Reviewerly/exHarmony huggingface dataset:
Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networks

Graph Neural Networks (GNNs) have been widely used for the representation learning of various structured graph data, typically through message passing among nodes by aggregating their neighborhood information via different operations. While promising, most existing GNNs oversimplify the complexity and diversity of the edges in the graph and thus are inefficient to cope with ubiquitous heterogeneous graphs, which are typically in the form of multi-relational graph representations. In this article, we propose RioGNN , a novel Reinforced, recursive, and flexible neighborhood selection guided multi-relational Graph Neural Network architecture, to navigate complexity of neural network structures whilst maintaining relation-dependent representations. We first construct a multi-relational graph, according to the practical task, to reflect the heterogeneity of nodes, edges, attributes, and labels. To avoid the embedding over-assimilation among different types of nodes, we employ a label-aware neural similarity measure to ascertain the most similar neighbors based on node attributes. A reinforced relation-aware neighbor selection mechanism is developed to choose the most similar neighbors of a targeting node within a relation before aggregating all neighborhood information from different relations to obtain the eventual node embedding. Particularly, to improve the efficiency of neighbor selecting, we propose a new recursive and scalable reinforcement learning framework with estimable depth and width for different scales of multi-relational graphs. RioGNN can learn more discriminative node embedding with enhanced explainability due to the recognition of individual importance of each relation via the filtering threshold mechanism. Comprehensive experiments on real-world graph data and practical tasks demonstrate the advancements of effectiveness, efficiency, and the model explainability, as opposed to other comparative GNN models.
A practical guide to multi-objective reinforcement learning and planning

Real-world decision-making tasks are generally complex, requiring trade-offs between multiple, often conflicting, objectives. Despite this, the majority of research in reinforcement learning and decision-theoretic planning either assumes only a single objective, or that multiple objectives can be adequately handled via a simple linear combination. Such approaches may oversimplify the underlying problem and hence produce suboptimal results. This paper serves as a guide to the application of multi-objective methods to difficult problems, and is aimed at researchers who are already familiar with single-objective reinforcement learning and planning methods who wish to adopt a multi-objective perspective on their research, as well as practitioners who encounter multi-objective decision problems in practice. It identifies the factors that may influence the nature of the desired solution, and illustrates by example how these influence the design of multi-objective decision-making systems for complex problems.
V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformer

In this paper, we investigate the application of Vehicle-to-Everything (V2X) communication to improve the perception performance of autonomous vehicles. We present a robust cooperative perception framework with V2X communication using a novel vision Transformer. Specifically, we build a holistic attention model, namely V2X-ViT, to effectively fuse information across on-road agents (i.e., vehicles and infrastructure). V2X-ViT consists of alternating layers of heterogeneous multi-agent self-attention and multi-scale window self-attention, which captures inter-agent interaction and per-agent spatial relationships. These key modules are designed in a unified Transformer architecture to handle common V2X challenges, including asynchronous information sharing, pose errors, and heterogeneity of V2X components. To validate our approach, we create a large-scale V2X perception dataset using CARLA and OpenCDA. Extensive experimental results demonstrate that V2X-ViT sets new state-of-the-art performance for 3D object detection and achieves robust performance even under harsh, noisy environments. The code is available at https://github.com/DerrickXuNu/v2x-vit .
DF-GAN: A Simple and Effective Baseline for Text-to-Image Synthesis

Synthesizing high-quality realistic images from text descriptions is a challenging task. Existing text-to-image Generative Adversarial Networks generally employ a stacked architecture as the backbone yet still remain three flaws. First, the stacked architecture introduces the entanglements between generators of different image scales. Second, existing studies prefer to apply and fix extra networks in adversarial learning for text-image semantic consistency, which limits the supervision capability of these networks. Third, the cross-modal attention-based text-image fusion that widely adopted by previous works is limited on several special image scales because of the computational cost. To these ends, we propose a simpler but more effective Deep Fusion Generative Adversarial Networks (DF-GAN). To be specific, we propose: (i) a novel one-stage text-to-image backbone that directly synthesizes high-resolution images without entanglements between different generators, (ii) a novel Target-Aware Discriminator composed of Matching-Aware Gradient Penalty and One-Way Output, which enhances the text-image semantic consistency without introducing extra networks, (iii) a novel deep text-image fusion block, which deepens the fusion process to make a full fusion between text and visual features. Compared with current state-of-the-art methods, our proposed DF-GAN is simpler but more efficient to synthesize realistic and text-matching images and achieves better performance on widely used datasets. Code is available at https://github.com/tobran/DF-GAN.
Neural Keyphrase Generation via Reinforcement Learning with Adaptive Rewards

Generating keyphrases that summarize the main points of a document is a fundamental task in natural language processing.Although existing generative models are capable of predicting multiple keyphrases for an input document as well as determining the number of keyphrases to generate, they still suffer from the problem of generating too few keyphrases.To address this problem, we propose a reinforcement learning (RL) approach for keyphrase generation, with an adaptive reward function that encourages a model to generate both sufficient and accurate keyphrases.Furthermore, we introduce a new evaluation method that incorporates name variations of the ground-truth keyphrases using the Wikipedia knowledge base.Thus, our evaluation method can more robustly evaluate the quality of predicted keyphrases.Extensive experiments on five real-world datasets of different scales demonstrate that our RL approach consistently and significantly improves the performance of the state-of-the-art generative models with both conventional and new evaluation methods. Document: DCE MRI data analysis for cancer area classification.The paper aims at improving the support of medical researchers in the context of in-vivo cancer imaging… The proposed approach is based on a three-step procedure: i) robust feature extraction from raw time-intensity curves, ii) voxel segmentation, and iii) voxel classification based on a learning-by-example approach… Finally, in the third step, a support vector machine (SVM) is trained to classify voxels according to the labels obtained by the clustering phase…
Sparse Adversarial Perturbations for Videos

Although adversarial samples of deep neural networks (DNNs) have been intensively studied on static images, their extensions in videos are never explored. Compared with images, attacking a video needs to consider not only spatial cues but also temporal cues. Moreover, to improve the imperceptibility as well as reduce the computation cost, perturbations should be added on as few frames as possible, i.e., adversarial perturbations are temporally sparse. This further motivates the propagation of perturbations, which denotes that perturbations added on the current frame can transfer to the next frames via their temporal interactions. Thus, no (or few) extra perturbations are needed for these frames to misclassify them. To this end, we propose the first white-box video attack method, which utilizes an l2,1-norm based optimization algorithm to compute the sparse adversarial perturbations for videos. We choose the action recognition as the targeted task, and networks with a CNN+RNN architecture as threat models to verify our method. Thanks to the propagation, we can compute perturbations on a shortened version video, and then adapt them to the long version video to fool DNNs. Experimental results on the UCF101 dataset demonstrate that even only one frame in a video is perturbed, the fooling rate can still reach 59.7%.
Graph Convolutional Network Hashing for Cross-Modal Retrieval

Deep network based cross-modal retrieval has recently made significant progress. However, bridging modality gap to further enhance the retrieval accuracy still remains a crucial bottleneck. In this paper, we propose a Graph Convolutional Hashing (GCH) approach, which learns modality-unified binary codes via an affinity graph. An end-to-end deep architecture is constructed with three main components: a semantic encoder module, two feature encoding networks, and a graph convolutional network (GCN). We design a semantic encoder as a teacher module to guide the feature encoding process, a.k.a. student module, for semantic information exploiting. Furthermore, GCN is utilized to explore the inherent similarity structure among data points, which will help to generate discriminative hash codes. Extensive experiments on three benchmark datasets demonstrate that the proposed GCH outperforms the state-of-the-art methods.
Novel image encryption by combining dynamic DNA sequence encryption and the improved 2D logistic sine map

The one-dimensional (2D) chaotic encryption algorithm has good encryption performance. For its properties, such as the excellent complexity, pseudo-randomness, and sensitivity to the initial value of the chaotic sequence. However, compared with other methods, its biggest drawback is that the key space is too small. To address these problems, in this study, the authors introduce an improved 2D logistic sine chaotic map (2D-LSMM). A novel image encryption scheme based on dynamic DNA sequences encryption and improved 2D-LSMM is presented. The logistic map is used to control the input of the sine map. And the encoding and operation rules of DNA sequences are determined by 2D-LSMM chaotic sequences. By implementing dynamic DNA sequence encryption, the encryption process becomes more complicated and harder to be attacked. Simulation experimental results and security analysis show that the authors’ encryption scheme not only achieves proper encryption but can also resist different attacks.
Integration of Blockchain Technology and Federated Learning in Vehicular (IoT) Networks: A Comprehensive Survey

The Internet of Things (IoT) revitalizes the world with tremendous capabilities and potential to be utilized in vehicular networks. The Smart Transport Infrastructure (STI) era depends mainly on the IoT. Advanced machine learning (ML) techniques are being used to strengthen the STI smartness further. However, some decisions are very challenging due to the vast number of STI components and big data generated from STIs. Computation cost, communication overheads, and privacy issues are significant concerns for wide-scale ML adoption within STI. These issues can be addressed using Federated Learning (FL) and blockchain. FL can be used to address the issues of privacy preservation and handling big data generated in STI management and control. Blockchain is a distributed ledger that can store data while providing trust and integrity assurance. Blockchain can be a solution to data integrity and can add more security to the STI. This survey initially explores the vehicular network and STI in detail and sheds light on the blockchain and FL with real-world implementations. Then, FL and blockchain applications in the Vehicular Ad Hoc Network (VANET) environment from security and privacy perspectives are discussed in detail. In the end, the paper focuses on the current research challenges and future research directions related to integrating FL and blockchain for vehicular networks.
Toward verified artificial intelligence

Making AI more trustworthy with a formal methods-based approach to AI system verification and validation.
Fast Visual Tracking With Siamese Oriented Region Proposal Network

Current oriented visual tracking depends on segmentation-driven framework brings about expensive computation cost, which becomes the bottleneck in the practical application. This paper proposes a simple and effective Siamese oriented Region Proposal Network (Siamese-ORPN) for visual tracking. Specifically, we propose to use oriented RPN on the similarity feature maps to directly generate high-quality oriented proposals in a nearly cost-free manner. Moreover, a top-down feature fusion network is proposed as the backbone for feature extraction and feature fusion, which can achieve substantial gains from the diversity of visual-semantic hierarchies. The Siamese-ORPN runs at 85 fps while achieving leading performance on the benchmark datasets including VOT2018 (44.6% EAO) and VOT2019 (39.6% EAO).
StyleGAN-XL: Scaling StyleGAN to Large Diverse Datasets

Computer graphics has experienced a recent surge of data-centric approaches for photorealistic and controllable content creation. StyleGAN in particular sets new standards for generative modeling regarding image quality and controllability. However, StyleGAN’s performance severely degrades on large unstructured datasets such as ImageNet. StyleGAN was designed for controllability; hence, prior works suspect its restrictive design to be unsuitable for diverse datasets. In contrast, we find the main limiting factor to be the current training strategy. Following the recently introduced Projected GAN paradigm, we leverage powerful neural network priors and a progressive growing strategy to successfully train the latest StyleGAN3 generator on ImageNet. Our final model, StyleGAN-XL, sets a new state-of-the-art on large-scale image synthesis and is the first to generate images at a resolution of 10242 at such a dataset scale. We demonstrate that this model can invert and edit images beyond the narrow domain of portraits or specific object classes. Code, models, and supplementary videos can be found at https://sites.google.com/view/stylegan-xl/ .
A 3D model encryption scheme based on a cascaded chaotic system

With the birth of the metaverse, 3D models have received extensive attention, and the security of information transmission continues to be an important issue. In this paper, we propose a 3D model encryption method based on a 2D chaotic system constructed via the coupling of the logistic map and infinite collapse (2D-LAIC) and on semi-tensor product (STP) theory. In terms of Lyapunov exponents, NIST test results, bifurcation diagrams, etc., 2D-LAIC exhibits better dynamical behavior than classical chaotic systems. 2D-LAIC can generate an unpredictable keystream, which is highly suitable for cryptography. Therefore, we propose a new 3D model encryption algorithm based on 2D-LAIC, named 3DME-SC. For a 3D model of the floating-point data type, XOR and STP processing are applied to the integer part and fractional part, respectively, of the model to obtain a 3D ciphertext model. The keystream required for XOR and STP processing is generated by 2D-LAIC. The results of a detailed security analysis and a comparative experimental analysis show that 3DME-SC exhibits good performance and effectiveness. (Code: https://github.com/Gao5211996/3D-model-encryption)
Designing the metaverse: A study on inclusion, diversity, equity, accessibility and safety for digital immersive environments

The metaverse, a digital immersive environment in which people interact by using immersive technologies, is shaping a new way for people to interact and socialise. Forecasts predict that by 2026 a quarter of the population will spend at least an hour a day in the metaverse. This projection requires the consideration of several challenges and opportunities that will influence the design of the metaverse. A qualitative ethnographic study with industry experts was carried out to explore the social impact of the metaverse through the lens of inclusion, diversity, equity, accessibility and safety with the goal to identify directions business have to undertake. The results indicated the vast nature of future research questions that will need to be answered and facilitated the creation of good practices for designing an inclusive, accessible, safe metaverse that guarantees equity and diversity. These good practices are a starting point to develop a narrative, spark conversations, brainstorm questions and eventually provide answers for designing a metaverse as a place for people that does not substitute the physical world but complements it.
Fick’s Law Algorithm: A physical law-based algorithm for numerical optimization

Recently, many metaheuristic optimization algorithms have been developed to address real-world issues. In this study, a new physics-based metaheuristic called Fick’s law optimization (FLA) is presented, in which Fick’s first rule of diffusion is utilized. According to Fick’s law of diffusion, molecules tend to diffuse from higher to lower concentration areas. Many experimental series are done to test FLA’s performance and ability in solving different optimization problems. Firstly, FLA is tested using twenty well-known benchmark functions and thirty CEC2017 test functions. Secondly, five real-world engineering problems are utilized to demonstrate the feasibility of the proposed FLA. The findings are compared with 12 well-known and powerful optimizers. A Wilcoxon rank-sum test is carried out to evaluate the comparable statistical performance of competing algorithms. Results prove that FLA achieves competitive and promising findings, a good convergence curve rate, and a good balance between exploration and exploitation. The source code is currently available for public from: https://se.mathworks.com/matlabcentral/fileexchange/121033-fick-s-law-algorithm-fla.
BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation

Multi-sensor fusion is essential for an accurate and reliable autonomous driving system. Recent approaches are based on point-level fusion: augmenting the LiDAR point cloud with camera features. However, the camera-to-LiDAR projection throws away the semantic density of camera features, hindering the effectiveness of such methods, especially for semantic-oriented tasks (such as 3D scene segmentation). In this paper, we propose BEVFusion, an efficient and generic multi-task multi-sensor fusion framework. It unifies multi-modal features in the shared bird's-eye view (BEV) representation space, which nicely preserves both geometric and semantic information. To achieve this, we diagnose and lift the key efficiency bottlenecks in the view transformation with optimized BEV pooling, reducing latency by more than <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{40}\times$</tex> . BEVFusion is fundamentally task-agnostic and seamlessly supports different 3D perception tasks with almost no architectural changes. It establishes the new state of the art on the nuScenes benchmark, achieving 1.3% higher mAP and NDS on 3D object detection and 13.6% higher mIoU on BEV map segmentation, with 1.9× lower computation cost. Code to reproduce our results is available at https://github.com/mit-han-lab/bevfusion.
Residual Non-local Attention Networks for Image Restoration

In this paper, we propose a residual non-local attention network for high-quality image restoration. Without considering the uneven distribution of information in the corrupted images, previous methods are restricted by local convolutional operation and equal treatment of spatial- and channel-wise features. To address this issue, we design local and non-local attention blocks to extract features that capture the long-range dependencies between pixels and pay more attention to the challenging parts. Specifically, we design trunk branch and (non-)local mask branch in each (non-)local attention block. The trunk branch is used to extract hierarchical features. Local and non-local mask branches aim to adaptively rescale these hierarchical features with mixed attentions. The local mask branch concentrates on more local structures with convolutional operations, while non-local attention considers more about long-range dependencies in the whole feature map. Furthermore, we propose residual local and non-local attention learning to train the very deep network, which further enhance the representation ability of the network. Our proposed method can be generalized for various image restoration applications, such as image denoising, demosaicing, compression artifacts reduction, and super-resolution. Experiments demonstrate that our method obtains comparable or better results compared with recently leading methods quantitatively and visually.
New Results About the Boomerang Uniformity of Permutation Polynomials

In EUROCRYPT 2018, Cid et al. introduced a new concept on the cryptographic property of S-boxes: boomerang connectivity table (BCT for short) for evaluating the subtleties of boomerang-style attacks. Very recently, BCT and the boomerang uniformity, the maximum value in BCT, were further studied by Boura and Canteaut. In this paper, aiming at providing new insights, we show some new results about BCT and the boomerang uniformity of permutations in terms of theory and experiment. First, we present an equivalent technique to compute BCT and the boomerang uniformity, which seems to be much simpler than the original definition by Cid et al. Second, thanks to Carlet's idea, we give a characterization of functions f from F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> to itself with boomerang uniformity δ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">f</sub> by means of the Walsh transform. Third, by our method, we consider boomerang uniformities of some specific permutations, mainly the ones with low differential uniformity. Finally, we obtain another class of 4-uniform BCT permutation polynomials over F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> n.
BreCaHAD: a dataset for breast cancer histopathological annotation and diagnosis

Histopathological tissue analysis by a pathologist determines the diagnosis and prognosis of most tumors, such as breast cancer. To estimate the aggressiveness of cancer, a pathologist evaluates the microscopic appearance of a biopsied tissue sample based on morphological features which have been correlated with patient outcome.This paper introduces a dataset of 162 breast cancer histopathology images, namely the breast cancer histopathological annotation and diagnosis dataset (BreCaHAD) which allows researchers to optimize and evaluate the usefulness of their proposed methods. The dataset includes various malignant cases. The task associated with this dataset is to automatically classify histological structures in these hematoxylin and eosin (H&E) stained images into six classes, namely mitosis, apoptosis, tumor nuclei, non-tumor nuclei, tubule, and non-tubule. By providing this dataset to the biomedical imaging community, we hope to encourage researchers in computer vision, machine learning and medical fields to contribute and develop methods/tools for automatic detection and diagnosis of cancerous regions in breast cancer histology images.