dataesr/scientific-paragraphs-categorization huggingface dataset:
For the selection bias, it might be possible to have location and language bias. For example, most of the original experiments on compliance were said to be made in the same street in the same city (Vannes, France), and others in Bordeaux (France). Also, it is possible that the "but you are free" and "vousêtes-libre-de" do not have the same meaning, most importantly once translated into the language in collec-tivistic countries. We found selective reporting in the Carpenter (2013) meta-analysis (reported in a commentary in the dataset). By looking at the original articles, we found lacking and inconsistent data we had to ask the author (we reported their answer in OSF). We also found several poor methodological qualities: a lot of the studies we found have very low power when compared to the effect size found in Carpenter's analysis.
Proliferation of data sources associated to Internet of Things (IoT) deployment as well as those bound to Open Data Portals (e. g. European Data Portal, Municipalities Open Data Portals, etc. ) and Social Media platforms is creating an abundance of information that is called to bring benefits for both the private and public sectors, through the development of added-value services, increasing administrations' transparency and availability or fostering efficiency of public services. However, pieces of information without a context are significantly less valuable. Raw data lacks semantics and it is highly heterogeneous from one data-source to another. This poses a challenge to make it useful. To turn all this data into valuable information it is necessary to enable its combination so that meaningful context can be created.
Moreover, it is fundamental to define the mechanisms enabling the adoption and orchestration of advanced (typically AIenabled) data processing techniques to be applied over the harmonized datasets and data-streams. This paper presents the Data Enrichment Toolchain (DET) that provides the necessary harmonization and enrichment to datasets and data-streams coming from heterogeneous sources. The value of the enriched data lies on the one hand in the transfer of the data into a semantically grounded knowledge graph and, on the other hand, in the creation of new data through linking, aggregating and reasoning on the data. In both cases, the benefit of employing linked-data modelling and semantics comes from the extension of the metadata that is associated to every piece of information. Furthermore, the experimental evaluation of the DET implementation that we have carried out is also presented in the paper.
I. INTRODUCTION Nowadays, data is becoming the new fuel for economic wealth and creation of novel business models. A multitude of technologies contributes to an abundance of information sources which are already the baseline for many different services. Among them, Internet of Things (IoT), Social Networks and Open Data are probably the most representative ones. However, for an economy of data to flourish there are still several challenges that have to be overcome. Current solutions are mainly based on centralized approaches where all the information is extracted from the field and forwarded to Data Management Platforms (DMP) in the cloud. Typically, such DMPs are associated with service platforms that allow the development of applications that employ the gathered information. However, existing DMPs mostly consider the provision of vertical solutions that do not This article has been accepted for publication in IEEE Access.
Our goal with the DET is to take existing high value data sources and create new value through the process of "Data Enrichment". Data Enrichment can be applied to a multitude of data formats and convert it to a standardized rich data description that make use of the advanced features of NGSI-LD. This approach facilitates data harmonization, and semantic annotation, and the use of Artificial Intelligence (AI) mechanisms to achieve data enrichment. The advantages that this solution brings forward are that the provided raw data and the enriched data will be more easily accessible to create new services and provide differentiated values that the simple publication of the datasets cannot provide. Additionally, the paper describes the DET implementation that actually enables homogeneous access to highly heterogeneous data sources (e.
g. IoT deployments, Open Data portals, Web content or Social Media). Our main contributions include: (1) the specification of a a modular architecture, based on context management standards, supporting decentralized operation and adaptation to heterogeneous data; (2) the implementation of a toolchain enabling heterogeneous data harmonization and enrichment by leveraging linked-data and AI technologies; and (3) proof of work services validating the proposed platform through actual integration of multiple data sources, and assessing its performance. Overall, the key innovations that are put forward through the DET that we are describing and evaluating in this paper relates to increasing data value by leveraging semantic annotation to provide some meaning to it, as well as to facilitating discovery of data (specifically, leveraging the NGSI-LD information model), and enabling more complex processing flows and event processing thanks to the establishment of links among pieces of data (existing and/or generated throughout the enrichment process).
Finally, we have found in the literature some works providing tools to enable interoperability. For example,  [15]  describes a framework to specify data according to adopted standards. Similarly, a lightweight model-based middleware to simplify interoperability of IoT services is described in  [16]. Although the scope of these propositions is different from ours, the formal definition of data model standards could be used by our framework in the future. It is also worth mentioning the development presented in  [17] , where authors proposed a set of mediation services to access resources in a uniform way in the context of IoT, thus enabling semantic and syntactic interoperability. In this case, the focus is on IoT only and considered the integration of the whole IoT platform, while the DET solution that we are proposing has been designed to enable interoperability and to add the processing steps for data enrichment of datasets and data-streams coming from heterogeneous data sources not limited to IoT platforms.
B. DATA SEMANTIC ENRICHMENT The key concept behind the so-called semantic enrichment of data is to augment the value of the data by increasing its contextualized characterization by annotating it with metadata referring to a specific domain of knowledge. An annotation is a form of metadata attached to a dataset or to any of the individual components of it usable to better describe the data type or its information content. Data enrichment has been widely used in data analysis applications in which the collected data contains limited information and needs to be correlated with existing knowledge to reveal higher-level insights. In  [18] , a framework for enriching sensor measurements with semantic concepts is introduced to generate new features. In the Big Active Data project  [19] , notifications delivered to users can be enriched with other existing data to provide actionable notifications that are individualized per user.
1) KEY REQUIREMENTS The architecture has been defined considering the following premises: • Data sources can be batch, providing data under request (e. g. RESTful interfaces from an Open Data portal); or real-time, providing data as soon as it is generated (e. g. under publish/subscribe based services). The DET architecture is flexible in order to collect and deal with data. Two operational modes are envisaged: o "batch mode" accesses already defined data sets and transform them; or o "real-time mode" collects and process data from sources while they are produced. • Data must be provided following the Linked Data Design Issues  [28]. • Data must be curated, limiting the data garbage provided to applications consuming data from the DET. • Data can be dynamically linked based on changes in the data (e.
The first three of these functions are grouped together in the so-called Injection Chain. Modules belonging to these first three functional categories team up to collect, format, and curate the data that is introduced into the platform. Afterwards, the architecture builds on the NGSI-LD standard, more specifically on its federated and de-centralized context data management API, and enables components from each of the other main functions to add/query or subscribe to data. Thus, the core component of the DET architecture is the NGSI-LD Context Broker. It enables the linking and enrichment process on top of curated NGSI-LD data, providing access to external applications retrieving and making use of the data. Considering this component as the core of the architecture, security procedures (e. g.
It is split into type identification, template selection, and transformation. In the first step, a trained AI model classifies the input data into multiple categories, corresponding to the potential output data models. Once selected, the corresponding template from the existing pool is used and the transformation is performed. For example, in the DET implementation that have been carried out (cf. Section IV), one of the implemented mapping modules, the IoT Data Mapper, combines two of the aforementioned approaches, the template-based mapping and AI-based mapping. It uses a Nearest Neighbor approach to individually map every field of the input document into its corresponding NSGI-LD property in the Smart Data Models. This mapping is based on the words used within that field. Once the NGSI-LD properties are identified, it uses a JMESpath template in order to transform the input document to the pertinent Smart Data Model.
These templates work in tandem with a Python class that includes several custom functions to interact with the templates, allowing us to modify the output NGSI-LD document in a more complex manner. For instance, we are able to do type conversion within the document, change the date format, or generate unique identifiers. However, the data received may not always be known. In order to deal with unknown data, we have implemented an AIaided type identification module with the Tensorflow and Keras packages for Python. Firstly, we train a model with domain-specific ontologies (in our case, the taxonomy defined in the SmartSantander project) and then this model is used to predict the type of the input heterogeneous data. After the type of input data has been identified, we send it to the Nearest Neighbor mapper along with the newly identified type, which then allows us to select the corresponding JMESpath template as mentioned before.
In the evaluation that was presented in  [29] , some differences can be observed between the behavior of the two alternatives. Mainly, the anomalous points detected with the Isolation Forest algorithm correspond to global outliers within the dataset, whereas those detected with the Local Outlier Factor algorithm respond to local anomalies related to the group of neighbors chosen for evaluation. The last step is dedicated to the acquisition of the Data Quality (DQ) dimensions associated to the incoming data points. Once the DQ features of the NGSI-LD entity being assessed are evaluated and obtained, they are linked to the data entity as a related metadata entity following the DataQualityAssessment data model  [30].
These formulas can be adjusted based on the availability of the data. For instance, the livability in a city can depend on multiple indices such as "RISK, SUSTAINABILITY, or MOBILITY". Each of these indices can take into various factors which would define the subindices. For example, MOBILITY may include multiple subindices such as PUBLIC_TRANSPORT_USAGE and BIKE_OWNERSHIP. FIGURE  4  illustrates the concept based on available datasets (FIGURE  4-bottom ) such as open datasets or data from European Data Portal (EDP). These datasets can be linked (FIGURE  4 -left) through a method such as semantic linking. Data is accessed by different interfaces such as City Livability Index Flexible Frontend (CLIFF) Dashboard or VOLUME XX, 2017 Chatbot (FIGURE  4 -top) Similarly, the value-augment data will be provided by the City Livability Indices that are described above.
A. DET DEPLOYMENT DETAILS The deployment architecture of the DET is based on a federated setup as illustrated in FIGURE  5. This setup includes Scorpio Context Broker Federator that connects the different "satellites" of partners in the data plane. In addition, there is a control broker that handles the control plane functions of the DET. Access to the Federator Scorpio Broker and the EMQX Control Broker is restricted using OAuth 2. 0. The technology used for the Identity and Access Management is Keycloak. We have deployed the Scorpio NGSI-LD Brokers from their latest docker images (i. e. scorpiobroker/all-in-onerunner:java-kafka-latest). On the other hand, we have used Python 3 as the programming language for the DET components. These are deployed as separate Python scripts acting independently, which enhances their modularity and reusability.
1) PERFORMANCE EVALUATION OF DET PHASES The performance indicator analyzed has been, in all cases (i. e. data mapping, curation and enrichment), the processing delay (i. e. the amount of time consumed to process a particular piece of data at each of the DET steps). This delay only has an impact on real-time data (i. e. , it does not have major implications for static datasets), and it is comprising the processing time of the single step and the delay to swap data among the DET modules. Moreover, the experiments were not meant to be exhaustive and comprehensively evaluate all the modules that have been implemented. Alternatively, we have evaluated three modules, one of each class. The Mapper took the measurements from the SmartSantander IoT platform  [36]. The Curator assessed several data quality dimensions of each of the measurements in that data stream.
Finally, the Linker and the Enricher VOLUME XX, 2017 processed each incoming measurement and added some metadata to them. The data used for the evaluation consists on temperature measurements generated by the IoT sensors deployed within the SmartSantander framework, in real-time, during several days. These measurements average 230 bytes in length and are formatted in JSON. Over 100 sensors were actively providing measurements every 5 minutes, which, eventually, resulted in roughly one measurement every 3 seconds in average. The content of the measurements comprises the identifier of the originating sensor, its location, the timestamp, the actual temperature value and its unit of measurement. We have chosen this dataset for several reasons. Firstly, the SmartSantander IoT platform is well-known to us and we are familiar with its reliability.
V. CONCLUSIONS The DET has been designed to enable heterogeneous data harmonization and enrichment. It leverages linked-data and AI-based data processing. Its modular architecture, for which the NGSI-LD context management standard is the key baseline, is meant to support decentralized operation and adaptation to heterogeneous data. As it has been described in the paper, the proposed solution is able to extract high value datasets from existing heterogeneous data sources (from public and private stakeholders) and publish enriched datasets and data-streams using NGSI-LD, which have been harmonized and aggregated using a data enrichment toolchain. Overall, we argue that the semantic data enrichment that is supported by the DET brings many advantages to data processing, such as: (1) making data more valuable by providing some meaning to it; (2) enabling more efficient and accurate discovery and reasoning; (3) establishing links among pieces of data; and, (4) allowing more complex processing flows and event processing.
Find the solution δ to Eq. (  27 ) starting from δ 0 = 0 and iterate it with + δ R = v ∞ v Stokes δ i+1 = 1 1+f R×(1+δ i ) -1 until the relative error on speed becomes smaller than ε: |δi+1-δi| 1+δ i+1 < ε. Then, v ∞ = v Stokes ∞ (1 + δ). -AerSett method. Directly calculate v ∞ = v Stokes ∞ × 1 -1 + R 2. 440 -0. 4335 -1. 905. The bisection and fixed-point methods depend on a tolerance parameter ε, which we have set to ε = 0. 02, corresponding to the maximal error of 2 % observed using the AerSett method (Fig. 3b ). A Fortran code has been designed to estimate the calculation cost for these three methods (Fig. 5 ). This has been done by performing the calculation of v ∞ for 10 8 random values of D, at a random altitude z in the atmosphere between 0 and 12 000 m. The pressure and temperature values P (z) and T (z) are estimated from the US Standard Atmospheric profile (NOAA/NASA/USAF, 1976).
Code availability. All the simulations and figures have been performed with Python scripts and Fortran code available according to the GNU General Public License v3. 0 at https://doi. org/10. 5281/zenodo. 7535171 (Mailler, 2023). All the available versions of the AerSett Fortran module are available according to the GNU General Public License v3. 0 from https://doi. org/10. 5281/zenodo. 7535114 (Mailler et al. , 2023).
Current proprietary and open-source serverless platforms follow opinionated, hardcoded scheduling policies to deploy the functions to be executed over the available workers. Such policies may decrease the performance and the security of the application due to locality issues (e. g. , functions executed by workers far from the databases to be accessed). These limitations are partially overcome by the adoption of APP, a new platform-agnostic declarative language that allows serverless platforms to support multiple scheduling logics. Defining the "right" scheduling policy in APP is far from being a trivial task since it often requires rounds of refinement involving knowledge of the underlying infrastructure, guesswork, and empirical testing. In this paper, we start investigating how information derived from static analysis could be incorporated into APP scheduling function policies to help users select the best-performing workers at function allocation.
However, the execution times of the functions are not independent of the workers since effects like data locality (the latencies to access data depending on the node position) can increase the run time of functions  [6]. We visualise the issue by commenting on the minimal scenario drawn in Figure  1. There, we have two workers, W1 and W2, located in distinct geographical Zones A and B, respectively. Both workers can run functions that interact with a database (db) located in Zone A. When the function scheduler -the Controller -receives a request to execute a function, it must determine which worker to use. To minimise response time, the function scheduler must take into account the different computational capabilities of the workers, as well as their current workloads, and, for functions that interact with the database, the time to access the database.
By featuring customised function scheduling policies, APP allows one to disentangle from platformdependent allocation rules. This opens the problem of finding the most appropriate scheduling for serverless applications. The approach currently adopted by APP is to feature only a few generic wellestablished strategies, like the foregoing best_first. The policies are selected manually, when the APP script is written, based on the developer's insights on the behaviour of their functions. In this paper, we propose the adoption of automatic procedures to define function scheduling policies based on information derived with a static analysis of the functions. Our approach relies on three main steps: (i) the definition of code analysis techniques for extracting meaningful scheduling information from function sources; (ii) the evaluation of scheduling information by a(n off-the-shelf) solver that returns cost expressions; (iii) the extension of APP to support allocation strategies depending on such expressions.
In particular, we discuss the applicability of our approach on a minimal language for programming functions in serverless applications. We start in Section 2 by defining our minimal language called miniSL (standing for mini Serverless Language) which includes constructs for specifying computation flow (via if and for constructs) and for service invocation (via a call construct). Then, by following  [5, 8] , we describe in Section 3 how to exploit a (behavioural) type system to automatically extract a set of equations from function source codes that define meaningful configuration costs. In Section 3 we also discuss how equations can be fed to off-the-shelf cost analyser (e. g. , PUBS  [1]  or CoFloCo  [4] ) to compute cost expressions quantifying over-approximations of the considered configuration costs.
The mini Serverless Language The mini Serverless Language, shortened into miniSL, is a minimal calculus that we use to define the functions' behaviour in serverless computing. In particular, miniSL focuses only on core constructs to define operations to access services, conditional behaviour with simple guards, and iterations. Function executions are triggered by events. At triggering time, a function receives a sequence of invocation parameters: for this reason, we assume a countable set of parameter names, ranged over by p, p ′. We also consider a countable set of counters, ranged over by i, j, used as indexes in iteration statements. Integer numbers are represented by n; service names are represented by h, g, • • •. The syntax of miniSL is as follows (we use over-lines to denote sequences, e.
We illustrate miniSL by means of three examples. As a first example, consider the code in Listing 1 representing the call of a function that selects a functionality based on the characteristic of the invoker. Listing 1: Function with a conditional statement guarded by an expression. This code may invoke either a PremiumService or a BasicService depending on whether it has been triggered by a premium user or not. The parameter isPremiumUser is a value indicating whether the user is a premium member (when the value is true) or not (when the value is false). The other invocation parameter par must be forwarded to the invoked service. For the purposes of this paper, this example is relevant because if we want to reduce the latency of this function, the best node to schedule it could be the one that reduces the latency of the invocation of either the service PremiumService or the service BasicService, depending on whether isPremiumUser is true or false, respectively.
The inference of cost expressions In this section, we formalise how one can extract a cost program from miniSL code. Once extracted, we can feed this program to off-the-shelf tools, such as  [4, 1] , to calculate the cost expression of the related miniSL code. Cost programs are lists of equations which are terms f (x) = e + ∑ i∈0. n f i (e i ) [ ϕ ] where variables occurring in the right-hand side and in ϕ are a subset of x and f and f i are (cost) function symbols. Every function definition has a right-hand side consisting of • a Presburger arithmetic expression e whose syntax is e ::= x | q | e + e | e -e | q * e | max(e 1 , • • • , e k ) where x is a variable and q is a positive rational number, • a number of cost function invocations f i (e i ) where e i are Presburger arithmetic expressions, • the Presburger guard ϕ is a linear conjunctive constraint, i.