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153 results on '"Ou, Zhonghong"'

1. RevGNN: Negative Sampling Enhanced Contrastive Graph Learning for Academic Reviewer Recommendation

Author

Liao, Weibin, Zhu, Yifan, Li, Yanyan, Zhang, Qi, Ou, Zhonghong, and Li, Xuesong

Subjects
Computer Science - Information Retrieval, Computer Science - Artificial Intelligence
Abstract

Acquiring reviewers for academic submissions is a challenging recommendation scenario. Recent graph learning-driven models have made remarkable progress in the field of recommendation, but their performance in the academic reviewer recommendation task may suffer from a significant false negative issue. This arises from the assumption that unobserved edges represent negative samples. In fact, the mechanism of anonymous review results in inadequate exposure of interactions between reviewers and submissions, leading to a higher number of unobserved interactions compared to those caused by reviewers declining to participate. Therefore, investigating how to better comprehend the negative labeling of unobserved interactions in academic reviewer recommendations is a significant challenge. This study aims to tackle the ambiguous nature of unobserved interactions in academic reviewer recommendations. Specifically, we propose an unsupervised Pseudo Neg-Label strategy to enhance graph contrastive learning (GCL) for recommending reviewers for academic submissions, which we call RevGNN. RevGNN utilizes a two-stage encoder structure that encodes both scientific knowledge and behavior using Pseudo Neg-Label to approximate review preference. Extensive experiments on three real-world datasets demonstrate that RevGNN outperforms all baselines across four metrics. Additionally, detailed further analyses confirm the effectiveness of each component in RevGNN., Comment: Accepted by ACM Transactions on Information Systems (TOIS)

Published
2024
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4. An Inclusive Finance Consortium Blockchain Platform for Secure Data Storage and Value Analysis for Small and Medium-Sized Enterprises

Author

Liu, Jiaxi, Liu, Peihang, Ou, Zhonghong, Zhang, Guangwei, Song, Meina, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Zu, Qiaohong, editor, Tang, Yong, editor, and Mladenović, Vladimir, editor

Published
2021
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5. LogEDL: Log Anomaly Detection via Evidential Deep Learning.

Author

Duan, Yunfeng, Xue, Kaiwen, Sun, Hao, Bao, Haotong, Wei, Yadong, You, Zhangzheng, Zhang, Yuantian, Jiang, Xiwei, Yang, Sangning, Chen, Jiaxing, Duan, Boya, and Ou, Zhonghong

Abstract

With advancements in digital technologies such as 5G communications, big data, and cloud computing, the components of network operation systems have become increasingly complex, significantly complicating system monitoring and maintenance. Correspondingly, automated log anomaly detection has become a crucial means to ensure stable network operation and protect networks from malicious attacks or failures. Conventional machine learning and deep learning methods assume consistent distributions between the training and testing data, adhering to a closed-set recognition paradigm. Nevertheless, in realistic scenarios, systems may encounter new anomalies that were not present in the training data, especially in log anomaly detection. Inspired by evidential learning, we propose a novel anomaly detector called LogEDL, which supervises the training of the model through an evidential loss function. Unlike traditional loss functions, the evidential loss function not only focuses on correct classification but also quantifies the uncertainty of predictions. This enhances the robustness and accuracy of the model in handling anomaly detection tasks while achieving functionality similar to open-set recognition. To evaluate the proposed LogEDL method, we conduct extensive experiments on three datasets, i.e., HDFS, BGL, and Thunderbird, to detect anomalous log sequences. The experimental results demonstrate that our proposed LogEDL achieves state-of-the-art performance in anomaly detection. [ABSTRACT FROM AUTHOR]

Published
2024
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6. Turn-Level Recurrence Self-attention for Joint Dialogue Action Prediction and Response Generation

Author

Tan, Yanxin, Ou, Zhonghong, Liu, Kemeng, Shi, Yanan, Song, Meina, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Wang, Xin, editor, Zhang, Rui, editor, Lee, Young-Koo, editor, Sun, Le, editor, and Moon, Yang-Sae, editor

Published
2020
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7. KGWD: Knowledge Graph Based Wide & Deep Framework for Recommendation

Author

Liu, Kemeng, Ou, Zhonghong, Tan, Yanxin, Zhao, Kai, Song, Meina, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Wang, Xin, editor, Zhang, Rui, editor, Lee, Young-Koo, editor, Sun, Le, editor, and Moon, Yang-Sae, editor

Published
2020
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8. Techniques and tools for measuring energy efficiency of scientific software applications

Author

Abdurachmanov, David, Elmer, Peter, Eulisse, Giulio, Knight, Robert, Niemi, Tapio, Nurminen, Jukka K., Nyback, Filip, Pestana, Goncalo, Ou, Zhonghong, and Khan, Kashif

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing, High Energy Physics - Experiment, Physics - Computational Physics
Abstract

The scale of scientific High Performance Computing (HPC) and High Throughput Computing (HTC) has increased significantly in recent years, and is becoming sensitive to total energy use and cost. Energy-efficiency has thus become an important concern in scientific fields such as High Energy Physics (HEP). There has been a growing interest in utilizing alternate architectures, such as low power ARM processors, to replace traditional Intel x86 architectures. Nevertheless, even though such solutions have been successfully used in mobile applications with low I/O and memory demands, it is unclear if they are suitable and more energy-efficient in the scientific computing environment. Furthermore, there is a lack of tools and experience to derive and compare power consumption between the architectures for various workloads, and eventually to support software optimizations for energy efficiency. To that end, we have performed several physical and software-based measurements of workloads from HEP applications running on ARM and Intel architectures, and compare their power consumption and performance. We leverage several profiling tools (both in hardware and software) to extract different characteristics of the power use. We report the results of these measurements and the experience gained in developing a set of measurement techniques and profiling tools to accurately assess the power consumption for scientific workloads., Comment: Submitted to proceedings of 16th International workshop on Advanced Computing and Analysis Techniques in physics research (ACAT 2014), Prague

Published
2014

9. DeepMEN: Multi-model Ensemble Network for B-Lymphoblast Cell Classification

Author

Xiao, Fenrui, Kuang, Ruifeng, Ou, Zhonghong, Xiong, Baiqiao, Lovell, Nigel H., Advisory Editor, Oneto, Luca, Advisory Editor, Piotto, Stefano, Advisory Editor, Rossi, Federico, Advisory Editor, Samsonovich, Alexei V., Advisory Editor, Babiloni, Fabio, Advisory Editor, Liwo, Adam, Advisory Editor, Magjarevic, Ratko, Advisory Editor, Gupta, Anubha, editor, and Gupta, Ritu, editor

Published
2019
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10. Computer-Aided Diagnosis of Ophthalmic Diseases Using OCT Based on Deep Learning: A Review

Author

Zhang, Ruru, He, Jiawen, Shi, Shenda, Kang, Xiaoyang, Chai, Wenjun, Lu, Meng, Liu, Yu, Haihong, E., Ou, Zhonghong, Song, Meina, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Milošević, Danijela, editor, Tang, Yong, editor, and Zu, Qiaohong, editor

Published
2019
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11. A Real-Time Update Approach for Visualizing Multidimensional Big Data

Author

Haihong, E., Kong, Huihui, Liu, Yunfeng, Song, Meina, Ou, Zhonghong, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Milošević, Danijela, editor, Tang, Yong, editor, and Zu, Qiaohong, editor

Published
2019
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16. Research and Implementation of Question Classification Model in Q&A System

Author

E, Haihong, Hu, Yingxi, Song, Meina, Ou, Zhonghong, Wang, Xinrui, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Ibrahim, Shadi, editor, Choo, Kim-Kwang Raymond, editor, Yan, Zheng, editor, and Pedrycz, Witold, editor

Published
2017
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17. A CNN-Based Supermarket Auto-Counting System

Author

Ou, Zhonghong, Lin, Changwei, Song, Meina, E, Haihong, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Ibrahim, Shadi, editor, Choo, Kim-Kwang Raymond, editor, Yan, Zheng, editor, and Pedrycz, Witold, editor

Published
2017
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20. Big-Little-Cell Based 'Handprint' Positioning System

Author

Ou, Zhonghong, Wu, Jun, Ylä-Jääski, Antti, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Xu, Kuai, editor, and Zhu, Haojin, editor

Published
2015
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21. Adaptive Packet Size Control for Bulk Data Transmission in IPv6 over Networks of Resource Constrained Nodes

Author

Deng, Yang, Ou, Zhonghong, Ylä-Jääski, Antti, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Abdelzaher, Tarek, editor, Pereira, Nuno, editor, and Tovar, Eduardo, editor

Published
2015
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23. Development and multi-center validation of machine learning model for early detection of fungal keratitis

Author

Wei, Zhenyu, primary, Wang, Shigeng, additional, Wang, Zhiqun, additional, Zhang, Yang, additional, Chen, Kexin, additional, Gong, Lan, additional, Li, Guigang, additional, Zheng, Qinxiang, additional, Zhang, Qin, additional, He, Yan, additional, Zhang, Qi, additional, Chen, Di, additional, Cao, Kai, additional, Pang, Jinding, additional, Zhang, Zijun, additional, Wang, Leying, additional, Ou, Zhonghong, additional, and Liang, Qingfeng, additional

Published
2023
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35. Development and Multi-Center Validation of Machine Learning Model for Early Detection of Fungal Keratitis

Author

Wei, Zhenyu, primary, Wang, Shigeng, additional, Chen, Di, additional, Zhang, Qi, additional, He, Yan, additional, Zheng, Qinxiang, additional, Li, Guigang, additional, Zhang, Qing, additional, Gong, Lan, additional, Zhang, Yang, additional, Chen, Kexin, additional, Wang, Zhiqun, additional, Cao, Kai, additional, Wang, Leying, additional, Ou, Zhonghong, additional, and Liang, Qingfeng, additional

Published
2022
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45. Performance evaluation of a Kademlia-based communication-oriented P2P system under churn

Author

Ou, Zhonghong, Harjula, Erkki, Kassinen, Otso, and Ylianttila, Mika

Subjects
Algorithm, Algorithms -- Analysis, Peer to peer computing -- Analysis, Androgens -- Analysis, Energy consumption -- Analysis
Abstract

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.comnet.2009.09.022 Byline: Zhonghong Ou (a)(b), Erkki Harjula (a), Otso Kassinen (a), Mika Ylianttila (a) Keywords: Communication-oriented; Churn; Distributed hash table (DHT); Kademlia; Energy consumption Abstract: The phenomenon of churn has a significant effect on the performance of Peer-to-Peer (P2P) networks, especially in mobile environments that are characterized by intermittent connections and unguaranteed network bandwidths. A number of proposals have been put forward to deal with this problem; however, we have so far not seen any thorough analysis to guide the optimal design choices and parameter configurations for structured P2P networks. In this article, we present a performance evaluation of a structured communication-oriented P2P system in the presence of churn. The evaluation is conducted using both simulation models and a real-life prototype implementation. In both evaluation environments, we utilize Kademlia with some modifications as the underlying distributed hash table (DHT) algorithm, and Peer-to-Peer Protocol (P2PP) as the signaling protocol. The results from the simulation models created using Nethawk EAST (a telecommunication simulator software) suggest that, in most situations, a lookup parallelism degree of 3 and resource replication degree of 3 are enough for guaranteeing a high resource lookup success ratio. We also notice that, with the parallel lookup mechanism, a good success ratio is achieved even without the KeepAlive traffic that is used for detecting the aliveness of nodes. A prototype system that works in mobile environment is implemented to evaluate the feasibility of mobile nodes acting as full-fledged peers. The measurements made using the prototype show that, from the viewpoints of CPU load and network traffic load, it is feasible for the mobile nodes to take part in the overlay. Through energy consumption measurements, we draw the conclusion that in general the UMTS access mode consumes slightly more power than the WLAN access mode. Protocol packets with sizes of 200bytes or less are observed to be the most energy efficient in the UMTS access mode. Author Affiliation: (a) Department of Electrical and Information Engineering, University of Oulu, Finland (b) School of Computer Engineering, Beijing University of Posts and Telecommunications, China Article History: Received 13 May 2009; Revised 17 September 2009; Accepted 30 September 2009 Article Note: (miscellaneous) Responsible Editor: N. Agoulmine

Published
2010

49. BMM-Net

Author

Zhang, Ruru, primary, He, Jiawen, additional, Shi, Shenda, additional, E, Haihong, additional, Ou, Zhonghong, additional, and Song, Meina, additional

Published
2020
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