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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Chen, Qian Zhang, Tianjian Yang, Linxin Han, Qingyu Wang, Akang Sun, Ruoyu Luo, Xiaodong Chang, Tsung-Hui School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data China School of Data Science The Chinese University of Hong Kong Shenzhen China Shenzhen International Center for Industrial and Applied Mathematics Shenzhen Research Institute of Big Data China

Integer linear programs (ILPs) are commonly employed to model diverse practical problems such as scheduling and planning. Recently, machine learning techniques have been utilized to solve ILPs. A straightforward idea is to train a model via supervised learning, with an ILP as the input and an optimal solution as the label. An ILP is symmetric if its variables can be permuted without changing the problem structure, resulting in numerous equivalent and optimal solutions. Randomly selecting an optimal solution as the label can introduce variability in the training data, which may hinder the model from learning stable patterns. In this work, we incorporate the intrinsic symmetry of ILPs and propose a novel training framework called SymILO. Specifically, we modify the learning task by introducing solution permutation along with neural network weights as learnable parameters and then design an alternating algorithm to jointly optimize the loss function. We conduct extensive experiments on ILPs involving different symmetries and the computational results demonstrate that our symmetry-aware approach significantly outperforms three existing methods-achieving 50.3%, 66.5%, and 45.4% average improvements, respectively. © 2024 Neural information processing systems foundation. All rights reserved.

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Research and Implementation of Lung Tumor Segmentation Method Based on Improved U-Net Network

Research and Implementation of Lung Tumor Segmentation Metho...

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2023 International Conference on Image Processing and Computer Vision, IPCV 2023

作者： Peng, Xiaoshu Peng, Junjie Ruan, Peiguang Lv, Wei Zhuhai College of Science and Technology Ali Cloud School of Big Data Application Guangdong Zhuhai China

ISBN: (纸本)9781665473941

In recent years, cancer has posed a serious threat to human health. Computer tomography is the most important auxiliary method for diagnosing cancer, and doctors can study the patient's CT images to determine the location of the lesion and determine treatment plans. But this method not only consumes a lot of doctors' energy, but also may lead to misdiagnosis. With the application of neural network technology in computer-aided diagnosis, it has greatly saved doctors' time reading images and improved the accuracy and accuracy of reading. © 2023 IEEE.

关键词： Diseases

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OOD Problem Research in Biochemistry Based on Backdoor Adjustment 5

OOD Problem Research in Biochemistry Based on Backdoor Adjus...

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5th International Conference on Artificial Intelligence Technologies and Applications, ICAITA 2023

作者： Gu, Pengfei Yu, Daao School of Data Science University of Science and Technology of China Hefei China

ISBN: (纸本)9781643684840

Due to its ability to deal well with the features of graph structure (social network, molecular structure), graph neural network (GNN) has recently shown amazing capabilities in many fields (biology, chemistry), which has aroused the attention of a large number of researchers on the operation mechanism of GNN model. In order to apply graph neural network to real environment, the problem of out-of-distribution generalization is solved. The difference of data distribution between training environment and real environment is an urgent problem to be solved. In the present study, from the point of view of data, the input graph data is processed to find the core part and filter out the noise part;From the perspective of model method, the parts related to this task (graph classification) in the model are extracted, so as to improve the accuracy and efficiency of the model. However, while these methods are valid from a theoretical point of view, they are using methods that are too simple to actually cut off the effects of non-causal components. However, the current methods of model Angle are to cut the network directly, without considering the information transfer between the model parameters. To solve these problems, the main work of this paper is to propose a distance-based environment selection method, which enables the backdoor adjustment to be implemented to the maximum extent and ensures the robustness of the model. At the same time, it proposes a way for the network to squeeze information during the clipping process, so that the effect of the model pruning algorithm can be optimized. Reduce the complexity of the model and improve the generalization of the model. The method presented in this paper has been validated on data sets in several biochemical fields, and the best performance has been achieved. © 2024 The Authors.

关键词： Graph neural networks

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Enhancing fairness of trading environment:discovering overlapping spammer groups with dynamic co‑review graph optimization

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Cybersecurity 2025年第2期8卷 242-269页

作者： Chaoqun Wang Ning Li Shujuan Ji Xianwen Fang Zhen Wang Shandong Provincial Key Laboratory of Wisdom Mine Information Technology Shandong University of Science&TechnologyQingdao 266590ShandongChina Anhui Province Engineering Laboratory for Big Data Analysis and Early Warning Technology of Coal Mine Safety Anhui University of Science and TechnologyHuainanChina

Within the thriving e-commerce landscape,some unscrupulous merchants hire spammer groups to post misleading reviews or ratings,aiming to manipulate public perception and disrupt fair market *** phenomenon has prompted a heightened research focus on spammer groups *** the e-commerce domain,current spammer group detection algorithms can be classified into three categories,i.e.,Frequent Item Mining-based,graph-based,and burst-based ***,existing graph-based algorithms have limitations in that they did not adequately consider the redundant relationships within co-review graphs and neglected to detect overlapping members within spammer *** address these issues,we introduce an overlapping spammer group detection algorithm based on deep reinforcement learning named ***,the algorithm filters out highly suspicious products and gets the set of reviewers who have reviewed these ***,taking these reviewers as nodes and their co-reviewing relationships as edges,we construct a homogeneous co-reviewing ***,to efficiently identify and handle the redundant relationships that are accidentally formed between ordinary users and spammer group members,we propose the Auto-Sim algorithm,which is a specifically tailored algorithm for dynamic optimization of the co-reviewing graph,allowing for adjustments to the reviewers’relationship network within the ***,candidate spammer groups are discovered by using the Ego-Splitting overlapping clustering algorithm,allowing overlapping members to exist in these ***,these groups are refined and ranked to derive the final list of spammer *** results based on real-life datasets show that our proposed DRL-OSG algorithm performs better than the baseline algorithms in Precision.

关键词： Spammer groups Homogeneous network Redundant relationships Overlapping members Deep reinforcement learning Ego-splitting algorithm

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Hybrid Evaluation for Occlusion-based Explanations on CNN Inference Queries 40

Hybrid Evaluation for Occlusion-based Explanations on CNN In...

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40th IEEE International Conference on data Engineering, ICDE 2024

作者： Ding, Guangyao Xu, Chen Qian, Weining School of Data Science and Engineering East China Normal University Shanghai200062 China Shanghai Engineering Research Center of Big Data Management China

ISBN: (纸本)9798350317152

Deep CNNs are increasingly prevalent in various application domains such as image processing. To explain a CNN prediction, it is popular to employ occlusion-based explanations (OBE). OBE helps users understand which parts of an image are important to a CNN prediction. Existing systems have explored incremental evaluation to accelerate CNN inference in OBE. However, they are oblivious that incremental evaluation does not always outperform full evaluation for certain layers. To address this issue, we propose a hybrid evaluation to efficiently interleave full and incremental evaluations during the CNN inference. Ad-ditionally, it employs a cost model to compare the overhead costs of two types of evaluations and a heuristic method to determine the efficient plan combination for common CNNs. More impor-tantly, hybrid evaluation adopts a dynamic programming-based method for attention-based CNNs. In particular, the dynamic programming-based method significantly reduces the overhead of searching for the efficient plan combination on the complex DAG structure. To demonstrate the efficiency of our techniques, we implement HyInJ, a hybrid CNN inf erence system based on PyTorch. Our experiments show that HyInf reduces execution time by up to 22% on GPU and 55% on CPU in comparison to the state-of-the-art incremental evaluation. © 2024 IEEE.

关键词： Dynamic programming

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Massively parallel algorithms for fully dynamic all-pairs shortest paths

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Frontiers of Computer science 2024年第4期18卷 201-203页

作者： Chilei WANG Qiang-Sheng HUA Hai JIN Chaodong ZHENG National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China

1 Introduction In recent years,the Massively Parallel Computation(MPC)model has gained significant ***,most of distributed and parallel graph algorithms in the MPC model are designed for static graphs[1].In fact,the graphs in the real world are constantly *** size of the real-time changes in these graphs is smaller and more *** graph algorithms[2,3]can deal with graph changes more efficiently[4]than the corresponding static graph ***,most studies on dynamic graph algorithms are limited to the single machine ***,a few parallel dynamic graph algorithms(such as the graph connectivity)in the MPC model[5]have been proposed and shown superiority over their parallel static counterparts.

关键词： dynamic shortest gained

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Fine extraction of multi-crop planting area based on deep learning with Sentinel- 2 time-series data

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Environmental science and Pollution Research 2025年第19期32卷 11931-11949页

作者： Jiang, Jingmin Zhang, Jiahua Wang, Xue Zhang, Shichao Kong, Delong Wang, Xiaopeng Ali, Shawkat Ullah, Hidayat Space Information and Big Earth Data Research Center School of Computer Science and Technology Qingdao University Qingdao266071 China Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China

Accurate and timely access to the spatial distribution of crops is crucial for sustainable agricultural development and food security. However, extracting multi-crop areas based on high-resolution time-series data and deep learning still faces challenges. Therefore, this study aims to provide an effective model for multi-crop classification using high-resolution remote sensing time-series data. We designed two deep learning models based on convolutional neural network-long short-term memory (CNN-LSTM) and bidirectional long short-term memory (Bi-LSTM). The monthly synthetic time series of the normalized difference vegetation index (NDVI) from Sentinel-2 data will be used as input features to extract the multi-crop planting area in Shandong province’s northwestern, southwestern, and eastern regions. The results showed that deep learning models achieved higher accuracy compared to the random forest (RF) and extreme gradient boosting (XGBoost) models, with CNN-LSTM achieving the highest overall accuracy of 96.48%. At the county level, the coefficients of determination (R2) for the CNN-LSTM model were 0.91 for wheat, 0.88 for maize, and 0.73 for spring cotton. This study demonstrates that the CNN-LSTM model combined with monthly synthetic time-series NDVI provides a feasible approach for accurately mapping high-resolution multi-crop planting areas and also contributes significantly to decision support and resource management in agricultural production. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Deep learning High-resolution Monthly synthetic time-series NDVI Multi-crop classification Remote sensing Sentinel- 2

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Dynamics of optical pulses in birefringent fibers without four-wave mixing effect via efficient computational techniques

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Journal of Ocean Engineering and science 2025年第2期10卷 181-196页

作者： Usman Younas Hadi Rezazadeh Jingli Ren Henan Academy of Big Data/School of Mathematics and Statistics Zhengzhou UniversityZhengzhou 450001China Faculty of Engineering Technology Amol University of Special Modern TechnologiesAmolIran

In this manuscript,we discuss the dynamical behavior of Chen-Lee-Liu(CLL)equation in birefringent fibers which is modeled by two-component nonlinear Schrodinger equation(NLSE)without four-wave mixing *** optical fibers and other wave-guide mediums this system models the propagation of soliton flow using group velocity dispersion(GVD)and self-steeping *** the realms of maritime transport,motion,and energy,the dynamics of deep-sea waves is one of oceanography’s greatest challenges.A mathematical model of the dynamics of solitary waves in the deep ocean under a twolayer stratification yields the NLSE,and resultantly,the interaction between the two can be described by a coupled *** two recently developed integration tools,namely the generalized exponential rational function method(GERFM)and the new extended direct algebraic method(NEDAM),the various optical pulses in the forms of bright,dark,combined,and complex solitons are ***,the hyperbolic,exponential,and trigonometric function solutions are *** addition,a comparison is made between our results and those that are well-known,and the study concludes that the solutions we’ve reached are *** choosing appropriate parameter values for numerical simulation and physical explanations,the significance of the results is *** results of this paper can enhance the nonlinear dynamical behavior of a given system and demonstrate the suitability of the methodology *** research,in our opinion,will be beneficial to a wide variety of engineering model specialists.

关键词： Chen-Lee-Liu equation Birefringent fibers Optical soliton NEDAM Integrability GERFM

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LF-DETR: Lightweight Detection Network for Floating Surface Garbage Based on Adaptive Channel Shuffling 22

LF-DETR: Lightweight Detection Network for Floating Surface ...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Xu, ShouKun Gong, Yan Yang, Gaochao Li, Ning School of Software Changzhou University School of Computer Science and Artificial Intelligence Aliyun School of Big Data Changzhou China

ISBN: (纸本)9798331509712

To address the issues of low accuracy and efficiency in water surface target detection caused by uneven lighting and water ripples, this study proposes a channel modeling-based DETR-like model. Starting from efficient partial convolutions and inverted residual blocks, the concept of dynamic sparsity is introduced, and a Flexible Masked Residual Block (FMRB) for a lightweight backbone is designed. Based on the design principle of channel grouping, an adaptive channel shuffling strategy is derived. Our model achieved an mAP of 79.8% on the FGDD dataset and 61.7% on the Pascal VOC2007 dataset. Compared to the original model, the FLOPs of our LF-DETR model are reduced by 24.9%, down to 42.8G, and the number of parameters is reduced by 29.4%. Extensive experimental results demonstrate that our model exhibits an excellent balance between efficiency and accuracy across multiple benchmarks, highlighting its great potential in practical applications. © 2024 IEEE.

关键词： Object detection

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Vision-based Multi-task Hybrid Model for Teacher-Student Behavior Recognition in Classroom Environment

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IEIE Transactions on Smart Processing and Computing 2024年第6期13卷 587-597页

作者： Zhou, Huan Zhu, Wenrui School of Big Data and Artificial Intelligence Anhui Xinhua University AH Hefei230088 China School of Civil Engineering Xi'an University of Architecture and Technology SN Xi'an710055 China

Teacher-student concentration in the teaching process is an essential indicator for evaluating teaching quality. Many researches assess students' learning interests by identifying their classroom behaviors but ignore the influence of teachers' behavior on students' behavior. Therefore, we collect classroom video data of teacher and student perspectives to analyse the interplay between their behaviors. Considering the particularity of data collection in classroom environments, we design a vision-based multi-task hybrid model for multi-mode data (RGB, optical flow and skeleton data). This model structure is divided into two parts. The RGB and optical flow are input into a spatio-temporal dual-stream framework for real-time action localization of the teacher. This dual-stream framework includes a 2D-CNN branch to extract spatial information and a Vision Transformer (ViT) branch to extract temporal information. In another part, skeleton data is obtained through the pose estimation method, and we propose a multi-level stacked spatio-temporal graph convolutional network (MSSTGCN) for skeleton-based student behavior recognition. This network can process the multi-order semantic information of the skeleton data and fuse the features at different scales through the Non-local block. © 2024 Institute of Electronics Engineers of Korea. All rights reserved.

关键词： Musculoskeletal system

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