dataesr/scientific-paragraphs-categorization huggingface dataset:
Chemo-informatics base of UManSysProp The discipline of chemo-informatics typically concerns the use of both software and computational hardware techniques applied to a range of problems in chemistry. The emergence of the open-source movement has lead to a wealth of chemoinformatics software made available, including OpenBabel, which acts as the molecular parsing software behind the service described here. OpenBabel  (O'Boyle et al. , 2008)  is a cross-platform suite of tools. Features include the ability to interchange chemical file formats and sub-structure searching, the latter particularly relevant. For more information, the reader is referred to the OpenBabel wiki (http: //openbabel. org/). OpenBabel comes with wrappers for numerous languages including Perl, Ruby, Java, and Python. Here we use the  ear notation using short ASCII strings is the SMILES format, a simplified chemical notation that allows a user to represent a two-dimensional (2-D) chemical structure in linear textual form.
For example, the SMILES notation for carbon dioxide is O=C=O, whereas cyclohexane is represented as C1CCCCC1. UMansSysProp uses SMILES for several reasons. The notation is commonly employed in commercial and public software for prediction of chemical properties. It can be imported by most molecule editors for conversion into 2-D/3-D models and has a wide base of software support and extensive theoretical backing (www. daylight. com). Common database searches for organic molecules include NIST (http://webbook. nist. gov/chemistry/) and the National Chemical Database service (http://cds. rsc. org/). From these, the SMILES representation of individual molecules can be found. In Table  1 , SMILES for common inorganic ions are provided along with a selection of organic compounds used by  O'meara et al.
Code availability In addition to providing the online portal for users, who do not want to use source code, and the JSON API for linking with our web portal without using a web browser, we also provide the source code for all predictive techniques provided on the site, covered by the GNU GPL license to encourage development of a user community. We have released this via a Github repository https://github. com/loftytopping/ UManSysProp_public. git, which has an associated DOI for the exact model version given in this paper as provided by the Zenodo service (doi:10. 5281/zenodo. 45143). The documentation for each tool can viewed on the UMan-SysProp API documentation page. Calling any of the tools will (in the event of success, given a valid SMILES or temperature quantity, for example) return a Result instance.
Acknowledgements. The authors would like to acknowledge  NERC  grants  NE/H002588/1 ,  NE/J009202/1 , and  NE/J02175X/1  for enabling  Mark Barley  to perform SMARTS library constructions and property prediction comparisons. David Topping  was similarly funded through the  National Centre for Atmospheric Science (NCAS). The authors would like to thank  Andreas Zuend , of  McGill University , for his discussions on automating functional group selections for the AIOMFAC method. The authors would also like to thank  Markus Petters , of  North Carolina State University , for discussions on the benefit of open source. Edited by:  A. Archibald.
To capture and characterize situations, we scan their environments (sensors and smart devices) and reason upon context changes in which multimodal and distributed behavioral adaptation is required. Our goal is to manage transparently all of the functionalities and additional modules that users may require in an ideal situation not available nowadays. Our proposed platform is implemented using an ontology-based approach. This ontology maintains semantic quality information in heterogeneous service providers. We particularly focus on context-aware e-health mobile applications. To keep capturing the context in an efficient way, we need to have a platform able to monitor and handle continuous context changes. The distributed/centralized context monitor and event manager collect and manage any important information that could be important for the context regardless of its source and store it in a database.
Our platform uses semantic technologies and the concept of multi-devices' context data representation to facilitate a seamless and interactive media service in a common contextual mobile environments. Section 2 deals with related works in context-aware software platforms and possible types of adaptations. Section 3 focuses on our contribution, i. e. , the ASSACR framework. In this section, we will detail our smart semantic-based context-aware service selection strategy. Section 4 describes our adaptation platform architecture, i. e. , the Kali-Smart platform. Section 5 validates our proposal, and Section 6 concludes the paper with some future works.
2. The Semantic Context-aware Services Management Layer is responsible for managing the adaptation process and providing a continuous response to a user by adapting dynamically its context provisioning paths according to the change happened during its execution. This core layer relies on the following components:  -Situation Reasoner: This is based on the JESS (Java Expert System Shell) inference engine  [14] , executes the SWRL (Semantic Web Rule Language) situation rules and infers the current situations. The SWRL situation rules include user-defined rules written and executed on the available context information in order to infer additional knowledge and conclusions about it. We choose to follow the rules-based approach for the basic advantage that a user can simply reconfigure the behavior of the environment by defining new rules, without having to change the source code.
-Action Reasoner: which is based on the JESS inference engine, as well  [15] , executes the SWRL service rules and determines the appropriate actions according to the deduced situations. -Prediction Reasoner: is responsible for generating predictions about the activity and situations happening in the environment. • Service Controller is responsible for re-routing the data transfer to another path when there is poor QoS, connection failure, low battery, etc. , in order to ensure service continuity and perform reconfiguration changes at the Kalimucho server. - -Service Deplorer: the configuration file created in the previous phase is copied in the Kalimucho cloud server, and all mobile devices affected by the equivalent path and the new services configuration are saved.
Functional Model of the Smart Semantic-Based Context-Aware Adaptation Platform The objective of the smart semantic-based context-aware platform is to provide a suitable service to users. This service has to fit with the usage context, such as: physical characteristics of the device, the bandwidth, the user's wishes, etc. An overview of the functional model of the smart semanticbased context-aware platform is provided in Figure  2. As shown in Figure  2 , the pervasive environment represents user, sensors, smart devices, smart objects and services. The pervasive environment is supervised by the Context Monitor and the Service Controller. The Context Monitor collects contextual information from the pervasive environment, which represents row and heterogeneous data, analyses and interprets these data with respect to high level information, which will present the adaptation of the system behavior and will be reported to the Service Controller.
Context Modeling The main objective of our approach is to improve the efficiency and accuracy of users' adaptations tasks. This objective is achieved through finite sets of semantic relevant adaptation services and various users' contexts. A user has a context in which he or she wishes to adapt his or her multimedia documents within a specific activity in a known time and location using one of the offered modalities; any smart service can be used in a local way or using the cloud, which allows him or her to handle the data storage that he or she needs to run his or her applications. As shown in Figure  2 , the pervasive environment represents user, sensors, smart devices, smart objects and services. The pervasive environment is supervised by the Context Monitor and the Service Controller. The Context Monitor collects contextual information from the pervasive environment, which represents row and heterogeneous data, analyses and interprets these data with respect to high level information, which will present the adaptation of the system behavior and will be reported to the Service Controller.
Implementation of Kali-Smart Firstly, based on OWL languages, we have implemented our ontology model using the Protégé tool. Protégé  [20]  is an open-source platform that provides a growing user community with a suite of tools to construct domain models and knowledge-based applications with ontologies. In addition, it implements a rich set of knowledge-modeling structures and actions that supports the creation, visualization and manipulation of OWL ontologies (see Figure  14 ). Future Internet 2016, 8, 48 17 of 26 implements a rich set of knowledge-modeling structures and actions that supports the creation, visualization and manipulation of OWL ontologies (see Figure  14 ). SWRL as a rule-based context inference language is used to infer situations that are related to the user's environment and user's devices, such as home temperature situations and battery situations.
system detects abnormal situations representing too high temperature and too low humidity, the "Fire Station Call" service is automatically triggered, and the service notifies the emergency status to the nearby fire station (Table  5 ). A first simple scenario is made of a User A, which is at home using a Samsung smartphone. User A can use our platform to monitor his or her health in a more practical way. He or she uses an intelligent environment (smart home), which includes smart devices, sensors (glucose meter, weight scale, video camera) and services. As proposed in our architecture, the principal role of the Context Monitor is to supervise User A in order to detect abnormal situations. The Context Monitor collects and preprocesses context information from the smart sensors and stores it in a database.
Then, it represents and reasons about this context using our ontology and Context Reasoner in order to deduce the current situations that are reported to the Service Controller. The latter is responsible for providing the appropriate service to the user according to the inferred situations. In fact, as illustrated in Figure  18 : (1) contextual information is collected from the smart home by the Context Collector, and it is stored in a database; this means that the information simulated via the prototype interfaces is stored automatically in a database; (2) this information is pre-processed by the Context Pre-processor and (3) converted into a triple pattern in OWL format and inserted in our ontology by the Context Translator; then, (4) the Context Reasoner uses the Situation Inference Engine, which is based on JESS, which executes the Situation Rules presented in Table  2  (translated in SWRL by Constraint Translator) and infers the current situations.
These situations are transmitted to the Service Controller. A first simple scenario is made of a User A, which is at home using a Samsung smartphone. User A can use our platform to monitor his or her health in a more practical way. He or she uses an intelligent environment (smart home), which includes smart devices, sensors (glucose meter, weight scale, video camera) and services. As proposed in our architecture, the principal role of the Context Monitor is to supervise User A in order to detect abnormal situations. The Context Monitor collects and preprocesses context information from the smart sensors and stores it in a database. Then, it represents and reasons about this context using our ontology and Context Reasoner in order to deduce the current situations that are reported to the Service Controller.
The latter is responsible for providing the appropriate service to the user according to the inferred situations. In fact, as illustrated in Figure  18:  (1) contextual information is collected from the smart home by the Context Collector, and it is stored in a database; this means that the information simulated via the prototype interfaces is stored automatically in a database;  (2)     Once User A is connected and clicks "My Space", the Kali-smart platform sends the initial actions configuration file to the Kalimucho Server, launching automatically the GPS location service. The script bellow is generated by the reconfiguration generator after scanning the environment with the help of Kalimucho in order to know the available devices and to orchestrate the best way to deploy the components (Figure  19 ).
Once User A is connected and clicks "My Space", the Kali-smart platform sends the initial actions configuration file to the Kalimucho Server, launching automatically the GPS location service. The script bellow is generated by the reconfiguration generator after scanning the environment with the help of Kalimucho in order to know the available devices and to orchestrate the best way to deploy the components (Figure  19 ). Once a user location is identified (e. g. , smart home, smart car, smart office), the Action Reasoner receives a notification that triggers the search for appropriate services according to the identified user location. The script bellow is generated by the reconfiguration generator after scanning the environment with the help of Kalimucho. Our Prediction Reasoner deploys interactive multimodal services in a smart TV using the voice modality and in a smart watch using the gesture modality.
The script bellow is generated by the reconfiguration generator after scanning the environment with the help of Kalimucho. Our Prediction Reasoner deploys interactive multimodal services in a smart TV using the voice modality and in a smart watch using the gesture modality. Once an abnormal situation is identified, the ASSACR is responsible for discovering available services around the user location. This is done thanks to the SNMP protocol and the middleware Kalimucho server side offering such a service; the ASSACR uploads different available services' profiles and starts semantic service matching that fulfills the user context and preferences according to Equation (1) (Section 4. 2). User A is looking for a quality multimedia health service with low cost with implicit constraints: the screen resolution of the smartphone is limited to = 400 × 600, which is less than the document video.
cs. ovgu. de/eduComponents/) module for student evaluation, which only provides a semiautomated and static assessment which is performed by the tutor under the guidance of the tool. Thus, the formative and summative assessments are carried out by the instructor. However, eduComponents offers only a semi-automated and static assessment which is done by the tutor and assisted by the tool during student evaluation process. In  (Nicol and Macfarlane-Dick, 2006; Nicol, 2007) , a large study of formative assessment and feedback has been carried out in order to identify how assessment processes can help the enhancement and the development of learner's self-direction and regulation. Our approach is actually a technical solution which covers the three assessment types, since time and number of attempts are useful traces for diagnostic processes to evaluate learners knowledge level.
Trace-Based Automated Scoring Process Figure  2  describes the components of the trace-based automated scoring process considered as follows: • e-Learning system, equipped with a user interface to interact with users, • e-Assessment module which implements the scoring and evaluation functions, and delivers the results to both learners and teachers/experts, • m-trace database which is put between the elearning system and the e-Assessment module. It is fed by the former with users' traces, and it feeds the latter with saved traces. Its steps are also described as follows. Users interact with the system (e-Learning platform, on-line laboratory, etc. ) in order to do a training activity. Their interaction actions are tracked and the traces are saved in trace database. The traces are then modeled based on the three phases of (i) data collection, (ii) data transformation, (iii) data analysis; this process is out of the scope of this paper.
These methods are demonstrated on open books, and assume there is no self-occlusion in the pages. Global conformal mapping is used in  Brown and Pisula (2005)  to unfold a document; however, this approach still makes strong assumptions about the type of deformations present. Samko et al. (2011 Samko et al. ( , 2014) )  present a method for scanning and virtually unrolling scrolled historical documents written on parchment. The scrolls are scanned using X-ray tomography to produce 3D volumetric data. These data are treated as a set of volumetric slices. However, this is also unsuitable for our problem because it assumes that the deformation to the document is equal throughout, where parchment (as previously discussed) is likely to buckle and twist in non-isometric patterns. In addition to the virtual unfolding of documents, a number of techniques have been developed to correct aspects of historical document degradation other than geometric distortion, namely, fading of the text as the ink degrades, and bleedthrough of the text as the writing support deteriorates.
Reconstruction As described above, we process the image sets with Wu's VisualSFM  (Wu, 2011)  software to generate a sparse 3D reconstruction using structure from motion. We then apply Furukawa and Ponce's Patch-based Multi View Stereo (PMVS) algorithm  (Furukawa and Ponce, 2010)  to generate a dense point reconstruction, examples of which are shown in  Fig. 7 (top). This process also computes, along with The reconstruction is computed up to an arbitrary scale, so the distances in the resulting object space do not correspond to the true distances in real-life space. To correct for this, we allow a user to mark points on the ColorChecker which are a known distance apart, and we then triangulate their positions in object space to compute a scaling factor to allow distances in the model to match those in real-life space.
Terras (2011)  discusses this issue at length, focussing mainly on imaging artefacts, and the London Charter for the Computer-based Visualization of Cultural Heritage  (Denard, 2012)  stresses the importance of storing paradata which documents the process of generating visualizations of cultural heritage. In our case the most likely source of error is the 3D reconstruction process. We therefore document the provenance of the reconstruction by providing the user with smart access to the original image collection: for a given 3D view, the system displays the portion of an original image that best depicts the currently observed part of the parchment. By comparing the 3D reconstruction with the original images the user can This system was used by the palaeographer who compiled the transcription of the Great Parchment Book, using our system alongside access to the original folios to gain a fuller understanding of the text contained within the parchment.
We have done so whilst considering palaeographic best practice and bearing data provenance in mind. The fact that our system allows users to interact with both the generated models, and continually to check the veracity of these models by comparing features in the 2D-captured images of the folios, builds trust in our approach. Additionally, we have developed a way in which our approach may be compared to established archival standards for the creation of digital surrogates, demonstrating that our resulting models are of similar spatial quality to 2D images acquired through more normative digitization procedures. Although we have not carried out detailed user testing of our software with a wide library and archive user community, the member of the project team who relied upon our software to aid in transcribing the Great Parchment Book was wholly positive about their experience in navigating and interrogating the surface of the document via the manipulation of its 3D surrogate, and we have had much successful feedback from the research community attending our events and workshops.
Acknowledgements This work was financially supported by the  UCL EngD VEIV Centre  for Doctoral Training; The  Engineering and Physical Sciences Research Council Digitally reconstructing the Great Parchment Book Digital Scholarship  in the Humanities, 2016 23 of 31 (grant  EP/G037159/1 ); The  European Research Council  (grant iModel  StG-2012-306877 ); Adobe Research;  Derry Heritage  and  Museums Service  (DHMS, now  Museum and Visitor Service, Derry City and Strabane District Council) , the  UK's National Manuscripts Conservation Trust ; the  Marc Fitch Fund ; The Honourable The Irish Society; the  City of London Corporation ,  London Metropolitan Archives , and a number of  London livery companies: Clothworkers' Company, Drapers' Company, Fishmongers' Company, Goldsmiths' Company, Ironmongers' Company, Mercers' Company, Merchant Taylors' Company, Skinners' Company.
The frequency data was processed by removing the linear drift first, as it can be easily compensated for using an acousto optical modulator (AOM) in the working clock setup. To express the fractional laser frequency instability we use the Allan deviation, which reached 2. 5 × 10 -15 in 1 s for the 24 hour measurement (Fig. 1  blue circles). To assess the smallest achieved instability of the laser, a script was written to find the most stable 1 hour slot in the 24 hour measurement. The green squares in Fig. 1  present Allan deviation for the best 1 hour slot, which reaches a minimum of 7. 9 × 10 -16 in less than 1 s, close to the estimated thermal noise limit of 5 × 10 -16. Thanks to the reference laser being part of a working strontium lattice clock, we could assess a long-term drift of the laser frequency under test to be 0.
Acknowledgements The research leading to these results has received funding from the  European Union Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement No. 263500. D. Ś. acknowledges the support from the  FP7 Marie Curie Actions  of the  European Commission , via the  Initial Training Network QTea  (contract-No. MCITN-317485 ). S. V. , S. H. and A. K. acknowledge funding from the  Deutsche Forschungsgemeinschaft DFG  via  RTG 1729. Authors also would like to thank  S. Schiller  for initiating and coordinating the  Space Optical Clock  project and  C. Gill  and  P. Knowles  for thorough proofreading.
Dataset 2. 3. 1. Document Collection In 2011, the document collection has been built based on a dump of the English Wikipedia from April 2011. For the 2012 edition a dump from November 2011 was used whereas for 2013 and 2014 a dump of the English Wikipedia from November 2012 was used. The date of the dump was anterior to all selected tweets. Whitney Elizabeth Houston (August 9, 1963 -February 11, 2012) was an American recording artist, actress, producer, and model. Houston was one of the world's best-selling music artists, having sold over 170 million albums, singles and videos worldwide. Robert Barisford "Bobby" Brown (born  February 5, 1969 ) is an American R&B singer-songwriter, occasional rapper, and dancer. After a three-year courtship, the two were married on July 18, 1992. On March 4, 1993, Houston gave birth to their daughter Bobbi Kristina Houston Brown, her only child, and his fourth.