dataesr/scientific-paragraphs-categorization huggingface dataset:
Results Before answering the three research questions, we first compare the performance of the base models per NLP technique. 5. 1. RQ1: Does adding textual features extracted from the company's textual website content increase the predictive performance of BFP models? Table  3  and Table  4  give insight whether incorporating textual features extracted from the company's website increases predictive performance for BFP modelling. Table  3  shows that all models including textual features (TF-IDF, Doc2Vec, CNN and BERT) better predict business failure than the model only considering structured input data (None). Further, Table  4  shows that incorporating textual features extracted by Doc2Vec, CNN and BERT significantly increases predictive performance for at least one of the evaluation metrics (p-value < 0.
Conclusion In this study, we presented the textual content of a company website as a valuable information source for BFP models. In line with other studies, we found that including textual features in BFP models improves predictive performance  (Ahmadi et al. , 2018; Cecchini et al. , 2010; Mai et al. , 2019). By introducing the company website as a textual data source, we extend the scope of considered companies in recent literature to firms that are not disclosing periodic financial reports to the respective authorities. This includes small-and medium-sized enterprises as well as publicly listed companies, all having a publicly available website. Moreover, we evaluated multiple approaches to extract textual features and measured their impact on predictive performance. We benchmarked vector-space, (deep) Additional information sources like market-based or macroeconomic data might provide rich data sources and improve predictive performance  (Shumway, 2001).
Second, recent studies also show the benefit of long short-term memory (LSTM) models in text processing. This opens up the ability to apply also these type of deep learning approaches for feature extraction in the BFP domain  (Fischer & Krauss, 2018). Third, the results presented in this study are based on one dataset. Expanding the applied framework to a multitude of datasets that include an international scope of companies and industries has considerable potential to derive insights on a wider scale of generalizability.
Tests specific to some libraries Interval arithmetic libraries may use different representations for the interval, for example, by their endpoints as in MPFI  6  or by their midpoint and radius as in Arb. 7  They may rely on usual floating-point arithmetic as Octave interval package,  8  or arbitrary precision floating-point arithmetic as MPFI, or exact rational arithmetic as JInterval. 9  Some libraries use usual floating-point arithmetic but avoid changing the rounding mode, as Filib 10 or JuliaInterval,  11  or as GAOL  12  which can use either rounding-to-nearest exclusively, or rounding-upwards exclusively. Each specificity must be tested through several different test cases. Typically, several examples using largely varied precisions must be tested for a library using arbitrary precision.
Our contributions Our contributions can be summarized as follows. • We introduce a JSON schema to describe interval tests in Section 2. This allows the tests to be easily integrated in existing libraries. • We present an extensive database of tests in Section 3. This builds on previous efforts such as ITF1788 presented in Section 1. 4. 2, though we expand it with new test cases. • In particular, we generate tests whose intervals have hard-to-round floating point numbers. These cases were generated using the program BaC-SeL and are particularly important to ensure that an interval implementation is robust to floating-point rounding issues. How these tests have been generated is detailed in Section 3. 1. • Finally, we run our tests on the INTERVALARITHMETIC. JL. As developed in Section 4, we show how our newly generated hard-to-round cases can help unveil hard to detect bugs, making hence our contribution valuable for interval arithmetic library developers.
Wrappers are available for Profil/BIAS, boost/interval, C-XSC, Filib, libieeep1788, libMoore, MPFI. The launcher reads tests from plain text files of simple human-readable format and writes the results computed using tested library and JInterval and their relation into a plain text report. Test set included in the JInterval P1788 Test Launcher consists of over 14,000 tests which partly originated from libieeep1788, Filib, libMoore while the other ones are original. Another attempt to introduce a systematic testing framework for interval arithmetic was the Interval Testing Framework 1788 (ITF1788). 20  Developers of ITF1788 introduced a domain specific language called ITL (Interval Testing Language) that could be used to express tests for interval arithmetic. ITF1788 is a Python engine that inputs ITL-files and converts tests into code for the specified language, test framework and library.
Parallel or distributed computations Testing correctness of interval libraries under parallel behavior is important, especially for libraries that handle floating-point error by changing the rounding mode. In several common processors, the rounding mode of the floating processing unit is controlled by a global state in the unit registers. If not handled with care, manually changing the rounding mode in parallel codes can lead to race conditions and undefined behavior. To test library soundness under multithreaded execution, the tests presented here could be run in parallel. In addition to speeding up the tests execution, this could also be used to test that correct rounding is preserved under parallel execution. Parallelism was also used to generate the hard-to-round cases: the code that determines hard-to-round cases, called BaCSeL, uses multithreading to explore in parallel many subintervals.
{ "function": "atanh", "precision": 23, "input": [ { "type": "interval", "inf": "-0xf. fe1e00@-1", "sup": "0xf. fe1e00@-1" "dec": "com" } ], "output": { "tight": { "type": "interval", "inf": "-0x4. 305fa0@0", "sup": "0x4. 305fa0@0", "dec": "com" }, "accurate": { "type": "interval", "inf": "-0x4. 306830@0", "sup": "0x4. 306830@0", "dec": "com" } } } In the above code snippet, the INF and SUP entries are hexadecimal numbers whose exponent is in base 16.
It has been developed since 2020 and it aims at determining hard-to-round cases for several mathematical functions, over specific subdomains. It is based on several algorithms that rely on LLL to prune easy cases and to focus on hard ones. The computing basis is either 2 or 10, in what follows we focus on the basis 2. The list of available functions contains exp, exp 2 , log and log 2 , sin, cos, 1∕x, √ x, 1∕ √ x, 3 √ x, acos where the subscript indicates the basis of the exponential or the logarithm: exp 2 (x) = 2 x and log 2 x = log x∕ log 2. Each subdomain is split into smaller intervals, that can be further subdivided to reach the required accuracy. These smaller intervals are explored in parallel, using OpenMP to distribute the work. Each function is internally approximated by a Taylor expansion on each small interval.
Four mathematical functions as illustration: cubic root, exp, sin, atanh As the IEEE 1788-2015 standard for interval arithmetic requires over 50 mathematical operations and functions and recommends 16 additional ones for the set-based flavor, we will not detail the test cases for each function but we will focus on a few selected ones. The ones we choose here are the cubic root, the exponential, the sine and the hyperbolic arc-tangent. These test cases can be found at https://github. com/lucaferranti/ IntervalArithmeticTests.
RESULTS WITH THE JULIA INTERVAL LIBRARY The IntervalArithmetic. jl package is written in the Julia programming language, motivated by Julia's mathematical-style syntax, near C-performance, high-level interactive usability, very robust type system with easy to exploit composability properties. These capabilities of the language permit to have parametric interval that depend on the underlying floating-point representation, which can be extended to incorporate IntervalArithmetic. jl implements interval-types in the form Interval{T}, where T refers to the underlying floating-point representation. For the tight accuracy mode, the package relies on the CRlibm library 28 for correctly rounded Binary64, and on the MPFR library  29  for Binary32 and arbitrary precision. The CRlibm library does not have all functions that the IEEE 1788-2015 standard requires, for instance, atanh is not included.
Cloud computing has become a popular delivery model service, offering several advantages. However, there are still challenges that need to be addressed when applying the cloud model to specific scenarios. Two of such challenges involve deploying and executing applications across multiple providers, each comprising several services with similar functionalities and different capabilities. Therefore, dealing with application distributions across various providers can be a complex task for a software architect due to the differing characteristics of the application components. Some works have proposed solutions to address the challenges discussed here, but most of them focus on service providers. To facilitate the decision-making process of software architects, we previously presented PacificClouds, an architecture for managing the deployment and execution of applications based on microservices and distributed in a multi-cloud environment.
According to  [11] , there are three cloud service delivery models: multi-cloud, cloud federation, and inter-cloud. In this work, we adopted the multi-cloud service delivery model, as it does not depend on an agreement between providers and, consequently, offers a higher number of providers and cloud services. Using multi-cloud providers can bring many economic benefits, especially if we consider the significant growth in the number of cloud providers and the number of resources they offer. However, many challenges remain, such as security, privacy, trust, legal issues, resource management, and the service-level agreement (SLA) of services  [9]. In order to address these problems, we proposed PacificClouds  [12] , an approach that aims to manage the entire process of deploying and executing a microservice-based application distributed in a multi-cloud environment from the software architect's perspective.
Availability Requirement The cloud provider availability for an application must at least meet the threshold set up by a software architect. MinAvbty is the minimum availability threshold set by a software architect. In (1), we define AP. a as the availability, which is the product among all the application terms. Moreover, (2) defines Term j. a as the application term j availability. The availability of Term j. a is the sum of all sequence availabilities that belong to term j. In (3), we name Seq ij. a as the availability of the sequence i of the term j, which is the product of all microservice availabilities, the availability of all communication links, and the sequence selection probability represented by α (3). Term j. a = num(Seq) ∑ i=1 Seq ij. a, ∀Seq ij | Seq ij ∈ Term j (2) Seq ij.
Setting Scenarios In this subsection, we describe the scenarios, experiments, and performance evaluation results for all approaches of the proposed model. We conducted each experiment 30 times to ensure a normal distribution, as recommended by  [39]. The scenarios were synthetically configured, aligning with common practices in the field, given the limited availability of datasets in the cloud computing research domain  [40]. The providers in all scenarios were organized into sets based on the number of providers or provider services. We described twelve applications based on microservices, varying in the number of microservices, as presented in Table  2. We assume that all microservices have the same size and require the same number of cloud services, but their requirements may vary. Each microservice requires three cloud services, which fall into different classes: computer, storage, and database.
Brain -Computer Interfaces and especially passive Brain-Computer interfaces (pBCI), with their ability to estimate and monitor user mental states, are receiving increasing attention from both the fundamental research and the applied research and development communities. testing new pipelines and benchmarking classifiers and feature extraction algorithms is central to further research within this domain. Unfortunately, data sharing in pBCI research is still scarce. the COG-BCI database encompasses the recordings of 29 participants over 3 separate sessions with 4 different tasks (MATB, N-Back, PVT, Flanker) designed to elicit different mental states, for a total of over 100 hours of open EEG data. This dataset was validated on a subjective, behavioral and physiological level, to ensure its usefulness to the pBCI community.
In particular, EEG signals and BCI performance have been reported to significantly change across days, tasks, users' mental states and contexts  [10] [11] [12] [13] [14] [15]. A relevant solution to tackle this issue is to develop transfer learning methods. Transfer learning is an overarching term for a group of problems that refer to the decrease in effectiveness/accuracy of machine-learning algorithms and pipelines when applying the algorithm to slightly different but related data  16. To that end, open datasets present not only a cost-effective alternative to collecting data individually for each experiment but also enables researchers to validate their algorithms and compare their performances on a similar set of data. Nonetheless, the number of freely available datasets is, with exceptions  17  , very limited see  10  for further details).
The advantages of data sharing and the simultaneous lack of free data were the motivating factors for the creation of the COG-BCI database  18. Hence, this database  18  was designed to propose a sufficient amount of fully informed data in a range of conditions representing various cognitive states, thus also allowing researchers to investigate transfer learning. Four tasks were carefully selected to allow the assessment of various mental states. The Flanker task is used for decision-making and conflict evaluation  19. The task consists of deciding the direction of an arrow, while either having distracting (incongruent) or non-distracting (congruent) stimuli presented at the same time. Afterwards, participants receive feedback, allowing researchers to investigate the effects of errors as well as trial-based feedback.
The N-Back task is known for taxing working memory, and has therefore been frequently used to elicit different levels of mental workload  20. Here, participants have to retain numbers that appear on the screen and decide if the currently presented letter is the same as the Nth letter before (N equal to 0, 1 or 2). Another more ecological way to measure mental workload is the MATB-II, in which participants have to simultaneously perform four aviation-related subtasks. Lastly, the Psychomotor Vigilance Task (PVT) was selected as a simple and well-established measure for robust estimation of vigilance and fatigue  21. In this task, participants have to react as fast as possible, by pressing a button, to a stimulus appearing pseudo-randomly on a computer screen. This article was meant as a description of a publicly available EEG database  18.
It details the methods used to create the tasks and database, the experimental choices and standards selected. Next, the analyses used for technical validation of the tasks are presented. The results (subjective, behavioral and physiological) are further interpreted to determine if the tasks were correctly implemented and elicited the expected mental states and their physiological markers. These results also include a proof of concept of their possible use for pBCI development with a three-class machine-learning estimation pipeline example. Lastly, usage notes are provided.
Methods Participants. Ethical Approval for the data collection and subsequent distribution was obtained from the Comité d'Éthique de la Recherche -CER at Université de Toulouse (CER number 2021-342). Based on the average number of participants presented in the pBCI literature (around 15 participants per experiment  5, 22  ), cognitive science literature (around 20 participants per experiment  23, 24  ) and similar databases (SEED  25  15 participants and DEAP  26  32 participants) we initially recruited 35 participants to perform this experiment. Due to participant dropout and technical issues with the data collection, a total of 29 participants were included in the final experimental setup. Participants (11 Female, 18 Male) were on average 23. 9 (Std. 3. 20) y. o. All but 4 participants were students, while the others were employees.
Experimental paradigm. The experiment was conducted over 3 sessions spaced one week apart during which participants had to perform 4 different tasks: the N-back task, the MATB-II, the PVT and the arrow-based Eriksen flanker task  19. Upon their arrival for the first session, participants were informed about the study procedure and asked to read and sign the informed consent. Participants consented to the collection and subsequent distribution of their subjective, behavioral and physiological (cardiac and EEG) data for the duration of the experiment. Participants also consented to having a 3D picture taken. Then they were asked to fill in the demographic questionnaires, as well as the Edinburgh Handedness Inventory  27. The EEG was set up while participants received task instructions. To ensure precise electrode location for within and between-subjects variability, a 3D picture of their head with the electrode cap was recorded at the beginning of each session.
Materials. The database was created by acquiring subjective, behavioral and physiological data from participants using the experimental protocol presented above. Details on the questionnaires, tasks and acquisition devices used are given in this section. Subjective questionnaires. All questionnaires were coded and administered with MATLAB v. 2021a (The Mathworks Inc. ), and presented on a 60 Hz LCD computer screen. The questionnaires can be found in Fig. 23. Demographics. The demographics questionnaire includes questions assessing age, gender, level of education and occupation. Edinburgh handedness inventory. The shortened version of the Edinburgh Handedness Inventory is administered to participants  30. It encompasses four items (e. g. , writing) scored on a 5-point scale ranging from: "Always right; Usually right; Both equally; Usually left; Always left".
Participants' reaction times and responses are collected throughout the whole task. Figure  2  contains an illustration of the task. N-Back task. The N-Back task is a widely used measure of both working memory and mental workload  20, 33. This task is selected to elicit different levels of mental workload across several conditions without changing the amount of visual information presented or the amount of motor responses required thus avoiding EEG data contamination. Fig. 1  Experimental setup with a participant equipped with the 64 electrode EEG system performing the MATB-II task. Consent for the publication of this picture was obtained from the participant. Fig. 2  The PVT task: Following an ISI of 2-10 seconds a red timer appears, which shows the time elapsed from trial onset until reaction.
For this study, combinations of four of the available subtasks of the MATB-II were used and presented with an adapted version of the MATB-II software (Fig. 5 ). This version is coded in MATLAB v. 2021a (The MathWorks Inc. ) and provides the same measures as the original MATB-II task  22. The four subtasks selected from the MTAB-II are tracking (TRACK), system monitoring (SYSMON), communication (COMM) and resource management (RESMAN); leaving task scheduling unused. In the tracking task (TRACK -top right corner), participants are presented with a moving target inside a window and have to keep the target within the central square area by controlling a joystick. The degree of difficulty can be adapted by modifying the degree and the speed at which the target moves. For the System monitoring task (SYSMON -top left corner), participants have to monitor gauges and warning lights.
For the difficult condition, the communications task was added, as well as the tracking task was made more difficult. Before the start of each run, the participants also received a short instruction. Data acquisition. Subjective, behavioral and physiological data are recorded throughout the entire session for each session independently and with each participant. Accurate data synchronization and stimuli recording is performed through the LabRecorder of the LabStreaming Layer software and its related API (https://labstreaminglayer. readthedocs. io/info/intro. html). Stimulus presentation and response acquisition. All tasks were coded in MATLAB v. 2021a (The Mathworks Inc. ), with the PsychToolBox-3 (http://psychtoolbox. org/). To display the stimuli, a desktop computer with a 60 Hz screen was used.
io/ and the get chanlocs plug-in developed specifically for electrode localisation purposes was used (GitHub. com/sccn/get chanlocs/wiki 37 ). For this, a 3D camera was located on an IPad (Apple Inc. ) and calibrated once for the entire experimental campaign. Then, after equipping the participant with the electrode cap, and right before the experiment, a 3D scanning of their head was realised as described on the Structure Sensor website and recommended specifically for EEG on the get_chanlocs wiki. Data was recorded continuously during each session. Reference was located at Fpz (Fig. 6 ). Before acquisition impedances were improved such that acquisition started with all electrode impedances lower than 25 kΩ. The signal was amplified, digitized at a 24-bit rate, and sampled at 500 Hz with a 0.
Data Records COG-BCI is available on Zenodo  18. In order to improve standardization and ease of use, the data is presented in the BIDS format, intended to become the new standard for neuroimaging studies  38  (https://bids. neuroimaging. io/). An exemplary folder tree can be seen in Fig. 25. Participants are numbered from 1 to 29. For each session, the behavioral results, as well as the exact electrode locations and individual datasets for each task, are provided. The EEG data is saved in the. set and. fdt file format (two files per dataset). The resting state is divided into 4 different  datasets: RS_Beg refers to the resting state at the beginning of the sessionand RS_End to the resting state at the end of the session; EC is the abbreviation for eyes closed and EO refers to eyes open. Furthermore, a notebook file is available within the database  18.
Technical Validation This section presents the analyses that were performed in order to ensure that the experimental protocol did elicit the mental states of interest to ensure the pBCI community of the relevance of the dataset. First, the processing applied to the data to enable statistical analysis of the results is presented. Next, the results obtained thanks to the statistical analyses are presented. Lastly, proof of usability for pBCI is provided with a simple pipeline and estimation results, followed by a conclusion on this technical validation. For all reported statistics a p-value of <0. 05 was considered significant. For multiple correction, the Bonferroni method was applied and the reported p-values are multiplied by the factor of tests. Following the GLM contrast analyses (t-test) were used to report statistical significance  39.