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A Method of Searching for Optimal Coalition Structure for Solving Resource Scheduling Problem of Overall Load Balancing in Edge Computing Environments 4

A Method of Searching for Optimal Coalition Structure for So...

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2020 4th International Workshop on Advanced Algorithms and Control Engineering, IWAACE 2020

作者： Yanan, Hu Chunsheng, Li Kejia, Zhang School of Computer and Information Technology Northeast Petroleum University Daqing163318 China Heilongjiang Provincial Key Laboratory of Oil Big Data and Intelligent Analysis Daqing163318 China

The cooperative computing resource scheduling problem in the distributed computing environment is usually transformed into the problem about how to find the optimal coalition structure. However, affected by factors such as high node density and large number of concurrent tasks in edge computing environments, it is difficult to formulate resource scheduling schemes for overall load balancing in a large strategic space by traditional methods. In order to solve this problem, we proposed a method of searching for optimal coalition structure based on discrete particle swarm optimization (DPSO). Firstly, we converted the resource scheduling problem of edge computing into an optimization problem model, constructed a new structure of coalition structure to express the resource scheduling scheme, and gave key elements such as optimization objectives, decision variables, and constraint conditions. Secondly, we improved DPSO and designed M-ary discrete particle swarm optimization (MDPSO): improved particle state determination method for adapting to the new expression of the coalition structure, and proposed a power series-based inertia weight computing method to improve the optimization speed and the optimal solution quality. Finally, we simulated real application scenarios and designed comparative experiments on the optimization speed, the optimal solution quality, stability, etc., which verifies that the method proposed in this paper has obvious advantages in solving the resource scheduling problem of overall load balancing in edge computing. © Published under licence by IOP Publishing Ltd.

关键词： Structural optimization

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Multi-session Joint Optimization for Accurate Mapping

Multi-session Joint Optimization for Accurate Mapping

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2021 China Automation Congress, CAC 2021

作者： Wang, He Li, Heping Wang, Liang Faculty of Information Technology Beijing University of Technology Beijing100124 China Research Institute of Mine Big Data China Coal Research Institute Beijing100013 China

ISBN: (纸本)9781665426473

Accurate maps play a vital role in the localization of mobile robots and autonomous vehicles. However, the traditional methods of creating precise maps of scenes involve special probe vehicles equipped with expensive devices, making the generalization and commercialization of accurate mapping difficult. This paper proposes a novel method to generate accurate sparse landmarks maps of the environment using a general commercial stereo camera in a multi-session pattern. It consists of a visual SLAM frontend for obtaining individual session maps and a backend for performing overlap detection and multi-session joint optimization. Experiments on the KITTI datasets validate the proposed method. It demonstrates that the proposed method can generate an accurate map only using a low-precision and low-cost stereo camera. Its accuracy is even on par with that of 3D LiDAR-based LOAM. © 2021 IEEE

关键词： Mapping

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A Probabilistic Framework for Temporal Cognitive Diagnosis in Online Learning Systems

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Journal of Computer Science & technology 2023年第6期38卷 1203-1222页

作者：刘嘉聿汪飞马海平黄振亚刘淇陈恩红苏喻 School of Data Science University of Science and Technology of ChinaHefei 230026China State Key Laboratory of Cognitive Intelligence Hefei 230088China Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China Hefei 230026China School of Computer Science and Technology University of Science and Technology of ChinaHefei 230026China Institutes of Physical Science and Information Technology Anhui UniversityHefei 230601China School of Computer Science and Artificial Intelligence Hefei Normal UniversityHefei 230061China

Cognitive diagnosis is an important issue of intelligent education systems,which aims to estimate students'proficiency on specific knowledge *** existing studies rely on the assumption of static student states and ig-nore the dynamics of proficiency in the learning process,which makes them unsuitable for online learning *** this paper,we propose a unified temporal item response theory(UTIRT)framework,incorporating temporality and random-ness of proficiency evolving to get both accurate and interpretable diagnosis ***,we hypothesize that stu-dents'proficiency varies as a Wiener process and describe a probabilistic graphical model in UTIRT to consider temporali-ty and randomness ***,based on the relationship between student states and exercising answers,we hy-pothesize that the answering result at time k contributes most to inferring a student's proficiency at time k,which also re-flects the temporality aspect and enables us to get analytical maximization(M-step)in the expectation maximization(EM)algorithm when estimating model *** UTIRT is a framework containing unified training and inferenc-ing methods,and is general to cover several typical traditional models such as Item Response Theory(IRT),multidimen-sional IRT(MIRT),and temporal IRT(TIRT).Extensive experimental results on real-world datasets show the effective-ness of UTIRT and prove its superiority in leveraging temporality theoretically and practically over TIRT.

关键词： cognitive diagnosis probabilistic graphical model item response theory(IRT) stochastic process expectation maximization(EM)algorithm

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FVL-OutLoc: Fusion of Vision and 2D LiDAR for Accurate Outdoor Localization

FVL-OutLoc: Fusion of Vision and 2D LiDAR for Accurate Outdo...

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2021 China Automation Congress, CAC 2021

作者： Liu, Zhengxuan Li, Heping Wang, Liang Faculty of Information Technology Beijing University of Technology Beijing100124 China Research Institute of Mine Big Data China Coal Research Institute Beijing100013 China

ISBN: (纸本)9781665426473

Precise localization is an essential issue for autonomous driving systems. 2D LiDAR has been widely used in various indoor localization systems because of its accuracy in measuring distances. However, the data of 2D LiDAR in the outdoor environment is very sparse and affected by dynamic objects greatly, which makes it very difficult to find the corresponding points. To overcome this defect, we propose a novel outdoor localization framework based on the fusion of stereo vision and 2D LiDAR, called FVL-OutLoc. This framework first adopts Dempster-Shafer theory to fuse several consecutive frames of 2D LiDAR data into a grid submap by using stereo odometry. Finally, Fourier-Mellin Transform is chosen to match the submap and prior map for further correcting the odometry. The experimental results on the KITTI dataset show that FVL-OutLoc can achieve high localization accuracy and run at a real-time performance. © 2021 IEEE

关键词： Stereo vision

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An attribute reduction framework for inconsistent decision tables from view of the boundary region

An attribute reduction framework for inconsistent decision t...

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2016 International Conference on Machine Learning and Cybernetics, ICMLC 2016

作者： Qian, Jin Chang, Yu-Hui Lv, Ping Key Laboratory of Cloud Computing and Intelligent Information Processing of Changzhou City Jiangsu University of Technology Changzhou213015 China Jiangsu Key Laboratory of Big Data Analysis Technology/B-DAT Nanjing University of Information Science and Technology Nanjing China

ISBN: (纸本)9781509003891

Attribute reduction is one of the key issues for data preprocess in data mining. Many heuristic attribute reduction algorithms based on discernibility matrix have been proposed for inconsistent decision tables. However, these methods are usually computationally time-consuming. To address this issue, the derived consistent decision tables are defined for different definitions of relative reducts. The computations for different reducts of the original inconsistent decision tables are converted into the computations for their corresponding reducts of the derived consistent datasets. The relative discernibility object pair and the more optimal relative discernibility degree from view of the boundary region are designed to accelerate the attribute reduction process. An efficient attribute reduction framework using relative discernibility degree is proposed for large datasets. Experimental results show that our attribute reduction algorithms are effective and feasible for large inconsistent datasets. © 2016 IEEE.

关键词： Decision tables

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Study of the Economic Growth of Oil and Gas Enterprises 16

Study of the Economic Growth of Oil and Gas Enterprises

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16th International Conference Management of Large-Scale System Development, MLSD 2023

作者： Tregub, Ilona Krasulin, Lev Financial University under the Government of the Russian Federation Department of Mathematics Moscow Russia Financial University under the Government of the Russian Federation Faculty of Information Technologies and Big Data Analysis Moscow Russia

ISBN: (纸本)9798350337907

Oil and gas have an impact on the Russian economy. On the one hand, oil prices affect the economic and political processes that determine the value of oil companies' shares, the speed of their economic growth, and the level of inflation in oil-importing countries, as well as the intensity of the processes of centralization and concentration of production. On the other hand, oil prices serve as a guide in the formation of energy carrier prices. The activity of the nation's financial institutions depends on the increase in oil prices. At the same time, increasing foreign exchange revenues have an impact on the banking industry's liquidity. This piques the attention of outside investors and aids in the inflow of foreign money into the national economy. The interest in the stock of Russian oil businesses is also impacted by the price increase, stimulating capital influx. A study of the impact of oil prices and the exchange rate of the national currency on the economic growth of oil and gas enterprises is presented in this article. © 2023 IEEE.

关键词： Regression analysis

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REQUIRED NUMBER OF ITERATIONS FOR SPARSE SIGNAL RECOVERY VIA ORTHOGONAL LEAST SQUARES

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Journal of Computational Mathematics 2023年第1期41卷 1-17页

作者： Haifeng Li Jing Zhang Jinming Wen Dongfang Li Henan Engineering Laboratory for Big Data Statistical Analysis and Optimal Control College of Mathematics and Information ScienceHenan Normal UniversityXinxiang 453007China College of Information Science and Technology Jinan UniversityGuangzhou 510632China School of Mathematics and Statistics Huazhong University of Science and TechnologyWuhan 430074China Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and TechnologyWuhan 430074China

In countless applications,we need to reconstruct a K-sparse signal x∈R n from noisy measurements y=Φx+v,whereΦ∈R^(m×n)is a sensing matrix and v∈R m is a noise *** least squares(OLS),which selects at each step the column that results in the most significant decrease in the residual power,is one of the most popular sparse recovery *** this paper,we investigate the number of iterations required for recovering x with the OLS *** show that OLS provides a stable reconstruction of all K-sparse signals x in[2.8K]iterations provided thatΦsatisfies the restricted isometry property(RIP).Our result provides a better recovery bound and fewer number of required iterations than those proposed by Foucart in 2013.

关键词： Sparse signal recovery Orthogonal least squares(OLS) Restricted isometry property(RIP)

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Efficient $\chi ^{2}$ Kernel Linearization via Random Feature Maps

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IEEE transactions on neural networks and learning systems 2016年第11期27卷 2448-2453页

作者： Xiao-Tong Yuan Zhenzhen Wang Jiankang Deng Qingshan Liu Jiangsu Province Key Laboratory of Big Data Analysis Technology Nanjing University of Information Science and Technology Nanjing China

Explicit feature mapping is an appealing way to linearize additive kernels, such as χ kernel for training large-scale support vector machines (SVMs). Although accurate in approximation, feature mapping could pose computational challenges in high-dimensional settings as it expands the original features to a higher dimensional space. To handle this issue in the context of χ kernel SVMs learning, we introduce a simple yet efficient method to approximately linearize χ kernel through random feature maps. The main idea is to use sparse random projection to reduce the dimensionality of feature maps while preserving their approximation capability to the original kernel. We provide approximation error bound for the proposed method. Furthermore, we extend our method to χ multiple kernel SVMs learning. Extensive experiments on large-scale image classification tasks confirm that the proposed approach is able to significantly speed up the training process of the χ kernel SVMs at almost no cost of testing accuracy.

关键词： Kernel Training Frequency modulation Approximation methods Accuracy Testing Sparse matrices

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Evaluation of Diagnostic Recommendations Embedded in Medication Alerts: Prospective Single-Arm Interventional Study

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Journal of medical Internet research 2025年 27卷 e70731页

作者： Yu-Chen Liu Guan-Ling Lin Jeremiah Scholl Yi-Chun Hung Yu-Jing Lin Yu-Chuan Li Hsuan-Chia Yang School of Nursing College of Medicine National Taiwan University Taipei Taiwan. Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei Taiwan. AESOP Technology Taipei Taiwan. Department of Pharmacy Tungs' Taichung MetroHarbor Hospital Taichung Taiwan. International Center for Health Information Technology (ICHIT) Taipei Medical University Taipei Taiwan. Clinical Big Data Research Center Taipei Medical University Hospital Taipei Taiwan. Research Center of Big Data and Meta-analysis Wanfang Hospital Taipei Medical University Taipei Taiwan.

BACKGROUND:Potentially inappropriate prescribing in outpatient care contributes to adverse outcomes and health care inefficiencies. Clinical decision support systems (CDSS) offer promising solutions, but their effectiveness is often constrained by incomplete medical records. OBJECTIVE:This study aims to evaluate the effectiveness of a machine learning-based CDSS for enhancing diagnostic recommendations, which are system-suggested diagnoses, ensuring that each prescribed medication has a corresponding diagnosis documented and meets medication appropriateness. METHODS:This prospective single-arm interventional study was conducted over 1 year in the outpatient departments of a hospital. The system provided diagnostic recommendations based on machine learning algorithms trained on data from the National Health Insurance Research database. Outcome measures included alert rates, acceptance rates of diagnostic recommendations, and variability in system performance across specialties. Descriptive and trend analyses were used to evaluate the system's effectiveness. RESULTS:This study included 438,558 prescriptions from 44 physicians across 23 specialties, involving 125,000 unique patients in the outpatient departments of a regional teaching hospital. MedGuard, embedded with diagnostic recommendations, achieved an overall alert rate of 2.28% and a diagnostic recommendation acceptance rate of 56.55%. All accepted recommendations resulted in actionable changes, including prescription adjustments or the addition of missing diagnoses. Ophthalmology achieved the highest acceptance rate at 96.59%, while rheumatology, surgery, psychiatry, and infectious disease recorded acceptance rates of 0%, 0%, 24.74%, and 35%, respectively. Over the years, acceptance rates for potentially inappropriate prescriptions stabilized at 51%, despite increasing prescription volumes. CONCLUSIONS:This study demonstrates the potential of embedding diagnostic recommendations into alerts within a machine lea

关键词： MedGuard alert fatigue clinical decision support system diagnostic recommendations machine learning outpatient care potentially inappropriate prescribing

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Flame detection via quaternion discrete cosine transform based spectral saliency

International Journal of Control and Automation

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International Journal of Control and Automation 2016年第9期9卷 343-352页

作者： Zhang, Haiyan Gao, Shangbing Xue, Jianxun Zheng, Hao Faculty of computer and software Jiangsu Provincial Key Laboratory for advanced manufacturing technology Huaiyin Institute of Technology Huai'an China The Key Laboratory for traffic and transportation security of Jiangsu Province Huai'an China Key Laboratory of Trusted Cloud Computing and Big Data Analysis Nanjing Xiaozhuang University Nanjing China

To address the problem of detecting the specific object of flame, this paper proposes quaternion discrete cosine transform based video flame detection algorithm in conjunction with the bottom-up visual attention mechanisms and the flame features. According to the characteristics of the flame to be detected, the brightness, color, texture and motion information of the image are respectively defined and extracted to form a quaternion image, and then the flame saliency map is produced by Quaternion discrete cosine Transform with this quaternion image. Extensive experimental results verify the accuracy and robustness of the proposed method on flame detection. © 2016 SERSC.

关键词： Discrete cosine transforms

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