United States

Artificial Intelligence (AI) continues to evolve rapidly, impacting numerous fields, including time series (TS) classification and human activity recognition (HAR). Despite the advancements in deep learning models, such as Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs), these models face several challenges, including the need for extensive labeled datasets, significant computational resources, and lack of interpretability. 
This research aims to address these limitations by developing an adaptive hierarchical deep neural network framework that integrates fuzzy logic principles and adaptive learning techniques for robust, computationally efficient, and interpretable real-time TS analysis. The reduction in the number of parameters and the efficient learning of hierarchical features mean that less training data is needed to achieve robust performance. The model&#39;s ability to generalize from hierarchical representations allows it to make effective use of smaller datasets, which is particularly advantageous in scenarios where data is limited or expensive to obtain.The proposed framework specifically targets HAR applications using data from wearable sensors.

AAAI 2025

Scalable Hierarchical Deep Neural Network for Time Series Analysis in Wearable Sensor-based Human Activity Recognition

Artificial Intelligence (AI) continues to evolve rapidly, impacting numerous fields, including time series (TS) classification and human activity recognition (HAR). Despite the advancements in deep learning models, such as Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs), these models face several challenges, including the need for extensive labeled datasets, significant computational resources, and lack of interpretability. 
This research aims to address these limitations by developing an adaptive hierarchical deep neural network framework that integrates fuzzy logic principles and adaptive learning techniques for robust, computationally efficient, and interpretable real-time TS analysis. The reduction in the number of parameters and the efficient learning of hierarchical features mean that less training data is needed to achieve robust performance. The model's ability to generalize from hierarchical representations allows it to make effective use of smaller datasets, which is particularly advantageous in scenarios where data is limited or expensive to obtain.The proposed framework specifically targets HAR applications using data from wearable sensors.

poster

We are pleased to announce the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI-25), which will be held in Philadelphia, Pennsylvania at the Pennsylvania Convention Center from February 25 to March 4, 2025.

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-25 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.

### [Invited Speakers](https://aaai.org/conference/aaai/aaai-25/aaai-25-invited-speakers/)

Register [here](https://aaai.org/conference/aaai/aaai-25/registration/)

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-25 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.



We provide improved upper and lower bounds for the Min-Sum-Radii (MSR) and Min-Sum-Diameters (MSD) clustering problems with a bounded number of clusters $k$. In particular, we propose an exact MSD algorithm with a running-time of $n^{O(k)}$. We also provide $(1+\epsilon)$ approximation algorithms for MSR with a running-time of $O(kn) +(1/\epsilon)^{O(dk)}$ in metrics spaces of doubling dimension $d$. For MSD we get a similar approximation bound (with an extra $2^{O(k/\epsilon)}$ factor in the second term).

Our algorithms extend to $\alpha$-MSR, where radii are raised to an $\alpha$ power, for $\alpha>1$, whereas for $\alpha$-MSD we prove an exponential time ETH-based lower bound for $\alpha>\log 3$. All algorithms can also be modified to handle outliers. 

Moreover, we can extend the results to variants  that observe \emph{fairness} constraints, as well as to the general framework of \emph{mergeable} clustering, which includes many other popular clustering variants. We complement these upper bounds with ETH-based lower bounds for these problems, in particular proving that $n^{O(k)}$ time is tight for MSR and $\alpha$-MSR even in doubling spaces, and that $2^{o(k)}$ bounds are impossible for MSD.

Improved Fixed-Parameter Bounds for Min-Sum-Radii and Diameters k-Clustering and Their Fair Variants

Large Language Models (LLMs) have demonstrated remarkable proficiency in generating code. However, the misuse of LLM-generated (synthetic) code has raised concerns in both educational and industrial contexts, underscoring the urgent need for synthetic code detectors. Existing methods for detecting synthetic content are primarily designed for general text and struggle with code due to the unique grammatical structure of programming languages and the presence of numerous ``low-entropy'' tokens. Building on this, our work proposes a novel zero-shot synthetic code detector based on the similarity between the original code and its LLM-rewritten variants. Our method is based on the observation that differences between LLM-rewritten and original code tend to be smaller when the original code is synthetic. We utilize self-supervised contrastive learning to train a code similarity model and evaluate our approach on two synthetic code detection benchmarks. Our results demonstrate a significant improvement over existing SOTA synthetic content detectors, with AUROC scores increasing by 20.5\% on the APPS benchmark and 29.1\% on the MBPP benchmark.

Uncovering LLM-Generated Code: A Zero-Shot Synthetic Code Detector via Code Rewriting

Deep learning models have demonstrated exceptional performance in a variety of real-world applications. These successes are often attributed to strong base models that can generalize to novel tasks with limited supporting data while keeping prior knowledge intact. However, these impressive results are based on the availability of a large amount of high-quality data, which is often lacking in specialized biomedical applications. In such fields, models are often developed with limited data that arrive incrementally with novel categories. This requires the model to adapt to new information while preserving existing knowledge. Few-Shot Class-Incremental Learning (FSCIL) methods offer a promising approach to addressing these challenges, but they also depend on strong base models that face the same aforementioned limitations. To overcome these constraints, we propose AnchorInv following the straightforward and efficient buffer-replay strategy. Instead of selecting and storing raw data, AnchorInv generates synthetic samples guided by anchor points in the feature space. This approach protects privacy and regularizes the model for adaptation. When evaluated on three public physiological time series datasets, AnchorInv exhibits efficient knowledge forgetting prevention and improved adaptation to novel classes, surpassing state-of-the-art baselines.

AnchorInv: Few-Shot Class-Incremental Learning of Physiological Signals via Feature Space-Guided Inversion

Building a universal multilingual automatic speech recognition (ASR) model that performs equitably across languages has long been a challenge due to its inherent difficulties.
To address this task we introduce a Language-Agnostic Multilingual ASR pipeline through orthography Unification and language-specific Transliteration (LAMA-UT).
LAMA-UT operates without any language-specific modules while matching the performance of state-of-the-art models trained on a minimal amount of data.
Our pipeline consists of two key steps.
First, we utilize a universal transcription generator to unify orthographic features into Romanized form and capture common phonetic characteristics across diverse languages. 
Second, we utilize a universal converter to transform these universal transcriptions into language-specific ones.
In experiments, we demonstrate the effectiveness of our proposed method leveraging universal transcriptions for massively multilingual ASR.
Our pipeline achieves a relative error reduction rate of 45\% when compared to Whisper and performs comparably to MMS, despite being trained on only 0.1\% of Whisper's training data.
Furthermore, our pipeline does not rely on any language-specific modules.
However, it performs on par with zero-shot ASR approaches which utilize additional language-specific lexicons and language models.
We expect this framework to serve as a cornerstone for flexible multilingual ASR systems that are generalizable even to unseen languages.

LAMA-UT: Language Agnostic Multilingual ASR Through Orthography Unification and Language-Specific Transliteration

Surface-from-gradients (SfG) aims to recover a three-dimensional (3D) surface from its gradients. Traditional methods encounter significant challenges in achieving high accuracy and handling high-resolution inputs, particularly facing the complex nature of discontinuities and the inefficiencies associated with large-scale linear solvers. Although recent advances in deep learning, such as photometric stereo, have enhanced normal estimation accuracy, they do not fully address the intricacies of gradient-based surface reconstruction. To overcome these limitations, we propose a Fourier neural operator-based Numerical Integration Network (FNIN) within a two-stage optimization framework. In the first stage, our approach employs an iterative architecture for numerical integration, harnessing an advanced Fourier neural operator to approximate the solution operator in Fourier space. Additionally, a self-learning attention mechanism is incorporated to effectively detect and handle discontinuities. In the second stage, we refine the surface reconstruction by formulating a weighted least squares problem, addressing the identified discontinuities rationally. Extensive experiments demonstrate that our method achieves significant improvements in both accuracy and efficiency compared to current state-of-the-art solvers. This is particularly evident in handling high-resolution images with complex data, achieving error fewer than 0.1 mm on tested objects.

FNIN: A Fourier Neural Operator-based Numerical Integration Network for Surface-from-gradients

Dependency-aware spatial crowdsourcing (DASC) addresses the unique challenges posed by subtask dependencies in spatial task assignment. This paper investigates the task assignment problem in DASC and proposes a two-stage Recommend and Match Optimization (RMO) framework, leveraging multi-agent reinforcement learning for subtask recommendation and a multi-dimensional utility function for subtask matching. The RMO framework primarily addresses two key challenges: credit assignment for subtasks with interdependencies and maintaining overall coherence between subtask recommendation and matching. Specifically, we employ meta-gradients to construct auxiliary policies and establish a gradient connection between two stages, which can effectively address credit assignment and joint optimization of subtask recommendation and matching, while concurrently accelerating network training. We further establish a unified gradient descent process through gradient synchronization across recommendation networks, auxiliary policies, and the matching utility evaluation function. Experiments on two real-world datasets validate the effectiveness and feasibility of our proposed approach.

Gradient-Guided Credit Assignment and Joint Optimization for Dependency-Aware Spatial Crowdsourcing

Offline safe reinforcement learning (RL) has emerged as a promising approach for learning safe behaviors without engaging in risky online interactions with the environment. Most existing methods in offline safe RL rely on cost constraints at each time step (derived from global cost constraints) and this can result in either overly conservative policies or violation of safety constraints. In this paper, we propose
to learn a policy that generates desirable trajectories and avoids undesirable trajectories. To be specific, we first partition the pre-collected dataset of state-action trajectories into desirable and undesirable subsets. Intuitively, the desirable set contains high reward and safe trajectories, and undesirable set contains unsafe trajectories and low-reward safe trajectories. Second, we learn a policy that generates desirable trajectories and avoids undesirable trajectories, where (un)desirability scores are provided by a classifier learnt from the dataset of desirable and undesirable trajectories. This approach bypasses the computational complexity and stability issues of a min-max objective that is employed in existing methods. Theoretically, we also show our approach’s strong connections to existing learning paradigms involving human feedback. Finally, we extensively evaluate our method using the DSRL benchmark for offline safe RL. Empirically, our method outperforms competitive baselines, achieving higher rewards and better constraint satisfaction across a wide variety of benchmark tasks.

Offline Safe Reinforcement Learning Using Trajectory Classification

My PhD research focuses on developing a highly accurate and explainable multi-output virtual metrology system for semiconductor manufacturing. Using machine learning, we predict the physical properties of metal layers from process parameters captured by production equipment sensors. Key contributions include a model-agnostic explanatory method based on projective operators, providing insights into the most influential features for multi-output predictions and feature selection algorithms for these tasks.

Human Interpretable Virtual Metrology in the Semiconductor Manufacturing

As modern network management grows increasingly complex, administrators are tasked with navigating vast volumes of log data, often resulting in inefficiencies, errors, and operational challenges. My doctoral research addresses these pressing issues by leveraging advanced AI techniques to minimize human intervention and pave the way for fully automated network operations. I propose a novel AI-driven framework that integrates Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) and a human-in-the-loop process to effectively automate key network management tasks, including log analysis, troubleshooting recommendations, and documentation generation. By enhancing the accuracy and efficiency of these tasks, this study aims to improve network reliability, reduce operational complexity, and contribute to the evolution of self-running networks.

Towards Autonomous Network Management: AI-Driven Framework for Intelligent Log Analysis, Troubleshooting and Documentation

As the use of autonomous mobile robots expands into dynamic and complex environments, the need for them to provide understandable explanations for their actions becomes crucial. This thesis addresses the challenge of developing explainability for robot navigation by leveraging a hybrid model that combines machine learning techniques with symbolic reasoning methods. Furthermore, the thesis explores the modeling of human explanation preferences and the impact of different explanation attributes on explanation recipients' understanding, satisfaction, and trust. The goal is to integrate different explanation aspects and approaches into a unified framework to support explainable navigation in robotics.

Content not yet available

Scalable Hierarchical Deep Neural Network for Time Series Analysis in Wearable Sensor-based Human Activity Recognition

Downloads

Next from AAAI 2025

Improved Fixed-Parameter Bounds for Min-Sum-Radii and Diameters k-Clustering and Their Fair Variants

Stay up to date with the latest Underline news!

PRODUCT

COMPANY

RESOURCES

Content not yet available

Scalable Hierarchical Deep Neural Network for Time Series Analysis in Wearable Sensor-based Human Activity Recognition

.css-70qvj9{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}Downloads

Next from AAAI 2025

Improved Fixed-Parameter Bounds for Min-Sum-Radii and Diameters k-Clustering and Their Fair Variants

Stay up to date with the latest Underline news!

PRODUCT

COMPANY

RESOURCES

Downloads