检索结果-内蒙古大学图书馆

Improving spatial clustering through a weight system on multilevel permanent museum attraction probability

Quality and Quantity 2025年 1-30页

作者： Distefano, Veronica De Iaco, Sandra Maggio, Sabrina Department of Economic Sciences University of Salento Lecce Italy European Centre for Living Technology Ca’ Foscari University of Venice Venice Italy National Centre for HPC Big Data and Quantum Computing Bologna Italy National Biodiversity Future Center Palermo Italy

Museums are extensively distributed all over the Italian territory. In this context, the identification of spatial patterns, referred to specific characteristics of museums evaluated at regional level, can support the enhancement of the cultural and natural heritage as well as the social and economic growth. In the literature, many studies were focused on the visitors’ profile or on the managerial performance and economic efficiency of the museums. However, none of them analysed the effects of the permanent presence of museums and their spatial contiguity by using both spatial machine learning models and statistical models. To this aim an innovative approach, which combines multilevel binary model and spatial clustering, as a machine learning unsupervised technique, is proposed to investigate the pattern recognition of the permanent museums all over the Italian territory and provide relevant information in terms of similarity among the spatial cluster formed. The logit of the museums to remain open all over the year, also with respect to different types of institution (private/public) and a different spatial/geographical constraints are jointly considered. In addition, a weight system is defined in order to introduce a regional measure of museums prevalence with respect to other types of cultural institutions. The ISTAT microdata concerning the Italian survey on museums and cultural entities are considered. The results highlight the great potentiality of this spatial clustering approach in delivering a better understanding of the role of museums as factor of challenge of urban development, providing in the meantime suggestions for tourism providers and museum managers. © The Author(s), under exclusive licence to Springer Nature B.V. 2024.

关键词： Bootstrap technique Clustering validation measures Multilevel model Odds ratios Spatial clustering

来源：评论

学校读者我要写书评

暂无评论

LLM Knows Geometry Better than Algebra: Numerical Understanding of LLM-Based Agents in A Trading Arena

arXiv

引用

arXiv 2025年

作者： Ma, Tianmi Du, Jiawei Huang, Wenxin Wang, Wenjie Xie, Liang Zhong, Xian Zhou, Joey Tianyi Hubei Key Laboratory of Transportation Internet of Things Wuhan University of Technology China Hubei Key Laboratory of Big Data Intelligent Analysis and Application Hubei University China Centre for Frontier AI Research Agency for Science Technology and Research Singapore Institute of High Performance Computing Agency for Science Technology and Research Singapore School of Computing National University of Singapore Singapore School of Science Wuhan University of Technology China

Recent advancements in large language models (LLMs) have significantly improved performance in natural language processing tasks. However, their ability to generalize to dynamic, unseen tasks, particularly in numerical reasoning, remains a challenge. Existing benchmarks mainly evaluate LLMs on problems with predefined optimal solutions, which may not align with real-world scenarios where clear answers are absent. To bridge this gap, we design the Agent Trading Arena, a virtual numerical game simulating complex economic systems through zero-sum games, where agents invest in stock portfolios. Our experiments reveal that LLMs, including GPT-4o, struggle with algebraic reasoning when dealing with plain-text stock data, often focusing on local details rather than global trends. In contrast, LLMs perform significantly better with geometric reasoning when presented with visual data, such as scatter plots or K-line charts, suggesting that visual representations enhance numerical reasoning. This capability is further improved by incorporating the reflection module, which aids in the analysis and interpretation of complex data. We validate our findings on NASDAQ STOCK dataset, where LLMs demonstrate stronger reasoning with visual data compared to text. Our code and data are publicly available at https://***/wekjsdvnm/***. © 2025, CC BY.

关键词： Natural language processing systems

来源：评论

学校读者我要写书评

暂无评论

N-Body Simulation Inspired by Metaheuristics Optimization

引用

Computer Systems Science & Engineering 2022年第6期41卷 1143-1155页

作者： Muhammad Ali Ismail Maria Waqas Farah Sadiq National Center in Big Data and Cloud Computing Department of Computer and Info Systems EngineeringNED University of Engineering and TechnologyKarachiPakistan

The N-body problem in classical physics, is the calculation of force ofgravitational attraction of heavenly bodies towards each other. Solving this problem for many heavenly bodies has always posed a challenge to physicists andmathematicians. Large number of bodies, huge masses, long distances and exponentially increasing number of equations of motion of the bodies have been themajor hurdles in solving this problem for large and complex galaxies. Adventof high performance computational machines have mitigated the problem to muchextent, but still for large number of bodies it consumes huge amount of resourcesand days for computation. Conventional algorithms have been able to reduce thecomputational complexity from O n2 ð Þ to O nlogn ð Þ by splitting the space into atree or mesh network, researchers are still looking for improvements. In thisresearch work we propose a novel solution to N-body problem inspired by metaheuristics algorithms. The proposed algorithm is simulated for various time periods of selected heavenly bodies and analyzed for speed and accuracy. Theresults are compared with that of conventional algorithms. The outcomes showabout 50% time saving with almost no loss in accuracy. The proposed approachbeing a metaheuristics optimization technique, attempts to find optimal solution tothe problem, searching the entire space in a unique and efficient manner in a verylimited amount of time.

关键词： N-body problem metaheuristics optimization particle swarm optimization heavenly bodies

来源：评论

学校读者我要写书评

暂无评论

Multi-DAGs Scheduling Integrating with Security and Availability in cloud Environment

引用

Chinese Journal of Electronics 2015年第4期24卷 709-716页

作者： LIU Yaqiu SHAO Hongrun JING Weipeng QIU Zhaowen The College of Information and Computer Engineering Northeast Forestry University Heilongjiang Province Engineering Technology Research Centre for Forestry Ecological Big Data Storage and High Performance (Cloud) Computing

In view of the issue concerns multiple Directed acyclic graphs(DAGs) scheduling in multi-tenant cloud computing environment, a scheduling strategy that integrate security and availability is proposed to satisfy the tenants’ requirements for resource security and availability, as thus it can not only protect the users’ privacy and data security but also advance the success rate. The proposal assesses resource reputation to ensure jobs can be scheduled onto relatively security nodes; during task scheduling, it classifies the DAGs to achieve fairness; in the process of resources allocation, the objective function would maximize the user’s security satisfaction and minimize the deviation of availability; meanwhile, it takes advantage of "time chips" flexibly to promote resource utilization rate; afterwards, we present a Greedy algorithm integrating with security and availability(GISA) to implement the strategy. The experimental results show the correctness and superior of the novel strategy.

关键词： Multi-DAGs scheduling Security Availability Resource utilization

来源：评论

学校读者我要写书评

暂无评论

Performance sensitivity of Operating System parameters in Microservice environments 9

Performance sensitivity of Operating System parameters in Mi...

引用

9th IEEE International Conference on cloud computing in Emerging Markets, CCEM 2020

作者： Goutham, K S Kempaiah, Bharani Ujjaini Mampilli, Ruben John Kalambur, Subramaniam Centre for Cloud Computing and Big Data Computer Science and Engineering Pes University Bangalore India

ISBN: (纸本)9781728195766

cloud services are undergoing a major shift from monolithic applications to graphs of hundreds of loosely-coupled microservices. Microservice architecture presents both opportunities and challenges while optimizing for system characterization and resource utilization. In this study, we characterize the performance sensitivity of Operating System parameters for two academic microservice benchmarks with respect to their thread scheduling, disk writes, and the TCP buffer. We demonstrate that through simple affinitization of kernel threads to cores of a Non-Uniform Memory Access processor and disk buffer configurations, there is a 26% and 19% decrease in Average Latency accompanied by a 27% decrease in 99% Tail latency for the benchmarks under consideration. While performance improvements can be gained, we also show that alternate nonoptimal configurations of the system parameters degrade the performance substantially. © 2020 IEEE.

关键词： Benchmarking

来源：评论

学校读者我要写书评

暂无评论

Characteristic Decomposition: From Regular Sets to Normal Sets

引用

Journal of Systems Science & Complexity 2019年第1期32卷 37-46页

作者： MOU Chenqi WANG Dongming LMIB–School of Mathematics and Systems Science/Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University LMIB–SKLSDE–School of Mathematics and Systems Science/Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Centre National de la Recherche Scientifique

In this paper it is shown how to transform a regular triangular set into a normal triangular set by computing the W-characteristic set of their saturated ideal and an algorithm is proposed for decomposing any polynomial set into ?nitely many strong characteristic pairs, each of which is formed with the reduced lexicographic Gr?bner basis and the normal W-characteristic set of a characterizable ideal.

关键词： Characteristic decomposition characteristic pair normal set regular set W-characteristic set

来源：评论

学校读者我要写书评

暂无评论

Pipelining acoustic model training for speech recognition using storm

Pipelining acoustic model training for speech recognition us...

引用

2013 5th International Conference on Computational Intelligence, Modelling and Simulation, CIMSim 2013

作者： Sitaram, Dinkar Srinivasaraghavan, Haripriya Agarwal, Kapish Agrawal, Amritanshu Joshi, Neha Ray, Debraj Centre for Cloud Computing and Big Data Computer Science Department PES Institute of Technology Bangalore India

ISBN: (纸本)9780769551555

Speech recognition has been increasingly used on mobile devices, which has in turn increased the need for creation of new acoustic models for various languages, dialects, accents, speakers and environmental conditions. This involves training and adapting a huge number of acoustic models, some of them in real-time. Training acoustic models is thus essential for speech recognition because these models determine the accuracy and quality of the recognition process. This paper, discusses the use of Storm, a distributed real time computational system, to pipeline the creation of acoustic models by CMU Sphinx, an open-source software project for speech recognition and training. Software pipelining reduces the time required for training and optimizes system resource utilization, thus enabling huge amounts of data to be trained in considerably less amount of time than taken by the conventional sequential process. Pipelining is achieved by grouping the stages of the training process into a set of five stages, and running each stage on individual nodes in a Storm cluster. Thus acoustic models are created by training multiple streams of speech samples using the same SphinxTrain setup, also resulting in improvement of training time and throughput. © 2013 IEEE.

关键词： Speech recognition

来源：评论

学校读者我要写书评

暂无评论

DeepFake Detection Using Deep Learning 1st

DeepFake Detection Using Deep Learning

引用

1st International Conference on data Science and Artificial Intelligence, DSAI 2023

作者： Rehman, Mariam Rasool, Mehran Safder, Sadaf Government College University Faisalabad Pakistan National Center in Big Data and Cloud Computing University of Agriculture Faisalabad Pakistan

ISBN: (纸本)9789819979684

A deepfake is a computer-generated video or image where one person's face is replaced with another face of a person which uses a generative adversarial network (GAN) to create and alter images that are practically impossible for humans to distinguish from authentic ones. The development of GAN technology has led to significant improvements in image generation. This progress has made it difficult for humans to differentiate between generated images and raised concerns about their misuse and also creating security threats for the society. Deepfake image detection is an essential challenge because the current methods often face inaccuracy and time-consuming issues. The research work employed GAN discriminators to address these issues. The experiment used the CelebA dataset, which contained diverse celebrity images. Moreover, the proposed model consists of components i.e., generator and discriminator. The generator creates fake data from the training images, while the discriminator distinguishes real from the fake images. The result shows that the proposed approach is out-performing with an accuracy of 95.8% in identifying deep fake images, even in the presence of various manipulations. The proposed model is helpful for enhancing trust and security in the society. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Programming Variational Quantum Circuits with Quantum-Train Agent

Programming Variational Quantum Circuits with Quantum-Train ...

引用

Quantum Communications, Networking, and computing (QCNC), International Conference on

作者： Chen-Yu Liu Samuel Yen-Chi Chen Kuan-Cheng Chen Wei-Jia Huang Yen-Jui Chang Graduate Institute of Applied Physics National Taiwan University Taipei Taiwan Hon Hai (Foxconn) Research Institute Taipei Taiwan Wells Fargo New York NY USA Department of Electrical and Electronic Engineering Imperial College London London UK Centre for Quantum Engineering Science and Technology (QuEST) Imperial College London London UK Quantum Information Center Chung Yuan Christian University Taoyuan City Taiwan Master Program in Intelligent Computing and Big Data Chung Yuan Christian University Taoyuan City Taiwan

ISBN: (数字)9798331531591

ISBN: (纸本)9798331531607

This study introduces the Quantum-Train Quantum Fast Weight Programmer (QT-QFWP) framework, enabling efficient and scalable programming of variational quantum circuits (VQCs) through quantum-driven parameter updates for the classical slow programmer controlling the fast programmer VQC. By optimizing quantum and classical parameter management, QT-QFWP significantly reduces parameters (by 70–90%) compared to Quantum Long Short-Term Memory (QLSTM) and Quantum Fast Weight Programmer (QFWP) while maintaining accuracy. Benchmarking on time-series tasks—including Damped Simple Harmonic Motion (SHM), NARMA5, and Simulated Gravitational Waves (GW)—demonstrates superior efficiency and predictive accuracy. QT-QFWP is particularly advantageous for near-term quantum systems, addressing qubit and gate fidelity constraints, enhancing VQC deployment in time-sensitive applications, and expanding quantum computing’s role in machine learning.

关键词： Quantum system Accuracy Computational modeling Qubit Neural networks Programming Predictive models Quantum circuit Physics Long short term memory

来源：评论

学校读者我要写书评

暂无评论

FUSDREAMER: Label-efficient Remote Sensing World Model for Multimodal data Classification

arXiv

引用

arXiv 2025年

作者： Wang, Jinping Song, Weiwei Chen, Hao Ren, Jinchang Zhao, Huimin School of Computer Sciences Guangdong Polytechnic Normal University Guangzhou510665 China Guangdong Provincial Key Laboratory of Intellectual Property and Big Data Guangdong Polytechnic Normal University Guangzhou510665 China Peng Cheng Laboratory Shenzhen518000 China Department of Applied Mathematics and Theoretical Physics University of Cambridge CambridgeCB3 0WA United Kingdom School of Computer Sciences Guangdong Polytechnic Normal University Guangzhou510640 China National Subsea Centre School of Computing Engineering and Technology Robert Gordon University AberdeenAB10 7AQ United Kingdom

World models significantly enhance hierarchical understanding, improving data integration and learning efficiency. To explore the potential of the world model in the remote sensing (RS) field, this paper proposes a label-efficient remote sensing world model for multimodal data fusion (FusDreamer). The FusDreamer uses the world model as a unified representation container to abstract common and high-level knowledge, promoting interactions across different types of data, i.e., hyperspectral (HSI), light detection and ranging (LiDAR), and text data. Initially, a new latent diffusion fusion and multimodal generation paradigm (LaMG) is utilized for its exceptional information integration and detail retention capabilities. Subsequently, an open-world knowledge-guided consistency projection (OK-CP) module incorporates prompt representations for visually described objects and aligns language-visual features through contrastive learning. In this way, the domain gap can be bridged by fine-tuning the pre-trained world models with limited samples. Finally, an end-to-end multitask combinatorial optimization (MuCO) strategy can capture slight feature bias and constrain the diffusion process in a collaboratively learnable direction. Experiments conducted on four typical datasets indicate the effectiveness and advantages of the proposed FusDreamer. The corresponding code will be released at https://***/ Cimy-wang/FusDreamer. © 2025, CC BY.

关键词： Contrastive Learning

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：