dataesr/scientific-paragraphs-categorization huggingface dataset:
To guarantee this, the Euro-Argo infrastructure needs to allocate sufficient resources for the storage of data, execution of service requests, and bandwidth for down and uploads. Analyzing access patterns of different data services will help Euro-Argo understand more about how its data products and services are used, particularly in the context of open access to scientific data. Like an ordinary data center, among access pattern analysis and optimization for quality of service, one critical and unavoidable factor is anomaly detection, which is, in data infrastructure context, to detect service failure, SLA unsatisfaction, traffic outliers, and network intrusion. The Euro-Argo The Computer Journal, Vol. ??, No. ??, ???? P. Chen et al. Integrated Data Service Log data, as can be seen from Figure  3 , is collected for one month's continuous data services running 24 hours per day.
Various anomalies occurred with different intents and divergent lasting durations from a few minutes to some hours. There are 49 variables from different data services measured for one month. There are 28311 samples collected by sampling every 10 minutes from the 4094157 raw log data. In the anomaly detection process, we subdivide the original long multiple sequences into smaller time series by taking a sliding window across raw streams. To enable effective adaptation, Euro-Argo requires that the point anomaly detection accuracy is no less than 90 percent. Meanwhile, another crucial requirement of Euro-Argo is real-time performance since the logs are generated continuously per each request. The time duration for analyzing the integrated service logs should be less than 10 minutes to empower Euro-Argo data infrastructures to adapt to real-time operation and dynamic provisioning.
This criterion is a trade-off between dynamic adaptation and resource utilization, e. g. , shorter duration may lead to better and faster adaptation but a lower resource utilization or even resource unpreparedness. Open Anomaly Detection Datasets: as for the open anomaly detection datasets, we choose two benchmark datasets: "vertebral" and "optdigits" to evaluate the performance for small sample sizes. These two benchmarks often appear in the anomaly or outlier detection literature with lower performance due to smaller sample sizes and ranged anomaly rates, e. g. , there are only 240 and 5216 samples for "vertebral" and "optdigits", respectively. Generally, the F1 Scores for most tested anomaly detection methods in existing experiments are lower than 0. 5. Synthetic Datasets: We adopt four synthetic datasets for further performance comparison.
Specifically, we use the PyOD 4 toolset to generate four random synthetic datasets with different volume, dimension, and anomaly rate combinations: 1) Data volume: training sample size considered from 3000 to 30000 with test sample size from 1000 to 3000 are used to evaluate the computational complexity of anomaly detectors on a group of datasets with different scale; 2) Data dimension: is designed to explore their influence on the performance of different algorithms. Here, from 50 to 200, which are most typical for highdimensional data, is synthesized for evaluation; 3) Anomaly Distribution and Percentage: the distribution and percentage of anomalies are used to assess the sensitivity for different anomaly class and ratios. Note that, for all synthetic datsets, normal data comply with multivariate Gaussian distribution, and anomalous data is generated by a Uniform distribution with an Anomaly Percentage, i.
Evaluation Measures Considering anomaly detection as a one-class classification, we follow generic measurements based on the confusion matrix. Generally, given anomalies are the rare part of the entire datasets, Precision, Recall, and F 1 score are adopted to measure the overall detection accuracy, which is more suitable for these highly skewed datasets. The definition of P recision, Recall, and F 1 scores are as follows: P recision = T P T P + F P ( 14 ) 4 https://pyod. readthedocs. io/en/latest/pyod. html The Computer Journal, Vol. ??, No. ??, ???? Effectively Detecting Operational Anomalies in Large-scale IoT Data Infrastructures by using a GAN-base Recall = T P T P + F N (15) F 1 = 2 × P recision × Recall P recision + Recall ( 16 ) Where T P is True Positives, F P is False Positives, T N is True Negatives and F N is False Negatives.
State-of-the-art Anomaly Detection Methods We evaluate the anomaly detection performance of HTA-GAN on the datasets mentioned earlier. We compare the anomaly detection performance with classic and popular K-Nearest Neighbour (KNN)  [11] , Angle-Based Outlier Detection (ABOD)  [14] , Isolation Forest (IF)  [15] , and Auto-Encoder (AE)  [18]  that are popular unsupervised anomaly detection methods on the datasets. As to the GAN-based method, we compare HTA-GAN with the Efficient GAN (EGAN)  [35]  method and Generative Adversarial Active Learning (GAAL) method  [5]. EGAN is a typical GAN-based generative approach, while GAAL is also based on anomaly generation and oneclass classification. Note that GAAL is actually including two variants: Single-Object Generative Adversarial Active Learning (SO GAAL) and Multi-Object Generative Adversarial Active Learning (MO GAAL).
Experimental Environments We perform all the experiments on a single server with Intel Xeon Processor 4 Core Skylake CPU and an Nvidia T4 GPU. The software environments include Anaconda 3, Python 3. 8, Cuda 11. 1, and necessary running libraries such as TensorFlow-GPU 2. 2. 0, Cudnn 7. 6. 5, PyOD 0. 8. 6, and Sktime 0. 5. 1. Implementation of our methods is based on Keras 5  , and all compared anomaly detection methods are implemented on a common anomaly detection framework PyOD. To better compare with those competing methods, we search their optimal parameters in a range of values. For KNN and ABOD, since their performance will be dramatically affected by the size of the neighborhood set and the number of base estimators, we tune it in the range of  2, 4, 8, 10, 20, 40, 80, 100, 200, 400.
(iv) The training epochs are all limited to 30. (v) Use the SGD optimizer with the learning rate of 0. 0001 for the generator G and the encoder E and 0. 01 for the discriminator D. (vi) a minibatch size m = min (500, n) for training, and (vii) stop training generator when the downward trend of its loss tends to be slow. To mitigate experiments' randomness, we run each method for each dataset ten times with different randomized seeds, and we record the average results of the ten runnings in the result tables.
Experimental Results In this section, we present results intending to demonstrate the global performance of HTA-GAN on both real-world and synthetic datasets and give some insights into the performance comparison among different anomaly detection methods. Figure  4  demonstrates the overall F1 Scores for the compared eight approaches on the seven datasets. Table  2  , Table  3 , and Table  4  show the detailed results for Euro-Argo, Anomaly benchmarks, and synthetic datasets, respectively. To make the result clear, we show the best performance among the 4 popular unsupervised methods, including K-Nearest Neighbour (KNN), Angle-Based Outlier Detection (ABOD), Isolation Forest (IF), and Auto-Encoder (AE) with underlines. The best result among the 4 GANbased methods, including EGAN, SO GAAL, MO GAAL, and the proposed HTA-GAN with double underlines, and the overall best performance in bold for all the compared 8 methods.
Results of Euro-Argo Datasets From Table  2 , we observe the following details: For the Euro-Argo dataset, we focused on the results chosen by best F1 since F1 could demonstrate a balance between precision and recall. Generally, with large-scale samples for feature learning, deep-learningbased models like AE or GAN perform better. AE The Computer Journal, Vol. ??, No. ??, ????  demonstrates the best performance among the four classic methods (KNN, ABOD, IF, and AE). We can see, the proposed HTA-GAN outperformed AE by 30. 12% and achieves almost 100% precision and recall. HTA-GAN detects anomalies with 36. 53% and 18. 65% more for precision and recall than AE, respectively, at the cost of longer time duration for training. As to the GAN-based method sets (SO GAAL, MO GAAL, and EGAN), according to previously mentioned reasonably similar hyper parameters' settings, although EGAN performs well both in recall and F1, HTA-GAN still obviously leads to an all-around better performance from all metrics except for time cost during training.
Results of Anomaly Benchmarks We compare performance between different methods for two popular anomaly benchmarks, and we put the result for both datasets together, as shown in Table  3. We can see, here following observations would be found: Compared methods, either the four popular or the four GAN-based ones, behave differently for the two benchmarks. Generally speaking, less samples will lead to lower performance because a larger sample size could be crucial for adequate feature learning. Specifically, for "vertebral", it is evident that HTA-GAN outperforms the others for all the metrics except for the time duration, which is always an inherent disadvantage for deep learning methods. The 0. 5519 F1 score is the only one more than 0. 5. However, there are not many differences among all these methods for "optdigits" because the variation is not quite noticeable, e.
g. , the F1 score ranges from 0. 4682 to 0. 5032 while Precision from 0. 486 to 0. 5105. The Computer Journal, Vol. ??, No. ??, ???? Effectively Detecting Operational Anomalies in Large-scale IoT Data Infrastructures by using a GAN-base  The "vertebral" benchmark is interesting because its sample number and its dimension are relatively small, which requires anomaly detection to be data-efficient. From the results, it can be learned that the five deeplearned-based unsupervised methods work pretty well as they beat classic models for this kind of data-efficient task. Among these methods, GAN-based ones behave even better than AE. What is more, the proposed HTA-GAN demonstrates the best detecting results: 9. 07% Precision, 5. 55% Recall, and 6. 53% F1 score better than the second-winner MO GAAL and shorter time by 12.
Results of Synthetic Datasets From Table  4 , it is easy to know: For the synthetic datasets by PyOD toolsets, a total of four datasets are generated for testing, with different datavolumes, dimensions, and contamination rates. As the PyOD toolset generates the synthetic datasets with multivariate Gaussian distribution for normal data and Uniform distribution for anomalous data, most anomaly detection methods work well since the data distributions are more straightforward to be captured than real-world complicated anomalous scenarios. Classic models such as KNN and IF could effectively capture such data regularities. However, this is challenging for GAN-based methods due to the generator's capability of learning data regularities via a mini-max game. As shown in Table  4 , existing GAN-based models still struggle in most cases.
The proposed HTA-GAN could perform well due to its heterogeneous structure, exploiting both temporal feature representation and one-class anomaly recognition. As can be seen from Table  4 , among those GAN-based methods, only HTA-GAN works fine to get as competitive results as other non-GAN-based methods. HTA-GAN provides the best metrics for three out of four datasets, proving that the proposed HTA-GAN can achieve better robustness. However, compared with other non-GAN-based models, the training would still be a tradeoff because it takes much longer than IF and AE, although it looks acceptable compared to KNN and ABOD. More precisely, on the one hand, classic unsupervised methods including IF, ABOD, KNN, and AE perform well, with good metrics such as more than 0. 85 for F1 score and relatively shorter time, e.
g. , just a few seconds. On the other hand, GAN-based methods, except for the proposed HTA-GAN, struggle a bit and behave inconsistently, e. g. , EGAN could catch up with main-stream metrics as ABOD and HTA-GAN for the fourth datasets but ranks the last for the other datasets. Only the proposed HTA-GAN, thanks to its heterogeneous BiGAN structure to better capture temporal dependencies and identify anomalies via an end-to-end one-class classifier, outperformed other methods consistently, especially for those datasets with large sample sizes. As for high-dimensional datasets, HTA-GAN can still achieve decent performance. Overall, as shown in Figure  4  HTA-GAN outperformed the popular unsupervised detection methods for most situations. To be more specific, among all these 7 test datasets, the proposed HTA-GAN ranks no.
1 for five times, and for remain two datasets, it ranks no. 2 and no. 4. The main drawback is that it takes a much longer time to deal with longer subsequences. For the Euro-Argo project, it is acceptable. However, it will be an issue for time-critical real-time applications. It will be worthwhile to explore using other Neural Networks to incorporate the temporal correlation and consider the choice of subsequence length for future work.
Experiments for Robustness Analysis As alluded to above, the robustness of HTA-GAN is an interesting issue to explore further. First of all, followed by previous synthetic datasets, we conduct more experiments for different data volumes, dimensions, and contamination rates, which could help demonstrate the proposed HTA-GAN's robustness. In addition to the existing four generated datasets, we have three more datasets with different volumes, dimensions, and contamination rates. There are seven datasets from D1 to D7. We plot the seven datasets' F1 score on Figure  5 (a), and we can see that the proposed HTA-GAN performs consistently well. To be more specific, all the F1 scores are generally decent, more than 0. 83. For lower contamination rates such as D5 and D6, The Computer Journal, Vol. ??, No.
??, ???? the performance is even better, although volume and dimension vary significantly. Another observation is that the runtime increases reasonably according to the scale, decided by both volume and dimension. In a word, HTA-GAN is robust to various datasets with different data volume, dimension, and contamination rate combinations. Next, HTA-GAN is based on deep neural networks. As mentioned by many pieces of literature, several hyperparameters of neural networks are involved. As for the representation learning ability of neural networks, we adjust the number of layers from 2 to 5 and the number of hidden layer neurons from 64 to 128, respectively, to get seven models of HTA-GAN of H1-H7. We test Euro-Argo real-world datasets on the above seven models with varied hyperparameters and plot their F1 scores on Figure  5(b).
DISCUSSION As for high-quality data services running on data infrastructures such as Euro-Argo, the proposed HTA-GAN is able to detect real-world operational anomalies accurately and timely, which would be practical to improve the quality of scientific data services. The main features of HTA-GAN are: i) a novel anomaly reconstruction error embedded into an end-to-end oneclass classification model. ii) adopting BiGAN structure for computational efficiency, and iii) heterogeneous generator and discriminator for different goals of feature learning. Moreover, here some interesting discussions would be involved further. As shown in Figure  6 , here is the complete service optimization pipeline for large-scale IoT data infrastructures. Anomaly detection is a fundamental module of the whole service optimization pipeline for data infrastructures.
A research data infrastructure is often constructed as an aggregation of many small-scale or regional data infrastructures, as we see in EPOS and many other examples in ENVRI. Detecting real-time anomalies at different parts of the infrastructure will thus be an important operation challenge. How to tackle anomaly detection in edgecloud continuum would be a fundamental problem here. Are we able to seamlessly migrate and embed HTA-GAN into edge systems as an anomaly detection service? Either communication cost or unavailability of complete system logs would cause massive troubles. The results achieved in this paper provide a good starting point to be applied in decentralized learning, e. g. , Decentralized Learning of Generative Adversarial Networks  [51]  to learn multiple data collections.
CONCLUSIONS This paper proposes a novel anomaly detection approach HTA-GAN to detecting real-world operational anomalies for high-quality data services running on The Computer Journal, Vol. ??, No. ??, ???? P. Chen et al. large-scale scientific research data infrastructures such as Euro-Argo, which can make better use of GAN's capability as an informative fake anomaly generator and end-to-end one-class discriminator. Moreover, we exploit the heterogeneous structure of GAN to improve multivariate time series representation learning and an efficient BiGAN-based anomaly scoring function leveraging not only binary crossentropy of discriminator but anomaly's reconstruction error. HTA-GAN achieves the best metrics and ranking on the real-world Euro-Argo datasets, anomaly benchmarks, and synthetic datasets compared to several state-of-the-art point anomaly detection methods for multivariate time series.
HTA-GAN demonstrates strong robustness to varying hyperparameters of neural networks and datasets with ranged data volume, dimension, and contamination rate based on further experiments. Although neural network training is involved, HTA-GAN can satisfy most realworld applications like Euro-Argo. Generally, HTA-GAN would be suitable for anomaly detection scenarios with complex anomaly distribution, large sample size, limited time cost, and high accuracy requirements. Typical applications would include operation & maintenance (O&M) in cloud platforms, data infrastructures, edge-cloud continuum, especially helpful when integrated into AIOps toolsets. For future work, as shown in Figure  6  for the Euro-Argo project, we plan to conduct further research on i) Introduce multiclassification into discriminator so that it could be capable of anomaly diagnosis and explanation, which would be of key importance for operating such data infrastructure; ii) Complex anomalies, such as contextual or collective anomalies, detection for Euro-Argo Data Service operations.
ACKNOWLEDGMENT The authors wish to thank  Erwan Bodere ,  Misha Mesarcik ,  Yu Wang ,  Tingyan Long ,  Lei Liu  and  Zhihao Gan  for their informative suggestions and help. This work has been partially supported by the  European Union 's  Horizon 2020 research and innovation programme  by the  ARTICONF  project grant agreement No. 825134 , by the  ENVRI-FAIR  project grant agreement No. 824068 , by the  BLUECLOUD  project grant agreement No. 862409 , by the  LifeWatch ERIC , by  China Scholarship Council, Science and Technology Program of Sichuan Province  under Grant No. 2020JDRC0067  and No. 2020YFG0326 , and  Talent Program of Xihua University  under Grant No. Z202047.
A. Data Sets We construct the corpus of earnings call transcripts using the Capital IQ Transcripts database, which is available through the Wharton Research Data Services (WRDS) platform. Various numerical variables are constructed based on the CRSP, Compustat, and IBES data sets available through the WRDS platform. 3 he details about data set construction, merging, abnormal returns calculation, and returns timing are in Appendix A of the Supplementary Material. The data set used to construct SUE. txt contains 108,704 observations between 2008Q1 and 2019Q4. The final data set after the construction of surprises contains 85,160 observations between 2010Q1 and 2019Q4. There are 4,701 unique firms in the data set.
14 While using analysts in such a way is unfeasible at scale, one can use a statistical model and some external measure of information relevance to infer the impact of new text content, expressed in a 13 To validate that our model separates unexpected textual information that drives market response from expected information that generates no market reaction, we performed a human annotation study. We produce a data set of 100 paragraphs, 50 from the top decile of unexpected paragraphs identified by our model and 50 from the bottom decile (most expected paragraphs have SUE. txt close to 0, and most surprising paragraphs have large positive or negative SUE. txt). A human annotator (a Research Assistant (RA) who did not participate in the project in any other capacity) was asked whether each of the paragraphs was "likely to contain unexpected good or bad news about the firm that is likely to cause a large market reaction" (note that this means we are asking the RA to annotate "unexpectedness" based on their judgment generally and not the full set of available information released before the earnings call specifically).
GWL time series were extracted from a database consisting of relatively undisturbed GWL time series regarding anthropogenic influence (water abstraction by either continuous or periodic pumping) over Metropolitan France. Based on this dataset, our approach consisted of exploring the effect of GWL low-frequency components on threshold exceedance and non-exceedance by successively filtering out low-frequency components of GWL signals using maximum overlap discrete wavelet transform (MODWT). Multi-annual (~7-yr) and decadal (~17-yr) variabilities were found to be the predominant LFVs in GWL signals, in accordance with previous studies in the northern France area. Filtering out these components (either independently or jointly) to (i) examine the proportion of high level (HL) and low level (LL) occurrences generated.
First, we propose estimating the proportion of high (HL) or low levels (LL) associated to the multi-annual or decadal variabilities, and then by the combination of both (Section 5). We also propose determining through a long groundwater time series (106 years of data available) if proportions of HL and LL associated to LFVs are consistent with those obtained in the short term. Second, we propose determining on four well-known historical events the contribution of multi-annual and decadal variabilities in the amplitude of threshold exceedance (ATE) and identify what parameters may control this contribution (Section 6). https://doi. org/10. 5194/hess-2022-10 Preprint. Discussion started: 10 January 2022 c Author(s) 2022. CC BY 4. 0 License.
Effective precipitation and groundwater data For this study, we used 78 boreholes in the Paris Basin (northern France), with GWL time series being little or not affected by pumping (Fig. 1 ). The study area was restricted to the Paris Basin in order to carry out the analysis over a relatively long period. Boreholes were selected from a BRGM database on boreholes not influenced by human activities  (Baulon et al. , 2020)  that was constituted in three steps: (i) a selection of boreholes with time series satisfying criteria of duration, minimum amount of data per month, maximum length of gaps; (ii) the crossing of pre-selected boreholes with other BRGM databases on known anthropogenic influences; (iii) numerous visualisations of time series with the hydrogeologists responsible for piezometric networks, in order to retain only non-influenced boreholes.
• The length of consecutive gaps must be <3 yr for time series starting after 1950 and <10 yr for time series starting before 1950. This allows time series in the new database to preserve LFV in the data. Several gaps in the time series can be allowed if these criteria are respected, and if the number of gaps and their lengths are small. Before data analysis, a visual check of the GWL time series served to remove or correct erroneous data. The analysis of the influence of LFV on HL and LL was conducted over the 1976-2019 period providing the best compromise between the spatial distribution of boreholes and time series length. In this work, all time series had a monthly time step to consider all possible GWL variation types from most reactive to most inertial ones. Then, monthly missing values were filled with linear interpolation to perform spectral analyses.