Reviewerly/exHarmony huggingface dataset:
Addressing Heterogeneity in Federated Learning with Client Selection via Submodular Optimization

Federated learning (FL) has been proposed as a privacy-preserving distributed learning paradigm, which differs from traditional distributed learning in two main aspects: the systems heterogeneity, meaning that clients participating in training have significant differences in systems performance including CPU frequency, dataset size, and transmission power, and the statistical heterogeneity, indicating that the data distribution among clients exhibits Non-Independent Identical Distribution. Therefore, the random selection of clients will significantly reduce the training efficiency of FL. In this article, we propose a client selection mechanism considering both systems and statistical heterogeneity, which aims to improve the time-to-accuracy performance by trading off the impact of systems performance differences and data distribution differences among the clients on training efficiency. First, client selection is formulated as a combinatorial optimization problem that jointly optimizes systems and statistical performance. Then, we generalize it to a submodular maximization problem with knapsack constraint, and propose the Iterative Greedy with Partial Enumeration (IGPE) algorithm to greedily select the suitable clients. Then, the approximation ratio of IGPE is analyzed theoretically. Extensive experiments verify that the time-to-accuracy performance of the IGPE algorithm outperforms other compared algorithms in a variety of heterogeneous environments.
Question-response representation with dual-level contrastive learning for improving knowledge tracing

Question-response representation (aka. embedding) lies at the core for knowledge tracing, which is pivotal to model students' evolving knowledge states for predicting their future performance. Existing efforts typically learn question-response representations in a supervised manner, thus inevitably neglecting to excavate implicit information existed in student-question-concept connections and suffering from the issue of data sparsity due to rare student-question interactions. Inspired by the recent success of self-supervised learning, in this paper, we propose a Question-Response representation with dual-level Contrastive Learning (QRCL) for improving knowledge tracing, to address the aforementioned problems. Our approach comprehensively considers three types of views for obtaining question-response representations: the native fold views derived from native interaction between students and questions, the augmented fold views generated by contrastive augmentation with Singular Value Decomposition (SVD), and the co-response relation views acquired by means of correlation among questions. By using delicately designed contrastive rules, Graph Neural Networks (GNNs) are adopted as backbone to encode the information from each view for better generating question-response representation. Extensive experimental results conducted on five datasets clearly show our proposed method has the capability in effectively predicting students' performances.
Finding hierarchy of clusters

In this work a novel hierarchical clustering technique is proposed which can be used to find a hierarchical structure of data items in a dataset. The emphasis is on maintaining consistency of the clusters at each level in the hierarchy as well as to have a natural number of clusters at each level. We propose a method by using Cluster Number Assisted k-Means (CNAK) to find the possible natural numbers of clusters for that dataset. Next, we find the association between clusters at different levels. We propose three criteria and use them to remove the insignificant levels to obtain a final hierarchical structure. This method can be used to find the number of clusters at each of the different levels, the clusters at those levels and their association with clusters at adjacent levels. Naïve Hierarchical k-means algorithm uses a pre-determined branching factor at all the levels. In most traditional agglomerative and divisive clustering, at each different level of the hierarchical structure, exactly one node is split into several nodes for the next lower level. Either this number is the same for each parent or decided independently. Clusters with multiple parents are not allowed in traditional hierarchical clustering. However, in reality a cluster may naturally have multiple parents. Experimentation have been carried out on multiple datasets to demonstrate the effectiveness of the proposed technique.
DARS: Decentralized Article Retrieval System

DARS is a decentralized article retrieval system designed to bring the community together for parallel and distributed extraction of article content from the web. The system is comprised of three types of components: web crawlers to extract the links from website pages, web scrapers to extract article information from the web pages identified as articles, and a retrieval core to manage the extraction process. To attain decentralization, multiple such systems can be deployed in different locations. When a client queries one of the nodes, the returned information can be an aggregate of data from multiple nodes. The system is flexible and can be adapted to extract different types of information in a decentralized manner.
Brightness and contrast corrections for stereocorrelation

Stereocorrelation (SC) is a versatile experimental method increasingly used to measure 3D surface displacements in complex mechanical tests. However, convergence issues may arise when gray-level conservation is no longer validated, particularly when local specular reflections are observed. Brightness and contrast corrections (BCCs) are added to the Global Stereocorrelation framework, including different spatial regularization strategies. A challenging experimental test case was chosen for validation purposes where extreme brightness and contrast changes were observed. An impact test on laminated glass was monitored with two high-speed cameras. It is shown that BCCs significantly improve the robustness and convergence rate for both calibration and kinematic measurements. Moreover, the numerical implementation cost into a finite element stereocorrelation code is negligible.
<scp>ArieL</scp> : Adversarial Graph Contrastive Learning

Contrastive learning is an effective unsupervised method in graph representation learning. The key component of contrastive learning lies in the construction of positive and negative samples. Previous methods usually utilize the proximity of nodes in the graph as the principle. Recently, the data-augmentation-based contrastive learning method has advanced to show great power in the visual domain, and some works have extended this method from images to graphs. However, unlike the data augmentation on images, the data augmentation on graphs is far less intuitive and it is much harder to provide high-quality contrastive samples, which leaves much space for improvement. In this work, by introducing an adversarial graph view for data augmentation, we propose a simple but effective method, Adversarial Graph Contrastive Learning ( ArieL ), to extract informative contrastive samples within reasonable constraints. We develop a new technique called information regularization for stable training and use subgraph sampling for scalability. We generalize our method from node-level contrastive learning to the graph level by treating each graph instance as a super-node. ArieL consistently outperforms the current graph contrastive learning methods for both node-level and graph-level classification tasks on real-world datasets. We further demonstrate that ArieL is more robust in the face of adversarial attacks.
Information-aware Multi-view Outlier Detection

With the development of multi-view learning, multi-view outlier detection has received increasing attention in recent years. However, the current research still faces two challenges: (1) The current research lacks theoretical analysis tools for multi-view outliers. (2) Most current multi-view outlier detection algorithms are based on shallow structural assumptions of the data, such as cluster assumptions and subspace assumptions, thus they are not suitable for more complex data distributions. In addressing these two issues, this article proposes three occurrence mechanisms of multi-view outlier, which serve as foundational theoretical analysis tools for multi-view outliers. Utilizing proposed mechanisms, we analyze the impact of multi-view outliers and the information structure of multi-view data and validate our findings through experiments. Finally, we propose a novel algorithm referred to as Information-Aware Multi-View Outlier Detection (IAMOD). In contrast to other methods, IAMOD focuses on the information structure of multi-view data without relying on shallow structural assumptions. By learning a compact representation of the sample that is semantically rich and non-redundant, IAMOD can accurately identify multi-view outliers by comparing the consistency of the representations’ neighbors and views. Extensive experimental results demonstrate that our approach outperforms several state-of-the-art multi-view outlier detection methods.
High-resolution spatiotemporal assessment of solar potential from remote sensing data using deep learning

Spatiotemporal assessment of solar potential is one of the most promising solutions to find suitable locations for future photovoltaic systems’ placement. However, accurate assessment of solar potential on large areas can be challenging due to missing data or computational complexity. In this paper, a fully convolutional neural network based method for high-resolution spatiotemporal assessment of solar potential by using remote sensing data is presented. The method is trained and validated on the area of 32 km2 of the Maribor city, Slovenia, and tested on the 6 different locations in Slovenia and Germany. The proposed method was tested against the simulation algorithm, which utilized isotropic Liu Jordan diffuse model or anisotropic Perez model with sky view factor based shading. On average, a normalized root mean square error (NRMSE) of 6.06% and mean absolute percentage error (MAPE) of 4.29% was achieved in test locations against the simulation based on Liu-Jordan model. When training the fully convolutional neural network models against the ground truth, generated with more advanced Perez diffuse model, the average NRMSE was 8.37% and MAPE of 10.90% was achieved across all test locations. Additionally, it was shown that the proposed method can assess solar potential in high-resolution from the input in lower resolution with higher accuracy than the simulation algorithm, while being up to 1500-times faster.
Improved modeling and fast in-field calibration of optical flow sensor for unmanned aerial vehicle position estimation

To better reveal the principle of off-the-shelf OFS that used for UAV position estimation, two measurement models are derived, namely the continuous-time and discrete-time models. Then the intrinsic equivalence of these two models is also proven. Based on these two models, a calibration method for OFS is introduced, which only makes use of in-situ rotation to determine the OFS’s parameters including its focal length and installation angles with respect to the UAV. Furthermore, an OFS-based UAV position estimation algorithm is presented, which stems from the proposed discrete-time model. The proposed OFS models, calibration method, and position estimation algorithm form a full solution for UAV displacement measurement via OFS, and its feasibility and effectiveness is verified through flight tests on a quadcopter UAV in outdoor environment.
LPIPS-AttnWav2Lip: Generic audio-driven lip synchronization for talking head generation in the wild

Researchers have shown a growing interest in Audio-driven Talking Head Generation. The primary challenge in talking head generation is achieving audio-visual coherence between the lips and the audio, known as lip synchronization. This paper proposes a generic method, LPIPS-AttnWav2Lip, for reconstructing face images of any speaker based on audio. We used the U-Net architecture based on residual CBAM to better encode and fuse audio and visual modal information. Additionally, the semantic alignment module extends the receptive field of the generator network to obtain the spatial and channel information of the visual features efficiently; and match statistical information of visual features with audio latent vector to achieve the adjustment and injection of the audio content information to the visual information. To achieve exact lip synchronization and to generate realistic high-quality images, our approach adopts LPIPS Loss, which simulates human judgment of image quality and reduces instability possibility during the training process. The proposed method achieves outstanding performance in terms of lip synchronization accuracy and visual quality as demonstrated by subjective and objective evaluation results.
Verifiable (t, n) Secret Image Sharing Scheme Based on Slim Turtle Shell Matrix

As Internet technology advances rapidly, safeguarding confidential messages has become an increasingly pressing concern. Among the various schemes available, secret image sharing (SIS) has gained considerable attention. Particularly, the (t, n) secret sharing scheme offers robust protection for hidden secret messages. However, it remains challenging to expand the quality of t and n while maintaining acceptable image quality for the share images and enhancing security features. To address these challenges, we have developed a variant turtle shell known as the slim turtle shell, which we incorporate into our innovative (t, n)-SIS scheme. The slim turtle shell technique not only facilitates the creation of meaningful shares but also ensures that the resulting meaningful shares maintain a high level of visual quality, thanks to its impressive embedding capabilities. Additionally, we have devised two authentication mechanisms to validate the integrity of both shares and secret images. These mechanisms facilitate the detection of tampering during the secret message extraction and verification phase, should the integrity be compromised. Empirical results from our experiments demonstrate an impressive embedding capacity of 1,572,864 bits in our proposed scheme, coupled with an average PSNR (Peak Signal-to-Noise Ratio) of 45.80 dB. Furthermore, our scheme demonstrates superior authentication capabilities when compared to other existing schemes.
SGF3D: Similarity-guided fusion network for 3D object detection

The representation of pseudo point cloud can significantly improve the precision of 3D object detection. However, existing pseudo point cloud-based methods typically fuse the processed features through coarse concatenation, which ignores the consistency between the point cloud and pseudo point cloud features. The inconsistency of features in different modal data can lead to detection bias. In this paper, we propose a novel pseudo point cloud-based network called SGF3D, which utilizes a cross-modal attention module cross-modal attention fusion (CMAF) to fuse point cloud and pseudo point cloud features. It can better learn the cross-modal similarity of output features, enabling the detection box to fit better with the target. We also designed a region of interest (RoI) head similarity attention head (SAH) to utilize the overlooked similarity to optimize training without increasing the complexity of the network. By using CMAF and SAH, the proposed method can obtain more accurate bounding boxes. Extensive experiments on KITTI dataset demonstrate that the proposed method can achieve competitive results. Training code and well trained weights are available at https://github.com/ChunZheng2022/SGF3D.
Hydrophobicity-Based Grading of Industrial Composite Insulators Images Using Cross Attention Vision Transformer With Knowledge Distillation

The hydrophobic property of composite insulators is a crucial attribute that prevents the accumulation of significant water content on the surface and ensures smooth operation and prevention of power line surges. However, since the weathering effect gradually reduces the hydrophobicity, periodic monitoring is necessary to ensure the smooth operation of the powerline. Efficient machine learning-based analysis of the composite insulator (CI) images collected by the spray method can potentially achieve very high efficacy. However, inconsistency in the shape and size of droplets, the intricate pattern of the droplets, the lack of color variation of the images, etc. pose a challenge to the prevalent computer vision techniques for categorizing the CIs. This treatise explored a novel, efficient deep learning paradigm, termed cross attention vision transformer (CA-ViT) for grading the CI images. The CA-ViT uses both small and large patches to blend spatial features corresponding to a different scale. The network uses a token fusion module to effectively combine the tokens obtained from small and large patches and understand the visual pattern, and characterize droplets of different shapes and sizes by exchanging cross-attentions, which demonstrates its ability to precisely detect small and large droplets, and accurately classify the droplet images. Further, the work also introduces a knowledge distillation strategy to reduce the computational run time of the model. Exhaustive experimental results have confirmed that our proposed work surpasses state-of-the-art methods.
Machine Learning Informed Diagnosis for Congenital Heart Disease in Large Claims Data Source

With an increasing interest in using large claims databases in medical practice and research, it is a meaningful and essential step to efficiently identify patients with the disease of interest. This study aims to establish a machine learning (ML) approach to identify patients with congenital heart disease (CHD) in large claims databases. We harnessed data from the Quebec claims and hospitalization databases from 1983 to 2000. The study included 19,187 patients. Of them, 3,784 were labeled as true CHD patients using a clinician developed algorithm with manual audits considered as the gold standards. To establish an accurate ML-empowered automated CHD classification system, we evaluated ML methods including Gradient Boosting Decision Tree, Support Vector Machine, Decision tree, and compared them to regularized logistic regression. The Area Under the Precision Recall Curve was used as the evaluation metric. External validation was conducted with an updated data set to 2010 with different subjects. Among the ML methods we evaluated, Gradient Boosting Decision Tree led the performance in identifying true CHD patients with 99.3% Area Under the Precision Recall Curve, 98.0% for sensitivity, and 99.7% for specificity. External validation returned similar statistics on model performance. This study shows that a tedious and time-consuming clinical inspection for CHD patient identification can be replaced by an extremely efficient ML algorithm in large claims database. Our findings demonstrate that ML methods can be used to automate complicated algorithms to identify patients with complex diseases.
An attachment recognition method based on semi-supervised video segmentation for tidal stream turbines

Tidal stream turbine (TST) is a reliable machine for converting tidal stream energy to electric energy. However, the efficiency of power generation will be severely affected by the increasing marine attachment on TST blades. Accurate identification of attachments is very important for their removal. This paper proposes a semi-supervised video segmentation network (SVSN) to recognize attachments in underwater video sources. To alleviate the labeling burden, only a small number of video frames need manual annotation, and sufficient labeled data are collected by a data augmentation algorithm. For utilizing the remaining video frames, these unlabeled data and the labeled data are used to train the SVSN based on semi-supervised adversarial learning. In this way, these two kinds of data benefit from each other, making the network to learn general features for better generalization ability. The SVSN includes a generator and a discriminator. The generator is a modified SegNet to obtain initial segmentation maps. The discriminator, a simple fully convolutional network, provides pixel-wise confidence maps for the semi-supervised adversarial learning. Besides, it enhances the generator segmentation quality via considering object boundary areas. Experimental results demonstrate that precise attachment recognition and fast uncertainty estimation can be accomplished under harsh submerged conditions.
Image encryption algorithm based on a new 3D chaotic system using cellular automata

Cryptography and steganography are usual methods for safe transfer of information, which encryption algorithms turn original images into unreadable noise-like images. This paper presents a novel symmetric cryptosystem designed for the transmission of RGB color images through open channels and our goal is to provide a secure cryptography algorithm against cropping and noise attacks. The encryption algorithm is based on a suitable 3D hybrid chaotic system, with high Lyapunov exponent value, leveraging the advantages of common chaos maps while addressing their inherent limitations. To further enhance security, a novel pixel shuffle operator is employed to eliminate any potential neighborhood relations between image pixels. The encryption process incorporates reversible second-order cellular automata, which are applied to the outputs of the chaotic system. Key generation is achieved through the utilization of irreversible cellular automata. The resulting key space is large enough to resist brute-force attacks and performs a high level of sensitivity. Experimental results, including analysis of histograms, entropies, and pixel correlations, confirm the effectiveness of the proposed image encryption scheme, and prove its resilient against statistical attacks and a remarkable resistance against data loss attacks.
Top-tuning: A study on transfer learning for an efficient alternative to fine tuning for image classification with fast kernel methods

The impressive performance of deep learning architectures is associated with a massive increase in model complexity. Millions of parameters need to be tuned, with training and inference time scaling accordingly, together with energy consumption. But is massive fine-tuning always necessary? In this paper, focusing on image classification, we consider a simple transfer learning approach exploiting pre-trained convolutional features as input for a fast-to-train kernel method. We refer to this approach as top-tuning since only the kernel classifier is trained on the target dataset. In our study, we perform more than 3000 training processes focusing on 32 small to medium-sized target datasets, a typical situation where transfer learning is necessary. We show that the top-tuning approach provides comparable accuracy with respect to fine-tuning, with a training time between one and two orders of magnitude smaller. These results suggest that top-tuning is an effective alternative to fine-tuning in small/medium datasets, being especially useful when training time efficiency and computational resources saving are crucial.