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

作者： Penglong Zhang Yongsheng Zhang Shaojun Fan Xiaoxiang Huang Jie Zhang School of Information Science and Engineering Shandong Normal University Jinan China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan China

In recent years, with the diversified development of education and training in China and the continuous "Internet +", enterprises and society have put forward higher requirements for the ability of skilled talents. However, in the vocational specialty colleges that transport a large number of skilled talents, it is increasingly difficult to meet the needs of students' learning and development. At the same time, it is difficult for students to select appropriate content for independent learning in the vast information on the Internet. Hybrid teaching is a teaching mode that combines the advantages of both traditional teaching and networked teaching. The specialty courses teaching system is based on hybrid teaching mode. It uses Java Web technology as the basis for adopting B/S development mode, MySQL database and eclipse, Navicat as Development tools. The research and implementation of the hybrid teaching system is conducive to the school to help bridging the communication between the students and the enterprise, so that students can learn more directly and provide high-skilled talents with basic professional knowledge and skilled skills. On the other hand, through practice, it also helps to promote the development of hybrid teaching reform, thus solving the complexity and particularity of the informationization process in higher vocational colleges.

关键词： Information technology Education Hafnium compounds

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Group Recommendation with Latent Voting Mechanism

Group Recommendation with Latent Voting Mechanism

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International Conference on Data Engineering

作者： Lei Guo Hongzhi Yin Qinyong Wang Bin Cui Zi Huang Lizhen Cui Shandong Normal University Jinan China School of Information Technology and Electrical Engineering The University of Queensland Australia Department of Computer Science & Key Laboratory of High Confidence Software Technologies (MOE) Peking University Shandong University Jinan China

ISBN: (数字)9781728129037

ISBN: (纸本)9781728129044

Group Recommendation (GR) is the task of suggesting relevant items/events for a group of users in online systems, whose major challenge is to aggregate the preferences of group members to infer the decision of a group. Prior group recommendation methods applied predefined static strategies for preference aggregation. However, these static strategies are insufficient to model the complicated decision making process of a group, especially for occasional groups which are formed adhoc. Compared to conventional individual recommendation task, GR is rather dynamic and each group member may contribute differently to the final group decision. Recent works argue that group members should have non-uniform weights in forming the decision of a group, and try to utilize a standard attention mechanism to aggregate the preferences of group members, but they do not model the interaction behavior among group members, and the decision making process is largely *** this work, we study GR in a more general scenario, that is Occasional Group Recommendation (OGR), and focus on solving the preference aggregation problem and the data sparsity issue of group-item interactions. Instead of exploring new heuristic or vanilla attention-based mechanism, we propose a new social self-attention based aggregation strategy by directly modeling the interactions among group members, namely Group Self-Attention (GroupSA). In GroupSA, we treat the group decision making process as multiple voting processes, and develop a stacked social self-attention network to simulate how a group consensus is reached. To overcome the data sparsity issue, we resort to the relatively abundant user-item and user-user interaction data, and enhance the representation of users by two types of aggregation methods. In the training process, we further propose a joint training method to learn the user/item embeddings in the group-item recommendation task and the user-item recommendation task simultaneously. Finally, we

关键词： Task analysis Decision making Training Aggregates Optimization Feeds Recommender systems

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Research on College Teachers Teaching Ability Evaluation Model Based on DHNN

Research on College Teachers Teaching Ability Evaluation Mod...

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

作者： Jie Zhang Yongsheng Zhang Yu Wei Zhihua Zheng Shaojun Fan School of Information Science and Engineering Shandong Normal University Jinan China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan China

The teaching ability of teachers is one of the core abilities of colleges. Its level has gradually become an important indicator for evaluating a school. However, the existing evaluation models of college teachers are timeconsuming, empirical, complex, subjective and speculative. Therefore, based on the teaching features of colleges in China, this paper constructs a fast and effective college teacher evaluation model by discrete Hopfield neural network technology, and introduces the basic ideas and steps of the model.

关键词： Conferences Information technology Education

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Research on Education Resource Recommendation Algorithm Based on Implicit Feedback and Pairwise

Research on Education Resource Recommendation Algorithm Base...

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

作者： Xiaoxiang Huang Yongsheng Zhang Zhihua Zheng Jie Zhang Jiaxi Duan School of Information Science and Engineering Shandong Normal University Jinan China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan China

In view of the problem of how to improve the accuracy of education resources recommendation in Internet era, a recommendation algorithm based on implicit feedback and pairwise education resources are proposed. Firstly, the feature extraction of learner-education resource is carried out, and the implicit features of learner-education resource are extracted by implicit semantic model, and the prediction results of factor decomposition are used as input to sorting learning algorithm. Converting sorting problems to classification problems uses the theory of ordering learning. Based on the experiment comparison with the traditional recommendation algorithm, it is compared with the comparison algorithm, which has better effect on the recommendation of educational resources. The algorithm increases by nearly 30% on metrics MAP, NDGG.

关键词： Sorting Classification algorithms Matrix decomposition Training Prediction algorithms Data models

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A embedding model for text classification

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Expert Systems 2019年第6期36卷 e12460-e12460页

作者： Liu, Wenfeng Liu, Peiyu Yang, Yuzhen Yi, Jing Zhu, Zhenfang School of Information Science and Engineering Shandong Normal University Jinan China School of Computer Heze University Heze China Shandong Normal University Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan China School of Computer Science and Technology Shandong Jianzhu University Jinan China School of Information Science and Electrical Engineering Shandong Jiaotong University Jinan China

Existing word embeddings learning algorithms only employ the contexts of words, but different text documents use words and their relevant parts of speech very differently. Based on the preceding assumption, in order to obtain appropriate word embeddings and further improve the effect of text classification, this paper studies in depth a representation of words combined with their parts of speech. First, using the parts of speech and context of words, a more expressive word embeddings can be obtained. Further, to improve the efficiency of look-up tables, we construct a two-dimensional table that is in the format to represent words in text documents. Finally, the two-dimensional table and a Bayesian theorem are used for text classification. Experimental results show that our model has achieved more desirable results on standard data sets. And it has more preferable versatility and portability than alternative models. © 2019 John Wiley & Sons, Ltd

关键词： Embeddings

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Cloud computing data security protection strategy 4th

Cloud computing data security protection strategy

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4th International Conference on Cloud Computing and Security, ICCCS 2018

作者： Wei, Yu Zhang, Yongsheng School of Information Science and Engineering Shandong Normal University Jinan250358 China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan250358 China

ISBN: (纸本)9783030000059

Cloud computing is a computing model that has emerged in recent years. It is of great significance for promoting massive information data calculations. We store large amounts of data in the cloud and use algorithms to make huge computations on large-scale data centers on the Internet. Big data cloud computing resources have a huge amount of information, which can provide convenient and fast computing while being a new type of storage. Due to the large amount of data that needs to be processed by cloud computing, many information processing methods are involved, and customer privacy data is difficult to be effectively protected in the cloud computing environment. Therefore, the protection of information security in the cloud computing environment becomes more important. Based on the analysis of the data security problems in the big data cloud computing environment, this paper analyzes the security protection of big data cloud computing data, which is of great significance for promoting the establishment of a complete data security system, and can also be further promoted the useful of cloud computing. © Springer Nature Switzerland AG 2018.

关键词： Data privacy

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Fire Video Image Detection Based on a Convolutional Neural Network

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Journal of Physics: Conference Series 2020年第1期1453卷

作者： Wen Wang Tianping Li Shandong Key Laboratory for Normal Distributed Computer Software Technology School of Physics and Electronic Science Shandong Normal University Jinan Shandong 250358 China School of Physics and Electronic Science Shandong Normal University Jinan Shandong 250358 China

computer vision-based and real-time flame detection is very important to inmodern surveillance system. At present, convolutional neural network (CNN) has become a topic discussed by more and more researchers because of its high recognition accuracy and wide application. The preprocessind process of the traditional image processing method is complicated and the false positive rate is high. So, in this paper we proposed an algorithm for detecting flame in real time using CNN technology. Firstly, to improve the accuracy of detection, we proposed a suspicious target regions segmentation for disposing the suspected flame regions. This algorithm could locate the target area and segment the target area to improve the flame detection and recognition accuracy. Then, we designed a model based on CNN to classify the extracted feature maps of candidate areas. Finally, we could get the detection of flame according to the the classification results. The experimental results show that the approach has high recognition accuracy.

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Fake comment detection based on time series and density peaks clustering 18th

Fake comment detection based on time series and density peak...

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18th International Conference on Algorithms and Architectures for Parallel Processing - ICA3PP 2018 Collocated Workshops: ICA3PP 2018 Workshop on Intelligent Algorithms for Large-scale Complex Optimization Problems, IALCOP 2018, ICA3PP 2018 Workshop on Security and Privacy in Data Processing, SPDP 2018

作者： Di, Ruitong Wang, Hong Fang, Youli Zhou, Ying School of Information Science and Engineering Shandong Normal University Jinan250014 China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan250014 China Institute of Biomedical Sciences Shandong Normal University Jinan250014 China

ISBN: (纸本)9783030052331

This paper proposes a fake comment recognition method based on time series and density peaks clustering. Firstly, an anomaly recognition model based on multi-dimensional time series is constructed. Secondly, according to the idea of multi-scale features, seven benchmark-scale and corresponding subdivision-scale features are extracted hierarchically, and further, 49 features are finally obtained. At last, an optimized detection model based on density peaks clustering is proposed for identifying the fake comments, so as to improve the anti-noise ability of our method. The effectiveness of our proposed method is verified by several experiments, with the AUC value reaching 92%. © Springer Nature Switzerland AG 2018.

关键词： Time series

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Multiple Photon Sampling Technique Based on Stochastic Progressive Photon Mapping

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Journal of Physics: Conference Series 2020年第1期1518卷

作者： Liyan Yang Chunmeng Kang 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

This paper introduces a multiple photon sampling technique based on stochastic progressive photon mapping. We use the image space concept to divide the scene into continuous sub-blocks and then we calculate our proposed distance function and photon number function in each of the sub-blocks. The distance function is used to calculate the distance error of the hit point and to determine whether each sub-block is located at a boundary between different objects. The photon number function is used to calculate the photon number error and to determine whether the photon distribution in each sub-block is uniform. Based on the values of the distance error and the photon number error, the multiple photon sampling technique is used to acquire multiple samples of the hit point in each sub-block. Instead of using a single radius for the radiance estimate, we use three different radii and compute the final radiance estimate as a weighted average of the three values. When compared with the existing stochastic progressive photon mapping method, our method provides a better solution to the photon distribution problem and can also reduce bias and noise, especially in the scene with drastic changes in light and dark.

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Data Deterministic Deletion Scheme Based on DHT Network and Fragmentation Deletion 5th

Data Deterministic Deletion Scheme Based on DHT Network and ...

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5th International Conference on Geo-Spatial Knowledge and Intelligence, GSKI 2017

作者： Zhang, Yongsheng Li, Nengneng Cui, Ranran Fan, Yueqin School of Information Science and Engineering Shandong Normal University Jinan China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan250358 China Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (纸本)9789811308925

During the life cycle of the cloud data, the technique of data deterministic deletion is designed to completely destroy the data and ensures that cloud data that is out of date or backed up in the cloud server is completely deleted or will never be decrypted or accessed. However, the existing scheme simply deletes the key or the cipher text. Once the key or the cloud backup of the cipher text is stolen, data privacy will be threatened. For this reason, this paper proposes a data deletion scheme that is dynamic deletion is based on fragmented cipher text and key. The core of this scheme is to use the dynamic characteristics of DHT network to realize the periodic deletion of fragmented cipher text. Using the idea of fragmented cipher text dynamic deletion, in the unauthorized time even if in the case that the key is leaked, illegal user can not get all the cipher text and key, and can not restore the complete data. The theoretical analysis and experimental results show that the scheme can effectively delete some fragmented cipher texts and key, and has low performance cost, which can meet the requirements for deterministic deletion of excessive data or backup files in cloud storage system. © 2018, Springer Nature Singapore Pte Ltd.

关键词： Data privacy

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