Singapore

With the rapid growth of the Internet of Things (IoT), integrating artificial intelligence (AI) on extremely weak embedded devices has garnered significant attention, enabling improved real-time performance and enhanced data privacy.
However, the resource limitations of such devices and unreliable network conditions necessitate error-resilient device-edge collaboration systems.
Traditional approaches often focus on bit-level transmission correctness, which can be inefficient under dynamic channel conditions. 
In contrast, we propose SemanticNN, a semantic codec that tolerates bit-level errors in pursuit of semantic-level correctness, enabling compressive and resilient collaborative inference offloading under strict computational and communication constraints.
It incorporates a Bit Error Rate (BER)-aware decoder %to sense the dynamically changing BERs that adapts to dynamic channel conditions and a Soft Quantization (SQ)-based encoder to learn compact representations.
Building on this architecture, we introduce Feature-augmentation Learning, a novel training strategy that enhances offloading efficiency.
Furthermore, to address the mismatch between encoder and decoder capabilities caused by asymmetric computational resources,
we propose eXplainable AI (XAI)-based Asymmetry Compensation, which improves semantic fidelity during decoding.
We conduct extensive experiments on STM32 using three models and six public datasets across image classification and object detection tasks.
Experimental results demonstrate that, under varying transmission error rates, SemanticNN significantly reduces feature transmission volume by 56.82–344.83× while maintaining superior inference accuracy. Code is available at https://anonymous.4open.science/r/semanticnn/.

AAAI 2026

SemanticNN: Compressive and Error-Resilient Semantic Offloading for Extremely Weak Devices

ml: distributed machine learning & federated learning

ml: learning on the edge & model compression

cv: applications

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
For the first time, ICPR has employed a two-round review process similar to journal submissions, with new papers allowed to be submitted in either the first or second round and papers submitted in the first round and not accepted allowed to be revised and re-submitted for second round review. 

In this edition of the **ICPR 2020** Conference, there are 1407 Oral/Poster presentations organized in five Tracks: 
- Artificial Intelligence, Machine Learning for Pattern Analysis; 
- Biometrics, Human Analysis and Behavior Understanding; Computer Vision, Robotics and Intelligent Systems; 
- Document and Media Analysis; and 
- Image and Signal Processing. 

The **ICPR 2020** Main Conference program also includes 7 Keynotes from prominent world-renowned scientists, 8 Tutorials, 28 Demos, 8 Challenges, 2 Panels, as well as an Industry/Sponsor Exhibition. In addition, 41 Workshops on a range of important current and emerging research topics complete the ICPR 2020 program. 


ICPR 2020

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.

Electromyography (EMG)-based gesture recognition has emerged as a promising approach for human-computer interaction. However, its performance is often limited by the scarcity of labeled EMG data, significant cross-user variability, and poor generalization to unseen gestures. To address these challenges, we propose SeqEMG-GAN, a conditional, sequence-driven generative framework that synthesizes high-fidelity EMG signals from hand joint angle sequences. Our method introduces a context-aware architecture composed of an angle encoder, a dual-layer context encoder featuring the novel Ang2Gist unit, a deep convolutional EMG generator, and a discriminator, all jointly optimized via adversarial learning. By conditioning on joint kinematic trajectories, SeqEMG-GAN is capable of generating semantically consistent EMG sequences, even for previously unseen gestures, thereby enhancing data diversity and physiological plausibility. Experimental results show that classifiers trained solely on synthetic data experience only a slight accuracy drop (from 57.77% to 55.71%). In contrast, training with a combination of real and synthetic data significantly improves accuracy to 60.53%, outperforming real-only training by 2.76%. These findings demonstrate the effectiveness of our framework, also achieves the state-of-art performance in augmenting EMG datasets and enhancing gesture recognition performance for applications such as neural robotic hand control, AI/AR glasses, and gesture-based virtual gaming systems.

New Synthetic Goldmine: Hand Joint Angle-Driven EMG Data Generation Framework for Micro-Gesture Recognition

Graphical User Interface (GUI) task automation constitutes a critical frontier in artificial intelligence research. While effective GUI agents synergistically integrate planning and grounding capabilities, current methodologies exhibit two fundamental limitations: (1) insufficient exploitation of cross-model synergies, and (2) over-reliance on synthetic data generation without sufficient utilization. To address these challenges, we propose Co-EPG, a self-iterative training framework for Co-Evolution of Planning and Grounding. Co-EPG establishes an iterative positive feedback loop: through this loop, the planning model explores superior strategies under grounding-based reward guidance via Group Relative Policy Optimization (GRPO), generating diverse data to optimize the grounding model. Concurrently, the optimized Grounding model provides more effective rewards for subsequent GRPO training of the planning model, fostering continuous improvement. Co-EPG thus enables iterative enhancement of agent capabilities through self-play optimization and training data distillation. On the Multimodal-Mind2Web and AndroidControl benchmarks, our framework outperforms existing state-of-the-art methods after just three iterations without requiring external data. The agent consistently improves with each iteration, demonstrating robust self-enhancement capabilities. This work establishes a novel training paradigm for GUI agents, shifting from isolated optimization to an integrated, self-driven co-evolution approach.

Co-EPG: A Framework for Co-Evolution of Planning and Grounding in Autonomous GUI Agents

In this paper, we explore the transferability of SSL by addressing two central questions: (i) what is the representation transferability of SSL, and (ii) how can we effectively model this transferability? Transferability is defined as the ability of a representation learned from one task to support the objective of another. Inspired by the meta-learning paradigm, we construct multiple SSL tasks within each training batch to support explicitly modeling transferability. Based on empirical evidence and causal analysis, we find that although introducing task-level information improves transferability, it is still hindered by task conflict. To address this issue, we propose a Task Conflict Calibration (TC$^2$) method to alleviate the impact of task conflict. Specifically, it first splits batches to create multiple SSL tasks, infusing task-level information. Next, it uses a factor extraction network to produce causal generative factors for all tasks and a weight extraction network to assign dedicated weights to each sample, employing data reconstruction, orthogonality, and sparsity to ensure effectiveness. Finally, TC$^2$
calibrates sample representations during SSL training and integrates into the pipeline via a two-stage bi-level optimization framework to boost the transferability of learned representations. Experimental results on multiple downstream tasks demonstrate that our method consistently improves the transferability of SSL models.

Exploring Transferability of Self-Supervised Learning by Task Conflict Calibration

Knowledge distillation from Artificial Neural Networks (ANNs) to Spiking Neural Networks (SNNs) is a prominent training paradigm. However, its efficacy is fundamentally limited by a spectral mismatch: SNNs, with their intrinsic low-pass filtering characteristics, struggle to learn high-frequency details from their ANN teachers, creating a bottleneck in knowledge transfer at both the feature and logit levels. To address this, we propose Bi-Spectrum Distillation (BSD), a novel framework that mitigates the mismatch from two complementary perspectives. First, at the feature level, our Spectral Residual Distillation (SRD) enhances the student SNN's features with a parameter-efficient, learnable filter that adaptively compensates for high-frequency information loss, which transforms the student's output to better match the teacher's rich spectral target. Second, at the logits level, our Spectral Semantic Distillation (SSD) enhances fine-grained classification by distilling high-frequency components from teacher-ordered logits. Extensive experiments on CIFAR-10/100, ImageNet, and CIFAR10-DVS demonstrate that BSD achieves new state-of-the-art performance across both CNN and Transformer-based SNNs, validating its effectiveness and broad applicability.

Bi-Spectrum Distillation: Addressing Spectral Mismatch in ANN-SNN Knowledge Transfer

Effective agent coordination is crucial in cooperative Multiagent Reinforcement Learning (MARL). While recent advances have significantly improved cooperation by modeling agent interactions through various graph structures, most existing approaches primarily focus on homogeneous agents. Despite the ubiquity of heterogeneous agents, constructing a comprehensive graph that captures their diverse attributes and relationships from scratch is notoriously labor-intensive for both humans and agents, which makes policy learning extremely challenging. To tackle this difficulty, we propose a novel method that utilizes a fuzzy human attention-guided graph to model inter-agent relationships. Instead of learning the graph entirely from scratch, we incorporate abstract human attention, with its uncertainty captured through fuzzy logic, to guide the graph development process. To further accommodate the varying attributes and objectives of heterogeneous agents while maintaining their learning capabilities, the attention-guided graph is fine-tuned through a hyper-network. Our proposed approach is end-to-end trainable and agnostic to specific MARL methods. Empirical evaluations conducted on challenging heterogeneous scenarios from the StarCraft Multiagent Challenge (SMAC) and SMACv2 validate the effectiveness of the proposed method.

Reinforcement Learning with Fuzzy Human Attention-Guided Graph for Heterogeneous Multiagent Systems

Diffusion-based Neural Combinatorial Optimization (NCO) has demonstrated effectiveness in solving NP-complete (NPC) problems by learning discrete diffusion models for solution generation, eliminating hand-crafted domain knowledge. Despite their success, existing NCO methods face significant challenges in both cross-scale and cross-problem generalization, and high training costs compared to traditional solvers. While recent studies on diffusion models have introduced training-free guidance approaches that leverage pre-defined guidance functions for conditional generation, such methodologies have not been extensively explored in combinatorial optimization. To bridge this gap, we propose a training-free inference time adaptation framework (DIFU-Ada) that enables both the zero-shot cross-problem transfer and cross-scale generalization capabilities of diffusion-based NCO solvers without requiring additional training. We provide theoretical analysis that helps understanding the cross-problem transfer capability. Our experimental results demonstrate that a diffusion solver, trained exclusively on the Traveling Salesman Problem (TSP), can achieve competitive zero-shot transfer performance across different problem scales on TSP variants, such as Prize Collecting TSP (PCTSP) and the Orienteering Problem (OP), through inference time adaptation.

Boosting Cross-problem Generalization in Diffusion-Based Neural Combinatorial Solver via Inference Time Adaptation

Nucleus detection in histopathology whole slide images (WSIs) is crucial for a broad spectrum of clinical applications. The gigapixel size of WSIs necessitates the use of sliding window methodology for nucleus detection. However, mainstream methods process each sliding window independently, which overlooks broader contextual information and easily leads to inaccurate predictions. To address this limitation, recent studies additionally crop a large Filed-of-View (LFoV) patch centered on each sliding window to extract contextual features. However, such methods substantially increase whole-slide inference latency. In this work, we propose an effective and efficient context-aware nucleus detection approach. Specifically, instead of using lFoV patches, we aggregate contextual clues from off-the-shelf features of historically visited sliding windows, which greatly enhances the inference efficiency. Moreover, compared to lFoV patches used in previous works, the sliding window patches have higher magnification and provide finer-grained tissue details, thereby enhancing the classification accuracy. To develop the proposed context-aware model, we utilize annotated patches along with their surrounding unlabeled patches for training. Beyond exploiting high-level tissue context from these surrounding regions, we design a post-training strategy that leverages abundant unlabeled nucleus samples within them to enhance the model's context adaptability. Extensive experimental results on three challenging benchmarks demonstrate the superiority of our method. Code will be available soon.

Towards Effective and Efficient Context-aware Nucleus Detection in Histopathology Whole Slide Images

The success of large-scale pre-training paradigm, exemplified by Large Language Models (LLMs), has inspired the development of Time Series Foundation Models (TSFMs). However, their application to financial candlestick (K-line) data remains limited, often underperforming non-pre-trained architectures. Moreover, existing TSFMs often overlook crucial downstream tasks such as volatility prediction and synthetic data generation. To address these limitations, we propose Kronos, a unified, scalable pre-training framework tailored to financial K-line modeling. Kronos introduces a specialized tokenizer that discretizes continuous market information into token sequences, preserving both price dynamics and trade activity patterns. We pre-train Kronos using an autoregressive objective on a massive, multi-market corpus of over 12 billion K-line records from 45 global exchanges, enabling it to learn nuanced temporal and cross-asset representations. Kronos excels in a zero-shot setting across a diverse set of financial tasks. On benchmark datasets, Kronos boosts price series forecasting RankIC by 93% over the leading TSFM and 87% over the best non-pre-trained baseline. It also achieves a 9% lower MAE in volatility forecasting and a 22% improvement in generative fidelity for synthetic K-line sequences. These results establish Kronos as a robust, versatile foundation model for end-to-end financial time series analysis.

Kronos: A Foundation Model for the Language of Financial Markets

Although existing backdoor defenses have gained success in mitigating backdoor attacks, they still face substantial challenges. In particular, most of them rely on large amounts of clean data to weaken the backdoor mapping but generally struggle with residual trigger effects, resulting in persistently high attack success rates (ASR). Therefore, in this paper, we propose a novel Backdoor defense method based on Directional mapping module and adversarial Knowledge Distillation (BeDKD), which balances the trade-off between defense effectiveness and model performance using a small amount of clean and poisoned data. We first introduce a directional mapping module to identify poisoned data, which destroys clean mapping while keeping backdoor mapping on a small set of flipped clean data. Then, the adversarial knowledge distillation is designed to reinforce clean mapping and suppress backdoor mapping through a cycle iteration mechanism between trust and punish distillations using clean and identified poisoned data. We conduct experiments to mitigate mainstream attacks on three datasets, and experimental results demonstrate that BeDKD surpasses the state-of-the-art defenses and reduces the ASR by 98% without significantly reducing the CACC.

BeDKD: Backdoor Defense Based on Directional Mapping Module and Adversarial Knowledge Distillation

Neural networks are increasingly important to learn action policies. Policy predicate abstraction (PPA) verifies safety of such a neural policy pi by over-approximating the state space subgraph induced by pi and using counterexample-guided abstraction refinement (CEGAR) to iteratively refine the abstraction. So far, PPA verifies safety in non-deterministic systems. This work extends PPA to probabilistic verification. Extending the abstract state space computation is relatively straightforward. Abstraction refinement, however, becomes substantially more complex, due to the more intricate form of counterexamples and the various sources of spuriousness it entails. We tackle this challenge by drawing inspiration from prior work on probabilistic CEGAR, empowering it to deal with neural pi. The resulting algorithm decides whether pi is safe with respect to a desired upper bound on unsafety probability. Invoking the algorithm incrementally, we can also derive upper and lower bounds automatically. Our experiments show that these algorithms can derive non-trivial bounds, whereas encodings into state-of-the-art probabilistic model checkers turn out to be ineffective.

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