dataesr/scientific-paragraphs-categorization huggingface dataset:
5. QADA (quality-driven architecture design and analysis): a product line architecture design method that provides traceability between the product quality and design time quality assessment. Semantic web  [29]  [30]  [31]  [32]  [33]  linked data is the most important concept to support Semantic Metadata Enrichment (SME) in a SECO architecture  [34] -  [40]. proach to searching and browsing data  [33]. Furthermore, the web is currently not contextually organized. Thus, to enrich web data by transforming it into knowledge accessible by users, we propose a multi-platform architecture, referred to as SMESE, which uses a CBSD approach to integrate distributed content management enterprise applications, such as libraries and the Software Product Line Engineering (SPLE) approach. Our SMESE architecture includes mobile first design (MFD) and semantic metadata enrichment (SME) engines that consist of metadata and meta-entity enrichment based on mapping ontologies and a semantic master metadata catalogue (SMMC).
where users select their preferences in the market place. The major contributions of this paper are: 1. Definition of a software ecosystem model that configures the application production process including software aspects based on a proposed CBSD and metadata-based SPLE approach. 2. Definition and partial implementation of semantic metadata enrichment using SPLE and a semantic master metadata catalogue (SMMC) to create a universal metadata knowledge gateway (UMKG). 3. Design and implementation of a SMESE prototype for a semantic digital library (Libër). This paper proposes a semantic metadata enrichment software ecosystem (SMESE) to support multi-platform metadata driven applications, such as a semantic digital library. Based on mapping ontologies SMESE also integrates and enriches data and metadata to create a semantic master metadata catalogue (SMMC).
Software Product Line Engineering (SPLE) The development of software involves requirements analysis, design, construction, testing, configuration management, quality assurance and more, where stakeholders always look for high productivity, low cost and low maintenance. This has led to software product line engineering (SPLE)  [24]  as a comprehensive model that helps software providers to build applications for organizations/ clients based on a common architecture and core assets. SPLE deals with the assembly of products from current core assets, commonly known as components, within a component-based architecture  [43]  [44], and involves the continuous growth of the core assets as production proceeds. Note that the following related works are organized according to two axes: organizational and technical.
3. Addressing the open world assumption in software product line settings. A survey of works on search based software engineering (SBSE) for SPLE is presented in Harman et al. [22]  [24]. Capilla et al. [24]  provide an overview of the state of the art of dynamic software product line architectures and identify current techniques that attempt to tackle some of the many challenges of runtime variability mechanisms. They also provide an integrated view of the challenges and solutions that are necessary to support runtime variability mechanisms in SPLE models and software architectures. According to them, the limitations of today's SPLE models are related to their inability to change the structural variability at runtime, provide the dynamic selection of variants, or handle the activation and deactivation of system features dynamically and/or autonomously.
SPLE is, therefore, the natural candidate within which to address these problems. Since it is impossible to predict all the expected variability in a product line, SPLE must be able to produce adaptable software where runtime variations can be managed in a controlled manner. Also, to ensure performance in systems that have strong real-time requirements, SPLE must be able to handle the necessary adaptations and current reconfiguration tasks after the original deployment due to the computational complexity during variants selection. Olyai and Rezaei  [23]  describe the issues and challenges surrounding SPLs, introduce some SPLE ecosystems and compare them, based on the issues and challenges, with a view to how each ecosystem might be improved. The issues and challenges are presented in terms of administrative and organizational aspects and technical aspects.
SECO Architecture Using Components Integration and Components Evolution Software ecosystems (SECO)  [  They define a SECO as the interaction of a set of actors on top of a common technological platform that results in a number of software solutions or services where each actor is motivated by a set of interests or business models while connected to the rest of the actors. They also identify three main components of SECO architecture: 1. SECO software engineering: focuses on technical issues related directly or indirectly to the technological platform. 2. SECO business and management: focuses on the business, organizational and management aspects. 3. SECO relationships: represent the social aspect of the architecture since it is essential for SPLE actors to interact among themselves and with the platform.
SMESE Multi-Platform Architecture This section presents the proposed semantic enriched metadata software ecosystem (SMESE) architecture based on SPLE and CBSD approaches to support metadata and entity social and semantic enrichment for semantic digital libraries and based on an MFD approach for user interface design. Each component of the SMESE architecture is based on existing approaches (SPLE and CBSD) and an SME concept (proposed in this work) to generate, extract, discover and enrich metadata based on mapping ontologies and making use of contents and linked data analysis. For the new generation of information and data management, metadata is a most efficient material for data aggregation. For example, it is easier to find a specific set of interests for users based on metadata such as content topics, or based on the sentiments expressed in a content.
Metadata Initiatives & Concordance Rules This section presents the details of the metadata initiatives & concordance rules, specifically the semantic metadata meta-catalogue (SMMC) as shown in Figure  2. Metadata is structured information that describes, explains, locates, accesses, retrieves, uses, or manages an information resource of any kind. Metadata refers to data about data. Some use it to refer to machine understandable information, while others employ it only for records that describe electronic resources. In the library ecosystem, metadata is commonly used for any formal scheme of resource description, applying to any type of object, digital or non-digital. Many metadata schemes exist to describe various types of textual and non-textual objects including published books, electronic documents, archival documents, art objects, educational and training materials, scientific datasets and, obviously, the web.
Dublin Core (DC): primarily designed to provide a simple resource description format for networked resources. DC does not have any coding to provide the necessary details for the specification of a record that could be converted to any machine readable coding like UNIMARC, MARC21. 2. UNIMARC: consists of data formulated by highly controlled cataloguing codes. This format is difficult to understand and unreadable for the end user. For this reason, MARC21 was proposed. In addition, there is no mapping model among these that would make them interoperable. The overall challenge is to develop: (1) a modeling of partial international standardization of entities, (2) a modeling of partial international standardization of metadata, and (3) a modeling of partial international standardization of metadata mapping ontology.
Unfortunately, the power of metadata is limited: indeed, large national and international digital library projects, such as Europeana and the Digital Public Library of America, have highlighted the importance of sharing metadata across silos. While both of these projects have been successful in harvesting collections data, they have had problems with rationalizing the data and forming a coherent and semantic understanding of the aggregation. In addition, organizations create digital collections and generate metadata in repository silos. Generally such metadata does not: 1. Connect the digitized items to their analogue sources. 2. Connect names to authority records (persons, organizations, places, etc. ) nor subject descriptions to controlled vocabularies. 3. Connect to related online items accessible elsewhere.
The semantic annotation process of SMESE creates relationships between semantic models, such as ontologies and persons. It may be characterized as the semantic enrichment of unstructured and semi-structured contents with new knowledge and linking these to relevant domain ontologies/knowledge bases. It typically requires annotating a potentially ambiguous entity mention (e. g. Justin Trudeau) with the canonical identifier of the correct unique entity (e. g. depending on the content, http://dbpedia. org/page/Justin_Trudeau). The benefit of social semantic enrichment is that by surfacing annotated terms derived from the full-text content, concepts buried within the body of the paper/report can be highlighted. Also, the addition of terms affects the relevance ranking in full-text searches. Moreover, users can be more specific by limiting the search criteria to the subject or interest or emotion metadata (e.
Rule-Based Semantic Metadata Internal Enrichments Engine This sub-section presents the details of the rule-based semantic metadata internal enrichment engine including software product line engineering (SPLE). This sub-system includes: 1. A rule-based semantic metadata internal enrichment engine. The different phases of the enrichment process by sentiments and emotions are: 1. Sentiment and emotion lexicon generation phase. 2. Sentiment and emotion discovery phase. 3. Sentiment and emotion refining phase. One of the contributions of the SMESE for digital libraries is that it is not specific to one software product but can be applied to many products dynamically. In addition, it includes a semantic metadata enrichment (SME) process to improve the quality of search and discovery engines. Indeed, our goal is to provide a SECO that offers a new way to share and learn knowledge.
In practice, with the emergence of Big Data, knowledge is not easy to find at the right time and place. The proposed ecosystem uses an SPLE architecture that is a combination of FORM and COPA approaches to catalogue semantically different contents. Furthermore, we introduce an SPLE decision support process (SPLE-DSP) in order to meet the SPLE characterization such as: 1. Runtime variability functionalities support. 2. Multiple and dynamic binding. 3. Context-awareness and self-adaptation. SPLE-DSP supports the activation and deactivation of features and changes in the structural variability at runtime and takes into account automatic runtime reconfiguration according to different scenarios. In addition, SPLE-DSP rebinds to new services dynamically based on the description of the relationships and transitions between multiple binding times under an SPLE when the software adapts its system properties to a new context.
To take into account context variability to model context-aware properties, SPLE-DSP makes use of an autonomous robot that exploits context information to adapt software behavior to varying conditions. Furthermore, SPLE-DSP integrates the adaptation of assets and products dy-namically. This helps products to evolve autonomously when the environment changes and provides self-adaptive and optimized reconfiguration. Additionally, SPLE-DSP exploits knowledge and context profiling as a learning capability for autonomic product evolution by enhancing self-adaptation. The SPLE-DSP model is an optimized metadata based reconfiguration model where users select their preferences in terms of configuration of interests. The dynamic and optimized metadata-based reconfiguration model (DOMRM) takes into account the preferences of several users who have distinct requirements in terms of desirable features and measurable criteria.
Our BMSFM constitutes an evolution of traditional stateful feature models  [51]  that includes the set of user metadata based configurations in the model itself, which allows the representation of user decisions with attributes and cardinalities. More specifically, we developed a metadata-based reconfiguration model that defines all possible metadata and all possible entities that users may need in a specific domain. When a user needs new metadata, he uses the metadata-based request creation tool. The DOMRM model analyses the request and checks whether the requested metadata is relevant and does not already exist. Thus when needed the model automatically creates the new metadata and reconfigures the ecosystem which then becomes available for all users. Figure  6  illustrates the DOMRM model we designed that is an optimized metadata based configuration for multiple users.
An Implementation of SMESE for a Large Semantic Digital Library in Industry The proposed SMESE architecture has been implemented for a large digital library. The product In Média V5 was implemented with a global metadata model defined with all the known entities and constraints. The catalogue contains more than 2 million items, with 18 entities and 132 defined metadata. SMMC identifies 1453 metadata and defines a metamodel that consists of a semantic classification of metadata into meta entities. In addition to semantic web technologies, the characteristics and challenges of SMESE for large digital libraries are: 1. Automatic cataloguing with the least human intervention. 2. Metadata enrichment. 3. Discovery and definition of semantic relationships between metadata and records. 4. Semi-automatic classification of bibliographic records.
5. Semantic cataloging and validated metadata making use of a multilingual thesaurus. First, we defined a list of entities, called Meta Entity, which introduced 193 items. These items represent all library materials. In addition, the structure of the model allows addition of new entities as may be required. Figure  7  shows the SMESE meta-entity model where for each entity there is: an ID, property Name, description, labels in different languages, and the domain that represents the logic group of the entity; for reason of formatting, Appendix C shows a readable version. The domain may be "user" as response value for a metadata. In this implementation, all instances of the entities of the domain can be the response value. The ID allows the user to uniquely identify the entity whatever the language, the source of entities or the metadata model (DC, UNIMARC, MARC21, RDA, BIBFRAME).
"property Name" is a comprehensive term that defines this metadata. 4. "UNIMARC", "MARC21", "property Name" allow users to create a mapping between them to make them interoperable. 5. "UNIMARC" and "MARC21" are codes such as 300$abcf. 6. "Expected type" represents the type of value that may be assigned to the metadata as response. 7. "isRelated" denotes that the response of the metadata is an entity where the identity is given by "related Content Type". 8. "thesaurus" mentions the thesaurus name that is used to control the metadata integrity. 9. "type" allows classification of the metadata as "descriptive", "structural", "administrative", "dimension", "longevity" or "identification". This classification allows users to do meta research. After the definition of entities of collections and harvesting of metadata from the dispersed collections, a metadata crosswalk is carried out.
The relationship "same As" is used to denote the mapping between distinct metadata or entity schema source. The output of the ontology is an OWL file. This OWL file is used by a crosswalk to automatically assign metadata values that are harvested from distinct sources. In the proposed ecosystem two sources are harvested: Discogs   A total of 94,015,090 metadata records were collected from these two sources: 1. From Discogs, we collected 7,983,288 entities: 2,621,435 music releases, 4,466,660 artists and 895,193 labels. 2. From researchGate, we collected 86,031,802 entities: 77,031,802 publications and more than 9,000,000 researchers. 3. In fact, SMESE contains more than 3. 4 billions triplets and growing.
Summary and Future Work In this paper, we proposed a design and implementation of a semantic enriched metadata software ecosystem (SMESE). The SMESE prototype, which was implemented at BiblioMondo, integrates data and metadata enrichment to support specific applications for distributed content management. To perform this integration, SMESE makes use of the software product line engineering (SPLE) approach, a component-based software development (CBSD) approach and our proposed new concept, called semantic metadata enrichment (SME) with distributed contents and mobile first design (MFD). In this implementation, the SPLE architecture is a combination of FORM and COPA approaches. We also presented our implementation of SMESE for digital libraries. This in- We also implemented the Meta Entity that represents all library materials and meta metadata.
The ontology mapping model was then implemented to make our models interoperable with existing metadata models such as Dublin Core, UNIMARC, MARC21, RDF/RDA and BIBFRAME. The major contributions of this paper are as follows: 1. Definition of a software ecosystem architecture (SMESE) that configures the application production process including software aspects based on CBSD and SPLE approaches. Future work will include: 1. An enhanced ecosystem of connecting engines and rule-based algorithms to enrich metadata semantically, including topics and sentiments/emotions. 2. Evaluation of the performance of an implementation of the SMESE ecosystem using different projects, comparing results against existing techniques of metadata enrichments. Exploring text summarization and automatic literature review as metadata enrichment, the semantic annotations could be used to enrich metadata and provide new types of visualizations by chaining documents backward and forward inside automated literature reviews.
The SMESE multiplatform prototype includes an engine to aggregate multiple world catalogues from libraries, universities, Bbookstores, #tag collections, museums, and cities. The collection of pre-harvested and processed metadata and full text comprises the searchable content. Central indexes typically include: full text and citations from publishers, full text and metadata from open source collections, full text, abstracting, and indexing from aggregators and subscription databases, and different formats (such as MARC) from library catalogues, also called the base index, unified index, or foundation index. The SMESE multiplatform framework must link bibliographic records and semantic metadata enrichments into a digital world library catalogue. SMESE must search and discover actual collections or novelties, including: works, books, DVDs, CDs, comics, games, pictures, videos peoples, legacy collections, organizations, rewards, TVs, radios, and museums.
The purpose of regression methods and supervised learning (artificial intelligence) is to predict the evolution of certain behaviours as a function of other variables. TDM software contains features comprising all or part of this processing chain, ranging from access through to the preparation of data, including the application of selected computing algorithms (learning), the exploitation of output, the exploitation of models, and the visualisation of results. The software specific to text-mining processes natural language by grammatical labelling, syntax rules, ontologies, learning from labelled corpora, etc. Based on the corpora of documents thus structured, different analytical algorithms can be used: automatic classification, analysis of trends, rules of association, etc. Many tools and techniques for the automatic analysis of texts and data have been developed by French research laboratories and are already used by researchers.
"Macro" functions within TDM Taking a functional macroscopic view of TDM provides another way of grasping the concept as regards its objectives. The development of TDM techniques opens new perspectives for the analysis of large volumes of texts and data (Big Data). TDM uses an inductive approach to analyse all this information grouped in corpora. Text mining encompasses a set of practices and a variety of methods that are difficult to treat in a unified way. 33 Yet it is possible to paint a comprehensible picture in broad strokes. 34 e vast increase in the number of scientific articles in digital format has made a wide variety of information available: tables, images, statistical data, sounds, corpora of text, and so on. The latter may be written in very different styles (language, expressions, etc.
Developing these innovations TDM techniques rely on innovative tools (software, supercomputing, technologies for the massive collection and processing of data), and French public research organisations and French companies contribute to their development. TDM is currently one of the major sectors with potential for innovation within the digital economy, and these technologies are central. This can be seen in the awards acknowledging work on TDM. Xavier Tannier, from the CNRS's LIMSI laboratory at Orsay (Laboratoire d'information pour la mécanique et les sciences de l'ingénieur) and Iona Manolescu of INRIA have received a "Google Award" 6 for their algorithm for text mining in print media. In the case of medical informatics, Pierre Zweigenbaum 7 was inducted into the American College of Medical Informatics in 2014.
10 The concept is divided into several branches: evidence-based decision, evidence-informed decision, and evidence-aware decision. TDM can optimise economic, social and environmental policies, as it is not based only on opinions and theoretical models, but also on factual analysis. TDM can be used for decision support in other fields (not necessarily public), for example in geography (economic geography, social geography, geomarketing, etc. ). The idea is to cross-reference geo-tagged data in order to identify the characteristics of geographical areas. To do this, "the descriptive techniques used in data mining and more precisely Agglomerative Hierarchical Clustering (AHC) were used to identify the number of homogeneous groups based on Ward's aggregation criterion and Euclidean distance, using the R Data Mining Software.
1. The final design of the telescope has a total mass of about 8 tons. The Telescope Control System's (TCS) main purpose is to drive the telescope's axes for tracking and pointing  [5, 9]. To achieve this, three embedded cabinets connected to a power supply and network (Internet and fieldbus) are implemented on the telescope. The hardware for slow-control and safety systems are COTS modules from Beckhoff. To control the motion of the prototype, the TCS software is distributed between the hardware (drives and motors provided by ETEL) on board the telescope and the workstation of the control room (see Fig. 2 ). To achieve pointing, the equatorial coordinates of a source have to be converted into altitude-azimuthor horizontalcoordinates, taking into account the observatory location and the date and time.
For tracking, the conversions have to be computed for real-time operation to allow accurate scientific observations. The pointing and tracking software (written in C/C++) were integrated in early 2017 in the prototype embedded PC, in accordance with the smart telescope design philosophy. The SOFA library, referenced by the International Astronomical Union, is used for astronomic calculations in compliance with the CTA recommendations. Following the Design Verification Document and the conformity matrix, 246 GCT technical specifications were defined in order to verify that the prototype meets the CTA requirements. Each specification has to be checked by one or more verification methodologies: analysis, certification, demonstration, inspection and testing  [5].