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Neural Networks, information and Communication engineering (NNICE), International Conference on

作者： Runzhu He Youli Qu Dept. Key Laboratory of Big Data & Artifcial Intelligence in Transportation Ministry of Education School of Computer and Information Technology Beijing Jiaotong University Beijing China

In large-scale search engines, the core data structure is the inverted index, essentially a collection of integer sequences within inverted lists. Precise partitioning of these sequences enables efficient query processing. The concept of partitioning by quantity and universe has been established for years in the field of inverted index compression algorithms. The volume of data within a post-partitioned area, defined as density, is a key determinant of the compression efficiency of partitioned inverted index algorithms. Previous studies have focused on creating denser partitioned areas atop existing inverted file indexes to improve compression efficiency. This paper proposes a novel approach that utilizes the Hierarchical Dirichlet Process model to extract document themes, categorizes documents based on these themes, and then constructs file indexes accordingly to create denser partitioned areas. Compared with other inverted index compression methods, this algorithm achieves a favorable balance between time and space efficiency.

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Understanding and Detecting Privacy Leakage Vulnerabilities in Hyperledger Fabric Chaincodes 35

Understanding and Detecting Privacy Leakage Vulnerabilities ...

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35th IEEE International Symposium on Software Reliability engineering, ISSRE 2024

作者： Chen, Ziming Li, Yue Gao, Jianbo Zhang, Jiashuo Wang, Ke Hu, Jianbin Guan, Zhi Chen, Zhong Peking University School of Computer Science Beijing China Taiyuan University of Technology College of Computer Science and Technology Taiyuan China Beijing Jiaotong University Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing China Peking University Chongqing Research Institute of Big Data Chongqing China Peking University National Engineering Research Center for Software Engineering Beijing China

ISBN: (纸本)9798350353884

The application on a blockchain cannot maintain secrecy because its data is replicated across all peers in the network. To remedy this problem, Hyperledger Fabric introduces private data collection (PDC) into its smart contract (i.e. chaincode) to facilitate applications that require privacy. However, recent studies have revealed that PDC is too complex for chaincode developers to fully understand and use correctly, leading to privacy leaks vulnerabilities. In this paper, we present an empirical study on the prevalence of PDC misuse in chaincodes by extracting privacy leakage cases from StackOverflow posts and Hyperledger Fabric repositories on GitHub. Subsequently, we systematically categorize the misuse of PDC into three categories of vulnerabilities resulting in the leakage of private data and provide formal definitions for them. Furthermore, we develop PDChecker, an automated security analysis framework for identifying the privacy and security vulnerabilities in Fabric chaincodes. We evaluated PDChecker on 956 real-world chaincodes applying PDC and found that 67.78% of them contain at least one privacy leakage vulnerability. In addition, PDChecker uncovered 10 zero-day vulnerabilities documented by the China National Vulnerability database. © 2024 IEEE.

关键词： Differential privacy

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Short-term traffic flow prediction based on BAS-VMD-CEEMDAN and IWOA-RF 11

Short-term traffic flow prediction based on BAS-VMD-CEEMDAN ...

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11th International Conference on information Systems and Computing Technology, ISCTech 2023

作者： Tian, Jia Wang, Deyong Fn, Yanyun Zhao, Xueyi Fang, Jian Shi, Wenxi Xinjiang University School of Information Science and Engineering Urumqi830017 China Xinjiang Lianhaichuangzhi Information Technology Co. Ltd Key Laboratory of Big Data of Xinjiang Social Security Risk Prevention and Control Urumqi830011 China Beijing Academy of Science and Technology Beijing100035 China

ISBN: (纸本)9798350342406

Short-term traffic flow forecasting is crucial for intelligent traffic management. In view of the non-linear and random nature of short-term traffic flow, a prediction model based on BAS-VMD-CEEMDAN and IWOA-RF is proposed. Firstly, the parameters of the VMD, namely the modal number (K) and the value of the quadratic penalty term (α), are optimized using the BAS algorithm, and then the traffic flow is decomposed to obtain several VMF components and one RES component, which extracts the smooth information of the traffic flow. Subsequently, the RES component is decomposed using the decomposition completeness of CEEMDAN to obtain several IMF components, refining the complex information of the RES component. Then, the RF model is constructed according to the characteristics of each component, and then the parameters of RF models are optimized using IWOA. Finally, overlay the predictions of each component superimposed to obtain the final predictions. Experimental results show that the short-term traffic flow forecasting model basedon BAS-VMD-CEEMDAN-IWOA-RF has a high accuracy compared to other decomposition models. © 2023 IEEE.

关键词： Forecasting

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Circular Decomposition and Cross-Modal Recombination for Multimodal Sentiment Analysis

Circular Decomposition and Cross-Modal Recombination for Mul...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Haijian Liang Weicheng Xie Xilin He Siyang Song Linlin Shen School of Computer Science & Software Engineering Computer Vision Institue Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University School of Computing and Mathematical Sciences University of Leicester National Engineering Laboratory for Big Data System Computing Technology Shenzhen University

Multimodal Sentiment Analysis is a burgeoning research area, leveraging various modalities to predict the sentiment score. Nevertheless, previous studies have disregarded the impact of noise interference on specific modal sentiments during video recording, thereby compromising the accuracy of sentiment prediction. In this paper, we propose the Guided Circular Decomposition and Cross-Modal Recombination (GCD-CMR) model, which aims to eliminate contaminated sentiment features in a fine-grained way. To achieve this, we utilize tailored global information specific to each modality to guide the circular decomposing process in the GCD module, to produce a set of sentiment prototypes. Subsequently, in the CMR module, we align cross-modal sentiment prototypes and remove the contaminated prototypes for recombination. Experimental results on two publicly available datasets demonstrate that our model surpasses state-of-the-art models, confirming the effectiveness of our proposed method. We release the code at: https://***/nianhua20/GCD-CMR.

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AUCSeg: AUC-oriented Pixel-level Long-tail Semantic Segmentation

arXiv

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arXiv 2024年

作者： Han, Boyu Xu, Qianqian Yang, Zhiyong Bao, Shilong Wen, Peisong Jiang, Yangbangyan Huang, Qingming Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS China School of Computer Science and Tech. University of Chinese Academy of Sciences China Peng Cheng Laboratory China Key Laboratory of Big Data Mining and Knowledge Management CAS China

The Area Under the ROC Curve (AUC) is a well-known metric for evaluating instance-level long-tail learning problems. In the past two decades, many AUC optimization methods have been proposed to improve model performance under long-tail distributions. In this paper, we explore AUC optimization methods in the context of pixel-level long-tail semantic segmentation, a much more complicated scenario. This task introduces two major challenges for AUC optimization techniques. On one hand, AUC optimization in a pixel-level task involves complex coupling across loss terms, with structured inner-image and pairwise inter-image dependencies, complicating theoretical analysis. On the other hand, we find that mini-batch estimation of AUC loss in this case requires a larger batch size, resulting in an unaffordable space complexity. To address these issues, we develop a pixel-level AUC loss function and conduct a dependency-graph-based theoretical analysis of the algorithm’s generalization ability. Additionally, we design a Tail-Classes Memory Bank (T-Memory Bank) to manage the significant memory demand. Finally, comprehensive experiments across various benchmarks confirm the effectiveness of our proposed AUCSeg method. The code is available at https://***/boyuh/AUCSeg. © 2024, CC BY.

关键词： Semantic Segmentation

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Fault Estimation for Nonlinear Systems with Disturbance Based on Reinforcement Iterative Learning Strategy

Fault Estimation for Nonlinear Systems with Disturbance Base...

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data Driven Control and Learning Systems (DDCLS)

作者： Zhengquan Chen Ruirui Huang Zhiheng Zhang Yandong Hou School of Computer and Information Engineering Henan University Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China School of Artificial Intelligence Henan University Zhengzhou China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

This paper addresses the problem of low fault estimation accuracy in Lipschitz nonlinear systems with external disturbances, presenting an innovative strategy based on a Reinforcement Iterative Learning (RIL) framework. Initially, we conducted precise mathematical modeling, comprehensively considering the impact of Lipschitz non-linearity and external disturbances. Subsequently, an iterative learning fault observer was designed, and the optimization problem for its parameters was reformulated into a reinforcement learning task, clearly defining the states, actions, and reward functions. Employing the Advantage Actor-Critic (A2C) reinforcement learning method, we optimized the gain parameters of the iterative learning fault estimator to approximate non-linearity. Simultaneously, with the increase in iteration count, the neural network weights were continually optimized, achieving accurate fault estimation. Experimental results demonstrate a significant improvement in fault estimation using the proposed method.

关键词： Sufficient conditions Accuracy Neural networks Estimation Reinforcement learning Observers Iterative methods

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Side-information Aided Adaptive Fusion Algorithm for Multiuser Detection in Grant-Free Non-Orthogonal Multiple Access Networks

Side-Information Aided Adaptive Fusion Algorithm for Multius...

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IEEE International Conference on Communications in China (ICCC)

作者： Han Yuan Congcong Ma Kun Yang Tong Peng School of Information Engineering Zhejiang Ocean University Key Laboratory of Oceanographic Big Data Mining and Application Zhoushan Zhejiang People’s Republic of China

ISBN: (数字)9798350378412

ISBN: (纸本)9798350378429

Grant-free non-orthogonal multiple access (GFNOMA), acting as a promising technique to provide high connection density with low latency, serves multiple users simultaneously with efficient spectrum sharing and simplified resource management. The sparsity of user activity naturally exists in the uplink GF-NOMA system, which makes compressed sensing (CS) technology applied to multi-user detection (MUD). In this paper, we propose a side-information aided adaptive fusion (SIAF) algorithm to improve the MUD performance in the uplink GFNOMA system. The proposed algorithm reconstructs the signal without knowing the signal sparsity, and makes full use of the structural characteristics of the temporal correlation in the GFNOMA system to obtain accurate active user estimation. In the post-processing of the algorithm, we obtain low data error rate by fusing two conventional CS algorithms. The simulation results show that our algorithm has better MUD performance than the conventional CS algorithms.

关键词： Multiuser detection NOMA Adaptive systems Accuracy Fuses Simulation Sensors

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SGSH: Stimulate Large Language Models with Skeleton Heuristics for knowledge Base Question Generation

arXiv

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arXiv 2024年

作者： Guo, Shasha Liao, Lizi Zhang, Jing Wang, Yanling Li, Cuiping Chen, Hong School of Information Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education China Singapore Management University Singapore Zhongguancun Laboratory China

knowledge base question generation (KBQG) aims to generate natural language questions from a set of triplet facts extracted from KB. Existing methods have significantly boosted the performance of KBQG via pre-trained language models (PLMs) thanks to the richly endowed semantic knowledge. With the advance of pre-training techniques, large language models (LLMs) (e.g., GPT-3.5) undoubtedly possess much more semantic knowledge. Therefore, how to effectively organize and exploit the abundant knowledge for KBQG becomes the focus of our study. In this work, we propose SGSH — a simple and effective framework to Stimulate GPT-3.5 with Skeleton Heuristics to enhance KBQG. The framework incorporates "skeleton heuristics", which provides more fine-grained guidance associated with each input to stimulate LLMs to generate optimal questions, encompassing essential elements like the question phrase and the auxiliary verb. More specifically, we devise an automatic data construction strategy leveraging ChatGPT to construct a skeleton training dataset, based on which we employ a soft prompting approach to train a BART model dedicated to generating the skeleton associated with each input. Subsequently, skeleton heuristics are encoded into the prompt to incentivize GPT-3.5 to generate desired questions. Extensive experiments demonstrate that SGSH derives the new state-of-the-art performance on the KBQG tasks. The code is now available on Github1 Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Multi-source Multi-label Feature Selection

Multi-source Multi-label Feature Selection

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International Joint Conference on Neural Networks (IJCNN)

作者： Xiulan Yuan Xuegang Hu Peipei Li Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei University of Technology School of Computer Science and Information Engineering Hefei University of Technology Hefei City China Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei City China

Feature selection for multi-source multi-label data has attracted much attention, because there are a lot of scenarios that produce multiple source data with multi-labels in the real-world applications, which aggravates the problems of dimensional disaster and the label skewness. However, most of existing multi-source feature selection methods miss the multi-label issue while existing multi-label feature selection methods can not select the optimal feature set in the multi-source environment. Motivated by this, we propose a novel feature selection method, called MSMLFS. To be specific, the Inf-FS algorithm is firstly introduced to handle multi-label feature selection for each data source, which considers the label weight in the feature selection. Secondly, the over-sampling mechanism and the inter-source feature fusion method are used to handle the label skewness of multi-label data and the feature selection in multiple sources respectively. Finally, extensive experiments conducted on synthetic and real-world multi-source multi-label data sets demonstrate that the proposed method outperforms several state-of-the-art multi-source or multi-label feature selection methods.

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Neural Adaptive Dynamic Surface Control of PMSMs with Input Saturation and output constraint

Neural Adaptive Dynamic Surface Control of PMSMs with Input ...

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2022 Chinese Automation Congress, CAC 2022

作者： Lv, Dongchao Li, Shaobo Zhang, Tao Wu, Fengbin Li, Menghan Zheng, Chaojie Guizhou University School of Mechanical Engineering Guiyang China Guizhou University State Key Laboratory Of Public Big Data Guiyang China Guizhou University School of Computer Science and Technology Guiyang China

ISBN: (纸本)9781665465335

This paper discusses an adaptive neural tracking control of permanent magnet synchronous motors subject to input saturation and output constraint. The difficulty is to consider output constraints and input saturation. Firstly, the adaptive dynamic surface control design process is systematized by embedding many existing tools into the classical backstepping framework. Then, a nonlinear transformation function is proposed to transform the output constrained system into an unconstrained system. Furthermore, using radial basis function neural networks to Process the unknown terms, the Gaussian error function is utilized to describe the continuously differentiable asymmetric saturation nonlinearity. It turns out that all signals in the proposed scheme are bounded. The simulation results are provided to further show the feasibility of the proposed method. © 2022 IEEE.

关键词： Backstepping

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