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International Conference on Information Science and Control engineering (ICISCE)

作者： Hao Liu Zhe Wang Lan Huang Kangping Wang School of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering Jilin University Changchun China

Computer science labs in universities have different demands in cloud computing compared with typical IT companies. They differ greatly in software requirements and resource utilization. As an instance, we propose building a private cloud based on microservices with Docker and Kubernetes in our laboratory to match our labs’ typical *** private cloud proposed in this paper is composed of multiple components such as authentication, service gateway, distributed storage, knowledge management, code management, etc. Furthermore, some typical research software systems are deployed as microservices in the private cloud. To satisfy complex software environment requirements, a multi version microservice is implemented. In our microservices platform, users can submit their own microservices and manage these microservices’ life cycle through a web interface. In addition, the platform will automatically start and stop low frequency services to improve resource utilization rate. We use laboratory resources to conduct experiments and build a platform. With deploying the platform in our laboratory, it shows that the platform meets most of typical demands in research work and improves research efficiency.

关键词： Computer science Cloud computing Information science Control engineering Buildings Companies Logic gates

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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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Acupuncture and Tuina knowledge Graph for Ancient Literature of Traditional Chinese Medicine

Acupuncture and Tuina Knowledge Graph for Ancient Literature...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xiaosong Han Xiaoran Li Yanchun Liang Xinghao Wang Dong Xu Renchu Guan Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Computer Science and Technology Jilin University Changchun China Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Software Jilin University Changchun China Christopher S. Bond Life Sciences Center University of Missouri Columbia USA

ISBN: (纸本)9781665429825

The Traditional Chinese Medicine’s ancient literature recorded the massive medical theories and abundant medical experiences. To better understand and utilize, the knowledge from the literature, the Acupuncture and Tuina knowledge Graph is proposed in this paper. Meanwhile, a deep learning network is established for acupuncture and tuina-related entity recognition and entity-relationship extraction. Finally, the trained network is able to reach an 82%+ F1-score for NER and 70%+ F1-score for relationship extraction.

关键词： Deep learning Conferences Labeling Bioinformatics

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Efficient Self-Learning Evolutionary Neural Architecture Search

SSRN

引用

SSRN 2023年

作者： Qiu, Zhengzhong Bi, Wei Xu, Dong Guo, Hua Ge, Hongwei Liang, Yanchun Lee, Heow Pueh Wu, Chunguo Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Jilin Changchun130012 China YGSOFT INC Guangdong Zhuhai519085 China Department of Electrical Engineering and Computer Science Informatics Institute Christopher S. Bond Life Sciences Center University of Missouri ColumbiaMO65211 United States College of Computer Science and Technology Dalian University of Technology Liaoning Dalian116081 China School of Computer Science Zhuhai College of Science and Technology Guangdong Zhuhai519041 China Department of Mechanical Engineering National University of Singapore 9 Engineering Drive 1 117575 Singapore

The evolutionary algorithm has become a major method for neural architecture search recently. However, the fixed probability distribution adopted by the traditional evolutionary algorithm makes the algorithm unable to control the increase or decrease of the individual architecture size and thus cannot guarantee the lightweight and inference efficiency of the candidate architecture. In addition, the existing approaches cannot learn the optimal sampling probability distribution relevant to the specific problem from the empirical information accumulated through the search process. What’s more, the algorithm needs to evaluate the performance of all the individual architectures, which requires huge computing resources and time overhead. To overcome these problems and challenges, an Efficient Self-learning Evolutionary Neural Architecture Search method, called ESE-NAS, is presented in this paper. Firstly, we propose a Model Size Control module, which generates the probability distribution for sampling the type of mutation operators according to the current model size, so as to control both the node number of network architecture and the sparsity of links, therefore ensuring that neural architectures can remain compact and efficient as they evolve. Thereafter, a Mutation Candidate Credit Assignment method is proposed, which enables the algorithm to dynamically adjust the probability distribution for available types of node operations and directed links according to the empirical information from individuals’ performance evaluation, which can guide the evolutionary direction of neural architecture and shorten the first hitting time of the optimal architecture. Finally, a neural architecture performance predictor is formulated, and the accuracy of each individual architecture is predicted by using the simplified input features, to further improve the efficiency of neural architecture search. Experiments show that the ESE-NAS method proposed in this paper can learn a highly i

关键词： Evolutionary algorithms

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Revisiting the efficacy of weak consistencies: a study of forward checking

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Science China(Information Sciences) 2021年第7期64卷 233-235页

作者： Zhe LI Zhezhou YU Hongbo LI Jinsong GUO Zhanshan LI Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Computer Science and Technology Jilin University College of Information Science and Technology Northeast Normal University Department of Computer Science University of Oxford

Dear editor,Arc consistency (AC)[1] is currently the most popular local consistency for solving constraint satisfaction problems(CSP). To solve CSPs more efficiently, extensive work [2–4]has been done to find local c... 详细信息

关键词： Information Systems and Communication Service

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Neural architecture search based on the cartesian genetic programming

arXiv

引用

arXiv 2021年

作者： Wu, Xuan Zhang, Xiuyi Jia, Linhan Chen, Liang Liang, Yanchun Zhou, You Wu, Chunguo Key Laboratory of Symbolic Computation Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun130012 China College of Software Jilin University Changchun130012 China School of Computer Science Zhuhai College of Science and Technology Zhuhai519041 China

Neural architecture search (NAS) is a hot topic in the field of automated machine learning and outperforms humans in designing neural architectures on quite a few machine learning tasks. Motivated by the natural representation form of neural networks by Cartesian Genetic Programming (CGP), we propose an evolutionary approach of NAS based on CGP, called CGPNAS, to solve sentence classification task. To evolve the architectures under the framework of CGP, the operations such as convolution are identified as the types of function nodes of CGP, and the evolutionary operations are designed based on Evolutionary Strategy. The experimental results show that the searched architectures are comparable with the performance of human-designed architectures. We verify the ability of domain transfer of our evolved architectures and the transfer experimental results show that the accuracy deterioration is lower than 2-5%. Finally, the ablation study identifies the Attention function as the single key function node and the linear transformations along could keep the accuracy similar with the full evolved architectures, which is worthy of investigation in the future. Copyright © 2021, The Authors. All rights reserved.

关键词： Genetic algorithms

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Crossed-Time Delay Neural Network for Speaker Recognition

arXiv

引用

arXiv 2020年

作者： Chen, Liang Liang, Yanchun Shi, Xiaohu Zhou, You Wu, Chunguo Key Laboratory of Symbolic Computation Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun130012 China Zhuhai Laboratory of Key Laboratory of Symbol Computation Knowledge Engineering of Ministry of Education School of Computer Zhuhai College Jilin University Zhuhai519041 China

Time Delay Neural Network (TDNN) is a well-performing structure for deep neural network-based speaker recognition systems. In this paper we introduce a novel structure, named Crossed-Time Delay Neural Network (CTDNN) to enhance the performance of current TDNN for speaker recognition. Inspired by the multi-filters setting of convolution layers from convolution neural networks, we set multiple time delay units with different context size at the bottom layer and construct a multilayer parallel network. The proposed CTDNN gives significant improvements over original TDNN on both speaker verification and identification tasks. It outperforms in VoxCeleb1 dataset in verification experiment with a 2.6% absolute Equal Error Rate improvement. In few shots condition, CTDNN reaches 90.4% identification accuracy, which doubles the identification accuracy of original TDNN. We also compare the proposed CTDNN with another new variant of TDNN, Factorized-TDNN, which shows that our model has a 36% absolute identification accuracy improvement under few shots condition. Moreover, the proposed CTDNN can handle training with a larger batch more efficiently and hence, utilize calculation resources economically. The code of the new model is released at https://***/chenllliang/CTDNN. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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Supervised topic models with weighted words:multi-label document classification

引用

Frontiers of Information Technology & Electronic engineering 2018年第4期19卷 513-523页

作者： Yue-peng ZOU Ji-hong OUYANG Xi-ming LI College of Computer Science and Technology Jilin University MOE Key Laboratory of Symbolic Computation and Knowledge Engineering Jilin University

Supervised topic modeling algorithms have been successfully applied to multi-label document classification *** models include labeled latent Dirichlet allocation(L-LDA)and ***,these models neglect the class frequency information of words(i.e.,the number of classes where a word has occurred in the training data),which is significant for *** address this,we propose a method,namely the class frequency weight(CF-weight),to weight words by considering the class frequency *** CF-weight is based on the intuition that a word with higher(lower)class frequency will be less(more)*** this study,the CF-weight is used to improve L-LDA and dependency-LDA.A number of experiments have been conducted on real-world multi-label *** results demonstrate that CF-weight based algorithms are competitive with the existing supervised topic models.

关键词： Supervised topic model Multi-label classification Class frequency Labeled latent Dirichlet allocation (L-LDA) Dependency-LDA

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An unmanned aerial vehicle pose estimation system guided by desired shot 2

An unmanned aerial vehicle pose estimation system guided by ...

引用

2nd IEEE International Conference on Automation, Electronics and Electrical engineering, AUTEEE 2019

作者： Dou, Xinglei Ding, Qijian Du, Qiao Yang, Min Huo, Yuzhou Wang, Xin College of Software Engineering Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineer Ministry of Education Jilin University Changchun China

ISBN: (纸本)9781728150291

Unmanned aerial vehicles (UAVs) are widely used in aerial photography nowadays for their strong maneuverability, good image quality and high cost performance, while they have limited battery capacity and difficulty in operation. It is difficult to reach the ideal pose and get the desired shot by manual manipulation in a short time. To solve these problems, an UAV pose estimation system guided by desired shot is constructed. Either a sequence of images or a video is taken as input. The user interacts with the system to express the desired shot. The 3D-reconstruction is done by Structure from Motion (SfM). Then the Perspective-n-Point (PnP) problem is solved to estimate the ideal pose of the UAV. The experimental results proves that the system is effective and valid. © 2019 IEEE.

关键词： Aerial photography

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GEOM-GCN: GEOMETRIC GRAPH CONVOLUTIONAL NETWORKS 8

GEOM-GCN: GEOMETRIC GRAPH CONVOLUTIONAL NETWORKS

引用

8th International Conference on Learning Representations, ICLR 2020

作者： Pei, Hongbin Wei, Bingzhe Chang, Kevin Chen-Chuan Lei, Yu Yang, Bo College of Computer Science and Technology Jilin University China Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign United States Department of Computer Science University of Illinois Urbana-Champaign United States Department of Computing Hong Kong Polytechnic University Hong Kong Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education China

Message-passing neural networks (MPNNs) have been successfully applied to representation learning on graphs in a variety of real-world applications. However, two fundamental weaknesses of MPNNs' aggregators limit their ability to represent graph-structured data: losing the structural information of nodes in neighborhoods and lacking the ability to capture long-range dependencies in disassortative graphs. Few studies have noticed the weaknesses from different perspectives. From the observations on classical neural network and network geometry, we propose a novel geometric aggregation scheme for graph neural networks to overcome the two weaknesses. The behind basic idea is the aggregation on a graph can benefit from a continuous space underlying the graph. The proposed aggregation scheme is permutation-invariant and consists of three modules, node embedding, structural neighborhood, and bi-level aggregation. We also present an implementation of the scheme in graph convolutional networks, termed Geom-GCN, to perform transductive learning on graphs. Experimental results show the proposed Geom-GCN achieved state-of-the-art performance on a wide range of open datasets of graphs. © 2020 8th International Conference on Learning Representations, ICLR 2020. All rights reserved.

关键词： Message passing

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