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The Application of PAD Teaching Method in computer Network Course

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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Optimal approximations of available states and a triple uncertainty relation

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Physical Review A 2020年第6期101卷 062106-062106页

作者： Xiao-Bin Liang Bo Li Liang Huang Biao-Liang Ye Shao-Ming Fei Shi-Xiang Huang School of Mathematics and Computer Science Shangrao Normal University Shangrao 334001 China Department of Computer and Software Engineering Wonkwang University Iksan 54538 Korea Quantum Information Research Center Shangrao Normal University Shangrao 334001 China School of Mathematical Sciences Capital Normal University Beijing 100048 China Max-Planck-Institute for Mathematics in the Sciences 04103 Leipzig Germany

We investigate the optimal convex approximation of the quantum state with respect to a set of available states. By isometric transformation, we have presented the general mathematical model and its solutions together with a triple uncertainty equality relation. Meanwhile, we show a concise inequality criterion for decomposing qubit mixed states. The new results include previous ones as special cases. Our model and method may be applied to solve similar problems in high-dimensional and multipartite scenarios.

关键词： Quantum foundations

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Fault Tolerance Impact on Near Field Communication for Data Storage of Mobile Commerce Technology in Cloud Computing 2nd

Fault Tolerance Impact on Near Field Communication for Data ...

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2nd International Conference on Advanced Data and information engineering, DaEng 2015

作者： Noraziah, Ahmad Herawan, Tutut Rahman, Mohd Tarmizi Abd Abdullah, Zailani Mustafa, Balsam A. Fakharaldien, Mohammed Adam Ibrahim Faculty of Computer Systems and Software Engineering University Malaysia Pahang GambangPahang26300 Malaysia Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur50603 Malaysia School of Informatics & Applied Mathematics University Malaysia Terengganu Kuala Terengganu21030 Malaysia

ISBN: (纸本)9789811317972

Fault tolerance is a major concern to guarantee availability and reliability of critical services as well as application execution. Recently, due to rapid expansion of smart phone market and wireless communication environment, mobile devices are considered as resource for large scale distributed processing but there are several drawbacks, such as unstable wireless connection, limitation of data storage capacity, low communication bandwidth and frequent location changes. Cloud systems differ from these previous frameworks in that they are designed to support large numbers of customer-oriented applications, each with different quality of service (QoS) requirements and resource consumption characteristics. As resource providers, mobile devices can join and leave the distributed computing environment unpredictably. This interrupts the undergoing operation, and the delay or failure of completing the operation may cause a system failure. In this paper, we propose a monitoring technique based on Load Balancing. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Mean time between failures

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Requirements Dependency Extraction by Integrating Active Learning with Ontology-Based Retrieval

Requirements Dependency Extraction by Integrating Active Lea...

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IEEE International Conference on Requirements engineering

作者： Gouri Deshpande Quim Motger Cristina Palomares Ikagarjot Kamra Katarzyna Biesialska Xavier Franch Guenther Ruhe Jason Ho dept. of Computer Science University of Calgary Calgary Canada dept. of Service and Information System Engineering (ESSI) Universitat Politècnica de Catalunya Barcelona Spain dept. of Electrical and Software Engineering University of Calgary Calgary Canada Blackline Safety. Inc Calgary Canada

ISBN: (数字)9781728174389

ISBN: (纸本)9781728174396

Context: Incomplete or incorrect detection of requirement dependencies has proven to result in reduced release quality and substantial rework. Additionally, the extraction of dependencies is challenging since requirements are mostly documented in natural language, which makes it a cognitively difficult task. Moreover, with ever-changing and new requirements, a manual analysis process must be repeated, which imposes extra hardship even for domain experts. Objective: The three main objectives of this research are: 1) Proposing a new dependency extraction method using a variant of Active Learning (AL). 2) Evaluating this AL and Ontology-based Retrieval (OBR) as baseline methods for dependency extraction on the two industrial data sets. 3) Analyzing the value gained from integrating these diverse approaches to form two hybrid methods. Method: Building on the general AL, ensemble and semi-supervised machine learning, a variant of AL was developed, which was further integrated with OBR to form two hybrid methods (Hybrid1, Hybrid2) for extracting three types of dependencies (requires, refines, other): Hybrid1 used OBR as a substitute for human expert; Hybrid2 used dependencies extracted through the OBR as an additional input for training set in AL. Results: For two industrial case studies, AL extracted more dependencies than OBR. Hybrid1 showed improvement for both data sets. For one of them, F1 score increased to 82.6% compared to the AL baseline score of 49.9%. Hybrid2 increased the accuracy by 25% to the level of 75.8% compared to the AL baseline accuracy. OBR also complemented the AL approach by reducing 50% of the human effort.

关键词： Training Ontologies Machine learning Data mining Electronic mail Requirements engineering Safety

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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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Feedforward neural network based on ensemble evolutionary algorithm with self-adaptive strategy and parameter for intrusion detection

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International Journal of Wireless and Mobile Computing 2019年第2期17卷 202-211页

作者： Xue, Yu Tang, Tao Liu, Alex X. School of Computer and Software Nanjing University of Information Science and Technology Pukou Nanjing China Department of Computer Science and Engineering Michigan State University East Lansing United States

The applications of Wireless Sensor Networks (WSNs) have driven the development of many fields, and the information security is an important issue in the WSNs. Intrusion Detection System (IDS) is a kind of effective system for identifying network attacks and protecting data security, which has a great significance for WSNs. In this paper, we transform the intrusion detection problem into a classification problem and introduce the Feedforward Neural Network (FNN) classifier to solve it. At the same time, we propose a novel Self-adaptive Parameter and Strategy Differential Particle Swarm (SPS-DPS) optimisation algorithm to find the optimal weights for the FNN. Experiments are performed on eight data sets which are constructed based on the intrusion detection data set KDDCUP99, and the results are compared with five Evolutionary Computation (EC) methods. The results show that the SPS-DPS based FNN method can solve the intrusion detection problem well compared with the other methods. Copyright © 2019 Inderscience Enterprises Ltd.

关键词： Wireless sensor networks

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Fine-grained image analysis with deep learning: A survey

arXiv

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arXiv 2021年

作者： Wei, Xiu-Shen Song, Yi-Zhe Aodha, Oisin Mac Wu, Jianxin Peng, Yuxin Tang, Jinhui Yang, Jian Belongie, Serge PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China University of Surrey United Kingdom The University of Edinburgh United Kingdom The State Key Laboratory for Novel Software Technology Nanjing University China Peking University China Nanjing University of Science and Technology China The University of Copenhagen The Pioneer Centre for AI Denmark

Fine-grained image analysis (FGIA) is a longstanding and fundamental problem in computer vision and pattern recognition, and underpins a diverse set of real-world applications. The task of FGIA targets analyzing visual objects from subordinate categories, e.g., species of birds or models of cars. The small inter-class and large intra-class variation inherent to fine-grained image analysis makes it a challenging problem. Capitalizing on advances in deep learning, in recent years we have witnessed remarkable progress in deep learning powered FGIA. In this paper we present a systematic survey of these advances, where we attempt to re-define and broaden the field of FGIA by consolidating two fundamental fine-grained research areas – fine-grained image recognition and fine-grained image retrieval. In addition, we also review other key issues of FGIA, such as publicly available benchmark datasets and related domain-specific applications. We conclude by highlighting several research directions and open problems which need further exploration from the community. © 2021, CC BY.

关键词： Image recognition

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SDN-based routing mechanism for industrial wireless sensor networks 14

SDN-based routing mechanism for industrial wireless sensor n...

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14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2018

作者： Cheng, Dandan Wang, Xingwei Zhang, Shuang Huang, Min College of Computer Science and Engineering China College of Software China College of Information Science and Engineering Northeastern University Shenyang China

ISBN: (纸本)9781538680971

With the development of industrial network, Industrial Wireless Sensor Networks (IWSNs) outperform traditional wired automation system in terms of flexibility, scalability, and efficiency, etc. And IWSNs have critical demands on reliable and real-time data transmission. To meet these demands and enhance efficiency, a SDN-based routing mechanism for IWSNs is proposed in this paper, to determine the global optimal routing strategy based on the cognition of real-time network status. Firstly, a topology discovery algorithm based on multi-controller collaboration is proposed to improve the efficiency of the SDN controllers' cognition of network status, and each controller in the network can perceive the global network status by synchronizing information. Then, combined with the cognition of controllers for network status, a link quality-aware routing algorithm (LQAR) is proposed, further the global optimal routing is calculated and distributed to the field devices. Additionally, a node failure response strategy and a controller failure response strategy are proposed to enhance the reliability of the network. The simulation results show that the proposed routing mechanism can guarantee real-time data transmission and significantly improve the reliability compared with the traditional industrial wireless network routing mechanisms. © 2018 IEEE.

关键词： Reliability

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Fair Down to the Device: A GC-Aware Fair Scheduler for SSD

Fair Down to the Device: A GC-Aware Fair Scheduler for SSD

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IEEE Non-Volatile Memory Systems and Applications Symposium (NVMSA)

作者： Cheng Ji Lun Wang Qiao Li Congming Gao Liang Shi Chia-Lin Yang Chun Jason Xue Department of Computer Science City University of Hong Kong Department of Computer Science and Information Engineering National Taiwan University College of Computer Science Chongqing University School of Computer Science and Software Engineering East China Normal University

ISBN: (纸本)9781728138558

Solid-state drives (SSD) are the mainstream solutions for massive data storage today. For modern computer systems, fair resource assignment is a critical design consideration and has drawn great interests in recent years. Although there are several I/O fairness schedulers proposed on the host side for SSDs, process fairness could still be dramatically degraded if garbage collection (GC) is triggered in the device side. A GC operation could block I/O requests, which causes unpredictable read/write latency variation and further impacts fairness between processes. This paper proposes Fair-GC, a novel coordinated host and device I/O scheduling strategy to achieve true fairness considering GC interferences. The key idea is to orchestrate GC operations inside SSDs carefully such that performance of a process is penalized by GC in the same degree (or comparable) as when it runs alone. In this way, the I/O fairness maintained by the host-side scheduler can be maintained in the presence of GC. Furthermore, our scheduler ensures that the timeslice of a process maintained at the host-side scheduler is updated in a timely manner to avoid unnecessary slowdown for maintaining fairness. Experimental results with a wide range of workloads verify that the proposed technique can achieve fairness as well as improve the throughput significantly. Compared to conventional fairness-based I/O scheduler, Fair-GC can reduce the slowdown of real applications by up to 99%, and improve the throughput by as much as 225%, respectively.

关键词： Performance evaluation computer science Throughput Flash memories Parallel processing Interference Quality of service

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Hierarchical Polygamy Inequality for Entanglement of Tsallis q-Entropy

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Communications in Theoretical Physics 2018年第5期69卷 532-536页

作者： Yu Luo Yong-Ming Li College of Computer Science Shaanxi Normal UniversityXi'an 710062China Centre for Quantum Software and Information(UTS:Q|SI>) Faculty of Engineering and Information TechnologyUniversity of Technology SydneyAustralia

In this paper, we study the polygamy inequality of quantum entanglement in terms of Tsallis *** first give a lower bound of Tsallis q-entropy entanglement of assistance(TOA) in the 2  d systems. The relationships between Tsallis q-entropy entanglement(TEE) and TOA are also given. Furthermore, we prove TOA follows a hierarchical polygamy inequality in a 2  2  2^(N-2) systems.

关键词： polygamy entanglement Tsallis q-entropy

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