GenAI4ELab/papercli-papers huggingface dataset:
Disentangled Table-Graph Representation for Interpretable Transmission Line Fault Location
Authors: Na Yu; Yutong Deng; Shunyu Liu; Kaixuan Chen; Tongya Zheng; Mingli Song
Venue: AAAI 2025 (main)

Abstract: The fault location task in power grids is crucial for maintaining social order and ensuring public safety. However, existing methods that rely on tabular state records often neglect the intrinsic topological influences of transmission lines, resulting in a segmented approach to fault location that consists of multiple stages. In this paper, we propose an Disentangled Table-Graph representation framework, termed DTG, which integrates fault location tasks at coarse-grained line levels and fine-grained point levels within an end-to-end learning paradigm. Our innovative disentanglement strategy produces interpretable attribution coefficients that connect tabular records and transmission line topology, thereby facilitating fault location at both line- and point-levels. The joint prediction tasks designed around our disentangled tabular graph representation promote mutual information exchange between features and topology of transmission lines in an interpretable manner. Experimental results on the 7-bus system, 36-bus system and a realistic 325-bus system in China demonstrate that the proposed method adapt to different topological structures and handle different types of faults. Compared to traditional methods, DTG4Power achieves high accuracy in both fault lines and fault points.
RaCMC: Residual-Aware Compensation Network with Multi-Granularity Constraints for Fake News Detection
Authors: Xinquan Yu; Ziqi Sheng; Wei Lu; Xiangyang Luo; Jiantao Zhou
Venue: AAAI 2025 (main)

Abstract: Multimodal fake news detection aims to automatically identify real or fake news, thereby mitigating the adverse effects caused by such misinformation. Although prevailing approaches have demonstrated their effectiveness, challenges persist in cross-modal feature fusion and refinement for classification. To address this, we present a residual-aware compensation network with multi-granularity constraints (RaCMC) for fake news detection, that aims to sufficiently interact and fuse cross-modal features while amplifying the differences between real and fake news. First, a multiscale residual-aware compensation module is designed to interact and fuse features at different scales, and ensure both the consistency and exclusivity of feature interaction, thus acquiring high-quality features. Second, a multi-granularity constraints module is implemented to limit the distribution of both the news overall and the image-text pairs within the news, thus amplifying the differences between real and fake news at the news and feature levels. Finally, a dominant feature fusion reasoning module is developed to comprehensively evaluate news authenticity from the perspectives of both consistency and inconsistency. Experiments on three public datasets, including Weibo17, Politifact and GossipCop, reveal the superiority of the proposed method.
Reinforced Multi-teacher Knowledge Distillation for Efficient General Image Forgery Detection and Localization
Authors: Zeqin Yu; Jiangqun Ni; Jian Zhang; Haoyi Deng; Yuzhen Lin
Venue: AAAI 2025 (main)

Abstract: Image forgery detection and localization (IFDL) is of vital importance as forged images can spread misinformation that poses potential threats to our daily life. However, previous methods still struggled to effectively handle forged images processed with diverse forgery operations in real-world scenarios. In this paper, we propose a novel Reinforced Multi-teacher Knowledge Distillation (Re-MTKD) framework for the IFDL task, structured around an encoder-decoder ConvNeXt-UperNet along with Edge-Aware Module, named Cue-Net. First, three Cue-Net models are separately trained for the three main types of image forgeries, i.e., copy-move, splicing and inpainting, which then serve as the multi-teacher models to train the target student model with Cue-Net through self-knowledge distillation. A Reinforced Dynamic Teacher Selection (Re-DTS) strategy is developed to dynamically assign weights to the involved teacher models, which facilitates specific knowledge transfer and enables the student model to effectively learn both the common and specific natures of diverse tampering traces. Extensive experiments demonstrate that, compared with other state-of-the-art methods, the proposed method achieves superior performance on several recently emerged datasets comprised of various kinds of image forgeries.
Accurate Nucleic Acid-Binding Residue Identification Based Domain-Adaptive Protein Language Model and Explainable Geometric Deep Learning
Authors: Wenwu Zeng; Liangrui Pan; Boya Ji; Liwen Xu; Shaoliang Peng
Venue: AAAI 2025 (main)

Abstract: Protein-nucleic acid interactions play a fundamental and critical role in a wide range of life activities. Accurate identification of nucleic acid-binding residues helps to understand the intrinsic mechanisms of the interactions. However, the accuracy and interpretability of existing computational methods for recognizing nucleic acid-binding residues need to be further improved. Here, we propose a novel method called GeSite based the domain-adaptive protein language model and E(3)-equivariant graph neural network. Prediction results across multiple benchmark test sets demonstrate that GeSite is superior or comparable to state-of-the-art prediction methods. The MCC values of GeSite are 0.522 and 0.326 for the one DNA-binding residue test set and one RNA-binding resi-due test set, which are 0.57 and 38.14% higher than that of the second-best method, respectively. Detailed experi-mental results suggest that the advanced performance of GeSite lies in the well-designed nucleic acid-binding pro-tein adaptive language model. Additionally, interpretabil-ity analysis exposes the perception of the prediction mod-el on various remote and close functional domains, which is the source of its discernment ability.
SWAMamba: A Sliding Window Attention Mamba Framework for Predicting Translation Elongation Rates
Authors: Xi Zeng; Fei Ni; Shaoqing Jiao; Dazhi Lu; Jianye Hao; Jiajie Peng
Venue: AAAI 2025 (main)

Abstract: Translation elongation is essential for cellular proteostasis and is implicated in cancer and neurodegeneration. Accurately predicting the rate of ribosome elongation in each codon (also called ribosomal A site) on mRNA is important for understanding and modulating protein synthesis. However, predicting elongation rates is challenging due to the trade-off between capturing distal codon interactions and focusing on proximal codon effects at the A site. Approaches capturing distal codon interactions in the coding sequences (CDS) of mRNA fail to effectively differentiate critical regions (codons near the A site) due to insufficient effective mechanisms for focusing on these regions. Conversely, due to the limitations of models when handling long mRNA sequences, some methods simplify inputs by conditioning solely on proximal codons surrounding the A site, leading to the loss of important information from distal codons. To address this issue, we leverage Mamba's success in capturing long-range dependencies to enable the consideration of distant codons' impact on the A site. Additionally, we introduce a sliding window attention mechanism to emphasize the proximal codons around the A site during ribosome elongation. Building on these advancements, we present Sliding Window Attention Mamba (SWAMamba), a novel framework that simultaneously leverages both proximal and distal codon effects on the A site. We conduct comprehensive evaluations on ribosome data across four species and find that SWAMamba significantly outperformed current state-of-the-art methods in predicting translation elongation rates.
Portcullis: A Scalable and Verifiable Privacy Gateway for Third-Party LLM Inference
Authors: Jiangou Zhan; Wenhui Zhang; Zheng Zhang; Huanran Xue; Yao Zhang; Ye Wu
Venue: AAAI 2025 (main)

Abstract: Businesses using third-party LLMs face privacy risks from exposed prompts. This paper presents Portcullis, a privacy-preserving gateway that safeguards sensitive data while supporting efficient and accurate LLM responses. Portcullis functions as a mediator, anonymizing sensitive data in prompts through parallel substitution, securely interacting with LLMs, and accurately reconstructing responses. It ensures all data processing occurs within secure encrypted memory. The gateway is attested to ensure trustworthiness and protect user privacy. Portcullis is the first of its kind, offering a verifiable and scalable privacy gateway for third-party LLM inferences.

We assess Portcullis's efficiency as a confidential container platform, demonstrating that its startup time scales linearly, ensuring scalability. Additionally, we evaluate its runtime performance using the PII and Enron Email Dataset. For masking and unmasking workloads, Portcullis outperforms Hide-and-Seek by 96x speed up, while maintaining equal or better false positive and false negative rates compared to existing solutions. On the Enron dataset, Portcullis achieves notably higher accuracy, surpassing Hide-and-Seek by over 0.1 for GPT-4o mini.
Is LLMs Hallucination Usable? LLM-based Negative Reasoning for Fake News Detection
Authors: Chaowei Zhang; Zongling Feng; Zewei Zhang; Jipeng Qiang; Guandong Xu; Yun Li
Venue: AAAI 2025 (main)

Abstract: The questionable responses caused by knowledge hallucination may lead to LLMs' unstable ability in decision-making. However, it has never been investigated whether the LLMs' hallucination is possibly usable for generating negative reasoning to assist fake news detection. In this paper, we propose a novel supervised self-reinforced reasoning rectification approach - SR^3 that not only yields common reasonable reasoning for news but also forces LLMs to generate the wrong understandings of news via LLMs reflection for semantic consistency learning. Upon that, we construct a negative reasoning-based news learning model called - NRFE, which leverages positive or negative news-reasoning pairs for learning the semantic consistency between them. To avoid the impact of label-implicated reasoning, we deploy a student model - NRFE-D that only takes news content as input to inspect the performance of our method by distilling the knowledge from NRFE. The experimental results verified on three popular fake news datasets demonstrate the superiority of our method compared with three kinds of baselines including prompting-based LLMs, fine-tuning-based PLMs, and other representative fake news detection methods.
BERT-Based Code Learning for Exception Localization and Type Prediction
Authors: Chongyu Zhang; Qiping Tao; Liangyu Chen; Min Zhang
Venue: AAAI 2025 (main)

Abstract: Exception handling is crucial but challenging in program development. It needs to identify and handle all potential exceptions within programs to ensure system security and stabilization. Traditional exception handling relies on the expertise and experience of programmers, which often leads to oversights. Therefore, identifying exceptional code and recommending handling solutions are hot research topics with significant practical value. This paper presents a model called CodeHunter for exception localization and type prediction. The model first utilizes BERT-based model to represent code features and then uses Bi-LSTM for sequence labeling to pinpoint exceptional code. Additionally, this model also considers contextual features of the exception code and learns weights for the code within the try block and its context through the self-attention mechanism. Subsequently, it performs exception localization and predicts exception types. We conduct experiments on three different datasets. The results demonstrate that in the task of exception localization, our model can achieve a maximum accuracy of 98.6%, exceeding SOTA baselines by 11.2%. In the task of exception type prediction, our model can surpass the accuracy of SOTA baselines by a maximum of 18.7%, achieving 92.0% Top-1 accuracy. The rationality of techniques used in our model is also proved by the ablation testing. The model is implemented as an IDE plugin for programming convenience.
Revolutionizing Encrypted Traffic Classification with MH-Net: A Multi-View Heterogeneous Graph Model
Authors: Haozhen Zhang; Haodong Yue; Xi Xiao; Le Yu; Qing Li; Zhen Ling; Ye Zhang
Venue: AAAI 2025 (main)

Abstract: With the growing significance of network security, the classification of encrypted traffic has emerged as an urgent challenge. Traditional byte-based traffic analysis methods are constrained by the rigid granularity of information and fail to fully exploit the diverse correlations between bytes. To address these limitations, this paper introduces MH-Net, a novel approach for classifying network traffic that leverages multi-view heterogeneous traffic graphs to model the intricate relationships between traffic bytes. The essence of MH-Net lies in aggregating varying numbers of traffic bits into multiple types of traffic units, thereby constructing multi-view traffic graphs with diverse information granularities. By accounting for different types of byte correlations, such as header-payload relationships, MH-Net further endows the traffic graph with heterogeneity, significantly enhancing model performance. Notably, we employ contrastive learning in a multi-task manner to strengthen the robustness of the learned traffic unit representations. Experiments conducted on the ISCX and CIC-IoT datasets for both the packet-level and flow-level traffic classification tasks demonstrate that MH-Net achieves the best overall performance compared to dozens of SOTA methods.
mRNA2vec: mRNA Embedding with Language Model in the 5'UTR-CDS for mRNA Design
Authors: Honggen Zhang; Xiangrui Gao; June Zhang; Lipeng Lai
Venue: AAAI 2025 (main)

Abstract: Messenger RNA (mRNA)-based vaccines are accelerating the discovery of new drugs and revolutionizing the pharmaceutical industry. However, selecting particular mRNA sequences for vaccines and therapeutics from extensive mRNA libraries is costly. Effective mRNA therapeutics require carefully designed sequences with optimized expression levels and stability. This paper proposes a novel contextual language model (LM)-based embedding method: mRNA2vec. In contrast to existing mRNA embedding approaches, our method is based on the self-supervised teacher-student learning framework of data2vec. We jointly use the 5' untranslated region (UTR) and coding sequence (CDS) region as the input sequences. We adapt our LM-based approach specifically to mRNA by 1) considering the importance of location on the mRNA sequence with probabilistic masking, 2) using Minimum Free Energy (MFE) prediction and Secondary Structure (SS) classification as additional pretext tasks. mRNA2vec demonstrates significant improvements in translation efficiency (TE) and expression level (EL) prediction tasks in UTR compared to SOTA methods such as UTR-LM. It also gives a competitive performance in mRNA stability and protein production level tasks in CDS such as CodonBERT.
Phoneme-Level Feature Discrepancies: A Key to Detecting Sophisticated Speech Deepfakes
Authors: Kuiyuan Zhang; Zhongyun Hua; Rushi Lan; Yushu Zhang; Yifang Guo
Venue: AAAI 2025 (main)

Abstract: Recent advancements in text-to-speech and speech conversion technologies have enabled the creation of highly convincing synthetic speech. While these innovations offer numerous practical benefits, they also cause significant security challenges when maliciously misused. Therefore, there is an urgent need to detect these synthetic speech signals. Phoneme features provide a powerful speech representation for deepfake detection. However, previous phoneme-based detection approaches typically focused on specific phonemes, overlooking temporal inconsistencies across the entire phoneme sequence. In this paper, we develop a new mechanism for detecting speech deepfakes by identifying the inconsistencies of phoneme-level speech features. We design an adaptive phoneme pooling technique that extracts sample-specific phoneme-level features from frame-level speech data. By applying this technique to features extracted by pre-trained audio models on previously unseen deepfake datasets, we demonstrate that deepfake samples often exhibit phoneme-level inconsistencies when compared to genuine speech. To further enhance detection accuracy, we propose a deepfake detector that uses a graph attention network to model the temporal dependencies of phoneme-level features. Additionally, we introduce a random phoneme substitution augmentation technique to increase feature diversity during training. Extensive experiments on four benchmark datasets demonstrate the superior performance of our method over existing state-of-the-art detection methods.
Multi-View Collaborative Learning Network for Speech Deepfake Detection
Authors: Kuiyuan Zhang; Zhongyun Hua; Rushi Lan; Yifang Guo; Yushu Zhang; Guoai Xu
Venue: AAAI 2025 (main)

Abstract: As deep learning techniques advance rapidly, deepfake speech synthesized through text-to-speech or voice conversion networks is becoming increasingly realistic, posing significant challenges for detection and raising potential threats to social security. This growing realism has prompted extensive research in speech deepfake detection. However, current detection methods primarily focus on extracting features from either the raw waveform or the spectrogram, often overlooking the valuable correspondences between these two modalities that could enhance the detection of previously unseen types of deepfakes. In this work, we propose a multi-view collaborative learning network for speech deepfake detection, which jointly learns robust speech representations from both raw waveforms and spectrograms. 
Specifically, we first design a Dual-Branch Contrastive Learning (DBCL) framework for learning different view features. DBCL consists of two branches that learn representations from the raw waveform or the spectrogram and utilizes contrastive learning to enhance inter- and inner-view correlations. Additionally, we introduce a Waveform-Spectrogram Fusion Module (WSFM) to exchange multi-view information for collaborative learning. In the feature learning process, WSFM converts features between views and merges them adaptively using waveform-spectrogram cross-attention. The final detection is conducted based on the concatenation of the waveform and spectrogram features. We conduct extensive experiments on four benchmark deepfake speech detection datasets, and the experimental results demonstrate that our method can achieve better detection performance than current state-of-the-art detection methods.
Multi-Edge Reinforced Collaborative Data Acquisition for Continuous Video Analytics by Prioritizing Quality over Quantity
Authors: Lei Zhang; Guanyu Gao; Haiyan Yin; Huaizheng Zhang
Venue: AAAI 2025 (main)

Abstract: Edge computing-based video analytics faces data drift issues due to the occurrence of unseen objects or scenes in ever-changing environments. To maintain accuracy, continuous learning (CL) retrains stale models periodically with newly obtained data. However, it leads to unaffordable costs, as we must keep labeling drift data and retraining models. Regarding this concern, we first investigate video patterns across multiple cameras within an area and reveal significant data redundancies. We find that many of the same objects can be captured by multiple edge cameras or appear many times on the same edges. Our quantitative findings suggest that selecting a subset of high-quality data for CL is preferable over using a larger quantity. Yet, existing efforts for data acquisition have only focused on a single static dataset. These methods are not suitable for multi-edge video analytics scenarios, where videos are captured from multiple sources with non-iid data distribution. Hence, we propose a multi-edge collaborative active video acquisition (AVA) framework to collaboratively learn a reinforced video acquisition strategy to identify informative video frames from multiple edge nodes that best enhance model accuracy, avoiding redundancy across edges. Extensive experiments on three video datasets demonstrate that, our method achieves comparable performance to full-set video training while utilizing only 20% of the data in classification tasks. In object detection tasks, our methods can maintain productive accuracy with a reduction of nearly 70% in training video frames.
Efficient Traffic Prediction Through Spatio-Temporal Distillation
Authors: Qianru Zhang; Xinyi Gao; Haixin Wang; Siu Ming Yiu; Hongzhi Yin
Venue: AAAI 2025 (main)

Abstract: Graph neural networks (GNNs) have gained considerable attention in recent years for traffic flow prediction due to their ability to learn spatio-temporal pattern representations through a graph-based message-passing framework. Although GNNs have shown great promise in handling traffic datasets, their deployment in real-life applications has been hindered by scalability constraints arising from high-order message passing. Additionally, the over-smoothing problem of GNNs may lead to indistinguishable region representations as the number of layers increases, resulting in performance degradation. To address these challenges, we propose a new knowledge distillation paradigm termed LightST that transfers spatial and temporal knowledge from a high-capacity teacher to a lightweight student. Specifically, we introduce a spatio-temporal knowledge distillation framework that helps student MLPs capture graph-structured global spatio-temporal patterns while alleviating the over-smoothing effect with adaptive knowledge distillation. 
Extensive experiments verify that LightST significantly speeds up traffic flow predictions by 5X to 40X compared to state-of-the-art spatio-temporal GNNs, all while maintaining superior accuracy.
Motif-Oriented Representation Learning with Topology Refinement for Drug-Drug Interaction Prediction
Authors: Ran Zhang; Xuezhi Wang; Guannan Liu; Pengyang Wang; Yuanchun Zhou; Pengfei Wang
Venue: AAAI 2025 (main)

Abstract: Drug-Drug Interaction (DDI) prediction has attracted considerable attention in designing multi-drug combination strategies and avoiding adverse reactions. Notably, Artificial Intelligence (AI)-driven DDI prediction methods have emerged as a pivotal research paradigm. However, most AI-driven DDI prediction methods fall short in exploring intra-molecular motifs, and heavily rely on the overly idealized assumption of the complete inter-molecular topology, limiting their expressive capacities. To this end, we propose a Motif-Oriented representation learning with TOpology Refinement for DDI prediction, namely MOTOR, to exploit both the multi-granularity motif information and the topological structure of DDI networks. Specifically, MOTOR effectively captures motif internal structures, motif local contexts, and motif global semantics. Furthermore, MOTOR employs an iterative learning strategy to continuously refine the DDI topology and optimize the corresponding drug representations. Extensive experimental results demonstrate that MOTOR exhibits superior performance with interpretable insights in DDI prediction tasks across three real-world datasets, thereby opening up new avenues in AI-driven DDI prediction.