Reviewerly/exHarmony huggingface dataset:
A Hybrid Robust ANFIS Based on Noise Fuzzy Clustering

Adaptive Network-based Fuzzy Inference System (ANFIS) is a promising model of explainable neural networks but rejection of illegal noise effects is an important issue in real application. In this paper, a novel approach for introducing noise clustering concepts into fuzzy <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$c$</tex> -means-based ANFIS is proposed for robust modeling. In the premise part, noise fuzzy clustering is performed in the input data space for estimating fuzzy membership functions removing noise inputs. Then, in the consequence part, rule-wise robust regression models are estimated by removing noise outputs. As a result, the proposed hybrid robust ANFIS model simultaneously considers two types of noise generation schemes of the input-level and the output-level. The characteristics of the proposed method are demonstrated through numerical experiments such that input-level noise are rejected by degrading premise fuzzy memberships of noise objects so that their ANFIS outputs have small absolute values while output-level noise observations are rejected through robust regression.
Target detection in color sonar image based on YOLOV5 network

Sonar is the main equipment used to explore underwater. The traditional sonar target detection technology has the problems of low intelligence, poor robustness, poor real-time performance and low recognition accuracy. In this paper, a high-precision target detection algorithm named RFB-SE-YOLOV5 is proposed. The data set was produced by sonar made in the laboratory based on the YOLOV5 network model. The SE attention mechanism was added to the original backbone network to improve the convergence of the model and the ability to extract effective features of models. Besides, the RFB-SE module is addedto the backbone network to increase the Receptive Field of the network, thereby enhancing the model for differentiability and robustness characteristics. RFB-SE module is the attention module added after concat of RFB (Receptive Field Block). After testing 660 sonar images, the mAP@0.5 of the improved RFB-SE- Yolov5 network is 0.972, which is 3.2% higher than the original network. The improved algorithm can be applied to underwater robot automatic detection of underwater targets in the future.
Brain Segmentation Based on UNet++ with Weighted Parameters and Convolutional Neural Network

The accurate segmentation of brain tumor contour and internal tissue is of great significance to the actual medical treatment. In the multimodal segmentation of brain tumor MRI, the 3D network model is superior to the 2D network model in the learning process. However, in the internal tissue segmentation of brain tumor, the effect is often unsatisfactory. At least at present, there is an urgent need for a method that can accurately segment the internal tissue of brain tumor. In this paper, we optimize the UNet++ network model. The improved UNet++ segmentation network was evaluated on the BraTS 2018 and BraTS 2019 datasets. The average Dice coefficient, Positive Predictive Value (PPV), Sensitivity, Hausdorff Distance and training time of the improved UNet++ network model after weight prediction are 0.87, 0.87, 0.89, 0.83 and 10h respectively.
Decoupling Autoencoders for Robust One-vs-Rest Classification

One-vs-Rest (OVR) classification aims to distinguish a single class of interest from other classes. The concept of novelty detection and robustness to dataset shift becomes crucial in OVR when the scope of the rest class extends from the classes observed during training to unseen and possibly unrelated classes. In this work, we propose a novel architecture, namely Decoupling Autoencoder (DAE) to tackle the common issue of robustness w.r.t. out-of-distribution samples which is prevalent in classifiers such as multi-layer perceptrons (MLP) and ensemble architectures. Experiments on plain classification, outlier detection, and dataset shift tasks show DAE to achieve robust performance across these tasks compared to the baselines, which tend to fail completely, when exposed to dataset shift. While DAE and the baselines yield rather uncalibrated predictions on the outlier detection and dataset shift task, we found that DAE calibration is more stable across all tasks. Therefore, calibration measures applied to the classification task could also improve the calibration of the outlier detection and dataset shift scenarios for DAE.
Assessing Evaluation Metrics for Speech-to-Speech Translation

Speech-to-speech translation combines machine translation with speech synthesis, introducing evaluation challenges not present in either task alone. How to automatically evaluate speech-to-speech translation is an open question which has not previously been explored. Translating to speech rather than to text is often motivated by unwritten languages or languages without standardized orthographies. However, we show that the previously used automatic metric for this task is best equipped for standardized high-resource languages only. In this work, we first evaluate current metrics for speech-to-speech translation, and second assess how translation to dialectal variants rather than to standardized languages impacts various evaluation methods.
Quantum Method for Finite Element Simulation of Electromagnetic Problems

Quantum simulation of electromagnetic (EM) structures is still in its infancy. In this paper, we investigate the possibility of applying quantum computing to solve the finite element equations for EM problems. Specifically, we propose to leverage a milestone in quantum computing, i.e., the Harrow/Hassidim/Lloyd (HHL) algorithm, to solve the finite element equations in the EM domain. To do this, we first present a systematic method to reformulate the finite element equations into quantum computation format and apply the HHL algorithm to solve the new equations. Taking advantage of the special properties of finite element matrices in EM problems, we then devise a stepwise algorithm to efficiently determine the suitable values for the hyperparameters of HHL. The proposed method can in theory solve the EM finite element equations in logarithmic time relative to the size of the finite element matrix. This is exponentially faster than that achievable by classical computation as the matrix size increases. A two-dimensional EM example is used to illustrate how the proposed method can be used to find the solutions to EM problems through quantum computation.
Dynamic feature weighting for data streams with distribution-based log-likelihood divergence

Data streams are expected to undergo changes in data distribution, a phenomenon called concept drift. Another closely related phenomenon is the feature drift of data streams. Feature drifts occur whenever a subset of features becomes, or ceases to be, relevant to the learning task. Identifying the most relevant feature subset from a high-dimensional feature space is challenging in the stream mining scenario. In this study, we propose an online dynamic feature weighting algorithm. Specifically, a feature drift detection scheme is introduced that monitors the changes in the class relevance of the features through a change-detection algorithm based on the log-likelihood divergence score. The score is computed via the kernel density estimator based on the information-theoretic feature merit values. The algorithm is evaluated on both synthetic and real-world datasets, and it is shown that the proposed distribution-based drift detection framework can boost the Nearest Neighbor and Naive Bayes classifier accuracy rates (an average of 2.7% for Nearest Neighbor and 5.5% for Naive Bayes). It also signals feature drifts much faster than traditional methods based on detecting changes in accuracy rates. Finally, the limitations of the proposed method are assessed, and future research directions are discussed.
Applying regression conformal prediction with nearest neighbors to time series data

In this paper, we apply conformal prediction to time series data. Conformal prediction is a method that produces predictive regions given a confidence level. The regions outputs are always valid under the exchangeability assumption. However, this assumption does not hold for the time series data because there is a link among past, current, and future observations. Consequently, the challenge of applying conformal predictors to the problem of time series data lies in the fact that observations of a time series are dependent and therefore do not meet the exchangeability assumption. This paper aims to present a way of constructing reliable prediction intervals by using conformal predictors in the context of time series. We use the nearest neighbors method based on the fast parameters tuning technique in the weighted nearest neighbors (FPTO–WNN) approach as the underlying algorithm. Data analysis demonstrates the effectiveness of the proposed approach.
A time-weighted metric for sets of trajectories to assess multi-object tracking algorithms

This paper proposes a metric for sets of trajectories to evaluate multi-object tracking algorithms that includes time-weighted costs for localisation errors of properly detected targets, for false targets, missed targets and track switches. The proposed metric extends the metric in [1] by including weights to the costs associated to different time steps. The time-weighted costs increase the flexibility of the metric [1] to fit more applications and user preferences. We first introduce a metric based on multi-dimensional assignments, and then its linear programming relaxation, which is computable in polynomial time and is also a metric. The metrics can also be extended to metrics on random finite sets of trajectories to evaluate and rank algorithms across different scenarios, each with a ground truth set of trajectories.
Heterogeneous Twin Dehazing Network for Visibility Enhancement in Maritime Video Surveillance

Visible images captured by maritime video surveillance system under hazy environment often suffer from low contrast and information loss, etc. These negative factors will significantly limit the development and progression of target recognition, detection, tracking, and other vision-based technologies in maritime applications. However, existing dehazing methods are mainly designed for massive inland images, resulting in inaccurate estimation of haze-free versions under the maritime scene. In this work, we propose a heterogeneous twin dehazing network (termed HTDNet) for enhancing visually-degraded maritime images. The HTDNet is mainly composed of two modules, i.e., twin feature extraction module (T-FEM) and feature fusion module (FFM). In particular, T-FEM is employed to collect coarse haze features from two views, and FFM is designed for feature integration and enhancement. To further improve the visual quality, the dataset containing massive maritime images is constructed to train our network. Both synthetic and real-world experiments have illustrated our superior performance compared with several state-of-the-art methods. There is thus a great potential to successfully extend our HTDNet to enable maritime intelligent transportation system.
Anatomical knowledge based level set segmentation of cardiac ventricles from MRI

This paper represents a novel level set framework for segmentation of cardiac left ventricle (LV) and right ventricle (RV) from magnetic resonance images based on anatomical structures of the heart. We first propose a level set approach to recover the endocardium and epicardium of LV by using a bi-layer level set (BILLS) formulation, in which the endocardium and epicardium are represented by the 0-level set and k-level set of a level set function. Furthermore, the recovery of LV endocardium and epicardium is achieved by a level set evolution process, called convexity preserving bi-layer level set (CP-BILLS). During the CP-BILLS evolution, the 0-level set and k-level set simultaneously evolve and move toward the true endocardium and epicardium under the guidance of image information and the impact of the convexity preserving mechanism as well. To eliminate the manual selection of the k-level, we develop an algorithm for automatic selection of an optimal k-level. As a result, the obtained endocardial and epicardial contours are convex and consistent with the anatomy of cardiac ventricles. For segmentation of the whole ventricle, we extend this method to the segmentation of RV and myocardium of both left and right ventricles by using a convex shape decomposition (CSD) structure of cardiac ventricles based on anatomical knowledge. Experimental results demonstrate promising performance of our method. Compared with some traditional methods, our method exhibits superior performance in terms of segmentation accuracy and algorithm stability. Our method is comparable with the state-of-the-art deep learning-based method in terms of segmentation accuracy and algorithm stability, but our method has no need for training and the manual segmentation of the training data.
Controlling Industrial Robots with High-Level Verbal Commands

Industrial robots today are still mostly pre-programmed to perform a specific task. Despite previous research in human-robot interaction in the academia, adopting such systems in industrial settings is not trivial and has rarely been done. In this paper, we introduce a robotic system that we control with high-level verbal commands, leveraging some of the latest neural approaches to language understanding and a cognitive architecture for goal-directed but reactive execution. We show that a large-scale pre-trained language model can be effectively fine-tuned for translating verbal instructions into robot tasks, better than other semantic parsing methods, and that our system is capable of handling through dialogue a variety of exceptions that happen during human-robot interaction including unknown tasks, user interruption, and changes in the world state.
InAugment: Improving Classifiers via Internal Augmentation

Image augmentation techniques apply transformation functions such as rotation, shearing, or color distortion on an input image. These augmentations were proven useful in improving neural networks’ generalization ability. In this paper, we present a novel augmentation operation, In-Augment, that exploits image internal statistics. The key idea is to copy patches from the image itself, apply augmentation operations on them, and paste them back at random positions on the same image. This method is simple and easy to implement and can be incorporated with existing augmentation techniques. We test InAugment on two popular datasets – CIFAR and ImageNet. We show improvement over state-of-the-art augmentation techniques. Incorporating InAugment with Auto Augment yields a significant improvement over other augmentation techniques (e.g., +1% improvement over multiple architectures trained on the CIFAR dataset). We also demonstrate an increase for ResNet50 and EfficientNet-B3 top-1’s accuracy on the ImageNet dataset compared to prior augmentation methods. Finally, our experiments suggest that training convolutional neural network using InAugment not only improves the model’s accuracy and confidence but its performance on out-of-distribution images.
Radar Signal Detection Based on Ambiguity Function and Deep Learning

The ambiguity function describes the joint characteristics of the signal in the time domain and the frequency domain, and is usually used for signal recognition. The time domain and frequency domain distribution of noise is more spurious than the signal. Its ambiguity function can also reflect its characteristics. However, the direct use of the ambiguity function as the signal feature will increase the amount of calculation of the detection system, so slice it, which reduces the burden on the system for detecting signal information while preserving the information. At the same time, the radar signal detection based on neural network can avoid the influence of artificially setting the threshold on the detection performance of the system. So, we propose an algorithm for inputting the extracted fuzzy function slice sequence into a neural network to realize signal detection. Simulation proves that when the signal-to-noise ratio of the received signal is very low, our proposed method still has a high detection probability.
Research on clothing patterns generation based on multi-scales self-attention improved generative adversarial network

Purpose Clothing patterns play a dominant role in costume design and have become an important link in the perception of costume art. Conventional clothing patterns design relies on experienced designers. Although the quality of clothing patterns is very high on conventional design, the input time and output amount ratio is relative low for conventional design. In order to break through the bottleneck of conventional clothing patterns design, this paper proposes a novel way based on generative adversarial network (GAN) model for automatic clothing patterns generation, which not only reduces the dependence of experienced designer, but also improve the input-output ratio. Design/methodology/approach In view of the fact that clothing patterns have high requirements for global artistic perception and local texture details, this paper improves the conventional GAN model from two aspects: a multi-scales discriminators strategy is introduced to deal with the local texture details; and the self-attention mechanism is introduced to improve the global artistic perception. Therefore, the improved GAN called multi-scales self-attention improved generative adversarial network (MS-SA-GAN) model, which is used for high resolution clothing patterns generation. Findings To verify the feasibility and effectiveness of the proposed MS-SA-GAN model, a crawler is designed to acquire standard clothing patterns dataset from Baidu pictures, and a comparative experiment is conducted on our designed clothing patterns dataset. In experiments, we have adjusted different parameters of the proposed MS-SA-GAN model, and compared the global artistic perception and local texture details of the generated clothing patterns. Originality/value Experimental results have shown that the clothing patterns generated by the proposed MS-SA-GAN model are superior to the conventional algorithms in some local texture detail indexes. In addition, a group of clothing design professionals is invited to evaluate the global artistic perception through a valence-arousal scale. The scale results have shown that the proposed MS-SA-GAN model achieves a better global art perception.
Fraud Detection under Multi-Sourced Extremely Noisy Annotations

Fraud detection in e-commerce, which is critical to protecting the capital safety of users and financial corporations, aims at determining whether an online transaction or other activity is fraudulent or not. This problem has been previously addressed by various fully supervised learning methods. However, the true labels for training a supervised fraud detection model are difficult to collect in many real-world cases. To circumvent this issue, a series of automatic annotation techniques are employed instead in generating multiple noisy annotations for each unknown activity. In order to utilize these low-quality, multi-sourced annotations in achieving reliable detection results, we propose an iterative two-staged fraud detection framework with multi-sourced extremely noisy annotations. In label aggregation stage, multi-sourced labels are integrated by voting with adaptive weights; and in label correction stage, the correctness of the aggregated labels are properly estimated with the help of a handful of exactly labeled data and the results are used to train a robust fraud detector. These two stages benefit from each other, and the iterative executions lead to steadily improved detection results. Therefore, our method is termed "Label Aggregation and Correction" (LAC). Experimentally, we collect millions of transaction records from Alipay in two different fraud detection scenarios, i.e., credit card theft and promotion abuse fraud. When compared with state-of-the-art counterparts, our method can achieve at least 0.019 and 0.117 improvements in terms of average AUC on the two collected datasets, which clearly demonstrate the effectiveness.
Fragile Watermark in Medical Image Based on Prime Number Distribution Theory

The research of medical image in the field of anti-counterfeiting and authentication plays a crucial role in the development of hospital digitization. In this paper, a new fragile medical watermarking scheme is proposed using the prime number distribution theorem, chaotic mapping, and Hash. Firstly, an approximate pixel set is constructed according to the distribution of prime numbers, and then the parity of the pixel value is re-granted with chaotic mapping, and Hash. The embedding and extraction of the watermark are done by using logical operations to adjust the pixel value according to the parity of the pixel value and the number of prime numbers contained in the pixel value. The experimental results demonstrate that the proposed scheme is imperceptible, efficient, and safe. Compared with the existing methods, the proposed scheme achieves the binding of medical image and patient information, as well as exhibits excellent detection and positioning capability, and will have good application prospects in medical image content authentication and tampering detection.
A Distributed Fall Detection Architecture Using Ensemble Learning

Falling accidents are serious and costly for people, especially for elders. Each year, millions of older people fall. Particularly, more than one out of four older people falls each year, and falling doubles the chance of falling again. Moreover, without immediate treatment, it would cause higher medical costs. Hence, we intend to design a novel distributed fall detection architecture, where the distributed nature and ensemble learning enhance the classical concept of fall detection based on computer vision. Lastly, in the experiments, we show that the prototype is accurate and stable enough to alert the emergency contact person.
On the Effect of Pruning on Adversarial Robustness

Pruning is a well-known mechanism for reducing the computational cost of deep convolutional networks. However, studies have shown the potential of pruning as a form of regularization, which reduces overfitting and improves generalization. We demonstrate that this family of strategies provides additional benefits beyond computational performance and generalization. Our analyses reveal that pruning structures (filters and/or layers) from convolutional networks increase not only generalization but also robustness to adversarial images (natural images with content modified). Such achievements are possible since pruning reduces network capacity and provides regularization, which have been proven effective tools against adversarial images. In contrast to promising defense mechanisms that require training with adversarial images and careful regularization, we show that pruning obtains competitive results considering only natural images (e.g., the standard and low-cost training). We confirm these findings on several adversarial attacks and architectures; thus suggesting the potential of pruning as a novel defense mechanism against adversarial images.
On classical and fuzzy Hough transform in colonoscopy image processing

Hough transform is used to find lines on edge-filtered images that are given in parametric form. As the fuzzy extension of the Hough transform has been proven to be more robust in environments where the lines to be found by them are not strictly following the formula given by the parametric equation of the Hough transform due to noise and weak and blurred contours, in the following considerations, we study the applicability of the circular fuzzy Hough transform for analyzing colonoscopy pictures and detecting colorectal polyps.