检索结果-内蒙古大学图书馆

Boosting Adversarial Training with Learnable Distribution

computers, Materials & Continua 2024年第3期78卷 3247-3265页

作者： Kai Chen Jinwei Wang James Msughter Adeke Guangjie Liu Yuewei Dai School of Electronics and Information Engineering Nanjing University of Information Science and TechnologyNanjing210044China Key Laboratory of Intelligent Support Technology for Complex Environments Ministry of EducationNanjing210044China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China Nanjing Center for Applied Mathematics Nanjing211135China

In recent years,various adversarial defense methods have been proposed to improve the robustness of deep neural *** training is one of the most potent methods to defend against adversarial ***,the difference in the feature space between natural and adversarial examples hinders the accuracy and robustness of the model in adversarial *** paper proposes a learnable distribution adversarial training method,aiming to construct the same distribution for training data utilizing the Gaussian mixture *** distribution centroid is built to classify samples and constrain the distribution of the sample *** natural and adversarial examples are pushed to the same distribution centroid to improve the accuracy and robustness of the *** proposed method generates adversarial examples to close the distribution gap between the natural and adversarial examples through an attack algorithm explicitly designed for adversarial *** algorithm gradually increases the accuracy and robustness of the model by scaling ***,the proposed method outputs the predicted labels and the distance between the sample and the distribution *** distribution characteristics of the samples can be utilized to detect adversarial cases that can potentially evade the model *** effectiveness of the proposed method is demonstrated through comprehensive experiments.

关键词： Adversarial training feature space learnable distribution distribution centroid

来源：评论

学校读者我要写书评

暂无评论

Raven: Benchmarking Monetary Expense and Query Efficiency of OLAP Engines on the Cloud 28th

Raven: Benchmarking Monetary Expense and Query Efficiency o...

引用

28th International Conference on Database Systems for Advanced Applications, DASFAA 2023

作者： Wu, Tongyu Gu, Rong Li, Yang Ma, Hongbin Chen, Yi Zhu, Ying Yu, Xiaoxiang Xu, Tengting Huang, Yihua State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Kyligence Inc. Shanghai China

ISBN: (纸本)9783031306778

Nowadays, it is prevalent to build OLAP services on cloud platforms. Cloud OLAP adopters are eager to understand and characterize the performance of OLAP engines on the cloud. However, traditional OLAP benchmarks are usually designed for on-premise environments. When evaluating cloud OLAP engines, they have limitations on cloud environment adaption and cloud scenario benchmark execution. To address these issues, this paper proposes Raven, a cloud-oriented OLAP benchmark with flexible system architecture and diversified workloads. Raven supports cloud service deployment and various cloud OLAP engine integration. In addition, to simulate complex cloud query scenarios, we design a group of timeline-based and service-oriented workloads. We implement Raven on the Amazon AWS cloud platform and use it to evaluate typical types of widely-used OLAP engines, including Presto, SparkSQL, Kylin, and Athena. Experimental results show that Raven can effectively benchmark diversified OLAP engines. Besides, Raven can benchmark various configuration settings of an identical OLAP engine. We also explore an OLAP case study on the cloud using Raven. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Engines

来源：评论

学校读者我要写书评

暂无评论

LIBALCHEMY: A Two-Layer Persistent Summary Design for Taming Third-Party Libraries in Static Bug-Finding Systems 24

LIBALCHEMY: A Two-Layer Persistent Summary Design for Taming...

引用

44th ACM/IEEE International Conference on software Engineering, ICSE 2024

作者： Wu, Rongxin He, Yuxuan Huang, Jiafeng Wang, Chengpeng Tang, Wensheng Shi, Qingkai Xiao, Xiao Zhang, Charles Xiamen Key Laboratory of Intelligent Storage and Computing School of Informatics Xiamen University Xiamen China The Hong Kong University of Science and Technology Hong Kong State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Person Hang Zhou China

ISBN: (纸本)9798400702174

Despite the benefits of using third-party libraries (TPLs), the misuse of TPL functions raises quality and security concerns. Using traditional static analysis to detect bugs caused by TPL function is nontrivial. One promising solution would be to automatically generate and persist the summaries of TPL functions offline and then reuse these summaries in compositional static analysis online. However, when dealing with millions of lines of TPL code, the summaries designed by existing studies suffer from an unresolved paradox. That is, a highly precise form of summary leads to an unaffordable space and time overhead, while an imprecise one seriously hurts its precision or recall. To address the paradox, we propose a novel two-layer summary design. The first layer utilizes a line-sized program representation known as the program dependence graph to compactly encode path conditions, while the second layer encodes bug-type-specific properties. We implemented our idea as a tool called Libalchemy and evaluated it on fifteen mature and extensively checked open-source projects. Experimental results show that Libalchemy can check over ten million lines of code within ten hours. Libalchemy has detected 55 true bugs with a high precision of 90.16%, eleven of which have been assigned CVE IDs. Compared to whole-program analysis and the conventional design of path-sensitively precise summaries, Libalchemy achieves an 18.56× and 12.77× speedup and saves 91.49% and 90.51% of memory usage, respectively. © 2024 ACM.

关键词： Static analysis

来源：评论

学校读者我要写书评

暂无评论

Critical Nodes Identification Algorithm Based on ResNet-CBAM

IEEE Networking Letters

引用

IEEE Networking Letters 2025年第2期7卷 103-107页

作者： Li, Xujie Shao, Fei Sun, Ying Li, Haotian Huang, Jiayi Hohai University College of Computer Science and Software Engineering Nanjing210098 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Hohai University College of Information Science and Engineering Nanjing210098 China

The identification of critical nodes in networks is of substantial practical significance. For instance, it can expedite information propagation within networks, target vulnerable links to enhance robustness, and optimize resource allocation by reducing redundancy and lowering costs. To improve the accuracy of critical node identification, we propose an algorithm that integrates complex networks, propagation models, and deep learning techniques. The algorithm generates low-complexity features that include the characteristics of nodes and their neighboring nodes. A ResNet-CBAM network is then designed to identify critical nodes. To assess node importance, a method has been proposed that considers both propagation range and propagation efficiency, using their product as the evaluation criterion. Experimental results show that, compared to various centrality-based algorithms and other deep learning methods, our proposed algorithm outperforms others in terms of recognition accuracy across different types of networks. © 2019 IEEE.

关键词： Small-world networks

来源：评论

学校读者我要写书评

暂无评论

Long-Tail Learning with Foundation Model: Heavy Fine-Tuning Hurts 41

Long-Tail Learning with Foundation Model: Heavy Fine-Tuning ...

引用

41st International Conference on Machine Learning, ICML 2024

作者： Shi, Jiang-Xin Wei, Tong Zhou, Zhi Shao, Jie-Jing Han, Xin-Yan Li, Yu-Feng National Key Laboratory for Novel Software Technology Nanjing University China School of Artificial Intelligence Nanjing University China School of Computer Science and Engineering Southeast University China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China

The fine-tuning paradigm in addressing long-tail learning tasks has sparked significant interest since the emergence of foundation models. Nonetheless, how fine-tuning impacts performance in long-tail learning was not explicitly quantified. In this paper, we disclose that heavy fine-tuning may even lead to non-negligible performance deterioration on tail classes, and lightweight fine-tuning is more effective. The reason is attributed to inconsistent class conditions caused by heavy fine-tuning. With the observation above, we develop a low-complexity and accurate long-tail learning algorithms LIFT with the goal of facilitating fast prediction and compact models by adaptive lightweight fine-tuning. Experiments clearly verify that both the training time and the learned parameters are significantly reduced with more accurate predictive performance compared with state-of-the-art approaches. The implementation code is available at https://***/shijxcs/LIFT. Copyright 2024 by the author(s)

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

SLAM: Towards Efficient Multilingual Reasoning via Selective Language Alignment 31

SLAM: Towards Efficient Multilingual Reasoning via Selective...

引用

31st International Conference on Computational Linguistics, COLING 2025

作者： Fan, Yuchun Mu, Yongyu Wang, Yilin Huang, Lei Ruan, Junhao Li, Bei Xiao, Tong Huang, Shujian Feng, Xiaocheng Zhu, Jingbo NLP Lab School of Computer Science and Engineering Northeastern University Shenyang China NiuTrans Research Shenyang China Harbin Institute of Technology Harbin China Meituan Inc. National Key Laboratory for Novel Software Technology Nanjing University China

ISBN: (纸本)9798891761964

Despite the significant improvements achieved by large language models (LLMs) in English reasoning tasks, these models continue to struggle with multilingual reasoning. Recent studies leverage a full-parameter and two-stage training paradigm to teach models to first understand non-English questions and then reason. However, this method suffers from both substantial computational resource computing and catastrophic forgetting. The fundamental cause is that, with the primary goal of enhancing multilingual comprehension, an excessive number of irrelevant layers and parameters are tuned during the first stage. Given our findings that the representation learning of languages is merely conducted in lower-level layers, we propose an efficient multilingual reasoning alignment approach that precisely identifies and fine-tunes the layers responsible for handling multilingualism. Experimental results show that our method, SLAM, only tunes 6 layers' feed-forward sub-layers including 6.5−8% of all parameters within 7B and 13B LLMs, achieving superior average performance than all strong baselines across 10 languages. Meanwhile, SLAM only involves one training stage, reducing training time by 4.1 − 11.9× compared to the two-stage method. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

来源：评论

学校读者我要写书评

暂无评论

Improved Regret for Bandit Convex Optimization with Delayed Feedback 38

Improved Regret for Bandit Convex Optimization with Delayed ...

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Wan, Yuanyu Yao, Chang Song, Mingli Zhang, Lijun School of Software Technology Zhejiang University Ningbo China State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Institute of Blockchain and Data Security Hangzhou China National Key Laboratory for Novel Software Technology Nanjing University Nanjing China

We investigate bandit convex optimization (BCO) with delayed feedback, where only the loss value of the action is revealed under an arbitrary delay. Let n, T, d¯ denote the dimensionality, time horizon, and average delay, respectively. Previous studies have achieved an O(√nT3/4 +(nd¯)1/3T2/3) regret bound for this problem, whose delay-independent part matches the regret of the classical non-delayed bandit gradient descent algorithm. However, there is a large gap between its delay-dependent part, i.e., O((nd¯)1/3T2/3), and an existing Ω(√d¯T) lower bound. In this paper, we illustrate that this gap can be filled in the worst case, where d¯is very close to the maximum delay d. Specifically, we first develop a novel algorithm, and prove that it enjoys a regret bound of O(√nT3/4 + √dT) in general. Compared with the previous result, our regret bound is better for d = O((nd¯)2/3T1/3), and the delay-dependent part is tight in the worst case. The primary idea is to decouple the joint effect of the delays and the bandit feedback on the regret by carefully incorporating the delayed bandit feedback with a blocking update mechanism. Furthermore, we show that the proposed algorithm can improve the regret bound to O((nT)2/3 log1/3 T + dlog T) for strongly convex functions. Finally, if the action sets are unconstrained, we demonstrate that it can be simply extended to achieve an O(n√T log T + dlog T) regret bound for strongly convex and smooth functions. © 2024 Neural information processing systems foundation. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Sparse representation scheme with enhanced medium pixel intensity for face recognition

引用

CAAI Transactions on Intelligence technology 2024年第1期9卷 116-127页

作者： Xuexue Zhang Yongjun Zhang Zewei Wang Wei Long Weihao Gao Bob Zhang State Key Laboratory of Public Big Data Institute for Artificial IntelligenceCollege of Computer Science and TechnologyGuizhou UniversityGuiyangGuizhouChina Department of Computer and Information Science University of MacaoAvenida da UniversidadeTaipaMacaoChina

Sparse representation is an effective data classification algorithm that depends on the known training samples to categorise the test *** has been widely used in various image classification *** in sparse representation means that only a few of instances selected from all training samples can effectively convey the essential class-specific information of the test sample,which is very important for *** deformable images such as human faces,pixels at the same location of different images of the same subject usually have different ***,extracting features and correctly classifying such deformable objects is very ***,the lighting,attitude and occlusion cause more *** the problems and challenges listed above,a novel image representation and classification algorithm is ***,the authors’algorithm generates virtual samples by a non-linear variation *** method can effectively extract the low-frequency information of space-domain features of the original image,which is very useful for representing deformable *** combination of the original and virtual samples is more beneficial to improve the clas-sification performance and robustness of the ***,the authors’algorithm calculates the expression coefficients of the original and virtual samples separately using the sparse representation principle and obtains the final score by a designed efficient score fusion *** weighting coefficients in the score fusion scheme are set entirely ***,the algorithm classifies the samples based on the final *** experimental results show that our method performs better classification than conventional sparse representation algorithms.

关键词： computer vision face recognition image classification image representation

来源：评论

学校读者我要写书评

暂无评论

Evolution of Web API Cooperation Network via Exploring Community Structure and Popularity

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6659-6671页

作者： Kang, Guosheng Wang, Yang Liu, Jianxun Cao, Buqing Xiao, Yong Xu, Yu Hunan University of Science and Technology Hunan Provincial Key Laboratory for Services Computing and Novel Software Technology School of Computer Science and Engineering Xiangtan411201 China University of Cambridge Cambridge Baker Systems Genomics Initiative British Heart Foundation Cardiovascular Epidemiology Unit Department of Public Health and Primary Care CambridgeCB1 8RN United Kingdom

With the growing popularity of the Internet, Web applications have become increasingly essential in our daily lives. Web application programming interfaces (Web APIs) play a crucial role in facilitating interaction between applications. However, most Web service platforms are suffering from the imbalance of Web services now, many services of good quality but low popularity are difficult to be invoked even once and do not create direct connections with the users. Some graph-based Web service recommendation methods also often present a long-tailed distribution of recommended Web services due to limited Mashup-API invocation relationships. To relieve this problem and promote service recommendation, in this article, we propose a community structure and popularity-based approach by constructing an evolving cooperation network for Web APIs. We leverage the Louvain algorithm in community detection to assign community structure to each Web API and consider both the popularity and community structure in constructing the network. By optimizing the Barabαsi-Albert (BA) evolving network model, we demonstrate that our approach outperforms the BA, Bianconi-Barabαsi (BB), and popularity-similarity optimization (PSO) models in Web service clustering. Based on our proposed evolutionary network model for the evolutionary extension of API cooperation network and used for downstream Web service recommendation tasks, the experimental results also show that our recommended approach outperforms some other baseline models for Web service recommendation. © 2020 IEEE.

关键词： Web services

来源：评论

学校读者我要写书评

暂无评论

An Empirical Study to Identify software Aging Indicators for Android OS 23

An Empirical Study to Identify Software Aging Indicators for...

引用

23rd IEEE International Conference on software Quality, Reliability, and Security, QRS 2023

作者： Chen, Yulei Nie, Yuge Yin, Beibei Zheng, Zheng Wu, Huayao Beihang University School of Automation Science and Electrical Engineering Beijing China Nanjing University State Key Laboratory for Novel Software Technology Nanjing China

ISBN: (纸本)9798350319583

Android mobile devices have been suffering from performance degradation and increased failure rates during long-term operation, known as software aging. With the major changes in performance optimization and resource management in Android, it is spotted that the aging behavior of Android devices in the 2020s differs significantly from previous studies in resource utilization and performance metrics, which makes some classic metrics difficult to measure aging well, and new metrics are required to better describe the new phenomenon. Thus, we propose thread- and interface-level metrics to portray aging at a finer granularity and conduct an empirical study to reidentify classic and new software aging metrics in Android. Analysis confirms that software aging in Android is less reflected in global resources metrics but in more fine-grained ones, so thread- and interface-level metrics combined with specific classic resource and process-level metrics are helpful as indicators of software aging. These metrics have been confirmed and deployed for aging monitoring by our mobile phone manufacturer collaborators. A new experimental method customized for metric studies has also been adopted in this paper, significantly reducing data costs and interference in measurements. © 2023 IEEE.

关键词： software reliability

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：