dataesr/scientific-paragraphs-categorization huggingface dataset:
Then, the Document Similarity between the two ontologies is calculated by taking the cosine dot product, as (4) shows, where V S O * the term weighting vectors of the ontologies. To compute DocSim, we use the class TFIDFVectorizer of the scikit-sklearn library of Python  33. T ermW eighting = T F × IDF (1) T F = t T (2) IDF = 1 2 × (1 + log 2 D d ) (3 DocSim(O i , O j ) = V S Oi • V S t Oj ∥V S Oi ∥∥V S Oj ∥ (4) According to DocSim(O i , O j ) metric, CURIOCITY ontology has a 71. 8% similar terms to ERLANGEN CRM; whereas the left percentage (28. 2%) corresponds to the inclusion of other concepts and properties which conform the proposed extensions.
B. STRUCTURAL LEVEL The structural level is mostly evaluated according to the relationships among entities, as ontologies are graphs. We use OQuaRE methodology  [87]  to conduct a structural evaluation. OQuaRE metrics are calculated according to (  5 ) to  (16). All OQuaRE's characteristics (i. e. , Structural, Functional Adequacy, Compatibility, Reliability, Transferability, Operability, and Maintainability) are scored according to the OQuaRE scale system (i. e. , 1 means not acceptable, 3 is minimally acceptable, and 5 represents exceeds the requirements). We implemented an application 34 to perform automated metrics calculation, the assignment of the score according to OQuaRE charts, the results presentation, as well as graphics that allow a better comparative analysis. The application was developed in Python 3.
• R Ci : Relations of class C i. • P ro Ci : Properties of class C i. • Anc Ci : Direct ancestor of class C i. • Sub Ci : Direct subconcept of class C i. 34 https://github. com/giulianodelagala/CURIOCITY/tree/master/ Evaluation/OquaRE • C T hing : Ontology root. Table  2  details the obtained metrics and their corresponding OQuaRE score to evaluate the structural level  35  , for CU-RIOCITY, ERLANGEN-CRM, and ArCo. Figure  10  depicts the comparison of the three ontologies according to each OQuaRE's characteristic. Figure  10 (a) depicts the OQuaRE's Structural characteristic, which evaluates ontology quality factors, such as Consistency, Formalization, and Entanglement. In this case, ontologies score similar for each sub-characteristic. The weakness of the ontologies is in Cohesion, whose LCOM Onto metric shows that there is a strong dependency between components, mainly due to the complexity of the relationships between concepts.
VI. CURIOCITY FRAMEWORK: AN APPLICATION CASE RUTAS project aims to develop a system of robots as tour guides in urban centers, solving the problem of connectivity between robots and providing access to tourist information through a semantic repository available on the cloud. In the context of this project, data from indoor spaces (e. g. , museums, historic churches, and libraries) and outdoor spaces (e. g. , historical sites, squares, monuments) are being collected for art specialists in a repository called D-RUTAS 36. Towards the accomplishment of RUTAS project goals, we have developed CURIOCITY framework  [36] , which is https://github. com/dulcineo roughly composed by three layers (see Figure  14 ): (i) Semantic Repository layer aimed at managing the semantic data, such as the CURIOCITY repository, with information about cultural heritage in urban tourism; other semantic repositories can also be included, such as an ontology to represent tourists (i.
e. , basic information, preferences, interests), an ontology to manage robots' tasks (e. g. , navigation, mapping, object detection); (ii) Data Processing layer in charge of processing sources of information (e. g. , D-RUTAS repository, web pages, databases of museums, databases of government cultural institutions) to automatically populate the semantic repositories and generate a rich knowledge network; it also processes and executes queries from the layer; and (iii) Application layer, which provides interfaces for maintenance, updates, and navigation through the semantic repositories; services such as online artwork catalogs, virtual museums, web forms to collect data, are offered at this layer. CURIOCITY framework code and documentation are available online  37. Next sections describe the components developed of the first prototype of CURIOCITY framework and explain how CURIOCITY ontology supports its functionality.
B. DATA PROCESSING LAYER This layer provides tools to automatically instantiate the Semantic Repository layer and generate SPARQL queries to support the Application layer. Nevertheless, for the current version of the framework, before using these tools, it is necessary to perform a manual mapping process of D-RUTAS concepts to CURIOCITY ontology classes and relations. This mapping configuration can be saved in a JSON file for later use. In the future, this mapping process will be also automated, by using, for example, string similarity, string matching, or natural language processing.
2) Instantiation: CSV Parser The implementation process of the instantiation begins with the spreadsheets containing the D-RUTAS museum data, which are exported to CSV format for manipulation. Parsing and generation of the RDF triplets is done through a parser developed with Python 3. 8, Pandas 1. 1. 2, and RDFlib 5. 0. 0 libraries. D-RUTAS data are processed line by line, starting with general concepts with multiple references (e. g. , crm:E55 Type, crm:E44 Material, crm:E58 Measurement Unit). Then, specific concepts are matched to the artifact (e. g. , crm:E22 Man-Made Object, crm:E54 Dimension). Lastly, the triplets corresponding to properties that relate the previous concepts are generated. During the instantiation, some drawbacks were detected and overcome: (i) incomplete data: some fields of D-RUTAS tables are identified as Unknown or Missing Data; since these data are not represented in the knowledge base, it is necessary to represent this empty attributes in the query response with some text such as 'unknown' to identify the missing information; (ii) fields that can refer to different classes (e.
C. APPLICATION LAYER The current version of CURIOCITY framework offers three services for end users. The first one (Figure  16  (a)), is a general user oriented web page that allows users to browse the semantic repository, perform queries for searching under combined criteria, and obtain details of artifacts contained in museums. The second application (Figure  16 (b) ) offers a 3D tour of virtual museums for end users, as well as a tour creator utility for developers to design and implement virtual visits to museums from the data and information kept in the semantic repositories. The third service (Figure  16 (c )) 40 https://github. com/giulianodelagala/CURIOCITY/tree/master/Querys is a desktop application oriented to the administration of the semantic repository. The functionalities of this application allow administrators to configure the mapping of CSV tables (entities of the ontology and the generation of instances in an automated way from the data), to make queries to the semantic repository under combined criteria, to make particular queries in SPARQL format, and to update the semantic repository manually.
We have shown that final users, as well as developers, can transparently access the Semantic Repository, through the utilities provided at the Processing Data layer (e. g. , SPARQL engine, scripts, API, parsers) from comfortable and easy-to-use graphical interfaces at the Application layer. Thus, it is possible to represent the knowledge of cultural heritage and urban tourism domains of a city; besides being a semantic base for CURIOCITY framework services, in order to conform a level of abstraction of knowledge and use of semantic web technologies for final users. Even though the current version of CURIOCITY ontology covers the initial requirements related to RUTAS project, we plan to evaluate the inclusion of other urban tourism topics, such as Languages and Traditional Medicine, to give greater coverage to the concept of cultural heritage.
It remains to evaluate CURIOCITY ontology through the applications proposed by the CURIOCITY framework, in order to know the level of satisfaction of the end user (expert and common user) in the activities supported by the ontology. We also are working on the integration of CURIOCITY ontology with other ontologies developed in parallel by the RUTAS project (e. g. , in the simultaneous location and mapping (SLAM) problem domain  [91] , in the user profile domain  [92] ), which will be part of the semantic repository of the CURIOCITY framework and the base for the development of a tourism recommendation system that takes into account preferences and interests of users. Additionally, we continue working on the development of CURIOCITY framework to including more general ontology population techniques to serve different heterogeneous databases.
This research was supported by the  FONDO NACIONAL DE DESARROLLO CIENTÍFICO, TECNOLÓGICO Y DE INNOVACIÓN TECNOLÓGICA -FONDECYT  as executing entity of  CONCYTEC  under grant agreement no. 01-2019-FONDECYT-BM-INC. INV  in the project  RUTAS: Robots for Urban Tourism , Autonomous and Semantic-based. This article is part of the  final dissertation in Computer Science program  at  Universidad Católica San Pablo  [1].
After two CCs of latency, the LLR block generates all the side information related to the received symbol ({I, Ĩe }). The data is stored in their appropriate location in the intrinsic memory RAMs and in the extrinsic memory RAMs. At cycle index 18, all the intrinsic information of the VNs connected to CN 0 are stored in memory. The processing of the layers L 1 and L 2 for frame k starts at cycle number 19, taking 24 cycles to complete at cycle number 19 + 24 -1 = 42. Then, 10 cycles after the beginning of the processing of the first CN of frame k, i. e. , at CC number 18+9 = 27 the decision on the VNs associated to the first CN are output ( X). After 18 CCs (see Parity Test block description), a codeword is said to be decoded (decod ok = 1) if all the decisions generated by layer L 1 verify all the M parity CNs (at CC number 47), just in time to start again the loading of a new codeword at cycle number 48.
This parallelism in the simultaneous processing of two consecutive frames requires the duplication of the intrinsic and extrinsic RAMs to store the data of two frames. To summarize, looking at the global execution of the decoder, the 19 CCs latency for preparing the data (shown in Fig. 9 ) and the 16 CCs latency of the CN are not considered when evaluating the execution time of the decoder (which has a direct impact on the throughput rate). We also note that without the duplication of the RAMs, that allowed the parallel processing of two consecutive frames, the 16 CCs latency of the CN has to be considered as a part of the execution time at each iteration. This is due to the fact that CN 23 and CN 0 share the same variable VN 12 , which prevents the start of the second iteration before the processing of CN 23 is ended.
Methods and analysis We will include systematic and scoping reviews of both quantitative and qualitative primary studies with or without meta-analysis focusing on assessing the effectiveness of interventions through smart living environments to support older adults in the community to age in place. The literature search will be done through the following biomedical, technological and sociological citation databases: MEDLINE, Embase, CINAHL, Web of Science and PsycINFO, and quality assessment of the reviews will be done thought AMSTAR2 checklist. The analysis of the results will be presented in narrative form. Ethics and dissemination Our review will rely exclusively on published data from secondary sources and will thus not involve any interactions with human subjects. The results will be presented at international conferences and publications.
There have been several breakthroughs in remote home monitoring over the years, especially for continuous physiological data monitoring (detection or prevention). For instance, urinary tract infections in older adults have been detected by monitoring the frequency of bathroom visits. 17 Studies have also shown the possibility of detecting mild cognitive impairments or dementia such as Alzheimer's disease based on simple markers such as walking speed  [18] [19] [20]  or time spent completing ADL. 21 22 In this respect, a recent literature review reported that cognitive deficits could be detected by smart home monitoring of general activity, outings, sleep habits and computer usage. 23 Other studies have shown that the information provided by this monitoring can be very useful to guide clinicians working in home care services.
How was the level of evidence determined? Q3. How was this evidence measured in terms of type of outcomes? Obj 2: among the reviews assessing the effectiveness of smart living environment, to gather evidence on what factors and strategies were identified as influencing the implementation process. Q4. What are the implementation strategies that were used to deploy such system in real-life settings? Q5. What are the obstacles of implementing SH/AAL in real-life settings? Q6. What types of technologies are included in SH/AAL? Q7. What is the information that is used from this system and how? Q8. Who is the main user of SH/AAL data to support older adults at home? Q9. How is the data used by the main user? (eg, clinical decision, etc). Q10. Who are the other actors involved? (eg, healthcare system, caregivers, neighbours, etc).
Data sources and search strategy An iterative search strategy for bibliometric databases will be developed to look for peer-reviewed systematic reviews evaluating smart living environment to support older adults to age in place, including quantitative and/ or qualitative syntheses of implementation studies and process evaluations alongside trials testing such technology. The search for this umbrella review will aim to identify all research syntheses relevant to our research questions. The literature search will be done through the following biomedical, technological and sociological citation databases: MEDLINE, Embase, CINAHL, Web of Science and PsycINFO. The search strategy will be guided by a librarian, comprehensively reported and the detailed search filters employed will be presented sequentially in the single appendix for all the databases that were searched and listed along with the search dates.
Data collection In line with recommendations to ensure the objectivity of systematic reviews, titles and citation abstracts will be reviewed by two independent, blinded reviewers using Covidence software (https://www. covidence. org/) developed by the Cochrane collaboration. Any discrepancies will be analysed by a third reviewer. Prior to selection, the inclusion criteria were tested in a pilot study. Similarly, the eligibility of the full texts of relevant papers will be independently checked by two independent reviewers and any disagreements will be resolved following reading by a third reviewer. Reviews excluded during the full-text review will be documented with the reason for exclusion.
Data extraction Data will be extracted independently by the reviewers according to an extraction grid previously tested on five random articles to check the correct completion of the data. To check inter-judge reproducibility, the first five journals will be double extracted, blinded. A discussion between the reviewers will allow the adjustment of the data extraction for the whole corpus. Extraction will be limited to the data reported in the included reviews, without judging the interpretation from the source studies. Nevertheless, the references included in source studies will be extracted to indicate overlap and avoid bias due to double counting.
Data synthesis The analysis of the results will be presented in narrative form and will refer to a detailed table of the characteristics of the included reviews. This presentation will contextualise the results in terms of relevance to supporting ageing in place, technological maturity, elements of implementation and evidence from the research syntheses included in the study, following our research questions (box 1). It will also describe the typologies of technologies used, as well as the different forms of interaction with users (residents, natural caregivers, professional caregivers). The data presented will be based on the elements and criteria used in the included reviews. Data from qualitative research will also be commented and synthesised directly from the primary authors' analyses using thematic trees.
In addition, the place of both older adults and caregivers will be analysed to show implication in codesign, if available. To perform these analyses, the data from the included review will be directly coded  39  to obtain summary tables containing, for each of the questions in box 1, reproducible keywords describing the major results of the included reviews. These tables will be included in our result section. Overlap is a key issue in the reporting of an umbrella review. It can lead to false assumptions about the evidence presented by a disproportion between sources. 40 It is therefore necessary to present an overlap analysis. In our study, this will be done by a quantitative approach, that is, with the calculation of the corrected covered area. 41 Additionally, our umbrella review will present, in graphic form, the interaction between the source studies.
This work was supported by the French National Research Agency (ANR)-through MRSEI process-grant number: ANR-196MRS3-0025-01. Competing interests None declared. Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research. on June 24, 2022 by guest. Protected by copyright. http://bmjopen. bmj. com/ BMJ Open: first published as 10. 1136/bmjopen-2021-054235 on 25 January 2022. Downloaded from.
Funding This work was supported by the French National Research Agency (ANR)-through MRSEI process-grant number: ANR-196MRS3-0025-01. Competing interests None declared. Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research. on June 24, 2022 by guest. Protected by copyright. http://bmjopen. bmj. com/ BMJ Open: first published as 10. 1136/bmjopen-2021-054235 on 25 January 2022. Downloaded from.
They called this method functional glasso, or simply fglasso, and the authors showed that, when m n approaches infinity, consistent estimation of the edge set is possible. Zhu et al. (2016)  proposed a Bayesian framework under the Gaussian assumption on the random functions for the analysis of functional graphical models, while  Li and Solea (2018)  relaxed this assumption by extending the concept of ACI to the functional setting. In this paper, we introduce an alternative approach to relax the Gaussian assumption in the functional graphical model. Our research is motivated by the fact that in many applications the relation between the functional principal scores is rarely linear as implied by the assumption of the FGGM. To illustrate this observation, we consider an electroencephalography (EEG) dataset that q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q -8 -6 -4 -2 0 2 4 -1.
0 0. 0 0. 5 1. 0 1. 5 2. 0 P8 AF1 q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q -0. 5 0. 0 0. 5 1. 0 -3 -2 -1 0 1 2 X O1 Figure  1 : Pairwise scatterplots for the control group between channels AF1 and P8 (left) and channels O1 and X (right). consists of two groups of subjects: 77 subjects in the alcoholic group, and 45 in the control group  (Zhang et al. , 1995; Ingber, 1997). For each subject, an EEG activity was recorded at 256 time points over a one second time interval using 64 electrodes placed on the subject's scalp. The goal is to construct a functional graphical model to characterize brain network connectivity for the two groups of subjects based on the functional data collected by the electrodes.
Section 2 describes the methodology and proposes the nonparametric functional graphical model. Section 3 presents the estimation procedure. In Section 4 we study the theoretical properties of the resulting estimator. In Section 5 we conduct simulation studies to evaluate the finite sample properties of the proposed methodology, and in Section 6 we apply the new model to the motivating EGG dataset. We conclude with some final remarks in Section 7, while all proofs of the theoretical results are deferred to the Appendix.
Real data application In this section we apply the new method to the EEG data set available at UCI Machine Learning Repository. The data involve 77 subjects in the alcoholic group and 45 subjects in the control group. Each subject was exposed to a stimulus while brain activities were recorded from the 64 electrodes placed on the subject's scalp, over a one-second period in which 256 time points were sampled. See  Zhang et al. (1995)  and  Ingber (1997)  for more backgrounds of this data. The goal is to characterize functional connectivity among the 64 nodes for the two groups, based on the functional data collected from the electrodes. We choose k n = 4 + √ n B-spline functions of order 4 and number of scores equal to m n = 5. Since our goal is to capture outstanding differences in brain connectivity between the alcoholic and control groups, we take the tuning constant λ n to be such that 5% of the 64 2 pairs of vertices are retained as edges.
Reproducibility crisis urge scientists to promote transparency which allows peers to draw same conclusions after performing identical steps from hypothesis to results. Growing resources are developed to open the access to methods, data and source codes. Still, the computational environment, an interface between data and source code running analyses, is not addressed. Environments are usually described with software and library names associated with version labels or provided as an opaque container image. This is not enough to describe the complexity of the dependencies on which they rely to operate on. We describe this issue and illustrate how open tools like Guix can be used by any scientist to share their environment and allow peers to reproduce it. Some steps of research might not be fully reproducible, but at least, transparency for computation is technically addressable.
Likewise, containers reproduce an environment but do not capture all dependencies used for their compilation thus cannot be rebuilt. In this example, a computational environment with R v4. 1. 1 (without packages) requires 304 binaries all bound by 935 edges. Ideally, the dependency tree should be described to allow reproducibility by any user at any-time. Though this huge list may seem daunting, tools like Guix can rebuilt dependency tree with the list of software used and the path to Git repositories with the all information to build them from scratch. Sharing these two files allow reproduction of computational environment byte-by-byte. For visualization purposes directed acyclic graph represents a cropped view of compiled dependencies of R version 4. 1. 1. A complete view of R dependency tree is available the GitLab repository with codes to reproduce graphs.
g. , install. packages from the R language ecosystem) or may be mixed as Conda or BioConda  9. When reproducing a computational environment using one package manager, the core question is the influence of the host computer system  9. Assuming the analysis was run on one specific host computer system, then the ability to re-run later on another host is strongly affected by the variation on the two different hosts. Most package managers defeat this criterion (Fig. 2a ). Thus, they fail to provide reproducible computational environment. Containers: keep under control the impact of the host. The aim of containers (e. g. , Docker 10 , Singularity 11 , or "virtual machines") is to capture the computational environment independently from the host computer system. They rely on stable low-level, light-weighted interactions with the host.