dataesr/scientific-paragraphs-categorization huggingface dataset:
org/),and by the  U. S. DOE Office of High Energy Physics  under grant number  DE-SC0012567. A. C. and C. A. M. acknowledge financial support by the  Swiss National Science Foundation , Project No. PP00P2_176884. M. H. is supported by the  Swiss National Science Foundation , Project No. PCEFP2_181117. MB is supported by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) under grant  396021762 -TRR 257. B. C. is supported by the  Italian Ministry of Research (MIUR)  under the Grant No. PRIN 20172LNEEZ. A. P. is supported by the  Spanish Government and ERDF  funds from the  EU Commission  [grant FPA2017-84445-P] and by the  Generalitat Valenciana  [grant  Prometeo/2017/053 ]. BM and XR are grateful for support from the  South African Department of Science and Innovation through the SA-CERN  programme and the  National Research Foundation  for various forms of support.
MK was supported by  MIUR (Italy)  under a contract  PRIN 2015P5SBHT  and by  INFN Sezione di Roma La Sapienza  and partially supported by the  ERC-2010  DaMESyFla Grant Agreement Number:  267985. Contribution by MB is based upon work supported by the  National Science Foundation  under Grant No. PHY-1913923. DM acknowledges support by  MIUR  grant  PRIN 2017L5W2PT  and the  INFN  grant SESAMO. The work of BD is supported in part by the  U. S. Department of Energy  under Grant No. DE-SC0017987. GB acknowledges the support of the  National Research Foundation of South Africa via Thuthuka  grant no. 117969.
TeV runs. Whereas LHCb has 4 datasets of very limited events to be used with some published analysis software. Several data releases for research-level data are available. The latest release in this series was in December 2020 and contains data from the CMS experiment including first 2010-11 heavy-ion data samples • Accessibility: A graphical user interface for manually downloading the data and an automated CERN open- data client is available. This command line client sup- ports downloading the dataset and metadata via HTTP and XRootD access protocols. • Interoperability: CERN open data portal offers several data formats and vocabularies as a community standard. In addition, data is also offered under common classifica- tion rather than formal vocabularies. The latter assists in physics interpretation, while the former is applicable in designing appropriate data processing chains.
In addition, the data variables are equipped with detailed semantics description. This helps identifying the variables which are of utmost importance for analysis design. For some examples of analysis development based on the principle of interoperability see Refs. [758,759]. • Reusability: A detailed record of data provenance i. e. how the data was generated is available in JSON for- mat. Instructions for processing of both RAW and recon- structed datasets (e. g. AOD format used by CMS) are provided and lastly, computing environment is preserved in the form of docker and singularity images. 4. 2. 2 Available data from the LHC collaborations The CERN Open data portal currently catalogues data from six experiments -ATLAS, ALICE, CMS, LHCb, OPERA and PHENIX. Focusing on the LHC experiments, of the four, CMS has the largest repository of research-level data available (currently at over 1.
In the DiffServ architecture edge nodes are responsible for packets classification and DSCP marking, so that core nodes can perform QoS procedures only looking at DSCP. Our work is meant to dynamically classify packets and up-date QoS classes, defining sub-classes within original DiffServ macro classes, while the legacy approach is to let the classification stay static  [6]. Our idea is to provide an automatic classification procedure able to finalize a 3-step operation: i) to dynamically identify DiffServ macro classes on the basis of the incoming traffic; ii) to increase the granularity of DiffServ classification, detecting QoS subclasses; iii) to exploit the data driven classification to improve network resources utilization. An interesting outcome of our investigation is that a limited number of header fields (the Protocol, Source Port number and Destination Port values for the dataset we analyzed), are sufficient to identify DiffServ macro classes / QoS sub-classes; in this way, the association of an incoming packet to a specific Diff-Serv macro class / QoS sub-class can be executed defining classical field-matching rules in edge nodes, allowing for a line-rate classification of incoming packets.
g. , the dominant one in terms of volume) to be exploited by network providers when performing resources management. In our case, taking into account the features of the dataset used for the numerical results, we provide a novel classification of Best Effort traffic, i. e. traffic with no QoS guarantees. • Resource management phase. We demonstrate that the fine grained classification of traffic obtained with Intra-L-DiffServ can be used to improve network performance. More in detail we focus on Best Effort traffic, providing a solution able to exploit Active Queue Management (AQM) strategy in network nodes to reduce the number of flows affected by losses. In this way, the network operator has an autonomous solution for concentrating packet losses, in cases of network overload, to a subset of low priority traffic, i.
Semi-supervised methods are also making surface in the literature on traffic management; while supervised learning methods are effective when fine granularity differentiation is already defined, and unsupervised methods detect unknown classes similarities, a semi-supervised approach is a combination of them offering the possibility of pursuing both objectives as in  [8] [9] [10] [11] [12] [13] [14]. Authors in  [8]  propose the RTC (Robust Traffic Classification) scheme, building on the first semisupervised framework developed in  [9, 10] , with the capability of identifying traffic of zero-day applications as well as accurately discriminating predefined application classes by exploiting the prior knowledge about the label of the traffic and detect the unknown traffic through the K-means. As in our work, this clustering technique is used to find the optimal number of centroids (classes) according to the training dataset, and the label assigned to a cluster is the one having the greater number of elements in it.
Preprocessing and oversampling The Inter-L-DiffServ training procedure is shown in Figure  2. It takes as input a traffic trace with labelled data, i. e. data with DiffServ codepoints available. The traffic trace, also referred to as training dataset, is a packet level trace, i. e. a file captured on wire by a packet sniffer. The first step of the Preprocessing phase is the characterization of input data in terms of features to be considered for the classification procedure. For each received packet, our approach performs a multi-layer features evaluation: all the IP and Layer 4 header fields are extracted and evaluated. Investigating the data-set used (more details in Section 6. 1), and exploiting the availability of the Diff-Serv codepoints, we are able to detect the correlation between each header field and the DiffServ classification.
We applied the One-Hot Encoding to the three categorical variables which are transformed into about 500 binary variables (the exact number of final variables depends on the training dataset); the huge number of binary variables shows the great heterogeneity of the traffic analyzed. The successive step is the evaluation of the traffic distribution among DiffServ macro classes for the training dataset. This is a classical step in any classification algorithm since it is very frequent to have an unbalanced dataset, that could lead to "cover" information related to classes with a limited amount of data. In our cases, as better explained in the Performance Evaluation section, the Best Effort class represents more than the 90% of the dataset. To cover with traffic unbalance within DiffServ macro classes, it is needed to adopt an Oversampling technique, i.
e. to increase the size of underrepresented classes. Two different approaches can be used to perform oversampling: i) Random Oversampling and ii) SMOTE  [18]. In the former case, the underrepresented class size is increased by adding new samples obtained by randomly selecting values already present in the dataset and belonging to the same class. In the latter case, used in  [16] , new samples are obtained by interpolating data values belonging to the same class. Considering the packet/session level traffic data to be processed, we decided to use Random Oversapling technique: i) Random Oversampling maintains the diversity of the training data, i. e. no novel values are added to the dataset; ii) SMOTE could lead to unfeasible data values, i. e. data having fields not feasible in a traffic trace.
Dimensionality reduction The outcome of the Oversampling phase is a new dataset, where new data is added to the original ones and variables are transformed. As a consequence, the size of the new dataset is higher with respect to the original one and this could be a prob- lem in terms of complexity. This aspect is known in the Machine Learning field as the "Curse of dimensionality". The classical approach to overcome this limitation is to extract the most informative variables reducing the space dimensionality, applying a so called dimensionality reduction technique. Exploiting a labelled dataset, it is possible to define a new space where the variance between different classes is maximized: to do that we used the known Linear Discriminant Analysis (LDA) classifier, able to perform space dimensionality reduction.
The main advantage with respect to unsupervised approaches, i. e. when no labelled data are available, is that LDA maximizes the variance between different Diff-Serv macro classes and not within them. In our solution we fix to three the new space dimension, since in this way more than the 90% of the total variance is obtained. Each new axis, referred to as Linear Discriminant (LD), leads to a new variable, obtained as a combination of the original ones. To have an idea about the LDA procedure, we report in Table  1  the composition of the three LDs after the execution of LDA on a reference training dataset. The final step of the dimensionality reduction phase is to represent the training dataset in the new space defined by LDA.
Intra-Class L-DiffServ The Inter-L-DiffServ algorithm is based on the availability of a labelled training dataset and allows to replicate the same DiffServ classification procedure statically realized today by service provider. The practical implementation of the proposed solution has two main requirements: i) the availability of a labelled dataset, and ii) the importance of delivering the correct traffic classification. This last aspect is the most problematic: if a high priority packet is not correctly detected as such by Inter-L-DiffServ, the SLA contracted with the customers is not respected, with a negative consequence on the ISP reputation. Moreover, the misclassification cannot be considered as a rare event, since the detection of high priority packets can be based on rules not related to headers fields; in such cases, Inter-L-DiffServ is not able to detect high priority packets.
In this work, as better explained in Section VII, we focus on BE traffic since the dataset we used is mainly composed of BE traffic. Intra-L-DiffServ is composed of the same steps of Inter-L-DiffServ, with two significant differences. The first one regards the training dataset: it is com-posed only of low priority packets. The second one regards the Dimensionality Reduction phase; considering that there is no difference among packets in terms of DSCP, it is completely unsupervised: the algorithm used is the Principal Component Analysis (PCA) one. The new variables computed by PCA, referred to as Principal Components, are linear combinations of the initial variables; PC computation is based on the Covariance matrix, composed of the correlations between all the possible variables pairs. The new space is obtained from the eigenvalues and eigenvectors of the Covariance matrix, ordering the eigenvectors in a decreasing order with respect to the eigenvalues.
Numerical results We numerically evaluate our proposal as follows. After describing the real dataset we used, we eval-uate the performance of Inter-L-DiffServ, showing the capability of our solution in detecting the proper QoS class when new traffic flows are managed by the network. Finally, we evaluate Intra-L-DiffServ showing how our unsupervised solution is able to define new QoS sub-classes to improve network performance. Our code is available at  [7].
Dataset description The dataset, referred to as MAWI archive  [21] , is an ongoing collection of daily Internet traffic traces captured within the WIDE backbone network (identified by AS 2500). WIDE interconnects universities and research institutes in Japan to the Internet, and peers with major Autonomous Systems. The analysis provided in  [22] , showed that traffic crossing WIDE includes both academic and commercial traffic. Each IP trace is a pcap file containing 15 minutes of traffic and its size is 20 GB, corresponding to about 200 millions packets. Traffic traces are anonymized to hide any personal information, removing application data and scrambling IP addresses with the Crypto-PAn Algorithm  [23]. In our analysis, we consider traces of multiple days across multiple weeks. The traffic traces are captured at samplepoint-G, that monitors a 10 Gbps link connecting WIDE network with a peer AS.
We work on the period from Apr. 10, 2019 to May 10, 2019; for that period MAWI provides file traces for a single day of the week. In the traffic analysis, we handle only IPv4 packets. For each trace we consider as training dataset the inial part of the trace, corresponding to 10 Million Packets 2  ; then, the classification is performed on the last 200,000 packets of the daily-trace. Observing the DSCP of packets in our dataset, we decided to modify the recommended DiffServ macro classes  [6]  in the following way: • adding a new DiffServ macro class, i. e. the Scavanger one, used to mark a not negligible number of packets in our trace; Scavanger class is used by network provider to identify services with a lower priority than BE class (in some enterprises, applications such as music download are marked with Scavanger label); • renaming the CS macro class as Network and Internetwork Control (NIC) class, since no other CS classes are present in our dataset.
Inter-L-DiffServ results We compare the Inter-L-DiffServ classification outcome to the DiffServ classification one. The Diff-Serv classification is available in the dataset since each packet is marked with its DSCP. As already described before, the training dataset is composed of an initial part of the trace, while the last 200 thousand packets represent the testing dataset. To provide a meaningful evaluation, we only focus on new sessions in the training part, i. e. packets belonging to sessions not already present in the training dataset: in this way, the capability of Inter-L-DiffServ to apply learned rules to new traffic sessions is really investigated. In Figure  3  we report the confusion matrices for four different days, focusing on DiffServ macro classes (we group our QoS sub-classes in the related DiffServ macro class).
The confusion matrix associates to each DiffServ macro class (True label in the figure) the true positive rate, i. e. the percentage of packets assigned to a specific QoS class by our algorithm (Predicted label). A first important observable outcome is that Inter-L-DiffServ is able to properly detect BE traffic: the true positive rate is always higher than 80%; moreover, this value increases to 94% if we also considered the Scavanger class, i. e. traffic with lower priority than BE. Another aspect regards the low performance related to high priority traffic. Looking at AF and EF QoS classes, our solution has high true positive rate in some days (Figure  3 (a)), while it has low ones in different days (AF for Figure  3(c) ). To better understand the low accuracy in classifying high priority traffic, we performed a detailed characterization of AF and EF traffic in our dataset.
The difference in EF accuracy between Abacus and Inter-L-DiffServ is due to two main reasons. Firstly, performing a deeper investigation on EF traffic, we found that it is characterized by flows generating a limited number of packets. Abacus is able to better classify EF traffic since it exploits the amount of packets/bytes generated by the flow as classification feature. A second aspect is a consequence of data preprocessing for Abacus: the different DiffServ classification of AF/EF packets of the same session, discussed previously. Considering that Abacus is a session-based classification, we removed from the input data all sessions having packets with more than a unique DSCP: in this way the Abacus performance reported in Figure  4  is not affected by the "unusual" multiple classification of a session performed by network operators, that degrades Inter-   L-DiffServ performance.
Conclusions In this work, we provide a Machine Learning based solution for the classification of incoming traffic in a service provider network supporting QoS differentiation. We defined the L-DiffServ framework composed of two engines: i) Inter-L-DiffServ, an algorithm able to learn an existing DiffServ classification and replicate it properly, and ii) Intra-L-DiffServ, providing a novel classification of a selected DiffServ macro class. In both cases, we show that the use of only three header fields (Protocol, Source Port, Destination port) as features is sufficient for the classification. We combine two preprocessing steps before feeding data into our Machine Learning solution: i) an oversampling strategy, to balance the training dataset, and ii) a dimensionality reduction algorithm, to reduce the problem complexity.
Once obtained the new dataset, we evaluate the clustering solution to detect QoS subclasses. An outcome of our work is that using our framework it is possible to provide a dynamic and thinner classification of best effort traffic following traffic changes. Moreover, we show how hard it is to classify high priority packets, mainly due to the limitation of header fields as algorithm features. We show that the method we propose to enhance the QoS classification can be exploited to improve network resource utilization. We defined two QoS aware active queue management solutions: the first one is based on WRED, the second one on CHOKe; in both cases, we show that it is possible to reduce the number of sessions affected by packet losses with respect to classical DiffServ classification. The definition of novel resource management strategies based on L-DiffServ represents the next step of our research activity.
The European Prevention of Alzheimer Dementia (EPAD) is a multi-center study that aims to characterize the preclinical and prodromal stages of Alzheimer's Disease. The EPAD imaging dataset includes core (3D T1w, 3D FLAIR) and advanced (ASL, diffusion MRI, and resting-state fMRI) MRI sequences. Here, we give an overview of the semi-automatic multimodal and multisite pipeline that we developed to curate, preprocess, quality control (QC), and compute image-derived phenotypes (IDPs) from the EPAD MRI dataset. This pipeline harmonizes DICOM data structure across sites and performs standardized MRI preprocessing steps. A semi-automated MRI QC procedure was implemented to visualize and flag MRI images next to site-specific distributions of QC featuresi. e. metrics that represent image quality. The value of each of these QC features was evaluated through comparison with visual assessment and step-wise parameter selection based on logistic regression.
IDPs were computed from 5 different MRI modalities and their sanity and potential clinical relevance were ascertained by assessing their relationship with biological markers of aging and dementia. The EPAD v1500. 0 data release encompassed core structural scans from 1356 participants 842 fMRI, 831 dMRI, and 858 ASL scans. From 1356 3D T1w images, we identified 17 images with poor quality and 61 with moderate quality. Five QC features -Signal to Noise Ratio (SNR), Contrast to Noise Ratio (CNR), Coefficient of Joint Variation (CJV), Foreground-Background energy Ratio (FBER), and Image Quality Rate (IQR) -were selected as the most informative on image quality by comparison with visual assessment. The multimodal IDPs showed greater impairment in associations with age and dementia biomarkers, demonstrating the potential of the dataset for future clinical analyses.
, 2019). Multimodal imaging data are acquired at each center and centrally stored and processed. This overview documents the methods and implementation details of the MRI data processing, QC procedures, and computation of several imaging-derived phenotypes (IDPs), as developed for the EPAD neuroimaging dataset. We then explored the sanity of IDPs by testing their sensitivity in their relationship with other biomarkers of neurodegeneration. The described pipeline is publicly available online ("https://github. com/ExploreASL/ExploreASL/tree/EPAD," n. d. ).
The European Prevention of Alzheimer's dementia longitudinal cohort study (EPAD LCS) EPAD eligibility criteria were age above 50 years and no history of dementia (clinical dementia rating (CDR) < 1). After providing written informed consent, participants underwent an extensive multimodal test battery including five outcome measurements: cognitive tests, demographics, cerebrospinal fluid (CSF) biomarkers, genetics, and brain MRI. Participants were followed up after 6 months, and after 12, 24, or 36 months, depending on their CDR score at baseline. Details on the EPAD rationale and study protocol are provided elsewhere  (Solomon et al. , 2019). Here, we considered the EPAD LCS v1500. 0 data release, which consists of the baseline data from the first 1500 participants included in the study.
The EPAD MRI acquisition protocol The v1500. 0 baseline data were acquired at 21 EPAD sites, including seven different scanner models from Siemens Healthineers, Philips Healthcare, and GE Healthcare. A common scanning protocol was developed during the preparation phase to keep between-site differences as small as possible while accommodating differences in scanner hardware and software limitations, and fit in local site-specific protocols and ongoing studies. The EPAD LCS imaging protocol was composed of core and advanced sequences (Table  1 ). The core sequences provided structural information and confirmed participants' eligibility status through baseline radiological assessment, and the advanced sequences were designed to investigate brain structure and function in greater detail. Whereas the core sequences were conducted at all sites (n = 21), the advanced sequences were performed in a subset of EPAD sites (n = 13) with 3 T scanners.
The EPAD imaging pipeline The EPAD image analysis pipeline consisted of four modules (Fig. 1 ): 1) Curation of raw DICOM files, including harmonization of DICOM structure among sites, initial DICOM quality control (QC), and conversion to NIfTI; 2) Image preprocessing for core and advanced sequences; 3) Semi-automatic QC of processed data through an in-house code-based toolbox; 4) Computation of image-derived phenotypes (IDP)  (Gong et al. , 2020) , i. e. extraction of numeric derivatives from images. Data sharing procedures are described in supplementary material section 2 and elsewhere (https://ep-ad. org/erap/). All preprocessing steps were implemented in ExploreASL  (Mutsaerts et al. , 2020) , an SPM-based toolbox designed to harmonize image processing for multi-center structural MRI and arterial spin labeling (ASL) studies.