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Analysis of Semi-Supervised Text Clustering Algorithm on Marine Data

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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computer-Aided Architectural Design Optimization Based on BIM Technology

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Informatica (Slovenia) 2022年第3期46卷 323-332页

作者： Fan, Haiyan Goyal, Bhawna Ghafoor, Kayhan Zrar Shandong Polytechnic Shandong Jinan250104 China Department of ECE University Centre for Research and Development Chandigarh University Punjab Mohali140413 India Department of Computer Science Knowledge University Erbil44001 Iraq Department of Software and Informatics Engineering Salahaddin University-Erbil Erbil Iraq

This article addresses the problem of the non-circulation of information in each stage of architectural design. This paper explores the architectural design process based on the BIM platform and puts forward the structural design method based on the BIM platform. It carries out the seismic analysis of a high-rise building with a transfer floor structure and compares the analysis results with the structural analysis software commonly used by the current design institute. The results obtained for experimentation show that the period ratio, displacement ratio, and the first six modes calculated by the two methods in the modal analysis are consistent. The error between calculation results and PKPM calculation results is within a reasonable range. In the analysis of the mode decomposition response spectrum method, the seismic forces in X and Y directions, floor shear, overturning moment, floor average displacement, and displacement angle obtained by the two models are compared respectively. The analysis results of the two methods accord with the mechanical characteristics of the transfer floor structure, and the calculation error is within the allowable range. The structural design based on the BIM platform has the advantages of high visualization, parameter-driven component size, and high model accuracy, improving design drawing efficiency. © 2022 Slovene Society Informatika. All rights reserved.

关键词： Architectural design

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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

引用

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

引用

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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Exploiting Microblog Conversation Structures to Detect Rumors

Exploiting Microblog Conversation Structures to Detect Rumor...

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28th International Conference on Computational Linguistics, COLING 2020

作者： Li, Jiawen Sujana, Yudianto Kao, Hung-Yu Intelligent Knowledge Management Lab Department of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan

ISBN: (纸本)9781952148279

As one of the most popular social media platforms, Twitter has become a primary source of information for many people. Unfortunately, both valid information and rumors are propagated on Twitter due to the lack of an automatic information verification system. Twitter users communicate by replying to other users' messages, forming a conversation structure. Using this structure, users can decide whether the information in the source tweet is a rumor by reading the tweet’s replies, which voice other users’ stances on the tweet. The majority of rumor detection researchers process such tweets based on time, ignoring the conversation structure. To reap the benefits of the Twitter conversation structure, we developed a model to detect rumors by modeling conversation structure as a graph. Thus, our model’s improved representation of the conversation structure enhances its rumor detection accuracy. The experimental results on two rumor datasets show that our model outperforms several baseline models, including a state-of-the-art model. © 2020 COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference. All rights reserved.

关键词： Computational linguistics

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EPRD: Exploiting prior knowledge for evidence-providing automatic rumor detection

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Neurocomputing 2024年 563卷

作者： Li, Jiawen Li, Ronghui Ni, Shiwen Kao, Hung-Yu Naval Architecture and Shipping College Guangdong Ocean University Guangdong Zhanjiang524000 China Technical Research Center for Ship Intelligence and Safety Engineering of Guangdong Province Guangdong Zhanjiang524000 China Intelligent Knowledge Management Lab National Cheng Kung University Tainan Taiwan Department of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

With the prevalence of social media platforms, rumors have been a serious social problem. Notably, existing rumor detection methods simply provide detection labels while ignoring their explanation. However, illustrating the reasons why a suspicious statement is a rumor is essential. To address this realistic scenario, we propose a novel Evidence-Providing Rumor Detection model called EPRD. EPRD incorporates a wide variety of information from both prior knowledge sources and current comments. It also learns bilaterally friendly representations for interpretable rumor detection. EPRD first retrieves evidence from prior knowledge sources and checks the relationship between the given statement and its evidence. Our model then constructs two heterogeneous graph objects to simulate the propagation layout of the current comments and evidence relationships. Finally, EPRD integrates the GraphSAGE component and attention mechanism to detect rumors. To the best of our knowledge, we propose the first model that incorporates prior knowledge to verify rumors and boost credibility. Experiments on two real-world Twitter datasets demonstrate that EPRD consistently exhibits the best rumor detection performance. Moreover, EPRD outperforms other baselines in the early rumor detection task. © 2023

关键词： Graph neural networks

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