GenAI4ELab/papercli-papers huggingface dataset:
AI-Powered Algorithm-Centric Quantum Processor Topology Design
Authors: Tian Li; Xiao-Yue Xu; Chen Ding; Tian-Ci Tian; Wei-You Liao; Shuo Zhang; He-Liang Huang
Venue: AAAI 2025 (main)

Abstract: Quantum computing promises to revolutionize various fields, yet the execution of quantum programs necessitates an effective compilation process. This involves strategically mapping quantum circuits onto the physical qubits of a quantum processor. The qubits' arrangement, or topology, is pivotal to the circuit's performance, a factor that often defies traditional heuristic or manual optimization methods due to its complexity. In this study, we introduce a novel approach leveraging reinforcement learning to dynamically tailor qubit topologies to the unique specifications of individual quantum circuits, guiding algorithm-driven quantum processor topology design for reducing the depth of mapped circuit, which is particularly critical for the output accuracy on noisy quantum processors. Our method marks a significant departure from previous methods that have been constrained to mapping circuits onto a fixed processor topology. Experiments demonstrate that we have achieved notable enhancements in circuit performance, with a minimum of 20% reduction in circuit depth in 60% of the cases examined, and a maximum enhancement of up to 46%. Furthermore, the pronounced benefits of our approach in reducing circuit depth become increasingly evident as the scale of the quantum circuits increases, exhibiting the scalability of our method in terms of problem size. This work advances the co-design of quantum processor architecture and algorithm mapping, offering a promising avenue for future research and development in the field.
Enhancing the Adversarial Robustness via Manifold Projection
Authors: Zhiting Li; Shibai Yin; Tai-Xiang Jiang; Yexun Hu; Jia-Mian Wu; Guowei Yang; Guisong Liu
Venue: AAAI 2025 (main)

Abstract: Deep learning has been widely applied to various aspects of computer vision, but the emergence of adversarial attacks raises concerns about its reliability.
Adversarial training (AT) is one of the most effective defense methods, which incorporates adversarial examples into the training data.
However, AT is typically employed in a discriminative learning manner, i.e., learning the mapping (conditional probability) from samples to labels, it essentially reinforces this mapping without considering the underlying data distribution.
It is notable that adversarial examples often deviate from the distribution of normal (clean) samples.
Therefore, building upon existing adversarial defense schemes, we propose to further exploit the distribution of normal samples, partly from the generative learning perspective, resulting in a novel robustness enhancement paradigm.
We train a simple autoencoder (AE) autoregressively on normal samples to learn their prior distribution, effectively serving as an image manifold. This AE is then used as a manifold projection operator to incorporate the distribution information of normal samples.
Specifically, we organically integrate the pretrained AE into the training process of both AT and adversarial distillation (AD), a method aiming at improving the robustness of small models with low capacity.
Since the AE captures the distribution of normal samples, it can adaptively pull adversarial examples closer to the normal sample manifold, weakening the attack strength of adversarial samples and easing the learning of mappings from adversarial samples to correct labels.
From the Pearson correlation coefficient (PCC) between the statistics on normal and adversarial examples, it’s validated that the AE indeed pulls adversarial samples closer to normal samples.
Extensive experiments illustrate that our proposed adversarial defense paradigm significantly improves the robustness compared with previous state-of-the-art AT and AD methods.
Whole Genome Transformer for Gene Interaction Effects in Microbiome Habitat Specificity
Authors: Zhufeng Li; Sandeep Suresh Cranganore; Nicholas Youngblut; Niki Kilbertus
Venue: AAAI 2025 (main)

Abstract: Leveraging the vast genetic diversity within microbiomes offers unparalleled insights into complex phenotypes, yet the task of accurately predicting and understanding such traits from genomic data remains challenging. We propose a framework taking advantage of existing large models for gene vectorization to predict habitat specificity from entire microbial genome sequences. Based on our model, we develop attribution techniques to elucidate gene interaction effects that drive microbial adaptation to diverse environments. We train and validate our approach on a large dataset of high quality microbiome genomes from different habitats. We not only demonstrate solid predictive performance, but also how sequence-level information of entire genomes allows us to identify gene associations underlying complex phenotypes. Our attribution recovers known important interaction networks and proposes new candidates for experimental follow up.
SCALM: Detecting Bad Practices in Smart Contracts Through LLMs
Authors: Zongwei Li; Xiaoqi Li; Wenkai Li; Xin Wang
Venue: AAAI 2025 (main)

Abstract: As the Ethereum platform continues to mature and gain widespread usage, it is crucial to maintain high standards of smart contract writing practices. While bad practices in smart contracts may not directly lead to security issues, they do elevate the risk of encountering problems. Therefore, to understand and avoid these bad practices, this paper introduces the first systematic study of bad practices in smart contracts, delving into over 35 specific issues. Specifically, we propose a large language models (LLMs)-based framework, SCALM. It combines Step-Back Prompting and Retrieval-Augmented Generation (RAG) to effectively identify and address various bad practices. Our extensive experiments using multiple LLMs and datasets have shown that SCALM outperforms existing tools in detecting bad practices in smart contracts.
An Evaluation Framework for Product Images Background Inpainting Based on Human Feedback and Product Consistency
Authors: Yuqi Liang; Jun Luo; Xiaoxi Guo; Jianqi Bi
Venue: AAAI 2025 (main)

Abstract: In product advertising applications, the automated inpainting of backgrounds utilizing AI techniques in product images has emerged as a significant task. However, the techniques still suffer from issues such as inappropriate background and inconsistent product in generated product images, and existing approaches for evaluating the quality of generated product images are mostly inconsistent with human feedback causing the evaluation for this task to depend on manual annotation. To relieve the issues above, this paper proposes Human Feedback and Product Consistency (HFPC), which can automatically assess the generated product images based on two modules. Firstly, to solve inappropriate backgrounds, human feedback on 44,000 automated inpainting product images is collected to train a reward model based on multi-modal features extracted from BLIP and comparative learning. Secondly, to filter generated product images containing inconsistent products, a fine-tuned segmentation model is employed to segment the product of the original and generated product images and then compare the differences between the above two. Extensive experiments have demonstrated that HFPC can effectively evaluate the quality of generated product images and significantly reduce the expense of manual annotation. Moreover, HFPC achieves state-of-the-art (96.4% in precision) in comparison to other open-source visual-quality-assessment models.
A Thorough Comparison Between Independent Cascade and Susceptible-Infected-Recovered Models
Authors: Panfeng Liu; Guoliang Qiu; Biaoshuai Tao; Kuan Yang
Venue: AAAI 2025 (main)

Abstract: We study cascades in social networks with the independent cascade (IC) model and the Susceptible-Infected-recovered (SIR) model. The well-studied IC model fails to capture the feature of node recovery, and the SIR model is a variant of the IC model with the node recovery feature. In the SIR model, by computing the probability that a node successfully infects another before its recovery and viewing this probability as the corresponding IC parameter, an equivalence between the two models is established, except that the events of the infections along different out-going edges of a node become dependent in the SIR model, whereas these events are independent in the IC model. In this paper, we thoroughly compare the two models and examine the effect of this extra dependency in the SIR model. By a carefully designed coupling argument, we show that the seeds in the IC model have a stronger influence spread than their counterparts in the SIR model, and sometimes it can be significantly stronger. Specifically, we prove that, given the same network, the same seed sets, and the parameters of the two models being set based on the above-mentioned equivalence, the expected number of infected nodes at the end of the cascade for the IC model is weakly larger than that for the SIR model, and there are instances where this dominance is significant.

We also study the influence maximization problem (the optimization problem of selecting a set of nodes as initial seeds in a social network to maximize their influence) with the SIR model. We show that the above-mentioned difference in the two models yields different seed-selection strategies, which motivates the design of influence maximization algorithms specifically for the SIR model. We design efficient approximation algorithms with theoretical guarantees by adapting the reverse-reachable-set-based algorithms, commonly used for the IC model, to the SIR model.
IDseq: Decoupled and Sequentially Detecting and Grounding Multi-Modal Media Manipulation
Authors: Runxin Liu; Tian Xie; Jiaming Li; Lingyun Yu; Hongtao Xie
Venue: AAAI 2025 (main)

Abstract: Detecting and grounding multi-modal media manipulation
aims to categorize the type and localize the region of manipulation for image-text pairs in both two modalities. Existing
methods have not sufficiently explored the intrinsic properties of the manipulated images, which contain both forgery and content features, leading to inefficient utilization. To address this problem, we propose an Image-Driven Decoupled
Sequential Framework (IDseq), designed to decouple image
features and rationally integrate them to accomplish different
sub-tasks effectively. Specifically, IDseq employs two specially designed disentangled losses to guide the disentangled
learning of forgery and content features. To efficiently leverage these features, we propose a Decoupled Image Manipulation Decoder (DIMD) that processes image tasks within a decoupled schema. We mitigate their exclusive competition by
separating the image tasks into forgery-relevant and content-relevant components and training them without gradient interaction. Additionally, we utilize content features enhanced by
the proposed Manipulation Indicator Generator (MIG) for the
text tasks, which provide the maximal visual information as a
reference while eliminating interference from unverified image data. Extensive experiments show the superiority of our
IDseq, where it notably outperforms SOTA methods on the
fine-grained classification by 3.8% in mAP and the forgery
face grounding by 8.7% in IoUmean, even 1.3% in F1 on the
most challenging manipulated text grounding.
Controllable Protein Sequence Generation with LLM Preference Optimization
Authors: Xiangyu Liu; Yi Liu; Silei Chen; Wei Hu
Venue: AAAI 2025 (main)

Abstract: Designing proteins with specific attributes offers an important solution to address biomedical challenges. Pre-trained protein large language models (LLMs) have shown promising results on protein sequence generation. However, to control sequence generation for specific attributes, existing work still exhibits poor functionality and structural stability. In this paper, we propose a novel controllable protein design method called CtrlProt. We finetune a protein LLM with a new multi-listwise preference optimization strategy to improve generation quality and support multi-attribute controllable generation. Experiments demonstrate that CtrlProt can meet functionality and structural stability requirements effectively, achieving state-of-the-art performance in both single-attribute and multi-attribute protein sequence generation.
Recoverable Facial Identity Protection via Adaptive Makeup Transfer Adversarial Attacks
Authors: Xiyao Liu; Junxing Ma; Xinda Wang; Qianyu Lin; Jian Zhang; Gerald Schaefer; Cagatay Turkay; Hui Fang
Venue: AAAI 2025 (main)

Abstract: Unauthorised face recognition (FR) systems have posed significant threats to digital identity and privacy protection. To alleviate the risk of compromised identities, recent makeup transfer-based attack methods embed adversarial signals in order to confuse unauthorised FR systems. However, their major weakness is that they set up a fixed image unrelated to both the protected and the makeup reference images as the confusion identity, which in turn has a negative impact on both attack success rate and visual quality of transferred photos. In addition, the generated images cannot be recognised by authorised FR systems once attacks are triggered. To address these challenges, in this paper, we propose a Recoverable Makeup Transferred Generative Adversarial Network (RMT-GAN) which has the distinctive feature of improving its image-transfer quality by selecting a suitable transfer reference photo as the target identity. Moreover, our method offers a solution to recover the protected photos to their original counterparts that can be recognised by authorised systems. Experimental results demonstrate that our method provides significantly improved attack success rates while maintaining higher visual quality compared to state-of-the-art makeup transfer-based adversarial attack methods. Our code and supplementary materials are available on Github.
Knowledge-Guided Domain Adaptation Model for Transferring Drug Response Prediction from Cell Lines to Patients
Authors: Xuan Liu; Menglu Li
Venue: AAAI 2025 (main)

Abstract: Drug response prediction (DRP) is a longstanding challenge in modern oncology that underpins personalized treatment. Early DRP methods, trained on label-rich cell line samples, suffer from performance degradation when applied to label-scarce patient samples due to the distribution shift. Recently, a few transfer learning efforts have addressed this issue by aligning cell line (source domain) and patient (target domain) data via unsupervised domain adaptation (UDA). However, these efforts often treat each drug's response prediction as an isolated task, requiring model retraining when the drug changes; and focus only on aligning data distributions as a whole, neglecting the category (e.g., different cancers or tissues) confusion problem. To address these limitations, we propose a knowledge-guided domain adaptation model to transfer the DRP from cell lines to patients, named TransDRP. Specifically, TransDRP operates in two phases: pre-training and adaptation. In the first phase, we pre-train a multi-label graph neural network using molecular knowledge, to simultaneously predict responses for various drugs and capture their interdependencies. In the second phase, we implement a global-local domain adversarial strategy with clinical knowledge, to encourage representation alignment within same cancer categories and separation among different cancer categories across domains. Extensive experiments demonstrate that TransDRP outperforms state-of-the-art UDA methods in both transfer efficiency and precision for the patient DRP.
TrojanDec: Data-free Detection of Trojan Inputs in Self-supervised Learning
Authors: Yupei Liu; Yanting Wang; Jinyuan Jia
Venue: AAAI 2025 (main)

Abstract: An image encoder pre-trained by self-supervised learning can be used as a general-purpose feature extractor to build downstream classifiers for various downstream tasks. However, many studies showed that an attacker can embed a trojan into an encoder such that multiple downstream classifiers built based on the trojaned encoder simultaneously inherit the trojan behavior. In this work, we propose TrojanDec, the first data-free method to identify and recover a test input embedded with a trigger. Given a (trojaned or clean) encoder and a test input, TrojanDec first predicts whether the test input is trojaned. If not, the test input is processed in a normal way to maintain the utility. Otherwise, the test input will be further restored to remove the trigger. Our extensive evaluation shows that TrojanDec can effectively identify the trojan (if any) from a given test input and recover it under state-of-the-art trojan attacks. We further demonstrate by experiments that our TrojanDec outperforms the state-of-the-art defenses.
BiDeV: Bilateral Defusing Verification for Complex Claim Fact-Checking
Authors: Yuxuan Liu; Hongda Sun; Wenya Guo; Xinyan Xiao; Cunli Mao; Zhengtao Yu; Rui Yan
Venue: AAAI 2025 (main)

Abstract: Complex claim fact-checking performs a crucial role in disinformation detection. However, existing fact-checking methods struggle with claim vagueness, specifically in effectively handling latent information and complex relations within claims. Moreover, evidence redundancy, where non-essential information complicates the verification process, remains a significant issue. To tackle these limitations, we propose Bilateral Defusing Verification (BiDeV), a novel fact-checking working-flow framework integrating multiple role-played LLMs to mimic the human-expert fact-checking process. BiDeV consists of two main modules: Vagueness Defusing identifies latent information and resolves complex relations to simplify the claim, and Redundancy Defusing eliminates redundant content to enhance the evidence quality. Extensive experimental results on two widely used challenging fact-checking benchmarks (Hover and Feverous-s) demonstrate that our BiDeV can achieve the best performance under both gold and open settings. This highlights the effectiveness of BiDeV in handling complex claims and ensuring precise fact-checking.
WiFi CSI Based Temporal Activity Detection via Dual Pyramid Network
Authors: Zhendong Liu; Le Zhang; Bing Li; Yingjie Zhou; Zhenghua Chen; Ce Zhu
Venue: AAAI 2025 (main)

Abstract: We address the challenge of WiFi-based temporal activity detection and  propose an efficient Dual Pyramid Network that integrates Temporal Signal Semantic Encoders and Local Sensitive Response Encoders. The Temporal Signal Semantic Encoder splits feature learning into high and low-frequency components, using a novel Signed Mask-Attention mechanism to emphasize important areas and downplay unimportant ones, with the features fused using ContraNorm. The Local Sensitive Response Encoder captures fluctuations without learning. These feature pyramids are then combined using a new cross-attention fusion mechanism. We also introduce a dataset with over 2,114 activity segments across 553 WiFi CSI samples, each lasting around 85 seconds. Extensive experiments show our method outperforms challenging baselines.
DAMMFND: Domain-Aware Multimodal Multi-view Fake News Detection
Authors: Weihai Lu; Yu Tong; Zhiqiu Ye
Venue: AAAI 2025 (main)

Abstract: Recently, multi-domain fake news detection has garnered increasing attention in academia. In particular, the integration of multimodal information into multi-domain fake news detection has emerged as a highly promising research direction. However, this field faces three main challenges: (1) Inaccurate domain identification, where predefined explicit identifiers fail to adapt to the inherent complexity of data; (2) Imbalanced multi-domain data distribution, which may induce negative transfer effects; and (3) Variable multi-domain modal contributions, indicating domain-specific differences in how various modalities influence news veracity assessments. To address these issues, we propose the Domain-Aware Multi-Modal Multi-View Fake News Detection (DAMMFND) framework. DAMMFND effectively extracts more accurate domain information through Domain Disentanglement, while simultaneously mitigating negative transfer between domains. Furthermore, DAMMFND introduces a Domain-Aware Multi-View Discriminator and a Domain-Enhanced Multi-view Decision Layer, which accurately quantify the contribution of domain information to multimodal, multi-view decision-making processes. Extensive experiments conducted on two real-world datasets demonstrate that the proposed model outperforms state-of-the-art baselines.
Graph-Based Cross-Domain Knowledge Distillation for Cross-Dataset Text-to-Image Person Retrieval
Authors: Bingjun Luo; Jinpeng Wang; Zewen Wang; Junjie Zhu; Xibin Zhao
Venue: AAAI 2025 (main)

Abstract: Video surveillance systems are crucial components for ensuring public safety and management in smart city. As a fundamental task in video surveillance, text-to-image person retrieval aims to retrieve the target person from an image gallery that best matches the given text description. Most existing text-to-image person retrieval methods are trained in a supervised manner that requires sufficient labeled data in the target domain. However, it is common in practice that only unlabeled data is available in the target domain due to the difficulty and cost of data annotation, which limits the generalization of existing methods in practical application scenarios. To address this issue, we propose a novel unsupervised domain adaptation method, termed Graph-Based Cross-Domain Knowledge Distillation (GCKD), to learn the cross-modal feature representation for text-to-image person retrieval in a cross-dataset scenario. The proposed GCKD method consists of two main components. Firstly, a graph-based multi-modal propagation module is designed to bridge the cross-domain correlation among the visual and textual samples. Secondly, a contrastive momentum knowledge distillation module is proposed to learn the cross-modal feature representation using the online knowledge distillation strategy. By jointly optimizing the two modules, the proposed method is able to achieve efficient performance for cross-dataset text-to-image person retrieval. Extensive experiments on three publicly available text-to-image person retrieval datasets demonstrate the effectiveness of the proposed GCKD method, which consistently outperforms the state-of-the-art baselines.