dataesr/scientific-paragraphs-categorization huggingface dataset:
Analysis setup We conduct the experiment in Python with the CPLEX solver  [39]  to evaluate the performance of the above-mentioned algorithms in a 5G network scenario. In this scenario, Base Stations (BSs) are equally distributed in a grid area, and there is a network link between neighboring BSs. There are also hosting nodes in different network parts (i. e. , BS, core, and cloud), and their capacities are reduced as they descend from cloud to BSs. We use the three types of applications specified for 5G networks, with the following characteristics  [40] : • massive Machine Type Communications (mMTC) has low resource usage, high deadline, and a large number of users; • Ultra Reliable Low Latency Communications (URLLC) has low resource usage, a strict deadline, and a small volume of users; • enhanced Mobile Broadband (eMBB) has high resource usage, a medium deadline, and an intermediate number of users.
Purpose -This study aims to explore US hotel guests' privacy concerns with a twofold aim as follows: to investigate the privacy categories, themes and attributes most commonly discussed by guests in their reviews and to examine the influence of cultural proximity on privacy concerns. Design/methodology/approach -This study combined automated text analytics with content analysis. The database consisted of 68,000 hotel reviews written by US guests lodged in different types of hotels in five European cities. Linguistic Inquiry Word Count, Leximancer and SPSS software were used for data analysis. Automated text analytics and a validated privacy dictionary were used to investigate the reviews by exploring the categories, themes and attributes of privacy concerns. Content analysis was used to analyze the narratives and select representative snippets.
Methodology Setting and data source This study used hotel reviews from TripAdvisor, which has proved to be the most suitable and popular data source for studying tourists' evaluations and preferences  (Filieri et al. , 2019; Ma et al. , 2018; Marine-Roig, 2019; Volo, 2019; Xie et al. , 2014). Recent studies have shown that social media is a reliable source of data that is representative not only of its users but also of the general population  (Ma and Kirilenko, 2021). The sample of the present study consisted of the OTRs of American guests staying at hotels across five major European cities (Rome, Paris, Amsterdam, Barcelona and Istanbul). These cities were selected with the aim of differentiating privacy concerns related to different hosting cultures. Furthermore, the selection of these cities was based on international overnight guests' volumes and destinations' revenues, as ranked by the Global Destination Cities Index (2015), which is a reliable metric for the hospitality industry  (D'Acunto et al.
Data collection and data set characteristics The collected data concerned hotel characteristics (e. g. independent hotels vs hotel chains), reviewer specifics (e. g. gender and age class), trip purpose, the text body of the reviews and star ratings. To investigate American guests' reviews, it was deemed appropriate to only use OTRs that were originally written in English, the language of the culture under investigation. The authors acknowledge that American passport holders can belong to other language groups, but this operational choice allowed for linear use of the privacy dictionary and considered that English is a mostly international language used by many firstgeneration and second-generation Americans during travel. The reviewer's country of origin was considered as a filter variable  (Filieri et al.
, 2011). This dictionary was constructed and validated by its authors through interviews and focus groups from different privacy-sensitive (offline and online) contexts. This privacy dictionary was used in the present study to track the presence of privacy elements in OTRs and to systematically measure specific psychological components (i. e. privacy categories) in such a way that words and phrases became the observed variables  (Lowe, 2004). Given that these variables have not yet been adopted in tourism research, the present study explored these variables within the hospitality context. Table  3  shows the categorization and scope of each privacy category in accordance with the codification of  Gill et al. (2011). This tool was specifically developed for the Linguistic Inquiry Word Count (LIWC) software application.
Following the original authors' suggestion, LIWC software Most Americans like their privacy  (Pennebaker et al. , 2007; Pennebaker et al. , 2015)  was adopted for the text analysis of guests' reviews, with the aim of detecting their reported privacy concerns. The privacy categories were contextualized by combining quantitative automated analysis with traditional qualitative content analysis. After a LIWC analysis was run with the privacy dictionary, a manual content analysis was performed on the reviews to explore the validity of the dictionary within the hospitality context. Examples of snippets covering each privacy variable are provided herein, offering context-specific narratives of single privacy categories. To operationalize the construct of cultural proximity, the reviews were grouped into three clusters, namely, independent hotels, American hotel chains (i.
Software and data analysis In the present study, LIWC was used to detect privacy elements using the privacy dictionary; LIWC is a text mining software application that allows researchers to detect terms belonging to predefined psychologically and cognitively coherent categories  (Pennebaker et al. , 2003; Pennebaker et al. , 2007). Leximancer software was adopted to determine and cluster the most frequently recurring concepts and themes regarding privacy embedded in the text of the guests' reviews. Finally, SPSS was chosen to carry out data elaborations on the LIWC results (e. g. analysis of variances (ANOVAs), post hoc tests and correlation analyses) and to produce descriptive statistics. This study was exploratory in nature, and therefore, did not test for any causal relationships at this stage.
To process documents, LIWC carries out text mining and converts words into numbers according to the presence and occurrence of specific words belonging to each dictionary category. Outputs are mostly expressed as percentages of total words or standardized composites (for summary variables only, i. e. analytic, clout, authentic and tone;  Pennebaker et al. , 2015). Leximancer software was used to analyze the content of the OTRs, cluster them and describe the privacy discourse by identifying the most frequently recurring privacy concepts and themes and by providing concept maps  (Leximancer, 2018). Leximancer software was chosen for this study because it can easily handle large volumes of unstructured data text, such as user-generated content. By converting text from natural language into semantic patterns in an unsupervised manner, this software identifies concepts within the text and extracts lexical co-occurrence information  (Robson et al.
, 2013). By means of an algorithm, Leximancer software analyzes a given text and depicts the relative importance of words, concepts and themes through size, space and color coding by outputting a conceptual heat map in which the main themes emerge in association with different colors according to their prominence  (Cheng and Jin, 2019; Robson et al. , 2013; Smith and Humphreys, 2006; Wu et al. , 2014). Finally, the SPSS package was used for mean value comparisons (i. e. ANOVAs), descriptive statistics and graphic elaborations.
The third identified theme was "breakfast" (6%), which included concepts such as "bar" (3%), "people" (2%) and "morning" (2%). For example, this female guest reported some privacy concerns with respect to her breakfast experience as follows: "The "buzz" from so many people made it impossible to use the Lounge as a place to relax for even a few minutes and sometimes, even to have breakfast comfortably" (female, age class: 50-64 years, Columbus, OH, a business trip to Amsterdam). Finally, the themes "building" and "problem" were isolated by the software as the farthest according to their association with privacy concerns discussed in guests' reviews. Figure  2  shows the Leximancer concept map with the main themes associated with privacy concerns and Figure  3  reports the ranked concept list.
The role of culture and privacy discourse across different hotel types (RQ3) To investigate the cultural proximity effect, OTRs were grouped into three clusters, namely, independent hotels, American hotel chains and non-American hotel chains. Descriptive statistics are provided in Table  5. A preliminary analysis of customer satisfaction levels (i. e. ratings) was performed to explore US guests' preferences when traveling to Europe. An ANOVA of the ratings showed that the American travelers tended to prefer independent hotels (4. 27) and American hotel chains (4. 26) when traveling to Europe while non-American chains had the lowest ratings overall (4. 12; F = 71. 051; p < 0. 0001). It was deemed appropriate to explore US guests' average ratings over time (Figure  5 ). The data showed an increasing preference for independent hotels (i.
Conclusions The present study was a response to the call for research on hotel guests' privacy concerns regarding their physical environments  (Tussyadiah et al. , 2019). This study investigated privacy concerns by exploring OTRs with text analytics, a reliable and validated privacy dictionary and content analysis. Previous studies have mainly investigated the role of physical privacy with regard to service experience  (Otto and Richie, 1996) , the relationship between guest privacy and employees  (Huang and To, 2018; Keung, 2000) , the importance of privacy for celebrities  (Goh and Law, 2007)  and families  (Schänzel and Lynch, 2016)  and special settings, such as automated motels  (Kim and Kim, 2018)  and shared accommodations  (Ranzini et al. , 2020; Wang et al. , 2020). Unlike previous studies, the present study adopted text analytics and big data to explore the sensitive topic of privacy, benefiting from the use of self-reported data  (Roster et al.
Tourists from different cultures may experience privacy concerns differently. Second, the data set was based solely on TripAdvisor reviews. Although TripAdvisor is the most frequently used travel review platform, comparisons with other online review platforms would be beneficial. Future investigations would benefit from addressing the aforementioned limitations by exploring different platforms, cultures and subcultures. Furthermore, future studies could explore privacy concerns in combination with the different languages used in reviews. Indeed, the literature has shown that review languages have some effects on hotel ratings  (Schuckert et al. , 2015). Appropriate text analytics would be needed to compare different languages using the privacy lexicon.
UMR 356 Economie Forestière is supported by a grant overseen by the French National Research Agency (ANR) as part of the "Investissements d'Avenir" program (ANR-11-LABX-0002-01, Lab of Excellence ARBRE). The authors would like to thank the attendees of the Laboratory of Forest Economics seminar (Nancy, France, 2014), the Climate Economics Chair seminar (Paris, France, 2014), the Paris-Ouest University seminar (Paris, France, 2014), the World Congress of Environmental and Resource Economists (Istanbul, Turkey, 2014), and the congress of the French Association of Environmental and Resource Economists (Montpellier, France, 2014), for their comments and feedbacks on this work. Authors also thank Eric Nazindigouba Kere.
Characterization of safe-by-design fluorescent PLA-NP Following a safe-by design strategy to produce nanocarriers as simply as possible, but having high biocompatibility, Poly(D,L lactic acid) particles, were prepared by nanoprecipitation without the addition of surfactant or stabilizer. During this process, the small length of the PLA (<50,000 g/mol) chains enables enough negatively charged carboxyl groups to be exposed at the surface of the PLA-NP, ensuring colloidal stabilization  [16]. Importantly, residual solvents (water and acetone) were gently evaporated until their minimal concentration respected the standards established by the European pharmacopeia for human use, as assessed by gas chromatography quantification (data not shown). The visualization of PLA-NP formulations was made possible by the encapsulation of different hydrophobic fluorophores into the polymeric matrix.
Stereo-microscope acquisitions of individual zebrafish were put through an ImageJ macro, from which an internalization score was calculated, based on the amount of red PLA-NP fluorescence associating with the green macrophage fluorescence signal, relative to the total amount of red PLA-NP fluorescence detected from the whole fish. Histograms represent the mean internalization score and each dot represents the score of an individual zebrafish. From 30 min to 24 h post-injection, more NP were internalized by macrophages (+56%). (B) Flow cytometry analysis of the fraction of macrophages that had internalized fluorescent PLA-NP relative to the whole population of macrophages, 10 min to 24 h after intravenous injection into 3 dpf zebrafish. Histograms represent the mean percentage of PLA-NP positive macrophages at different time-points following administration, with each dot representing the data acquired from 10 pooled larvae.
Live-imaging Following PLA-NP (0,5%) administration, stereomicroscope (Leica DFC365FX -1. 0× planapo lens) acquisitions were performed on 3 dpf zebrafish larvae that were maintained under anesthesia (buffered tricaine: 120 μg/mL) and placed onto an agarose support. For the high-resolution live-imaging acquisitions, zebrafish larvae were maintained under anesthesia (buffered tricaine: 120 μg/mL), embedded in 1,5% low-melting agarose onto the coverslip of a round petridish. Images/videos were then acquired at 28 • C using the Zyla camera of a Dragonfly spinning disk confocal microscope (Andor), 40 μm pinholes and a 60×/1. 2-water immersion objective. Acquisitions were performed using the Fusion software, while image/video analysis was made using both IMARIS and ImageJ softwares.
Acquisitions were made using the Zyla camera of a Dragonfly spinning disk confocal microscope (Andor), 40 μm pinholes and a 60×/1. 4-oil immersion objective. Acquisitions were performed using features from the Fusion software, while image analysis was made using both IMARIS and ImageJ softwares. Some acquisitions were made using a SP5 confocal microscope (Leica).
Acknowledgement We thank the  Norwegian Research Counsil  for funding ( FRIMEDBIO-No 144642 ). This work was also supported by the  ANR  project  Fish-RNAVax  ( ANR-16-CE20-0002-01  and  ANR-16-CE20-0002-03 ) and by  Euronanomed III (Flunanoair). We thank the  PRECI fish facility  ( L. Bernard  and  R. Renard ), the  AniRA-Cytométrie platform  ( T. Andrieu  and  S. Dussurgey ), the  PLATIM-Microscopy plateform of SFR Biosciences Gerland-Lyon Sud  ( UMS3444/US8 ). We would also like to thank the Primatiss histology plateform ( C. Lethias  and  N. El Kholti ) from  IBCP , the  CTμ (Centre Technologique des Microstructures ) electron microscopy plateform from  UCBL , the  EM-lab  from the  university of Oslo  ( N. Roos  and head engineers), the fish facility (AC. Tavara and  J. Santana ) of NCMM, the  Oslo NorMIC imaging plateform  ( O.
At the same time, query provenance is essential for some applications given the central role of data integration in KG construction and maintenance. The provenance of a query result is an expression that encodes the "sources", i. e. , relationships/triples, and processes that led to its computation. Reification is a prerequisite for provenance, as we need to identify the relationships in a KG to annotate query answers. For example, the triples in the RDF graph of Figure  1  have been reified with identifiers of the form. Notable paradigms for query provenance are lineage, why-, and how-provenance  [10]. Consider, for instance, the query that asks the KG in Figure  1  for people with an occupation who were awarded a Nobel Prize in Literature (NPL). The answers to this query are Gabriela Mistral (GM) and Olga Tokarczuk (OT).
This property makes polynomials solid proofs that can be used in multiple applications  [18]. It has been shown that commutative semirings cannot explain the provenance of non-monotonic queries, that is, queries where an answer can cease to be an answer after adding data to the database  [19, 20]. To guarantee this property, Geerts et al. [20]  propose the spm-semiring formalism. Spm-semirings extend polynomial semirings with a difference operation ⊖ that can model non-monotonic SPARQL operators ("spm" stands for SPARQL minus). Geerts et al. prove that spm-semirings can model provenance for monotonic and non-monotonic SPARQL queries, however they do not show how to compute such provenance.
Furthermore, TripleProv counts on its own customized engine that stores data as molecules, i. e. , star patterns. Hence, it cannot be deployed on top of existing engines in contrast to our approach. By relying on query rewriting, solutions such as Perm  [21]  and GProM  [4]  can be deployed on top of existing engines. Nevertheless, their annotations are not always suitable for SPARQL queries because these solutions are designed for relational databases. Moreover, both GProM and Perm rely on the semirings framework, thus their annotations do not commute with homomorphisms for nonmonotonic queries. Other solutions exploit provenance annotations for specific applications. Avgoustaki et al. [6]  propose a framework to capture the triple and attribute provenance polynomials of data added via SPARQL monotonic insert updates.
The algebra defined above is called K-annotated SPARQL algebra, which is shown to hold the property of commutation of homorphisms according to Geerts et al. [20]. Definition 3. 2. Given an spm-semiring homomorphism ℎ : K → K ′ , a query  is said to hold the commutation with homomorphisms property if for any graph  it satisfies ℎ(   ) =  ℎ (). Commutation with homomorphisms is a desired property because it allows specializing abstract provenance polynomials for different applications such as multiplicities, trust scores, cost, deletion propagation, and probabilistic databases  [2]. For example, consider the K-graph  depicted in Figure  1 , and the query  = select ?person where (?person, award, NPL) diff (?person, occupation, novelist) 𝑟 = 𝑞(𝑎 1 , 𝑎 2 , 𝑎 3 ), and  is 𝑞 𝑎 1 𝑎 2 𝑎 3 𝑢 𝑎 1 𝑎 2 𝑎 3 𝑞 1 𝑞 2 𝑞 3 and let  M = {?person ↦ → GM} and  T = {?person ↦ → OT} be two mappings corresponding to the solutions of the first triple pattern.
RDF and SPARQL allow multiple ways to reify the data  [16, 27]. A reification scheme is a function Reify that receives an -relationship  and a term  ∈ I, and returns a graph Reify(, ), called the encoding of  reified as. A reification scheme defines an encoding of N[ ]-graphs  as regular graphs   (i. e. , without annotations). We write Reify -1 (  ) to denote the N[ ] graph defined as follows  4  :  1  depicts two RDF reification schemes. The Wikidata scheme is shown in the first row. Wikidata uses the prefix wdt for the original predicates, i. e. , predicates in a non-reified relationship such as wdt: (left cell). The prefix wdt is replaced by the prefixes p and ps to create the new relationships p:, ps: that link the new reification entity  with the subject and the object of the original relationship (right cell).
The second row illustrates a scheme to codify -ary relations in RDF (depicted as an hyper-edge on the left), and is similar to the codification used to represent relational databases in RDF according to the direct mapping strategy  [39]. Reify -1 (𝐺 𝑟 ) ((𝑠, 𝑝, 𝑜)) = ⨁︁ 𝜇 ∈ Reify(𝑝 (𝑠,𝑜),?u) 𝐺 𝑟 𝜇 (?u). Table Reification schemes are generalized for basic graph pattern by admitting variables in the input relationships and basic graph patterns in the output. For example, the relationship queries  1 = wdt:q(?x, ) and  2 = q( 1 , ?x,  3 ) that result from introducing variables in the relationships in Table  1  are reified as follows: Reify( 1 , ?u) = (?x, p:q, ?u) and (?u, ps:q, ), Reify( 2 , ?u) = (?u, q:1,  1 ) and (?u, q:2, ?x) and (?u, q:3,  3 ).
Extending the K-annotated sparql Algebra The K-annotated SPARQL algebra proposed by Geerts et al. [20]  -and described in Section 3 -is not defined for all the SPARQL operators considered in this paper. For instance, the empty basic graph pattern and the operator optional  (defined in SPARQL 1. 0) were not studied by Geerts et al. Moreover, the operators minus and bind succeed the publication of the spm-semirings formalism. In this section we extend the K-annotated SPARQL algebra to account for the missing operators, except for aggregate queries because they are not expressible with the spm-semiring  [2]. Given a K-graph , the semantics of the empty basic graph pattern are given by the identities {}  ( ∅ ) = 1 K and {}  () = 0 K if  ≠  ∅ , where  ∅ is the empty mapping. We highlight that producing answers from no data is a distinctive feature of SPARQL with respect to relational algebra, since in relational algebra all answers are generated from at least one tuple in the database.
EVALUATION We evaluate the viability of SPARQLprov to compute howprovenance by measuring the runtime overhead w. r. t. the original query. The evaluation is carried out on Virtuoso and Fuseki, two standard RDF/SPARQL engines, using real and synthetic data. In Section 5. 1, we evaluate the response times of each phase of SPAR-QLprov (rewriting, execution, and decoding) as well as the impact of the reification scheme used to model the data. In Section 5. 2, we conduct a scalability analysis and consider queries with aggregates. Section 5. 3 evaluates our approach on a real-world dataset, i. e. , Wikidata  [42]. Finally, in Section 5. 4 we compare our solution with two state-of-the-art approaches for how-provenance. All experiments were conducted on a machine with an AMD EPYC 7281 16-Core Processor, 256GB of RAM, and an 8 TB HDD disk.
Algorithm phases and reification scheme For this evaluation we use Watdiv  [1] , a state-of-the-art performance benchmark for RDF/SPARQL engines. Watdiv offers a data generator to build synthetic datasets of different sizes, and 20 query templates each containing 10 instantiated queries. The query templates are divided in four categories; linear queries (L), star queries (S), snowflake-shaped queries (F), and complex queries (C). We also introduce 5 new query templates to account for non-monotonic queries (O). These queries were constructed by surrounding one of the triple patterns in the linear queries with an optional clause.
This happens because non-monotonic operators translate into more complex rewritten queries. We also find that the effect of reification is particularly pronounced for the standard reification scheme. We observe that on average, 82% of the provenance queries' runtime corresponds to the overhead induced by the reification scheme in Fuseki (52% for Virtuoso). In contrast, the average overhead for the named graphs (0. 3% Virtuoso, 44% Fuseki) and the Wikidata (18% Virtuoso, 30% Fuseki) reification schemes are lower. This can be explained by the fact that reification introduces additional triples that complexify query execution. Besides, standard reification introduces more triples than any of the other schemes. Although in general Virtuoso outperforms Fuseki, there are some cases where the provenance query is slower on Virtuoso.