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An Interview system Based on Large Language Models and Multi-Agent Interactions

An Interview System Based on Large Language Models and Multi...

引用

International conference on Fuzzy systems and Knowledge Discovery (FSKD)

作者： Quan Xinyue Zhang Yao Xie Xiang Wang Ya School of information and Electronics Beijing Institute of Technology Beijing China Jiangsu Automation Research Institute Lianyungang

ISBN: (数字)9798350356328

ISBN: (纸本)9798350356335

To address the increasing demand for interviews, we have proposed an intelligent interview system. this system has the capability to generate interview questions and design interview processes. this system introduces a ChatGPT-based iterative enhancement algorithm for seed datasets to create a high-quality prompt-question dataset specific to the interview domain. through large-scale language model fine-tuning, the system can generate more accurate and expected interview questions, demonstrating significant performance improvements across multiple evaluation metrics. Additionally, the study introduces an innovative multi-agent interaction algorithm to enhance interview process efficiency and comprehensive information gathering. Experimental results indicate improvements in text coherence and contextual understanding with the fine-tuned model, and subjective testing confirms the system's fluency and practicality.

关键词： Measurement Training Large language models Computational modeling Chatbots Knowledge discovery Question generation Iterative algorithms Interviews Testing

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Cellular Traffic Prediction Based on CVAE and Transformer

Cellular Traffic Prediction Based on CVAE and Transformer

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International conference on Fuzzy systems and Knowledge Discovery (FSKD)

作者： Jianwu Li Yiguang Liu Qingyao Liu Zhixiao Ni Bin Liu Mingyue Qi School of Computer Science and Technology Beijing Institute of Technology Beijing China China Mobile Network Planning and Design Optimization R&D Center China Mobile Group Design Institute Co. Ltd. Beijing China School of Economics and Management Hebei University of Science and Technology Shijiazhuang Hebei China Hebei Yuesi Information Technology Co. Ltd. Shijiazhuang Hebei China

ISBN: (数字)9798350356328

ISBN: (纸本)9798350356335

Cellular traffic prediction is an important problem in the field of communication management. In this paper, a cellular traffic prediction method CTSTN based on spatio-temporal learning is designed combining Transformer and Conditional Variational Auto-Encoder (CVAE). this paper explores the application of CVAE in the spatial states modeling and generating of cellular traffic data, constructs a spatial states dataset from the raw dataset, and designs an S-CVAE module to learn spatial characteristics of traffic data. Meanwhile, the Transformer is used to model temporal features of cellular traffic data. We conduct experiments and analyses on CTSTN and its sub-modules on real datasets. the results show the effectiveness of applying CVAE to spatial status prediction and validate the accuracy of CTSTN predictions.

关键词： Accuracy Computational modeling Time series analysis Predictive models Transformers Knowledge discovery Data models Spatial databases Spatiotemporal phenomena Information technology

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Enhancing Emotion Perception in Sentiment Analysis through Large-Scale Language Models

Enhancing Emotion Perception in Sentiment Analysis through L...

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International conference on Fuzzy systems and Knowledge Discovery (FSKD)

作者： Qiang Li Feng Zhao Jianning Zheng Hui Xiang Hong Ouyang Linlin Zhao Yongyuan Chen Ting Guo State Grid Information and Telecommunication Group Co. Ltd State Grid Corporation of China Beijing China Beijing SGITG Accenture Information Technology Center Co. Ltd. State Grid Corporation of China Beijing China

ISBN: (数字)9798350356328

ISBN: (纸本)9798350356335

Traditional sentiment analysis methods often overlook the sensitivity to emotional changes in text, which limits their applicability in real-world scenarios. To address this issue, We presents a deep learning based Sentiment-Aware Analysis Model(SAAM), leveraging the powerful representation capability of large-scale language models, namely the sentiment-aware world model introduced herein, in conjunction with the sentiment-sensitive SAAM. through experimental validation, we demonstrate the effectiveness and performance advantages of the proposed algorithm in sentiment analysis tasks, highlighting the significant role of large-scale language models in sentiment-aware analysis. this approach provides new insights and methods for the field of sentiment analysis.

关键词： Analytical models Sentiment analysis Sensitivity Computational modeling Diversity reception Training data Knowledge discovery Data models optimization Fuzzy systems

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Problem of Improving the Environmental and Socio-Economic system of the Volga-Akhtuba Floodplain: Position Examination of Large Dam in the Volga River Based on Computational Fluid Dynamics

Problem of Improving the Environmental and Socio-Economic Sy...

引用

International conference on Control systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA)

作者： Anna Yu. Klikunova Ekaterina O. Agafonnikova Alexander V. Khoperskov Dept. Information Systems and Computer Modeling Volgograd State University Volgograd Russia

the system of hydroelectric power stations on the Volga River, built in the 20th century, completely changed the hydrological regime of the Volga River. the current over-regulation of the water flow is the cause of the accelerating degradation of the Volga-Akhtuba floodplain. Modern analysis of water availability indicates a critical point associated with the spring filling of the Akhtuba binnacle. Various projects suggest a solution to this problem. this study is aims at identifying the most optimal location of a large dam below the Volzhsky hydroelectric power stations. We use a new model of hydraulic resistance based on the combined effect of both the friction force in the Manning model and the turbulent viscosity. Our study involves varying of the dam’s location in a numerical shallow water model. We calculate the hydrological relationship between the release of water through the Volzhsky dam and the source of the Akhtuba, as a target function for analyzing the effectiveness of the project to construction such a dam.

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Brain State Classification Based on Magnetoencephalography via Hybrid Linear-Dot Attention Mechanisms

Brain State Classification Based on Magnetoencephalography v...

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International conference on Fuzzy systems and Knowledge Discovery (FSKD)

作者： Helong Zhao Yanling Yang Xufeng Yao University of Shanghai for Science and Technology Shanghai China Shanghai University of Medicine and Health Sciences Shanghai China

ISBN: (数字)9798350356328

ISBN: (纸本)9798350356335

the decoding of specific task activity patterns embedded in magnetoencephalography (MEG) is crucial for understanding brain function. As a kind of useful neuroimaging technology, MEG decoding plays a key role in Brain-Computer Interfaces (BCI). In our study, the public MEG data were downloaded from the Human Connectivity Group Project (HCP), and the cascade model based on Hybrid Linear-Dot Attention Mechanics was proposed to conduct the brain decoding. the performance of the proposed model was evaluated by conducting cross-topic multiclassification verification on the MEG dataset under different states of subjects. Learned from the experiment, the highest accuracy and the F1 score were 69% and 63% on dataset 1, 92% and 92% on dataset 2, respectively. the proposed model has been demonstrated to enhance the classification performance of the Cascade model, proving its efficiency and practicality in the classification of MEG data.

关键词： Neuroimaging Attention mechanisms Accuracy Magnetoencephalography Brain modeling Knowledge discovery Data models Hybrid power systems Decoding optimization

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Self-Triggered Model Predictive Control for Signal Temporal Logic Tasks

Self-Triggered Model Predictive Control for Signal Temporal ...

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IEEE International conference on Automation Science and Engineering (CASE)

作者： Junyue Huang Chuwei Wang Xinyi Yu Xiang Yin Department of Automation Shanghai Jiao Tong University Shanghai China Zhiyuan College Shanghai Jiao Tong University Thomas Lord Department of Computer Science University of Southern California Los Angeles CA USA

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

In this paper, we investigate the reactive control synthesis problem for discrete-time dynamic systems under signal temporal logic (STL) specifications. Our focus is on addressing this problem within the model predictive control (MPC) framework, which involves iteratively solving optimization problems at each time instant. Traditional MPC controllers for STL tasks necessitate sampling the system state at every time instant. To mitigate sensing costs and conserve communication bandwidth during sensor measurement transmission, we introduce a novel concept termed self-triggered MPC for STL tasks. Our proposed approach aims to reduce the overall sampling rate of the system, resulting in considerable energy savings. We provide case studies to demonstrate the effectiveness of the proposed algorithm.

关键词： Costs systematics Computer aided software engineering Energy conservation Prediction algorithms Sensors Logic Dynamical systems optimization Predictive control

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Design, Implementation and Testing of an EtherCAT-based Network for Multi-modal Distributed Sensing Architectures

Design, Implementation and Testing of an EtherCAT-based Netw...

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IEEE International conference on Automation Science and Engineering (CASE)

作者： Francesco Giovinazzo Alessandro Perri Marco Staiano Francesco Grella Marco Sartore Manuela Adami Riccardo Galletti Giorgio Cannata Department of Informatics Bioengineering Robotics and Systems Engineering (DIBRIS) Università di Genova Genova Italy ElbaTech Srl Marciana Italy

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

this paper introduces an EtherCAT-based communication infrastructure designed for large-area multi-modal sensor networks in robotic applications. the system addresses critical application-oriented challenges, such as bandwidth optimization, cable routing efficiency, network scalability and fault tolerance. the proposed architecture is experimentally validated with ProxySKIN, a distributed sensing technology integrating arrays of tactile and proximity sensors, designed to cover large areas of robotic manipulators. the implemented solution leverages a redundant EtherCAT network driven by custom-made miniaturized slave devices to collect comprehensive data regarding the robot’s surrounding space. Bench-test experiments conducted on over 6400 pressure sensors and 20 multi-zone proximity sensors show reliable data collection with minimal communication latency through a unique EtherCAT backbone.

关键词： Pressure sensors Scalability Robot sensing systems Manipulators Routing Real-time systems Sensors optimization Sensor arrays Testing

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optimization of First-Person Shooter Game Control Using Heart Rate Sensor 20th

Optimization of First-Person Shooter Game Control Using Hear...

引用

20th International conference on Entertainment Computing (ICEC)

作者： Zhou, Shuo Segawa, Norihisa Kyoto Sangyo Univ Kyoto Japan

ISBN: (纸本)9783030893934;9783030893941

As first-person shooter (FPS) games become highly competitive game genres, various supports are needed to improve the gaming environment. In the past, players have focused on gaming mouses and displays, which are expensive and challenging to select from. To understand the influence of the player's tension on FPS game control, we developed a system that can automatically adjust the speed of the mouse according to the player's real-time heartbeat during the game. In this study, we developed and evaluated a system to improve the FPS game environment based on the heart rate.

关键词： Game control Heart rate Emotion Sensor First person shooter game Control optimization system Mouse sensitivity Arduino

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Efficient Stimuli Generation using Reinforcement Learning in Design Verification

Efficient Stimuli Generation using Reinforcement Learning in...

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International conference on Synthesis, modeling, Analysis and Simulation Methods and Applications to Circuit Design (SMACD)

作者： Deepak Narayan Gadde thomas Nalapat Aman Kumar Djones Lettnin Wolfgang Kunz Sebastian Simon Infineon Technologies Dresden GmbH & Co. KG Germany Infineon Technologies AG Germany Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau Germany

ISBN: (数字)9798350351927

ISBN: (纸本)9798350351934

the increasing design complexity of system-on-Chips (SoCs) has led to significant verification challenges, particularly in meeting coverage targets within a timely manner. At present, coverage closure is heavily dependent on constrained random and coverage driven verification methodologies where the randomized stimuli are bounded to verify certain scenarios and to reach coverage goals. this process is said to be exhaustive and to consume a lot of project time. In this paper, a novel methodology is proposed to generate efficient stimuli with the help of Reinforcement Learning (RL) to reach the maximum code coverage of the Design Under Verification (DUV). Additionally, an automated framework is created using metamodeling to generate a systemVerilog testbench and an RL environment for any given design. the proposed approach is applied to various designs and the produced results proves that the RL agent provides effective stimuli to achieve code coverage faster in comparison with baseline random simulations. Furthermore, various RL agents and reward schemes are analyzed in our work.

关键词： Analytical models Codes Metamodeling Reinforcement learning system-on-chip Complexity theory Circuit synthesis Integrated circuit modeling

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Joint optimization of Wind Farm Layout Considering Yaw Control 8th

Joint Optimization of Wind Farm Layout Considering Yaw Cont...

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8th International conference on Life system modeling and Simulation, LSMS 2024 and 8th International conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2024

作者： Wang, Yiqun Jiang, Ruolin Chen, Yuanye Fang, Fang School of Control and Computer Engineering North China Electric Power University Beijing102206 China

ISBN: (纸本)9789819602247

As wind farms continue to grow in size, minimizing wake losses between turbines in layout design becomes increasingly important. Current wind farm layout strategies often focus on the performance of individual turbines without considering wake interactions. this study introduces a coordinated control approach that considers wake effects and demonstrates an improved yaw control results to reduce the impact of wake interactions. By implementing cooperative yaw control among turbines and using the NSGA-II algorithm, the study establishes a dual optimization problem of minimizing turbine spacing and maximizing power output, ultimately yielding the optimal layout for wind turbines. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Wind turbines

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