bluuebunny/crossref_metadata_2025 huggingface dataset:
Towards Universal Backward-Compatible Representation Learning
Authors: Zhang Binjie , Ge Yixiao , Shen Yantao , Su Shupeng , Wu Fanzi , Yuan Chun , Xu Xuyuan , Wang Yexin , Shan Ying
Published: July 2022

Conventional model upgrades for visual search systems require offline refresh of gallery features by feeding gallery images into new models (dubbed as “backfill”), which is time-consuming and expensive, especially in large-scale applications. The task of backward-compatible representation learning is therefore introduced to support backfill-free model upgrades, where the new query features are interoperable with the old gallery features. Despite the success, previous works only investigated a close-set training scenario (i.e., the new training set shares the same classes as the old one), and are limited by more realistic and challenging open-set scenarios. To this end, we first introduce a new problem of universal backward-compatible representation learning, covering all possible data split in model upgrades. We further propose a simple yet effective method, dubbed as Universal Backward-Compatible Training (UniBCT) with a novel structural prototype refinement algorithm, to learn compatible representations in all kinds of model upgrading benchmarks in a unified manner. Comprehensive experiments on the large-scale face recognition datasets MS1Mv3 and IJB-C fully demonstrate the effectiveness of our method. Source code is available at https://github.com/TencentARC/OpenCompatible.
Plane Geometry Diagram Parsing
Authors: Zhang Ming-Liang , Yin Fei , Hao Yi-Han , Liu Cheng-Lin
Published: July 2022

Geometry diagram parsing plays a key role in geometry problem solving, wherein the primitive extraction and relation parsing remain challenging due to the complex layout and between-primitive relationship. In this paper, we propose a powerful diagram parser based on deep learning and graph reasoning. Specifically, a modified instance segmentation method is proposed to extract geometric primitives, and the graph neural network (GNN) is leveraged to realize relation parsing and primitive classification incorporating geometric features and prior knowledge. All the modules are integrated into an end-to-end model called PGDPNet to perform all the sub-tasks simultaneously. In addition, we build a new large-scale geometry diagram dataset named PGDP5K with primitive level annotations. Experiments on PGDP5K and an existing dataset IMP-Geometry3K show that our model outperforms state-of-the-art methods in four sub-tasks remarkably. Our code, dataset and appendix material are available at https://github.com/mingliangzhang2018/PGDP.
CATrans: Context and Affinity Transformer for Few-Shot Segmentation
Authors: Zhang Shan , Wu Tianyi , Wu Sitong , Guo Guodong
Published: July 2022

Few-shot segmentation (FSS) aims to segment novel categories given scarce annotated support images. The crux of FSS is how to aggregate dense correlations between support and query images for query segmentation while being robust to the large variations in appearance and context. To this end, previous Transformer-based methods explore global consensus either on context similarity or affinity map between support-query pairs. In this work, we effectively integrate the context and affinity information via the proposed novel Context and Affinity Transformer (CATrans) in a hierarchical architecture. Specifically, the Relation-guided Context Transformer (RCT) propagates context information from support to query images conditioned on more informative support features. Based on the observation that a huge feature distinction between support and query pairs brings barriers for context knowledge transfer, the Relation-guided Affinity Transformer (RAT) measures attention-aware affinity as auxiliary information for FSS, in which the self-affinity is responsible for more reliable cross-affinity. We conduct experiments to demonstrate the effectiveness of the proposed model, outperforming the state-of-the-art methods.
Visual Emotion Representation Learning via Emotion-Aware Pre-training
Authors: Zhang Yue , Ding Wanying , Xu Ran , Hu Xiaohua
Published: July 2022

Despite recent progress in deep learning, visual emotion recognition remains a challenging problem due to ambiguity of emotion perception, diverse concepts related to visual emotion and lack of large-scale annotated dataset. In this paper, we present a large-scale multimodal pre-training method to learn visual emotion representation by aligning emotion, object, attribute triplet with a contrastive loss. We conduct our pre-training on a large web dataset with noisy tags and fine-tune on visual emotion classification datasets. Our method achieves state-of-the-art performance for visual emotion classification.
HifiHead: One-Shot High Fidelity Neural Head Synthesis with 3D Control
Authors: Zhu Feida , Zhu Junwei , Chu Wenqing , Tai Ying , Xie Zhifeng , Huang Xiaoming , Wang Chengjie
Published: July 2022

We propose HifiHead, a high fidelity neural talking head synthesis method, which can well preserve the source image's appearance and control the motion (e.g., pose, expression, gaze) flexibly with 3D morphable face models (3DMMs) parameters derived from a driving image or indicated by users. Existing head synthesis works mainly focus on low-resolution inputs. Instead, we exploit the powerful generative prior embedded in StyleGAN to achieve high-quality head synthesis and editing. Specifically, we first extract the source image's appearance and driving image's motion to construct 3D face descriptors, which are employed as latent style codes for the generator. Meanwhile, hierarchical representations are extracted from the source and rendered 3D images respectively to provide faithful appearance and shape guidance. Considering the appearance representations need high-resolution flow fields for spatial transform, we propose a coarse-to-fine style-based generator consisting of a series of feature alignment and refinement (FAR) blocks. Each FAR block updates the dense flow fields and refines RGB outputs simultaneously for efficiency. Extensive experiments show that our method blends source appearance and target motion more accurately along with more photo-realistic results than previous state-of-the-art approaches.
Combining Constraint Solving and Bayesian Techniques for System Optimization
Authors: Brauße Franz , Khasidashvili Zurab , Korovin Konstantin
Published: July 2022

Application domains of Bayesian optimization include optimizing black-box functions or very complex functions.

The functions we are interested in describe complex real-world systems applied in industrial settings.

Even though they do have explicit representations, standard optimization techniques fail to provide validated solutions and correctness guarantees for them.

In this paper we present a combination of Bayesian optimization and SMT-based constraint solving to achieve safe and stable solutions with optimality guarantees.
Goal Consistency: An Effective Multi-Agent Cooperative Method for Multistage Tasks
Authors: Chen Xinning , Liu Xuan , Zhang Shigeng , Ding Bo , Li Kenli
Published: July 2022

Although multistage tasks involving multiple sequential goals are common in real-world applications, they are not fully studied in multi-agent reinforcement learning (MARL). To accomplish a multi-stage task, agents have to achieve cooperation on different subtasks. Exploring the collaborative patterns of different subtasks and the sequence of completing the subtasks leads to an explosion in the search space, which poses great challenges to policy learning. Existing works designed for single-stage tasks where agents learn to cooperate only once usually suffer from low sample efficiency in multi-stage tasks as agents explore aimlessly. Inspired by human’s improving cooperation through goal consistency, we propose Multi-Agent Goal Consistency (MAGIC) framework to improve sample efficiency for learning in multi-stage tasks. MAGIC adopts a goal-oriented actor-critic model to learn both local and global views of goal cognition, which helps agents understand the task at the goal level so that they can conduct targeted exploration accordingly. Moreover, to improve exploration efficiency, MAGIC employs two-level goal consistency training to drive agents to formulate a consistent goal cognition. Experimental results show that MAGIC significantly improves sample efficiency and facilitates cooperation among agents compared with state-of-art MARL algorithms in several challenging multistage tasks.
DPSampler: Exact Weighted Sampling Using Dynamic Programming
Authors: Dudek Jeffrey M. , Shrotri Aditya A. , Vardi Moshe Y.
Published: July 2022

The problem of exact weighted sampling of solutions of Boolean formulas has applications in Bayesian inference, testing, and verification. The state-of-the-art approach to sampling involves carefully decomposing the input formula and compiling a data structure called d-DNNF in the process. Recent work in the closely connected field of model counting, however, has shown that smartly composing different subformulas using dynamic programming and Algebraic Decision Diagrams (ADDs) can outperform d-DNNF-style approaches on many benchmarks. In this work, we present a modular algorithm called DPSampler that extends such dynamic-programming techniques to the problem of exact weighted sampling.

DPSampler operates in three phases. First, an execution plan in the form of a project-join tree is computed using tree decompositions. Second, the plan is used to compile the input formula into a succinct tree-of-ADDs representation. Third, this tree is traversed to generate a random sample. This decoupling of planning, compilation and sampling phases enables usage of specialized libraries for each purpose in a black-box fashion. Further, our novel ADD-sampling algorithm avoids the need for expensive dynamic memory allocation required in previous work. Extensive experiments over diverse sets of benchmarks show DPSampler is more scalable and versatile than existing approaches.
Large Neighbourhood Search for Anytime MaxSAT Solving
Authors: Hickey Randy , Bacchus Fahiem
Published: July 2022

Large Neighbourhood Search (LNS) is an algorithmic framework for optimization problems that can yield good performance in many domains. In this paper, we present a method for applying LNS to improve anytime maximum satisfiability (MaxSAT) solving by introducing a neighbourhood selection policy that shows good empirical performance. We show that our LNS solver can often improve the suboptimal solutions produced by other anytime MaxSAT solvers. When starting with a suboptimal solution of reasonable quality, our approach often finds a better solution than the original anytime solver can achieve. We demonstrate that implementing our LNS solver on top of three different state-of-the-art anytime solvers improves the anytime performance of all three solvers within the standard time limit used in the incomplete tracks of the annual MaxSAT Evaluation.
Online Matching with Controllable Rewards and Arrival Probabilities
Authors: Hikima Yuya , Akagi Yasunori , Marumo Naoki , Kim Hideaki
Published: July 2022

Online bipartite matching has attracted much attention due to its importance in various applications such as advertising, ride-sharing, and crowdsourcing. In most online matching problems, the rewards and node arrival probabilities are given in advance and are not controllable. However, many real-world matching services require them to be controllable and the decision-maker faces a non-trivial problem of optimizing them. In this study, we formulate a new optimization problem, Online Matching with Controllable Rewards and Arrival probabilities (OM-CRA), to simultaneously determine not only the matching strategy but also the rewards and arrival probabilities. Even though our problem is more complex than the existing ones, we propose a fast 1/2-approximation algorithm for OM-CRA. The proposed approach transforms OM-CRA to a saddle-point problem by approximating the objective function, and then solves it by the Primal-Dual Hybrid Gradient (PDHG) method with acceleration through the use of the problem structure. In simulations on real data from crowdsourcing and ride-sharing platforms, we show that the proposed algorithm can find solutions with high total rewards in practical times.
Encoding Probabilistic Graphical Models into Stochastic Boolean Satisfiability
Authors: Hsieh Cheng-Han , Jiang Jie-Hong R.
Published: July 2022

Statistical inference is a powerful technique in various applications. Although many statistical inference tools are available, answering inference queries involving complex quantification structures remains challenging. Recently, solvers for Stochastic Boolean Satisfiability (SSAT), a powerful formalism allowing concise encodings of PSPACE decision problems under uncertainty, are under active development and applied in more and more applications. In this work, we exploit SSAT solvers for the inference of Probabilistic Graphical Models (PGMs), an essential representation for probabilistic reasoning. Specifically, we develop encoding methods to systematically convert PGM inference problems into SSAT formulas for effective solving. Experimental results demonstrate that, by using our encoding, SSAT-based solving can complement existing PGM tools, especially in answering complex queries.
Degradation Accordant Plug-and-Play for Low-Rank Tensor Completion
Authors: Hu Yexun , Jiang Tai-Xiang , Zhao Xi-Le
Published: July 2022

Tensor completion aims at estimating missing values from an incomplete observation, playing a fundamental role for many applications. This work proposes a novel low-rank tensor completion model, in which the inherent low-rank prior and external degradation accordant data-driven prior are simultaneously utilized. Specifically, the tensor nuclear norm (TNN) is adopted to characterize the overall low-dimensionality of the tensor data. Meanwhile, an implicit regularizer is formulated and its related subproblem is solved via a deep convolutional neural network (CNN) under the plug-and-play framework. This CNN, pretrained for the inpainting task on a mass of natural images, is expected to express the external data-driven prior and this plugged inpainter is consistent with the original degradation process. Then, an efficient alternating direction method of multipliers (ADMM) is designed to solve the proposed optimization model. Extensive experiments are conducted on different types of tensor imaging data with the comparison with state-of-the-art methods, illustrating the effectiveness and the remarkable generalization ability of our method.
Automated Program Analysis: Revisiting Precondition Inference through Constraint Acquisition
Authors: Menguy Grégoire , Bardin Sébastien , Lazaar Nadjib , Gotlieb Arnaud
Published: July 2022

Program annotations under the form of function pre/postconditions are crucial for many software engineering and program verification applications. Unfortunately, such annotations are rarely available and must be retrofit by hand. In this paper, we explore how Constraint Acquisition (CA), a learning framework from Constraint Programming, can be leveraged to automatically infer program preconditions in a black-box manner, from input-output observations. We propose PreCA, the first ever framework based on active constraint acquisition dedicated to infer memory-related preconditions. PreCA overpasses prior techniques based on program analysis and formal methods, offering well-identified guarantees and returning more precise results in practice.
Non-Euclidean Self-Organizing Maps
Authors: Celińska-Kopczyńska Dorota , Kopczyński Eryk
Published: July 2022

Self-Organizing Maps (SOMs, Kohonen networks) belong to neural network models of the unsupervised class. In this paper, we present the generalized setup for non-Euclidean SOMs. Most data analysts take it for granted to use some subregions of a flat space as their data model; however, by the assumption that the underlying geometry is non-Euclidean we obtain a new degree of freedom for the techniques that translate the similarities into spatial neighborhood relationships. We improve the traditional SOM algorithm by introducing topology-related extensions. Our proposition can be successfully applied to dimension reduction, clustering or finding similarities in big data (both hierarchical and non-hierarchical).
Two-Sided Matching over Social Networks
Authors: Cho Sung-Ho , Todo Taiki , Yokoo Makoto
Published: July 2022

A new paradigm of mechanism design, called mechanism design over social networks, investigates agents’ incentives to diffuse the information of mechanisms to their followers over social networks. In this paper we consider it for two-sided matching, where the agents on one side, say students, are distributed over social networks and thus are not fully observable to the mechanism designer, while the agents on the other side, say colleges, are known a priori. The main purpose of this paper is to clarify the existence of mechanisms that satisfy several properties that are classified into four criteria: incentive constraints, efficiency constraints, stability constraints, and fairness constraints. We proposed three mechanisms and showed that no mechanism is better than these mechanisms, i.e., they are in the Pareto frontier according to the set of properties defined in this paper.
Robust High-Dimensional Classification From Few Positive Examples
Authors: Chakrabarti Deepayan , Fauber Benjamin
Published: July 2022

We tackle an extreme form of imbalanced classification, with up to 105 features but as few as 5 samples from the minority class. This problem occurs in predicting predicting tumor types and fraud detection, among others. Standard imbalanced classification methods are not designed for such severe data scarcity. Sampling-based methods need too many samples due to the high-dimensionality, while cost-based methods must place too high a weight on the limited minority samples. Our proposed method, called DIRECT, bypasses sample generation by training the classifier over a robust smoothed distribution of the minority class. DIRECT is fast, simple, robust, parameter-free, and easy to interpret. We validate DIRECT on several real-world datasets spanning document, image, and medical classification. DIRECT is up to 5x − 7x better than SMOTE-like methods, 30−200% better than ensemble methods, 3x − 7x better than cost-sensitive methods. The greatest gains are for settings with the fewest samples in the minority class, where DIRECT’s robustness is most helpful.
Meta-Learning Based Knowledge Extrapolation for Knowledge Graphs in the Federated Setting
Authors: Chen Mingyang , Zhang Wen , Yao Zhen , Chen Xiangnan , Ding Mengxiao , Huang Fei , Chen Huajun
Published: July 2022

We study the knowledge extrapolation problem to embed new components (i.e., entities and relations) that come with emerging knowledge graphs (KGs) in the federated setting. In this problem, a model trained on an existing KG needs to embed an emerging KG with unseen entities and relations. To solve this problem, we introduce the meta-learning setting, where a set of tasks are sampled on the existing KG to mimic the link prediction task on the emerging KG. Based on sampled tasks, we meta-train a graph neural network framework that can construct features for unseen components based on structural information and output embeddings for them. Experimental results show that our proposed method can effectively embed unseen components and outperforms models that consider inductive settings for KGs and baselines that directly use conventional KG embedding methods.
Towards Robust Dense Retrieval via Local Ranking Alignment
Authors: Chen Xuanang , Luo Jian , He Ben , Sun Le , Sun Yingfei
Published: July 2022

Dense retrieval (DR) has extended the employment of pre-trained language models, like BERT, for text ranking. However, recent studies have raised the robustness issue of DR model against query variations, like query with typos, along with non-trivial performance losses. Herein, we argue that it would be beneficial to allow the DR model to learn to align the relative positions of query-passage pairs in the representation space, as query variations cause the query vector to drift away from its original position, affecting the subsequent DR effectiveness. To this end, we propose RoDR, a novel robust DR model that learns to calibrate the in-batch local ranking of query variation to that of original query for the DR space alignment. Extensive experiments on MS MARCO and ANTIQUE datasets show that RoDR significantly improves the retrieval results on both the original queries and different types of query variations. Meanwhile, RoDR provides a general query noise-tolerate learning framework that boosts the robustness and effectiveness of various existing DR models. Our code and models are openly available at https://github.com/cxa-unique/RoDR.
Filtration-Enhanced Graph Transformation
Authors: Chen Zijian , Li Rong-Hua , Qin Hongchao , Duan Huanzhong , Lu Yanxiong , Dai Qiangqiang , Wang Guoren
Published: July 2022

Graph kernels and graph neural networks (GNNs) are widely used for the classification of graph data. However, many existing graph kernels and GNNs have limited expressive power, because they cannot distinguish graphs if the classic 1-dimensional Weisfeiler-Leman (1-WL) algorithm does not distinguish them. To break the 1-WL expressiveness barrier, we propose a novel method called filtration-enhanced graph transformation, which is based on a concept from the area of topological data analysis. In a nutshell, our approach first transforms each original graph into a filtration-enhanced graph based on a certain pre-defined filtration operation, and then uses the transformed graphs as the inputs for graph kernels or GNNs. The striking feature of our approach is that it is a plug-in method and can be applied in any graph kernel and GNN to enhance their expressive power. We theoretically and experimentally demonstrate that our solutions exhibit significantly better performance than the state-of-the art solutions for graph classification tasks.
Triformer: Triangular, Variable-Specific Attentions for Long Sequence Multivariate Time Series Forecasting
Authors: Cirstea Razvan-Gabriel , Guo Chenjuan , Yang Bin , Kieu Tung , Dong Xuanyi , Pan Shirui
Published: July 2022

A variety of real-world applications rely on far future information to make decisions, thus calling for efficient and accurate long sequence multivariate time series forecasting. While recent attention-based forecasting models show strong abilities in

capturing long-term dependencies, they still suffer from two key limitations. First, canonical self attention has a quadratic complexity w.r.t. the input time series length, thus falling short in efficiency. Second, different variables’ time series often have

distinct temporal dynamics, which existing studies fail to capture, as they use the same model parameter space, e.g., projection matrices, for all variables’ time series, thus falling short in accuracy. To ensure high efficiency and accuracy, we propose Triformer, a triangular, variable-specific attention. (i) Linear complexity: we introduce a novel patch attention with linear complexity. When stacking multiple layers of the patch attentions, a triangular structure is proposed such that the

layer sizes shrink exponentially, thus maintaining linear complexity. (ii) Variable-specific parameters: we propose a light-weight method to enable distinct sets of model parameters for different variables’ time series to enhance accuracy

without compromising efficiency and memory usage. Strong empirical evidence on four datasets from multiple domains justifies our design choices, and it demonstrates that Triformer outperforms state-of-the-art methods w.r.t. both accuracy and

efficiency. Source code is publicly available at https://github.com/razvanc92/triformer.
Private Semi-Supervised Federated Learning
Authors: Fan Chenyou , Hu Junjie , Huang Jianwei
Published: July 2022

We study a federated learning (FL) framework to effectively train models from scarce and skewly distributed labeled data. We consider a challenging yet practical scenario: a few data sources own a small amount of labeled data, while the rest mass sources own purely unlabeled data. Classical FL requires each client to have enough labeled data for local training, thus is not applicable in this scenario. In this work, we design an effective federated semi-supervised learning framework (FedSSL) to fully leverage both labeled and unlabeled data sources. We establish a unified data space across all participating agents, so that each agent can generate mixed data samples to boost semi-supervised learning (SSL), while keeping data locality. We further show that FedSSL can integrate differential privacy protection techniques to prevent labeled data leakage at the cost of minimum performance degradation. On SSL tasks with as small as 0.17% and 1% of MNIST and CIFAR-10 datasets as labeled data, respectively, our approach can achieve 5-20% performance boost over the state-of-the-art methods.
A Formal Model for Multiagent Q-Learning Dynamics on Regular Graphs
Authors: Chu Chen , Li Yong , Liu Jinzhuo , Hu Shuyue , Li Xuelong , Wang Zhen
Published: July 2022

Modeling the dynamics of multi-agent learning has long been an important research topic. The focus of previous research has been either on 2-agent settings or well-mixed infinitely large agent populations. In this paper, we consider the scenario where n Q-learning agents locate on regular graphs, such that agents can only interact with their neighbors. We examine the local interactions between individuals and their neighbors, and derive a formal model to capture the Q-value dynamics of the entire population. Through comparisons with agent-based simulations on different types of regular graphs, we show that our model describes the agent learning dynamics in an exact manner.
MetaER-TTE: An Adaptive Meta-learning Model for En Route Travel Time Estimation
Authors: Fan Yu , Xu Jiajie , Zhou Rui , Li Jianxin , Zheng Kai , Chen Lu , Liu Chengfei
Published: July 2022

En route travel time estimation (ER-TTE) aims to predict the travel time on the remaining route. Since the traveled and remaining parts of a trip usually have some common characteristics like driving speed, it is desirable to explore these characteristics for improved performance via effective adaptation. This yet faces the severe problem of data sparsity due to the few sampled points in a traveled partial trajectory. Since trajectories with different contextual information tend to have different characteristics, the existing meta-learning method for ER-TTE cannot fit each trajectory well because it uses the same model for all trajectories. To this end, we propose a novel adaptive meta-learning model called MetaER-TTE. Particularly, we utilize soft-clustering and derive cluster-aware initialized parameters to better transfer the shared knowledge across trajectories with similar contextual information. In addition, we adopt a distribution-aware approach for adaptive learning rate optimization, so as to avoid task-overfitting which will occur when guiding the initial parameters with a fixed learning rate for tasks under imbalanced distribution. Finally, we conduct comprehensive experiments to demonstrate the superiority of MetaER-TTE.
Modeling Precursors for Temporal Knowledge Graph Reasoning via Auto-encoder Structure
Authors: Gao Yifu , Feng Linhui , Kan Zhigang , Han Yi , Qiao Linbo , Li Dongsheng
Published: July 2022

Temporal knowledge graph (TKG) reasoning that infers missing facts in the future is an essential and challenging task. When predicting a future event, there must be a narrative evolutionary process composed of closely related historical facts to support the event's occurrence, namely fact precursors. However, most existing models employ a sequential reasoning process in an auto-regressive manner, which cannot capture precursor information. This paper proposes a novel auto-encoder architecture that introduces a relation-aware graph attention layer into transformer (rGalT) to accommodate inference over the TKG. Specifically, we first calculate the correlation between historical and predicted facts through multiple attention mechanisms along intra-graph and inter-graph dimensions, then constitute these mutually related facts into diverse fact segments. Next, we borrow the translation generation idea to decode in parallel the precursor information associated with the given query, which enables our model to infer future unknown facts by progressively generating graph structures. Experimental results on four benchmark datasets demonstrate that our model outperforms other state-of-the-art methods, and precursor identiﬁcation provides supporting evidence for prediction.
Constrained Adaptive Projection with Pretrained Features for Anomaly Detection
Authors: Gui Xingtai , Wu Di , Chang Yang , Fan Shicai
Published: July 2022

Anomaly detection aims to separate anomalies from normal samples, and the pretrained network is promising for anomaly detection. However, adapting the pretrained features would be confronted with the risk of pattern collapse when finetuning on one-class training data. In this paper, we propose an anomaly detection framework called constrained adaptive projection with pretrained features (CAP). Combined with pretrained features, a simple linear projection head applied on a specific input and its k most similar pretrained normal representations is designed for feature adaptation, and a reformed self-attention is leveraged to mine the inner-relationship among one-class semantic features. A loss function is proposed to avoid potential pattern collapse. Concretely, it considers the similarity between a specific data and its corresponding adaptive normal representation, and incorporates a constraint term slightly aligning pretrained and adaptive spaces. Our method achieves state-of-the-art anomaly detection performance on semantic anomaly detection and sensory anomaly detection benchmarks including 96.5% AUROC on CIFAR-100 dataset, 97.0% AUROC on CIFAR-10 dataset and 89.9% AUROC on MvTec dataset.
Quaternion Ordinal Embedding
Authors: Hou Wenzheng , Xu Qianqian , Ma Ke , Hao Qianxiu , Huang Qingming
Published: July 2022

Ordinal embedding (OE) aims to project objects into a low-dimensional space while preserving their ordinal constraints as well as possible. Generally speaking, a reasonable OE algorithm should simultaneously capture a) semantic meaning and b) the ordinal relationship of the objects. However, most of the existing methods merely focus on b). To address this issue, our goal in this paper is to seek a generic OE method to embrace the two features simultaneously. We argue that different dimensions of vector-based embedding are naturally entangled with each other. To realize a), we expect to decompose the D dimensional embedding space into D different semantic subspaces, where each subspace is associated with a matrix representation. Unfortunately, introducing a matrix-based representation requires far more complex parametric space than its vector-based counterparts. Thanks to the algebraic property of quaternions, we are able to find a more efficient way to represent a matrix with quaternions. For b), inspired by the classic chordal Grassmannian distance, a new distance function is defined to measure the distance between different quaternions/matrices, on top of which we construct a generic OE loss function. Experimental results for different tasks on both simulated and real-world datasets verify the effectiveness of our proposed method.
A Sparse-Motif Ensemble Graph Convolutional Network against Over-smoothing
Authors: Jiang Xuan , Yang Zhiyong , Wen Peisong , Su Li , Huang Qingming
Published: July 2022

The over-smoothing issue is a well-known challenge for Graph Convolutional Networks (GCN). Specifically, it is often observed that increasing the depth of GCN ends up in a trivial embedding subspace where the difference among node embeddings belonging to the same cluster tends to vanish. This paper believes that the main cause lies in the limited diversity along the message passing pipeline. Inspired by this, we propose a Sparse-Motif Ensemble Graph Convolutional Network (SMEGCN). We argue that merely employing the original graph Laplacian as the spectrum of the graph cannot capture the diversified local structure of complex graphs. Hence, to improve the diversity of the graph spectrum, we introduce local topological structures of complex graphs into GCN by employing the so-called graph motifs or the small network subgraphs. Moreover, we find that the motif connections are much denser than the edge connections, which might converge to an all-one matrix within a few times of message-passing. To fix this, we first propose the notion of sparse motif to avoid spurious motif connections. Subsequently, we propose a hierarchical motif aggregation mechanism to integrate the graph spectral information from a series of different sparse-motif message passing paths. Finally, we conduct a series of theoretical and experimental analyses to demonstrate the superiority of the proposed method.
RAW-GNN: RAndom Walk Aggregation based Graph Neural Network
Authors: Jin Di , Wang Rui , Ge Meng , He Dongxiao , Li Xiang , Lin Wei , Zhang Weixiong
Published: July 2022

Graph-Convolution-based methods have been successfully applied to representation learning on homophily graphs where nodes with the same label or similar attributes tend to connect with one another. Due to the homophily assumption of Graph Convolutional Networks (GCNs) that these methods use, they are not suitable for heterophily graphs where nodes with different labels or dissimilar attributes tend to be adjacent. Several methods have attempted to address this heterophily problem, but they do not change the fundamental aggregation mechanism of GCNs because they rely on summation operators to aggregate information from neighboring nodes, which is implicitly subject to the homophily assumption. Here, we introduce a novel aggregation mechanism and develop a RAndom Walk Aggregation-based Graph Neural Network (called RAW-GNN) method. The proposed approach integrates the random walk strategy with graph neural networks. The new method utilizes breadth-first random walk search to capture homophily information and depth-first search to collect heterophily information. It replaces the conventional neighborhoods with path-based neighborhoods and introduces a new path-based aggregator based on Recurrent Neural Networks. These designs make RAW-GNN suitable for both homophily and heterophily graphs. Extensive experimental results showed that the new method achieved state-of-the-art performance on a variety of homophily and heterophily graphs.
Physics-Informed Long-Sequence Forecasting From Multi-Resolution Spatiotemporal Data
Authors: Meng Chuizheng , Niu Hao , Habault Guillaume , Legaspi Roberto , Wada Shinya , Ono Chihiro , Liu Yan
Published: July 2022

Spatiotemporal data aggregated over regions or time windows at various resolutions demonstrate heterogeneous patterns and dynamics in each resolution. Meanwhile, the multi-resolution characteristic provides rich contextual information, which is critical for effective long-sequence forecasting. The importance of such inter-resolution information is more significant in practical cases, where fine-grained data is usually collected via approaches with lower costs but also lower qualities compared to those for coarse-grained data. However, existing works focus on uni-resolution data and cannot be directly applied to fully utilize the aforementioned extra information in multi-resolution data. In this work, we propose Spatiotemporal Koopman Multi-Resolution Network (ST-KMRN), a physics-informed learning framework for long-sequence forecasting from multi-resolution spatiotemporal data. Our method jointly models data aggregated in multiple resolutions and captures the inter-resolution dynamics with the self-attention mechanism. We also propose downsampling and upsampling modules among resolutions to further strengthen the connections among data of multiple resolutions. Moreover, we enhance the modeling of intra-resolution dynamics with physics-informed modules based on Koopman theory. Experimental results demonstrate that our proposed approach achieves the best performance on the long-sequence forecasting tasks compared to baselines without a specific design for multi-resolution data.
Poisoning Deep Learning Based Recommender Model in Federated Learning Scenarios
Authors: Rong Dazhong , He Qinming , Chen Jianhai
Published: July 2022

Various attack methods against recommender systems have been proposed in the past years, and the security issues of recommender systems have drawn considerable attention.

Traditional attacks attempt to make target items recommended to as many users as possible by poisoning the training data.

Benifiting from the feature of protecting users' private data, federated recommendation can effectively defend such attacks.

Therefore, quite a few works have devoted themselves to developing federated recommender systems.

For proving current federated recommendation is still vulnerable, in this work we probe to design attack approaches targeting deep learning based recommender models in federated learning scenarios.

Specifically, our attacks generate poisoned gradients for manipulated malicious users to upload based on two strategies (i.e., random approximation and hard user mining).

Extensive experiments show that our well-designed attacks can effectively poison the target models, and the attack effectiveness sets the state-of-the-art.
Multi-Agent Intention Progression with Reward Machines
Authors: Dann Michael , Yao Yuan , Alechina Natasha , Logan Brian , Thangarajah John
Published: July 2022

Recent work in multi-agent intention scheduling has shown that enabling agents to predict the actions of other agents when choosing their own actions can be beneficial. However existing approaches to 'intention-aware' scheduling assume that the programs of other agents are known, or are "similar" to that of the agent making the prediction. While this assumption is reasonable in some circumstances, it is less plausible when the agents are not co-designed. In this paper, we present a new approach to multi-agent intention scheduling in which agents predict the actions of other agents based on a high-level specification of the tasks performed by an agent in the form of a reward machine (RM) rather than on its (assumed) program. We show how a reward machine can be used to generate tree and rollout policies for an MCTS-based scheduler. We evaluate our approach in a range of multi-agent environments, and show that RM-based scheduling out-performs previous intention-aware scheduling approaches in settings where agents are not co-designed
Beyond Homophily: Structure-aware Path Aggregation Graph Neural Network
Authors: Sun Yifei , Deng Haoran , Yang Yang , Wang Chunping , Xu Jiarong , Huang Renhong , Cao Linfeng , Wang Yang , Chen Lei
Published: July 2022

Graph neural networks (GNNs) have been intensively studied in various real-world tasks. However, the homophily assumption of GNNs' aggregation function limits their representation learning ability in heterophily graphs.

In this paper, we shed light on the path level patterns in graphs that can explicitly reflect rich semantic and structural information.

We therefore propose a novel Structure-aware Path Aggregation Graph Neural Network (PathNet) aiming to generalize GNNs for both homophily and heterophily graphs. Specifically, we first introduce a maximal entropy path sampler, which helps us sample a number of paths containing structural context. Then, we introduce a structure-aware recurrent cell consisting of order-preserving and distance-aware components to learn the semantic information of neighborhoods. Finally, we model the preference of different paths to target node after path encoding.

Experimental results demonstrate that our model achieves superior performance in node classification on both heterophily and homophily graphs.
Positive-Unlabeled Learning with Adversarial Data Augmentation for Knowledge Graph Completion
Authors: Tang Zhenwei , Pei Shichao , Zhang Zhao , Zhu Yongchun , Zhuang Fuzhen , Hoehndorf Robert , Zhang Xiangliang
Published: July 2022

Most real-world knowledge graphs (KG) are far from complete and comprehensive. This problem has motivated efforts in predicting the most plausible missing facts to complete a given KG, i.e., knowledge graph completion (KGC). However, existing KGC methods suffer from two main issues, 1) the false negative issue, i.e., the sampled negative training instances may include potential true facts; and 2) the data sparsity issue, i.e., true facts account for only a tiny part of all possible facts. To this end, we propose positive-unlabeled learning with adversarial data augmentation (PUDA) for KGC. In particular, PUDA tailors positive-unlabeled risk estimator for the KGC task to deal with the false negative issue. Furthermore, to address the data sparsity issue, PUDA achieves a data augmentation strategy by unifying adversarial training and positive-unlabeled learning under the positive-unlabeled minimax game. Extensive experimental results on real-world benchmark datasets demonstrate the effectiveness and compatibility of our proposed method.
Language Models as Knowledge Embeddings
Authors: Wang Xintao , He Qianyu , Liang Jiaqing , Xiao Yanghua
Published: July 2022

Knowledge embeddings (KE) represent a knowledge graph (KG) by embedding entities and relations into continuous vector spaces. Existing methods are mainly structure-based or description-based. Structure-based methods learn representations that preserve the inherent structure of KGs. They cannot well represent abundant long-tail entities in real-world KGs with limited structural information. Description-based methods leverage textual information and language models. Prior approaches in this direction barely outperform structure-based ones, and suffer from problems like expensive negative sampling and restrictive description demand. In this paper, we propose LMKE, which adopts Language Models to derive Knowledge Embeddings, aiming at both enriching representations of long-tail entities and solving problems of prior description-based methods. We formulate description-based KE learning with a contrastive learning framework to improve efficiency in training and evaluation. Experimental results show that LMKE achieves state-of-the-art performance on KE benchmarks of link prediction and triple classification, especially for long-tail entities.
An Analysis of the Linear Bilateral ANAC Domains Using the MiCRO Benchmark Strategy
Authors: De Jonge Dave
Published: July 2022

The Automated Negotiating Agents Competition (ANAC) is an annual competition that compares the state-of-the-art algorithms in the field of automated negotiation. Although in recent years ANAC has given more and more attention to more complex scenarios, the linear and bilateral negotiation domains that were used for its first few editions are still widely used as the default benchmark in automated negotiations research. In this paper, however, we argue that these domains should no longer be used, because they are too simplistic. We demonstrate this with an extremely simple new negotiation strategy called MiCRO, which does not employ any form of opponent modeling or machine learning, but nevertheless outperforms the strongest participants of ANAC 2012, 2013, 2018 and 2019. Furthermore, we provide a theoretical analysis which explains why MiCRO performs so well in the ANAC domains. This analysis may help researchers to design more challenging negotiation domains in the future.
Multi-Graph Fusion Networks for Urban Region Embedding
Authors: Wu Shangbin , Yan Xu , Fan Xiaoliang , Pan Shirui , Zhu Shichao , Zheng Chuanpan , Cheng Ming , Wang Cheng
Published: July 2022

Learning the embeddings for urban regions from human mobility data can reveal the functionality of regions, and then enables the correlated but distinct tasks such as crime prediction. Human mobility data contains rich but abundant information, which yields to the comprehensive region embeddings for cross domain tasks. In this paper, we propose multi-graph fusion networks (MGFN) to enable the cross domain prediction tasks. First, we integrate the graphs with spatio-temporal similarity as mobility patterns through a mobility graph fusion module. Then, in the mobility pattern joint learning module, we design the multi-level cross-attention mechanism to learn the comprehensive embeddings from multiple mobility patterns based on intra-pattern and inter-pattern messages. Finally, we conduct extensive experiments on real-world urban datasets. Experimental results demonstrate that the proposed MGFN outperforms the state-of-the-art methods by up to 12.35% improvement. https://github.com/wushangbin/MGFN
FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining Competitive Performance in Federated Learning
Authors: Wu Yuezhou , Kang Yan , Luo Jiahuan , He Yuanqin , Fan Lixin , Pan Rong , Yang Qiang
Published: July 2022

Federated learning (FL) aims to protect data privacy by enabling clients to build machine learning models collaboratively without sharing their private data. Recent works demonstrate that information exchanged during FL is subject to gradient-based privacy attacks and, consequently, a variety of privacy-preserving methods have been adopted to thwart such attacks. However, these defensive methods either introduce orders of magnitudes more computational and communication overheads (e.g., with homomorphic encryption) or incur substantial model performance losses in terms of prediction accuracy (e.g., with differential privacy). In this work, we propose FEDCG, a novel federated learning method that leverages conditional generative adversarial networks to achieve high-level privacy protection while still maintaining competitive model performance. FEDCG decomposes each client's local network into a private extractor and a public classifier and keeps the extractor local to protect privacy. Instead of exposing extractors, FEDCG shares clients' generators with the server for aggregating clients' shared knowledge aiming to enhance the performance of each client's local networks. Extensive experiments demonstrate that FEDCG can achieve competitive model performance compared with FL baselines, and privacy analysis shows that FEDCG has a high-level privacy-preserving capability.
T-SMOTE: Temporal-oriented Synthetic Minority Oversampling Technique for Imbalanced Time Series Classification
Authors: Zhao Pu , Luo Chuan , Qiao Bo , Wang Lu , Rajmohan Saravan , Lin Qingwei , Zhang Dongmei
Published: July 2022

Time series classification is a popular and important topic in machine learning, and it suffers from the class imbalance problem in many real-world applications. In this paper, to address the class imbalance problem, we propose a novel and practical oversampling method named T-SMOTE, which can make full use of the temporal information of time-series data. In particular, for each sample of minority class, T-SMOTE generates multiple samples that are close to class border. Then, based on those samples near class border, T-SMOTE synthesizes more samples. Finally, a weighted sampling method is called on both generated samples near class border and synthetic samples. Extensive experiments on a diverse set of both univariate and multivariate time-series datasets demonstrate that T-SMOTE consistently outperforms the current state-of-the-art methods on imbalanced time series classification. More encouragingly, our empirical evaluations show that T-SMOTE performs better in the scenario of early prediction, an important application scenario in industry, which indicates that T-SMOTE could bring benefits in practice.