dataesr/scientific-paragraphs-categorization huggingface dataset:
It is from this point onward that the SPP configuration works differently with the available solutions and systems. Most of the available solutions generate quotations, including prices and delivery dates, based on the historic costing and cycle time data of features. In generating quotations, the availability of manufacturing resources is ignored, leading to the current and future orders competing for the same resources. In fact, the traditional way of quotation generation assumes infinite manufacturing resources available on shop floors. As a result, quotations obtained are often inaccurate  (Lan et al. , 2008). Subsequently, the configured alternatives are evaluated in terms of production cost and completion time. The evaluation is accomplished by incorporating the real time data from companies' legacy systems, such as production planning and control systems and shop floor execution systems.
As pricing and delivery lead-time are calculated based on the latest data about manufacturing resource availabilities, the quotation is accurate. Together with the quotation obtained, it presents the preliminary sales configuration as the formal sales configuration to the customer. Upon the customer's acceptance, it generates the BOM and BOO for the configured product and production process, respectively. Also generated is the customer order. In case, the customer is not satisfied with either the price, delivery lead-time or the features included in the sales configuration, the process will start again with modified answers and/or online questions. While we summarize above the major steps involved in the SPP configuration, we elaborate the configuration details in terms of system modules below.
If the customer is satisfied with the product, price and delivery date, he can also place an order through the user interface module. In case there are errors in the customer input or changes in the quoted prices and/or delivery dates (see Quotation preparation module), the dialog boxes with the explanations are also presented to the customer through the user interface module. Input evaluation module 2 : The input evaluation module evaluates user inputs from several aspects, such as data validity and completeness, customer historic information, product manufacturability and functionality. For instance, if the module detects that the input data are incomplete or invalid, it prompts the user to make necessary additions or changes. The module also checks if the existing design and manufacturing capabilities are capable to produce a specific feature in accordance with the inputs.
For example, a customer requires a 15-inched LCD monitor. The module evaluates this requirement to be unachievable as the company's design and manufacturing capabilities are not able to produce such a monitor. Furthermore, the module assesses if customer requirements conflict with one another. Similarly, if it finds incompatible or conflicting requirements, the system informs the user about the conflict and further asks modifications. Sales/product/production configuration module: To enable sales, product, and production configurations in one system, the configuration model: GBoFMO organizes all the data and knowledge related to customers, sales, design, planning, production and process, as shown in Figure  2. Built upon the GBoFMO, three submodules, including the sales configuration, product configuration, and production configuration submodules, form the configuration module.
The configuration evaluation module performs this task. It takes the result of the configuration module: pairs of configured product and production process alternatives as input and evaluates each pair with respect to production cost and completion time. It outputs a list of product and process pairs along with the cost and time values. Based on such result, the company can make decision on the products to be offered by considering, e. g. , the trade-off between cost and delivery time, the relationship with the customer, its strategic objectives (see details in Section 4). To ensure the accuracy, the evaluation is based on the latest data about manufacturing resource availabilities that are obtained from the company's existing production planning and scheduling systems and shop floor execution systems, as shown Figure  2.
The additional factors might be saved in customer databases or sales databases, as shown in Figure  2. As the quotation is prepared based on the latest information about manufacturing resources, if the customer does not accept it within a given period, the quotation may not be accurate due to the possible status change of manufacturing resources. In such situations, the quotation evaluation module recalculates the price and delivery date based on the re-evaluated product and production process alternatives. Order/BOM/BOO generation module: To generate error-free BOMs, companies turn to configuration systems  (Forza and Salvador, 2002). In response to the lack of studies in the generation of error-free BOOs, which together with BOMs contribute to smooth production, high product quality and reduced production lead time, the SPP configuration system is designed to automatically generate both BOMs and BOOs for the configured products and production processes.
The order/BOM/BOO generation module performs this task and generates customer orders, BOMs, and BOOs. Upon receiving the customer acceptance signal from the user interface module, the generation module will generate the customer order including the basic customer information, his requirements, the sales configuration, and the price and delivery date. It then saves the customer order into relevant databases, such as the customer, sales, product and process databases, for future configuration. The module also generates BOMs and BOOs for the final selected products and production processes after configuration evaluation. Similarly, the BOMs and BOOs data will be saved in the corresponding databases for future configuration. To ensure the accuracy of BOMs and BOOs generated, the generation module gets the necessary data from the legacy systems, such as product data management systems, process planning systems, design systems.
The transport cost is determined by the unit transport cost and the total number of transports. The total number of transports is determined by the machines and their precedence relationships described in a BOO. Similarly, the unit transport cost is considered as the average transport cost obtained based on the historical data and is the same for the transport between any two machines. For the set of product alternatives and production process alternative sets, the transport cost is computed as follows: out by certain manufacturing resources (e. g. , operators), the change of machines takes time, thus incurring cost. This cost factor is determined by the unit machine change cost and the total number of machine changes. Similarly, the total number of machine changes can be determined based on a BOO; the unit machine change cost is considered the same.
A temperature controller has many child components, such as plastic cases, surface mount components, and printed circuit board, as shown in Figure  5. The challenge that the company faces is numerous temperature controller alternatives can be specified to meet the same customer requirements, and multiple production process alternatives can be planned to produce a temperature controller. With the presence of many alternatives, the company needs to determine the most suitable one to reduce costs or delivery lead time or both. The company adopts the configuration evaluation proposed to determine customized temperature controllers to be offered and production processes to be adopted on their shop floor. The prototypical system is developed based on C#, XML, open API and JSON data format. Thanks to the use of C#, open API and JSON data format, the prototype can be easily integrated with the company's legacy systems for 3D visualization of temperature controllers and for obtaining the latest data about manufacturing resource capacities and availability.
Thus, built on top of the GBoFMO, the SPP configuration can configure the suitable product variants and production processes for given customer requirements. These product variants and production processes, in turn, can be used as feedback for developers to fine tune design and planning activities. In this regard, as a development tool, the SPP configuration enables a company to continuously improve its product offerings while taking other factors into account, such as the manufacturing capabilities. Additionally, contemporary business operations are exponentially inundated with data and analytics. In this regard, managers can make more evidence-based configuration decisions by utilizing data-driven capabilities and resources. They can update customer orders or their requirements in a timely fashion, possible in real time.
Integration between SPP configuration with legacy systems In the current family-based product development, both products and processes can be designed by adopting the similar product and process structures. Without the support of an automatic tool, designers and process engineers are troubled by a lot of time-consuming but less value-added work. For example, the process engineer may need to modify the entire set of production process only for a small change in the design. However, such trivial activities in the traditional design cannot be avoided in order to satisfy customers. The SPP configuration is, thus, proposed to automatically generate product and production process alternatives. In addition, to avoid the human-made errors in manually generated BOMs and BOOs, the SPP configuration needs to get the real-time data pertaining to manufacturing resource availabilities.
Procedure Participants started by filling out a general information and music abilities questionnaire. We then fitted a headcap on participants' heads and placed EEG electrodes in it using a conductive gel. The experiment took place in a sound-attenuated, electromagnetically shielded room. Participants were asked to relax their upper body, close their eyes, avoid body movements and to pay attention to the beat throughout the experiment  (Schwarz and Taylor, 2005). We recorded data from five experimental blocks: Monaural beats are created by digitally summing these tones and presenting the resulting signal diotically. FFT of signal, Stimuli were analyzed using a Fourier transform to obtain their frequency composition. FFT of Hilbert transform, The FFT of the Hilbert transform (i. e. , the analytic signal) was computed to tap into the spectral information of the envelope of the signal (the beat frequency).
The frequency of the envelope of the summed tones encodes beat frequency (e. g. , 403. 5-396. 5 = 7 Hz for u ). This information, however, is only encoded in monaural beats because they are digitally summed. an 8 min baseline (no stimulus presentation; eyes-closed) followed by the four pseudorandomized experimental conditions (binaural g , monaural g , binaural u , monaural u ), each lasting for 8 min. After each recording block, participants were asked to rate their experience using two visual analogue scales. They were also given the opportunity to take a break in the middle of the experiment. Auditory stimuli (both binaural and monaural beats) were generated live (i. e. , during the recording block) to ensure submillisecond phase accuracy using a signal processing system (RX6, Tucker-Davis Technologies) controlled with MATLAB software (The MathWorks) and delivered via insert earphones (ER3, Etymotic Research).
EEG data acquisition EEG was recorded using 64 active sintered Ag-AgCl electrodes placed on the scalp according to the International 10/10 system (ActiveTwo, BioSemi). The active electrodes contain the first amplifier stage within the electrode cover and provide impedance transformation on the electrode to prevent interference currents from generating significant impedance-dependent nuisance voltages. We, therefore, did not control electrode impedances but rather kept direct-current offset close to zero during electrode placement. Vertical and horizontal eye movements were monitored using three additional electrodes placed on the outer canthus of each eye and on the inferior area of the left orbit. Reference-free electrode signals were amplified, sampled at 2048 Hz (ActiveTwo amplifier, BioSemi), and stored using BioSemi ActiView Software for offline analysis.
Visual analogue scales Data from pen and paper scales was measured manually and stored digitally in CSV files for further statistical analysis using R (v3. 6. 1, R Development Core team, 2008), setting the significance level at 0. 05. We first determined the data distribution using a Shapiro-Wilk test. Wherever data were not normal, the specified statistic was compared against a distribution created by permuting the data 1000 times (i. e. , scrambling the label of the data), as opposed to comparing the statistic against a parametric distribution  (Ernst, 2004). We also report confidence intervals obtained from the null distribution (obtained by permuting the data). Given the within-nature of our study, we only performed permutations within-subjects (e. g. , if data were arranged in a matrix were each row is one subject and each column is a measurement, we only permuted the labels of the values within each row).
EEG The data were processed using the EEGLAB toolbox  (Delorme and Makeig, 2004)  and in-house developed scripts in MATLAB. Two different analyses were conducted on the raw EEG data: subcortical (FFR) and cortical (ASSR and functional connectivity). The preprocessing procedures for either subcortical or cortical analysis differed in filtering process, ICA decomposition and re-referencing. For subcortical analysis, data were high-pass filtered at 100 Hz and re-referenced to linked mastoids. Data used for cortical analysis was bandpass filtered between 1-100 Hz, decomposed using ICA for artifact correction purposes  (Jung et al. , 1998)  and re-referenced to linked mastoids as a first step and then to common average reference as a final step.
FFR Data were re-referenced to linked mastoids and highpass filtered at 100 Hz using a zero-phase Butterworth filter Order 4. Data were visually inspected for noisy electrodes, which were then removed and interpolated using spherical interpolation. Finally, data were epoched into 60 events (from À1 to 7 s with respect to trigger onset) and exported for further analysis. Epochs from each participant were averaged and transformed into the frequency domain using an fast Fourier transform (FFT). From these, power was calculated as the square of the magnitude normalized using a factor of 2/N, N being the length of the epoch. Frequencies of interest were extracted as the mean of 1-Hz bins around the carrier frequencies (pure tones: 380, 397. 5, 403. 5, and 420 Hz) for the baseline and each experimental condition.
A baseline normalization (decibel change from baseline) was performed to disentangle background dynamics from actual stimulation-related oscillations  (Cohen, 2014). The equation used was as follows: dB f ¼ 10log 10 activity f baseline f ; (1) where activity f is a specific frequency power in a given experimental condition and baseline f is the average activity across the whole baseline at a given frequency  (Cohen, 2014). The unit of these data is decibel change from baseline. After baseline normalization, all the scalp channels were averaged together to output one normalized power score per experimental condition per participant. Frequency relevant scores were averaged together in each experimental condition. For example, power scores for 396. 5 and 403. 5 Hz were averaged together for u frequency relevant scores.
Functional connectivity Two complementary measurements of functional connectivity were used as indices of long-range synchronization: phase-locking value (PLV;  Lachaux et al. , 2000)  and imaginary coherence (iCOH;  Nolte et al. , 2004). On top of that, per electrode, the amplitude of the Hilbert Transform and the Power of the Fourier transform were computed as local indices of synchronization. Analyses were done over all traditional frequency bands (d : 1-4 Hz; u : 5-8 Hz, a: 9-12 Hz, b : 13-30 Hz, g : 32-48 Hz) and the specific beat frequencies (1-Hz bins around 7 and 40 Hz). ICA corrected data (i. e. , the same files used for ASSR) was imported to MATLAB to compute these metrics.
Procedure The experiment was programmed in the open-source PsychoPy environment  [49]  and the code and data have been made available on open science framework https://osf. io/7j5qk/ The stimuli and nature of experimental trials are shown in Figure  2. First a central fixation cross appeared for 0. 8 seconds. This was followed by a 0. 1 second warning tone (400 Hz) which signalled the start of the trial. The visible target-counter then appeared at the top 12 o'clock position, set a zero, and remained static for 0. 5 seconds. It then moved clockwise as the counter incremented in integers (e. g. 1, 2, 3, 4…). Visible motion was followed by a variable occlusion period. The occlusion period was terminated by anther 0. 1 second auditory warning tone (800Hz). The visible and occlusion intervals were identical in all blocks.
Design and Procedure After the first 6 practice items, participants were presented with a sequence of 65 pictures for naming. Following the procedure of  Howard et al. (2006) , five exemplars from each of the categories under study were embedded within the sequence. Pictures belonging to the same category were separated by a lag of 2, 4, 6, or 8 intervening items; each interval was present once for every category. Hence, 23 items intervened between the first and last items in each category. Thirty-seven different lists were created with the following constraints. Items intervening between members of a category might either be drawn from other 1 Categories were chosen following  Levin (1993)  and  Levin and Rappaport (1995). The inclusion of verbs within each category was done using the semantic classification of the ADESSE database (Base de datos de verbos, alternancias de diátesis y esquemas sintáctico-semánticos del español  [ADESSE].
Data Analysis Two types of responses were excluded from the analysis: naming errors and omissions. Semantically non related verbs and words from other lexical classes were considered naming errors. Latencies of more than 2000 ms. (responses initiated after the picture disappeared) were considered omissions. Acceptable alternatives (e. g. "entregar" instead of "dar;" see Appendix 1 for the full list of items and acceptable alternatives) were considered right answers and included in the analysis. Data were analysed for the number of unrelated items between the occurrence of two items in a category (lag) and for its ordinal position within the category. Following the protocol of  Howard et al. (2006) , separate analyses were carried out treating subjects and categories as random factors, leading to F1 and F2 statistics.
Acknowledgement This research was supported by three grants from the  Spanish Government  ( SEJ 2005/SEJ2005-01223/CONSOLIDER-INGENIO ) and by grants  PSIC  and  PSI2008-00464/PSIC , as well as by  UBACyT   F436 , from the  University of Buenos Aires. Yamila Sevilla  and  Diego E. Shalóm  were supported by the  National Research Council (Conicet) ,  Argentina. Elin Runnqvist  was supported by a fellowship from the  Catalan Government (FI). SEVILLA, Y. ; SHALÓM, D. ; RUNNQVIST, E. ; COSTA,  A. Inhibición  semántica acumulativa en la producción de verbos en español. Alfa,  São Paulo , v. 64, 2020.
g. , is location information available?), and their temporal and spatial coverage. These issues must be explored before a reliable assessment of the utility of these untapped resources can be made. AI naturalists, just like their human counterparts, may have their own biases which must be fully understood if the information that they generate is to be trusted and suitably utilized. For example, most AI systems can only detect or recognize already seen (or learned) objects or concepts. Benchmark datasets of images can be organized to precisely assess the limits of AI systems' ability, highlighting where human expertise is still required. Deep learning models (some of the most advanced AI algorithms) are developed with training datasets that allow them to capture discriminant visual patterns.
Their performances are then strongly correlated to the quality and completeness of the datasets on which they are trained. Unbalanced, biased, or otherwise poor-quality training datasets will lead to underperforming algorithms in real conditions. 27 During the learning phases, particular attention must be given to any relevant limitations of the training data, and the gap between these and the test data on which the developed algorithms will be evaluated. 28 e present an AI naturalist developed to create biodiversity datasets from social media image data. We use Flickr to collect images from two locations in England, one rural (Peak district) and one urban,(London) and classified images to species using the Pl@ntNet image classier. We explore the biases and challenges inherent to the image dataset and the AI classification algorithm using an expert assessment approach.
Images Are Spatially Clustered Flickr searches returned a far greater number of images for central London (n = 55,176; 1,200 images/km 2 ) than for the Peak District (n = 5,486; 46 images/km 2 ). Images were taken between and April 26, 2003, and August 23, 2019 (Figure  1 ). By definition these are only the subset of images taken in these locations that had location data available. To obtain an indication of the proportion of images that did not have location data, we searched for all images that contained the word ''flower'' taken in the first week of July 2019, regardless of location information. July was chosen because it is the month in which Flickr records the greatest number of uploads. 29 This search returned 23,140 images, of which 25% had location information, indicating that the majority of Flickr images of flowers likely do not have location information.
The Urban Area Has a Lower Proportion of Well-Classified Images We see a clear difference between the distribution of classification scores in the rural and urban datasets (Figure  3 ). In London the scores have a unimodal left-skewed distribution (Dip test of multi-modality, D = 0. 001, p = 0. 997), while in the Peak District the distribution is bimodal with peaks near 0 and 1 (D = 0. 015, p < 0. 01). This suggests that in the urban environment there is a larger proportion of images that are either not of flowers, are not of sufficient quality, or are of species that the classifier is currently not as good at classifying.
DISCUSSION By combining social media APIs with AI classifiers, we were able to build an AI naturalist capable of creating biodiversity datasets from previously unexploited data sources. However, we demonstrate that there are a number of biases in the data produced, some of which may be able to be mitigated against, that must be carefully considered before the data could be used in certain types of analyses. Image data are being collected in vast quantities all over the world, and we have looked at only one repository. We focused on Flickr because of its accessibility and rich metadata, which allowed us to filter images using text and spatial searches. Other notable sources of image data include Facebook, Twitter, Reddit, and Instagram; however, these all pose greater challenges in terms of collating and filtering, with geolocation of images perhaps being the greatest challenge.