Reviewerly/exHarmony huggingface dataset:
A novel hybrid particle swarm optimization using adaptive strategy

Particle swarm optimization (PSO) has been employed to solve numerous real-world problems because of its strong optimization ability and easy implementation. However, PSO still has some shortcomings in solving complicated optimization problems, such as premature convergence and poor balance between global exploration and local exploitation. A novel hybrid particle swarm optimization using adaptive strategy (ASPSO) is developed to address associated difficulties. The contribution of ASPSO is threefold: (1) a chaotic map and an adaptive position updating strategy to balance exploration behavior and exploitation nature in the search progress; (2) elite and dimensional learning strategies to enhance the diversity of the population effectively; (3) a competitive substitution mechanism to improve the accuracy of solutions. Based on various functions from CEC 2017, the numerical experiment results demonstrate that ASPSO is significantly better than the other 16 optimization algorithms. Furthermore, we apply ASPSO to a typical industrial problem, the optimization of melt spinning progress, where the results indicate that ASPSO performs better than other algorithms.
Imbalanced image classification with complement cross entropy

Recently, deep learning models have achieved great success in computer vision applications, relying on large-scale class-balanced datasets. However, imbalanced class distributions still limit the wide applicability of these models due to degradation in performance. To solve this problem, in this paper, we concentrate on the study of cross entropy which mostly ignores output scores on incorrect classes. This work discovers that neutralizing predicted probabilities on incorrect classes improves the prediction accuracy for imbalanced image classification. This paper proposes a simple but effective loss named complement cross entropy based on this finding. The proposed loss makes the ground truth class overwhelm the other classes in terms of softmax probability, by neutralizing probabilities of incorrect classes, without additional training procedures. Along with it, this loss facilitates the models to learn key information especially from samples on minority classes. It ensures more accurate and robust classification results on imbalanced distributions. Extensive experiments on imbalanced datasets demonstrate the effectiveness of the proposed method compared to other state-of-the-art methods.
Decentralized Federated Learning With Unreliable Communications

Decentralized federated learning, inherited from decentralized learning, enables the edge devices to collaborate on model training in a peer-to-peer manner without the assistance of a server. However, existing decentralized learning frameworks usually assume perfect communication among devices, where they can reliably exchange messages, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , gradients or parameters. But the real-world communication networks are prone to packet loss and transmission errors. Transmission reliability comes with a price. The commonly-used solution is to adopt a reliable transportation layer protocol, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , transmission control protocol (TCP), which however leads to significant communication overhead and reduces connectivity among devices that can be supported. For a communication network with a lightweight and unreliable communication protocol, user datagram protocol (UDP), we propose a robust decentralized stochastic gradient descent (SGD) approach, called Soft-DSGD, to address the unreliability issue. Soft-DSGD updates the model parameters with partially received messages and optimizes the mixing weights according to the link reliability matrix of communication links. We prove that the proposed decentralized training system, even with unreliable communications, can still achieve the same asymptotic convergence rate as vanilla decentralized SGD with perfect communications. Moreover, numerical results confirm the proposed approach can leverage all available unreliable communication links to speed up convergence.
Gaussian Anomaly Detection by Modeling the Distribution of Normal Data in Pretrained Deep Features

Anomaly detection (AD) in images is a fundamental computer vision problem and refers to identifying images that deviate significantly from normality. State-of-the-art AD algorithms commonly learn a model of normality from scratch using task-specific datasets in either semisupervised or self-supervised manner. We follow an alternative approach and model the distribution of normal data in deep feature representations learned from ImageNet via a multivariate Gaussian (MVG). This lightweight approach achieves a new state of the art in AD on the public MVTec AD dataset. In addition to the empirical benefits, we give a clear motivation for the seemingly simplistic approach via the ties between deep generative and discriminative modeling revealed recently. We further elucidate why ImageNet representations are discriminative in the transfer learning AD setting using the principal component analysis. Here, we find that the principal components containing little variance in normal data are the ones crucial for discriminating between normal and anomalous instances, giving an explanation for the unreasonable effectiveness of our approach. We also investigate setting the working point of our approach by selecting acceptable false-positive rate thresholds based on the MVG assumption and the resistance of our approach to unlabeled anomalies in the dataset. Finally, we investigate whether our approach is prone to exploiting spurious correlations using explainable AI techniques. Code is publicly available at https://github.com/ORippler/gaussian-ad-mvtec.
An intuitionistic fuzzy kernel ridge regression classifier for binary classification

Kernel ridge regression (KRR) is a widely accepted efficient machine learning paradigm that has been fruitfully implemented for solving both classification and regression problems. KRR solves a set of linear equations instead of solving a quadratic programming problem. However, KRR gives equal importance to each sample which leads to giving the same significance to the important and non-important samples. That might lead to low classification accuracy. To resolve this issue, this paper suggests a novel kernel ridge regression based on intuitionistic fuzzy membership (IFKRR) for binary classification. In IFKRR, there is an intuitionistic fuzzy number linked to each training sample which is framed by either its membership or non-membership. A pattern’s membership degree considers its distance from the corresponding class center.​ However, the non-membership degree is provided by the ratio of the number of heterogeneous points to the total number of its neighborhood points. The proposed IFKRR model can efficiently reduce the influence of noise in datasets. To evaluate the efficiency of IFKRR, its performance is compared with support vector machine (SVM), twin SVM (TWSVM), intuitionistic fuzzy SVM (IFSVM), intuitionistic fuzzy TWSVM (IFTSVM), random vector functional link with univariate trees (RFL), KRR and Co-trained KRR average (CoKRR-avg) on an artificial and a few really interesting real world datasets using Gaussian kernel. Computational results reveal the efficacy of the IFKRR model on the real world as well as noisy datasets.
The power of ensemble learning in sentiment analysis

An ensemble of models is a set of learning models whose individual predictions are combined in such a way that component models compensate for each other’s weaknesses. Although there has been a growing interest in ensemble learning techniques in the general machine learning community, the use of ensembles in sentiment classification is still limited. Moreover, much of the research activity on ensemble learning is centred around homogeneous ensembles, although heterogeneous ensembles may prove very useful when combining pre-trained models, which are often readily available. In this paper, several techniques for constructing heterogeneous ensembles are applied and comparatively evaluated in respect of benchmark sentiment classification data sets across four different domains. Median performance improvements of up to 5.53% over the best individual model are observed for several ensemble configurations in respect of all four validation data sets, and clear trends are identified that may prove useful to other researchers in the field. Furthermore, a novel ensemble selection approach is proposed that avoids the storage of individual predictions, as well as the costly retraining of all candidate models for an ensemble, that are often required by other similar approaches.
Optimization-Based Trajectory Planning for Autonomous Parking With Irregularly Placed Obstacles: A Lightweight Iterative Framework

This paper is focused on planning fast, accurate, and optimal trajectories for autonomous parking. Nominally, this task should be described as an optimal control problem (OCP), wherein the collision-avoidance constraints guarantee travel safety and the kinematic constraints guarantee tracking accuracy. The dimension of the nominal OCP is high because it requires the vehicle to avoid collision with each obstacle at every moment throughout the entire parking process. With a coarse trajectory guiding a homotopic route, the intractably scaled collision-avoidance constraints are replaced by within-corridor constraints, whose scale is small and independent from the environment complexity. Constructing such a corridor sacrifices partial free spaces, which may cause loss of optimality or even feasibility. To address this issue, our proposed method reconstructs the corridor in an iterative framework, where a lightweight OCP with only box constraints is quickly solved in each iteration. The proposed planner, together with several prevalent optimization-based planners are tested under 115 simulation cases w.r.t. the success rate and computational time. Real-world indoor experiments are conducted as well.
Mixed reality in design prototyping: A systematic review

The integration of virtual and physical workflow is a major ongoing trend in engineering, with substantial potential for benefit during design development stages. Within this trend, the emerging field of Mixed Reality technologies provide capability to couple the virtual and physical realms in design prototyping. Applications to date remain ad-hoc however, with little clarity of the breadth of benefits afforded by Mixed Reality. Through a systematic review of 108 publications, this paper presents a classification of the benefits of Mixed Reality technology afforded to prototyping, the design process, and designers. This paper elicits and characterises five evidence-based dimensions of value. These are analysed in detail against design processes and prototyping to extract benefits of application, implementation challenges, and sets directions for future work with implications for design researchers, computer scientists and designers.
Research and improvement of feature detection algorithm based on FAST

At present, in the application of feature-based medical 3D reconstruction technology, there are still problems such as low matching accuracy of feature points in endoscope images and slow processing speed of image data. Therefore, the feature-based 3D reconstruction theory is of great research value and has great application value. This paper proposed a new feature detection method to improve the problems. This paper divides feature detection into two parts for further improvements: feature extraction and feature description. For feature extraction, the FAST algorithm shows a poor classification effect, so this paper adds the decision tree based on the C4.5 algorithm into the traditional FAST. The original data are divided into two decision trees to make the feature extraction performance more stable and feature point extraction more efficient. For the feature description part, the FREAK descriptor is used, combined with this paper's improved feature extraction algorithm. The feature points are extracted in scale space. The second-order function fitting is carried out according to the feature points' response scores in different scales. The scale-invariant descriptor of sub-pixel precision is obtained. The experimental results on the endoscope image show that the feature extraction method has a higher extraction accuracy and faster extraction speed. In addition, the feature description algorithm has higher calculation efficiency.
Bots vs. humans: how schema congruity, contingency-based interactivity, and sympathy influence consumer perceptions and patronage intentions

Artificial intelligence (AI) is rapidly reconstructing consumer experiences with brands in recent years. However, there have been the unsettled debates on whether humans react to robots (e.g., chatbots) in the same way as they do to other humans, and how the intrinsic strength of AI (i.e., autonomous processing and synthetization of information) and humans (i.e., emotional intelligence) factor in the human-AI interactions in brand communication settings. Hence, this study investigates the conditions under which a service entity of a brand can optimize their potential. To this end, the current study conceptualizes and operationalizes two dimensions that define chatbots' capabilities – message contingency (i.e., contingency-based interactivity) and emotional intelligence (i.e., sympathy). Based on two experiments, we found that, regarding the same online customer service of an apparel brand, participants rated the human employee to be more competent and warmer than a chatbot. When a human employee who expresses sympathy to the afflicted customer during the conversation, participants considered the employee to be more competent when he/she also exhibits contingency (vs. no contingency) during the conversation, which in turn, elicited higher patronage intentions among participants.
Generalization Ability of MOS Prediction Networks

Automatic methods to predict listener opinions of synthesized speech remain elusive since listeners, systems being evaluated, characteristics of the speech, and even the instructions given and the rating scale all vary from test to test. While automatic predictors for metrics such as mean opinion score (MOS) can achieve high prediction accuracy on samples from the same test, they typically fail to generalize well to new listening test contexts. In this paper, using a variety of networks for MOS prediction including MOSNet and self-supervised speech models such as wav2vec2, we investigate their performance on data from different listening tests in both zero-shot and fine-tuned settings. We find that wav2vec2 models fine-tuned for MOS prediction have good generalization capability to out-of-domain data even for the most challenging case of utterance-level predictions in the zero-shot setting, and that fine-tuning to in-domain data can improve predictions. We also observe that unseen systems are especially challenging for MOS prediction models.
Context Reasoning Attention Network for Image Super-Resolution

Deep convolutional neural networks (CNNs) are achieving great successes for image super-resolution (SR), where global context is crucial for accurate restoration. However, the basic convolutional layer in CNNs is designed to extract local patterns, lacking the ability to model global context. With global context information, lots of efforts have been devoted to augmenting SR networks, especially by global feature interaction methods. These works incorporate the global context into local feature representation. However, recent advances in neuroscience show that it is necessary for the neurons to dynamically modulate their functions according to context, which is neglected in most CNN based SR methods. Motivated by those observations and analyses, we propose context reasoning attention network (CRAN) to modulate the convolution kernel according to the global context adaptively. Specifically, we extract global context descriptors, which are further enhanced with semantic reasoning. Channel and spatial interactions are then introduced to generate context reasoning attention mask, which is applied to modify the convolution kernel adaptively. Such a modulated convolution layer is utilized as basic component to build the blocks and networks. Extensive experiments on benchmark datasets with multiple degradation models show that CRAN obtains superior results and favorable trade-off between performance and model complexity.
LayoutTransformer: Layout Generation and Completion with Self-attention

We address the problem of scene layout generation for diverse domains such as images, mobile applications, documents, and 3D objects. Most complex scenes, natural or human-designed, can be expressed as a meaningful arrangement of simpler compositional graphical primitives. Generating a new layout or extending an existing layout re- quires understanding the relationships between these primitives. To do this, we propose LayoutTransformer, a novel framework that leverages self-attention to learn contextual relationships between layout elements and generate novel layouts in a given domain. Our framework allows us to generate a new layout either from an empty set or from an initial seed set of primitives, and can easily scale to support an arbitrary of primitives per layout. Furthermore, our analyses show that the model is able to automatically capture the semantic properties of the primitives. We propose simple improvements in both representation of layout primitives, as well as training methods to demonstrate competitive performance in very diverse data domains such as object bounding boxes in natural images (COCO bounding box), documents (PubLayNet), mobile applications (RICO dataset) as well as 3D shapes (Part-Net). Code and other materials will be made available at https://kampta.github.io/layout.
Foreground-Action Consistency Network for Weakly Supervised Temporal Action Localization

As a challenging task of high-level video understanding, weakly supervised temporal action localization has been attracting increasing attention. With only video annotations, most existing methods seek to handle this task with a localization-by-classification framework, which generally adopts a selector to select snippets of high probabilities of actions or namely the foreground. Nevertheless, the existing foreground selection strategies have a major limitation of only considering the unilateral relation from foreground to actions, which cannot guarantee the foreground-action consistency. In this paper, we present a framework named FAC-Net based on the I3D backbone, on which three branches are appended, named class-wise foreground classification branch, class-agnostic attention branch and multiple instance learning branch. First, our class-wise foreground classification branch regularizes the relation between actions and foreground to maximize the foreground-background separation. Besides, the class-agnostic attention branch and multiple instance learning branch are adopted to regularize the foreground-action consistency and help to learn a meaningful foreground classifier. Within each branch, we introduce a hybrid attention mechanism, which calculates multiple attention scores for each snippet, to focus on both discriminative and less-discriminative snippets to capture the full action boundaries. Experimental results on THUMOS14 and ActivityNet1.3 demonstrate the state-of-the-art performance of our method.
Modality-Aware Mutual Learning for Multi-modal Medical Image Segmentation

Liver cancer is one of the most common cancers worldwide. Due to inconspicuous texture changes of liver tumor, contrast-enhanced computed tomography (CT) imaging is effective for the diagnosis of liver cancer. In this paper, we focus on improving automated liver tumor segmentation by integrating multi-modal CT images. To this end, we propose a novel mutual learning (ML) strategy for effective and robust multi-modal liver tumor segmentation. Different from existing multi-modal methods that fuse information from different modalities by a single model, with ML, an ensemble of modality-specific models learn collaboratively and teach each other to distill both the characteristics and the commonality between high-level representations of different modalities. The proposed ML not only enables the superiority for multi-modal learning but can also handle missing modalities by transferring knowledge from existing modalities to missing ones. Additionally, we present a modality-aware (MA) module, where the modality-specific models are interconnected and calibrated with attention weights for adaptive information exchange. The proposed modality-aware mutual learning (MAML) method achieves promising results for liver tumor segmentation on a large-scale clinical dataset. Moreover, we show the efficacy and robustness of MAML for handling missing modalities on both the liver tumor and public brain tumor (BRATS 2018) datasets. Our code is available at https://github.com/YaoZhang93/MAML.
Conditional Deformable Image Registration with Convolutional Neural Network

Recent deep learning-based methods have shown promising results and runtime advantages in deformable image registration. However, analyzing the effects of hyperparameters and searching for optimal regularization parameters prove to be too prohibitive in deep learning-based methods. This is because it involves training a substantial number of separate models with distinct hyperparameter values. In this paper, we propose a conditional image registration method and a new self-supervised learning paradigm for deep deformable image registration. By learning the conditional features that are correlated with the regularization hyperparameter, we demonstrate that optimal solutions with arbitrary hyperparameters can be captured by a single deep convolutional neural network. In addition, the smoothness of the resulting deformation field can be manipulated with arbitrary strength of smoothness regularization during inference. Extensive experiments on a large-scale brain MRI dataset show that our proposed method enables the precise control of the smoothness of the deformation field without sacrificing the runtime advantage or registration accuracy.
DSSL: Deep Surroundings-person Separation Learning for Text-based Person Retrieval

Many previous methods on text-based person retrieval tasks are devoted to learning a latent common space mapping, with the purpose of extracting modality-invariant features from both visual and textual modality. Nevertheless, due to the complexity of high-dimensional data, the unconstrained mapping paradigms are not able to properly catch discriminative clues about the corresponding person while drop the misaligned information. Intuitively, the information contained in visual data can be divided into person information (PI) and surroundings information (SI), which are mutually exclusive from each other. To this end, we propose a novel Deep Surroundings-person Separation Learning (DSSL) model in this paper to effectively extract and match person information, and hence achieve a superior retrieval accuracy. A surroundings-person separation and fusion mechanism plays the key role to realize an accurate and effective surroundings-person separation under a mutually exclusion constraint. In order to adequately utilize multi-modal and multi-granular information for a higher retrieval accuracy, five diverse alignment paradigms are adopted. Extensive experiments are carried out to evaluate the proposed DSSL on CUHK-PEDES, which is currently the only accessible dataset for text-base person retrieval task. DSSL achieves the state-of-the-art performance on CUHK-PEDES. To properly evaluate our proposed DSSL in the real scenarios, a Real Scenarios Text-based Person Reidentification (RSTPReid) dataset is constructed to benefit future research on text-based person retrieval, which will be publicly available.