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Conference of the North American Fuzzy Information Processing Society - NAFIPS

作者： Ying Yu Witold Pedrycz Duoqian Miao Hongyun Zhang Tongji University Shanghai Shanghai CN University of Alberta Edmonton AB CA Department of Computer Science and Technology Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China

ISBN: (纸本)9781479903467

Nowadays, multi-label classification methods are of growing interest. Due to the relationships among the labels, traditional single-label classification methods are not directly applicable to the multi-label classification problem. This paper presents a novel multi-label classification framework based on the variable precision neighborhood rough sets, called Multi-Label classification using Rough Sets (MLRS) which considers the impact of correlation among the labels and the uncertainty that exists in the mapping between the feature space and label space. A series of experiments reported for seven multi-label datasets show that MLRS achieves promising performance when compared with some famous multi-label learning algorithms.

关键词： Classification algorithms Rough sets Training Testing Approximation methods Uncertainty Prediction algorithms

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Multi-modal Large Language Model Enhanced Pseudo 3D Perception Framework for Visual Commonsense Reasoning

arXiv

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arXiv 2023年

作者： Zhu, Jian Wang, Hanli Shi, Miaojing The Department of Computer Science & Technology The Key Laboratory of Embedded System & Service Computing Ministry of Education Tongji University Shanghai200092 China The College of Electronic and Information Engineering Tongji University Shanghai201804 China

The visual commonsense reasoning (VCR) task is to choose an answer and provide a justifying rationale based on the given image and textural question. Representative works first recognize objects in images and then associate them with key words in texts. However, existing approaches do not consider exact positions of objects in a human-like three-dimensional (3D) manner, making them incompetent to accurately distinguish objects and understand visual relation. Recently, multi-modal large language models (MLLMs) have been used as powerful tools for several multi-modal tasks but not for VCR yet, which requires elaborate reasoning on specific visual objects referred by texts. In light of the above, an MLLM enhanced pseudo 3D perception framework is designed for VCR. Specifically, we first demonstrate that the relation between objects is relevant to object depths in images, and hence introduce object depth into VCR frameworks to infer 3D positions of objects in images. Then, a depth-aware Transformer is proposed to encode depth differences between objects into the attention mechanism of Transformer to discriminatively associate objects with visual scenes guided by depth. To further associate the answer with the depth of visual scene, each word in the answer is tagged with a pseudo depth to realize depth-aware association between answer words and objects. On the other hand, BLIP-2 as an MLLM is employed to process images and texts, and the referring expressions in texts involving specific visual objects are modified with linguistic object labels to serve as comprehensible MLLM inputs. Finally, a parameter optimization technique is devised to fully consider the quality of data batches based on multi-level reasoning confidence. Experiments on the VCR dataset demonstrate the superiority of the proposed framework over state-of-the-art approaches. Copyright © 2023, The Authors. All rights reserved.

关键词： Computational linguistics

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Optimal scheduling of single-arm multi-cluster tools with two-space buffering modules

Optimal scheduling of single-arm multi-cluster tools with tw...

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2014 IEEE International Conference on Automation Science and Engineering, CASE 2014

作者： Yang, Fajun Wu, Naiqi Qiao, Yan Zhou, Mengchu Department of Industrial Engineering School of Electro-Mechanical Engineering Guangdong University of Technology Guangzhou510006 China Institute of Systems Engineering Macau University of Science and Technology China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai201804 China Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102-1982 United States

A multi-cluster tool is composed of a number of single-cluster tools linked by buffering modules. The capacity of a buffering module can be one or two. Aiming at finding an optimal one-wafer cyclic schedule, this work explores the effect of two-space buffering modules on the performance of a multi-cluster tool. The tool is modeled by a kind of Petri nets. The dynamic behavior of robot waiting and tasks, process modules, and buffers is well captured by the net model. With the model, this work shows that there is always a one-wafer cyclic schedule that reaches the lower bound of the cycle time of a process-dominant tool. Furthermore, a constant-time algorithm is revealed to find such a schedule for the first time for such multi-cluster tools. An illustrative example is given to show the application and power of this new method. © 2014 IEEE.

关键词： Petri nets

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A Recommender system for Ordering Platform Based on an Improved Collaborative Filtering Algorithm

A Recommender System for Ordering Platform Based on an Impro...

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International Conference on Audio, Language and Image Processing, ICALIP

作者： Chengchao Yu Qingshi Tang Zheng Liu Bin Dong Zhihua Wei Department of Computer Science and Technology Tongji University Shanghai China Shanghai Zhuoji Information Technology ltd. Shanghai China Key Laboratory of Embedded System and Service Computing Tongji University Shanghai China

With the development of ordering platform, an increasing number of people are paying their attention to design a suitable recommender system. Most of the traditional recommender systems are based on the abundant rating information of users. However, Only historical order data can be provided to the recommender system in ordering platform as training data. This paper proposes an improved Collaborative Filtering algorithm based on historical order data of restaurants. The recommender system includes two parts: 1) rule generation module, we define a new method for measuring the similarity between dishes. Furthermore, we incorporate an incremental learning method in this module. 2) recommendation module, we design user interest vector and propose a noise filtering method. Experimental results demonstrate that the proposed algorithm can effectively improve the performance of recommendation in terms of the accuracy and coverage ratio. Moreover, our recommender system has been successfully put into service.

关键词： Recommender systems Collaboration Training Measurement Filtering algorithms Training data Learning systems

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Mining Hierarchical Decision Rules from Hybrid Data with Categorical and Continuous Valued Attributes

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浙江海洋学院学报（自然科学版） 2010年第5期29卷 420-427页

作者： MIAO Duo-qian QIAN Jin LI Wen ZHANG Ze-hua Department of Computer＇Science and Technology Tongji University Shanghai 201804 China College of Computer Engineering Jiangsu Teachers University of Technology Changzhou 213015 China Key Laboratory of Embedded System and Service Computing Ministry of Education of China Tongji University Shanghai 201804 China

Decision rules mining is an important issue in machine learning and data ***,most proposed algorithms mine categorical data at single level,and these rules are not easily understandable and really useful for ***,a new approach to hierarchical decision rules mining is provided in this paper,in which similarity direction measure is introduced to deal with hybrid *** approach can mine hierarchical decision rules by adjusting similarity measure parameters and the level of concept hierarchy trees.

关键词： Similarity relation Attribute reduction Hierarchical decision rules Hybrid data

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New reachability trees for unbounded Petri nets

New reachability trees for unbounded Petri nets

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IEEE International Conference on Robotics and Automation (ICRA)

作者： ShouGuang Wang MengChu Zhou MengDi Gan Dan You Yue Li School of Information & Electronic Engineering Zhejiang Gongshang University Hangzhou China State Key Laboratory for Manufacturing Systems Engineering Xi'an Jiaotong University Xi'an China MoE Key Laboratory of Embedded System and Service Computing Tongji University Shanghai China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

ISBN: (纸本)9781479969241

Reachability is an important dynamic property of Petri nets. Its determination given an unbounded net and initial marking has remained an open problem since 1960s. Due to its extreme difficulty, a great deal of research has been focused on some subclasses of unbounded nets. For arbitrary ones, this work intends to propose a new reachability tree (NRT), which consists of only but all reachable markings from its initial marking. Furthermore, an NRT-based method to decide deadlocks of unbounded nets is presented. An example is provided to show the new results.

关键词： system recovery Petri nets Manufacturing systems Solids Gallium nitride embedded systems

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An Energy-efficient Traffic Scheduling Method based on Slime Mould Algorithm for SDN

An Energy-efficient Traffic Scheduling Method based on Slime...

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International Conference on Information systems and Computer Networks (ISCON)

作者： Zheyuan Wang Junli Wang Chungang Yan Ministry of Education Key Laboratory of Embedded System and Service Computing (Tongji University) Shanghai China National (Province-Ministry Joint) Collaborative Innovation Center for Financial Network Security Tongji University Shanghai China

Software defined network (SDN) enables efficient and green traffic management by separating the control and data planes. However, the existing itemized scheduling approach is prone to waste of network resources and energy consumption due to the different sensitivity of traffic to time delay. To address these problems, we design a Slime Mould Algorithm based Energy-efficient Traffic Scheduling Method (SMA-ETSM) for SDN. First, we model the traffic scheduling problem with delay requirements to evaluate the decision. Second, in order to minimize the generation of invalid solutions in traffic scheduling, the encoding of the slime mould algorithm is improved, and a slime mould adaptation and update mechanism suitable for energy-efficient traffic scheduling is designed. The experiments show that SMA-ETSM can effectively reduce the energy consumption and improve the overall bandwidth utilization of the network compared with ECMP and ACO algorithm, and it also has some improvement in the operation efficiency compared with the original slime mould algorithm.

关键词： Energy consumption Sensitivity Processor scheduling Scheduling Energy efficiency Real-time systems Time factors

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Reader Activation Scheduling in Multi-reader RFID systems: A Study of General Case

Reader Activation Scheduling in Multi-reader RFID Systems: A...

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International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Shaojie Tang Cheng Wang Xiang-Yang Li Changjun Jiang Department of Computer Science Illinois Institute of Technology Chicago IL USA Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai China Department of Computer Science University of Tongji Shanghai China

Radio frequency identification (RFID) is a technology where a reader device can "sense'' the presence of a close by object by reading a tag device attached to the object. To guarantee the coverage quality, multiple RFID readers can be deployed in the given region. In this paper, we consider the problem of activation schedule for readers in a multi-reader environment. In particular, we try to design a schedule for readers to maximize the number of served tags per time-slot while avoiding various interferences. We first develop a centralized algorithm under the assumption that different readers may have different interference and interrogation radius. Next, we propose a novel algorithm which does not need any location information of the readers. Finally, we extend the previous algorithm in distributed manner in order to suit the case where no central entity exists. We conduct extensive simulations to study the performances of our proposed algorithm. And our evaluation results corroborate our theoretical analysis.

关键词： Interference Radiofrequency identification Schedules Scheduling Processor scheduling Geometry Algorithm design and analysis

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FedWNS: Data Distribution-Wise Node Selection in Federated Learning via Reinforcement Learning

FedWNS: Data Distribution-Wise Node Selection in Federated L...

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International Conference on Computer Supported Cooperative Work in Design

作者： Chengwu Tu Shengjie Zhao Hao Deng School of Software Engineering Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing Shanghai China Engineering Research Center of Key Software Technologies for Smart City Perception and Planning Ministry of Education Shanghai China

To deal with the discrepancy between global and local objectives in the federated learning invoked by the non-independent, identically distributed (non-IID) data and mitigate the impact of catastrophic forgetting in the training phase, we propose a federated learning framework with data distribution-wise reinforcement learning to perform node selection to accelerate the convergence process and alleviate the accuracy degradation. In this framework, the agent on the central server observes the number of samples every node owns, the derived distribution information of every dataset, and the current local and global accuracy. Then infer the selected node-set to participate in the current federated learning round through policy network in reinforcement learning. Finally, we conduct simulations with publicly data sets. Simulation results indicate that our FedWNS outperforms the existing FedAvg and CSFedAvg on the testing accuracy and the communication rounds to reach target accuracy under different settings.

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A Rough Set Approach to Grouping Goods in Electronic Commerce

A Rough Set Approach to Grouping Goods in Electronic Commerc...

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International Conference on Advanced Communication Technology (ICACT)

作者： Qiguo Duan Duoqian Miao The Key Laboratory of Embedded System and Service Computing Ministry of Education Shanghai China Department of Computer Science and Technology University of Tongji Shanghai China Department ofComputer Science and Technology University of Tongji Shanghai China

As electronic commerce (EC) become more and more prevalent to people, it is very critical to provide the right information to the right customers. The EC sites are generating large amount of data on customer purchases. Data mining techniques in EC domain is currently a hot research area. This paper proposes a rough set based approach to obtaining patterns of goods and classifying associated goods into groups, which can be used by the sites to group their goods according to the customers' preference and adopt appropriate selling policies.

关键词： Electronic commerce Set theory Data mining Computer science Laboratories embedded system embedded computing Web pages Transaction databases Information systems

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