Reviewerly/exHarmony huggingface dataset:
Self-supervised heterogeneous graph learning with iterative similarity distillation

This paper focuses on the self-supervised learning issue on heterogeneous graph. Currently, contrastive learning has become the dominant approach on various heterogeneous graph tasks. Its performance is highly dependent on the elaborate selection strategy of negative samples. However, it is difficult to design such an ideal strategy giving sufficient consideration to the characteristics of graph, such as structural similarity and feature similarity. To circumvent this problem, we propose a novel negative sample-free self-supervised framework named HGISD (Heterogeneous Graph learning based on Iterative Similarity Distillation), where the student improves itself by imitating the teacher's behavior and the teacher updates itself as a slow-moving average of the student. Moreover, metapath-based HGNN is employed as the built-in information encoder. We argue that previous metapath-based HGNNs neglect the correlation among metapaths and the locality within metapaths. To tackle these issues, we respectively propose IMCD (Inter-Metapath Correlation Distillation) and IMSD (Intra-Metapath Similarity Distillation) within the self-distillation framework. Both IMCD and IMSD treat the embeddings in the shallow layers as the knowledge source, which well preserves the inter-metapath correlation and the intra-metapath locality. Finally, we conduct extensive experiments on several real-world heterogeneous graph datasets. The results show that the proposed HGISD achieves superior performance compared with the state-of-the-art methods.
Classification and nomenclature of volcanic rocks using immobile elements: A novel approach based on big data analysis

The proper classification and nomenclature of igneous rocks are cornerstones of geology. In contrast to intrusive rocks, volcanic rocks cannot be named after their mineral assemblages, due to their glassy and dominantly glassy nature. Thus, they are commonly classified based on their major elemental compositions, such as the Total Alkali-Silica (TAS) classification. Unfortunately, these major elements (especially K and Na) are commonly mobile during metamorphism and alteration, making the use of the TAS diagram problematic for altered or metamorphosed volcanic rocks. Though it can be replaced by diagrams employing immobile or less mobile elements, the accuracy is relatively low. By compiling data on volcanic rocks from GEOROC dataset, we confirmed that the classification based on Zr/TiO2-Nb/Y relationship has low accuracy (45.3%), so the results can be misleading. Although we improve this classification to 68.84% accuracy through refinement based on big data, the accuracy is still not satisfying. Such a deficiency can be compensated by the application of Machine Learning methods on a large dataset. Employing the Random Forest algorithm for model training based on 13 immobile elements, we propose a new classification method where the training accuracy increases to 74% for the verification dataset. Our results show that the prediction accuracy of several major rock types, such as basalt and rhyolite, increase to 89.2% and 97.5%, respectively.
Automatized localization of induced geothermal seismicity using robust time-domain array processing

The surveillance of geothermal seismicity is typically conducted using seismic networks, deployed around the power plants and subject to noise conditions in often highly urbanized areas. In contrast, seismic arrays can be situated at greater distances and allow monitoring of different power plants from one central location, less affected by noise interference. However, the effectiveness of arrays to monitor geothermal reservoirs is not well investigated and the increased distance to the source coincides with a decreased accuracy of the earthquake localizations. It is therefore essential to establish robust data processing and to obtain precise estimates of the location uncertainties. Here, we use time-domain array data processing and solve for the full 3-D slowness vector using robust linear regression. The approach implements a Biweight M-estimator, which yields stable parameter estimates and is well suited for real-time applications. We compare its performance to conventional least squares regression and frequency wavenumber analysis. Additionally, we implement a statistical approach based on changepoint analysis to automatically identify P- and S-wave arrivals within the recorded waveforms. The method can be seen as a simplification of autoregressive prediction. The estimated onsets facilitate reliable calculations of epicentral distances. We assess the performance of our methodology by comparison to network localizations for 77 induced earthquakes from the Landau and Insheim deep-geothermal reservoirs, situated in Rhineland-Palatinate, Germany. Our results demonstrate that we can differentiate earthquakes originating from both reservoirs and successfully localize the majority of events within the magnitude range of M L -0.2 to M L 1.3. The discrepancy between the two localization methods is mostly less than 1 km, which falls within the statistical errors. However, a few localizations deviate significantly, which can be attributed to poor observations during the winter of 2021/2022.
Visual transformer with stable prior and patch-level attention for single image dehazing

Single-image dehazing aims to recover blurred image details and improve image quality, which is a challenging ill-posed problem due to severe information degradation. In the image dehazing task, extracting local features from adjacent regions is particularly important. However, Transformer-based methods lack relative awareness of patch-level features. Furthermore, due to the sensitivity of self-attention to textcolorreddata distribution, the model suffers severe performance degradation when migrating from synthetic domain to real domain. To alleviate the above problems, we propose visual transformer with stable prior and patch-level attention (VSPPA) for image dehazing. Firstly, we propose a region-aware patch-level attention module to obtain the positional correlation between local patches and contexts, which can enhance the concentration of local patch-related features. Next, due to the instability problem caused by distribution shifts, we introduce dataset-independent prior to guide the transformer model, thereby preventing feature drift thus to improve the robustness of the model. Finally, domain-drift leads to insufficient dehazing when the model trained on synthetic data while migrates to the real environment, we come up with a introduce a patch filling strategy (PFS) for fuzzy data to narrow the domain gap and realize the generalization in real scenes. Extensive experiments show that the model achieves State-of-the-Art on the SOTS synthetic dataset and effective generalization to real-world scenarios.
Election predictions in the news: how users perceive and respond to visual election forecasts

Political journalism often tries to predict the future, especially the outcomes of elections. This has historically been accomplished through written articles or opinion pieces. A more recent development involves the publication of data-driven predictions in online news media. These news items not only contain an estimate for the election results but also often try to visualize potential uncertainties of the prediction. However, the ways in which users react to these forms of journalism have not yet been studied extensively. In this work, we survey users of a predictive journalism piece on the 2021 German federal elections published by the German newspaper Süddeutsche Zeitung to better understand their reactions. While we found an alignment between the designers' intention to show the inherent uncertainty of election predictions with the audience's reception, we encountered mixed results in users' ability to interpret the uncertainty visualizations presented. Most respondents indicated that the predictions did not influence their thinking about the race, and some remained skeptical toward such predictions published by journalists for various reasons. Based on these findings, we suggest the need for rigorous user testing of visualizations for election prediction and increased awareness and future research on ways to increase the transparency of methods and data to develop appropriate trust toward predictive journalism.
One-Class Learning for AI-Generated Essay Detection

Detection of AI-generated content is a crucially important task considering the increasing attention towards AI tools, such as ChatGPT, and the raised concerns with regard to academic integrity. Existing text classification approaches, including neural-network-based and feature-based methods, are mostly tailored for English data, and they are typically limited to a supervised learning setting. Although one-class learning methods are more suitable for classification tasks, their effectiveness in essay detection is still unknown. In this paper, this gap is explored by adopting linguistic features and one-class learning models for AI-generated essay detection. Detection performance of different models is assessed in different settings, where positively labeled data, i.e., AI-generated essays, are unavailable for model training. Results with two datasets containing essays in L2 English and L2 Spanish show that it is feasible to accurately detect AI-generated essays. The analysis reveals which models and which sets of linguistic features are more powerful than others in the detection task.
Online Voting Scheme Using IBM Cloud-Based Hyperledger Fabric with Privacy-Preservation

The current traditional paper ballot voting schemes suffer from several limitations such as processing delays due to counting paper ballots, lack of transparency, and manipulation of the ballots. To solve these limitations, an electronic voting (e-voting) scheme has received massive interest from both governments and academia. In e-voting, individuals can cast their vote online using their smartphones without the need to wait in long lines. Additionally, handicapped voters who face limited wheelchair access in many polling centers could now participate in elections hassle-free. The existing e-voting schemes suffer from several limitations as they are either centralized, based on public blockchains, or utilize local private blockchains. This results in privacy issues (using public blockchains) or large financial costs (using local/private blockchains) due to the amount of computing power and technical knowledge needed to host blockchains locally. To address the aforementioned limitations, in this paper, we propose an online voting scheme using IBM cloud-based Hyperledger Fabric. Our scheme allows voters to cast their encrypted votes in a secure manner. Then any participant can obtain the ballot results in a decentralized and transparent manner, without sacrificing the privacy of individual voters. We implement the proposed scheme using IBM cloud-based Hyperledger Fabric. The experimental results identify the performance characteristics of our scheme and demonstrate that it is feasible to run an election consisting of thousands of participants using cloud-based Fabric.
Rewarded Semi-Supervised Re-Identification on Identities Rarely Crossing Camera Views

Semi-supervised person re-identification (Re-ID) is an important approach for alleviating annotation costs when learning to match person images across camera views. Most existing works assume that training data contains abundant identities crossing camera views. However, this assumption is not true in many real-world applications, especially when images are captured in nonadjacent scenes for Re-ID in wider areas, where the identities rarely cross camera views. In this work, we operate semi-supervised Re-ID under a relaxed assumption of identities rarely crossing camera views, which is still largely ignored in existing methods. Since the identities rarely cross camera views, the underlying sample relations across camera views become much more uncertain, and deteriorate the noise accumulation problem in many advanced Re-ID methods that apply pseudo labeling for associating visually similar samples. To quantify such uncertainty, we parameterize the probabilistic relations between samples in a relation discovery objective for pseudo label training. Then, we introduce reward quantified by identification performance on a few labeled data to guide learning dynamic relations between samples for reducing uncertainty. Our strategy is called the Rewarded Relation Discovery (R <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> D), of which the rewarded learning paradigm is under-explored in existing pseudo labeling methods. To further reduce the uncertainty in sample relations, we perform multiple relation discovery objectives learning to discover probabilistic relations based on different prior knowledge of intra-camera affinity and cross-camera style variation, and fuse the complementary knowledge of different probabilistic relations by similarity distillation. To better evaluate semi-supervised Re-ID on identities rarely crossing camera views, we collect a new real-world dataset called REID-CBD, and perform simulation on benchmark datasets. Experiment results show that our method outperforms a wide range of semi-supervised and unsupervised learning methods.
Multibranch Attention Graph Convolutional Networks for 3-D Human Pose Estimation

The human pose has a natural graphic structure. Therefore, in recent years, many studies on human pose estimation is based on Graph Convolutional Networks (GCN). However, the classical GCN method has a small receptive field and the transformation matrix is shared by all nodes, which limits their feature extraction ability. To address these issues, a novel multi-branch attention graph convolution (MultiBA_GConv) operation is proposed and a regression network model for human 3D position estimation based on the MultiBA_GConv operation is developed in this paper. Several different transformation matrices are used to extract the feature information contributing to the node itself, its neighbors, and other global nodes, and the corresponding attention mechanism is used to focus on these features in the MultiBA_GConv operation. The ablation experiments show that the multi-branch processing and multi-attention mechanism proposed in the MultiBA_GConv operation can greatly improve the feature extraction ability of graph convolution. The comparison experiments with other GCN methods show that MultiBA_GConv has the best feature extraction performance in all graph convolution operations. Based on the MultiBA_GConv, a 3D human pose regression network architecture is designed and the proposed method is evaluated on a challenging benchmark dataset for 3D human pose estimation. The experimental results of the comparison with the state of the art show that our method is highly competitive.
A novel video magnification method for revealing subtle signals based on adaptive Laplacian pyramid

Numerous difficult to perceive minor signals, such as minute color variations and modest movements, carry crucial information. The ability to see small signals in video has been demonstrated to be greatly aided by video magnification techniques. The Eulerian video magnification (EVM) method produces excellent results when magnifying minute colors and motions; but, it is susceptible to artifacts and outcomes that are blurry, especially when bigger magnifications are used. In this research, we propose a novel video magnification method based on adaptive Laplacian pyramid to achieve non-contact optical measurements. Experiments were conducted on real video sequences containing biological or mechanical motion signals, the results demonstrated that our approach achieves video amplification and extraction of subtle signals, supports larger amplification factors, and obtains sharper video quality with reduced artifacts and blurring.
Three-dimensional collaborative path planning for multiple UCAVs based on improved artificial ecosystem optimizer and reinforcement learning

This study proposes a multi-strategy evolutionary artificial ecosystem optimizer based on reinforcement learning (MEAEO-RL) to tackle the collaborative path-planning problem of multiple unmanned combat aerial vehicles (UCAVs) in complex environments with multiple constraints. The objective is to generate optimal candidate paths for each UCAV, ensuring they reach the destination simultaneously while considering time variables and obstacle avoidance. To overcome the limitations of the standard artificial ecosystem optimizer (AEO), such as local optimality and slow convergence, a learning framework inspired by brain-like perception is constructed. This framework enhances swarm agents with greater intelligence by fusing swarm intelligence and human cognitive mechanisms. Meanwhile, a multi-strategy database is implemented within the evolutionary learning framework to replace the single-update method of the AEO during the consumption phase. To reduce the computational complexity of the algorithm, agents in the consumption stage utilize experience accumulation from reinforcement learning to select an effective update strategy for obtaining the latest consumer location. Path-planning simulation experiments are conducted in a series of complex three-dimensional environments, demonstrating the algorithm’s robustness, improved convergence accuracy, and ability to plan collaborative paths for multiple UCAVs while satisfying various constraints.
Automatic modulation recognition of radiation source signals based on two-dimensional data matrix and improved residual neural network

Automatic modulation recognition (AMR) of radiation source signals is a research focus in the field of cognitive radio. However, the AMR of radiation source signals at low SNRs still faces a great challenge. Therefore, the AMR method of radiation source signals based on two-dimensional data matrix and improved residual neural network is proposed in this paper. First, the time series of the radiation source signals are reconstructed into two-dimensional data matrix, which greatly simplifies the signal preprocessing process. Second, the depthwise convolution and large-size convolutional kernels based residual neural network (DLRNet) is proposed to improve the feature extraction capability of the AMR model. Finally, the model performs feature extraction and classification on the two-dimensional data matrix to obtain the recognition vector that represents the signal modulation type. Theoretical analysis and simulation results show that the AMR method based on two-dimensional data matrix and improved residual network can significantly improve the accuracy of the AMR method. The recognition accuracy of the proposed method maintains a high level greater than 90% even at −14 dB SNR.
A framework for designing vulnerability metrics

Vulnerability analysis has long been used to evaluate the security posture of a system. Different approaches, including vulnerability graphs and various vulnerability metrics, have been used to study the vulnerability landscape and provide security analysts with cyber situational awareness. However, most current solutions still lack a principled approach to quantifying various dimensions of known vulnerabilities in a way that can easily adapt to different applicative domains and operating conditions. To address this limitation, we introduce a vulnerability metrics framework that extends and generalizes our previous metrics for evaluating the exploitation likelihood of a vulnerability and the exposure factor of system components to vulnerability exploits. We argue that the factors influencing these metrics and their relative weights depend on the specific applicative domain, defender’s priorities, and attacker’s knowledge. Thus, instead of providing a static set of equations, we establish a framework for instantiating the equations that best model the scenario being considered. We combine likelihood and exposure factor metrics into a severity score that allows us to rank vulnerabilities. In our evaluation, we demonstrate that ranking vulnerabilities solely based on their CVSS scores is not sufficient for effective prioritization, due to the limited number of possible distinct severity values compared to the sheer number of existing vulnerabilities. We define a ranking quality score and show that considering additional information about vulnerabilities helps refine their ranking, providing more actionable intelligence to security analysts.
Integral cryptanalysis: a new key determination technique for 3-phase Kuznyechik encryption

Abstract The primary objective of this work is to develop an algorithm for detecting the secret round key used in encryption by analyzing a collection of plaintexts and ciphertexts encrypted with the three-round Kuznyechik algorithm. In addition, the fundamentals of using the integral cryptanalysis method for the Substitution-permutation (SP) network-based Kuznyechik encryption have been investigated. It has been found that the Kuznyechik three-round encryption algorithm is useful for creating a balanced set before the third round and an unbalanced set at the end of the round, using a set in which the first byte is active, and the remaining bytes are passive. Based on the findings above, an efficient algorithm to determine the key of the final round for the 3-round Kuznyechik algorithm is proposed utilizing the method of integral cryptanalysis. According to simulation results, the proposed approach offers approximately 90% improvement compared to baseline algorithms in terms of time complexity, data, and memory overhead.