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Xin Xin

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10 papers

1 author row

EAAI Journal 2025 Journal Article

Cross-domain fault diagnosis of marine diesel engines based on stepwise diffusion and iterative bidirectional optimization

Zhen Zhao
Ziru Jin
Xin Xin
Yutong Fu
Xiaotong Huang
Liang Li
Hongyan Qin
Chong Wei

Cross-domain fault diagnosis of marine diesel engines presents significant challenges due to variations in data distribution and the limited availability of labeled fault samples under different operating conditions. To address this, an unsupervised domain-adaptive diagnostic framework is proposed, integrating stepwise diffusion and iterative bidirectional optimization to enhance fault identification. First, the quadratic axial attention transformer introduces a fourth weight in the axial computation to effectively capture the long-range spatio-temporal correlations in the time–frequency representations and strengthen the cross-axis contextual dependence. Next, the domain stepwise diffusion bridge utilizes Markov transform to gradually refine the significant distributional differences across domains into continuous sub-distributions, ensuring a smoother adaptation process. Finally, an iterative bidirectional optimization strategy is proposed to dynamically coordinate the interaction between stepwise diffusion and fault classification, where two complementary learning directions are alternately executed to preserve the semantic integrity of features. Experimental validation on a self-constructed dataset covering multiple operating conditions demonstrates the effectiveness of the proposed approach, achieving 93. 80 % average accuracy, 93. 75 % precision, and 93. 45 % recall. This approach not only breaks through the limitations of existing domain alignment methods and provides a brand new solution for cross-domain fault diagnosis, but also provides a wide range of implications for future research and applications in this field. The code and model are available at: https: //github. com/lazyJzr/UDAtask.

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NeurIPS Conference 2025 Conference Paper

How Does Topology Bias Distort Message Passing in Graph Recommender? A Dirichlet Energy Perspective

Yanbiao Ji
Yue Ding
Dan Luo
Chang Liu
Yuxiang Lu
Xin Xin
Hongtao Lu

Graph-based recommender systems have achieved remarkable effectiveness by modeling high-order interactions between users and items. However, such approaches are significantly undermined by popularity bias, which distorts the interaction graph’s structure—referred to as topology bias. This leads to overrepresentation of popular items, thereby reinforcing biases and fairness issues through the user-system feedback loop. Despite attempts to study this effect, most prior work focuses on the embedding or gradient level bias, overlooking how topology bias fundamentally distorts the message passing process itself. We bridge this gap by providing an empirical and theoretical analysis from a Dirichlet energy perspective, revealing that graph message passing inherently amplifies topology bias and consistently benefits highly connected nodes. To address these limitations, we propose Test-time Simplicial Propagation (TSP), which extends message passing to higher-order simplicial complexes. By incorporating richer structures beyond pairwise connections, TSP mitigates harmful topology bias and substantially improves the representation and recommendation of long-tail items during inference. Extensive experiments across five real-world datasets demonstrate the superiority of our approach in mitigating topology bias and enhancing recommendation quality. The implementation code is available at https: //github. com/sotaagi/TSP.

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AAAI Conference 2025 Conference Paper

Reverse Region-to-Entity Annotation for Pixel-Level Visual Entity Linking

Zhengfei Xu
Sijia Zhao
Yanchao Hao
Xiaolong Liu
Lili Li
Yuyang Yin
Bo Li
Xi Chen

Visual Entity Linking (VEL) is a crucial task for achieving fine-grained visual understanding, matching objects within images (visual mentions) to entities in a knowledge base. Previous VEL tasks rely on textual inputs, but writing queries for complex scenes can be challenging. Visual inputs like clicks or bounding boxes offer a more convenient alternative. Therefore, we propose a new task, Pixel-Level Visual Entity Linking (PL-VEL), which uses pixel masks from visual inputs to refer to objects, supplementing reference methods for VEL. To facilitate research on this task, we have constructed the MaskOVEN-Wiki dataset through an entirely automatic reverse region-entity annotation framework. This dataset contains over 5 million annotations aligning pixel-level regions with entity-level labels, which will advance visual understanding towards fine-grained. Moreover, as pixel masks correspond to semantic regions in an image, we enhance previous patch-interacted attention with region-interacted attention by a visual semantic tokenization approach. Manual evaluation results indicate that the reverse annotation framework achieved a 94.8% annotation success rate. Experimental results show that models trained on this dataset improved accuracy by 18 points compared to zero-shot models. Additionally, the semantic tokenization method achieved a 5-point accuracy improvement over the trained baseline.

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AAAI Conference 2024 Conference Paper

Confucius: Iterative Tool Learning from Introspection Feedback by Easy-to-Difficult Curriculum

Shen Gao
Zhengliang Shi
Minghang Zhu
Bowen Fang
Xin Xin
Pengjie Ren
Zhumin Chen
Jun Ma

Augmenting large language models (LLMs) with external tools has emerged as a promising approach to extending the capability of LLMs. Although there are some works that employ open-source LLMs for the tool-learning task, most of them are trained in a controlled environment in which LLMs only learn to execute the human-provided tools. However, selecting proper tools from the large toolset is also a crucial ability for the tool-learning model to be applied in real-world applications. Existing methods usually directly employ self-instruction methods to train the model, which ignores differences in tool complexity. In this paper, we propose the Confucius a novel tool-learning framework to train LLM to use complicated tools in real-world scenarios, which contains two main phases: (1) We first propose a multi-stage learning method to teach the LLM to use various tools from an easy-to-difficult curriculum; (2) thenceforth, we propose the Iterative Self-instruct from Introspective Feedback (ISIF) to dynamically construct the dataset to improve the ability to use the complicated tool. Extensive experiments conducted on both controlled and real-world settings demonstrate the superiority of our tool-learning framework in the real-world application scenario compared to both tuning-free (e.g., ChatGPT, Claude) and tuning-based baselines (e.g., GPT4Tools).

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NeurIPS Conference 2024 Conference Paper

PSL: Rethinking and Improving Softmax Loss from Pairwise Perspective for Recommendation

Weiqin Yang
Jiawei Chen
Xin Xin
Sheng Zhou
Binbin Hu
Yan Feng
Chun Chen
Can Wang

Softmax Loss (SL) is widely applied in recommender systems (RS) and has demonstrated effectiveness. This work analyzes SL from a pairwise perspective, revealing two significant limitations: 1) the relationship between SL and conventional ranking metrics like DCG is not sufficiently tight; 2) SL is highly sensitive to false negative instances. Our analysis indicates that these limitations are primarily due to the use of the exponential function. To address these issues, this work extends SL to a new family of loss functions, termed Pairwise Softmax Loss (PSL), which replaces the exponential function in SL with other appropriate activation functions. While the revision is minimal, we highlight three merits of PSL: 1) it serves as a tighter surrogate for DCG with suitable activation functions; 2) it better balances data contributions; and 3) it acts as a specific BPR loss enhanced by Distributionally Robust Optimization (DRO). We further validate the effectiveness and robustness of PSL through empirical experiments. The code is available at https: //github. com/Tiny-Snow/IR-Benchmark.

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IJCAI Conference 2019 Conference Paper

CFM: Convolutional Factorization Machines for Context-Aware Recommendation

Xin Xin
Bo Chen
Xiangnan He
Dong Wang
Yue Ding
Joemon Jose

Factorization Machine (FM) is an effective solution for context-aware recommender systems (CARS) which models second-order feature interactions by inner product. However, it is insufficient to capture high-order and nonlinear interaction signals. While several recent efforts have enhanced FM with neural networks, they assume the embedding dimensions are independent from each other and model high-order interactions in a rather implicit manner. In this paper, we propose Convolutional Factorization Machine (CFM) to address above limitations. Specifically, CFM models second-order interactions with outer product, resulting in ''images'' which capture correlations between embedding dimensions. Then all generated ''images'' are stacked, forming an interaction cube. 3D convolution is applied above it to learn high-order interaction signals in an explicit approach. Besides, we also leverage a self-attention mechanism to perform the pooling of features to reduce time complexity. We conduct extensive experiments on three real-world datasets, demonstrating significant improvement of CFM over competing methods for context-aware top-k recommendation.

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AAAI Conference 2019 Conference Paper

Chinese NER with Height-Limited Constituent Parsing

Rui Wang
Xin Xin
Wei Chang
Kun Ming
Biao Li
Xin Fan

In this paper, we investigate how to improve Chinese named entity recognition (NER) by jointly modeling NER and constituent parsing, in the framework of neural conditional random fields (CRF). We reformulate the parsing task to heightlimited constituent parsing, by which the computational complexity can be significantly reduced, and the majority of phrase-level grammars are retained. Specifically, an unified model of neural semi-CRF and neural tree-CRF is proposed, which simultaneously conducts word segmentation, part-ofspeech (POS) tagging, NER, and parsing. The challenge comes from how to train and infer the joint model, which has not been solved previously. We design a dynamic programming algorithm for both training and inference, whose complexity is O(n·4h ), where n is the sentence length and h is the height limit. In addition, we derive a pruning algorithm for the joint model, which further prunes 99. 9% of the search space with 2% loss of the ground truth data. Experimental results on the OntoNotes 4. 0 dataset have demonstrated that the proposed model outperforms the state-of-the-art method by 2. 79 points in the F1-measure.

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IJCAI Conference 2015 Conference Paper

Cross-Domain Collaborative Filtering with Review Text

Xin Xin
Zhirun Liu
Chin-Yew Lin
Heyan Huang
Xiaochi Wei
Ping Guo

Most existing cross-domain recommendation algorithms focus on modeling ratings, while ignoring review texts. The review text, however, contains rich information, which can be utilized to alleviate data sparsity limitations, and interpret transfer patterns. In this paper, we investigate how to utilize the review text to improve cross-domain collaborative filtering models. The challenge lies in the existence of non-linear properties in some transfer patterns. Given this, we extend previous transfer learning models in collaborative filtering, from linear mapping functions to non-linear ones, and propose a cross-domain recommendation framework with the review text incorporated. Experimental verifications have demonstrated, for new users with sparse feedback, utilizing the review text obtains 10% improvement in the AUC metric, and the nonlinear method outperforms the linear ones by 4%.

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AAAI Conference 2015 Conference Paper

Forecasting Collector Road Speeds Under High Percentage of Missing Data

Xin Xin
Chunwei Lu
Yashen Wang
Heyan Huang

Accurate road speed predictions can help drivers in smart route planning. Although the issue has been studied previously, most existing work focus on arterial roads only, where sensors are configured closely for collecting complete real-time data. For collector roads where sensors sparsely cover, however, speed predictions are often ignored. With GPS-equipped floating car signals being available nowadays, we aim at forecasting collector road speeds by utilizing these signals. The main challenge compared with arterial roads comes from the missing data. In a time slot of the real case, over 90% of collector roads cannot be covered by enough floating cars. Thus most traditional approaches for arterial roads, relying on complete historical data, cannot be employed directly. Aiming at solving this problem, we propose a multi-view road speed prediction framework. In the first view, temporal patterns are modeled by a layered hidden Markov model; and in the second view, spatial patterns are modeled by a collective matrix factorization model. The two models are learned and inferred simultaneously in a co-regularized manner. Experiments conducted in the Beijing road network, based on 10K taxi signals in 2 years, have demonstrated that the approach outperforms traditional approaches by 10% in MAE and RMSE.

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IJCAI Conference 2015 Conference Paper

Re-Ranking Voting-Based Answers by Discarding User Behavior Biases

Xiaochi Wei
Heyan Huang
Chin-Yew Lin
Xin Xin
Xianling Mao
Shangguang Wang

The vote mechanism is widely utilized to rank answers in community-based question answering sites. In generating a vote, a user’s attention is influenced by the answer position and appearance, in addition to real answer quality. Previously, these biases are ignored. As a result, the top answers obtained from this mechanism are not reliable, if the number of votes for the active question is not sufficient. In this paper, we solve this problem by analyzing two kinds of biases; position bias and appearance bias. We identify the existence of these biases and propose a joint click model for dealing with both of them. Our experiments in real data demonstrate how the ranking performance of the proposed model outperforms traditional methods with biases ignored by 15. 1% in precision@1, and 11. 7% in the mean reciprocal rank. A case st-udy on a manually labeled dataset futher supports the effectiveness of the proposed model.

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