检索结果-内蒙古大学图书馆

SSRN 2023年

作者： Tang, Chunhua Zhao, Shuangyao Chen, Binbin Lu, Xiaonong Zhang, Qiang School of Management Hefei University of Technology Hefei230009 China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Hefei230009 China

Owing to an increasing number of functionally equivalent services on the cloud computing platform, quality-of-service (QoS) prediction and service recommendation have been developed using collaborative filtering (CF) techniques. However, each individual QoS is related to service status and user perceptions and thus instantly varies over time. QoS instantaneity often makes service predicted QoS deviate from actual values in traditional CF approaches. In addition, existing CF approaches ignore inauthentic QoS values given by untrustworthy users. To address these problems, we develop a two-dimensional time-aware and trust-aware service recommendation approach (TaTruSR). First, considering QoS instantaneity, both time weight (time dimension) and temporal certainty (QoS dimension) are designed to determine the contribution of co-invoked services. Second, a set of most similar and trusted neighbors can be identified from the view of the time-aware similarity model and trust model. In models, the direct similarity and local trust are calculated based on the QoS ratings and contribution of co-invoked services to improve the prediction accuracy and eliminate unreliable QoS. The indirect similarity and global trust are estimated based on the social network to alleviate the data sparsity problem. Finally, missing QoS prediction and service recommendation for the active user can be achieved based on enhanced similarity and trust. A case study and experimental evaluation on real-world datasets demonstrate the practicality and accuracy of the proposed approach. © 2023, The Authors. All rights reserved.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Learning Adaptive Mapless Navigation Skills Through Evidential Deep Learning

Learning Adaptive Mapless Navigation Skills Through Evidenti...

引用

IEEE International Conference on Real-time Computing and Robotics (RCAR)

作者： Cancan Zhao Yufeng Yan Xuhao Liu Shuai Zhang Bo Ouyang School of Management Hefei University of Technology Hefei China The Key Laboratory of Process Optimization and Intelligent Decision-Making(Ministry of Education) Hefei University of Technology Hefei China

Traditional navigation methods tend to design and train navigation skills based on the consideration of multi-scenario applicability and perform well in general scenarios. However, this results in sub-optimal navigation performance in some context-specific scenarios, such as navigating through open or crowded scenarios. While some existing navigation skills can achieve satisfactory performance in context-specific scenarios, e.g., using PID in open scenarios and reinforcement learning-based training methods in crowded scenarios. Most methods currently do without consideration of the integration of navigation skills and contextual understanding. To address this gap, we propose an adaptive mapless navigation method to utilize the optimal capabilities of each skill corresponding to the context-specific scenario. Specifically, we package several existing navigation skills and train a scenario classifier to enable the agent to automatically select the most appropriate skill based on the current scenario. Furthermore, we incorporate evidential deep learning (EDL) to assess classification uncertainty and ensure the safe switching of navigation skills. Our method was tested in both simulated and real scenarios, and the results of the experiments confirmed the effectiveness and generalization of our method. Moreover, the proposed method exhibits notably enhanced, context-sensitive efficiency within a hybrid multi-scenario setting, in comparison to a single navigation skill.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Earth Observation Satellite Scheduling Problem for Multitemporal Revisit Tasks: A Variable Neighborhood Search Algorithm

SSRN

引用

SSRN 2024年

作者： Xing, Ligang Hu, Xiaoxuan Zhu, Waiming Zhu, Xutong Xia, Wei School of Management Hefei University of Technology Hefei230009 China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Hefei230009 China Engineering Research Center for Intelligent Management of Aerospace System Hefei230009 China

After natural disasters such as floods and earthquakes, Earth observation satellites (EOSs) often need to revisit affected areas multiple times to acquire multitemporal images. Such observation tasks are referred to as multitemporal revisit tasks. In this paper, we study the Earth observation satellite scheduling problem for multitemporal revisit tasks, which is a combinatorial optimization problem in continuous space. This problem exhibits more complex constraints and is more challenging to solve than the traditional Earth observation satellite scheduling problem. Firstly, we formally define the problem and formulate it with a mixed integer nonlinear programming model. Secondly, we develop a variable neighborhood search algorithm to search for the optimal solution. This algorithm embeds a dynamic greedy heuristic, which can efficiently generate a schedule for satellites. Thirdly, computational experiments demonstrate the efficiency and stability of the algorithm. Moreover, the CPU time of the algorithm increases linearly with both task count and revisit number, indicating good scalability. © 2024, The Authors. All rights reserved.

关键词： Integer programming

来源：评论

学校读者我要写书评

暂无评论

An Evidence-Based Multimodal Fusion Approach for Predicting Review Helpfulness with Human-Ai Complementarity

SSRN

引用

SSRN 2023年

作者： Yang, Ying Zhang, Ling-Feng Ren, Gang Wang, Gang School of Management Hefei University of Technology Hefei230009 China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Hefei230009 China Ministry of Education Engineering Research Center for Intelligent Decision-Making & Information System Technologies Hefei230009 China

Online product reviews have been increasingly adopted by consumers to express their evaluation for products on e-commercial platforms. Consumers are encouraged to evaluate the review quality by helpful votes according to their human intelligence, and considerable machine learning approaches have been developed to help detect helpful reviews automatically. However, previous studies largely depend on machine intelligence and neglect the complementary strength between machine algorithms and human intelligence. This study proposes an evidence-based multimodal fusion approach for predicting the helpfulness of reviews by aggregating the human intelligence from user votes and machine intelligence from texts and images. Review helpfulness is measured by a belief distribution and represented by the evidence obtained from each modality data. An evidential reasoning rule-based fusion strategy is proposed to combine the hybrid intelligence considering their complementarity. A new dataset is also developed for the task of multimodal review helpfulness prediction by crawling online product reviews from *** and annotated by human scoring. Experimental results on the dataset show that the proposed approach for predicting review helpfulness has superior predictive power and outperforms existing methods. This study also contributes to e-commerce by providing an enhanced method for predicting review helpfulness from both human and machine intelligence. © 2023, The Authors. All rights reserved.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Three-Dimensional Collision Avoidance Method for Robot-Assisted Minimally Invasive Surgery

引用

Cyborg and Bionic Systems 2023年第1期4卷 121-135页

作者： Ling Li Xiaojian Li Bo Ouyang Hangjie Mo Hongliang Ren Shanlin Yang School of Management Hefei University of TechnologyHefeiChina Key Laboratory of Process Optimization and Intelligent Decision-Making(Ministry of Education) Hefei University of TechnologyHefeiChina Philosophy and Social Sciences Laboratory of Data Science and Smart Society Governance(Ministry of Education) Hefei University of TechnologyHefeiChina Department of Biomedical Engineering Faculty of EngineeringNational University of SingaporeSingaporeSingapore Department of Electronic Engineering Shun Hing Institute of Advanced EngineeringThe Chinese University of Hong KongHong KongChina National Engineering Laboratory for Big Data Distribution and Exchange Technologies ShanghaiChina

In the robot-assisted minimally invasive surgery,if a collision occurs,the robot system program could be damaged,and normal tissues could be *** avoid collisions during surgery,a 3-dimensional collision avoidance method is proposed in this *** proposed method is predicated on the design of 3 strategic vectors:the collision-with-instrument-avoidance(CI)vector,the collision-with-tissues-avoidance(CT)vector,and the constrained-control(CC)*** CI vector demarcates 3 specific directions to forestall collision among the surgical *** CT vector,on the other hand,comprises 2 components tailored to prevent inadvertent contact between the robot-controlled instrument and nontarget ***,the CC vector is introduced to guide the endpoint of the robot-controlled instrument toward the desired position,ensuring precision in its movements,in alignment with the surgical *** results verify the proposed collision avoidance method for robot-assisted minimally invasive *** code and data are available at https://***/cynerelee/collision-avoidance.

关键词： collision desired verify

来源：评论

学校读者我要写书评

暂无评论

Multi-Objective optimization of Cloud Manufacturing Service Combination Based on Manufacturing and Logistics Coordinate

Multi-Objective Optimization of Cloud Manufacturing Service ...

引用

Chinese Control and decision Conference, CCDC

作者： Sining Han Bingfeng Ge Wanying Wei Xiaonong Lu Zeqiang Hou Yuming Huang College of Systems Engineering National University of Defense Technology National University of Defense Technology Changsha China Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of Education University of Science and Technology of China Hefei China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

With the continuous emergence of new technologies, the manufacturing industry is facing unprecedented challenges. Manufacturing services typically involve various types of production equipment, diverse suppliers, and varied customer demands. optimization in this context often encompasses multi pie objectives, necessitating timely adjustments in service comb ¡nations to meet customer needs. Traditional manufacturing models evidently fall short in resource allocation within manufacturing supply chains. Cloud manufacturing, proposed as a novel manufacturing model, aims to optimize resource allocation through the combination and configuration of resources, there by enhancing the efficiency of manufacturing supply chains. Logistics, as a crucial component within manufacturing supply chains, underscores the significance of coordinating the optimizad on of manufacturing and logistics services. This research focus es on the multi-objective optimization problem of cloud manufacturing service combinations. Building upon the execution process of manufacturing and logistics services for complex product manufacturing tasks, a novel mathematical model for the optimization of manufacturing resources and logistics resources i s constructed. The model utilizes the NSGA-H algorithm to coordinate the optimization processes of manufacturing and legistics resources. Finally, a case study demonstrates the effectiveness of the proposed model.

关键词： Costs Supply chains Production equipment Pareto optimization Mathematical models Manufacturing Resource management

来源：评论

学校读者我要写书评

暂无评论

The impacts of content and source factors on consumers’ liking toward advertisements: an hsm-based framework

The impacts of content and source factors on consumers’ lik...

引用

International Conference on Applications and Techniques in Cyber Intelligence, ATCI 2020

作者： Chu, Xiumin Liu, Yezheng Chen, Xiayu School of Management Hefei University of Technology HefeiAnhui230009 China Key Laboratory of Process Optimization and Intelligent Decision Making Ministry of Education HefeiAnhui230009 China

ISBN: (纸本)9783030539795

Drawing on heuristic-systematic model (HSM), this study develops a research model to examine how content factors (i.e., persuasive and informative cues) and source factors (i.e., brand popularity and brand reputation) affect consumers’ liking behavior toward advertisements in microblogs, specifically on Weibo. Brand type is regarded as a moderator in the research model. Manual coding and machine learning algorithms are integrated to develop a classification model that tags tweets. Our study enhances the current understanding of consumers’ liking behavior on social media and provides managerial insights for brands seeking to facilitate consumer engagement. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Learning algorithms

来源：评论

学校读者我要写书评

暂无评论

Pain Intensity Recognition from Masked Facial Expressions using Swin-Transformer

Pain Intensity Recognition from Masked Facial Expressions us...

引用

IEEE International Conference on Robotics and Biomimetics

作者： Xin Yuan Shuai Zhang Cancan Zhao Xian He Bo Ouyang Shanlin Yang Key Laboratory of Process Optimization and Intelligent Decision-Making(Ministry of Education) School of Management Hefei University of Technology Hefei China

ISBN: (纸本)9781665481106

Previous studies recognize pain expressions based on the entire face, for example, Prkachin and Solomon Pain intensity (PSPI). However, the patients face is often masked by instruments in an intensive care unit (ICU), such as respirator, and gauzes, just name a few, which causes the agent cannot measure the pain intensity using PSPI directly. To tackle this problem, we explore the pain intensity recognition from masked face. First, we conducted four levels of pain measurement experiments with four types of masked face using Swin-Transformer. Experiment results show that the accuracy of pain measurement is more than 90%, even masking a large part of facial Action Units (AU) related to the pain on the UNBC-McMaster dataset. Furthermore, we pre-train the pain recognition model with masked face dataset, such that the model can capture facial features contributed to the pain. Results on binary and four-level pain intensity measurement tasks show our model outperforms recent state-of-the-art performance, achieving 97.38% and 95.25% accuracy, respectively.

关键词： Gold Pain Face recognition Instruments Ventilation Robustness Physiology

来源：评论

学校读者我要写书评

暂无评论

A Transfer-Based decision-making Method with Criterion Weight and Reliability Transfer

SSRN

引用

SSRN 2023年

作者： Jia, Xuefei Chang, Wenjun Liao, Baoyu School of Management Hefei University of Technology Hefei Box 270 Anhui Hefei230009 China Key Laboratory of Process Optimization and Intelligent Decision-making Ministry of Education Anhui Hefei230009 China Ministry of Education Engineering Research Center for Intelligent Decision-Making & Information System Technologies Hefei230009 China

The accumulated historical data not only brings benefits to social life, but also increases the complexity of decision-making. Data-driven multi-criteria decision-making (D2MCDM) can extract parameter information from historical data to help decision-makers generate acceptable and satisfactory decision solutions. When little historical data in one domain is available, the extracted parameter information may be inaccurate or inconsistency. To deal with this issue, the parameter-based transfer learning is considered to integrate into D2MCDM. The domain with little historical data is called the target domain (TD). One related domain with sufficient data is found, which is called the SD (SD). The sufficient data in the SD can help extract accurate parameter information of the TD. This paper proposes a transfer-based decision-making (TBDM) method with criterion weight and reliability transfer based on the Tri-training algorithm. The Tri-training algorithm is used to construct a data selection stage for the SD, which improves the data similarity between the SD and TD. The data-driven decision-making models are then constructed to learn criterion weights and reliabilities. The proposed TBDM method not only deals with the problem of insufficient data for decision model training, but also improves the acceptability of decision solutions to decision-makers. A case study of the diagnosis of breast lesions is analyzed to demonstrate the advantages of the TBDM method. The UCI datasets are used to conduct the comparative experiments to further validate the advantages of the TBDM method. © 2023, The Authors. All rights reserved.

关键词： Reliability

来源：评论

学校读者我要写书评

暂无评论

A novel centralized cross-efficiency evaluation via explainable artificial intelligence in the context of big data

引用

European Journal of Operational Research 2025年

作者： Yang, Min Wang, Zixuan Liang, Liang Management School Hefei University of Technology Hefei230009 China Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of Education Hefei230009 China Intelligent Interconnection System Anhui Provincial Laboratory Hefei230009 China NSFC Fundamental Research Center on Smart Interconnected System Engineering Hefei230009 China

Cross-efficiency evaluation in data envelopment analysis (DEA) assumes that decision making units (DMUs) have full flexibility in choosing weights according to their individual preferences. However, this total autonomy may be inapplicable in some centralized organizational scenarios. To address this problem, this paper introduces a novel centralized cross-efficiency evaluation which considers both individual and organizational preferences with assistance of explainable artificial intelligence (XAI) in the context of big data. Specifically, XAI is first applied to approach the organizational efficiency function and then calculate the marginal contribution of each variable as the variable importance, which represents the organizational preference. Furthermore, we propose a centralized secondary goal model to select the unique optimal weight profile from the candidate weights that remain self-efficiency as Pareto-optimal, such that the deviation between individual and organizational preferences is minimized. In addition, a centralization factor is introduced to ensure that the model's centralization degree corresponds to the actual centralization level in organizational management. Finally, the proposed method is applied to evaluate the efficiency of DMUs within three different centralized organizations sequentially. The results verify that the proposed method yields more discriminative and robust efficiency scores within organizations compared to several previous cross-efficiency evaluation methods. © 2025 Elsevier B.V.

关键词： Risk perception

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：