dataesr/scientific-paragraphs-categorization huggingface dataset:
VI. VALIDATION OF ONTOLOGY This ontology was designed and built based on multiple layers. Hence, the validation should be based on layer by layer or component to components. The aim of this section is to determine a concise and acceptance of the ontology to the experts and users. We identified a set of ontology quality criteria and ontology aspects that must be achieved, including accuracy, adaptability, clarity, completeness, computational efficiency, and consistency. There are several methods of ontology validation using tools and other techniques. While tools are good with ontology consistency validation, we consider several techniques that are more consistent with knowledge and content validation. The dataset validation was performed in two ways.
A. Dataset Based Validation This type of validation is done by using fraud-related datasets to validate the ontology, in this section we elaborate some of the dataset. The challenge with this type of validation is finding the correct data that is in line with the concepts of the ontology. Hence, we explore several options and various data sources. Financial fraud data: The dataset obtained from Kaggle, has various features simulated for mobile money transactions. The technique of concept extraction from the dataset was done by means of distributing every feature into a particular class as ontology. From the features we extracted sub concepts, entities, properties, as well as individuals. • Matching the semantic content of the ontology concept with the dataset features. This process explored the similarity function to find the matching words that appear in both files.
While using a dictionary function to identify words with several meanings to avoid missing matching. This process of validation is considered the most acceptable type of ontology content validation. • Concept extraction validation. In this process, we studied the features of the dataset in order to extract a possible ontology concept from the dataset, then we checked the ontology if the concept is existing. Else it will be added into the ontology. Using this type of validation, we were able to add several concepts into the ontology, which include transaction fraud, forex fraud, and crypto currency fraud concepts. • Insurance fraud data: The dataset contains insurance claim data for fraud detection, in the ontology we built several concepts including insurance fraud. Some examples of features in this dataset are employee data which include the data of the employee or agent working in the insurance company.
This data is used to validate the claim of internal insurance fraud in our ontology. While the feature of vendor data is for the external agent who assists the insurance company in investigating the claims. As suggested in our ontology vendors are sometimes involved in external insurance fraud. This feature was used to validate the broker's collision class in the ontology. • Bank Fraud Data: The banks are often exposed to fraud transactions in various forms, we use the dataset to validate the concept of banking fraud in the ontology. From the various features in the dataset, we construct the concept of banking fraud and it exactly tallies with the concept of and entities of banking fraud that exist in the ontology. • Fraudulent Email Data: This dataset contains criminally deceptive information, usually with the intent of convincing the recipient to give the sender a large amount of money.
This dataset is a huge collection of fraudulent letters sent to various recipients. The features include sender, receiver, message id, date, subject, etc. We explore this dataset and extract the concept of email fraud, in our ontology email fraud is a sub concept of identity theft. We also used this dataset to validate Nigerian scam, which is a class of fraud under sub concept termed scam. • Cryptocurrency Fraud Data: This dataset contains rows of known fraud and valid transactions made over ethereum, a type of cryptocurrency. The various features in the dataset makes it possible to have enough entities to create the cryptocurrency concept in our ontology. Hence, we proposed a class of cryptocurrency fraud concept in the ontology. The properties of individuals as well as entities of this class were not in the ontology before we started the validation process.
• Financial fraud data: The dataset obtained from Kaggle, has various features simulated for mobile money transactions based on a sample of real. We explore these features. Concept extraction from the dataset was done by means of distributing every feature into a particular class as ontology. From the features we have those in sub concepts, entities, properties, as well as individuals. From the features we extracted, the transaction fraud concept is extracted from the dataset. • Insurance fraud data: The dataset contains insurance claim data for fraud detection. In our ontology we built several concepts including insurance fraud. From the features, employee data include the data of the employee or agent working in the insurance company. This data is used to validate the claim of internal insurance fraud in our ontology.
VIII. CONCLUSION AND FUTURE WORK The paper proposed an ontological approach to various concepts of financial fraud, which could serve as first of its kind. The elaboration of the ontology provides a comprehensive and explicit terminology of financial fraud activities, we explored creating an ontological catalogue that defines the various types of fraud committed. This was largely helped by the dataset validation technique adopted. The technique allows the flexibly created fraud ontology to be modified and additional definitions made. The validation approach used in this research work opens doors to the ontology developer's community to implement such an approach for better accuracy of ontology developed. The availability of datasets makes it possible to achieve this type of validation. But in the case of fraud and other cyber security-related ontologies, there could be a difficulty in obtaining the correct data and information needed, due to the sensitivity of the research.
We found a number of the dataset's attributes in our ontology. Using a dataset classified as transaction fraud, we perform concepts and properties extraction in the dataset, and we find a match with the number of properties in our ontology. But not enough as the unmatched attributes are more than the matched. Hence, we create the concept of transaction fraud in the dataset. Few improvements could be included in the future work. The paper lacks semantic detection reasoning, this shall be implemented in the future work. The detection reasoning will allow for real time detection of various kinds of financial fraud. Obviously, comparison of this method with other machine learning approaches will as well be implemented. The future work identified in this section (semantic detection reasoning) will make the work an asset to financial institutions globally.
Querying someone who has information is often a great way to access the information yourself. During a series of interviews, information about our research subject was obtained. This method of oral data collection relates to facts or representations  (Baumard, 2007). The 26 individual interviews in the series, 'intended to collect, in the perspective of their analysis, discursive data reflecting in particular the conscious or unconscious mental universe of individuals'  (Baumard, 2007) , allowed us to complete a preliminary documentary study to better understand the study context. The selection of interviewees was determined based on the objective of our study. Within the value chain studied, experts have been appointed and served as advisers. Within the limits of everyone's availability, we have therefore chosen to interview players in the brewing sector representing organizations of all sizes, all geographic representations, and with the most diverse activities possible to provide a representative portrayal of the profession.
This guide was completed, adapted, and modified iteratively as the interviews progressed. The corpus made up of the entire speech of the interviewees was coded and then analyzed using the IRaMuTeQ tool. IRaMuTeQ (for 'R Interface for Multidimensional Analyzes of Texts and Questionnaires') is free and open software for analyzing textual data or textual statistics that works in interface with the R language. It allows statistical analyses on text corpora and on individual/character tables based on the Reinert classification method (hierarchical classification descending on a table crossing solid forms and text segments)  (Reinert, 2015). IRaMuTeQ is based on the free R language and the Python language. It is developed within the Laboratory of Studies and Applied Research in Social Sciences (LÉRASS, University of Toulouse) and supported by the LabEx 'Structuring of social worlds' and is distributed under GNU GPL v2 license.
Results Among the 26 interviews, 22 texts were analyzed for reasons of data homogeneity. A total of 2,600 text segments were processed by the tool, 90. 77% of which were analyzed using a simple classification to optimize the sensitivity settings of the software. For comparison, a dual classification was also implemented, and the results were similar. The results obtained using IRaMuTeQ, that is, the number of word classes, their composition and their distribution according to the profile of the respondents, informed us about the point of view of brewers and stakeholders on the revaluation of the spent grain and its implications. In the end, four classes of words were analyzed, which is quite weak and may require a subsequent manual analysis of the content of the speech. The first of these classes, which contains more than a third (33.
One could imagine that it is because of a higher real estate pressure exerted on urban breweries in Quebec or because more urban microbreweries were visited in Quebec, despite the fact that the corpus processing software should allow us to eliminate this kind of bias. In addition, the graphical representations given by IRaMuTeQ seem to show that, in the case of two separate persons, this decision is made independently by the manager and the brewer. It appears that in these structures, perhaps due to the culture of the brewing environment or due to the small size of the craft breweries, strategic decisions concern both the tactical players who manage and operational players who brew. For the vast majority of interviewees, the current fate of the spent grain is not a waste. This remark was sometimes made spontaneously, by the brewers who bring the grain themselves to the cattle and noting the important value of this raw material to many species (some brewers described whole herds of cattle or goats recognizing the van delivery and rushing toward it) or even more indirectly, by indicating that using the spent grain for human nutrition would reduce the need for more crops grown for fodder.
For startups, this flexibility of enabling MCSs might play an important role in keeping processes under control despite potentially frequent pivoting. Technological developments are argued to play a key role in allowing flexibility: enterprise resource planning (ERP) systems permit tailored reports to be generated for different recipients from one single database, and computer applications allow ad hoc customization of control routines  (Ahrens and Chapman 2004; Chapman and Chua 2003). More recent technological developments such as mobile devices, cloud computing, software as a service (SaaS), and application programming interface (API) software integration might even push this flexibility to another level. Wouters and Roijmans (2011;  see also  Wouters and Wilderom 2008; Goretzki et al. 2018 ) emphasize that, next to the above design features, the development process of MCSs can also contribute to their enabling character.
e. , interview transcripts, notes, and additional documents stored in a physical folder) and digital matrices to realize the depth of the research approach. To illustrate the results of our intensive data analysis, we present representative quotations. For data triangulation  (Yin 2003) , we build on additional data sources. First, we analyzed company websites and the profiles of interviewees on professional social networks; startup documents and reports provided by interviewees; startup and incubator visits; and software, dashboard, and product presentations that occurred before, during, or after interviews. Second, during and immediately after the interviews, we took notes to document additional observations and pre-and postinterview discussions, to make sense of the data collected, and to identify emerging patterns  (Baxter and Chua 1998).
In other words, the BML feedback loop represents a quasi-scientific approach of systematically testing entrepreneurial hypotheses-for example, which product features customers want, which sales channels they prefer, and which services they are willing to pay for-on real customers. So you really need a product or a way of testing one of your hypotheses in one week. [As an example,] I send a commercial on Facebook, I do A/B testing, just to know who are my customers, and who is interested in which product. (ES12) In that sense, in a resource-efficient (i. e. , lean) process, these experiments break down largescale intuitions of founders into small-scale hypotheses that can be quickly tested on real customers. The resulting validated learning allows startups to replace their intuition with databased decision-making, or as one interviewee puts it, "Making decisions that are based on.
". It's about, at any time, being sure that everyone in the team and in the surrounding of the teams is able to understand what the team is trying to do. And all those collaborative tools, the power around those tools is that it helps the teams being accountable on what they do. And this is the real power of those tools: Trello, Asana, Slack. (ES11) And indeed, the use of advanced information technologies in general and the propagated software tools allow startups not only to collaboratively use MCSs but also to foster a culture of mutual accountability to ensure continuous progress in areas such as product and service development. This concerns both the vertical collaboration between managers and employees, and also, importantly, the horizontal collaboration between employees cross-functionally.
Sampling and procedures The data for this study were collected from three countries: France, the United Arab Emirates (UAE) and Malaysia using convenience sampling. Top three innovation economies by income group is the rationale behind selecting these countries. For example, Malaysia leads the middle-income group rankings. The UAE is taking from the high-income economies. France is one of the world's largest IMF advanced economies. In terms of Global Innovation index (GII), all of them belong to the top 40 of the most innovative economies (i. e. , France (11th), the UAE (33rd) and Malaysia (36th))  (WIPO, 2021). The units of analysis in this study were companies based in these countries. The data came from six (6) different industry categories: pharmaceuticals, office machinery and equipment, medical, precision and optical instruments, transport equipment, chemical products and other industries.
172 usable responses were collected from France, which constituted a response rate of 32. 5%. In total, 195 usable responses were obtained from the UAE, representing a response rate of 35. 5%. A total of 163 usable responses were collected from Malaysia, which constituted a response rate of 30. 8%. Table  1  displays the country wise respondent distribution of the study. To maintain consistency and ensure appropriate responses, the survey questionnaire was distributed among middle and top managers. All three datasets were collected using the same instrument at three different times. Appropriate data cleaning techniques were used. Accordingly, responses with more than 10 percent missing values were discarded and the responses with disengaged answers and outliers were removed  (Hair, Black, Babin & Anderson, 2010).
Respondent profile Table  2  illustrates the respondent profile. Multiple organizations in different industries in France, the UAE and Malaysia were approached for data collection. Of the 530 usable responses received in the three countries, 28. 3% responses were collected from the pharmaceuticals industry, 36. 2% from the office machinery and equipment industry, 21. 9% from the medical, precision and optical instruments industry, 7. 7% from the transport equipment industry, 4% from the chemical products and 1. 9% from other industries. Most responses (64. 7%) came from middle managers, while 35. 3% were from top managers. 23. 8% of respondents had worked for the current organizations for 0-5 years, 43. 2% for 6-10 years, 27. 5% for 11 -15 years, and 5. 5% for more than 16 years. Most of the surveyed organizations were operational for several decades.
Descriptive statistics and bivariate correlations Table  3  presents the mean, SD, Cronbach alpha (in parenthesis along diagonals) and correlation values for the variables of current study. The table shows the presence of a positive correlation of managerial ties with absorptive capacity (r = 0. 28, p < 0. In addition to computing descriptive statistics and correlations between variables for the whole dataset (N = 530), we also report correlations and descriptive statistics for each surveyed country, as shown in Table  4.
The model with all the measurement items showed a poor fit with the data (x 2 /df =3. 57, CFI = 0. 87 and RMSEA=0. 070), while the refined model revealed an acceptable model fit (x 2 /df =2. 70, CFI = 0. 937 and RMSEA=0. 033) and was thus retained for further analyses. During the CFA, configural invariance was examined and evidenced. An acceptable model fit was obtained by estimating the model with three groups (data from France, the UAE, and Malaysia) freely without any constraints. In addition, to check for metric invariance, we examined the chi-square difference between a constrained model and an unconstrained model, which was observed to be nonsignificant. It is concluded thus that the measurements were invariant across the three-country groups. We further checked the discriminant validity and convergent validity of the study variables.
Hypothesis testing We employed linear regression in SPSS Ò v. 21 to test the direct relationships proposed in this study. Regression results of all the direct hypotheses are shown in Table  6. As the data were collected from three different countries, the analyses were performed separately for each country to understand the underlying nuances. Simple regression was performed three times to test Hypothesis 1 and Hypothesis 2. Hypothesis 1 (H1) proposed a significant and positive relationship between managerial ties and inbound open innovation. The results revealed that for all the three countries H1 was supported (France: b = 0. 37 p < 0. 00; UAE: b = 0. 67, p < 0. 00; and Malaysia: b = 0. 21, p < 0. 00). Thus, managerial ties positively affected inbound open innovation in all the three surveyed countries.
Implications Theoretically, this study contributes by providing a multi-country perspective to the relationship of managerial ties with inbound and outbound open innovation. A rich dataset compiled by collecting data from three countries was used to test the study's hypotheses. Being one of the first studies to do so, this study adds value by improving the generalizability of the findings and bringing greater clarity to the issue. The findings are crucial since open innovation research is maturing and themes and patterns that can apply crossculturally need identification. The findings of the paper can be expected to benefit managers in diverse cultural setting since the data are sourced from varied cultural and country contexts. The findings provide valuable insights for managers who may be particularly working across country borders or cultural contexts with suppliers, partners, or other external entities in a collaborative arrangement.
Limitations and future research directions While the current study explores an important issue using a rich dataset and following established research standards, it is constrained by a few limitations. Firstly, the study does not segregate the three types of managerial ties and instead clubs them together as 'managerial ties'. Future research may establish the links between the three types of managerial ties and the other variables of interest to establish a better understanding of the relationships. Secondly, the dataset used in this study was collected cross-sectionally. Cross-sectional data has its limitations in that it may not be the most appropriate data for testing causal hypotheses  (Naqshbandi, Singh & Ma, 2016). Future research may use longitudinal data to test the associations that form the subject matter of this study.
The sequence flow category can in turn be decomposed into normal sequence flows, conditional sequence flows (expressing the condition for some branch to be activated), and default sequence flows (the default branch to activate if all others, conditional ones, cannot be). BPMN defines three main kinds of models: process diagrams, collaboration diagrams, and choreography diagrams. In this work, we deal with the first two. A process diagram is used to model the activities of a single organization. Collaboration diagrams can be defined with a set of different processes (for different organizations), exchanging messages and cooperating to reach a shared objective. In our work, we abstract away from data (data objects, data stores and message payloads). Therefore, in the sequel, message (resp. event instance) and message type (resp.
The TLA + specification language and verification framework TLA + [9] is a formal specification language based on untyped Zermelo-Fraenkel set theory for specifying data structures, and on the temporal logic of actions (TLA) for specifying dynamic behaviors. TLA + allows one to specify symbolic transition systems with variables and actions. An action is a transition predicate between a state and a successor state. It is an arbitrary first-order predicate with quantifiers, set and arithmetic operators, and functions. In an action, x denotes the value of a variable x in the origin state, and x ′ denotes its value in the next state. Functions are primitive objects in TLA +. The application of function f to an expression e is written as f [e]. The expression [x ∈ X ↦ → e] denotes the function with domain X that maps any x ∈ X to e.
nonestart_complete(n) ≜ ∧ CatN[n] = NoneStartEvent ∧ nodemarks[n] >= 1 ∧ LET p == ContainRelInv(n) IN ∨ ∧ CatN[p] = Process ∧ nodemarks[p] = 0 ∧ nodemarks ′ = [nodemarks except ![n] = @ -1, ![p] = 1] ∨ ∧ CatN[p] = SubProcess ∧ nodemarks ′ = [nodemarks except ![n] = @ -1] ∧ edgemarks ′ = [e ∈ domain edgemarks ↦ → if e ∈ outtype(SeqFlowType, n) then edgemarks[e] + 1 else edgemarks[e]] ∧ Network!unchanged Intuitively, the translation is done syntactically as shown in Table  2. Here, we can easily observe that the translation is straightforward, and this holds for all the elements of BPMN. The resulting theories, for the translation of the whole elements, are available at  [6]  under theories/tla. Module PWSTypes defines the abstract constants that correspond to the node and edge types. Module PWSDefs specifies the constants that describe a BPMN graph (Definition 2.
The fbpmn tool suite Our FOL formal semantics for communication parametric BPMN collaboration, and the verification of properties, realized over TLA + and the TLC model checker, as presented in Section 5, are implemented in the fbpmn tool suite. This open source, freely available, suite is made up of : • the fbpmn program and several accompanying scripts, to perform verification in one's command line and to graphically animate counter examples, • a Web application version of the above, with a client-side front-end (for BPMN modeling and for giving communication and verification parameters) that runs in one's browser, The URL to get fbpmn is https://github. com/pascalpoizat/fbp mn.