pwc-archive/papers-with-abstracts huggingface dataset:
Few-Shot Generation of Brain Tumors for Secure and Fair Data Sharing
Authors: Yongyi Shi, Ge Wang
Date: 2025-03-31 00:00:00
Tasks: Brain Tumor Segmentation, Data Augmentation, Fairness, Federated Learning, Memorization, Tumor Segmentation

Leveraging multi-center data for medical analytics presents challenges due to privacy concerns and data heterogeneity. While distributed approaches such as federated learning has gained traction, they remain vulnerable to privacy breaches, particularly in sensitive domains like medical imaging. Generative models, such as diffusion models, enhance privacy by synthesizing realistic data. However, they are prone to memorization, especially when trained on small datasets. This study proposes a decentralized few-shot generative model (DFGM) to synthesize brain tumor images while fully preserving privacy. DFGM harmonizes private tumor data with publicly shareable healthy images from multiple medical centers, constructing a new dataset by blending tumor foregrounds with healthy backgrounds. This approach ensures stringent privacy protection and enables controllable, high-quality synthesis by preserving both the healthy backgrounds and tumor foregrounds. We assess DFGM's effectiveness in brain tumor segmentation using a UNet, achieving Dice score improvements of 3.9% for data augmentation and 4.6% for fairness on a separate dataset.
Sim-is-More: Randomizing HW-NAS with Synthetic Devices
Authors: Francesco Capuano, Gabriele Tiboni, Niccolò Cavagnero, Giuseppe Averta
Date: 2025-04-01 00:00:00

Existing hardware-aware NAS (HW-NAS) methods typically assume access to precise information circa the target device, either via analytical approximations of the post-compilation latency model, or through learned latency predictors. Such approximate approaches risk introducing estimation errors that may prove detrimental in risk-sensitive applications. In this work, we propose a two-stage HW-NAS framework, in which we first learn an architecture controller on a distribution of synthetic devices, and then directly deploy the controller on a target device. At test-time, our network controller deploys directly to the target device without relying on any pre-collected information, and only exploits direct interactions. In particular, the pre-training phase on synthetic devices enables the controller to design an architecture for the target device by interacting with it through a small number of high-fidelity latency measurements. To guarantee accessibility of our method, we only train our controller with training-free accuracy proxies, allowing us to scale the meta-training phase without incurring the overhead of full network training. We benchmark on HW-NATS-Bench, demonstrating that our method generalizes to unseen devices and searches for latency-efficient architectures by in-context adaptation using only a few real-world latency evaluations at test-time.
Towards Responsible and Trustworthy Educational Data Mining: Comparing Symbolic, Sub-Symbolic, and Neural-Symbolic AI Methods
Authors: Danial Hooshyar, Eve Kikas, Yeongwook Yang, Gustav Šír, Raija Hämäläinen, Tommi Kärkkäinen, Roger Azevedo
Date: 2025-04-01 00:00:00
Tasks: Decision Making

Given the demand for responsible and trustworthy AI for education, this study evaluates symbolic, sub-symbolic, and neural-symbolic AI (NSAI) in terms of generalizability and interpretability. Our extensive experiments on balanced and imbalanced self-regulated learning datasets of Estonian primary school students predicting 7th-grade mathematics national test performance showed that symbolic and sub-symbolic methods performed well on balanced data but struggled to identify low performers in imbalanced datasets. Interestingly, symbolic and sub-symbolic methods emphasized different factors in their decision-making: symbolic approaches primarily relied on cognitive and motivational factors, while sub-symbolic methods focused more on cognitive aspects, learned knowledge, and the demographic variable of gender -- yet both largely overlooked metacognitive factors. The NSAI method, on the other hand, showed advantages by: (i) being more generalizable across both classes -- even in imbalanced datasets -- as its symbolic knowledge component compensated for the underrepresented class; and (ii) relying on a more integrated set of factors in its decision-making, including motivation, (meta)cognition, and learned knowledge, thus offering a comprehensive and theoretically grounded interpretability framework. These contrasting findings highlight the need for a holistic comparison of AI methods before drawing conclusions based solely on predictive performance. They also underscore the potential of hybrid, human-centered NSAI methods to address the limitations of other AI families and move us closer to responsible AI for education. Specifically, by enabling stakeholders to contribute to AI design, NSAI aligns learned patterns with theoretical constructs, incorporates factors like motivation and metacognition, and strengthens the trustworthiness and responsibility of educational data mining.
MetaLoRA: Tensor-Enhanced Adaptive Low-Rank Fine-tuning
Authors: Maolin Wang, Xiangyu Zhao
Date: 2025-04-01 00:00:00
Tasks: Computational Efficiency, Meta-Learning, parameter-efficient fine-tuning

There has been a significant increase in the deployment of neural network models, presenting substantial challenges in model adaptation and fine-tuning. Efficient adaptation is crucial in maintaining model performance across diverse tasks and domains. While Low-Rank Adaptation (LoRA) has emerged as a promising parameter-efficient fine-tuning method, its fixed parameter nature limits its ability to handle dynamic task requirements effectively. Adapting models to new tasks can be challenging due to the need for extensive fine-tuning. Current LoRA variants primarily focus on general parameter reduction while overlooking the importance of dynamic parameter adjustment and meta-learning capabilities. Moreover, existing approaches mainly address static adaptations, neglecting the potential benefits of task-aware parameter generation in handling diverse task distributions. To address these limitations, this Ph.D. research proposes a LoRA generation approach to model task relationships and introduces MetaLoRA, a novel parameter-efficient adaptation framework incorporating meta-learning principles. This work develops a comprehensive architecture that integrates meta-parameter generation with adaptive low-rank decomposition, enabling efficient handling of both task-specific and task-agnostic features. MetaLoRA accurately captures task patterns by incorporating meta-learning mechanisms and dynamic parameter adjustment strategies. To our knowledge, this research represents the first attempt to provide a meta-learning enhanced LoRA variant, offering improved adaptation capability while maintaining computational efficiency in model fine-tuning.
VerifiAgent: a Unified Verification Agent in Language Model Reasoning
Authors: Jiuzhou Han, Wray Buntine, Ehsan Shareghi
Date: 2025-04-01 00:00:00
Tasks: Language Modeling, Language Modelling, Mathematical Reasoning

Large language models demonstrate remarkable reasoning capabilities but often produce unreliable or incorrect responses. Existing verification methods are typically model-specific or domain-restricted, requiring significant computational resources and lacking scalability across diverse reasoning tasks. To address these limitations, we propose VerifiAgent, a unified verification agent that integrates two levels of verification: meta-verification, which assesses completeness and consistency in model responses, and tool-based adaptive verification, where VerifiAgent autonomously selects appropriate verification tools based on the reasoning type, including mathematical, logical, or commonsense reasoning. This adaptive approach ensures both efficiency and robustness across different verification scenarios. Experimental results show that VerifiAgent outperforms baseline verification methods (e.g., deductive verifier, backward verifier) among all reasoning tasks. Additionally, it can further enhance reasoning accuracy by leveraging feedback from verification results. VerifiAgent can also be effectively applied to inference scaling, achieving better results with fewer generated samples and costs compared to existing process reward models in the mathematical reasoning domain. Code is available at https://github.com/Jiuzhouh/VerifiAgent
MetaCLBench: Meta Continual Learning Benchmark on Resource-Constrained Edge Devices
Authors: Sijia Li, Young D. Kwon, Lik-Hang Lee, Pan Hui
Date: 2025-03-31 00:00:00
Tasks: Continual Learning

Meta-Continual Learning (Meta-CL) has emerged as a promising approach to minimize manual labeling efforts and system resource requirements by enabling Continual Learning (CL) with limited labeled samples. However, while existing methods have shown success in image-based tasks, their effectiveness remains unexplored for sequential time-series data from sensor systems, particularly audio inputs. To address this gap, we conduct a comprehensive benchmark study evaluating six representative Meta-CL approaches using three network architectures on five datasets from both image and audio modalities. We develop MetaCLBench, an end-to-end Meta-CL benchmark framework for edge devices to evaluate system overheads and investigate trade-offs among performance, computational costs, and memory requirements across various Meta-CL methods. Our results reveal that while many Meta-CL methods enable to learn new classes for both image and audio modalities, they impose significant computational and memory costs on edge devices. Also, we find that pre-training and meta-training procedures based on source data before deployment improve Meta-CL performance. Finally, to facilitate further research, we provide practical guidelines for researchers and machine learning practitioners implementing Meta-CL on resource-constrained environments and make our benchmark framework and tools publicly available, enabling fair evaluation across both accuracy and system-level metrics.
Knowledge-enhanced Prompt-tuning for Stance Detection
Authors: Hu Huang, BoWen Zhang, Yangyang Li, Baoquan Zhang, Yuxi Sun, CHUYAOLUO, Cheng Peng
Date: 2023-06-16 00:00:00
Proceeding: journal 2023 6
Tasks: Opinion Mining, Stance Detection

Investigating public attitudes on social media is important in opinion mining systems. Stance detection aims
 to analyze the attitude of an opinionated text (e.g., favor, neutral, or against) toward a given target. Ex
isting methods mainly address this problem from the perspective of fine-tuning. Recently, prompt-tuning
 has achieved success in natural language processing tasks. However, conducting prompt-tuning methods for
 stance detection in real-world remains a challenge for several reasons: (1) The text form of stance detection
 is usually short and informal, which makes it difficult to design label words for the verbalizer. (2) The tweet
 text may not explicitly give the attitude. Instead, users may use various hashtags or background knowledge
 to express stance-aware perspectives. In this article, we first propose a prompt-tuning-based framework that
 performs stance detection in a cloze question manner. Specifically, a knowledge-enhanced prompt-tuning
 framework (KEprompt) method is designed, which consists of an automatic verbalizer (AutoV) and back
ground knowledge injection (BKI). Specifically, in AutoV, we introduce a semantic graph to build a better
 mapping from the predicted word of the pretrained language model and detection labels. In BKI, we first pro
pose atopic model for learning hashtag representation and introduce ConceptGraph as the supplement of the
 target. At last, we present a challenging dataset for stance detection, where all stance categories are expressed
 in an implicit manner. Extensive experiments on a large real-world dataset demonstrate the superiority of
 KEprompt over state-of-the-art methods.
Knowledge-enhanced Prompt Tuning for Dialogue-based Relation Extraction with Trigger and Label Semantic
Authors: Hao An, Zhihong Zhu, Xuxin Cheng, Zhiqi Huang, Yuexian Zou∗
Date: 2024-05-20 00:00:00
Proceeding: Conference 2024 5
Tasks: Language Modeling, Language Modelling, Masked Language Modeling, Relation, Relation Extraction, Representation Learning

Dialogue-based relation extraction (DRE) aims to determine the semantic relation of a given pair of arguments from a
 piece of dialogue, which has received increasing attention. Due to the low information density of dialogue text, it is
 difficult for the model to focus on key information. To this end, in this paper, we propose a Knowledge-Enhanced
 Prompt-Tuning (KEPT) method to effectively enhance DRE model by exploiting trigger and label semantic.
 Specifically, we propose two beneficial tasks, masked trigger prediction, and verbalizer representation learning,
 to effectively inject trigger knowledge and label semantic knowledge respectively. Furthermore, we convert the
 DREtask to a masked language modeling task to unify the format of knowledge injection and utilization, aiming to
 better promote DRE performance. Experimental results on the DialogRE dataset show that our KEPT achieves
 state-of-the-art performance in F1 and F1c scores. Detailed analyses demonstrate the effectiveness and efficiency of
 our proposed approach. Code is available at https://github.com/blackbookay/KEPT.
Swin-MSTP: Swin transformer with multi-scale temporal perception for continuous sign language recognition
Authors: Sarah Alyami, Hamzah Luqman
Date: 2025-02-07 00:00:00
Proceeding: Neurocomputing 2025 2
Tasks: Sign Language Recognition

Continuous sign language recognition (CSLR) aims to recognize and interpret sequences of sign language gestures in videos. Currently, most CSLR frameworks combine spatial feature extractors based on convolutional neural networks (CNNs) with temporal convolutional networks (TCNs) for sequence learning. However, CNN-based spatial feature extractors apply the same convolutional kernel uniformly across all regions of an image, which limits their capacity to extract complex details such as fingers and facial features, which are essential for CSLR. In addition, sign languages include signs of varying lengths that cannot be accurately modeled using a fixed-size TCN. To address these issues, we present the Swin multiscale temporal perception (Swin-MSTP) framework, in which the Swin Transformer is utilized as the spatial feature extractor, capable of capturing fine spatial details and providing a stronger contextual understanding between sign language elements in video frames. The Swin Transformer was integrated with the MSTP module to extract time-wise features. Experimental results show that our single-modality system outperformed existing methods on the CSL dataset, including multimodal frameworks. The model also achieved competitive performance on the Phoenix2014, Phoenix2014-T, and CSL-Daily datasets. The code is available at https://github.com/snalyami/Swin-MSTP
Hurshch A Pakistani Choreographer And Director
Authors: Hurshch, Rana Tahir Faisal
Date: 2022-12-13 00:00:00
Proceeding: 14-12 2022 12

Hurshch is a Pakistani choreographer, model groomer, and director known for being the first model to work with the world's largest fashion company, FMD (The Fashion Model Directory).
Systematic Literature Review: Explainable AI Definitions and Challenges in Education
Authors: Zaid M. Altukhi, Sojen Pradhan
Date: 2025-04-03 00:00:00
Tasks: Ethics, Systematic Literature Review

Explainable AI (XAI) seeks to transform black-box algorithmic processes into transparent ones, enhancing trust in AI applications across various sectors such as education. This review aims to examine the various definitions of XAI within the literature and explore the challenges of XAI in education. Our goal is to shed light on how XAI can contribute to enhancing the educational field. This systematic review, utilising the PRISMA method for rigorous and transparent research, identified 19 relevant studies. Our findings reveal 15 definitions and 62 challenges. These challenges are categorised using thematic analysis into seven groups: explainability, ethical, technical, human-computer interaction (HCI), trustworthiness, policy and guideline, and others, thereby deepening our understanding of the implications of XAI in education. Our analysis highlights the absence of standardised definitions for XAI, leading to confusion, especially because definitions concerning ethics, trustworthiness, technicalities, and explainability tend to overlap and vary.
Enhancing Traffic Sign Recognition On The Performance Based On Yolov8
Authors: Baba Ibrahim, Zhou Kui
Date: 2025-04-02 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, Data Augmentation, object-detection, Object Detection, Traffic Sign Recognition

This paper Traffic sign recognition plays a crucial role in the development of autonomous vehicles and advanced driver-assistance systems (ADAS). Despite significant advances in deep learning and object detection, accurately detecting and classifying traffic signs remains challenging due to their small sizes, variable environmental conditions, occlusion, and class imbalance. This thesis presents an enhanced YOLOv8-based detection system that integrates advanced data augmentation techniques, novel architectural enhancements including Coordinate Attention (CA), Bidirectional Feature Pyramid Network (BiFPN), and dynamic modules such as ODConv and LSKA, along with refined loss functions (EIoU and WIoU combined with Focal Loss). Extensive experiments conducted on datasets including GTSRB, TT100K, and GTSDB demonstrate marked improvements in detection accuracy, robustness under adverse conditions, and real-time inference on edge devices. The findings contribute actionable insights for deploying reliable traffic sign recognition systems in real-world autonomous driving scenarios.
Learning Lie Group Generators from Trajectories
Authors: Lifan Hu
Date: 2025-04-04 00:00:00

This work investigates the inverse problem of generator recovery in matrix Lie groups from discretized trajectories. Let $G$ be a real matrix Lie group and $\mathfrak{g} = \text{Lie}(G)$ its corresponding Lie algebra. A smooth trajectory $\gamma($t$)$ generated by a fixed Lie algebra element $\xi \in \mathfrak{g}$ follows the exponential flow $\gamma($t$) = g_0 \cdot \exp(t \xi)$. The central task addressed in this work is the reconstruction of such a latent generator $\xi$ from a discretized sequence of poses $ \{g_0, g_1, \dots, g_T\} \subset G$, sampled at uniform time intervals. This problem is formulated as a data-driven regression from normalized sequences of discrete Lie algebra increments $\log\left(g_{t}^{-1} g_{t+1}\right)$ to the constant generator $\xi \in \mathfrak{g}$. A feedforward neural network is trained to learn this mapping across several groups, including $\text{SE(2)}, \text{SE(3)}, \text{SO(3)}, and \text{SL(2,$\mathbb{R})$}$. It demonstrates strong empirical accuracy under both clean and noisy conditions, which validates the viability of data-driven recovery of Lie group generators using shallow neural architectures. This is Lie-RL GitHub Repo https://github.com/Anormalm/LieRL-on-Trajectories. Feel free to make suggestions and collaborations!
Integrating Notch Filtering and Statistical Methods for Improved Cardiac Diagnostics Using MATLAB
Authors: Lohit Bibar, Samali bose, Tribeni Prasad Banerjee
Date: 2025-03-20 00:00:00
Tasks: Arrhythmia Detection, Classification, ECG Classification

A Notch Filter is essential in ECG signal processing to eliminate narrowband noise, especially powerline interference at 50 Hz or 60 Hz. This interference overlaps with vital ECG signal features, affecting the accuracy of downstream classification tasks (e.g., arrhythmia detection). A properly designed notch filter enhances signal quality, preserves essential ECG components (P, QRS, T waves), and improves the performance of machine learning or deep learning models used for ECG classification.
Classification of epileptic seizures in EEG data based on iterative gated graph convolution network
Authors: Yue Hu, Jian Liu, Rencheng Sun, Yongqiang Yu and Yi Sui
Date: 2024-08-29 00:00:00
Proceeding: Frontiers in Computational Neuroscience 2024 8
Tasks: EEG, Electroencephalogram (EEG)

Introduction: The automatic and precise classification of epilepsy types using electroencephalogram (EEG) data promises significant advancements in diagnosing patients with epilepsy. However, the intricate interplay among multiple electrode signals in EEG data poses challenges. Recently, Graph Convolutional Neural Networks (GCN) have shown strength in analyzing EEG data due to their capability to describe complex relationships among different EEG regions. Nevertheless, several challenges remain: (1) GCN typically rely on predefined or prior graph topologies, which may not accurately reflect the complex correlations between brain regions. (2) GCN struggle to capture the long-temporal dependencies inherent in EEG signals, limiting their ability to effectively extract temporal features.
Methods: To address these challenges, we propose an innovative epileptic seizure classification model based on an Iterative Gated Graph Convolutional Network (IGGCN). For the epileptic seizure classification task, the original EEG graph structure is iteratively optimized using a multi-head attention mechanism during training, rather than relying on a static, predefined prior graph. We introduce Gated Graph Neural Networks (GGNN) to enhance the model's capacity to capture long-term dependencies in EEG series between brain regions. Additionally, Focal Loss is employed to alleviate the imbalance caused by the scarcity of epileptic EEG data.
Results: Our model was evaluated on the Temple University Hospital EEG Seizure Corpus (TUSZ) for classifying four types of epileptic seizures. The results are outstanding, achieving an average F1 score of 91.5% and an average Recall of 91.8%, showing a substantial improvement over current state-of-the-art models.
Discussion: Ablation experiments verified the efficacy of iterative graph optimization and gated graph convolution. The optimized graph structure significantly differs from the predefined EEG topology. Gated graph convolutions demonstrate superior performance in capturing the long-term dependencies in EEG series. Additionally, Focal Loss outperforms other commonly used loss functions in the TUSZ classification task.
AutoSSVH: Exploring Automated Frame Sampling for Efficient Self-Supervised Video Hashing
Authors: Niu Lian, Jun Li, Jinpeng Wang, Ruisheng Luo, YaoWei Wang, Shu-Tao Xia, Bin Chen
Date: 2025-04-04 00:00:00
Proceeding: CVPR 2025 1
Tasks: Contrastive Learning, Retrieval

Self-Supervised Video Hashing (SSVH) compresses videos into hash codes for efficient indexing and retrieval using unlabeled training videos. Existing approaches rely on random frame sampling to learn video features and treat all frames equally. This results in suboptimal hash codes, as it ignores frame-specific information density and reconstruction difficulty. To address this limitation, we propose a new framework, termed AutoSSVH, that employs adversarial frame sampling with hash-based contrastive learning. Our adversarial sampling strategy automatically identifies and selects challenging frames with richer information for reconstruction, enhancing encoding capability. Additionally, we introduce a hash component voting strategy and a point-to-set (P2Set) hash-based contrastive objective, which help capture complex inter-video semantic relationships in the Hamming space and improve the discriminability of learned hash codes. Extensive experiments demonstrate that AutoSSVH achieves superior retrieval efficacy and efficiency compared to state-of-the-art approaches. Code is available at https://github.com/EliSpectre/CVPR25-AutoSSVH.