Reviewerly/exHarmony huggingface dataset:
Deep discriminative causal domain generalization

Domain generalization aims to generalize knowledge learned from multi-domains sources to a target domain whose statistical distribution is unknown. The mainstream approach to domain generalization involves learning domain-invariant features by aligning them across the domain distribution. However, these methods usually neglect the causal relationship between the instances and class labels; thus, the domain generalization model may learn the features that are not causally related to the label predictions and lead to potential misclassification. To address this problem, we propose a deep discriminative causal domain generalization (DDCDG), which can effectively learn causal feature representations. Specifically, we first imitate various possible intervention styles by applying an enriched data augmentation technique while preserving causal features relevant to the prediction task. We then designed a new regularization term for causal feature disentanglement, aiming to separate the causal and non-causal features. Additionally, we introduce centre alignment to approximate the representations of cross-domain homogeneous objects and enhance their discriminative ability. Extensive cross-domain visual recognition experiments were conducted, and the results show that our method can achieve a significant improvement in classification accuracy when compared with existing state-of-the-art methods on the Domainbed benchmark.
Part-attentive kinematic chain-based regressor for 3D human modeling

As the demand for realistic representation and its applications increases rapidly, 3D human modeling via a single RGB image has become the essential technique. Owing to the great success of deep neural networks, various learning-based approaches have been introduced for this task. However, partial occlusions still give the difficulty to accurately estimate the 3D human model. In this letter, we propose the part-attentive kinematic regressor for 3D human modeling. The key idea of the proposed method is to predict body part attentions based on each body center position and estimate parameters of the 3D human model via corresponding attentive features through the kinematic chain-based decoder in a one-stage fashion. One important advantage is that the proposed method has a good ability to yield natural shapes and poses even with severe occlusions. Experimental results on benchmark datasets show that the proposed method is effective for 3D human modeling under complicated real-world environments. The code and model are publicly available at: https://github.com/DCVL-3D/PKCN_release
Time-series prediction using a regularized self-organizing long short-term memory neural network

Industrial process data are naturally in the form of complex time-series with high nonlinearities and dynamics. Long short-term memory (LSTM) networks are suitable for developing prediction models to handle nonlinear and dynamic process. However, LSTM neural networks have typically large and predefined structures, which may result in overfitting, and an optimal hidden neurons for a given problem cannot be automatically obtained. For this reason, a regularized self-organizing LSTM (RSO-LSTM) is proposed to optimize both the structure and the parameters of the network. First, an adaptive learning algorithm based on l2-norm regularization is introduced for parameter adjustment. Thereafter, both the prediction accuracy and weight dispersion are considered to avoid overfitting. Second, a growing strategy is designed based on hidden neuronal sensitivity. The structure of the LSTM can then be determined automatically with improved compactness. Finally, a convergence analysis is performed to ensure the feasibility of RSO-LSTM. To demonstrate the merits of the proposed RSO-LSTM for time-series​ prediction, its results for three benchmark experiments and real industrial data of a municipal solid waste incineration process were examined and compared with those of other methods. The results indicated the superiority and potential of RSO-LSTM for industrial applications.
Effect of temporal resolution on the reproduction of chaotic dynamics via reservoir computing

Reservoir computing is a machine learning paradigm that uses a structure called a reservoir, which has nonlinearities and short-term memory. In recent years, reservoir computing has expanded to new functions such as the autonomous generation of chaotic time series, as well as time series prediction and classification. Furthermore, novel possibilities have been demonstrated, such as inferring the existence of previously unseen attractors. Sampling, in contrast, has a strong influence on such functions. Sampling is indispensable in a physical reservoir computer that uses an existing physical system as a reservoir because the use of an external digital system for the data input is usually inevitable. This study analyzes the effect of sampling on the ability of reservoir computing to autonomously regenerate chaotic time series. We found, as expected, that excessively coarse sampling degrades the system performance, but also that excessively dense sampling is unsuitable. Based on quantitative indicators that capture the local and global characteristics of attractors, we identify a suitable window of the sampling frequency and discuss its underlying mechanisms.
Detection of GAN generated image using color gradient representation

With the development of generative adversarial network (GANs) technology, the technology of GAN generates images has evolved dramatically. Distinguishing these GAN generated images is challenging for the human eye. Moreover, the GAN generated fake images may cause some behaviors that endanger society and bring great security problems to society. Research on GAN generated image detection is still in the exploratory stage and many challenges remain. Motivated by the above problem, we propose a novel GAN image detection method based on color gradient analysis. We consider the difference in color information between real images and GAN generated images in multiple color spaces, and combined the gradient information and the directional texture information of the generated images to extract the gradient texture features for GAN generated images detection. Experimental results on PGGAN and StyleGAN2 datasets demonstrate that the proposed method achieves good performance, and is robust to other various perturbation attacks.
Benchmarking the Utility of <i>w</i> -Event Differential Privacy Mechanisms - When Baselines Become Mighty Competitors

The w -event framework is the current standard for ensuring differential privacy on continuously monitored data streams. Following the proposition of w -event differential privacy, various mechanisms to implement the framework are proposed. Their comparability in empirical studies is vital for both practitioners to choose a suitable mechanism, and researchers to identify current limitations and propose novel mechanisms. By conducting a literature survey, we observe that the results of existing studies are hardly comparable and partially intrinsically inconsistent. To this end, we formalize an empirical study of w -event mechanisms by re-occurring elements found in our survey. We introduce requirements on these elements that ensure the comparability of experimental results. Moreover, we propose a benchmark that meets all requirements and establishes a new way to evaluate existing and newly proposed mechanisms. Conducting a large-scale empirical study, we gain valuable new insights into the strengths and weaknesses of existing mechanisms. An unexpected - yet explainable - result is a baseline supremacy, i.e., using one of the two baseline mechanisms is expected to deliver good or even the best utility. Finally, we provide guidelines for practitioners to select suitable mechanisms and improvement options for researchers.
Underwater Target Detection Algorithm Based on Feature Fusion Enhancement

Underwater robots that use optical images for dynamic target detection often encounter image blurring, poor contrast, and indistinct target features. As a result, the underwater robots have poor detection performance with a high rate of missed detections. To overcome these issues, a feature-enhanced algorithm for underwater target detection has been proposed in this paper. Based on YOLOv7, a feature enhancement module utilizing a triple-attention mechanism is developed to improve the network’s feature extraction ability without increasing the computational or algorithmic parameter quantity. Moreover, comprehensively considering the impact of a redundant feature in the images on detection accuracy, the ASPPCSPC structure was built. A parallel spatial convolutional pooling structure based on the original feature pyramid fusion structure, SPPCSPC, is introduced. The GhostNet network was utilized to optimize its convolution module, which reduces the model’s parameter quantity and optimizes the feature map. Furthermore, a Cat-BiFPN structure was designed to address the problem of fine-grained information loss in YOLOv7 feature fusion by adopting a weighted nonlinear fusion strategy to enhance the algorithm’s adaptability. Using the UPRC offshore dataset for validation, the algorithm’s detection accuracy was increased by 2.9%, and the recall rate was improved by 2.3% compared to the original YOLOv7 algorithm. In addition, the model quantity is reduced by 11.2%, and the model size is compressed by 10.9%. The experimental results significantly establish the validity of the proposed algorithm.
Unsupervised deep homography with multi‐scale global attention

Abstract Homography estimation serves an important role in many computer vision tasks. Depending heavily on hand‐craft feature quality, traditional methods degenerate sharply in scenes with low texture. Existing deep homography methods can handle the low‐texture problem but are not robust for scenes with low overlap rates and/or illumination changes. This paper proposes a novel unsupervised homography estimation method that can simultaneously handle such low overlap and illumination change. Specifically, a powerful module, named global transformer contextual encoder (GTCE) is first designed, together with a correlation encoder to effectively aggregate global contextual information and reduce matching ambiguity between feature maps. Moreover, a hybrid photo‐perceptual loss for unsupervised homography estimation is proposed. The proposed loss function considers alignment information on both pixel level and perceptual level thus helping this network to be more adaptive to various scenes, including normal cases and illumination change cases. The results of extensive experiments on synthetic and real‐world datasets demonstrate the superiority of this proposed method over current state‐of‐the‐art solutions especially on challenging scenes with low overlap rates, repetitive patterns and illumination changes.
Local Adaptive Image Filtering Based on Recursive Dilation Segmentation

This paper introduces a simple but effective image filtering method, namely, local adaptive image filtering (LAIF), based on an image segmentation method, i.e., recursive dilation segmentation (RDS). The algorithm is motivated by the observation that for the pixel to be smoothed, only the similar pixels nearby are utilized to obtain the filtering result. Relying on this observation, similar pixels are partitioned by RDS before applying a locally adaptive filter to smooth the image. More specifically, by directly taking the spatial information between adjacent pixels into consideration in a recursive dilation way, RDS is firstly proposed to partition the guided image into several regions, so that the pixels belonging to the same segmentation region share a similar property. Then, guided by the iterative segmented results, the input image can be easily filtered via a local adaptive filtering technique, which smooths each pixel by selectively averaging its local similar pixels. It is worth mentioning that RDS makes full use of multiple integrated information including pixel intensity, hue information, and especially spatial adjacent information, leading to more robust filtering results. In addition, the application of LAIF in the remote sensing field has achieved outstanding results, specifically in areas such as image dehazing, denoising, enhancement, and edge preservation, among others. Experimental results show that the proposed LAIF can be successfully applied to various filtering-based tasks with favorable performance against state-of-the-art methods.
Multiparameter Regression of a Photovoltaic System by Applying Hybrid Methods with Variable Selection and Stacking Ensembles under Extreme Conditions of Altitudes Higher than 3800 Meters above Sea Level

The production of solar energy at altitudes higher than 3800 m above sea level is not constant because the relevant factors are highly varied and complex due to extreme solar radiation, climatic variations, and hostile environments. Therefore, it is necessary to create efficient prediction models to forecast solar production even before implementing photovoltaic systems. In this study, stacking techniques using ElasticNet and XGBoost were applied in order to develop regression models that could collect a maximum number of features, using the LASSO, Ridge, ElasticNet, and Bayesian models as a base. A sequential feature selector (SFS) was used to reduce the computational cost and optimize the algorithm. The models were implemented with data from a string photovoltaic (PV) system in Puno, Peru, during April and August 2021, using 15 atmospheric and photovoltaic system variables in accordance with the European standard IEC 61724-20170. The results indicate that ElasticNet reduced the MAE by 30.15% compared to the base model, and that the XGBoost error was reduced by 30.16% using hyperparameter optimization through modified random forest research. It is concluded that the proposed models reduce the error of the prediction system, especially the stacking model using XGBoost with hyperparameter optimization.
Zero-referenced low-light image enhancement with adaptive filter network

The current strive toward efficient intelligent visual systems suffers from challenges in the task of low-light image enhancement. To improve image perception, the low-light scenes in different illumination conditions must be properly focused. However, typical CNN-based methods use the same set of parameters for all images, which limits the capability for handling complex scenes. Meanwhile, the existing deep models integrate the low-level and high-level features by simply adding or concatenating operations, lacking unique designs for the low-light image enhancement task. To address the above challenges, we propose a zero-referenced adaptive filter network (ZAFN) for low-light image enhancement. Specifically, the adaptive filters are generated by the integration of high-level contents from multiple partial scenes. The iterative enlightening process is then conducted using the low-level features that are dynamically modulated with the adaptive filters. To alleviate the requirement of paired training data and enable zero-referenced learning, we propose a color enhancement loss, a global consistency loss, and a self-regularized denoising loss for high-quality results. Our ZAFN model, which has a light model size and low computational cost, outperforms other state-of-the-art zero-referenced methods on four popular datasets.
Dynamic Robust Particle Swarm Optimization Algorithm Based on Hybrid Strategy

Robust optimization over time can effectively solve the problem of frequent solution switching in dynamic environments. In order to improve the search performance of dynamic robust optimization algorithm, a dynamic robust particle swarm optimization algorithm based on hybrid strategy (HS-DRPSO) is proposed in this paper. Based on the particle swarm optimization, the HS-DRPSO combines differential evolution algorithm and brainstorms an optimization algorithm to improve the search ability. Moreover, a dynamic selection strategy is employed to realize the selection of different search methods in the proposed algorithm. Compared with the other two dynamic robust optimization algorithms on five dynamic standard test functions, the results show that the overall performance of the proposed algorithm is better than other comparison algorithms.
Beyond high-tech versus low-tech: A tentative framework for sustainable urban data governance

Technological imaginaries have been increasingly shaping the future perceptions of cities. From artificial intelligence and distributed ledger technology to three-dimensional printing, high-tech artifacts are very often the premises of such imaginaries. However, technology does not only refer to artifacts. Technology also encompasses the processes around the artifacts: how the artifacts are designed, manufactured, used, maintained, and disposed. From this perspective, high-tech visions often disregard problems that pertain to resource extraction, labor exploitation, energy use, and material flows. On the contrary, low-tech and localized alternatives incite lower impact and higher resilience visions. However, they fail to offer solutions of the desired scale and intensity. To address this tension, we provide an alternative vision for mid-tech: a balance between the opposite extreme qualities of low-tech and high-tech. Through a case of open-source prosthetics, we illustrate how to synergistically combine the efficiency and versatility of high-tech solutions with the potential for autonomy and resilience that low-tech offers. Then we discuss a mid-tech approach for distributed ledger technology from a city as a license lens. We provide connections with existing or conceptual applications to show how distributed ledger technology could support more socially and ecologically responsible data practices for city governance.
The Making of Argumentation Theory: A Pragma-dialectical View

Abstract In ‘The making of argumentation theory’ van Eemeren and van Haaften describe the contributions made to the five components of a full-fledged research program of argumentation theory by four prominent approaches to the discipline: formal dialectics, rhetoric/pragmalinguistics, informal logic, and pragma-dialectics. Most of these approaches do not contribute to all components, but to some in particular. Starting from the pragma-dialectical view of the relationship between dialectical reasonableness and rhetorical effectiveness – the crucial issue in argumentation theory – van Eemeren and van Haaften explain the positions taken by representatives from the approaches discussed and indicate where they differ from the pragma-dialectical approach. It transpires that approaches focusing on dialectical reasonableness are, next to pragma-dialectics, formal dialectics and informal logic; approaches focusing on rhetorical effectiveness are, next to pragma-dialectics, rhetoric and pragmalinguistics, and the informal logician Tindale. When it comes to the relationship between dialectical reasonableness and rhetorical effectiveness, some interest in it is shown in rhetoric and pragmalinguistics, but only in pragma-dialectics and in Tindale’s work is it a real focus. The main difference between Tindale’s view and the pragma-dialectical view is that in pragma-dialectics the decisive role in deciding about reasonableness is assigned to a code of conduct for reasonable argumentative discourse and in Tindale’s approach this role is assigned to Tindale’s interpretation of the Perelmanian universal audience.
Low Latency Extended Dijkstra Algorithm with Multiple Linear Regression for Optimal Path Planning of Multiple AGVs Network

Dijkstra algorithms are typically used to find the shortest path from a source node to a destination node. It is widely used in various applications due to its reliability and less complexity. This paper presents the extended Dijkstra Algorithm with lower latency and consumes less computing memory intended for implementation in many AGVs networks for effective decentralized task distribution path planning. This paper proposed linear regression normalization across the node network in Dijkstra architecture to reduce computing time and memory consumption. The issue addressed through this optimization focused on reducing the possibilities of collision between AGVs and deadlock. The extended Dijkstra algorithm significantly reduces computing time compared to the traditional Dijkstra algorithm. In addition, the proposed solutions suggest better AGV routing for collision avoidance and deadlock prevention possibilities.
Incremental Fault Diagnosis Method Based on Metric Feature Distillation and Improved Sample Memory

Incremental learning-based fault diagnosis systems (IFD) are widely used because of their ability to handle constantly updated fault data and types.However, the catastrophic forgetting problem remains the most crucial contemporary challenge facing IFD.This paper proposes an incremental fault diagnosis method based on metric feature distillation (MFD) and improved sample memory to solve this problem.First, the metric feature distillation is designed with metric learning methods and feature distillation.It uses distillation and triplet loss to constrain the network parameters of old and new tasks in the same feasible region, effectively alleviating catastrophic forgetting.Then, for a small amount of data that can be stored scenario, an improved sample memory strategy is introduced to reduce catastrophic forgetting further, called the center and hard sample memory (CAHM).It can better preserve the global information of the data, reducing the forgetting of old data information that needs to be preserved during the training process.Experimental results on CWRU and MFPT datasets verify the proposed method's effectiveness.
Facade Style Mixing Using Artificial Intelligence for Urban Infill

Artificial intelligence and machine learning, in particular, have made rapid advances in image processing. However, their incorporation into architectural design is still in its early stages compared to other disciplines. Therefore, this paper addresses the development of an integrated bottom–up digital design approach and describes a research framework for incorporating the deep convolutional generative adversarial network (GAN) for early stage design exploration and the generation of intricate and complex alternative facade designs for urban interiors. In this paper, a novel facade design is proposed using the architectural style, size, scale, and openings of two adjacent buildings as references to create a new building design in the same neighborhood for urban infill. This newly created building contains the outline, style and shape of the two main buildings. A 2D building design is generated as an image, where (1) neighboring buildings are imported as a reference using the cell phone and (2) iFACADE decodes their spatial neighborhood. It is illustrated that iFACADE will be useful for designers in the early design phase to create new facades in relation to existing buildings in a short time, saving time and energy. Moreover, building owners can use iFACADE to show their preferred architectural facade to their architects by mixing two building styles and creating a new building. Therefore, it is presented that iFACADE can become a communication platform in the early design phases between architects and builders. The initial results define a heuristic function for generating abstract facade elements and sufficiently illustrate the desired functionality of the prototype we developed.