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442 results on '"Gu, Tianlong"'

56. Assembly Sequence Planning Based on Hybrid Artificial Bee Colony Algorithm

Author

Yuan, Wenbing, Chang, Liang, Zhu, Manli, Gu, Tianlong, Rannenberg, Kai, Editor-in-chief, Sakarovitch, Jacques, Series editor, Goedicke, Michael, Series editor, Tatnall, Arthur, Series editor, Neuhold, Erich J., Series editor, Pras, Aiko, Series editor, Tröltzsch, Fredi, Series editor, Pries-Heje, Jan, Series editor, Whitehouse, Diane, Series editor, Reis, Ricardo, Series editor, Furnell, Steven, Series editor, Furbach, Ulrich, Series editor, Gulliksen, Jan, Series editor, Rauterberg, Matthias, Series editor, Shi, Zhongzhi, editor, Vadera, Sunil, editor, and Li, Gang, editor

Published
2016
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57. An Improved Reconfiguration Algorithm for VLSI Arrays with A-Star

Author

Qian, Junyan, Zhou, Zhide, Zhao, Lingzhong, Gu, Tianlong, 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, Gervasi, Osvaldo, editor, Murgante, Beniamino, editor, Misra, Sanjay, editor, Rocha, Ana Maria A.C., editor, Torre, Carmelo M., editor, Taniar, David, editor, Apduhan, Bernady O., editor, Stankova, Elena, editor, and Wang, Shangguang, editor

Published
2016
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58. Collusive Model Poisoning Attack in Decentralized Federated Learning

Author

Tan, Shouhong, Hao, Fengrui, Gu, Tianlong, Li, Long, and Liu, Ming

Abstract

As a privacy-preserving machine learning paradigm, federated learning (FL) has attracted widespread attention from both academia and industry. Decentralized FL (DFL) overcomes the problems of untrusted aggregation server, single point of failure and poor scalability in traditional FL, making it suitable for industrial Internet of Things (IIoT). However, DFL provides more convenient conditions for malicious participants to launch attacks. This article focuses on the model poisoning attack in DFL for the first time, and proposes a novel attack method called collusive model poisoning attack (CMPA). To implement CMPA, we propose the dynamic adaptive construction mechanism, in which malicious participants can dynamically and adaptively construct malicious local models that meet distance constraints, reducing the convergence speed and accuracy of consensus models. Furthermore, we design the collusion-based attack enhancement strategies, where multiple participants can collude in the process of constructing malicious local models to improve the strength of attack. Empirical experiments conducted on MNIST and CIFAR-10 datasets reveal that CMPA significantly impacts the training process and results of DFL. Attack tests against representative defense methods show that CMPA not only invalidates statistical-based defenses but also skillfully overcomes performance-based methods, further proving its effectiveness and stealthiness. In addition, experiments based on practical IIoT scenario have also shown that CMPA can effectively disrupt system functionality.

Published
2024
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59. PROTECT: Parameter-Efficient Tuning for Few-Shot Robust Chinese Text Correction

Author

Feng, Xuan, Gu, Tianlong, Chang, Liang, and Liu, Xiaoli

Abstract

Non-normative texts and euphemisms are widely spread on the Internet, making it more difficult to moderate the content. These phenomena result from misspelling errors or deliberate textual attacks by users. However, current methods lack a robust design and are vulnerable to adversarial attacks. Thus, exploring robust text correction is of great significance. Text correction aims to automatically detect and correct errors in sentences, which can be employed as a defensive method against adversarial attacks. In this work, we provide PROTECT: a robust Chinese language model for more general Chinese text correction. In particular, we propose a simple but effective self-supervised learning approach for calibrating generalizable representations, which is robust to adapt to the changing adversarial texts. S pecifically, we develop an adversarial-aware multi-feature representation method that establishes auxiliary supervision. To the best of our knowledge, the PROTECT is the first model to represent perfect pinyin, abbreviation pinyin, character split, visual, and phonetic features simultaneously. Based on the generated adversarial examples, PROTECT is trained from scratch through a unified text-to-text generation paradigm. This empowers the model to correct multi-type text errors and inconsistent lengths simultaneously. After obtaining adversarially robust representations, we design a novel parameter-efficient tuning method consisting of context-specific adaptive prefix and semantic-consistent low-rank adaptation modules to implement zero-shot and few-shot learning. Extensive experimental results have shown that by tuning the parameters by 0.2%, PROTECT achieves best performance in the full-data and in the low-resource setting.

Published
2024
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71. Behavior-Based Ethical Understanding in Chinese Social News.

Author

Feng, Xuan, Gu, Tianlong, Bao, Xuguang, and Li, Long

Abstract

Ethical understanding aims at morally analyzing and discriminating ethical scenarios described in natural language. By classifying behaviors that occur in ethical scenarios as ethical or unethical, ethical understanding empowers artificial intelligence systems to understand human values so as to discern right from wrong morally. However, most existing ethical understanding methods lack fine-grained analysis and cannot handle the problem that an ethical scenario may contain multiple behaviors with multiple polarities. In this paper, we introduce a novel natural language processing task, behavior-based ethical understanding (BEU), for mining the purpose relation(s) and ethical polarity of a specific behavior from the social news. It contains three subtasks: behavior term extraction (BTE) to extracts behavior terms, purpose relation inference (PRI) to identifies purposive relations among behaviors, and polarity discrimination (PD) to predicts the ethical polarities of behaviors, respectively. To perform this task, we constructed a Chinese BEU dataset, named FG-ETHICS. Besides, we propose a three-stage framework, BEU-BERT, based on the pre-trained language model BERT and deliberately designed downstream models for three subtasks. Experimental results show that the proposed framework achieves the best performance from the BTE and PD tasks, and achieves a promising performance of 75% on the PRI task. [ABSTRACT FROM AUTHOR]

Published
2023
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85. Using ASP to Improve the Information Reuse in Mechanical Assembly Sequence Planning

Author

Zhao, Lingzhong, Wang, Xuesong, Qian, Junyan, Gu, Tianlong, Hutchison, David, Kanade, Takeo, Kittler, Josef, Kleinberg, Jon M., Mattern, Friedemann, Mitchell, John C., Naor, Moni, Nierstrasz, Oscar, Pandu Rangan, C., Steffen, Bernhard, Sudan, Madhu, Terzopoulos, Demetri, Tygar, Doug, Vardi, Moshe Y., Weikum, Gerhard, Goebel, Randy, Siekmann, Jörg, Wahlster, Wolfgang, Zhang, Byoung-Tak, editor, and Orgun, Mehmet A., editor

Published
2010
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86. Formula-Dependent Abstraction for CTL Model Checking

Author

Qian, Junyan, Zhao, Lingzhong, Cai, Guoyong, Gu, Tianlong, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Gervasi, Osvaldo, editor, Murgante, Beniamino, editor, Laganà, Antonio, editor, Taniar, David, editor, Mun, Youngsong, editor, and Gavrilova, Marina L., editor

Published
2008
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87. A Novel Test Case Generation Method for Prolog Programs Based on Call Patterns Semantics

Author

Zhao, Lingzhong, Gu, Tianlong, Qian, Junyan, Cai, Guoyong, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, and Shao, Zhong, editor

Published
2007
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88. On the New Application of Call Patterns to CPM Testing of Prolog Programs

Author

Zhao, Lingzhong, Gu, Tianlong, Qian, Junyan, Cai, Guoyong, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Sattar, Abdul, editor, and Kang, Byeong-ho, editor

Published
2006
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89. Designing Prolog Semantics for a Class of Observables

Author

Zhao, Lingzhong, Gu, Tianlong, Qian, Junyan, Cai, Guoyong, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Yang, Qiang, editor, and Webb, Geoff, editor

Published
2006
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