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Personality Enhanced Emotion Generation Modeling for Dialogue Systems

COGNITIVE COMPUTATION 2024年第1期16卷 293-304页

作者： Liu, Zhiqiang Jia, Wenchao Zhou, Yutong Xu, Biqi Liu, Zhiqiang Wu, Zhuoyi College of Data Science and Application Inner Mongolia University of Technology Hohhot 010080 Inner Mongolia China Inner Mongolia Autonomous Region Engineering & Technology Research Centre of Big Data Based Software Service Hohhot 010080 Inner Mongolia China

Emotion plays a crucial role in human communication, as it adds depth and richness to conversations. In recent years, there has been growing interest in developing conversation systems with the ability to generate emotions. However, to create more engaging and realistic interactions, it is essential to consider the influence of personality on emotion generation. This paper proposes a novel approach that combines personality modeling with emotion generation for conversation systems. By incorporating personality traits into the emotion generation process, we aim to create more personalized and contextually appropriate emotional responses. Drawing from BigFive model and emotion computation techniques, our model takes into account individual differences in personality to generate emotions that align with each user's unique characteristics. Experiments show that combining emotion modeling with personality in a dialogue system helps improve the performance of emotion generation models. Additionally, it is also verified that our approach outperforms other baselines on several metrics.

关键词： Dialogue system Emotion generation Personality Anthropomorphism

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Multi-Task Computation Offloading based on Vehicular Blockchain 5

Multi-Task Computation Offloading based on Vehicular Blockch...

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5th International Conference on Frontiers technology of Information and Computer, ICFTIC 2023

作者： Meng, Zhen Liu, Chuyi Ren, Guanyu Wan, Jianxiong Li, Leixiao Inner Mongolia University of Technology Hohhot China Inner Mongolia Autonomous Region Engineering & Technology Research Center of Big Data Based Software Service Hohhot China

ISBN: (纸本)9798350309034

With the increase of vehicular applications, which aimed at improving traffic safety and driving experience, the limited computing resources of vehicles face the challenge of the real-time task requirements. Vehicular Edge Computing is an efficient way to augment the computational capabilities of vehicles. However, there are few researches have simultaneously considered the vehicle reliability, task prioritization, and timely scheduling of tasks. To bridge this gap and motivate vehicles to share computing resources, this paper proposed a multi-task offloading method. We formulated a revenue maximization problem and designed a control algorithm for this problem based on deep reinforcement learning. Meanwhile, we employed blockchain technology to ensure vehicle reliability. Simulation results validate the effectiveness of the proposed method, demonstrating a profits improvement of 67.98%. © 2023 IEEE.

关键词： blockchain computation offloading deep reinforcement learning vehicular edge computing

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Review of Water Quality Prediction Methods 8th

Review of Water Quality Prediction Methods

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8th International Conference on Water Resource and Environment, WRE 2022

作者： Chen, Zhen Liu, Limin Wang, Yongsheng Gao, Jing College of Data Science and Application Inner Mongolia University of Technology Hohhot China Inner Mongolia Autonomous Region Software Service Engineering Technology Research Center Based on Big Data Inner Mongolia Autonomous Region Hohhot China School of Computer and Information Engineering Inner Mongolia Agricultural University Hohhot China

ISBN: (纸本)9789819919185

Water quality prediction plays a crucial role in environmental monitoring, ecosystem sustainability, and aquaculture, and it plays an important role in both economic and ecological benefits. For many years, researchers have been working on how to improve the accuracy of water quality prediction. However, at this stage, with the increase in external climate change, external noise, precipitation, and many other uncertainties, water quality prediction is facing the problem of insufficient accuracy. This paper analyzes the research methods of water quality prediction at home and abroad in recent years and summarizes the introduction through two aspects: mechanical water quality prediction methods and non-mechanical water quality prediction methods. Firstly, the mechanism of water quality prediction method is introduced, which uses various hydrological data information such as initial water head, bottom slope, hydraulic radius, etc. to predict water quality;Secondly, it introduces the non-mechanical water quality prediction method that uses historical water quality index data to analyze and mine to predict water quality. Finally, after introducing the existing prediction methods, the development direction of water quality prediction is analyzed and summarized. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Water quality

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Automatic Inspection and Reading System of Pointer Instrument Based on Computer Vision 3

Automatic Inspection and Reading System of Pointer Instrumen...

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3rd International Symposium on Computer engineering and Intelligent Communications, ISCEIC 2022

作者： Xue, Liming Qin, Junping Gao, Tong Wang, Chao Inner Mongolia University of Technology College of Data Science and Application Inner Mongolia Hohhot China Inner Mongolia Autonomous Region Engineering & Technology Research Center of Big Data Based Software Service Inner Mongolia Hohhot China

ISBN: (纸本)9781510660298

Aiming at the problems of background interference and incorrect angles in the images collected by inspection robots, a computer vision-based automatic inspection and reading system for pointer-type instruments is proposed, which can automatically obtain pointer readings on the basis of instrument image detection and correction. First, use the Centernet algorithm to detect the target of the pointer instrument image, and cut out the instrument image with the background removed according to the detected position information;then, perform key point detection, select a pair of symmetrical key points to rotate the instrument using affine transformation Correction, and then select two pairs of symmetrical key points through template matching to correct the inclination of the meter using perspective transformation;finally, complete the reading of the meter by using Otsu segmentation and Hough transform circle and line detection. The experimental results show that the proportion of images to be corrected is 93%, the average error rate of the corrected instrument image is reduced by 10.19%, and the average accuracy of readings reaches 97.02%, which can meet the practical application. © The Authors. Published under a Creative Commons Attribution CC-BY 3.0 License.

关键词： Hough transforms

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Distributed Energy Management for Carbon Neutral Data Centers 22

Distributed Energy Management for Carbon Neutral Data Center...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Liu, Chuyi Wan, Jianxiong Li, Leixiao Ren, Guanyu Wang, Xiaolei Inner Mongolia University of Technology Huhhot China Inner Mongolia Autonomous Region Engineering Technology Research Center of Big Data Based Software Service Huhhot China Research Center of Large-Scale Energy Storage Technologies Huhhot China

ISBN: (纸本)9798331509712

With the continuous expansion of data centers, their carbon emission becomes a serious issue. A number of studies are committing to reduce the carbon emission of data centers. Carbon trading is a promising emission reduction technique which is, however, seldom applied to data centers. To bridge this gap, we propose a carbon-neutral architecture DC2 for distributed data centers, where each data center consists of three subsystems, i.e., energy subsystem for energy supply, thermal subsystem for data center cooling and the carbon subsystem for carbon trading. Then, we formulate the energy management problem as a Decentralized Partially Observable Markov Decision Processe (Dec-POMDP) and develop a distributed solution framework using Multi-Agent Deep Deterministic Policy Gradient (MAD-DPG). Finally, simulations on real-world data shows that the DC2 provides a cost saving of 8.16%, and applying MADDPG saves 10.04% of the overall cost compared to Independent MADDPG. © 2024 IEEE.

关键词： Carbon sequestration

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Resource Management in Cloud Based on Deep Reinforcement Learning 4

Resource Management in Cloud Based on Deep Reinforcement Lea...

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4th International Conference on Computer Communication and the Internet, ICCCI 2022

作者： Liu, Chuyi Li, Wenjing Wan, Jianxiong Li, Leixiao Ma, Zhiqiang Wang, Yongsheng Inner Mongolia University of Technology China Inner Mongolia Autonomous Region Engineering & Technology Research Center of Big Data Based Software Service Huhhot China

ISBN: (数字)9781665469920

ISBN: (纸本)9781665469920

In order to provide users timely and reliable services, resources and facilities in cloud data centers are often more than actual needs, which leads to low resource utilization and high operating costs. However, controling the quantity of resources and facilities may cause low throughput. Therefore, many researches are devoted to increasing resource utilization, improving jobs throughput and reducing operating costs through effective cloud resource management. The cloud resource management in cloud data center can be regarded as a multi-resource demand job allocation problem. This paper proposed an allocation algorithm of jobs based on deep reinforcement learning. The algorithm allocates waiting jobs to clusters respectively, so as to optimize throughput and improve resource utilization. The algorithm is implemented in cloud data center simulation model CloudSim. The experimental results show that the method based on Deep Reinforcement Learning is better than traditional method in multi-resource job allocation problem. © 2022 IEEE.

关键词： Reinforcement learning

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Data Center Microgrid Energy Management Based on Model-Based Reinforcement Learning 4

Data Center Microgrid Energy Management Based on Model-Based...

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4th International Conference on Frontiers technology of Information and Computer, ICFTIC 2022

作者： Zhao, He Wan, Jianxiong Li, Leixiao Inner Mongolia University of Technology Hohhot China Inner Mongolia University of Technology Inner Mongolia Autonomous Region Engineering &technology Research Center of Big Data Based Software Service Hohhot China

ISBN: (纸本)9798350321951

With the increasing of energy consumption and price, energy management is becoming increasingly important for data center with larger and larger scale. Microgrid can provide reliable, stable and continuous power services for data centers, and improve energy efficiency and reduce the operating cost. In this paper, we study the energy management problem for data center microgrid (DCMG) integrated with renewable energy. We introduce the energy storage device to mediate the intermittent nature of renewable energy. The energy management problem is formulated as a Markov Decision Process (MDP). Traditional methods greedy choice actions does not consider the impact of the actions on future cumulative operating cost. Model-free reinforcement learning is a candidate solution to this problem. However, model-free reinforcement learning methods converge slowly. Therefore, in this paper, we propose a model- based deep reinforcement learning algorithms to solve the energy management problem (Model-Based Reinforcement Learning Energy Management, MBRL-EM) of DCMG. We use long short-term memory (LSTM) model to design a system transition model, which predicts the future renewable energy production and energy demand. Then we obtain the optimal policy based on model predictive control to minimize the long-term operation cost. The simulation results show that MBRL-EM outperforms the baseline algorithms of model-based PPO and model-free PPO and reduces the average cost by up to 23% and 38% respectively. © 2022 IEEE.

关键词： Operating costs

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Resource Allocation of IoT systems Integrated with Blockchain and Mobile Edge Computing 4

Resource Allocation of IoT systems Integrated with Blockchai...

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4th International Conference on Frontiers technology of Information and Computer, ICFTIC 2022

作者： Bai, Zihan Wan, Jianxiong Li, Leixiao Liu, Chuyi Duan, Mingda Inner Mongolia University of Technology Hohhot China Inner Mongolia Autonomous Region Engineering & Technology Research Center of Big Data Based Software Service Hohhot China

ISBN: (纸本)9798350321951

With the development of the Internet of Things (IoT), IoT devices have been widely applied into several fields to collect and transmit data. However, it is crucial to ensure to the security of the collected data. On the other hand, computing resources of IoT devices have obvious constraint. Blockchain and Mobile Edge Computing (MEC) can significantly improved the security of the IoT system and the efficiency of the consensus process of IoT devices. However, they also bring a lot of energy consumption and computational overhead. Reasonable allocation of computational resources is an effective method to reduce the energy consumption and the computational overhead. The resource allocation problem of the IoT system supported by blockchain and MEC can be formulated as an Markov Decision Process (MDP). In the existing studies, Deep Q-Network (DQN)-based approaches are adopted to optimize the energy consumption and the computational overhead. However, as the dimensions of the actions become larger, the action space of DQN-based approaches will have scalability limitations. Therefore, we propose the Branching Dueling Q-Network Resource Allocation (BDQ-RA) algorithm to address the problem of scalability limitations. In this article, we consider the profit between the earnings of computational tasks and the weight cost as the reward. Simulation results show that our algorithm can reduce the action space to one ninth and improve the reward about 12% compared with DQN solutions. © 2022 IEEE.

关键词： Blockchain

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An Alternative Algorithm for the Symmetry Classification of Ordinary Differential Equations

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Sound & Vibration 2022年第1期56卷 65-76页

作者： Yi Tian Jing Pang College of Data Science and Application Inner Mongolia University of TechnologyHohhot010080China Inner Mongolia Autonomous Region Engineering and Technology Research Center of Big Data Based Software Service Hohhot010080China College of Science Inner Mongolia University of TechnologyHohhot010051China Inner Mongolia Key Laboratory of Statistical Analysis Theory for Life Data and Neural Network Modeling Inner Mongolia University of TechnologyHohhot010051China

This is the first paper on symmetry classification for ordinary differential equations(ODEs)based on Wu’s *** carry out symmetry classification of two ODEs,named the generalizations of the Kummer-Schwarz equations which involving arbitrary ***,Lie algorithm is used to give the determining equations of symmetry for the given equations,which involving arbitrary ***,differential form Wu’s method is used to decompose determining equations into a union of a series of zero sets of differential characteristic sets,which are easy to be solved *** branch of the decomposition yields a class of symmetries and associated *** algorithm makes the classification become direct and *** Dimitrov Bozhkov,and Pammela Ramos da Conceição have used the Lie algorithm to give the symmetry classifications of the equations talked in this paper in *** this paper,we can find that the differential form Wu’s method for symmetry classification of ODEs with arbitrary function(parameter)is effective,and is an alternative method.

关键词： Kummer-Schwarz equation ordinary differential equations(ODEs) differential form Wu’s method

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Vehicle Tracking Method Based on Pyramid Layered Optical Flow Network 22

Vehicle Tracking Method Based on Pyramid Layered Optical Flo...

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4th World Symposium on software engineering, WSSE 2022

作者： Cheng, Jingya Ma, Zhiqiang Bao, Caijilahu Gao, Wenjun Li, Leixiao Wang, Hongbin College of Data Science and Application Inner Mongolia University of Technology China Inner Mongolia Autonomous Region Engineering Technology Research Centre of Big Data Based Software Service China

ISBN: (纸本)9781450396950

In order to solve the problem that the vehicles associated with the track cannot be uniquely identified when tracking a moving vehicle by MOT approaches in computer vision field, a moving vehicle tracking method based on pyramid hierarchical optical flow network was proposed. The Pyramid Optical Flow Net (POFN) was used to estimate the motion of moving vehicles, and the optical flow map containing the movement information of vehicles was obtained. The action refined net was designed to identify the optical flow map and estimate the more accurate position of vehicles. The motion information of the vehicle was correlated with the Intersection over Union (IOU), and the cosine similarity algorithm was used to verify the correlation of the appearance information of the vehicle boundary frame after the correlation of the motion information, so as to complete the vehicle tracking. The test results of UA-DETRAC data set show that the number of track ID switching of POFN model is less than that of current advanced tracking methods. © 2022 ACM.

关键词： Optical flows

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