bluuebunny/crossref_metadata_2025 huggingface dataset:
AttExplainer: Explain Transformer via Attention by Reinforcement Learning
Authors: Niu Runliang , Wei Zhepei , Wang Yan , Wang Qi
Published: July 2022

Transformer and its variants, built based on attention mechanisms, have recently achieved remarkable performance in many NLP tasks. Most existing works on Transformer explanation tend to reveal and utilize the attention matrix with human subjective intuitions in a qualitative manner. However, the huge size of dimensions directly challenges these methods to quantitatively analyze the attention matrix. Therefore, in this paper, we propose a novel reinforcement learning (RL) based framework for Transformer explanation via attention matrix, namely AttExplainer. The RL agent learns to perform step-by-step masking operations by observing the change in attention matrices. We have adapted our method to two scenarios, perturbation-based model explanation and text adversarial attack. Experiments on three widely used text classification benchmarks validate the effectiveness of the proposed method compared to state-of-the-art baselines. Additional studies show that our method is highly transferable and consistent with human intuition. The code of this paper is available at https://github.com/niuzaisheng/AttExplainer .
ICGNet: Integration Context-based Reverse-Contour Guidance Network for Polyp Segmentation
Authors: Du Xiuquan , Xu Xuebin , Ma Kunpeng
Published: July 2022

Precise segmentation of polyps from colonoscopic images is extremely significant for the early diagnosis and treatment of colorectal cancer. However, it is still a challenging task due to: (1)the boundary between the polyp and the background is blurred makes delineation difficult; (2)the various size and shapes causes feature representation of polyps difficult. In this paper, we propose an integration context-based reverse-contour guidance network (ICGNet) to solve these challenges. The ICGNet firstly utilizes a reverse-contour guidance module to aggregate low-level edge detail information and meanwhile constraint reverse region. Then, the newly designed adaptive context module is used to adaptively extract local-global information of the current layer and complementary information of the previous layer to get larger and denser features. Lastly, an innovative hybrid pyramid pooling fusion module fuses the multi-level features generated from the decoder in the case of considering salient features and less background. Our proposed approach is evaluated on the EndoScene, Kvasir-SEG and CVC-ColonDB datasets with eight evaluation metrics, and gives competitive results compared with other state-of-the-art methods in both learning ability and generalization capability.
Exploring Fourier Prior for Single Image Rain Removal
Authors: Guo Xin , Fu Xueyang , Zhou Man , Huang Zhen , Peng Jialun , Zha Zheng-Jun
Published: July 2022

Deep convolutional neural networks (CNNs) have become dominant in the task of single image rain removal. Most of current CNN methods, however, suffer from the problem of overfitting on one single synthetic dataset as they neglect the intrinsic prior of the physical properties of rain streaks. To address this issue, we propose a simple but effective prior - Fourier prior to improve the generalization ability of an image rain removal model. The Fourier prior is a kind of property of rainy images. It is based on a key observation of us - replacing the Fourier amplitude of rainy images with that of clean images greatly suppresses the synthetic and real-world rain streaks. This means the amplitude contains most of the rain streak information and the phase keeps the similar structures of the background. So it is natural for single image rain removal to process the amplitude and phase information of the rainy images separately. In this paper, we develop a two-stage model where the first stage restores the amplitude of rainy images to clean rain streaks, and the second stage restores the phase information to refine fine-grained background structures. Extensive experiments on synthetic rainy data demonstrate the power of Fourier prior. Moreover, when trained on synthetic data, a robust generalization ability to real-world images can also be obtained. The code will be publicly available at https://github.com/willinglucky/ExploringFourier-Prior-for-Single-Image-Rain-Removal.
Representation Learning for Compressed Video Action Recognition via Attentive Cross-modal Interaction with Motion Enhancement
Authors: Li Bing , Chen Jiaxin , Zhang Dongming , Bao Xiuguo , Huang Di
Published: July 2022

Compressed video action recognition has recently drawn growing attention, since it remarkably reduces the storage and computational cost via replacing raw videos by sparsely sampled RGB frames and compressed motion cues (e.g., motion vectors and residuals). However, this task severely suffers from the coarse and noisy dynamics and the insufficient fusion of the heterogeneous RGB and motion modalities. To address the two issues above, this paper proposes a novel framework, namely Attentive Cross-modal Interaction Network with Motion Enhancement (MEACI-Net). It follows the two-stream architecture, i.e. one for the RGB modality and the other for the motion modality. Particularly, the motion stream employs a multi-scale block embedded with a denoising module to enhance representation learning. The interaction between the two streams is then strengthened by introducing the Selective Motion Complement (SMC) and Cross-Modality Augment (CMA) modules, where SMC complements the RGB modality with spatio-temporally attentive local motion features and CMA further combines the two modalities with selective feature augmentation. Extensive experiments on the UCF-101, HMDB-51 and Kinetics-400 benchmarks demonstrate the effectiveness and efficiency of MEACI-Net.
TCCNet: Temporally Consistent Context-Free Network for Semi-supervised Video Polyp Segmentation
Authors: Li Xiaotong , Xu Jilan , Zhang Yuejie , Feng Rui , Zhao Rui-Wei , Zhang Tao , Lu Xuequan , Gao Shang
Published: July 2022

Automatic video polyp segmentation (VPS) is highly valued for the early diagnosis of colorectal cancer. However, existing methods are limited in three respects: 1) most of them work on static images, while ignoring the temporal information in consecutive video frames; 2) all of them are fully supervised and easily overfit in presence of limited annotations; 3) the context of polyp (i.e., lumen, specularity and mucosa tissue) varies in an endoscopic clip, which may affect the predictions of adjacent frames. To resolve these challenges, we propose a novel Temporally Consistent Context-Free Network (TCCNet) for semi-supervised VPS. It contains a segmentation branch and a propagation branch with a co-training scheme to supervise the predictions of unlabeled image. To maintain the temporal consistency of predictions, we design a Sequence-Corrected Reverse Attention module and a Propagation-Corrected Reverse Attention module. A Context-Free Loss is also proposed to mitigate the impact of varying contexts. Extensive experiments show that even trained under 1/15 label ratio, TCCNet is comparable to the state-of-the-art fully supervised methods for VPS. Also, TCCNet surpasses existing semi-supervised methods for natural image and other medical image segmentation tasks.
Hypertron: Explicit Social-Temporal Hypergraph Framework for Multi-Agent Forecasting
Authors: Tian Yu , Huang Xingliang , Niu Ruigang , Yu Hongfeng , Wang Peijin , Sun Xian
Published: July 2022

Forecasting the future trajectories of multiple agents is a core technology for human-robot interaction systems. To predict multi-agent trajectories more accurately, it is inevitable that models need to improve interpretability and reduce redundancy. However, many methods adopt implicit weight calculation or black-box networks to learn the semantic interaction of agents, which obviously lack enough interpretation. In addition, most of the existing works model the relation among all agents in a one-to-one manner, which might lead to irrational trajectory predictions due to its redundancy and noise. To address the above issues, we present Hypertron, a human-understandable and lightweight hypergraph-based multi-agent forecasting framework, to explicitly estimate the motions of multiple agents and generate reasonable trajectories. The framework explicitly interacts among multiple agents and learns their latent intentions by our coarse-to-fine hypergraph convolution interaction module. Our experiments on several challenging real-world trajectory forecasting datasets show that Hypertron outperforms a wide array of state-of-the-art methods while saving over 60% parameters and reducing 30% inference time.
Augmenting Anchors by the Detector Itself
Authors: Wan Xiaopei , Li Guoqiu , Yang Yujiu , Guo Zhenhua
Published: July 2022

Usually, it is difficult to determine the scale and aspect ratio of anchors for anchor-based object detection methods. Current state-of-the-art object detectors either determine anchor parameters according to objects' shape and scale in a dataset, or avoid this problem by utilizing anchor-free methods, however, the former scheme is dataset-specific and the latter methods could not get better performance than the former ones. In this paper, we propose a novel anchor augmentation method named AADI, which means Augmenting Anchors by the Detector Itself. AADI is not an anchor-free method, instead, it can convert the scale and aspect ratio of anchors from a continuous space to a discrete space, which greatly alleviates the problem of anchors' designation. Furthermore, AADI is a learning-based anchor augmentation method, but it does not add any parameters or hyper-parameters, which is beneficial for research and downstream tasks. Extensive experiments on COCO dataset demonstrate the effectiveness of AADI, specifically, AADI achieves significant performance boosts on many state-of-the-art object detectors (eg. at least +2.4 box AP on Faster R-CNN, +2.2 box AP on Mask R-CNN, and +0.9 box AP on Cascade Mask R-CNN). We hope that this simple and cost-efficient method can be widely used in object detection. Code and models are available at https://github.com/WanXiaopei/aadi.
Socially Intelligent Genetic Agents for the Emergence of Explicit Norms
Authors: Agrawal Rishabh , Ajmeri Nirav , Singh Munindar
Published: July 2022

Norms help regulate a society. Norms may be explicit (represented in structured form) or implicit.

We address the emergence of explicit norms by developing agents who provide and reason about explanations for norm violations in deciding sanctions and identifying alternative norms. These agents use a genetic algorithm to produce norms and reinforcement learning to learn the values of these norms.

We find that applying explanations leads to norms that provide better cohesion and goal satisfaction for the agents. Our results are stable for societies with differing attitudes of generosity.
QCDCL with Cube Learning or Pure Literal Elimination - What is Best?
Authors: Böhm Benjamin , Peitl Tomáš , Beyersdorff Olaf
Published: July 2022

Quantified conflict-driven clause learning (QCDCL) is one of the main approaches for solving quantified Boolean formulas (QBF). We formalise and investigate several versions of QCDCL that include cube learning and/or pure-literal elimination, and formally compare the resulting solving models via proof complexity techniques. Our results show that almost all of the QCDCL models are exponentially incomparable with respect to proof size (and hence solver running time), pointing towards different orthogonal ways how to practically implement QCDCL.
BandMaxSAT: A Local Search MaxSAT Solver with Multi-armed Bandit
Authors: Zheng Jiongzhi , He Kun , Zhou Jianrong , Jin Yan , Li Chu-Min , Manyà Felip
Published: July 2022

We address Partial MaxSAT (PMS) and Weighted PMS (WPMS), two practical generalizations of the MaxSAT problem, and propose a local search algorithm called BandMaxSAT, that applies a multi-armed bandit to guide the search direction, for these problems. The bandit in our method is associated with all the soft clauses in the input (W)PMS instance. Each arm corresponds to a soft clause. The bandit model can help BandMaxSAT to select a good direction to escape from local optima by selecting a soft clause to be satisfied in the current step, that is, selecting an arm to be pulled. We further propose an initialization method for (W)PMS that prioritizes both unit and binary clauses when producing the initial solutions. Extensive experiments demonstrate that BandMaxSAT significantly outperforms the state-of-the-art (W)PMS local search algorithm SATLike3.0. Specifically, the number of instances in which BandMaxSAT obtains better results is about twice that obtained by SATLike3.0. We further combine BandMaxSAT with the complete solver TT-Open-WBO-Inc. The resulting solver BandMaxSAT-c also outperforms some of the best state-of-the-art complete (W)PMS solvers, including SATLike-c, Loandra and TT-Open-WBO-Inc.
A Strengthened Branch and Bound Algorithm for the Maximum Common (Connected) Subgraph Problem
Authors: Zhou Jianrong , He Kun , Zheng Jiongzhi , Li Chu-Min , Liu Yanli
Published: July 2022

We propose a new and strengthened Branch-and-Bound (BnB) algorithm for the maximum common (connected) induced subgraph problem based on two new operators, Long-Short Memory (LSM) and Leaf vertex Union Match (LUM). Given two graphs for which we search for the maximum common (connected) induced subgraph, the first operator of LSM maintains a score for the branching node using the short-term reward of each vertex of the first graph and the long-term reward of each vertex pair of the two graphs. In this way, the BnB process learns to reduce the search tree size significantly and boost the algorithm performance. The second operator of LUM further improves the performance by simultaneously matching the leaf vertices connected to the current matched vertices, and allows the algorithm to match multiple vertex pairs without affecting the optimality of solution. We incorporate the two operators into the state-of-the-art BnB algorithm McSplit, and denote the resulting algorithm as McSplit+LL. Experiments show that McSplit+LL outperforms McSplit+RL, a more recent variant of McSplit using reinforcement learning that is superior than McSplit.
Can Abnormality be Detected by Graph Neural Networks?
Authors: Chai Ziwei , You Siqi , Yang Yang , Pu Shiliang , Xu Jiarong , Cai Haoyang , Jiang Weihao
Published: July 2022

Anomaly detection in graphs has attracted considerable interests in both academia and industry due to its wide applications in numerous domains ranging from finance to biology. Meanwhile, graph neural networks (GNNs) is emerging as a powerful tool for modeling graph data. A natural and fundamental question that arises here is: can abnormality be detected by graph neural networks?

In this paper, we aim to answer this question, which is nontrivial. As many existing works have explored, graph neural networks can be seen as filters for graph signals, with the favor of low frequency in graphs. In other words, GNN will smooth the signals of adjacent nodes. However, abnormality in a graph intuitively has the characteristic that it tends to be dissimilar to its neighbors, which are mostly normal samples. It thereby conflicts with the general assumption with traditional GNNs. To solve this, we propose a novel Adaptive Multi-frequency Graph Neural Network (AMNet), aiming to capture both low-frequency and high-frequency signals, and adaptively combine signals of different frequencies. Experimental results on real-world datasets demonstrate that our model achieves a significant improvement comparing with several state-of-the-art baseline methods.
Mutual Distillation Learning Network for Trajectory-User Linking
Authors: Chen Wei , Li ShuZhe , Huang Chao , Yu Yanwei , Jiang Yongguo , Dong Junyu
Published: July 2022

Trajectory-User Linking (TUL), which links trajectories to users who generate them, has been a challenging problem due to the sparsity in check-in mobility data. Existing methods ignore the utilization of historical data or rich contextual features in check-in data, resulting in poor performance for TUL task.

In this paper, we propose a novel Mutual distillation learning network to solve the TUL problem for sparse check-in mobility data, named MainTUL.

Specifically, MainTUL is composed of a Recurrent Neural Network (RNN) trajectory encoder that models sequential patterns of input trajectory and a temporal-aware Transformer trajectory encoder that captures long-term time dependencies for the corresponding augmented historical trajectories.

Then, the knowledge learned on historical trajectories is transferred between the two trajectory encoders to guide the learning of both encoders to achieve mutual distillation of information. Experimental results on two real-world check-in mobility datasets demonstrate the superiority of \model against state-of-the-art baselines. The source code of our model is available at https://github.com/Onedean/MainTUL.
CADET: Calibrated Anomaly Detection for Mitigating Hardness Bias
Authors: Deng Ailin , Goodge Adam , Ang Lang Yi , Hooi Bryan
Published: July 2022

The detection of anomalous samples in large, high-dimensional datasets is a challenging task with numerous practical applications. Recently, state-of-the-art performance is achieved with deep learning methods: for example, using the reconstruction error from an autoencoder as anomaly scores. However, the scores are uncalibrated: that is, they follow an unknown distribution and lack a clear interpretation. Furthermore, the reconstruction error is highly influenced by the `hardness' of a given sample, which leads to false negative and false positive errors. In this paper, we empirically show the significance of this hardness bias present in a range of recent deep anomaly detection methods. To mitigate this, we propose an efficient and plug-and-play error calibration method which mitigates this hardness bias in the anomaly scoring without the need to retrain the model. We verify the effectiveness of our method on a range of image, time-series, and tabular datasets and against several baseline methods.
Feature and Instance Joint Selection: A Reinforcement Learning Perspective
Authors: Fan Wei , Liu Kunpeng , Liu Hao , Zhu Hengshu , Xiong Hui , Fu Yanjie
Published: July 2022

Feature selection and instance selection are two important techniques of data processing. However, such selections have mostly been studied separately, while existing work towards the joint selection conducts feature/instance selection coarsely; thus neglecting the latent fine-grained interaction between feature space and instance space. To address this challenge, we propose a reinforcement learning solution to accomplish the joint selection task and simultaneously capture the interaction between the selection of each feature and each instance. In particular, a sequential-scanning mechanism is designed as action strategy of agents and a collaborative-changing environment is used to enhance agent collaboration. In addition, an interactive paradigm introduces prior selection knowledge to help agents for more efficient exploration. Finally, extensive experiments on real-world datasets have demonstrated improved performances.
End-to-End Open-Set Semi-Supervised Node Classification with Out-of-Distribution Detection
Authors: Huang Tiancheng , Wang Donglin , Fang Yuan , Chen Zhengyu
Published: July 2022

Out-Of-Distribution (OOD) samples are prevalent in real-world applications. The OOD issue becomes even more severe on graph data, as the effect of OOD nodes can be potentially amplified by propagation through the graph topology. Recent works have considered the OOD detection problem, which is critical for reducing the uncertainty in learning and improving the robustness. However, no prior work considers simultaneously OOD detection and node classification on graphs in an end-to-end manner. In this paper, we study a novel problem of end-to-end open-set semi-supervised node classification (OSSNC) on graphs, which deals with node classification in the presence of OOD nodes. Given the lack of supervision on OOD nodes, we introduce a latent variable to indicate in-distribution or OOD nodes in a variational inference framework, and further propose a novel algorithm named as Learning to Mix Neighbors (LMN) which learns to dampen the influence of OOD nodes through the messaging-passing in typical graph neural networks. Extensive experiments on various datasets show that the proposed method outperforms state-of-the-art baselines in terms of both node classification and OOD detection.
Personalized Federated Learning with Contextualized Generalization
Authors: Tang Xueyang , Guo Song , Guo Jingcai
Published: July 2022

The prevalent personalized federated learning (PFL) usually pursues a trade-off between personalization and generalization by maintaining a shared global model to guide the training process of local models. However, the sole global model may easily transfer deviated context knowledge to some local models when multiple latent contexts exist across the local datasets. In this paper, we propose a novel concept called contextualized generalization (CG) to provide each client with fine-grained context knowledge that can better fit the local data distributions and facilitate faster model convergence, based on which we properly design a framework of PFL, dubbed CGPFL. We conduct detailed theoretical analysis, in which the convergence guarantee is presented and a speedup of order 1/2 w.r.t the number of contexts over most existing methods is granted. To quantitatively study the generalization-personalization trade-off, we introduce the generalization error measure and prove that the proposed CGPFL can achieve a better trade-off than existing solutions. Moreover, our theoretical analysis further inspires a heuristic algorithm to find a near-optimal trade-off in CGPFL. Experimental results on multiple real-world datasets show that our approach surpasses the state-of-the-art methods on test accuracy by a significant margin.
The Limits of Morality in Strategic Games
Authors: Cao Rui , Naumov Pavel
Published: July 2022

An agent, or a coalition of agents, is blameable for an outcome if she had a strategy to prevent it. In this paper we introduce a notion of limited blameworthiness, with a constraint on the amount of sacrifice required to prevent the outcome. The main technical contribution is a sound and complete logical system for reasoning about limited blameworthiness in the strategic game setting.
Epistemic Logic of Likelihood and Belief
Authors: Delgrande James P. , Sack Joshua , Lakemeyer Gerhard , Pagnucco Maurice
Published: July 2022

A major challenge in AI is dealing with uncertain information. While probabilistic approaches have been employed to address this issue, in many situations probabilities may not be available or may be unsuitable. As an alternative, qualitative approaches have been introduced to express that one event is no more probable than another. We provide an approach where an agent may reason deductively about notions of likelihood, and may hold beliefs where the subjective probability for a belief is less than 1. Thus, an agent can believe that p holds (with probability <1); and if the agent believes that q is more likely than p, then the agent will also believe q. Our language allows for arbitrary nesting of beliefs and qualitative likelihoods. We provide a sound and complete proof system for the logic with respect to an underlying probabilistic semantics, and show that the language is equivalent to a sublanguage with no nested modalities.
Inverse Problems for Gradual Semantics
Authors: Oren Nir , Yun Bruno , Vesic Srdjan , Baptista Murilo
Published: July 2022

Gradual semantics with abstract argumentation provide each argument with a score reflecting its acceptability. Many different gradual semantics have been proposed in the literature, each following different principles and producing different argument rankings. A sub-class of such semantics, the so-called weighted semantics, takes, in addition to the graph structure, an initial set of weights over the arguments as input, with these weights affecting the resultant argument ranking. In this work, we consider the inverse problem over such weighted semantics. That is, given an argumentation framework and a desired argument ranking, we ask whether there exist initial weights such that a particular semantics produces the given ranking. The contribution of this paper are: (1) an algorithm to answer this problem, (2) a characterisation of the properties that a gradual semantics must satisfy for the algorithm to operate, and (3) an empirical evaluation of the proposed algorithm.
DDDM: A Brain-Inspired Framework for Robust Classification
Authors: Chen Xiyuan , Li Xingyu , Zhou Yi , Yang Tianming
Published: July 2022

Despite their outstanding performance in a broad spectrum of real-world tasks, deep artificial neural networks are sensitive to input noises, particularly adversarial perturbations. On the contrary, human and animal brains are much less vulnerable. In contrast to the one-shot inference performed by most deep neural networks, the brain often solves decision-making with an evidence accumulation mechanism that may trade time for accuracy when facing noisy inputs. The mechanism is well described by the Drift-Diffusion Model (DDM). In the DDM, decision-making is modeled as a process in which noisy evidence is accumulated toward a threshold. Drawing inspiration from the DDM, we propose the Dropout-based Drift-Diffusion Model (DDDM) that combines test-phase dropout and the DDM for improving the robustness for arbitrary neural networks. The dropouts create temporally uncorrelated noises in the network that counter perturbations, while the evidence accumulation mechanism guarantees a reasonable decision accuracy. Neural networks enhanced with the DDDM tested in image, speech, and text classification tasks all significantly outperform their native counterparts, demonstrating the DDDM as a task-agnostic defense against adversarial attacks.
Representation Matters: Characterisation and Impossibility Results for Interval Aggregation
Authors: Endriss Ulle , Novaro Arianna , Terzopoulou Zoi
Published: July 2022

In the context of aggregating intervals reflecting the views of several agents into a single interval, we investigate the impact of the form of representation chosen for the intervals involved. Specifically, we ask whether there are natural rules we can define both as rules that aggregate separately the left and right endpoints of intervals and as rules that aggregate separately the left endpoints and the interval widths. We show that on discrete scales it is essentially impossible to do so, while on continuous scales we can characterise the rules meeting these requirements as those that compute a weighted average of the endpoints of the individual intervals.
RoboGNN: Robustifying Node Classification under Link Perturbation
Authors: Guan Sheng , Ma Hanchao , Wu Yinghui
Published: July 2022

Graph neural networks (GNNs) have emerged as powerful approaches for graph representation learning and node classification. Nevertheless, they can be vulnerable (sensitive) to link perturbations due to structural noise or adversarial attacks. This paper introduces RoboGNN, a novel framework that simultaneously robustifies an input classifier to a counterpart with certifiable robustness, and suggests desired graph representation with auxiliary links to ensure the robustness guarantee. (1) We introduce (p,θ)-robustness, which characterizes the robustness guarantee of a GNN-based classifier if its performance is insensitive for at least θ fraction of a targeted set of nodes under any perturbation of a set of vulnerable links up to a bounded size p. (2) We present a co-learning framework that interacts model learning with graph structural learning to robustify an input model M to a (p,θ)-robustness counterpart. The framework also outputs the desired graph structures that ensure the robustness. Using real-world benchmark graphs, we experimentally verify that roboGNN can effectively robustify representative GNNs with guaranteed robustness, and desirable gains on accuracy.
FLS: A New Local Search Algorithm for K-means with Smaller Search Space
Authors: Huang Junyu , Feng Qilong , Huang Ziyun , Xu Jinhui , Wang Jianxin
Published: July 2022

The k-means problem is an extensively studied unsupervised learning problem with various applications in decision making and data mining. In this paper, we propose a fast and practical local search algorithm for the k-means problem. Our method reduces the search space of swap pairs from O(nk) to O(k^2), and applies random mutations to find potentially better solutions when local search falls into poor local optimum. With the assumption of data distribution that each optimal cluster has "average" size of \Omega(n/k), which is common in many datasets and k-means benchmarks, we prove that our proposed algorithm gives a (100+\epsilon)-approximate solution in expectation. Empirical experiments show that our algorithm achieves better performance compared to existing state-of-the-art local search methods on k-means benchmarks and large datasets.
Libra-CAM: An Activation-Based Attribution Based on the Linear Approximation of Deep Neural Nets and Threshold Calibration
Authors: Lee Sangkyun , Han Sungmin
Published: July 2022

Universal application of AI has increased the need to explain why an AI model makes a specific decision in a human-understandable form. Among many related works, the class activation map (CAM)-based methods have been successful recently, creating input attribution based on the weighted sum of activation maps in convolutional neural networks. However, existing methods use channel-wise importance weights with specific architectural assumptions, relying on arbitrarily chosen attribution threshold values in their quality assessment: we think these can degrade the quality of attribution. In this paper, we propose Libra-CAM, a new CAM-style attribution method based on the best linear approximation of the layer (as a function) between the penultimate activation and the target-class score output. From the approximation, we derive the base formula of Libra-CAM, which is applied with multiple reference activations from a pre-built library. We construct Libra-CAM by averaging these base attribution maps, taking a threshold calibration procedure to optimize its attribution quality. Our experiments show that Libra-CAM can be computed in a reasonable time and is superior to the existing attribution methods in quantitative and qualitative attribution quality evaluations.
Learning General Gaussian Mixture Model with Integral Cosine Similarity
Authors: Li Guanglin , Li Bin , Chen Changsheng , Tan Shunquan , Qiu Guoping
Published: July 2022

Gaussian mixture model (GMM) is a powerful statistical tool in data modeling, especially for unsupervised learning tasks. Traditional learning methods for GMM such as expectation maximization (EM) require the covariance of the Gaussian components to be non-singular, a condition that is often not satisfied in real-world applications. This paper presents a new learning method called G$^2$M$^2$ (General Gaussian Mixture Model) by fitting an unnormalized Gaussian mixture function (UGMF) to a data distribution. At the core of G$^2$M$^2$ is the introduction of an integral cosine similarity (ICS) function for comparing the UGMF and the unknown data density distribution without having to explicitly estimate it. By maximizing the ICS through Monte Carlo sampling, the UGMF can be made to overlap with the unknown data density distribution such that the two only differ by a constant scalar, and the UGMF can be normalized to obtain the data density distribution. A Siamese convolutional neural network is also designed for optimizing the ICS function. Experimental results show that our method is more competitive in modeling data having correlations that may lead to singular covariance matrices in GMM, and it outperforms state-of-the-art methods in unsupervised anomaly detection.
Projected Gradient Descent Algorithms for Solving Nonlinear Inverse Problems with Generative Priors
Authors: Liu Zhaoqiang , Han Jun
Published: July 2022

In this paper, we propose projected gradient descent (PGD) algorithms for signal estimation from noisy nonlinear measurements. We assume that the unknown signal lies near the range of a Lipschitz continuous generative model with bounded inputs. In particular, we consider two cases when the nonlinear link function is either unknown or known. For unknown nonlinearity, we make the assumption of sub-Gaussian observations and propose a linear least-squares estimator. We show that when there is no representation error, the sensing vectors are Gaussian, and the number of samples is sufficiently large, with high probability, a PGD algorithm converges linearly to a point achieving the optimal statistical rate using arbitrary initialization. For known nonlinearity, we assume monotonicity, and make much weaker assumptions on the sensing vectors and allow for representation error. We propose a nonlinear least-squares estimator that is guaranteed to enjoy an optimal statistical rate. A corresponding PGD algorithm is provided and is shown to also converge linearly to the estimator using arbitrary initialization. In addition, we present experimental results on image datasets to demonstrate the performance of our PGD algorithms.
A Few Seconds Can Change Everything: Fast Decision-based Attacks against DNNs
Authors: Mou Ningping , Zheng Baolin , Wang Qian , Ge Yunjie , Guo Binqing
Published: July 2022

Previous researches have demonstrated deep learning models' vulnerabilities to decision-based adversarial attacks, which craft adversarial examples based solely on information from output decisions (top-1 labels). However, existing decision-based attacks have two major limitations, i.e., expensive query cost and being easy to detect. To bridge the gap and enlarge real threats to commercial applications, we propose a novel and efficient decision-based attack against black-box models, dubbed FastDrop, which only requires a few queries and work well under strong defenses. The crux of the innovation is that, unlike existing adversarial attacks that rely on gradient estimation and additive noise, FastDrop generates adversarial examples by dropping information in the frequency domain. Extensive experiments on three datasets demonstrate that FastDrop can escape the detection of the state-of-the-art (SOTA) black-box defenses and reduce the number of queries by 13~133× under the same level of perturbations compared with the SOTA attacks. FastDrop only needs 10~20 queries to conduct an attack against various black-box models within 1s. Besides, on commercial vision APIs provided by Baidu and Tencent, FastDrop achieves an attack success rate (ASR) of 100% with 10 queries on average, which poses a real and severe threat to real-world applications.
Search-based Reinforcement Learning through Bandit Linear Optimization
Authors: Peelman Milan , Bronselaer Antoon , De Tré Guy
Published: July 2022

The development of AlphaZero was a breakthrough in search-based reinforcement learning, by employing a given world model in a Monte-Carlo tree search (MCTS) algorithm to incrementally learn both an action policy and a value estimation. When extending this paradigm to the setting of simultaneous move games we find that the selection strategy of AlphaZero has theoretical shortcomings, including that convergence to a Nash equilibrium is not guaranteed. By analyzing these shortcomings, we find that the selection strategy corresponds to an approximated version of bandit linear optimization using Tsallis entropy regularization with α parameter set to zero, which is equivalent to log-barrier regularization. This observation allows us to refine the search method used by AlphaZero to obtain an algorithm that has theoretically optimal regret as well as superior empirical performance on our evaluation benchmark.
Multi-Player Multi-Armed Bandits with Finite Shareable Resources Arms: Learning Algorithms & Applications
Authors: Wang Xuchuang , Xie Hong , Lui John C. S.
Published: July 2022

Multi-player multi-armed bandits (MMAB) study how decentralized players cooperatively play the same multi-armed bandit so as to maximize their total cumulative rewards. Existing MMAB models mostly assume when more than one player pulls the same arm, they either have a collision and obtain zero rewards or have no collision and gain independent rewards, both of which are usually too restrictive in practical scenarios. In this paper, we propose an MMAB with shareable resources as an extension of the collision and non-collision settings. Each shareable arm has finite shareable resources and a “per-load” reward random variable, both of which are unknown to players. The reward from a shareable arm is equal to the “per-load” reward multiplied by the minimum between the number of players pulling the arm and the arm’s maximal shareable resources. We consider two types of feedback: sharing demand information (SDI) and sharing demand awareness (SDA), each of which provides different signals of resource sharing. We design the DPE-SDI and SIC-SDA algorithms to address the shareable arm problem under these two cases of feedback respectively and prove that both algorithms have logarithmic regrets that are tight in the number of rounds. We conduct simulations to validate both algorithms’ performance and show their utilities in wireless networking and edge computing.
Hierarchical Diffusion Scattering Graph Neural Network
Authors: Zhang Ke , Pu Xinyan , Li Jiaxing , Wu Jiasong , Shu Huazhong , Kong Youyong
Published: July 2022

Graph neural network (GNN) is popular now to solve the tasks in non-Euclidean space and most of them learn deep embeddings by aggregating the neighboring nodes. However, these methods are prone to some problems such as over-smoothing because of the single-scale perspective field and the nature of low-pass filter. To address these limitations, we introduce diffusion scattering network (DSN) to exploit high-order patterns. With observing the complementary relationship between multi-layer GNN and DSN, we propose Hierarchical Diffusion Scattering Graph Neural Network (HDS-GNN) to efficiently bridge DSN and GNN layer by layer to supplement GNN with multi-scale information and band-pass signals. Our model extracts node-level scattering representations by intercepting the low-pass filtering, and adaptively tunes the different scales to regularize multi-scale information. Then we apply hierarchical representation enhancement to improve GNN with the scattering features. We benchmark our model on nine real-world networks on the transductive semi-supervised node classification task. The experimental results demonstrate the effectiveness of our method.
A Smart Trader for Portfolio Management based on Normalizing Flows
Authors: Yang Mengyuan , Zheng Xiaolin , Liang Qianqiao , Han Bing , Zhu Mengying
Published: July 2022

In this paper, we study a new kind of portfolio problem, named trading point aware portfolio optimization (TPPO), which aims to obtain excess intraday profit by deciding the portfolio weights and their trading points simultaneously based on microscopic information. However, a strategy for the TPPO problem faces two challenging problems, i.e., modeling the ever-changing and irregular microscopic stock price time series and deciding the scattering candidate trading points. To address these problems, we propose a novel TPPO strategy named STrader based on normalizing flows. STrader is not only promising in reversibly transforming the geometric Brownian motion process to the unobservable and complicated stochastic process of the microscopic stock price time series for modeling such series, but also has the ability to earn excess intraday profit by capturing the appropriate trading points of the portfolio. Extensive experiments conducted on three public datasets demonstrate STrader's superiority over the state-of-the-art portfolio strategies.
Propositional Gossip Protocols under Fair Schedulers
Authors: Livesey Joseph , Wojtczak Dominik
Published: July 2022

Gossip protocols are programs that can be used by a group of agents to synchronize what information they have. Namely, assuming each agent holds a secret, the goal of a protocol is to reach a situation in which all agents know all secrets. Distributed epistemic gossip protocols use epistemic formulas in the component programs for the agents. In this paper, we study the simplest classes of such gossip protocols: propositional gossip protocols, in which whether an agent wants to initiate a call depends only on the set of secrets that the agent currently knows. It was recently shown that such a protocol can be correct, i.e., always terminates in a state where all agents know all secrets, only when its communication graph is complete. We show here that this characterization dramatically changes when the usual fairness constraints are imposed on the call scheduler used. Finally, we establish that checking the correctness of a given propositional protocol under a fair scheduler is a coNP-complete problem.
PCVAE: Generating Prior Context for Dialogue Response Generation
Authors: Cai Zefeng , Cai Zerui
Published: July 2022

Conditional Variational AutoEncoder (CVAE) is promising for modeling one-to-many relationships in dialogue generation, as it can naturally generate many responses from a given context. However, the conventional used continual latent variables in CVAE are more likely to generate generic rather than distinct and specific responses. To resolve this problem, we introduce a novel discrete variable called prior context which enables the generation of favorable responses. Specifically, we present Prior Context VAE (PCVAE), a hierarchical VAE that learns prior context from data automatically for dialogue generation. Meanwhile, we design Active Codeword Transport (ACT) to help the model actively discover potential prior context. Moreover, we propose Autoregressive Compatible Arrangement (ACA) that enables modeling prior context in autoregressive style, which is crucial for selecting appropriate prior context according to a given context. Extensive experiments demonstrate that PCVAE can generate distinct responses and significantly outperforms strong baselines.
Parameterized Algorithms for Kidney Exchange
Authors: Maiti Arnab , Dey Palash
Published: July 2022

In kidney exchange programs, multiple patient-donor pairs each of whom are otherwise incompatible, exchange their donors to receive compatible kidneys. The Kidney Exchange problem is typically modelled as a directed graph where every vertex is either an altruistic donor or a pair of patient and donor; directed edges are added from a donor to its compatible patients. The computational task is to find if there exists a collection of disjoint cycles and paths starting from altruistic donor vertices of length at most l_c and l_p respectively that covers at least some specific number t of non-altruistic vertices (patients). We study parameterized algorithms for the kidney exchange problem in this paper. Specifically, we design FPT algorithms parameterized by each of the following parameters: (1) the number of patients who receive kidney, (2) treewidth of the input graph + max{l_p, l_c}, and (3) the number of vertex types in the input graph when l_p <= l_c. We also present interesting algorithmic and hardness results on the kernelization complexity of the problem. Finally, we present an approximation algorithm for an important special case of Kidney Exchange.
Lyra: A Benchmark for Turducken-Style Code Generation
Authors: Liang Qingyuan , Sun Zeyu , Zhu Qihao , Zhang Wenjie , Yu Lian , Xiong Yingfei , Zhang Lu
Published: July 2022

Recently, neural techniques have been used to generate source code automatically. While promising for declarative languages, these approaches achieve much poorer performance on datasets for imperative languages. Since a declarative language is typically embedded in an imperative language (i.e., the turducken-style programming) in real-world software development, the promising results on declarative languages can hardly lead to significant reduction of manual software development efforts.

In this paper, we define a new code generation task: given a natural language comment, this task aims to generate a program in a base imperative language with an embedded declarative language. To our knowledge, this is the first turducken-style code generation task. For this task, we present Lyra: a dataset in Python with embedded SQL. This dataset contains 2,000 carefully annotated database manipulation programs from real usage projects. Each program is paired with both a Chinese comment and an English comment. In our experiment, we adopted Transformer, BERT-style, and GPT-style models as baselines. In the best setting, GPT-style model can achieve 24% and 25.5% AST exact matching accuracy using Chinese and English comments, respectively. Therefore, we believe that Lyra provides a new challenge for code generation. Yet, overcoming this challenge may significantly boost the applicability of code generation techniques for real-world software development.
Automatic Noisy Label Correction for Fine-Grained Entity Typing
Authors: Pan Weiran , Wei Wei , Zhu Feida
Published: July 2022

Fine-grained entity typing (FET) aims to assign proper semantic types to entity mentions according to their context, which is a fundamental task in various entity-leveraging applications. Current FET systems usually establish on large-scale weaklysupervised/distantly annotation data, which may contain abundant noise and thus severely hinder the performance of the FET task. Although previous studies have made great success in automatically identifying the noisy labels in FET, they usually rely on some auxiliary resources which may be unavailable in real-world applications (e.g., pre-defined hierarchical type structures, humanannotated subsets). In this paper, we propose a novel approach to automatically correct noisy labels for FET without external resources. Specifically, it first identifies the potentially noisy labels by estimating the posterior probability of a label being positive or negative according to the logits output by the model, and then relabel candidate noisy labels by training a robust model over the remaining clean labels. Experiments on two popular benchmarks prove the effectiveness of our method. Our source code can be obtained from https://github.com/CCIIPLab/DenoiseFET.
MGAD: Learning Descriptional Representation Distilled from Distributional Semantics for Unseen Entities
Authors: Wang Yuanzheng , Cheng Xueqi , Fan Yixing , Zhu Xiaofei , Liang Huasheng , Yan Qiang , Guo Jiafeng
Published: July 2022

Entity representation plays a central role in building effective entity retrieval models. Recent works propose to learn entity representations based on entity-centric contexts, which achieve SOTA performances on many tasks. However, these methods lead to poor representations for unseen entities since they rely on a multitude of occurrences for each entity to enable accurate representations. To address this issue, we propose to learn enhanced descriptional representations for unseen entities by distilling knowledge from distributional semantics into descriptional embeddings. Specifically, we infer enhanced embeddings for unseen entities based on descriptions by aligning the descriptional embedding space to the distributional embedding space with different granularities, i.e., element-level, batch-level and space-level alignment. Experimental results on four benchmark datasets show that our approach improves the performance over all baseline methods. In particular, our approach can achieve the effectiveness of the teacher model on almost all entities, and maintain such high performance on unseen entities.