Reviewerly/exHarmony huggingface dataset:
Analysis of technical features in basketball video based on deep learning algorithm

The research of video-based sports movement analysis technology has an important application value. The introduction of digital video, human–computer interaction and other technologies in sports training can greatly improve training efficiency. This paper studies the technical characteristics of the players in the basketball game video and proposes a behavior analysis method based on deep learning. We first design a method to automatically extract the basketball court and stadium marking line. Subsequently, key frames in the video are captured using a spatiotemporal scoring mechanism. Afterward, we develop a behavior recognition and prediction method based on an encoder–decoder framework. The analysis results can be fed back to coaches and data analysts in real-time to help them analyze the tactics and technical choices. Experiments on the proposed method are carried out on a large basketball video dataset. The results show that the proposed method can effectively identify the motion of video characters while achieving high behavior analysis accuracy.
A Data-Driven Motion Prior for Continuous-Time Trajectory Estimation on <i>SE(3)</i>

Simultaneous trajectory estimation and mapping (STEAM) is a method for continuous-time trajectory estimation in which the trajectory is represented as a Gaussian Process (GP). Previous formulations of STEAM used a GP prior that assumed either white-noise-on-acceleration (WNOA) or white-noise-on-jerk (WNOJ). However, previous work did not provide a principled way to choose the continuous-time motion prior or its parameters on a real robotic system. This letter derives a novel data-driven motion prior where ground truth trajectories of a moving robot are used to train a motion prior that better represents the robot's motion. In this approach, we use a prior where latent accelerations are represented as a GP with a Matérn covariance function and draw a connection to the Singer acceleration model. We then formulate a variation of STEAM using this new prior. We train the WNOA, WNOJ, and our new latent-force prior and evaluate their performance in the context of both lidar localization and lidar odometry of a car driving along a 20 km route, where we show improved state estimates compared to the two previous formulations.
Heuristics for interesting class association rule mining a colorectal cancer database

Colorectal cancer affects many people and is one of the most frequent causes of cancer-related deaths in many countries. Professionals of the Reina Sofia University Hospital have fed a database about this pathology, with 1516 patients and 126 attributes, for more than 10 years. Finding useful knowledge therein has shown to be a difficult endeavor. We present four heuristic operators and a complete methodology for searching for interesting rules that describe cases with complications and recurrences. Our proposal has shown some advantages over the well-known Apriori algorithm, for class association rule mining, and the adaptation of three representatives of associative classification. Besides, it has allowed us to identify rules with practical interest among the vast amount of trivial and sporadic associations.
Deep Learning Adaptation with Word Embeddings for Sentiment Analysis on Online Course Reviews

Online educational platforms are enabling learners to consume a great variety of content and share opinions on their learning experience. The analysis of the sentiment behind such a collective intelligence represents a key element for supporting both instructors and learning institutions on shaping the offered educational experience. Combining Word Embedding representations and deep learning architectures has made possible to design sentiment analysis systems able to accurately measure the text polarity on several contexts. However, the application of such representations and architectures on educational data still appears limited. Therefore, considering the over-sensitiveness of the emerging models to the context where the training data is collected, conducting adaptation processes that target the e-learning context becomes crucial to unlock the full potential of a model. In this chapter, we describe a deep learning approach that, starting from Word Embedding representations, measures the sentiment polarity of textual reviews posted by learners after attending online courses. Then, we demonstrate how Word Embeddings trained on smaller e-learning-specific resources are more effective with respect to those trained on bigger general-purpose resources. Moreover, we show the benefits achieved by combining Word Embeddings representations with deep learning architectures instead of common machine learning models. We expect that this chapter will help stakeholders to get a clear view and shape the future research on this field.
A novel robust Student’s t-based Gaussian approximate filter with one-step randomly delayed measurements

In this paper, a novel robust Student’s t-based Gaussian approximate filter (RSTGAF) is proposed to solve the filtering problem of the nonlinear system with one-step randomly delayed measurements (ORDM) and heavy-tailed measurement noise. The conditional likelihood function is transformed into an exponential multiplication form after using state augmentation approach and introducing a Bernoulli random variable, and then the augmentation state vector, the auxiliary random variables and the Bernoulli random variable are jointly estimated based on the variational Bayesian (VB) approach. The simulation results demonstrate the superiority of the proposed filter, as compared with the existing filters, to address the ORDM and heavy-tailed measurement noise.
CASIA-AHCDB: A Large-Scale Chinese Ancient Handwritten Characters Database

This paper introduces a Chinese Ancient Handwritten Characters Database (CASIA-AHCDB) for character recognition research. The database was built by annotating 11,937 pages of Chinese ancient handwritten documents. It consists of more than 2.2 million annotated handwritten character samples of 10,350 categories. According to the source of these documents, the database is divided into two datasets of different styles: Complete Library in Four Sections (AHCDB-style1) and Ancient Buddhist Scriptures (AHCDB-style2). Each dataset can be divided into three parts based on its applications. The first part, called basic category set, contains samples of common categories in two datasets, and is suitable for basic character recognition task. The second part, called enhanced category set, is mainly used for open-set character recognition task based on the basic character recognition. The third part, called the reserved category set, can be used in many pattern recognition tasks in the future. Based on the large category set, the various writing styles and the imbalanced sample number per category, CASIA-AHCDB can also be used for various classification and learning tasks such as transfer learning, few-shot learning. We performed experiments of basic character recognition on the basic category set, and report the results for benchmark. More techniques can be evaluated on this challenging database in the future.
Cost-sensitive joint feature and dictionary learning for face recognition

Dictionary learning (DL) for classification aims to learn a codebook from training samples to enhance the discriminative capability of their coding vectors. But how to determine appropriate features that can best work with the learned dictionary remains an open question. Recently, several joint feature and dictionary learning method have been proposed and achieved impressive performance. The purpose of these methods is to achieve low classification errors by implicitly assuming the costs for all misclassifications, regardless of the original labels, are the same. However, in real applications, this assumption may not hold as different kinds of mistake could produce different losses. Motivated by this concern, we propose a cost-sensitive joint feature and dictionary learning (CS-JFDL) method, in which the features are concurrently learned with the dictionaries. Our method considers the separate misclassification cost objectives during the feature and dictionary learning stages to achieve a minimum overall recognition loss. Thus, the derived feature and dictionary attain cost-sensitive constraints throughout the learning process. Extensive experimental results on both image based face recognition and image set based face recognition demonstrate that the proposed algorithm is better than many state-of-the-art methods.
Recaptured Screen Image Demoiréing

In many situations, such as transferring data between devices and recording precious moments, we would like to capture the contents on screens using digital cameras for convenience. These recaptured screen images and videos suffer from a special type of degradation called “moiré pattern”, which is caused by the aliasing between the grid of display screen and the array of camera sensor. However, few works are proposed to tackle this problem. Considering the great success of convolutional neural networks (CNNs) in image restoration, we propose a CNN-based moiré removal method for recaptured screen images. There are mainly two contributions in this paper. First, for the generation of training data, we propose an image registration algorithm via global homography transform and local patch matching to compensate the significant viewpoint disparity between the recaptured screen image and the moiré-free image obtained via screenshot. We construct a moiré removal and brightness improvement (MRBI) database with aligned moiré-free and moiré images. Second, we propose a convolutional neural Network with Additive and Multiplicative modules (termed as AMNet) to transfer the low light moiré image to the bright moiré-free image. The proposed network is trained with pixel-wise loss, perceptual loss, and adversarial loss. Extensive experiments on 340 test images demonstrate that the proposed method outperforms state-of-the-art moiré removal methods.
Mixture-Kernel Graph Attention Network for Situation Recognition

Understanding images beyond salient actions involves reasoning about scene context, objects, and the roles they play in the captured event. Situation recognition has recently been introduced as the task of jointly reasoning about the verbs (actions) and a set of semantic-role and entity (noun) pairs in the form of action frames. Labeling an image with an action frame requires an assignment of values (nouns) to the roles based on the observed image content. Among the inherent challenges are the rich conditional structured dependencies between the output role assignments and the overall semantic sparsity. In this paper, we propose a novel mixture-kernel attention graph neural network (GNN) architecture designed to address these challenges. Our GNN enables dynamic graph structure during training and inference, through the use of a graph attention mechanism, and context-aware interactions between role pairs. We illustrate the efficacy of our model and design choices by conducting experiments on imSitu benchmark dataset, with accuracy improvements of up to 10% over the state-of-the-art.
Trajectory Optimization for Unknown Constrained Systems using Reinforcement Learning

In this paper, we propose a reinforcement learning-based algorithm for trajectory optimization for constrained dynamical systems. This problem is motivated by the fact that for most robotic systems, the dynamics may not always be known. Generating smooth, dynamically feasible trajectories could be difficult for such systems. Using sampling-based algorithms for motion planning may result in trajectories that are prone to undesirable control jumps. However, they can usually provide a good reference trajectory which a model-free reinforcement learning algorithm can then exploit by limiting the search domain and quickly finding a dynamically smooth trajectory. We use this idea to train a reinforcement learning agent to learn a dynamically smooth trajectory in a curriculum learning setting. Furthermore, for generalization, we parameterize the policies with goal locations, so that the agent can be trained for multiple goals simultaneously. We show result in both simulated environments as well as real experiments, for a 6-DoF manipulator arm operated in position-controlled mode to validate the proposed idea. We compare the proposed ideas against a PID controller which is used to track a designed trajectory in configuration space. Our experiments show that our RL agent trained with a reference path outperformed a model-free PID controller of the type commonly used on many robotic platforms for trajectory tracking.
Optimal Binary Linear Codes From Maximal Arcs

The binary Hamming codes with parameters [2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> -1, 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> -1- m, 3] are perfect. Their extended codes have parameters [2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> , 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> - 1 - m, 4] and are distance-optimal. The first objective of this paper is to construct a class of binary linear codes with parameters [2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m+s</sup> + 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sup> - 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> , 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m+s</sup> + 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sup> - 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> - 2m -2, 4], which have better information rates than the class of extended binary Hamming codes, and are also distance-optimal. The second objective is to construct a class of distance-optimal binary codes with parameters [2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> +2, 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> -2m, 6]. Both classes of binary linear codes have new parameters.
Models for estimating daily photosynthetically active radiation in oceanic and mediterranean climates and their improvement by site adaptation techniques

In this work Photosynthetically Active Radiation (PAR) in oceanic and mediterranean climates is modeled. Twenty-two different models have been developed and tested: eleven Multilinear Regression (MR) models and eleven Artificial Neuron Network (ANN) models, using combinations of variables such as Global Horizontal Irradiance (GHI), Global Extraterrestrial Irradiance (G0), Temperature (T) and Relative Humidity (RH). Data provided by Satellite Application Facility on Climate Monitoring (CM SAF) are used to develop and train the models, while the models have been validated using field data from four stations located in Spain, covering the different study climates. According to the results, zones with different climate conditions need different models, both for the case of MR and ANN. The results show the need of including the GHI in all models in order to obtain accurate estimates; in fact, the presence of more variables only improves slightly the results in mediterranean climate, while in oceanic climate no improvement is observed. On the other hand, comparing MR and ANN models, ANN models did not show better results than those of MR models in no one of the cases studied. Regarding the climate, both types of models are clearly better for the mediterranean case than for the oceanic one. In order to improve the performance of the model for oceanic climate a correction based on the site adaptation technique was carried out. The good results obtained by this technique fully justify its use. The best proposed models provide better performance than other models which are restricted to certain locations. Besides, the clustering technique based on the PAR variable, used in this work, allows obtaining useful models for a whole region. Finally, another advantage of this methodology is that there is no need of ground measurements for its development, except for the site adaptation technique.
A GAN-Based Feature Generator for Table Detection

Table detection is of great significance for the documents analysis and recognition. Although many methods have been proposed and great progress have been made, it is still a great challenge to recognize the less-ruled tables due to the lack of table line features. In this paper, we propose a novel network to generate the layout features for table text to improve the performance of less-ruled table recognition. This feature generator model is similar to the Generative Adversarial Networks (GAN). We force the feature generator model to extract similar features for both ruling tables and less-ruled tables. It can be added into some common object detection and semantic segmentation models such as Mask R-CNN, U-Net. Extensive experiments are conducted on the dataset of ICDAR2017 Page Object Detection Competition dataset and a closed dataset full of the less-ruled tables and non-ruled tables. The primary experimental results show that the proposed GAN-based feature generator is very helpful for less-ruled table detection.
Incremental Cluster Validity Indices for Online Learning of Hard Partitions: Extensions and Comparative Study

Validation is one of the most important aspects of clustering, particularly when the user is designing a trustworthy or explainable system.However, most clustering validation approaches require batch calculation.This is an important gap because of the value of clustering in real-time data streaming and other online learning applications.Therefore, interest has grown in providing online alternatives for validation.This paper extends the incremental cluster validity index (iCVI) family by presenting incremental versions of Calinski-Harabasz (iCH), Pakhira-Bandyopadhyay-Maulik (iPBM), WB index (iWB), Silhouette (iSIL), Negentropy Increment (iNI), Representative Cross Information Potential (irCIP), Representative Cross Entropy (irH), and Conn_Index (iConn_Index).This paper also provides a thorough comparative study of correct, under-and over-partitioning on the behavior of these iCVIs, the Partition Separation (PS) index as well as four recently introduced iCVIs: incremental Xie-Beni (iXB), incremental Davies-Bouldin (iDB), and incremental generalized Dunn's indices 43 and 53 (iGD43 and iGD53).Experiments were carried out using a framework that was designed to be as agnostic as possible to the clustering algorithms.The results on synthetic benchmark data sets showed that while evidence of most under-partitioning cases could be inferred from the behaviors of the majority of these iCVIs, over-partitioning was found to be a more challenging problem, detected by fewer of them.Interestingly, over-partitioning, rather then under-partitioning, was more prominently detected on the real-world data experiments within this study.The expansion of iCVIs provides significant novel opportunities for assessing and interpreting the results of unsupervised lifelong learning in real-time, wherein samples cannot be reprocessed due to memory and/or application constraints.
Inferring Distributions Over Depth from a Single Image

When building a geometric scene understanding system for autonomous vehicles, it is crucial to know when the system might fail. Most contemporary approaches cast the problem as depth regression, whose output is a depth value for each pixel. Such approaches cannot diagnose when failures might occur. One attractive alternative is a deep Bayesian network, which captures uncertainty in both model parameters and ambiguous sensor measurements. However, estimating uncertainties is often slow and the distributions are often limited to be uni-modal. In this paper, we recast the continuous problem of depth regression as discrete binary classification, whose output is an un-normalized distribution over possible depths for each pixel. Such output allows one to reliably and efficiently capture multi-modal depth distributions in ambiguous cases, such as depth discontinuities and reflective surfaces. Results on standard benchmarks show that our method produces accurate depth predictions and significantly better uncertainty estimations than prior art while running near real-time. Finally, by making use of uncertainties of the predicted distribution, we significantly reduce streak-like artifacts and improves accuracy as well as memory efficiency in 3D map reconstruction. Video and code can be found on the project website <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
CycleGAN With an Improved Loss Function for Cell Detection Using Partly Labeled Images

The object detection, which has been widely applied in the biomedical field already, is of real significance but technically challenging. In practice, the object detection accuracy is vulnerable to labeling quality, which is usually not a big headache for simple algorithm or model verification since there are a bunch of ideal public available datasets whose classes and tags are all well-marked. However, in real scenarios, image data is often partially or even incorrectly labeled. Particularly, in cell detection, this becomes a thorny issue since the labelling of the dataset is incomplete and inaccurate. To address this issue, we propose a data-augmentation algorithm that can generate full labeled cell image data from incomplete labeled ones. First of all, we randomly extract the labeled objects from raw cell images, and meanwhile, keep their corresponding position information. Next, we employ the framework of cycle-consistent adversarial network, but significantly distinguished from the original one, to generate fully labeled data including both objects and backgrounds. We conduct extensive experiments on a blood cell classification dataset called BCCD to evaluate our model, and experimental results show that our proposed method can successfully address the weak annotation problem and improve the performance of object detection.
Expressure: Detect Expressions Related to Emotional and Cognitive Activities Using Forehead Textile Pressure Mechanomyography

We investigate how pressure-sensitive smart textiles, in the form of a headband, can detect changes in facial expressions that are indicative of emotions and cognitive activities. Specifically, we present the Expressure system that performs surface pressure mechanomyography on the forehead using an array of textile pressure sensors that is not dependent on specific placement or attachment to the skin. Our approach is evaluated in systematic psychological experiments. First, through a mimicking expression experiment with 20 participants, we demonstrate the system's ability to detect well-defined facial expressions. We achieved accuracies of 0.824 to classify among three eyebrow movements (0.333 chance-level) and 0.381 among seven full-face expressions (0.143 chance-level). A second experiment was conducted with 20 participants to induce cognitive loads with N-back tasks. Statistical analysis has shown significant correlations between the Expressure features on a fine time granularity and the cognitive activity. The results have also shown significant correlations between the Expressure features and the N-back score. From the 10 most facially expressive participants, our approach can predict whether the N-back score is above or below the average with 0.767 accuracy.