Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by integrating up-to-date external knowledge, yet real-world web environments present unique challenges. These limitations manifest as two key challenges: pervasive misinformation in the web environment, which introduces unreliable or misleading content that can degrade retrieval accuracy, and the underutilization of web tools, which, if effectively employed, could enhance query precision and help mitigate this noise, ultimately improving retrieval results in RAG systems. To address these issues, we propose WebFilter, a novel RAG framework that generates source-restricted queries and filters out unreliable content. This approach combines a retrieval filtering mechanism with a behavior- and outcome-driven reward strategy, optimizing both query formulation and retrieval outcomes. Extensive experiments demonstrate that WebFilter improves answer quality and retrieval precision, outperforming existing RAG methods on both in-domain and out-of-domain benchmarks.

Mixing services run on blockchain trading systems, enhancing the transaction privacy of blockchain users. Yet, in recent years, the mixing services provide fertile ground for concealing illicit fund flows. Therefore, security experts make great efforts to find an effective de-anonymization of mixing services. Unfortunately, current de-anonymization technologies are constrained by a fundamental issue, i.e., the lack of a comprehensive, extensive, and reliably labeled benchmark dataset. To address this problem, we propose a new method for acquiring mixing transaction data. We design and implement a method named TGweaver, which actively executes the complete mixing workflow within a simulated blockchain environment. Furthermore, to enhance the realism of the dataset, we introduce a ''behavioral fingerprint'' mapping strategy. Ultimately, the proposed dataset includes over 891K transactions, scaling existing benchmark sizes by 2 to 4 orders of magnitude. In experiments, we use the proposed data to systematically evaluate existing de-anonymization techniques. Experimental results reveal that the current mixing address linking methods, based on heuristic rules, lacks generalization capability in complex scenarios, exhibiting low precision. In contrast, the methods utilizing supervised learning demonstrate significant advantages.

Predictive modeling on web-scale tabular data presents significant scalability challenges for industrial applications, often involving billions of instances and hundreds of heterogeneous numerical features. The inherent complexities of these features—characterized by anisotropy, heavy-tailed distributions, and non-stationarity—not only impose bottlenecks on the training efficiency and scalability of mainstream models like Gradient Boosting Decision Trees (GBDTs), but also compel practitioners into laborious, inefficient, and expert-dependent manual feature engineering. To systematically address this challenge, we introduce KMLP, a novel hybrid deep architecture. KMLP synergistically integrates a shallow Kolmogorov-Arnold Network (KAN) as a front-end with a Gated Multilayer Perceptron (gMLP) as the backbone. The KAN front-end leverages its learnable activation functions to automatically model complex non-linear transformations for each input feature in an end-to-end manner, thereby automating feature representation learning. Subsequently, the gMLP backbone efficiently captures high-order interactions among these refined representations. Extensive experiments on multiple public benchmarks and an ultra-large-scale industrial web dataset with billions of samples demonstrate that KMLP achieves state-of-the-art (SOTA) performance. Crucially, our findings reveal that KMLP's performance advantage over strong baselines like GBDTs becomes more pronounced as the data scale increases. This validates KMLP as a scalable and adaptive deep learning paradigm, offering a promising path forward for modeling large-scale, dynamic web tabular data.

A critical limitation of conventional sequential recommendation models (SRMs) is their reliance on observed user-item interaction sequences within a closed-world setting, which hinders their ability to generalize to unseen or infrequent items. Recently, Large Language Models (LLMs) have shown remarkable promise in recommendation systems due to their vast world knowledge and advanced reasoning capabilities. Current research has predominantly explored two approaches: using LLMs to directly generate recommendations and distilling knowledge from LLMs to enhance conventional SRMs. However, these approaches face two major challenges: (1) high inference costs, as they require LLM responses during inference, either for generating predictions or as supplementary input; (2) inadequate distillation of the reasoning process, as existing methods focus mainly on improving embeddings or aligning outputs, without fully integrating LLMs' inherent reasoning capabilities. To address these issues, we propose LCKD-SR, anLLM-drivenCascadedKnowledgeDistillation framework forSequentialRecommendation. In this framework, an LLM, a Teacher SRM, and a Student SRM form a hierarchical distillation structure, enabling an LLM-free inference by using only the Student model. Beyond traditional embedding and ranking distillation, our framework abstracts the LLM's sequential reasoning abilities by identifying key interactions that subsequently guide the Teacher's attention using learnable markers. The Student model, which mirrors the architecture of the Teacher, achieves seamless knowledge alignment from the Teacher across all three aspects. Extensive experiments demonstrate the effectiveness and efficiency of the proposed LCKD-SR, showcasing its scalability to perform multi-level knowledge transfer while enabling LLM-independent inference, thereby overcoming the inference cost and reasoning limitations of existing methods.

The malicious misuse and widespread dissemination of AI-generated images pose a significant threat to the authenticity of online information. Current detection methods often struggle to generalize to unseen generative models, and the rapid evolution of generative techniques continuously exacerbates this challenge. Without adaptability, detection models risk becoming ineffective in real-world applications. To address this critical issue, we propose a novel three-stage domain continual learning framework designed for continuous adaptation to evolving generative models. In the first stage, we employ a strategic parameter-efficient fine-tuning approach to develop a transferable offline detection model with strong generalization capabilities. Building upon this foundation, the second stage integrates unseen data streams into a continual learning process. To efficiently learn from limited samples of novel generated models and mitigate overfitting, we design a data augmentation chain with progressively increasing complexity. Furthermore, we leverage the Kronecker-Factored Approximate Curvature (K-FAC) method to approximate the Hessian and alleviate catastrophic forgetting. Finally, the third stage utilizes a linear interpolation strategy based on Linear Mode Connectivity, effectively capturing commonalities across diverse generative models and further enhancing overall performance. We establish a comprehensive benchmark of 27 generative models, including GANs, deepfakes, and diffusion models, chronologically structured up to August 2024 to simulate real-world scenarios. Extensive experiments demonstrate that our initial offline detectors surpass the leading baseline by +5.51% in terms of mean average precision. Our continual learning strategy achieves an average accuracy of 92.20%, outperforming state-of-the-art methods.

The development of text-to-image generative models has enabled the creation of images so realistic that distinguishing between AI-generated images and real photos is becoming a challenge. This progress offers new possibilities but also raises concerns over privacy, authenticity, and security. Detecting AI-generated images is crucial to prevent misuse. To assess the generalizability and robustness of AI-generated image detection, we present a large-scale dataset, referred to as WildFake. This dataset features cutting-edge image generators, a wide variety of generator categories, and generators for various applications, organized in a hierarchical framework. WildFake collects fake images from the open-source community, enriching its diversity with a broad range of image classes and image styles. Its design significantly improves the effectiveness of detection algorithms, making it a valuable resource for enhancing AI-generated image detection in practical applications. Our evaluations offer insights into the performance of generative models at various levels, showcasing WildFake's unique hierarchical structure's benefits.

In the domain of merchant-oriented risk control decisions within e-commerce, balancing the effectiveness of risk management with merchant satisfaction remains a critical challenge. Strict risk control strategies, while effectively mitigating risks, often lead to increased merchant dissatisfaction. Conversely, loose policies could enhance the merchant experience but raise the likelihood of incidents, potentially incurring substantial financial losses. Additionally, determining personalized risk control strategies for different merchants to achieve optimal overall risk management effectiveness is crucial. Given the high uncertainty in the outcomes of different risk control decisions, manual strategy allocation and real-time adjustments are commonly implemented in practice, leading to significant human and resource costs. In this work, we present a novel automated risk control decision framework that utilizes unbiased data-driven decision-making and dynamic optimization to automate the allocation and adjustment of risk control strategies. Our proposed solution adapts to various online business requirements, demonstrating exceptional risk management performance and significantly reducing overall costs. This approach has been extensively deployed and validated in Alibaba's risk control operations, achieving large-scale automated risk control decisions.

Anti-fraud machine learning systems are perpetually confronted with the significant challenge of concept drift, driven by the continuous and intense evolution of fraudulent techniques. That is, outdated models trained on historical fraudulent behaviors often fall short in addressing the evolving tactics of malicious users over time. The key issue lies in effectively tackling the rapid and significant evolution of fraudsters' behaviors to detect these emerging and unforeseen anomalies. In this paper, we propose a solution by directly accessing real-time data and introducing a lightweight plug-in approach named TRE (Test-time Retrieval-based Representation Enrichment). Considering the similarity among samples, TRE employs a retriever to efficiently identify the top-K most relevant recent samples and implements an aggregation strategy to provide neighboring embeddings to the predictor. It thus adjusts the trained classifiers during the test time, providing them with the information from the latest unlabeled data. Extensive experiments on three large-scale real-world datasets demonstrate the superiority of TRE. By consistently incorporating information from the nearest neighbors, TRE demonstrates high adaptability and surpasses existing methods in performance.

RAG systems rely on rerankers to identify relevant documents. However, fine-tuning these models remains challenging due to the scarcity of annotated query-document pairs. Existing distillation-based approaches suffer from training-inference misalignment and fail to capture interdependencies among candidate documents. To overcome these limitations, we reframe the reranking process as an attention-mask problem and propose Gumbel Reranking, an end-to-end training framework for rerankers aimed at minimizing the training-inference gap. In our approach, reranker optimization is reformulated as learning a stochastic, document-wise Top-k attention mask using the Gumbel Trick and Relaxed Top-k Sampling. This formulation enables end-to-end optimization by minimizing the overall language loss. Experiments across various settings consistently demonstrate performance gains, including a 10.4% improvement in recall on HotpotQA for distinguishing indirectly relevant documents.

The financial industry faces a substantial workload in verifying document images.Existing methods based on visual features struggle to identify fraudulent document images due to the lack of visual clues on the tampering region.This paper proposes CSIAD (Cross-Sample Image Anomaly Detection) by leveraging LLMs to identify logical inconsistencies in similar images.This novel framework accurately detects forged images with slight tampering traces and explains anomaly detection results.Furthermore, we introduce CrossCred, a new benchmark of real-world fraudulent images with fine-grained manual annotations.Experiments demonstrate that CSIAD outperforms state-of-the-art image fraud detection methods by 79.6% (F 1) on CrossCred and deployed industrial solutions by 21.7% (F 1) on business data.The benchmark is available at https://github.com/XMUDM/CSIAD.

Chunlei Xin, Shuheng Zhou, Huijia Zhu, Weiqiang Wang, Xuanang Chen, Xinyan Guan, Yaojie Lu, Hongyu Lin, Xianpei Han, Le Sun. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Accurate personalized headline generation hinges on precisely capturing user interests from historical behaviors. However, existing methods neglect personalized-irrelevant click noise in entire historical clickstreams, which may lead to hallucinated headlines that deviate from genuine user preferences. In this paper, we reveal the detrimental impact of click noise on personalized generation quality through rigorous analysis in both user and news dimensions. Based on these insights, we propose a novel Personalized Headline Generation framework via Denoising Fake Interests from Implicit Feedback (PHG-DIF). PHG-DIF first employs dual-stage filtering to effectively remove clickstream noise, identified by short dwell times and abnormal click bursts, and then leverages multi-level temporal fusion to dynamically model users' evolving and multi-faceted interests for precise profiling. Moreover, we release DT-PENS, a new benchmark dataset comprising the click behavior of 1,000 carefully curated users and nearly 10,000 annotated personalized headlines with historical dwell time annotations. Extensive experiments demonstrate that PHG-DIF substantially mitigates the adverse effects of click noise and significantly improves headline quality, achieving state-of-the-art (SOTA) results on DT-PENS. Our framework implementation and dataset are available at https://github.com/liukejin-up/PHG-DIF.

Explainable transaction risk analysis is a challenge for traditional deep learning models, which only predict suspicious transactions without explanations. Current explainable methods rely on hand-crafted rules and lack the ability to automatically generate language-based explanations. Large Language Models (LLMs) offer promise due to their reasoning and text generation abilities but struggle with domain knowledge and hallucinations, making risk analysis difficult. Specifically, LLMs face: (1) insufficient adaptation to transaction data analysis, and (2) ineffective knowledge retrieval methods that ignore the rich graph structure of transaction data. To address these issues, we propose the Dual Graph Retrieval-Augmented Generation (Dual-gRAG) framework, which utilizes dual retrieval: expert knowledge and reasoning case retrieval. Expert knowledge compensates for domain gaps, while reasoning case retrieval provides step-wise analysis guidance. We incorporate both graph-structured features and semantic features into the retrieval process to enhance the effectiveness of the retrieval. Extensive experiments show that Dual-gRAG improves LLMs' risk analysis capabilities, achieving a 15% increase in different metrics.

Open-Domain Question Answering (ODQA) systems often struggle with the quality of retrieved passages, which may contain conflicting information and be misaligned with the reader’s needs. Existing retrieval methods aim to gather relevant passages but often fail to prioritize consistent and useful information for the reader. In this paper, we introduce a novel Reader-Centered Passage Selection (R-CPS) method, which enhances the performance of the retrieve-then-read pipeline by re-ranking and clustering passages from the reader’s perspective. Our method re-ranks passages based on the reader’s prediction probability distribution and clusters passages according to the predicted answers, prioritizing more useful and relevant passages to the top and reducing inconsistent information. Experiments on ODQA datasets demonstrate the effectiveness of our approach in improving the quality of evidence passages under zero-shot settings.

Tabular data, which accounts for over 80% of enterprise data assets, is vital in various fields. With growing concerns about privacy protection and data-sharing restrictions, generating high-quality synthetic tabular data has become essential. Recent advancements show that large language models (LLMs) can effectively generate realistic tabular data by leveraging semantic information and overcoming the challenges of high-dimensional data that arise from one-hot encoding. However, current methods do not fully utilize the rich information available in tables. To address this, we introduce AI Generative Table based on prompt enhancement, a novel approach that utilizes metadata information, such as table descriptions and schemas, as prompts to generate ultra-high-quality synthetic data. To overcome the token limit constraints of LLMs, we propose long-token partitioning algorithms that enable AIGT to model tables of any scale. AIGT achieves state-of-the-art performance on 14 out of 20 public datasets and two real industry datasets within the Alipay risk control system.

In advertising recommendation systems, click-through rate (CTR) prediction is a critical task. Capturing users' interests from their rich historical behaviors is key to improving prediction results. Although traditional deep learning methods can capture users' interests to some extent, they fail to account for the local and global interests reflected in users' historical behaviors and the dynamic relationships between them. In this paper, we propose a novel architecture - Dual-Interest Adaptive Network (DIAN), which adaptively extracts both local and global interests of users. Specifically, to better explore users' interests in depth, we propose an Adaptive Interest Extraction Block applied to users' historical behavior sequences. By introducing an attention mechanism, this module can flexibly allocate weights to users' local and global interests after decoupling user behaviors. Additionally, to capture complex feature interactions, our model introduces two feature extractors: one combines a Multi-Layer Perceptron (MLP) with a Cross Network for high-order feature extraction, and the other incorporates an Attention Factorization Machine (AFM) for low-order feature extraction. We conducted extensive experiments on the Movielens-1M and Amazon Electronics datasets, validating the effectiveness of DIAN.

Root cause analysis (RCA) is crucial for the stability and reliability of large-scale microservice architectures. Existing multi-source RCA methods primarily rely on logs, traces, and metrics data to detect anomalies and identify abnormal services and root causes. And most multi-source methods focus only on service-level operations and inter-service dependencies, neglecting transactions and their dynamic changes. Furthermore, these approaches typically address only a subset of the RCA tasks, such as anomaly detection, root cause service localization, or root cause type determination. To address these limitations, we propose MicroTR, a transaction reproduction fault diagnosis framework for multi-source RCA in microservice environments. MicroTR deeply analyzes transaction execution logic and service states, utilizing multi-source data to reproduce the dynamic changes of transaction execution states in knowledge graph. This approach enables efficient and synchronized anomaly detection, root cause service localization, and root cause type determination. Experimental evaluations on two widely-adopted open-source microservice platforms demonstrate that MicroTR outperforms state-of-the-art multi-source methods, achieving an average F1 of 97.3% for anomaly detection, an average Hit@l of 90.8% for root cause service localization, and an average Hit@1 of 89.2% for root cause type determination. These results highlight the effectiveness of reproducing transaction execution states for RCA.

This paper proposes a new autoregressive model as an effective and scalable monocular depth estimator. Our idea is simple: We tackle the monocular depth estimation (MDE) task with an autoregressive prediction paradigm, based on two core designs. First, our depth autoregressive model (DAR) treats the depth map of different resolutions as a set of tokens, and conducts the low-to-high resolution autoregressive objective with a patch-wise causal mask. Second, our DAR recursively discretizes the entire depth range into more compact intervals, and attains the coarse-to-fine granularity autoregressive objective in an ordinal-regression manner. By coupling these two autoregressive objectives, our DAR establishes new state-of-the-art (SOTA) on KITTI and NYU Depth v2 by clear margins. Further, our scalable approach allows us to scale the model up to 2.0B and achieve the best RMSE of 1.799 on the KITTI dataset (5% improvement) compared to 1.896 by the current SOTA (Depth Anything). DAR further showcases zero-shot generalization ability on unseen datasets. These results suggest that DAR yields superior performance with an autoregressive prediction paradigm, providing a promising approach to equip modern autoregressive large models (e.g., GPT-4o) with depth estimation capabilities. Project page: https://depth-ar.github.io/.

Large language models (LLMs) have increasingly been explored as powerful text embedders.Existing LLM-based text embedding approaches often leverage the embedding of the final token, typically a reserved special token such as [EOS].However, these tokens have not been intentionally trained to capture the semantics of the whole context, limiting their capacity as text embeddings, especially for retrieval and re-ranking tasks.We propose to add a new training stage before contrastive learning to enrich the semantics of the final token embedding.This stage employs bidirectional generative reconstruction tasks, namely EBQ2D (Embedding-Based Query-to-Document) and EBD2Q (Embedding-Based Document-to-Query), which interleave to anchor the [EOS] embedding and reconstruct either side of Query-Document pairs.Experimental results demonstrate that our additional training stage significantly improves LLM performance on the Massive Text Embedding Benchmark (MTEB), achieving new state-ofthe-art results across different LLM base models and scales. 1

Recent Vision Mamba (Vim) models exhibit nearly linear complexity in sequence length, making them highly attractive for processing visual data. However, the training methodologies and their potential are still not sufficiently explored. In this paper, we investigate strategies for Vim and propose Stochastic Layer-Wise Shuffle (SLWS), a novel regularization method that can effectively improve the Vim training. Without architectural modifications, this approach enables the non-hierarchical Vim to get leading performance on ImageNet-1K compared with the similar type counterparts. Our method operates through four simple steps per layer: probability allocation to assign layer-dependent shuffle rates, operation sampling via Bernoulli trials, sequence shuffling of input tokens, and order restoration of outputs. SLWS distinguishes itself through three principles: \textit{(1) Plug-and-play:} No architectural modifications are needed, and it is deactivated during inference. \textit{(2) Simple but effective:} The four-step process introduces only random permutations and negligible overhead. \textit{(3) Intuitive design:} Shuffling probabilities grow linearly with layer depth, aligning with the hierarchical semantic abstraction in vision models. Our work underscores the importance of tailored training strategies for Vim models and provides a helpful way to explore their scalability. Code and models are available at https://github.com/huangzizheng01/ShuffleMamba

Open World Object Detection (OWOD) requires the detector to continuously identify and learn new categories. Existing methods rely on the large language model (LLM) to describe the visual attributes of known categories and use these attributes to mark potential objects. The performance of such methods is influenced by the accuracy of LLM descriptions, and selecting appropriate attributes during incremental learning remains a challenge. In this paper, we propose a novel OWOD framework, termed OW-VAP, which operates independently of LLM and requires only minimal object descriptions to detect unknown objects. Specifically, we propose a Visual Attribute Parser (VAP) that parses the attributes of visual regions and assesses object potential based on the similarity between these attributes and the object descriptions. To enable the VAP to recognize objects in unlabeled areas, we exploit potential objects within background regions. Finally, we propose Probabilistic Soft Label Assignment (PSLA) to prevent optimization conflicts from misidentifying background as foreground. Comparative results on the OWOD benchmark demonstrate that our approach surpasses existing state-of-the-art methods with a +13 improvement in U-Recall and a +8 increase in U-AP for unknown detection capabilities. Furthermore, OW-VAP approaches the unknown recall upper limit of the detector.

Due to the high similarity between camouflaged instances and the surroundings and the widespread camouflage-like scenarios, the recently proposed camouflaged instance segmentation (CIS) is a challenging and relevant task. Previous approaches achieve some progress on CIS, while many overlook camouflaged objects’ color and contour nature and then decide on each candidate instinctively. In this paper, we contribute a Mixture-of-Queries Transformer (MoQT) in an end-to-end manner for CIS based on two key designs (a Frequency Enhancement Feature Extractor and a Mixture-of-Queries Decoder). First, the Frequency Enhancement Feature Extractor is responsible for capturing the camouflaged clues in the frequency domain. To expose camouflaged instances, the extractor enhances the effectiveness of contour, eliminates the interference color, and obtains suitable features simultaneously. Second, a Mixture-of-Queries Decoder utilizes multiple newly initialized experts of queries (a group of queries considered an expert) in each layer for spotting camouflaged characteristics with cooperation. These experts collaborate to generate outputs with the mixture-of-queries mechanism, refined hierarchically to a fine-grained level for more accurate instance masks. Coupling these two components enables MoQT to use multiple experts to integrate effective clues of camouflaged objects in both spatial and frequency domains. Extensive experimental results demonstrate our MoQT outperforms 19 state-of-the-art CIS approaches on both COD10K and NC4K datasets.

Sequential recommendation (SR) aims to predict the next purchasing item according to users' dynamic preference learned from their historical user-item interactions. To improve the performance of recommendation, learning dynamic heterogeneous cross-type behavior dependencies is indispensable for recommender system. However, there still exists some challenges in Multi-Behavior Sequential Recommendation (MBSR). On the one hand, existing methods only model heterogeneous multi-behavior dependencies at behavior-level or item-level, and modeling interaction-level dependencies is still a challenge. On the other hand, the dynamic multi-grained behavior-aware preference is hard to capture in interaction sequences, which reflects interaction-aware sequential pattern. To tackle these challenges, we propose a Multi-Grained Preference enhanced Transformer framework (M-GPT). First, M-GPT constructs an interaction-level graph of historical cross-typed interactions in a sequence. Then graph convolution is performed to derive interaction-level multi-behavior dependency representation repeatedly, in which the complex correlation between historical cross-typed interactions at specific orders can be well learned. Secondly, a novel multifaceted transformer architecture equipped with multi-grained user preference extraction is proposed to encode the interaction-aware sequential pattern enhanced by capturing temporal behavior-aware multi-grained preference . Experiments on the real-world datasets indicate that our method M-GPT consistently outperforms various state-of-the-art recommendation methods. Our code is available at: https://github.com/hchchchchchchc/MGPT.

The Quadratic Assignment Problem (QAP) has attracted lasting attention for its wide applications and computational challenges. Despite the rich literature in machine learning for QAP, most works often address the problem in the setting of image matching, whereby deep networks could play a vital role in extracting useful features for the subsequent matching. While its power on pure numerical QAP instances is limited in node embedding, often with a vanilla graph neural network. This paper tries to tap the potential of deep nets for QAP, specifically by modifying the input instance, which is orthogonal to previous efforts. Specifically, we develop a bi-level unsupervised framework, where the inner optimization involves trying to solve the modified instance with entropic regularization that can be solved iteratively using the Sinkhorn algorithm without affecting backpropagation by truncating gradients during training. The outer minimization deals with the quadratic objective function of the original QAP. In particular, seeing the intractable scale of the most general form, i.e., Lawler's QAP and the practical utility of the more efficient Koopmans-Beckmann QAP (KBQAP) form for solving other graph and combinatorial problems like TSP and graph edit distance, we embody our network on the KBQAP, and show its strong performance on various benchmarks in our experiments.

Large Language Models (LLMs) hold significant potential for advancing fact-checking by leveraging their capabilities in reasoning, evidence retrieval, and explanation generation. However, existing benchmarks fail to comprehensively evaluate LLMs and Multimodal Large Language Models (MLLMs) in realistic misinformation scenarios. To bridge this gap, we introduce RealFactBench, a comprehensive benchmark designed to assess the fact-checking capabilities of LLMs and MLLMs across diverse real-world tasks, including Knowledge Validation, Rumor Detection, and Event Verification. RealFactBench consists of 6K high-quality claims drawn from authoritative sources, encompassing multimodal content and diverse domains. Our evaluation framework further introduces the Unknown Rate (UnR) metric, enabling a more nuanced assessment of models' ability to handle uncertainty and balance between over-conservatism and over-confidence. Extensive experiments on 7 representative LLMs and 4 MLLMs reveal their limitations in real-world fact-checking and offer valuable insights for further research. RealFactBench is publicly available at https://github.com/kalendsyang/RealFactBench.git.

Progress in image generation raises significant public security concerns. We argue that fake image detection should not operate as a "black box". Instead, an ideal approach must ensure both strong generalization and transparency. Recent progress in Multi-modal Large Language Models (MLLMs) offers new opportunities for reasoning-based AI-generated image detection. In this work, we evaluate the capabilities of MLLMs in comparison to traditional detection methods and human evaluators, highlighting their strengths and limitations. Furthermore, we design six distinct prompts and propose a framework that integrates these prompts to develop a more robust, explainable, and reasoning-driven detection system. The code is available at https://github.com/Gennadiyev/mllm-defake.

With the rapid advancement of AIGC technologies, audio deepfakes have become increasingly realistic, posing serious threats to information security and biometric authentication. Therefore, audio deepfake detection (ADD) has emerged as a critical and fast-evolving research area, particularly requiring superior generalization in out-of-domain scenarios. However, existing ADD methods suffer from constrained generalization and limited access to target data. To address these challenges, we propose Risk-Aware Style Alignment (RASA), a novel generalizable ADD framework that projects the style of any input feature into a shared style space through similarity-based projection. This alignment reduces both inter-domain and intra-source discrepancies without requiring target data during training. In addition, we adopt Structural Empirical Risk Minimization (SERM) in the Poincaré ball model to capture the hierarchical structure of the data and further minimize source risk. By jointly optimizing RASA and SERM, the proposed method effectively tightens the theoretical upper bound of target risk across three key dimensions: source risk, inter-domain divergence, and intra-source discrepancy. Extensive experiments demonstrate that our approach achieves superior generalization and outperforms existing state-of-the-art methods.

In recent years, a large number of on-chain attacks have emerged in the blockchain empowered Web3 ecosystem. In the year of 2023 alone, on-chain attacks have caused losses of over 585 million. Attackers use blockchain transactions to carry out on-chain attacks, for example, exploiting vulnerabilities or business logic flaws in Web3 applications. A wealth of efforts have been devoted to detecting on-chain attack transactions through expert patterns and machine learning techniques. However, in this ever-evolving ecosystem, the performance of current methods is limited in detecting new on-chain attacks, due to the obsoleting of attack recognition patterns or the reliance on on-chain attack samples. In this paper, we propose a universal approach for detecting on-chain attacks even when there are few or even no new on-chain attack samples. Specifically, an in-depth analysis of the transaction characteristics is conducted, and we propose a new insight to train a generic attack transaction detecting model, i.e., transaction reconstruction. Particularly, to overcome the over-fitting in the transaction reconstruction task, we use the web-scale function comments related to transactions as supervision information, rather than expert-confirmed labels. Experimental results demonstrate that the proposed approach surpasses the supervised state-of-the-art by 13% in AUC, with just 30 known on-chain attack samples. Moreover, without any known attack samples, our method can still detect new on-chain attacks in the wild (with a precision of 61.83%). Among attacks detected in the wild, we confirm 1,692 address poisoning attacks, a new type of on-chain attack targeting token holders. Our code is available at: https://github.com/wuzhy1ng/attack_trans_detection_www25.

Pre-trained large-scale vision-language models (VLMs) have acquired profound understanding of general visual concepts. Recent advancements in efficient transfer learning (ETL) have shown remarkable success in fine-tuning VLMs within the scenario of limited data, introducing only a few parameters to harness task-specific insights from VLMs. Despite significant progress, current leading ETL methods tend to overfit the narrow distributions of base classes seen during training and encounter two primary challenges: (i) only utilizing uni-modal information to modeling task-specific knowledge; and (ii) using costly and time-consuming methods to supplement knowledge. To address these issues, we propose a Conditional Prototype Rectification Prompt Learning (CPR) method to correct the bias of the base examples and augment limited data in an effective way. Specifically, we alleviate over-fitting on base classes from two aspects. First, each input image acquires knowledge from both textual and visual prototypes and then generates sample-conditional text tokens. Second, we extract utilizable knowledge from unlabeled data to further refine the prototypes. These two strategies mitigate biases that stem from base classes, yielding a more effective classifier. Extensive experiments on 11 benchmark datasets show that our CPR achieves state-of-the-art performance on few-shot classification, base-to-new generalization, and cross-dataset generalization tasks. Our code is available at https://github.com/chenhaoxing/CPR.

Knowledge Graph Embedding (KGE) has been widely studied as an important semantic enhancement technique that extracts expressive representation from Knowledge Graph (KG) to facilitate various downstream applications, such as knowledge reasoning, Semantic Web, and question answering. The advent of Federated KGE (FKGE) allows for collaborative training across distributed KGs without revealing clients’ private raw KGs that contain sensitive knowledge graph triples. Despite this, FKGE remains susceptible to privacy threats as demonstrated in previously studied federated learning models. However, utilizing and addressing these vulnerabilities remain uninvestigated for FKGE which exhibits unique characteristics distinct from other models. In this work, we conduct the first comprehensive study of the privacy issues in FKGE from both attack and defense perspectives. On the attack side, we introduce five new inference attacks, highlighting the privacy vulnerabilities by successfully deducing the presence of KG triples from the targeted dataset. On the defense side, we present PDP-Flames, a novel differentially private FKGE scheme that leverages the sparse gradient nature of FKGE for better privacy-utility trade-off by integrating advanced private selection techniques. We further introduce a dynamic defense policy based on the observation that the privacy risk fluctuates throughout the training procedure. Additionally, we incorporate a personalized procedure to provide a customized model tailored to the unique data distributions of individual clients. Joint differential privacy is introduced to guarantee the privacy of the personalized models. Comprehensive experiments demonstrate that PDP-Flames effectively mitigates privacy concerns, notably diminishing the attack success rate while maintaining decent model utility.

The Crowd-edge (CE) computing paradigm facilitates the utilization of the computational resources through simultaneously relying the edge computing and the collaboration among various mobile devices (MDs). Most existing works, focusing on offloading tasks from device to edge servers by centralized solutions, are unable to distribute tasks to massive MDs in CE. Meanwhile, designing a decentralized task offloading solution enabling task subscribers to individually make offloading decisions can be challenging given the randomness of crowd resource provisioning and limited knowledge of global status variations. In this paper, we propose a decentralized crowd-edge task offloading solution that enables users to optimally offload tasks to the CE in a distributed manner. Specifically, we formulate the corresponding problem as a stochastic optimization with partially observable status. By observing network and process delays at the crowd side, we further reform the optimization forms and provide a novel approximation policy, enabling users to optimize their offloading strategy based on local observations without interaction with each other. We then solve this task offloading problem by developing a Mixed Multi-Agent Proxy Policy Optimization algorithm (mixed MAPPO). Extensive testing, including numerical and system-level simulations, was conducted to validate the performance of the proposed algorithm in terms of task delay (including the processing delay and transmission delay), load rate, and resource utilization.

Discrete-Time Dynamic Graphs (DTDGs), which are prevalent in real-world implementations and notable for their ease of data acquisition, have garnered considerable attention from both academic researchers and industry practitioners. The representation learning of DTDGs has been extensively applied to model the dynamics of temporally changing entities and their evolving connections. Currently, DTDG representation learning predominantly relies on GNN+RNN architectures, which manifest the inherent limitations of both Graph Neural Networks (GNNs) and Recurrent Neural Networks (RNNs). GNNs suffer from the over-smoothing issue as the models architecture goes deeper, while RNNs struggle to capture long-term dependencies effectively. GNN+RNN architectures also grapple with scaling to large graph sizes and long sequences. Additionally, these methods often compute node representations separately and focus solely on individual node characteristics, thereby overlooking the behavior intersections between the two nodes whose link is being predicted, such as instances where the two nodes appear together in the same context or share common neighbors.

In low-resource Named Entity Recognition (NER) scenarios, only a limited quantity of strongly labeled data is available, while a vast amount of weakly labeled data can be easily acquired through distant supervision. However, weakly labeled data may fail to improve the model performance or even harm it due to the inevitable noise. While training on noisy data, only certain parameters are essential for model learning, termed safe parameters, whereas the other parameters tend to fit noise. In this paper, we propose a noise-robust learning framework where safe parameters can be identified with guidance from the small set of strongly labeled data, and non-safe parameters are suppressed during training on weakly labeled data for better generalization. Our method can effectively mitigate the impact of noise in weakly labeled data, and it can be easily integrated with data level noise-robust learning methods for NER. We conduct extensive experiments on multiple datasets and the results show that our approach outperforms the state-of-the-art methods.

Modeling social media users is the core of social governance in the digital society. Existing works have incorporated different digital traces to better learn the representations of social media users, including text information encoded by pre-trained language models and social network information encoded by graph models. However, limited by overloaded text information and hard-to-collect social network information, they cannot utilize global text information and cannot be generalized without social relationships. In this paper, we propose a Pre-training Architecture for Social Media User Modeling based on Text Graph(PASUM). We aggregate all microblogs to represent social media users based on the text graph model and learn the mapping from microblogs to user representation. We further design inter-user and intra-user contrastive learning tasks to inject general structural information into the mapping. In different scenarios, we can represent users based on text, even without social network information. Experimental results on various downstream tasks demonstrate the effectiveness and superiority of our framework.

Low-Rank Adaptation (LoRA) is a widespread parameter-efficient fine-tuning algorithm for large-scale language models. It has been commonly accepted that LoRA mostly achieves promising results in single-task, low-resource settings, and struggles to handle multi-task instruction tuning scenarios. In this paper, we conduct a systematic study of LoRA on diverse tasks and rich resources with different learning capacities, examining its performance on seen tasks during training and its cross-task generalization on unseen tasks. Our findings challenge the prevalent assumption that the limited learning capacity will inevitably result in performance decline. In fact, our study reveals that when configured with an appropriate rank, LoRA can achieve remarkable performance in high-resource and multi-task scenarios, even comparable to that achieved through full fine-tuning. It turns out that the constrained learning capacity encourages LoRA to prioritize conforming to instruction requirements rather than memorizing specialized features of particular tasks or instances. This study reveals the underlying connection between learning capacity and generalization capabilities for robust parameter-efficient fine-tuning, highlighting a promising direction for the broader application of LoRA across various tasks and settings.

Mobile Crowd Sensing (MCS) represents an emerging paradigm for collecting sensory data, leveraging the extensive sensing capabilities of widely used mobile devices to execute sensing tasks. Among the array of challenges facing current MCS systems, the incentive mechanism for data requesters and participants consistently stands out as a paramount concern. Existing incentive mechanisms often rely on model-based approaches, assuming a certain degree of prior knowledge about the MCS system, such as expected pricing for data requesters and participants. However, these assumptions are impractical in real-world scenarios. To address this challenge, we endeavor to explore a wholly model-free incentive mechanism. Specifically, we propose a Simulated Annealing Deep Q-learning (SADQ-learning) algorithm to dynamically generate the pricing policy for the sensing platform. Furthermore, to accommodate diverse incentive needs, we devise three distinct incentive modes: one focuses on maximizing the profit of the sensing platform, another dedicates to maximizing the successful matching amount of sensing tasks, and an equilibrium mode seeks a balance between the aforementioned objectives. Finally, numerical results demonstrate the superiority of SADQ-learning through comparisons with baseline algorithms.

Prompt learning has emerged as a valuable technique in enhancing vision-language models (VLMs) such as CLIP for downstream tasks in specific domains. Existing work mainly focuses on designing various learning forms of prompts, neglecting the potential of prompts as effective distillers for learning from larger teacher models. In this paper, we introduce an unsupervised domain prompt distillation framework, which aims to transfer the knowledge of a larger teacher model to a lightweight target model through prompt-driven imitation using unlabeled domain images. Specifically, our framework consists of two distinct stages. In the initial stage, we pre-train a large CLIP teacher model using domain (few-shot) labels. After pretraining, we leverage the unique decoupled-modality characteristics of CLIP by pre-computing and storing the text features as class vectors only once through the teacher text encoder. In the subsequent stage, the stored class vectors are shared across teacher and student image encoders for calculating the predicted logits. Further, we align the logits of both the teacher and student models via KL divergence, encouraging the student image encoder to generate similar probability distributions to the teacher through the learnable prompts. The proposed prompt distillation process eliminates the reliance on labeled data, enabling the algorithm to leverage a vast amount of unlabeled images within the domain. Finally, the well-trained student image encoders and pre-stored text features (class vectors) are utilized for inference. To our best knowledge, we are the first to (1) perform unsupervised domain-specific prompt-driven knowledge distillation for CLIP, and (2) establish a practical pre-storing mechanism of text features as shared class vectors between teacher and student. Extensive experiments on 11 datasets demonstrate the effectiveness of our method. Code is publicly available at https://github.com/zhengli97/PromptKD.

Deep learning-based classifiers have been widely used in the field of financial fraud transaction detection. However, training a high-performance classifier for fraud detection is challenging due to the lack of sufficient labeled fraud data. Particularly, it is difficult to detect stealthy fraud transactions that closely mimic benign user behaviors. We observe that the suspicious transactions identified by the online detection system can augment the feature space to improve the detection performance of machine learning-based models. In this paper, we propose a new framework GIANTESS to leverage suspicious transactions to augment the feature space and thus enhance the detection of stealthy fraud transactions. Our semi-supervised approach combines both labeled transactions and unlabeled suspicious transactions to train a detection model. Specifically, it first estimates pseudo labels of suspicious transactions and then combines the pseudo labels with ground truth labels to train the detection model. We conduct experiments on two real-world datasets to demonstrate the effectiveness of our proposed method on detecting stealthy fraud transactions. The experimental results show that GIANTESS successfully improves the recall by up to 6.3% at the fixed low false positive rate of 1%. We also perform a 9-week deployment test of our system in a real-world online payment platform to demonstrate the performance of GIANTESS.

Graph clustering, a fundamental and challenging task in graph mining, aims to classify nodes in a graph into several disjoint clusters. In recent years, graph contrastive learning (GCL) has emerged as a dominant line of research in graph clustering and advances the new state-of-the-art. However, GCL-based methods heavily rely on graph augmentations and contrastive schemes, which may potentially introduce challenges such as semantic drift and scalability issues. Another promising line of research involves the adoption of modularity maximization, a popular and effective measure for community detection, as the guiding principle for clustering tasks. Despite the recent progress, the underlying mechanism of modularity maximization is still not well understood. In this work, we dig into the hidden success of modularity maximization for graph clustering. Our analysis reveals the strong connections between modularity maximization and graph contrastive learning, where positive and negative examples are naturally defined by modularity. In light of our results, we propose a community-aware graph clustering framework, coined øurs, which leverages modularity maximization as a contrastive pretext task to effectively uncover the underlying information of communities in graphs, while avoiding the problem of semantic drift. Extensive experiments on multiple graph datasets verify the effectiveness of øurs in terms of scalability and clustering performance compared to state-of-the-art graph clustering methods. Notably, øurs easily scales a sufficiently large graph with 100M nodes while outperforming strong baselines.

The recent progress in text-to-image models pretrained on large-scale datasets has enabled us to generate various images as long as we provide a text prompt describing what we want. Nevertheless, the availability of these models is still limited when we expect to generate images that fall into a specific domain either hard to describe or just unseen to the models. In this work, we propose DomainGallery, a few-shot domain-driven image generation method which aims at finetuning pretrained Stable Diffusion on few-shot target datasets in an attribute-centric manner. Specifically, DomainGallery features prior attribute erasure, attribute disentanglement, regularization and enhancement. These techniques are tailored to few-shot domain-driven generation in order to solve key issues that previous works have failed to settle. Extensive experiments are given to validate the superior performance of DomainGallery on a variety of domain-driven generation scenarios.

Due to the heterogeneous architectures and class skew, the global representation models training in resource-adaptive federated self-supervised learning face with tricky challenges: $\textit{deviated representation abilities}$ and $\textit{inconsistent representation spaces}$. In this work, we are the first to propose a multi-teacher knowledge distillation framework, namely $\textit{FedMKD}$, to learn global representations with whole class knowledge from heterogeneous clients even under extreme class skew. Firstly, the adaptive knowledge integration mechanism is designed to learn better representations from all heterogeneous models with deviated representation abilities. Then the weighted combination of the self-supervised loss and the distillation loss can support the global model to encode all classes from clients into a unified space. Besides, the global knowledge anchored alignment module can make the local representation spaces close to the global spaces, which further improves the representation abilities of local ones. Finally, extensive experiments conducted on two datasets demonstrate the effectiveness of $\textit{FedMKD}$ which outperforms state-of-the-art baselines 4.78\% under linear evaluation on average.

Graph representation learning (GRL) is critical for extracting insights from complex network structures, but it also raises security concerns due to potential privacy vulnerabilities in these representations. This paper investigates the structural vulnerabilities in graph neural models where sensitive topological information can be inferred through edge reconstruction attacks. Our research primarily addresses the theoretical underpinnings of similarity-based edge reconstruction attacks (SERA), furnishing a non-asymptotic analysis of their reconstruction capacities. Moreover, we present empirical corroboration indicating that such attacks can perfectly reconstruct sparse graphs as graph size increases. Conversely, we establish that sparsity is a critical factor for SERA's effectiveness, as demonstrated through analysis and experiments on (dense) stochastic block models. Finally, we explore the resilience of private graph representations produced via noisy aggregation (NAG) mechanism against SERA. Through theoretical analysis and empirical assessments, we affirm the mitigation of SERA using NAG . In parallel, we also empirically delineate instances wherein SERA demonstrates both efficacy and deficiency in its capacity to function as an instrument for elucidating the trade-off between privacy and utility.

Federated Recommendation (FedRec) is popularly investigated in personalized recommenders for preserving user privacy. However, due to the distributed training paradigm, FedRec is vulnerable to model poisoning attacks. In this paper, we focus on the targeted model poisoning attack against FedRec, which aims at effectively attacking the FedRec via uploading poisoned gradients to raise the exposure ratio of a multi-target item set. Previous attack methods excel with fewer target items but suffer performance decline as the amount of target items increases, which reveals two perennially neglected issues: (i) The simple promotion of prediction scores without considering intrinsic collaborations between users and items is ineffective in multi-target cases. (ii) Target items are heterogeneous, which requires discriminative attacking users and strategies for different targets. To address the issues, we propose a novel Heterogeneous Multi-target Transfer Attack framework named HMTA which consists of two stages, i.e., (1) diverse user agent generation and (2) optimal multi-target transport attack. The former stage leverages collaboration-aware manifold learning to extract latent associations among users and items, and develops a differentiable contrastive sorting to generate user agents from both difficulty and diversity scale. The latter stage conducts poisoning in a fine-grained and distinguishing way, which first completes distribution mapping from target items to generated user agents and then achieves a hybrid multi-target attack. Extensive experiments on benchmark datasets demonstrate the effectiveness of HMTA.

Anti-money laundering (AML) systems play a critical role in safeguarding global economy. As money laundering is considered as one of the top group crimes, there is a crucial need to discover money laundering sub-network behind a particular money laundering transaction for a robust AML system. However, existing rule-based methods for money laundering sub-network discovery is heavily based on domain knowledge and may lag behind the modus operandi of launderers. Therefore, in this work, we first address the money laundering sub-network discovery problem with a neural network based approach, and propose an AML framework AMAP equipped with an adaptive sub-network proposer. In particular, we design an adaptive sub-network proposer guided by a supervised contrastive loss to discriminate money laundering transactions from massive benign transactions. We conduct extensive experiments on real-word datasets in AliPay of Ant Group. The result demonstrates the effectiveness of our AMAP in both money laundering transaction detection and money laundering sub-network discovering. The learned framework which yields money laundering sub-network from massive transaction network leads to a more comprehensive risk coverage and a deeper insight to money laundering strategies.

Most state-of-the-art deep domain adaptation techniques align source and target samples in a global fashion. That is, after alignment, each source sample is expected to become similar to any target sample. However, global alignment may not always be optimal or necessary in practice. For example, consider cross-domain fraud detection, where there are two types of transactions: credit and non-credit. Aligning credit and non-credit transactions separately may yield better performance than global alignment, as credit transactions are unlikely to exhibit patterns similar to non-credit transactions. To enable such fine-grained domain adaption, we propose a novel Knowledge-Inspired Subdomain Adaptation (KISA) framework. In particular, (1) We provide the theoretical insight that KISA minimizes the shared expected loss which is the premise for the success of domain adaptation methods. (2) We propose the knowledge-inspired subdomain division problem that plays a crucial role in fine-grained domain adaption. (3) We design a knowledge fusion network to exploit diverse domain knowledge. Extensive experiments demonstrate that KISA achieves remarkable results on fraud detection and traffic demand prediction tasks.

In many practical binary classification applications, such as financial fraud detection or medical diagnosis, it is crucial to optimize a model's performance on high-confidence samples whose scores are higher than a specific threshold, which is calculated by a given false positive rate according to practical requirements. However, the proportion of high-confidence samples is typically extremely small, especially in long-tailed datasets, which can lead to poor recall results and an alignment bias between realistic goals and loss. To address this challenge, we propose a novel loss reweighting framework called Momentum Threshold-Oriented Loss (MTOL) for binary classification tasks and propose two instantiated losses of it. Given a limited FPR range, MTOL aims to improve the recall of binary classification models at that FPR range by incorporating a batch memory queue and momentum estimation mechanisms. The MTOL adaptively estimates thresholds of FPR during the model training iterations and up-weights the loss of samples in the threshold range, with little consumption of storage and computation. Our experimental results on various datasets, including CIFAR-10, CIFAR-100, Tiny-ImageNet, demonstrate the significant effect of MTOL in improving the recall at low FPR especially in class imbalance settings. These results suggest that MTOL is a promising approach in scenarios where the model's performance in the low FPR range is of utmost importance.

Graph convolutional networks (GCNs) have been shown to be vulnerable to small adversarial perturbations, which becomes a severe threat and largely limits their applications in security-critical scenarios. To mitigate such a threat, considerable research efforts have been devoted to increasing the robustness of GCNs against adversarial attacks. However, current defense approaches are typically designed to prevent GCNs from untargeted adversarial attacks and focus on overall performance, making it challenging to protect important local nodes from more powerful targeted adversarial attacks. Additionally, a trade-off between robustness and performance is often made in existing research. Such limitations highlight the need for developing an effective and efficient approach that can defend local nodes against targeted attacks, without compromising the overall performance of GCNs. In this work, we present a simple yet effective method, named Graph Universal AdveRsarial Defense (GUARD). Unlike previous works, GUARD protects each individual node from attacks with a universal defensive patch, which is generated once and can be applied to any node (node-agnostic) in a graph. GUARD is fast, straightforward to implement without any change to network architecture nor any additional parameters, and is broadly applicable to any GCNs. Extensive experiments on four benchmark datasets demonstrate that GUARD significantly improves robustness for several established GCNs against multiple adversarial attacks and outperforms state-of-the-art defense methods by large margins.

Recent object detection approaches rely on pretrained vision-language models for image-text alignment. However, they fail to detect the Mobile User Interface (MUI) element since it contains additional OCR information, which describes its content and function but is often ignored. In this paper, we develop a new MUI element detection dataset named MUI-zh and propose an Adaptively Prompt Tuning (APT) module to take advantage of discriminating OCR information. APT is a lightweight and effective module to jointly optimize category prompts across different modalities. For every element, APT uniformly encodes its visual features and OCR descriptions to dynamically adjust the representation of frozen category prompts. We evaluate the effectiveness of our plug-and-play APT upon several existing CLIP-based detectors for both standard and open-vocabulary MUI element detection. Extensive experiments show that our method achieves considerable improvements on two datasets. The datasets is available at github.com/antmachineintelligence/MUI-zh.

Deep neural networks are vulnerable to adversarial examples, dictating the imperativeness to test the model’s robustness before deployment. Transfer-based attackers craft adversarial examples against surrogate models and transfer them to victim models deployed in the black-box situation. To enhance the adversarial transferability, structure-based attackers adjust the backpropagation path to avoid the attack from overfitting the surrogate model. However, existing structure-based attackers fail to explore the convolution module in CNNs and modify the backpropagation graph heuristically, leading to limited effectiveness. In this paper, we propose backPropagation pAth Search (PAS), solving the aforementioned two problems. We first propose SkipConv to adjust the backpropagation path of convolution by structural reparameterization. To overcome the drawback of heuristically designed backpropagation paths, we further construct a Directed Acyclic Graph (DAG) search space, utilize one-step approximation for path evaluation and employ Bayesian Optimization to search for the optimal path. We conduct comprehensive experiments in a wide range of transfer settings, showing that PAS improves the attack success rate by a huge margin for both normally trained and defense models.

Recently, collaborative learning is proposed to amortize massive computation costs of highly sophisticated artificial intelligence (AI) tasks. To attract lots of participants, researchers investigate blockchains ‘ economic incentives with proof of useful work (PoUW) consensus protocols to motivate substantial numbers of miners in a mining pool to complete AI tasks. However, participants might be untrusted and defraud rewards with as less as possible efforts. In the paper, we propose a robust and efficient proof of learning scheme called RPoL that enables pool managers to verify the training integrity of pool workers for secure pooled mining. Specifically, we devise an address-encoded model and employ a commitment-based secure sampling method to prevent malicious participants from abusing well-trained models or evading the sampling-based verification. Besides, we optimize RPoL via locality-sensitive hashing (LSH) to achieve communication-efficient verification while tolerating inherent reproduction errors of AI tasks. Furthermore, we conduct theoretical analysis and extensive evaluations. The results demonstrate that RPoL preserves high model performance against adversaries with acceptable costs and thus helps the pool win the mining competition among consensus nodes.

The last years have witnessed the emergence of a promising self-supervised learning strategy, referred to as masked autoencoding. However, there is a lack of theoretical understanding of how masking matters on graph autoencoders (GAEs). In this work, we present masked graph autoencoder (MaskGAE), a self-supervised learning framework for graph-structured data. Different from standard GAEs, MaskGAE adopts masked graph modeling (MGM) as a principled pretext task - masking a portion of edges and attempting to reconstruct the missing part with partially visible, unmasked graph structure. To understand whether MGM can help GAEs learn better representations, we provide both theoretical and empirical evidence to comprehensively justify the benefits of this pretext task. Theoretically, we establish close connections between GAEs and contrastive learning, showing that MGM significantly improves the self-supervised learning scheme of GAEs. Empirically, we conduct extensive experiments on a variety of graph benchmarks, demonstrating the superiority of MaskGAE over several state-of-the-arts on both link prediction and node classification tasks.

Image harmonization is a critical task in computer vision, which aims to adjust the foreground to make it compatible with the background. Recent works mainly focus on using global transformations (i.e., normalization and color curve rendering) to achieve visual consistency. However, these models ignore local visual consistency and their huge model sizes limit their harmonization ability on edge devices. In this paper, we propose a hierarchical dynamic network (HDNet) to adapt features from local to global view for better feature transformation in efficient image harmonization. Inspired by the success of various dynamic models, local dynamic (LD) module and mask-aware global dynamic (MGD) module are proposed in this paper. Specifically, LD matches local representations between the foreground and background regions based on semantic similarities, then adaptively adjust every foreground local representation according to the appearance of its K-nearest neighbor background regions. In this way, LD can produce more realistic images at a more fine-grained level, and simultaneously enjoy the characteristic of semantic alignment. The MGD effectively applies distinct convolution to the foreground and background, learning the representations of foreground and background regions as well as their correlations to the global harmonization, facilitating local visual consistency for the images much more efficiently. Experimental results demonstrate that the proposed HDNet significantly reduces the total model parameters by more than 80% compared to previous methods, while still attaining state-of-the-art performance on the popular iHarmony4 dataset. Additionally, we introduced a lightweight version of HDNet, i.e., HDNet-lite, which has only 0.65MB parameters, yet it still achieve competitive performance. Our code is avaliable at https://github.com/chenhaoxing/HDNet.

Federated learning (FL) is a distributed machine learning paradigm that needs collaboration between a server and a series of clients with decentralized data. To make FL effective in real-world applications, existing work devotes to improving the modeling of decentralized non-IID data. In non-IID settings, there are intra-client inconsistency that comes from the imbalanced data modeling, and inter-client inconsistency among heterogeneous client distributions, which not only hinders sufficient representation of the minority data, but also brings discrepant model deviations. However, previous work overlooks to tackle the above two coupling inconsistencies together. In this work, we propose FedRANE, which consists of two main modules, i.e., local relational augmentation (LRA) and global Nash equilibrium (GNE), to resolve intra-and inter-client inconsistency simultaneously. Specifically, in each client, LRA mines the similarity relations among different data samples and enhances the minority sample representations with their neighbors using attentive message passing. In server, GNE reaches an agreement among inconsistent and discrepant model deviations from clients to server, which encourages the global model to update in the direction of global optimum without breaking down the clients' optimization toward their local optimums. We conduct extensive experiments on four benchmark datasets to show the superiority of FedRANE in enhancing the performance of FL with non-IID data.

Diffusion model based language-guided image editing has achieved great success recently. However, existing state-of-the-art diffusion models struggle with rendering correct text and text style during generation. To tackle this problem, we propose a universal self-supervised text editing diffusion model (DiffUTE), which aims to replace or modify words in the source image with another one while maintaining its realistic appearance. Specifically, we build our model on a diffusion model and carefully modify the network structure to enable the model for drawing multilingual characters with the help of glyph and position information. Moreover, we design a self-supervised learning framework to leverage large amounts of web data to improve the representation ability of the model. Experimental results show that our method achieves an impressive performance and enables controllable editing on in-the-wild images with high fidelity. Our code will be avaliable in \url{https://github.com/chenhaoxing/DiffUTE}.

We present neural frailty machine (NFM), a powerful and flexible neural modeling framework for survival regressions. The NFM framework utilizes the classical idea of multiplicative frailty in survival analysis as a principled way of extending the proportional hazard assumption, at the same time being able to leverage the strong approximation power of neural architectures for handling nonlinear covariate dependence. Two concrete models are derived under the framework that extends neural proportional hazard models and nonparametric hazard regression models. Both models allow efficient training under the likelihood objective. Theoretically, for both proposed models, we establish statistical guarantees of neural function approximation with respect to nonparametric components via characterizing their rate of convergence. Empirically, we provide synthetic experiments that verify our theoretical statements. We also conduct experimental evaluations over $6$ benchmark datasets of different scales, showing that the proposed NFM models achieve predictive performance comparable to or sometimes surpassing state-of-the-art survival models. Our code is publicly availabel at https://github.com/Rorschach1989/nfm

Knowledge Graph Embedding (KGE) is a fundamental technique that extracts expressive representation from knowledge graph (KG) to facilitate diverse downstream tasks. The emerging federated KGE (FKGE) collaboratively trains from distributed KGs held among clients while avoiding exchanging clients’ sensitive raw KGs, which can still suffer from privacy threats as evidenced in other federated model trainings (e.g., neural networks). However, quantifying and defending against such privacy threats remain unexplored for FKGE which possesses unique properties not shared by previously studied models. In this paper, we conduct the first holistic study of the privacy threat on FKGE from both attack and defense perspectives. For the attack, we quantify the privacy threat by proposing three new inference attacks, which reveal substantial privacy risk by successfully inferring the existence of the KG triple from victim clients. For the defense, we propose DP-Flames, a novel differentially private FKGE with private selection, which offers a better privacy-utility tradeoff by exploiting the entity-binding sparse gradient property of FKGE and comes with a tight privacy accountant by incorporating the state-of-the-art private selection technique. We further propose an adaptive privacy budget allocation policy to dynamically adjust defense magnitude across the training procedure. Comprehensive evaluations demonstrate that the proposed defense can successfully mitigate the privacy threat by effectively reducing the success rate of inference attacks from to on average with only a modest utility decrease.

In the era of deep learning, it is critical to protect the intellectual property of high-performance deep neural network (DNN) models. Existing proposals, however, are subject to adversarial ownership forgery (e.g., methods based on watermarks or fingerprints) or require full access to the original training dataset for ownership verification (e.g., methods requiring the replay of the learning process). In this paper, we propose a novel Provenance of Training (PoT) scheme, the first empirical study towards verifying DNN model ownership without accessing any original dataset while being robust against existing attacks. At its core, PoT relies on a coherent model chain built from the intermediate checkpoints saved during model training to serve as the ownership certificate. Through an in-depth analysis of model training, we propose six key properties that a legitimate model chain shall naturally hold. In contrast, it is difficult for the adversary to forge a model chain that satisfies these properties simultaneously without performing actual training. We systematically analyze PoT’s robustness against various possible attacks, including the adaptive attacks that are designed given the full knowledge of PoT’s design, and further perform extensive empirical experiments to demonstrate our security analysis.

Data augmentation has been widely studied as it can be used to improve the generalizability of graph representation learning models. However, existing works focus only on the data augmentation on homogeneous graphs. Data augmentation for heterogeneous graphs remains under-explored. Considering that heterogeneous graphs contain different types of nodes and links, ignoring the type information and directly applying the data augmentation methods of homogeneous graphs to heterogeneous graphs will lead to suboptimal results. In this paper, we propose a novel Multi-Aspect Heterogeneous Graph Augmentation framework named MAHGA. Specifically, MAHGA consists of two core augmentation strategies: structure-level augmentation and metapath-level augmentation. Structure-level augmentation pays attention to network schema aspect and designs a relation-aware conditional variational auto-encoder that can generate synthetic features of neighbors to augment the nodes and the node types with scarce links. Metapath-level augmentation concentrates on metapath aspect, which constructs metapath reachable graphs for different metapaths and estimates the graphons of them. By sampling and mixing up based on the graphons, MAHGA yields intra-metapath and inter-metapath augmentation. Finally, we conduct extensive experiments on multiple benchmarks to validate the effectiveness of MAHGA. Experimental results demonstrate that our method improves the performances across a set of heterogeneous graph learning models and datasets.

Recently, various multimodal networks for Visually-Rich Document Understanding(VRDU) have been proposed, showing the promotion of transformers by integrating visual and layout information with the text embeddings. However, most existing approaches utilize the position embeddings to incorporate the sequence information, neglecting the noisy improper reading order obtained by OCR tools. In this paper, we propose a robust layout-aware multimodal network named XYLayoutLM to capture and leverage rich layout information from proper reading orders produced by our Augmented XY Cut. Moreover, a Dilated Conditional Position Encoding module is proposed to deal with the input sequence of variable lengths, and it additionally extracts local layout information from both textual and vi-sual modalities while generating position embeddings. Experiment results show that our XYLayoutLM achieves competitive results on document understanding tasks.

In recent years, knowledge graph (KG) has obtained many achievements in both research and industrial fields. However, most KG algorithms consider node embedding with only structure and node features, but not relation features. In this paper, we propose a novel Heterogeneous Attention (HAT) algorithm and use both node-based and path-based attention mechanisms to learn various types of nodes and edges on the KG. To better capture representations, multi-hop relation features are involved to generate edge embeddings and help the model obtain more semantic information. To capture a more complex representation, we design different encoder parameters for different types of nodes and edges in HAT. Extensive experiments validate that our HAT significantly outperforms the state-of-the-art methods on both the public datasets and a large-scale real-world fintech dataset1.

The application of graph representation learning techniques to the area of financial risk management (FRM) has attracted significant attention recently. However, directly modeling transaction networks using graph neural models remains challenging: Firstly, transaction networks are directed multigraphs by nature, which could not be properly handled with most of the current off-the-shelf graph neural networks (GNN). Secondly, a crucial problem in FRM scenarios like anti-money laundering (AML) is to identify risky transactions and is most naturally cast into an edge classification problem with rich edgelevel features, which are not fully exploited by the prevailing GNN design that follows node-centric message passing protocols. In this paper, we present a systematic investigation of design aspects of neural models over directed multigraphs and develop a novel GNN protocol that overcomes the above challenges via efficiently incorporating directional information, as well as proposing an enhancement that targets edge-related tasks using a novel message passing scheme over an extension of edge-to-node dual graph. A concrete GNN architecture called GRANDE is derived using the proposed protocol, with several further improvements and generalizations to temporal dynamic graphs. We apply the GRANDE model to both a real-world anti-money laundering task and public datasets. Experimental evaluations show the superiority of the proposed GRANDE architecture over recent state-of-the-art models on dynamic graph modeling and directed graph modeling.

Deep sequence networks such as multi-head self-attention networks provide a promising way to extract effective representations from raw sequence data in an end-to-end fashion and have shown great success in various domains such as natural language processing, computer vision, $etc$. However, in domains such as financial risk management and anti-fraud where expert-derived features are heavily relied on, deep sequence models struggle to dominate the game.In this paper, we introduce a simple framework called symbolic testing to verify the learnability of certain expert-derived features over sequence data. A systematic investigation over simulated data reveals the fact that the self-attention architecture fails to learn some standard symbolic expressions like the count distinct operation. To overcome this deficiency, we propose a novel architecture named SHORING, which contains two components:event network andsequence network. Theevent network efficiently learns arbitrary high-orderevent-level conditional embeddings via a reparameterization trick while thesequence network integrates domain-specific aggregations into the sequence-level representation, thereby providing richer inductive biases compare to standard sequence architectures like self-attention. We conduct comprehensive experiments and ablation studies on synthetic datasets that mimic sequence data commonly seen in anti-fraud domain and three real-world datasets. The results show that SHORING learns commonly used symbolic features well, and experimentally outperforms the state-of-the-art methods by a significant margin over real-world online transaction datasets. The symbolic testing framework and SHORING have been applied in anti-fraud model development at Alipay and improved performance of models for real-time fraud-detection.

Based on the significant improvement of model robustness by AT (Adversarial Training), various variants have been proposed to further boost the performance. Well-recognized methods have focused on different components of AT (e.g., designing loss functions and leveraging additional unlabeled data). It is generally accepted that stronger perturbations yield more robust models.However, how to generate stronger perturbations efficiently is still missed. In this paper, we propose an efficient automated attacker called A2 to boost AT by generating the optimal perturbations on-the-fly during training. A2 is a parameterized automated attacker to search in the attacker space for the best attacker against the defense model and examples. Extensive experiments across different datasets demonstrate that A2 generates stronger perturbations with low extra cost and reliably improves the robustness of various AT methods against different attacks.

Existing system dealing with online complaint provides a final decision without explanations. We propose to analyse the complaint text of internet fraud in a fine-grained manner. Considering the complaint text includes multiple clauses with various functions, we propose to identify the role of each clause and classify them into different types of fraud element. We construct a large labeled dataset originated from a real finance service platform. We build an element identification model on top of BERT and propose additional two modules to utilize the context of complaint text for better element label classification, namely, global context encoder and label refiner. Experimental results show the effectiveness of our model.

Typically, learning a deep classifier from massive cleanly annotated instances is effective but impractical in many real-world scenarios. An alternative is collecting and aggregating multiple noisy annotations for each instance to train the classifier. Inspired by that, this paper proposes to learn deep classifier from multiple noisy annotators via a coupled-view learning approach, where the learning view from data is represented by deep neural networks for data classification and the learning view from labels is described by a Naive Bayes classifier for label aggregation. Such coupled-view learning is converted to a supervised learning problem under the mutual supervision of the aggregated and predicted labels, and can be solved via alternate optimization to update labels and refine the classifiers. To alleviate the propagation of incorrect labels, small-loss metric is proposed to select reliable instances in both views. A co-teaching strategy with class-weighted loss is further leveraged in the deep classifier learning, which uses two networks with different learning abilities to teach each other, and the diverse errors introduced by noisy labels can be filtered out by peer networks. By these strategies, our approach can finally learn a robust data classifier which less overfits to label noise. Experimental results on synthetic and real data demonstrate the effectiveness and robustness of the proposed approach.

Positive and Unlabeled learning (PU learning) aims to build a binary classifier where only positive and unlabeled data are available for classifier training. However, existing PU learning methods all work on a batch learning mode, which cannot deal with the online learning scenarios with sequential data. Therefore, this paper proposes a novel positive and unlabeled learning algorithm in an online training mode, which trains a classifier solely on the positive and unlabeled data arriving in a sequential order. Specifically, we adopt an unbiased estimate for the loss induced by the arriving positive or unlabeled examples at each time. Then we show that for any coming new single datum, the model can be updated independently and incrementally by gradient based online learning method. Furthermore, we extend our method to tackle the cases when more than one example is received at each time. Theoretically, we show that the proposed online PU learning method achieves low regret even though it receives sequential positive and unlabeled data. Empirically, we conduct intensive experiments on both benchmark and real-world datasets, and the results clearly demonstrate the effectiveness of the proposed method.

Community detection is an important task with many applications. However, there is no universal definition of communities, and a variety of algorithms have been proposed based on different assumptions. In this paper, we instead study the semi-supervised community detection problem where we are given several communities in a network as training data and aim to discover more communities. This setting makes it possible to learn concepts of communities from data without any prior knowledge. We propose the Seed Expansion with generative Adversarial Learning (SEAL), a framework for learning heuristics for community detection. SEAL contains a generative adversarial network, where the discriminator predicts whether a community is real or fake, and the generator generates communities that cheat the discriminator by implicitly fitting characteristics of real ones. The generator is a graph neural network specialized in sequential decision processes and gets trained by policy gradient. Moreover, a locator is proposed to avoid well-known free-rider effects by forming a dual learning task with the generator. Last but not least, a seed selector is utilized to provide promising seeds to the generator. We evaluate SEAL on 5 real-world networks and prove its effectiveness.

A metascalable (or ldquodesign once, scale on new architecturesrdquo) parallel computing framework has been developed for large spatiotemporal-scale atomistic simulations of materials based on spatiotemporal data locality principles, which is expected to scale on emerging multipetaflops architectures. The framework consists of: (1) an embedded divide-and-conquer (EDC) algorithmic framework based on spatial locality to design linear-scaling algorithms for high complexity problems; (2) a space-time-ensemble parallel (STEP) approach based on temporal locality to predict long-time dynamics, while introducing multiple parallelization axes; and (3) a tunable hierarchical cellular decomposition (HCD) parallelization framework to map these O(N) algorithms onto a multicore cluster based on hybrid implementation combining message passing and critical section-free multithreading. The EDC-STEP-HCD framework exposes maximal concurrency and data locality, thereby achieving: (1) inter-node parallel efficiency well over 0.95 for 218 billion-atom molecular-dynamics and 1.68 trillion electronic-degrees-of-freedom quantum-mechanical simulations on 212,992 IBM BlueGene/L processors (superscalability); (2) high intra-node, multithreading parallel efficiency (nanoscalability); and (3) nearly perfect time/ensemble parallel efficiency (eon-scalability). The spatiotemporal scale covered by MD simulation on a sustained petaflops computer per day (i.e. petaflopsmiddotday of computing) is estimated as NT = 2.14 (e.g. N = 2.14 million atoms for T = 1 microseconds).