Multilingual Visual Document Retrieval (VDR) aims to retrieve relevant document screenshots given a textual query across different languages. Recent late-interaction VDR models estimate relevance via token-level maximum similarity aggregation (MaxSim), but this heuristic can introduce semantic bias in multilingual settings. In particular, linguistic differences such as morphological variation, functional word distribution, and tokenization granularity can lead to heterogeneous query token representations. When aggregated under MaxSim, such heterogeneity may result in disproportionate token contributions and inconsistent relevance estimation across languages. To address these limitations, we propose Language-Vision-Aware Late Interaction (LVALI), a lightweight calibration framework without modifying the original embeddings. LVALI introduces two complementary mechanisms: (1) a language-aware token weighting scheme that calibrates token-level contributions at the vocabulary level, and (2) a visual alignment stability-based correction term that downweights low-confidence token-patch matches according to the dispersion of similarity distributions in the document image space. This design preserves the underlying similarity structure of MaxSim while improving robustness to cross-lingual variability. Experiments on multiple multilingual VDR benchmarks demonstrate consistent improvements in robustness and cross-lingual consistency, achieved with only lightweight token-level calibration training.

Multimodal large language models (MLLMs) demonstrate strong vision–language understanding, yet their safety enforcement often lags behind their reasoning capability. Existing multimodal jailbreak methods, such as HADES and FigStep, primarily rely on visual obfuscation or OCR-based injection, and often show limited robustness across models and safety policies. We identify a deeper vulnerability, termed structure-induced safety gaps, in which models retain semantic comprehension but safety enforcement weakens when generation is guided by structured layouts. By embedding harmful cues into tabular templates and framing tasks as benign completion operations, structured reasoning can weaken refusal triggering and enable harmful outputs even when the underlying content is recognized. Building on this insight, we propose SISG, a layout-driven jailbreak framework that exploits structured multimodal reasoning. Experiments on two multimodal safety benchmarks and six advanced MLLMs, including GPT-4o, show that SISG consistently outperforms strong baselines in attack success while maintaining semantic plausibility. Further behavioral analysis reveals reduced refusal responses and elevated harmful scores in failed cases, indicating systematic weaknesses in current safety pipelines. These findings highlight the need for structure-aware safety alignment in multimodal reasoning systems.

Wei Tang, Yixin Cao, Yang Deng, Jiahao Ying, Bo Wang, Yizhe Yang, Yuyue Zhao, Qi Zhang, Xuanjing Huang, Yu-Gang Jiang, Yong Liao. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Federated Learning (FL) allows several clients to cooperatively train machine learning models without disclosing the raw data. In practical applications, asynchronous FL (AFL) can address the straggler effect compared to synchronous FL. However, Non-Iid data and stale models pose significant challenges to AFL, as they can diminish the practicality of the global model and even lead to training failures. In this work, we propose a novel AFL framework called Federated Historical Learning (FedHist), which effectively addresses the challenges posed by both Non-Iid data and gradient staleness based on the concept of knowledge rumination. FedHist enhances the stability of local gradients by performing weighted fusion with historical global gradients cached on the server. Relying on hindsight, it assigns aggregation weights to each participant in a multi-dimensional manner during each communication round. To further enhance the efficiency and stability of the training process, we introduce an intelligent ℓ2-norm amplification scheme, which dynamically regulates the learning progress based on the ℓ2-norms of the submitted gradients. Extensive experiments indicate FedHist outperforms state-of-the-art methods in terms of convergence performance and test accuracy.

The increasing realism of AI-generated videos has raised potential security concerns, making it difficult for humans to distinguish them from the naked eye. Despite these concerns, limited research has been dedicated to detecting such videos effectively. To this end, we propose an open-source AI-generated video detection dataset. Our dataset spans diverse objects, scenes, behaviors, and actions by organizing input prompts into independent dimensions. It also includes various generation models with different generative models, featuring popular commercial models such as OpenAI’s Sora, Google’s Veo, and Kwai’s Kling. Furthermore, we propose a simple yet effective Detection model based on Concistency of Frame (DeCoF), which learns robust temporal artifacts across different generation methods. Extensive experiments demonstrate the generality and efficacy of the proposed DeCoF in detecting AI-generated videos, including those from nowadays’ mainstream commercial generators.

Secure aggregation of user update vectors (e.g. gradients) has become a critical issue in the field of federated learning. Many Secure Aggregation Protocols (SAPs) face exorbitant computation costs, severely constraining their applicability. Given the observation that a considerable portion of SAP's computation burden stems from processing each entry in the private vectors, we propose Partial Vector Freezing (PVF), a portable module for compressing computation costs without introducing additional communication overhead.$\lambda$-SecAgg, which integrates SAP with PVF, “freezes” a substantial portion of the private vector through specific transformations, requiring only$\frac{1}{\lambda}$of the original vector to participate in SAP. Eventually, users can “thaw” the public sum of the “frozen entries” by the result of SAP. To avoid potential privacy leakage, we devise Disrupting Variables Extension for PVF. We demonstrate that PVF can seamlessly integrate with various SAPs and it poses no threat to user privacy in the semi-honest and active adversary settings. We include 7 baselines, encompassing 5 distinct types of masking schemes, and explore the acceleration effects of PVF on these SAPs. Empirical investigations indicate that when$\lambda=100$, PVF yields up to$99.5 \times$speedup and up to$32.3 \times$communication reduction.

Detecting social media bots is essential for maintaining the security and trustworthiness of social networks. While contemporary graph-based detection methods demonstrate promising results, their practical application is limited by label reliance and poor generalization capability across diverse communities. Generative Graph Self-Supervised Learning (GSL) presents a promising paradigm to overcome these limitations, yet existing approaches predominantly follow the homophily assumption and fail to capture the global patterns in the graph, which potentially diminishes their effectiveness when facing the challenges of interaction camouflage and distributed deployment in bot detection scenarios. To this end, we propose BotHP, a generative GSL framework tailored to boost graph-based bot detectors through heterophily-aware representation learning and prototype-guided cluster discovery. Specifically, BotHP leverages a dual-encoder architecture, consisting of a graph-aware encoder to capture node commonality and a graph-agnostic encoder to preserve node uniqueness. This enables the simultaneous modeling of both homophily and heterophily, effectively countering the interaction camouflage issue. Additionally, BotHP incorporates a prototype-guided cluster discovery pretext task to model the latent global consistency of bot clusters and identify spatially dispersed yet semantically aligned bot collectives. Extensive experiments on two real-world bot detection benchmarks demonstrate that BotHP consistently boosts graph-based bot detectors, improving detection performance, alleviating label reliance, and enhancing generalization capability.

Multi-view clustering has garnered increasing research attention due to its capacity to learn common semantics across views for enhanced performance. Numerous methods have been proposed to effectively learn multi-view common semantics, among which deep learning-based approaches have gradually become mainstream owing to their superior representation capabilities. However, most existing methods only fuse multi-view information at the final clustering stage, causing underutilization of multi-view data during training. To address this limitation, we propose a novel framework that treats fused multi-view features as an additional view during model training (FADE). Our autoencoder-based method first integrates deep features from multiple views to generate new representations, which are incorporated as the (V+1)-th view. Through dedicated MLPs, we extract high-level features and cluster assignments from all views. To learn common semantics, contrastive learning is first conducted between the (V+1)-th view and other views, followed by comprehensive cross-view contrastive learning to enforce multi-view consistency. Finally, we fine-tune the model by jointly optimizing high-level semantic features and cluster assignments. Comparative experiments demonstrate that our method effectively extracts common information while mitigating the impact of view-private information, outperforming multiple approaches.

Recently, federated multi-view clustering has gained attention as an effective approach for exploring the clustering structure of multi-view/multi-modal data distributed across multiple clients. However, most existing methods primarily focus on sample-related client scenarios with limited research on sample-unrelated settings. These scenarios face severe data heterogeneity, while missing data further reduces available knowledge, making the problem more challenging. To address these challenges, we propose FedHMAI, a novel horizontal federated incomplete multi-view clustering method. Specifically, on the server side, we perform heterogeneity matching by leveraging Maximum Mean Discrepancy (MMD) to identify and distribute to each client the features with the least heterogeneity. Then on the client side, local cluster structure learning is conducted to alleviate data heterogeneity and reinforce cluster discriminability. Furthermore, we design an attention-based dual-level imputation mechanism that jointly leverages data- and feature-level imputation, along with matching features, to ensure imputation accuracy and consistency. Extensive experimental results validate that FedHMAI effectively overcomes the challenges of incomplete multi-view data in horizontal federated settings, consistently achieving superior clustering performance.

Detecting deepfakes has been an increasingly important topic, especially given the rapid development of AI generation techniques. In this paper, we ask: How can we build a universal detection framework that is effective for most facial deepfakes? One significant challenge is the wide variety of deepfake generators available, resulting in varying forgery artifacts (e.g., lighting inconsistency, color mismatch, etc). But should we ``teach" the detector to learn all these artifacts separately? It is impossible and impractical to elaborate on them all. So the core idea is to pinpoint the more common and general artifacts across different deepfakes. Accordingly, we categorize deepfake artifacts into two distinct yet complementary types: Face Inconsistency Artifacts (FIA) and Up-Sampling Artifacts (USA). FIA arise from the challenge of generating all intricate details, inevitably causing inconsistencies between the complex facial features and relatively uniform surrounding areas. USA, on the other hand, are the inevitable traces left by the generator's decoder during the up-sampling process. This categorization stems from the observation that all existing deepfakes typically exhibit one or both of these artifacts. To achieve this, we propose a new data-level pseudo-fake creation framework that constructs fake samples with only the FIA and USA, without introducing extra less-general artifacts. Specifically, we employ a super-resolution to simulate the USA, while utilise image-level self-blending on diverse facial regions to create the FIA. We surprisingly found that, with this intuitive design, a standard image classifier trained only with our pseudo-fake data can non-trivially generalize well to previously unseen deepfakes.

Multimodal large language models (MLLMs) have shown remarkable capability in vision–language understanding but remain vulnerable to jailbreak attacks that bypass safety alignment. Existing methods often rely on structured input manipulations such as cross-modal signal shifting, adversarial visual injection, or semantic rephrasing, which compromise naturalness and degrade under stronger defenses. To address this, we propose Consistent Cross-Modal Backgrounds Attack (CCB-A), a jailbreak strategy that embeds benign and semantically aligned background cues across both image and text modalities. Leveraging such consistency, CCB-A dilutes safety-sensitive signals and subtly redirects model attention away from harmful content while maintaining semantic coherence. We evaluate CCB-A on six representative MLLMs, including both commercial and open-source systems, across MM-SafetyBench and HADES. Experiments show that CCB-A consistently surpasses strong baselines in harmfulness and attack success rate. Ablation and defense analyses further reveal that cross-modal semantic consistency is the key driver of its effectiveness, while current defense mechanisms remain insufficiently robust, suggesting that exploiting consistency across modalities constitutes an effective yet previously overlooked jailbreak strategy that exposes a critical vulnerability in current alignment techniques.

Under stringent privacy constraints, whether federated recommendation systems can achieve group fairness remains an inadequately explored question. Taking gender fairness as a representative issue, we identify three phenomena in federated recommendation systems: performance difference, data imbalance, and preference disparity. We discover that the state-of-the-art methods only focus on the first phenomenon. Consequently, their imposition of inappropriate fairness constraints detrimentally affects the model training. Moreover, due to insufficient sensitive attribute protection of existing works, we can infer the gender of all users with 99.90% accuracy even with the addition of maximal noise. In this work, we propose Privacy-Preserving Orthogonal Aggregation (PPOA), which employs the secure aggregation scheme and quantization technique, to prevent the suppression of minority groups by the majority and preserve the distinct preferences for better group fairness. PPOA can assist different groups in obtaining their respective model aggregation results through a designed orthogonal mapping while keeping their attributes private. Experimental results on three real-world datasets demonstrate that PPOA enhances recommendation effectiveness for both females and males by up to 8.25% and 6.36%, respectively, with a maximum overall improvement of 7.30%, and achieves optimal fairness in most cases. Extensive ablation experiments and visualizations indicate that PPOA successfully maintains preferences for different gender groups.

Cooperative multiagent reinforcement learning (CMARL) strategies are well known to be vulnerable to adversarial perturbations. Previous works on adversarial attacks have primarily focused on glass-box attacks that directly perturb the states or actions of victim agents, often in scenarios with a limited number of attacks. However, gaining complete access to victim agents in real-world environments is exceedingly difficult. To create more realistic adversarial attacks, we introduce a novel method that involves injecting traitor agents into the CMARL system. We model this problem as a traitor Markov decision process (TMDP), where traitors cannot directly attack the victim agents but can influence their formation or positioning through collisions. In TMDP, traitors are trained using the same MARL algorithm as the victim agents, with their reward function set as the negative of the victim agents' reward. Despite this, the training efficiency for traitors remains low because it is challenging for them to directly associate their actions with the victim agents' rewards. To address this issue, we propose the curiosity-driven adversarial attack (CuDA2) framework. CuDA2 enhances the efficiency and aggressiveness of attacks on the specified victim agents' policies while maintaining the optimal policy invariance of the traitors. Specifically, we employ a pretrained random network distillation module, where the extra reward generated by the RND module encourages traitors to explore states unencountered by the victim agents. Extensive experiments on various scenarios from SMAC demonstrate that our CuDA2 framework offers comparable or superior adversarial attack capabilities compared to other baselines.

In many RFID-enabled applications, objects are classified into different categories, and the information associated with each object's category (called category information) is written into the attached tag, allowing the reader to access it later. The category information sampling in such RFID systems, which is to randomly choose (sample) a few tags from each category and collect their category information, is fundamental for providing real-time monitoring and analysis in RFID. However, to the best of our knowledge, two technical challenges, i.e., how to guarantee a minimized execution time and reduce collection failure caused by missing tags, remain unsolved for this problem. In this paper, we address these two limitations by considering how to use the shortest possible time to sample a different number of random tags from each category and collect their category information sequentially in small batches. In particular, we first obtain a lower bound on the execution time of any protocol that can solve this problem. Subsequently, we present a near-OPTimalCategory information sampling protocol (OPT-C) that solves the problem with an execution time close to the lower bound. Finally, extensive simulation results demonstrate the superiority of OPT-C over existing protocols, while real-world experiments further validate its practicality.

Neural Radiance Field (NeRF) has been proposed as an innovative advancement in 3D reconstruction techniques. However, little research has been conducted on the issues of information confidentiality and security to NeRF, such as steganography. Existing NeRF steganography solutions have shortcomings in low steganography quality, model weight damage, and limited amount of steganographic information. This paper proposes Noise-NeRF, a novel NeRF steganography method employing Adaptive Pixel Selection strategy and Pixel Perturbation strategy to improve the quality and efficiency of steganography via trainable noise. Extensive experiments validate the state-of-the-art performances of Noise-NeRF on both steganography quality and rendering quality, as well as effectiveness in super-resolution image steganography.

Albeit presenting great capabilities for graph node representations, Graph neural networks (GNNs) face biases and discrimination issues. Existing solutions mostly focus on one aspect of fairness, failing to thoroughly consider the multiple dimensions of fairness. To break this limitation, we propose a novel fairness-aware framework, FacGNN, which establishes connections between group fairness, counterfactual fairness, and stability fairness for the first time. Specifically, FacGNN enhances both group fairness and counterfactual fairness while extending stability metrics beyond prior works. FacGNN employs a phased nested iterative training process with self-supervised contrastive learning, to model fairness relationships and improve discriminatory detection in adversarial frameworks. Experimental results show that FacGNN outperforms previous methods across group fairness, counterfactual fairness, and model stability while maintaining model performance.

Recent advancements in social bot detection have been driven by the adoption of Graph Neural Networks. The social graph, constructed from social network interactions, contains benign and bot accounts that influence each other. However, previous graph-based detection methods that follow the transductive message-passing paradigm may not fully utilize hidden graph information and are vulnerable to adversarial bot behavior. The indiscriminate message passing between nodes from different categories and communities results in excessively homogeneous node representations, ultimately reducing the effectiveness of social bot detectors. In this paper, we propose \SEBot, a novel multi-view graph-based contrastive learning-enabled social bot detector. In particular, we use structural entropy as an uncertainty metric to optimize the entire graph's structure and subgraph-level granularity, revealing the implicitly existing hierarchical community structure. And we design an encoder to enable message passing beyond the homophily assumption, enhancing robustness to adversarial behaviors of social bots. Finally, we employ multi-view contrastive learning to maximize mutual information between different views and enhance the detection performance through multi-task learning. Experimental results demonstrate that our approach significantly improves the performance of social bot detection compared with SOTA methods.

Multi-view clustering has proven to be highly effective in exploring consistency information across multiple views/modalities when dealing with large-scale unlabeled data. However, in the real world, multi-view data is often distributed across multiple entities, and due to privacy concerns, federated multi-view clustering solutions have emerged. Existing federated multi-view clustering algorithms often result in misalignment in feature representations among clients, difficulty in integrating information across multiple views, and poor performance in heterogeneous scenarios. To address these challenges, we propose HFMVC, a heterogeneity-aware federated deep multi-view clustering method. Specifically, HFMVC adaptively perceives the degree of heterogeneity in the environment and employs contrastive learning to explore consistency and complementarity information across clients' multi-view data. Besides, we seek consensus among clients where local data originates from the same view, incorporating a contrastive loss between local models and the global model during local training to adjust consistency among local models. Furthermore, we elucidate the sample representation logic for local clustering in different heterogeneous environments, identifying the degree of heterogeneity by computing the within-cluster sum of squares (WCSS) and the average inter-cluster distance (AICD). Extensive experiments verify the superior performance of HFMVC across both IID and Non-IID settings.

Heterogeneous Federated Learning (HFL) is a challenging task that allows clients with different model structures to engage in collaborative training. In real-world scenarios, due to the difficulty of accurate annotation, clients may have different proportions of noisy labels. Incorrect labels can lead to performance degradation of models in FL. In this paper, we formulate a more realistic federated learning problem with noisy labels where client data, client model and the noisy labels are heterogeneous. Furthermore, we propose a multi-stage framework, FedUIG, designed to identify clean clients and quantify the importance of clients. In particular, it contains three modules: (1) Local Robust Learning Module: we apply a noise-tolerant loss function based on semi-supervised training. (2) User Importance Guidance Module: FedUIG identifies clean clients based on the independently measured training loss reduction rate for each client, and then FedUIG re-weights noisy clients by measuring prediction difference between noisy and clean clients on public dataset. (3) Robust Knowledge Distillation Module: heterogeneous clients engage in collaborative training through knowledge distillation based on public dataset. Extensive experiments conducted on the widely-used CIFAR-IO and ANIMAL-ION datasets, with varying noise levels, demonstrate that FedUIG is robust to noisy datasets and outperforms state-of-the-art methods across multiple noise levels.

Privacy protection in recommendation systems is gaining increasing attention, for which federated learning has emerged as a promising solution. Current federated recommendation systems grapple with high communication overhead due to sharing dense global embeddings, and also poorly reflect user preferences due to data heterogeneity. To overcome these challenges, we propose a two-stage Federated Low-rank Coordinated Adaptation (FedLoCA) framework to decouple global and client-specific knowledge into low-rank embeddings, which significantly reduces communication overhead while enhancing the system’s ability to capture individual user preferences amidst data heterogeneity. Further, to tackle gradient estimation inaccuracies stemming from data sparsity in federated recommendation systems, we introduce an adversarial gradient projected descent approach in low-rank spaces, which significantly boosts model performance while maintaining robustness. Remarkably, FedLoCA also alleviates performance loss even under the stringent constraints of differential privacy. Extensive experiments on various real-world datasets demonstrate that FedLoCA significantly outperforms existing methods in both recommendation accuracy and communication efficiency.

The real-world recommender system needs to be regularly retrained to keep with the new data. In this work, we consider how to efficiently retrain graph convolution network (GCN)-based recommender models that are state-of-the-art techniques for the collaborative recommendation. To pursue high efficiency, we set the target as using only new data for model updating, meanwhile not sacrificing the recommendation accuracy compared with full model retraining. This is nontrivial to achieve since the interaction data participates in both the graph structure for model construction and the loss function for model learning, whereas the old graph structure is not allowed to use in model updating. Toward the goal, we propose a causal incremental graph convolution (IGC) approach, which consists of two new operators named IGC and colliding effect distillation (CED) to estimate the output of full graph convolution. In particular, we devise simple and effective modules for IGC to ingeniously combine the old representations and the incremental graph and effectively fuse the long- and short-term preference signals. CED aims to avoid the out-of-date issue of inactive nodes that are not in the incremental graph, which connects the new data with inactive nodes through causal inference. In particular, CED estimates the causal effect of new data on the representation of inactive nodes through the control of their collider. Extensive experiments on three real-world datasets demonstrate both accuracy gains and significant speed-ups over the existing retraining mechanism.

We study the out-of-distribution generalization of active learning that adaptively selects samples for annotation in learning the decision boundary of classification.Our empirical study finds that increasingly annotating seen samples may hardly benefit the generalization.To address the problem, we propose Counterfactual Active Learning (CounterAL) that empowers active learning with counterfactual thinking to bridge the seen samples with unseen cases.In addition to annotating factual samples, Coun-terAL requires annotators to answer counterfactual questions to construct counterfactual samples for training.To achieve CounterAL, we design a new acquisition strategy that selects the informative factual-counterfactual pairs for annotation; and a new training strategy that pushes the model update to focus on the discrepancy between factual and counterfactual samples.We evaluate CounterAL on multiple public datasets of sentiment analysis and natural language inference.The experiment results show that CounterAL requires fewer acquisition rounds and outperforms existing active learning methods by a large margin in OOD tests with comparable IID performance.* Wenjie Wang is the corresponding author.This work is

ChatGPT shows the enormous potential of large language models (LLMs). These models can easily reach the size of billions of parameters and create training difficulties for the majority. We propose a paradigm to train LLMs using distributed in-network computation on routers. Our preliminary result shows that our design allows LLMs to be trained at a reasonable learning rate without demanding extensive GPU resources.

The knowledge graphs have found widespread use in numerous areas. However, as its applications expand, privacy concerns have been raised due to its ability to reveal links between entities in the real world. This brief work introduces an innovative approach to address the concern by considering the hierarchical structure of the knowledge graph to safeguard the privacy of information presented in knowledge graphs.

This study aims to establish a comprehensive knowledge graph that spans domains and networks, with a specific focus on legal cases and their applications. The proposed methodology enables efficient collection and storage of large volumes of structured, semi-structured, and unstructured data related to cases from various sources including organizations, the government, and the internet. To analyze the relationships between roles in cases, a multimodal model is proposed to process and collect data for domain-specific knowledge graphs. Furthermore, to support social governance and public safety, a knowledge-driven intelligent recommendation algorithm is proposed in the form of question-answering, providing multiple strategies such as causal analysis, similar case matching and pre-disaster response. This work contributes to the field of artificial intelligence and natural language processing, with potential applications in legal and governmental domains, as well as in disaster response and prevention.

The rapidly expanding neural network models are becoming increasingly challenging to run on a single device. Hence, model parallelism over multiple devices is critical to guaranteeing the efficiency of training large models. Recent proposals either have long processing time or poor performance. Therefore, we propose Mercury, a fast framework for optimizing device placement for large models. Mercury employs a simple but efficient model parallelization strategy in the baseline measurement, and generates placement policies through a series of scheduling algorithms. We conduct experiments to deploy and evaluate Mercury on numerous large models. The results show that Mercury not only reduces the placement policy generation time by 26.4% but also improves the model throughput by 218.5% compared to the most advanced methods.

Social bot detection is of paramount importance to the resilience and security of online social platforms. The state-of-the-art detection models are siloed and have largely overlooked a variety of data characteristics from multiple cross-lingual platforms. Meanwhile, the heterogeneity of data distribution and model architecture make it intricate to devise an efficient cross-platform and cross-model detection framework. In this paper, we propose FedACK, a new federated adversarial contrastive knowledge distillation framework for social bot detection. We devise a GAN-based federated knowledge distillation mechanism for efficiently transferring knowledge of data distribution among clients. In particular, a global generator is used to extract the knowledge of global data distribution and distill it into each client’s local model. We leverage local discriminator to enable customized model design and use local generator for data enhancement with hard-to-decide samples. Local training is conducted as multi-stage adversarial and contrastive learning to enable consistent feature spaces among clients and to constrain the optimization direction of local models, reducing the divergences between local and global models. Experiments demonstrate that FedACK outperforms the state-of-the-art approaches in terms of accuracy, communication efficiency, and feature space consistency.

Relational triple extraction is challenging for its difficulty in capturing rich correlations between entities and relations.Existing works suffer from 1) heterogeneous representations of entities and relations, and 2) heterogeneous modeling of entity-entity interactions and entity-relation interactions.Therefore, the rich correlations are not fully exploited by existing works.In this paper, we propose UniRel to address these challenges.Specifically, we unify the representations of entities and relations by jointly encoding them within a concatenated natural language sequence, and unify the modeling of interactions with a proposed Interaction Map, which is built upon the off-the-shelf self-attention mechanism within any Transformer block.With comprehensive experiments on two popular relational triple extraction datasets, we demonstrate that UniRel is more effective and computationally efficient.The source code is available at https://github.com/wtangdev/UniRel.

It has been shown that named entity recognition (NER) could benefit from incorporating the long-distance structured information captured by dependency trees.However, dependency trees built by tools usually have a certain percentage of errors.Under such circumstances, how to better use relevant structured information while ignoring irrelevant or wrong structured information from the dependency trees to improve NER performance is still a challenging research problem.In this paper, we propose the Attention and Edge-Label guided Graph Convolution Network (AELGCN) model.Then, we integrate it into BiLSTM-CRF to form BiLSTM-AELGCN-CRF model.We design an edge-aware node joint update module and introduce a node-aware edge update module to explore hidden structured information entirely and solve the wrong dependency label information to some extent.After two modules, we apply attention-guided GCN, which automatically learns how to attend to the relevant structured information selectively.We conduct extensive experiments on several standard datasets across four languages and achieve better results than previous approaches.Through experimental analysis, it is found that our proposed model can better exploit the structured information on the dependency tree to improve the recognition of long entities.

Recommender systems should answer the intervention question "if recommending an item to a user, what would the feedback be", calling for estimating the causal effect of a recommendation on user feedback. Generally, this requires blocking the effect of confounders that simultaneously affect the recommendation and feedback. To mitigate the confounding bias, a strategy is incorporating propensity into model learning. However, existing methods forgo possible unmeasured confounders (e.g., user financial status), which can result in biased propensities and hurt recommendation performance. This work combats the risk of unmeasured confounders in recommender systems.

Most recommender systems evaluate model performance offline through either: 1) normal biased test on factual interactions; or 2) debiased test with records from the randomized controlled trial. In fact, both tests only reflect part of the whole picture: factual interactions are collected from the recommendation policy, fitting them better implies benefiting the platform with higher click or conversion rate; in contrast, debiased test eliminates system-induced biases and thus is more reflective of user true preference. Nevertheless, we find that existing models exhibit trade-off on the two tests, and there lacks methods that perform well on both tests.

The fast pace of global urbanization is drastically changing the population distributions over the world, which leads to significant changes in geographical population densities. Such changes in turn alter the underlying geographical power demand over time, and drive power substations to become over-supplied (demand z capacity) or under-supplied (demand ≈ capacity). In this paper, we make the first attempt to investigate the problem of power substation-user assignment by analyzing large-scale power grid data. We develop a Scalable Power User Assignment (SPUA) framework, that takes large-scale spatial power user/substation distribution data and temporal user power consumption data as input, and control the assignments between users and substations, in a manner that minimizes the maximum substation utilization among all substations. To evaluate the performance of our SPUA framework, we conduct evaluations on real power consumption data and user/substation location data collected from a northwestern province in China for 35 days in 2015. The evaluation results demonstrate that our SPUA framework can achieve a 20-65 percent reduction on the maximum substation utilization, and 2 to 3.7 times reduction on total transmission loss over other baseline methods.

The fast pace of global urbanization is drastically changing the population distributions over the world, which leads to significant changes in geographical population densities. Such changes in turn alter the underlying geographical power demand over time, and drive power substations to become over-supplied (demand << capacity) or under-supplied (demand ≈ capacity). In this paper, we make the first attempt to investigate the problem of power substation-user assignment by analyzing large-scale power grid data. We develop a Scalable Power User Assignment (SPUA) framework, that takes large-scale spatial power user/substation distribution data and temporal user power consumption data as input, and assigns users to substations, in a manner that minimizes the maximum substation utilization among all substations. To evaluate the performance of our SPUA framework, we conduct evaluations on real power consumption data and user/substation location data collected from a province in China for 35 days in 2015. The evaluation results demonstrate that our SPUA framework can achieve a 20%--65% reduction on the maximum substation utilization, and 2 to 3.7 times reduction on total transmission loss over other baseline methods.

Cloud service providers are building out geographically distributed networks of datacenters around the world. It is customary for cloud service providers to distribute their data replicas at multiple geographic locations to mitigate user latency and to increase service availability. In this paper, we treat the content distributed from one datacenter to multiple datacenters as a multicast session. We investigate the problem of maximizing the capacity utilization of inter-datacenter networks while maintaining fairness among multiple multicast sessions. A bandwidth allocation algorithm based on max-min fairness is developed, named Blossom. Blossom leverages a fully polynomial time approximation scheme to accelerate the bandwidth allocation, while achieving an approximation that is (1- ϵ)-optimal. Through trace-driven simulation, we show that our approach is substantially more efficient than prior work.