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arXiv 2020年

作者： Wang, Yida Ke, Pei Zheng, Yinhe Huang, Kaili Jiang, Yong Zhu, Xiaoyan Huang, Minlie Tsinghua Shenzhen International Graduate School Tsinghua University China Institute for Artifical Intelligence State Key Lab of Intelligent Technology and Systems Beijing National Research Center for Information Science and Technology Department of Computer Science and Technology Tsinghua University Beijing China Beijing China

The advancements of neural dialogue generation models show promising results on modeling short-text conversations. However, training such models usually needs a large-scale high-quality dialogue corpus, which is hard to access. In this paper, we present a large-scale cleaned Chinese conversation dataset LCCC, which contains a base version (6.8 million dialogues) and a large version (12.0 million dialogues). The quality of our dataset is ensured by a rigorous data cleaning pipeline, which is built based on a set of rules and a classifier that is trained on manually annotated 110K dialogue pairs. We also release pre-training dialogue models which are trained on LCCC-base and LCCC-large respectively. The cleaned dataset and the pre-training models will facilitate the research of short-text conversation modeling. All the models and datasets are available at https://***/thu-coai/CDial-GPT. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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Large language models in urban planning

Nature Cities

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Nature Cities 2025年 1-8页

作者： Xinyu Fu Chaosu Li Steven Jige Quan Tan Yigitcanlar David Wasserman Environmental Planning Program University of Waikato Hamilton New Zealand Urban Governance and Design Thrust The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China Environmental Planning Institute Graduate School of Environmental Studies Seoul National University Seoul South Korea Graduate School of Environmental Studies Seoul National University Seoul South Korea City 4.0 Lab School of Architecture and Built Environment Queensland University of Technology Brisbane Queensland Australia Civic Analytics Discipline Alta Planning + Design Seattle WA USA

The advanced computational capabilities of artificial intelligence, particularly large language models such as OpenAI’s ChatGPT, offer great potential for addressing the complex developmental challenges faced by cities globally. These challenges are those that traditional urban planning methods often struggle to tackle. We explore how large language models can be leveraged to automate and support various urban planning tasks, providing nuanced computational support and advanced analytical capabilities. We highlight potential applications throughout the planning process and discuss the barriers and challenges involved. By setting a research agenda, we aim to foster the integration of artificial intelligence in urban planning, enhancing the field’s ability to create positive, inclusive and effective urban solutions.

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Analyzing the Factors that Influence Learning Experience through Game Based Learning using Visual Novel Game for Learning Pancasila

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Procedia computer science 2019年 157卷 353-359页

作者： J Andrew S Henry A.N. Yudhisthira Yulyani Arifin Syarifah Diana Permai Game Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Statistics Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480

Games are not only used as a means of entertainment but also can be used as a learning tool. Attractive learning tools can increase the interest of young people or millennial generations, especially for learning conceptual or too theoretical. Pancasila as the basis of the Indonesian nation’s life view needs to be understood by the Indonesian people, especially the younger generation. But the way of learning about the Pancasila is currently too theoretical and does not attract young people. Therefore, by making the game that is liked and favorited by young people today is used to study the Pancasila. Through this research the author wants to analyze the factors that can affect learning experience using game-based learning. Researchers use multiple linear regression methods to analyze these factors. The game prototype developed in this study uses the visual novel genre which has an interesting story. There are 101 respondents as samples. The analysis showed that there are two factors that influence game-based learning, namely the gameplay factor and the story of the game. So, it can be concluded that for developing game based learning we need to pay attention to these two factors and through this visual novel prototype game, it can be concluded that the prototype of this game has an interesting game play and a story that is easy to understand.

关键词： Game Based Learning Gamification Visual Novel Pancasila Regression Analysis

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Human Emergency Behaviour Characteristics in Large-scale Emergencies Based on Mobile Communication Data

Research Square

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Research Square 2021年

作者： Wei, XiaoLin Luo, Yi Chen, ZhenZhen Zhao, XuMing Li, HongYan Wang, Tao Yu, Wei Zhu, Lin Liu, FangYuan Zhejiang Yuexiu University Shaoxing China School of International Business Zhejiang Yuexiu University Shaoxing China Shaoxing Key Laboratory for Smart Society Monitoring Prevention Control Shaoxing China Library Zhejiang Yuexiu University Shaoxing China Department of Computer Science and Engineering Shaoxing University Shaoxing China College of Economic and Management Zhejiang University of Science and Technology Hangzhou China UCSI Graduate Business School UCSI University Kuala Lumpur Malaysia

The characteristics of human emergency behaviour under the emergency are a crucial scientific issue in basic emergency management research. The analysis of time dynamic aspects of human behaviour based on electronic footprint data provides a new method for quantitative investigation of this problem. Previous studies generally assumed that human behaviours were randomly distributed in time, but few studies studied the impact of emergencies and carried out prediction methods through social media data. Using mobile QQ space communication data, this paper from four kinds of emergencies and one kind of conventional event data, digging out the statistical characteristic on the time dimension of human communication behaviour, and in case of any emergencies, such as public security mode of evolution, to explore intrinsic emergency regularity of the impact of human communication behaviour model and further predict human behaviour characteristics. We found that the communication peaks accompanying an emergency are local in time, resulting in a communication avalanche that importantly engages eyewitness social networks. In mobile QQ space communication, the probability distribution of the interval time of the Posting behaviour sequence shows the statistical characteristics of power-law and approximate exponential tail. Compared with most of the typical Posting behaviour, the probability distribution of the interval time of the Posting behaviour sequence is higher. At the same time, the mnemonic is lower than most of the typical Posting behaviour, with a weak anti- mnemonic. These result are theoretically helpful in understanding the regularity of the impact of emergencies on human communication behaviour patterns and have potential application value in predicting the impact degree of crises and the analysis and classification of human social attributes. © 2021, CC BY.

关键词： Time series analysis

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Muscle Vectors as Temporally Dense "Labels"

Muscle Vectors as Temporally Dense "Labels"

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International Joint Conference on Neural Networks (IJCNN)

作者： Xiang Wu Juyang Weng School of Automation Nanjing University of Science and Technology Nanjing China Department of Computer Science and Engineering Michigan State University East Lansing MI USA Cognitive Science Program Michigan State University East Lansing MI USA Neuroscience Program Michigan State University East Lansing MI USA

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

Consider a human who interacts with the physical world to autonomously learn in a task non-specific way through lifetime. It seems obvious that the fully autonomous learner does not have the luxury to have the mother to provide temporally dense state labels, but the context/state at every frame is beneficial (e.g., to generate attention for the next frame). How can we enable the learner to generate frame-wise contexts/states on the fly? Our past work on Developmental Network (DN-1) has shown that frame-wise state labels (e.g., stages within a phoneme) are useful to generate temporally sparse label (the type of the phoneme). However, such dense and sparse labels were handcrafted from a static data set, using human identified frame-wise equivalence. In this paper, we study a conceptually challenging problem - how to enable an autonomous learner to generate frame-wise states autonomously without human handcrafting dense labels at all. We propose that frame-wise muscle actions (e.g., producing a sound) are not only temporally dense and high-dimensional, but also natural as dense labels. However, it is unknown how a neural network can use such high-dimensional vectors as dense labels. In this work, we provide a model for this new issue and experiment with Developmental Network-2 (DN-2) for imitation of audio sequences. Our experimental results showed DN-2 can successfully emerge high-dimensional real-valued vector actions. These actions provide DN-2 with frame-wise temporal context information. This work corresponds to a key step toward our goal to enable the agent to fully autonomously generate frame-wise actions without human-provided dense labels and with only a few human-provided sparse labels.

关键词： Task analysis Turing machines Hidden Markov models Neurons Robot sensing systems Bayes methods Natural languages

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Autonomous programming for General Purposes: Theory and Experiments

Autonomous Programming for General Purposes: Theory and Expe...

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International Joint Conference on Neural Networks (IJCNN)

作者： Juyang Weng Zejia Zheng Xiang Wu Juan Castro-Garcia Department of Computer Science and Engineering Michigan State University East Lansing MI USA Cognitive Science Program Michigan State University East Lansing MI USA Neuroscience Program Michigan State University East Lansing MI USA School of Automation Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

Since the birth of AI, symbols have been well accepted as an abstract representation for intelligent systems, even when neural networks are employed. This theoretical work shows the following new methods: (1) Symbols are probably not used by biological brains because their behaviors are not limited by symbols. (2) Autonomous programming For General Purposes (APFGP) is necessary for scaling-up AI to animal level intelligence. "Autonomous " means inside the skull (or network). (3) A Developmental Network (DN) performs APFGP by learning a super Turing machine, called Grounded, Emergent, Natural, Incremental, Skull-closed, Attentive, Motivated, and Abstractive (GENISAMA) Turing machine. (4) A DN is free of any central controller (e.g., Master Map, convolution, or error back-propagation). (5) The GENISAMA DN does APFGP without using symbols. Experiments are reported for vision guided navigation, auditory recognition, and natural language learning. This is the first conference paper on APFGP.

关键词： Task analysis Turing machines Brain modeling Artificial intelligence programming Biological neural networks

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First measurement of the strange axial coupling constant using neutral-current quasielastic interactions of atmospheric neutrinos at KamLAND

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Physical Review D 2023年第7期107卷 072006-072006页

作者： S. Abe S. Asami M. Eizuka S. Futagi A. Gando Y. Gando T. Gima A. Goto T. Hachiya K. Hata K. Ichimura S. Ieki H. Ikeda K. Inoue K. Ishidoshiro Y. Kamei N. Kawada Y. Kishimoto M. Koga M. Kurasawa T. Mitsui H. Miyake T. Nakahata K. Nakamura R. Nakamura H. Ozaki T. Sakai I. Shimizu J. Shirai K. Shiraishi A. Suzuki Y. Suzuki A. Takeuchi K. Tamae H. Watanabe Y. Yoshida S. Obara A. K. Ichikawa S. Yoshida S. Umehara K. Fushimi K. Kotera Y. Urano B. E. Berger B. K. Fujikawa J. G. Learned J. Maricic S. N. Axani Z. Fu J. Smolsky L. A. Winslow Y. Efremenko H. J. Karwowski D. M. Markoff W. Tornow J. A. Detwiler S. Enomoto M. P. Decowski C. Grant A. Li H. Song S. Dell’Oro T. O’Donnell Research Center for Neutrino Science Tohoku University Sendai 980-8578 Japan Department of Human Science Obihiro University of Agriculture and Veterinary Medicine Obihiro 080-8555 Japan Kavli Institute for the Physics and Mathematics of the Universe (WPI) The University of Tokyo Institutes for Advanced Study The University of Tokyo Kashiwa Chiba 277-8583 Japan Graduate Program on Physics for the Universe Tohoku University Sendai 980-8578 Japan Frontier Research Institute for Interdisciplinary Sciences Tohoku University Sendai 980-8578 Japan Department of Physics Tohoku University Sendai 980-8578 Japan Graduate School of Science Osaka University Toyonaka Osaka 560-0043 Japan Research Center for Nuclear Physics Osaka University Ibaraki Osaka 567-0047 Japan Department of Physics Tokushima University Tokushima 770-8506 Japan Graduate School of Integrated Arts and Sciences Tokushima University Tokushima 770-8502 Japan Nuclear Science Division Lawrence Berkeley National Laboratory Berkeley California 94720 USA Department of Physics and Astronomy University of Hawaii at Manoa Honolulu Hawaii 96822 USA University of Delaware Newark Delaware 19716 USA Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Department of Physics and Astronomy University of Tennessee Knoxville Tennessee 37996 USA Triangle Universities Nuclear Laboratory Durham North Carolina 27708 USA Physics Departments at Duke University Durham North Carolina 27708 USA North Carolina Central University Durham North Carolina 27707 USA and The University of North Carolina at Chapel Hill Chapel Hill North Carolina 27599 USA Center for Experimental Nuclear Physics and Astrophysics University of Washington Seattle Washington 98195 USA Nikhef and the University of Amsterdam Science Park Amsterdam The Netherlands Boston University Boston Massachusetts 02215 USA Center for Neutrino Physics Virginia Polytechnic Institute and State University Blacksburg Virginia 24061 US

We report a measurement of the strange axial coupling constant gAs using atmospheric neutrino data at KamLAND. This constant is a component of the axial form factor of the neutral-current quasielastic (NCQE) interaction. The value of gAs significantly changes the ratio of proton and neutron NCQE cross sections. KamLAND is suitable for measuring NCQE interactions as it can detect nucleon recoils with low-energy thresholds and measure neutron multiplicity with high efficiency. KamLAND data, including the information on neutron multiplicity associated with the NCQE interactions, makes it possible to measure gAs with a suppressed dependence on the axial mass MA, which has not yet been determined. For a comprehensive prediction of the neutron emission associated with neutrino interactions, we establish a simulation of particle emission via nuclear deexcitation of C12, a process not considered in existing neutrino Monte Carlo event generators. Energy spectrum fitting for each neutron multiplicity gives gAs=−0.14−0.26+0.25, which is the most stringent limit obtained using NCQE interactions without MA constraints. The two-body current contribution considered in this analysis relies on a theoretically effective model and electron scattering experiments and requires future verification by direct measurements and future model improvement.

关键词： Neutrinos

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Improvement of the ungrounded force display using grooved cam 17

Improvement of the ungrounded force display using grooved ca...

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17th IEEE International Conference on Industrial Informatics, INDIN 2019

作者： Kakuda, Emi Yokota, Sho Matsumoto, Akihiro Chugo, Daisuke Hashimoto, Hiroshi Toro University Graduate School of Science and Eng Kawagoe Japan Toyo University Dept. of Mechanical Engineering Kawagoe Japan Kwansei Gakuin University School of Science and Technology Hyogo Japan Advanced Institute of Industrial Technology Master Program of Innovation for Design and Eng. Tokyo Japan

ISBN: (纸本)9781728129273

This paper proposes the ungrounded force display using the grooved cam. Using the human illusion, the user can feel the force in the one direction when they use proposed device. It is mainly configured the cam and the slider. The slider follows the cam and moves translational movement. In this paper, we create the new prototype which solving the problems that emerged from the previous prototype. We evaluate the device using the two experiments. One is the subjective evaluation. Second one is the force presentation from the new prototype as the quantitative evaluation. We could solve problems of previous prototype by changing the cam type from plate to groove and blushless motor, and the new one produce the force more clearly than the previous prototype. © 2019 IEEE.

关键词： Navigation systems

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On Performance Prediction of Big Data Transfer in High-performance Networks

On Performance Prediction of Big Data Transfer in High-perfo...

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IEEE International Conference on Communications (ICC)

作者： Wuji Liu Daqing Yun Chase Q. Wu Nageswara S.V. Rao Aiqin Hou Wei Shen Department of Computer Science New Jersey Institute of Technology Newark NJ USA Computer and Information Sciences Program Harrisburg University Harrisburg PA USA Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak Ridge TN USA School of Information Science and Technology Northwest University Xi’an Shaanxi China School of Informatics Science and Technology Zhejiang Sci-Tech University Hangzhou Zhejiang China

ISBN: (数字)9781728150895

ISBN: (纸本)9781728150901

Big data generated by large-scale scientific and industrial applications need to be transferred between different geographical locations for remote storage, processing, and analysis. High-speed dedicated connections provisioned in High-performance Networks (HPNs) are increasingly utilized to carry out such big data transfer. HPN management highly relies on an important capability of performance (mainly throughput) prediction to reserve sufficient bandwidth and meanwhile avoid over-provisioning that may result in unnecessary resource waste. This capability is critical to improving the resource (mainly bandwidth) utilization of dedicated connections and meeting various user requests for data transfer. Conventional methods conduct performance prediction by fitting prior observed transfer history with predefined loss functions, without considering unobservable latent factors such as competing loads on end hosts. Such latent factors also have a significant impact on the application-level data transfer performance, which may result in an inaccurate prediction model. In this paper, we first investigate the impact of latent factors and propose a clustering-based method to eliminate their negative impact on performance prediction. We then develop a robust machine learning-based performance predictor by: i) incorporating the proposed latent factor elimination method into data preprocessing, and ii) adopting a customized domain guided loss function. Extensive experimental results show that our predictor achieves significantly higher prediction accuracy than several other state-of-the-art methods.

关键词： Data transfer Big Data Throughput Bandwidth Measurement Predictive models Protocols

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Self-Supervised WiFi-Based Identity Recognition in Multi-User Smart Environments

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Sensors 2025年第10期25卷 3108-3108页

作者： Rizk, Hamada Elmogy, Ahmed Computers & Control Engineering Department Faculty of Engineering Tanta University Tanta31527 Egypt Graduate School of Information Science and Technology Osaka University Osaka565-0871 Japan RIKEN Center for Computational Science Kobe650-0047 Japan Faculty of Computer Engineering & Sciences Prince Sattam Ibn Abdelaziz University Alkharj16273 Saudi Arabia Faculty of Engineering Tanta University Tanta31527 Egypt

The deployment of autonomous AI agents in smart environments has accelerated the need for accurate and privacy-preserving human identification. Traditional vision-based solutions, while effective in capturing spatial and contextual information, often face challenges related to high deployment costs, privacy concerns, and susceptibility to environmental variations. To address these limitations, we propose IdentiFi, a novel AI-driven human identification system that leverages WiFi-based wireless sensing and contrastive learning techniques. IdentiFi utilizes self-supervised and semi-supervised learning to extract robust, identity-specific representations from Channel State Information (CSI) data, effectively distinguishing between individuals even in dynamic, multi-occupant settings. The system’s temporal and contextual contrasting modules enhance its ability to model human motion and reduce multi-user interference, while class-aware contrastive learning minimizes the need for extensive labeled datasets. Extensive evaluations demonstrate that IdentiFi outperforms existing methods in terms of scalability, adaptability, and privacy preservation, making it highly suitable for AI agents in smart homes, healthcare facilities, security systems, and personalized services. © 2025 by the authors.

关键词： Self-supervised learning

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