dataesr/scientific-paragraphs-categorization huggingface dataset:
For example, the QBS approach makes the dump of DBpedia dataset 99% smaller than the original dataset, while by GBS approach it gets only 32. 2% smaller than the original one. Therefore, the compactness based on QBS approach is more than GBS approach. Also, Table  1  provides the Summarization Time (ST) which is the time required to generate the summary graph in both approaches based on the given datasets. Here, it should be mentioned that summarizing the RDF graph in QBS approach is not only related to the size of dataset but also is based on the query of the user. Therefore, the size and time provided here for generating the summarization graph is the average of the size and time among different queries. Moreover, Table  2  shows the result of applying the SPARQL queries with multi triple patterns over the original RDF graph and compares them with the result of applying the transformation of these queries over summarized graphs provided by both approaches.
The execution time is the summation of time for summarizing the RDF graph (SUM) and time for query answering (QA). As seen, execution time in summarized graph provided by GBS specially in larger datasets ( Fig. 12 c  and d ) is much higher than the execution time in original RDF graph. Fig. 13  shows the same experiments but over summarized RDF graph generated based on Query Based Summary Graph (QBS) approach in four different size of datasets. As seen in Fig. 13  , the execution time in summarized graph provided by QBS specially in larger datasets ( Fig. 13 c  and d ) is much less than the execution time in original RDF graph. The approach of QBS speeds up the execution time by up to 80% by summarizing the RDF graph. Therefore, the execution time of query processing in the summarized graph of QBS approach is much less than in the summarized graph of GBS approach.
Algorithm 1 : 1 The Grouping Based Summarization (GBS) Algorithm, Offline phase-Summarizing Graph. Input : RDF dataset has been loaded as RDF graph (G) Output : Semantic Summary Graph ( 𝐺 ′ ) 1 G ← RDFGraphLoader. loadFromDisk(spark, input, parallelism) ; 2 inferredGraph ← TransitiveReasoner. apply(G) ; 3 SN ← RDD[(List[Subjects])] from inferredGraph where Subjects have similar Predicates and Objects ; /*SN is a list of Super-Nodes*/ 4 newTriples ← Triple. create(SN, Predicate, Object) ; 5 Buffer ← new ArrayBuffer(triple. length) ; 6 foreach (SN, Predicate, Object) ∈ inferredGraph do 7 if newTriples not exists in Buffer then 8 Buffer += newTriples ; end end 9 𝐺 ′ ← Buffer ; 10 return 𝐺 ′.
100M 𝑄 16∕ 𝑄 ′ 16 321 802 274 88 109 𝑄 17∕ 𝑄 ′ 17 147 541 349 42 56 𝑄 18∕ 𝑄 ′ 18 192 627 482 41 86 𝑄 19∕ 𝑄 ′ 19 401 1,190 850 63 168 𝑄 20∕ 𝑄 ′ 20 182 778 321 34 105 Fig. Comparing execution time in original RDF graph and summary RDF graph by Grouping Based Summary Graph (GBS) approach over four different datasets with different sizes. Q and Q' are executed over the original and summarized RDF graphs, respectively. Red dots show the execution time (in seconds) to answer a query over the original RDF graph. Blue and green dots show the time for summarizing RDF graph (SUM) and the time for query answering (QA) over summary graph, respectively. As seen, by increasing the size of datasets, execution time in the summarized graph is much higher than the original graph. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.
Measuring the influence of users in social networks is key for numerous applications. A recently proposed influence metric, coined as ψ-score, allows to go beyond traditional centrality metrics, which only assess structural graph importance, by further incorporating the rich information provided by the posting and re-posting activity of users. The ψ-score is shown in fact to generalize PageRank for non-homogeneous node activity. Despite its significance, it scales poorly to large datasets; for a network of N users, it requires to solve N linear systems of equations of size N. To address this problem, this work introduces a novel scalable algorithm for the fast approximation of ψscore, named Power-ψ. The proposed algorithm is based on a novel equation indicating that it suffices to solve one system of equations of size N to compute the ψ-score.
B. Goal and contributions The problem we address in this paper is that the ψ-score calculation from  [10]  does not scale to large graphs since it needs the resolution of N linear systems of N equations each, N being the number of users in the network, whereas the PageRank would only require to solve one system with N unknowns. We thus propose a new algorithm based on an equation that revamps these N system into a single one, still with N equations. Our algorithm, which accepts a recursive and distributed implementation, allows to approximate the ψ-score of the OSP with a similar speed as the traditional approximation for PageRank via the power method. Hence, the ψ-score can be used as a more expressive alternative to PageRank in the task of ranking users by their influence. Finally, we provide an open source software library 1 in order to allow anyone to implement the ψ-score in their own projects.
D. Outline of the paper The paper is structured as follows. Section II presents the recently proposed ψ-score and discusses its relation to classical centrality metrics. Section III contains our main contribution: a new algorithm for the fast approximation of the ψ-score that scales to massive datasets. We show that the algorithm runtime is comparable to PageRank, thus providing a morediverse alternative. Section IV briefly presents our software contribution: an open source Python library for the ψ-score computation. Section V evaluates the performance of our new algorithm on several real-world networks. Conclusions and future work are discussed in Section VI.
II. THE ψ-SCORE Recently, a new score of users' influence over their social network (platform) has been introduced in  [10]. The calculation of this score involves information not only about the users' 1 https://github. com/NouamaneA/psi-score impressions of i on the Newsfeed of n q (n) i (influence) impressions of i on the Wall of n p i vector with every p (n) i q i vector with every q (n) i P matrix with p i as column i Q matrix with q i as column i b i input vector of normalised posting activity positions inside the graph structure, but additionally their posting and sharing activity. These are two extra important features that enrich previous graph-based metrics: when a user posts more frequently he/she should have a higher influence because his/her posts appear more often on the newsfeed of others; furthermore, when a user shares content rarely, very few posts (other than of his/her own creation) pass through to reach his/her followers.
IV. THE P S I-S C O R E PYTHON PACKAGE The psi-score project 2  is a software library that we have developed in Python. It is open source and licensed under the terms of the MIT license, allowing free usability. This package is a practical tool for the community to easily use the ψ-score metric in Network Science projects without the need to develop each algorithm individually. Both methods mentioned in this work are accessible. Inspired by scikit-network  [19]  and scikit-learn  [20] , the project has a similar Application Programming Interface (API) in order to be user-friendly.
Hence, when we set some value ε for the x-tolerance in a specific method, we are interested to evaluate the resulting relative error in the result ψ ε of this method compared to the true value ψ true , defined as follows error = ∥ψ true -ψ ε ∥ 2 ∥ψ true ∥ 2 (23) Table II lists the datasets used for our experiment, which are available in the Konect repository  [21]. Twitter and Facebook graphs, where users have posting and re-posting activity, are natural application scenarios for our method. The two citation datasets (DBLP and HepPh arXiv) are also relevant application cases for our algorithm. This is because paper publications per author can be seen as equivalent to posting activity, and paper citations by author can be seen as equivalent to reposting activity. In all datasets, only the graph topology is available, thus user activity has been generated either at random for (i) or chosen for (ii) in order to have µ λ+µ = 0.
85. In order to be able to assess the error with respect to a true ψ-score, for experiments 1 and 2 we focus entirely on the DBLP citation network  [22]  that has a moderate size and it is thus realistic to exactly solve the linear system. Experiment 3 addresses the remainder of datasets from Table  II. Our implementation is available on GitHub 3  as well as the code for numerical evaluation. relative error calculated with  (23)  in order to compare the algorithms within a given precision. Figure  4  shows the result in the case where we compute the ψ-score with (i) heterogeneous activity and Figure  5  shows the result when computing ψ-score in the specific case of (ii) homogeneous activity that gives the PageRank vector. We see clearly that the proposed algorithm Power-ψ outperforms the old one Power-NF by several orders of magnitude.
We have proposed a new method to compute the ψ-score, which is influence of users in a social network (or in any network with nodes having a posting a sharing activity). We have shown that the proposed algorithm converges faster than the state-of-the-art method. The algorithm enables scalability for real-world datasets for the ψ-score. We have also provided an open source software library that implements the existing algorithm for the ψ-score calculation as well as the novel approach. The proposed method gives directly the influence of every user over the entire network. But in some cases, one might need to use the influence of a user on a specific user, which is giver by the OSP model but skipped by the proposed Power-ψ to go faster to the point. In the future, we are interested in further study on the ψ-score in order to explore the possibility to get these other information potentially valuable for some applications.
ACKNOWLEDGEMENTS The work of N. A. is funded directly by  Sorbonne University ,  CNRS -LIP6 laboratory by an internal project. The research of A. G. is supported by the  French National Agency of Research (ANR)  through the  FairEngine  project under Grant  ANR-19-CE25-0011. E. B. is funded by the  French National Agency of Research (ANR)  under the  FiT LabCom grant.
relationship between the different challenges that have hindered biomedical research and the generation of novel scientific knowledge during the COVID-19 pandemic. Methods: During the early stages of the pandemic, research conducted on hydroxychloroquine (HCQ) sparked several heated debates with respect to the scientific methods used and the quality of knowledge generated. Research on HCQ is used as a case study in the papers. The authors explored biomedical databases, peer-reviewed journals, preprint servers and media articles to identify relevant literature on HCQ and COVID-19, and examined philosophical perspectives on medical research in the context of this pandemic and previous global health challenges. Results: These papers demonstrate that a lack of research prioritization and methodological rigour resulted in the generation of fleeting and inconsistent evidence that complicated the development of public health guidelines.
Acknowledgements We would like to thank:  Dr. Mark Solovey  for his participation in conference planning, his participation as a panelist, and his help in reviewing the abstract book;  Dr. Hakob Barseghyan  for his participation in planning the conference and his help reviewing the abstract book;  Dr. Maya Goldenberg  for her keynote speech on " Public Trust in Science ";  Dr. Jamie Shaw  for his participation as a panelist;  Ava Spurr  and  Joshua Allen  for their participation in conference planning;  Dr. Edward Jones-Imhotep , the director of the  IHPST ,  Dr. Angela Esterhammer , the principal of  Victoria College , and  Dr. Ira Wells , Academic Programs Director,  Victoria College , for their support in hosting the conference; and  Dr. Nahyan Fancy  for founding the  International Undergraduate Conference in Science, Technology, Medicine , and Society six years ago and for his assistance in promoting this year's event.
Symbolic drivers of IS Adoption A few studies have demonstrated the influence of symbolic drivers on IS adoption (e. g. ,  Arbore et al. , 2014; Hong & Tam, 2006; Moore & Benbasat, 1991; Venkatesh & Brown, 2001). In fact, technologies 4 and digital possessions are status symbols that may lead to admiration and perhaps respect. For instance, during the 1990s, purchasing a personal computer (PC) for home use was a way to increase one's prestige and status  (Venkatesh & Brown, 2001). Similarly, innovations play a role of signaling one's need for uniqueness  (Hong & Tam, 2006). Berger and Heath (2007)  explain that people use products to differentiate themselves (divergence strategy) in order to effectively communicate their desired identities. As such, people look for unique products to signal their distinct identities.
Research design The data were collected in Ghana, where one of the researchers spent a few weeks for our study. The data were obtained from paper-based questionnaires in English, which were distributed to and completed by the respondents in specific cities: Accra (the capital city, with approximately 2. 2 million inhabitants), Tema (with the largest seaport in Ghana), and Takoradi. This study relied on a convenience sampling method, which can be defined as "a sample in which research participants are selected based on their ease of availability"  (Saumure & Given, 2008, p. 124). This technique allows researchers to survey the individuals who are the most ready, willing, and able to participate in the study. According to  Malhotra (2010) , intercept surveys correspond to one type of convenience sampling.
The participants received a reward (e. g. , a small gift) of less than one euro in phone credit. We acknowledge that non-probabilistic methods have several limitations, such as time bias, generalizability issues, desirability, and self-selection bias. First, to limit the bias of time, we collected data at different times of the day (morning and afternoon principally) to account for the fact that people who respond in the morning may be substantially different than those available to take the study in the afternoon. Second, regarding the generalizability of our results, we tried to survey the respondents in different urban areas of Ghana. We collected data from areas where various groups in the country were well-represented (major cities and university campuses). Owusu and Agyei-Mensah (2011)  noted that all Ghanaian ethnic groups and socio-economic levels are represented in Accra.
Collecting data at the capital as well as other big cities ensured that we gathered the views as well as the reasons for ownership and usage of mobile phones by many of the ethnic and socio-economic groups in the country. However, we admit that our results may not be generalizable to people living in the countryside. As stated in our limitations, future research can compare mobile phone adoption in urban vs. rural areas. Third, we recognize the bias of self-selection with intercept surveys. Nonetheless, we wanted to make sure that the respondents owned a smartphone, which was directly observed by the researcher on the field. In our self-selection process, we did not exclude subjects willing to participate to our survey on any criteria other than possession of a mobile phone. Fourth, we tried to reduce the desirability bias by assuring the participants that they could share their personal opinions and that no answers were right or wrong when completing the questionnaire.
, 2017)  and mobile apps  (Wu et al. , 2020). Furthermore,  Choi et al. (2007)  recommend measuring mobile systems' impacts on the overall quality of life because these technologies are pervasive. As such, other variables, such as happiness and well-being, could be measured to gain a better understanding of the impacts of mobile phone technologies on users in developing countries. Sey and Ortoleva (2014)  argue that research on mobile phone usage in developing countries should also account for play and leisure-related activities, while  Ameen et al. (2018)  highlight the gender gap in smartphone adoption in emerging countries. Second, the data for this research were collected from participants in urban areas in Ghana. In the future, we can survey users in suburban and rural areas since their need for mobile technology and their use of it may differ from those of residents in urban areas  (Tognisse & Degila, 2021).
This includes a general paradigm shift in relation to copyrights, a central component of the conventional publishing industry  (Bachrach et al. 1998 , Walker 1998, Creative Commons). Generalizing the use of non-exclusive and royalty-free publisher licences, an already viable and ongoing system since 2003 (Public Library of Science), will allow authors to reproduce and distribute their findings independently, and encourage the publishers that actually provide additional contributions to the work performed by authors, such as improved content, complementary media design, or outreach support  (Odlyzko 1997 , Bachrach et al. 1998). Various journals already embrace open and author-led reviewing processes as ways to save the present demise of the scientific reviewing system (Aarssen and Lortie 1998, Peerage of Science), with several of them also publishing reviewer names and/or comments research articles  (Warne 2015 , Eisen 2019, F1000Research, Ideas in Ecology and Evolution).
, academics can avoid publishing in and evaluating for journals with unsustainable practices as prescribed by several previous initiatives, including The Cost of Knowledge initiated in 2012 (Whitfield 2012). A large spectrum of new alternatives has emerged, from community driven media with established ethical publishing strategies (e. g. Peer Community in, SCOAP3, Zenodo) to author-led, open peer-reviewing (Aarssen and  Lortie 2010,   F1000Research, Ideas in Ecology and Evolution, Peerage of Science) and public disobedience initiatives for free universal access to scientific literature (Sci-Hub), all of which could be more broadly embraced as a means to furthering scientific contributions to society. As a general rule, given the relatively low costs of archiving and distributing articles in electronic formats in the present digital era as printing and mailing are rapidly vanishing (Odlyzko 1997 , Walker 1998) , working with journals that charge minimal publication fees can encourage transition to a new economic model.
We extend this to data to the whole weekend by assuming the timetable and original duties for Sunday to be the same. For each instance, we aim at covering all tasks in the (now infeasible) original duties which are still in the plan. The result are four challenging instances, where more than 500 tasks per day are to be covered. Furthermore, to assure sufficient flexibility, the remaining tasks are also added to the problem. This implies that for each of the four instances, there are 2519 possible tasks (i. e. , tasks that are not necessarily to be covered but can be used as passenger tasks) and 87514 possible connections per day. We construct original rosters covering all of the original duties. When constructing the rosters, we assume that each employee works both days in the weekend. The rosters are obtained by solving a stylized rostering problem, where we minimize the occurrence of short rest times and high workload, while assuring that the roster rules (as discussed in Section 2) are satisfied.
This is evidenced by the fact that the same original damage breach distributions, derived in Project HARDER, based on largely cargo ships with the age spread over the last three decades of the previous century, are still being used today for all ship types, including modern passenger ships. Filling this gap, a new database for passenger ships developed in the EC-funded Project FLARE, is briefly presented, leading to new damage breach distributions specifically for passenger ships. It is believed that this paper will throw considerable light in enhancing understanding on the p-factor, which has been cluttered with unnecessary complexity from the outset.
SOLAS is still referring to "compartments" for collision damages only, using statistical data of breaches that relate to the last 3 decades of the previous century, the majority of which relate to cargo vessels, consequently disregarding all related information of modern passenger ships, ship size, speed and structural arrangements. Hence, material and speed for the vessel is under consideration. Moreover, the statistical database available in SOLAS includes collisions and contacts as part of the hazards. If there is no breach, then there is no p-factor as its definition is conditional on having a breach of given dimensions, location, and position.
Specific developments include an accident database addressing all ha ards  [18]  and leading to new damage breach distributions  [19]. Directly related to p-fa tors determination in probabilistic damage stability calculations, a non-zonal approac for breach generation has been developed, e. g. ,  [20, 21]  as well as calculations of all pert nent indices and their combination, based on the current IMO framework and accountin consistently for all hazards  [22, 23].
Specific developments include an accident database addressing all hazards  [18]  and leading to new damage breach distributions  [19]. Directly related to p-factors determination in probabilistic damage stability calculations, a non-zonal approach for breach generation has been developed, e. g. ,  [20, 21]  as well as calculations of all pertinent indices and their combination, based on the current IMO framework and accounting consistently for all hazards  [22, 23].
However, what is currently adopted by SOLAS regulations still pertains only to the earliest of these projects, namely Project HARDER. To address this gap, a concerted effort in the EU Project FLARE  [12] , focused on devising new damage breach distributions, specifically for large passenger ships, whilst addressing both collision and grounding accidents. To this end, use is made of a newly developed accident database undertaken within this project, leading to the development of pertinent damage distributions for damage length, height, penetration, and damage location.
Overview of the FLARE Accident Database This section provides a brief discussion of the flooding database developed in FLARE  [18, 19]. Figure  13  illustrates the distribution of flooding cases for different types of accidents, spanning the period 1999-2020 for Cruise and RoPax ships, extracted from the IHS Sea-web. The record shows that the number of hull/machinery damages and groundings dominate, followed by collisions. This study focuses mainly on ship flooding due to the initiating events developing external to the ship, namely collision and grounding, disregarding contact where only a few flooding events (8) are registered. Keeping in mind the 1995 SOLAS Conference and scope of the FLARE project, worldwide accidents during the last 20 years have been investigated. The identification of different accidents into collision and grounding is in line with the definition of accident types mentioned in IMO MSC/Circ.
953, i. e. , Collision: striking or being struck by another ship (regardless of whether underway, anchored, or moored); stranding (or grounding): being aground, or hitting/touching shore or sea bottom or underwater objects (wrecks, etc. ). To filter large passenger ships from the database, a lower threshold value of 3500 GT is selected, representing an average value based on a simple comparison of Cruise and RoPax ships having an overall length of 100 m. It is, essentially, a compromise between having enough data in the database for meaningful statistical analysis while focusing on large passenger ships. For the same reason, the filter for the ship-built year in the accident period has not been applied in this study.