Reviewerly/exHarmony huggingface dataset:
Scene Text Image Super-Resolution via Parallelly Contextual Attention Network

Optical degradation blurs text shapes and edges, so existing scene text recognition methods have difficulties in achieving desirable results on low-resolution (LR) scene text images acquired in real-world environments. The above problem can be solved by efficiently extracting sequential information to reconstruct super-resolution (SR) text images, which remains a challenging task. In this paper, we propose a Parallelly Contextual Attention Network (PCAN), which effectively learns sequence-dependent features and focuses more on high-frequency information of the reconstruction in text images. Firstly, we explore the importance of sequence-dependent features in horizontal and vertical directions parallelly for text SR, and then design a parallelly contextual attention block to adaptively select the key information in the text sequence that contributes to image super-resolution. Secondly, we propose a hierarchically orthogonal texture-aware attention module and an edge guidance loss function, which can help to reconstruct high-frequency information in text images. Finally, we conduct extensive experiments on TextZoom dataset, and the results can be easily incorporated into mainstream text recognition algorithms to further improve their performance in LR image recognition. Besides, our approach exhibits great robustness in defending against adversarial attacks on seven mainstream scene text recognition datasets, which means it can also improve the security of the text recognition pipeline. Compared with directly recognizing LR images, our method can respectively improve the recognition accuracy of ASTER, MORAN, and CRNN by 14.9%, 14.0%, and 20.1%. Our method outperforms eleven state-of-the-art (SOTA) SR methods in terms of boosting text recognition performance. Most importantly, it outperforms the current optimal text-orient SR method TSRN by 3.2%, 3.7%, and 6.0% on the recognition accuracy of ASTER, MORAN, and CRNN respectively.
Stereo matching algorithm based on improved Census transform and texture filtering

To solve the problem that existing binocular stereo matching algorithms have low matching accuracy in discontinuous disparity and low texture area, a stereo matching algorithm based on Census transform and texture filtering is proposed. The weighted Census transform circular template is used to carry out the matching cost, which reflects the influence of the distance between neighborhood pixels and target pixels on the calculation and expands the perception range of target pixels; the texture filtering method is used for cost aggregation, which highlights the image structure information and smooths the internal texture. The experimental results show that the stereo matching algorithm proposed in this paper can effectively reduce the mismatching rate of images, the disparity map obtained has less noise, and the matching effect is better when the pattern texture is relatively dense.
Controllable Sketch-to-Image Translation for Robust Face Synthesis

In this paper, we propose a novel controllable sketch-to-image translation framework that allows users to interactively and robustly synthesize and edit face images with hand-drawn sketches. Inspired by the coarse-to-fine painting process of human artists, we propose a novel dilation-based sketch refinement method to refine sketches at varied coarse levels without the need for real sketch training data. We further investigate multi-level refinement that enables users to flexibly define how "reliable" the input sketch should be considered for the final output through a refinement level control parameter, which helps balance between the realism of the output and its structural consistency with the input sketch. It is realized by leveraging scale-aware style transfer to model and adjust the style features of sketches at different coarse levels. Moreover, advanced user controllability in terms of the editing region control, facial attribute editing, and spatially non-uniform refinement is further explored for fine-grained and semantic editing. We demonstrate the effectiveness of the proposed method in terms of visual quality and user controllability through extensive experiments including qualitative and quantitative comparison with state-of-the-art methods, ablation studies and various applications.
Agile Robot Navigation through Hallucinated Learning and Sober Deployment

Learning from Hallucination (LfH) is a recent machine learning paradigm for autonomous navigation, which uses training data collected in completely safe environments and adds numerous imaginary obstacles to make the environment densely constrained, to learn navigation planners that produce feasible navigation even in highly constrained (more dangerous) spaces. However, LfH requires hallucinating the robot perception during deployment to match with the hallucinated training data, which creates a need for sometimes-infeasible prior knowledge and tends to generate very conservative planning. In this work, we propose a new LfH paradigm that does not require runtime hallucination—a feature we call "sober deployment"—and can therefore adapt to more realistic navigation scenarios. This novel Hallucinated Learning and Sober Deployment (HLSD) paradigm is tested in a benchmark testbed of 300 simulated navigation environments with a wide range of difficulty levels, and in the real-world. In most cases, HLSD outperforms both the original LfH method and a classical navigation planner.
Adapting Membership Inference Attacks to GNN for Graph Classification: Approaches and Implications

In light of the wide application of Graph Neural Networks (GNNs), Membership Inference Attack (MIA) against GNNs raises severe privacy concerns, where training data can be leaked from trained GNN models. However, prior studies focus on inferring the membership of only the components in a graph, e.g., an individual node or edge. In this paper, we take the first step in MIA against GNNs for graph-level classification. Our objective is to infer whether a graph sample has been used for training a GNN model. We present and implement two types of attacks, i.e., training-based attacks and threshold-based attacks from different adversarial capabilities. We perform comprehensive experiments to evaluate our attacks in seven real-world datasets using five representative GNN models. Both our attacks are shown effective and can achieve high performance, i.e., reaching over 0.7 attack F1 scores in most cases <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . Our findings also confirm that, unlike the node-level classifier, MIAs on graph-level classification tasks are more co-related with the overfitting level of GNNs rather than the statistic property of their training graphs.
Object-Region Video Transformers

Recently, video transformers have shown great success in video understanding, exceeding CNN performance; yet existing video transformer models do not explicitly model objects, although objects can be essential for recognizing actions. In this work, we present Object-Region Video Transformers (ORViT), an object-centric approach that extends video transformer layers with a block that directly incorporates object representations. The key idea is to fuse object-centric representations starting from early layers and propagate them into the transformer-layers, thus affecting the spatio-temporal representations throughout the network. Our ORViT block consists of two object-level streams: appearance and dynamics. In the appearance stream, an “Object-Region Attention” module applies self-attention over the patches and object regions. In this way, visual object regions interact with uniform patch tokens and enrich them with contextualized object information. We further model object dynamics via a separate “Object-Dynamics Module”, which captures trajectory interactions, and show how to integrate the two streams. We evaluate our model on four tasks and five datasets: compositional and few-shot action recognition on SomethingElse, spatio-temporal action detection on AVA, and standard action recognition on Something-Something V2, Diving48 and Epic-Kitchen100. We show strong performance improvement across all tasks and datasets considered, demonstrating the value of a model that incorporates object representations into a transformer architecture. For code and pretrained models, visit the project page at https://roeiherz.github.io/ORViT/
A Quantum Secure and Noninteractive Identity-Based Aggregate Signature Protocol From Lattices

The aggregate signature is a special signature mode widely used in the wireless medical sensor network. The aggregate signature can reduce bandwidth and storage space in this sensor network by aggregating multiple signatures from different signers into a short signature. Traditional aggregate signature protocols based on the discrete logarithm problem and the great integer factorization problem cannot resist quantum attacks. In this article, we propose a new noninteractive identity-based aggregate signature protocol from lattices. We prove that the proposed protocol is secure in the quantum random oracle model. Additionally, the performance of the proposed protocol is much better than most previous protocols according to our experiment. Meanwhile, the size of the aggregate signature in our protocol is a logarithmic function of the number of signatures being aggregated.
Making and taking information

Abstract Information behavior theory covers different aspects of the totality of information‐related human behavior rather unevenly. The transitions or trading zones between different types of information activities have remained perhaps especially under‐theorized. This article interrogates and expands a conceptual apparatus of information making and information taking as a pair of substantial concepts for explaining, in part, the mobility of information in terms of doing that unfolds as a process of becoming rather than of being, and in part, what is happening when information comes into being and when something is taken up for use as information. Besides providing an apparatus to describe the nexus of information provision and acquisition, a closer consideration of the parallel doings opens opportunities to enrich the inquiry of the conditions and practice of information seeking, appropriation, discovery, and retrieval as modes taking, and learning and information use as its posterities.
Efficient Search of First-Order Nash Equilibria in Nonconvex-Concave Smooth Min-Max Problems

Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 12 May 2020Accepted: 15 July 2021Published online: 21 October 2021Keywordsfirst-order Nash equilibria, stationary points, nonconvex min-max problemsAMS Subject Headings90C06, 90C25, 90C26, 91A99Publication DataISSN (print): 1052-6234ISSN (online): 1095-7189Publisher: Society for Industrial and Applied MathematicsCODEN: sjope8
Adaptive Graph Guided Disambiguation for Partial Label Learning

In partial label learning, a multi-class classifier is learned from the ambiguous supervision where each training example is associated with a set of candidate labels among which only one is valid. An intuitive way to deal with this problem is label disambiguation, i.e., differentiating the labeling confidences of different candidate labels so as to try to recover ground-truth labeling information. Recently, feature-aware label disambiguation has been proposed which utilizes the graph structure of feature space to generate labeling confidences over candidate labels. Nevertheless, the existence of noises and outliers in training data makes the graph structure derived from original feature space less reliable. In this paper, a novel partial label learning approach based on adaptive graph guided disambiguation is proposed, which is shown to be more effective in revealing the intrinsic manifold structure among training examples. Other than the sequential disambiguation-then-induction learning strategy, the proposed approach jointly performs adaptive graph construction, candidate label disambiguation and predictive model induction with alternating optimization. Furthermore, we consider the particular human-in-the-loop framework in which a learner is allowed to actively query some ambiguously labeled examples for manual disambiguation. Extensive experiments clearly validate the effectiveness of adaptive graph guided disambiguation for learning from partial label examples.
Towards fully verifiable forward secure privacy preserving keyword search for IoT outsourced data

As cloud computing and Internet of Things (IoT) are becoming more and more deeply integrated, the new secure challenge for IoT data has attracted great attention. Privacy preserving keyword search, as one important secure technique, achieves search over encrypted cloud-based IoT data. Forward security and verifiability are two important security properties for privacy preserving keyword search for cloud-based IoT data. Forward security makes privacy preserving keyword search schemes for cloud-based IoT data able to resist file injection attack, which is one new attack on privacy preserving keyword search when IoT data is updated. Verifiability makes privacy preserving keyword search schemes for cloud-based IoT data able to ensure the validity of the search results returned by cloud. However, to the best of our knowledge, all of existing privacy preserving keyword search schemes at most achieve forward security and partial verifiability. In order to solve this problem, we explore how to simultaneously achieve the forward security and the full verification for intelligent encrypted data processing in cloud-based IoT and propose the first fully verifiable forward secure privacy preserving keyword search scheme for IoT outsourced data. In order to achieve forward security, we construct the secure index based on the hash chain. In each update, the IoT data owner randomly chooses a string as a new state to generate the new trapdoor. The new trapdoor cannot match previous trapdoors. In order to achieve the full verifiability while still maintaining forward security for intelligent encrypted data processing in cloud-based IoT, we design novel authentication construction called Forward Secure Accumulative Authentication Tag (FSAAT). This kind of tag has the incremental property and supports the verification on the basis of efficient data update. We prove that our scheme is secure and efficient through detailed security analysis and experiments.
APPLR: Adaptive Planner Parameter Learning from Reinforcement

Classical navigation systems typically operate using a fixed set of hand-picked parameters (e.g. maximum speed, sampling rate, inflation radius, etc.) and require heavy expert re-tuning in order to work in new environments. To mitigate this requirement, it has been proposed to learn parameters for different contexts in a new environment using human demonstrations collected via teleoperation. However, learning from human demonstration limits deployment to the training environment, and limits overall performance to that of a potentially-suboptimal demonstrator. In this paper, we introduce APPLR, Adaptive Planner Parameter Learning from Reinforcement, which allows existing navigation systems to adapt to new scenarios by using a parameter selection scheme discovered via reinforcement learning (RL) in a wide variety of simulation environments. We evaluate APPLR on a robot in both simulated and physical experiments, and show that it can outperform both a fixed set of hand-tuned parameters and also a dynamic parameter tuning scheme learned from human demonstration.
Linking the Characters

Recent years have witnessed the booming of online video platforms. Along this line, a graph to illustrate social relation among characters has been long expected to not only benefit the audiences for better understanding the story, but also support the fine-grained video analysis task in a semantic way. Unfortunately, though we humans could easily infer the social relations among characters, it is still an extremely challenging task for intelligent systems to automatically capture the social relation by absorbing multi-modal cues. Besides, they fail to describe the relations among multiple characters in a graph-generation perspective. To that end, inspired by the human inference ability on social relationship, we propose a novel Hierarchical- Cumulative Graph Convolutional Network (HC-GCN) to generate the social relation graph for multiple characters in the video. Specifically, we first integrate the short-term multi-modal cues, including visual, textual and audio information, to generate the frame-level graphs for part of characters via multimodal graph convolution technique. While dealing with the video-level aggregation task, we design an end-to-end framework to aggregate all frame-level subgraphs along the temporal trajectory, which results in a global video-level social graph with various social relationships among multiple characters. Extensive validations on two real-world large-scale datasets demonstrate the effectiveness of our proposed method compared with SOTA baselines.
Cybersecurity capabilities for critical infrastructure resilience

Purpose For many innovative organisations, Industry 4.0 paves the way for significant operational efficiencies, quality of goods and services and cost reductions. One of the ways to realise these benefits is to embark on digital transformation initiatives that may be summed up as the intelligent interconnectivity of people, processes, data and cyber-connected things. Sadly, this interconnectivity between the enterprise information technology (IT) and industrial control systems (ICS) environment introduces new attack surfaces for critical infrastructure (CI) operators. As a result of the ICS cybersecurity risk introduced by the interconnectivity between the enterprise IT and ICS networks, the purpose of this study is to identify the cybersecurity capabilities that CI operators must have to attain good cybersecurity resilience. Design/methodology/approach A scoping literature review of best practice international CI protection frameworks, standards and guidelines were conducted. Similar cybersecurity practices from these frameworks, standards and guidelines were grouped together under a corresponding National Institute of Standards and Technology (NIST) cybersecurity framework (CF) practice. Practices that could not be categorised under any of the existing NIST CF practices were considered new insights, and therefore, additions. Findings A CI cybersecurity capability framework comprising 29 capability domains (cybersecurity focus areas) was developed as an adaptation of the NIST CF with an added dimension. This added dimension emphasises cloud computing and internet of things (IoT) security. Each of the 29 cybersecurity capability domains is executed through various capabilities (cybersecurity processes and procedures). The study found that each cybersecurity capability can further be operationalised by a set of cybersecurity controls derived from various frameworks, standards and guidelines, such as COBIT®, CIS®, ISA/IEC 62443, ISO/IEC 27002 and NIST Special Publication 800-53. Practical implications CI sectors are immediately able to adopt the CI cybersecurity capability framework to evaluate their levels of resilience against cyber-attacks, given new attack surfaces introduced by the interconnectivity of cyber-connected things between the enterprise and ICS levels. Originality/value The authors present an added dimension to the NIST framework for CI cyber protection. In addition to emphasising cryptography, IoT and cloud computing security aspects, this added dimension highlights the need for an integrated approach to CI cybersecurity resilience instead of a piecemeal approach.
Weakly supervised learning for classification of lung cytological images using attention-based multiple instance learning

In cytological examination, suspicious cells are evaluated regarding malignancy and cancer type. To assist this, we previously proposed an automated method based on supervised learning that classifies cells in lung cytological images as benign or malignant. However, it is often difficult to label all cells. In this study, we developed a weakly supervised method for the classification of benign and malignant lung cells in cytological images using attention-based deep multiple instance learning (AD MIL). Images of lung cytological specimens were divided into small patch images and stored in bags. Each bag was then labeled as benign or malignant, and classification was conducted using AD MIL. The distribution of attention weights was also calculated as a color map to confirm the presence of malignant cells in the image. AD MIL using the AlexNet-like convolutional neural network model showed the best classification performance, with an accuracy of 0.916, which was better than that of supervised learning. In addition, an attention map of the entire image based on the attention weight allowed AD MIL to focus on most malignant cells. Our weakly supervised method automatically classifies cytological images with acceptable accuracy based on supervised learning without complex annotations.
A Simple and Strong Baseline for Universal Targeted Attacks on Siamese Visual Tracking

Siamese trackers are shown to be vulnerable to adversarial attacks recently. However, the existing attack methods craft the perturbations for each video independently, which comes at a non-negligible computational cost. In this paper, we show the existence of universal perturbations that can enable the targeted attack, e.g., forcing a tracker to follow the ground-truth trajectory with specified offsets, to be video-agnostic and free from inference in a network. Specifically, we attack a tracker by adding a universal translucent perturbation to the template image and adding a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fake target</i> , i.e., a small universal adversarial patch, into the search images adhering to the predefined trajectory, so that the tracker outputs the location and size of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fake target</i> instead of the real target. Our approach allows perturbing a novel video to come at no additional cost except the mere addition operations – and not require gradient optimization or network inference. Experimental results on several datasets demonstrate that our approach can effectively fool the Siamese trackers in a targeted attack manner. We show that the proposed perturbations are not only universal across videos, but also generalize well across different trackers. Such perturbations are therefore doubly universal, both with respect to the data and the network architectures. Our code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/lizhenbang56/SiamAttack</uri> .