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2022 IEEE International Conference on Artificial Intelligence and Computer Applications, ICAICA 2022

作者： Yin, Zhiqian Cao, Wei Song, Tao Yang, Xu Zhang, Tianhao Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education College of Software Changchun130012 China

ISBN: (数字)9781665499910

ISBN: (纸本)9781665499910

With the rapid development of artificial intelligence, people have put forward higher requirements for robot path planning. As a more commonly used algorithm, reinforcement learning learns from experience by imitating the process of human learning skills and continuously iterates to do. In this paper, an improved Step Batch Q-Learning algorithm is proposed to solve the problem that the traditional Q-Learning algorithm has a slow convergence speed in discrete states. Prediction can detect obstacles and target points earlier, reduce the number of iterations, and significantly speed up the convergence time. Under the same experimental conditions, the method in this paper is compared with the traditional Q-Learning algorithm. The experimental results show that the improved Step Batch Q-Learning algorithm has significantly improved convergence speed and good adaptability. © 2022 IEEE.

关键词： Reinforcement learning

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BSDIFF Difference Algorithm Based on LZMA2 for In-Vehicle ECUs 7th

BSDIFF Difference Algorithm Based on LZMA2 for In-Vehicle EC...

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7th International Conference on computing, Control and Industrial engineering, CCIE 2023

作者： Li, Zhihao Qin, Guihe Liang, Yanhua Danzengouzhu Yang, Lifeng College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China

ISBN: (纸本)9789819927296

With cars becoming the primary choice of people’s daily travel, on-board OTA upgrade technology has arisen a common concern in the automotive industry. Traditional on-board OTA upgrade technology, which adopts a method of updating the whole package, successfully avoids offline upgrade. However, it takes too long to download the whole update package and upgrade in-vehicle ECUs because of the huge data transmission. In addition, packet loss and disconnection are easy to occur in case of network congestion. As a result, it will seriously affect users’ online upgrade experience. Therefore, incremental update technology should be used instead of updating the whole package in on-board OTA upgrade technology. In order to solve above problems, in this paper, we propose an improved BSDIFF difference algorithm based on LZMA2 compression algorithm to realize the incremental update of on-board OTA. Traditional BSDIFF can generate a patch file according to an old app and a new app. And a patch file is far smaller than the corresponding new app, which is conducive to remote transmission and reduces the download time of users. Through our experiments, LZMA2-based BSDIFF has a better compression ratio than traditional BSDIFF, which is more suitable for the incremental update of in-vehicle ECUs with limited memory resources. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Automotive industry

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Polynomial-Time Assignment-Based Cell Association with Generic Utility Functions

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China Communications 2022年第9期19卷 214-228页

作者： Lusheng Wang Chao Fang Hai Lin Min Peng Caihong Kai Key Laboratory of Knowledge Engineering with Big Data Ministry of Education.School of Computer Science and Information EngineeringHefei University of TechnologyHefei 230601China Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education.School of Cyber Science and EngineeringWuhan UniversityWuhan 430072China

Cell association is a significant research issue in future mobile communication systems due to the unacceptably large computational time of traditional *** article proposes a polynomial-time cell association scheme which not only completes the association in polynomial time but also fits for a generic optimization objective *** the one hand,traditional cell association as a non-deterministic polynomial(NP)hard problem with a generic utility function is heuristically transformed into a 2-dimensional assignment optimization and solved by a certain polynomial-time algorithm,which significantly saves computational *** the other hand,the scheme jointly considers utility maximization and load balancing among multiple base stations(BSs)by maintaining an experience pool storing a set of weighting factor values and their corresponding *** an association optimization is required,a suitable weighting factor value is taken from the pool to calculate a long square utility matrix and a certain polynomial-time algorithm will be applied for the *** with several representative schemes,the proposed scheme achieves large system capacity and high fairness within a relatively short computational time.

关键词： mobile communication system 2-dimensional assignment problem hungarian algorithm fairness cell association

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Flow Monitoring Alarm Module Application for In-Vehicle CAN Bus Networks 7th

Flow Monitoring Alarm Module Application for In-Vehicle CAN ...

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7th International Conference on computing, Control and Industrial engineering, CCIE 2023

作者： Liu, Wanning Qin, Guihe Yang, Lifeng Liang, Yanhua College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China

ISBN: (纸本)9789819927296

As control modules in cars become intelligent and automated, electrical systems become more complex. Dozens or even hundreds of electronic control units (ECUs) are used in many cars. In this case, the information security problem of the car is also latent in the dark. When the actual application load ratio of the CAN bus exceeds more than 30%, the delay of data communication and the probability of error frames multiply, causing serious communication congestion. A malicious intruder only needs to affect the bus data traffic to cause the collapse of the CAN bus network, which in turn changes the behavior of the car and has irreversible consequences. In order to enable the CAN bus to maintain its advantages, it can adapt to the new needs of contemporary in-vehicle networks with higher performance and more reliable security protection. In the paper, we propose a flow monitoring alarm module built into the in-vehicle CAN. This module embeds a statistical algorithm based on information theory. The algorithm has a small amount of computation and clear logic, which meets the lightweight requirements of embedded algorithms. Through multiple experiments, the module can monitor all Dos for in-vehicle CAN, as well as other traffic abnormalities with a monitoring alarm rate of more than 98%. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Microcontrollers

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Discovering Skyline Periodic Itemset Patterns in Transaction Sequences 9th

Discovering Skyline Periodic Itemset Patterns in Transactio...

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19th International Conference on Advanced Data Mining and Applications, ADMA 2023

作者： Chen, Guisheng Li, Zhanshan College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China

ISBN: (纸本)9783031466601

As an extended version of frequent itemset patterns, periodic itemset patterns concern both the frequency and periodicity of itemsets at the same time, so they contain more information than frequent itemset patterns, which only concern the frequency. With further research, we found that, in some cases, the periodic itemset patterns with higher frequency, or with optimal periodicity, or with both higher frequency and optimal periodicity have higher application value. However, there is currently no work focusing on such a kind of periodic itemset patterns. In view of this, this paper first proposes a new concept of skyline periodic itemset patterns, and states the problem of skyline periodic itemset pattern mining, then presents an algorithm called SLPIM (SkyLine Periodic Itemset pattern Miner) for skyline periodic itemset pattern mining. SLPIM first adopts the well-known FP-Growth algorithm to mine all frequent itemset patterns, and then uses an effective judgment strategy to determine which frequent itemset patterns are skyline periodic itemset patterns. Finally, experiments are conducted on two real-world and two simulated datasets. The results show that SLPIM is competent for mining skyline periodic itemset patterns. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Data mining

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A Pruning Based Continuous RkNN Query Algorithm for Large k

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Chinese Journal of Electronics 2023年第3期21卷 523-527页

作者： Shengsheng WANG Sheng CHAI Qiannan LV College of Computer Science and Technology Key Laboratory of Symbolic Computing and Knowledge Engineering of Ministry of Education Jilin University Changchun China

Reverse k-nearest neighbor (RkNN) query is a hot-spot in spatio-temporal database. With the developing of mobile devices, continuous RkNN query becomes more and more important. Most of the previous methods use the two-step (filter-refinement) processing. However, for large k, the amount of calculation becomes very heavy, especially in the filter step. This is not acceptable for most mobile devices. This paper presents a novel algorithm for continuous RkNN queries based on pruning heuristics. The experiments show that the processing time of our method is still acceptable for most mobile devices when k is large.

关键词： Location awareness Databases Heuristic algorithms Filtering algorithms Mobile handsets Mobile applications

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Osteoporotic Vertebral Fracture Classification in X-rays Based on a Multi-modal Semantic Consistency Network

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Journal of Bionic engineering 2022年第6期19卷 1816-1829页

作者： Yuzhao Wang Tian Bai Tong Li Lan Huang College of Computer Science and Technology Jilin UniversityChangchun130012China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityChangchun130012China Department of Orthopedics The Second Hospital of Jilin UniversityChangchun130012China

Osteoporotic Vertebral Fracture(OVFs)is a common lumbar spine disorder that severely affects the health of *** a clear bone blocks boundary,CT images have gained obvious advantages in OVFs *** with CT images,X-rays are faster and more inexpensive but often leads to misdiagnosis and miss-diagnosis because of the overlapping *** how to transfer CT imaging advantages to achieve OVFs classification in X-rays is *** this purpose,we propose a multi-modal semantic consistency network which could do well X-ray OVFs classification by transferring CT semantic consistency *** from existing methods,we introduce a feature-level mix-up module to get the domain soft labels which helps the network reduce the domain offsets between CT and *** the meanwhile,the network uses a self-rotation pretext task on both CT and X-ray domains to enhance learning the high-level semantic invariant *** employ five evaluation metrics to compare the proposed method with the state-of-the-art *** final results show that our method improves the best value of AUC from 86.32 to 92.16%.The results indicate that multi-modal semantic consistency method could use CT imaging features to improve osteoporotic vertebral fracture classification in X-rays effectively.

关键词： Osteoporotic vertebral fracture classification Cross-modality Unsupervised domain adaptation Transfer learning Convolutional neural network Computer-aided diagnosis

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Positive and Unlabeled Learning with Controlled Probability Boundary Fence 41

Positive and Unlabeled Learning with Controlled Probability ...

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41st International Conference on Machine Learning, ICML 2024

作者： Li, Changchun Dai, Yuanchao Feng, Lei Li, Ximing Wang, Bing Ouyang, Jihong College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore

Positive and Unlabeled (PU) learning refers to a special case of binary classification, and technically, it aims to induce a binary classifier from a few labeled positive training instances and loads of unlabeled instances. In this paper, we derive a theorem indicating that the probability boundary of the asymmetric disambiguation-free expected risk of PU learning is controlled by its asymmetric penalty, and we further empirically evaluated this theorem. Inspired by the theorem and its empirical evaluations, we propose an easy-to-implement two-stage PU learning method, namely Positive and Unlabeled Learning with Controlled Probability Boundary Fence (PUL-CPBF). In the first stage, we train a set of weak binary classifiers concerning different probability boundaries by minimizing the asymmetric disambiguation-free empirical risks with specific asymmetric penalty values. We can interpret these induced weak binary classifiers as a probability boundary fence. For each unlabeled instance, we can use the predictions to locate its class posterior probability and generate a stochastic label. In the second stage, we train a strong binary classifier over labeled positive training instances and all unlabeled instances with stochastic labels in a self-training manner. Extensive empirical results demonstrate that PUL-CPBF can achieve competitive performance compared with the existing PU learning baselines. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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Learning Stable Task-Level Manifold for Few-Shot Learning

Learning Stable Task-Level Manifold for Few-Shot Learning

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2023 International Conference on Digital Image computing: Techniques and Applications, DICTA 2023

作者： Yu, Siyi Luo, Wei Li, Gang Yang, Bo Jilin University College of Computer Science and Technology China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education China Deakin University School of Information Technology Geelong Australia

ISBN: (纸本)9798350382204

Few-shot learning (FSL) aims to learn to new concepts based on very limited data. One of the main challenges in FSL is the use of pretrained embeddings whose dimension is too high for the small sample size. While the large dimensionality helps capture diverse features in the training example, not all the features are relevant to the task at hand. Such task-irrelevant features from pre-trained embeddings can negatively impact the performance of the FSL model. To address this challenge, we propose a simple yet effective way to learn task-level manifolds that explicitly eliminate the negative effect of nuisance features. One key novelty of our approach is to address class imbalance in the training data by injecting supervised information through key virtual samples, which we named Manifold Support Points. Empirically, we demonstrate that the stability of the learnt task-level manifolds and their effectiveness in challenging tasks in FSL. Our approach shows competitive performance on several benchmark datasets, including tieredImageNet and CUB. © 2023 IEEE.

关键词： Embeddings

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SS-MAF: Semi-Supervised Echocardiographic Segmentation Using Multi-Atlas Fusion 8

SS-MAF: Semi-Supervised Echocardiographic Segmentation Using...

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8th International Conference on Communication, Image and Signal Processing, CCISP 2023

作者： Yang, Shuaihua Zheng, Caixia College of Information Science and Technology Northeast Normal University Changchun130117 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun130012 China

ISBN: (纸本)9798350305838

Left ventricle (LV) segmentation in echocardiography is of paramount significance in cardiac function analysis. Recently, semi-supervised segmentation has garnered considerable attention due to its potential mitigation of labeling costs. Despite this, most semi-supervised methodologies tend to overlook the specialized anatomical priors specific to echocardiography, often yielding suboptimal outcomes. Simultaneously, atlas priors have been validated on other organs and imaging modalities. However, their direct application in echocardiography is fraught with challenges, primarily attributed to the indistinct boundaries of the LV and the presence of fan-shaped image edges, which impede atlas image registration. This paper presents a new method of semi-supervised segmentation using multi-atlas fusion, named SS- MAF, which is designed to incorporate atlas priors tailored for echocardiography while circumventing the intricate image registration process in traditional atlas-based methods. The SS-MAF comprises two components: a Multi-Task Learning (MTL) module and a Multi-Atlas Fusion (MAF) module. The MTL module serves precise landmark localization and segmentation estimation. The MAF module is primarily focused on the generation of probabilistic atlases, utilizing annotated atlas data with the guidance of key landmarks to facilitate the automatic annotation of unlabeled data, avoiding the traditional image-level registration, thus enhancing the performance of the MTL module. Our SS- MAF is rigorously evaluated on the publicly available CAMUS dataset, with results demonstrating its superiority over other state-of-the-art semi-supervised segmentation techniques in echocardiography. © 2023 IEEE.

关键词： Image registration

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