Reviewerly/exHarmony huggingface dataset:
Level-wise Dynamic Label Assignment for Oriented Object Detection

Oriented object detection has attracted extensive attention due to its wide range of applications. Most oriented detectors predefine multi-scale anchors on feature pyramids to cover objects of various sizes, and assign labels for all anchors to train the detectors. Recent advanced label assignment strategies take the quality of anchors' predictions as the indicator to assign positive/negative samples across all pyramid levels. However, since the model is not sufficiently optimized in early training stages, the predictions might be unreliable, which might make the positive samples arbitrarily selected from various pyramid levels, further reducing the detection performance. To mitigate the interference of unreliable predictions, this paper proposes a Level-wise Dynamic Label Assignment (LDLA) algorithm, which decouples the selection of pyramid levels from unreliable predictions and selects positive/negative samples level by level. Concretely, LDLA first estimates the number of positive samples on each pyramid level based on the statistics of anchors instead of the predictions. Afterwards, LDLA defines score functions to measure the quality of anchors, and selects anchors with higher scores from each level as positive samples. Extensive experiments conducted on DOTA and HRSC2016 datasets demonstrate the effectiveness of the proposed LDLA.
Exploring Beyond-Demonstrator via Meta Learning-Based Reward Extrapolation

Extrapolating beyond-demonstrator (BD) performance through the imitation learning (IL) algorithm aims to learn from and subsequently outperform the demonstrator. To that end, a representative approach is to leverage inverse rein-forcement learning (IRL) to infer a reward function from demon-strations before performing RL on the learned reward function. However, most existing reward extrapolation methods require massive demonstrations, making it difficult to be applied in tasks of limited training data. To address this problem, one simple solution is to perform data augmentation to artificially generate more training data, which may incur severe inductive bias and policy performance loss. In this paper, we propose a novel meta learning-based reward extrapolation (MLRE) algorithm, which can effectively approximate the ground-truth rewards using limited demonstrations. More specifically, MLRE first learns an initial reward function from a set of tasks that have abundant training data. Then the learned reward function will be fine-tuned using data of the target task. Extensive simulation results demonstrated that the proposed MLRE can achieve impressive performance improvement as compared to other similar BDIL algorithms. Our code is available at GitHub <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://github.com/yuanmingqi/MLRE.
Bayesian Folding-In Using Generalized Dirichlet and Beta-Liouville Kernels for Information Retrieval

Probabilistic latent semantic indexing (PLSI) has been proposed to represent textual documents as mixture proportions of latent topics. Compared to the standard latent semantic indexing (LSI), PLSI has a solid statistical foundation. However, the need of folding new documents into the latent topic space led to the definition of PLSI folding-in. Previous studies have shown that in case of short queries, poor vocabulary results in small sample of words with non-zero frequencies. Therefore, PLSI folding-in tends to produce topic mixtures that are predominated by a single latent aspect. As a result, folding-in is unable to take into consideration different mapping. Thus, Bayesian folding-in was introduced to involve the topic mixtures of the known document and the mixture proportions of topics for a new document were estimated by maximizing the posterior. Hence, the prior was defined as a kernel density estimate using a Dirichlet distribution. Although Bayesian folding-in overcomes PLSI folding-in, there are still some drawbacks since the Dirichlet distribution has negative covariance structure that makes it restrictive, especially in the case of count data. To improve previous works, we propose using generalized Dirichlet (GD) distribution and Beta-Liouville (BL) distribution as kernel densities in a Bayesian framework for information retrieval.
Label Clustering for Particle Swarm Optimisation based Multi-Label Feature Selection

Feature selection is an important pre-processing step for multi-label classification, which selects a small subset of complementary features and improves the classification performance. Particle Swarm Optimisation (PSO) has recently been widely applied to achieve multi-label feature selection. However, when the number of labels is large, existing PSO-based multi-label approaches are computationally intensive. In multi-label classification, many labels usually appear together (for example, “sea” and “ship” labels), which are regarded as similar labels. This work proposes to group similar labels together to form a label cluster. By considering each label cluster as a super label, the number of labels is significantly reduced, and thus using the super labels can significantly reduce the computational cost. The experimental results on nine real-world datasets show that the proposed label clustering mechanism can maintain or even improve the multi-label classification performance while being much more efficient and selecting smaller numbers of features than using a large number of original labels.
Satellite-derived Photovoltaic Power Forecasting Based on Various Attention Inputs for Higher Self-explainability

Renewable power generation is beneficial to handle the global energy shortage problem. However, the intermittent generation output is still the major drawback of renewable energy sources, such as solar energy. Forecasting technology can efficiently deal with these stochastic energy sources, and numerous studies have developed state-of-the-art forecasting models in order to increase the prediction accuracy. Among those, satellite-derived models perform well in short- term photovoltaic (PV) power forecasting, since they can capture the local cloud motion by using historical satellite images. Nevertheless, the explainability of these novel models decreases as the price of accuracy improvements, especially for those intelligent black-box forecast models. The limited geographical resolution of satellite measurements may also lead to the miss or over-forecast of a solar power ramp, causing higher economic loss and model distrust. It is necessary to understand how the models obtain the forecast results, whereas this problem still attracts little attention nowadays. As a result, this study develops a novel model structure focusing on satellite- derived PV power forecasting. The model can measure the effects of different cloud motion displacement by involving various input combinations. The results denote that the proposed method can improve the model explainability while retaining the accuracy level.
Data Augmentation Meta-Classifier Scheme for imbalanced data sets

Categorical data obtained from real-world domains are commonly imbalanced, as they often present more number of samples belonging to one of the classes. Imbalanced data tends to be a problem for classifiers, as the majority class biased them and affects overall performance. Among the techniques used for dealing with imbalanced data sets, data augmentation (DA) constitutes an alternative, as it can improve the accuracy of prediction for the minority class (usually the relevant one), but usually at the cost of a loss regarding predictions of the majority one. To benefit from both behaviours, we introduce in this study a meta-classifier scheme that works as a mixture of two classifiers, one trained with the original data and the second one trained using augmented data. The experiments carried out with 12 imbalanced data sets, 5 of them obtained from the TCGA database related to cancer survival prediction, show an improvement in accuracy, area under the ROC curve and Matthews correlation coefficient values compared to the results obtained using the original data sets.
Data squashing as preprocessing in association rule mining

Data squashing is a well-known preprocessing method in Machine Learning that enables construction of smaller datasets from the original ones and provides approximately the same results of data analysis as the original. The paper proposes a new data squashing method for Association Rule Mining based on the Cosine similarity and Euclidean distance similarity. The method was applied to three datasets from the UCI Machine Learning repository. The results showed that the proposed data squashing method is effective, scalable, and easy to use, and therefore represents a huge potential for use in practice.
On Reducing Adversarial Vulnerability with Data Dependent Stochastic Resonance

Neural networks are vulnerable to adversarial attacks formed by minuscule perturbations to the original data. These perturbations lead to significant performance degradation. Previous works on defenses against adversarial evasion attacks typically involve pre-processing input data at training or testing time, or modifications to the objective function optimized during the training. In contrast, relatively fewer defense methods focus on modifying the topology and functionality of the underlying defended neural network. Additionally, prior theoretical examinations of the geometry of adversarial examples reveal a challenging and intrinsic trade-off between adversarial and benign accuracy. We introduce a novel modification to a traditional feed-forward convolutional neural network that embeds uncertainty within the network's hidden representations in a learned and data-dependent manner. Our proposed alteration renders the network significantly more resilient than comparably computationally expensive alternatives. Further, the empirical investigation of the proposed defense demonstrates that, unlike prior defense techniques that are comparable to state-of-the-art, the stochastic resonance effect improves adversarial accuracy without significant degradation in benign accuracy.
Detection, Explanation and Filtering of Cyber Attacks Combining Symbolic and Sub-Symbolic Methods

Machine learning (ML) on graph-structured data has recently received deepened interest in the context of intrusion detection in the cybersecurity domain. Due to the increasing amounts of data generated by monitoring tools as well as more and more sophisticated attacks, these ML methods are gaining traction. Knowledge graphs and their corresponding learning techniques such as Graph Neural Networks (GNNs) with their ability to seamlessly integrate data from multiple domains using human-understandable vocabularies, are finding application in the cybersecurity domain. However, similar to other connectionist models, GNNs are lacking transparency in their decision making. This is especially important as there tend to be a high number of false positive alerts in the cybersecurity domain, such that triage needs to be done by domain experts, requiring a lot of man power. Therefore, we are addressing Explainable AI (XAI) for GNNs to enhance trust management by exploring combining symbolic and sub-symbolic methods in the area of cybersecurity that incorporate domain knowledge. We experimented with this approach by generating explanations in an industrial demonstrator system. The proposed method is shown to produce intuitive explanations for alerts for a diverse range of scenarios. Not only do the explanations provide deeper insights into the alerts, but they also lead to a reduction of false positive alerts by 66% and by 93% when including the fidelity metric.
Teleoperation Control for Mobile Robot via Leap Motion Device

Human-computer interaction can be divided into touch and non-touch, of which somatosensory control technology is a new non-touch technology. Gesture control is a method of somatosensory interaction, which refers to the shaking operation control of the system by recognizing hand movements. Leap Motion is a gesture-controlled somatosensory sensor with ultra-high resolution and fast recognition performance. In this paper, we construct a straightforward and efficient gesture detection method for the Leap Motion gadget. We introduce the steering model and Kinematic model of the mobile robot. Then, we introduce the controller development. Next, we build a wireless network environment and design a teleoperation control system for experimental verification. Finally, we experimentally achieve the control of the mobile robot with straightforward gestures.
3D Point Cloud Matching without Projection Constraint based on Gray Gradient Optimization

Stereo vision measurement is the main method of 3D reconstruction. However, it relies on projection methods, such as structured light projection, grating projection, phase shift projection, to achieve point matching in different shooting positions, which is not conducive to portable application. In this paper, the idea of 3D reconstruction without projection constraint was proposed, which can be realized by the process of mark points recognition and matching, feature edge extraction, matching region division, and point cloud matching. In order to improve the accuracy of point cloud matching, the matching algorithm based on gray gradient optimization was adopted. To verify its effectiveness, the checkerboard with $5*7$ squares was used in the 3D reconstruction experiment, the proposed method was compared with the interpolation method. The results show that our method outperformed the interpolation method in flatness ($\lt 0.06 {\mathrm{mm}}$). Moreover, it can provide a reference for stereo vision measurement without projection constraints.
Manipulators 3D printing trajectory tracking control combined with RBFNNs and visual feedback

Manipulators are used in the 3D printing due to their flexibility and wide working range. However, due to the tandem structure themselves, the motion accuracy of the end-effector is low, which affects the geometric accuracy and quality of printed products. Therefore, it is proposed to use visual servo control to improve the trajectory accuracy, and combine the radial basis function neural networks (RBFNNs) to approximate the robot dynamic parameters, so as to avoid the influence of the manipulators’ mass inertia and gravity properties on the motion accuracy under high speed and heavy load. Aiming at the visual measurement feedback method, a combined method of error similarity theory in continuous space and inverse distance weighting is proposed to estimate the actual error of the endeffector. The controller of RBFNNs dynamics feedforward coupled visual servo sliding mode control (SMC) is designed, and Lyapunov stability theory is used to prove the stability of the control system. Combined with Matlab/Simulink to build a simulation model to verify the effectiveness of the controller.
Training and Testing Cascades for Imbalanced Data Classification

Although prediction models can provide high accuracy with massive data, learning from imbalanced data is a great challenge. Existing imbalanced-data solutions change the data distributions or the minority class's impact on the models by increasing the false positive rate to achieve a better recall. To overcome these issues, we develop an approach based on the cascade structure (a set of sequential learners). Our approach comprises training and testing cascades. The entire cascade is first trained on the actual data distribution by removing many negative data based on the probability thresholds and the desired class ratio. The obtained cascade is then used to predict unseen data. We validate our approach on two highly imbalanced datasets. The empirical results show our approach superiority when compared to existing solutions.
Learning CNN architecture for multi-view text classification using genetic algorithms

Natural Language Processing is a sub-field of Artificial Intelligence focusing on the interaction of human languages and computers. As the way which computers communicate and treat data is different to the way natural languages are represented, there is a range of unique challenges in this field. Text Classification is a fundamental NLP task, in which Convolutional Neural Networks (CNN) have shown state of the art performance in. However, designing an optimal CNN model architecture can often require specialist knowledge, as well as be an arduous process for each distinct problem. This paper presents two Genetic Algorithms (GA), single-view and multi-view, which can automatically learn CNN architectures for text classification problems.
Learning from coarsely-labeled images for semantic segmentation

Image segmentation aims to assign a semantic label to each pixel of an image and thus requires accurate pixel-wise annotations of massive images to train high-performance models. This paper aims to alleviate the arduous annotations by learning from coarser polygonal annotations that can be acquired at a lower cost. A novel self-training framework is proposed to build a robust semantic segmentation model in the presence of noisy labels. A superpixel-based relabeling strategy is developed to refine the original annotations according to the local context. Then multiple segmentation models are trained on the revised annotations and produce multiple pseudo labels for model re-training. Finally, a new segmentation model is re-trained by fusing multiple pseudo-labels in terms of their confidences. Experimental results on the Cityscapes dataset demonstrate the effectiveness of the proposed method.
Preserving Fine-Grained Style Consistency for Universal Image Style Transfer

Universal image style transfer requires not only maintaining the semantic content but also transferring arbitrary visual styles. Recent progress has been made through processing an image as a whole, but without considering fine-grained styles of different semantic regions in the image. In this paper, we propose a Fine-Grained Style Transfer (FGST) model, which renders different content image regions into different fine-grained styles, thus improving the comprehensibility and visual effect of the stylized image. Specifically, we segment the input images into different semantic regions first, and then select the style and content image with the same semantic regions for training to preserve the fine-grained style consistency. In addition, we design a new style loss function to evaluate style consistency between the output stylized image and the input style image. Compared with the state-of-the-art models, experiments show that our model obtains better visual effects.
WiM-Based Group Navigation for Collaborative Virtual Reality

Group navigation is essential for arranging group members in a certain configuration, as it supports team communication and interaction in collaborative virtual reality (VR). We propose a group World-in-Miniature (group WiM) navigation technique that allows a guide to control and navigate a team within a miniature replica of the entire virtual environment. We evaluated the technique’s usability, user performance, and discomfort considering both user roles: the group guide, who controls navigation and arrangement, and group members, who are the ones being navigated and arranged. In addition, we compared it with a state-of-the-art group teleportation technique in a user study (n = 20). No significant differences were observed in the placement error and for the interaction effect. Qualitative feedback on the perceived advantages and disadvantages of both techniques was collected and summarized. The participants highlighted that the group WiM provides an overview and full control for navigation in complex virtual environments, while group teleportation provides a good understanding of the spatial environment during the navigation.
Efficient and Iterative Training for High-Performance Light Field Synthesis

Light field displays are considered a promising technique for future augmented reality (AR) displays since they provide a fully natural visual experience by reproducing light rays in space. However, the acquisition of a high-resolution light field is a great challenge. In this paper, we propose a high-performance, efficient, and iterative training framework that helps synthesize a light field from a pair of stereo images. Our iterative training framework combines the advantage of different input image disparity measures and performs favorably against state-of-the-art algorithms for light field synthesis from extremely sparse (only one, two, or four) views on real and synthetic light field datasets. Our model structure consists of a convolutional neural network (CNN) that enforces a left-right consistency constraint on the light fields synthesized from left and right stereo views, a stage that merges light fields synthesized from left and right stereo views with a novel alpha blending technique, and a final refinement network using a unique 3D convolution operation. Our method also speeds up the process of light field synthesis, realizing real-time display of light fields for AR.
Group Activity Recognition based on Dual Channel Feature Processing Network

Spatiotemporal modeling is the key element of group activity recognition. Most of the proposed methods deduce the relationship between characters based on spatiotemporal information, while ignoring the inter frame information. This paper proposes a dual channel feature processing model. In the first channel, a temporal semantic relation subgraph network is proposed to explore the relationship between video frames. In the other channel, a Personal Feature Walk Module (PFWM) is designed to capture the dynamic information of character features in the video frame. The outputs of the two channels are combined to classify the group activity. In addition, Backbone Exciting Module (BEM) stimulates the backbone’s motion sensitive channel from three aspects of space-time, channel and motion is used. Mixed Pooling Module (MPM) makes the network more efficient to obtain the information in a wide range of receptive fields, and enhances the basic feature extraction of characters in video frames. The model is tested on the most widely used datasets in the field of group activity recognition, and excellent results are obtained, which proves the effectiveness of the model.
A line segment detector based on subanchor point and line segment combination method

Linear segment features contains rich geometric information and it is crucial to perform accurate linear segment detection. We propose a temporal line segment detector based on subanchor point and line segment combination approach, which gives more accurate results without adjusting the parameters. The speed remains essentially the same as the fastest running line segment detectors (EDLines) existing and is 89’ faster compared to the conventional LSD line extraction algorithm. The proposed algorithm introduces bilateral filtering and line segment combination methods based on EDLines. The bilateral filter is used after the image input to remove the noise and retain the edge information of the image well. The short line segments that exist homologous are combined to generate clearer long line segments, which overcomes the problem of broken lines in LSD algorithm and excessive filtering of short line segments in EDLines algorithm. We validate the effectiveness of the proposed algorithm in experiments of line feature extraction for multiple sets of images in different scenes.