dataesr/scientific-paragraphs-categorization huggingface dataset:
g. , gouping multiple block-sections  (Meng & Zhou, 2014). Some approaches focus on the solution process to properly drive the search of good solutions. For example, initial solutions are computed for the scheduling problem considering the timetable route that are then improved by either enlarging the search space adding rerouting variables  (Pellegrini et al. , 2015)  or iteratively solving the scheduling and routing Selection Problem (TRSP) aims to do so, by limiting the number of routing variables through a pre-processing that selects the most promising routing alternatives among the available ones for each train in order to reduce the size of rtRTMP instances. This paper studies the performance of an Ant Colony Optimization (ACO) algorithm for the same problem. An integer linear programming formulation for the TRSP is presented and solved using a commercial software, and it is considered as a benchmark.
This paper studies the performance of an Ant Colony Optimization (ACO) algorithm for the same problem. An integer linear programming formulation for the TRSP is presented and solved using a commercial software, and it is considered as a benchmark. Computational experiments are performed on two practical case studies of the French railway infrastructure: the line near the city of Rouen and the Lille terminal station area. ACO and the commercial solver perform comparably only on small instances and both are able to find optimal solutions. However, on larger instances, the ACO algorithm outperforms the commercial software, both in terms of computation time and solution quality.
Geometry reconstruction A healthy subject (male, 40 years old, 94 kg, 1. 73 m) was volunteered and gave his informed consent for participating in the experimental data collection that was part of a pilot study approved by an ethical committee (MammoBio MAP-VS pilot study, IRMaGe platform, Univ. Grenoble Alpes). The subject underwent a total of 5 sessions of non-weight-bearing MRI using a clinical MRI system (Achieva 3. 0T dStream Philips Healthcare) and one session of computed tomography (CT) scan. The CT scan was taken to capture the surfaces of the bones precisely to be used as a reference for registering the bones segmented from different MRI sets. The first MRI session was performed to obtain the full geometry of the lower limb muscles while the subject was lying still in the supine position (6.
99×0. 97×0. 97 mm 3 intervals respectively in the axial, sagittal, and coronal views). The second MRI was obtained from the subject's right knee to capture the articulation, the connective tissues, muscle insertions, and tendons more precisely (0. 31×0. 7×0. 31 mm 3 intervals in the axial, sagittal and coronal views). The subject was lying in the supine position with an approximate 25° knee flexion. The third to fifth MRI sessions were conducted to obtain the required data for simulating passive deep knee flexion as well as model validation. For this purpose, the subject was lying in the left lateral recumbent position and positioned his knee at three different flexion angles for each acquisition. The MRI of the right knee was captured having 0. 5 mm intervals in all planes. The three different knee positions during the third to fifth MRIs are depicted from the sagittal view in Figure  1.
Moreover, the recording of spontaneous emotions must be applied while the subjects are un-aware of it, which is extremely difficult to establish because the subjects should deliver authorization for the recording and use of their images/videos. Therefore, there are only a few studies that have been interested in spontaneous emotion recognition and have focused solely on verifying the assigned labels and whether they match the corresponding observation, as reported by  [15]. The majority of available databases for spontaneous facial expressions have been collected from the web, based on saving images from search engines (mainly Google and Flicker) by specifying the emotion-related keywords. The well-known and widely used databases of this kind are FER and AFEW. By contrast, posed expressions are obtained by requesting the subjects to perform the facial expressions in a uniform way in order to avoid intra-class similarities that would confuse the classification task.
Afterwards, the 49 feature vectors are concatenated to form the final appearance feature. During the classification step, we use the SVC library preprocessed by the PCA technique intended to reduce the dimensionality of the feature vectors. The framework is evaluated on five of the most widely used state-of-the-art benchmarks: KDEF, CK+, RaFD, JAFFE, and OuluCasia. To ensure the person-independent evaluation, the leave-one-subject-out (LOSO) protocol is adopted on all five datasets. To prove the superiority of the proposed OPD-GQMBP descriptor for the FER, we conducted a comprehensive experimental comparison against 12 recent and powerful LBP variants and 10 state-of-the-art deep features (e. g. , VGG, ResNet, and Inception) using our FER framework on the five datasets. We also compared our FER framework perfor-mance to those reported in the literature (from articles in journals with a high impact factor and from highly indexed conferences).
The major contributions of our study can be summarized as follows: • An FER system based on the coupling shape and appearance information is proposed. This system applies the Dlib package to detect 49 landmarks on the facial image and then extracts the shape and textural features before proceeding to the SVM-based classification stage. • New handcrafted LBP variants our applied for a texture analysis based on the orthogonality and parallelism geometry concepts, which are referred to as OPD-GQMBP. This is a generic descriptor that takes the neighborhood size as a parameter. This parameter offers the opportunity to balance the computational speed with the discrimination performance. • We considered five widely used benchmarks from the literature for evaluation: CK+, KDEF, JAFFE, Oulu Casia, and RaFD.
We adopted an experimental person-independent evaluation on each dataset, using the LOSO protocol. • The proposed FER framework has outperformed many recent studies published in journals with a high impact factor, including deep-learning and handcrafted frameworks, following the LOSO protocol. • The performance of the OPD-GQMBP descriptor is fairly evaluated based on its comparison to 12 recent handcrafted LBP variants and 10 deep features. The results showed the superiority of OPD-GQMBP against all of the tested handcrafted and deep features. To provide readers and field-interested researchers with a better reading experience, this paper is organized as follows. Section 2 presents some state-of-the-art approaches devoted to the FER problem, covering handcrafted and deep-based features. Section 3 introduces the proposed OPD-GQMBP descriptor and highlights its process to compute the textural feature vector.
Moreover, this section shows the overall proposed FER framework and explains how the proposed OPD-GQMBP and HOG operators are applied and combined to obtain the textural and shape features from the detected landmarks of a given input face image. Section 4 provides comprehensive experiments on five widely used databases and shows a comparative evaluation of state-of-the-art descriptors (handcrafted-based and deep-based) and FER systems. The last section provide some concluding remarks regarding our study and areas of future research.
Related works The computer vision community has conducted many studies devoted to facial expression recognition (FER) by applying machine learning techniques. In this section, we briefly present some state-of-the-art FER frameworks to highlight some of the proposed architectures that rely on either handcrafted descriptors or deep-learning methods. Shan et al. [31]  proposed an approach, referred to as Boosted-LBP, based on combining a basic LBP descriptor with the Adaboost algorithm to enhance the classification performance. They conducted experiments on CK+, MMI, and JAFFE databases, and found that the Boosted-LBP outperforms the basic LBP combined with a multi-class SVM classifier. Moreover, they reported that local methods (LBP) perform better than global methods (Gabor filters). Zhang et al.
They evaluated the LBP, LTP, and CLBP texture descriptors and their combination with the HOG operator cascading with a linear SVM classifier. They found that the concatenation of LTP and HOG leads to the best FER performance on three datasets (CK, FEED, and KDEF). Their framework strengths rely on extracting the appearance features from seven sub-images defined from landmarks carrying information about the facial expression class, in addition to combining the LBP-Like descriptor with the HOG operator. However, this architecture presents certain drawbacks that we can point out. The seven extracted sub-images have different sizes and orientations, but their computed features have the same length. We found that the nose region of interest is vertically oriented compared to the eyebrow regions, which are horizontal, and the eye regions, which are almost square.
Afterwards, a feature selection method is applied to reduce the dimensionality of the final feature vector because they use the spatial division on the input image. However, this system takes as input the entire face image, which makes it inconvenient for person-independent FER applications. In addition, no shape descriptor has been adopted in the overall framework, relying only on LPDP extracted features. Minchul et al. [33]  used a convolutional neural network model to achieve facial expression recognition. They adopted and aligned cropped faces from FER-2013, SFEW2. 0, CK+, KDEF, and Jaffe with respect to the landmark position of the eyes. The training data were augmented 10 times by flipping them. Five types of data input (raw, histogram equalization, isotropic smoothing, diffusion-based normalization, and difference of Gaussian) were tested.
They then selected the one that showed the highest accuracy as a target structure for fineparameter tuning. Finally, the CNN network with histogram equalization images was chosen as the baseline CNN model for further research. Yu et al. [45]  proposed a method that contains a face detection module based on an ensemble of three stateof-the-art face detectors, JDA, DCNN, and MoT. Subsequently, a classification module composed of an ensemble of deep convolutional neural networks (CNNs) was adopted based on averaging the output responses. Each CNN model is initialized randomly and pretrained on the Facial Expression Recognition (FER) Challenge 2013 database. The pretrained models were then fine-tuned on the training set of SFEW 2. 0. To combine multiple CNN models, they presented two schemes for learning the ensemble weights of the network responses: minimizing the log-likelihood loss and minimizing the hinge losses.
According to the results reported in their study, the hinge loss performs slightly better than the log-like and single CNN models on the validation and test sets of the FER2013 and SEFW databases. Therefore, their framework is computationally heavy, and the outcomes are not very promising. Jung et al. [13]  proposed a new CNN framework based on combining the temporal appearance and temporal geometry extracted from two CNN models. The faces in the input image sequences are detected, cropped, and rescaled to a pixel resolution of 64 × 64, and 49 landmark points are then extracted using the IntraFace algorithm. Finally, these two models are combined using an element-wise sum of the outputs of the last fully connected layers from the two temporal CNN models. Through several experiments conducted on the CK+, MMI, and Oulu-CASIA databases as well as numerous data from various data augmentation techniques, the framework built showed that the two models cooperate with each other.
However, the joint model did not improve the recognition of all of the facial expressions and achieved the same performance as the temporal appearance and temporal geometry models of the Disgusted, Fear, Happy, and Surprised classes. In addition, the temporal appearance CNN model outperformed the geometry model on all tested databases. Most of the previous methods have considered the entire facial region as the input information, and have paid less attention to the sub-regions of human faces, which may lead to a large difference between the extracted and expected representations. Indeed, when the extracted information obtained from the entire face image is irrelevant, the final recognition result will be affected. Because the judgment of the facial expression is usually based on the information of several sensitive components in some areas of the face, such as the eyes, nose, and mouth, in this paper, a new method is proposed that concentrates the feature extraction on these sub-regions, which not only allows for the extraction of more relevant features, but will also further improve the overall recognition rate.
Note that spatial division was adopted to compute the OPD-GQMBP feature vector by dividing each sub-image into non-overlapping blocks of size w × w, as illustrated in Figure  5. The number of spatial blocks that divide the sub-image depends on the considered dataset and is related to the camera resolution and image blur. Indeed, blurred images require fewer blocks than clear images, which present more details to be detected. At the end of the feature extraction stage, the HOG and OPD-GQMBP computed histogram vectors are concatenated to compose the final image descriptor that is further fed to a dimensionality reduction using the PCA method before proceeding to the classification phase based on an SVM. We used the LIBLINEAR 2. 30 library as a multiclass kernel-based vector machine implementation for MATLAB/Python environments.
Initially, the model was trained endto-end on a big dataset, mainly the Facial Expression Recognition 2013 (FER2013) database. Afterwards, the model is expected to achieve a good training performance using the validation set. We then proceed to the transfer learning technique to extract the features of the subject database that belongs to the same application as the database used for the initial training (same classes). Once the features are obtained, we train the SVM classifier and evaluate the performance of each deep feature. To further improve this deep-based FER architecture, the calculated features be concatenated with other descriptors such as HOG, Gabor filters, and LBP descriptors before proceeding to the classification step.
Evaluation of OPD-GQMBP neighborhood size configuration The proposed OPD-GQMBP descriptor is a generic method defined by the neighborhood size n, which can be seen as a userspecified parameter depending on the needs of the considered application. To find the best value for FER, we conducted an experiment evaluating the performance of four configurations: n = 3, 5, 7, and 9. For each, we evaluated the FER framework on the five datasets using the LOSO protocol. The smaller neighborhood sizes provide less computational cost, but with weak discriminative power, and larger sizes enhance the discriminative power, but require more resources to store and classify the extracted features. For example, a neighborhood size of 3 (OPD-GQMBP 3 ) generates only 4×2 3 = 32 possible patterns, whereas neighborhood sizes of 5 (OPD-GQMBP 5 ), 7 (OPD-GQMBP 7 ), and 9 (OPD -GQMBP 9 ) produce 128, 512, and 2048 patterns, respectively.
Comparative analysis against state-of-the-art handcrafted and deep feature methods The goal of this comprehensive analysis is to compare the performance of the proposed OPD-GQMBP descriptor to those recorded in the literature on feature extraction methods, including handcrafted and deep-based approaches. We considered 12 recent and powerful LBP variants published in high-impact journals and proposed for various applications, mainly texture classification and face recognition. In addition, we evaluated the top-10 state-of-the-art deep learning models proposed thus far. These models were initially trained on the FER2013 database, each of them reached a validation accuracy of above 60% on 25,000 images, which can be considered extremely significant. We then use transfer learning to extract the features of the five databases adopted in this study.
We respected the same evaluation protocol (LOSO) to provide a fair and systematic analysis. Table  3  lists the performance achieved by each method or model. We provide two metrics. The first metric is the average accuracies recorded for all runs of each database depending on the number of individuals, of which JAFFE has 10 runs, CK+ has 106, KDEF has 70, OuluCasia has 80, and the RaFD database has 67. The second metric is the maximum accuracy reached for all runs per database. We highlight the top 3 average values with green color. It can be seen from the average accuracies that the proposed OPD-GQMBP descriptor managed to score the top performance on all tested datasets. For the CK+ database, the OPD-GQMBP descriptor achieved a score of 96. 48% on 106 runs with a maximum recognition accuracy of 100%, maintaining more than a 2% gap over the next best method, i.
e. , the handcrafted MNTCDP descriptor, which managed to secure an average accuracy of 94. 36%. All of the evaluated methods were capable of reaching a maximum accuracy of 100% on the CK+ dataset. Moreover, the majority of the handcrafted methods performed at above 87. 81% when applying the DCP descriptor, whereas the deep features reached a maximum average of 88. 76% with VGG19 only and 54. 18% (AlexNet) as the minimum average for CK+. As indicated in Table2, we found that n=9 for the configuration of the OPD-GQMBP method scored 97. 53% on CK+, which was a 1% improvement compared to OPD-GQMBP with n=7 adopted in this evaluation. In addition, OPD-GQMBP reached 77. 62% on JAFFE with a lead of 3% over the rest of the methods, with the second highest average accuracy being 74. 76% for the DCP descriptor followed by ELGS as the third-best performing method at 73.
81%. According to the maximum accuracy on the JAFFE database, only the OPD-GQMBP and DCP methods managed to score 100%. The Japanese females with high facial similarities make the JAFFE an extremely tough benchmark for FER systems. The results demonstrate that the JAFFE database is quite challenging, particularly for deep features where the top average accuracy was limited to 56. 67%, as obtained by VGG16, whereas the handcrafted features granted an average accuracy of above 64. 76%, which was reached by the LOOP descriptor. For the KDEF database, the first remark to be concluded is that the OPD-GQMBP descriptor is the only method that breaks the 90% average performance ceiling, and the best state-of-the-art method reached 88. 37% (LDTP). The performance of the handcrafted methods and deep methods varied from 78.
57% to 88. 37% and from 65. 31% to 76. 73%, respectively. The lead gap on this database was again approximately 2% in favor of the OPD-GQMBP descriptor, which proves its discriminative power for FER applications. Despite the quality of the recorded images in terms of resolution, lighting conditions, and a uniform background, the KDEF database is also challenging in face of CK+. It presents fewer individuals (fewer runs) expressing the seven emotions in different manners, resulting in more intraclass similarities and approaching spontaneous facial expressions. Oulu-Casia can be considered as the toughest among the adopted benchmarks owing to the blur images and the South Asian individuals composing this database. The OPD-GQMBP descriptor scored 77. 32% as the overall best performing method, followed by ARCS-LBP (75.
5%) and DC (74. 64%) descriptors. The lowest accuracy was recorded by the AlexNet deep network, reaching only 29. 64%. In addition, all methods achieved a 100% maximum accuracy except for MNTCDP, AlexNet, GoogLeNet, and Inceptionv3. Moreover, only the ELGS, DC, ARCS-LBP, and OPD-GQMBP methods exceeded a 70% average accuracy, whereas the DCP descriptor, which had the second highest rank on JAFFE, found its performance limited to 45. 89% as the lowest average accuracy over all handcrafted LBP-like methods. [4]  Multi-Directional Multi-Level Dual-OPD-GQMBPs Patterns (Face recognition) 2015 3 DRLBP  [23]  Dominant Rotated Local Binary Patterns (Texture classification) 2016 4 QBP  [47]  Quad Binary Pattern (Target tracking) 2016 5 ELGS  [9]  Extended Local Graph Structure (Texture classification) 2016 6 LNDP  [28]  Local neighborhood difference pattern (Texture classification) 2017 7 DC  [42]  Rotation-invariant features based on directional coding (Texture classification) 2018 8 LCCMSP  [8]  Local Concave-and-Convex Micro-Structure Patterns (Texture classification) 2018 9 LDTP  [16]  Local directional ternary pattern (Texture classification) 2018 10 LOOP  [3]  Local Optimal Oriented Pattern (Spieces recognition) 2018 11 MNTCDP  [14]  Mixed Neighborhood Topology OPD-GQMBPs Decoded Patterns (Face recognition) 2018 12 ARCS-LBP  [9]  Attractive-and The RaFD database is collected by a set of well-trained individuals that clearly express eight emotions (basic emotions + contempt).
Indeed, the peak average accuracy exceeded 97% by the proposed OPD-GQMBP descriptor with a minor lead (0. 19%) against the LDTP and LCCMSP methods (97. 01%). Moreover, all of the handcrafted descriptors reached above 90%, except for DCP (87. 31%). By contrast, ResNet50 was the best among the deep feature methods, with an average accuracy of 84. 51%. In terms of stability, the OPD-GQMBP descriptor performed well on the five datasets, always reaching the top average accuracies and 100% at all times. The ELGS method also presented a stable performance across all databases. By contrast, DCP suffered a performance decrease on the KDEF and OuluCasia datasets. For deep feature methods, VGG16 can be considered as the best performing deep feature method. The deep learning networks did not perform well on the five benchmarks, despite reaching a validation accuracy of above 60% on 25,000 images of the FER2013 database.
The problem here is that the deep learning methods should be fine-tuned on each dataset before extracting the features to obtain satisfactory results. The applied application in this paper is personindependent, and to ensure that the probe images of a given person are unseen by the framework, we should perform fine tuning on each run and for each deep method. Hence, because we have a set of 333 persons on the five datasets and we consider 10 deep learning models, we need 3330 fine tunes to expect satisfying results from these models, which are time-and resource-consuming. Moreover, such frameworks are intended for real implementations and deployments, and fine tuning is not always a possible option. In addition, the probe images will have generally different characteristics than the trained images.
Comparison against state-of-the-art FER systems In this subsection, we compare the results obtained by our proposed FER framework to those achieved by previous studies in the field of facial expression recognition. Tables  4, 5 , 6, 7 and 8 list the highest accuracies on the five datasets reported in wellindexed journals and conferences of the literature. We tried to collect the maximum number of studies that followed the same adopted evaluation protocol (person-independent). As can be seen in Table  4 , the proposed FER framework outperforms all listed systems, including both handcrafted and deep-based features in the CK+ database. We reached an accuracy of 96. 48% (97. 53% with the OPD-GQMBP at a neighborhood size of n=9), whereas the best accuracy of the other state-of-the art methods was 94.