Reviewerly/exHarmony huggingface dataset:
High-accuracy three-dimensional measurement based on multi-directional cooperative target with weighted SfM algorithm

Large-scale three-dimensional (3D) precision measurement is limited by the high-accuracy single-view measurement range. Therefore, it is necessary to perform multi-station coordinate transformation through high-precision 3D cooperative target to complete the measurement for large-scale objects. However, high-precision 3D cooperative target is difficult to be manufactured. The physical features on the cooperative target are also easy to occlude each other in complex measurement environments. To solve these problems and reduce the number of coordinate transformations in the process of global coordinate unification, a Multi-directional Cooperative (MDC) target consisting of designed coded planar feature target and polyhedron skeletons is designed. The designed MDC target is arbitrarily placed in the Field of View (FOV) and images of the target are obtained from multiple views. Based on the captured target images, a new Weighted SfM algorithm is proposed to realize the high-accuracy 3D measurement for the MDC target. The experimental results show that the proposed algorithm has good robustness and high accuracy for the designed MDC target.
Predicting PV Areas in Aerial Images with Deep Learning

Data on the location of distributed photovoltaic installations are valuable to a variety of research activities. We have trained and applied a Fully Convolutional Neural Network to identify PV sites from aerial images of Oldenburg, Germany acquired from Google Maps. The architecture used was U-net, which was trained on a set of manually labelled images, and verified against a test dataset. The model is able to accurately estimate location and shape of PV plants in the north European town of Oldenburg. In addition, the model is able to estimate its own uncertainty, breaking the black box assumption of Deep Learning.
Distributed Training of Graph Convolutional Networks

The aim of this work is to develop a fully-distributed algorithmic framework for training graph convolutional networks (GCNs).The proposed method is able to exploit the meaningful relational structure of the input data, which are collected by a set of agents that communicate over a sparse network topology.After formulating the centralized GCN training problem, we first show how to make inference in a distributed scenario where the underlying data graph is split among different agents.Then, we propose a distributed gradient descent procedure to solve the GCN training problem.The resulting model distributes computation along three lines: during inference, during backpropagation, and during optimization.Convergence to stationary solutions of the GCN training problem is also established under mild conditions.Finally, we propose an optimization criterion to design the communication topology between agents in order to match with the graph describing data relationships.A wide set of numerical results validate our proposal.To the best of our knowledge, this is the first work combining graph convolutional neural networks with distributed optimization.
Discovering High Utility Itemsets Using Set-Based Particle Swarm Optimization

Mining high utility itemsets (HUIs) is a hot research topic in data mining. Algorithms based on evolutionary computation are attracting increasing attention because they have the advantage of avoiding the combinatorial explosion of the HUI search space. Among evolutionary methods used for mining HUIs, particle swarm optimization (PSO) is the most popular. Existing PSO-based HUI mining (HUIM) algorithms transform positions according to the result of applying the sigmoid function to the velocity. In this paper, we propose an HUIM algorithm based on set-based PSO (S-PSO) called HUIM-SPSO, which mainly considers elements in positions whose velocities are high. We introduce the modeling of HUIM using S-PSO, and explain HUIM-SPSO in detail. To reflect the diversity of the mining results, we propose the measure of the bit edit distance. Extensive experimental results show that the HUIM-SPSO algorithm is efficient and can discover more HUIs with a high degree of diversity.
3D Human Pose and Shape Estimation Through Collaborative Learning and Multi-view Model-fitting

3D human pose and shape estimation plays a vital role in many computer vision applications. There are many deep learning based methods attempting to solve the problem only relying on single-view RGB images for training the network. However, since some public datasets are captured from multi-view cameras system, we propose a novel method to tackle the problem by putting optimization-based multi-view model-fitting into a regression-based learning loop from multi-view images. Firstly, a convolutional neural network (CNN) regresses the pose and shape of a parametric human body model (SMPL) from multi-view images. Then, utilizing the regressed pose and shape as initialization, we propose an improved multi-view optimization method based on the SMPLify method (MV-SMPLify) to fit the SMPL model to the multi-view images simultaneously. Subsequently, the optimized parameters can be adopted to supervise the training of the CNN model. This whole process forms a self-supervising framework which can combine the advantages of the CNN approach and the optimization- based approach through a collaborative process. In addition, the multi-view images can provide more comprehensive supervision for the training. Experiments on public datasets qualitatively and quantitatively demonstrate that our method outperforms previous approaches in a number of ways.
Opening New Dimensions: Vehicle Motion Planning and Control using Brakes while Drifting

Autonomous vehicles should be able to maintain control in scenarios that push them beyond the limits of handling. In case of unintended rear tire force saturation while driving, the vehicle should be able to decelerate while ensuring the navigation of an obstacle free path. With that objective, this paper presents a novel architecture capable of controlling a rear-wheel drive vehicle in a drift using brakes in addition to steering and throttle. We demonstrate the existence of another dimension of drift equilibria which allow motion planning algorithms to prescribe vehicle states independently even while drifting. A tangent space analysis illustrates the transformation from an under-actuated to a fully-actuated system with the use of front-wheel braking. Minimal modifications to existing state of the art in drifting can exploit the additional actuation to significantly increase the set of feasible actions for the vehicle. The framework is then experimentally validated for two different trajectories on MARTY, an electric DeLorean drift research platform.
HEMlets PoSh: Learning Part-Centric Heatmap Triplets for 3D Human Pose and Shape Estimation

Estimating 3D human pose from a single image is a challenging task. This work attempts to address the uncertainty of lifting the detected 2D joints to the 3D space by introducing an intermediate state - Part-Centric Heatmap Triplets (HEMlets), which shortens the gap between the 2D observation and the 3D interpretation. The HEMlets utilize three joint-heatmaps to represent the relative depth information of the end-joints for each skeletal body part. In our approach, a Convolutional Network (ConvNet) is first trained to predict HEMlets from the input image, followed by a volumetric joint-heatmap regression. We leverage on the integral operation to extract the joint locations from the volumetric heatmaps, guaranteeing end-to-end learning. Despite the simplicity of the network design, the quantitative comparisons show a significant performance improvement over the best-of-grade methods (e.g., 20 percent on Human3.6M). The proposed method naturally supports training with "in-the-wild" images, where only weakly-annotated relative depth information of skeletal joints is available. This further improves the generalization ability of our model, as validated by qualitative comparisons on outdoor images. Leveraging the strength of the HEMlets pose estimation, we further design and append a shallow yet effective network module to regress the SMPL parameters of the body pose and shape. We term the entire HEMlets-based human pose and shape recovery pipeline HEMlets PoSh. Extensive quantitative and qualitative experiments on the existing human body recovery benchmarks justify the state-of-the-art results obtained with our HEMlets PoSh approach.
Methods for Hyperparameters Optimization in Learning Approaches: An Overview

Automatic learning research focuses on the development of methods capable of extracting useful information from a given dataset. A large variety of learning methods exists, ranging from biologically inspired neural networks to statistical methods. A common trait in these methods is that they are parameterized by a set of hyperparameters, which must be set appropriately by the user to maximize the usefulness of the learning approach. In this paper we review hyperparameter tuning and discuss its main challenges from an optimization point of view. We provide an overview on the most important approaches for hyperparameter optimization problem, comparing them in terms of advantages and disadvantages, focusing on Gradient-based Optimization.
Reinforcement Learning in Nonzero-sum Linear Quadratic Deep Structured Games: Global Convergence of Policy Optimization

We study model-based and model-free policy optimization in a class of nonzero-sum stochastic dynamic games called linear quadratic (LQ) deep structured games. In such games, players interact with each other through a set of weighted averages (linear regressions) of the states and actions. In this paper, we focus our attention to homogeneous weights; however, for the special case of infinite population, the obtained results extend to asymptotically vanishing weights wherein the players learn the sequential weighted mean-field equilibrium. Despite the non-convexity of the optimization in policy space and the fact that policy optimization does not generally converge in game setting, we prove that the proposed model-based and model-free policy gradient descent and natural policy gradient descent algorithms globally converge to the sub-game perfect Nash equilibrium. To the best of our knowledge, this is the first result that provides a global convergence proof of policy optimization in a nonzero-sum LQ game. One of the salient features of the proposed algorithms is that their parameter space is independent of the number of players, and when the dimension of state space is significantly larger than that of the action space, they provide a more efficient way of computation compared to those algorithms that plan and learn in the action space. Finally, some simulations are provided to numerically verify the obtained theoretical results.
Effect of the Affordances of the FM New Media Communication Interface Design for Smartphones

Smartphone equipment has promoted the widespread use of new media communication, and users have changed from being passive to actively receiving information. This has changed people’s lifestyles, and has enriched the convenience and entertainment value of knowledge acquisition. However, the new media communication systems can be too complex, and the interface design for users to interact with the new media may not be sufficiently intuitive, which causes interface usability problems. Therefore, this research study focuses on the concept of affordance and the impact of user perception pertinent to smartphone applications on frequency modulation (FM) new media interface design. The experiment is a between-subjects design using one-way ANOVA to examine three different operation modes, namely the Litchi, Himalayas and Archimedes types. The experimental data were obtained through task performance and subjective evaluation. The results indicate that: (1) Visual information presentation methods, such as viewing and deleting, affect user perception. The three operating modes revealed significant differences, with the Himalayas type taking the least amount of task performance time. (2) There were significant differences among the different operation modes, with the Himalayas type the best in terms of the users’ subjective evaluation. (3) The overall analysis of task performance and satisfaction consistently showed that the Himalayas type was better than the Litchi and Archimedes types in all aspects. (4) Smartphone user interface applications provide users with cognitive support, objects, functions, and sensory affordance, which enhance the user’s interactive experience of FM new media.
Arbitrary-Oriented Ship Detection via Feature Fusion and Visual Attention for High-Resolution Optical Remote Sensing Imagery

Ship detection for high-resolution optical remote sensing (HRORS) imagery is widely applicable to Navy construction, maritime transportation, maritime safety, port management, and many other fields. Optical remote-sensing images can easily be disturbed by thin cloud, light, and some complex environmental conditions, which makes the task of ship detection challenging. In this paper, we propose a new general detection network framework to recognize ships with arbitrary-orientation, in which we use a novel way to define the rotating rectangular box, remove the dependence on methods with angle parameters of the target. We propose a new feature fusion module (FFM) to integrate the semantic information, which enhances the feature extraction from the shallow network, and effectively improve the detection of small targets. We also employ one attention module (AM) to adaptively select meaningful features, which enhance the ability of feature extraction for complex environmental conditions. The multi-directional anchor size and length-width ratio are designed based on the clustering technique to improve the convergence speed and accuracy. Experiments on the public HRORS ship detection dataset HRSC2016 and the ship dataset SSD2020 collected from Google Earth have shown better performance compared with some conventional methods, which prove the feasibility and effectiveness of our proposed network architecture.
Adaptive Streaming of 360-Degree Videos with Reinforcement Learning

For bandwidth-efficient streaming of 360-degree videos, the streaming technique must adapt both to the changing viewport of the user and variations of the available network bandwidth. The state-of-the-art streaming techniques for this problem attempt to solve an optimization using simplified rules that do not adapt very well to the uncertainties related to the viewport or network. We adopt a 3D-Convolutional Neural Networks (3DCNN) model to extract spatio-temporal features of videos and predict the viewport. Given the sequential decision-making nature of such streaming technique, we then apply a Reinforcement Learning (RL) based adaptive streaming approach. We address the challenges of using RL in this scenario, such as large action space and delayed reward evaluation. Comprehensive evaluations with real network traces show that the proposed method outperforms three tile-based streaming techniques for 360-degree videos. Compared to the tile-based streaming techniques, the average user-perceived bitrate of the proposed method is 1.3-1.7 times higher and the average quality of experience of the proposed method is also 1.6-3.4 times higher. Subjective user studies further confirm the superiority of the proposed approach.
k- Means-MIND: An Efficient Alternative to Repetitive k-Means Runs

The problem of local minimum in k-means clustering, is commonly addressed by running the algorithm repeatedly in order to choose the best run. Although effective, the approach is computationally expensive. In this paper, we observe that the approach is effectively a comparison among different initializations. Thus, if there is a way to compare these initializations ab initio, there will be no need for repeated clustering. We propose such a technique in this paper. Specifically, we choose the initialization with the largest minimum inter-center distance (MIND), as the `best' one. In other words, our technique is a general approach to improving existing seeding techniques. We demonstrate the concept with MIND-optimized versions of two standard algorithms: k-means and k-means++. Experiments show that in addition to drastic efficiency gain when compared to repetitive k-means, our approach improves the accuracy of the standard versions of these algorithms.
Conditional image generation with One-Vs-All classifier

This paper explores conditional image generation with a One-Vs-All classifier based on the Generative Adversarial Networks (GANs). Instead of the real/fake discriminator used in vanilla GANs, we propose to extend the discriminator to a One-Vs-All classifier (GAN-OVA) that can distinguish each input data to its category label. Specifically, we feed certain additional information as conditions to the generator and take the discriminator as a One-Vs-All classifier to identify each conditional category. Our model can be applied to different divergence or distances used to define the objective function, such as Jensen-Shannon divergence and Earth-Mover (or called Wasserstein-1) distance. We evaluate GAN-OVAs on MNIST and CelebA-HQ datasets, and the experimental results show that GAN-OVAs improve generation quality and the stability of training.
RETRACTED: Development of tourism resources based on fpga microprocessor and convolutional neural network

Accuracy of inbound tourism demand, it is important for the development and implementation of inbound tourism strategy. Use the FPGA, as in the common traditional mechanical, convolution neural network, learn how they are wide, it is being used to traffic demand forecasting model. This over fitting, the difficulty of the parameters, as such convolution neural network, drawbacks that receive, and due to the minimum number of problem set. The proposed system has a combination of neural networks and FPGA convolution to establish tourism demand forecasting neural network. The most advanced methods for the country's tourism research for traditional statistical methods, predictive models and artificial intelligence soft computing techniques. Please improve tourism research and modelling method introduces significant prediction accuracy of artificial intelligence methods. The results showed that, compared with, including FPGA traditional neural network neural network convolution essence of traditional statistical methods and methods can improve the prediction accuracy. This approach provides a more accurate forecast travel demand model is a better choice. Develop and compare various classifiers based on convolutional neural networks (CNN) and long short-term memory networks (LSTM). These classifiers were trained and validated with data from hotels located on the island of Tenerife. An analysis of our findings shows that the most accurate and robust estimators are those based on LSTM recurrent neural networks.
Motion Planning for Autonomous Grain Carts

When harvesting grain crops on large farms, a combine collects the grain while a grain cart transports the grain by commuting between the combine and a semi-trailer parked by the roadside. There are several issues associated with human-operated grain carts: labor shortage and increasing labor cost, operational imprecision and inefficiency as well as safety hazards, all of which can potentially be addressed if grain carts were autonomous. This paper presents a motion planning algorithm and the associated navigation solution for autonomous grain carts. The algorithm features a novel integration of Artificial Potential Field (APF) with Fuzzy Logic Control (FLC). A set of simulation tests were carried out, comparing the proposed APF+FLC planner with a simple APF planner. The test results verified the effectiveness, robustness, and efficiency of the proposed planning algorithm in performing the logistical tasks in harvest operations where unharvested crops were the only obstacles as well as when random static or dynamic obstacles existed. In addition, a set of mobile robot tests implementing the proposed navigation solution were conducted, in which the robot representing the grain cart autonomously accomplished the logistical tasks in the harvest operations, verifying the effectiveness and practicality of the navigation solution.
Weighted visual secret sharing for general access structures based on random grids

The traditional visual secret sharing (VSS) schemes share a secret image into multiple shares, and all the shares have the same importance. However, it is occasionally necessary to differentiate participants based on their different levels of importance in certain applications. In previous works, some weighted VSS schemes were implemented only for the (k,n) threshold access structure and were not applicable to other access structures. In this paper, a weighted VSS scheme is proposed for general access structures (GAS) based on random grids (RG). For the different qualified sets of shares, the amount of information we get about the secret image is different because the total weight of the shares participating in the recovery process is different. Moreover, both OR and XOR operations can be applied to recover the secret image, which can extend the applications of our scheme. For the same minimal qualified set, we can obtain better visual quality by stacking (XOR-ing) more shares. In addition, we can obtain the lossless secret image by performing the XOR operation on all shares. More importantly, our scheme is suitable for GAS; however, it has certain limitations in the weight assignment of participants for the non-threshold access structures. Finally, experiments and comparisons demonstrate the effectiveness of our scheme.
A Novel Genetic Algorithm Approach to Simultaneous Feature Selection and Instance Selection

With advancements in technology, the amount of collected data has been sharply increased. Most of the existing learning algorithms do not perform well on such huge datasets due to useless data such as irrelevant/redundant features and noisy instances. Feature selection and instance selection are two common data reduction approaches to address this issue. However, most of existing data reduction algorithms focus solely on either feature selection or instance selection. In this paper, based on a Genetic Algorithm, we propose a novel data reduction algorithm that can perform feature selection and instance selection simultaneously, which considers the complementary interaction between selected features and instances. The proposed algorithm is examined on nine real-world datasets with varying difficulties. The experimental results show that the proposed algorithm can successfully reduce both the number of features and the number of instances while maintaining or even improving the learning performance over using the original data.
An Adaptive Reversible Data Hiding Scheme Using AMBTC and Quantization Level Difference

Hiding a message in compression codes can reduce transmission costs and simultaneously make the transmission more secure. This paper presents an adaptive reversible data hiding scheme that is able to provide large embedding capacity while improving the quantity of modified images. The proposed scheme employs the quantization level difference (QLD) and interpolation technique to adaptively embed the secret information into pixels of each absolute moment block truncation coding (AMBTC)-compressed block, except for the positions of two replaced quantization levels. The values of QLD tend to be much larger in complex areas than in smooth areas. In other words, our proposed method can obtain good performance for embedding capacity and still meets the requirement for better modified image quality when the image is complex. The performance of the proposed approach was compared to previous image hiding methods. The experimental results show that our approach outperforms referenced approaches.