dataesr/scientific-paragraphs-categorization huggingface dataset:
M del elec i XGB E a a R a the prediction correctly identifies the absence of a class; False Positive (FP), where the prediction wrongly identifies the presence of a class, and False Negatives (FN), where the prediction wrongly identifies the absence of a class. The classification matrix (Figure  S2 ) is typically used in ML to show the proportions of TP, TN, FP and FN for each class. Based on these proportions, we can calculate four well-known metrics to evaluate the models' performance (e. g. ,  Verdhan, 2020) : Accuracy = !"#!$ !"#!$#%"#%$ (1) Precision = !" !"#%" (2) Recall = !" !"#%$ (3) F1-score = & * !" & * !"#%"#%$ (4) Classification scores in this study are reported based on the F1-score, as it combines the precision, dependent on the FP, and recall, dependent on the FN, into a single metric  (Verdhan, 2020) , and is recommended for imbalanced datasets when FN and FP are equally important  (Brownlee, 2020).
Explaining the model's predictions Explainable AI (xAI) is a set of methods that provide explanations on the variables that drive the model's predictions  (Gianfagna & Di Cecco, 2021; Mishra, 2022; Molnar, 2021). We used the method called "permutation feature importance" to assess the contribution of the 33 features to the model's prediction across all instances (i. e. , the feature values from all particles), and the SHapley Additive exPlanations (SHAP; Lundberg and Lee, 2017) method to estimate the contribution of the features for each particle and, by aggregation, their global importance  (Molnar, 2021). In the permutation feature importance, the values of each feature from the dataset are shuffled to measure the increase in prediction error. We used Scikit-learn's function permutation on the test set from which we obtained a ranking of the features' contribution between two end-members: "important" features, which cause an increase in prediction error when shuffled, and "unimportant" features, where the error remains unchanged or decreases  (Molnar, 2021).
, preprint) , it is difficult to decide whether these are False Negatives, or instead, petrographic classification errors (Figure  15D ), especially when ML-based image classifiers have surpassed human performances in other fields  (He et al. , 2015). (3) Ash particles from lava fountaining are generally accurately classified (macro F1score of 0. 94), between juvenile (F1-score of 0. 94) and lithic (F1-score of 0. 88) types. Most of the lithic particles belong to recycled juvenile particles, which are critical to avoid overestimating the amount of juvenile component  (D'Oriano et al. , 2022)  and their identification typically requires examination in the SEM  (D'Oriano et al. , 2014). The high score suggests that the ViT can discriminate between them to some extent (Figure  15E ), but a more robust labelling by a team of experts and a larger dataset containing SEM images is necessary to obtain more robust conclusions.
Acknowledgments I am grateful to  Caroline Bouvet de Maisonneuve ,  John Pallister ,  Jacopo Taddeucci  and  Alison Rust  for insightful discussions, to  Do Xuan Long  for help on the use of the Hugging Face API for image classification, to  Sébastien Biass  for advice and help on the use of the SHAP method for this study, and to  Edwin Tan  for support on the Gekko cluster. This research was supported by the  Earth Observatory of Singapore  via its funding from the  National Research Foundation Singapore  and the  Singapore Ministry of Education  under the  Research Centres of Excellence initiative. Open Research Particle images and features can be downloaded through the publicly available VolcAshDB web database at https://volcash. wovodat. org/. Details on the feature measurement and image acquisition are described in Benet et al.
, preprint. The GitHub repository https://github. com/dbenet-max/volcashdb_classification contains two relevant codes: the Python code for hyperparameter optimization, development, and interpretation via xAI of the XGBoost, and the code for deployment via the API Hugging Face of the ViT. Acknowledgments I am grateful to  Caroline Bouvet de Maisonneuve ,  John Pallister ,  Jacopo Taddeucci  and  Alison Rust  for insightful discussions, to  Do Xuan Long  for help on the use of the Hugging Face API for image classification, to  Sébastien Biass  for advice and help on the use of the SHAP method for this study, and to  Edwin Tan  for support on the Gekko cluster. This research was supported by the  Earth Observatory of Singapore  via its funding from the  National Research Foundation Singapore  and the  Singapore Ministry of Education  under the  Research Centres of Excellence initiative.
Open Research Particle images and features can be downloaded through the publicly available VolcAshDB web database at https://volcash. wovodat. org/. Details on the feature measurement and image acquisition are described in Benet et al. , preprint. The GitHub repository https://github. com/dbenet-max/volcashdb_classification contains two relevant codes: the Python code for hyperparameter optimization, development, and interpretation via xAI of the XGBoost, and the code for deployment via the API Hugging Face of the ViT.
This work was supported in part by  National Natural Science Foundation of China  (Grant No. 62306086 ), in part by  Science, Technology and Innovation Commission of Shenzhen Municipality  (Grant No. GXWD20220818191018001 ), in part by  Guangdong Basic and Applied Basic Research Foundation  (Grant No. 2019A1515110177 ), and in part by a public grant as part of the  Investissement d'avenir project , reference  ANR-11-LABX-0056-LMH ,  LabEx LMH.
Such experimental insight provides an in-depth understanding of specific interactions within proteins in terms of biochemical and biophysical mechanisms. However, the experimental study of functional constraints at a family-wide level by comparing residue-level mutational epistasis across homologous sequences remains scarce. This is because only a small number of protein families have been studied  13  , including a few large-scale datasets acquired through DMS  [26] [27] [28] [29] [30] [31]. Furthermore, most analyses are restricted to higher-level, broader trends between homologs with limited examinations of detailed mechanistic bases for epistasis. In this study, we combine these two complementary approaches to improve our understanding of epistatic networks. We apply both approaches to the same system, the class B1 metallo-ꞵ-lactamases (MBL), which is a family of highly diversified antibiotic degrading enzymes with a long evolutionary history  32.
There appears to be trends of mutational behavior in the structure, but the exact intramolecular network appears much more complex upon further testing, as predicted by the DCA model. Overall, the combination of DCA and DMS allows us to greatly enhance our understanding of epistatic networks. Class B1 metallo-ꞵ-lactamases are a highly diversified family of enzymes (as low as 20% identity within the family) that degrade ꞵ-lactam antibiotics  1, 33, 34  , a function for which there is likely a long evolutionary history  32, 35. Given the high sequence divergence, this family is an excellent model for epistatic networks, as different homologs likely formed different sets of intramolecular interactions over the course of their evolution. To create a comprehensive dataset of all B1 sequences, we collected ~5000 sequences that belong to B1 MBLs, from the MBL domain superfamily (Interpro ID: IPR001279) along with metagenomic data from the Joint Genomics Institute Integrated Microbial Genomes & Microbiomes (JGI IMG/M) database (see methods).
A complete DMS dataset of all single amino acid mutants of VIM-2 was previously published by us  6, 39. To conduct a comparison, we performed DMS on NDM-1 in an identical manner to VIM-2 (Fig. 2a ). Briefly, all single amino acid mutants were generated for NDM-1 and placed under selection under three different antibiotics: ampicillin (AMP), cefotaxime (CTX), and meropenem (MEM). The plasmid DNA was isolated after selection and sent for deep sequencing. The fitness conferred by each mutant relative to wtNDM-1 was then characterized as the fitness score in Eq. (1):.
Heterogeneity in mutational behavior is supported by DMS data of NDM-1 and VIM-2 𝑓𝑓 𝑚𝑚𝑚𝑚𝑚𝑚 = 𝑙𝑙𝑙𝑙𝑙𝑙 2 ( 𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓 𝑣𝑣𝑣𝑣𝑓𝑓 𝑠𝑠𝑓𝑓𝑠𝑠 𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓 𝑣𝑣𝑣𝑣𝑓𝑓 𝑛𝑛𝑛𝑛𝑠𝑠𝑓𝑓𝑠𝑠 ) -𝑙𝑙𝑙𝑙𝑙𝑙 2 ( 𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓 𝑤𝑤𝑚𝑚 𝑠𝑠𝑓𝑓𝑠𝑠 𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓 𝑤𝑤𝑚𝑚 𝑛𝑛𝑛𝑛𝑠𝑠𝑓𝑓𝑠𝑠 ) (1) The experiments were conducted in duplicate on separate days, and replicates typically show good correlation (R 2 of 0. 77-0. 95 across conditions) (Fig. 2b , Supp. Fig. 1 ). As was the case for VIM-2, the NDM-1 data shows that fitness scores of -4 or lower are below the fitness of nonsense variants. As such, we also limit the fitness scores to a minimum of -4 for analyses, and scores below -4 are set to be -4 instead. To ensure the best possible direct comparison, we use the datasets between NDM-1 and VIM-2 with the most similar selection pressure in AMP, evidenced by the similarity in distribution of fitness effects (Supp.
3. Interestingly, this contrasts with the almost total absence of mutational heterogeneity observed between very exposed residues of VIM-2 and NDM-1, especially in the DMS data. A possible cause of this difference is the numerous intramolecular interactions occurring around buried residues, suggesting that a rich intramolecular network not only reduces the mutability of residues, but it also leads to homolog-specific differences of such constraints. However, it is the intermediately exposed region that exhibits the largest variability in behaviors for all datasets. This ASA range corresponds to positions that have more freedom to mutate than the fully buried positions while still being capable of forming interactions with other residues. The possibility of a mutation is therefore strongly dependent on the sequence context and is, therefore, homologspecific.
Sequence collection for the B1 MBL family In an effort to comprehensively isolate all B1 MBLs, we used a broad sweep approach through a sequence similarity network (SSN) using data from genomic (UniProt) and metagenomic (JGI) databases. Initially, an SSN was constructed using the EFI-EST tool using the InterPro ID IPR001279 (MBL domain superfamily) as the input. The network was analyzed at different alignment score cutoffs to find a cut-off where all clinically isolated B1 enzymes fell within the same isolated cluster (~1,800 sequences at EFI alignment score cut-off of 25). The sequences that were shorter than 220aa were removed, and the remainder (~1,300) were clustered by cd-hit to 60% identity to reduce redundancy (187 sequences final) then used to generate an HMM, i. e. , sequence profile, of the B1 family.
The HMM was used to search for B1 sequences in the JGI database resulting in ~2. 5Mil sequences from JGI, with 1,859,503 non-redundant sequences determined by cd-hit clustering at 100%. To construct an initial SSN, all sequences of the MBL domain superfamily were downloaded from UniProt using the InterPro IPR001279 definition (434,623 sequences, accessed 2019). The UniProt and JGI data were combined, sequences with ambiguous characters were removed, and the length was restricted to between 100-1500aa. Cd-hit was used to successively cluster the sequences to 100%, 90%, 70% and finally 50% identity to reduce redundancy, resulting in 86,947 representative sequences for the entire MBL superfamily. A SSN was generated by performing 'all by all' BLASTP with an e-value cutoff of 1e-5 and 1000 maximum hits per sequence, giving a raw network of ~90Mil pairwise bitscore values (edges) between all sequences (nodes).
The network was processed using an inhouse SSN analysis pipeline (MetaSSN: https://github. com/johnchen93/MetaSSN) to identify the lowest bitscore cutoff at which all clinically isolated B1 sequences break off into an isolated cluster (10,252 sequences). Finally, to identify sequences that are most likely to be active B1 sequences, the dataset was filtered by length to between 200-350aa (6,828) and used to build a multiple sequence alignment (MSA) using Clustal Omega on default settings. The MSA was manually curated to exclude any mis-aligned sequences, resulting in 6308 curated sequences. Finally, to ensure the sequences were likely to have B1-like function, only sequences aligned with the conserved B1 active-site metal binding residues (H116/H118/H196 and D120/C221/H263, BBL numbering) were kept, resulting in a final dataset of 5035 B1 family sequences, with a roughly 50/50 split between sequences from UniProt and JGI.
Direct coupling analysis of the B1 family To generate an alignment of conserved structure, crystal structures of 14 homologs were aligned using mTM-align (https://yanglab. nankai. edu. cn/mTM-align/)  51. The homologs (PDB IDs) are: NDM-1 (3spu), VIM-2 (5yd7), DIM-1 (4wd6), ECV-1 (6t5k), FIM-1 (6v3q), GIM-1 (2ynt), IMP-1(4uam), IND-7 (3l6n), MYO-1 (6t5l), TMB-1 (5mmd), VMB-1 (6jv4), bc-II (1bc2), blaB (1m2x) and cfiA (1znb). An HMM sequence profile was trained on this curated dataset by using HMMER via the hmmbuild command. We used a -symfrac value of 0. 3 to control the maximum number of gaps in each alignment column. The 5035 B1 sequences were then aligned to this profile via the hmmsearch command to produce the MSA for training the DCA model. After converting the alignment to FASTA format, all sequences exhibiting more than 10% gaps were removed from the alignment.
After deep sequencing, the forward and reverse reads are merged together using our previously published pipeline (https://github. com/johnchen93/DMS-FastQ-processing), and we discard reads with greater than 20 mismatches between forward and reverse reads or with a posterior Q score of less than 10. We use the wtNDM-1 samples as an estimate for error rates arising from the deep sequencing process, and we filter the non-selected libraries to remove variants with frequencies less than 2x of the expected frequency from sequencing noise alone, or variants with less than 5 reads. We then calculate the fitness scores for each variant in all conditions according to eq (1). All variants that pass filtering in the non-selected condition are considered to truly exist in the library, and if the same variants are not observed in conditions undergoing selection they are assumed to have been depleted by selection and are given a dummy count of 1 to simulate the lowest possible frequency.
Related work In the context of quantum-resistant communications based on QKD and PQC, numerous studies have emerged for finding resilient alternatives to classical authentication and key exchange mechanisms inside TLS. These studies are summarized in Table  1. Paquin et al. [38]  presented a hybrid classical-PQ solution for securing TLS connections within an emulated network scenario developed via the Linux kernel. The analysis presented determined that the TLS handshake completion time was mainly dominated by the cost of both classical and PQ PKC. It was also mentioned that unreliable networks are affected by maximum transmission unit (MTU) size, normally set at 1500 bytes, which is an issue to take into account when implementing PQ primitives with large message sizes. This translated into an increase in the number of network packets exchanged per handshake.
Acknowledgments This work was supported by the  EC   H2020 MSCA ITN-ETN   Io-Talentum  (grant no. 953442 ) and  ECSEL JU  project  BRAINE  (grant no. 876967 ) projects and the  Dutch Ministry of Economic Affairs and Climate Policy (EZK) , as part of the  Quantum Delta NL programme. C. Rubio Garcia  thanks  Dr. Sebastian Verschoor  from  Eindhoven University of Technology  for fruitful discussions regarding TLS and the work in this article.
The model employs Neural Factorization Machines (NFM) and an Autoencoder (AE) to model calling patterns and a memory module to adapt to changing customer behaviour. We evaluate our approach on a large dataset of real-world call detail records and compare it with several state-of-the-art methods. Our results show that our approach outperforms the baselines, with an AUC of 91. 06%, a TPR of 91. 89%, an FPR of 14. 76%, and an F1-score of 95. 45%. These results demonstrate the effectiveness of our approach in detecting fraud in real-time and suggest that it can be a valuable tool for preventing fraud in telecommunications networks.
Contributions The key contributions of our paper are summarized as follows: Real-time Telephony Fraud Detection: We design and develop a new fraud detection system based on the customers' calling patterns. Identifying Frauds Pattern: We leverage the power of the Neural Factorization Machine (NFM) and the Autoencoder (AE) to model customers' calling patterns. Memory Module: In order to detect fraudulent calls, we develop a memory module that tracks fraud patterns and updates it regularly using a First In First Out (FIFO) memory update policy. Effectiveness: As part of our validation of our proposed system, we performed extensive experiments on real CDR datasets (of 670,211 call records) provided by our industrial partners 1. In addition, we demonstrate that the proposed system is more effective than baseline approaches in combating telephony fraud.
Therefore, searching for fraud patterns in streaming datasets is challenging in order to mobilize expert knowledge effectively. Recently, there has been a surge in the number of researchers who have presented sequence-based approaches to the problem of fraud detection. When a sequence, sub-sequence, or sequential pattern deviates significantly from normality, it is classified as an abnormal  [10]. In Ref. [11] , the authors proposed a highly intuitive method for computing pairwise similarities between sequences. The study  [12]  focused on window-based mining and detecting partial anomalies in entire sequences. In some works, such as  [13, 14] , Recurrent Neural Networks (RNN) was used to detect fraud based on sequences. In Ref. [15] , These articles focus on sequential information rather than effectively using the intrinsic information associated with each calling behavior.
Methodology This section discusses the methodology proposed for real-time fraud detection in the telecommunications industry. The focus of this section is to address the challenge of real-time fraud detection in the telecom industry. We propose a new method that combines Neural Factorization Machines (NFM) and Autoencoders (AE) to detect fraudulent calls that deviate from normal calling patterns in real time. The following sections detail our Neural Factorization Autoencoder (NFA) based detection model, the algorithms utilized, and the functional architecture proposed. Traditional machine learning models tend to suffer from overfitting due to the multidimensional nature of CDR data and many missing feature values. Although manual feature engineering has been used to enhance the performance of machine learning models, this approach becomes impractical for interactions between higher-order features.
In high-dimension and sparse settings, NFM and autoencoders are used to find an effective latent representation of the data that is regularized by adversarial learning. Fig. 3  illustrates the flow diagram of NFA that uses NFM, AE, and a memory module that is initially trained on a subset of a normal dataset. The memory modules store the patterns associated with normal phone calls. A feature extractor is used to highlight each call event's structure. The current call is analyzed and scored by computing the fraudulent score based on the similarity between its encoding and that stored in memory. Depending on this score, the calling event is used to update the memory if it is deemed normal; otherwise, it is discarded. Memory is required to continuously maintain the update process in order to keep track of the changing patterns of the current data distribution.
Modelling calling patterns Fraud detection uses two types of patterns: profile-based and anomaly detection  [30] [31] [32] [33]. Profile-based methods have a database of aberrant attacks, while anomaly detection uses each call's pattern as the baseline to detect fraud by comparing new traffic against individual patterns. If an activity deviates significantly from normal, it may indicate compromise. This article focuses on the profile-based method for fraud detection. Fig. 4  demonstrates NFM and AE learning the current event's calling pattern (e t ) represented as Zðe 0 t Þ. NFM captures higher-order feature interactions by feeding dense embeddings into FM. An autoencoder (encoder and decoder, as in Ref. [29] ) is used to learn feature representations, with a pattern being the intermediate representation of input data encoded by the encoder.
We employed the look-up embedding method to handle the dense representation of our dataset. This approach has proven effective in modelling user behavior and making predictions, as shown in previous studies like  [16, 26, 34]. In practice, there are two types of fields: categorical fields with a limited number of distinct values (e. g. Network Service Provider or Calling Country) and numerical fields with continuous data. These two types of fields require different look-up embedding techniques. The embedding matrix for the i th field can be expressed as follows: ψ i 2 R dÂl ; if i th field is a categorical field R d ; if i th field is a numerical field (1) Here d is the embedding vector dimension, and l is how many distinct values are in a categorical field {1, 2, …, m}. Next, we compute the embedding vector for the i th feature ðf t i Þ as ϑ t i ¼ ( ψ i ½f t i ; if i th field is a categorical field f t i Â ψ i ; if i th field is a numerical field (2) where ψ i ½f t i is the f t i row of ψ i.
Algorithm The NFM and AE utilize a limited set of CDR datasets to recognize typical call patterns, and the memory component is initialized. When a new call event (e t ) takes place, the NFM and AE model the call pattern and produce a compressed representation (line 3). Using this compact output, z(e t ), they compute the l 2 distance (line 4). Then, they calculate the final score (line 5). This score is compared to the user-defined threshold (β) (line 6). If the score is below the threshold, the memory is updated with a new pattern based on the FIFO update policy. If the fraudulent score surpasses the threshold, the alarm is triggered, and the call is blocked (lines 9 and 10). Finally, the score for the current call e t is returned (line 11). Based on the patterns of the fraudulent calls, they are classified into various categories, such as Smishing, Wangiri, MTRA, etc.
Dataset description For our experimental study, we focus on Wangiri fraud. This type of fraud, also known as "one ring and cut" or "ping-calls" or "call-back scams," involves fraudsters calling thousands of random numbers and disconnecting after a few rings in hopes that the recipients will call back. If they do, they are then exposed to a pre-recorded message and charged an exorbitant fee. Detecting Wangiri fraud is challenging due to the lack of a chargeable duration and visibility, as the terminating carrier cannot determine the origin country's risk. To collect a real-world Call Detail Record (CDR) dataset, we utilized a telecom honeypot from our French industrial partners. The dataset includes millions of CDRs, each with information such as the caller and recipient's phone numbers, country of origin, target country, start time, etc.