Reviewerly/exHarmony huggingface dataset:
A feature engineering method for machine learning inspired by quantum mechanics

This work proposes a quantum-state-based feature engineering (QSFE) method for machine learning. QSFE uses wave functions that describe microscopic particle systems as mappings. By QSFE, original inputs or features extracted by neural networks are processed as quantum states to train wave function parameters. The experiments demonstrate that QSFE can improve the feature recalibration ability in deep neural networks. QSFE has a low computational cost, almost no additional parameters, and is easy to integrate with other modules. This work unfolds two effectiveness of QSFE: firstly, QSFE can enhance the expression ability of the model and make full use of the features extracted from the previous network; secondly, QSFE can extract complex spatial and temporal interactions, following the self-organization theory. The validations on various machine learning tasks, including a classical self-organization model, indicate that QSFE is valuable in interdisciplinary machine learning applications.
Underexplored Subspace Mining for Sparse-Reward Cooperative Multi-Agent Reinforcement Learning

Learning cooperation in sparse-reward multi-agent reinforcement learning is challenging, since agents need to explore in the large joint-state space with sparse feedback. However, in cooperative games, the cooperative target is often related to partial attributes, hence there is no need to treat the whole state space equally. Therefore, we propose Underexplored Subspace Mining (USM), a novel type of intrinsic reward that encourages agents to selectively explore partial attributes instead of wasting time on the whole state space to accelerate learning. Specially, considering that the target-related attributes are varying in different games and hard to predefine, we choose to focus on the underexplored subspace as an alternative, which is an automatic aggregation of the underexplored bottom-level dimensions without any human design or learning parameters. We evaluate our method in cooperative games with discrete and continuous state space separately. Results demonstrate that USM consistently outperforms existing state-of-the-art methods, and becomes the only method that has succeeded in sparse-reward games evaluated with larger state space or more complicated cooperation dynamics.
Zero-shot Adaptive Low Light Enhancement with Retinex Decomposition and Hybrid Curve Estimation

The low-light image enhancement has long been a critical need in practical applications. Existing methods require either paired or unpaired datasets. Zero-shot methods avoid the requirement of datasets, but they simply enhance the illumination component of the entire image with traditional gamma transformation, which causes color deviation and fails to process low-light images with uneven illumination. Also, these methods often do not take noise into account. We propose a zero-shot low-light image enhancement method. First, we decompose the image into illumination and reflectance according to the Retinex theory. The decomposed reflectance usually contains noise, so we estimate and remove the noise from the reflectance. To enhance the illumination, we design a hybrid illumination enhancement curve that combines gamma transformation and linear transformation. Also, we use a convolutional neural network to estimate the parametric maps in the curve to achieve pixel-level illumination enhancement, so our method can robustly process low-light images with uneven illumination. Extensive experiments demonstrate that our method outperforms recent state-of-the-art methods qualitatively and quantitatively.
Adversarial Domain Adaptation Network with Enhanced Feature Discriminability for Thyroid Ultrasound Images

In recent years, computer-aided diagnosis technology has made breakthroughs in clinical medicine. However, due to the different models and configurations of ultrasound instruments, the thyroid ultrasound images collected by different medical centers have different visual characteristics. It leads to domain shift in multi-center data and the lower generalization of computer-aided diagnosis models. We consider this limitation may attribute to the minor inter-class differences in thyroid ultrasound images, resulting in confusion space before and after domain feature alignment. Therefore, we propose an adversarial domain adaptation network with enhanced feature discriminability method. Among them, the discriminative feature learning module strengthens the discriminability of the learned features, so that the class distribution in each domain presents a high cohesion and low coupling effect; the class alignment module reduces the distance between the same class samples across domains to achieve class alignment and further strengthen the discriminability of each class. Experiments show that our method outperforms other state-of-the-art algorithms on the internal thyroid ultrasound image dataset. The method can effectively improve the model's generalization ability on multi-center thyroid ultrasound images.
Bi-ClueMVSNet: Learning Bidirectional Occlusion Clues for Multi-View Stereo

Deep learning-based multi-view stereo (MVS) meth-ods have achieved promising results in recent years. However, very few existing works take the occlusion issues into consideration, leading to poor reconstruction results on the boundaries and occluded areas. In this paper, the Bidirectional Occlusion Clues-based Multi-View Stereo Network (Bi-ClueMVSNet) is proposed as an end-to-end MVS framework that explicitly models the occlusion obstacle for depth map inference and 3D modeling. To this end, we use bidirectional projection for the first time to reduce the propagation and accumulation of incorrect matches and build the occlusion-enhanced network to further advance the representational ability from 2D visibility maps to 3D occlusion clues. As for depth map estimation, we combine the characteristics of both regression and classification approaches to propose the adaptive depth map inference strategy. Besides, the robustness of the training process is further guaranteed and elevated by the occlusion clues-based loss function. The proposed method significantly improves the accuracy of depth map inference in boundaries and heavily occluded areas and brings the overall quality of the reconstructed point cloud to a new altitude. Extensive experiments are performed on DTU, Tanks and Temples, and BlendedMVS datasets to demonstrate the persuasiveness of the proposed framework.
Modal Neural Network: Robust Deep Learning with Mode Loss Function

Neural networks have been successfully applied in numerous domains with the help of high-quality training samples. However, datasets containing noises and outliers (i.e., corrupted samples) are ubiquitous in the real world. When using these datasets as training samples, most neural networks exhibit poor predictive performance. In this paper, motivated by the modal regression, we propose a Modal Neural Network, which is robust to corrupted samples. Specifically, the modal neural network can reveal the most likely trends of training samples without overfitting the corrupted samples. On the theoretical side, we establish the generalization error bounds of the proposed method with Rademacher complexity. On the experimental side, the numerical results demonstrate our method yields substantial effectiveness and robustness to different levels of corruption on both synthetic and real-world benchmark datasets. Furthermore, our method, as a plug-and-play algorithm, can be readily applied to most neural network architectures and optimizers.
Multi Feature Representation and Aggregation Network for Accurate and Robust Visual Tracking

Segmentation-based tracking paradigm has been successfully applied to tracking field and significantly improves the tracking performance. Although segmentation-based trackers are effective for target scale estimation, it makes the trackers have high requirements for the extracted target features due to the need for pixel-level segmentation. Therefore, in this article, we propose a novel multi feature representation and aggregation network and introduce it into tracking-by-segmentation framework to extract and integrate rich features for segmentation-based tracking. To be specific, the proposed approach firstly models three complementary feature representations through cross-attention, cross-correlation and dilated involution mechanisms respectively and employ a simple feature aggregation network to fuse these features. And then feeding those fusion features into a segmentation network obtains the accurate target state estimation. In addition, we introduce a bounding box refinement module to further refine the target box to alleviate the issues of partial occlusion and surrounding distractors. The extensive experimental results show that the proposed tracker achieves very promising tracking performance on seven challenging visual tracking benchmarks. Code and models are available at https://github.com/Yang428/FEAST.
An Autonomous Non-monolithic Agent with Multi-mode Exploration based on Options Framework

Most exploration research on reinforcement learning (RL) has paid attention to ‘the way of exploration’, which is ‘how to explore’. The other exploration research, ‘when to explore’, has not been the main focus of RL exploration research. The issue of ‘when’ of a monolithic exploration in the usual RL exploration behaviour binds an exploratory action to an exploitational action of an agent. Recently, a non-monolithic exploration research has emerged to examine the mode-switching exploration behaviour of humans and animals. The ultimate purpose of our research is to enable an agent to decide when to explore or exploit autonomously. We describe the initial research of an autonomous multi-mode exploration of non-monolithic behaviour in an options framework. The higher performance of our method is shown against the existing non-monolithic exploration method through comparative experimental results.
Flexible BERT with Width- and Depth-dynamic Inference

Pre-trained Language Models bring about an in-creasing computational and memory cost. The recently proposed computation-flexible BERT models facilitate their deployment in varied computational environments. Training such flexible BERT models involves jointly optimizing multiple BERT subnets that inevitably interfere with one another. Besides, the performance of large sub nets is limited when there is a significant performance gap between the smallest sub net and the supernet, despite methods managing to enhance the smaller subnets. We propose layer-wise Neural grafting to boost BERT subnets, particularly the larger ones. The proposed method improves the average performance of BERT sub nets on six out of eight GLUE tasks. Furthermore, we build a flexible BERT model that enables practical width- and depth-dynamic inference regarding different inputs by combining width-dynamic gating modules and early exit off-ramps in the depth dimension. Experimental results demonstrate that the proposed framework achieves a better dynamic inference range than other methods in the trade-off between performance and computational complexity on four GLUE tasks and the SQuAD data set. Our optimal-tradeoff inference result, in particular, outperforms related fixed-size models with comparable computational complexity. Compared to the supernet, BERT-Base, this inference result improves the average GLUE score and Fl score on SQuAD by 1.3 and 2.2 absolute points, respectively, and decreases computations by around 45%.
MobileNet-Light: A Lightweight TCT Image Classification Model for Cervical Cancer

Cervical cancer is one of the most common malignant tumors in women. Clinical observations have demonstrated that early screening and treatment can prevent, detect, and cure cervical cancer. TCT image detection has been widely used in cervical disease screening, yet the numerous cell counts in TCT image samples make traditional manual diagnostic screening consume a significant amount of human resources. Accurately and rapidly classifying TCT images is essential for cervical cancer screening. This paper proposed a MobileNet-Light model for cervical cancer TCT image classification based on lightweight design. The Ghost module was introduced in the lightweight model design to optimize redundant image features in the network. Moreover, the model design included an efficient channel attention mechanism based on ECA, which improved the classification accuracy while significantly reducing the parameter count. Finally, to address the black-box problem of the classification model, we used the Grad-Cam based interpretable method to provide some interpretability to the model's classification results as diagnostic basis for doctors. Experimental results demonstrated that our lightweight classification model remarkably performs in cervical cancer TCT image classification tasks.
Weighted Keywords-Guided feature Extraction and Reasoning Network for Visual Grounding

In recent years, as the basis for the downstream image-text task, Visual Grounding has attracted more and more attention. Existing models treat the feature extraction process of image and text as two completely independent parts. However, due to a lack of effective interaction between text and image, especially the understanding deviation of keywords. It tends to weaken the alignment between the extracted visual features and the target. Therefore, in this paper, we propose a Weighted Keywords-Guided feature Extraction and Reasoning Network(WKG-Net) to enhance the interaction of text and image. We design a Weighted Keywords-Guided Extractor(WKG-Extractor), which can add extra keywords information as the input to guide the further extraction from general visual features to guided visual features. Then a Weighted Keywords-Guided Reasoner(WKG-Reasoner) is introduced to fuse weighted keywords, original text and visual features together to enhance the alignment between the image and text. Enhancing the understanding of keywords will be helpful for all tasks of text and image alignment. Extensive experiments show that our method has a greater performance compared with the baseline on the three mainstream datasets.
LGWAE: Label-Guided Weighted Autoencoder Network for Flexible Targeted Attacks of Deep Hashing

Deep hashing is frequently utilized in large-scale image retrieval because of its strong representation learning capabilities and effective processing capacity. However, deep hashing models are susceptible to adversarial examples. We propose a label-guided weighted autoencoder network (LGWAE) to generate adversarial examples for targeted hashing attacks. Specifically, we first introduce a multi-label learning network to extract the semantic features and the category code of different labels. Then, the semantic features and the benign image are collectively fed into an autoencoder in order to generate a natural-looking adversarial example. The category code is used as target hash code to supervise the generation of the adversarial example. To efficiently generate better-performing adversarial examples, we design a weighted Hamming distance loss that dynamically adjusts the loss value based on the label similarity between the benign image label and the target label. Numerous experiments demonstrate that we are capable of efficiently and effectively generating better quality adversarial samples.
Conformal Prediction Enhanced SVMs for Swarm Behavior Classification

Collective swarm behavior recognition is the state-of-the-art to identify and observe a moving self-organized swarm or an object. However, because of the complex and harsh environment, current swarm behavior recognition systems remain either need large numbers of training samples or lack reliable measurements enough. In this paper, we solve the two issues in different directions, with the novel Support Vector Machines to provide guaranteed classification under the limited sample and a new conformal prediction algorithm to enhance the accuracy of the prediction. We report two classification schemes, both the simple prediction corresponding to confidence and credibility measures, and provide set prediction with any significance levels. We show why our approach performs better than other solutions through empirical studies with two benchmark datasets, both simulation and real-life swarm behavior datasets. To the best of our knowledge, we are the first to apply conformal prediction for the collective swarm behavior recognition purpose in general, and for the real-life swarm behavior in particular.
MASZSL: A Multi-Block Attention-Based Description Generative Adversarial Network for Knowledge Graph Zero-Shot Relational Learning

In the real world, the Knowledge Graph(KG) is dynamic and new entities are added at any time. Therefore, open-world Knowledge Graph Completion(KGC) was proposed to approach new-added entities, but previous approaches often introduced too much noise when introducing external text resources for new entities. To alleviate this problem, knowledge graph zero-shot relational learning (KGZSL) has recently attracted more attention. Generative Adversarial Networks (GANs) are frequently used in KGZSL to connect existing relation descriptions to the domain of knowledge graphs. However, these methods ignore the impact of existing entities on embeddings for unidentified relational representations and in-stead concentrate on examining the connection between relational textual texts and knowledge network structures. In this work, we propose a multi-block attention framework using relation Description Generative Adversarial Networks (desGAN) jointing KG and text representation and address model collapse and training stability problems in previous studies. The core idea of our method is to obtain the background knowledge graph information and the relation representation through and multi-block attention layer and the desGAN, then a connection between the structured KG semantic space and the unstructured text semantic space of the new entity is established, forcing the entity pair to be closer to their real relation. Experimental results on the knowledge graph zeroshot relational learning dataset demonstrate that our MASZSL has a faster convergence speed and achieves state-of-the-art performance on this task.
Interaction-Focused Anomaly Detection on Bipartite Node-and-Edge-Attributed Graphs

Many anomaly detection applications naturally pro-duce datasets that can be represented as bipartite graphs (user-interaction-item graphs). These graph datasets are usually sup-plied with rich information on both the entities (nodes) and the interactions (edges). Unfortunately, previous graph neural network anomaly models are unable to fully capture the rich information and produce high-performing detections on these graphs, as they mostly focus on homogeneous graphs and node attributes only. To overcome the problem, we propose a new graph anomaly detection model that focuses on the rich interactions in bipartite graphs. Specifically, our model takes a bipartite node-and-edge-attributed graph and produces anomaly scores for each of its edges and then for each of its bipartite nodes. We design our model as an autoencoder-type model with a customized encoder and decoder to facilitate the compression of node features, edge features, and graph structure into node-level latent representations. The reconstruction errors of each edge and node are then leveraged to spot the anomalies. Our network architecture is scalable, enabling large real-world applications. Finally, we demonstrate that our method significantly outper-forms previous anomaly detection methods in the experiments.
User Recognition of Devices on the Internet based on Heterogeneous Graph Transformer with Partial Labels

Recognizing the users of devices can easily enable numerous security applications. Due to the lot's kinds of device data and a large number of missing values, it takes work to recognize the users of devices well. The community detection methods based on Graph Neural Networks (GNN) can integrate multi-source data well and cluster devices into communities with the same users. While existing GNN methods face several issues. The methods on homogeneous graphs could not utilize the multi-source data of devices, and most methods on heterogeneous graphs need specific knowledge to design meta paths. Also, the Internet-scale data of devices make it hard to learn the representation thoroughly. Further, most methods need to consider the known partial labels in the early stage of the training process. To improve the performance of user recognition, this paper proposes HGT-PL, namely a Heterogeneous Graph Transformer with Partial Labels, to calculate the representation of devices on the Internet. Then cluster methods are used to realize user recognition. Using graph transformers, HGT-PL deeply learns node features and graph structure on the heterogeneous graph of devices. By Label Encoder, HGT-PL fully utilizes the users of partial devices from preliminary rules with high confidence. Moreover, cluster methods carefully divide and modify the communities with different users. The paper conducts experiments on the web-scale data collected from the Internet. The results show that HGT-PL can recognize users of devices more accurately and effectively, with 0.5121 NMI and 0.3554 ARI, compared with existing GNN methods.
An Empirical Study on Data Balancing in Machine Learning Based Software Traceability Methods

Machine learning (ML) has been widely used in trace link recovery (TLR) to reduce the manual maintenance cost of trace links by developers. However, the imbalanced distribution of valid links and invalid links seriously affects the performance of classifiers. Although a few studies have applied data balancing techniques (DBT) to ML-based TLR, none of them has systematically analyzed more effective combinations of them. Therefore, we perform an empirical study on three groups of control experiments to explore the impact of the combination of different ML methods with and without DBT on TLR efficiency. We compare the performance of supervised ML-based TLR and unsupervised ML-based TLR with and without DBT respectively. Then, we analyze the performance of the ensemble learning model (EM) with DBT on TLR. The experimental results on the 7 imbalance datasets of CoEST indicate that DBT has a positive effect on ML-based TLR. Specifically, the recall of the LR model increased by 0.5517 after combining with most DBTs on EasyClinic(ID-TC), while Tomek-link significantly improves the precision of K-Nearest Neighbor (KNN), Decision Tree (DT), LR, Support Vector Machine (SVM). The precision of LR increased from 0.5036 to 1.0. BalanceRF is best at increasing recall, reaching 1.0 on 4 datasets. Moreover,the improvement degree of ML-based TLR with DBT shows differences in terms of the size of datasets and the proportion of valid links.