dataesr/scientific-paragraphs-categorization huggingface dataset:
Planktonic foraminifera dataset (core MD97-2138) As for the benthic dataset, the dynamics of each abundance signal calculated for the planktonic dataset using out method were similar to those from manual counting. The two main discrepancies in the abundance records are the significant underestimation of G. bulloides and some poor recognition in the highest peaks of P. obliquiloculata. With regards to the underestimation (by 5 % to 10 %) of G. bulloides, the dataset does include a lot of images of foraminifera whose umbilical aperture is not fully cleaned and is infilled with remaining nannofossil ooze. Such infilling often precludes the CNN from classifying these image correctly. The other factor that might explain this underestimation is that the G. bulloides in the western Pacific warm pool are smaller and not as well imaged as the larger species investigated here.
Our study, due to its short time span (less than 150 kyr), cannot fully test this approach, with the only major datum being the disappearance of G. ruber pink at 130 kyr BP  (Thompson et al. , 1979) ; moreover, our CNN was not trained on colour images. The CNN trained on the Endless Forams dataset gave similar accuracy to the CNN trained on the MD97-2138 dataset for G. ruber; however, for other species it did not estimate the abundance well. This failure to generalize is possibly due to the different imaging conditions and different species present int the set. It reinforces our approach of randomly sampling the full dataset to create the classification training set, as this ensures the same distribution of morphometric variation in the images to be classified, and also the same range of taphonomical biases (dissolution, etc).
Conclusions In this article we have presented a method for analysing large foraminifera image sets using deep convolutional neural networks. The performance of transfer learning and full network training for publicly available CNNs, as well as our custom Base-Cyclic and ResNet-Cyclic designs, were demonstrated on the Endless Forams image set, as well as our core-specific benthic and planktonic training sets. The transfer learning approach is fast to train and gives good accuracy without augmentation. Full network training is much slower to train, but our Base-Cyclic design gives as good or better accuracy with faster inference time. Our approach has been to use transfer learning when a CNN is needed quickly to aid with manual labelling and to use full network training to create the final network used for analysis of the entire dataset.
Code and data availability. The ParticleTrieur software program, which was used to manually label the training set and automatically classify foraminifera images, is available with tutorials from http://particle-classification. readthedocs. io (Marchant, 2020a). The latest version of the Python scripts (https://doi. org/10. 5281/zenodo. 3996358, Marchant, 2020b) to train the CNNs in this paper are available from http://www. github. com/microfossil/particle-classification (last access: 2 October 2020) and can be installed using the "pip" Python package. The Endless Forams (Hsiang et al. , 2019), MD022508 and MD972138 training sets (https://doi. org/10. 5281/zenodo. 3996436, Marchant et al. , 2020)  are available from https://github. com/ microfossil/datasets-and-models (last access: 2 October 2020).
A contact was defined as either a physical contact (e. g. a kiss or a handshake), or a close contact (e. g. face to face conversation at less than 1 meter). Data regarding childcare arrangements, lifestyle habits (shopping frequency, mode of transport, etc), and individual protective behaviours (hand washing, mask wearing, etc) were also collected. Participants were asked to provide this information for the previous day, which was during lockdown, and on a typical day before the lockdown. Data were collected in accordance with the regulation in force in France for the protection and security of personal data. Aggregated data is accessible online (link here). The complete dataset can be provided on demand, subject to prior determination of the terms and conditions of the request and in respect for the compliance with the applicable regulation.
Statistical methods We analysed data collected between April 10 and April 28, 2020. We grouped study participants and contacts into the following age groups: 18-20, 21-25, 26-30, 31-35, 36-40, 41-45, 46-50, 51-55, 56-60, 61-65, 66-70, 71-75, 76-80, 81-85, 86-90, >90 and computed descriptive statistics for the responses related to age, household, location, activity, and individual preventative measures. We present either the distributions of numbers and percentages or means and standard deviations as appropriate. Contact matrices. We computed the average number of contacts per person per day stratified by age group, gender, work activity and place of contact and built contact matrices. For home matrices, all individuals of a household were considered as close contacts. For other locations (work, shops, public transports), close contacts and age of contacts were computed by summing those reported by the participant in the corresponding setting.
Comparison of contacts with pre-lockdown situation We compare the frequency of contacts during the lockdown with a non-epidemic period, using direct reports of participants from the SocialCov survey (the part related to their contacts on a day preceding lockdown) and data from a previous survey in France, the COMES-F (5). Comparison with SocialCov pre-lockdown survey. Participants involved in the SocialCov survey were also asked to fill an additional pre-lockdown survey and to depict their contact at the workplace for a day preceding the national lockdown. A total of 35982 participants completed this second questionnaire. We used the full dataset to compute the distribution of the number of contacts. We considered only the participants that declared to work outside home in the pre-lockdown period and during lockdown (3186 participants) to assess the individual change between the number of contacts before and under the lockdown.
COMES-F survey. The COMES-F study was the first French large-scale population survey performed by Ipsos in 2012  (5). The aim of the study was to describe the mixing pattern by age in the French population. In total 2033 participants reported their contacts over 2 days, one during the week day and one during weekends. To match and compare the two datasets, individual contacts in SocialCov were censored at 40 daily contacts to comply with COMES-F constraints. Also, only contacts during weekdays were considered for the COMES-F survey. Censoring all contacts >40 generated a reduction of 0. 2 in the average number of contacts in our survey, from 3. 3 without censoring to 3. 1 with censoring.
8 contacts per day from 1356 participants in the UK  (17). Similar reductions were observed in China, from 636 study participants in Wuhan, and 557 participants in Shanghai, where the average number of daily contacts was estimated at 2, representing a reduction of 7-8-fold during the COVID-19 social distancing period, with most interactions restricted to the household  (18). The data and results presented here should be considered in the light to the following limitations. First, participants were recruited online. As a consequence, the study population may not be a representative sample of the French population. First, as frequently observed in this type of studies, two third of survey participants were women. However, the work situation (Figure  S9 ) and contact matrices (Figure  S10 ) did not differ significantly between males and females.
In order to bypass this overrepresentation, the global contact matrix was computed by weighting according to gender and professional status (telework, unemployed, retired) estimated from the Covid 19 Barometre. This article provides novel data on social patterns during a strict lockdown. Following lockdown relaxing and school and workplaces reopening, intermediate and progressive changes in behaviour and social patterns are expected to have happened. Monitoring contacts, risk perception and behaviours following the ease of the restrictions will be key to assess the dynamics of contact patterns over time. In low and middle income countries, very little data related to social mixing in the community are available. These data are all the more needed to analyse the spread of the virus and the impact of interventions in these countries where social distancing and barrier gestures are potentially more difficult to implement and health care capacity, particularly intensive care, is limited.
Orange and red curves represent the cumulative distributions of the number of contacts for participants in the SocialCov study for ages between 18 and 60 years old and more than 60 years old, respectively. In order to compare the two dataset, individuals with more than 40 contacts per day were not considered. d) Distribution of the number of contacts at work before lockdown (grey), and under lockdown (red) only using observations up to the 95 percentile. Inset shows the change in the number of contacts before and under lockdown at the workplace for participants working outside home during the lockdown (orange).
This objective will be achieved by autonomously generating the nominal data space, considering key operational characteristics parameters of each sensor category (e. g. , nominal range of values, sensitivity, accuracy, and drift). Based on this information, the sensing uncertainties will be automatically estimated, exploiting their representation in lower-dimensional spaces, which will both automatically update the nominal operational bounds, as well as indicate when specific regions of data exceed these bounds, due to the existence of short (e. g. outlier) or longer (e. g. , hardware) failure of the sensing infrastructure. To accommodate the expected increased volume of the data collected and the expectation of providing reduced, yet equivalently informational observations, we will employ cutting-edge machine learning algorithms, focusing on deep learning architectures, for inferring the optimal anomaly detection policies in a completely unsupervised way  [250].
Furthermore, we will extend state-of-the-art by simultaneously considering observations from multiple modalities/sources and time-instances  [251]  into the anomaly detection policies. For supporting the above goals, the data storage and access needs of ELGAR will be studied in order to propose the proper storage and computing infrastructure to meet them. In order to estimate infrastructure requirements, the landscape of the ELGAR's data will be analyzed in four dimensions: a) their size, b) their access patterns, c) their processing needs, and d) the robustness of the proposed approach(es). Then the serving model of the data will be analyzed along with the different computing infrastructure options that can better serve the data requirements. User-friendly and customizable ways to access the data and formulate information needs will also be consider, mainly based on the Ontology-Based Data Access (OBDA) paradigm and exploratory approaches, allowing the integration and interoperation among different scientists and data repositories.
Digital internationalization Since the late 1990s, online sales turned to a new internationalization model. By dematerializing borders and reducing costs, e-commerce fundamentally changed the way business was conducted  (Tiessen et al. , 2001). Exporting to foreign markets through online sales became a significant competitive strategy. is particularly true for SMEs  (Gabrielsson & Gabrielsson, 2011). Internationalization and Digitalization: Applying digital technologies to the internationalization process of small and medium-sized enterprises Often faced with a lack of resources, the internationalization path has increased SMEs' agility in targeting markets and expanding their network  (Watson et al. , 2018; Mathews et al. , 2016; Bell & Loane, 2010; Foscht et al. , 2006). A study by  Coviello and colleagues (2017)  recognised that nascent digital technologies have democratised global consumption, paved the way to a wide database for knowledge acquisition in foreign markets, improved communication and information exchange, and facilitated cross-border transactions by increasing intangible flows and reducing location dependencies.
Data collection This review comprehensively presents the relevant literature and synthesizes the key insights on how digital technologies impact the internationalization process of established SMEs. Little previous research has focused on the integration of purely digital technologies into the internationalization process of established small firms. Therefore, due to an insufficient and highly fragmented research basis, a systematic approach could not be pursued. The limited number of articles corresponding to our criteria led the data collection towards a rather systematically performed traditional review. The literature search was bound by specific factors. First, an articles search was performed with SAGE Journals, Google Scholar and Ulysses search engines, and came mainly from scholarly publishers, like Emerald, Springer, and Elsevier.
Results: We performed a stratified 5-fold cross-validation experiment comparing automated predictions with ground truth readouts from one radiologist resulting in an area under the Receiver Operating Characteristic (ROC) curve of 0. 93±0. 01. Conclusions: Our automated vertebral fracture demonstrated the potential for automated early identification of vertebral fractures in patients aged N50 years by opportunistically screening spinecontaining CT images. Confirmatory analyses and additional methodological improvements (e. g. automatic Genant grading, fracture location) using more extensive datasets and method validation are ongoing.
Vertebral fractures (VFs) are under-diagnosed in Computed Tomography (CT). There is also a need to develop fracture detection tools that can diagnose osteoporosis. We describe the development of a VF detection tool, SlicePick-MT, that uses quantitativemorphometry to assess suspected VFs in CT datasets collected during a previous audit of 325 patients who had undergone CT scanning for reasons unrelated to osteoporosis. The detection rate of SlicePick-MT was compared against the current standard, comprising Optasia's ASPIRE™ fracture detection service, which includes a radiologist over-read. Local radiologists verified the Optasia diagnoses. Bone Mineral Density (BMD) in each patient was estimated by spine Quantitative CT(QCT) and hip CT X-ray absorptiometry (CTXA) using Mindways software. The SlicePick-MT method detected 50 true VF patients compared with 46 VF patients detected by ASPIRE™.
SlicePick-MT had a higher sensitivity (98. 0%) and specificity (99. 6%) than ASPIRE™ (93. 9%, 98. 4% respectively). Patients with VFs had lower average spine BMD (76. 75mg/cm 3 ± 24. 58) compared with those without VFs (111. 15mg/cm 3 ± 34. 97, pN0. 0001). Femoral neck BMD was lower in VF patients, 0. 600g/cm 2 than non-fracture patients, 0. 696g/cm 2 (pN0. 005). Spine BMD was a better discriminator of VFs than femoral neck BMD, by Area Under the Curve analysis (AUC, Spine BMD = 0. 79, Femoral Neck BMD = 0. 7). Eighty-five patients were found to have osteoporosis by either Spine QCT or CTXA of the hip. However, only 30 of these patients were previously known to have osteoporosis. When applied to routinely-acquired CT datasets, the SlicePick-MT method detected more fractures than the current standard of VF detection.
Therefore, the pricing problem PP b in this paper is not a classical knapsack problem that can be efficiently solved by dynamic programming algorithms. Our preliminary numerical experiments show that the commercial optimization solver GUROBI can solve the pricing problem PP b efficiently, hence, we employ GUROBI to solve PP b directly in this paper. Furthermore, the following additional SICU capacity constraints can be added to PP b to tighten the formulation: z n it 1 -α iξ , ∀ i ∈ I b , t ∈ T b \T n ib , n = 1, 2,. , N (48) z n it α iξ , ∀ i ∈ I b , t ∈ T b , n = 1, 2,. , N (49) Constraints (48) specify that surgery i does not stay in SICU before the surgery date t b or after the end of LOS t b + l n i -1, while constraints (49) imply that the untreated patients do not need SICU beds. These constraints are not incorporated into our original formulations TSSP-R and DILP, because they significantly increase the problem size and the computation time; whereas in the column-oriented formulation, constraints (  48 ) and (  49 ) help to accurately calculate the occupation of SICU beds for each column, so that the CG procedure can converge faster.
Comparison of different CG strategies and heuristic rules To test the accuracy and efficiency of the proposed CGBH approaches, we perform numerical experiments on randomly generated cases with different numbers of surgeries and SICU beds. Each case is solved by different approaches with the same sampled scenarios. GUROBI optimizer is used to solve the DILP problem directly and serves as a benchmark of the CGBH approaches. We first evaluate the performances of the three CG strategies presented in Section 4. 1, while the RMP heuristic rule introduced in Section 4. 2. 1 is used to derive feasible solutions. The results are presented in Table  5. We notice that the computational complexity of DILP depends on the problem size, and the efficiency of GUROBI in solving DILP drastically decreases as the size of I and the value of N increase.
For the large cases with |I| 50 and N 50, GUROBI cannot even provide the optimal solutions before reaching the maximum computation time (one hour). By contrast, the proposed CGBH approaches can efficiently solve all the cases and provide near-optimal solutions of high quality. In some large cases, e. g. , 75-15-100 and 75-20-100, the solutions of CGBH approaches are even better than those of GUROBI-DILP. Comparing the computational results of the three CG strategies in Table  5 , it can be seen that the all-negative strategy outperforms the other two strategies in terms of robustness and efficiency. The gaps between the exact solutions of GUROBI-DILP and the near-optimal solutions of CGBH-RMP-All-Negative are within 1%. More importantly, the CGBH-RMP-All-Negative approach consumes less than one minute to compute a near-optimal solution, even for the large cases that cannot be solved to optimality by GUROBI.
Specifically, the near-optimal solutions computed by the CGBH-RMP-All-Negative approach (hereinafter referred to as CGBH) are very close to the exact solutions given by GUROBI (errors < 1%), and the CGBH approach consumes considerably less CPU time than GUROBI does when solving the cases with large number of sampled scenarios. The experimental results also illustrate that the combination of SAA and CGBH, i. e. , the SAA-CGBH algorithm, significantly outperforms the original SAA algorithm in terms of computational efficiency and does not lead to any deterioration of the solution quality. Furthermore, we perform numerical experiments on large-sized cases and the results validate the capability of the SAA-CGBH algorithm in solving realistically sized advance surgery scheduling problems. Given that the stochastic programming model TSSP-R captures the major elements involved in surgical activities and that the SAA-CGBH algorithm is proved to be efficient in coping with realistically sized cases, they can be used by hospital managers to optimize the advance scheduling of elective surgeries in real life.
For instance, this arises in industrial applications to manage assets and in smart homes to monitor home appliances, such as the control of home alarm systems, the monitoring of the temperature and humidity levels, turning on/off lights, etc. The rapid development of IoT was accompanied with the generation of a large amount of data, which leads to a new big data era  [32]. However, the produced data are exposed to many security vulnerabilities and threats, such as eavesdropping, data modification, man-in-the middle-attack, DoS attack, etc. This challenge is amplified by the dynamic and heterogeneous structure of IoT where applying static and traditional security solutions is inefficient. In this context, adaptive security mechanisms become mandatory to meet application requirements in terms of security and privacy preservation.
The main purpose of the proposed approach is to dynamically and efficiently assign access rights under a given situation. Specifically, we aim to ensure context-aware and secure remote control of smart things. Thus, we design a context-aware attributebased access control (CAABAC) scheme that integrates the contextual information with attributes into the access policy. Specifically, we exploit the worthiness of the CP-ABE scheme to grant fine-grained authorization. In addition, we use a contextual token concept to define the different contextual parameters characterizing a given context. Particularly, the contextual information is involved in the access policy as contextual tokens whose corresponding secret can be revealed only under a predefined context by a context manager (CM). The CM is deployed to verify the validity of data consumer's requests and generate access tokens for them.
2) The key escrow problem of CP-ABE is addressed. Specifically, both KGC and attribute authority (AA) cooperate to generate users' private keys, so that no authority can reveal the users' secret keys. Therefore, data confidentiality and privacy are ensured. 3) A rigorous security proof and an informal security analysis are carried out on the proposed scheme to demonstrate its capacity and its security strength against well-known attacks. 4) A thorough comparative analysis of the performance in terms of security and functionality properties, storage, communication, and computation overhead is performed. The comparative study proves the effectiveness and efficiency of the proposed scheme compared to the benchmark schemes. 5) A design of an extended CAABAC scheme, where multiple contextual parameters can be considered and appended to arbitrary nodes, is proposed.
To tackle these issues, distributed CapBAC was proposed  [15] ,  [16]  to delegate the authorization decisions to the IoT devices themselves. However, IoT objects are often resource-impoverished and may be easily compromised. Therefore, distributed CapBAC is ill-equipped to address access control in untrustworthy IoT environment. Attribute-based cryptography (ABC) is considered as a promising technique to ensure fine-grained access control. In such a scheme, data is encrypted according to an access policy, and only the user whose attribute set fits the access structure can decrypt the ciphertext. In this context, several studies looked into the feasibility of applying CP-ABE to resourceconstrained devices. For instance, Hu et al. [17]  proposed to implement the CP-ABE scheme in sensor nodes to secure the communications between them and the data sink/data consumers.
A. System Model We consider an IoT remote control system (presented in Fig. 2 ) that consists of the following parties: KGC, AA, CM, IoT gateway, smart things, and data consumers. The different functions of each entity are presented as follows. 1) The KGC and the AA are semi-trusted entities. They are responsible for system initialization where public parameters and users' secret key are generated. They cooperate in delivering users' attributes secret keys to solve the key escrow problem. 2) CM is responsible for the control of the dynamic context changes. It verifies the validity of a user's request to access data under a given context and generates access tokens to enable authorized data consumers decrypting the ciphertext. 3) IoT gateway is deployed as a powerful node that cooperates with the IoT device to define the appropriate access rules and encrypt data based on the CP-ABE scheme.