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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Chniter, Malak Abid, Abir Kallel, Ilhem Kanoun, Slim Mir@cl Lab: Multimedia InfoRmation Systems and Advanced Computing Laboratory University of Sfax ReGIM-Lab: Research Groups in Intelligent Machines Tunisia

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

The task of assessing, grouping and arranging data into meaningful groups or clusters based on their similarities/dissimilarities measures known as cluster analysis. Thereby, there are numerous clustering algorithms: hierarchical and partitional. In the last decade, clustering using bio-inspired algorithms received more attention, specifically the ant clustering algorithms. Regardless, they have required a lot of processing power due to the massive amount of data that has been generated during the last years. As a consequence, determining the computational cost of these algorithms is one of the most interesting tasks in the quest for optimal clustering solutions in a real-time system. This study presents a research guide for the researchers working in the same field. A series of experiments are elaborated to investigate the computational complexity of the most promising algorithms applied to students grouping problem. The results indicate two challenges that arise when using ant clustering algorithms: the difficulty in adjusting parameters and extended computation time. © 2022 IEEE.

关键词： Computational complexity

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Recommendation in heterogeneous information network via dual similarity regularization

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International Journal of Data Science and Analytics 2017年第1期3卷 35-48页

作者： Zheng, Jing Liu, Jian Shi, Chuan Zhuang, Fuzhen Li, Jingzhi Wu, Bin Beijing Key Lab of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China Southern University of Science and Technology Shenzhen China

Recommender system has caught much attention from multiple disciplines, and many techniques are proposed to build it. Recently, social recommendation becomes a hot research direction. The social recommendation methods tend to leverage social relations among users obtained from social network to alleviate data sparsity and cold-start problems in recommender systems. It employs simple similarity information of users as social regularization on users. Unfortunately, the widely used social regularization suffers from several aspects: (1) The similarity information of users only stems from social relations of related users;(2) it only has constraint on users without considering the impact of items for recommendation;(3) it may not work well for dissimilar users. To overcome the shortcomings of social regularization, we design a novel dual similarity regularization to impose the constraint on users and items with high and low similarities simultaneously. With the dual similarity regularization, we further propose an optimization function to integrate the similarity information of users and items under different semantic meta-paths, and a gradient descend solution is derived to optimize the objective function. Experiments with different meta-paths validate the superiority of integrating much available information, and the experiments conducted on three real data sets validate the effectiveness of the proposed solution. © 2016, Springer International Publishing Switzerland.

关键词： Recommender systems

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Aspect mining with rating bias 15th

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15th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2016

作者： Li, Yitong Shi, Chuan Zhao, Huidong Zhuang, Fuzhen Wu, Bin Beijing Key Lab of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and Telecommunications Beijing China Institute of Computing Technology CAS Beijing China

ISBN: (纸本)9783319462264

Due to the personalized needs for specific aspect evaluation on product quality, these years have witnessed a boom of researches on aspect rating prediction, whose goal is to extract ad hoc aspects from online reviews and predict rating or opinion on each aspect. Most of the existing works on aspect rating prediction have a basic assumption that the overall rating is the average score of aspect ratings or the overall rating is very close to aspect ratings. However, after analyzing real datasets, we have an insightful observation: there is an obvious rating bias between overall rating and aspect ratings. Motivated by this observation, we study the problem of aspect mining with rating bias, and design a novel RAting-center model with BIas (RABI). Different from the widely used review-center models, RABI adopts the overall rating as the center of the probabilistic model, which generates reviews and topics. In addition, a novel aspect rating variable in RABI is designed to effectively integrate the rating bias priori information. Experiments on two real datasets (Dianping and TripAdvisor) validate that RABI significantly improves the prediction accuracy over existing state-of-the-art methods. © Springer International Publishing AG 2016.

关键词： Forecasting

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Beyond Knowledge Engineering

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Journal of Computer Science & Technology 2006年第5期21卷 790-799页

作者：陆汝钤金芝 Academy of Mathematics and System Science Chinese Academy of Sciences Beijing 100080 P.R. China Institute of Computing Technology Chinese Academy of Sciences Beijing 100080 P.R. China Shanghai Key Lab of Intelligent Information Processing Fudan University Shanghai 200333 P.R. China Beijing Key Lab of Multimedia and Intelligent Software Beijing University of Technology Beijing 100083 P.R. China

Knowledge engineering stems from E. A. Figenbaum＇s proposal in 1977, but it will enter a new decade with the new challenges. This paper first summarizes three knowledge engineering experiments we have undertaken to show possibility of separating knowledge development from intelligent software development. We call it the ICAX mode of intelligent application software generation. The key of this mode is to generate knowledge base, which is the source of intelligence of ICAX software, independently and parallel to intelligent software development. That gives birth to a new and more general concept ＂knowware＂. Knowware is a commercialized knowledge module with documentation and intellectual property, which is computer operable, but free of any built-in control mechanism, meeting some industrial standards and embeddable in software/hardware. The process of development, application and management of knowware is called knowware engineering. Two different knowware life cycle models are discussed： the furnace model and the crystallization model. Knowledge middleware is a class of software functioning in all aspects of knowware life cycle models. Finally, this paper also presents some examples of building knowware in the domain of information system engineering.

关键词： knowledge engineering knowware knowware engineering knowledge middleware domain knowware

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Adaptive tag selection for image annotation 15

Adaptive tag selection for image annotation

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15th Pacific-Rim Conference on multimedia, PCM 2014

作者： He, Xixi Li, Xirong Yang, Gang Xu, Jieping Jin, Qin Key Lab of DEKE Renmin University of China 100872 China Multimedia Computing Lab Renmin University of China 100872 China Shanghai Key Laboratory of Intelligent Information Processing 200443 China

ISBN: (纸本)9783319131672

Not all tags are relevant to an image, and the number of relevant tags is image-dependent. Although many methods have been proposed for image auto-annotation, the question of how to determine the number of tags to be selected per image remains open. The main challenge is that for a large tag vocabulary, there is often a lack of ground truth data for acquiring optimal cutoff thresholds per tag. In contrast to previous works that pre-specify the number of tags to be selected, we propose in this paper adaptive tag selection. The key insight is to divide the vocabulary into two disjoint subsets, namely a seen set consisting of tags having ground truth available for optimizing their thresholds and a novel set consisting of tags without any ground truth. Such a division allows us to estimate how many tags shall be selected from the novel set according to the tags that have been selected from the seen set. The effectiveness of the proposed method is justified by our participation in the ImageCLEF 2014 image annotation task. On a set of 2,065 test images with ground truth available for 207 tags, the benchmark evaluation shows that compared to the popular top-k strategy which obtains an F-score of 0.122, adaptive tag selection achieves a higher Fscore of 0.223. Moreover, by treating the underlying image annotation system as a black box, the new method can be used as an easy plug-in to boost the performance of existing systems. © Springer International Publishing Switzerland 2014.

关键词： Image annotation

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Watershed-based weighted graph partitioning active contours

Watershed-based weighted graph partitioning active contours

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2010 3rd International Congress on Image and Signal Processing, CISP 2010

作者： Wang, Shaorong Chen, Yisong Li, Sheng Wang, Guoping Li, Hua Key Lab. of Machine Perception and Intelligent MOE Beijing 100871 China HCI and Multimedia Lab. Dept. of Computer Science Peking University Beijing 100871 China National Research Center for Intelligent Computing Systems Institute of Computing Technology Chinese Academy of Sciences Beijing 100080 China

A novel Weighted Graph Partitioning Active Contours method based on weighted dissimilarity is introduced. This method is easy to be extended by defining different types of similarities. And it has been greatly acceler... 详细信息

ISBN: (纸本)9781424465149

关键词： Image segmentation

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A CONSTRAINT-BASED REASONING FRAMEWORK FOR BEHAVIORAL ANIMATION

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COMPUTER GRAPHICS FORUM 1994年第5期13卷 315-324页

作者： MAH, S CALVERT, TW HAVENS, W Graphics and Multimedia Research Lab / Intelligent Systems Lab School of Computing Science Simon Fraser University Burnaby B.C. Canada V5A 1S6 e-mail: sang@cs.sfu.ca tom@cs.sfu.ca havens@cs.sfu.ca

Behaviour is a reflection of a reasoning process that must deal with constraints imposed by an external environment, internal knowledge and physical structure. This paper proposes a framework for behavioural animation that is based on the next generation of object-oriented, constraint-based expert systems technology, and applies a control structure of knowledge agents and knowledge units to determine the behaviour of objects to be animated. Knowledge agents are responsible for planning, plan implementation and information extraction from the environment. The activity of an agent is dependent on the knowledge units ascribed to them by the animator. The interaction between agents and knowledge units is resolved by the reasoning engine, and thus, influences the eventual motion displayed. An example given in NSAIL, a pilot implementation using the model-based ECHIDNA constraint logic programming shell. With this approach, the motion for a sailing scenario and other behavioural domains can be specified at a high level through the characterization of the knowledge agents.

关键词： BEHAVIORAL ANIMATION CONSTRAINT-BASED REASONING KNOWLEDGE-BASED ANIMATION CONSTRAINT LOGIC PROGRAMMING EXPERT SYSTEMS AGENTS SAILING

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Bark texture feature extraction based on statistical texture analysis

Bark texture feature extraction based on statistical texture...

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2004 International Symposium on intelligent multimedia, Video and Speech Processing, ISIMP 2004

作者： Wan, Yuan-Yuan Du, Ji-Xiang Huang, De-Shuang Chi, Zheru Cheung, Yiu-Ming Wang, Xiao-Feng Zhang, Guo-Jun Intelligent Computing Lab. Institute of Intelligent Machines Chinese Academy of Sciences P.O.Box 1130 Hefei Anhui 230021 China Ctr. for Multimedia Sign. Processing Hong Kong Polytechnic University Hong Kong Hong Kong Department of Computer Science Hong Kong Baptist University

ISBN: (纸本)0780386884

This paper quantitatively describes and discusses the usefulness of texture analysis methods for the recognition of bark Comparative studies of bark texture feature extraction are performed for the four texture analysis methods such as the gray level Run-Length method (RLM), Co-occurrence Matrices method (COMM) and Histogram method (HM) as well as Auto-Correlation method (ACM). Specifically, we use three classifiers of Nearest Neighbor (1-NN), k-Nearest Neighbor (k-NN) and Moving Median Centers (MMC) Hypersphere classifiers to verify the validity of the extracted bark texture features. To gain good result we added the color information that proved very efficient. Moreover, the experimental results also demonstrate that from the viewpoint of the recognition accuracy and computational complexify, the COMM method is superior to the other three methods.

关键词： Feature extraction

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A hypersphere method for plant leaves classification

A hypersphere method for plant leaves classification

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2004 International Symposium on intelligent multimedia, Video and Speech Processing, ISIMP 2004

作者： Zhang, Guo-Jun Wang, Xiao-Feng Huang, De-Shuang Chi, Zheru Cheung, Yiu-Ming Du, Ji-Xiang Wan, Yuan-Yuan Intelligent Computing Lab. Institute of Intelligent Machines Chinese Academy of Sciences P.O.Box 1130 Hefei Anhui 230021 China Ctr. for Multimedia Sign. Processing Hong Kong Polytechnic University Hong Kong Hong Kong Department of Computer Science Hong Kong Baptist University

ISBN: (纸本)0780386884

The recognition of tens of thousands kinds of plants on earth is an important and difficult task. If we use nearest neighbour classifier to solve it, millions of training data are needed to obtain a high correct recognition rate. However, the corresponding classification process would be quite time-consuming. In this paper, we propose a new approach using Moving Median Centers Hypersphere technique to recognize different plant leaves. With this new method, we can successfully decrease the classification time, and reduce the storage size without sacrificing the classification accuracy. The experimental results using real data show that this method is really efficient and effective in classifying the different plant leaves.

关键词： Plants (botany)

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Endmember extraction by exemplar finder

Endmember extraction by exemplar finder

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9th International Conference on Advanced Data Mining and Applications, ADMA 2013

作者： Guo, Yi Gao, Junbin Sun, Yanfeng CSIRO Division of Computational Informatics North Ryde NSW 1670 Australia School of Computing and Mathematics Charles Sturt University Bathurst NSW 2795 Australia Beijing Municipal Key Lab. of Multimedia and Intelligent Software Technology Beijing University of Technology Beijing 100124 China

ISBN: (纸本)9783642539169

We propose a novel method called exemplar finder (EF) for spectral data endmember extraction problem, which is also known as blind unmixing in remote sensing community. Exemplar finder is based on data self reconstruction assuming that the bases (endmembers) generating the data exist in the given data set. The bases selection is fulfilled by minimising a l2/l1 norm on the reconstruction coefficients, which eliminates or suppresses irrelevant weights from non-exemplar samples. As a result, it is able to identify endmembers automatically. This algorithm can be further extended, for example, using different error structures and including rank operator. We test this method on semi-simulated hyperspectral data where ground truth is available. Exemplar finder successfully identifies endmembers, which is far better than some existing methods, especially when signal to noise ratio is high. © 2013 Springer-Verlag.

关键词： Signal to noise ratio

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