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Performance Evaluation of an Anomaly-Detection Algorithm for Keystroke-Typing Based Insider Detection

Tsinghua science and technology 2018年第5期23卷 513-525页

作者： Liang He Zhixiang Li Chao Shen National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu 610041 China Ministry of Education Key Lab for Intelligent Networks and Network Security Xi'an Jiaotong University Xi'an 710004 China

Keystroke dynamics is the process to identify or authenticate individuals based on their typing rhythm behaviors. Several classifications have been proposed to verify a user＇s legitimacy, and the performances of these classifications should be confirmed to identify the most promising research direction. However, classification research contains several experiments with different conditions such as datasets and methodologies. This study aims to benchmark the algorithms to the same dataset and features to equally measure all performances. Using a dataset that contains the typing rhythm of 51 subjects, we implement and evaluate 15 classifiers measured by Fl-measure, which is the harmonic mean of a false-negative identification rate and false-positive identification rate. We also develop a methodology to process the typing data. By considering a case in which the model will reject the outsider, we tested the algorithms on an open set. Additionally, we tested different parameters in random forest and k nearest neighbors classifications to achieve better results and explore the cause of their high performance. We also tested the dataset on one-class classification and explained the results of the experiment. The top-performing classifier achieves an Fl-measure rate of 92% while using the normalized typing data of 50 subjects to train and the remaining data to test. The results, along with the normalization methodology, constitute a benchmark for comparing the classifiers and measuring the performance of keystroke dynamics for insider detection.

关键词： keystroke dynamics insider identification Fl-measure normalization one-class classification

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Convergence Analysis of Whale Optimization Algorithm

Convergence Analysis of Whale Optimization Algorithm

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2020 International Conference on Computer Big Data and Artificial Intelligence, ICCBDAI 2020

作者： Feng, Wentao National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu Sichuan610041 China

The whale optimization algorithm (WOA) has been widely used in different applications. It has simple control parameters and novel population updating mechanism. However, there is few theoretical analysis of WOA and the convergence property of WOA is ambiguity. This paper analyzes the convergence property of WOA by using the Markov chain of the stochastic process theory. The Markov chain model of the WOA algorithm is established. The one step transition probabilities and convergence properties of different population updating mechanisms in WOA are given. It's proved that the convergence property of WOA is determined by its shrinking encircling mechanism. Finally, three algorithms with different population updating mechanisms are tested with thirteen benchmark functions on accuracy and convergence speed. The simulation results on benchmark functions verify the validity of the theoretical analysis of WOA. © Published under licence by IOP Publishing Ltd.

关键词： Stochastic systems

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A self-adaptive modified whale optimization algorithm for optimizing the linear antenna array 12

A self-adaptive modified whale optimization algorithm for op...

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12th International Conference on Measuring technology and Mechatronics Automation, ICMTMA 2020

作者： Feng, Wentao Song, Kekang National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu Sichuan610041 China

ISBN: (纸本)9781728170817

The whale optimization algorithm (WOA) is an effective algorithm for solving complex optimization problem. Its unique search mechanism results in better exploitation than exploration capability which means that it's easy to get trapped in the local optima. A self-adaptive modified whale optimization algorithm named of SAMWOA is proposed. A self-adaptive quasi-opposition method and novel population updating mechanism is integrated into the whale optimization algorithm to improve the solution accuracy. Finally, the proposed algorithm is utilized in optimizing the radiation pattern of a 16-element linear antenna array. The experimental results show that SAMWOA algorithm can reduce the sidelobe level obviously. © 2020 IEEE.

关键词： Optimization

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A modified multi-objective whale optimization algorithm with dynamic leader selection mechanism 12

A modified multi-objective whale optimization algorithm with...

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12th International Conference on Measuring technology and Mechatronics Automation, ICMTMA 2020

作者： Feng, Wentao Deng, Bing National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu Sichuan610041 China

ISBN: (纸本)9781728170817

The whale optimization algorithm (WOA) has less control parameters and it is easier to implement. The multi-objective whale optimization algorithm (MOWOA) also shows good exploration and exploitation capability. A modified multi-objective whale optimization algorithm with dynamic leader selection mechanism (MMOWOA-DLS) is proposed. First, the opposition-based learning (OBL) is employed to accelerate the convergence speed. Second, a dynamic leader selection mechanism is utilized to improve the solution accuracy. Third, a modified archive grid controller is proposed to delete redundant solutions in external archive. The simulation results show that the performance of MMOWOA-DLS outperforms other algorithms. © 2020 IEEE.

关键词： Dynamics

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MCFTNet: Multimodal Cross-layer Fusion Transformer Network for Hyperspectral and LiDAR Data Classification

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年

作者： Huang, Wei Wu, Tianren Zhang, Xueyu Li, Liangliang Lv, Ming Jia, Zhenhong Zhao, Xiaobin Ma, Hongbing Vivone, Gemine Xinjiang University School of Computer Science and Technology Urumqi830017 China Xinjiang University Key Laboratory of Signal Detection and Processing Urumqi830017 China Guangxi University School of Computer and Electronic Information Nanning530004 China Guangxi University Guangxi Key Laboratory of Multimedia Communications and Network Technology Nanning530004 China Beijing Institute of Technology School of Information and Electronics Beijing100081 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Tsinghua University Department of Electronic Engineering Beijing100084 China National Research Council Tito85050 Italy NBFC National Biodiversity Future Center Palermo90133 Italy

Remote sensing image classification is a popular yet challenging field. Many researchers have combined convolutional neural networks (CNNs) and Transformers for hyperspectral image (HSI) classification tasks. However, in traditional Transformers, shallow-level information does not propagate well to deeper layers, which can lead to spatial variations and overfitting. Moreover, traditional Transformer models use an external classification (CLS) token that is randomly initialized and often struggles to generalize effectively. In this paper, we combine the strengths of HSI and light detection and ranging (LiDAR) data, using LiDAR as an external CLS token, which significantly enhances classification accuracy and reliability. We propose a new multimodal cross-layer fusion transformer network (MCFTNet), integrating CNNs with the latest Transformer networks. It includes a CNN for extracting spatial features and a hybrid cross-patch attention mechanism (HyCrossAttention) for land cover classification, leveraging LiDAR data to generate CLS and HSI patch tokens. More importantly, to reduce the loss of valuable information during the layer-by-layer propagation, we designed cross-layer skip connections. Through adaptive learning, these cross-layer fusions help address the gradient vanishing problem in deep networks while preserving early-layer features. This enables the model to better integrate information from different layers, enhancing both its stability and performance. We carried out in - depth experiments on commonly - used benchmark datasets, specifically, the University of Houston dataset, the Trento dataset, and the University of Southern Mississippi Gulf Park (MUUFL) dataset. We compared the results of the proposed MCFTNet model with those obtained from state-of-the-art Transformer models, classical CNNs, and traditional classifiers. As a result, the MCFTNet model outshone them all in terms of performance. © 2008-2012 IEEE.

关键词： Convolutional neural networks

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Unsupervised Domain-Adaptive Semantic Segmentation for Surgical Instruments Leveraging Dropout-Enhanced Dual Heads and Coarse-Grained Classification Branch

IEEE Transactions on Medical Robotics and Bionics

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IEEE Transactions on Medical Robotics and Bionics 2025年

作者： Li, Ziqian Wang, Zhengyu Xu, Xinzhou Chen, Yongfa Schuller, Bjorn W. Hefei University of Technology School of Mechanical Engineering Hefei China Nanjing University of Posts and Telecommunications School of Internet of Things Nanjing China Graz University of Technology Signal Processing and Speech Communication Laboratory Graz Austria Chair of Health Informatics Munich Germany Munich Data Science Institute Munich Germany Munich Center for Machine Learning Munich Germany Imperial College London GLAM – the Group on Language Audio and Music London United Kingdom

Accurate semantic segmentation for surgical instruments is crucial in robot-assisted minimally invasive surgery, mainly regarded as a core module in surgical-instrument tracking and operation guidance. Nevertheless, it is usually difficult for existing semantic surgical-instrument segmentation approaches to adapt to unknown surgical scenes, particularly due to their insufficient consideration for reducing the domain gaps across different scenes. To address this issue, we propose an unsupervised domain-adaptive semantic segmentation approach for surgical instruments, leveraging Dropout-enhanced Dual Heads and Coarse-Grained classification branch (D2HCG). The proposed approach comprises dropout-enhanced dual heads for diverse feature representation, and a coarse-grained classification branch for capturing complexities across varying granularities. This incorporates consistency loss functions targeting fine-grained features and coarse-grained granularities, aiming to reduce crossscene domain gaps. Afterwards, we perform experiments in crossscene surgical-instrument semantic segmentation cases, with the experimental results reporting the effectiveness for the proposed approach, compared with state-of-the-art semantic segmentation ones. © 2018 IEEE.

关键词： coarse-grained classification dropout enhancement Surgical instruments unsupervised domain-adaptive semantic segmentation

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An Optimization Algorithm for Service Composition Based on an Improved FOA

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Tsinghua science and technology 2015年第1期20卷 90-99页

作者： Yiwen Zhang Guangming Cui Yan Wang Xing Guo Shu Zhao the School of Computer Science and Technology Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University

Large-scale service composition has become an important research topic in Service-Oriented Computing（SOC）. Quality of Service（Qo S） has been mostly applied to represent nonfunctional properties of web services and to differentiate those with the same functionality. Many studies for measuring service composition in terms of Qo S have been completed. Among current popular optimization methods for service composition, the exhaustion method has some disadvantages such as requiring a large number of calculations and poor scalability. Similarly,the traditional evolutionary computation method has defects such as exhibiting slow convergence speed and falling easily into the local optimum. In order to solve these problems, an improved optimization algorithm, WS FOA（Web Service composition based on Fruit Fly Optimization Algorithm） for service composition, was proposed, on the basis of the modeling of service composition and the FOA. Simulated experiments demonstrated that the algorithm is effective, feasible, stable, and possesses good global searching ability.

关键词： service composition Fruit Fly Optimization Algorithm(FOA) Quality of Service(QoS) index

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MR-IDPSO: A Novel Algorithm for Large-Scale Dynamic Service Composition

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Tsinghua science and technology 2015年第6期20卷 602-612页

作者： Yanping Zhang Zihui Jing Yiwen Zhang School of Computer Science and Technology Key Laboratory of Intelligent Computing and Signal ProcessingMinistry of EducationAnhui University

In the era of big data, data intensive applications have posed new challenges to the field of service composition. How to select the optimal composited service from thousands of functionally equivalent services but different Quality of Service(Qo S) attributes has become a hot research in service computing. As a consequence,in this paper, we propose a novel algorithm MR-IDPSO(Map Reduce based on Improved Discrete Particle Swarm Optimization), which makes use of the improved discrete Particle Swarm Optimization(PSO) with the Map Reduce to solve large-scale dynamic service composition. Experiments show that our algorithm outperforms the parallel genetic algorithm in terms of solution quality and is efficient for large-scale dynamic service composition. In addition,the experimental results also demonstrate that the performance of MR-IDPSO becomes more better with increasing number of candidate services.

关键词： Map Reduce service composition Quality of Service(

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Semantic social network analysis with text corpora

Semantic social network analysis with text corpora

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16th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2012

作者： Yang, Dong-Mei Zheng, Hui Yan, Ji-Kun Jin, Ye Science and Technology on Blind Signal Processing Laboratory China Southwest Electronics and Telecommunication Technology Research Institute China

ISBN: (纸本)9783642302169

We present the Document-Entity-Topic (DET) model for semantic social network analysis which tries to find out the interested entities through the topics we aim at, detect groups according to the entities which concern the similar topics, and rank the plentiful entities in a document to figure out the most valuable ones. DET model learns the topic distributions by the literal descriptions of entities. The model is similar to Author-Topic (AT) model, adding the key attribute that the distribution of entities in a document is not uniform but Dirichlet allocation. We experiment on the "Libya Event" data set which is collected from the Internet. DET model increases the precision on tasks of social network analysis and gives much lower perplexity than AT model. © 2012 Springer-Verlag.

关键词： Semantic Web

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Controlling auto-focusing chirped perfect Laguerre-Gaussian beam to mitigate crosstalk in atmospheric turbulence

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Optical and Quantum Electronics 2025年第4期57卷 1-11页

作者： Ma, Yangbin Wang, Xinguang Shen, Yangbo Zhao, Ben Wang, Le Zhao, Shengmei College of Science Nanjing University of Posts and Telecommunications Wenyuan Road Jiangsu Nanjing210023 China Jiangsu Engineering Research Center of Communication and Network Technology Nanjing University of Posts and Telecommunications Wenyuan Road Jiangsu Nanjing210023 China Portland Institute Nanjing University of Posts and Telecommunications Wenyuan Road Jiangsu Nanjing210023 China School of Communications and Information Engineering Nanjing University of Posts and Telecommunications Wenyuan Road Jiangsu Nanjing210023 China Institute of Signal Processing and Transmission Nanjing University of Posts and Telecommunications New model road Jiangsu Nanjing210003 China Nanjing University of Posts and Telecommunications New model road Jiangsu Nanjing210003 China National Laboratory of Solid State Microstructures Nanjing University Hankou Road Jiangsu Nanjing210093 China

Auto-focusing beams can effectively mitigate orbital angular momentum (OAM) crosstalk in atmospheric turbulence. In this paper, we focus on the effect of the chirp factor on the crosstalk mitigation of chirped perfect Laguerre-Gaussian beam (CPLGB) based on the propagation model of CPLGB. The results demonstrate that a positive first-order chirp factor can significantly promote the auto-focusing of CPLGB, and increasing it can effectively mitigate the intermodal crosstalk of OAM modes in atmospheric turbulence, whereas a positive second-order chirp factor has a relatively weak effect on them. By comparing with Bessel Gaussian beam, perfect Laguerre-Gaussian beam, and perfect optical vortex beam, the CPLGB shows superior turbulence resistance when transmitted in atmospheric turbulence. Our results will be useful for optimizing the performance of free-space optical (FSO) communication systems with CPLGBs. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Gaussian beams

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