Reviewerly/exHarmony huggingface dataset:
Multi-view graph imputation network

Graph data in the real world is often accompanied by the problem of missing attributes. Recently, self-supervised graph representation learning, implementing data imputation according to observable nodes, has become a paradigm for studying attribute-missing graphs. However, existing methods directly encode attribute-missing graphs and then impute attribute-missing nodes in the latent space, which often increases the uncertainty of node embedding information, thereby limiting the performance of attribute imputation. To address this issue, we propose a novel method named Multi-view GrAph impuTation nEtwork (MATE), which performs attribute imputation in the input space for attribute-missing graphs. Specifically, we first employ parameter initialization and graph diffusion in the input space to generate relatively complete multi-view from both the attribute and structure levels. To provide reliable guidance in each epoch for parameter initialization, we propose a Dual Constraint Strategy (DCS) that maximizes the consistency of node embeddings between two views. In this way, the learning of parameter initialization and node embedding promotes each other, thus effectively improving the quality of attribute imputation. Extensive experiments on four benchmark datasets demonstrate that our proposed MATE achieves state-of-the-art performance. The corresponding code is available at https://github.com/XinPeng97/MATE.
Adaptive Trust Model for Multi-Agent Teaming Based on Reinforcement-Learning-Based Fusion

The performance of agents is highly influenced by multiple factors, including ability, decision, and states. Trust modeling is widely used to boost the performance of multiagent teaming (MAT). However, most existing trust models rely on statistical methods or preset parameters to assess the trust value in the MAT scenario. In this article, an adaptive trust model is proposed to evaluate comprehensive trust values based on multiple pieces of evidence from variant sources. The proposed trust model leverages information fusion and RL to properly fuse multiple pieces of evidence to generate trust value for every agent in MAT. The trust value is then used in an interaction protocol with MAT to increase the efficiency of cooperation. To verify the performance of the proposed trust model, a ball-collection experiment is designed for MAT to work cooperatively in simulation environments. Two different scenario settings are used to demonstrate the adaptability and robustness of the proposed trust model. The results are further compared with human-designed fusion methods. The comparison shows that the proposed trust model has a better representation of agent performance, namely convergence speed, than human-designed methods in different scenario settings.
CS-GAC: Compressively sensed geodesic active contours

This paper proposes an edge based compressively sensed (CS) geodesic active contour (GAC) model, termed CS-GAC, to ensure faithful edge detection and accurate object segmentation. The motivation behind this paper is that edge information driving the contour evolution can be iteratively obtained by incomplete CS measurements. In each iteration, the CS-GAC is a three-step process including edge detection, active contouring and sparse reconstruction. Instead of working on the final reconstructed images themselves, the evolution of the CS-GAC is driven by a few CS measurements and guided by updatable edge information. The edge information is generated by a complex shearlet transform (CST) based edge map. In the framework, reconstruction and edge detection work alternately. The iterative update property that takes advantages of both edge sparsity and edge detection can largely improve the evolution precision. Numerical experiments show that the CS-GAC can obtain challenging segmentation results in comparisons with the state of the art methods, and has competitive prospects.
Convolutional neural networks rarely learn shape for semantic segmentation

Shape learning, or the ability to leverage shape information, could be a desirable property of convolutional neural networks (CNNs) when target objects have specific shapes. While some research on the topic is emerging, there is no systematic study to conclusively determine whether and under what circumstances CNNs learn shape. Here, we present such a study in the context of segmentation networks where shapes are particularly important. We define shape and propose a new behavioral metric to measure the extent to which a CNN utilizes shape information. We then execute a set of experiments with synthetic and real-world data to progressively uncover under which circumstances CNNs learn shape and what can be done to encourage such behavior. We conclude that (i) CNNs do not learn shape in typical settings but rather rely on other features available to identify the objects of interest, (ii) CNNs can learn shape, but only if the shape is the only feature available to identify the object, (iii) sufficiently large receptive field size relative to the size of target objects is necessary for shape learning; (iv) a limited set of augmentations can encourage shape learning; (v) learning shape is indeed useful in the presence of out-of-distribution data.
Weighted side-window based gradient guided image filtering

Image filtering under guidance image, known as guided filtering (GF), has been successfully applied to a variety of applications. Existing GF methods utilize either conventional full window-based framework (FWF) or simple uniformly weighted aggregation strategy (UWA); thereby they suffer from edge-blurring. In this paper, based upon gradient guided filtering (GGF), a weighted side-window based gradient guided filtering (WSGGF) is proposed to address the aforementioned problem. First, both regression and adaptive regularization terms in GGF are improved upon eight side windows by introducing side window-based framework (SWF). L1 norm is adopted to choose the results calculated in side windows. Second, UWA strategy in GGF is replaced by a refined variance-based weighted average (VWA) aggregation. In VWA, the value of each weight is chosen inversely proportional to the corresponding estimator. We show that with these improvements our method can well retain the edge sharpness and is robust to visual artifacts. To cut down the time consumption, a fast version of WSGGF (FWSGGF) is further proposed by incorporating a simple but effective down-sampling strategy, which is about four times faster while maintaining the superior performance. By comparing with the state-of-the-art (SOTA) methods on edge-aware smoothing, detail enhancement, high dynamic range image (HDR) compression, image luminance adjustment, depth map upsampling and single image haze removal, the effectiveness and flexibility of our proposed methods are verified. The source code is available at: https://github.com/weimin581/WSGGF
Matrix randomized autoencoder

Randomized autoencoder (RAE) has attracted much attention due to its strong capability of representation with fast learning speed. However, the mainstream RAEs are still designed for scalar/vector data, which inevitably destroys the structure information of tensor data. To alleviate this deficiency, a novel convolutions based matrix randomized autoencoder (MRAE) is developed for two-dimensional (2D) data in this paper, including a one-side MRAE (OMRAE) exploiting the row or column information and a double-side MRAE (DMRAE) that simultaneously extracts the row and column information by 2 parallel OMRAEs. To reduce meaningless encoded features, the within-class scatter matrix (WSI) and within-class interaction distance (WID) constraints are added into OMRAE resulting WSI-OMRAE and WID-OMRAE, respectively. To demonstrate the superiority, stacked MRAEs are embedded into hierarchical regularized least squares for one-class classification and comparisons with several state-of-the-art methods are provided. The source code would be available at https://github.com/ML-HDU/MRAE.
Semi-supervised clustering with two types of background knowledge: Fusing pairwise constraints and monotonicity constraints

This study addresses the problem of performing clustering in the presence of two types of background knowledge: pairwise constraints and monotonicity constraints. Pairwise constraint relate pair of instances, indicating if they must or must not belong to the same cluster, while monotonicity constraints impose and order relationship between the cluster labels, and are applied to the dataset globally. To fuse these to types of background knowledge, the formal framework to perform clustering under monotonicity constraints is, firstly, defined, resulting in a specific distance measure. Pairwise constraints are integrated afterwards by designing an objective function which combines the proposed distance measure and a pairwise constraint-based penalty term, in order to fuse both types of information. This objective function can be optimized with an EM optimization scheme. The proposed method serves as the first approach to the problem it addresses, as it is the first method designed to work with the two types of background knowledge mentioned above. Our proposal is tested with previous approaches from different clustering paradigms in monotonic datasets, including a real-world case of study. Bayesian statistical tests are used to provide empirical evidence in favor of our proposal, and fund subsequent conclusions: the proposed method can be used obtain better results than previous approaches within the monotonic constrained clustering paradigm.
Domain generalization via Inter-domain Alignment and Intra-domain Expansion

The performance of traditional deep learning models tends to drop dramatically during being deployed in real-world scenarios when the distribution shift between the seen training and unseen test data occurs. Domain Generalization methods are designed to achieve generalizability to deal with the above issue. Since the features extracted by softmax cross-entropy loss are not adequately domain-invariant, previous works in Domain Generalization have attempted to overcome this problem by employing contrastive-based losses which pull positive pairs (i.e., samples with the same class label) from different domains closer. Unfortunately, these approaches tend to produce an extremely small feature space, which is not robust facing unseen domain and easily overfits to source domains. To address the aforementioned issue, we propose a novel loss named IAIE Loss to simultaneously perform Inter-domain Alignment and Intra-domain Expansion for positive pairs, which facilitates the model to extract domain-invariant features and mitigates overfitting. Specifically, we design two sets of positive samples named "easy positive samples" and "hard positive samples". IAIE Loss pulls the hard positive pairs closer (alignment) while pushing the easy positive pairs apart (expansion). The state-of-the-art results on multiple DG benchmark datasets verify the effectiveness of our method.
A segmentation method based on the deep fuzzy segmentation model in combined with SCANDLE clustering

To enhance the low clustering accuracy of the fuzzy clustering segmentation algorithm for analyzing high spatial resolution remote sensing images (HSRRSIs), a deep fuzzy segmentation model (DFSM)combined with Spectral Clustering with Adaptive Neighbors for Deep Learning (SCANDLE) clustering is proposed. The DFSM is used to over-segment the image, and the automatic coding structure is used to adaptively fuse the image features, minimizing the internal compactness and maximizing the external separability of the clustering, yielding better results. Meanwhile, the SCANDLE clustering model is used to cluster the over-segmentation results, and the matrix construction algorithm for adaptive neighborhood allocation is used to map the frame of the connected layer and optimally combine the over-segmentation images to realize the final segmentation results. The new method can accurately segment HSRRSIs with good segmentation performance.
Medoid Silhouette clustering with automatic cluster number selection

The evaluation of clustering results is difficult, highly dependent on the evaluated data set and the perspective of the beholder. There are many different clustering quality measures, which try to provide a general measure to validate clustering results. A very popular measure is the Silhouette. We discuss the efficient medoid-based variant of the Silhouette, perform a theoretical analysis of its properties, provide two fast versions for the direct optimization, and discuss the use to choose the optimal number of clusters. We combine ideas from the original Silhouette with the well-known PAM algorithm and its latest improvements FasterPAM. One of the versions guarantees equal results to the original variant and provides a run speedup of O(k2). In experiments on real data with 30000 samples and k= 100, we observed a 10464× speedup compared to the original PAMMEDSIL algorithm. Additionally, we provide a variant to choose the optimal number of clusters directly.
Adaptive Network Configuration for Efficient and Accurate Neural Video Inference

Cameras are widely used in many fields, e.g., intelligent transportation, autonomous driving, surveillance, etc. It is thus vital to conduct video analytics in an efficient and accurate manner. However, camera’s built-in capacity is insufficient to support neural network processing, while offloading video streams incurs prohibitive latency and communication cost. In this paper, we find that frame rate, resolution, and neural network inference model, have an intertwined impact on network resource demand. The optimal configuration of these factors also varies with video content feature. To address these challenges, we propose a dynamic configuration update scheme based on predictive video perception using a long short-term memory (LSTM) neural network, to adapt configuration to content changes. This scheme consists of a change detector and a configuration profiler. Through theoretical modeling and analysis, we derive the detection thresholds for both dynamic and stationary video contents, considering the LSTM prediction error. The configuration profiler then updates system by solving an optimization problem, which maximizes the overall utility considering analytics accuracy and resource consumption. Extensive real-world traces-based experiments show that the proposed scheme can save profiling resources by up to 95% while ensuring high accuracy compared with other benchmarks.
Coarse-to-Fine Low-Light Image Enhancement With Light Restoration and Color Refinement

Low-light image enhancement aims to improve the illumination intensity while restoring color information. Despite recent advancements using deep learning methods, they still struggle with over or under-exposure in complex lighting scenes and poor color recovery in dark regions. To address these drawbacks, we propose a novel pipeline (called LRCR-Net) to perform Light Restoration and Color Refinement in a coarse-to-fine manner. In the coarse step, we focus on improving the illumination adaptively while avoiding inappropriate enhancement in the brighter or darker regions. This is achieved by introducing a region-calibrated residual block (RCRB) that balances local and global dependencies among different image regions. In the fine step, we aim to retouch the color of the images enhanced in the coarse step. To achieve this goal, we propose learnable image processing operators (LIPOs), including contrast and saturation operators, to refine the color according to the input images' color and contrast information. The final result is an image with proper illumination and rich color. Experiments on four benchmark datasets (NASA, LIME, MEF, and NPE) show that our model outperforms state-of-the-art methods.
Reinforcement learning with time intervals for temporal knowledge graph reasoning

Temporal reasoning methods have been successful in temporal knowledge graphs (TKGs) and are widely employed in many downstream application areas. Most existing TKG reasoning models transform time intervals into continuous time snapshots, with each snapshot representing a subgraph of the TKG. Although such processing can produce satisfactory outcomes, it ignores the integrity of a time interval and increases the amount of data. Meanwhile, many previous works focuses on the logic of sequentially occurring facts, disregarding the complex temporal logics of various time intervals. Consequently, we propose a Reinforcement Learning-based Model for Temporal Knowledge Graph Reasoning with Time Intervals (RTTI), which focuses on time-aware multi-hop reasoning arising from complex time intervals. In RTTI, we construct the time learning part to obtain the time embedding of the current path. It simulates the temporal logic with relation historical encoding and compute the time interval between two facts through the temporal logic feature matrix. Furthermore, we propose a new method for representing time intervals that breaks the original time interval embedding method, and express the time interval using median and embedding changes of two timestamps. We evaluate RTTI on four public TKGs for the link prediction task, and experimental results indicate that our approach can still perform well on more complicated tasks. Meanwhile, our method can search for more interpretable paths in the broader space and improve the reasoning ability in sparse spaces.
Feature incremental learning with causality

With the emerging of new data collection ways, the features are incremental and accumulated gradually. Due to the expansion of feature spaces, it is more common that there are unknown biases between the distribution of training and testing datasets. It is known as the unknown data selection bias, which belongs to the learning scenario with non-i.i.d samples. The performance of traditional approaches, which need the i.i.d. assumption, will be aggravated seriously. How to design an algorithm to address the problem of data selection bias in this feature incremental scenario is crucial but rarely studied. In this paper, we propose a feature incremental classification algorithm with causality. Firstly, we embed the confounding variable balance algorithm in causal learning into the prediction modeling and utilize the logical regression algorithm with balancing regular terms as a baseline. Then, to satisfy the special requirement of feature increment, we design a new regularizer, which maintains the consistency of the regression coefficients between the data in the current and previous stages. It retains the correlation between the old features and labels. Finally, we propose the Multiple Balancing Logistic Regression model (MBRLR) to jointly optimize the balancing regularizer and weighted logistic regression model with multiple feature sets. We also present theoretical results to show that our proposed algorithm can make precise and stable predictions. Besides, the numerical results also demonstrate that our MBRLR algorithm is superior to other methods.
Deep Contrastive Clustering for Signal Deinterleaving

In the complex electromagnetic environment, radar signal deinterleaving (RSD) is a challenging task. In this paper, a deep contrastive clustering algorithm (DCCA) is advanced in a new self-supervised paradigm, for accurate RSD without any prior information of radar emitters. First, a contrastive self-supervised deep attention network (CSDAN) is constructed to learn signal representations, by using self-defined pseudo-labels of augmented signals as supervision. We use CSDAN to learn the differences between different radiation source data and generate deep features suitable for clustering. Three metrics are then used to automatically determine the number of clusters for the subsequent clustering. Extensive experiments are performed on several datasets containing different numbers of emitters. The results show that the proposed DCCA can accurately determine the number of emitters and deinterleave radar pulses. Furthermore, CSDAN can extract discriminative features of emitters with low intra-class similarity and high inter-class similarity
A Novel Deep Belief Network Architecture with Interval Type-2 Fuzzy Set Based Uncertain Parameters Towards Enhanced Learning

This paper proposes a novel Deep Belief Network (DBN) architecture, the ‘Interval Type-2 Fuzzy DBN (IT2FDBN)’, which models the weights and biases with IT2 FSs. Thus, it introduces a novel algorithm for augmented deep leaning, which has the capability to address all the limitations of the classical DBN (CDBN) and T1 fuzzy DBN (T1FDBN). We comparatively evaluate the performance of the IT2FDBN by conducting experiments using the popular MNIST handwritten digit recognition datasets. Additionally, to demonstrate its robustness and generalization capabilities, we also conduct experiments taking two noisy variants of MNIST dataset, viz. the MNIST with AWGN (additive white Gaussian noise) and the MNIST with motion blur. We conduct extensive simulations by considering different combinations of nodes in the hidden layers of the DBN for better model selection. We thoroughly compare the results using well-known performance measures such as root mean square error (RMSE) and Error rate. We show that, in terms of RMSE values and error rates, the proposed IT2FDBN outperforms both T1FDBN and CDBN across all the three datasets. Further, we also provide the results of convergence, runtime-based comparison, and statistical analysis in support of our proposal.
Auto-weighted multi-view clustering with the use of an augmented view

Multi-view clustering is a powerful technique that leverages both consensus and complementary information from multiple perspectives to achieve impressive results. However, existing methods have not fully explored the inherent structural information contained in multi-view data, such as inadequately harnessing the consistency among views or neglecting to explore their high-order correlations. To address these limitations, we propose a novel auto-weighted multi-view clustering (AWMVC) method with the use of an augmented view. The main motivation of AWMVC is that each view naturally embodies consensus information, and concatenating the views maximizes the consistency properties. AWMVC first connects multiple views via feature concatenation to form a newly augmented view. Then, we stack the self-representation matrices of the original views and the newly augmented view, i.e., the concatenated view into a 3rd-order tensor, subject to a low-rank tensor constraint. Additionally, this approach automatically assigns weights to each view and constructs a more reliable affinity matrix. Our method unifies the feature concatenation process, high-order information, and a weighted strategy into one model, and is efficiently optimized through an iterative algorithm. Experimental results on benchmark datasets demonstrate that AWMVC outperforms state-of-the-art methods, highlighting the effectiveness of our approach.
Generative adversarial networks via a composite annealing of noise and diffusion

Generative adversarial network (GAN) is a framework for generating fake data using a set of real examples. However, GAN is unstable in the training stage. In order to stabilize GANs, the noise injection has been used to enlarge the overlap of the real and fake distributions at the cost of increasing variance. The diffusion process (or data smoothing in its spatial domain) removes fine details in order to capture the structure and important patterns in data but it suppresses the capability of GANs to learn high-frequency information in the training procedure. Based on these observations, we propose a data representation for the GAN training, called noisy scale-space (NSS), that recursively applies the smoothing with a balanced noise to data in order to replace the high-frequency information by random data, leading to a coarse-to-fine training of GANs. We experiment with NSS using DCGAN and StyleGAN2 based on benchmark datasets in which the NSS-based GANs outperforms the state-of-the-arts in most cases.