Singapore

Early warning of intraoperative adverse events plays a vital role in reducing surgical risk and improving patient safety. While deep learning has shown promise in predicting the single adverse event, several key challenges remain: overlooking adverse event dependencies, underutilizing heterogeneous clinical data, and suffering from the class imbalance inherent in medical datasets. To address these issues, we construct the first multi-label adverse events dataset (MuAE) for intraoperative adverse events prediction, covering six critical events. Next, we propose a novel Transformer-based multi-label learning framework (IAENet) that combines an improved Time-Aware Feature-wise Linear Modulation (TAFiLM) module for static covariates and dynamic variables robust fusion and complex temporal dependencies modeling. Furthermore, we introduce a label-constrained reweighting loss (LCRLoss) with co-occurrence regularization to effectively mitigate intra-event imbalance and enforce structured consistency among frequently co-occurring events. Extensive experiments demonstrate that IAENet consistently outperforms strong baselines on 5, 10, and 15-minute early warning tasks, achieving improvements of +5.05\%, +2.82\%, and +7.57\% on average F1-score. These results highlight the potential of IAENet for supporting intelligent intraoperative decision-making in clinical practice.

AAAI 2026

Early Warning of Intraoperative Adverse Events via Transformer-Driven Multi-Label Learning

ml: deep learning algorithms

ml: multi-class/multi-label learning & extreme classification

ml: time-series/data streams

The International Conference on Computational Processing of Portuguese (PROPOR) is the main event in the area of human language processing that is focused on theoretical and technological issues of written and spoken Portuguese and Galician. The meeting has been a very rich forum for the exchange of ideas and partnerships for the research and industry communities dedicated to the automated processing of Portuguese, promoting the development of methodologies, resources and projects that can be shared among researchers and practitioners in the field.

Find more information and pay the registration fee on [event organizer’s website](https://sites.universidadedefortaleza.com/propor2022/?page_id=401)

PROPOR 2022

The International Conference on Computational Processing of Portuguese (PROPOR) is the main event in the area of human language processing that is focused on theoretical and technological issues of written and spoken Portuguese and Galician.

poster

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-26 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.<br><br>

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We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

Learning procedural-aware video representations is a key step towards building agents that can reason about and execute complex tasks. Existing methods typically address this problem by aligning visual content with textual descriptions at the task and step levels to inject procedural semantics into video representations. However, due to their high level of abstraction, "task" and "step" descriptions fail to form a robust alignment with the concrete, observable details in visual data. To address this, we introduce "states", i.e., textual snapshots of object configurations, as a visually-grounded semantic layer that anchors abstract procedures to what a model can actually see. We formalize this insight in a novel Task-Step-State (TSS) framework, where tasks are achieved via steps that drive transitions between observable states. To enforce this structure, we propose a progressive pre-training strategy that unfolds the TSS hierarchy, forcing the model to first ground representations in states before associating them with steps and, ultimately, high-level tasks.
Extensive experiments on the COIN and CrossTask datasets show that our method outperforms baseline models on multiple downstream tasks, including task recognition, step recognition, and next step prediction. Ablation studies show that introducing state supervision is a key driver of performance gains across all tasks. Additionally, our progressive pretraining strategy proves more effective than standard joint training, as it better enforces the intended hierarchical structure.

Learning Procedural-Aware Video Representations Through State-Grounded Hierarchy Unfolding

Sparse Inertial Measurement Units (IMUs) based human motion capture has gained significant momentum, driven by the adaptation of fundamental AI tools such as recurrent neural networks (RNNs) and transformers that are tailored for temporal and spatial modeling. Despite these achievements, current research predominantly focuses on pipeline and architectural designs, with comparatively little attention given to regularization methods, highlighting a critical gap in developing a comprehensive AI toolkit for this task. To bridge this gap, we propose {\it motion label smoothing}, a novel method that adapts the classic label smoothing strategy from classification to the sparse IMU-based motion capture task. Specifically, we first demonstrate that a naive adaptation of label smoothing, including simply blending a uniform vector or a ``uniform'' motion representation (e.g., dataset-average motion or a canonical T-pose), is suboptimal; and argue that a proper adaptation requires increasing the {\it entropy} of the smoothed labels. Second, we conduct a thorough analysis of human motion labels, identifying three critical properties: 1) Temporal Smoothness, 2) Joint Correlation, and 3) Low-Frequency Dominance, and show that conventional approaches to entropy enhancement (e.g., blending Gaussian noise) are ineffective as they disrupt these properties. Finally, we propose the blend of a novel skeleton-based Perlin noise for motion label smoothing, designed to raise label entropy while satisfying motion properties. Extensive experiments applying our motion label smoothing to three state-of-the-art methods across four real-world IMU datasets demonstrate its effectiveness and robust generalization (plug-and-play) capability.

Improving Sparse IMU-based Motion Capture with Motion Label Smoothing

Modern oriented object detectors typically predict a set of bounding boxes and select the top-ranked ones based on estimated localization quality. Achieving high detection performance requires that the estimated quality closely aligns with the actual localization accuracy. To this end, existing approaches predict the Intersection over Union (IoU) between the predicted and ground-truth (GT) boxes as a proxy for localization quality. However, box-level IoU prediction suffers from a structural coupling issue: since the predicted box is derived from the detector’s internal estimation of the GT box, the predicted IoU—based on their similarity—can be overestimated for poorly localized boxes. To overcome this limitation, we propose a novel Pixel-level Quality Assessment (PQA) framework, which replaces box-level IoU prediction with the integration of pixel-level spatial consistency. PQA measures the alignment between each pixel’s relative position to the predicted box and its corresponding position to the GT box. By operating at the pixel level, PQA avoids directly comparing the predicted box with the estimated GT box, thereby eliminating the inherent similarity bias in box-level IoU prediction. Furthermore, we introduce a new integration metric that aggregates pixel-level spatial consistency into a unified quality score, yielding a more accurate approximation of the actual localization quality. Extensive experiments on HRSC2016 and DOTA demonstrate that PQA can be seamlessly integrated into various oriented object detectors, consistently improving performance (e.g., +5.96% AP$_{50:95}$ on Rotated RetinaNet and +2.32% on STD).

Pixel-level Quality Assessment for Oriented Object Detection

Numerous studies have demonstrated that Visual Question Answering (VQA) models are vulnerable to language priors and dataset biases, often leading to spurious correlations between questions and answers. As a result, these models excessively rely on linguistic cues, neglecting essential visual information and causing representational distortions. To address this challenge, we propose a novel Bayesian debiasing framework termed BayesVQA, which integrates three carefully designed mechanisms: Energy-guided Prior Variance (EPV), Energy-guided Posterior Sampling (EPS), and Energy-guided Likelihood Reweighting (ELR). Specifically, we explicitly decompose each sample's latent representation into a biased feature and a stochastic corrective perturbation δ. Using a Bayesian formulation, we model the posterior distribution of the perturbation δ conditioned on the predictive uncertainty, quantified via calibrated energy scores. To mitigate language bias, the posterior is optimized through energy-driven variational inference with an uncertainty-adaptive prior and sampling strategy. Moreover, the ELR mechanism incorporates an energy-based weighting of the reconstruction objective and enforces an energy-coherence constraint to emphasize challenging, high-uncertainty instances and align model confidence before and after debiasing. 
Extensive experiments conducted across multiple standard VQA benchmarks consistently validate the superior performance of our BayesVQA method over state-of-the-art competitors under distributional shifts and challenging bias conditions.
The source code is provided in the Supplementary Material.

BayesVQA: Energy-Guided Bayesian Debiasing for Language-Bias-Robust Visual Question Answering

As a typical information medium, images are widely utilized across various application scenarios. Measuring image quality accurately is meaningful for the subsequent usability of images. However, significant variations exist in image types and distortion types in different application scenarios. And, acquiring labeled images for each specific scenario is time-consuming and labor-intensive. Consequently, designing cross-domain image quality assessment (IQA) that generalizes across different scenarios remains a substantial challenge. Existing cross-domain IQA methods primarily focus on content relevance while neglecting distortion differences between the source and target domains, leading to limited applicability while distortion fluctuates. To address these limitations, a Graph-Driven Domain Co-adaptation framework for cross-domain IQA (GDCIQA) is proposed. Firstly, a graph knowledge sharing (GKS) module that constructs graphs based on inter-domain distortion relevance has been proposed. GKS employs graph neural networks to update quality-aware features in the source domain by leveraging target-domain representations. Secondly, the proposed co-adaptation learning (CAL) mechanism can enable joint optimization of different modules, which ensures comprehensive sharing of quality-aware and distortion-related information. Finally, a domain adaptation framework that enables effective training on labeled source images to gain target-domain-optimized IQA models has been designed. Experimental results demonstrate that GDCIQA achieves enhanced accuracy and stability in cross-domain scenarios. The proposed GKS and CAL can benefit for advancing cross-domain IQA research.

Graph-Driven Domain Co-Adaptation for Cross-Domain Image Quality Assessment

Hashing techniques are widely adopted in large-scale cross-modal retrieval due to their efficiency and low storage cost. However, semantic ambiguities, including polysemy, multi-object images, and missing semantic descriptions, significantly degrade the accuracy of alignment and retrieval performance. Most existing methods rely on one-to-one mappings that preserve only global average semantics, which fail to capture the intrinsic polysemous structures embedded within individual samples. To address this issue, we propose a novel Deep Polysemic Semantic Instance Hashing (DPSIH) method and design a Diverse Semantic Instance Embedding Module (DSIE). This module integrates local and global features through multi-head self-attention and residual learning, generating multiple diverse embeddings per sample to effectively capture fine-grained and polysemous semantic structures. Furthermore, we design a multi-embedding semantic correlation constraint that relaxes strict alignment restrictions to improve robustness under partial alignment, and introduce Maximum Mean Discrepancy (MMD) regularization to alleviate cross-modal distribution shifts. Additionally, an embedding diversity mechanism is proposed to prevent all embeddings from collapsing into a central or averaged representation, thereby enhancing semantic diversity. Extensive experiments on four benchmark datasets demonstrate that DPSIH significantly outperforms state-of-the-art methods and effectively improves the modeling of semantic ambiguity in cross-modal retrieval tasks.

Polysemic Semantic Instance Network for Cross-Modal Hashing

The rapid advancement of Large Language Models (LLMs) has driven significant progress in Natural Language Interface to Database (NLIDB). However, the widespread adoption of LLMs has raised critical privacy and security concerns. During interactions, LLMs may unintentionally expose confidential database contents or be manipulated by attackers to exfiltrate data through seemingly benign queries. While current efforts typically rely on rule-based heuristics or LLM agents to mitigate this leakage risk, these methods still struggle with complex inference-based attacks, suffer from high false positive rates, and often compromise the reliability of SQL queries. To address these challenges, we propose \textsc{SafeNlidb}, a novel privacy-security alignment framework for LLM-based NLIDB. The framework features an automated pipeline that generates hybrid chain-of-thought interaction data from scratch, seamlessly combining implicit security reasoning with SQL generation. Additionally, we introduce reasoning warm-up and alternating preference optimization to overcome the multi-preference oscillations of Direct Preference Optimization (DPO), enabling LLMs to produce security-aware SQL through fine-grained reasoning without the need for human-annotated preference data. Extensive experiments demonstrate that our method outperforms both larger-scale LLMs and ideal-setting baselines, achieving significant security improvements while preserving high utility. WARNING: This work may contain content that is offensive and harmful!

SafeNLIDB: A Privacy-Preserving Safety Alignment Framework for LLM-based Natural Language Database Interfaces

Person search is a challenging computer vision task that aims to simultaneously detect and re-identify individuals from uncropped gallery images. However, most existing approaches are limited by restricted receptive fields, leading to distorted local feature representations under occlusions or complex poses. Additionally, scale variations hinder model generalization in real-world scenarios. To address these limitations, we introduce a novel E-Bike Rider Search (EBRS) dataset, which comprises 27,501 images capturing 963 distinct IDs across 8 camera views at a large urban intersection in a Chinese city. Furthermore, we propose a Context-aware Dynamic Contrastive Learning (CDCL) framework that dynamically adjusts convolutional weights and performs hard sample mining based on contextual cues, thereby improving discriminative capability for both local details and global features. Extensive experiments show our method achieves state-of-the-art performance on CUHK-SYSU and PRW benchmarks, with competitive results on the challenging EBRS dataset, demonstrating its effectiveness.

Context-aware Dynamic Contrastive Learning Network and E-Bike Rider Benchmark for Person Search

AIGC-based image editing technology has greatly simplified the realistic-level image modification, causing serious potential risks of image forgery. This paper introduces a new approach to tampering detection using the Segment Anything Model (SAM). Instead of training SAM to identify tampered areas, we propose a novel strategy. The entire image is transformed into a blank canvas from the perspective of neural models. Any modifications to this blank canvas would be noticeable to the models. To achieve this idea, we introduce adversarial perturbations to prevent SAM from ``seeing anything'', allowing it to identify forged regions when the image is tampered with. Due to SAM's powerful perceiving capabilities, naive adversarial attacks cannot completely tame SAM. To thoroughly deceive SAM and make it blind to the image, we introduce a frequency-aware optimization strategy, which further enhances the capability of tamper localization. Extensive experimental results demonstrate the effectiveness of our method.

Creating Blank Canvas Against AI-enabled Image Forgery

Multivariate time series forecasting is essential in domains such as finance, transportation, climate, and energy. However, existing patch-based methods typically adopt fixed-length segmentation, overlooking the heterogeneity of local temporal dynamics and the decoding heterogeneity of forecasting. Such designs lose details in information-dense regions, introduce redundancy in stable segments, and fail to capture the distinct complexities of short-term and long-term horizons. We propose TimeMosaic, a forecasting framework that aims to address temporal heterogeneity. TimeMosaic employs adaptive patch embedding to dynamically adjust granularity according to local information density, balancing motif reuse with structural clarity while preserving temporal continuity. In addition, it introduces segment-wise decoding that treats each prediction horizon as a related subtask and adapts to horizon-specific difficulty and information requirements, rather than applying a single uniform decoder. Extensive evaluations on benchmark datasets demonstrate that TimeMosaic delivers consistent improvements over existing methods, and our model trained on the large-scale corpus with 321 billion observations achieves performance competitive with state-of-the-art TSFMs.

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