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13th International Multi-Conference on Complexity, Informatics and Cybernetics, IMCIC 2022

作者： Meng, Hang Li, Fei Yang, Zhen Zhu, Qiang Fan, Chaogang Zhan, Shu Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology Anhui Province Hefei230009 China The Second Affiliated Hospital of Anhui Medical University Anhui Province Hefei230601 China Tongcheng Teachers College Anhui Province Tongcheng231400 China Department of General Surgery The Affiliated BenQ Hospital of Nanjing Medical University Jiangsu Province Nanjing210021 China

ISBN: (纸本)9781950492619

Digital histopathological tissue images are gold material for cancer diagnosis and grades. Convolutional Neural Networks (CNNs) are state-of-the-art models in many image classification tasks. However, considering the limited computing resources, it is not practical to directly train CNNs on gigapixel resolution histopathological tissue images. Therefore, the entire histopathological image is commonly divided into a grid of patches and then fed each patch to CNNs, but not all patches are necessary for specific tasks. Besides, although CNNs are excellent image classifiers, many pre-trained models are based on natural images. Neural Architecture Search (NAS) can realize automatic search, construction, and training specific problemoriented networks. We present salient patch based NAS for grading colorectal cancer (CRC) histology images with these motivations. The proposed framework first applies the classic classification network to extract significant patches from the whole image, then use the network obtained by NAS to grade the patches, and finally aggregates the patch-level predictions to generate the entire CRC histological image label. Our experiments on microscopic colorectal tissue images demonstrate that the proposed method is sufficient to obtain more accurate classification results compared to its counterparts. We also conducted a comprehensive analysis of the different scenarios of the proposed method. © by the International Institute of Informatics and Systemics.

关键词： Grading

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Author Name Disambiguation Based on Rule and Graph Model 1

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9th CCF International Conference on Natural Language Processing and Chinese Computing, NLPCC 2020

作者： Zhang, Lizhi Ban, Zhijie Inner Mongilia A.R. Key Laboratory of Data Mining and Knowledge Engineering College of Computer Science Inner Mongolia University Huhhot China

ISBN: (数字)9783030604509

ISBN: (纸本)9783030604493

Author name disambiguation has long been viewed as a challenging problem in scientific literature management, and with the substantial growth of the scientific literature, the solution to this problem has become increasingly difficult and urgency. In this paper, we conduct research on the author name disambiguation problem in large-scale academic papers. In our method, we combine the paper feature information and the relation information between the papers for disambiguation. Based on the Aminers disambiguation framework, we present a novel method to constructing the paper relation graph based on atomic cluster and propose an efficient post processing algorithm, aiming to improve the disambiguation performance by rule-based clustering, this algorithm utilizes similarity features based on metadata information and implement two types of disambiguation rules. We carefully evaluate the proposed disambiguation method on real-world large data and experimental result shows that our method achieves clearly better performance than the state-of-the-art methods. © 2020, Springer Nature Switzerland AG.

关键词： Clustering algorithms

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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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USER: A unified information search and recommendation model based on integrated behavior sequence

arXiv

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arXiv 2021年

作者： Yao, Jing Dou, Zhicheng Xie, Ruobing Lu, Yanxiong Wang, Zhiping Wen, Ji-Rong Gaoling School of Artificial Intelligence Renmin University of China WeChat Search Application Department Tencent China Beijing Key Laboratory of Big Data Management and Analysis Methods Key Laboratory of Data Engineering and Knowledge Engineering MOE

Search and recommendation are the two most common approaches used by people to obtain information. They share the same goal - satisfying the user's information need at the right time. There are already a lot of Internet platforms and Apps providing both search and recommendation services, showing us the demand and opportunity to simultaneously handle both tasks. However, most platforms consider these two tasks independently - they tend to train separate search model and recommendation model, without exploiting the relatedness and dependency between them. In this paper, we argue that jointly modeling these two tasks will benefit both of them and finally improve overall user satisfaction. We investigate the interactions between these two tasks in the specific information content service domain. We propose first integrating the user's behaviors in search and recommendation into a heterogeneous behavior sequence, then utilizing a joint model for handling both tasks based on the unified sequence. More specifically, we design the Unified Information SEarch and Recommendation model (USER), which mines user interests from the integrated sequence and accomplish the two tasks in a unified way. Experiments on a dataset from a real-world information content service platform verify that our model outperforms separate search and recommendation baselines. Copyright © 2021, The Authors. All rights reserved.

关键词： Recommender systems

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BADet: Boundary-Aware 3D Object Detection from Point Clouds

arXiv

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arXiv 2021年

作者： Qian, Rui Lai, Xin Li, Xirong Key Lab of Data Engineering and Knowledge Engineering Renmin University of China Beijing100872 China School of Mathematics Renmin University of China Beijing100872 China

Currently, existing state-of-the-art 3D object detectors are in two-stage paradigm. These methods typically comprise two steps: 1) Utilize a region proposal network to propose a handful of high-quality proposals in a bottom-up fashion. 2) Resize and pool the semantic features from the proposed regions to summarize RoI-wise representations for further refinement. Note that these RoI-wise representations in step 2) are considered individually as uncorrelated entries when fed to following detection headers. Nevertheless, we observe these proposals generated by step 1) offset from ground truth somehow, emerging in local neighborhood densely with an underlying probability. Challenges arise in the case where a proposal largely forsakes its boundary information due to coordinate offset while existing networks lack corresponding information compensation mechanism. In this paper, we propose BADet for 3D object detection from point clouds. Specifically, instead of refining each proposal independently as previous works do, we represent each proposal as a node for graph construction within a given cut-off threshold, associating proposals in the form of local neighborhood graph, with boundary correlations of an object being explicitly exploited. Besides, we devise a lightweight Region Feature Aggregation Module to fully exploit voxel-wise, pixel-wise, and point-wise features with expanding receptive fields for more informative RoI-wise representations. We validate BADet both on widely used KITTI dataset and highly challenging nuScenes dataset. As of Apr. 17th, 2021, our BADet achieves on par performance on KITTI 3D detection leaderboard and ranks 1st on Moderate difficulty of Car category on KITTI BEV detection leaderboard. The source code is available at https://***/rui-qian/BADet. © 2021, CC BY.

关键词： Object detection

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3D Object Detection for Autonomous Driving: A Survey

arXiv

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arXiv 2021年

Autonomous driving is regarded as one of the most promising remedies to shield human beings from severe crashes. To this end, 3D object detection serves as the core basis of perception stack especially for the sake of path planning, motion prediction, and collision avoidance etc. Taking a quick glance at the progress we have made, we attribute challenges to visual appearance recovery in the absence of depth information from images, representation learning from partially occluded unstructured point clouds, and semantic alignments over heterogeneous features from cross modalities. Despite existing efforts, 3D object detection for autonomous driving is still in its infancy. Recently, a large body of literature have been investigated to address this 3D vision task. Nevertheless, few investigations have looked into collecting and structuring this growing knowledge. We therefore aim to fill this gap in a comprehensive survey, encompassing all the main concerns including sensors, datasets, performance metrics and the recent state-of-the-art detection methods, together with their pros and cons. Furthermore, we provide quantitative comparisons with the state of the art. A case study on fifteen selected representative methods is presented, involved with runtime analysis, error analysis, and robustness analysis. Finally, we provide concluding remarks after an in-depth analysis of the surveyed works and identify promising directions for future work. © 2021, CC BY.

关键词： Object recognition

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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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Detection of ChatGPT Fake Science with the xFakeSci Learning Algorithm

arXiv

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arXiv 2023年

作者： Hamed, Ahmed Abdeen Wu, Xindong Complex Adaptive Systems and Computational Intelligence Laboratory State University of New York at Binghamton BinghamtonNY13902 United States Sano Centre for Computational Medicine Clinical Data Science Cracow30-072 Poland Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data The Ministry of Education of China Hefei230009 China China Electronics Data Industry Group Shenzhen518057 China

Generative AI tools exemplified by ChatGPT are becoming a new reality. This study is motivated by the premise that "AI generated content may exhibit a distinctive behavior that can be separated from scientific articles". In this study, we show how articles can be generated using means of prompt engineering for various diseases and conditions. We then show how we tested this premise in two phases and prove its validity. Subsequently, we introduce xFakeSci, a novel learning algorithm, that is capable of distinguishing ChatGPT-generated articles from publications produced by scientists. The algorithm is trained using network models driven from both sources. To mitigate overfitting issues, we incorporated a calibration step that is built upon data-driven heuristics, including proximity and ratios. Specifically, from a total of a 3952 fake articles for three different medical conditions, the algorithm was trained using only 100 articles, but calibrated using folds of 100 articles. As for the classification step, it was performed using 300 articles per condition. The actual label steps took place against an equal mix of 50 generated articles and 50 authentic PubMed abstracts. The testing also spanned publication periods from 2010 to 2024 and encompassed research on three distinct diseases: cancer, depression, and Alzheimer’s. Further, we evaluated the accuracy of the xFakeSci algorithm against some of the classical data mining algorithms (e.g., Support Vector Machines, Regression, and Naive Bayes). The xFakeSci algorithm achieved F1 scores ranging from 80% to 94%, outperforming common data mining algorithms, which scored F1 values between 38% and 52%. We attribute the noticeable difference to the introduction of calibration and a proximity distance heuristic, which underscores this promising performance. Indeed, the prediction of fake science generated by ChatGPT presents a considerable challenge. Nonetheless, the introduction of the xFakeSci algorithm is a significant st

关键词： Learning algorithms

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Beam Tracking for High-Speed UAV Via Generative Diffusion Model-Enabled Joint Optimization Approach

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IEEE Transactions on Vehicular Technology 2025年

作者： Zhang, Jing Zhang, Chaofeng Feng, Xin Yang, Hongwei Liang, Shuang Sun, Geng Wang, Jiacheng Niyato, Dusit Changchun University of Science and Technology College of Computer Science and Technology Changchun130022 China Jilin University College of Communication Engineering Changchun130012 China Northeast Normal University School of Information Science and Technology Changchun130117 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun130012 China Jilin University College of Computer Science and Technology Changchun130012 China Nanyang Technological University College of Computing and Data Science 639798 Singapore Nanyang Technological University 639798 Singapore

Beam tracking is crucial for maintaining stable data transmission in unmanned aerial vehicle (UAV) communications. However, a communication link can be disrupted by frequent switching of narrow beams between a base station and a UAV at certain moments. In this study, we propose a position prediction-based beam tracking algorithm with adaptive beam reconstruction (PPBT-AR) for high-speed UAV. Specifically, long short-term memory (LSTM) recurrent neural networks are utilized to predict the nonlinear flight trajectory during high-speed flight. Moreover, we employ the generative diffusion model (GDM) to jointly optimize the beam width and signal strength, thereby reducing the number of beam switches. In addition, we design an adaptive beam reconstruction (ABR) mechanism to mitigate communication interruptions caused by prediction errors. Simulation results demonstrate that the proposed PPBT-AR reduces the number of beam switches by 66.6% and 76.9% in low-speed and high-speed scenarios compared to the traditional phased array beam tracking algorithm, respectively. © 1967-2012 IEEE.

关键词： Long short-term memory

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Joint Resource Management for Energy-efficient UAV-Assisted SWIPT-MEC: A Deep Reinforcement Learning Approach

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IEEE Internet of Things Journal 2025年

作者： Chen, Yue Kang, Hui Li, Jiahui Sun, Geng Wang, Boxiong Wang, Jiacheng Liang, Cong Liang, Shuang Niyato, Dusit Jilin University College of Computer Science and Technology Changchun130012 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun130012 China Nanyang Technological University College of Computing and Data Science 639798 Singapore China Mobile Communications Group Jilin Co. Ltd China Northeast Normal University School of Information Science and Technology Changchun130117 China

The integration of simultaneous wireless information and power transfer (SWIPT) technology in 6G Internet of Things (IoT) networks faces significant challenges in remote areas and disaster scenarios where ground infrastructure is unavailable. This paper proposes a novel unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system enhanced by directional antennas to provide both computational resources and energy support for ground IoT terminals. However, such systems require multiple trade-off policies to balance UAV energy consumption, terminal battery levels, and computational resource allocation under various constraints, including limited UAV battery capacity, non-linear energy harvesting characteristics, and dynamic task arrivals. To address these challenges comprehensively, we formulate a bi-objective optimization problem that simultaneously considers system energy efficiency and terminal battery sustainability. We then reformulate this non-convex problem with a hybrid solution space as a Markov decision process (MDP) and propose an improved soft actor-critic (SAC) algorithm with an action simplification mechanism to enhance its convergence and generalization capabilities. Simulation results have demonstrated that our proposed approach outperforms various baselines in different scenarios, achieving efficient energy management while maintaining high computational performance. Furthermore, our method shows strong generalization ability across different scenarios, particularly in complex environments, validating the effectiveness of our designed boundary penalty and charging reward mechanisms. © 2014 IEEE.

关键词： Resource allocation

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