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arXiv 2024年

作者： Wang, Bo Sun, Tianxiang Yan, Hang Wang, Siyin Cheng, Qingyuan Qiu, Xipeng School of Computer Science Fudan University Shanghai Artificial Intelligence Laboratory China

The exploration of whether agents can align with their environment without relying on human-labeled data presents an intriguing research topic. Drawing inspiration from the alignment process observed in intelligent organisms, where declarative memory plays a pivotal role in summarizing past experiences, we propose a novel learning framework. The agents adeptly distill insights from past experiences, refining and updating existing notes to enhance their performance in the environment. This entire process transpires within the memory components and is implemented through natural language, so we character this framework as In-memory Learning. We also delve into the key features of benchmarks designed to evaluate the self-improvement process. Through systematic experiments, we demonstrate the effectiveness of our framework and provide insights into this problem. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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Local Neural Descriptor Fields: Locally Conditioned Object Representations for Manipulation

Local Neural Descriptor Fields: Locally Conditioned Object R...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Ethan Chun Yilun Du Anthony Simeonov Tomas Lozano-Perez Leslie Kaelbling Computer Science and Artificial Intelligence Laboratory MIT USA

A robot operating in a household environment will see a wide range of unique and unfamiliar objects. While a system could train on many of these, it is infeasible to predict all the objects a robot will see. In this paper, we present a method to generalize object manipulation skills acquired from a limited number of demonstrations, to novel objects from unseen shape categories. Our approach, Local Neural Descriptor Fields (L-NDF), utilizes neural descriptors defined on the local geometry of the object to effectively transfer manipulation demonstrations to novel objects at test time. In doing so, we leverage the local geometry shared between objects to produce a more general manipulation framework. We illustrate the efficacy of our approach in manipulating novel objects in novel poses - both in simulation and in the real world. Project website, videos, and code: https://***/lndf/.

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OpenDrift: Online Evolving Fraud Detection for Open-Category and Concept-Drift Transactions

OpenDrift: Online Evolving Fraud Detection for Open-Category...

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2023 IEEE International Conference on Web Services, ICWS 2023

作者： Wang, Cheng Chai, Songyao Zhu, Hangyu Department of Computer Science and Technology Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing Ministry of Education China Shanghai Artificial Intelligence Laboratory Shanghai China

ISBN: (纸本)9798350304855

The rapid growth of electronic commerce brings convenience to modern life but comes with security risks by various cybercrimes in online payment services. Most existing security methods for fraud detection depend on the static learning paradigm, which trains a model over a static training dataset and deploys the trained model for inference with the frozen model parameters under the i.i.d. assumption. Unfortunately, this paradigm becomes incommensurate with the increasingly complicated and varying fraud patterns due to the untimely and delayed responses in the offline environment. Without sensing the evolution of fraud timely, it is challenging to train and deploy targeted countermeasures. The emerging means of fraud are not only reflected in the openness of their category, but also in the drift of their superimposed risk features. The interweaving of open-category and concept drift accelerates the process of existing methods becoming powerless. In this paper, we propose EvoFD, an online evolving fraud detection framework to enable continual learning to cope with undercurrent surges of evolving fraud. The core idea of EvoFD is to weaken the bias caused by the anchoring effect on the learned information. It learns in an online streaming fashion by using instructive representations as anchors. Specially, we maintain the progressively updatable class anchors and optimize the representation network to embed features and class anchors into a unified normalized space, where the training and predicting can be conducted simultaneously or independently. In the framework, we preserve the balanced replay memory for each class to accumulate knowledge and avoid forgetting. The advantages of our method are validated by extensive experiments over the real-world dataset from a prestigious bank. © 2023 IEEE.

关键词： Crime

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Long-Tail Learning with Foundation Model: Heavy Fine-Tuning Hurts 41

Long-Tail Learning with Foundation Model: Heavy Fine-Tuning ...

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41st International Conference on Machine Learning, ICML 2024

作者： Shi, Jiang-Xin Wei, Tong Zhou, Zhi Shao, Jie-Jing Han, Xin-Yan Li, Yu-Feng National Key Laboratory for Novel Software Technology Nanjing University China School of Artificial Intelligence Nanjing University China School of Computer Science and Engineering Southeast University China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China

The fine-tuning paradigm in addressing long-tail learning tasks has sparked significant interest since the emergence of foundation models. Nonetheless, how fine-tuning impacts performance in long-tail learning was not explicitly quantified. In this paper, we disclose that heavy fine-tuning may even lead to non-negligible performance deterioration on tail classes, and lightweight fine-tuning is more effective. The reason is attributed to inconsistent class conditions caused by heavy fine-tuning. With the observation above, we develop a low-complexity and accurate long-tail learning algorithms LIFT with the goal of facilitating fast prediction and compact models by adaptive lightweight fine-tuning. Experiments clearly verify that both the training time and the learned parameters are significantly reduced with more accurate predictive performance compared with state-of-the-art approaches. The implementation code is available at https://***/shijxcs/LIFT. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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DeepGCN based on variable multi‐graph and multimodal data for ASD diagnosis

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CAAI Transactions on intelligence Technology 2024年第4期9卷 879-893页

作者： Shuaiqi Liu Siqi Wang Chaolei Sun Bing Li Shuihua Wang Fei Li College of Electronic and Information Engineering Hebei UniversityBaodingChina Machine Vision Technology Innovation Center of Hebei Province BaodingChina The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijingChina School of Computer Science and Technology Henan Polytechnic UniversityJiaozuoChina

Diagnosing individuals with autism spectrum disorder(ASD)accurately faces great chal-lenges in clinical practice,primarily due to the data's high heterogeneity and limited sample *** tackle this issue,the authors constructed a deep graph convolutional network(GCN)based on variable multi‐graph and multimodal data(VMM‐DGCN)for ASD ***,the functional connectivity matrix was constructed to extract primary ***,the authors constructed a variable multi‐graph construction strategy to capture the multi‐scale feature representations of each subject by utilising convolutional filters with varying kernel ***,the authors brought the non‐imaging in-formation into the feature representation at each scale and constructed multiple population graphs based on multimodal data by fully considering the correlation between *** extracting the deeper features of population graphs using the deep GCN(DeepGCN),the authors fused the node features of multiple subgraphs to perform node classification tasks for typical control and ASD *** proposed algorithm was evaluated on the Autism Brain Imaging Data Exchange I(ABIDE I)dataset,achieving an accuracy of 91.62%and an area under the curve value of 95.74%.These results demon-strated its outstanding performance compared to other ASD diagnostic algorithms.

关键词： machine learning medical image processing medical signal processing

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HUSS:A Heuristic Method for Understanding the Semantic Structure of Spreadsheets

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Data intelligence 2023年第3期5卷 537-559页

作者： Xindong Wu Hao Chen Chenyang Bu Shengwei Ji Zan Zhang Victor S.Sheng Key Laboratory of Knowledge Engineering with Big Data(the Ministry of Education of China) Hefei University of TechnologyChinaSchool of Computer Science and Information EngineeringHefei University of TechnologyHefeiChina Research Institute of Artificial Intelligence Zhejiang LabHangzhouChina Department of Computer Science Texas Tech UniversityLubbockTX 79409USA

Spreadsheets contain a lot of valuable data and have many practical *** key technology of these practical applications is how to make machines understand the semantic structure of spreadsheets,e.g.,identifying cell function types and discovering relationships between cell *** existing methods for understanding the semantic structure of spreadsheets do not make use of the semantic information of cells.A few studies do,but they ignore the layout structure information of spreadsheets,which affects the performance of cell function classification and the discovery of different relationship types of cell *** this paper,we propose a Heuristic algorithm for Understanding the Semantic Structure of spreadsheets(HUSS).Specifically,for improving the cell function classification,we propose an error correction mechanism(ECM)based on an existing cell function classification model[11]and the layout features of *** improving the table structure analysis,we propose five types of heuristic rules to extract four different types of cell pairs,based on the cell style and spatial location *** experimental results on five real-world datasets demonstrate that HUSS can effectively understand the semantic structure of spreadsheets and outperforms corresponding baselines.

关键词： Spreadsheet semantic structure Information extraction Heuristics Cell function analysis Table structure analysis

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NABP: Social Network Alignment Algorithm based on Pre-trained Language Models 3

NABP: Social Network Alignment Algorithm based on Pre-traine...

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3rd International Conference on artificial intelligence, Automation, and High-Performance Computing, AIAHPC 2023

作者： Ding, Linjun Chen, Jing School of Artificial Intelligence and Computer Science Jiangnan University Jiangsu Wuxi214122 China Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computing Intelligence Jiangnan University Jiangsu Wuxi214122 China

ISBN: (数字)9781510666580

ISBN: (纸本)9781510666573

The existing social network matching algorithms have problems in processing text attribute information, as they cannot handle polysemy issues of word meanings well and cannot effectively extract deep semantic information from the text. To address the aforementioned issues, this paper introduces pretrained models to extract information from attribute text and proposes a social network alignment algorithm called NABP (Network Alignment based on Pre-trained Language Models). NABP first preprocesses the attribute text data to extract the text feature vectors;then, it uses the DeepWalk algorithm to perform graph representation learning on the two networks to extract their structural features. Next, the attribute feature vectors and structural feature vectors are concatenated to form the final feature vector. Then, a projection function is learned using supervised information to further improve the algorithm's performance. To verify the effectiveness of NABP algorithm, this study conducted experiments on two real social network datasets. The results show that the NABP algorithm performs better than existing algorithms in both the Accuracy and Precision@k metrics, demonstrating the effectiveness of the algorithm and pre-trained language models. © 2023 SPIE.

关键词： Semantics

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Design of a Virtual Simulation Experimental Teaching Platform Based on the Internet of Things 13

Design of a Virtual Simulation Experimental Teaching Platfor...

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13th International Conference on Information Technology in Medicine and Education, ITME 2023

作者： Xiehua, Yu Shengjie, You Rentao, Lin School of Computer and Information MinNan Science and Technology University Quanzhou China University of Fujian Province Key Laboratory of Big Data and Artificial Intelligence Quanzhou China

ISBN: (纸本)9798350319156

The virtual simulation experiment teaching system based on the Internet of Things simulates the Internet of Things sensing device in the form of software, and integrates relatively new teaching methods and theoretical knowledge, designs and completes a characteristic, can adapt to various needs of the Internet of Things experiment teaching system, and strives to provide more efficient protection and help for teachers' teaching tasks and students' learning tasks. To a large extent, it can solve the problems in the design and implementation of the Internet of Things application system, provide students with practical operation opportunities in the way of virtual simulation, combine theory and practice, and cultivate application-oriented talents urgently needed by the industry. © 2023 IEEE.

关键词： Internet of things

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Review and Prospect: Applications of Exponential Signals with Machine Learning in Nuclear Magnetic Resonance

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Spectroscopy (Santa Monica) 2023年第9期38卷 22-32页

作者： Guo, Di Chen, Xianjing Lu, Mengli He, Wangfeng Luo, Sihui Lin, Yanqin Huang, Yuqing Xiao, Lizhi Qu, Xiaobo School of Computer and Information Engineering Xiamen University of Technology Xiamen China School of Electronic Science Xiamen University Xiamen China College of Artificial Intelligence State Key Laboratory of Petroleum Resources and Prospecting China University of Petroleum Beijing China

Nuclear magnetic resonance (NMR) spectroscopy presents an important analytical tool for composition analysis, molecular structure elucidation, and dynamic study in the fields of chemistry, biomedicine, food science, energy and more. As a basic function, exponential functions can be applied to model NMR signals of free induction decay, relaxation, and diffusion. In this paper, we will review Fourier and Laplace NMR exponential signals separately, as well as the performance of state-of-the-art machine learning on NMR applications. © 2023 Advanstar Communications Inc.. All rights reserved.

关键词： Nuclear magnetic resonance spectroscopy

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3D Reconstruction and Texture Optimization Based on Differentiable Rendering 8

3D Reconstruction and Texture Optimization Based on Differen...

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8th International Conference on Intelligent Computing and Signal Processing, ICSP 2023

作者： Liu, Yongxin Zhao, Haifeng Zhang, Shaojie Fu, Yanping Anhui University Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China

ISBN: (纸本)9798350302455

Due to noise and quantization errors inherent in depth cameras, RGB-D based 3D reconstruction algorithms inevitably suffer from defects such as texture blurring and ghosting. However, current texture optimization methods tend to only focus on optimizing one aspect, such as camera pose, geometry, or texture, without considering the highly coupled relationship between them. As a result, it is difficult to achieve global optimality. To address this issue, we propose a joint optimization framework based on differentiable rendering that simultaneously optimizes camera pose, geometry, and texture. By fully exploiting the complementary relationship between these aspects, we are able to reconstruct textures with high fidelity while recovering fine geometric details. First, we propose an optimized geometry model combined with adaptive subdivision, where we selectively subdivide the model only in texture-rich regions to increase the number of vertex facets. Then, we perform geometry optimization to recover high-frequency geometric details evenly in the presence of large geometric errors without incurring computational costs. After the pose correction and geometric model optimization, we use pixel regeneration pipelines to generate clear texture atlases. Comparative evaluations with state-of-the-art texture optimization methods on a public dataset demonstrate the effectiveness of our proposed approach in reducing errors in the reconstruction of 3D models, generating clear textures, and recovering high-frequency geometric details. © 2023 IEEE.

关键词： Textures

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