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A Balanced Cloudlet Management Method for Wireless Metropolitan Area Networks 13th

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A Balanced Cloudlet Management Method for Wireless Metropoli...

13th EAI International Conference on Testbeds and Research Infrastructures for the Development of Networks and Communities, TridentCom 2018

作者： Xu, Xiaolong Chen, Yuhao Qi, Lianyong He, Jing Zhang, Xuyun School of Computer and Software Nanjing University of Information Science and Technology Nanjing China Jiangsu Engineering Centre of Network Monitoring Nanjing University of Information Science and Technology Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Information Science and Engineering Qufu Normal University Jining China Department of Electrical and Computer Engineering University of Auckland Auckland New Zealand

ISBN: (纸本)9783030129705

With the rapid development of wireless communication technology, cloudlet-based wireless metropolitan area network, which provides people with more convenient network services, has become an effiective paradigm to meet the growing demand for requirements of wireless cloud computing. Currently, the energy consumption of cloudlets can be reduced by migrating tasks, but how to jointly optimize the time consumption and energy consumption in the process of migrations is still a significant problem. In this paper, a balanced cloudlet management method, named BCM, is proposed to address the above challenge. Technically, the Simple Additive Weighting (SAW) and Multiple Criteria Decision Making (MCDM) techniques are applied to optimize virtual machine scheduling strategy. Finally, simulation results demonstrate the effectiveness of our proposed method. © 2019, ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Energy utilization

Epilepsy Classification for Mining Deeper Relationships between EEG Channels based on GCN

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Epilepsy Classification for Mining Deeper Relationships betw...

computer Vision, Image and Deep Learning (CVIDL), International Conference on

作者： Xin Chen Yuanjie Zheng Yi Niu Chengiiang Li School of Information Science and Engineering Shandong Normal University Jinan China Key Lab of Intelligent Computing and Information Security Universities of Shandong Jinan China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Institute of Biomedical Sciences Jinan China Shandong University of Science and Technology Jinan China

ISBN: (数字)9781728194813

ISBN: (纸本)9781728194820

Epilepsy is a brain disorder caused by abnormal discharges of neurons in brain. It is one of the most commonly studied disorders in neurology. The research of epilepsy electroencephalogram (EEG) has become a hot research topic. We find that in epilepsy EEG detection task, many previous methods focused on directly collecting the data of each channel, but these methods seldom analyse relationships between signals. Therefore, we propose the Epilepsy EEG Graph Convolutional Network EGCN, which makes full use of correlations between channels to deeply mine data information. We specifically design 5-layer graph convolutional network structure for classification of healthy and epileptic patients. The method is applied to public data set (Boon and CHB-MIT) to establish a reasonable classification model. And we compare it with some advanced algorithms. The experimental results show that the E-GCN method is superior to many existing methods in classification accuracy. In brief, the E-GCN method can be effectively used in classification and detection for epilepsy. This provides new ideas for colleagues, who study epilepsy EEG. In addition, this also provides richer experience for diagnosis of epilepsy.

关键词： Epilepsy Electroencephalography Convolution Brain modeling Feature extraction Correlation Task analysis

Neural Entity Summarization with Joint Encoding and Weak Supervision

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arXiv 2020年

作者： Li, Junyou Cheng, Gong Liu, Qingxia Zhang, Wen Kharlamov, Evgeny Gunaratna, Kalpa Chen, Huajun National Key Laboratory for Novel Software Technology Nanjing University China College of Computer Science and Technology Zhejiang University China Bosch Center for Artificial Intelligence Robert Bosch GmbH Germany Department of Informatics University of Oslo Norway Samsung Research America Mountain ViewCA United States

In a large-scale knowledge graph (KG), an entity is often described by a large number of triple-structured facts. Many applications require abridged versions of entity descriptions, called entity summaries. Existing solutions to entity summarization are mainly unsupervised. In this paper, we present a supervised approach NEST that is based on our novel neural model to jointly encode graph structure and text in KGs and generate high-quality diversified summaries. Since it is costly to obtain manually labeled summaries for training, our supervision is weak as we train with programmatically labeled data which may contain noise but is free of manual work. Evaluation results show that our approach significantly outperforms the state of the art on two public benchmarks. Copyright © 2020, The Authors. All rights reserved.

关键词： Knowledge graph

TransN: Heterogeneous Network Representation Learning by Translating Node Embeddings

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TransN: Heterogeneous Network Representation Learning by Tra...

International Conference on Data Engineering

作者： Zijian Li Wenhao Zheng Xueling Lin Ziyuan Zhao Zhe Wang Yue Wang Xun Jian Lei Chen Qiang Yan Tiezheng Mao Computer Science and Engineering Department HKUST Hong Kong China National Key Laboratory for Novel Software Technology Nanjing University Nanjing China Wechat Group Tencent Corporation Guangzhou China Shenzhen Institute of Computing Sciences Shenzhen University Shenzhen China

ISBN: (数字)9781728129037

ISBN: (纸本)9781728129044

Learning network embeddings has attracted growing attention in recent years. However, most of the existing methods focus on homogeneous networks, which cannot capture the important type information in heterogeneous networks. To address this problem, in this paper, we propose TransN, a novel multi-view network embedding framework for heterogeneous networks. Compared with the existing methods, TransN is an unsupervised framework which does not require node labels or user-specified meta-paths as inputs. In addition, TransN is capable of handling more general types of heterogeneous networks than the previous works. Specifically, in our framework TransN, we propose a novel algorithm to capture the proximity information inside each single view. Moreover, to transfer the learned information across views, we propose an algorithm to translate the node embeddings between different views based on the dual-learning mechanism, which can both capture the complex relations between node embeddings in different views, and preserve the proximity information inside each view during the translation. We conduct extensive experiments on real-world heterogeneous networks, whose results demonstrate that the node embeddings generated by TransN outperform those of competitors in various network mining tasks.

关键词： Heterogeneous networks Task analysis Color Conferences Data engineering computer science software

Weighted Linear Multiple Kernel Learning for Saliency Detection 2nd

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Weighted Linear Multiple Kernel Learning for Saliency Detect...

2nd EAI International Conference on Robotic Sensor Networks, ROSENET 2018

作者： Zhou, Quan Wu, Jinwen Fan, Yawen Zhang, Suofei Wu, Xiaofu Zheng, Baoyu Jin, Xin Lu, Huimin Latecki, Longin Jan National Engineering Research Center of Communications and Networking Nanjing University of Posts and Telecommunications Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China College of Information Engineering China University of Geosciences Wuhan China School of Internet of Things Nanjing University of Posts and Telecommunications Nanjing China National Engineering Research Center of Communications and Networking Nanjing University of Posts and Telecommunications Nanjing China Department of Cyber Security Beijing Electronic Science and Technology Institute Beijing China CETC Big Data Research Institute Co. Ltd. Guizhou Guiyang China Department of Mechanical and Control Engineering Kyushu Institute of Technology Kitakyushu Japan Department of Computer and Information Sciences Temple University PhiladelphiaPA United States

ISBN: (纸本)9783030177621

This paper presents a novel saliency detection method based on weighted linear multiple kernel learning (WLMKL), which is able to adaptively combine different contrast measurements in a supervised manner. Three commonly used bottom-up visual saliency operations are first introduced, including corner-surround contrast (CSC), center-surround contrast (CESC), and global contrast (GC). Then these contrast measures are fed into our WLMKL framework to produce the final saliency map. We show that the assigned weights for each contrast feature maps are always normalized in our WLMKL formulation. In addition, the proposed approach benefits from the advantages of the contribution of each individual contrast operation, and thus produces more robust and accurate saliency maps. The extensive experimental results show the effectiveness of the proposed model, and demonstrate the combination is superior to individual subcomponent. © 2020, Springer science and Business Media Deutschland GmbH. All rights reserved.

关键词： Center-surround contrast Corner-surround contrast Global contrast Multiple kernel learning Saliency detection

Remove-Win: a Design Framework for Conflict-free Replicated Data Types

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arXiv 2019年

作者： Zhang, Yuqi Wei, Hengfeng Huang, Yu The State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University 210023 China

Distributed storage systems employ replication to improve performance and reliability. To provide low latency data access, replicas are often required to accept updates without coordination with each other, and the updates are then propagated asynchronously. This brings the critical challenge of conflict resolution among concurrent updates. Conflict-free Replicated Data Type (CRDT) is a principled approach to addressing this challenge. However, existing CRDT designs are tricky, and hard to be generalized to other data types. A design framework is in great need to guide the systematic design of new CRDTs. To address this challenge, we propose RWF – the Remove-Win design Framework for CRDTs. RWF leverages the simple but powerful remove-win strategy to resolve conflicting updates, and provides generic design for a variety of data container types. Two exemplar implementations following RWF are given over the Redis data type store, which demonstrate the effectiveness of RWF. Performance measurements of our implementations further show the efficiency of CRDT designs following RWF. © 2019, CC BY.

关键词： Multiprocessing systems

Inversion-based measurement of data consistency for read/write registers

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arXiv 2019年

作者： Huang, Yu Wei, Hengfeng Huang, Maosen Ouyang, Lingzhi State Key Laboratory for Novel Software Technology Nanjing University Department of Computer Science and Technology Nanjing University

Both providers and consumers of distributed storage services benefit from the quantification of the severity of consistency violations. However, existing methods fail to capture a typical pattern of violation - the disorder among operations with reference to the ideally strong sequential execution. Such disorder is often seen in Internet applications based on distributed storage services, such as instant messaging. To this end, we use inversions in a permutation with reference to the ideally strong sequential execution to measure the consistency of data. The i-atomicity model is defined and a (pseudo-)polynomial verification algorithm for a restricted type of histories is proposed. The basic idea of the verification algorithm is brute-force search for the permutation with less than i inversions, which requires exponential cost. Reasonable assumptions abstracted from application scenarios can be leveraged to prune the search space, which yields an efficient polynomial verification algorithm. Copyright © 2019, The Authors. All rights reserved.

关键词： Digital storage

MAT: A simple yet strong baseline for identifying selfadmitted technical debt

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arXiv 2019年

作者： Guo, Zhaoqiang Liu, Shiran Liu, Jinping Li, Yanhui Chen, Lin Lu, Hongmin Zhou, Yuming Xu, Baowen State Key Laboratory for Novel Software Technology Nanjing University Department of Computer Science and Technology Nanjing University

In the process of software evolution, developers often sacrifice the long-term code quality to satisfy the short-term goals due to specific reasons, which is called technical debt. In particular, self-admitted technical debt (SATD) refers to those that were intentionally introduced and remarked by code comments. Those technical debts reduce the quality of software and increase the cost of subsequent software maintenance. Therefore, it is necessary to find out and resolve these debts in time. Recently, many approaches have been proposed to identify SATD. However, those approaches either have a low accuracy or are complex to implementation in practice. In this paper, we propose a simple unsupervised baseline approach that fuzzily matches task annotation tags (MAT) to identify SATD. MAT does not need any training data to build a prediction model. Instead, MAT only examines whether any of four task tags (i.e. TODO, FIXME, XXX, and HACK) appears in the comments of a target project to identify SATD. In this sense, MAT is a natural baseline approach, which has a good understandability, in SATD identification. In order to evaluate the usefulness of MAT, we use 10 open-source projects to conduct the experiment. The experimental results reveal that MAT has a surprisingly excellent performance for SATD identification compared with the state-of-the-art approaches. As such, we suggest that, in the future SATD identification studies, MAT should be considered as an easy-to-implement baseline to which any new approach should be compared against to demonstrate its usefulness. Copyright © 2019, The Authors. All rights reserved.

关键词： Open source software

MIPI 2022 Challenge on Under-Display Camera Image Restoration: Methods and Results

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arXiv 2022年

作者： Zhu, Yurui Wang, Xi Fu, Xueyang Hu, Xiaowei Hu, Jinfan Liu, Xina Chen, Xiangyu Dong, Chao Zhang, Dafeng Huang, Feiyu Liu, Shizhuo Wang, Xiaobing Jin, Zhezhu Jiang, Xuhao Shao, Guangqi Wang, Xiaotao Lei, Lei Zhang, Zhao Zhao, Suiyi Zheng, Huan Gao, Yangcheng Wei, Yanyan Ren, Jiahuan Huang, Tao Fang, Zhenxuan Huang, Mengluan Xu, Junwei Zhang, Yong Yang, Yuechi Shu, Qidi Yang, Zhiwen Li, Shaocong Yao, Mingde Xu, Ruikang Guan, Yuanshen Huang, Jie Xiong, Zhiwei Zhu, Hangyan Liu, Ming Liu, Shaohui Zuo, Wangmeng Jia, Zhuang Song, Binbin Song, Ziqi Mao, Guiting Hou, Ben Liu, Zhimou Ke, Yi Ouyang, Dengpei Han, Dekui Zhang, Jinghao Zhu, Qi Zheng, Naishan Zhao, Feng Jin, Wu Conde, Marcos Nathan, Sabari Timofte, Radu Xu, Tianyi Xu, Jun Hrishikesh, P.S. Puthussery, Densen Jiji, C.V. Biao, Jiang Yuhan, Ding Zhang, Li Wan Xiaoyue, Feng Sijing, Chen Tianheng, Zhong Lu, Jiyang Chen, Hongming Fan, Zhentao Chen, Xiang University of Science and Technology of China China Shanghai Ai Laboratory China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China University of Macau China Samsung Research China Beijing China Xiaomi Beijing China Hefei University of Technology China School of Artificial Intelligence Xidian University China School of Remote Sensing and Information Engineering Wuhan University China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University China Harbin Institute of Technology China Changsha Research Institute of Mining and Metallurgy China Tianjin University China University of Wurzburg Computer Vision Lab Germany Couger Inc. Japan School of Statistics and Data Science Nankai University China Founding Minds Software India Department of Electronics and Communication Srm University Fudan University China Shenyang Aerospace University China Nanjing University of Science and Technology China

Developing and integrating advanced image sensors with novel algorithms in camera systems are prevalent with the increasing demand for computational photography and imaging on mobile platforms. However, the lack of high-quality data for research and the rare opportunity for in-depth exchange of views from industry and academia constrain the development of mobile intelligent photography and imaging (MIPI). To bridge the gap, we introduce the first MIPI challenge including five tracks focusing on novel image sensors and imaging algorithms. In this paper, we summarize and review the Under-Display Camera (UDC) Image Restoration track on MIPI 2022. In total, 167 participants were successfully registered, and 19 teams submitted results in the final testing phase. The developed solutions in this challenge achieved state-of-the-art performance on Under-Display Camera Image Restoration. A detailed description of all models developed in this challenge is provided in this paper. More details of this challenge and the link to the dataset can be found at https://***/mipi-challenge/MIPI2022. Copyright © 2022, The Authors. All rights reserved.

关键词： Image reconstruction