检索结果-内蒙古大学图书馆

arXiv 2025年

作者： Zhang, Boya Andrussow, Iris Zell, Andreas Martius, Georg Department of Computer Science University of Tübingen Germany Max Planck Institute for Intelligent Systems Germany

Stable and robust robotic grasping is essential for current and future robot applications. In recent works, the use of large datasets and supervised learning has enhanced speed and precision in antipodal grasping. However, these methods struggle with perception and calibration errors due to large planning horizons. To obtain more robust and reactive grasping motions, leveraging reinforcement learning combined with tactile sensing is a promising direction. Yet, there is no systematic evaluation of how the complexity of force-based tactile sensing affects the learning behavior for grasping tasks. This paper compares various tactile and environmental setups using two model-free reinforcement learning approaches for antipodal grasping. Our findings suggest that under imperfect visual perception, various tactile features improve learning outcomes, while complex tactile inputs complicate training. Copyright © 2025, The Authors. All rights reserved.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Towards Understanding the Critical Role of Human Behavior in Smart and Cognitive City Cybersecurity 16

Towards Understanding the Critical Role of Human Behavior in...

引用

16th International Conference on Ambient systems, Networks and Technologies Networks, ANT 2025 / 8th International Conference on Emerging Data and Industry 4.0, EDI40 2025

作者： Alraddadi, Rawan Alshayeb, Mohammad Mahmood, Sajjad Niazi, Mahmood Information and Computer Science Department King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia College of Computer Science and Engineering Taibah University Yanbu Saudi Arabia Interdisciplinary Research Center for Intelligent Secure Systems Dhahran Saudi Arabia

The rapid development of cognitive cities, driven by IoT and AI technologies, introduces unique cybersecurity challenges aggravated by human-centric vulnerabilities. The objective of this paper is to examine the role of human factors in the cybersecurity landscape of both smart and cognitive cities. Through a literature review, we investigate how human interactions with digital infrastructures impact security and privacy. We highlight the need to address human errors, social engineering attacks, and insider threats while distinguishing how differences in infrastructure, intelligence, and citizen roles between smart and cognitive cities shape the management of human-centric vulnerabilities. Our review of existing research on integrating human behavioral data into AI systems suggests significant threat detection and response improvements. The findings suggest that human-centric AI detection tools not only mitigate risks of human errors and latent failures but also significantly improve protection for vulnerable populations, thereby promoting stronger human-AI collaboration. Ultimately, this paper advocates for advancing these tools to bolster urban cybersecurity frameworks. © 2025 The Author(s).

关键词： AI cognitive cities cybersecurity human-centric vulnerabilities smart city

来源：评论

学校读者我要写书评

暂无评论

Chat with MES: LLM-driven user interface for manipulating garment manufacturing system through natural language

引用

Journal of Manufacturing systems 2025年 80卷 1093-1107页

作者： Yuan, Zhaolin Li, Ming Liu, Chang Han, Fangyuan Huang, Haolun Dai, Hong-Ning Institute of Artificial Intelligence University of Science and Technology Beijing Beijing China Shunde Innovation School University of Science and Technology Beijing Guangdong China Department of Industrial and Systems Engineering The Hong Kong Polytechnic University Hong Kong Research Institute of Advanced Manufacturing The Hong Kong Polytechnic University Hong Kong Research Centre for Digital Transformation of Tourism The Hong Kong Polytechnic University Hung Hom Hong Kong Centre for Transformative Garment Production Hong Kong Science Park Pak Shek Kok Hong Kong Department of Computer Science Hong Kong Baptist University Hong Kong School of Intelligent Systems Science and Engineering Jinan University Zhuhai China

This paper presents Chat with MES (CWM), an AI agent system, which integrates LLMs into the Manufacturing Execution System (MES), serving as the "ears, mouth, and the brain". This system promotes a paradigm shift in MES interactions from Graphical User Interface (GUI) to natural language interface", offering a more natural and efficient way for workers to manipulate the manufacturing system. Compared with the traditional GUI, both the maintenance costs for developers and the learning costs and the complexity of use for workers are significantly reduced. This paper also contributes two technical improvements to address the challenges of using LLM-Agent in serious manufacturing scenarios. The first one is Request Rewriting, designed to rephrase or automatically follow up on non-standardized and ambiguous requests from users. The second innovation is the Multi-Step Dynamic Operations Generation, which is a pre-execution planning technique similar to Chain-of-Thought (COT), used to enhance the success rate of handling complex tasks involving multiple operations. A case study conducted on a simulated garment MES with 55 manually designed requests demonstrates the high execution accuracy of CWM (80%) and the improvement achieved through query rewriting (9.1%) and Multi-Step Dynamic operations generation (18.2%). The source code of CWM, along with the simulated MES and benchmark requests, is publicly accessible. © 2025 The Authors

关键词： Graphical user interfaces

来源：评论

学校读者我要写书评

暂无评论

Systematic Literature Review of Topic Labeling

引用

IEEE Access 2025年 13卷 93124-93147页

作者： Salma Mekaoui Ilham Chaker Arsalane Zarghili Nikola S. Nikolov Department of Computer Science and Information Systems University of Limerick Limerick Ireland Laboratory of Intelligent Systems and Applications Faculty of Sciences and Technology Sidi Mohamed Ben Abdellah University Fes Morocco

The rapid growth of textual data on the web has led researchers to develop methods in Natural Language Processing (NLP) to process, understand, and identify topics. Among these methods, Topic Modeling helps extract relevant topics, represented as clusters of words. However, interpreting these clusters into meaningful topics remains a challenge. This limitation has led to further research into topic labeling, an approach for assigning comprehensive and semantically meaningful labels to topic modeling results, ensuring that they are interpretable and understandable from a human perspective. In this paper, we present a Systematic Literature Review (SLR) on topic labeling. This review explores its definition, geographical and time distribution, methodologies, datasets, evaluation methods, successes, and challenges. This paper presents an SLR on topic labeling, synthesizing insights from 41 high-quality studies. It serves as a rich source of information for researchers interested in investigating different approaches for discovering topics within textual data. It addresses the various aspects of topic labeling and includes discussions that highlight the challenges of this approach, encouraging further research in this field.

关键词： Labeling Systematic literature review Search problems Probabilistic logic Computational modeling Databases Data models Context modeling Usability Semantics

来源：评论

学校读者我要写书评

暂无评论

The Information systems and Model for Succession Planning in Higher Education Institutions

The Information Systems and Model for Succession Planning in...

引用

computer Applications (ICCA), IEEE Conference on

作者： Devyano Luhukay Ford Lumban Gaol Meyliana Harjanto Prabowo Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331534592

ISBN: (纸本)9798331534608

Succession planning is important in organization management. It is a strategic level management that involves not only finding the best successor but also including the development program for the successor so that they will be ready for the new prospective position. The integration and collaboration between all related stakeholders are important. Specifically, in succession planning at higher education, key positions play an important role so that institutional operations can run well. In this research, a succession planning model in higher education was built using the Systematic Literature Review(SLR), and the Software Development Lifecycle(SDLC) method was used for the application development, also, to be able to evaluate the implementation of one of the steps of the model, the simulation technique used machine learning (ML) to find the best algorithm for predicting the readiness of potential successors at higher education. Six algorithms were used: Logistic Regression, Decision Tree, K-Nearest Neighbor, Linear Discriminant Analysis, Gaussian Naïve Bayes, and Support Vector Machine. The data prepared for the simulation is based on the factors for the best successor candidate. The results obtained in this research are the succession planning model and the application design, as well as the Support Vector Machine (SVM) algorithm as the best algorithm for predicting the readiness status of successor candidates based on SVM’s accuracy score that is compared to the other algorithm in some variance amounts of data. The Succession Planning Model that is actualized in the application design provides a reference for the higher education institution to implement the Succession Model in their institution.

关键词： Support vector machines Machine learning algorithms Systematics Education Software algorithms Machine learning Predictive models Prediction algorithms Planning Information systems

来源：评论

学校读者我要写书评

暂无评论

A Two-stage Reconfiguration in Network Function Virtualization: Toward Service Function Chain Optimization

引用

IEEE Transactions on Network and Service Management 2025年

作者： Assis, Karcius D. R. Almeida, Raul C. Baghban, Hojjat Santos, Alex F. Boutaba, Raouf Electrical and Computer Engineering Department Salvador Brazil University of Waterloo David R. Cheriton School of Computer Science Canada Department of Electronics and Systems Recife Brazil Chang Gung University Department of Artificial Intelligence Taiwan Chang Gung University College of Intelligent Computing Taiwan Center of Science and Technology in Energy and Sustainability Brazil Postgraduate Program in Computer Science Bahia Brazil

Network Function Virtualization (NFV), as a promising paradigm, speeds up the service deployment by separating network functions from proprietary devices and deploying them on common servers in the form of software. Any service in NFV-enabled networks is achieved as a Service Function Chain (SFC) which consists of a series of ordered Virtual Network Functions (VNFs). However, migration of VNFs for more flexible services within a dynamic NFV-enabled network is a key challenge to be addressed. Current VNF migration studies mainly focus on single VNF migration decisions without considering the sharing and concurrent migration of VNF instances. In this paper, we assume that each deployed VNF is used by multiple SFCs and deal with the optimal placement for the contemporaneous migration of VNFs based on the actual network situation. We formalize the VNF migration and SFC reconfiguration problem as a mathematical model, which aims to minimize the VNF migration between nodes or the total number of core changes per node. The approach is a two-stage MILP based on optimal order to solve the reconfiguration. Extensive evaluation shows that the proposed approach can reduce the change in terms of location or number of cores per node in a 6-node and 14-node networks while ensuring network latency compared with the model without reconfiguration. © 2004-2012 IEEE.

关键词： Network function virtualization

来源：评论

学校读者我要写书评

暂无评论

Worst-Case Integrity Attacks and Resilient State Estimation With Partially Secured Measurements

引用

IEEE Transactions on Control of Network systems 2025年

作者： Zhou, Jing Shang, Jun Chen, Tongwen University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada Tongji University Department of Control Science and Engineering Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Shanghai200092 China

This paper examines the problem of optimal deception attacks against state estimation with partially secured measurements, where smart sensors transmit innovation sequences to the remote end for information fusion. Due to resource limitations or defensive countermeasures, the adversary can only modify data packets transmitted through unreliable channels. Meanwhile, the attack should be synthesized with sophistication to deceive an anomaly detector. To investigate the vulnerabilities of such estimation systems without feedback links and enhance security performance, the optimal attack policy is derived by formulating and explicitly solving a convex optimization problem, with the goal of maximizing the sum of estimation errors. Subsequently, a novel attack detection and resilient state estimation algorithm is proposed to ensure an acceptable level of estimation accuracy. The theoretical performance metrics, including false alarm rates for the proposed detector, are provided. Finally, the effectiveness of the results is confirmed through numerical examples. © 2014 IEEE.

关键词： Optimal detection

来源：评论

学校读者我要写书评

暂无评论

The Sample Complexity of Online Reinforcement Learning: A Multi-model Perspective

arXiv

引用

arXiv 2025年

作者： Muehlebach, Michael He, Zhiyu Jordan, Michael I. Max Planck Institute for Intelligent Systems Tuebingen Germany Department of Computer Science University of California Berkeley United States

We study the sample complexity of online reinforcement learning for nonlinear dynamical systems with continuous state and action spaces. Our analysis accommodates a large class of dynamical systems ranging from a finite set of nonlinear candidate models to models with bounded and Lipschitz continuous dynamics, to systems that are parametrized by a compact and real-valued set of parameters. In the most general setting, our algorithm achieves a policy regret of O(Nǫ2+ln(m(ǫ))/ǫ2), where N is the time horizon, ǫ is a user-specified discretization width, and m(ǫ) measures the complexity of the function class under consideration via its packing number. In the special case where the dynamics are parametrized by a compact and real-valued set of parameters (such as neural networks, transformers, etc.), we prove a policy regret of O(√Np), where p denotes the number of parameters, recovering earlier sample-complexity results that were derived for linear time-invariant dynamical systems. While this article focuses on characterizing sample complexity, the proposed algorithms are likely to be useful in practice, due to their simplicity, the ability to incorporate prior knowledge, and their benign transient behavior. © 2025, CC BY.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Prescriptive Analytics for Logistics: Bridging Predictive Models and Actionable Decisions Through Advanced Optimization Techniques

Prescriptive Analytics for Logistics: Bridging Predictive Mo...

引用

Innovative Research in Applied science, Engineering and Technology (IRASET),Conference on

作者： Oussama El Azzouzy Tarik Chanyour Said Jai Andaloussi Department of Mathematics and Computer Science Laboratory of Data Engineering and Intelligent Systems(LIDSI) Hassan II University Casablanca Morocco

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

Modern logistics is facing increasing challenges due to the growing complexity of supply chains, high consumer expectations, and economic and environmental pressures. While predictive analytics, powered by artificial intelligence (AI) and massive data, has transformed the ability to forecast trends and risks, its application remains limited by the lack of effective mechanisms to convert these predictions into actionable decisions in dynamic and constrained environments. This paper proposes an innovative hybrid approach that combines advanced predictive models, notably the Temporal Fusion Transformer (TFT), with prescriptive algorithms, such as linear programming and deep reinforcement learning (DRL). The TFT demonstrated superior performance in terms of predictive accuracy (significant reduction in MAE, RMSE, and MAPE), while the prescriptive algorithms optimized complex logistics decisions in real time, even in uncertain environments. This integrated methodology was validated through use cases simulating critical logistics scenarios, such as delivery route optimization. Experimental results show a substantial improvement in operational efficiency, with reductions in costs, lead times and resource utilization. The prescriptive approach made it possible to automate complex decisions, while taking into account multiple constraints, such as costs, lead times and sustainability objectives. By integrating real-time data collected via IoT devices and logistics management systems, this architecture offers a robust, adaptable and scalable solution. It thus meets the requirements of modern logistics by enabling proactive, optimized and resilient decision-making. This research makes a significant contribution by bridging the gap between predictive and prescriptive analysis, while laying the foundations for new applications in the field of intelligent logistics.

关键词： Costs Supply chains Predictive models Prediction algorithms Linear programming Transformers Real-time systems Thin film transistors Resource management Optimization

来源：评论

学校读者我要写书评

暂无评论

Exploring Curricular Synergy Between ACM CS2023 Knowledge Areas and the UN Sustainable Development Goals

Exploring Curricular Synergy Between ACM CS2023 Knowledge Ar...

引用

IT Innovation and Knowledge Discovery (ITIKD), International Conference on

作者： El-Sayed M. El-Alfy Information and Computer Science Department Computer Engineering Department IRC-Intelligent Secure Systems King Fahd University of Petroleum & Minerals (KFUPM) Dhahran Saudi Arabia

ISBN: (数字)9798350355468

ISBN: (纸本)9798350355475

The United Nations' Sustainable Development Goals (SDGs) call for global action to address interconnected challenges such as poverty, inequality, climate change, economic growth, and access to quality education and healthcare systems. As computing disciplines and technologies increasingly underpin critical sectors of sustainable development and play a vital role in solving complex problems, integrating the SDGs into computing education has become imperative. This paper briefly summarize the key points of a keynote speech presented at the International Conference on IT Innovation and Knowledge Discovery (ITIKD 2025). It explores the conceptual and curricular synergy between the ACM computer science 2023 (CS2023) Knowledge Areas and the SDGs. By aligning SDGs with CS2023 domains, the paper provides insights for a structured perspective to incorporate sustainability into undergraduate computing curricula. This proposed alignment aims to serve as a foundation to guide educators, curriculum designers, and institutions in rethinking computing education to equip future professionals and leaders with both technical expertise and sustainability awareness and competencies for systemic thinking, and interdisciplinary impact.

关键词： computer science Technological innovation Education Refining Pipelines Employment Medical services Knowledge discovery Problem-solving 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：