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arXiv 2023年

作者： Zhang, Zijian Zhao, Xiangyu Miao, Hao Zhang, Chunxu Zhao, Hongwei Zhang, Junbo College of Computer Science and Technology Jilin University China School of Data Science City University of Hong Kong Hong Kong Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University China Department of Computer Science Aalborg University Denmark JD Intelligent Cities Research China JD iCity JD Technology China Hong Kong Institute for Data Science City University of Hong Kong Hong Kong

Spatio-temporal prediction plays a critical role in smart city construction. Jointly modeling multiple spatio-temporal tasks can further promote an intelligent city life by integrating their inseparable relationship. However, existing studies fail to address this joint learning problem well, which generally solve tasks individually or a fixed task combination. The challenges lie in the tangled relation between different properties, the demand for supporting flexible combinations of tasks and the complex spatio-temporal dependency. To cope with the problems above, we propose an Automated Spatio-Temporal multi-task Learning (AutoSTL) method to handle multiple spatio-temporal tasks jointly. Firstly, we propose a scalable architecture consisting of advanced spatio-temporal operations to exploit the complicated dependency. Shared modules and feature fusion mechanism are incorporated to further capture the intrinsic relationship between tasks. Furthermore, our model automatically allocates the operations and fusion weight. Extensive experiments on benchmark datasets verified that our model achieves state-of-the-art performance. As we can know, AutoSTL is the first automated spatio-temporal multi-task learning method. Copyright © 2023, The Authors. All rights reserved.

关键词： Smart city

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Performance of magnetic dipole contribution on electromagnetic Ellis tetra hybrid nanofluid with the applications of surface tension gradient: A Xue model exploration

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International Journal of Thermofluids 2024年 24卷

作者： Abbas, Munawar Marzouki, Riadh Ameen, Hawzhen Fateh M. Dilsora, Abduvalieva Younis, Jihad Akgül, Ali Department of Mathematics The Islamia University of Bahawalpur Bahawalpur63100 Pakistan Department of Chemistry College of Science King Khalid University P.O. Box 9004 Abha61413 Saudi Arabia Fuel and Energy Techniques Engineering College of Oil and Gas Techniques Engineering Northern Technical University Kirkuk Iraq Department of Petroleum Engineering College of Engineering Knowledge University Erbil Iraq Department of Mathematics and Information Technologies Tashkent State Pedagogical University Bunyodkor avenue 27 Tashkent100070 Uzbekistan Department of Mathematics Aden University P.O.Box 6014 Aden Yemen Siirt University Art and Science Faculty Department of Mathematics Siirt56100 Turkey Department of Computer Engineering Biruni University Istanbul Topkapı34010 Turkey Near East University Mathematics Research Center Department of Mathematics Near East Boulevard PC: 99138 Nicosia /Mersin 10 Turkey

The objective of this work is to examine the enhancement of thermal energy transfer in Ellis THNF (tetra hybrid nanofluid) flow with magnetic dipole permits on a vertical surface. Using the Xue thermal conductivity model, the THNF (tetra hybrid nanofluid) is organized according to the diffusion of TNPs (tetra nanoparticles) (Mgo, Cu, Ag, and TiO2)in the engine oil liquid. The flow, mass and heat transportation have been observed in the occurrence of the Marangoni convection impact and CattaneoChristove mass and heat flux model. In biomedical engineering, the model can also be used for targeted drug delivery and hyperthermia treatment, where precise temperature and fluid flow control are essential. Gradient-driven surface tension in order to enhance the possibility for enhancing surface-driven flows in microfluidic devices and material processing techniques, such as welding and crystal formation, where regulated mass transport and consistent temperature distribution are crucial, Marangoni convection is included. Using the Bvp4c, the obtained dimensionless equations are mathematically resolved. It is found that a magnetic dipole significantly increases the generation of the thermal energy field and exhibits an opposing trend with respect to the flow profile. The addition Marangoni convection factor increases the velocity distribution while decline the solutal and thermal distribution. The heat transfer rate is increased by 19.71% for the tetra hybrid nanofluid and 13.43% for the trihybrid nanofluid when the nanoparticles volume fraction is improved from 0.01 to 0.04. © 2024 The Authors

关键词： Hyperthermia therapy

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Predicting ionic conductivity in solids from the machine-learned potential energy landscape

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Physical Review Research 2025年第2期7卷 023167-023167页

作者： Artem Maevskiy Alexandra Carvalho Emil Sataev Volha Turchyna Keian Noori Aleksandr Rodin A. H. Castro Neto Andrey Ustyuzhanin National University of Singapore Institute for Functional Intelligent Materials NUS S9 Building 4 Science Drive 2 117544 Singapore National University of Singapore Centre for Advanced 2D Materials 6 Science Drive 2 117546 Singapore HSE University Faculty of Computer Science Pokrovsky Boulevard 11 109028 Moscow Russian Federation Yale-NUS College 16 College Avenue West 138527 Singapore National University of Singapore Department of Materials Science Engineering 9 Engineering Drive 1 117575 Singapore National University of Singapore Department of Physics 2 Science Drive 3 117551 Singapore Constructor Knowledge Labs Campus Ring 1 28759 Bremen Germany Constructor University Campus Ring 1 28759 Bremen Germany

Discovering new superionic materials is essential for advancing solid-state batteries, which offer improved energy density and safety compared to traditional lithium-ion batteries with liquid electrolytes. Conventional computational methods for identifying such materials are resource-intensive and not easily scalable. Recently, universal interatomic potential models have been developed using equivariant graph neural networks. These models are trained on extensive datasets of first-principles force and energy calculations. One can achieve significant computational advantages by leveraging them as the foundation for traditional methods of assessing the ionic conductivity, such as molecular dynamics or nudged elastic band techniques. However, the generalization error from model inference on diverse atomic structures arising in such calculations can compromise the reliability of the results. In this work, we propose an approach for the quick and reliable screening of ionic conductors through the analysis of a universal interatomic potential. Our method incorporates a set of heuristic structure descriptors that effectively employ the rich knowledge of the underlying model while requiring minimal generalization capabilities. Using our descriptors, we rank lithium-containing materials in the Materials Project database according to their expected ionic conductivity. Eight out of the ten highest-ranked materials are confirmed to be superionic at room temperature in first-principles calculations. Notably, our method achieves a speed-up factor of approximately 50 compared to molecular dynamics driven by a machine-learning potential, and it is at least 3000 times faster compared to first-principles molecular dynamics.

关键词： Lithium-ion batteries

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Analysis of Semi-Supervised Text Clustering Algorithm on Marine Data

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computers, Materials & Continua 2020年第7期64卷 207-216页

作者： Yu Jiang Dengwen Yu Mingzhao Zhao Hongtao Bai Chong Wang Lili He College of Computer Science and Technology Jilin UniversityChangchun130012China A Key Laboratory of Symbolic Computation and Knowledge Engineering Jilin UniversityChangchun130012China Department of Engineering Mechanics State Marine Technical University of St.PetersburgSt.Petersburg190008Russia

Semi-supervised clustering improves learning performance as long as it uses a small number of labeled samples to assist un-tagged samples for *** paper implements and compares unsupervised and semi-supervised clustering analysis of BOA-Argo ocean text *** K-Means and Affinity Propagation(AP)are two classical clustering *** Election-AP algorithm is proposed to handle the final cluster number in AP clustering as it has proved to be difficult to control in a suitable ***-supervised samples thermocline data in the BOA-Argo dataset according to the thermocline standard definition,and use this data for semi-supervised cluster *** semi-supervised clustering algorithms were chosen for comparison of learning performance:Constrained-K-Means,Seeded-K-Means,SAP(Semi-supervised Affinity Propagation),LSAP(Loose Seed AP)and CSAP(Compact Seed AP).In order to adapt the single label,this paper improves the above algorithms to SCKM(improved Constrained-K-Means),SSKM(improved Seeded-K-Means),and SSAP(improved Semi-supervised Affinity Propagationg)to perform semi-supervised clustering analysis on the data.A DSAP(Double Seed AP)semi-supervised clustering algorithm based on compact seeds is proposed as the experimental data shows that DSAP has a better clustering *** unsupervised and semi-supervised clustering results are used to analyze the potential patterns of marine data.

关键词： Unsupervised learning semi-supervised learning text clustering

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Summarising Historical Text in Modern Languages

arXiv

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arXiv 2021年

作者： Peng, Xutan Zheng, Yi Lin, Chenghua Siddharthan, Advaith Department of Computer Science The University of Sheffield United Kingdom School of Computer Science and Engineering Beihang University China Knowledge Media Institute The Open University United Kingdom

We introduce the task of historical text summarisation, where documents in historical forms of a language are summarised in the corresponding modern language. This is a fundamentally important routine to historians and digital humanities researchers but has never been automated. We compile a high-quality gold-standard text summarisation dataset, which consists of historical German and Chinese news from hundreds of years ago summarised in modern German or Chinese. Based on cross-lingual transfer learning techniques, we propose a summarisation model that can be trained even with no cross-lingual (historical to modern) parallel data, and further benchmark it against state-of-the-art algorithms. We report automatic and human evaluations that distinguish the historic to modern language summarisation task from standard cross-lingual summarisation (i.e., modern to modern language), highlight the distinctness and value of our dataset, and demonstrate that our transfer learning approach outperforms standard cross-lingual benchmarks on this task. Copyright © 2021, The Authors. All rights reserved.

关键词： Text processing

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Deep Reinforcement Learning-Based LSTM Model for Traffic Flow Forecasting in Internet of Vehicles

Deep Reinforcement Learning-Based LSTM Model for Traffic Flo...

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2021中国智能自动化大会(CIAC 2021)

作者： Zekuan Chen Xiong Luo Ting Wang Weiping Wang Wenbing Zhao School of Computer and Communication Engineering University of Science and Technology Beijing Beijing Key Laboratory of Knowledge Engineering for Materials Science Shunde Graduate School University of Science and Technology Beijing Department of Electrical Engineering and Computer Science Cleveland State University

Nowadays many large cities often suffer from road congestion. Then,it is necessary to improve the working efficiency of urban road network with the help of computational intelligence technologies, within the Internet of vehicles(Io V) environment. We are committed to designing traffic flow prediction methods through the use of machine learning models. In this paper, we predict the traffic flow and traffic situation of the selected road segment. Specifically, we propose a deep reinforcement learning(DRL)-based long short-term memory(LSTM)model to predict the traffic flow. On the basis of it, we characterize the traffic situation with fuzzy comprehensive evaluation(FCE)-based model. The experimental results show that the constructed DRL-based LSTM model can accurately predict the traffic flow data. Meanwhile, the traffic situation characterization model can represent the road traffic situation well.

关键词： Traffic flow forecasting Long short-term memory(LSTM) Deep reinforcement learning(DRL) Traffic situation Characterization model

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A design approach for traffic surveillance using real time computer vision for vehicle tracking

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AIP Conference Proceedings 2023年第1期2851卷

作者： Lakshman Kumar Md Samir Ansari Niharika Raj Pallavi Kumari Saibal Majumder Bijoy Kumar Mandal Department of Computer Science and Engineering NSHM Knowledge Campus Durgapur West Bengal India

In this paper, we are trying to make Surveillance Car using real time computer vision. Our model will focus on intelligence gathering and react according to prevention of crime or the investigation of crime with the help of feed which we are getting from image and the feed is then processed with the help of image processing. In this paper, we proposed a model to save time as we have a lack of time to be involved in society. The surveillance car is used as a robotic car, it can control the car through our web page from anywhere anytime. It can be used in parking if we are not able to come and do parking work on our own then we can control it by Web page. In this paper, we are going to make a camera surveillance car using a real time computer vision concept. We will capture images using a camera present on the car and send these images to our mobile phone using Web Socket through Wi-Fi connection. We will also control the car with the web page. We build a robot using a pre-built custom robot chassis. For movement DC geared motors are used along with small wheels to get the required torque. To generate the power to the motor driver module and ESP32-CAM we have used the battery for its objectives.

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An empirical examination of balancing strategy for counterfactual estimation on time series 24

An empirical examination of balancing strategy for counterfa...

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Proceedings of the 41st International Conference on Machine Learning

作者： Qiang Huang Chuizheng Meng Defu Cao Biwei Huang Yi Chang Yan Liu School of Artificial Intelligence and International Center of Future Science Jilin University Changchun Jilin China Department of Computer Science University of Southern California California Los Angeles Halicioğlu Data Science Institute University of California San Diego San Diego California School of Artificial Intelligence and International Center of Future Science Jilin University Changchun Jilin China and Engineering Research Center of Knowledge-Driven Human-Machine Intelligence MOE Changchun Jilin China

Counterfactual estimation from observations represents a critical endeavor in numerous application fields, such as healthcare and finance, with the primary challenge being the mitigation of treatment bias. The balancing strategy aimed at reducing covariate disparities between different treatment groups serves as a universal solution. However, when it comes to the time series data, the effectiveness of balancing strategies remains an open question, with a thorough analysis of the robustness and applicability of balancing strategies still lacking. This paper revisits counterfactual estimation in the temporal setting and provides a brief overview of recent advancements in balancing strategies. More importantly, we conduct a critical empirical examination for the effectiveness of the balancing strategies within the realm of temporal counterfactual estimation in various settings on multiple datasets. Our findings could be of significant interest to researchers and practitioners and call for a reexamination of the balancing strategy in time series settings.

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Zero-knowledge Proof-based Practical Federated Learning on Blockchain

arXiv

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arXiv 2023年

作者： Xing, Zhibo Zhang, Zijian Li, Meng Liu, Jiamou Zhu, Liehuang Russello, Giovanni Asghar, Muhammad Rizwan The School of Computer Science The University of Auckland Auckland New Zealand The School of Cyberspace Science and Technology Beijing Institute of Technology Beijing100081 China Southeast Institute of Information Technology Beijing Institute of Technology Fujian351100 China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Anhui Hefei230601 China Anhui Province Key Laboratory of Industry Safety and Emergency Technology Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology China The Department of Computer Science University of Surrey The School of Computer Science The University of Auckland Auckland New Zealand

Since the concern of privacy leakage extremely discourages user participation in sharing data, federated learning has gradually become a promising technique for both academia and industry for achieving collaborative learning without leaking information about the local data. Unfortunately, most federated learning solutions cannot efficiently verify the execution of each participant's local machine learning model and protect the privacy of user data, simultaneously. In this article, we first propose a Zero-knowledge Proof-based Federated Learning (ZKP-FL) scheme on blockchain. It leverages zero-knowledge proof for both the computation of local data and the aggregation of local model parameters, aiming to verify the computation process without requiring the plaintext of the local data. We further propose a Practical ZKP-FL (PZKP-FL) scheme to support fraction and non-linear operations. Specifically, we explore a Fraction-Integer mapping function, and use Taylor expansion to efficiently handle non-linear operations while maintaining the accuracy of the federated learning model. We also analyze the security of PZKP-FL. Performance analysis demonstrates that the whole running time of the PZKP-FL scheme is approximately less than one minute in parallel execution. Copyright © 2023, The Authors. All rights reserved.

关键词： Blockchain

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A Survey on Fairness in Large Language Models

arXiv

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arXiv 2023年

作者： Li, Yingji Du, Mengnan Song, Rui Wang, Xin Wang, Ying College of Computer Science and Technology Jilin University Changchun130012 China Department of Data Science New Jersey Institute of Technology Newark United States School of Artificial Intelligence Jilin University Changchun130012 China Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Ministry of Education Changchun130012 China

Large Language Models (LLMs) have shown powerful performance and development prospects and are widely deployed in the real world. However, LLMs can capture social biases from unprocessed training data and propagate the biases to downstream tasks. Unfair LLM systems have undesirable social impacts and potential harms. In this paper, we provide a comprehensive review of related research on fairness in LLMs. Considering the influence of parameter magnitude and training paradigm on research strategy, we divide existing fairness research into oriented to medium-sized LLMs under pre-training and fine-tuning paradigms and oriented to large-sized LLMs under prompting paradigms. First, for medium-sized LLMs, we introduce evaluation metrics and debiasing methods from the perspectives of intrinsic bias and extrinsic bias, respectively. Then, for large-sized LLMs, we introduce recent fairness research, including fairness evaluation, reasons for bias, and debiasing methods. Finally, we discuss and provide insight on the challenges and future directions for the development of fairness in LLMs. Copyright © 2023, The Authors. All rights reserved.

关键词： Computational linguistics

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