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Solving the maximum planar subgraph problem using a two-part permutation encoding genetic approach

ICIC Express Letters 2011年第11期5卷 4107-4113页

作者： Li, Sheng Chen, Huiqin Gao, Shangce Tang, Zheng Graduate School of Innovation Life Science University of Toyama Gofuku 930-8555 Toyama-shi Japan Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University No. 1239 Siping Road Shanghai 200092 China

Genetic algorithms (GAs) have been shown to be very useful in a variety of search and optimization problems. In this paper, we present a new genetic algorithm for the maximum planar subgraph problem (MPSP). Previous studies investigated solving the MPSP with genetic algorithms using standard bit string chromosomes. This paper proposes a new two-part permutation encoding with a greedy heuristic decoding to represent the chromosomes. The first part represents the ordering of the edges in the graph, while the second part deals with the starting vertex based on which a vertex sequence can be determined. Computational testing shows the new approach produces better solutions than previous techniques. © 2011 ICIC International.

关键词： Chromosomes

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A MapReduce-based parallel K-means clustering for large-scale CIM data verification

A MapReduce-based parallel K-means clustering for large-scal...

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作者： Deng, Chuang Liu, Yang Xu, Lixiong Yang, Jie Liu, Junyong Li, Siguang Li, Maozhen School of Electrical Engineering and Information Sichuan University Chengdu610065 China Department of Electronic and Computer Engineering Brunel University London UxbridgeUB8 3PH United Kingdom The Key Laboratory of Embedded Systems and Service Computing Tongji University Shanghai200092 China

The Common Information Model (CIM) has been heavily used in electric power grids for data exchange among a number of auxiliary systems such as communication systems, monitoring systems, and marketing systems. With a rapid deployment of digitalized devices in electric power networks, the volume of data continuously grows, which makes verification of CIM data a challenging issue. This paper presents a parallel K-means clustering algorithm for large-scale CIM data verification. The parallel K-means builds on the MapReduce computing model which has been widely taken up by the community in dealing with data-intensive applications. A genetic algorithm-based load-balancing scheme is designed to balance the workloads among the heterogeneous computing nodes for a further improvement in computation efficiency. The performance of the parallel K-means is initially evaluated in a small-scale in-house MapReduce cluster and subsequently evaluated in a commercial cloud computing platform. Finally, the parallel K-means is evaluated in large-scale simulated MapReduce environments. Both the experimental and simulation results show that the parallel K-means reduces the CIM data-verification time significantly compared with the sequential K-means clustering, while generating a high level of precision in data verification. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

关键词： MapReduce

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Rotational invariant LBP-SURF for fast and robust image matching

Rotational invariant LBP-SURF for fast and robust image matc...

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International Conference on Signal Processing and Communication systems, ICSPCS

作者： Penghui Jiang Shengjie Zhao Samuel Cheng School of Electronic and Information Engineering Tongji University Shanghai China Key Lab of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China

Speeded Up Robust Features (SURF) is one of the most robust and widely used image matching algorithms based on local features. However, the performance for rotation image is poor when one image is a rotated version of the other. To improve the matching accuracy of rotation image, we present an modified image matching algorithm combining Haar wavelet and the rotation invariant Local Binary Patterns (LBP). Firstly, keypoints are extracted from the images for matching by applying the Hessian matrix and integral images. Secondly, each keypoint is described by the Rotation Invariant LBP patterns and Haar wavelet, which are computed from the image patch centered at the keypoint. Finally, the matching pairs between the two sets of keypoints are determined by using the nearest neighbor distance based on matching strategy. The experimented results show that in comparison with prior works the proposed algorithm is efficient when tested on the images of scaling, rotation, blurring and brightening.

关键词： Robustness Feature extraction Image matching Brightness Lighting Euclidean distance embedded systems

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Neighborhood Rough Sets based Multi-Label classification

Neighborhood Rough Sets based Multi-Label classification

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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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Conceptual model of real-time IoT systems

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Frontiers of Information Technology & Electronic Engineering 2019年第11期20卷 1457-1464页

作者： Bo YUAN De-ji CHEN Dong-mei XU Ming CHEN Department of Computer Science and Technology Tongji UniversityShanghai 201804China School of Electronics and Information Engineering Jinggangshan UniversityJi'an 343009China MOE Key Laboratory of Embedded System and Service Computing Tongji UniversityShanghai 201804China China Electronics Standardization Institute Beijing 100007China Chinesisch-Deutsche Hochschule for Angewandte Wissenschaften Tongji UniversityShanghai 201804China

We address a special kind of Internet of Things (IoT) systems that are also real-time. We call them real-time IoT (RT-IoT) systems. An RT-IoT system needs to meet timing constraints of system delay, clock synchronization, deadline, and so on. The timing constraints turn to be more stringent as we get closer to the physical things. Based on the reference architecture of IoT (ISO/IEC 30141), the RT-IoT conceptual model is established. The idea of edge subsystem is introduced. The sensing & con-trolling domain is the basis of the edge subsystem, and the edge subsystem usually must meet the hard real-time constraints. The model includes four perspectives, the time view, computation view, communication view, and control view. Each view looks, from a different angle, at how the time parameters impact an RT-IoT system.

关键词： Internet of Things(IoT) Real-time system Conceptual model View Hard/Soft real-time

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Feature selection for semi-supervised multi-label learning with application to gene function analysis 10

Feature selection for semi-supervised multi-label learning w...

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Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology

作者： Li, Guo-Zheng You, Mingyu Ge, Lei Yang, Jack Y. Yang, Mary Qu MOE Key Laboratory of Embedded System and Service Computing Tongji University Shanghai 201804 China Department of Control Science and Engineering Tongji University Shanghai 201804 China School of Computer Science and Engineering Shanghai University Shanghai 200072 China Purdue Univ. ECE West Lafayette IN 47907 United States Indiana Univ. Bioinformatics Center Indianapolis IN 46202 United States

ISBN: (纸本)9781450304382

This paper investigates gene function annotation of Yeast by using semi-supervised multi-label learning. Multi-label learning has been a hot topic in the bioinformatics field, but there are many samples unlabeled. Semi-supervised learning may be employed to utilize the unlabeled data. This paper proposes a novel semi-supervised multi-label learning algorithm COMN by combining Co-Training with ML-kNN to utilize the unlabeled yeast gene data to improve modeling accuracy of function annotation. Furthermore, an embedded feature selection algorithm PRECOMN is proposed to perform feature selection for COMN to remove the irrelevant and redundant features. Experimental results on one benchmark data set of Yeast show COMN and PRECOMN perform better than the original multi-label learning algorithm ML-kNN. Furthermore PRECOMN improves generalization performance of COMN. Copyright © 2010 ACM.

关键词： Feature Selection

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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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Mobile message-aware enhancement using fuzzy lattice reasoning

Mobile message-aware enhancement using fuzzy lattice reasoni...

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International Conference on Fuzzy systems and Knowledge Discovery (FSKD)

作者： Ghaidaa Al-Sultany Maozhen Li Mahesh Ponraj Hamid Al-Raweshidy School of Engineering and Design Brunel University Uxbridge UK The Key Laboratory of Embedded Systems and Service Computing Ministry of Education University of Tongji China

Developing service discovery, information retrieval and context-aware to adapt to user everywhere/anytime and attain the ambient intelligence arise the issue of handling misclassification, imperfect reasoning and estimation with respect to the current context. To deal with this issue, we propose the use of Fuzzy set theory with the purpose of inferring and reasoning that adopt fuzzy lattice classifier for decision making. It is applied on top of SOIM framework to increase the accuracy of the classification process with clearer decisions.

关键词： Lattices Cognition Context Ontologies Semantics Decision making Mobile communication

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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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