. The result only holds if the static analysis S P is admissible for the given program, i. e. , it safely overapproximates all aliasing in the program when started from a given memory configuration. Proof sketch. The proof of the soundness theorem proceeds by rule induction on the semantics. We will sketch the main idea of the soundness argument for assignments to scalars and for one branch of the evaluation of the If statement. In either case, the idea is to show preservation of s-equivalence and the 'less restrictive up to' relation by considering how the value and label of an arbitrary memory block b is modified by the program. Rule ASSIGNSCALAR. We may assume that there exist derivations in the semantics showing both E, S P , pc 1 ⊢ x ::= e, M 1 , Γ 1 ⇒ M ′ 1 , Γ ′ 1 and E, S P , pc 2 ⊢ x ::= e, M 2 , Γ 2 ⇒ M ′ 2 , Γ ′ 2.
Implementation notes We have implemented the program transformation sketched above as a plugin in the modular C analysis and transformation framework Frama-C  [12]. The current prototype handles programs with arbitrary data structures composed of arrays, pointers, and struct types. For the alias analysis S P needed by the transformation, we rely on Frama-C's built-in Value analysis, which computes both aliases for pointers and value approximations for numeric variables using intervals and other domains. The transformation is implemented as a transformation of the Frama-C AST, which can then be output as C code. At the time of writing, some details of real-world C programs are not yet handled by the analysis, which precludes us from giving a detailed experimental evaluation of our approach.
The main missing feature is the treatment of goto statements. These are inserted by Frama-C's AST normalization for early return statements inside conditionals and for continue statements in for loops. We are currently working on finalizing the handling of the information flows due to this kind of non-compositional control flow. Using Frama-C's support for code annotations, we allow security levels of variables to be specified as /*@ public */ or /*@ private */ at the point of declaration. The corresponding label variables are then initialized accordingly. Labels are tracked as integer values of 0 (public) and 1 (private) and are efficiently combined using the bitwise-or operator |. We do not currently support more general lattices; however, extending the current implementation to lattices that can be represented as bitvectors (up to 64 bits) is straightforward.
[11]  handles arrays, but the details are not described; the authors only mention that an array may 'consist[. ] of heterogeneously labeled fields'. This heterogeneous labeling is something our approach consciously avoids for soundness reasons, to avoid information leaks through array indices. In our approach, reading an array element always involves reading the array's summary label (see  Example 4). The authors do not describe any formal or informal proof of non-interference for their analysis. The other well-developed analysis for JavaScript is JSFlow  [9]  with its extended hybrid version  [8]. Both track the labels of array elements precisely, but a different notion of non-interference from ours is used: In this variant, it is not allowed to assign secret values to locations that previously held public values (the converse, overwriting a secret value by a public value, is allowed).
The monitor aborts the program if a violation of this policy is detected. In our approach, this would correspond to adding an assertion to every assignment statement. In contrast, our approach is more permissive and only uses such constraints at user-defined program points; as discussed above, our analysis is completely independent of any specific flow policy. The authors prove non-interference of both versions of JSFlow. In the literature, there are various static information flow analyses, often formulated as flow-sensitive type systems  [17, 10] , as well as further hybrid static/dynamic analyses that are somewhat comparable to ours  [13, 16]. Arrays are occasionally mentioned in connection with type systems  [17]  but, to our knowledge, never for the systems involving some dynamic monitoring.
In particular, the Pearson and Spearman correlation coefficients, root-mean-square error, and outlier ratio are computed to estimate the linearity, monotonicity, accuracy, and consistency of the metric's estimation of MOS, respectively. Finally, to compare the performance of two metrics, their performance index values are compared using statistical tests. Because of the classical performance evaluation procedure, almost all public image and video databases for quality assessment  [2]  were obtained using a test method that consists of a direct scaling of the stimuli, e. g. , the ACR/SS, DCR/DSIS, or DSCQS test methods  [3] ,  [4]  to obtain MOS (of differential MOS). Reciprocally, most performance evaluations of objective quality metrics, such as the studies from Sheikh The work presented in this paper was possible thanks to COST Action IC1003 (Qualinet).
In summary, indirect scaling is more natural and requires little to no training while providing more reliable results, which is particularly beneficial for crowdsourcing evaluations for example. There are very few databases for quality assessment that were obtained using the PC method. The major exceptions are the TID2008  [7]  and TID2013  [8]  databases, which were created using a test methodology derived from the PC method. However, the subjective scores provided in the TID databases still consist of MOS, which were derived from the different comparisons. Another exception is the EPFL scalable video database  [9] , which was created using both the PC method by considering all possible pairs and the more standard SSCQS method. Despite that both PC and MOS values are provided in the EPFL database, most papers that use this database to benchmark objective metrics only use the MOS values.
We use a common statistical test to compare the AUC values of two metrics. We believe that the classification errors and proposed ROC analyses provide a meaningful way to benchmark objective metrics from preference scores, without the need to convert them to MOSlike values. A MATLAB script to easily apply the proposed analysis tools can be downloaded via the following DOI link: https://doi. org/10. 5281/zenodo. 50493 The remainder of this paper is organized as follows. Different possible analysis tools to evaluate objective metrics from PC data are described in Sec. II. An example of application of the analysis tools is presented in Sec. III. Finally, concluding remarks are given in Sec. IV.
In this case, only data points corresponding to pairs with significant difference are considered. 3) Better/Equal-Worse ROC Analysis: this analysis illustrates the ability of the metric to determine whether stimulus A has similar or worse visual quality than stimulus B or if it has significantly better visual quality than stimulus B. In this case, all data points are considered. Figure  1  illustrates the procedure. First, the dataset is split into pairs with and without significant differences in terms of visual quality based on the pair comparison test. Then, different classes, namely, A = B, A = B, A < B, A > B, and A ≤ B are formed. For each ROC analysis, the histogram of the two corresponding classes is constructed as a function of the metric difference ∆OM between the two stimuli. Note that for the Different/Similar ROC Analysis, the absolute metric difference is used.
E. Significance of the Difference between Areas Under the Curve Various tests to determine the significance of the difference between two AUC values have been proposed. The methods can be parametric  [22]  or non-parametric  [23]. More recently, techniques based on bootstrap have been proposed as well  [24]. Similarly to Sec. II-C, comparison of the different approaches exceeds the scope of this paper. Most statistical toolboxes and packages implement several methods for both paired and unpaired ROC curves. In our example, we use the popular method proposed by DeLong et al. [23]. The provided MATLAB script includes the fast implementation of the DeLong's algorithm proposed in  [25] , as well as the parametric test proposed by  Hanley and McNeil [22].
III. EXAMPLE In this section, we show one example of application of the proposed analysis tools. For this purpose, we used a dataset of 5 high dynamic range (HDR) video sequences, which were encoded at 4 bit rates with HEVC HDR Profile and 9 algorithms in competitions submitted in response to the MPEG Call for Evidence on HDR video coding  [26]. The subjective evaluations were conducted on a full HD 42" Dolby Research HDR RGB backlight dual modulation display (aka Pulsar). Two video sequences were presented simultaneously in side-by-side fashion. One of the two video sequences was always the HEVC anchor. The other video sequence was the proponent to be evaluated, at the same (targeted) bit rate as the anchor. Subjects were asked to judge which video sequence in a pair ('left' or 'right') has the best overall quality.
N. Ponomarenko, L. Jin, O. Ieremeiev, V. Lukin, K. Egiazarian, J. Astola, B. Vozel, K. Chehdi, M. Carli, F. Battisti, and C. -C. J. Kuo, "Image database TID2013: Peculiarities, results and perspectives," Signal Processing: Image Communication, vol. 30, pp. 57-77, 2015.
This is the scope of the modular approach developed in ProRegio project to support frugal innovation process  [3]. According to this approach, a critical task on using modularity for co-evolution perspective is the definition of the mapping strategy between designed product modules and related possible production capabilities, which inform about the standard manufacturing processes and technologies able to provide the designed module with the desired features. In practice, the problem of mapping between product modules and related production capabilities can be extended to the more generic problem of system interoperability that aim to support the communication between two or more systems at conceptual, organizational and/or technical level  [4]. The first category of tools is dedicated to store and manage product architectures and features, based on a modular organization.
e. Audros PLM [6]) to facilitate the exchange of information needed to support a co-evolution strategy between product architecture and production system. Audros solution is French PLM systems which provide a set of flexible tools able to be adapted to any functional domain through an intelligent merge of the business process model, the data model generator and the user interface design. The next section presents a survey of the literature related to the problem of interoperability between enterprise software; the VFF and PLM tools taken into considerations are also described. Section 3 describes the proposed model-driven approach used for the definition of the connector framework. The last section describes the mapping and query mechanisms used for the implementation of the interoperability between the RDF store (used for the assessment of production capabilities) and the Audros tool.
The VFDM acts as a semantic repository supporting MPM applications to represent the characteristics and classify the types of a manufacturing system in terms of products types, manufacturing processes, and involved resources. The current version of the VFDM  [17]  is mainly based and extends Industry Foundation Classes (IFC) standard  [18] , automatically converted from an EXPRESS schema specification into an OWL ontology (named ifcOWL)  [19]. Thus, a production system is formalized as an RDF graph according to the VFDM ontology. RDF graphs can be serialized into files or can be stored into more advanced storage solutions (named triplestore or RDF store) providing also remote access and SPARQL endpoint to ease the input/output data exchanges (Fig  1 ). The VFDM facilitates the interoperability between different software tools if they are endowed with a specific a software connector taking care of input/output data conversion from the ontology format to proprietary data structures and vice-versa.
Since both the scope of product design and production system design are very broad, the mapping of data and the interactions between the two systems of data storage must be limited and flexible. For instance, data will be transferred from the PLM system to the RDF store as soon as a M. A. or a product module is created or modified. Specifically, in a PLM system, the completion of the validation workflow to endorse and formalize the new (or first) version of an M. A is the right moment to disseminate information within the connected Information Systems. Global interoperability scheme.
Implementation strategy based on SPARQL If the employed RDF store provides a SPARQL endpoint, then it is possible to query and update the knowledge base remotely using the SPARQL protocol. SPARQL  [20, 21]  is a query language developed and validated by the W3C (World Wide Web Consortium) allowing reading and writing semantic queries on RDF stores. Using the SPARQL protocol, this section presents the implementation process of different mapping rules defined in the section 3. 2. 2. The first step concerns the definition of mapping variables that implement concepts' mapping. These variables are used for the declaration of prefixes and objects. The last stage concerns the characterization of operations necessary for the assessment of production capabilities. RDF syntax consists of triples composed of a subject, a predicate and an object.
Product and related processes data export to "feed" the RDF store To implement the semantic mapping with the SPARQL language, mapping variables are defined based on PLM and RDF store objects ID (Table  1 ). Except for moduleID, manufAlterID and opeID, all the IDs characterize concepts exclusive to the RDF store. For reuse possibility those IDs will be constructed based on the objects they link and the type of property/relation they characterize. Thus it will be possible to retrieve them for further modification. (i. e. declaring new M. A for existing module).
Conclusion The presented approach allows an asynchronous and flexible data exchange between a PLM system and an RDF store and gives an answer to the problem of interoperability between corporate information systems. The strength of this approach comes from the use of standards as the ifcOWL ontology and SPARQL language. In future work, we foresee studying the possibility to develop domain ontology to better represent manufacturing-specific concepts while taking advantage of ifcOWL and SPARQL standards. Being developed in the project "ProRegio", the approach will be prototypically implemented together with industrial partners in several use cases. Thus, its feasibility and potentiality to support co-evolution strategy can be test on real use cases. Further works will concerns the extension of the mapping table with the ontology models to support more complex interoperability situations including other tools than MPM.
5 Thus, Pecht et al. 5  first proposed to use deterministic PoF models to predict reliability of electronic products, which comprises three steps: first, a deterministic model is developed based on PoF models to predict the TTF of the system; then, parametric uncertainties of the system TTF model are identified and represented by their Probability Density Functions (PDFs); finally, system reliability is predicted by propagating the parametric uncertainties based on probability theory. 6 CALCE at the University of Maryland further elaborated on the method and developed software to aid its application in electronic equipment. 7 In 2003, IEEE adopted the PoF-based reliability prediction method and recommended it as a general procedure for system reliability predictions. 8, 9 ] [12] [13] [14]  Using PoF models to predict the TTF of the system is the first and most important step in PoF-based system reliability prediction.
In reality, there are also larger-thebetter (LTB) parameters where p 6 p th indicates a failure, and nominal-the-best (NTB) parameters where both p 6 p th;L and p P p th;U indicate a failure. In practice, p S can be obtained by developing Physical Functional Models (PFMs). The PFM is a physical model which describes how the system functions. A PFM can be described conceptually as p S ¼ f PFM ðzÞ ð 7Þ where p S denotes the system's performance parameter, f PFM ðÁÞ denotes the PFM, and z denotes the input parameter for the PFM. A variety of software is available to help develop the PFMs for most commonly encountered engineering systems. For example, the PFM of electrical circuits can be developed by software like PSpice; for control systems, MATLAB Simulink can be used to develop the PFMs; for hydraulic systems, the PFM can be developed by software like AMESim.
Some elements of z are influenced by failure mechanisms and therefore degrade over time. It is the degradation of these elements that causes the degradation of p S over time. These elements are regarded as degradation sensitive parameters and denoted by z d. The degradation of z d can be modeled by corresponding PoF models. By substituting the degradation of z d into the PFM, we will be able to model the variation of p S over time. This motivates the system failure behavior modeling method presented in Fig. 3. The method comprises four steps. First, the PFM for the system is obtained. The PFM should be obtained through the corresponding domain knowledge. Commercial software such as PSpice, MATLAB Simulink and AMESim could be used to develop the PFMs. Then, the degradation sensitive parameters, z d , are identified.
Acknowledgements This work has been performed within the initiative of the  Center for Resilience and Safety of Critical Infrastructures (CRESCI , http://cresci. cn). The researches of  Dr. Zeng  and  Prof. Kang  are supported by the  National Natural Science Foundation of China  (No. 61573043 ). The research of  Prof. Chen  is supported by  National Natural Science Foundation of China  (No. 51675025 ). The authors would like to thank  Miss Xiao Ying ,  Mr. Fan Kejia  from  Beihang University  for their help in developing the AMESim model for the HSA, and  Mr. Liao Xun  from  Beihang University  for his generous discussions and suggestions.
iLOVECLIM climate model The climate model iLOVECLIM version 1. 0 is an earth system model of intermediate complexity 115 and a code fork of the LOVECLIM 1. 2 model  (Goosse et al. , 2010). iLOVECLIM has been further developed to include the explicit computation of water isotopes in the atmosphere, ocean, continental surface  (Roche, 2013)  and icebergs. Following the description of  Bügelmayer et al. (2015a)  we state the main characteristics of the ocean, atmosphere and vegetation models. The included atmospheric model ECBilt  (Opsteegh et al. , 1998 ) is a quasi-geostrophic, spectral 120 model calculated with a time step of 4 hours on a horizontal T21 truncation (5. 6 • in latitude/longitude) and three vertical pressure levels  (800, 500, 200 hPa). The cloud cover is prescribed according to climatology (ISCCP D2 dataset,  Rossow et al.
set-upUsing the iLOVECLIM model, Roche et al. (2014)  found the best agreement between simulated and observed δ 18 O calcite of Heinrich event 1 when applying a strong freshwater forcing of about 0. 2 Sv over 300 years. This value depends of course on the model and the duration of the applied forcing. 5 Clim. Past Discuss. , doi:10. 5194/cp-2016-31, 2016 Manuscript under review for journal Clim. Past Published: 17 March 2016 c Author(s) 2016. CC-BY 3. 0 License.
enth Framework Programme (FP7/2007-2013)/ERC grant agreement no 339108. M. Bügelmayer-Blaschek is supported by NWO under project number n • 864. 09. 013, D. M. Roche is supported by NWO under project number n • 864. 09. 013 and by CNRS-INSU, C. Waelbroeck by CNRS-INSU. The authors wish to thank Catherine Ritz for the use of the GRISLI ice sheet model. Institute Pierre Simon Laplace is acknowledged for hosting 14 Clim. Past Discuss. , doi:10. 5194/cp-2016-31, 2016 Manuscript under review for journal Clim. Past Published: 17 March 2016 c Author(s) 2016. CC-BY 3. 0 License. the iLOVECLIM model code under the-LUDUS framework project (https://forge. ipsl. jussieu. fr/ludus). This is 485 NWO/AC2ME contribution number 11. 15 Clim. Past Discuss. , doi:10. 5194/cp-2016-31, 2016 Manuscript under review for journal Clim.
Acknowledgements. This work is a contribution to the  NWO/VIDI  " AC2ME " project (n •  864. 09. 013 ) and to the  ACCLIMATE ERC  project funded by the  European Research Council  under the  European Union's Sev-Clim. Past Discuss. , doi:10. 5194/cp-2016-31,2016 Manuscript under review for journal Clim. Past Published: 17 March 2016 c Author(s)  2016. CC-BY 3. 0 License.
Examples of the algorithms applied to offshore pressure data from DART buoys are described by  Rabinovich et al. (2011)  and  Rabinovich and Eblé (2015) , whilst other authors focus on the application of this kind of algorithm to coastal tide gauge data such as  Beltrami et al. (2011) ,  Bressan et al. (2013)  and  Pérez-Gómez et al. (2013). Several years ago, Puertos del Estado (hereafter PdE) implemented such software for the Spanish REDMAR tide gauge network, in response to the small tsunami generated by the Algerian earthquake, in May 2003, in the Western Mediterranean  (Alasset et al. , 2006; Sahal et al. , 2009; Vich and Monserrat, 2009; Vela et al. , 2014). The objective was real time tsunami detection and the transmission of alert messages to the network and harbor operators during future events.