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IEEE International Conference on data Mining (ICDM)

作者： Bo Wang Zhao Zhang Jicong Fan Mingbo Zhao Choujun Zhan Mingliang Xu School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) & Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China School of Data Science The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China School of Information Science and Technology Donghua University Shanghai China School of Electrical and Computer Engineering Nanfang College Guangzhou Guangzhou China School of Information Engineering Zhengzhou University Zhengzhou China

Image captioning is still a challenging task aiming at describing the contents of image by words. Current image caption methods usually assume the object relation to be important if the semantic and spatial geometric relationships between objects are close and large, but the relations meeting this assumption are not necessarily important to describe the contents of image in a fine-grained way. That is, the importance of fine-grained object relations is not properly taken into account. Besides, current Transformer based image caption models also fail to consider the importance of fine-grained objects, since they generate all the words of a sentence at one time, which cannot Figure out which objects are more important and vice versa. In this paper, we propose a novel Fine-grained Adaptive Object Transformer (FineFormer) network, which can jointly discover the importance of fine-grained objects and object relations for image captioning. Specifically, a new concept of adaptive soft-foreground attention is proposed to highlight the fine-grained objects dominating the descriptive contents. To characterize and calculate the important relations between fine-grained objects, we also propose an adaptive object relation attention to refine the object relation from the generation process of relation. As such, FineFormer can describe the contents of image more accurately, by reducing the interference of unimportant objects in the background. Extensive experiments on the highly-competitive MS-COCO dataset demonstrated the superiority of our FineFormer.

关键词： Adaptation models Adaptive systems Current transformers Semantics Interference data mining Task analysis

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Counterfactual Faithful data Generation Based on Disentangled Representation for Compound Fault Diagnosis of Rolling Bearings

Counterfactual Faithful Data Generation Based on Disentangle...

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Sensing, Measurement & data Analytics in the era of Artificial Intelligence (ICSMD), International Conference on

作者： Xu Ding Qiang Zhu Song Wang Yiqi Zhang Hui Wang Juan Xu Institute of Industry and Equipment Technology Hefei University of Technology Hefei China School of Mechanical Engineering Hefei University of Technology Hefei China Bengbu Triumph Engineering Technology Co. Ltd Bengbu China Key Laboratory of Knowledge Engineering with Big Data School of Computer and Information Hefei University of Technology Hefei China

Recently, deep learning and human-out-of-the-loop methods enjoy their prosperous applications in mechanical fault diagnosis. Nonetheless, the None-IID(independent and identically distributed) issue radicated in acquired data severely limits the stability and accuracy of compound fault diagnosis of rolling bearings. This paper proposes a sample augmentation method for generating simulated signals based on the concept of counterfactuals. Firstly, disentangled representations and counterfactual faithful theory are applied to classify the original signal into two categories of properties. One is the fault semantics encoded from the original vibration signal. And the other is the sample attribute encoded by the encoder of Variational Autoencoders (VAEs). Secondly, the counterfactual faithful pseudo-samples are conjured through the Generative Adversarial Network(GAN) using the seeds of the “factual” sample attributes and “counterfactual” fault semantics to compensate for the drawback of distribution shift. Finally, the original samples and pseudo-samples are used as the CNN classifier dataset to realize bearing fault diagnosis. Experiments show that this method can generate counterfactual signals that are highly consistent with the original data distribution and can achieve better classification accuracy after balancing the dataset.

关键词： Fault diagnosis Wavelet transforms Vibrations Semantics Rolling bearings Feature extraction Stability analysis

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Injecting Numerical Reasoning Skills into knowledge Base Question Answering Models

arXiv

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arXiv 2021年

作者： Feng, Yu Zhang, Jing Zhang, Xiaokang Liu, Lemao Li, Cuiping Chen, Hong Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education China School of Information Renmin University of China China Tencent AI Lab United States

Embedding-based methods are popular for knowledge Base Question Answering (KBQA), but few current models have numerical reasoning skills and thus struggle to answer ordinal constrained questions. This paper proposes a new embedding-based KBQA framework which particularly takes numerical reasoning into account. We present NumericalTransformer on top of NSM, a state-of-the-art embedding-based KBQA model, to create NT-NSM. To enable better training, we propose two pre-training tasks with explicit numerical-oriented loss functions on two generated training datasets and a template-based data augmentation method for enriching ordinal constrained QA dataset. Extensive experiments on KBQA benchmarks demonstrate that with the help of our training algorithm, NT-NSM is empowered with numerical reasoning skills and substantially outperforms the baselines in answering ordinal constrained questions. Copyright © 2021, The Authors. All rights reserved.

关键词： Embeddings

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Multiagent Q-Learning for Multicrew Dynamic Scheduling and Routing in Road Network Restoration

Multiagent Q-Learning for Multicrew Dynamic Scheduling and R...

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Chinese Control and Decision Conference, CCDC

作者： Yufeng Shen Lianqi Duan Qianshun Zhu Zhaopin Su Guofu Zhang School of Computer Science and Information Engineering Hefei University of Technology Hefei 230601 School of Computer Science and Information Engineering Hefei University of Technology Hefei Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education Hefei Intelligent Interconnected Systems Laboratory of Anhui Province (Hefei University of Technology) Hefei Anhui Province Key Laboratory of Industry Safety and Emergency Technology (Hefei University of Technology) Heifei

ISBN: (纸本)9781665478977

Road network restoration is an important issue in the post-disaster disposal and rescue, especially when extraordinarily serious natural disasters (e.g., floods and earthquakes) occur. Central to this endeavour is the problem of determining how to reasonably schedule and route the repair crew to quickly restore the damaged road network and establish reliable supply lines from supply nodes to demand nodes. However, most existing work focuses on the activities of the single repair crew in the static road network, which is unable to adapt to the dynamic changes of the road network caused by secondary disasters, such as rock slides and debris flows. Consequently, this work is concentrated on multicrew dynamic scheduling and routing in road network restoration. Specifically, a model of multicrew dynamic scheduling and routing is first presented. Next, the Markov decision process is adopted to construct a decision model for repair crews, based on which a multiagent Q-learning algorithm is developed to find the best scheduling and routing strategy. Finally, experimental results demonstrate that the proposed method can make repair crews adjust their scheduling and routing strategies according to the dynamic changes of the damaged road network and provides a useful attempt to restore the damaged road network in complex, dynamic emergency scenarios of post-disaster.

关键词： Schedules Q-learning Roads Heuristic algorithms Maintenance engineering Markov processes Dynamic scheduling

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A Review of Multimodal Explainable Artificial Intelligence: Past, Present and Future

arXiv

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arXiv 2024年

作者： Sun, Shilin An, Wenbin Tian, Feng Nan, Fang Liu, Qidong Liu, Jun Shah, Nazaraf Chen, Ping School of Computer Science and Technology Xi’an Jiaotong University Xi’an710049 China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an710049 China Faculty of Electronic and Information Engineering Xi’an Jiaotong University Xi’an710049 China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University Xi’an710049 China Institute for Future Transport and Cities Coventry University Priory Street CoventryCV1 5FB United Kingdom Department of Engineering University of Massachusetts Boston BostonMA02125 United States

Artificial intelligence (AI) has rapidly developed through advancements in computational power and the growth of massive datasets. However, this progress has also heightened challenges in interpreting the "black-box" nature of AI models. To address these concerns, eXplainable AI (XAI) has emerged with a focus on transparency and interpretability to enhance human understanding and trust in AI decision-making processes. In the context of multimodal data fusion and complex reasoning scenarios, the proposal of Multimodal eXplainable AI (MXAI) integrates multiple modalities for prediction and explanation tasks. Meanwhile, the advent of Large Language Models (LLMs) has led to remarkable breakthroughs in natural language processing, yet their complexity has further exacerbated the issue of MXAI. To gain key insights into the development of MXAI methods and provide crucial guidance for building more transparent, fair, and trustworthy AI systems, we review the MXAI methods from a historical perspective and categorize them across four eras: traditional machine learning, deep learning, discriminative foundation models, and generative LLMs. We also review evaluation metrics and datasets used in MXAI research, concluding with a discussion of future challenges and directions. A project related to this review has been created at https://***/ShilinSun/mxai_review. Copyright © 2024, The Authors. All rights reserved.

关键词： data fusion

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Recovering accurate labeling information from partially valid data for effective multi-label learning 29

Recovering accurate labeling information from partially vali...

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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Li, Ximing Wang, Yang College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation Knowledge Engineering of Ministry of Education Jilin University China Key Laboratory of Knowledge Engineering With Big Data Ministry of Education Hefei University of Technology China School of Computer Sci and Information Engineering Hefei University of Technology China

ISBN: (纸本)9780999241165

Partial Multi-label Learning (PML) aims to induce the multi-label predictor from datasets with noisy supervision, where each training instance is associated with several candidate labels but only partially valid. To address the noisy issue, the existing PML methods basically recover the ground-truth labels by leveraging the ground-truth confidence of the candidate label, i.e., the likelihood of a candidate label being a ground-truth one. However, they neglect the information from non-candidate labels, which potentially contributes to the ground-truth label recovery. In this paper, we propose to recover the ground-truth labels, i.e., estimating the ground-truth confidences, from the label enrichment, composed of the relevance degrees of candidate labels and irrelevance degrees of non-candidate labels. Upon this observation, we further develop a novel two-stage PML method, namely Partial MultiLabel Learning with Label Enrichment-Recovery (PML3ER), where in the first stage, it estimates the label enrichment with unconstrained label propagation, then jointly learns the ground-truth confidence and multi-label predictor given the label enrichment. Experimental results validate that PML3ER outperforms the state-of-the-art PML methods. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Learning systems

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Prompt Tuning Pushes Farther, Contrastive Learning Pulls Closer: A Two-Stage Approach to Mitigate Social Biases

arXiv

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arXiv 2023年

作者： Li, Yingji Du, Mengnan Wang, Xin Wang, Ying College of Computer Science and Technology Jilin University Changchun China Department of Data Science New Jersey Institute of Technology Newark United States School of Artificial Intelligence Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China

As the representation capability of Pre-trained Language Models (PLMs) improve, there is growing concern that they will inherit social biases from unprocessed corpora. Most previous debiasing techniques used Counterfactual data Augmentation (CDA) to balance the training corpus. However, CDA slightly modifies the original corpus, limiting the representation distance between different demographic groups to a narrow range. As a result, the debiasing model easily fits the differences between counterfactual pairs, which affects its debiasing performance with limited text resources. In this paper, we propose an adversarial training-inspired two-stage debiasing model using Contrastive learning with Continuous Prompt Augmentation (named CCPA) to mitigate social biases in PLMs’ encoding. In the first stage, we propose a data augmentation method based on continuous prompt tuning to push farther the representation distance between sample pairs along different demographic groups. In the second stage, we utilize contrastive learning to pull closer the representation distance between the augmented sample pairs and then fine-tune PLMs’ parameters to get debiased encoding. Our approach guides the model to achieve stronger debiasing performance by adding difficulty to the training process. Extensive experiments show that CCPA outperforms baselines in terms of debiasing performance. Meanwhile, experimental results on the GLUE benchmark show that CCPA retains the language modeling capability of PLMs. Copyright © 2023, The Authors. All rights reserved.

关键词： Encoding (symbols)

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Order-preserving pattern mining with forgetting mechanism

arXiv

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arXiv 2024年

作者： Li, Yan Ma, Chenyu Gao, Rong Wu, Youxi Li, Jinyan Wang, Wenjian Wu, Xindong School of Economics and Management Hebei University of Technology Tianjin300400 China School of Artificial Intelligence Hebei University of Technology Tianjin300400 China School of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen518055 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China School of Computer and Information Technology Shanxi University Taiyuan237016 China Key Laboratory of Knowledge Engineering with Big Data The Ministry of Education of China Hefei University of Technology Hefei230009 China

Order-preserving pattern (OPP) mining is a type of sequential pattern mining method in which a group of ranks of time series is used to represent an OPP. This approach can discover frequent trends in time series. Existing OPP mining algorithms consider data points at different time to be equally important;however, newer data usually have a more significant impact, while older data have a weaker impact. We therefore introduce the forgetting mechanism into OPP mining to reduce the importance of older data. This paper explores the mining of OPPs with forgetting mechanism (OPF) and proposes an algorithm called OPF-Miner that can discover frequent OPFs. OPF-Miner performs two tasks, candidate pattern generation and support calculation. In candidate pattern generation, OPF-Miner employs a maximal support priority strategy and a group pattern fusion strategy to avoid redundant pattern fusions. For support calculation, we propose an algorithm called support calculation with forgetting mechanism, which uses prefix and suffix pattern pruning strategies to avoid redundant support calculations. The experiments are conducted on nine datasets and 12 alternative algorithms. The results verify that OPF-Miner is superior to other competitive algorithms. More importantly, OPF-Miner yields good clustering performance for time series, since the forgetting mechanism is employed. Copyright © 2024, The Authors. All rights reserved.

关键词： Miners

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GPTR: Gestalt-Perception Transformer for Diagram Object Detection

arXiv

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arXiv 2022年

作者： Hu, Xin Zhang, Lingling Liu, Jun Fan, Jinfu You, Yang Wu, Yaqiang Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering School of Computer Science and Technology Xi’an Jiaotong University China National Engineering Lab for Big Data Analytics Xi’an Jiaotong University China Department of Control Science and Engineering Tongji University Shanghai China Department of Computer Science National University of Singapore Singapore Lenovo Research Beijing China

Diagram object detection is the key basis of practical applications such as textbook question answering. Because the diagram mainly consists of simple lines and color blocks, its visual features are sparser than those of natural images. In addition, diagrams usually express diverse knowledge, in which there are many low-frequency object categories in diagrams. These lead to the fact that traditional data-driven detection model is not suitable for diagrams. In this work, we propose a gestalt-perception transformer model for diagram object detection, which is based on an encoder-decoder architecture. Gestalt perception contains a series of laws to explain human perception, that the human visual system tends to perceive patches in an image that are similar, close or connected without abrupt directional changes as a perceptual whole object. Inspired by these thoughts, we build a gestalt-perception graph in transformer encoder, which is composed of diagram patches as nodes and the relationships between patches as edges. This graph aims to group these patches into objects via laws of similarity, proximity, and smoothness implied in these edges, so that the meaningful objects can be effectively detected. The experimental results demonstrate that the proposed GPTR achieves the best results in the diagram object detection task. Our model also obtains comparable results over the competitors in natural image object detection. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

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An Empirical Study on Information Extraction using Large Language Models

arXiv

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arXiv 2023年

作者： Han, Ridong Yang, Chaohao Peng, Tao Tiwari, Prayag Wan, Xiang Liu, Lu Wang, Benyou College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China College of Software Jilin University China Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China School of Data Science The Chinese University of Hong Kong Shenzhen China School of Information Technology Halmstad University Sweden

Human-like large language models (LLMs), especially the most powerful and popular ones in OpenAI’s GPT family, have proven to be very helpful for many natural language processing (NLP) related tasks. Therefore, various attempts have been made to apply LLMs to information extraction (IE), which is a fundamental NLP task that involves extracting information from unstructured plain text. To demonstrate the latest representative progress in LLMs’ information extraction ability, we assess the information extraction ability of GPT-4 (the latest version of GPT at the time of writing this paper) from four perspectives: Performance, Evaluation Criteria, Robustness, and Error Types. Our results suggest a visible performance gap between GPT-4 and state-of-the-art (SOTA) IE methods. To alleviate this problem, considering the LLMs’ human-like characteristics, we propose and analyze the effects of a series of simple prompt-based methods, which can be generalized to other LLMs and NLP tasks. Rich experiments show our methods’ effectiveness and some of their remaining issues in improving GPT-4’s information extraction ability. Copyright © 2023, The Authors. All rights reserved.

关键词： Information retrieval

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