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arXiv 2020年

作者： Jubran, Ibrahim Tukan, Murad Maalouf, Alaa Feldman, Dan Robotics and Big Data Lab Department of Computer Science University of Haifa Israel

The input to the sets-k-means problem is an integer k ≥ 1 and a set P = {P1;Pn} of sets in Rd. The goal is to compute a set C of k centers (points) in Rd that minimizes the sum Σ P2P minp2P;c2C p - c2 of squared distances to these sets. An ϵ-core-set for this problem is a weighted subset of P that approximates this sum up to 1 ± ϵ factor, for every set C of k centers in Rd. We prove that such a core-set of O(log2 n) sets always exists, and can be computed in O(n log n) time, for every input P and every fixed d;k ≥ 1 and ϵ 2 (0;1). The result easily generalized for any metric space, distances to the power of z > 0, and M-estimators that handle outliers. Applying an inefficient but optimal algorithm on this coreset allows us to obtain the first PTAS (1 + ϵ approximation) for the sets-k- means problem that takes time near linear in n. This is the first result even for sets-mean on the plane (k = 1, d = 2). Open source code and experimental results for document classification and facility locations are also provided. Copyright © 2020, The Authors. All rights reserved.

关键词： Open systems

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Developing Global Digital Economy after COVID-19: Preface for ITQM 2020 & 2021

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Procedia Computer science 2021年 199卷 1-9页

作者： Liu, Yubin Shi, Yong Wang, Yong Ergu, Daji Berg, Daniel Tien, James Li, Jianping Tian, Yingjie Southwest Minzu University Sichuan Chengdu610041 China College of Information Science and Technology University of Nebraska at Omaha OmahaNE68182 United States Key Lab of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China College of Engineering University of Miami Coral GablesFL33124 United States School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China

Welcome to the Eighth International Conference on Information Technology and Quantitative Management (ITQM 2020 & 2021), July 9-11, Chengdu, China. The theme of ITQM 2020 & 2021 is “Developing Global Digital Economy after COVID-19”. ITQM 2020 & 2021 is organized by International Academy of Information Technology and Quantitative Management (IAITQM), Southwest Minzu University, Chinese Academy of sciences and University of Nebraska at Omaha. IAITQM was formally inaugurated on June 3, 2012 with more than 50 founding members from China, United States, Australia, Japan, Lithuania, Poland, Romania, Spain, Singapore, South Korea, The Netherlands, Turkey and other countries. The International Conference on Information Technology and Quantitative Management (ITQM), established by IAITQM, is a global forum for exchanging research findings and case studies that bridge the latest information technology and quantitative management techniques. It explores how the use of information technology techniques to improve quantitative management and how the development of management tools can reshape the development of information technology. The First International Conference on Information Technology and Quantitative Management (ITQM 2013) took place in Suzhou, China. The Second International Conference on Information Technology and Quantitative Management (ITQM 2014) was held in Moscow, Russia. The Third International Conference on Information Technology and Quantitative Management (ITQM 2015) was held at Rio de Janeiro, Brazil. The Forth International Conference on Information Technology and Quantitative Management (ITQM 2016) was held at Asan, Korea. The Fifth International Conference on Information Technology and Quantitative Management (ITQM 2017) was held at New Delhi, India. The Sixth International Conference on Information Technology and Quantitative Management (ITQM 2018) was hosted at Omaha, USA. The Seventh International Conference on Information Technology and Quantitative

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Text-augmented long-term relation dependency learning for knowledge graph representation

High-Confidence Computing

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High-Confidence Computing 2025年

作者： Quntao Zhu Mengfan Li Yuanjun Gao Yao Wan Xuanhua Shi Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China

Knowledge graph (KG) representation learning aims to map entities and relations into a low-dimensional representation space, showing significant potential in many tasks. Existing approaches follow two categories: (1) Graph-based approaches encode KG elements into vectors using structural score functions. (2) Text-based approaches embed text descriptions of entities and relations via pre-trained language models (PLMs), further fine-tuned with triples. We argue that graph-based approaches struggle with sparse data, while text-based approaches face challenges with complex relations. To address these limitations, we propose a unified Text-Augmented Attention-based Recurrent Network, bridging the gap between graph and natural language. Specifically, we employ a graph attention network based on local influence weights to model local structural information and utilize a PLM based prompt learning to learn textual information, enhanced by a mask-reconstruction strategy based on global influence weights and textual contrastive learning for improved robustness and generalizability. Besides, to effectively model multi-hop relations, we propose a novel semantic-depth guided path extraction algorithm and integrate cross-attention layers into recurrent neural networks to facilitate learning the long-term relation dependency and offer an adaptive attention mechanism for varied-length information. Extensive experiments demonstrate that our model exhibits superiority over existing models across KG completion and question-answering tasks.

关键词： Knowledge graph representation Graph attention network Pre-trained language model Attention-based recurrent network Masked autoencoder Contrastive learning

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Deep Learning for Code Intelligence: Survey, Benchmark and Toolkit

arXiv

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arXiv 2023年

作者： Wan, Yao He, Yang Bi, Zhangqian Zhang, Jianguo Zhang, Hongyu Sui, Yulei Xu, Guandong Jin, Hai Yu, Philip S. National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Simon Fraser University Vancouver Canada School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Salesforce Research United States Chongqing University China University of New South Wales Australia University of Technology Sydney Australia University of Illinois at Chicago Chicago United States

Code intelligence leverages machine learning techniques to extract knowledge from extensive code corpora, with the aim of developing intelligent tools to improve the quality and productivity of computer programming. Currently, there is already a thriving research community focusing on code intelligence, with efforts ranging from software engineering, machine learning, data mining, natural language processing, and programming languages. In this paper, we conduct a comprehensive literature review on deep learning for code intelligence, from the aspects of code representation learning, deep learning techniques, and application tasks. We also benchmark several state-of-the-art neural models for code intelligence, and provide an open-source toolkit tailored for the rapid prototyping of deep-learning-based code intelligence models. In particular, we inspect the existing code intelligence models under the basis of code representation learning, and provide a comprehensive overview to enhance comprehension of the present state of code intelligence. Furthermore, we publicly release the source code and data resources to provide the community with a ready-to-use benchmark, which can facilitate the evaluation and comparison of existing and future code intelligence models (https://***). At last, we also point out several challenging and promising directions for future research. Copyright © 2023, The Authors. All rights reserved.

关键词： Computer programming languages

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Research on Multiscale Information Storage of MEG of Depression Based on ARFI Model 21

Research on Multiscale Information Storage of MEG of Depress...

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5th International Conference on Computer science and Application Engineering, CSAE 2021

作者： Luo, Dayou Yan, Wei Li, Jin Hou, Fengzhen Wang, Jun Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing China Department of Psychiatry Affiliated Brain Hospital of Nanjing Medical University Nanjing China College of Physics and Information Technology Shaanxi Normal University Xi'an China School of Science China Pharmaceutical University Nanjing China

ISBN: (纸本)9781450389853

As a noninvasive brain function detection technique, Magnetoencephalography (MEG) has been widely used in the research of depression. By analyzing the amount of information storage, the difference of MEG information storage between patients with depression and healthy people was studied. Our analysis was carried out in the popular multiscale entropy framework, in which the time series were first "coarse-grained" on the selected time scale by low-pass filtering and down-sampling, and then its complexity was evaluated ac-cording to conditional entropy. Within this framework, we used the linear fractional integral autoregressive (ARFI) model to derive the analytical expression of information storage calculated at multiple time scales. We used the information storage expression derived from the ARFI model and then collected the information storage of MEG through positive, negative and neutral stimuli and finally calculate it. The experimental results showed that it was best to distinguish between patients with depression and healthy people through the information storage of MEG through positive stimuli, and it was best to distinguish healthy people from patients with depression at a higher frequency if it was negative or neutral stimuli.. © 2021 Association for Computing Machinery. All rights reserved.

关键词： Magnetoencephalography

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Suppress content shift: better diffusion features via off-the-shelf generation techniques 24

Suppress content shift: better diffusion features via off-th...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Benyuan Meng Qianqian Xu Zitai Wang Zhiyong Yang Xiaochun Cao Qingming Huang Institute of Information Engineering CAS and School of Cyber Security University of Chinese Academy of Sciences Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS and Peng Cheng Laboratory Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS School of Computer Science and Tech. University of Chinese Academy of Sciences School of Cyber Science and Tech. Shenzhen Campus of Sun Yat-sen University School of Computer Science and Tech. University of Chinese Academy of Sciences and Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS and Key Laboratory of Big Data Mining and Knowledge Management CAS

ISBN: (纸本)9798331314385

Diffusion models are powerful generative models, and this capability can also be applied to discrimination. The inner activations of a pre-trained diffusion model can serve as features for discriminative tasks, namely, diffusion feature. We discover that diffusion feature has been hindered by a hidden yet universal phenomenon that we call content shift. To be specific, there are content differences between features and the input image, such as the exact shape of a certain object. We locate the cause of content shift as one inherent characteristic of diffusion models, which suggests the broad existence of this phenomenon in diffusion feature. Further empirical study also indicates that its negative impact is not negligible even when content shift is not visually perceivable. Hence, we propose to suppress content shift to enhance the overall quality of diffusion features. Specifically, content shift is related to the information drift during the process of recovering an image from the noisy input, pointing out the possibility of turning off-the-shelf generation techniques into tools for content shift suppression. We further propose a practical guideline named GATE to efficiently evaluate the potential benefit of a technique and provide an implementation of our methodology. Despite the simplicity, the proposed approach has achieved superior results on various tasks and datasets, validating its potential as a generic booster for diffusion features. Our code is available at https://***/Darkbblue/diffusion-content-shift.

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VNF Service Chain Deployment Algorithm in 5G Communication based on Reinforcement Learning

IAENG International Journal of Computer Science

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IAENG International Journal of Computer science 2021年第1期48卷 1页

作者： Xuan, Hejun Zhao, Xuelin Fan, Jianwei Xue, Yahui Zhu, Fangfang Li, Yanling School of Computer Science and Technology Xinyang Normal University Xinyang464000 China Henan Key Lab. of Analysis and Application of Education Big Data Xinyang Normal University XinyangHenan464000 China

With the rapidly deployment of 5G communication, network functional virtualization has became one of key technology. However, how to achieve virtual network function service chaining (VNF-SC) adaptively and cost-effectively in an inter datacenter elastic optical network (interDC-EON) has become an interesting and challenging problem. In addition, network function virtualization has became one of the key technology for 5G Commons. In this paper, we propose a resource scheduling framework based on artificial intelligence. In addition, an effective VNF-SC deployment algorithm based on deep reinforcement learning (RL) is designed. In this algorithm, VNF-SC deployed scheme is obtained by using deep Q-learning according to the state of the network and thereward of the action. To verify the efficient of the proposed algorithm, a large number of experiments have been conducted. Experimental results demonstrate that the RL achieves better performance than several benchmarks, in terms of balancing the tradeoff among the overall resource utilization, the vNF-SC request blocking probability. © 2021, IAENG International Journal of Computer science. All Rights Reserved.

关键词： Reinforcement learning

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Understanding Online Public Sentiments: A Machine Learning-Based Analysis of English and Chinese Twitter Discourse during the 2019 Chinese National Day 2

Understanding Online Public Sentiments: A Machine Learning-B...

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2nd International Multidisciplinary Information Technology and Engineering Conference, IMITEC 2020

作者： Xu, Yekai He, Qingqian Ni, Shiguang Big Data Lab Tsinghua-Berkeley Shenzhen Institute Tsinghua University Shenzhen China Tsinghua International Relations Data Computing Lab School of Social Sciences Tsinghua University Beijing China School of Political Science and Public Administration China University of Political Science and Law Beijing China Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China

ISBN: (纸本)9781728195209

As the Internet gradually penetrates people's daily lives, individual citizens are empowered to demonstrate and exchange opinions and sentiments at any time anywhere. Online communities are increasingly participating in the agenda-setting of public affairs and official policies. However, how to depict online public opinion and to what degree does it influence the real world are still unclear. This study addresses the above problems by analyzing Twitter discourse during the 2019 Chinese National Day with a machine learning-based approach. Over 300,000 English and Chinese tweets were collected between Sept 30 and Oct 3, and a hybrid method of support vector machine (SVM) and dictionary was applied to evaluate the sentiments of the collected tweets. This method avoids complex structures while yielding an average accuracy of over 96% in most classifiers used in the study. The results indicate alignment between the time of National Day celebration activities and the peak of sentiments revealed in both English and Chinese tweets, although the sentiments of the two languages tend to be in opposite directions. The sentiments of tweets also diverge from nation to nation, but are generally consistent with the country's official relations with China. The linguistic features of the tweets suggest different concerns for Twitter users who have different sentiments towards China. Future studies should prolong the collecting period and refine the algorithms used. Specific attention can also be paid to important countries pairs like China and the United States. © 2020 IEEE.

关键词： Sentiment analysis

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Semantic and syntactic enhanced aspect sentiment triplet extraction

arXiv

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arXiv 2021年

作者： Chen, Zhexue Huang, Hong Liu, Bang Shi, Xuanhua Jin, Hai National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab Huazhong University of Science and Technology China RALI & Mila University of Montreal

Aspect Sentiment Triplet Extraction (ASTE) aims to extract triplets from sentences, where each triplet includes an entity, its associated sentiment, and the opinion span explaining the reason for the sentiment. Most existing research addresses this problem in a multi-stage pipeline manner, which neglects the mutual information between such three elements and has the problem of error propagation. In this paper, we propose a Semantic and Syntactic Enhanced aspect Sentiment triplet Extraction model (S3E2) to fully exploit the syntactic and semantic relationships between the triplet elements and jointly extract them. Specifically, we design a Graph-Sequence duel representation and modeling paradigm for the task of ASTE: we represent the semantic and syntactic relationships between word pairs in a sentence by graph and encode it by Graph Neural Networks (GNNs), as well as modeling the original sentence by LSTM to preserve the sequential information. Under this setting, we further apply a more efficient inference strategy for the extraction of triplets. Extensive evaluations on four benchmark datasets show that S3E2 significantly outperforms existing approaches, which proves our S3E2’s superiority and flexibility in an end-to-end fashion. Copyright © 2021, The Authors. All rights reserved.

关键词： Extraction

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Mining the value characteristics of one-way car-sharing users based on empirical data 4

Mining the value characteristics of one-way car-sharing user...

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2020 4th International Conference on Traffic Engineering and Transportation System, ICTETS 2020

作者： Mi, Xue Wang, Ying-Qi Xie, Dong-Fan Institute of System Science School of Traffic and Transportation Beijing Jiaotong University Key Lab. of Transport Industry of Big Data Application Technologies for Comprehensive Transport Beijing Jiaotong University Beijing China

Car-sharing has been emergent in many cities all over the world due to the environmental sustainability and the diverse service for users. However, the huge investment and the long payback period hinder the development of car-sharing. To this end, this study attempts to mine the value of car-sharing users in terms of empirical data, aiming to provide suggestions for companies to enhance user management. Based on the order data of a car-sharing company, several indicators are defined. On the basis, the users are divided into three categories, namely high-value users, low-value users and potential users. Furthermore, with the proposed indicators and the personal attributes, a prediction model is developed based on random forest method to predict the category of users in advance. The results show that with the observation period of 7 weeks, the long-term value type of a user can be predicted with accuracy of more than 80%. © Published under licence by IOP Publishing Ltd.

关键词： Decision trees

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