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2022 CCF International Conference on Service Science, CCF ICSS 2022

作者： Ji, Chao Li, Tuo Zou, Xiaofeng State Key Laboratory of High-End Server & Storage Technology China Shandong Inspur Artificial Intelligence Research Institute Co. Ltd Jinan China

ISBN: (数字)9781665498616

ISBN: (纸本)9781665498616

With the rapid development of computer technology, logical circuits play an important role in many fields. Semiconductor transistors, as the basic logic circuit unit of modern computer hardware structure, also encounter more and more technical limitations in the highly integrated field such as computer chips. In order to continue Moore's Law effectively, higher requirements need to be put forward for the basic logic unit. The emergence of memristor opens up a new way to explore advanced computing architecture. This paper presents a MRL- based logic circuit in which input and output logic is provided by high and low voltage. Logic gate circuit elements such as XOR, NAND, NOR are implemented in conjunction with CMOS inverters. Through the construction of basic ratio logic gate, the adder and multiplier is simplified, and the result of signal simulation using LTspice software is completely correct. Finally, a multifunctional logic module is innovatively designed. © 2022 IEEE.

关键词： Timing circuits

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Layer-wise based Adabelief Optimization Algorithm for Deep Learning 22

Layer-wise based Adabelief Optimization Algorithm for Deep L...

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Proceedings of the 4th International Conference on Advanced Information Science and System

作者： Zhiyong Qiu Zhenhua Guo Li Wang Yaqian Zhao Rengang Li State Key Laboratory of High-end Server and Storage Technology Shandong massive information technology research institute China State Key Laboratory of High-end Server and Storage Technology Inspur Electronic Information Industry Co. Ltd. China

ISBN: (纸本)9781450397933

For the optimization problem of deep learning, it is important to formulate a optimization method that can improve the convergence rate without sacrificing generalization ability. This paper proposes a layer-wise based Adabelief optimization algorithm to solve the deep learning optimization problems more efficiently. In the proposed algorithm, each layer of the deep neural network is set different learning rate appropriately in order to achieve a faster convergence rate. We also give the theorems that can guarantee the convergence property of Layer-wised AdaBelief method. Finally, we evaluate the effectiveness and efficiency of the proposed algorithm on experimental examples. Experimental results show that the converges speed of the layer-wised AdaBelief algorithm is the fastest compared with the mainstream algorithms. Besides, the new algorithm also maintaining an excellent convergence result in all numerical examples.

关键词： Adaptive Learning Rate

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Operator Optimization Oriented Person Re-Identification

Operator Optimization Oriented Person Re-Identification

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2022 International Conference on Machine Vision, Automatic Identification and Detection, MVAID 2022

作者： Cao, Qichun Wang, Binqiang Dong, Gang Hu, Kekun Yang, Hongbin Electronic Information Industry Co. Ltd. Beijing100085 China State Key Laboratory of High-End Server & Storage Technology Beijing100085 China

Using FPGA to accelerate the application of convolutional neural networks has attracted more and more attention. However, the design technology of the FPGA accelerator cannot follow up the latest achievements of the convolutional neural networks, nor can it consider optimizing the complex network structure combined with the characteristics of hardware to improve the accuracy of the network and simplify the implementation of hardware. To address these issues, in this paper, we adopt the development idea of algorithm-hardware co-design, compress the structure and optimize the operator for a state-of-the-art lightweight person re-identification network, and design a new network model SGCNet (Shift Gaussian Convolution Network). Specifically, SGCNet is obtained by structure optimization and operator replacement according to the optimization degree of the operator in the FPGA hardware environment. For convolution operation, only 1 × 1 kernel convolution operator is adopted in SGCNet, while spatial convolution is replaced by a more effective shift operation. SGCNet is more concise and clear in the operation types and complex structure of the network. Experimental evaluations demonstrate that SGCNet's top-5 accuracy of 92.7% on ImageNet and R1 accuracy of 74.6% on CUHK03 of person re-identification data set, is higher than CMSNet network. © Published under licence by IOP Publishing Ltd.

关键词： Field programmable gate arrays (FPGA)

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Data-Driven Crowd Motion Modeling With Group Properties Analysis 25

Data-Driven Crowd Motion Modeling With Group Properties Anal...

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25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Jia, Chuanhua Lu, Dianjie Zhang, Guijuan Liu, Hong School of Information Science and Engineering Shandong Normal University Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology State Key Laboratory of High-end Server and Storage Technology Shandong Jinan China

ISBN: (纸本)9781665405270

Traditional crowd evacuation simulation methods focus on the analysis of individual behavior in the crowd, but ignore the analysis of potential group properties in the process of crowd motion, which reduces the visual realism of the crowd evacuation simulation process. During crowd motion, crowds cause unconscious self-organization due to common destinations and social relationships, thus forming groups. Considering the influence of group properties on crowd motion is important to improve the visual realism of crowd motion simulation. To address this problem, we propose a crowd motion modeling method for group properties analysis (GPA-CMM). First, we build a data-driven group properties quantification (DGPQ) model to describe the characteristics of group motion accurately. In the model, we divide the crowd into several groups, extract motion properties of the crowd, and quantify the features of intra-group stability and inter-group conflict. Then, in order to analyze their influences of group properties, we build a stability and conflict based crowd motion analysis (SC-CMA) model. Finally, we implement a crowd simulation system based on SC-CMA, visualize the results of the theoretical analysis in a graphical way. The experimental results show that the method can simulate crowd motion more realistically. © 2022 IEEE.

关键词： Behavioral research

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Blockchain-based Crowd-sensing Trust Management Mechanism for Crowd Evacuation 25

Blockchain-based Crowd-sensing Trust Management Mechanism fo...

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25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Yu, Jiawen Zhang, Guijuan Lu, Dianjie Liu, Hong School of Information Science and Engineering Shandong Normal University Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology State Key Laboratory of High-end Server and Storage Technology Shandong Jinan China

ISBN: (纸本)9781665405270

With the continuous urban expansion, the safety of public places has attracted more and more attention. Once an accident occurs and people distrust their surroundings, it will cause congestion, trampling and other events. Individual trust value is particularly important in crowd evacuation. However, it is very difficult to evaluate the individual credibility with existing methods. We propose a blockchain-based crowd-sensing trust management mechanism (B-CSTM) to address this problem. First, we provide a message credibility evaluation mechanism that can evaluate the credibility of messages sent by individuals on spot. Second, we developed a trust calculation method that uses a distributed Evacuation Perception Unit (EPU) to calculate individuals' trust values by perception scores. Finally, we build a blockchain-based trust management mechanism model that uses blockchain to store and query trust values. Through experimental analysis, we visualize the trust results in the evacuation scenario. It is shown that the mechanism is feasible for the management of trust values (collection, calculation and storage, etc.) in crowd evacuation. © 2022 IEEE.

关键词： Blockchain

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Towards Interpreting Computer Vision Based on Transformation Invariant Optimization 17th

Towards Interpreting Computer Vision Based on Transformatio...

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17th European Conference on Computer Vision, ECCV 2022

作者： Li, Chen Jiang, Jinzhe Zhang, Xin Zhang, Tonghuan Zhao, Yaqian Jiang, Dongdong Li, Rengang State Key Laboratory of High-End Server & Storage Technology Jinan China Shandong Hailiang Information Technology Institutes Jinan China Inspur Electronic Information Industry Co. Ltd Jinan China International Office Shenzhen University Shenzhen China

ISBN: (纸本)9783031250743

Interpreting how deep neural networks (DNNs) make predictions is a vital field in artificial intelligence, which hinders wide applications of DNNs. Visualization of learned representations helps we humans understand the causality in vision of DNNs. In this work, visualized images that can activate the neural network to the target classes are generated by back-propagation method. Here, rotation and scaling operations are applied to introduce the transformation invariance in the image generating process, in which we find a significant improvement on visualization effect. Finally, we show some cases that such method can give a causal discovery of neural networks. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep neural networks

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Traditional Chinese Medicine Automated Diagnosis Based on Knowledge Graph Reasoning

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Computers, Materials & Continua 2022年第4期71卷 159-170页

作者： Dezheng Zhang Qi Jia Shibing Yang Xinliang Han Cong Xu Xin Liu Yonghong Xie School of Computer&Communication Engineering University of Science&Technology BeijingBeijing100083China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing100083China Inspur Electronic Information Industry Co. Ltd.&State Key Laboratory of High-End Server&Storage TechnologyJinan250101China Surgical Simulation Research Lab Department of SurgeryUniversity of AlbertaEdmontonT6G 2E1AlbertaCanada

Syndrome differentiation is the core diagnosis method of Traditional Chinese Medicine(TCM).We propose a method that simulates syndrome differentiation through deductive reasoning on a knowledge graph to achieve automated diagnosis in *** analyze the reasoning path patterns from symptom to syndromes on the knowledge *** are two kinds of path patterns in the knowledge graph:one-hop and *** one-hop path pattern maps the symptom to syndromes *** two-hop path pattern maps the symptom to syndromes through the nature of disease,etiology,and pathomechanism to support the diagnostic *** the different support strengths for the knowledge paths in reasoning,we design a dynamic weight *** utilize Naïve Bayes and TF-IDF to implement the reasoning method and the weighted score *** proposed method reasons the syndrome results by calculating the possibility according to the weighted score of the path in the knowledge graph based on the reasoning path *** evaluate the method with clinical records and clinical practice in *** preliminary results suggest that the method achieves high performance and can help TCM doctors make better diagnosis decisions in ***,the method is robust and explainable under the guide of the knowledge *** could help TCM physicians,especially primary physicians in rural areas,and provide clinical decision support in clinical practice.

关键词： Traditional Chinese medicine automated diagnosis knowledge graph Naïve Bayes syndrome differentiation

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Federated Learning based Path Planning Method for Crowd Evacuation 25

Federated Learning based Path Planning Method for Crowd Evac...

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25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Yuan, Zihao Lu, Dianjie Zhang, Guijuan Liu, Hong School of Information Science and Engineering Shandong Normal University Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology State Key Laboratory of High-end Server and Storage Technology Shandong Jinan China

ISBN: (纸本)9781665405270

The surveillance videos captured by cameras deployed in large-scale places can provide help for path planning of crowd evacuation in emergencies. However, most surveillance videos are not allowed to be shared in order to avoid privacy disclosure. Thus, it is difficult to obtain the video data of the whole scenario for path planning which greatly reduces effectiveness of path planning. How to provide global path planning for crowd evacuation while ensuring privacy protection is a challenging problem. To solve this problem, we propose a federated learning based path planning method for crowd evacuation. In this method, the potential field model are introduced into the federated learning framework to provide the privacy protection since only the potential field information is aggregated between the camera terminal and the central server, instead of the surveillance videos. First, we construct the local potential field for the surveillance scene of each camera by extracting the scene information and the crowds information. Second, the central server periodically performs the global potential field information aggregation and integrates these information to plan the optimal global path dynamically for crowd evacuation. Finally, we implemente a simulation platform to simulate the process of crowd movement and visualize the dynamic change of the potential energy field. Experimental results prove that the proposed method can provide privacy-preserving path planning for multi-camera evacuation scenarios. © 2022 IEEE.

关键词： Motion planning

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Collaborative Teams Recruitment Based on Dual Constraints of Willingness and Trust for Crowd Sensing 25

Collaborative Teams Recruitment Based on Dual Constraints of...

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25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Song, Nianyun Lu, Dianjie Shi, Yepeng Zhang, Guijuan Liu, Hong School of Information Science and Engineering Shandong Normal University Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology State Key Laboratory of High-end Server and Storage Technology Shandong Jinan China

ISBN: (纸本)9781665405270

As crowd sensing tasks become more complex, it is increasingly needed for participants to form teams to collaborate interactively in order to complete tasks more efficiently. It is valuable to fully understand user behavior, the organizational principles of crowd collaboration teams and incorporate them into the user recruitment process. In fact, there is a two-way process of subjective and objective selection for building a team in crowd sensing. From the participant's perspective, the willingness of users to participate in the task is a subjective factor;from the platform's perspective, the trust relationship between users is an objective factor. However, existing work has not well recognized the value of including collaborative teams in the crowd sensing recruitment process and has not considered the willingness and trust relationships between participants simultaneously, which results in poor quality of service (QoS) among recruited users. To address this problem, we propose a willingness and trust-based collaborative team recruitment method (WT-CTRM). First, we construct a willingness network (WN) and a trust network (TN) to describe the interaction of willingness and trust among the collaborated users respectively. Based on these networks, we then predict the unknown willingness and trust relationships between users for collaboration by using graph convolutional networks (GCNs). Finally, we build collaborative teams for the user recruitment process of crowd sensing based on the consensus constraints of the predicted willingness network and trust network. Simulation results show that the recruitment scheme significantly improves the QoS in task scenarios with collaborative requirements. © 2022 IEEE.

关键词： Quality of service

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Transport Layer Optimization Strategy to Prepare high Efficiency Perovskite Photovoltaic Device

Transport Layer Optimization Strategy to Prepare High Effici...

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2022 International Conference on Electrical, Electronics and Information Engineering, EEIE 2022

作者： Ji, Chao Li, Tuo Zou, Xiaofeng Zhang, Lu State Key Laboratory of High-End Server & Storage Technology Jinan250013 China Shandong Inspur Artificial Intelligence Research Institute Co. Ltd Jinan250013 China

Perovskite photovoltaic devices have attracted widespread attention. Despite the spectacular advances in power conversion efficiency (PCE), the unsatisfying stability of the perovskite devices is still a great challenge, which requires a deeper understanding of the device physics, in particular, the interfacial behavior and the junction structure of the perovskite semiconductor devices. Here we demonstrate the continuous decrease of ionic interface charge (IIC) density, weakening of current-voltage hysteresis, decrease of leakage current as well as constant increase of PCE from ∼10% to ∼19% were achieved through step-by-step modification of the hole transport layer (HTL) and electron transport layer (ETL) of the devices. A new semiconductor device junction device model is presented to understand the correlation between the IIC density and the photovoltaic performance. The work shows that although the IIC is originated from mobile ions in perovskite layer, the IIC density is determined by the properties of the charge transport layer. These conclusions and the proposed device model have important implication for future study in pursuing efficient and stable perovskite photovoltaic devices. © Published under licence by IOP Publishing Ltd.

关键词： Perovskite

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