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19th IEEE International Conference on Communication Technology, ICCT 2019

作者： Yi, Zexuan Fu, Wenlong Cao, Lihong Neuroscience and Intelligent Media Institute Communication University of China Beijing China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China

ISBN: (纸本)9781728105352

For the study of new computer architecture inspired by brain, implementing neuron function on hardware has become an emerging research direction. To study the feasibility of biological neuron implementation and acceleration characteristics on the hardware platform, the basic Hodgkin-Huxley (HH) neuron model and a small scale of synapse connection model is implemented on Xilinx Zynq7 Zedboard FPGA, high-level-synthesis method is used to reduce the burden of hardware design. The Hodgkin-Huxley model simulation result of FPGA are validated through high level software simulation tool in Brian2, furthermore, resource utilization and calculation time cost are provided in this paper, finally, a pipeline design of HH neuron model is used to accelerate calculations and reduce resource utilization, the result proves that this design is suitable for HH model simulation. © 2019 IEEE.

关键词： Neurons

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Leveraging Slot-Aware Enhancements for Improved Zero-Shot Dialogue State Tracking 6

Leveraging Slot-Aware Enhancements for Improved Zero-Shot Di...

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6th International Conference on Frontier Technologies of Information and Computer, ICFTIC 2024

作者： Song, Tianhang Hu, Haolei Chen, Xinxin Zi, Kangli Henan Institute of Advanced Technology Zhengzhou University Zhengzhou China Institute of Computing Technology Key Laboratory of Intelligent Information Processing Beijing China

ISBN: (纸本)9798331541750

Task-oriented dialogue systems (TOD) aim to help users complete specific tasks through multiple rounds of dialogue, in which Dialogue State Tracking (DST) is a key component. The training of DST models typically necessitates the availability of a substantial corpus of dialogue data with turn-level annotations. However, the collection of such data is both costly and inefficient. Despite the impressive performance of systems based on advanced large language models (LLMs), their closed-source nature restricts the potential for local deployment. To address this challenge, this paper proposes a new zero-shot slot-aware enhanced model (SAE), which introduces a novel coded prompting framework to enhance the DST capability of small parameter models in new domains and task scenarios. In particular, we have devised a prompting framework that employs code to refine task instructions. Furthermore, we have proposed a slot-aware question generation mechanism that generates slot-aware questions based on contextual details in accordance with the dialogue context, thereby enhancing the model's perception and matching capabilities for slots. In order to verify the effectiveness of the model, we conducted experiments on the MultiWOZ dataset. The results show that the SAE model has achieved significant performance improvements in zero-shot DST tasks. © 2024 IEEE.

关键词： Zero-shot learning

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Chinese microblogging emotion classification based on support vector machine 5

Chinese microblogging emotion classification based on suppor...

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5th International Conference on computing Communication and Networking Technologies, ICCCNT 2014

作者： Sun, Xiao Li, Chengcheng Ye, Jiaqi Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei University of Technology Hefei230009 China

ISBN: (纸本)9781479926961

Through the analysis and study of the emotional characteristics in Chinese micro-blog, this paper proposed a multidimensional sentiment classification method based micro-blog emotion classification, emoticon in micro-blog is adopted to train the support vector machine and divide micro-blog into seven types of emotions: happiness, fondness, sorrow, anger, fear, detestation and surprise. We used micro-blog emoticon to initial screen large-scale unmarked data, and automatically labeled them into seven types, then used this emotional corpus as training set to train the emotion classifier to classify micro-blog data into multiple emotion categories. The experimental results show that precision of unigram model for each type can reach 63.7%. Meanwhile, different feature selection methods for support vector machines and Naive Bayes classifier experiment have been adopted in the experiment, the precision and recall has reached more than 71%. © 2014 IEEE.

关键词： Support vector machines

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Axiomatic approaches based on the software trustworthiness measure

Axiomatic approaches based on the software trustworthiness m...

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International Conference on Applied System Innovation, ICASI 2015

作者： Tao, H.W. Chen, Y.X. Pang, J.M. State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China MoE Engineering Center for Software/Hardware Co-Design Technology and Application East China Normal University Shanghai China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781138028937

Due to the outstanding problems of software trustworthiness, the measurement of software trustworthiness has aroused more and more concern. However, few researches pay attention to using more rigorous approaches in the measurement of software trustworthiness and carrying out the theoretical validation of measures. Axiomatic approaches by formally defining the desirable properties of the measures for software attributes can provide precise, formal, and unambiguous terms for the quantification of software attributes. We once applied axiomatic approaches to the measured software trustworthiness, presented four properties for software trustworthiness measures and gave three measures for software trustworthiness. In this paper, we propose a new property-related user expectation to establish an improved software trustworthiness measure and validate this measure from the theory point of view by proving that it complies with the five properties presented by us. A comparative study shows that this measure is better than the popular software trustworthiness measures in terms of our properties. © 2016 Taylor & Francis Group.

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Hierarchically porous Cu current collector with lithiophilic CuxO interphase towards high-performance lithium metal batteries

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Journal of Energy Chemistry 2021年第7期30卷 292-299页

作者： Yaya Wang Zexu Zhao Wei Zeng Xingbo Liu Lei Wang Jian Zhu Bingan Lu State Key Laboratory for Chemo/Biosensing and Chemometrics and College of Chemistry and Chemical EngineeringHunan Key Laboratory of Two-Dimensional MaterialsHunan UniversityChangsha 410082HunanChina State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan Provincial Key Laboratory of Intelligent Laser ManufacturingHunan UniversityChangsha 410082HunanChina School of Materials and Engineering Hunan UniversityChangsha 410082HunanChina School of Physics and Electronics Hunan UniversityChangsha 410082HunanChina

Lithium metal is one of the most promising anode materials for next-generation electrochemical energy storage due to low electrochemical potential and high specific ***,large volume change and uncontrollable formation of lithium dendrite during cycling severely hinder the practical application of rechargeable Li metal ***,we report a hierarchically porous Cu covered with lithiophilic CuxO(HPCu-CuxO) via femtosecond laser strategy in about 2 min as current collector for highperformance Li metal *** precisely tunable pore volume and depth as well as lithiophilic CuxO interphase,the HPCu-CuxO not only guides homogeneous Li nucleation,resulting in a smooth and dendrite-free lithium surface,but also provides space to alleviate the volume expansion of Li metal anode,achieving excellent structure ***,highly stable Coulombic efficiency and ultralow overpotential of 15 mV even up to 1000 h were achieved at the current density of 1 mA cm^(-2).Moreover,the resultant Li@HPCu-CuxO//LiFePO_(4) full battery delivered outstanding cycle stability and rate *** results offer a pathway toward high-energy-density and safe rechargeable Li metal batteries.

关键词： Lithium metal batteries Femtosecond laser strategy Hierarchically porous structure Lithiophilic CuxO interphase

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Dual space multi-granular model for multi-interest sequential recommendation

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Knowledge-Based Systems 2025年 323卷

作者： Yijun Sheng Puiieng Lei Yanyan Liu Ximing Chen Qiwen Xu Zhiguo Gong State Key Laboratory of Internet of Things for Smart City University of Macau 999078 Macao Special Administrative Region of China Guangdong-Macau Joint Laboratory for Advanced and Intelligent Computing University of Macau 999078 Macao Special Administrative Region of China Department of Computer Information Science University of Macau 999078 Macao Special Administrative Region of China

Sequential recommendation aims to predict the next item for a user given his historical interaction sequence. Recently, multi-interest and Graph Neural Network (GNN) based paradigms are two new directions in such task. Multi-interest learning encompasses extracting diverse user interests through historical item clustering, while GNN refines user preferences through correlations among historical items. Recent research suggests the synergistic potential of combining these methods to aggregate user preferences at multiple levels, enhancing the accuracy of multi-interest extraction for improved recommendations. However, existing GNN-based multi-interest models can only achieve local smoothing for node embeddings through neighbor information aggregating, thus, they could not match remote items (far-apart items on the item–item graph) even though those remote items show similar local patterns and the items may reflect some niche preferences. It is unreasonable in the context of multi-interest recommendation as the objective is to capture the user’s interests in a more comprehensive manner. To tackle this issue, we propose a D ual S pace M ulti- G ranular Rec ommendation model ( DSMGRec ), where a Graph Deconvolutional Network (GDcN) is designed to disentangle local structure-based patterns of items as their additional embeddings. Then, we adopt a dual framework that combines the traditional GNN with our novel GDcN to encode multi-granular representations for items in the dual space. Such dual item representations can match items by not only their primary patterns but also their secondary patterns. Experiments on four real-world datasets with different densities show that our model outperforms state-of-the-art baselines.

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LSTM based classification model and its application for doctor-patient relationship evaluation 19

LSTM based classification model and its application for doct...

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19th IEEE International Conference on e-Health Networking, Applications and Services, Healthcom 2017

作者： Kuang, Hongrui Che, Chao Zhang, Qiang Wei, Xiaopeng Key Lab of Advanced Design and Intelligent Computing Dalian University Ministry of Education Dalian China Dalian University of Technology School of Computer Science and Technology Dalian China

ISBN: (纸本)9781509067046

The emergence of medical social media has made it possible for more and more patients to share their views and experiences on the medical care platform. These subjective texts contains patients' evaluation information for doctors and can be analyzed to provide rich decision-making information for patients and hospitals. Therefore, we propose a LSTM (Long Short Term Memory) based text sentiment classification method to evaluate the relationship between doctors and patients through the comment data from medical social media. The classification model of doctor-patient relationship can also be performed to evaluate the hospital by calculating the praise rate to help people choose hospitals. We perform two experiments on the patients' evaluation data from the website of Haodaifu. The experiment of doctor-patient relationship classification confirms the effectiveness of the classification model. In the experiment of hospital evaluation, we calculate the praise rate of 11 hospitals in Jinan of Shandong province based on the doctor-patient relationship classification results. The consistency between the results obtained by our method and the data of Mingyihui shows that our evaluation method for hospitals is reasonable and effective. © 2017 IEEE.

关键词： Hospitals

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Blockchain-Based Privacy-Preserving and Accountable Mobile Edge Outsourcing computing Framework for the Metaverse

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2024年第2期9卷 711-724页

作者： Li, Chunpei Liu, Chen Liu, Peng Li, Xianxian Qiu, Wangjie Lei, Lei Jin, Yanli Guangxi Normal University Key Laboratory of Educational Blockchain and Intelligent Technology Ministry of Education Guangxi Key Lab of Multi-Source Information Mining and Security Guilin541004 China Zhongguancun Laboratory China Beihang University Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing Beijing China

Mobile Edge computing (MEC) enables Metaverse Terminal Devices (MTD) to perform complex tasks, including graphic rendering and physical simulation, by leveraging low-latency outsourced computing. However, existing research has not fully addressed the challenge of establishing an efficient outsourced computing service within an open and dynamic MEC environment that simultaneously ensures privacy and accountability. To address this, we proposes a blockchain-based privacy-preserving and accountable mobile edge outsourcing computing framework for the Metaverse, termed Meta-BMEOC. Specifically, we have designed an outsourcing computing protocol based on smart contracts and threshold secret sharing, enabling MTD to outsource tasks to multiple edge servers while preserving privacy. Furthermore, we have developed an off-chain smart contract protocol based on a Trusted Execution Environment. This protocol is designed to reduce the risk of malicious edge servers colluding to reconstruct the computational tasks of MTD, and it enables accountability for servers that return erroneous results. Additionally, we designed an incentive mechanism to resist malicious attacks and ensure system security and stability. Security analysis and experimental evaluation show that Meta-BMEOC not only ensures the privacy and accountability of outsourced computing but also provides outsourced computing services with lower computational latency. © 2017 IEEE.

关键词： Differential privacy

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Analysis of Influencing Factors on Carbon Monoxide Utilization Rate of Blast Furnace Based on Multi-Timescale Characteristics 36

Analysis of Influencing Factors on Carbon Monoxide Utilizati...

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第36届中国控制会议

作者： Jianqi An Yuwan Yang Min Wu Takao Terano School of Automation China University of Geosciences Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Department of Computer Science School of ComputingTokyo Institute of Technology

ISBN: (纸本)9781538629185

In the blast furnace,due to the different changing frequency of different operations,and the different reaction time of gas,liquid and solid materials,there exists multi-timescale characteristics in the iron-making ***,not enough attention has been paid to this characteristic,most of the analyses on the blast furnace state before are under a fixed time ***,this paper makes a analysis of influencing factors on carbon monoxide utilization rate of blast furnace based on multi-timescale ***,the factors that affect the carbon monoxide utilization rate are analyzed from different time ***,in the short time scale,the individual influencing time scale of the permeability index,total pressure difference and top temperature is found by the support vector machine（SVM） *** in the long time scale,the influencing time scale of burdening is *** the validity of each model is verified by the field data of the blast furnace.

关键词： Multi-time scales Carbon monoxide utilization Support vector machine Blast furnace

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Robust Iterative Learning Control for Alloy Batch Weighing Process in Refining Furnace 36

Robust Iterative Learning Control for Alloy Batch Weighing P...

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第36届中国控制会议

作者： Yibo Huang Jianqi An Yong He Min Wu Takao Terano School of Automation China University of Geosciences Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Department of Computer Science School of ComputingTokyo Institute of Technology

ISBN: (纸本)9781538629185

This paper proposes a robust iterative learning control method for the refining furnace alloy weighing process to solve the problem of the poor control accuracy and stability caused by the changing of alloy properties and the frequency of the vibration feeder. First, a two dimensional（2D） weighing model was established based on the analysis of the dynamic characteristics of alloy weighing process. Second, a control scheme is proposed for the 2D model of alloy weighing ***, a robust iterative learning controller is developed and a stability condition of the 2D system is derived through linear matrix inequality（LMI） obtained by a 2D Lyapunov-Krasovskii function. Finally, the simulation results show that the proposed method can sufficiently improve the control precision of the alloy weighing process.

关键词： Iterative learning control 2D weighing model Batch process Alloy weighing process Robust control

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