Reviewerly/exHarmony huggingface dataset:
Semantic-Reconstructed Graph Transformer Network for Event Detection

Event detection (ED) is a key subtask of information extraction to extract key events, such as stock rise and fall and social public opinion, from news or social media. Although current GCN-based event detection methods achieve remarkable success via building graphs with dependency trees, they typically suffer from two challenges: 1) They use sequence models to learn contextual information of sentences, ignoring the longterm dependencies problem of sequence models might learn ineffective information and make it propagate in GCN layers. 2) Most methods do not exploit global dependency label information and grammatical structure information that convey rich linguistic knowledge directly, and only consider local dependency label information. To cope with these challenges, we propose a novel event detection model via semantic-reconstructed graph transformer networks (SRGTNED), which incorporates semantic reconstruction and path information collection methods. Using the semantic reconstruction method, we assign a pruned sequence to each word based on the path information to capture contextual information consistent with sentence semantics. Moreover, to better utilize global dependency label information and grammatical structure information, a Graph Transformer Network (GTN)-based heterogeneous graph embedding framework is introduced to automatically learn path information between important words by converting sentences as heterogeneous graphs. We conduct experiments on the ACE2005 dataset and the Commodity News dataset, and the experimental results demonstrate that our method significantly outperforms 11 state-of-the-art baselines in terms of the F1-score.
Stacked Attention-based Networks for Accurate and Interpretable Health Risk Prediction

Predicting the health risks of patients based on electronic health records (EHRs) has recently attracted considerable research interest. Health risk refers to the probability of the occurrence of a specific health outcome for a specific patient. The predicted risks of a specific health outcome can be used to support decisions by healthcare professionals. Various predictive models have been developed. Compared with traditional machine learning models, deep learning-based models have achieved more promising performance. However, due to the lack of transparency, the acceptance of deep learning-based models are often limited. This paper proposes a Stacked Attention-based Network, SANet, for accurate and interpretable health risk prediction. Two novel attention-based modules, named Convolutional Attention Module and Sequential Attention Module respectively, are designed to capture patient-specific contextual information at both feature and sequence levels. Particularly, Sequential Attention Module can flexibly learn the impact of the time interval between sequential visits and significantly enhance the interpretability and robustness of learning outcomes from sequences. Experimental results on two real-world EHR datasets demonstrate the superior predictive accuracy of our method, as well as interpretability and robustness, compared to existing state-of-the-art methods. The findings extracted by this approach are also empirically confirmed by relevant literature and medical experts.
Connections Between Pairs of Filters Improve the Accuracy of Convolutional Neural Networks

While researchers continue to find new and improved network structures for CNNs, most of the newly invented architectures still rely on the traditional pattern of stacking convolutional blocks and separating them with pointwise activation functions. However, there are drawbacks to a network purely building on pointwise nonlinearities. One alternative is to introduce a pairwise connection between two filters of a network. Typical connection functions use multiplications or the minimum operation to realize logical AND connections. In this paper, we go one step further by demonstrating that CNNs can benefit from more general connections, which include parameters that are learned. With such parameters, the network is able to implement different connections in different network layers and better adapt the connection function to the task at hand.
ConsE: Consistency Exploitation for Semi-Supervised Anomaly Detection in Graphs

Graph anomaly detection has attracted considerable interest due to the wide use of graph structure data. Several GNN-based anomaly detection methods discover anomalies through the powerful node representation ability of GNNs. However, real-world graphs are typically rarely labeled, which leads these deep learning methods to face the challenge of under-fitting. A fundamental question here is: can anomalies in graphs be detected with few annotations? In this paper, we propose a novel semi-supervised anomaly detection method in graphs based on Consistency Exploitation (ConsE). First, ConsE adopts a consistency-based neighbor sampler, which ensures the consistency of a central node and its neighbors on attributes and categories during the aggregation process through attribute similarity and soft pseudo-labels. Afterward, ConsE encourages the consistency of node representations generated by a dedicated neighbor sampler and a generic neighbor sampler to improve its robustness in complex neighborhoods. Experimental results on real-world datasets demonstrate that our model significantly outperforms several state-of-the-art baseline methods.
Global and Adaptive Local Label Correlation for Multi-label Learning with Missing Labels

Label missing is a major challenge in multi-label learning. Many existing methods try to use label correlation to recover ground-truth labels, but they only focus on the label correlation within the original label space, however, the label correlation learned in this way is incomplete. Thus, inspired <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$b$</tex> y the matrix adaptive column correlation, we propose a method to continuously adjust the label correlation matrix while the labels are filled in by adaptive column correlation learning method. Specifically, to reduce the impact of the missing label <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sup> on label correlation, the label space is firstly completed through manifold regularization while learning the local label information by adaptive column correlation learning in the complemented label space. Secondly, the global label correlation is utilized by adding a low-rank constraint to the entire label space. Finally, by jointly taking advantage of the global and adaptive local label correlation, our proposed approach achieves superior performance on both synthetic and real-world data sets from diverse domains compared to state-of-the art baselines.
Lorentzian Graph Convolution Networks for Collaborative Filtering

Recently, recommendation methods based on graph convolutional networks (GCNs) have received much attention and have shown competitive performance. These methods usually perform graph convolutions of user and item embeddings in Euclidean space. However, user-item interaction graphs usually present a tree-like hierarchy in large-scale recommender systems. To better fit user and item data to the embedding space, hyperbolic space becomes a good choice, which grows exponentially in volume with radius and is well suited for embedding hierarchical data. In addition, most previous works aggregate neighborhoods through projection operations on tangent spaces, which inevitably cause distortion. In this paper, we propose a Lorentzian graph convolutional network model for collaborative filtering (LCF), which makes the learned user and item representations strictly follow hyperbolic geometry, and the learned embeddings are optimized based on margin ranking loss and geometric views. Specifically, we first pass the initialized user and item information through multiple Lorentzian graph convolutional layers, and ensure that the learned node embeddings are not out of the hyperbolic space, while capturing rich high-order neighborhood information. Then, the model is effectively learned under the composite loss function to take full advantage of the hyperbolic space. We conduct extensive experiments on three datasets and compare with many baselines. Experimental results show that our method performs better than many state-of-the-art baseline methods.
Modality-Aware Negative Sampling for Multi-modal Knowledge Graph Embedding

Negative sampling (NS) is widely used in knowledge graph embedding (KGE), which aims to generate negative triples to make a positive-negative contrast during training. However, existing NS methods are unsuitable when multi-modal information is considered in KGE models. They are also inefficient due to their complex design. In this paper, we propose Modality-Aware Negative Sampling (MANS) for multi-modal knowledge graph embedding (MMKGE) to address the mentioned problems. MANS could align structural and visual embeddings for entities in KGs and learn meaningful embeddings to perform better in multi-modal KGE while keeping lightweight and efficient. Empirical results on two benchmarks demonstrate that MANS outperforms existing NS methods. Meanwhile, we make further explorations about MANS to confirm its effectiveness.
Convolutional Neural Networks Do Work with Pre-Defined Filters

We present a novel class of Convolutional Neural Networks called Pre-defined Filter Convolutional Neural Networks (PFCNNs), where all <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n\times n$</tex> convolution kernels with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n &gt; 1$</tex> are pre-defined and constant during training. It involves a special form of depthwise convolution operation called a Pre-defined Filter Module (PFM). In the channel-wise convolution part, the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1\times n\times n$</tex> kernels are drawn from a fixed pool of only a few (16) different pre-defined kernels. In the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1\times 1$</tex> convolution part linear combinations of the pre-defined filter outputs are learned. Despite this harsh restriction, complex and discriminative features are learned. These findings provide a novel perspective on the way how information is processed within deep CNNs. We discuss various properties of PFCNNs and prove their effectiveness using the popular datasets Caltech101, CIFAR10, CUB-200-2011, FGVC-Aircraft, Flowers102, and Stanford Cars. Our implementation of PFCNNs is provided on Github https://github.com/Criscraft/PredefinedFilterNetworks.
The Third Common Interface for Graph Neural Networks

Although graph neural networks (GNNs) are powerful tools for analyzing data that can be represented as a graph, they generally suffer from the problem of over-smoothing as the number of layers in the networks increases. This paper focuses on two recent methods for overcoming this problem, namely JKNet-lstm and TwinGNN, and presents a simpler GNN interface that achieves a similar reduction in over-smoothing and its implementations that require much lower computational cost. The proposed approach utilizes the relationship between the convolution operation in GNNs and the reproductive property of the normal distribution. In particular, our method does not require any computationally intensive long short-term memory calculations, and achieves complexity that is independent of the number of layers in the GNN. We present mathematical arguments to prove that our proposed interface achieves the same functionality as JKNet-lstm and TwinGNN, and perform experiments on six benchmark datasets to demonstrate the remarkable performance of our method in terms of the classification accuracy, computational cost, and degree of over-smoothing. Overall, the proposed interface enables complex graph network structures to be analyzed at reasonable computational cost with no loss of accuracy.
MultiDrop: A Local Rademacher Complexity-Based Regularization for Multitask Models

Multitask learning combines both commons among a group of tasks and each task's individual characters to achieve better performance. However, different tasks may lead to contrary gradient directions or imbalance gradient values disrupting the training process. We propose a new regularization algorithm based on multitask local Rademacher complexity called MultiDrop, dropping some random tasks in optimization. A new regularization function has been derived based on the upper bound of local Rademacher complexity in multitask learning. Experiments on different image classification datasets have been implemented to demonstrate the effectiveness of MultiDrop compared with other regularization algorithms, and links between theoretical analyses and experiments.
Online Automated Machine Learning for Class Imbalanced Data Streams

Automated machine learning (AutoML) has achieved great success in offline class imbalance learning where data are static. However, many real world applications data nowadays tend to evolve over time in the form of data streams and involve class imbalance distributions, e.g., intrusion detection, fault diagnosis systems, and fraud detection. These learning tasks require AutoML processing the instances instantly and adapting to the dynamic data changes. Nevertheless, existing AutoML research either only focuses on class imbalance in static data sets, or discusses data streams with concept drift. No existing work studied the joint learning challenges of class imbalance and online data stream learning in AutoML. To close the gap, this paper focuses on learning dynamic data streams with a skewed class distribution in AutoML. In this paper, we propose two new AutoML approaches, UEvoAutoML and OEvoAutoML, which integrate adaptive resampling techniques into an existing online AutoML framework. Their performance is investigated through a set of synthetic imbalanced data streams under various stationary and non-stationary scenarios and 5 real-world data streams. As the pioneering work of exploring how class imbalance techniques benefit online AutoML, this paper demonstrated that the effectiveness of adaptive resampling in AutoML frameworks.
Consensus-Based Distributed Kernel One-class Support Vector Machine for Anomaly Detection

One-class support vector machine (OCSVM) is one of the most widely used methods for learning from imbalanced data and has been successfully applied to numerous tasks such as anomaly detection. However, the study on decentralized OCSVM is currently limited to linear cases. The main challenge is how to communicate the non-parametric and local-data-dependent decision functions between neighboring nodes. To tackle it, this paper proposes a projection consensus constraint to formulate a decentralized OCSVM, where local solutions are assumed to be the projection of the global optimum on local reproducing kernel Hilbert spaces. A fast non-parametric solving algorithm is then designed based on alternating direction method of multipliers. Experiments on real-world anomaly datasets indicate that our method outperforms the existing distributed OCSVM methods while reducing the communication cost.
EDANAS: Adaptive Neural Architecture Search for Early Exit Neural Networks

Early Exit Neural Networks (EENNs) endow neural network architectures with auxiliary classifiers to progressively process the input and make decisions at intermediate points of the network. This leads to significant benefits in terms of effectiveness and efficiency such as the reduction of the average inference time as well as the mitigation of overfitting and vanishing gradient phenomena. Currently, the design of EENNs, which is a very complex and time-consuming task, is carried out manually by experts. This is where Neural Architecture Search (NAS) comes into play by automatically designing neural network architectures focusing also on the optimization of the computational demand of these networks. These requirements are crucial in the design of machine and deep learning solutions meant to operate in devices constrained by the technology (computation, memory, and energy) such as Internet-Of-Things and embedded systems. Interestingly, few NAS solutions have taken into account the design of early exiting mechanisms. This work introduces, for the first time in the literature, a framework called Early exit aDAptive Neural Architecture Search (EDANAS) for the automatic design of both the EENN architecture and the parameters that manage its early exit mechanism in order to optimize both the accuracy in the classification tasks and the computational demand. EDANAS has proven to compete with expert-designed early exit solutions paving the way for a new era in the prominent field of NAS.
Adaptive Learning Rate for Unsupervised Learning of Deep Neural Networks

In this paper an approach for adaptive learning step calculation using ReLU transfer function in neural network is proposed. This adaptive learning rate aims to automatically choose the step size that minimizes the objective function of neural network. We give a theoretical justification for the proposed adaptive learning rate approach, which is based on the steepest descent method. The main contribution of this paper is a novel technique for adaptive learning rate calculation, if we use ReLU transfer function. The experiments in data compression datasets show that proposed approach provides better generalization capability (test set accuracy) and permits to choose the learning rate automatically.
Improving The Local Stability of Deep Model With Margin Losses

One of the serious problems encountered by deep models is their high sensitivity to small perturbations on the input samples. It is mainly manifested in two aspects, one is the problem of adversarial samples and the other is that the gradient interpretation results of similar samples are quite different, which seriously affects the reliability and dependence of the model. In this paper, we first obtain a new loss by deriving an upper bound of the expectation of the CE loss on the local Gaussian sampled data, which is called Margin Losses. Further, under the inspiration of this margin loss and in order to improve the computational efficiency, we designed another Margin Losses based on the loss gradient. The idea of these two Margin Losses is to force samples away from the decision-making boundary to ensure local stability. Empirical results on MNIST, CIFAR-IO, and CIFAR-IOO demonstrate that our losses can indeed improve the model's resistance to adversarial noise, and at the same time can promote the local consistency of the model gradient.
DDA: A Dynamic Difficulty-aware Data Augmenter for Image Super-resolution

Deep neural networks (DNNs) have been recently widely used in image super-resolution (SR) and have achieved remarkable performance. However, most existing methods focus on elaborate network design, while rarely considering the training strategy, which affects the model performance and training efficiency. In practice, most SR methods still train the networks with the commonly-used data augmentation (e.g., random crop and sampling), which is shown to converge slowly for deep SR networks. To address this issue, in this paper, we propose a dynamic difficulty-aware data augmenter, named DDA, by considering the restoration difficulty and distribution of input patches. Our DDA mainly consists of difficulty-aware divider, dynamic sampler, and adaptive re-weighter. Specifically, our DDA first uses the difficulty-aware divider to divide the input image into small over-lapping patches, followed by classification into <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N$</tex> different classes based on the restoration difficulty. Next, dynamic sampler samples the training patches from each class with probability based on training loss. Furthermore, to remedy the imbalance of training patches between different classes, adaptive re-weighter updates the weight of each training patch according to the accumulated training loss. Extensive experiments demonstrate the effectiveness of our DDA on different SR methods by improving training efficiency and model performance across a wide range of scenarios.
Iterative Fusion Method Based on Heterogeneous Graph Neural Network for Entity Alignment

Entity alignment aims to link equivalent entities across different knowledge graphs. The knowledge graph contains multiple types of information, such as entities, relations, and attributes. Most existing studies have focused on entity information and ignored other information. Some of the latest approaches introduce information such as relations, but the interactivity could be stronger. Otherwise, it will lead to bias in the extracted features when the central entities have the same or similar neighbours. Thus, the correct entity alignment seed pair cannot be found. In this paper, by introducing heterogeneous graph neural networks to model entities and relations as nodes on the graph simultaneously, the relevant information of relations is fully considered. Furthermore, an iterative fusion method is proposed to enhance the interaction of these two types of semantic nodes to obtain a node representation more suitable for the entity alignment task. The model performs entity alignment after updating the node representations. The experimental results show that all Hits@1 values of this model are greater than 75%, all Hits@10 values are greater than 95%, and all MRR values are greater than 0.83 on the dataset DBP15K.
MA-YOLO: Multi-Scale Information Prediction Network Based on the Multi-Direction Weighted Pyramid for UAV Scene

Object detection on unmanned aerial vehicles (DAVs)-captured scenarios play an essential role in several applications such as surveillance, environmental monitoring, security, disaster response strategies, and construction of transportation systems. Images captured by DAVs are all overhead vision including too many small objects, which are difficult to detect. Besides, the high-speed and low-altitude flight process of DAVs brings in the motion blur on the densely packed objects. The average, scale transformation and scene coverage are large, which brings difficulties in extracting and identifying useful information. To address these challenges, we propose a lightweight detection model named MA-YOLO. This article has made the following improvements based on YOLOv5:1)a multi-directional weighted pyramid structure (MiFPN) is proposed for fusing information of different scales and improves the ability to detect small objects.2) A learning-capable decoupling head (AD-head) is proposed to obtain small object information in a complex environment. Extensive experiments are conducted on the challenging VisDrone-DET2021 dataset to evaluate the performance of MA-YOLO. The obtained results show that the accuracy is better than other detection algorithms and the detection speed of VisDrone-DET2021 is improved from 85FPS to 109FPS. Thus, the MA-YOLO method pursues a trade-off between speed and accuracy compared to the state-of-the-art small object detection methods and ensures practicality on drones.