检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

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

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

432 篇 期刊文献
394 篇 会议

馆藏范围

826 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

586 篇 工学
- 480 篇 计算机科学与技术...
- 390 篇 软件工程
- 112 篇 信息与通信工程
- 51 篇 电气工程
- 48 篇 网络空间安全
- 46 篇 控制科学与工程
- 36 篇 电子科学与技术（可...
- 35 篇 生物工程
- 24 篇 机械工程
- 23 篇 安全科学与工程
- 20 篇 光学工程
- 15 篇 动力工程及工程热...
- 15 篇 化学工程与技术
- 15 篇 轻工技术与工程
- 12 篇 生物医学工程（可授...
- 11 篇 仪器科学与技术
- 11 篇 土木工程
- 11 篇 交通运输工程
231 篇 理学
- 137 篇 数学
- 46 篇 生物学
- 44 篇 物理学
- 41 篇 统计学（可授理学、...
- 24 篇 系统科学
- 15 篇 化学
137 篇 管理学
- 84 篇 管理科学与工程(可...
- 63 篇 图书情报与档案管...
- 31 篇 工商管理
36 篇 法学
- 24 篇 社会学
- 13 篇 法学
22 篇 经济学
- 22 篇 应用经济学
13 篇 军事学
10 篇 医学
9 篇 农学
6 篇 教育学
3 篇 文学

主题

29 篇 privacy
28 篇 semantics
27 篇 blockchain
23 篇 security
21 篇 federated learni...
18 篇 deep learning
17 篇 codes
17 篇 feature extracti...
17 篇 accuracy
16 篇 data privacy
15 篇 cloud computing
14 篇 cryptography
13 篇 differential pri...
13 篇 training
12 篇 computational mo...
12 篇 protocols
12 篇 data models
12 篇 robustness
11 篇 machine learning
10 篇 public key crypt...

机构

165 篇 the state key la...
104 篇 state key labora...
76 篇 institute of blo...
74 篇 state key labora...
71 篇 data assurance a...
70 篇 school of cyber ...
53 篇 zhejiang univers...
46 篇 university of ch...
42 篇 state key labora...
31 篇 college of compu...
29 篇 state key labora...
26 篇 zhejiang univers...
20 篇 school of softwa...
18 篇 state key labora...
18 篇 data assurance a...
18 篇 information secu...
15 篇 singapore manage...
15 篇 huawei
15 篇 ant group
14 篇 zhejiang univers...

作者

37 篇 ren kui
32 篇 song mingli
23 篇 xia xin
23 篇 qin zhan
21 篇 yang xiaohu
20 篇 tian youliang
20 篇 youliang tian
20 篇 hu xing
19 篇 chen yuling
18 篇 lin jingqiang
17 篇 chen jiawei
17 篇 chen chun
17 篇 li yiming
16 篇 chen gang
16 篇 liu zhongxin
16 篇 wang can
15 篇 lo david
14 篇 shou lidan
14 篇 chen ke
13 篇 xin xia

语言

725 篇 英文
96 篇 其他
6 篇 中文

检索条件"机构=The State Key Laboratory of Blockchain and Data Security"

共 826 条记录，以下是11-20 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

Ada-MSHyper: Adaptive Multi-Scale Hypergraph Transformer for Time Series Forecasting 38

Ada-MSHyper: Adaptive Multi-Scale Hypergraph Transformer for...

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Shang, Zongjiang Chen, Ling Wu, Binqing Cui, Dongliang State Key Laboratory of Blockchain and Data Security College of Computer Science and Technology Zhejiang University China

Although transformer-based methods have achieved great success in multi-scale temporal pattern interaction modeling, two key challenges limit their further development: (1) Individual time points contain less semantic information, and leveraging attention to model pair-wise interactions may cause the information utilization bottleneck. (2) Multiple inherent temporal variations (e.g., rising, falling, and fluctuating) entangled in temporal patterns. To this end, we propose Adaptive Multi-Scale Hypergraph Transformer (Ada-MSHyper) for time series forecasting. Specifically, an adaptive hypergraph learning module is designed to provide foundations for modeling group-wise interactions, then a multi-scale interaction module is introduced to promote more comprehensive pattern interactions at different scales. In addition, a node and hyperedge constraint mechanism is introduced to cluster nodes with similar semantic information and differentiate the temporal variations within each scales. Extensive experiments on 11 real-world datasets demonstrate that Ada-MSHyper achieves state-of-the-art performance, reducing prediction errors by an average of 4.56%, 10.38%, and 4.97% in MSE for long-range, short-range, and ultra-long-range time series forecasting, respectively. Code is available at https://***/shangzongjiang/Ada-MSHyper. © 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

DECRL: A Deep Evolutionary Clustering Jointed Temporal Knowledge Graph Representation Learning Approach 38

DECRL: A Deep Evolutionary Clustering Jointed Temporal Knowl...

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Chen, Qian Chen, Ling State Key Laboratory of Blockchain and Data Security College of Computer Science and Technology Zhejiang University China

Temporal Knowledge Graph (TKG) representation learning aims to map temporal evolving entities and relations to embedded representations in a continuous low-dimensional vector space. However, existing approaches cannot capture the temporal evolution of high-order correlations in TKGs. To this end, we propose a Deep Evolutionary Clustering jointed temporal knowledge graph Representation Learning approach (DECRL). Specifically, a deep evolutionary clustering module is proposed to capture the temporal evolution of high-order correlations among entities. Furthermore, a cluster-aware unsupervised alignment mechanism is introduced to ensure the precise one-to-one alignment of soft overlapping clusters across timestamps, thereby maintaining the temporal smoothness of clusters. In addition, an implicit correlation encoder is introduced to capture latent correlations between any pair of clusters under the guidance of a global graph. Extensive experiments on seven real-world datasets demonstrate that DECRL achieves the state-of-the-art performances, outperforming the best baseline by an average of 9.53%, 12.98%, 10.42%, and 14.68% in MRR, Hits@1, Hits@3, and Hits@10, respectively. © 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

Towards More Practical Automation of Vulnerability Assessment 24

Towards More Practical Automation of Vulnerability Assessmen...

引用

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

作者： Pan, Shengyi Bao, Lingfeng Zhou, Jiayuan Hu, Xing Xia, Xin Li, Shanping Zhejiang University The State Key Laboratory of Blockchain and Data Security Zhejiang Hangzhou China Centre for Software Excellence Huawei KingstonON Canada Zhejiang University The State Key Laboratory of Blockchain and Data Security Zhejiang Ningbo China Huawei Zhejiang Hangzhou China

ISBN: (纸本)9798400702174

It is increasingly suggested to identify emerging software vulner-abilities (SVs) through relevant development activities (e.g., issue reports) to allow early warnings to open source software (OSS) users. However, the support for the following assessment of the de-tected SVs has not yet been explored. SV assessment characterizes the detected SVs to prioritize limited remediation resources on the critical ones. To fill this gap, we aim to enable early vulnerability assessment based on SV-related issue reports (SIR). Besides, we observe the following concerns of the existing assessment techniques: 1) the assessment output lacks rationale and practical value;2) the associations between Common Vulnerability Scoring System (CVSS) metrics have been ignored;3) insufficient evaluation sce-narios and metrics. We address these concerns to enhance the prac-ticality of our proposed early vulnerability assessment approach (namely proEVA). Specifically, based on the observation of strong associations between CVSS metrics, we propose a prompt-based model to exploit such relations for CVSS metrics prediction. More-over, we design a curriculum-learning (CL) schedule to guide the model better learn such hidden associations during training. Aside from the standard classification metrics adopted in existing works, we propose two severity-aware metrics to provide a more compre-hensive evaluation regarding the prioritization of the high-severe SVs. Experimental results show that proEVA significantly outper-forms the baselines in both types of metrics. We further discuss the transferability of the prediction model regarding the upgrade of the assessment system, an important yet overlooked evaluation scenario in existing works. The results verify that proEVA is more efficient and flexible in migrating to different assessment systems. © 2024 ACM.

关键词： Open source software

来源：评论

学校读者我要写书评

暂无评论

BushStore: Efficient B+Tree Group Indexing for LSM-Tree in Non-Volatile Memory 40

BushStore: Efficient B+Tree Group Indexing for LSM-Tree in N...

引用

40th IEEE International Conference on data Engineering, ICDE 2024

作者： Wang, Zhenghao Shou, Lidan Chen, Ke Zhou, Xuan Zhejiang University The State Key Laboratory of Blockchain and Data Security China East China Normal University China

ISBN: (纸本)9798350317152

Non-Volatile Memory (NVM) offers low-latency, non-volatility, and byte-addressability, positioning it as a highly promising device for database performance enhancement. Cur-rent research primarily focuses on utilizing LSM-Tree in con-junction with NVM to reduce write amplification and alleviate write stall issues. However, the comprehensive potential of NVM in simultaneously augmenting both read and write performances remains underexplored. And the previous NVM-enhanced LSM-Tree also ignores the sensitivity of NVM to small-grained random reads and writes, which we believe is the key to further improving read and write performance. To address these issues, we propose BushStore, an innovative LSM-Tree variant specifically optimized for NVM. BushStore is designed with a three-level architecture, where the higher levels of BushStore contain a group of immutable, non-clustered B+Trees, replacing traditional SSTables. By storing the non-leaf nodes in the DRAM and the leaf nodes in NVM, and separating the data pages from the indexes, these B+Trees are able to exhibit high performance for diverse read and write operations. Our approach encompasses four key techniques to significantly boost system efficiency: First, we develop novel data structures that localize read/write operations to confined NVM areas, enhancing access speed. Second, we optimize the key-value data handling during flushing and compaction phases, leveraging the superior scanning and sequantial writing capabilities of B+Trees to ex-pedite write and compaction processes. Third, we dynamically adjust the B+Tree sizes, enabling a balanced and optimized flushing and compaction process, thereby improving overall write performance. Fourth, we implement a lazy-delete Cuckoo filtering and lazy-persistent allocation strategy to accelerate query and compaction processes. Evaluations show that BushStore exhibits high performance and scalability under synthetic and real work-loads, and achieves an average performance i

关键词： Dynamic random access storage

来源：评论

学校读者我要写书评

暂无评论

Pre-training by Predicting Program Dependencies for Vulnerability Analysis Tasks 24

Pre-training by Predicting Program Dependencies for Vulnerab...

引用

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

作者： Liu, Zhongxin Tang, Zhijie Zhang, Junwei Xia, Xin Yang, Xiaohu Zhejiang University The State Key Laboratory of Blockchain and Data Security China Zhejiang University China Huawei China

ISBN: (纸本)9798400702174

Vulnerability analysis is crucial for software security. Inspired by the success of pre-trained models on software engineering tasks, this work focuses on using pre-training techniques to enhance the understanding of vulnerable code and boost vulnerability analysis. The code understanding ability of a pre-trained model is highly related to its pre-training objectives. The semantic structure, e.g., control and data dependencies, of code is important for vulnerability analysis. However, existing pre-training objectives either ignore such structure or focus on learning to use it. The feasibility and benefits of learning the knowledge of analyzing semantic structure have not been investigated. To this end, this work proposes two novel pre-training objectives, namely Control Dependency Prediction (CDP) and data Dependency Prediction (DDP), which aim to predict the statement-level control dependencies and token-level data dependencies, respectively, in a code snippet only based on its source code. During pre-training, CDP and DDP can guide the model to learn the knowledge required for analyzing fine-grained dependencies in code. After pre-training, the pre-trained model can boost the understanding of vulnerable code during fine-tuning and can directly be used to perform dependence analysis for both partial and complete functions. To demonstrate the benefits of our pre-training objectives, we pre-train a Transformer model named PDBERT with CDP and DDP, fine-tune it on three vulnerability analysis tasks, i.e., vulnerability detection, vulnerability classification, and vulnerability assessment, and also evaluate it on program dependence analysis. Experimental results show that PDBERT benefits from CDP and DDP, leading to state-of-the-art performance on the three downstream tasks. Also, PDBERT achieves F1-scores of over 99% and 94% for predicting control and data dependencies, respectively, in partial and complete functions. © 2024 ACM.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Can Graph Neural Networks Expose Training data Properties? An Efficient Risk Assessment Approach 38

Can Graph Neural Networks Expose Training Data Properties? A...

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Yuan, Hanyang Xu, Jiarong Huang, Renhong Song, Mingli Wang, Chunping Yang, Yang Zhejiang University China Fudan University China Finvolution Group China Institute of Blockchain and Data Security China State Key Laboratory of Blockchain and Security Zhejiang University China

Graph neural networks (GNNs) have attracted considerable attention due to their diverse applications. However, the scarcity and quality limitations of graph data present challenges to their training process in practical settings. To facilitate the development of effective GNNs, companies and researchers often seek external collaboration. Yet, directly sharing data raises privacy concerns, motivating data owners to train GNNs on their private graphs and share the trained models. Unfortunately, these models may still inadvertently disclose sensitive properties of their training graphs (e.g., average default rate in a transaction network), leading to severe consequences for data owners. In this work, we study graph property inference attack to identify the risk of sensitive property information leakage from shared models. Existing approaches typically train numerous shadow models for developing such attack, which is computationally intensive and impractical. To address this issue, we propose an efficient graph property inference attack by leveraging model approximation techniques. Our method only requires training a small set of models on graphs, while generating a sufficient number of approximated shadow models for attacks. To enhance diversity while reducing errors in the approximated models, we apply edit distance to quantify the diversity within a group of approximated models and introduce a theoretically guaranteed criterion to evaluate each model's error. Subsequently, we propose a novel selection mechanism to ensure that the retained approximated models achieve high diversity and low error. Extensive experiments across six real-world scenarios demonstrate our method's substantial improvement, with average increases of 2.7% in attack accuracy and 4.1% in ROC-AUC, while being 6.5× faster compared to the best baseline. © 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

TimeChain: A Secure and Decentralized Off-chain Storage System for IoT Time Series data 25

TimeChain: A Secure and Decentralized Off-chain Storage Syst...

引用

34th ACM Web Conference, WWW 2025

作者： Teng, Yixiao Lv, Jiamei Wang, Ziping Gao, Yi Dong, Wei The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China

ISBN: (纸本)9798400712746

blockchain-based distributed storage systems offer enhanced security, transparency, and lower costs compared to traditional centralized storage, making them ideal for peer-to-peer collaboration. However, with the trend towards the Web of Things (WoT), lower transaction speeds and higher computational requirements limit their access to high-density data such as IoT. To address this, we propose TimeChain, an efficient off-chain blockchain storage system for IoT time series data. TimeChain batches discrete time series data, storing only the hash value of each batch on-chain while keeping the complete data off-chain. This significantly reduces storage overhead on the blockchain and storage latency by 37.4 times. TimeChain adopts an adaptive packaging mechanism to reduce the additional latency in range queries by converting the batch processing problem into a graph partitioning problem. To reduce the overhead of node selection, TimeChain integrates a node selection mechanism based on consensus protocol, combining node selection and consensus processes together. TimeChain also proposes a Locality-Sensitive Hashing tree-based data integrity verification mechanism to reduce transmission size. Our evaluation shows a reduction in query latency by 64.6% and storage latency by 35.3% compared to existing systems. © 2025 Copyright held by the owner/author(s).

关键词： Query languages

来源：评论

学校读者我要写书评

暂无评论

Distributionally Robust Graph-based Recommendation System 24

Distributionally Robust Graph-based Recommendation System

引用

33rd ACM Web Conference, WWW 2024

作者： Wang, Bohao Chen, Jiawei Li, Changdong Zhou, Sheng Shi, Qihao Gao, Yang Feng, Yan Chen, Chun Wang, Can The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Zhejiang University Hangzhou China

ISBN: (纸本)9798400701719

With the capacity to capture high-order collaborative signals, Graph Neural Networks (GNNs) have emerged as powerful methods in Recommender Systems (RS). However, their efficacy often hinges on the assumption that training and testing data share the same distribution (\aka IID assumption), and exhibits significant declines under distribution shifts. Distribution shifts commonly arises in RS, often attributed to the dynamic nature of user preferences or ubiquitous biases during data collection in RS. Despite its significance, researches on GNN-based recommendation against distribution shift are still sparse. To bridge this gap, we propose Distributionally Robust GNN (DR-GNN) that incorporates Distributional Robust Optimization (DRO) into the GNN-based recommendation. DR-GNN addresses two core challenges: 1) To enable DRO to cater to graph data intertwined with GNN, we reinterpret GNN as a graph smoothing regularizer, thereby facilitating the nuanced application of DRO;2) Given the typically sparse nature of recommendation data, which might impede robust optimization, we introduce slight perturbations in the training distribution to expand its support. Notably, while DR-GNN involves complex optimization, it can be implemented easily and efficiently. Our extensive experiments validate the effectiveness of DR-GNN against three typical distribution shifts. The code is available at https://***/WANGBohaO-jpg/DR-GNN. © 2024 ACM.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

PSL: Rethinking and Improving Softmax Loss from Pairwise Perspective for Recommendation 38

PSL: Rethinking and Improving Softmax Loss from Pairwise Per...

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Yang, Weiqin Chen, Jiawei Xin, Xin Zhou, Sheng Hu, Binbin Feng, Yan Chen, Chun Wang, Can Shandong University China Ant Group China State Key Laboratory of Blockchain and Data Security Zhejiang University China College of Computer Science and Technology Zhejiang University China Institute of Blockchain and Data Security China

Softmax Loss (SL) is widely applied in recommender systems (RS) and has demonstrated effectiveness. This work analyzes SL from a pairwise perspective, revealing two significant limitations: 1) the relationship between SL and conventional ranking metrics like DCG is not sufficiently tight;2) SL is highly sensitive to false negative instances. Our analysis indicates that these limitations are primarily due to the use of the exponential function. To address these issues, this work extends SL to a new family of loss functions, termed Pairwise Softmax Loss (PSL), which replaces the exponential function in SL with other appropriate activation functions. While the revision is minimal, we highlight three merits of PSL: 1) it serves as a tighter surrogate for DCG with suitable activation functions;2) it better balances data contributions;and 3) it acts as a specific BPR loss enhanced by Distributionally Robust Optimization (DRO). We further validate the effectiveness and robustness of PSL through empirical experiments. The code is available at https://***/Tiny-Snow/IR-Benchmark. © 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

Vision Mamba Mender 38

Vision Mamba Mender

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Hu, Jiacong Cao, Anda Feng, Zunlei Zhang, Shengxuming Wang, Yi Jia, Lingxiang Song, Mingli College of Computer Science and Technology Zhejiang University China School of Software Technology Zhejiang University China State Key Laboratory of Blockchain and Data Security Zhejiang University China Institute of Blockchain and Data Security China

Mamba, a state-space model with selective mechanisms and hardware-aware architecture, has demonstrated outstanding performance in long sequence modeling tasks, particularly garnering widespread exploration and application in the field of computer vision. While existing works have mixed opinions of its application in visual tasks, the exploration of its internal workings and the optimization of its performance remain urgent and worthy research questions given its status as a novel model. Existing optimizations of the Mamba model, especially when applied in the visual domain, have primarily relied on predefined methods such as improving scanning mechanisms or integrating other architectures, often requiring strong priors and extensive trial and error. In contrast to these approaches, this paper proposes the Vision Mamba Mender, a systematic approach for understanding the workings of Mamba, identifying flaws within, and subsequently optimizing model performance. Specifically, we present methods for predictive correlation analysis of Mamba's hidden states from both internal and external perspectives, along with corresponding definitions of correlation scores, aimed at understanding the workings of Mamba in visual recognition tasks and identifying flaws therein. Additionally, tailored repair methods are proposed for identified external and internal state flaws to eliminate them and optimize model performance. Extensive experiments validate the efficacy of the proposed methods on prevalent Mamba architectures, significantly enhancing Mamba's performance. For more information, please visit https://***/. © 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

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共83页 << < 1 2 3 4 5 6 7 8 9 10 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

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：