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Improved Bound of the Fourth Hankel Determinant for a Class of Analytic Functions with Bounded Turnings Involving Cardioid Domain

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Journal of Mathematical Research with Applications 2025年第1期 39-55页

作者： Dong GUO Huo TANG Xi LUO Zongtao LI School of Mathematical sciences Yangzhou Polytechnic College college of Mathematics and computer science Chifeng University School of Mathematics Jiaying University Department of Mathematics Teaching Guangzhou Civil Aviation College

In the paper, a class of functions with bounded turnings involving cardioid domain,are studied in the region of the unit disc. The bounds of |a5|, |a6|, |a7| and the fourth Hankel determinant are obtained, which are m...

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Practical cloud storage auditing using serverless computing

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science China(Information sciences) 2024年第3期67卷 33-47页

作者： Fei CHEN Jianquan CAI Tao XIANG Xiaofeng LIAO college of computer science and Software Engineering Shenzhen University college of computer science Chongqing University

Cloud storage auditing research is dedicated to solving the data integrity problem of outsourced storage on the cloud. In recent years, researchers have proposed various cloud storage auditing schemes using different tech.iques. While these studies are elegant in theory, they assume an ideal cloud storage model;that is, they assume that the cloud provides the storage and compute interfaces as required by the proposed schemes. However, this does not hold for mainstream cloud storage systems because these systems only provide read and write interfaces but not the compute interface. To bridge this gap, this work proposes a serverless computing-based cloud storage auditing system for existing mainstream cloud object storage. The proposed system leverages existing cloud storage auditing schemes as a basic building block and makes two adaptations. One is that we use the read interface of cloud object storage to support block data requests in a traditional cloud storage auditing scheme. Another is that we employ the serverless computing paradigm to support block data computation as traditionally required. Leveraging the characteristics of serverless computing, the proposed system realizes economical, pay-as-you-go cloud storage auditing. The proposed system also supports mainstream cloud storage upper layer applications(e.g., file preview) by not modifying the data formats when embedding authentication tags for later auditing. We prototyped and open-sourced the proposed system to a mainstream cloud service, i.e., Tencent Cloud. Experimental results show that the proposed system is efficient and promising for practical use. For 40 GB of data, auditing takes approximately 98 s using serverless computation. The economic cost is 120.48 CNY per year, of which serverless computing only accounts for 46%. In contrast, no existing studies reported cloud storage auditing results for real-world cloud services.

关键词： cloud storage auditing serverless computing object storage usability

Student Performance Prediction Based on Behavior Process Similarity

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Chinese Journal of Electronics 2020年第6期29卷 1110-1118页

作者： BAO Yunxia LU Faming WANG Yanxiao ZENG Qingtian LIU Cong college of computer science and Engineering Shandong University of Science and Technology college of computer science and tech.ology Shandong University of Technology

Student performance prediction plays an important role in improving education *** that students,exercise-answering processes exhibit different characteristics according to their different performance levels,this paper aims to mine the performance-related information from students’ exercising logs and to explore the possibility of predicting students’ performance using such process-characteristic information.A formal model of student-shared exercising processes and its discovery method from students’ exercising logs are *** similarity measures between students’ individual exercising behavior and student-shared exercising processes are presented.A prediction method of students’ performance level considering these similarity measures is explored based on classification *** experiment on real-life exercise-answering event logs shows the effectiveness of the proposed prediction method.

关键词： computer aided instruction data mining educational courses real-life exercise-answering event logs similarity measures student-shared process-characteristic information performance-related information performance levels behavior process similarity student performance prediction prediction method Process mining Process similarity Process discovery Student performance prediction

A Short Text Classification Method Based on N-Gram and CNN

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Chinese Journal of Electronics 2020年第2期29卷 248-254页

作者： WANG Haitao HE Jie ZHANG Xiaohong LIU Shufen college of computer science and tech.ology Henan Polytechnic University college of computer science and tech.ology Jilin University

Text classification is a fundamental task in Nature language process(NLP) application. Most existing research work relied on either explicate or implicit text representation to settle this kind of problems, while these tech.iques work well for sentence and can not simply apply to short text because of its shortness and sparseness feature. Given these facts that obtaining the simple word vector feature and ignoring the important feature by utilizing the traditional multi-size filter Convolution neural network(CNN) during the course of text classification task, we offer a kind of short text classification model by CNN, which can obtain the abundant text feature by adopting none linear sliding method and N-gram language model, and picks out the key features by using the concentration mechanism, in addition employing the pooling operation can preserve the text features at the most certain as far as possible. The experiment shows that this method we offered, comparing the traditional machine learning algorithm and convolutional neural network, can markedly improve the classification result during the short text classification.

关键词： Short text Classification Convolution neural network N-gram Concentration mechanism

Multi-Level Cross-Lingual Attentive Neural Architecture for Low Resource Name Tagging

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Tsinghua science and tech.ology 2017年第6期22卷 633-645页

作者： Xiaocheng Feng Lifu Huang Bing Qin Ying Lin Heng Ji Ting Liu college of computer science and tech.ology Harbin Institute of Technology college of computer science Rensselaer Polytechnic Institute

Neural networks have been widely used for English name tagging and have delivered state-of-the-art results. However, for low resource languages, due to the limited resources and lack of training data, taggers tend to have lower performance, in comparison to the English language. In this paper, we tackle this challenging issue by incorporating multi-level cross-lingual knowledge as attention into a neural architecture, which guides low resource name tagging to achieve a better performance. Specifically, we regard entity type distribution as language independent and use bilingual lexicons to bridge cross-lingual semantic mapping. Then, we jointly apply word-level cross-lingual mutual influence and entity-type level monolingual word distributions to enhance low resource name tagging. Experiments on three languages demonstrate the effectiveness of this neural architecture： for Chinese,Uzbek, and Turkish, we are able to yield significant improvements in name tagging over all previous baselines.

关键词： name tagging deep learning recurrent neural network cross-lingual information extraction

Degradation of Aniline Wastewater Using Dielectric Barrier Discharges at Atmospheric Pressure

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Plasma science and tech.ology 2015年第3期17卷 228-234页

作者：武海霞方志徐炎华 college of Environment Nanjing Tech.University School of Automation and Electrical Engineering Nanjing Tech. University

Aniline is a toxic water pollutant detected in drinking water and surface water, and this chemical is harmful to both human and aquatic life. A dielectric barrier discharge （DBD） reactor was designed in this study to investigate the treatment of aniline in aqueous solution. Discharge characteristics were assessed by measuring voltage and current waveforms, capturing light emission images, and obtaining optical emission spectra. The effects of several parameters were analyzed, including treatment distance, discharge power, DBD treatment time, initial pH of aniline solutions, and addition of sodium carbonate and hydrogen peroxide to the treatment. Aniline degradation increased with increasing discharge power. Under the same conditions, higher degradation was obtained at a treatment distance of 0 mm than at other treatment distances. At a discharge power of 21.5 W, 84.32% of aniline was removed after 10 rain of DBD treatment. Initial pH significantly influenced aniline degradation. Adding a certain dosage of sodium carbonate and hydrogen peroxide to the wastewater can accelerate the degradation rate of aniline. Possible degradation pathways of aniline by DBD plasmas were proposed based on the analytical data of GC/MS and TOC.

关键词： Non-thermal plasma (NTP) dielectric barrier discharge (DBD) aniline degradation wastewater treatment

Image-based 3D model retrieval using manifold learning

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Frontiers of Information tech.ology & Electronic Engineering 2018年第11期19卷 1397-1408页

作者： Pan-pan MU San-yuan ZHANG Yin ZHANG Xiu-zi YE Xiang PAN college of computer science and tech.ology Zhejiang University college of Mathematics and Information science Wenzhou University college of computer science and tech.ology Zhejiang University of Technology

We propose a new framework for image-based three-dimensional(3D) model retrieval. We first model the query image as a Euclidean point. Then we model all projected views of a 3D model as a symmetric positive definite(SPD) matrix, which is a point on a Riemannian manifold. Thus, the image-based 3D model retrieval is reduced to a problem of Euclid-to-Riemann metric learning. To solve this heterogeneous matching problem, we map the Euclidean space and SPD Riemannian manifold to the same high-dimensional Hilbert space, thus shrinking the great gap between them. Finally, we design an optimization algorithm to learn a metric in this Hilbert space using a kernel trick. Any new image descriptors, such as the features from deep learning, can be easily embedded in our framework. Experimental results show the advantages of our approach over the state-of-the-art methods for image-based 3D model retrieval.

关键词： Model retrieval Euclidean space Riemannian manifold Hilbert space Metric learning

Energy-Aware Scheduling with Uncertain Execution Time for Real-Time Systems

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Chinese Journal of Electronics 2017年第1期26卷 42-49页

作者： ZHANG Longxin TONG Zhao ZHU Ningbo XIAO Zheng LI Kenli college of computer and Communication Hunan University of Technology college of computer science and Electronic Engineering Hunan University college of Mathematic and computer science Hunan Normal University

Energy-saving is extraordinary important for real-time systems. Dynamic voltage scaling(DVS) is an important tech.ique to reduce the energy consumption of processors that support voltage scaling. It has been exploited extensively in task scheduling. However, many approaches take simple treatments and some of them even neglect the large voltage transition overheads. Although some strategies consider the penalty, frequency switching produces extra large time overhead and deadline misses occur frequently. In our paper, we propose an energy efficient soft real-time dynamic program scheme by using quantitative switching overhead and communication penalty for multitask scheduling with uncertain execution time. The experiments show that our approaches significantly outperform existing solutions both on simple-core and multi-core systems in terms of energy-saving.

关键词： Dynamic voltage scaling(DVS) Energy minimization Real-time Task scheduling

Trusted Attestation Architecture on an Infrastructure-as-a-Service

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Tsinghua science and tech.ology 2017年第5期22卷 469-477页

作者： Xin Jin Xingshu Chen Cheng Zhao Dandan Zhao the college of computer science Sichuan University

Trusted attestation is the main obstruction preventing large-scale promotion of cloud computing. How to extend a trusted relationship from a single physical node to an Infrastructure-as-a-Service （laaS） platform is a problem that must be solved. The laaS platform provides the Virtual Machine （VM）, and the Trusted VM, equipped with a virtual Trusted Platform Module （vTPM）, is the foundation of the trusted laaS platform. We propose a multi-dimensional trusted attestation architecture that can collect and verify trusted attestation information from the computing nodes, and manage the information centrally on a cloud management platform. The architecture verifies the laaS＇s trusted attestation by apprising the VM, Hypervisor, and host Operating System＇s （OS） trusted status. The theory and the tech.ology roadmap were introduced, and the key tech.ologies were analyzed. The key tech.ologies include dynamic measurement of the Hypervisor at the process level, the protection of vTPM instances, the reinforcement of Hypervisor security, and the verification of the laaS trusted attestation. A prototype was deployed to verify the feasibility of the system. The advantages of the prototype system were compared with the Open CIT （Intel Cloud attestation solution）. A performance analysis experiment was performed on computing nodes and the results show that the performance loss is within an acceptable range.

关键词： dynamic measurement trusted cloud vTPM trusted attestation