Reviewerly/exHarmony huggingface dataset:
Deployment of interpretable machine learning in a water treatment device - feasibility exploration of performance enhancement

Machine learning (ML) provides a promising tool for predictive control and unattended operation of water treatment processes, especially in decentralized water supply due to its high variability of water quality and scarcity of manpower. However, model deployment after ML development is a challenge that hinders their implementation. Herein, this study constructed a novel electrocoagulation membrane cathode reactor (ECMCR) as an example of decentralized water treatment due to its highly integratable. Nine ML models were established to determine the ECMCR operational current density (CD) and the Bayesian technique was employed for adaptive hyperparameters tuning of each ML model. Light gradient boosting machine (LightGBM) with turbidity, humic acid (HA), pH, temperature and time as input performed best (R2 = 0.99, MSE = 0.0004) during training, whereas the random forest (RF) was effective and robust for test data (R2 = 0.98, MSE = 0.0056) with same inputs. The mean decrease impurity (MDI) and Shapley Additive exPlanation (SHAP) method indicated that the RF model made predictions based primarily on influent turbidity, which implied a correct "understanding" of the RF model. Finally, the model was deployed to the ECMCR to explore the regulation capability, and the HA removal and turbidity removal of the ECMCR increased by 23.68 % and 23.83 %, respectively. The results demonstrate that ML technology could assist in achieving the desired performance of drinking water treatment devices without manual involvement.
Asymmetric Learning for Graph Neural Network based Link Prediction

Link prediction is a fundamental problem in many graph-based applications, such as protein-protein interaction prediction. Recently, graph neural network (GNN) has been widely used for link prediction. However, existing GNN-based link prediction (GNN-LP) methods suffer from scalability problem during training for large-scale graphs, which has received little attention from researchers. In this paper, we first analyze the computation complexity of existing GNN-LP methods, revealing that one reason for the scalability problem stems from their symmetric learning strategy in applying the same class of GNN models to learn representation for both head nodes and tail nodes. We then propose a novel method, called a sym m etric l earning (AML), for GNN-LP. More specifically, AML applies a GNN model to learn head node representation while applying a multi-layer perceptron (MLP) model to learn tail node representation. To the best of our knowledge, AML is the first GNN-LP method to adopt an asymmetric learning strategy for node representation learning. Furthermore, we design a novel model architecture and apply a row-wise mini-batch sampling strategy to ensure promising model accuracy and training efficiency for AML. Experiments on three real large-scale datasets show that AML is 1.7×∼7.3× faster in training than baselines with a symmetric learning strategy while having almost no accuracy loss.
Semi-supervised possibilistic c-means clustering algorithm based on feature weights for imbalanced data

The possibilistic c-means clustering (PCM) algorithm improves the robustness of fuzzy c-means clustering (FCM) to noise and outliers by releasing the probabilistic constraint of memberships. The semi-supervised possibilistic c-means clustering (SSPCM) algorithm improves the clustering effect on datasets with imbalanced sizes by introducing a small amount of label information. However, the traditional semi-supervised algorithm still faces the problem of low utilization of supervision information for datasets with large differences in sample sizes. Moreover, the Euclidean distance, which treats features equally, cannot handle feature-imbalanced data. Therefore, this paper proposes a semi-supervised possibilistic c-means clustering algorithm based on feature weights (FW-SSPCM) by introducing the ideas of supervised centers. First, the algorithm introduces the supervised center into the objective function of the SSPCM to improve the utilization rate of supervision information and thus guide the center iteration of small clusters. Second, the feature weighting strategy is introduced in the objective function to adaptively assign feature weights according to the importance of different features in different clusters, thus improving the adaptability of the algorithm to feature-imbalanced datasets. In addition, to improve the robustness of the antinoise effect and retain additional image details, a new image segmentation algorithm based on FW-SSPCM and local information (LFW-SSPCM) is proposed by introducing local spatial information obtained by bilateral filtering. Finally, through clustering experiments on synthetic data, UCI datasets and on color images characteristic of multiple features, including imbalanced sizes, imbalanced features and strong noise injection, the clustering performances of the proposed FW-SSPCM and LFW-SSPCM proposed in this paper are significantly better than those of several related clustering algorithms.
Multi-granularity Knowledge Sharing in Low-resource Neural Machine Translation

As the rapid development of deep learning methods, neural machine translation (NMT) has attracted more and more attention in recent years. However, lack of bilingual resources decreases the performance of the low-resource NMT model seriously. To overcome this problem, several studies put their efforts on knowledge transfer from high-resource language pairs to low-resource language pairs. However, these methods usually focus on one single granularity of language and the parameter sharing among different granularities in NMT is not well studied. In this article, we propose to improve the parameter sharing in low-resource NMT by introducing multi-granularity knowledge such as word, phrase and sentence. This knowledge can be monolingual and bilingual. We build the knowledge sharing model for low-resource NMT based on a multi-task learning framework, three auxiliary tasks such as syntax parsing, cross-lingual named entity recognition, and natural language generation are selected for the low-resource NMT. Experimental results show that the proposed method consistently outperforms six strong baseline systems on several low-resource language pairs.
Improving extreme learning machine model using deep learning feature extraction and grey wolf optimizer: Application to image classification

The Extreme Learning Machine (ELM) is a highly efficient model for real-time network retraining due to its fast learning speed, unlike traditional machine learning methods. However, the performance of ELM can be negatively impacted by the random initialization of weights and biases. Moreover, poor input feature quality can further degrade performance, particularly with complex visual data. To overcome these issues, this paper proposes optimizing the input features as well as the initial weights and biases. We combine both Convolutional Neural Network (CNN) and Convolutional AutoEncoder (CAE) extracted features to optimize the quality of the input features. And we use our hybrid Grey Wolf Optimizer-Multi-Verse Optimizer (GWO-MVO) metaheuristic for initializing weights and biases by applying four fitness functions based on: the norm of the output weights, the error rate on the training set, and the error rate on the validation set. Our method is evaluated on image classification tasks using two benchmark datasets: CIFAR-10 and CIFAR-100. Since image quality may vary in real-world applications, we trained and tested our models on the dataset’s original and noisy versions. The results demonstrate that our method provides a robust and efficient alternative for image classification tasks, offering improved accuracy and reduced overfitting.
Event-Driven Fringe Projection Structured Light 3-D Reconstruction Based on Time–Frequency Analysis

Event-based structured light 3D measurement systems (eSL) overcome intrinsic limitations of frame-based structured light 3D measurement systems (fSL) such as sensor dynamic range, pixel bandwidth, and system optical power. However, most of the existing methods are based on laser scanning, and they are not only limited by the speed of acquisition, but are also very sensitive to noise events. This paper proposes an efficient event-driven fringe projection structured light 3D reconstruction system (eFPSL), aiming for high-precision 3D measurements at increased acquisition speeds. Leveraging a digital light processing fringe projection system, eFPSL enhances the acquisition speed. We further develop a time-frequency analysis algorithm capable of efficiently converting event streams into 2D fringe intensity images with high signal-to-noise ratio and excellent sinusoidality thus mitigating noise event impacts. Moreover, a novel events-count-based shadow mask extraction (eSME) strategy is proposed to eliminate shadow noise. Experimental validations demonstrate eFPSL’s ability to produce higher precision 3D reconstructions, reducing mean absolute error (MAE) by 64% compared to existing eSL methods. In addition, eFPSL impressively safeguards comprehensive 3D results while reducing system power consumption, achieving an 82% reduction in system optical power compared to traditional fSL systems. Most importantly, for high dynamic range (HDR) scenes, eFPSL achieves visual representation and measurement accuracy comparable to eight measurements of fSL systems using only a single measurement, indicating an 87.5% improvement in measurement efficiency. Hence, our experiments confirm eFPSL’s adaptability can extend to HDR scenes involving high-speed motion.
Motion segmentation using Ward’s hierarchical agglomerative clustering for crowd disaster risk mitigation

Motion segmentation has gained increasing attention due to its significance for many public surveillance applications, such as behavior understanding and density estimation. Thus ensuring crowd risk mitigation and safety. Although numerous solutions have been put out to deal with this issue, it remains challenging due to factors like scene fluctuations, cluttered backgrounds, and severe occlusions. The present paper proposes a novel framework, spatio-angular-shape Ward linkage (SAS-WL), to segment human motion based on tracked trajectories features in crowd scenes. In the proposed SAS-WL, first the motion features are captured from the crowd video through the generalized-KLT (gKLT) keypoint tracker. The tracklets are then examined to keep only the significant and long-term tracklets. Then, spatio-angular-shape motion features are obtained from those long-term tracklets. Next, Ward's linkage was utilized for performing motion pattern segmentation. In addition, we propose an improved Ward's linkage method and integrate it with the obtained features to increase the quality of the clustering results. One of the significant advantages of the SAS-WL is its capability to address various densities in different crowd scenes. Moreover, the SAS-WL achieves accurate segmentation results. We validated our findings with videos of real-world crowd scenes. We introduce a novel dataset along with its manually annotated ground truth, comprising 21 diverse crowd videos. This dataset aims to enhance the assessment of our framework's performance. Obtaining results on a diversity of challenging crowd scenarios demonstrates that this framework provides precise motion segmentation results.
Generalized GHZ entanglement states for optimal rotation detection of quantum spin systems using majorana representation

Abstract A multitude of engineering and scientific applications require the estimation of an unknown axis’ rotation, known as the reference frame alignment problem. These problems can be categorized using the quantum Cramer–Rao bound. Detecting rotations is typically focused on smaller angles, as errors are more significant in this range. Therefore, selecting the optimal combination of measurement techniques based on a quantum entangled state for a particular setup is a critical step. This paper proposes a linear combination of anticoherence and coherence measures based on the generalized GHZ entanglement states. The anticoherence measures, optimizes the spin system’s average fidelity in detecting rotated states, while the overlap between states in any spin number is minimal. The optimal quantum rotosensors are characterized by the minimal average fidelity given by the overlap before and after averaging over all directions. The individual subsystems’ states have substantial overlap with GHZ states, making the generalized GHZ state the entanglement quantum spin-j state with coherence decay rate ϵ beneficial in finding the optimal rotation detection on noncoherent entanglement state.
Time-aware and task-transferable adversarial attack for perception of autonomous vehicles

With rapid development of self-driving vehicles, recent work in adversarial machine learning started to study adversarial examples (AEs) for perception of autonomous driving (AD). However, generating practical AEs for the perception module remains a significant challenge. Traditional adversarial attacks tend to focus on a single computer vision task, making it difficult to compromise multiple perception tasks such as object detection and segmentation simultaneously. Additionally, the limited computational resources available on-board and the necessity for online operation pose further obstacles to deploying adversarial attacks on real autonomous driving platforms. To address the aforementioned issues, we propose the Time-aware Perception Attack (TPA), which is a real-time cross-task adversarial attack for perception of autonomous driving. In particular, we propose a novel backbone based adversarial attack method to modify input images to approach Lipschitz Constant Point (LCP), which results in erroneous inferences for all the sub-models in perception module. The novel part of this work is proposing an efficient yet effective LCP approaching algorithm. Comparing to conventional LCP based attacks, which consume significant amount of computation resources and can be only applied on small DNNs, TPA generates AEs on an intermediate layer of surrogate backbone, significantly enhancing the cross-task transferability and accelerates the attack process. Evaluation results on Berkeley Driving Dataset 100k (BDD100k) show that, comparing to the state-of-the-art baselines, the proposed TPA achieves higher attack effectiveness and faster processing speed and outperforms the baselines by a large margin.
GPL-GNN: Graph prompt learning for graph neural network

Despite the impressive results achieved in many areas of graph machine learning, through graph representation learning using supervised learning techniques, the limited availability of labeled training data has led to a bottleneck in terms of performance. To address this challenge, transfer learning has been proposed as an effective solution. It involves designing pre-training methods in an unsupervised manner to learn representations, which are then adapted to downstream tasks with limited labeled data. However, transfer learning can suffer from negative transfer when there is a major gap between the objectives of pre-training and the downstream tasks. To overcome these challenges, we introduce a novel framework, graph prompt learning-graph neural network (GPL-GNN), to narrow the gap between different tasks. GPL-GNN employs unsupervised methods, which require no labeled data, and incorporates unsupervised pre-trained structural representations into downstream tasks as prompt information. This information is combined with downstream data to train GNNs adapting them to the downstream tasks, and resulting in more adaptive, task-specific representations. Furthermore, the ability of GPL-GNN to learn graph representations without the constraints of pre-training and fine-tuning for model consistency increases the flexibility in choosing task-specific GNNs. In addition, the introduction of prototype networks as classification heads enables quick adaptation of GPL-GNNs to downstream tasks. Finally, we conduct extensive experiments on a benchmark dataset to demonstrate the effectiveness of GPL-GNN. The code is available in: https://github.com/chenzihaoww/GPL-GNN.
Deep learning-based fusion networks with high-order attention mechanism for 3D object detection in autonomous driving scenarios

In the realm of autonomous driving, accurately detecting 3D objects is both vital and challenging. Recently, deep convolutional networks have been successfully implemented in the fusion of LiDAR and camera data, delivering impressive results. However, prevailing approaches tend to concentrate on basic architectural designs and the use of fixed 3D bounding boxes, overlooking the exploration of feature interrelations and the varying scales of 3D objects. In this paper, we propose High-order Attention Mechanism Fusion Networks (HAMFNs) for image expression and multi-scale learning, based on a novel high-order attention mechanism with multi-scale detection and scale linear regression. High-order convolution layers are built for tenser filtering with discriminative representations of the holistic image. Multi-scale query module further characterizes the saliency properties of the 3D objects. Our tests on the nuScenes dataset show that HAMFNs outperform the latest top-performing methods, achieving a 0.7% increase in mean Average Precision (mAP). We further integrated high-order convolutional layers into ResNet-50, ResNet-101, and ResNet-152 architectures, enhancing their performance with minimal parameter increase. The Top-1 error rates were reduced by 1.65%, 1.63%, and 1.60% for each network, respectively.
An effective deep learning adversarial defense method based on spatial structural constraints in embedding space

Deep neural networks are highly vulnerable to adversarial samples. Most existing adversarial defense methods do not consider the distribution of adversarial samples. We argue that very few adversarial samples in the natural sample set prevent the deep neural networks from learning a complete and effective representation of the adversarial samples. This causes the spatial structures between the natural and the adversarial samples to be vastly different from that of the input space, thus making the models vulnerable to adversarial attacks. Based on this viewpoint, this paper proposes an effective deep-learning adversarial defense method, which incorporates information about the spatial structures of the natural and the adversarial samples in the embedding space during the training process. This proposed approach improves the deep learning model's generalization to new adversarial samples and achieves the purpose of defending against adversarial attacks. Four deep neural networks with different scales are used and experimentally verified on four typical publicly available image data. The experimental results show that our method effectively improves the defense ability of deep learning models against adversarial attacks.
Stereo vision-based health monitoring method for wind turbine towers

Wind turbines are subjected to complex aerodynamic loads, which results in potential sway and tilt, even causing the collision between blades and the tower. Therefore, it is imperative to monitor the condition of the tower to ensure safety. In this paper, we propose a method to monitor the health of wind turbine towers based on stereo vision. Our approach extracts the tower central axis from the image based on the structural information. Then the tower's sway magnitude is measured by binocular intersection, and the tilt angle is computed by exploiting the angle information provided by an inclinometer. Moreover, we detect blade tips using their geometric shape, which can be used to estimate the clearance distance between blades and the tower. The proposed method can simultaneously monitor the tower tilt and sway, while also identifying instances of blade collision with the tower. Experiments demonstrate the accuracy and feasibility of the designed system.
Debiased Visual Question Answering via the perspective of question types

Visual Question Answering (VQA) aims to answer questions according to the given image. However, current VQA models tend to rely solely on textual information from the questions and ignore the visual information in the images to get answers, which is caused by bias that is generated during the training phase. Previous studies have shown that bias in VQA is mainly caused by the text modality, and our analysis suggests that question type is a crucial factor in bias formation. To address this bias, we proposed a self-supervised method including the Against Biased Samples(ABS) module that performs targeted debiasing by selecting samples that are prone to bias, and the Shuffle Question types(SQT) module that constructs negative samples by randomly replacing the question types of the samples selected by the ABS, to interrupting the shortcuts from question type to answer. Our approach mitigates the question-to-answer bias without using external annotations, overcoming the prior language problem. Additionally, we designed a new objective function for negative samples. Experimental results indicate that our method outperforms both self-supervised-based and supervised-based state-of-the-art approaches, achieving 70.36% accuracy on the VQA-CP v2 dataset.
Blockchain-based multi-institution collaborative credit evaluation modeling method

Multi-institution credit data sharing-aggregation can improve the accuracy of credit evaluation, however, it also encounters the problems of data fraud, and the privacy, compliance of collaborative modeling. Faced with increasingly stringent and comprehensive privacy protection regulations, the potential security risk call into question the current data sharing-aggregation mode. Blockchain has shown that trusted and auditable computing is possible using a decentralized network of peers, and the immutable distributed ledger. Moreover, oblivious transfer (OT) guarantees the confidentiality of cross-institution transmission and calculation. The integration of blockchain with OT is rapidly increasing in computing environment. In this paper, we design and implement a blockchain-OT-based credit evaluation system dubbed as MEChain. Blockchain acts as a distributed ledger which facilitates efficient and credible data sharing-aggregation. Combined the 1-out-of-N OT, data transformation is used for raw data encryption achieving private, secure, and compliant sharing-aggregation operations. The calculation case and security analysis, prototype system implementation, performance and accuracy rate analysis are presented to validate the proposed solution.
Sequential visual and semantic consistency for semi-supervised text recognition

Scene text recognition (STR) is a challenging task that requires large-scale annotated data for training. However, collecting and labeling real text images is expensive and time-consuming, which limits the availability of real data. Therefore, most existing STR methods resort to synthetic data, which may introduce domain discrepancy and degrade the performance of STR models. To alleviate this problem, recent semi-supervised STR methods exploit unlabeled real data by enforcing character-level consistency regularization between weakly and strongly augmented views of the same image. However, these methods neglect word-level consistency, which is crucial for sequence recognition tasks. This paper proposes a novel semi-supervised learning method for STR that incorporates word-level consistency regularization from both visual and semantic aspects. Specifically, we devise a shortest path alignment module to align the sequential visual features of different views and minimize their distance. Moreover, we adopt a reinforcement learning framework to optimize the semantic similarity of the predicted strings in the embedding space. We conduct extensive experiments on several standard and challenging STR benchmarks and demonstrate the superiority of our proposed method over existing semi-supervised STR methods.