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17th Genetic and Evolutionary Computation Conference, GECCO 2015

作者： Wang, Jia-Bin Chen, Wei-Neng Zhang, Jun Lin, Ying School of Advanced Computing Sun Yat-sen University Guangzhou China Key Lab. Machine Intelligence and Advanced Computing Ministry of Education China Collaborative Innovation Center of High Performance Computing China Engineering Research Center of Supercomputing Engineering Software Ministry of Education China Department of Psychology Sun Yat-sen University Guangzhou China

ISBN: (纸本)9781450334884

Portfolio optimization problems are challenging as they contain different kinds of constrains and their complexity becomes very high when the number of assets grows. In this paper, we develop a dimension-decreasing particle swarm optimization (DDPSO) for solving multi-constrained portfolio optimization problems. DDPSO improves the efficiency of PSO for solving portfolio optimization problems with a lot of asset and it can easily handle the cardinality constraint in portfolio optimization. To improve search diversity, the dimension-decreasing method is coupled with the comprehensive learning particle swarm optimization (CLPSO) algorithm. The proposed method is tested on benchmark problems from the OR library. Experimental results show that the proposed algorithm performs well. Copyright is held by the owner/author(s).

关键词： Particle swarm optimization (PSO)

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Continual learning of new diseases with dual distillation and ensemble strategy 23rd

Continual learning of new diseases with dual distillation an...

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23rd International Conference on Medical Image computing and Computer-Assisted Intervention, MICCAI 2020

作者： Li, Zhuoyun Zhong, Changhong Wang, Ruixuan Zheng, Wei-Shi School of Data and Computer Science Sun Yat-sen University Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing MOE Guangzhou China Pazhou Lab Guangzhou China

ISBN: (纸本)9783030597092

Most intelligent diagnosis systems are developed for one or a few specific diseases, while medical specialists can diagnose all diseases of certain organ or tissue. Since it is often difficult to collect data of all diseases, it would be desirable if an intelligent system can initially diagnose a few diseases, and then continually learn to diagnose more and more diseases with coming data of these new classes in the future. However, current intelligent systems are characterised by catastrophic forgetting of old knowledge when learning new classes. In this paper, we propose a new continual learning framework to alleviate this issue by simultaneously distilling both old knowledge and recently learned new knowledge and by ensembling the class-specific knowledge from the previous classifier and the learned new classifier. Experiments showed that the proposed method outperforms state-of-the-art methods on multiple medical and natural image datasets. © Springer Nature Switzerland AG 2020.

关键词： Distillation

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Software Defined Networking Based On-Demand Routing Protocol in Vehicle Ad Hoc Networks 12

Software Defined Networking Based On-Demand Routing Protocol...

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12th International Conference on Mobile Ad-Hoc and Sensor Networks, MSN 2016

作者： Dong, Baihong Wu, Weigang Yang, Zhiwei Li, Junjie School of Data and Computer Science Sun Yat-Sen University China Guangdong Key Lab of Big Data Analysis and Processing China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou China

ISBN: (纸本)9781509056965

This paper comes up with a SDN based On-Demand Routing Protocol, SVAO, which separates data forwarding layer and network control layer, as in SDN, to enhance the data transmission efficiency within VANETs. The Roadside Service Unit plays the role of Local Controller and is in charge of selecting vehicles to forward packet within a road segment. All the vehicles state in the road. Correspondingly, a two-level design is used. Global Level is distributed and adopts a ranked query scheme to collect vehicle information and determine the road segments along which a message should be forwarded. And the Local Level is in charge of selecting forwarding vehicles in each road segment determined by the global level. We compare SVAO with popular ad-hoc network routing protocols, including OLSR, DSR, DSDV, and DB via simulations. We consider the impact of vehicle density, speed on data transmission rate and average packet delay. The simulation results show that in case of large network scales or high vehicle speed, SVAO performs better than the others. © 2016 IEEE.

关键词： Routing protocols

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Detection of driving fatigue based on grip force on steering wheel with wavelet transformation and support vector machine

Detection of driving fatigue based on grip force on steering...

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20th International Conference on Neural Information Processing, ICONIP 2013

作者： Li, Fan Wang, Xiao-Wei Lu, Bao-Liang Center for Brain-like Computing and Machine Intelligence Department of Computer Science and Engineering China MOE-Microsoft Key Lab. for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong University 800 Dongchuan Road Shanghai 200240 China

ISBN: (纸本)9783642420504

This paper proposes an unobtrusive way to detect fatigue for drivers through grip forces on steering wheel. Simulated driving experiments are conducted in a refitted passenger car, during which grip forces of both hands are collected. Wavelet transformation is introduced to extract fatigue-related features from wavelet coefficients. We compare the performance of k-nearest neighbours, linear discriminant analysis, and support vector machine (SVM) on the task of discriminating drowsy and awake states. SVM with radial basis function reaches the best accuracy, 75% on average. The results show that variation in grip forces on steering wheel can be used to effectively detect drivers' fatigue. © Springer-Verlag 2013.

关键词： Support vector machines

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Syntax-enhanced pre-trained model 59

Syntax-enhanced pre-trained model

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Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, ACL-IJCNLP 2021

作者： Xu, Zenan Guo, Daya Tang, Duyu Su, Qinliang Shou, Linjun Gong, Ming Zhong, Wanjun Quan, Xiaojun Jiang, Daxin Duan, Nan School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China Microsoft Research Asia Beijing China Microsoft Search Technology Center Asia Beijing China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Key Lab. of Machine Intelligence and Advanced Computing Ministry of Education China

ISBN: (纸本)9781954085527

We study the problem of leveraging the syntactic structure of text to enhance pre-trained models such as BERT and RoBERTa. Existing methods utilize syntax of text either in the pre-training stage or in the fine-tuning stage, so that they suffer from discrepancy between the two stages. Such a problem would lead to the necessity of having human-annotated syntactic information, which limits the application of existing methods to broader scenarios. To address this, we present a model that utilizes the syntax of text in both pre-training and fine-tuning stages. Our model is based on Transformer with a syntax-aware attention layer that considers the dependency tree of the text. We further introduce a new pre-training task of predicting the syntactic distance among tokens in the dependency tree. We evaluate the model on three downstream tasks, including relation classification, entity typing, and question answering. Results show that our model achieves state-of-the-art performance on six public benchmark datasets. We have two major findings. First, we demonstrate that infusing automatically produced syntax of text improves pre-trained models. Second, global syntactic distances among tokens bring larger performance gains compared to local head relations between contiguous tokens. © 2021 Association for Computational Linguistics

关键词： Syntactics

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Real-time head detection with kinect for driving fatigue detection

Real-time head detection with kinect for driving fatigue det...

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20th International Conference on Neural Information Processing, ICONIP 2013

作者： Cao, Yang Lu, Bao-Liang Center for Brain-like Computing and Machine Intelligence Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai 200240 China MOE-Microsoft Key Lab. for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong University 800 Dongchuan Road Shanghai 200240 China

ISBN: (纸本)9783642420504

Nowadays, depth cameras such Microsoft Kinect make it easier and cheaper for us to capture depth images. It becomes practical to use depth images for detection in consumer-grade products. In this paper, we propose a novel and simple real-time method to detect human head in depth image for our driving fatigue detection system, based on the elliptical shape of human head. Experiments show that our method can successfully detect human head in different light conditions and across different head poses. We integrate this detection algorithm into our driving fatigue detection system, and see remarkable improvements both in detection rate and detection speed. © Springer-Verlag 2013.

关键词： Computers

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Current Situation and Development of intelligence Robots

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ZTE Communications 2016年第B12期14卷 25-34页

作者： REN Fuji SUN Xiao Anhui Key Lab of Affective Computing and Advanced Intelligence Machine Hefei University of Technology Hefei 230009 China Tokushima University Tokushima 770800 Japan

Industrial intelligent robots are treated as a measure of na- tional scientific level and technology innovation, and also the important symbol of high-level manufacturing, while service intelligent robots can directly affect people＇ s daily lives. The development of artificial robots in different areas is at- tracting much attention around the world. This article re- views the current situation and development of Chinese and international intelligent robot markets including industrial ro- bots and service robots. The intelligent robot technology and the classification of robots are also discussed. Finally, appli- cations of intelligent robots in various fields are concluded and the development trends and outlook of intelligent robots are explored.

关键词： intelligent robot artificial intelligence development trends

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A Parallel Implementation of Multiobjective Particle Swarm Optimization Algorithm Based on Decomposition

A Parallel Implementation of Multiobjective Particle Swarm O...

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IEEE Symposium Series on Computational intelligence

作者： Jin-Zhou Li Wei-Neng Chen Jun Zhang Zhi-hui Zhan Sun Yat-sen University Collaborative Innovation Center of High Performance Computing Sun Yat-sen University Key Lab. Machine Intelligence and Advanced Computing Ministry of Education

ISBN: (纸本)9781479975617

Multiobjective particle swarm optimization based on decomposition (MOPSO/D) is an effective algorithm for multiobjective optimization problems (MOPs). This paper proposes a parallel version of MOPSO/D algorithm using both message passing interface (MPI) and OpenMP, which is abbreviated as MO-MOPSO/D. It adopts an island model and divides the whole population into several subspecies. Based on the hybrid of distributed and shared-memory programming models, the proposed algorithm can fully use the processing power of today's multicore processors and even a cluster. The experimental results show that MO-MOPSO/D can achieve speedups of 2× on a personal computer equipped with a dual-core four-thread CPU. In terms of the quality of solutions, it can perform similarly to the serial MOPSO/D but greatly outperform NSGA-II. An additional experiment is done on a cluster, and the results show the speedup is not obvious for small-scale MOPs and it is more suitable for solving highly complex problems.

关键词： Particle swarm optimization decomposition Speeding MOP Mops processing power efficient algorithm Personal computers message passing subspecies Parallel Lines Distribute degradation

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Glad: Global And Local Anomaly Detection

Glad: Global And Local Anomaly Detection

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Lihai Nie Laiping Zhao Keqiu Li Tianjin Key Lab. of Advanced Networking (TANKLab) College of Intelligence and Computing (CIC) Tianjin University

ISBN: (数字)9781728113319

ISBN: (纸本)9781728113326

Detecting anomaly in images is challenging due to the high dimension nature of image data. While the previous learning-based anomaly detection approaches can detect a particular type of anomaly precisely, they often fail in detecting multiple types of abnormal samples *** identify the two specific types of anomalies that can be precisely detected by either compress-based or reconstruction-based anomaly detection approaches, named global anomaly and local anomaly. We then propose Glad, an anomaly detector that can precisely detect both of them at the same time. Glad adopts a joint approach combining the density estimation and auto-encoder. Firstly, it designs a multimodal density estimation model to derive the latent representation probability for identifying the global anomaly. Then, it uses structural similarity to measure the reconstruction loss for characterizing local anomaly. Finally, both anomalies can be diagnosed according to the joint density of latent representation and reconstruction loss. Experimental results on public benchmark datasets demonstrate that Glad outperforms the state-of-the-art methods significantly.

关键词： Image reconstruction Anomaly detection Estimation Decoding Training Neural networks Convolution

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PPLSA: Parallel probabilistic latent semantic analysis based on MapReduce

PPLSA: Parallel probabilistic latent semantic analysis based...

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7th IFIP International Conference on Intelligent Information Processing, IIP 2012

作者： Li, Ning Zhuang, Fuzhen He, Qing Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China Graduate University Chinese Academy of Sciences Beijing China Key Lab. of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding China

ISBN: (纸本)9783642328909

PLSA(Probabilistic Latent Semantic Analysis) is a popular topic modeling technique for exploring document collections. Due to the increasing prevalence of large datasets, there is a need to improve the scalab.lity of computation in PLSA. In this paper, we propose a parallel PLSA algorithm called PPLSA to accommodate large corpus collections in the MapReduce framework. Our solution efficiently distributes computation and is relatively simple to implement. © 2012 IFIP International Federation for Information Processing.

关键词： MapReduce

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