dataesr/scientific-paragraphs-categorization huggingface dataset:
I. INTRODUCTION Vector-based descriptors are massively used in many domains including mathematics, physics, computer science, etc. For this reason, several multidisciplinary repositories containing datasets where data are represented as vectors are available. This is the case of the widespread UCI Machine Learning Repository 1 which contains more than 550 datasets from various domains. A vector -→ u = [u 1 , u 2 ,. , u n ] is basically an ordered list of n elements belonging to diverse datatypes. In this paper, the vector components belong to N or to R. The number n of components of -→ u is its dimension. Vectors can be manipulated by different operations including vector comparison which is crucial for popular machine learning tasks like classification or clustering. Given a dataset composed of many classes, each class containing several vectors, classification refers to the process of assigning a class label to a vector based on its components  [1].
D. DIMENSIONALITY REDUCTION An alternative to the use of ZP techniques for comparing vectors having different dimensions, is the use of DR techniques. DR techniques are thoroughly used in several application domains including face/image/pattern recognition, image/video classification and clustering, signal processing, etc. 9 In these domains, DR techniques are generally used for extracting only relevant components while eliminating redundant and unnecessary components. Given a dataset U = { -→ u 1 ,. , -→ u x } where -→ u i ∈R n (1≤i≤x), DR techniques aim at transforming U into the set U ′ = { -→ u 1 ′ ,. , -→ u x ′ } such that -→ u i ′ ∈R m (1≤i≤x ) with (m ≪ n), while preserving as much as possible a property of the original vectors. This property can be the distance, the variance, the classification accuracy, etc.
V. EXPERIMENTAL RESULTS As it can be observed in Equation  12 , the current work does not propose any new similarity or distance measure for comparing vectors. It rather enhances existing measures by giving them the ability: 1) To perform the comparison according to a specific set P of targeted properties. 2) To compare two finite sets of vectors, each containing vectors of various dimensions. The goal of this section is to demonstrate that there are situations where it is preferable to compare vectors according to other criteria than the exact values of their components and to evaluate the performances of the proposed approach in these conditions. In order to show how the proposed approach can efficiently enhance existing techniques when the suitable set of targeted properties is selected, flat classification experiments were conducted on three synthetic datasets especially constructed for this purpose.
B. EXPERIMENTAL SETTINGS The classification experiments performed in this work were realized on a personal computer having 16 GB of main 21 https://webperso. etis-lab. fr/sylvain. iloga/Vectors/index. html memory and the following processor: Intel(R) Core(TM) i7-8665U CPU @ 1. 90 GHz 2. 11 GHz. The maximum number of iterations γ = 100 was selected here for the Baum-Welch algorithm 22 and the constant r = 100 was selected here for computing the stationary distributions of our HMMs 23 following  [9]. Each vector -→ u of each dataset was considered as the singleton U = { -→ u }. Given that the three synthetic experimental datasets contain vectors having components belonging to N, Equation 10 was used for the generation of the final feature vectors. It is not systematically necessary to determine the most relevant and informative content of the set P of targeted properties when using the proposed approach since the comparison result always remains consistent according to P, whatever it contains.
23 See Section V-B of  [9]. they compare original vectors on the one hand, and when they compare the final feature vectors generated by the proposed approach on the other hand. Classification results are presented in Table  9  where each cell contains the best classification performance for the considered dataset and considering all the four selected classifiers. These results experimentaly demonstrate that the final feature vectors generated by the proposed approach according the sets of selected properties enable to obtain good performances for the three experimental datasets.
D. COMPARISONS In order to compare the proposed approach to existing techniques, we evaluated the flat classification performances when each original vector -→ u of each dataset is directly used as input for the classifier. The resulting best classification performances are extremely poor for the three synthetic datasets. When the best accuracies obtained by existing techniques are compared to those exhibited by the proposed approach in Table  10 , we observe that the proposed approach outperforms existing techniques with accuracy gains reaching +82. 3%.
Flat classification experiments conducted on three online available custom datasets demonstrated that when the suitable set of targeted properties is selected, the proposed approach outperforms existing techniques with accuracy gains reaching +82. 3%. The following perspectives can be considered in future work: 1) Future work can analyze the impact of the gradual modification of the user-defined parameters r, γ , N and M on the performances of the proposed approach. 2) Future work must focus on the objective discovery of the most relevant and informative set of properties by analyzing the vector components using machine learning or data mining techniques when it is required by the final goal of the comparison. Given two finite vector sets U and V , future work can analyze the content of U in order to discover of the subset P U of targeted properties that is suitable for the characterization of the vectors in U.
4) A taxonomy is an efficient navigating and browsing mechanism of a dataset. It organizes the content of the dataset into a hierarchy where broad concepts are at the top and more specific concepts are further down. Future work must explore the possibility of designing datasets taxonomies based on the feature vectors generated by the proposed approach. More formally, given a dataset composed of many classes, a unique feature vector can be generated for each class using the proposed approach. The resulting feature vectors can be finally taken as input by a hierarchical clustering algorithm in order to generate a taxonomy of the dataset. 5) Future work must focus on the implementation of an extension of the proposed approach that will consider vectors having nominal components or a mixture of numeric and nominal components.
VOLUME 11, 2023 TABLE 6. TABLE 7. TABLE 8. 2023678 Properties in Modulo10. For each vector -→ u = [u 1 ,. , u 10 ], the item located at line c and column u k defines the value of (|u k | mod 10) in class c. Properties in OddOrEven. For each vector -→ u = [u 1 ,. , u 10 ], the item located at line c and column u k indicates if |u k | is even e or odd o in class c. Properties in FirstOrNot. For each vector -→ u = [u 1 ,. , u 10 ], the item located at line c and column u k indicates if |u k | is a first number + or notin class c. A. EXPERIMENTAL DATABASES The three synthetic datasets are Modulo10, OddOrEven and FirstOrNot. Each dataset contains 10 classes composed of 100 vectors, each having 10 components. The components of the vectors in Modulo10 are integers from the interval [-200, 200], while those of the two remaining datasets are integers from the interval [0, 200].
Detecting sharp features in raw 3D point clouds is an essential step for designing efficient priors in several 3D Vision applications. This paper presents a deep learning-based approach that learns to detect and consolidate sharp feature points on raw 3D point clouds. We devise a multi-task neural network architecture that identifies points near sharp features and predicts displacement vectors toward the local sharp features. The so-detected points are thus consolidated via relocation. Our approach is robust against noise by utilizing a dynamic labeling oracle during the training phase. The approach is also flexible and can be combined with several popular point-based network architectures. Our experiments demonstrate that our approach outperforms the previous work in terms of detection accuracy measured on the popular ABC dataset.
Introduction For scanned or Computer-Aided Design (CAD) 3D models, a sharp feature usually refers to creases or corners where the surface is not smooth. Recognizing such sharp features from raw point cloud is a preliminary step for several point cloud processing tasks such as surface reconstruction  ( Öztireli et al. , 2009; Gal et al. , 2007; Amenta et al. , 1998; Guennebaud and Gross, 2007; Salman et al. , 2010) , extraction of feature graphs  (Lee and Bo, 2016; Ni et al. , 2016)  or semantic segmentation  (Gong et al. , 2021; Loizou et al. , 2020). With the development of point cloud acquisition techniques and the release of well-annotated 3D datasets  (Koch et al. , 2019) , many data-driven approaches have been proposed in recent years. Most previous approaches reformulate sharp feature detection as a classification problem, which leads to several issues: (1) Feature points are not located exactly on sharp features in general, which leads to incomplete features; (2) To deal with this issue, several approaches introduce a distance threshold that defines the maximum distance between a feature point and the nearest sharp feature.
Using such a distance threshold in common classification problems highly impacts the final prediction and it is delicate to choose (see Figure  1 ); (3) Noise has a large impact over the classification results; and (4) The network can be easily overfitted given patches or point clouds with ground truth labels. Inspired by displacement-based point cloud denoising methods, we propose a deep learning framework for detecting and consolidating sharp features from a raw 3D point cloud, namely SFCNet. Our framework contains two main components: (1) A point-to-feature oracle that largely improves the data augmentation quality so as to prevent models from overfitting; (2) A multi-task neural network architecture that predicts altogether a binary label (sharp/smooth) for each input point and a displacement vector for each sharp feature point.
Our approach outperforms popular unsupervised methods and supervised methods on the ABC dataset  (Koch et al. , 2019). We also evaluate our method on noisy point clouds and real scanned models. Quantitative and qualitative results are provided in Section 4. In summary, our insights and technical contributions are: • We propose a novel end-to-end multi-task neural network architecture that performs accurate sharp feature detection and consolidation; • Our neural network has a flexible structure that can be combined with many popular backbones and adapted to many datasets; • We propose a point-to-feature oracle that largely improves the data augmentation quality during training to prevent models from overfitting; • Our approach has a good resilience to noise.
More recently,  Qian et al. (2021)  proposed ASSANet , a novel separable set abstraction module that further improves PointNet++. Another family of methods is spatial-based. Wang et al. (2019)  introduced a graph neural network, referred to as DGCNN, which learns features on KNN neighbors using successive EdgeConv layers. The So-Net  (Li et al. , 2018 ) is devised to model the spatial distribution of the input points. The SPLATNet  (Su et al. , 2018)  computes hierarchical and spatially-aware features of input points with sparse and efficient lattice filters. All the above networks can be utilized as a backbone of our framework, as long as they produce pointwise descriptors.
Point-to-feature oracle Most data-driven methods use pre-defined labels for point clouds during the training session. This operation would probably lead to overfitting, or designing complicated loss functions to compute gradients, which makes the network hard to adapt to other datasets or architectures. Instead, we propose a point-to-feature oracle. It updates dynamically the ground truth classification labels and the ground truth displacement vectors according to the current noisy point cloud and the ground truth mesh before forwarding inputs to the network. Our network can deal with, but is not limited to, four types of sharp creases: segments, circles, ellipsoids and B-splines. Figure  3  shows the principle of our oracle. It takes two user-define parameters, a noise level n and a distance threshold d max , whose influences are well studied in Section 4.
Experiments We implemented our approach using the Geomdl library  (Bingol and Krishnamurthy, 2019)  for B-spline distance computation and the PyTorch deep learning framework  (Paszke et al. , 2019). We first introduce the setup of our experiments. Then we study the robustness of our approach to the hyper-parameters, followed by an ablation study of each component of our approach to demonstrate their functionalities. We show both the quantitative and the qualitative results, as well as comparisons with unsupervised and supervised methods. Dataset. The ABC dataset  (Koch et al. , 2019 ) is a collection of CAD models with parameterized curves and annotated surfaces. We selected 5,093 models which contain at least one sharp crease. We split them into three non-overlapping sets used for training (3,623 models), evaluating (471 models) and testing (999 models).
Metrics. We selected two lines of quantitative metrics to evaluate our approach: (1) Common metrics used for binary classification and (2) Distance metrics used for regression. Similarly to previous approaches  (Himeur et al. , 2022; Loizou et al. , 2020; Wang et al. , 2020) , we adopt the following metrics for classification: Precision, Recall, F1-score, Accuracy and Intersection over Union score (IoU). Besides, we estimate two metrics based on the Euclidean distance. More specifically, we compute the distance between detected sharp feature points and continuous ground truth sharp features, by taking advantage of the annotation of the ABC dataset and our point-to-feature oracle, which evaluates the correctness of sharp feature points. We refer to this distance with PtF distance. We then estimate the Chamfer distance between the detected sharp feature points and a set of uniformly-sampled points on the ground truth sharp features, which evaluates the completeness of detected features.
Comparisons Point-based approaches. We compare our approach with PB-DGCNN  (Loizou et al. , 2020)  (re-trained from scratch on our dataset for 50 epochs, since there is no released pre-trained model), ECNet  (Yu et al. , 2018a)  (using the released pre-trained model), PIE-Net  (Wang et al. , 2020)  (using the released pre-trained model), together with two unsupervised methods: Feature edge estimation via Voronoi Covariance Measure (VCM) provided by the CGAL Library  (Mérigot et al. , 2010)  and Covariance Analysis (CA)  (Bazazian et al. , 2015). Table  2  records the error metrics on our test set. We observe that both of our networks have smaller Chamfer distances and smaller PtF distances compared to other methods. SFCNet-P performs best on the various metrics. Figure  7  offers a visual comparison of all methods.
VCM and CA are sensitive to user-defined parameters: we find it hard to define through trial-and-error parameters that fit all point clouds in our test set. Compared to PB-DGCNN, our SFCNet-D achieves similar scores for classification metrics while performing better in terms of distance, owing to the displacement learning. EC-Net seems to have more missing parts than our models and both distance criteria are worse than the ours. The results show that PIE-Net is not noise-resilient and the detected sharp feature points may distribute over the whole point cloud. While the paper mentions that it also predicts 3D offset vectors to relocate points onto edges, such a prediction step is commented out in the code and no pre-trained model contains such a relocation operation. We evaluate the average inference time on our test set.
VCM  (Mérigot et al. , 2010) , implemented in C++, takes around 1. 877s for 1 model with 10k points. All the other methods are implemented in Python, and the approximate test times are: 0. 134s for CA  (Bazazian et al. , 2015) , 1. 26s for PB-DGCNN  (Loizou et al. , 2020) , 2. 4s for EC-Net  (Yu et al. , 2018a) , 0. 177s for PIE-Net  (Wang et al. , 2020) , 0. 174s for SFCNet-D and 5s for SFCNet-P. Mesh-based approaches. Feature-preserving mesh denoising approaches can be utilized for sharp feature detection and consolidation from point clouds, combining with an extra surface reconstruction step. We compared our approach with Bilateral Normal Filtering  (Zheng et al. , 2010) , Mesh Denoising via L0 Optimization  (He and Schaefer, 2013) , Guided Mesh Normal Filtering  (Zhang et al. , 2015) , Edge Sharpener  (Attene et al.
Results on real scans We evaluate our SFCNet-P model on real scanned 3D point clouds taken from the online Visionair repository  (Kopecki et al. , 2011). These point clouds have never been seen during the training phase. Compared to the training dataset, we highlight three major differences in real scanned point clouds: (1) The cardinality of the point clouds (from 460k to 1M) is substantially larger than that of our training dataset (10k); (2) The intrinsic noise in scanned point clouds originates from the laser, instead of being simulated randomly; (3) The models scanned by real-world scanners are often open, while all models of our training dataset are closed. Despite the above differences, our model predicts accurate sharp feature points from the real scanned point clouds with displacement learning, see Figure  10.
Quantitative comparison with unsupervised and deep learning sharp feature points detection methods on the ABC dataset. Note that we cannot compute classification metrics of EC-Net since the produced point clouds are upsampled. CA: Bazazian et al. (2015), VCM: Mérigot et al. (2010), PB-DGCNN: Loizou et al. (2020), EC-Net: Yu et al. (2018a), PIE-Net: Wang et al. (2020).
A. Process and Principle CSI-based authentication is a technique used to verify the identity of a device. The process involves the following steps: 1) The device collects CSI information, which is a set of measurements that describe the state of the wireless channel. 2) The device either processes the information locally or sends it to the authentication server, which can be located in the cloud, edge or on a local network. 3) The CSI information is used to determine the authenticity of the device by comparing it with a reference database of known and trusted CSI patterns. 4) If the CSI information is consistent with that of a known and trusted device, the authentication server grants access to the wireless network. Otherwise, access is denied. Therefore, authentication requires a verifiable source of uniqueness, related, for example, to node positioning and fingerprinting.
A. Quadriga synthetic dataset We considered single-antenna legitimate nodes, referred to as Alices and a base station referred to as Bob; Alices' spatial locations are denoted by {x n } N n=1 n = 1,. , N , where {x n } N n=1 ∈ R L and L denotes the spatial dimensions considered (typically L = 2). We obtain the channel response at N = 400 equi-distant (1 m) spatial locations within a square area on the ground, between x = 100 and x = 290 and y = -100 and y = 90 and a base station located at (x, y, z) = (0, 0, 10) using the "Berlin-UMa-NLOS" configuration in Quadriga channel models  [14] , [42], as depicted in Fig. 5. We assume that for any specific Alice, all other Alices can act as attackers (Eves). This configuration's terrestrial Urban Macrocell parameters are extracted from measurements in Berlin, Germany.
B. Nokia experimental dataset A massive multiple-input multiple-output (mMIMO) channel measurement campaign was conducted on the Nokia campus in Stuttgart, Germany. The campaign area consisted of multiple roads with high buildings (15 m high approximately), acting as reflectors and blockers for the radio wave propagation. The transmit antenna array was placed on the roof-top of one of these buildings. The geometry of 64-element transmit-array was such that there were 4 rows with 16 singlepolarization patch antennas, with a horizontal spacing of λ/2, and vertical spacing of λ. The transmit antenna array transmitted 64 time-frequency orthogonal pilot signals at 2. 18 GHz carrier frequency, using orthogonal frequency division multiplexing (OFDM) waveforms according to the 10 MHz LTE numerology (i.
5 mm. Post-processing was used to extract, for each pilot burst and subband, the 64-dimensional CSI vector. In this work, we used datasets on tracks 6 and 12, depicted in Fig. 6 , that are parallel at a vertical distance of 1 m. In detail, we assumed that Bob is the base station and Alice walks along track-6, while Eve performs an "on the shoulder attack" and walks in parallel to Alice on track-12, i. e. , legitimate and adversarial nodes are at all times 1 m away. In order to remove frequency domain (within the coherence bandwidth) and antenna domain correlations, we have downsampled the dataset. In detail, we kept the measurements from every 10th subcarrier and every 4th antenna, i. e. , we used sub-sampling factors of 5 and 6, respectively. Furthermore, as the Nokia dataset consists of only uplink data, we used alternate consecutive measurements to approximate downlink data and have further downsampled the data in the time domain, keeping every 5th channel sample.
C. Analysis of datasets We fitted the empirical distributions of the amplitude and the phase for the Quadriga and the Nokia datasets (tracks 6 and 12) to 16 parametric probability density functions (PDFs). Table  I , summarizes the estimated PDFs' parameters of the amplitude resulting in the lowest Akaike's information criterion (AIC) values, along with the p-values of the Kolmogorov-Smirnov (KS) goodness-of-fit test (the null hypothesis being that the data follow the specified distribution). Also, Fig. 7 , depict the fitted PDFs of the distributions tabulated Table I on the amplitude (left column) and the fitted PDF of the uniform distribution on the phase (right column), for the Quadriga and NOKIA datasets. According to the p-values of the KS test, the amplitude of the Quadriga-based channel follows a Rician distribution, while the phase follows a Uniform(-π, π) distribution (see Fig.
7 ). However, the amplitude of Nokia's track 6 CSIs is bimodal (mixture distribution) and does not fit any of the chosen distributions, as shown in Fig. 7  and confirmed by the p-values of the KS test. On the other hand, for Nokia track 12, the p-value of the Nakagami(1. 52,0. 0004) distribution (pvalue = 0. 4) implies that the channel is likely to follow the above distribution while the phase is uniformly distributed. In conclusion, Table  I  and Fig. 7  indicate that real datasets might not be well fitted to any of the usual distributions customarily used for the evaluation of SKG rates in literature, is the case for track 6. In the future, we will consider non-parametric analysis, mixture distributions and the use of generative models for fitting real datasets.
VI. NUMERICAL RESULTS In this section, we provide a comprehensive evaluation of the proposed approaches using both synthetic and real datasets. We first begin with results on maximising the separability of CSI fingerprints. We analyze the variation of TVD with respect to D. Since the results for CSI-based authentication are similar for all proposed approaches, we present them solely for the PCA for conciseness. Subsequently, in the second half of the section, we turn our attention to unpredictable components used for SKG. We examine the reciprocity versus correlation trade-off of the PCA, KPCA, and AE by analyzing two key metrics: the average CC between locations and their nearest neighbours, and the average MP between Alice and Bob. To investigate this trade-off, we plot the variations of these metrics as the pair  D1 , D2 changes.