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arXiv 2021年

作者： Tang, Gaigai Meng, Lianxiao Wang, Huiqiang Ren, Shuangyin Wang, Qiang Yang, Lin Cao, Weipeng School of computer science and technology Harbin Engineering University Harbin China National Key Laboratory of Science and Technology on Information System Security Institute of System Engineering Chinese Academy of Military Science Beijing China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

software vulnerabilities are usually caused by design flaws or implementation errors, which could be exploited to cause damage to the security of the system. At present, the most commonly used method for detecting software vulnerabilities is static analysis. Most of the related technologies work based on rules or code similarity (source code level) and rely on manually defined vulnerability features. However, these rules and vulnerability features are difficult to be defined and designed accurately, which makes static analysis face many challenges in practical applications. To alleviate this problem, some researchers have proposed to use neural networks that have the ability of automatic feature extraction to improve the intelligence of detection. However, there are many types of neural networks, and different data preprocessing methods will have a significant impact on model performance. It is a great challenge for engineers and researchers to choose a proper neural network and data preprocessing method for a given problem. To solve this problem, we have conducted extensive experiments to test the performance of the two most typical neural networks (i.e., Bi-LSTM and RVFL) with the two most classical data preprocessing methods (i.e., the vector representation and the program symbolization methods) on software vulnerability detection problems and obtained a series of interesting research conclusions, which can provide valuable guidelines for researchers and engineers. Specifically, we found that 1) the training speed of RVFL is always faster than Bi-LSTM, but the prediction accuracy of Bi-LSTM model is higher than RVFL;2) using doc2vec for vector representation can make the model have faster training speed and generalization ability than using word2vec;and 3) multi-level symbolization is helpful to improve the precision of neural network models. © 2021, CC BY.

关键词： Long short-term memory

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Gaussian Decay Function-based Improved Moment Matching for Ocular Artifacts Removal

Gaussian Decay Function-based Improved Moment Matching for O...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Hua Jiang Qiuxia Shi Siying Liu Jiuying Zhang Qinglin Zhao Bin Hu Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Key Laboratory of Special Functional Materials and Structural Design Ministry of Education Lanzhou University Lanzhou China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences Chinese Academy of Sciences Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University & Institute of Semiconductors Engineering Research Center of Open Source Software and Real-Time System (Lanzhou University) Ministry of Education Key Laboratory of Special Functional Materials and Structural Design (Lanzhou University) Ministry of Education Lanzhou China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Electroencephalogram (EEG) equipped with high time resolution that distracted by incoherent brain sources which including ocular artifacts (OAs) generating by blinks is of great significance. Therefore, these OAs got corrected before extracting information from EEG is indispensable. Improved Moment Matching (IMM) is a high-speed denoising algorithm suitable for removing OA in multi-channel EEG, which is an improvement of the moment matching method used to remove stripe noise in hyperspectral images. On foundation of this, this paper proposes an optimization algorithm for IMM based on Gaussian decay function (IMM_G). In the first place, the construction of the reference signal is optimized via utilizing a Gaussian decay function, thereby preventing the signal distortion caused by the filter. And then, a method for judging the to-be-processed interval is proposed to realizes the individualized processing of different channels. As a result, through quantitative comparison experiments with simulated data and real data from the UAIS laboratory, it was identified that IMM_G showed less time complexity, significantly enhanced arithmetic speed and denoising consequence while the detail retention ability of the original method for the non-blinking region of multi-channel EEG data is maintained. Hence, this method could be extensively used in High-density EEG (hdEEG) OAs removal scene.

关键词： Electrooculography Noise reduction Filtering algorithms Distortion Electroencephalography Recording Time-domain analysis

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Research on the Influence of Improved K-shell Algorithm on Commodity Profit

Research on the Influence of Improved K-shell Algorithm on C...

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作者： Jing Chen Jincheng Huang College of Information Science and Engineering Yanshan University Key Laboratory for Computer Virtual Technology and system Integration of Hebei Province Software Engineering Laboratory in Hebei Province

In order to maximize the influence of commodity profits in e-commerce platforms, designing and improving the K-shell algorithm to select the more influential seed node sets in this paper. The new algorithm improves the number of active nodes by setting node threshold and edge weight attributes. To obtain more commodity profits, a strategy IRDSN(Strategy for Improving Repeat Degree of Seed Nodes) is proposed to select initial seed nodes and improve the repeat degree of seed nodes. The profit maximization based on linear threshold model is realized by setting different propagation modes. The improved algorithm and strategy IRDSN are analysed and verified in real data set and e-commerce platform. The results show that the algorithm effectively improves the profit of commodities.

关键词： Influence threshold K-shell influence maximization profit

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Path-following Control of Fish-like Robots: A Deep Reinforcement Learning Approach ⁎

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IFAC-PapersOnLine 2020年第2期53卷 8163-8168页

作者： Tianhao Zhang Runyu Tian Chen Wang Guangming Xie National Engineering Research Center for Software Engineering Peking University Beijing 100871 China The State Key Laboratory of Turbulence and Complex Systems Intelligent Biomimetic Design Lab College of Engineering Peking University Beijing 100871 China Institute of Ocean Research Peking University Beijing 100871 China

In this paper, we propose a deep reinforcement learning (DRL) approach for path-following control of a fish-like robot. The desired path may be a randomly generated Bézier curve. First, to implement the locomotion control of the fish-like robot, we design a modified Central Pattern Generated (CPG) model, using which the fish achieves varied swimming behaviors just by adjusting a single control input. To reduce the reality gap between simulation and the physical system, using the experimental data of the real fish-like robot, we build a surrogate simulation environment, which also well balances the accuracy and the speed of training. Second, for the path-following control, we select the advantage actor-critic (A2C) approach and train the control policy in the surrogate simulation environment with a straight line as the desired path. Then the trained control policy is directly deployed on a physical fish-like robot to follow a randomly generated Bézier curve. The experimental results show that our proposed approach has good practical applicability in view of its efficiency and feasibility in controlling the physical fishlike robot. This work shows a novel and promising way to control biomimetic underwater robots in the real world.

关键词： Reinforcement learning control autonomous underwater vehicles biomimetic underwater robots path following deep learning

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Many-to-one distribution learning and K-nearest neighbor smoothing for thoracic disease identification

arXiv

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arXiv 2021年

作者： Zhou, Yi Huang, Lei Zhou, Tianfei Shao, Ling School of Computer Science and Engineering Southeast University Nanjing China State Key Laboratory of Software Development Environment Beihang University Beijing China Computer Vision Laboratory ETH Zurich Switzerland Inception Institute of Artificial Intelligence Abu Dhabi United Arab Emirates

Chest X-rays are an important and accessible clinical imaging tool for the detection of many thoracic diseases. Over the past decade, deep learning, with a focus on the convolutional neural network (CNN), has become the most powerful computer-aided diagnosis technology for improving disease identification performance. However, training an effective and robust deep CNN usually requires a large amount of data with high annotation quality. For chest X-ray imaging, annotating large-scale data requires professional domain knowledge and is time-consuming. Thus, existing public chest X-ray datasets usually adopt language pattern based methods to automatically mine labels from reports. However, this results in label uncertainty and inconsistency. In this paper, we propose many-to-one distribution learning (MODL) and Knearest neighbor smoothing (KNNS) methods from two perspectives to improve a single model’s disease identification performance, rather than focusing on an ensemble of models. MODL integrates multiple models to obtain a soft label distribution for optimizing the single target model, which can reduce the effects of original label uncertainty. Moreover, KNNS aims to enhance the robustness of the target model to provide consistent predictions on images with similar medical findings. Extensive experiments on the public NIH Chest X-ray and CheXpert datasets show that our model achieves consistent improvements over the state-of-the-art methods. © 2021, CC0.

关键词： Computer aided diagnosis

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Generating superpixels for high-resolution images with Decoupled Patch Calibration

arXiv

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arXiv 2021年

作者： Wang, Yaxiong Wei, Yunchao Qian, Xueming Zhu, Li Yang, Yi The School of Software Engineering Xi'an Jiaotong University Xi'an710049 China School of Computer and Information Technology Beijing Jiaotong University Beijing100000 China The Key Laboratory for Intelligent Networks and Network Security Ministry of Education Xi'an Jiaotong University Xi'an710049 China The SMILES Laboratory Xi'an Jiaotong University Xi'an710049 China Zhibian Technology Co. Ltd. Taizhou317000 China The School of Software Xi'an Jiaotong University Xi'an710049 China School of Computer Science and Technology Zhejiang University Hangzhou310000 China

Superpixel segmentation has recently seen important progress benefiting from the advances in differentiable deep learning. However, the very high-resolution superpixel segmentation still remains challenging due to the expensive memory and computation cost, making the current advanced superpixel networks fail to process. In this paper, we devise Patch Calibration Networks (PCNet), aiming to efficiently and accurately implement high-resolution superpixel segmentation. PCNet follows the principle of producing high-resolution output from low-resolution input for saving GPU memory and relieving computation cost. To recall the fine details destroyed by the down-sampling operation, we propose a novel Decoupled Patch Calibration (DPC) branch for collaboratively augment the main superpixel generation branch. In particular, DPC takes a local patch from the high-resolution images and dynamically generates a binary mask to impose the network to focus on region boundaries. By sharing the parameters of DPC and main branches, the fine-detailed knowledge learned from high-resolution patches will be transferred to help calibrate the destroyed information. To the best of our knowledge, we make the first attempt to consider the deep-learning-based superpixel generation for high-resolution cases. To facilitate this research, we build evaluation benchmarks from two public datasets and one new constructed one, covering a wide range of diversities from fine-grained human parts to cityscapes. Extensive experiments demonstrate that our PCNet can not only perform favorably against the state-of-the-arts in the quantitative results but also improve the resolution upper bound from 3K to 5K on 1080Ti GPUs. © 2021, CC BY.

关键词： Image segmentation

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Missing Data Recovery for Human Mocap Data Based on A-LSTM and LS Constraint

Missing Data Recovery for Human Mocap Data Based on A-LSTM a...

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IEEE International Conference on Signal and Image Processing (ICSIP)

作者： Lu Ji Rui Liu Dongsheng Zhou Qiang Zhang Xiaopeng Wei Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Dalian China School of Computer Science and Technology Dalian University of Technology Dalian China

ISBN: (数字)9781728168968

ISBN: (纸本)9781728168975

Human mocap (motion capture) data has been widely used in many fields. However, influenced by the occlusion and falling, missing of markers is inevitable in optical motion capture system. According to the temporal and spatial characteristics of mocap data, a missing data recovery method based on attention-based LSTM network (A-LSTM) and least-squares (LS) constraint is proposed in this paper. First, the LSTM is chosen as the basic network to estimate the position of the missing markers, in which an attention mechanism is introduced to learn the temporal continuity of human mocap data. Then, a space affine transformation based on least-squares fitting is adopted as the spatial constraint to refine the estimated positions, after which the restored error can be reduced significantly. Experiments show that the proposed A-LSTM+LS method is robust to the length of missing gap and the number of missing markers. Under the same conditions, our method achieves smaller restore error than the state of the art.

关键词： Legged locomotion Deep learning Image recognition Correlation Fitting Neural networks Image restoration

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Research on a Novel Influence Maximization Algorithm Based on Community Structure

Research on a Novel Influence Maximization Algorithm Based o...

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作者： Jing Chen Jiangchuan Liu College of Information Science and Engineering Yanshan University Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Software Engineering Laboratory in Hebei Province

With the research of influence maximization algorithm, many researchers have found that the existing algorithm has the problem of overlapping influence of seed nodes. In order to solve the problem of overlapping influence of seed nodes, this paper proposes an IMCS algorithm based on community structure. Firstly, we divide the community through the central node, and the quality of community division is ensured by defining community fitness and node contribution. Then through the analysis of the community division results, the seed node selects the one with the largest degree. Since most of the nodes activated by seed nodes of different communities also belong to different communities, this method solves the problem of overlapping influence to a certain extent. The experimental results show that the effectiveness of the IMCS algorithm is verified under the real network, cooperative network and artificial network, and the IMCS algorithm has a better effect than IEIR and Degree algorithms in most networks under the IC model.

关键词： Seed node influence maximization community division overlapping influence community structure independent cascade model

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Multi-scale feature fusion for breast tumor grading using dual-adaptive attention mechanisms

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Pattern Recognition 2026年 169卷

作者： Israr Hussain Zhihui Lai Heng Kong Fazil Hussain College of Computer Science and Software Engineering Shenzhen University Shenzhen 518060 China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen 518060 China Department of Thyroid and Breast Surgery BaoAn Central Hospital of Shenzhen Shenzhen 518102 China Department of Medicine Provincial Head Quarter (PHQ) Hospital Gilgit 15711 Pakistan

Breast cancer is the most common malignant tumor in women worldwide. Early detection and timely treatment are crucial for improving survival rates and ensuring effective treatment planning. While existing methods primarily focus on classifying benign versus malignant tumors, accurate tumor grading remains underexplored. The scarcity of high-quality annotated datasets for advanced tumor grades, combined with their inherent similarity, limits model robustness and precise grading. To address these challenges, we propose a novel framework for breast tumor grading diagnosis that leverages multi-scale feature fusion with dual-adaptive attention mechanisms. This framework enhances grading performance by capturing both local and global information. It integrates information across multiple scales, improves feature representation, and enables the detection of subtle differences between grades. The framework consists of three primary modules: deep hybrid feature extraction, multi-scale feature fusion, and dual-adaptive attention mechanisms. Together, these modules enhance feature representation, capture salient information, and highlight discriminative features, ultimately improving grading accuracy. In addition, we constructed two distinct image datasets consisting of 17,058 ultrasound images and 1571 MRI images for breast tumor grading. Experimental results demonstrated the effectiveness of our framework, achieving a test accuracy of 0.89, a macro-average F1-score of 0.82, and a micro-average precision of 0.93, outperforming existing state-of-the-art methods.

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Runtime resource management for microservices-based applications: A congestion game approach (Short Paper) 14th

Runtime resource management for microservices-based applicat...

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14th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2018

作者： Luo, Ruici Ye, Wei Sun, Jinan Liu, Xueyang Zhang, Shikun School of Electronics Engineering and Computer Science Peking University Beijing China National Engineering Research Center for Software Engineering Peking University Beijing China Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing China

ISBN: (纸本)9783030129804

The term "Microservice Architecture" has sprung up in recent years as a new style of software design that gains popularity as cloud computing prospers. In microservice-based applications, different microservices collaborate with one another via interface calls, but they may also compete for resources when an increase of users’ need renders the resources insufficient. This poses new challenges for allocating resources efficiently during runtime. To tackle the problem, we propose a novel approach based on Congestion Game in this paper. Firstly, we use a weighted directed acyclic graph to model the inter-relationship of the microservices that compose an application. Then we use M/G/1 Queue in Queue Theory to describe the arrival process of access requests, and combine it with the above graph to calculate the arrival rate of access requests to each microservice, which in turn is used to estimate response time in a newly-designed microservice revenue function. Finally, we define resources competing problem as a congestion game where each microservice is a player aiming to maximize its revenue, and propose an algorithm to find Nash equilibrium in polynomial time. Experiment results show that our approach can effectively improve the overall performance of the system with limited resources, and outperform Binpack and Spread, two scheduling strategies used in Docker Swarm. © 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Game theory

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