dataesr/scientific-paragraphs-categorization huggingface dataset:
In addition, we note that workto-family conflict was assessed only at time 2 although it would have been preferable to also assess it during time 1. While not optimal, we attempted to alleviate this shortcoming by incorporating a relevant proxy; specifically, we controlled for the number of hours worked per week at time 1. However, future research should replicate our study by capturing work-tofamily conflict at both points in time. Additionally, because we did not directly capture if a manager's supervisor had changed between time 1 and time 2 in order to ensure anonymity, future research should investigate if a change in supervisor affects our study's results. The low response rate of our study should also be noted as a limitation. Because we relied on randomly retrieved email addresses of managers from two professional data providers, we were not able to capitalize on any type of affiliation relationship with the potential respondents (e.
or Starbucks? Accordingly, the purpose of this study is to determine the impact of intangible assets, firm size, effective tax rate, and leverage on the transfer pricing intensity of French listed firms in the CAC-40 index. We collect data for the period from 2012 to 2015 and apply appropriate regression analysis controlled for time fixed-effects. The results of the study explain that intangible assets and effective tax rate negatively effects transfer pricing intensity while firm size and leverage positively effects transfer pricing intensity. This study contributes to the academic literature in this area as to the best of authors' knowledge no similar study has been conducted in the French perimeter. The paper is structured as follows: section 2 theoretical framework for the study; section 3 presents the data and methodology; section 4 and 5, respectively, cover the findings and the conclusion of the empirical analysis.
The methodology developed by Hines and Rice is based on the hypothesis that the observed profits equal to the sum of the "real" profits, which come out of tangible economic activities, and the shifted benefits. The regression analysis allows to measure the sensitivity of profits to the tax rates differentials between parent firms and their subsidiaries, considering factors that have a direct and material impact on an enterprise profits such as workforce, leverage, industry, level of development of the host country, etc. Therefore, these factors are used to estimate the counterfactual level of profits, i. e. the profits which would have been observed if no shifting was possible. The initial approach by Hines and Rice (ibid. ) used country-by-country aggregated data on U. S. -based MNEs to isolate the effect of tax rates variations between the parent firm and its subsidiary on the reported earnings of the affiliate.
S. -based parent entity and that R&D operations were a major determinant of settling in low-tax jurisdictions. As we mentioned in the introduction, the global economy has shifted to a dematerialized form and it raises one of the major challenges for transfer pricing. The golden rule being the arm's length principle, firms must find comparable transactions to price their own, but it is much more difficult when dealing with highly valued intangible assets rather than common goods for which transfer pricing managers can use public data or private databases which gather comparable. It is also a great challenge for tax authorities when examining transactions of such assets because of the lack of similar transactions in an active market  (Gravelle, 2010). Therefore, as those valuations are subject to the corporations' own analysis, it allows management to take advantage of discrepancies in tax rates among jurisdictions by moving those assets between countries  (Dyreng, Hanlon, & Maydew, 2008; Markle & Shackelford, 2011).
Data and methodology The sample analysed in the study is the listed firms on the CAC-40 French index during the period from 2012 to 2015. Our initial sample included all the CAC-40 publicly listed firms. However, firms in the financial industry were removed from our sample because of material variations in their accounting policies and derivation of accounting estimates. Further, during the period from 2012 to 2015, a few firms were retreated or suspended from the index (e. g. STMicroelectronics which was replaced by Alcatel S. A. on the 23 rd of December 2013), thus these firms were also excluded from our sample. Accordingly, our final sample comprised of 33 firms with 132 firm-year observations over the period of 4 years. The sample period was chosen represents the in-between period right after the global financial crisis and the OECD's BEPS projects and guidelines implementation.
The final experimental results on real-world dataset show that the approach we used achieve promising results on finding similar problems from a large amount of patent documents as well as corresponding solutions. This greatly speeds up the whole efficiency for seeking innovative solutions and emerging IDM-related knowledge. Particularly, we show that our work successfully finds out two typical similar problems that are just matched with the vibration and data retention problems as well as corresponding solutions from different domain's patents. The contribution of this paper is that we mix the use of neural networks and IDM-knowledge to facilitate the merging of IDMknowledge of different domains' patent documents in order to promote R&D activities. Besides, no other work, to the best of our knowledge, has been introduced so far.
Datasets and Evaluation Metrics In this paper, we use the clean version of the English Wikipedia dataset  [27]  to train our Word2vec model. It only contains regular article text but removes tables and links to foreign language versions. Also, in this dataset, citations, footnotes, and markup were removed as well as hypertext links were converted to ordinary text. Furthermore, we also evaluate the similarity computation method on the Utility Patent datasets of US Patent Grant dataset  [28]. US patents are mainly classified to three types: utility patent, design patent, and plant patent. According to the USPTO, utility patents are granted to anyone who invents or discovers any new and useful process, machine, article of manufacture, or composition of matter, or any new and useful improvement.
Design patents are granted to anyone who invents a new, original, and ornamental design for an article of manufacture. Plant patents are granted to anyone who invents or discovers and asexually reproduces any distinct and new variety of plant. But 90% of US patents are utility patents, which protect the utility or functional aspects of an invention and they normally contain some kind of similarity on invention domains with each other compared to other types of patents. Therefore, in this paper, we use utility patent dataset as the test dataset to check the performance of the similarity computation approach. Utility patent dataset contains a total of 6,161 documents. Finding the "gold-standard" ground truth of verifying the similarity among different sentences always is an open problem, especially for the sentences that are from different domains, and no work solved this problem.
Experimental Settings In order to achieve an optimal word vector model, in this paper, we tune our Word2vec model on the training dataset by using different parameters. At the same time, we also optimize the efficiency and accuracy for training the model. The final parameters of our Word2vec we set are shown in Table  1. The number of dimensions in the created vectors is defined by #size. Hence size here means each document receives a 100-dimensional vector from training. More dimensions usually mean slower training and we may risk having overfitting when the model works on small-sized datasets. The #window indicates how many words before and after a given word would be included as context words of the given word. Those words in training corpus that the minimum frequency of words is below the threshold of frequency will be discarded by #min_count.
It is helpful to filter out those extremely rare or wrong words in the corpus. Word2vec model will use the hierarchical softmax as the loss function when we set #negative > 0 and #hs = 1 as well as we set 3 noise words in the model when the value of #negative is 3. The #sample represents the threshold of sampling. Those words with higher frequency in the training dataset will be randomly down-sampled. Furthermore, in the cosine similarity computation, as mentioned in chapter 4, if the similarity value between the two sentences is closer to 1, they are considered as similar. We finally fix the threshold of the similarity to 0. 95 for performance reasons upon carrying out several tests and optimizing the size of the output.
Overall Results In this part, we carry out the performance analysis of our model on the US patent dataset and offer some further analysis. At first, as illustrated in Table  2 , Patent Extractor  [24]  we used has extracted three types of IDM-related knowledge from 6,161 US patent documents. We used 4,574 problems among them as input dataset to Sentence2vec model. We compute the similarity between any two different problems via IDM-Similar model. The performance of our model on US patent dataset is shown in Table  3. From the results, we can observe that IDM-Similar model finally gives us 1,121 pairs of similar problems when the similarity threshold is set as 0. 95. Through three experts' crosschecking, the number of true positive (TP) and false positive (FP) of final results are 1,000 and 121 respectively so that the precision of similarity is 89.
" and "The ECC block 1224 may detect and correct errors of data which are read out from the memory device 1210. " We think there is a kind of possibility to add ECC block using US9536897B2 patent into the left device to solve the data retention errors that mentioned in US9536619B2 patent. This case is from two similar domains and also was extracted by our model successfully. In conclusion of these cases, we note that the final similar problems our IDM-Similar model found from different domains' patents have a significant practical value for inventive solutions. In fact, these experimental results on the real-world dataset are a great encouragement for us.
By comparing the firms' strategies between the retailer-led and manufacturer-led cases, we obtain proportions to show the distinct strategies and performances. We use the software Mathematica to obtain the comparison of relationships among the terms discussed. Let γ6 = k(k+t+tβ) 2(1-α)(1+β) 2 (α-β+αβ) and γ7 = 3(k+t+tβ) 2 8(1-α)(1+β) 2 (α-β+αβ) (0 < α < 1); γ6 = k(k+t+tβ) 2(α-1)(1+β) 2 and γ7 = 3(k+t+tβ) 2 8(α-1)(1+β) 2 (α > 0). Then we have the following propositions. Proposition 5. A threshold γ6 exists, such that we have the following relationships: i) e * R > e * M. ii) q * R > q * M. iii) p * R > p * M , if γ < γ6 ; and p * R ≤ p * M , if γ ≥ γ6. Proposition 5 shows that the supply chain provides a greener product to consumers in the retailer-led case than in the manufacturer-led case case, i.
Sampling Strategy Data has been primarily collected through in-depth semi-structured interviews. The participants were of a high stature within the companies in the supply chain. 25 managers were interviewed in total. Of those, 60% were senior executives including the director of the company's UK operations. In order to avoid a polarised perspective, a number of more junior managers were also chosen to offer complementarities and comparison. All interviews were recorded and transcribed and then analysed through the development of a codification system for supply chain responsiveness. All the interviewees participated voluntarily, and it was agreed that their details would be kept confidential, so the companies are referred to throughout this paper by pseudonyms. The interviews lasted between 60 and 90 minutes.
Application Developer's Kit (ADK) This module is the main component of a PaaS product delivered by the SCM service provider. It involves a set of application development tools that allows cloud clients to design, develop, test and deploy their own customised applications. Its main target users are IT staff within customer organisations. The development tools can adopt a developing graphical interface and supported by a technical library. Within the technical library, a large group of underlying services is included as service components for the further application. These service components involve every aspect of application design and development, including integration, database, security, user interface, and application logic. With the help of service components, developers do not have to develop from scratch.
Database This module is responsible for storing and retrieving data that is used in C-SCM. It involves two components: a storage and a database management system (DBMS). The storage is where the data is stored in and retrieved from. It is provided by the infrastructure provider within its IaaS services. The space of storage is provided in an on-demand manner through a large pool of storage devices (e. g. , physical disk and main memory) installed in its data centres. The DBMS is a software system that mediates storage and users or other applications. It uses a standard method to store into and retrieve data from storage on request of people or other applications. Three main types of data are stored in the database: transactional data (e. g. , purchase/shipping orders, invoices. ), master data (e.
Business Intelligence The need for real-time monitoring, analysis of information and quick response to supply chain risks, necessitates the use of a business intelligence module. This module provides a group of applications and tools for managers and business users to monitor supply chain performance and conducts various data analyses to capture business trends, make decisions, resolve problems, as well as optimise business processes  (Dubey et al. , 2019; Azvine et al. , 2006). The analysis and reporting conducted are based on real-time data collected from various trading partners and history data stored in the database. The Business Intelligence module is the bridge with the Big data analytics modules and can provide multiple additional opportunities for the SCM  (Ashrafi et al, 2019; Dubey et al.
, 2018). This module includes four sub-modules: KPIs management, dashboard, reports, and analytics tools. The KPIs Management sub-module generates and maintains various KPIs data and business targets used for performance monitoring and reporting. It provides graphical tools that help managers and business users to define KPIs and business targets and could include data from all the supply chain partners. All KPIs and business targets are stored in KPIs repository. KPIs and business targets are used when designing business rules of BPM. The dashboards module is a data visualisation tools that provide an intuitive overview of what is happening in the supply chain. It consolidates a collection of analytics on arranged metrics, KPIs, as well as performance scorecards on a single screen. It enables users to efficiently and proactively capture trends and find exceptions in their supply chain as they occur, thus giving organisations the advantage of rapid reaction time.
They allow business users to reconfigure dynamically business process to realise certain business requirements, without having to involve IT staff and redeploying processes. That could eliminate the latency between business requirements initialisation and technical realisation. Moreover, a collection of best practices can be provided in rule templates to simplify implementation. With these functions and enabled capabilities, this module could effectively exploit the flexible services architecture and real-time integration platform to realise the capability of rapidly business processes creation and reconfiguration. The Business Rule Repository is a database of various business rules. The Business rule repository and the KPI repository are interconnected so that the various KPI definitions can be used in defining business rules, and BI can use decision logic for automatic decision making as well.
Real-Time End-to-end Visibility With the ESB module, the C-SCM integrates all organisations' back-end systems (e. g. , ERPs) and devices (e. g. , mobile, GPS). Figure  4  depicts the integration overview for K&E's supply chain. The large range of adapters ensures that the ESB can "talk" to various systems and applications. The route and mediation services enable systems' integration to be loose-coupled (without the need of hardcode). Each organisation only needs to maintain an interface with the C-SCM. Up-todate information is always maintained in the C-SCM's database, which is accessible to all participants through the ESB. In this way, data latency that is caused by manual intervention is reduced, and end-to-end supply chain visibility regarding material movements, as well as current and projected inventory levels, are provided.
The crawler developed in Olmedilla et al. [57]  has been applied to gather the data. More specifically, the programming language Python has been used to crawl the web combined with an open source web crawler framework called Scrapy. To begin with the scraping cycle of all the categories, an item "review" was defined that contains the fields "main category" and "subcategory" to be collected. Next, to crawl the information of each category, a class named spider was programmed. The spider browses the product webpage and, by using XPath selectors to perform data extractions from the HTML source, all the information about the main categories and subcategories was extracted. To this end, the method response. xpath('text_to_gather'). extract() was called and a list with all the elements (vector) of categories was built.
Collection of data In this study, we considered two types of data: 1) potential consumers and their characteristics, and 2) a collection of cars and their characteristics. Potential consumers and characteristics. Table  1  shows the set of characteristics and corresponding data considering the societal aspects of consumers. Cars and characteristics. For the identification of potential styles that represent specific brand identities, the cars are chosen from countries with car design tradition. The set of chosen countries are: {France, Germany, Italy, United States, Japan}. For each country, we selected car models from the late 1990s that had achieved commercial success. In this study, we selected French car models, German car models, Italian car models, American car models and Japanese car models.
Research design and case selection We investigated how types of uncertainty develop and influence behavioral logic through a comparative process study of four new technology-based ventures over a two-year period at the founding team level. We coded uncertainty perception, behavioral logics, and time occurrence to address the research question. Our data included interpretations, feelings and events. Because we had process data and were dealing with a nonlinear dynamic perspective on organizational processes, we applied both quantification and temporal bracket strategies to analyze and theorize the process data  (Langley, 1999). We used purposive sampling to select cases  (Eisenhardt, 1989; Eisenhardt and Graebner, 2007). The similarities and variations in cases enable more robust theory development  (Eisenhardt and Graebner, 2007)  and are more beneficial than using a random sample  (Santos and Eisenhardt, 2009).
to predict and prevent chronic diseases. The firm offered to collect personal health data through electronic devices including two types of smart bracelets, and a smart scale, and to provide its clients with personal healthcare advice. Virgin Ent was founded in 2012. The firm aimed at collection of personal physiological information (including body temperature, sleep cycles, etc. ) through different devices including a smart ring, a smart watch, and a smart phone. To address the retrospective problem, the research team conducted interviews at two points in time: between November and December 2013 and between November and December 2014. We visited the same venture several times during each visit whenever we had the opportunity. Finally, we presented our data analysis to the entrepreneurs of each new venture in December 2015.
Empirical indicators Exemplary Effectuation Basis for action: means oriented Founders of Virgin Ent knew that big data related business becomes very popular, but he had no idea what to develop as concrete product. One day, when the founders met one of their friends, they soon decided to develop a business around smart watches based completely on their personal preferences. (Virgin Ent, 2013) Attitude toward unexpected events: leverage By coincidence, the main founder in Virgin Ent met an influential person in the portable device market. The founder soon asked the influential person to post their product idea on his personal web page. This resulted in sales recorded of 20,000 watches in less than 72 hours. (Virgin Ent, 2014) Attitude towards outsiders: partnerships The founder of Education Ent decided to design the product together with the help of their users.
Transforming qualitative codes into a bit-map database In the third step, we quantified the qualitative data to visualize the sequence of events. We transformed qualitative codes into dichotomous variables in order to adopt comparative methods for examining sequences and patterns among the coded variables (Van de Ven and  Poole, 1990). In transforming coded uncertainty types, we used the dichotomous indicator "2" to represent perceived uncertainty, "1" to represent the absence of perceived uncertainty, and left the row empty when respondents did not mention uncertainty. We applied the same process to code behavioral logic, using the dichotomous indicator "2" to represent the presence of a behavior, "1" to represent the absence of a behavior, and left the row empty when respondents did not mention any type of effectuation or causation action.
Analyzing patterns and relationships In the final step, we analyzed how the overall perceived uncertainty and behavior develop over time by applying a temporal bracket strategy  (Barley, 1986; Denis et al. , 1996)  and using the bit-map database (Van de Ven and Poole, 1990) to transform the process data into a series of more discrete but connected blocks. We started by identifying breakpoints to define "phases" of perceived uncertainty and behavior for each case. These phases do not have any particular theoretical significance; they are not "phases" in the sense of a predictable sequential process, but as a way to structure the description of events  (Barley, 1986; Denis et al. , 1996; Langley, 1999; Langley and Truax, 1997). The idea was to identify phases in which data described the processes as a relatively stable or a linearly evolving pattern  (Langley, 1999).
All severe sickle cell disease patients were provided with hydroxyurea medicine. A complete hemogram, renal function tests, and liver function tests were performed annually. A web-based management information system (MIS) captured the above information at baseline and also during follow-up visits every three months. Hospitalization data were updated in MIS by a dedicated counselor who visited the patients during their admission. Details of hospitalization in other health facilities and incidences of pain crisis were collected during the follow-up visits and updated in MIS. The counselor educated patients during visits to the sickle cell clinic about the disease, its complications, including pain crisis, and adherence to the care. If any patient missed the scheduled visit, MIS sent an alert to the counselor for retrieval action.