Reviewerly/exHarmony huggingface dataset:
Discrete Wavelet Transform-Based Gaussian Mixture Model for Remote Sensing Image Compression

High-ratio image compression is difficult because remote sensing images have complex background and rich information, and the correlation between features is weak. An accurate entropy model is an important way to solve the problem by enhancing the representation ability of the compression models. The entropy model is more suited to estimate the probability distributions with the sparse latent representations. This study proposes a novel entropy model (DWTGMM) based on discrete wavelet transform (DWT) and Gaussian mixture model (GMM) for remote sensing image compression. The method uses DWT to transform the latent representations into wavelet domain and obtains four sparse representations, and then uses the proposed DWTGMM to model them separately to estimate the probability distribution of each element. It is noteworthy that the DWT used in our approach does not require learning parameters and can be combined with other entropy models to acquire the distribution of latent representations. To evaluate our method, we construct three remote sensing image datasets, i.e., GoogleMap, GF1, and GF7. We compare our method with several popular learned compression models and traditional codecs. Experimental results show that the proposed method can achieve excellent performance with low complexity. Especially with the same model architecture, the DWTGMM achieves the best compression performance.
A CNN-Transformer Hybrid Recognition Approach for sEMG-Based Dynamic Gesture Prediction

As a unique physiological electrical signal in the human body, surface electromyography (sEMG) signals always include human movement intention and muscle state. Through the collection of sEMG signals, different gestures can be effectively recognized. At present, the convolutional neural network (CNN) has been widely applied to different gesture recognition systems. However, due to its inherent limitations in global context feature extraction, it exists a certain shortcoming on high-precision prediction tasks. To solve this issue, a CNN-transformer hybrid recognition approach is proposed for high-precision dynamic gesture prediction. In addition, the continuous wavelet transform (CWT) is proposed for to acquire the time-frequency maps. To realize effective feature representation of local features from the time-frequency maps, an attention fusion block (AFB) is proposed to build the deep CNN network branch to effectively extract key channel information and spatial information from local features. Faced with the inherent limitations in global context feature extraction of CNNs, a transformer network branch is proposed to model the global relationship between pixels, called convolution and transformer (CAT) network branch. In addition, a multi-scale feature attention block (MFA) is proposed for effective feature aggregation of local features and global contexts by learning adaptive multi-scale features and suppressing irrelevant scale information. The experimental results on the established multi-channel sEMG signal time-frequency map dataset show that the proposed CNN transformer hybrid recognition network has competitive recognition performance compared with other state-of-the-art recognition networks, and the average recognition speed of each spectrogram on the test set is only 14.7ms. The proposed network can effectively improve network performance and identification efficiency.
Multi-semantic hypergraph neural network for effective few-shot learning

Recently, Graph-based Few-Shot Learning (FSL) methods exhibit good generalization by mining relations among few samples with Graph Neural Networks. However, most Graph-based FSL methods consider only binary relations and ignore the multi-semantic information of the global context knowledge. We propose a framework of Multi-Semantic Hypergraph for FSL (MSH-FSL) to explore complex latent high-order multi-semantic relations among the few samples. By mining the complex relationship structure of multi-node and multi-semantics, more refined feature representation can be learned, which yields better classification robustness. Specifically, we first construct a novel Multi-Semantic Hypergraph by obtaining associated instances with different semantic features via orthogonal mapping. With the constructed hypergraph, we then develop the Hyergraph Neural Network along with a novel multi-generation hypergraph message passing so as to better leverage the complex latent semantic relations among samples. Finally, after a number of generations, the hyper-node representations embedded in the learned hypergraph become more accurate for obtaining few-shot prediction. In the 5-way 1-shot task of ResNet-12 on mini-Imagenet dataset, the multi-semantic hypergraph outperforms single-semantic graph by 3.1%, and with the proposed semantic-distribution message passing, the improvement can further reach 6.1%.
A Deep Analysis of Transfer Learning Based Breast Cancer Detection Using Histopathology Images

Breast cancer is one of the most common and dangerous cancers in women, while it can also afflict men. Breast cancer treatment and detection are greatly aided by the use of histopathological images since they contain sufficient phenotypic data. A Deep Neural Network (DNN) is commonly employed to improve accuracy and breast cancer detection. In our research, we have analyzed pre-trained deep transfer learning models such as ResNet50, ResNetl01, VGG16, and VGG19 for detecting breast cancer using the 2453 histopathology images dataset. Images in the dataset were separated into two categories: those with invasive ductal carcinoma (IDC) and those without IDC. After analyzing the transfer learning model, we found that ResNet50 outperformed other models, achieving accuracy rates of 90.2%, Area under Curve (AUC) rates of 90.0%, recall rates of 94.7%, and a marginal loss of 3.5%.
Exploring better target for shadow detection

Shadow detection aims to identify shadow regions from images, which plays a significant role in scene understanding. Existing approaches tend to ignore the annotation noises in ground truths, which will be overfitted in the later training phase and potentially degrade detection performance. To alleviate the impact of such noisy labels, this work proposes a framework for robust shadow detection (RSD) by locating and correcting them. Specifically, we first introduce a noise-rate blind sample selection scheme based on the prediction-level stability to identify the reliable parts from all pixel-level samples. Next, we design a label correction strategy based on the graph convolutional network, which can propagate the label information between reliable and unreliable parts. Finally, we enable subsequent robust learning by using a new training target with fewer noisy labels for each image. Experimental results on public benchmarks (i.e., SBU, ISTD, UCF and CUHK-Shadow) show that our method can be favorable against SOTAs. Our source code is available at https://github.com/wuwen1994/RSD.
Multiobjective bilevel programming model for multilayer perceptron neural networks

The architecture of multilayer perceptron (MLP) neural networks dictates the network's performance. However, aiming at the specific classification problems, suitable architectures of MLPs must be determined beforehand. More often, the design decisions rely on a trial and error learning and the experience knowledge. To automatically design an MLP's network architecture and optimize network parameters, a multiobjective bilevel programming model is built. In this model, a multiobjective optimization problem is constructed in the upper level for obtaining a set of Pareto optimal architectures of the MLPs, considering network complexity, training error rate, and validation error rate, while a single-objective optimization problem is established in the lower level to search for the optimum network parameters for a given network architecture. For dealing with this model efficiently, a novel multiobjective hierarchical learning algorithm (MOHLA) is proposed, in which an integer-coding NSGA-II is developed as the upper-level optimizer for a set of Pareto optimal network structures of the MLPs, while a non-iterative method is regarded as the lower-level solver for the MLPs' connection parameters. After a set of trained MLPs is obtained finally by using MOHLA, a selective ensemble strategy is adopted for improving identification accuracy. Three types of multiobjective bilevel programming models are investigated and compared in the experiments. Moreover, the proposed MOHLA is compared with several state-of-the-art learning approaches on various classification problems. The experimental results confirm that MOHLA performs well.
Reconfigurable refraction manipulation at synthetic temporal interfaces with scalar and vector gauge potentials

Photonic gauge potentials, including scalar and vector ones, play fundamental roles in emulating photonic topological effects and for enabling intriguing light transport dynamics. While previous studies mainly focus on manipulating light propagation in uniformly distributed gauge potentials, here we create a series of gauge-potential interfaces with different orientations in a nonuniform discrete-time quantum walk and demonstrate various reconfigurable temporal-refraction effects. We show that for a lattice-site interface with the potential step along the lattice direction, the scalar potentials can yield total internal reflection (TIR) or Klein tunneling, while vector potentials manifest direction-invariant refractions. We also reveal the existence of penetration depth for the temporal TIR by demonstrating frustrated TIR with a double lattice-site interface structure. By contrast, for an interface emerging in the time-evolution direction, the scalar potentials have no effect on the packet propagation, while the vector potentials can enable birefringence, through which we further create a "temporal superlens" to achieve time-reversal operations. Finally, we experimentally demonstrate electric and magnetic Aharonov-Bohm effects using combined lattice-site and evolution-step interfaces of either scalar or vector potential. Our work initiates the creation of artificial heterointerfaces in synthetic time dimension by employing nonuniformly and reconfigurable distributed gauge potentials. This paradigm may find applications in optical pulse reshaping, fiber-optic communications, and quantum simulations.
Ethical scaling for content moderation: Extreme speech and the (in)significance of artificial intelligence

In this article, we present new empirical evidence to demonstrate the severe limitations of existing machine learning content moderation methods to keep pace with, let alone stay ahead of, hateful language online. Building on the collaborative coding project “AI4Dignity” we outline the ambiguities and complexities of annotating problematic text in AI-assisted moderation systems. We diagnose the shortcomings of the content moderation and natural language processing approach as emerging from a broader epistemological trapping wrapped in the liberal-modern idea of “the human”. Presenting a decolonial critique of the “human vs machine” conundrum and drawing attention to the structuring effects of coloniality on extreme speech, we propose “ethical scaling” to highlight moderation process as political praxis. As a normative framework for platform governance, ethical scaling calls for a transparent, reflexive, and replicable process of iteration for content moderation with community participation and global parity, which should evolve in conjunction with addressing algorithmic amplification of divisive content and resource allocation for content moderation.
Double handed dynamic Turkish Sign Language recognition using Leap Motion with meta learning approach

Sign language is one of the most important communication tools for hearing impaired people. In this study, the recognition of two-handed dynamic words in Turkish Sign Language (TSL) was studied using the LMC device. As a result of the repetition of 26 dynamic words, which were determined by considering the similarities and differences between them, by 6 participants, two data sets were obtained by extracting two types of feature sets. By applying a three-stage strategy to these feature sets, word recognition performances are presented by considering many aspects. These stages are data regularization, feature selection and dimension reduction. By performing these stages, new datasets with less dimension were obtained. The recognition performance was tested with six different ELM networks and the results were compared. Five-fold cross-validation was used to test the validity of the proposed system and accuracy of the obtained results. According to the results obtained with a comprehensive analysis, it has been seen that the Meta-ELM classifier maintains its performance rate and gives the highest performance. At the same time it has been observed that the Meta-ELM classifier has a stable structure that offers less user intervention.
Learning discriminative multi-relation representations for multimodal sentiment analysis

Modality representation learning is a critical issue in multimodal sentiment analysis (MSA). A good sentiment representation should contain as much effective information as possible while being discriminative enough to be better recognized. Previous attention-based MSA methods mainly rely on word-level feature interactions to capture intra-modality and inter-modality relations, which may lead to the loss of essential sentiment information. Furthermore, they primarily focus on information fusion but do not give enough importance to feature discrimination. To address these challenges, we propose a modal-utterance-temporal attention network with multimodal sentiment loss (MUTA-Net) for learning discriminative multi-relation representations, where the modal-utterance-temporal attention (MUTA) and multimodal sentiment loss (MMSL) are the two core units. First, we propose MUTA incorporate utterance-level feature vectors into the interactions of different modalities, which can help extract more useful relationships as the utterance-level vectors may contain sentiment information complementary to word-level vectors. Second, MMSL is designed to achieve a large inter-class distance and a small intra-class feature distance simultaneously in multimodal scenes to enhance the discriminative power of feature representations. Our experiments on four public multimodal datasets show that MUTA-Net outperforms previous baselines significantly.
MathNet: Haar-like wavelet multiresolution analysis for graph representation learning

Graph neural networks (GNNs) have recently attracted great attention and achieved significant progress in graph-level applications. In this paper, we propose a framework for graph neural networks with multiresolution Haar-like wavelets, or MathNet, with interrelated convolution and pooling strategies. The rendering method takes graphs in different structures as input and assembles consistent graph representations for readout layers, which then accomplishes label prediction. To achieve this, multiresolution graph representations are first constructed and fed into graph convolutional layers for processing. The hierarchical graph pooling layers are then involved to downsample graph resolution while simultaneously removing redundancy of graph signals. The whole workflow could be formed with a multilevel graph analysis, which not only helps embed the intrinsic topological information of each graph into the GNN, but also supports fast computation of forward and adjoint graph transforms. Extensive experiments present notable accuracy gains of the proposed MathNet on graph classification and regression tasks with performance stability. MathNet outperforms various existing GNN models, especially on big datasets.
Influence-Driven Data Poisoning for Robust Recommender Systems

Recent studies have shown that recommender systems are vulnerable, and it is easy for attackers to inject well-designed malicious profiles into the system, resulting in biased recommendations. We cannot deprive these data's injection right and deny their existence's rationality, making it imperative to study recommendation robustness. Despite impressive emerging work, threat assessment of the bi-level poisoning problem and the imperceptibility of poisoning users remain key challenges to be solved. To this end, we propose Infmix, an efficient poisoning attack strategy. Specifically, Infmix consists of an influence-based threat estimator and a user generator, Usermix. First, the influence-based estimator can efficiently evaluate the user's harm to the recommender system without retraining, which is challenging for existing attacks. Second, Usermix, a distribution-agnostic generator, can generate unnoticeable fake data even with a few known users. Under the guidance of the threat estimator, Infmix can select the users with large attacking impacts from the quasi-real candidates generated by Usermix. Extensive experiments demonstrate Infmix's superiority by attacking six recommendation systems with four real datasets. Additionally, we propose a novel defense strategy, adversarial poisoning training (APT). It mimics the poisoning process by injecting fake users (ERM users) committed to minimizing empirical risk to build a robust system. Similar to Infmix, we also utilize the influence function to solve the bi-level optimization challenge of generating ERM users. Although the idea of "fighting fire with fire" in APT seems counterintuitive, we prove its effectiveness in improving recommendation robustness through theoretical analysis and empirical experiments.
Temporal dynamics of the Rubber Hand Illusion

It is widely accepted that the representation of the body is not fixed and immutable, but rather flexible and constantly updated based on a continuous stream of multisensory information. This mechanism can be very useful to adapt to several situations, but it would not be adaptive if the body representation was too malleable or if it wasn't capable of restoring its integrity after a transient modification. Here we used the Rubber Hand Illusion (RHI) to investigate how quickly the body representation can be modified. Previous studies have investigated the timing of the onset and offset of the illusion, however, they did not assess a fine temporal resolution. Here, we used a potentiometer to record a moment-by-moment rating of the feeling of owning the RH for two minutes during the visuo-tactile stimulation and two minutes following the stimulation. Our results suggest that the feeling of Ownership is already established during the first 19 s of stimulation then it continues to grow, but at a much slower pace. The feeling of Ownership disappears within 66 s from the end of the stimulation. This work sheds new light on the temporal dynamics of the RHI and the malleability of the body self-consciousness.
Dual-Recommendation Disentanglement Network for View Fuzz in Action Recognition

Multi-view action recognition aims to identify action categories from given clues. Existing studies ignore the negative influences of fuzzy views between view and action in disentangling, commonly arising the mistaken recognition results. To this end, we regard the observed image as the composition of the view and action components, and give full play to the advantages of multiple views via the adaptive cooperative representation among these two components, forming a Dual-Recommendation Disentanglement Network (DRDN) for multi-view action recognition. Specifically, 1) For the action, we leverage a multi-level Specific Information Recommendation (SIR) to enhance the interaction among intricate activities and views. SIR offers a more comprehensive representation of activities, measuring the trade-off between global and local information. 2) For the view, we utilize a Pyramid Dynamic Recommendation (PDR) to learn a complete and detailed global representation by transferring features from different views. It is explicitly restricted to resist the fuzzy noise influence, focusing on positive knowledge from other views. Our DRDN aims for complete action and view representation, where PDR directly guides action to disentangle with view features and SIR considers mutual exclusivity of view and action clues. Extensive experiments have indicated that the multi-view action recognition method DRDN we proposed achieves state-of-the-art performance over powerful competitors on several standard benchmarks. The code will be available at https://github.com/51cloud/DRDN.
Multi-layered semantic representation network for multi-label image classification

Multi-label image classification is a fundamental and practical task, which aims to assign multiple possible labels to an image. In recent years, many deep convolutional neural network (CNN) based approaches have been proposed which model label correlations to discover semantics of labels and learn semantic representations of images. This paper advances this research direction by improving both the modeling of label correlations and the learning of semantic representations. On the one hand, besides the local semantics of each label, we propose to further explore global semantics shared by multiple labels. On the other hand, existing approaches mainly learn the semantic representations at the last convolutional layer of a CNN. But it has been noted that the image representations of different layers of CNN capture different levels or scales of features and have different discriminative abilities. We thus propose to learn semantic representations at multiple convolutional layers. To this end, this paper designs a Multi-layered Semantic Representation Network (MSRN) which discovers both local and global semantics of labels through modeling label correlations and utilizes the label semantics to guide the semantic representations learning at multiple layers through an attention mechanism. Extensive experiments on five benchmark datasets including VOC2007, VOC2012, MS-COCO, NUS-WIDE, and Apparel show a competitive performance of the proposed MSRN against state-of-the-art models.