dataesr/scientific-paragraphs-categorization huggingface dataset:
This can also drive an interest in having a platform that can collect and organize this data. Another challenge is having the right expertise in place to be able to deploy such a platform for a small academic research group or facility. While cloud platforms offer a GUI that can help launch the right servers, networking tools, databases, etc. , it can be quite cumbersome to repeat this task as well as debug it if something goes awry. To address this, we have developed the platform using an infrastructure-ascode approach. This effectively means that there is a codebase that, when run, deploys the entire platform readily into different cloud environments, for example, each belonging to a different team or group. This reduces the time required to replicate the deployment of the platform from days to minutes.
How can I try MLFlow? Most current open-source ML experiment managers can run locally on a personal computer where the object and database storage are also hosted locally. This configuration can be installed as a Python package in the typical manner (using the Python package management software 'pip') and executed from the command line. However, the experiment manager system can also be deployed in a more scalable manner onto a remote server with long-term object storage and a database. In this case, it is preferable to build a Docker container to deploy on the remote server. At the time of this writing, we prefer using MLFlow as our experiment manager, but there are many other similar opensource experiment managers that serve a similar purpose.
Leveraging pre-trained language models and different learning frameworks, we perform hateful content classification of exchanged messages according to a binary scheme (online hate or no online hate). Then, -from these bullying tracesbullying behavioural cues (repetition and intention to harm) are derived and formalised into a role scoring function. As a result, the proposed pipeline identifies the bully and the victim among chat participants. Evaluated against state-ofthe-art methods, the proposed pipeline achieves better performances considering all the datasets and roles to predict. In addition, the error analysis confirms that deriving bullying behavioural cues is beneficial to the task of participant role identification.
Then, learning from these bullying traces we derive bullying behavioural cues allowing to distinguish bullies from victims among chat participants, i. e. , repetition and intention to harm. The contribution of this work is twofold: (1) it introduces a full pipeline portable to multiple languages, and (2) it formalises bullying behavioural cues to reinforce OHD tasks. Evaluations of each task were conducted on both French and Italian using two datasets mimicking cyber aggression situations on private instant messaging platforms  (Sprugnoli et al. , 2018; Ollagnier et al. , 2022). Concerning the task (1), fine-tuned models relying on the augmented cross-lingual learning framework (models trained using both the French and Italian data) reach the best balance on both languages. Supported by post-hoc explanations, this learning setting demonstrates better robustness in facing different types of aggression and verbal abuse unlike zero and few-shot settings.
To overcome this limitation, other studies have built beyond existing works to other social media including online forums (e. g. Reddit) and question asking and answering services (e. g. ASKfm). In this context, most of the proposed approaches have applied machine learning techniques using linguistic-based features ranging from shallow (surface form of the post) to deeplevel (theoretical and descriptive linguistic analysis) as well as sentiment analysis  (Kim et al. , 2021). Among them,  Ratnayaka et al. (2020)  propose multiple role modelling relying on the pre-trained language model BERT aiming at distinguishing posts written by harasser/bystander assistant and victim/bystander defender. Recently, Jacobs, Hee, and Hoste (2022) investigate both supervised and ensemble learning relying on various pre-trained language models (RoBERTa, XLNet, BERTje, RobBERT) to build a multiclass classifier performing fine-grained role classification.
Concerning available resources, most of the proposed studies rely on "easy to access" data (Facebook, YouTube or Instagram) annotated using binary schemes (e. g. content or behaviours categories as abusive or not abusive)  (Milosevic, Van Royen, and Davis, 2022). Whilst  Van Hee et al. (2015)  introduce fine-grained annotation guidelines to improve cyberbullying detection, few datasets were released using a more nuanced description of the type of abuse involved, or the severity of the case; or roles played by those involved. Concerning the latest, only  Xu et al. (2012)  and Jacobs, Hee, and Hoste (2022) use publicly available datasets providing fine-grained role labelling. Recently, two datasets collected through role-playing games aiming at mimicking cyber aggression situations on private instant messaging platforms in French and Italian were released  (Sprugnoli et al.
, 2018; Ollagnier et al. , 2022). These datasets provide a multi-level annotation scheme including the different participant roles, i. e. , bullies, victims and bystanders. Currently, methods addressing the task of PRI occurring on instant messaging platforms is only reported in  Ollagnier et al. (2023). In this work, a pipeline aiming at predicting a unique role to chat participants is introduced. Despite promising results, this task remains challenging due to the ambivalence in the use of OH among participants  (Haddock and Jimerson, 2017). Indeed, OH can be used with different intentions considering the role of participants in bullying episodes; it can be used as a means to cause harm (harassers), to defend (victims), to encourage (bystanders), and so on. In response to this limitation, we propose to explore OHD methods coupled to a scoring function relying on behavioural cues (i.
Here, both mono and multi-lingual models are assessed using different learning settings. The lack of models and labelled corpora to address OHD in multi-party setting led us to investigate mono and multi-lingual learning as well as zero-shot, few-shot and cross-lingual frameworks. The proposed settings are both evaluated on the French and the Italian datasets, respectively introduced in Ollagnier et al. (  2022 ) and  Sprugnoli et al. (2018). The investigated learning settings are: 1) zero-shot, crosslingual, i. e. , training on one language and testing on unseen languages; 2) mono-lingual, i. e. , training and testing on the same language; 3) few-shot, cross-lingual, i. e. , training on one language and a small percentage of samples from the test language and testing on the test language; 4) augmented cross-lingual, i.
e. , training on several languages and testing on a language included in the training. In detail, as multilingual models the XLM Roberta base model from  Conneau et al. (2020)  (XLM-Base) and multilingual BERT base  (Devlin et al. , 2019)  (mBERT) are used. As mono-lingual models we use respectively for French and Italian CamemBERT base  (Martin et al. , 2020)  and ITA-Base 1. In addition, we use the mono-lingual version of DeHateBert  (Aluru et al. , 2020).
Data To evaluate the proposed pipeline on both tasks of OHD and PRI, we use two datasets in French and Italian respectively introduced in Ollagnier et al. (  2022 ) and  Sprugnoli et al. (2018). These two datasets contain conversations collected through role-playing games aiming at mimicking cyber aggression situations on private instant messaging platforms. Both of them consist of conversations manually annotated using a multi-level annotation scheme. Table  1  presents a sample of a conversation extracted from the French dataset. French This resource, named CyberAgressionAdo-v1 dataset 2  , is composed of 19 conversations including 4 addressing the topic of homophobia, 7 the topic of obesity, 3 the topic of religion and 6 about ethnicity. The conversations provide annotations such as the participant roles, the presence of hate speech, the type of verbal abuse present in the message, and whether utterances use different humour figurative devices (e.
g. , sarcasm or irony). To evaluate the task of OHD, we have transposed the annotations corresponding to the different types of verbal abuse into a binary scheme (online hate or no online hate), i. e. , exchanged messages containing any type of verbal abuse are considered hateful while the others are annotated as non-hateful. Concerning the task of bullying participant role detection, performances are assessed using the provided labels corresponding to the participant roles, i. e. , bully, victim, bystander-assistant, bystanderdefender and conciliator. Italian This dataset, hereinafter referred to CyberViolenzaAdo-v1 3  , consists of 10 conversations including 4 about gendered division of sport practices and 2 conversations per cyber aggression situations addressing the topics of interference in others' businesses, lack of independence, parental intromission and web virality.
The provided annotation layers correspond to: (i) the cyberbullying role of the message's author; (ii) the cyberbullying type of the expression; (iii) the presence of sarcasm in the expression; (iv) whether the expression containing insults is not really offensive but a joke. As carried out for the CyberAgressionAdo-v1 dataset, we have transposed the annotation corresponding to the layer (ii) (i. e. , bullying entity) into a binary scheme distinguishing the presence or absence of OH. Here, the labels Threat or blackmail, General Insult, Body Shame, Sexism, Racism, Curse or Exclusion, Insult Attacking Relatives, Defamation and Sexual Harassment are considered hateful while the others are annotated as non-hateful. To evaluate the task of PRI, we use the provided labels corresponding to the annotation layer (i), i.
Experimental Methodology Our experimental setup illustrates four aspects: (1) the performance of the different models and learning frameworks on the task of OHD on conversational data, (2) the qualitative evaluation section in which we use explainability methods to observe model limitations, (3) the performance of the best OHD classifier on the task of PRI, and (4) the error analysis of PRI predictions to assess the reliability of the proposed scoring function. To evaluate the task of OHD, training sets are composed of messages extracted from the aforementioned datasets, i. e. 2,921 entries in French and 2,074 in Italian. Concerning preprocessing, all the messages are lower-cased and tokenized. In turn, they are respectively truncated/padded to a length of 100 in both languages. Then, they are encoded using the pre-trained model required according to the learning setting.
Next, generated sentence vector-based features are used to fine-tune the given pre-trained model to perform OHD according to the used binary scheme. Here, we reproduce the same setting used to perform the task of OHD on Italian introduced in  Nozza, Bianchi, and Attanasio (2022). Models are trained for 5 epochs and evaluated every 50 steps, and we select the best checkpoint considering the validation loss. For each dataset, the train consists of 70% of stratified sampling (2336 messages in French and 1451 in Italian) and both the validation and the testing sets represent 15% of the remaining, i. e. , 293 messages for both sets in French and 311 messages in Italian. To fine-tune the pretrained models, we run different experimental frameworks: (1) mono-lingual (MONO), in which we fine-tune monolingual pre-trained models on the source language and test on the same language; (2) zero-shot, cross-lingual (ZERO), on which we fine-tune the best MONO model on each language and test on the unseen language; (3) few-shot, crosslingual (FEW), in which we fine-tune mono-lingual models either on French or Italian and add 20% of the training set of the test language, then testing on the test language accordingly; (4) augmented cross-lingual (AUG), in which we fine-tune multi-lingual models using both languages and test on each separately.
OHD Results Table  2  presents the results obtained on the task of OHD using different pre-trained models and learning frameworks Table  2 : Results obtained on the OHD task considering different learning settings. We report F1 score the for hateful and nonhateful cases, and the macro-averaged F1 score. on both the French and the Italian datasets. For both languages, the best performances are observed using the MONO and AUG settings. In detail, the AUG setting combined with mBERT and the MONO setting combined with ITA-Base are the first-ranked models, respectively in French and Italian. Overall, detecting the presence of OH performs better than identifying non-hateful comments, except with the AUG setting combined with XML-base in French. On average, the difference between the two classes is 8 points in French and 21 in Italian considering the two first-ranked models for each language.
For both languages, the poorest performances are observed using the ZERO setting combined with ITA-Base and CamemBERT, respectively in French and Italian. For both languages, results obtained on the hateful class are improved using the ZERO setting combined with DeHateBert, especially in French. Such a finding suggests that either using data from social media or related to the task of OHD can be considered in the case of lowresource settings. However, it has to be evaluated empirically considering the targeted language; the gap between the F1-h scores of the two monolingual pre-trained models on both languages suggests that transferring knowledge from some languages to others can be confusing regarding the conceptualisation of meaning, idioms, and language structures, to name a few. Moreover, adding samples of the target resources to fine-tune models (the FEW setting) improves performances whatever the source of the data used for the original training.
As a part of cyber aggression situations, hate speech can be identified as the most central, albeit not the most frequent, mode of use. In the datasets, slur terms/taboo expressions are frequently exchanged among friends, e. g. between bullies and their bystanders. Conversely, most hateful content misclassification is due to the absence of slurs/taboo expressions; La differenza tra @user e martedi ? Il martedì è grasso una sola volta all'anno (EN: The difference between @user and Tuesday? Tuesday is only Fat Tuesday once a year), to go beyond the literal meaning it requires commonsense knowledge (the festival martedí grasso) and the bullying topical-focus (obesity). Hate can be conveyed in an insidious way using various victimisation strategies relying on verbal abuse  (Hee et al. , 2018; Ollagnier et al.
PRI Results This evaluation was conducted using the AUG setting combined with mBERT which is the learning framework reach-  ing the best balance considering all evaluation metrics on both languages. Table  3  presents the performances obtained by our pipeline against the baseline introduced in Ollagnier et al. (  2023 ) on the task of PRI for the roles of bully and victim. As we can observe, our pipeline significantly outperforms the baseline on most of the labels suggesting that bullying traces are pertinent features to determine participant roles of involvement in bullying. Moreover, our pipeline proves its ability to deal with short and noisy texts (6 tokens on average per message) which is beneficial considering the nature of social media data, especially in multiparty chats. In detail, the detection of victim performs better than bully, especially in Italian.
This gap between the two languages is due to the presence in the French dataset of the role of conciliator, i. e. , a friend of both the bully and the victim mediating the disagreement among active participants. For this role, we observe few occurrences of OH considering the whole French conversations, namely 0. 013%; the percentage for the victims is 0. 048% which suggests similarities in their behavioural practices in facing bullying. For the Italian dataset, reported misclassifications for victims and bullies are between victims and bystander-assistants and victim and bystander-defenders, respectively representing 100% and 85. 7% of the model's mistakes. Concerning the French dataset, misclassifications are observed between bullies and bystander-assistants (75%) and between victims and conciliators (72.
7%). Observations on the datasets have shown different aggressive tendencies and variations in bullying engagements suggesting that the bullying behavioural cues need to be nuanced to establish contrasts between aggressive behaviours, i. e. , the types of aggression and verbal abuse as suggested in  Ollagnier et al. (2022)  and  Sprugnoli et al. (2018). Considering the derived behavioural cues, they contribute to understanding the construction of aggressive behaviours regarding the role of involvement in bullying; for instance, bullies have a ratio of repetition and intention to harm of 0. 084 and 0. 566 on average against 0. 025 and 0. 376 for victims in Italian.
Conclusion and Future directions This paper presents a pipeline aiming at identifying bullies and victims in chats. Consisting of two tasks (OHD and PRI), the proposed pipeline outperforms the existing baseline considering all the datasets and roles to predict. Despite these promising results, training and testing are carried out on data "mimicking" cyber aggression situations occurring in chats. Whilst role-play data argue to be similar to naturalistic interactions, they can include biased data (e. g. personal student interactions not related to the scenario or extrapolated aggressive behaviours). The proposed pipeline should be tested in real-world settings to ensure its reliability. In this study, we have focused on identifying bullies and victims. However, other social roles can be involved and play a key role in bullying episodes such as bystanders and conciliators.
Acknowledgments This work is supported by the  French government , through the  3IA Côte d ' Azur Investments  in the Future project managed by the  National Research Agency (ANR)  with the reference number  ANR-19-P3IA-0002  and the  EFELIA Côte d'Azur  project  ANR-22-CMAS-0004. The authors are grateful to the OPAL infrastructure from  Universite Cote d'Azur  for providing resources and support.
X-IFU/Athena mock observations In order to investigate the observability of the gravitational redshift from the X-ray emission of galaxy clusters, we used a cluster toy model, based on the simulations presented in  Cucchetti et al. (2018b) , to produce simulated observations with Athena X-IFU using the SIXTE instrument end-to-end simulator  (Wilms et al. 2014; Dauser et al. 2019). The emission models and spectral fitting rely on the xspec software  (Arnaud 1996).
Mock data analysis The main output of the SIXTE simulator is a mock event list of the X-IFU observation. For all the recorded events, namely, the X-ray photons that have been detected, the measured energy,  detector and sky coordinates, time of arrival, and so on, are provided. From the SIXTE mock event lists, we generated count images. The spectra were computed within concentric annuli of constant width or over full X-IFU FoV. Each spectrum is fitted using xspec with the phabs*bapec model and the aforementioned model for the background, with the redshift, the cluster emission normalisation, temperature, abundance, and the background emission normalisations as free parameters. The velocity broadening of the bapec model, which is set to 0 when simulating the cluster emission, is also set to 0 during the fit.
Conclusions and discussion In this work, we evaluated the possibility of observing the gravitational redshift in galaxy clusters in X-rays with future integral field spectrometers such as the Athena X-IFU. To that end, we created mock observations of an idealized massive and nearby galaxy cluster (the targets with the highest probability to be A24, page 7 of 11 detected) with X-IFU, by using the SIXTE software. We analyzed the data with the xspec spectral analysis software. We reconstructed the gravitational redshift profile that we modeled through the shape of the cluster potential well and the X-ray emission of its gas content. We showed that: (1) X-IFU could recover the gravitational redshift for massive (M 200 ∼ 10 15 M ) and nearby (z ∼ 0. 1) clusters within a quite large, but still achievable exposure time; and (2) the measurement if the gravitational redshift profile can be used to derive properties of the halo gravitational potential, such as its total mass.
There is hope that future data analysis methods will also be able to make full use of such spectra, allowing for combinations of spectral an spatial information in the cluster, and perhaps allow for the inclusion of the gravitational redshift as another useful probe to our understanding of these large structures. At the time of publishing this paper, the European Space Agency has sponsored a full reformulation of the Athena mission science case and specifications. We thus stress that the results of our study may have to be reconsidered according to the future new instrumental requirements of the Athena mission.
Acknowledgements. We are grateful to the anonymous referee for fruitful comments that helped improving this paper. A. M. , E. P. and N. C. acknowledge the support of  CNRS/INSU  and  CNES. The following python packages have been used throughout this work : astropy (Astropy Collaboration 2013, 2018, 2022), chainconsumer (Hinton 2016), emcee (Foreman-Mackey et al. 2013), matplotlib (Hunter 2007) and cmasher (van der Velden 2020).
Here we assess these systematics with data from the MeerKAT Absorption Line Survey (MALS), a blind search for absorption lines with pointings centred on bright radio sources. With the sensitivity and field of view of MeerKAT, thousands of sources are observed in each pointing, allowing for the possibility of measuring the cosmic radio dipole given enough pointings. We present the analysis of ten MALS pointings, focusing on systematic effects that could lead to an inhomogeneous catalogue. We describe the calibration and creation of full band continuum images and catalogues, producing a combined catalogue containing 16 307 sources and covering 37. 5 square degrees of sky down to a sensitivity of 10 µJy beam -1. We measure the completeness, purity, and flux recovery statistics for these catalogues using simulated data.
As a blind search for absorption lines, every MALS pointing is centred on a bright AGN (>200 mJy at 1. 4 GHz). The targets have been chosen from the NVSS and SUMSS catalogues and are cross-checked with Wide-field Infrared Survey Explorer (WISE) data in order to build a dust-unbiased sample of AGN  (Gupta et al. 2022). Early results show that MALS is able to attain unprecedented sensitivity to absorption lines in these bright AGN  (Gupta et al. 2021; Combes et al. 2021 ). In addition to the search for absorption lines, the data taken will be sensitive enough to produce deep continuum images, down to 10 µJy beam -1. With a full width at half maximum (FWHM) field of view of 1 degree in the L band (1. 27 GHz), each MeerKAT pointing presents a few square degrees and potentially thousands of sources.
All observations have a correlator integration time of 8 s, with observations carried out in 32 K mode, providing 32 768 channels with a channel width of 26. 123 kHz. With a frequency range of 856-1712 MHz, the total bandwidth is 856 MHz, with a central frequency of 1. 285 GHz. The MeerKAT data are shipped to the Inter-University Centre for Astronomy and Astrophysics (IUCAA) in India and processed by the Automated Radio Telescope Imaging Pipeline (ARTIP). The complete deployment of ARTIP in MALS is described in  Gupta et al. (2021). ARTIP presents an environment where data can be processed according to user specifications and is based on the Common Astronomy Software Applications (CASA) tasks  (The CASA Team et al. 2022). Each dataset undergoes a round of basic flagging, removing known radio frequency interference (RFI) frequencies.