Reviewerly/exHarmony huggingface dataset:
Offline Reinforcement Learning via Policy Regularization and Ensemble Q-Functions

Offline reinforcement learning aims to learn effective policies from a fixed set of data collected in advance and without further interaction with the environment during learning. This setting will promote the applications of reinforcement learning in the real world, in which interaction is costly or dangerous. However, existing off-policy algorithms can fail in offline settings due to the distributional shift between the learned policy and the policy that collected the dataset. To solve the problem, we develop a lightweight and effective algorithm, policy regularization with behavior model (PRBM). Firstly, PRBM trains a behavior model as the regularization term during policy optimization to avoid choosing out-of-distribution (OOD) actions. Secondly, to avoid the overestimation of OOD actions, PRBM trains multiple Q- functions and uses their min-max mixture to compute Q-values. Our experiments on different datasets from various continuous control tasks demonstrate that PRBM outperforms most baselines (especially on medium quality datasets) and requires only half of their training time.
Discriminative Graph Convolutional Networks for Semi-supervised Node Classification

Graph Convolutional Networks (GCNs) gain remarkable success in graph-based semi-supervised node classification task. Despite their success, most GCNs still exist several challenges. For this task, it is necessary to draw nodes in the same class close and push ones from different classes apart. However, GCNs smooth the node's representation by aggregating information within node neighborhoods, despite whether the connected nodes are from the same class or not. The smooth property overlooks the intra-class similarity and inter-class diversity, which leads to GCNs failing especially on low homophilic or heterophilic graphs where most nodes have neighbors from different classes. In this paper, we propose the Discriminative Graph Convolution Networks (DGCN), an extension of GCN model with discriminant modules: intra-class smoothness and inter-class sharpness. The modules effectively strengthen the intra-class similarity and the inter-class differences by introducing available label information into the convolution process. Extensive experiments on six benchmark datasets demonstrate the effectiveness of DGCN in semi-supervised node classification. Code available at https://github.com/AIG22/DGCN.
Multiple Feature Mining Based on Local Correlation and Frequency Information for Face Forgery Detection

As facial image manipulation techniques developed, deep fake detection attracted extensive attentions. Although researchers have made remarkable progresses in deepfake detection recently, which is still suffering from two limitations: a) current detectors achieve high accuracy in the high-quality videos and images, but it is hard to capture local and subtle artifacts in the low-quality and high-compression media; b) few of deep fake detection methods gain satisfying performance under cross-database scenario, because detector overfit to specific color textures producing by same manipulation algorithm. Inspired the above issues, this paper proposes a novel framework fusing local related features and frequency information to mine the forgery patterns. Firstly, we design multi-feature enhancement module, which amplifies implicit local disc repancies and capture spatial correlation from three shallow feature layers and high-level semantic layer guided by attention maps. Secondly, dual frequency decomposition module is proposed for disassembling high-frequency and low-frequency features, the forgery artifacts are exposed after dual cross attention block processing in the frequency spectrum. Features from the two streams are fused to the classification for the final result. Comprehensive experiments demonstrate the superior performance of our proposed approach in the low-quality benchmark database and cross-dataset sce-nario.
FIXC: A Method for Data Distribution Shifts Calibration via Feature Importance

With the rapid development of Artificial Intelligence (AI), a long line of past papers have shown concerns about the data distribution shifts problem in image classification models via deep learning. Moreover, there is also an Out-of-Distribution (OOD) problem in perturbation-based explanation methods for DNNs ineXplainable Artificial Intelligence (XAI), because the generated perturbation samples may be not the same distribution as the original dataset. We explore the limitations of post-hoc Learning to Explain (L2X) explanation methods that use approximators to mimic the behavior of DNNs. We propose a training pipeline called Feature Importance eXplanation(-based) Calibration (FIXC), which efficiently extracts feature importance without using imitations of existing DNNs. We use feature importance as additional information to calibrate data distribution shifts. The evaluation of the corrupted dataset and DNNs benchmarks shows that the FIXC effectively improves the classification accuracy of corrupted images. Experiments on popular vision datasets show that the FIXC outperforms state-of-the-art methods on calibration metrics While the training pipeline provides a calibrated feature importance explanation. We also provide an analysis of our method based on game interaction theory.
Detecting Backdoor Attacks on Deep Neural Networks Based on Model Parameters Analysis

With the introduction of the backdoor in deep neural networks (DNNs), much research focuses on backdoor attacks and defenses against DNNs. Since many DNN models are developed based on public datasets and pre-trained models often published by untrusted third parties, backdoors can be easily injected. The defender usually cannot access training data and does not know the target class or the triggers of the backdoor injected by the attacker. All these make it challenging to guarantee the security of decision guidance and support systems. In this paper, we proposed to detect backdoor attacks on DNNs based on model parameters analysis (MPA). We extracted and selected parameters related to the backdoor in the model's hidden layer and decision layer and trained the MPA classifier based on these parameters. We evaluated the effectiveness of the MPA classifier on various target models. The results show that the area under the receiver operating characteristic curve of the MPA classifier reaches 0.96 and 0.86 on the CIFAR10 and Troj target models, respectively. The MPA classifier improved the detection rate of backdoor attacks by 2%-6% compared with other advanced methods, with less prior knowledge and more relaxed constraints.
VS-Net: Multiscale Spatiotemporal Features for Lightweight Video Salient Document Detection

Video Salient Document Detection (VSDD) is an essential task of practical computer vision, which aims to highlight visually salient document regions in video frames. Previous techniques for VSDD focus on learning features without considering the cooperation among and across the appearance and motion cues and thus fail to perform in practical scenarios. Moreover, most of the previous techniques demand high computational resources, which limits the usage of such systems in resource-constrained settings. To handle these issues, we propose VS-Net, which captures multi-scale spatiotemporal information with the help of dilated depth-wise separable convolution and Approximation Rank Pooling. VS-Net extracts the key features locally from each frame across embedding sub-spaces and forwards the features between adjacent and parallel nodes, enhancing model performance globally. Our model generates saliency maps considering both the background and foreground simultaneously, making it perform better in challenging scenarios. The immense experiments regulated on the benchmark MIDV-500 dataset show that the VS-Net model outperforms state-of-the-art approaches in both time and robustness measures.
Improved Graph-Based Model for Recovering Superpoly on Trivium

Recovering superpoly for a given cube is the key step in cube attacks - an algebraic cryptanalysis method for symmetric ciphers. Since 2015, division property, monomial prediction, and enhanced techniques have been proposed to recover the exact superpoly by converting the problem into Mixed Integer Linear Programming (MILP) model, whose feasible solutions should be enumerated exactly. To penetrate more rounds, cryptanalysts try their best to reduce the scale of deduced MILP model to alleviate the bottleneck of computational cost for solving the model. In this paper, we investigate the graph-based modeling approach proposed in SAC 2021 to further reduce the number of feasible solutions for the model to handle and reduce the model’s scale in cube attacks on Trivium. Specifically, we develop an algorithm to search for pruning patterns and reveal a budget way to add the constraints concerning pruning patterns, thus eliminating a large number of solutions by adding fewer additional constraints. Under our measurement method, the pruning efficiency of added constraints is improved by 7 to 10 times more effective than in previous work. We also embed this modified graph-based model to the nested superpoly recovery framework proposed in ASIACRYPT 2021 and improve graph-based cube attack on Trivium by one round. The improved graph-based model performs better than monomial prediction with nested framework on 842- and 843-round cube attack of Trivium.
Evaluating a New Attention Framework Based on Matrix Blocking for Attention Models on FPGAs

The attention mechanism has recently shown superior performance in natural language processing and computer vision tasks. But its complex dataflow and large-scale matrix calculation with huge computing and memory overhead pose a great challenge for the design of hardware accelerators. And previous solutions that benefited from matrix partitioning are bounded by the softmax function. In this paper, we propose a new attention framework that can dramatically improve the performance of attention model inference for long sequence tasks on FPGAs. We design a novel accelerator architecture that employs two systolic arrays and a ping-pong structure to accelerate attention calculation. Meanwhile, we propose an analytical model to predict resource usage and performance, which guides a fast design space exploration. Experiments using the state-of-the-art BERT demonstrate the design achieves 4.61 and 1.24× improvement in speed and energy efficiency compared to CPU and GPU on the Xilinx XCZU11EG platform.
Doing Analogical Reasoning in Dynamic Assumption-based Argumentation Frameworks

Analogical reasoning is one of the most common methods by which human beings use to understand the world and make decisions. It is a dynamic process that explores an analogy between two states of affairs to claim further properties that might be shared between them. This paper tackles the question of how such arguments can be formulated in an assumption-based argumentation (ABA) framework. For that purpose, we introduce a notion of argument-based similarity measure between two argument's structures and a set of principles that such a measure should satisfy. Then, we propose an intuitive extension into an ABA framework, called ABA <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">≈</sup> , to deal with analogical reasoning properly. Finally, we also propose a formalization extended from ABA <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">≈</sup> , to deal with the dynamics in analogical argumentation and show how a structure can be handled.
Privacy-preserving Cost-sensitive Federated Learning from Imbalanced Data

Federated learning allows multiple clients to collab-oratively train a global deep learning model without revealing their local data to a centralized server. However, the existence of clients whose datasets have imbalanced class distribution has a significant impact on model accuracy. Imbalance makes it challenging for a model to distinguish between the majority and minority classes without accessing clients' local data. In this paper, we aim to tackle this problem by privacy-preserving cost-sensitive federated learning. We design the joint cost-sensitive and differentially private model parameter optimization mechanism which maintains the model accuracy while satisfying differential privacy constraints. Moreover, this mechanism does not alter the original data distribution. Experimental results demonstrate the superior performance of our proposed scheme in terms of model accuracy and privacy preservation.
Multi-branch selection fusion fine-grained classification algorithm based on coordinate attention localization

Object localization has been the focus of research in FGVC(Fine-Grained Visual Categorization). With the aim of improving the accuracy and precision of object localization in multi-branch networks, as well as the robustness and universality of object localization methods, our study mainly focus on how to combines coordinate attention and feature activation map for target localization. The model in this paper is a three-branch model including raw branch, object branch and part branch. The images are fed directly into the raw branch. CAOLM is used to localize and crop objects in the image to generate the input for the object branch. APPM is used to propose part regions at different scales. The three classes of input images undergo end-to-end weakly supervised learning through different branches of the network. The model expands the receptive field to capture multi-scale features by SB-ASPP. It can fuse the feature maps obtained from the raw branch and the object branch with SBBlock, and the complete features of the raw branch are used to supplement the missing information of the object branch. Extensive experimental results on CUB-200-2011, FGVC-Aircraft and Stanford Cars datasets show that our method has the best classification performance on FGVC-Aircraft and also has competitive performance on other datasets. Few parameters and fast inference speed are also the advantages of our model.
Actor-Aware Contrastive Learning for Semi-Supervised Action Recognition

The unique features of existing video datasets have led self-supervised contrastive learning to scene correlations and background biases, resulting in poor generalization in scene-invariant action recognition. Therefore, we propose Actor-aware Contrastive Learning for semi-supervised action recognition (ActorCLR). We employ localized actors to encourage the model to learn discriminative regions and mitigate the model's reliance on the video background during contrastive training. Furthermore, we introduce Inter-video Background Mixing (iBM) augmentation strategy to inject scene-invariance into the model. For iBM, we patch inter-video crops of four randomly selected frames to create a distinct frame for each video individually. The patched frame is mixed with the target video frames to produce a spatially distorted sample. Then, we jointly optimize contrastive loss and consistency regularization with localized actors and corresponding iBM-augmented videos in a semi-supervised manner. iBM also mixes the one-hot-encoded labels of patches with the target video's label, which softens the decision boundaries of the semi-supervised model. Our experimental results show that ActorCLR significantly improved action recognition on Kinetics-400, UCF101, and HMDB51 datasets under a low-label regime.
Approximating Element-Wise Functions of Matrix with Improved Streaming Randomized SVD

The element-wise functions of a matrix are widely used in machine learning. For the applications with large matrices, efficiently computing the matrix-vector multiplication of matrix element-wise function without explicitly constructed matrix is very desired. In this work, we aim to develop an efficient low-rank approximation of the element-wise function of matrix with the time/memory cost linear to the matrix dimension. We first propose a sparse-sign streaming randomized SVD (ssrSVD) algorithm based on a streaming singular value decomposition (SVD) algorithm and the sparse-sign random projection for the approximation of element-wise function of general asymmetric matrix. For symmetric positive semi-definite (SPSD) matrix, for which the existing Nyström [1] and FastSPSD [2] method do not perform well if the matrix's singular value decays slowly, we propose a theoretically proved shift skill to improve the approximation accuracy. Combining with the ssrSVD, we obtain the sparse-sign streaming SPSD matrix approximation with shift (S3SPSD) algorithm. Experiments are carried out to evaluate the proposed algorithms' performance in approximating element-wise functions of matrix. With the color transfer task based on the Sinkhorn algorithm, the ssrSVD algorithm largely reduces the approximation error (up to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$10^{5}\times$</tex> ) compared with the state-of-the-art baselines, and results in high-quality color transfer result. For the kernel matrix approximation, the proposed S3SPSD algorithm also consistently outperforms the state-of-the-art baselines. Experimental results finally validate the linear time complexity of the proposed algorithms.
Fully Parameterized Dueling Mixing Distributional Q-Leaning for Multi-Agent Cooperation

Multi-agent reinforcement learning (MARL) has been applied to many multi-agent team tasks, such as multi-robot swarm control. Distributional Value Function Factorization (DFAC) follows the Distributional-Individual-Global-Max (DIGM) principle, which forces the individual's optimal action to be in accordance with the optimal joint action at all times. However, this principle leads to grade inflation, which limits agents to exploring better strategies than before. We focus on this issue and propose a novel MARL method named dueling mixing distributional Q-learning with fully parameters (FDMIX). Firstly, a parametric individual value network generates an individual distribution function and a utility value function, while the fractions are obtained through a fraction proposal network. Secondly, the conversion mixing network obeys a new advantage-based DIGM principle to generate a joint distribution action value based on the global state. Finally, we incorporate an N-step return-based loss function to achieve stable and efficient training. Our extensive tests on the multiple-particle environment and StarCraft II show that our method performs better than state-of-the-art algorithms noticeably.
DeComa: Dictionary Enhanced Chinese NER Based on Confidence Estimation

Many recent Chinese NER approaches have adopted Dictionary Enhanced and achieved state-of-the-art results. However, the Dictionary Enhanced approach introduces noise because the recall strategy of the augmented set is string matching rather than semantic matching, which causes the incorporation of non-normal distribution of noise in the character vector. To solve this problem, we proposed a semantically adaptive Dictionary Enhanced augmentation structure that not only losslessly incorporates dictionary information into the word vector, but also has faster inference speed. In addition, we proposed a label discrimination structure based on confidence estimation to address the problem that the NER model cannot be widely used in real industrial scenarios due to the lack of training data. Using this strategy, our model not only improves the F1 value by up to 2.9% on the publicly available corpus in academia, but also achieves the best results on the noisy dataset after artificial random perturbation.
Classical and Quantum Security of Elliptic Curve VRF, via Relative Indifferentiability

Verifiable random functions ( VRFs ) are essentially pseudorandom functions for which selected outputs can be proved correct and unique, without compromising the security of other outputs. VRFs have numerous applications across cryptography, and in particular they have recently been used to implement committee selection in the Algorand protocol. Elliptic Curve VRF (ECVRF) is an elegant construction, originally due to Papadopoulos et al., that is now under consideration by the Internet Research Task Force. Prior work proved that ECVRF possesses the main desired security properties of a VRF, under suitable assumptions. However, several recent versions of ECVRF include changes that make some of these proofs inapplicable. Moreover, the prior analysis holds only for classical attackers, in the random-oracle model (ROM); it says nothing about whether any of the desired properties hold against quantum attacks, in the quantumly accessible ROM. We note that certain important properties of ECVRF, like uniqueness, do not rely on assumptions that are known to be broken by quantum computers, so it is plausible that these properties could hold even in the quantum setting. This work provides a multi-faceted security analysis of recent versions of ECVRF, in both the classical and quantum settings. First, we motivate and formally define new security properties for VRFs, like non-malleability and binding, and prove that recent versions of ECVRF satisfy them (under standard assumptions). Second, we identify a subtle obstruction in proving that recent versions of ECVRF have uniqueness via prior indifferentiability definitions and theorems, even in the classical setting. Third, we fill this gap by defining a stronger notion called relative indifferentiability, and extend prior work to show that a standard domain extender used in ECVRF satisfies this notion, in both the classical and quantum settings. This final contribution is of independent interest and we believe it should be applicable elsewhere.