Reviewerly/exHarmony huggingface dataset:
Image Inpainting via Conditional Texture and Structure Dual Generation

Deep generative approaches have recently made considerable progress in image inpainting by introducing structure priors. Due to the lack of proper interaction with image texture during structure reconstruction, however, current solutions are incompetent in handling the cases with large corruptions, and they generally suffer from distorted results. In this paper, we propose a novel two-stream network for image inpainting, which models the structure-constrained texture synthesis and texture-guided structure reconstruction in a coupled manner so that they better leverage each other for more plausible generation. Furthermore, to enhance the global consistency, a Bi-directional Gated Feature Fusion (Bi-GFF) module is designed to exchange and combine the structure and texture information and a Contextual Feature Aggregation (CFA) module is developed to refine the generated contents by region affinity learning and multi-scale feature aggregation. Qualitative and quantitative experiments on the CelebA, Paris StreetView and Places2 datasets demonstrate the superiority of the proposed method. Our code is available at https://github.com/Xiefan-Guo/CTSDG.
Co-Scale Conv-Attentional Image Transformers

In this paper, we present Co-scale conv-attentional image Transformers (CoaT), a Transformer-based image classifier equipped with co-scale and conv-attentional mechanisms. First, the co-scale mechanism maintains the integrity of Transformers’ encoder branches at individual scales, while allowing representations learned at different scales to effectively communicate with each other; we design a series of serial and parallel blocks to realize the co-scale mechanism. Second, we devise a conv-attentional mechanism by realizing a relative position embedding formulation in the factorized attention module with an efficient convolution-like implementation. CoaT empowers image Transformers with enriched multi-scale and contextual modeling capabilities. On ImageNet, relatively small CoaT models attain superior classification results compared with similar-sized convolutional neural networks and image/vision Transformers. The effectiveness of CoaT’s backbone is also illustrated on object detection and instance segmentation, demonstrating its applicability to downstream computer vision tasks.
Real-Time Traffic Sign Recognition Based on Efficient CNNs in the Wild

Both unmanned vehicles and driver assistance systems require solving the problem of traffic sign recognition. A lot of work has been done in this area, but no approach has been presented to perform the task with high accuracy and high speed under various conditions until now. In this paper, we have designed and implemented a detector by adopting the framework of faster R-convolutional neural networks (CNN) and the structure of MobileNet. Here, color and shape information have been used to refine the localizations of small traffic signs, which are not easy to regress precisely. Finally, an efficient CNN with asymmetric kernels is used to be the classifier of traffic signs. Both the detector and the classifier have been trained on challenging public benchmarks. The results show that the proposed detector can detect all categories of traffic signs. The detector and the classifier proposed here are proved to be superior to the state-of-the-art method. Our code and results are available online.
A Novel Ensemble ELM for Human Activity Recognition Using Smartphone Sensors

Human activity recognition plays a unique role in many important applications, including ubiquitous computing, health-care services, and smart buildings. Due to the nonintrusive property of smartphones, smartphone sensors are widely used for the identification of human activities. Since the signals of smartphone sensors are quite noisy, feature engineering will be performed to extract more discriminant representations. Then, various machine learning algorithms can be employed to recognize different human activities. Extreme learning machine (ELM) has been shown to be effective in classification tasks with extremely fast learning speed. Due to its randomness property, it is naturally suitable for ensemble learning. In this paper, we propose a novel ensemble ELM algorithm for human activity recognition using smartphone sensors. Gaussian random projection is employed to initialize the input weights of base ELMs. By doing this, more diversities can be generated to boost the performance of ensemble learning. Real experimental data has been applied to evaluate the performance of our proposed approach. We also conduct a comparison of the proposed approach with some state-of-the-art approaches in the literature. The experimental results indicate that our proposed ensemble ELM approach outperforms these approaches and can achieve recognition accuracies of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{97.35}\%$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{98.88}\%$</tex-math></inline-formula> on two datasets.
Optimisation of colour generation from dielectric nanostructures using reinforcement learning

Recently, a novel machine learning model has emerged in the field of reinforcement learning known as deep Q-learning. This model is capable of finding the best possible solution in systems consisting of millions of choices, without ever experiencing it before, and has been used to beat the best human minds at complex games such as, Go and chess, which both have a huge number of possible decisions and outcomes for each move. With a human-level intelligence, it has solved the problems that no other machine learning model has done before. Here, we show the steps needed for implementing this model to an optical problem. We investigate the colour generation by dielectric nanostructures and show that this model can find geometrical properties that can generate much purer red, green and blue colours compared to previously reported results. The model found these results in 9000 steps from a possible 34.5 million solutions. This technique can easily be extended to predict and optimise the design parameters for other optical structures.
A 3D CNN-LSTM-Based Image-to-Image Foreground Segmentation

The video-based separation of foreground (FG) and background (BG) has been widely studied due to its vital role in many applications, including intelligent transportation and video surveillance. Most of the existing algorithms are based on traditional computer vision techniques that perform pixel-level processing assuming that FG and BG possess distinct visual characteristics. Recently, state-of-the-art solutions exploit deep learning models targeted originally for image classification. Major drawbacks of such a strategy are the lacking delineation of FG regions due to missing temporal information as they segment the FG based on a single frame object detection strategy. To grapple with this issue, we excogitate a 3D convolutional neural network (3D CNN) with long short-term memory (LSTM) pipelines that harness seminal ideas, viz., fully convolutional networking, 3D transpose convolution, and residual feature flows. Thence, an FG-BG segmenter is implemented in an encoder-decoder fashion and trained on representative FG-BG segments. The model devises a strategy called double encoding and slow decoding, which fuses the learned spatio-temporal cues with appropriate feature maps both in the down-sampling and up-sampling paths for achieving well generalized FG object representation. Finally, from the Sigmoid confidence map generated by the 3D CNN-LSTM model, the FG is identified automatically by using Nobuyuki Otsu's method and an empirical global threshold. The analysis of experimental results via standard quantitative metrics on 16 benchmark datasets including both indoor and outdoor scenes validates that the proposed 3D CNN-LSTM achieves competitive performance in terms of figure of merit evaluated against prior and state-of-the-art methods. Besides, a failure analysis is conducted on 20 video sequences from the DAVIS 2016 dataset.
Barrage of Random Transforms for Adversarially Robust Defense

Defenses against adversarial examples, when using the ImageNet dataset, are historically easy to defeat. The common understanding is that a combination of simple image transformations and other various defenses are insufficient to provide the necessary protection when the obfuscated gradient is taken into account. In this paper, we explore the idea of stochastically combining a large number of individually weak defenses into a single barrage of randomized transformations to build a strong defense against adversarial attacks. We show that, even after accounting for obfuscated gradients, the Barrage of Random Transforms (BaRT) is a resilient defense against even the most difficult attacks, such as PGD. BaRT achieves up to a 24x improvement in accuracy compared to previous work, and has even extended effectiveness out to a previously untested maximum adversarial perturbation of ε=32.
A Survey of Sentiment Analysis Based on Transfer Learning

With the rapid development of the Internet industry, sentiment analysis has grown into one of the popular areas of natural language processing (NLP). Through it, the implicit emotion in the text can be effectively mined, which can help enterprises or organizations to make an effective decision, and the explosive growth of data undoubtedly brings more opportunities and challenges to the sentiment analysis. At the same time, transfer learning has emerged as a new machine learning technique that uses the existing knowledge to solve different domain problems and produces state-of-the-art prediction results. Many scholars apply transfer learning to the field of the sentiment analysis. This survey summarizes the relevant research results of the sentiment analysis in recent years and focuses on the algorithms and applications of transfer learning in the sentiment analysis, and we look forward to the development trend of the sentiment analysis.
Low-Level Control of a Quadrotor With Deep Model-Based Reinforcement Learning

Designing effective low-level robot controllers often entail platform-specific implementations that require manual heuristic parameter tuning, significant system knowledge, or long design times. With the rising number of robotic and mechatronic systems deployed across areas ranging from industrial automation to intelligent toys, the need for a general approach to generating low-level controllers is increasing. To address the challenge of rapidly generating low-level controllers, we argue for using model-based reinforcement learning (MBRL) trained on relatively small amounts of automatically generated (i.e., without system simulation) data. In this letter, we explore the capabilities of MBRL on a Crazyflie centimeter-scale quadrotor with rapid dynamics to predict and control at ≤50 Hz. To our knowledge, this is the first use of MBRL for controlled hover of a quadrotor using only on-board sensors, direct motor input signals, and no initial dynamics knowledge. Our controller leverages rapid simulation of a neural network forward dynamics model on a graphic processing unit enabled base station, which then transmits the best current action to the quadrotor firmware via radio. In our experiments, the quadrotor achieved hovering capability of up to 6 s with 3 min of experimental training data.
Monocular 3D Human Pose Estimation by Generation and Ordinal Ranking

Monocular 3D human-pose estimation from static images is a challenging problem, due to the curse of dimensionality and the ill-posed nature of lifting 2D-to-3D. In this paper, we propose a Deep Conditional Variational Autoencoder based model that synthesizes diverse anatomically plausible 3D-pose samples conditioned on the estimated 2D-pose. We show that CVAE-based 3D-pose sample set is consistent with the 2D-pose and helps tackling the inherent ambiguity in 2D-to-3D lifting. We propose two strategies for obtaining the final 3D pose- (a) depth-ordering/ordinal relations to score and weight-average the candidate 3D-poses, referred to as OrdinalScore, and (b) with supervision from an Oracle. We report close to state-of-the-art results on two benchmark datasets using OrdinalScore, and state-of-the-art results using the Oracle. We also show that our pipeline yields competitive results without paired image-to-3D annotations. The training and evaluation code is available at https://github.com/ssfootball04/generative_pose.
A meta-analysis of virtual reality training programs for social skill development

Recent years have seen an increase in the number of researchers and practitioners applying virtual reality (VR) to develop social skills, but varying levels of success have been observed when applying these VR training programs. Due to these disparities, review and summary work is needed to determine (a) whether these programs are effective and (b) the attributes of these programs that lead to success. In the current article, we perform a meta-analysis to determine the overall effectiveness of VR training programs for developing social skills, and we also study the effect of several moderating variables that may influence the effectiveness of these programs. We test whether certain aspects of the applied hardware (e.g. input devices, output devices), applied software (e.g. game elements, Second Life), participant population (e.g. general/specialized), and study design (e.g. type of control group, type of measure, others) influence the success of these programs. Sources were identified using EBSCO and Google Scholar, and, after our inclusion criteria were applied, 23 samples were included in our meta-analysis. Using a random-effects approach, we show that VR training programs, on average, perform better than alternative training programs for developing social skills, but almost all the other findings contradict current notions regarding these programs. Of note, gamified programs produced slightly worse outcomes than non-gamified programs, and programs utilizing immersive technologies (e.g. head-mounted displays) produced slightly worse outcomes than programs utilizing non-immersive displays (e.g. monitors). We provide suggestions regarding the effectiveness of VR training for social skills in comparison to alternative training approaches, the attributes of VR training programs that produce better outcomes, and directions for future research and practice.
Tortoise, not the hare: Digital transformation of supply chain business processes

With the rapid development of digital technologies, many supply chain professionals are wondering how to move forward. Three technologies are poised to change supply chain business processes: robotic process automation (RPA), artificial intelligence (AI)/machine learning (ML) and blockchain. Based on interviews with supply chain professionals in 14 large, mature manufacturing and service organizations, we outline the promise of each technology and forecast their broad-scale adoption potential. Organizations should take the following measures to ensure their readiness to adopt and effectively use one or more of these technologies: (1) identify a supply chain technology visionary who can lead through the maze of technologies and the changing digital landscape, (2) develop a digital technology roadmap for their supply chain processes, and (3) update foundational information systems.
MedChain: A Design of Blockchain-Based System for Medical Records Access and Permissions Management

Although the blockchain technology was first introduced through Bitcoin, extending its usage to non-financial applications, such as managing electronic medical records, is an attractive mission for recent research to balance the needs for increasing data privacy and the regular interaction among patients and health providers.Various systems that adopts the blockchain in managing medical records have been proposed.However, there is a need for more work to better characterize, understand and evaluate the employment of blockchain technology in the healthcare industry.In this paper, a design of blockchain based system, namely MedChain, for managing medical records is proposed.MedChain is designed to improve the current systems as it provides interoperable, secure, and effective access for medical records by patients, health care providers, and other third parties, while keeping the patients' privacy.MedChain employs timed-based smart contracts for governing transactions and controlling accesses to electronic medical records.It adopts advanced encryption techniques for providing further security.This work proposes a new incentive mechanism that leverages the degree of health providers regarding their efforts on maintaining medical records and creating new blocks.Extensive experiments are conducted to evaluate the MedChain performance, and results indicate the efficiency of our proposal in handling a large dataset at low latency.
A multi-strategy enhanced sine cosine algorithm for global optimization and constrained practical engineering problems

The Sine Cosine Algorithm (SCA) has received much attention from engineering and scientific fields since it was proposed. Nevertheless, when solving multimodal or complex high dimensional optimization tasks, the conventional SCA still has a high probability of falling into the local optimal stagnation or failing to obtain the global optimum solution. Additionally, it performspoorly in convergence. Therefore, in this study, a multi-strategy enhanced SCA, a memetic algorithm termed MSCA, is proposed, which combines multiple control mechanisms including Cauchy mutation operator, chaotic local search mechanism, opposition-based learning strategy and two operators based on differential evolution to achieve a better balance between exploration and exploitation. To verify its performance, MSCA was compared with 11 state-of-the-art original optimizers and variant algorithms on 23 continuous benchmark tasks including 7 unimodal tasks, 6 multimodal tasks, 10 various fixed-dimension multimodal functions, and several typical CEC2014 benchmark problems. Furthermore, MSCA was utilized to solve three constrained practical engineering problems including tension/compression spring design, welded beam design, and pressure vessel design. The experimental results demonstrate that the proposed algorithm MSCA is superior to other competitors in terms of quality of solutions and convergence speed and can serve as an effective andefficient computer-aided tool for practical tasks with complex search space.
A Comprehensive Review of Path Planning Algorithms for Autonomous Underwater Vehicles

Abstract The underwater path planning problem deals with finding an optimal or sub-optimal route between an origin point and a termination point in marine environments. The underwater environment is still considered as a great challenge for the path planning of autonomous underwater vehicles (AUVs) because of its hostile and dynamic nature. The major constraints for path planning are limited data transmission capability, power and sensing technology available for underwater operations. The sea environment is subjected to a large set of challenging factors classified as atmospheric, coastal and gravitational. Based on whether the impact of these factors can be approximated or not, the underwater environment can be characterized as predictable and unpredictable respectively. The classical path planning algorithms based on artificial intelligence assume that environmental conditions are known apriori to the path planner. But the current path planning algorithms involve continual interaction with the environment considering the environment as dynamic and its effect cannot be predicted. Path planning is necessary for many applications involving AUVs. These are based upon planning safety routes with minimum energy cost and computation overheads. This review is intended to summarize various path planning strategies for AUVs on the basis of characterization of underwater environments as predictable and unpredictable. The algorithms employed in path planning of single AUV and multiple AUVs are reviewed in the light of predictable and unpredictable environments.
Adaptive parameter estimation for a general dynamical system with unknown states

Summary This paper is concerned with the design of a state filter for a time‐delay state‐space system with unknown parameters from noisy observation information. The key is to investigate new identification algorithms for interactive state and parameter estimation of the considered system. Firstly, an observability canonical state‐space model is derived from the original model by linear transformation for the purpose of simplifying the model structure. Secondly, a direct state filter is formulated by minimizing the state estimation error covariance matrix on the basis of the Kalman filtering principle. Thirdly, once the unknown states are estimated, a state filter–based recursive least squares algorithm is proposed for parameter estimation using the least squares principle. Then, a state filter–based hierarchical least squares algorithm is derived by decomposing the original system into several subsystems for improving the computational efficiency. Finally, the numerical examples illustrate the effectiveness and robustness of the proposed algorithms.
Efficient and Interpretable Deep Blind Image Deblurring Via Algorithm Unrolling

Blind image deblurring remains a topic of enduring interest. Learning based approaches, especially those that employ neural networks have emerged to complement traditional model based methods and in many cases achieve vastly enhanced performance. That said, neural network approaches are generally empirically designed and the underlying structures are difficult to interpret. In recent years, a promising technique called algorithm unrolling has been developed that has helped connect iterative algorithms such as those for sparse coding to neural network architectures. In this article, we propose a neural network architecture based on this idea. We first present an iterative algorithm that may be considered as a generalization of the traditional total-variation regularization method in the gradient domain. We then unroll the algorithm to construct a neural network for image deblurring which we refer to as Deep Unrolling for Blind Deblurring (DUBLID). Key algorithm parameters are learned with the help of training images. Our proposed deep network DUBLID achieves significant practical performance gains while enjoying interpretability and efficiency at the same time. Extensive experimental results show that DUBLID outperforms many state-of-the-art methods and in addition is computationally faster.
Quantum based Whale Optimization Algorithm for wrapper feature selection

In this paper, we propose the Quantum Whale Optimization Algorithm (QWOA) for feature selection, which is an amalgamation of the Quantum Concepts and the Whale Optimization Algorithm (WOA). The proposed method enhances the exploratory and exploitation power of the classical WOA, with the use of quantum bit representation of the individuals of the population and the quantum rotation gate operator as a variation operator. Modified mutation and crossover operators are also introduced for quantum-based exploration, shrinking and spiral movement of the whales in the proposed QWOA. The efficacy of the proposed method is compared with that of the conventional WOA and with well-known evolutionary, swarm and quantum algorithms with fourteen datasets from diversified domains. Experimental results demonstrate the superior performance of the proposed QWOA method. Statistical tests also demonstrate the significantly better performance of the QWOA in comparison to eight well-known meta-heuristic algorithms.