Reviewerly/exHarmony huggingface dataset:
Mutual Boost Network for attributed graph clustering

Attributed graph clustering is an essential research topic on real-world data. However, the heterogeneous gap between node and structure features limits the existing approaches for discriminative representation. Besides, how to ensure the consistency of clustering assignments produced by the node and structure features is still a challenging problem, which always causes inferior clustering performance. To overcome above problems, a dual-channel network for attributed graph clustering named Mutual Boost Network (MBN) is proposed, which consists of auto-encoder and graph auto-encoder that can interact and learn from each other to achieve mutual boost of clustering performance. First, a novel representation enhancement module is proposed to disseminate the heterogeneous information from both node and structure features for learning comprehensive representation. Then, a consistency constraint by contrasting clustering assignments is devised to provide a mutual guide to make them tend to be consistent. Finally, the procedures of representation learning and clustering assignment are simultaneously optimized under a unified framework in a self-supervised manner. Experimental results on six graph collections show that the MBN performs better than the SOTA approaches by a statistically significant margin. The MBN code can be found at: https://github.com/Xiaoqiang-Yan/MBN.
Informed expansion for informative path planning via online distribution learning

Mobile robots are essential tools for gathering knowledge of the environment and monitoring areas of interest as well as industrial assets. Informative Path Planning methodologies have been successfully applied making robots able to autonomously acquire information and explore unknown surroundings. Rapidly-exploring Information Gathering approaches have been validated in real-world applications, proving they are the way to go when aiming for Information Gathering tasks. In fact, RIG can plan paths for robots with several degrees of freedom and rapidly explore complex workspaces by using the state-of-the-art Voronoi-biased expansion. Nevertheless, it is an efficient solution when most of the area is unknown but its effectiveness decreases as the exploration/gathering evolves. This paper introduces an innovative informed expansion for IG tasks that combines the Kernel Density Estimation technique and a rejection sampling algorithm. By learning online the distribution of the acquired information (i.e., the discovered map), the proposed methodology generates samples in the unexplored regions of the workspace, and thus steers the tree toward the most promising areas. Realistic simulations and an experimental campaign, conducted in the underwater robotics domain, provide a proof-of-concept validation for the developed informed expansion methodology and demonstrate that it enhances the performance of the RIG algorithm.
A label noise filtering method for regression based on adaptive threshold and noise score

The quality of training data plays a decisive role in the establishment of intelligent models. Since raw data obtained from the real world are usually entwined with noise due to variety of causes, noise filtering has become an important aspect of machine learning techniques. In contrast with the extensive research conducted on noise elimination for classification purposes, papers addressing this problem for regression tasks are rather scarce. In this paper, we propose a novel noise filter to clean noisy instances with real-valued label noise. Aiming at the deficiency of the existing noise determination criterion, a new adaptive threshold-based method is first proposed. It allows a noisy instance to be adaptively defined according to the fitting difficulty levels of different datasets, and areas with different densities. Embedded with this criterion, an effective noise filtering procedure is also designed. An ensemble filtering scheme and an iterative filtering process are combined to detect as many potential noisy samples as possible from the original training set. According to the acquire noise detection information, a noise score for evaluating the noise level is specifically developed. The potential noisy samples whose scores exceed a reasonable threshold are further filtered, which can compensate for the possible errors incurred during the previous procedure, and contribute to more reliable filtering results. The validity of the proposed method is studied in exhaustive experiments. We discuss reasonable hyperparameters, and compare the developed method with several state-of-the-art noise filters. The outcomes show that the prediction accuracy of the utilized regressor can greatly benefit from preprocessing the given raw dataset by using our method. Simultaneously, the method is able to acquire a good balance between the elimination of noisy samples and the retention of clean samples, and consistently achieves a better noise filtering performance.
Towards automated homomorphic encryption parameter selection with fuzzy logic and linear programming

Homomorphic Encryption (HE) is a set of powerful properties of certain cryptosystems that allow privacy-preserving operation over the encrypted text. Still, HE is not widespread due to limitations in terms of efficiency and usability. Among the challenges of HE, scheme parametrization (i.e., the selection of appropriate parameters within the algorithms) is a relevant multi-faced problem. First, the parametrization needs to comply with a set of properties to guarantee the security of the underlying scheme. Second, parametrization requires a deep understanding of the low-level primitives since the parameters have a confronting impact on the scheme’s precision, performance, and security. Finally, the circuit to be executed influences, and it is influenced by, the parametrization. Thus, there is no general optimal selection of parameters, and this selection depends on the circuit and the scenario of the application. Currently, most existing HE frameworks require cryptographers to address these considerations manually. It requires a minimum of expertise acquired through a steep learning curve. In this paper, we propose a unified solution for the aforementioned challenges. Concretely, we present an expert system combining Fuzzy Logic and Linear Programming. The Fuzzy Logic Modules receive a user selection of high-level priorities for the security, efficiency, and performance of the cryptosystem. Based on these preferences, the expert system generates a Linear Programming Model that obtains optimal combinations of parameters by considering those priorities while preserving a minimum level of security for the cryptosystem. We conduct an extended evaluation showing that an expert system generates optimal parameter selections that maintain user preferences without undergoing the inherent complexity of analyzing the circuit.
Peering through the lens of high‐reliability theory: A competencies driven security culture model of high‐reliability organisations

Abstract To improve organisational safety and enhance security efficiency, organisations seek to establish a culture of security that provides a foundation for how employees should approach security. There are several frameworks and models that provide a set of requirements for forming security cultures; however, for many organisations, the requirements of the frameworks are difficult to meet, if not impossible. In this research, we take a different perspective and focus on the core underlying competencies that high‐reliability organisations (HROs) have shown to be effective in achieving levels of risk tolerance consistent with the goals of a security culture. In doing so we draw on high‐reliability theory to develop a Security Culture Model that explains how a firm's supportive and practical competencies form its organisational security culture. To refine and test the model, we conducted a developmental mixed‐method study using interviews and survey data with professional managers involved in the information security (InfoSec) programs within their respective HROs. Our findings emphasise the importance of an organisation's supportive and practical competencies for developing a culture of security. Our results suggest that organisations' security cultures are a product of their InfoSec practices and that organisational mindfulness, top management involvement and organisational structure are key to the development of those practices.
Transformer-based image generation from scene graphs

Graph-structured scene descriptions can be efficiently used in generative models to control the composition of the generated image. Previous approaches are based on the combination of graph convolutional networks and adversarial methods for layout prediction and image generation, respectively. In this work, we show how employing multi-head attention to encode the graph information, as well as using a transformer-based model in the latent space for image generation can improve the quality of the sampled data, without the need to employ adversarial models with the subsequent advantage in terms of training stability. The proposed approach, specifically, is entirely based on transformer architectures both for encoding scene graphs into intermediate object layouts and for decoding these layouts into images, passing through a lower dimensional space learned by a vector-quantized variational autoencoder. Our approach shows an improved image quality with respect to state-of-the-art methods as well as a higher degree of diversity among multiple generations from the same scene graph. We evaluate our approach on three public datasets: Visual Genome, COCO, and CLEVR. We achieve an Inception Score of 13.7 and 12.8, and an FID of 52.3 and 60.3, on COCO and Visual Genome, respectively. We perform ablation studies on our contributions to assess the impact of each component. Code is available at https://github.com/perceivelab/trf-sg2im.
Implementing and evaluating a GDPR-compliant open-source SIEM solution

Security Information and Event Management (SIEM) solutions collect events from the IT infrastructure and concentrate information from the various components in a single place, allowing the detection of anomalous situations and attacks, and helping to protect confidential data. But real-world network environments may be complex and heterogeneous (e.g., in terms of devices, applications, and operating systems), and the attack surface can be vast, which makes increases the amount that a SIEM solution must collect and analyze. The General Data Protection Regulation (GDPR) has increased the level of complexity in such context, as organizations must ensure the monitoring of access to personal data and various levels of security in their infrastructure. In this work, we deal with the implementation of an open-source SIEM solution that incorporates technical measures for the protection and control of personal data, ensuring compliance with the GDPR. We identify the main functionalities and describe a solution based on the Elastic Stack and additional open-source external tools. To validate our proposals, we implemented a prototype of our solution in a real-world environment. We simulated internal and external attacks that show the solution capacity to deal in real-time with the detection of threats and incidents. We also evaluated the performance and resource consumption of personal data pseudonymization processes. Obtained results show our solution presents good performance and scalability.
Students’ perception on immersive learning through 2D and 3D metaverse platforms

The main purpose of this paper is to add empirical data to the nascent field of metaverse learning and teaching by examining factors affecting student participation and their perceived experiences of different metaverse platforms. For data collection, 57 Korean undergraduates participated in a self-administered questionnaire and a short reflective essay regarding their experiences on three metaverse platforms (ifland, Gather Town, & Frame VR). For data analysis, exploratory factor analysis was first executed to derive the underlying factors that can explain student participation in metaverse platforms. The social and interactive learning as well as individualized and behavioral learning were identified as two main contributing factors. While the three platforms had no statistical difference in terms of social presence, students' sentimentally perceived differences among them. The sentiment analysis shows that 60.00% of ifland users were positive, followed by 53.66% of Frame VR users and 51.22% of Gather Town users. Furthermore, the additional keyword analysis shows why students expressed the perceived experiences of each platform in a different way. Given that the success of metaverse instruction can be dependent upon whether students regard it as beneficial, such measurements of student perception on the effectiveness of learning on metaverse platforms can offer meaningful recommendations for tech-savvy educators.
Using multiobjective optimization to reconstruct interferometric data. Part I

Imaging in radioastronomy is an ill-posed inverse problem. Particularly the Event Horizon Telescope (EHT) Collaboration investigated the fidelity of their image reconstructions convincingly by large surveys solving the problem with different optimization parameters. This strategy faces a limitation for the existing methods when imaging the active galactic nuclei (AGN): large and expensive surveys solving the problem with different optimization parameters are time-consumptive. We present a novel nonconvex, multiobjective optimization modeling approach that gives a different type of claim and may provide a pathway to overcome this limitation. To this end we used a multiobjective version of the genetic algorithm (GA): the Multiobjective Evolutionary Algorithm Based on Decomposition, or MOEA/D. GA strategies explore the objective function by evolutionary operations to find the different local minima, and to avoid getting trapped in saddle points. First, we have tested our algorithm (MOEA/D) using synthetic data based on the 2017 Event Horizon Telescope (EHT) array and a possible EHT + next-generation EHT (ngEHT) configuration. We successfully recover a fully evolved Pareto front of non-dominated solutions for these examples. The Pareto front divides into clusters of image morphologies representing the full set of locally optimal solutions. We discuss approaches to find the most natural guess among these solutions and demonstrate its performance on synthetic data. Finally, we apply MOEA/D to observations of the black hole shadow in Messier 87 (M87) with the EHT data in 2017. MOEA/D is very flexible, faster than any other Bayesian method and explores more solutions than Regularized Maximum Likelihood methods (RML).
Visual–inertial object tracking: Incorporating camera pose into motion models

Visual object tracking for autonomy of aerial robots could become challenging especially in the presence of target or camera fast motions and long-term occlusions. This paper presents a visual–inertial tracking paradigm by incorporating the camera kinematics states into the visual object tracking pipelines. We gathered a dataset of image sequences with the addition of camera’s position and orientation measurements as well as the object’s position measurement. For the cases of long-term object occlusion, we provide ground-truth boxes derived from mapping the measured object position onto the image frame. A search zone proposal method is developed based on the estimation of object future position represented in the inertial frame and projected back onto the image frame using the camera states. This search zone, which is robust to fast camera/target motions, is fused into the original search zone settings of the base tracker. Also proposed is a measure indicating the confidence of a tracking structure in keeping track of a correct target and reporting the tracking failure in-time. Accordingly, the model updating mechanism of base tracker is modulated to avoid recovering of wrong objects as the target. The proposed modifications are benchmarked on nine visual object tracking algorithms including five state-of-art deep learning structures, namely DiMP, PrDiMP, KYS, ToMP, and MixFormer. Most of the trackers are remarkably improved by the modifications with up to 8% increase in precision. Modified PrDiMP tracker yields the best precision of 68.4%, more than all considered original (and modified) trackers. Source code and dataset are made available online.1
Fraud detection on multi-relation graphs via imbalanced and interactive learning

In recent years, the application of Graph Neural Networks (GNNs) in fraud detection has gained considerable attention. GNNs have demonstrated their efficacy in leveraging the abundant relational information inherent in graph-structured data for such tasks. However, despite the remarkable progress achieved thus far, several challenges still need to be addressed in the current GNN-based algorithms. On the one hand, GNNs exhibit limited performance when confronted with imbalanced label distribution between fraudsters and benign users. On the other hand, the embedding representations learned for nodes and their neighbors in the last layer are frequently processed in a simplistic manner, such as concatenation or averaging, which may compromise the performance of downstream tasks. To alleviate the above problems, we propose a novel imbalanced and interactive learning framework for fraud detection on multi-relation graphs (IMINF for short). Concretely, firstly, we design a novel neighbor sampler that can be trained using supervised contrastive learning. This sampler selects the most similar and consistent neighbors to update the target node's features, taking into account label information from multiple perspectives. Secondly, we introduce the learnable contrastive embedding and an improved relational aggregator to enhance the representation of the target node. Thirdly, we propose a new interactive learning module, which consists of explicit and implicit transformation layers, with the aim of capturing deep interactive relations between a target node and its neighbors. Additionally, we devise a feature mapping module prior to the relation aggregation step, transferring the raw input features to a latent feature subspace. Extensive experiments on two real-world fraud detection datasets demonstrate that the proposed model outperforms existing baselines across all evaluation metrics, especially for the Amazon fraud detection dataset.
Deep incremental random vector functional-link network: A non-iterative constructive sketch via greedy feature learning

The incremental version of randomized neural networks provides a greedy constructive algorithm for the shallow network, which adds new nodes through different stochastic methods rather than gradient optimization. However, the potential of the random incremental mechanism is still underutilized in deep structures. To address this research gap, we propose an unsupervised algorithm termed the incremental randomization-based autoencoder (IR-AE) for greedy feature learning, which applies an integrated optimized constructive algorithm to train the feature extractor. Using IR-AE as a hierarchical stacked block, we synthesize the deep incremental random vector functional-link (DI-RVFL) network that builds a deep structure with overall feature-output links through a feedforward approach. Furthermore, it is a novel data-driven initialization to implement the feedforward constructive sketch (CoSketch) as a pre-trained model for multi-layer perceptron. The simulation results empirically demonstrate that the proposed IR-AE can realize a higher reconstruction efficiency than AE and randomization-based AE. Moreover, DI-RVFL shows the advantages of deep structures in higher-level feature extraction compared to other stacked random structures. The overall performance of deep RVFLs outperforms those of multi-layer extreme learning machines. As data-driven initialization, CoSketch significantly improves the convergence performance of gradient descent.
Variable-hyperparameter visual transformer for efficient image inpainting

Image inpainting has shown a great evolution in the reconstruction of damaged regions or holes since the advent of deep neural networks. Recently, transformers have been used in the field of computer vision to capture global information about the image, which cannot be done with convolutional neural networks due to the limitation of their local receptive fields. Therefore, the transformer may be essential to achieve realistic results when damaged regions cover a large part of the image. However, the quadratic computational and memory costs in the self-attention layer have led to its prohibited usage in high-resolution images and restricted devices, especially for image inpainting when the method must deal with large masks. To overcome this problem, we propose a variable-hyperparameter visual transformer architecture that (i) subdivides the feature maps into a variable number of multi-scale patches, (ii) distributes the feature map into a variable number of heads to balance the complexity of the self-attention operation, and (iii) includes a new strategy based on depth-wise convolution to reduce the number of channels of the feature map sent to each transformer block. We conduct experiments on three datasets from the literature. Our experiments show that our method consistently achieved the best results for the FID and LPIPS metrics on the CelebA dataset. We obtained competitive results for Places2 and Paris StreetView datasets compared to state-of-the-art methods. Moreover, our model presents the best performance in terms of model size, number of parameters, and FLOPS. Our qualitative results indicate that our proposed method is capable of reconstructing semantic content, such as parts of human faces.
A novel reversible fragile watermarking method in DWT domain for tamper localization and digital image authentication

Medical images are extremely valuable and sensitive data because of their significance in research, diagnosis, and treatment. However, the transmission of medical images over an unsecured network can lead to security issues such as modification, removal, copying, and unauthorized access. For this reason, the protection of medical information is of great importance. This paper proposes a novel reversible fragile digital watermarking method for tamper localization and authenticity and integrity protection in medical images. In the proposed method, the host image is firstly divided into 8×8 non-overlapped blocks. Then the image is transformed to the frequency domain by applying a Discrete Wavelet Transform, and the watermark information is embedded. Finally, the image is converted from the frequency domain to the spatial domain using an inverse Discrete Wavelet Transform. During this transformation, an intelligent optimization technique is used to correct the rounding errors and obtain the watermarked image with the highest level of imperceptibility. The experimental results demonstrate that the proposed method offers a watermarked image with high imperceptibility, extracts the host image lossless during the watermark extraction phase, and performs tamper localization with high precision.