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作者： Gong, Wenwen Lv, Chao Duan, Yucong Liu, Zengguang Khosravi, Mohammad R. Qi, Lianyong Dou, Wanchun College of Information and Electrical Engineering China Agricultural University Beijing China China Telecom Smart Home Competence Center Nanjing China E-Surfing Smart Home Technology Co. Ltd Nanjing China School of Computer Science and Cyberspace Security Hainan University Haikou China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China Department of Computer Engineering Persian Gulf University Bushehr Iran Telecommunications Group Department of Electrical and Electronic Engineering Shiraz University of Technology Shiraz Iran School of Information Science and Engineering Qufu Normal University Rizhao China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Computer Science and Technology Nanjing University Nanjing China

With the ever-increasing popularity of web application programming interfaces (APIs) sharing communities, it is becoming a promising way for software developers to design and create their interesting Apps through composing a set of selected web APIs that can collectively fulfill the App functions expected by the App developer. However, the App developer's web APIs selection decision-makings are often nontrivial due to the massive candidate APIs as well as their diverse functions. Furthermore, it is difficult to guarantee that the selected web APIs are compatible enough. Moreover, traditional web APIs recommendation approaches only return a recommended APIs list, which are often not sufficient to accommodate the App developer's undetermined and fuzzy personalized preferences. Considering the above challenges, a novel keywords-driven web APIs recommendation approach called keywords-driven and compatibility-aware multiple API group recommendation is proposed in this article for green and compatible software, which cannot only satisfy the App developer's functional requirements, but also return a group of web APIs recommended lists. Each returned list includes a set of compatible web APIs. Finally, we design a series of experiments based on a real-world web APIs dataset, that is, PW dataset crawled from ***. Experimental reports compared with other competitive approaches in existing literatures indicate the effectiveness and efficiency of our proposal in this work. © 2020 John Wiley & Sons Ltd

关键词： Application programs

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An RBF neural network–based dynamic virtual network embedding algorithm

An RBF neural network–based dynamic virtual network embeddi...

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作者： Zheng, Xiangwei Zhang, Yuang Zhang, Hui Xue, Qingshui School of Information Science and Engineering Shandong Normal University Jinan250014 China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan250014 China School of Computer Science and Information Engineering Shanghai Institute of Technology Shanghai201418 China

Network virtualization is a promising technology, which is used to solve the existing shortcomings of the Internet architecture and in which virtual network (VN) embedding plays a vital role in resource allocation. However, current virtual network embedding algorithms mainly deal with static VN embedding;the allocations of network resources are constant and excessive in the VN requests' lifetime. In reality, the user's resource requirements usually change with time;hence, static VN embedding algorithms can lead to low utilization of substrate resources and reduction of the operators' revenues. To solve the aforementioned problem, we focus on the virtual network embedding considering dynamic demands in this paper. We apply supervised radial basis function (RBF) neural network to predict the changes of virtual requests and dynamically reallocate substrate resources by adjusting already allocated resources. Simulation experiments show that compared with the static algorithms, dynamic VN embedding schemes with supervised RBF can more accurately predict the demand changes of virtual networks, which adaptively adjusts resources allocation and management and therefore improves resource utilization of substrate networks. © 2018 John Wiley & Sons, Ltd.

关键词： Resource allocation

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Epilepsy Classification for Mining Deeper Relationships between EEG Channels based on GCN

Epilepsy Classification for Mining Deeper Relationships betw...

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computer Vision, Image and Deep Learning (CVIDL), International Conference on

作者： Xin Chen Yuanjie Zheng Yi Niu Chengiiang Li School of Information Science and Engineering Shandong Normal University Jinan China Key Lab of Intelligent Computing and Information Security Universities of Shandong Jinan China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Institute of Biomedical Sciences Jinan China Shandong University of Science and Technology Jinan China

ISBN: (数字)9781728194813

ISBN: (纸本)9781728194820

Epilepsy is a brain disorder caused by abnormal discharges of neurons in brain. It is one of the most commonly studied disorders in neurology. The research of epilepsy electroencephalogram (EEG) has become a hot research topic. We find that in epilepsy EEG detection task, many previous methods focused on directly collecting the data of each channel, but these methods seldom analyse relationships between signals. Therefore, we propose the Epilepsy EEG Graph Convolutional Network EGCN, which makes full use of correlations between channels to deeply mine data information. We specifically design 5-layer graph convolutional network structure for classification of healthy and epileptic patients. The method is applied to public data set (Boon and CHB-MIT) to establish a reasonable classification model. And we compare it with some advanced algorithms. The experimental results show that the E-GCN method is superior to many existing methods in classification accuracy. In brief, the E-GCN method can be effectively used in classification and detection for epilepsy. This provides new ideas for colleagues, who study epilepsy EEG. In addition, this also provides richer experience for diagnosis of epilepsy.

关键词： Epilepsy Electroencephalography Convolution Brain modeling Feature extraction Correlation Task analysis

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The Application of PAD Teaching Method in computer Network Course

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IOP Conference Series: Earth and Environmental Science 2021年第1期693卷

作者： Lining Liu Yongsheng Zhang Penglong Zhang Wenqian Sun School of Information Science and Engineering Shandong Normal University Jinan 250358 China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology

As a new teaching mode and method, PAD has been developed in recent years. This teaching mode has been applied in the teaching of universities, ordinary primary and secondary schools and vocational colleges. It has achieved good teaching results. In order to improve teaching effect, improve the students' autonomy and enthusiasm and cultivate the independent innovative talents needed by the society, more and more computer teachers have also begun to apply PAD to the computer classroom. 'computer Network' is a basic and compulsory course for learning computers. 'transmission media' in this course is selected as the specific content, and PAD is used for teaching design.

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A Dynamic Network Resource Demand Predicting Algorithm Based on Incremental Design of RBF

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Procedia computer Science 2019年 147卷 29-35页

作者： Xiancui Xiao Xiangwei Zheng School of Information Science and Engineering Shandong Normal University Jinan 250014 China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan 250014 China

Nowadays, the research on network mapping is mostly limited to static resource allocation. In fact, the user’s demand of network resources changes dynamically over time. Therefore, how to predict the time-varying demand of users and allocate appropriate resources becomes an important way to improve resource utilization. As a fully connected artificial neural network (ANN), the RBFN (Radial Basis Function Network) has diagnostic, predictive and classification functions. However, due to the excessive use of hidden RBF units during training process, it suffers from expensive core inner product calculations and long training time. This paper proposes a dynamic network resource demand predicting algorithm based on RBF incremental design (GSO-INC-RBFDM). In the network mapping, the group search optimizer (GSO) is used to optimize the mapping scheme, and then the radial basis function (RBF) of the incremental construction is used to predict the time-varying demand of the user. GSO-INC-RBFDM based on incremental design of RBF can construct a compact neural network structure, which not only accelerates the training speed, but also improves the predictive accuracy. Simulation experiments show, compared with traditional algorithms and the original RBF, GSO-INC-RBFDM have lower cost, higher acceptance rate and network revenue.

关键词： Static resource allocation Dynamically Time-varying RBFN GSO-INC-RBFDM

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A biometric-based IoT device identity authentication scheme 1st

A biometric-based IoT device identity authentication scheme

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1st EAI International Conference on Artificial Intelligence for Communications and Networks, AICON 2019

作者： Xue, Qingshui Ju, Xingzhong Zhu, Haozhi Zhu, Haojin Li, Fengying Zheng, Xiangwei School of Computer Science and Information Engineering Shanghai Institute of Technology Shanghai201418 China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China School of Information Science and Engineering Shandong Normal University Jinan250014 China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan250014 China

ISBN: (纸本)9783030229702

IoT is an important part of the new generation of information technologies and the next big thing in the IT industry after the computer and the internet. The IoT has great development potential and a wide range of possible applications, especially commercial applications. And information security of the IoT is the key to the long-term development of the whole industry. Currently, the two most significant factors in the development of the IoT are user identity authentication and privacy protection. This paper contains an analysis on the current picture of inter-device user identity authentication in the IoT and proposes an inter-device biometric authentication solution for the IoT that’s designed to work with larger devices, addressing the shortcomings of the traditional user identity authentication technologies including security and efficiency problems. A strategy for further solution optimization is also included. This paper elaborates on the specific process of user identity authentication carried out by users on devices and between devices making use of fingerprints. We’ll demonstrate the security of this solution against existing attack methods and in the last part, we enumerate various possible applications of this solution in smart homes. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2019 Published by Springer Nature Switzerland AG 2019. All Rights Reserved.

关键词： Biometrics

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A Sensor Data Processing and Storage Method for Wireless Body Area Networks

A Sensor Data Processing and Storage Method for Wireless Bod...

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IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Lu Han Lulu Xun Jinfa Liu Wenjun Yin Xiangwei Zheng Zhuoran Zheng Shandong Normal University Ji’nan China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Shandong Normal University Ji’nan China

This paper proposes an adaptive processing method and a dynamic storage method of pulsed wave signal based on wireless body area network. Firstly, the ZigBee star network model is adopted as the basic network architecture of the system. Secondly, an adaptive Gaussian filter model is designed to eliminate the influence of environmental and man-made factors. Finally, the dynamic storage algorithm is used to map the pulsed wave signal to a new stored sample, and the regression model is used to restore the new sample to the original signal. Simulation experiments show that: 1) The adaptive Gaussian model minimizes the signal noise and better maintains the characteristics of the pulsed wave signal. 2) On the premise of using ARIMA regression model of neural network, dynamic storage algorithm improves the regression accuracy compared with fixed storage algorithm.

关键词： Information technology Education

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A new parallel simulation method for massive crowd

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Procedia computer Science 2019年 147卷 283-287页

作者： Xiukai Zhao Lei Lyu Chen Lyu Cun Ji School of Information Science and Engineering Shandong Normal University Jinan 250014 China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan 250014 China

With the advent of multi-core and even many-core processors, massively parallel computing processing has presented a trend of low cost and popularity. It provides new technical means and feasible solutions for the rapid simulation calculation of large-scale group movements and the simulable problem of super-large group movements. In the process of parallel computing, the design of parallel architecture and parallel algorithm is closely related to the group motion simulation algorithm itself due to the original task needs to be decomposed. A parallel simulation algorithm for the continuum crowds has been proposed in this paper. We use the transition block-based scene segmentation algorithm on the management node to divide the simulated scene after initializing the virtual scene, and assign each block to a different computing node for processing. Then, the compute node gets information about the individual it is responsible for. Based on the method, a parallel simulation prototype system was developed on the Sugon high-performance computing platform. Experimental results show that our parallel simulation algorithm can increase the efficiency of scene-rendering and solve the bottleneck in group scale.

关键词： parallel computing massive crowd compute node scene segmentation

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A dynamic level interaction method based on trusted value

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Journal of computers (Taiwan) 2019年第1期30卷 142-152页

作者： Zhang, Yong-sheng Fan, Yue-qin Cui, Ran-ran Wei, Yu Department of Information Science and Engineering Shandong Normal University Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan China

With the rapid development of the cloud computing, there is a large amount of service providers with low quality in the marketplace. The mendacious service providers cause a serious loss for the users. In order to select an efficient cloud service, it can be trust and meet the user’s privacy protection requirements. In this paper, a dynamic level interaction method based on credible value is indicated. The cloud service providers and cloud users are divided into different dynamic levels according to their historical records. The security level of privacy data is decided according to the users’ trust level. This model introduces the center degree factor to filter the mendacious service and it can get a more accurate service evaluation by limiting the malicious users to make false evaluation for the service and it can control the providers’ access rights to protect the users’ information. The results of simulation experiment show that this model can improve the interaction successful rate and strengthen the safety of user privacy data. It demonstrates the efficiency and effectiveness of our approach in trusted interaction with other counterparts. 2019 © computer Society of the Republic of China. All Rights Reserved.

关键词： Grading

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Corrigendum to “Fractional-order quaternion exponential moments for color images” [Applied Mathematics and Computation 400 (2021) 126061]

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Applied Mathematics and Computation 2021年 402卷

作者： Chunpeng Wang Qixian Hao Bin Ma Jian Li Hongling Gao School of Computer Science and Technology (School of Cyber Security) Qilu University of Technology (Shandong Academy of Sciences) Jinan 250353 China Shandong Provincial Key Laboratory of Computer Networks Jinan 250353 China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan 250358 China

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