检索结果-内蒙古大学图书馆

37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Sun, Yifei Song, Cheng Lu, Feng Li, Wei Jin, Hai Zomaya, Albert Y. National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Centre for Distributed and High Performance Computing School of Computer Science University of Sydney Australia

ISBN: (纸本)9781577358800

Machine learning models are increasingly used in time series prediction with promising results. The model explanation of time series prediction falls behind the model development and makes less sense to users in understanding model decisions. This paper proposes ES-Mask, a post-hoc and model-agnostic evolutionary strip mask-based saliency approach for time series applications. ES-Mask designs the mask consisting of strips with the same salient value in consecutive time steps to produce binary and sustained feature importance scores over time for easy understanding and interpretation of time series. ES-Mask uses an evolutionary algorithm to search for the optimal mask by manipulating strips in rounds, thus is agnostic to models by involving no internal model states in the search. The initial experiments on MIMIC-III data set show that ES-Mask outperforms state-of-the-art methods. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Time series

来源：评论

学校读者我要写书评

暂无评论

Road network representation learning with the third law of geography 24

Road network representation learning with the third law of g...

引用

Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Haicang Zhou Weiming Huang Yile Chen Tiantian He Gao Cong Yew-Soon Ong College of Computing and Data Science Nanyang Technological University Singapore Institute of High-Performance Computing and Centre for Frontier Al Research Agency for Science Technology and Research Singapore College of Computing and Data Science Nanyang Technological University Singapore and Institute of High-Performance Computing and Centre for Frontier Al Research Agency for Science Technology and Research Singapore

ISBN: (纸本)9798331314385

Road network representation learning aims to learn compressed and effective vectorized representations for road segments that are applicable to numerous tasks. In this paper, we identify the limitations of existing methods, particularly their overemphasis on the distance effect as outlined in the First Law of Geography. In response, we propose to endow road network representation with the principles of the recent Third Law of Geography. To this end, we propose a novel graph contrastive learning framework that employs geographic configuration-aware graph augmentation and spectral negative sampling, ensuring that road segments with similar geographic configurations yield similar representations, and vice versa, aligning with the principles stated in the Third Law. The framework further fuses the Third Law with the First Law through a dual contrastive learning objective to effectively balance the implications of both laws. We evaluate our framework on two real-world datasets across three downstream tasks. The results show that the integration of the Third Law significantly improves the performance of road segment representations in downstream tasks. Our code is available at https://***/Haicang/Garner.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Simulation of the Quantum Key Distribution Algorithm Using the Intel Quantum SDK 13

Simulation of the Quantum Key Distribution Algorithm Using t...

引用

13th International Conference on Advanced Computer Information Technologies, ACIT 2023

作者： Gambo, Yaknan John Shinde, Tejas Rasch, Kevin Liebelt, Helena Li, Rui Deggendorf Institute of Technology High Performance Computing and Quantum Computing Deggendorf Germany Quantum Computing Research Intel Inc HillsboroOR United States Deggendorf Institute of Technology Department of Computer Science Deggendorf Germany

Information security is an important fabric of our world today. Technologies used in the development of information security have constantly been evolving in response to new threats. Cryptographic algorithms like the RSA, DSA, AES etc. are currently being used in securing information even over insecure channels. The coming of quantum computers has now posed a great threat to classical cryptosystems which can be broken in polynomial time thanks to Shor's algorithm. A good hacker with good quantum computing power can break into encrypted messages easily. The Quantum Key Distribution (QKD) algorithm based on the BB84 protocol provides a solution to this problem. It makes it possible for communicating parties to exchange secret keys securely before a conversation can ensue. Verifying if these keys have been tampered with is a good first step to start before any other. QKD technology has made it possible to exchange these keys via an insecure channel and lets communicating parties know if an eavesdropper has tempered with the message. In this work, we discuss the QKD algorithm and simulated it on the newly released Intel quantum SDK. © 2023 IEEE.

关键词： Quantum cryptography

来源：评论

学校读者我要写书评

暂无评论

Towards Green HPC Job Outsourcing: Energy Models for Molecular Dynamics Simulations on Three Clusters

Towards Green HPC Job Outsourcing: Energy Models for Molecul...

引用

International Symposium on Parallel and Distributed computing

作者： Ruben Horn Philipp Neumann Chair for High Performance Computing Helmut Schmidt University Hamburg Germany Research Group High Performance Computing and Data Science Universitä Hamburg Deutsches Elektronen-Synchroton Hamburg Germany

ISBN: (数字)9798350369199

ISBN: (纸本)9798350369205

In order to facilitate outsourcing of HPC jobs to remote computing resources powered directly by renewable energy, we aim to model the energy requirement of MD simulations on a single node. First, three different clusters are compared using an example simulation scenario with varying volume using a simple linear model. Then we investigate the impact of the density and simulated substance (number of LJ sites) and confirm their linear effect on the energy consumption of the simulation. Combining these insights with direct energy measurements could allow the prediction of the total energy required for a given job. Other simulation parameters, such as the temperature, and hardware parameters like FLOPS and cache size, should be added to such a model in the future to improve its generalizability.

关键词： Temperature measurement Solid modeling Energy consumption Renewable energy sources Computational modeling Green products Energy measurement Hardware Outsourcing Distributed computing

来源：评论

学校读者我要写书评

暂无评论

BERD+: A Generic Sequential Recommendation Framework by Eliminating Unreliable data with Item- and Attribute-level Signals

引用

ACM Transactions on Information Systems 2023年第2期42卷 1-33页

作者： Sun, Yatong Yang, Xiaochun Sun, Zhu Wang, Bin School of Computer Science and Engineering Northeastern University School of Computing Macquarie University China Australia Institute of High Performance Computing Centre for Frontier AI Research A*STAR Singapore School of Computer Science and Engineering Northeastern University China National Frontiers Science Center for Industrial Intelligence and Systems Optimization China Key Laboratory of Data Analytics and Optimization for Smart Industry [Northeastern University Ministry of Education China

A preliminary work of our study was published at IJCAI’21 [47], which is substantially extended in the following aspects: (1) In Section 1, we analyze the necessity of introducing item attributes for detecting unreliable instances, together with the problems and challenges that attributes may bring in. (2) In Section 2, we add discussions about the limitations of existing attribute-aware recommender systems (Section 2.2) and denoising methods (Section 2.3) in the context of detecting unreliable instances. (3) In Section 4.2, we further conduct an in-depth analysis at the attribute level to demonstrate the capability of attributes for rectifying instance loss and uncertainty, as well as the disturbance caused by attributes. (4) We generalize BERD to a generic framework BERD+ in Section 5.1, equipped with novel modules, i.e., HU-GCN (Section 5.2) and EPE (Section 5.4), which properly incorporate item attributes while reducing their disturbance for rectifying instance uncer tainty (Section 5.5) and loss (Section 5.6). The generic BERD+ can be flexibly plugged into existing SRSs for performance enhanced recommendation via eliminating unreliable data. (5) In Section 6.2, we apply our BERD+ framework to seven state-of-the-art SRSs on five real-world datasets to illustrate its superiority. (6) To avoid unfair comparison caused by item attributes, we build and compare with the baseline that combines the original BERD and an advanced attribute-aware recommender system, KSR [19]. (7) For more comprehensive comparison, in Section 6.2.2, we compare BRED+ with two state-of-the-art denoising approaches;in Section 6.2.3, to examine the efficacy of HU-GCN and EPE, we compare HU-GCN with various attribute embedding techniques, i.e., variants of graph neural networks, and compare EPE with different attribute fusing methods, i.e., adding, concatenation, and weighted sum. (8) In Section 6.2.4, a detailed ablation study is conducted to verify the effectiveness of each module of BERD+. (

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Demystifying Ethereum account diversity:observations,models and analysis

引用

Frontiers of Computer science 2022年第4期16卷 119-130页

作者： Chaofan Wang Xiaohai Dai Jiang Xiao Chenchen Li Ming Wen Bingbing Zhou Hai Jin Clusters and Grid Computing Lab National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabWuhan430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan430074China School of Cyber Science and Engineering Huazhong University of Science and TechnologyWuhan430074China Centre for Distributed and High Performance Computing School of Information TechnologiesThe University of SydneyCamperdownNew South Wales2006Australia

Blockchain platform Ethereum has involved millions of accounts due to its strong potential for providing numerous services based on smart *** massive accounts can be divided into diverse categories,such as miners,tokens,and exchanges,which is termed as account diversity in this *** benefit of investigating diversity are multi-fold,including understanding the Ethereum ecosystem deeper and opening the possibility of tracking certain abnormal ***,the exploration of blockchain account diversity remains *** the most relevant studies,which focus on the deanonymization of the accounts on Bitcoin,can hardly be applied on Ethereum since their underlying protocols and user idioms are *** this end,we present the first attempt to demystify the account diversity on *** key observation is that different accounts exhibit diverse behavior patterns,leading us to propose the heuristics for classification as the *** then raise the coverage rate of classification by the statistical learning model Maximum Likelihood Estimation(MLE).We collect real-world data through extensive efforts to evaluate our proposed method and show its ***,we make an in-depth analysis of the dynamic evolution of the Ethereum ecosystem and uncover the abnormal arbitrage *** for the former,we validate two sweeping statements reliably:(1)standalone miners are gradually replaced by the mining pools and cooperative miners;(2)transactions related to the mining pool and exchanges take up a large share of the total *** latter analysis shows that there are a large number of arbitrage transactions transferring the coins from one exchange to another to make a price difference.

关键词： blockchain Ethereum classification account diversity network analysis

来源：评论

学校读者我要写书评

暂无评论

Solving 2 by 2 Grid Sudoku Problem using Grover's Algorithm with Intel Quantum SDK 13

Solving 2 by 2 Grid Sudoku Problem using Grover's Algorithm ...

引用

13th International Conference on Advanced Computer Information Technologies, ACIT 2023

作者： Shinde, Tejas Gambo, Yaknan John Rasch, Kevin Liebelt, Helena Li, Rui Deggendorf Institute of Technology High Performance Computing and Quantum Computing Deggendorf Germany Quantum Computing Research Intel Inc HillsboroOR United States Deggendorf Institute of Technology Department of Applied Computer Science Deggendorf Germany

Quantum technologies are moving towards enabling real world uses and as these technologies develop. The most important thing that differs Quantum from classical computers is that Quantum Computers can do 'multiple' tasks at the same time as opposed to the classical computers doing a single task at a time. This phenomenon has led to the discovery of a few algorithms that are faster than regular computers. One such algorithm is the Grover's Algorithm[l]. This algorithm has shown a few real-world applications such as Quantum Search Algorithm, Travelling salesman, etc[2]. In this paper, Grover's algorithm is introduced, and a 2 x 2 Sudoku example is solved using Intel Quantum Software Development Kit[3]. The Intel Quantum SDK is quite new and in this paper we show how a simple Grover's Problem can be implemented in the SDK. Sudoku is a popular number-based puzzle game played on a grid consisting of nine squares subdivided into smaller 3x3 grids. The objective is to fill the grid with numbers from 1 to 9, ensuring that each row, column, and 3x3 grid contains all numbers exactly once. © 2023 IEEE.

关键词： Qubits

来源：评论

学校读者我要写书评

暂无评论

AIF-SFDA: Autonomous Information Filter-driven Source-Free Domain Adaptation for Medical Image Segmentation 39

AIF-SFDA: Autonomous Information Filter-driven Source-Free D...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Li, Haojin Li, Heng Chen, Jianyu Zhong, Rihan Niu, Ke Fu, Huazhu Liu, Jiang Research Institute of Trustworthy Adaptive Systems Southern University of Science and Technology China Department of Computer Science and Engineering Southern University of Science and Technology China Beijing Information Science & Technology University China Institute of High Performance Computing Agency for Science Technology and Research China

ISBN: (纸本)157735897X

Decoupling domain-variant information (DVI) from domain-invariant information (DII) serves as a prominent strategy for mitigating domain shifts in the practical implementation of deep learning algorithms. However, in medical settings, concerns surrounding data collection and privacy often restrict access to both training and test data, hindering the empirical decoupling of information by existing methods. To tackle this issue, we propose an Autonomous Information Filter-driven Source-free Domain Adaptation (AIF-SFDA) algorithm, which leverages a frequency-based learnable information filter to autonomously decouple DVI and DII. Information Bottleneck (IB) and Self-supervision (SS) are incorporated to optimize the learnable frequency filter. The IB governs the information flow within the filter to diminish redundant DVI, while SS preserves DII in alignment with the specific task and image modality. Thus, the autonomous information filter can overcome domain shifts relying solely on target data. A series of experiments covering various medical image modalities and segmentation tasks were conducted to demonstrate the benefits of AIF-SFDA through comparisons with leading algorithms and ablation studies. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

MHGRL: An Effective Representation Learning Model for Electronic Health Records 30

MHGRL: An Effective Representation Learning Model for Electr...

引用

Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Liu, Feiyan Li, Liangzhi Wang, Xiaoli Liu, Chang Luo, Feng Su, Jinsong Qian, Yiming School of Informatics Xiamen University Xiamen China Meetyou AI Lab Xiamen China National Institute for Data Science in Health and Medicine Xiamen University Xiamen China RMIT University Melbourne Australia Institute of High Performance Computing Agency for Science Technology and Research Singapore

ISBN: (纸本)9782493814104

Electronic health records (EHRs) serve as a digital repository storing comprehensive medical information about patients. Representation learning for EHRs plays a crucial role in healthcare applications. In this paper, we propose a Multimodal Heterogeneous Graph-enhanced Representation Learning, denoted as MHGRL, aimed at learning effective EHR representations. To address the challenge posed by data insufficiency of EHRs, MHGRL utilizes a multimodal heterogeneous graph to model an EHR. Specifically, we construct a heterogeneous graph for each EHR and enrich it by incorporating multimodal information with medical ontology and textual notes. With the integration of pre-trained model, graph neural network, and attention mechanism, MHGRL effectively incorporates both node attributes and structural information across a multimodal heterogeneous graph. Moreover, we employ contrastive learning to ensure the consistency of representations for similar EHRs and improve the model robustness. The experimental results show that MHGRL outperforms all baselines on two real clinical datasets in downstream tasks, including EHR clustering and disease prediction. The code is available at https://***/emmali808/MHGRL. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Records management

来源：评论

学校读者我要写书评

暂无评论

Neurosymbolic AI for Mining Key Aspects of Socially Responsible Investing

Neurosymbolic AI for Mining Key Aspects of Socially Responsi...

引用

IEEE International Conference on data Mining Workshops (ICDM Workshops)

作者： Wihan Van Der Heever Ranjan Satapathy Erik Cambria Rick Siow Mong Goh College of Computing and Data Science Nanyang Technological University Singapore Institute of High Performance Computing (IHPC) Agency for Science Technology and Research (A*STAR) Singapore

ISBN: (数字)9798331530631

ISBN: (纸本)9798331530648

Environmental, Social, and Governance (ESG) factors have become critical for assessing corporate sustainability and ethical responsibility. However, the vast volume of unstructured data available across corporate reports, social media, and news sources poses a challenge for systematic ESG analysis. This paper explores the application of neurosymbolic AI, which combines neural networks’ pattern recognition capabilities with the structured reasoning of symbolic AI, to mine key aspects of ESG from large-scale, diverse data sources. By leveraging SenticNet for concept parsing and deep learning for sentiment analysis, we extract relevant ESG metrics, classify corporate practices, and identify trends. This hybrid approach enhances both the interpretability and scalability of ESG analysis, providing more accurate insights into corporate behaviors and their impact on sustainability goals. Results demonstrate that neurosymbolic AI not only improves the extraction of meaningful ESG aspects but also enables real-time monitoring, supporting data-driven decision-making for investors, regulators, and stakeholders.

关键词： Ethics Technological innovation Sentiment analysis Systematics Decision making Companies Real-time systems Stakeholders Artificial intelligence Sustainable development

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：