dataesr/scientific-paragraphs-categorization huggingface dataset:
Analysing a large scale data set of 74 million tweets, they showed that the number of followers and followees, the age of the user account and the number of favourites strongly correlate with the retweetability. Conversely, the total of the user's past tweets and the presence of hashtag/URL do not highly impact on the number of retweets. Hoang et al. proposed a model to predict whether a tweet is going to be diffused and the level of diffusion using machine learning algorithms. Their model is based on various features including Suh's that were shown effective for diffusion prediction evaluation. For this reason, we extract the features proposed in Suh and Hoang studies and release them to the community. Two twitter-based data sets have been made available on information cascade. Domenico et al.
That make these data sets of limited use for information diffusion prediction evaluation. In this paper we describe the TwitCID collection consisting of 5 Twitter data sets (around 18M tweets) that we make available for research purposes. It is designed to evaluate studies on predicting information diffusion on social networks. In addition, we provide a set of extracted features corresponding to data sets, along with results of predictive models of 80% accuracy which can be used as a baseline for other works in this area.
Data Sets on General and Brand Marketing Information The TwitCID collection consists of five Twitter data sets which were extracted from the 1% of the tweets that Twitter agrees to be collected. The Firstweek and Secondweek data set were collected during the first week and second week of January 2017 while the Iphone, Gucci and Galaxy data sets were collected from 21 September 2015 to 31 May 2017 using the keywords "iphone", "gucci" and "galaxys" respectively. We designed the TwitCID collection so that it contains "general" information (Firstweek and Secondweek data sets) and more "marketing"-oriented information (the three other data sets). In that way, information diffusion can be studied in different scenario using the TwitCID collection. Table  1  reports the number of tweets, the number of non-retweeted and the number of unique retweeted tweets for the five data sets.
Firstweek Secondweek Iphone Gucci Galaxy Class-0 4,025,157 4,058,066  proportion of tweets in each of these classes is about the same for the first and second week data sets, while they differ in the three other data sets. Table  2  presents the distribution of tweets when we consider the level of retweets on the five data sets. In the Table  2 , class-0 corresponds to the tweets that are not retweeted at all, class-1 are tweets that are retweeted less than 100 times, class-2 from 100 times to 10,000 times, and class-3 are tweets retweeted more than 10,000 times. To make the distribution more visual, we plot it in the Fig. 1. FirstWeek and SecondWeek data sets are very similar in terms of class distribution. Moreover, in general, the tweets that are not retweeted are the majority (from 59% to 87% depending on the data sets).
The second most populated class is class-1, whatever the data set is and corresponds to from 11% to 33%. Class-2 is from 1% to 8%. Unsurprisingly, there are very few tweets in Fig. 2. Spear correlations between the number of times a tweet is retweeted in the data sets and retweet counts in the tweet objects. class-3, less than 1% for all the data sets. Having data sets with different distributions is of interest to be able to evaluate the information diffusion prediction in different contexts. Since we are using the 1% of tweets from Twitter, it is important to check whether our data sets keeps the original distribution of re-tweets. In Fig. 2 , we consider the Spearman correlation between the number of times each tweet is retweeted in our data sets and the number of times this tweet is retweeted on Twitter (stored in retweet count field of the tweet object).
Tweet Features In this section, we shortly describe the features representing a tweet that are released at the same time as the collection and that could be of use for research in the domain of microblog information diffusion. We also briefly present a descriptive analysis of the released data sets focusing on features that have been shown to be the most important to predict retweetability  [7, 19]. There are three categories of features: related to the tweets' authors (features F1 to F9), the tweet content (feature F14 to F29) and to the time when the tweet was created (features F10 to F13). Seven features come from  [19]  (features with a + ) and 24 features from  [6, 7]. Table  3  presents a short description of all the features. Followers and Followees. In Twitter, followers are people who follow a user and followees are people whom a user follows.
This is shown by the fact that the rate of F1000 tweets from class-RT is very high in all data sets, from 73% to 79%. In addition, there is a low proportion of these F1000 tweets that are not retweeted. The rate of F1000 tweets from class-0 is 19% for the Galaxy data set and 22% for the Iphone data set. Similarly, for the Followees feature, the rate of tweets created by authors who have 1,000 followees or more (called Fe1000) in class-RT is pretty high, about 34% to 38% depending on the data sets while this rate from class-0 is very low, around 18%. This means tweets created by users who are followed by many other users have a high possibility to be retweeted and low possibility to be non-retweeted. Groups. Groups denote the of lists or groups that a Twitter user is a member of. In our five data sets, the number of groups strongly correlate with retweet.
We studied the rate of tweets created by authors who are members of at least 50 groups (called Gr50) in each class. This rate is low for class-0 but very high for other classes for all data sets, especially for class-3. For the Galaxy data set, there 34% Gr50 tweets from class-0 but 76% Gr50 tweets from class-2 and 93% from class-3 respectively. For other data sets, the percentage of Gr50 from class-0 is around 20% while this rate is from 39% to 83% for other classes. This result shows that the tweets of authors who join in at least 50 groups have high possibility to be retweeted and there is a high percentage of tweets that are retweeted more than 100 times belong to these users. Image and Video. The image (resp. video) feature specifies whether a tweet contains an image (resp. video) or not.
Favourite. Favourite is the number of tweets that a user has liked in his timeline. In our collection, the rate of tweets of which the authors have at least 1,000 favourites (called Fav1000) is pretty high: around 50% for the Firstweek, Secondweek and Gucci data sets, and around 30% for the Iphone data set. For the Galaxy data set, although there is only 15% Fav1000 tweets in the whole data set, this rate on class-3 is 77%. The other data sets also have high rate of Fav1000 tweets in class-3: 77% for the Gucci data set and around 60% for the Iphone, Firstweek and Secondweek data sets. This result shows that tweets of authors who have likes at least 1,000 posts on their timeline are highly likely to be retweeted several times. Age of Account. The Age of an account presents the number of days a user account is registered from.
We analyzed the relationship between the age of the author account and the retweet rate for the data sets of the TwitCID. We found that whatever the data set is, the age of account and retweet rate is linearly strongly correlated (see plot in Fig. 5 ). Figure  5  presents the normalized retweet rate of different types of users. The junior users: who have registered their account less than 1,000 days, the intermediate users: from 1,000 days to 3,000 days and the senior users: greater than 3,000 days. In this figure, the retweet rate represents a normalized ratio of the Fig. 5. Retweet rate vs Age of account on the five data sets. Junior are users who have created their account less than 1,000 days; Intermediate: from 1,000 days to 3,000 days; Senior: greater than 3,000 days. number of retweeted tweets to the number of tweets.
Released Data Being compliant with Twitter developer agreement and policy 1 , we publish our TwitCID collection on behalf of our academic institution IRIT 2 for the sole purpose of non commercial research, under the licence CC BY-NC-SA (Attribution -NonCommercial -ShareAlike). For each data set, we release all tweet IDs accompanied by a script so that researchers can retrieve the tweets in JSON from the Twitter API. We also provide extracted features described in the Table  3  corresponding to each data set in arff form. All these materials are available at http://doi. org/10. 5281/zenodo. 3246705.
(b) SMOTE 4  technique is used the to synthetic minority over sampling. From the Table  4 , we can see that using the full set of features remarkably increases the F-measure on each class and on average compared to when using the reduced feature set for all the data sets both for binary classification. On average, the complete model improves the F-measure by 4% (from 0. 78 to 0. 82) for the Firstweek, Secondweek and the Gucci data sets; by 3% (from 0. 82 to 0. 85) for the Iphone and by 2% (from 0. 86 to 0. 88) for the Galaxy data set compared to the reduced model. All these improvement are statistically significant. One interesting point is that the improvement on class-RT is equal or higher than on class-0 although the number of tweets in class-RT is smaller than the number of tweets in class-0.
For the Gucci data set, F-measure is increased by 3% on class-0 while this increase is by 4% on class-RT. The complete model also achieved higher improvement on class-RT than on class-0 for the Galaxy data set that the reduced one (Table  4 ). Similarly to the case of binary classification, the complete model increases the F-measure on average and on every class compared to the reduced model for all the data sets for multi-class classification. On average, F-measure is increased by 4% for the Firstweek, Secondweek and Iphone data sets, by 3% for the Gucci data set and by 2% for the Galaxy data set compared to the reduced model. All these improvements are significantly different. When considering each class, the complete model obtains higher F-measure on class-0, class-1 and class-2 but lower F-measure on class-3 compared to the reduced model.
The reason may be the number of tweets from class-3 which is extremely small compared to those from the other classes (see Table  2 ). For the Gucci data set, the complete model achieves 0. 79, 0. 65 and 0. 62 (F-measure) on class-1, class-2 and class-3 respectively but only 0. 02 on class-3. The highest F-measure is obtained on class-0 and class-1 for the Iphone data set which are 0. 85 and 0. 76 respectively.
Conclusions In this paper, we introduced the TwitCID collection of 18 million tweets that is designed to evaluate studies on predicting information diffusion on social networks, both for 'general' information and 'marketing' information. We also provide extracted features corresponding to each data set regarding the user profile, tweet content and temporal information. In addition, we report results of information diffusion prediction models evaluated on our collection (around 80% accuracy) which could be strong baselines for comparative studies.
Introduction Information systems are increasingly exposed to a variety of security threats that need constant attention from corporate decision makers. Despite being an effective approach to measure security risks, current risk management approaches lack in some areas such as the need for very detailed knowledge about the company environment and the cybersecurity body of knowledge  [1]. To stay current with the latest security knowledge, a security professional has to stay abreast with the latest information about security aspects such as vulnerabilities, attacks, etc. , which is a challenging task as information is updated on a daily basis. The public disclosure of important security information often first occurs in different online resources like blogs, vendor bulletins, and online databases  [2].
The information is then collected and stored in semi-structured vulnerability databases such as the National Vulnerability Database (NVD)  [3]. The timely analysis of cyber-security information necessitates automated information extraction from web sources. This is particularly difficult because the information is unstructured and dispersed over the web, which is the first source of information and not present in one location. It is useful and more practical for the cybersecurity community if the relevant information is recognized, extracted, and represented as an integrated, shared, and structured form like databases or ontologies. A potential immediate benefit is the increase of situation awareness and enabling of intrusion detection systems to detect and prevent potential "zero-day" attacks.
The aim of relation extraction, on the other hand, is to extract knowledge about related entities from unstructured language sources such as text or speech and represent that knowledge usually as a triplet that has the form (subject, predicate, object). For instance, from the vulnerability description in Figure  1 , we can extract the tuple (IBM, is_vendor_of, WebSphere), which is an instance of the relation type: (Software vendor, is_vendor_of, Software product). Appl. Sci. 2019, 9, x FOR PEER REVIEW 2 of 15 The two main tasks of information extraction are Named Entity Recognition (NER) and Relation Extraction (RE). NER can be generic where the aim is to extract general entities such as the names of places, people, etc. , or can be specific to a domain. For instance, products, vendors, vulnerabilities are names of entities in the cybersecurity domain.
Nowadays, the mainstream tools used for NER that give the best results rely on feature engineering to describe entities. These tools are usually domain specific, thus, depending on the features that characterize the entities in the domain. For instance, a sequence of entities in the financial domain are likely to be a company name, whereas in cybersecurity, they are more likely to be a software name. Figure  1  highlights some entities from the cybersecurity domain. The aim of relation extraction, on the other hand, is to extract knowledge about related entities from unstructured language sources such as text or speech and represent that knowledge usually as a triplet that has the form (subject, predicate, object). For instance, from the vulnerability description in Figure  1 , we can extract the tuple (IBM, is_vendor_of, WebSphere), which is an instance of the relation type: (Software vendor, is_vendor_of, Software product).
Models To evaluate the performance of LSTMs for the NER tasks, we applied the LSTM-CRF method introduced by Lample et al. [19]  to the domain of cybersecurity vulnerability management. This architecture is a combination of LSTMs, CRFs, and word embeddings. Its input is an annotated corpus in a format similar to the format of the CoNLL-2000 dataset  [20]. This dataset contains a list of words with a tag denoting the type of the word. There is no description of the features of entities, hence it is domain and entity agnostic. We compared the results we obtained with a notably fast and accurate CRFs implementation called CRFSuite  [21]. Annotated corpus is not widely available in the cybersecurity domain as opposed to other domains such as the biomedical domain. To train the models, we used the annotated corpora provided by Bridges et al.
Data for Named Entity Recognition The corpus used in the training of the NER model is based mainly on the NVD data and contains 40 entity types. The corpus contains descriptions of the cybersecurity vulnerabilities and was generated as part of the Stucco project  [31]. It mainly contains the descriptions of entries in the Common Vulnerabilities and Exposures (CVE)  [32]  and NVD databases from 2010. Each word in the corpus is annotated with an entity type. Since not all the entity types are common, we concentrated our analysis of the model performance on the seven most common entities of the domain. The The common ancestor node of two entities is used to separate the SDP into a left and right sub-path. These two sub-paths are picked up by two RNNs. In each RNN, LSTM units are used for information propagation.
Data for Named Entity Recognition The corpus used in the training of the NER model is based mainly on the NVD data and contains 40 entity types. The corpus contains descriptions of the cybersecurity vulnerabilities and was generated as part of the Stucco project  [31]. It mainly contains the descriptions of entries in the Common Vulnerabilities and Exposures (CVE)  [32]  and NVD databases from 2010. Each word in the corpus is annotated with an entity type. Since not all the entity types are common, we concentrated our analysis of the model performance on the seven most common entities of the domain. The statistics in Table  1  show the number of entities for the most significant entities in the training and test corpora:.
Data for Relation Extraction The corpus for RE training was challenging because there was no annotated textual data available for cybersecurity vulnerability descriptions and the manual annotation of the NVD corpus is costly and impractical. This is a common case when applying NLP to specific domains. For this reason, we applied the bootstrapping algorithm developed by Jones et al. [10, 33]  for the cybersecurity domain that obtained a precision of 82% to generate an automatically annotated corpus. The algorithm follows a semi-supervised approach for extracting relations by querying the users to assist in labelling a seed of few important relations and the algorithm iteratively builds on the seed to generate a bigger corpus. We applied the bootstrapping algorithm on the NVD corpus using the seed provided by Jones et al.
as an input to generate the corpus. We then trained the model on the data for the year 2015. The NVD database for 2015 consisted of 20,125 sentences divided into training and test data. The training data constituted 80% of the corpus with 13,569 sentences and test data consisted of 6556 sentences. The aim of our experiment is to concentrate initially on the most important relations in the domain. The list of studied relations and the statistics of the final data used in our experiments is shown in Table  2 : The word embeddings needed by the models were generated from the text of the whole NVD corpus using the google word2vec tool  [34]. The reason for not using general embeddings available online is that these embeddings are based on general English text and having embedding generated from the text of a specific domain improves the quality of the results.
SOFTWARE_VENDOR-is_vendor_of-SOFTWARE_PRODUCT (e1, e2). The algorithm detected that 'Microsoft' is a vendor of 'Windows' and denotes that with the tuple (e1, e2). Each of the three evaluated models then has its own preprocessing depending on the model architecture and its expected input. The common preprocessing steps such as POS tagging and dependency parsing were carried out using Stanford CoreNLP toolkit version 3. 7. The main preprocessing work for all the models is extracting the shortest path for the least common ancestor for candidate entities. Some processing errors occurred when extracting the information needed by corresponding models such as the dependency paths, mapping words to their embeddings id, etc. These errors include unsupported dependency types, unsupported POS types, etc.
Evaluation Metrics The evaluation metrics used for the NER and RE models' evaluation are the accuracy, precision, recall, and F1 score. For the NER task, we calculated these metrics for the full set of entity tags as well as for the subset of the seven common entities. The evaluation metrics are calculated as follows: P = TP TP + FP , R = TP TP + FN , and F1 = 2 × P × R P + R. As for RE, a correctly extracted relation is considered as true positive (TP) if the type of relation is correct and the type of the entities in this relation match the type of entities in the training data. Otherwise, the extracted relation is considered as a false positive (FP). False negative instances were computed as the total number of gold relations that our model did not identify. We evaluated the model using the 20% test data we set aside from the corpus.
We read with great interest the paper published by Nannan Panday et al. [1], who reported that, first, culturepositive sepsis patients have a higher mortality rate than those with culture-negative and, second, culture-positive sepsis patients more often have multiple organ systems affected during the sepsis episode. Firstly, we compliment the authors for this very interesting study. Nevertheless, to our opinion, some methodological issues deserve their attention. Firstly, the database used for the analysis comes from another study (PHANTASi trial)  [2] , in which, eligible patients were those with a suspected of (severe) sepsis and septic shock  [2]. Thus, we cannot be certain.
The edge computing paradigm has been adopted for implementation purpose. The EMH 2 architecture is designed to facilitate asynchronous and progressive onboard and off-board deployments. A realworld application of EMH 2 to a fleet of ten trains currently in use, in collaboration with our industrial partner, Bombardier Transport, is presented. Three key performances indicators have been estimated by comparing EMH 2 with the current industrial situation. These indicators are (1) the number of fleet maintenance visits, (2) the time needed by a maintenance operator to investigate and diagnose, and (3) the time needed by the system to update data regarding the health status and monitoring of trains. Results obtained outperformed industrial expectations. The paper finally discusses feedbacks from experience and limitations of the work.
Some recent technologies are pushing this relatively new kind of architecture. There are a few applications in the service sector, for example, including AWS Lambda @ Edge, 1 Microsoft Azure IoT Edge, 2 EdgeConneX, 3 and Equinix. 4 These applications can support any mobile or fixed system such as drones and software applications using Internet of things and augmented reality. However, there are currently no applications in the transportation sector. Advantages The advantages of this kind of architecture include its ability to limit and control the transmission of large volumes of data and events to the ground MC. It is also compatible with modern technological solutions and enables the integration of a basic level of modularity through the decoupling of functions and algorithms. Drawbacks Transposed to the transportation sector, the drawbacks may be the same as those identified for the centralized type, except regarding modularity.
Motivations of the work The literature review has shown that none of the existing types of fleet EMADM provides a comprehensive solution  to all the issues introduced previously. We believe that an effective fleet EMADM developed using a standardized modeling of events coupled with a cooperative, hierarchized management process of these events, from monitoring raw data of a component to high-level knowledge at fleet level, can become the backbone of the fleet CBM system. The remainder of this paper presents this fleet EMADM called EMH 2.
SurfEvent model In EMH 2 , every event is modeled and handled using a single model. One key idea is to model and manage every event in the same way thus rendering our approach more generic. For example, with this approach, expert rules can be designed independently of the monitoring domain and the real-time diagnostic processing performance, thus contributing to the specification related to modularity. This generic model of an event is called a ''SurfEvent. '' Every event entering or traveling within the EMH 2 is a ''SurfEvent. '' Interoperability of SurfEvents with other information systems must then be addressed but this is not within the scope of this paper. A SurfEvent is modeled as a set of the following parameters: • A unique identification (its name). • Two possible data types: quantitative (e.