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The 6th International Conference on Computer Research and Development

作者： Yubao Liu Guihe Qin College of Computer Science and Technology Jilin UniversityChangchunChina College of Computer Science and TechnologyChangchun University College of Computer Science and Technology Jilin UniversityChangchunChina Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of EducationJilin University

In this paper, a hybrid TS-DE algorithm based on Tabu search and differential evolution algorithm is proposed to solve the reliability redundancy optimization problem. A differential evolution algorithm is embedded in Tabu search algorithm. TS is applied for searching solutions space, and DE is used for generating neighborhood solutions. The advantages of both algorithms are considered simultaneously. And an adaptive hybrid TS-DE approach is developed to solve three benchmark reliability redundancy allocation problems. By comparing with other algorithms reported in previous literatures, experimental results show that the proposed method is effective and efficient for solving the reliability redundancy optimization problem.

关键词： nonlinear programming Tabu search differential evolution reliability optimization redundancy allocation

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FastClass: A Time-Efficient Approach to Weakly-Supervised Text Classification

arXiv

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arXiv 2022年

作者： Xia, Tingyu Wang, Yue Tian, Yuan Chang, Yi School of Artificial Intelligence Jilin University China School of Information and Library Science University of North Carolina Chapel Hill United States Key Laboratory of Symbolic Computation and Knowledge Engineering Jilin University China International Center of Future Science Jilin University China

Weakly-supervised text classification aims to train a classifier using only class descriptions and unlabeled data. Recent research shows that keyword-driven methods can achieve state-of-the-art performance on various tasks. However, these methods not only rely on carefully-crafted class descriptions to obtain class-specific keywords but also require substantial amount of unlabeled data and takes a long time to train. This paper proposes FastClass, an efficient weakly-supervised classification approach. It uses dense text representation to retrieve class-relevant documents from external unlabeled corpus and selects an optimal subset to train a classifier. Compared to keyword-driven methods, our approach is less reliant on initial class descriptions as it no longer needs to expand each class description into a set of class-specific keywords. Experiments on a wide range of classification tasks show that the proposed approach frequently outperforms keyword-driven models in terms of classification accuracy and often enjoys orders-of-magnitude faster training speed. Copyright © 2022, The Authors. All rights reserved.

关键词： Classification (of information)

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A Task Processing Efficiency Improvement Scheme Based on Cloud-Edge Architecture in Dense Computing Environments

SSRN

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SSRN 2022年

作者： Feng, Jiahui Qi, Jingze Liu, Yuanning Liu, Zhen College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China Graduate School of Engineering Nagasaki Institute of Applied Science 536 Aba-machi Nagasaki851-0193 Japan

As cloud-edge architecture combines low latency and high-performance computing, it has a wider range of application scenarios. Considering that, how to reasonably allocate tasks to the edge cloud or the central cloud and how to select appropriate virtual machines (VMs) for tasks are two key aspects affecting the efficiency of cloud-edge architecture. For the former, the ARDT algorithm is proposed, which dynamically adjusts the optimal threshold by predicting the number of real-time edge-end devices. For the latter, we design the DBMR algorithm based on the Max-Fit algorithm, in which we propose a structure of blocking to reduce the time complexity of the original algorithm from O(n2) to O(n(n-m)). Experimental results show that the ARDT-DBMR algorithm optimizes the average processing time and task failure rate by 30%-40% compared with the baseline algorithms and other advanced algorithms and significantly improves the problem of abnormal fluctuations in VM utilization in dense computing environments. © 2022, The Authors. All rights reserved.

关键词： Virtual machine

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Using prior knowledge to guide BERT's attention in semantic textual matching tasks

arXiv

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arXiv 2021年

作者： Xia, Tingyu Tian, Yuan Wang, Yue Chang, Yi School of Artificial Intelligence Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering Jilin University China School of Information and Library Science University of North Carolina Chapel Hill United States International Center of Future Science Jilin University China

We study the problem of incorporating prior knowledge into a deep Transformer-based model, i.e., Bidirectional Encoder Representations from Transformers (BERT), to enhance its performance on semantic textual matching tasks. By probing and analyzing what BERT has already known when solving this task, we obtain better understanding of what task-specific knowledge BERT needs the most and where it is most needed. The analysis further motivates us to take a different approach than most existing works. Instead of using prior knowledge to create a new training task for fine-tuning BERT, we directly inject knowledge into BERT’s multi-head attention mechanism. This leads us to a simple yet effective approach that enjoys fast training stage as it saves the model from training on additional data or tasks other than the main task. Extensive experiments demonstrate that the proposed knowledge-enhanced BERT is able to consistently improve semantic textual matching performance over the original BERT model, and the performance benefit is most salient when training data is scarce. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep neural networks

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Aspect Based Fine-Grained Sentiment Analysis for Public Policy Opinion Mining 21st

Aspect Based Fine-Grained Sentiment Analysis for Public Poli...

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21st International Symposium on knowledge and Systems Sciences, KSS 2022

作者： Zhao, Yueming Li, Ying Liu, Yijun Li, Qianqian College of Computer Science and Technology Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China Institutes of Science and Development Chinese Academy of Sciences Beijing100190 China School of Public Policy and Management University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9789811936098

Fine-grained mining has substantial practical significance for understanding public opinion toward public policies and optimizing relevant decision-making processes. In this study, an aspect-based sentiment analysis model integrated with both a sentiment lexicon for the policy domain and mutual information (MI) is established from the perspective of attitudinal orientations toward several elements of public policies, including subjects, objects, and tools to analyze comments. First, terms related to the aspects of comments on different policy elements are extracted using the association rules in conjunction with the word2vec algorithm. In addition, the contextual neighbor principle is employed to extract terms that express evaluative opinions and to extricate aspect-based two-tuples from comments. Subsequently, the sentiment lexicon for the policy domain is expanded using the sentiment orientation pointwise mutual information (SO-PMI) algorithm. Four machine learning models (i.e., the support vector machine, logistic regression, naive Bayes, and k-nearest neighbors models) and two deep learning models (i.e., the convolutional neural network (CNN) and long short-term memory models) are compared in terms of their classification performance based on 87,304 comments on public policies crawled from the Weibo platform. The results show that the CNN model integrated with the domain-specific sentiment lexicon and mutual information is effective at facilitating policy element-oriented fine-grained sentiment analysis of public opinion toward policies and therefore has practical decision-making value for the government to better understand the policy demands of the public. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Public policy

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Medicine composition concentration analysis based on least square support vector machine

Medicine composition concentration analysis based on least s...

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International Conference on Machine Learning and Cybernetics, ICMLC 2005

作者： Guo, Xin-Chen Chen, Zhou-Yi Teng, Li-Rong Wu, Chun-Guo Du, Tian-Bao Lu, Jia-Hui Meng, Qing-Fan Liang, Yan-Chun Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Ministry of Education Changchun 130012 China College of Science NortheastChinaInstituteofElectricPowerEngineering Jilin 132012 China Institute of General Development and Evaluation of Equipment EAAF PLA Beijing 100076 China College of Life Science Jilin University Changchun 130012 China

ISBN: (纸本)078039092X

This paper presents a method of medicine composition concentration analysis based on least square support vector machines (LS-SVMs) and examines the importance of the hyperparameter choice in improvement of algorithm performance. Simulation results show that the proposed method obtains high quality precision in the generalization, compared with multiple linear regression, and that it is an efficient approach to regression estimation. © 2005 IEEE.

关键词： Medicine

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An Adversarial Complementary Erasing Network for Salient Object Detection in Optical Remote Sensing Images

SSRN

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SSRN 2023年

作者： Liang, Yanhua Qin, Guihe Sun, Minghui Qin, Jun Yan, Jie Zhang, Zhonghan College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China School of Computer Science and Technology Changchun University of Science and Technology Changchun130012 China

Salient object detection in optical remote sensing images (RSI-SOD) aims to locate and extract visually discriminative objects or regions, which has attracted more and more attention from the community. The key challenge of RSI-SOD lies in the characteristics of optical RSIs, such as scale diversification of salient objects, irregular edges, and cluttered backgrounds. In this paper, we strive to embrace challenges towards effective and efficient RSI-SOD. To this end, we develop a framework, termed Adversarial Complementary Erasing Network (ACENet), which comprehensively mines discriminative features of different layers from foreground, background, local and global perspectives to perform robust and accurate saliency detection. Specifically, ACENet is a typical encoder-decoder structure that embeds Adversarial Erasing Synthesis Module (AESM) in multi-scale encoding features to fully explore more useful and complete representations. In AESM, we introduce Adversarial Discriminant Information Mining (ADIM) component to facilitate adaptive adjustment between salient features and background, further enabling more representative and strong integrity feature generation. Experimental results on three datasets prove that our ACENet significantly outperforms cutting-edge SOD models both qualitatively and quantitatively with a quick reasoning speed of 76 FPS. Code will be available at https://***/Liangyh18/ACENet. © 2023, The Authors. All rights reserved.

关键词： Optical remote sensing

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Modeling and Mining Spatiotemporal Social Contact of Metapopulation from Heterogeneous Data

Modeling and Mining Spatiotemporal Social Contact of Metapop...

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IEEE International Conference on Data Mining (ICDM)

作者： Bo Yang Hongbin Pei Hechang Chen Jiming Liu Shang Xia School of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education China Department of Computer Science Hong Kong Baptist University Hong Kong National Institute of Parasitic Diseases Chinese CDC Shanghai China

ISBN: (纸本)9781479943012

During an epidemic, the spatial, temporal and demographical patterns of disease transmission are determined by multiple factors. Besides the physiological properties of pathogenes and hosts, the social contacts of host population, which characterize individuals' reciprocal exposures of infection in view of demographical structures and various social activities, are also pivotal to understand and further predict the prevalence of infectious diseases. The means of measuring social contacts will dominate the extent how precisely we can forecast the dynamics of infections in the real world. Most current works focus their efforts on modeling the spatial patterns of static social contacts. In this work, we address the problem on how to characterize and measure dynamical social contacts during an epidemic from a novel perspective. We propose an epidemic-model-based tensor deconvolution framework to address this issue, in which the spatiotemporal patterns of social contacts are represented by the factors of tensors, which can be discovered by a tensor deconvolution procedure with an integration of epidemic models from rich types of data, mainly including heterogeneous outbreak surveillance, social-demographic census and physiological data from medical reports. Taking SIR model as a case study, the efficacy of the proposed method is theoretically analyzed and empirically validated through a set of rigorous experiments on both synthetic and real-world data.

关键词： Educational institutions Diseases Sociology Statistics Tensile stress Surveillance Data models

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Understanding the Runtime Overheads of Deep Learning Inference on Edge Devices

Understanding the Runtime Overheads of Deep Learning Inferen...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Xiu Ma Guangli Li Lei Liu Huaxiao Liu Xiaobing Feng College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China SKL of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China

With the growing ubiquity of the Internet of Things, in-the-edge inference of deep neural network models has been a major driver for promoting the widespread use of intelligent applications. As model inference characteristics are crucial for optimizing and deploying deep neural networks on hardware platforms, many studies focus on analyzing the performance of neural networks such as latency, accuracy, throughput, and energy consumption. However, few existing works have ever discussed the runtime overheads hidden in neural network inference, despite the overheads are non-negligible for edge applications. The lack of in-depth analysis of the overheads hinders the understanding of how hardware designs and model structures impact on-device inference performance. In this paper, we characterize the runtime overheads of deep learning inference on representative edge devices by leveraging state-of-the-art neural network models, performing a systematical analysis from the perspective of end-to-end performance, hardware platforms, memory bandwidth, and neural network model structures. Based on experimental results, the crucial insights are offered to facilitate the design and configure of resource-efficient networks and pick appropriate models on the specific platform, which provides a comprehensive view of runtime overheads of in-the- edge neural network inference for architects and developers.

关键词： Deep learning Performance evaluation Analytical models Runtime Roads Neural networks Throughput

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Density and sliding window-based clustering over evolving data streams

引用

ICIC Express Letters, Part B: Applications 2015年第8期6卷 2275-2283页

作者： Yu, Yanwei Zhao, Jindong Zhang, Yonggang Wen, Changci School of Computer and Control Engineering Yantai University No. 32 Qingquan Road Laishan District Yantai China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University No. 2699 Qianjin Street Changchun China Jingchang Office of Chinese Airforce No. 11 Shuangyuan Road Shijingshan District Beijing China

Density-based clustering over huge volumes of evolving data streams is critical for many modern applications ranging from network traffic monitoring to moving object management. In this work, we propose an efficient density-based clustering algorithm under sliding window environments to handle evolving data streams. First, we use cell partition optimization to fast determine points’ status instead of expensive completed neighbor query for core points. Second, we cluster new and expired points with cell-cluster structure. Cell-cluster leverages relationships of connected cells around points to complete classification of the points in the cells. Under the effect analysis of new and expired points, we then design an exact clustering framework with cell partition and cell-cluster in streaming windows. Our experimental evaluation, using both real and synthetic streaming data, demonstrates that our algorithm shows effectiveness in capturing the clusters with noise and performs faster than state-of-the art methods, and scales to high volume data streams. © 2015, ICIC International.

关键词： Cells

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