pwc-archive/papers-with-abstracts huggingface dataset:
HGATE: Heterogeneous Graph Attention Auto-Encoders
Authors: Wei Wang, Xiaoyang Suo, Xiangyu Wei, Bin Wang, Hao Wang, Hong-Ning Dai, and Xiangliang Zhang, Senior Member, IEEE
Date: 2021-11-28 00:00:00
Proceeding: IEEE Transactions on Knowledge and Data Engineering 2021 11
Tasks: Graph Attention, Graph Representation Learning, Inductive Learning, Link Prediction, Node Classification, Representation Learning, Transductive Learning

Graph auto-encoder is considered a framework for unsupervised learning on graph-structured data by representing graphs in a low dimensional space. It has been proved very powerful for graph analytics. In the real world, complex relationships in various entities can be represented by heterogeneous graphs that contain more abundant semantic information than homogeneous graphs. In general, graph auto-encoders based on homogeneous graphs are not applicable to heterogeneous graphs. In addition, little work has been done to evaluate the effect of different semantics on node embedding in heterogeneous graphs for unsupervised graph representation learning. In this work, we propose a novel Heterogeneous Graph Attention Auto-Encoders (HGATE) for unsupervised representation learning on heterogeneous graph-structured data. Based on the consideration of semantic information, our architecture of HGATE reconstructs not only the edges of the heterogeneous graph but also node attributes, through stacked encoder/decoder layers. Hierarchical attention is used to learn the relevance between a node and its meta-path based neighbors, and the relevance among different meta-paths. HGATE is applicable to transductive learning as well as inductive learning. Node classification and link prediction experiments on real-world heterogeneous graph datasets demonstrate the effectiveness of HGATE for both transductive and inductive tasks.
HetReGAT-FC: heterogeneous residual graph attention network via feature completion
Authors: Chao Li, Yeyu Yan, Jinhu Fu, Zhongying Zhao ∗, Qingtian Zeng ∗
Date: 2023-06-01 00:00:00
Proceeding: Information Sciences 2023 6
Tasks: Graph Attention, Graph Embedding

Heterogeneous graph embedding is receiving increasing attention from researchers due to the ubiquity of heterogeneous graphs (HGs). How to effectively handle the problem of missing features in HGs has become a hot research topic in recent years. However, the existing heterogeneous graph neural networks (HGNNs) with feature completion are facing two challenges: (1) Pre-training is required; and (2) Heterogeneous information in the HG is not fully explored. To this end, we propose a Heterogeneous Residual Graph Attention Network via Feature Completion (HetReGAT-FC). Specifically, we first design a Heterogeneous Residual Graph Attention Network (HetReGAT) to learn topological information. Then, the attention mechanism is adopted to complete the missing features. Finally, HetReGAT is used to learn the final node embeddings on the feature completed heterogeneous graph. To prove the effectiveness of this work, we conduct extensive experiments on three real-world datasets and compare it with ten competitive baselines. The results demonstrate that the proposed HetReGAT-FC significantly outperforms state-of-the-art methods. The codes and data of this work are available at https:// github.com/ZZY-GraphMiningLab/HetReGAT-FC.
Blockchain Meets Adaptive Honeypots: A Trust-Aware Approach to Next-Gen IoT Security
Authors: Yazan Otoum, Arghavan Asad, Amiya Nayak
Date: 2025-04-22 00:00:00
Tasks: Anomaly Detection, CPU, Edge-computing, Intrusion Detection

Edge computing-based Next-Generation Wireless Networks (NGWN)-IoT offer enhanced bandwidth capacity for large-scale service provisioning but remain vulnerable to evolving cyber threats. Existing intrusion detection and prevention methods provide limited security as adversaries continually adapt their attack strategies. We propose a dynamic attack detection and prevention approach to address this challenge. First, blockchain-based authentication uses the Deoxys Authentication Algorithm (DAA) to verify IoT device legitimacy before data transmission. Next, a bi-stage intrusion detection system is introduced: the first stage uses signature-based detection via an Improved Random Forest (IRF) algorithm. In contrast, the second stage applies feature-based anomaly detection using a Diffusion Convolution Recurrent Neural Network (DCRNN). To ensure Quality of Service (QoS) and maintain Service Level Agreements (SLA), trust-aware service migration is performed using Heap-Based Optimization (HBO). Additionally, on-demand virtual High-Interaction honeypots deceive attackers and extract attack patterns, which are securely stored using the Bimodal Lattice Signature Scheme (BLISS) to enhance signature-based Intrusion Detection Systems (IDS). The proposed framework is implemented in the NS3 simulation environment and evaluated against existing methods across multiple performance metrics, including accuracy, attack detection rate, false negative rate, precision, recall, ROC curve, memory usage, CPU usage, and execution time. Experimental results demonstrate that the framework significantly outperforms existing approaches, reinforcing the security of NGWN-enabled IoT ecosystems
SaENeRF: Suppressing Artifacts in Event-based Neural Radiance Fields
Authors: Yuanjian Wang, Yufei Deng, Rong Xiao, Jiahao Fan, Chenwei Tang, Deng Xiong, Jiancheng Lv
Date: 2025-04-23 00:00:00
Tasks: NeRF

Event cameras are neuromorphic vision sensors that asynchronously capture changes in logarithmic brightness changes, offering significant advantages such as low latency, low power consumption, low bandwidth, and high dynamic range. While these characteristics make them ideal for high-speed scenarios, reconstructing geometrically consistent and photometrically accurate 3D representations from event data remains fundamentally challenging. Current event-based Neural Radiance Fields (NeRF) methods partially address these challenges but suffer from persistent artifacts caused by aggressive network learning in early stages and the inherent noise of event cameras. To overcome these limitations, we present SaENeRF, a novel self-supervised framework that effectively suppresses artifacts and enables 3D-consistent, dense, and photorealistic NeRF reconstruction of static scenes solely from event streams. Our approach normalizes predicted radiance variations based on accumulated event polarities, facilitating progressive and rapid learning for scene representation construction. Additionally, we introduce regularization losses specifically designed to suppress artifacts in regions where photometric changes fall below the event threshold and simultaneously enhance the light intensity difference of non-zero events, thereby improving the visual fidelity of the reconstructed scene. Extensive qualitative and quantitative experiments demonstrate that our method significantly reduces artifacts and achieves superior reconstruction quality compared to existing methods. The code is available at https://github.com/Mr-firework/SaENeRF.
Robotics and Computer-Integrated Manufacturing
Authors: Yu Huang, Daxin Liu  *, Zhenyu Liu, Ke Wang, Qide Wang, Jianrong Tan
Date: 2024-04-01 00:00:00
Proceeding: Robotics and Computer-Integrated Manufacturing 2024 4
Tasks: Robotic Grasping

To grasp the randomly moving objects in unstructured environment, a novel robotic grasping method based on 
multi-agent TD3 with high-quality memory (MA-TD3H) is proposed. During the grasping process, the MA-TD3H 
algorithm obtains the object’s motion state from the vision detection module and outputs the velocity of the 
gripper. The quality of the sampled memory plays a crucial role in reinforcement learning models. In MA-TD3H, 
transitions are saved in the memory buffer and high-quality memory (H-memory) buffer respectively. When 
updating the actor network, transitions are adaptively sampled from the two buffers by a set ratio according to 
the current grasping success rate of the algorithm. Also, the multi-agent mechanism enables the MA-TD3H al
gorithm to control multiple agents for simultaneous training and experience sharing. In the simulation, MA- 
TD3H improves the success rate of grasping the moving object by around 25 percent, compared with TD3, 
DDPG and SAC. While in most cases, MA-TD3H spends 80 percent of the time of the other algorithms. In real- 
world experiments on grasping objects in different shapes and trajectories, the average grasping prediction 
success rate (GPSR) and grasping reaching success rate (GRSR) of MA-TD3H are above 90 percent and 80 percent 
respectively, and the average GRSR is improved by 20–30 percent compared with the other algorithms. In 
summary, simulated and real-world experiments validate that the MA-TD3H algorithm outperforms the other 
algorithms in robotic grasping for moving objects.
Case Study: Fine-tuning Small Language Models for Accurate and Private CWE Detection in Python Code
Authors: Md. Azizul Hakim Bappy, Hossen A Mustafa, Prottoy Saha, Rajinus Salehat
Date: 2025-04-23 00:00:00
Tasks: Instruction Following, Privacy Preserving, Synthetic Data Generation, Vulnerability Detection

Large Language Models (LLMs) have demonstrated significant capabilities in understanding and analyzing code for security vulnerabilities, such as Common Weakness Enumerations (CWEs). However, their reliance on cloud infrastructure and substantial computational requirements pose challenges for analyzing sensitive or proprietary codebases due to privacy concerns and inference costs. This work explores the potential of Small Language Models (SLMs) as a viable alternative for accurate, on-premise vulnerability detection. We investigated whether a 350-million parameter pre-trained code model (codegen-mono) could be effectively fine-tuned to detect the MITRE Top 25 CWEs specifically within Python code. To facilitate this, we developed a targeted dataset of 500 examples using a semi-supervised approach involving LLM-driven synthetic data generation coupled with meticulous human review. Initial tests confirmed that the base codegen-mono model completely failed to identify CWEs in our samples. However, after applying instruction-following fine-tuning, the specialized SLM achieved remarkable performance on our test set, yielding approximately 99% accuracy, 98.08% precision, 100% recall, and a 99.04% F1-score. These results strongly suggest that fine-tuned SLMs can serve as highly accurate and efficient tools for CWE detection, offering a practical and privacy-preserving solution for integrating advanced security analysis directly into development workflows.
AI-Based Vulnerability Analysis of NFT Smart Contracts
Authors: Xin Wang, Xiaoqi Li
Date: 2025-04-18 00:00:00
Tasks: Vulnerability Detection

With the rapid growth of the NFT market, the security of smart contracts has become crucial. However, existing AI-based detection models for NFT contract vulnerabilities remain limited due to their complexity, while traditional manual methods are time-consuming and costly. This study proposes an AI-driven approach to detect vulnerabilities in NFT smart contracts. We collected 16,527 public smart contract codes, classifying them into five vulnerability categories: Risky Mutable Proxy, ERC-721 Reentrancy, Unlimited Minting, Missing Requirements, and Public Burn. Python-processed data was structured into training/test sets. Using the CART algorithm with Gini coefficient evaluation, we built initial decision trees for feature extraction. A random forest model was implemented to improve robustness through random data/feature sampling and multitree integration. GridSearch hyperparameter tuning further optimized the model, with 3D visualizations demonstrating parameter impacts on vulnerability detection. Results show the random forest model excels in detecting all five vulnerabilities. For example, it identifies Risky Mutable Proxy by analyzing authorization mechanisms and state modifications, while ERC-721 Reentrancy detection relies on external call locations and lock mechanisms. The ensemble approach effectively reduces single-tree overfitting, with stable performance improvements after parameter tuning. This method provides an efficient technical solution for automated NFT contract detection and lays groundwork for scaling AI applications.
UAV-Mounted IRS (UMI) in the Presence of Hovering Fluctuations: 3D Pattern Characterization and Performance Analysis
Authors: Mohammad Javad Zakavi, Mahtab Mirmohseni, Farid Ashtiani, Masoumeh Nasiri-Kenari
Date: 2025-04-23 00:00:00

This paper investigates unmanned aerial vehicle (UAV)-mounted intelligent reflecting surfaces (IRS) to leverage the benefits of this technology for future communication networks, such as 6G. Key advantages include enhanced spectral and energy efficiency, expanded network coverage, and flexible deployment. One of the main challenges in employing UAV-mounted IRS (UMI) technology is the random fluctuations of hovering UAVs. Focusing on this challenge, this paper explores the capabilities of UMI with passive/active elements affected by UAV fluctuations in both horizontal and vertical angles, considering the three-dimensional (3D) radiation pattern of the IRS. The relationship between UAV fluctuations and IRS pattern is investigated by taking into account the random angular vibrations of UAVs. A tractable and closed-form distribution function for the IRS pattern is derived, using linear approximation and by dividing it into several sectors. In addition, closed-form expressions for outage probability (OP) are obtained using central limit theorem (CLT) and Gamma approximation. The theoretical expressions are validated through Monte Carlo simulations. The findings indicate that the random fluctuations of hovering UAVs have a notable impact on the performance of UMI systems. To avoid link interruptions due to UAV instability, IRS should utilize fewer elements, even though this leads to a decrease in directivity. As a result, unlike terrestrial IRS, incorporating more elements into aerial IRS systems does not necessarily improve performance due to the fluctuations in UAV. Numerical results show that the OP can be minimized by selecting the optimal number of IRS elements and using active elements.
A Diff-Attention Aware State Space Fusion Model for Remote Sensing Classification
Authors: Wenping Ma, Boyou Xue, Mengru Ma, Chuang Chen, Hekai Zhang, Hao Zhu
Date: 2025-04-23 00:00:00
Tasks: image-classification, Image Classification, Mamba, Remote Sensing Image Classification

Multispectral (MS) and panchromatic (PAN) images describe the same land surface, so these images not only have their own advantages, but also have a lot of similar information. In order to separate these similar information and their respective advantages, reduce the feature redundancy in the fusion stage. This paper introduces a diff-attention aware state space fusion model (DAS2F-Model) for multimodal remote sensing image classification. Based on the selective state space model, a cross-modal diff-attention module (CMDA-Module) is designed to extract and separate the common features and their respective dominant features of MS and PAN images. Among this, space preserving visual mamba (SPVM) retains image spatial features and captures local features by optimizing visual mamba's input reasonably. Considering that features in the fusion stage will have large semantic differences after feature separation and simple fusion operations struggle to effectively integrate these significantly different features, an attention-aware linear fusion module (AALF-Module) is proposed. It performs pixel-wise linear fusion by calculating influence coefficients. This mechanism can fuse features with large semantic differences while keeping the feature size unchanged. Empirical evaluations indicate that the presented method achieves better results than alternative approaches. The relevant code can be found at:https://github.com/AVKSKVL/DAS-F-Model
On feature representations for marmoset vocal communication analysis
Authors: Eklavya Sarkar, Kaja Wierucka, Alexandra B. Bosshard, Judith Burkart, Mathew Magimai. -Doss
Date: 2025-04-21 00:00:00
Tasks: Self-Supervised Learning

The acoustic analysis of marmoset (Callithrix jacchus) vocalizations is often used to understand the evolutionary origins of human language. Currently, the analysis is largely carried out in a manual or semi-manual manner. Thus, there is a need to develop automatic call analysis methods. In that direction, research has been limited to the development of analysis methods with small amounts of data or for specific scenarios. Furthermore, there is lack of prior knowledge about what type of information is relevant for different call analysis tasks. To address these issues, as a first step, this paper explores different feature representation methods, namely, HCTSA-based hand-crafted features Catch22, pre-trained self supervised learning (SSL) based features extracted from neural networks trained on human speech and end-to-end acoustic modeling for call-type classification, caller identification and caller sex identification. Through an investigation on three different marmoset call datasets, we demonstrate that SSL-based feature representations and end-to-end acoustic modeling tend to lead to better systems than Catch22 features for call-type and caller classification. Furthermore, we also highlight the impact of signal bandwidth on the obtained task performances.
Breaking scaling relations with inverse catalysts: a machine learning exploration of trends in $\mathrm{CO_2}$ hydrogenation energy barriers
Authors: Luuk H. E. Kempen, Marius Juul Nielsen, Mie Andersen
Date: 2025-04-23 00:00:00

The conversion of $\mathrm{CO_2}$ into useful products such as methanol is a key strategy for abating climate change and our dependence on fossil fuels. Developing new catalysts for this process is costly and time-consuming and can thus benefit from computational exploration of possible active sites. However, this is complicated by the complexity of the materials and reaction networks. Here, we present a workflow for exploring transition states of elementary reaction steps at inverse catalysts, which is based on the training of a neural network-based machine learning interatomic potential. We focus on the crucial formate intermediate and its formation over nanoclusters of indium oxide supported on Cu(111). The speedup compared to an approach purely based on density functional theory allows us to probe a wide variety of active sites found at nanoclusters of different sizes and stoichiometries. Analysis of the obtained set of transition state geometries reveals different structure--activity trends at the edge or interior of the nanoclusters. Furthermore, the identified geometries allow for the breaking of linear scaling relations, which could be a key underlying reason for the excellent catalytic performance of inverse catalysts observed in experiments.
Do Large Language Models know who did what to whom?
Authors: Joseph M. Denning, Xiaohan, Guo, Bryor Snefjella, Idan A. Blank
Date: 2025-04-23 00:00:00
Tasks: Sentence

Large Language Models (LLMs) are commonly criticized for not understanding language. However, many critiques focus on cognitive abilities that, in humans, are distinct from language processing. Here, we instead study a kind of understanding tightly linked to language: inferring who did what to whom (thematic roles) in a sentence. Does the central training objective of LLMs-word prediction-result in sentence representations that capture thematic roles? In two experiments, we characterized sentence representations in four LLMs. In contrast to human similarity judgments, in LLMs the overall representational similarity of sentence pairs reflected syntactic similarity but not whether their agent and patient assignments were identical vs. reversed. Furthermore, we found little evidence that thematic role information was available in any subset of hidden units. However, some attention heads robustly captured thematic roles, independently of syntax. Therefore, LLMs can extract thematic roles but, relative to humans, this information influences their representations more weakly.
Towards Generalized and Training-Free Text-Guided Semantic Manipulation
Authors: Yu Hong, Xiao Cai, Pengpeng Zeng, Shuai Zhang, Jingkuan Song, Lianli Gao, Heng Tao Shen
Date: 2025-04-24 00:00:00
Tasks: Style Transfer

Text-guided semantic manipulation refers to semantically editing an image generated from a source prompt to match a target prompt, enabling the desired semantic changes (e.g., addition, removal, and style transfer) while preserving irrelevant contents. With the powerful generative capabilities of the diffusion model, the task has shown the potential to generate high-fidelity visual content. Nevertheless, existing methods either typically require time-consuming fine-tuning (inefficient), fail to accomplish multiple semantic manipulations (poorly extensible), and/or lack support for different modality tasks (limited generalizability). Upon further investigation, we find that the geometric properties of noises in the diffusion model are strongly correlated with the semantic changes. Motivated by this, we propose a novel $\textit{GTF}$ for text-guided semantic manipulation, which has the following attractive capabilities: 1) $\textbf{Generalized}$: our $\textit{GTF}$ supports multiple semantic manipulations (e.g., addition, removal, and style transfer) and can be seamlessly integrated into all diffusion-based methods (i.e., Plug-and-play) across different modalities (i.e., modality-agnostic); and 2) $\textbf{Training-free}$: $\textit{GTF}$ produces high-fidelity results via simply controlling the geometric relationship between noises without tuning or optimization. Our extensive experiments demonstrate the efficacy of our approach, highlighting its potential to advance the state-of-the-art in semantics manipulation.
AUTHENTICATION: Identifying Rare Failure Modes in Autonomous Vehicle Perception Systems using Adversarially Guided Diffusion Models
Authors: Mohammad Zarei, Melanie A Jutras, Eliana Evans, Mike Tan, Omid Aaramoon
Date: 2025-04-24 00:00:00
Tasks: Autonomous Vehicles, object-detection, Object Detection

Autonomous Vehicles (AVs) rely on artificial intelligence (AI) to accurately detect objects and interpret their surroundings. However, even when trained using millions of miles of real-world data, AVs are often unable to detect rare failure modes (RFMs). The problem of RFMs is commonly referred to as the "long-tail challenge", due to the distribution of data including many instances that are very rarely seen. In this paper, we present a novel approach that utilizes advanced generative and explainable AI techniques to aid in understanding RFMs. Our methods can be used to enhance the robustness and reliability of AVs when combined with both downstream model training and testing. We extract segmentation masks for objects of interest (e.g., cars) and invert them to create environmental masks. These masks, combined with carefully crafted text prompts, are fed into a custom diffusion model. We leverage the Stable Diffusion inpainting model guided by adversarial noise optimization to generate images containing diverse environments designed to evade object detection models and expose vulnerabilities in AI systems. Finally, we produce natural language descriptions of the generated RFMs that can guide developers and policymakers to improve the safety and reliability of AV systems.