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作者： Liu, Lu College of Software Jilin University Changchun China Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Changchun China College of Computer Science and Technology Jilin University Changchun China

Obtaining interesting and topic-relevant information is a very important task in Web mining. Text classification using a small proportion of labeled data and a large proportion of unlabeled data, also called semi-supervised learning, is a well-known problem. Despite plenty of research on text classification, however, how to effectively and efficiently apply valuable frequent patterns and deal with high-dimensional data in text classification is still an open issue. Due to the increasing data volumes and plenty of high-dimensional data, both distance measures and time complexity could be influenced by the noisy data. This paper targets on this problem and presents a novel method for text classification called CTFP (Classification based on TFP-tree), which uses TFP-tree (Text-Frequent-Pattern-tree) to generate frequent patterns in tremendous amount of texts and conduct text classification in a relatively low dimensional data space. It effectively reduces the data dimensionality during constructing the classifier. Substantial experiments on three datasets (RCV1, SRAA and Reuters-21578) show that our proposed method can achieve better performance than many existing state-of-the-art methods on precision, efficiency and many other evaluation metrics. © 2018 - IOS Press and the authors. All rights reserved.

关键词： Support vector machines

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An improved genetic algorithm to optimize vehicle scheduling for relief efforts

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International Journal of Performability engineering 2019年第9期15卷 2356-2363页

作者： Zhou, You Sun, Lecheng Zhou, Xu Parmar, Milan Wang, Liupu College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbol Computation Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China Cancer Hospital Attached Xinjiang Medical University Urumqi830000 China Faculty of Engineering and Computing Coventry University CoventryCV1 5FB United Kingdom

The vehicle scheduling problem is a typical multi-objective optimization problem. It is a core logistics dispatching problem and the key to realize system optimization. Optimizing vehicle scheduling for relief efforts has important practical significance and can be applied to a broader range of applications. Compared with other vehicle scheduling problems, vehicle optimal scheduling for relief efforts is mostly used to perform spot delivery or security tasks. In this paper, we propose an improved genetic algorithm and apply it to solve the vehicle scheduling problem for relief efforts in the equipment technical area. The experimental results demonstrate the effectiveness and robustness of our proposed algorithm. © 2019 Totem Publisher, Inc. All rights reserved.

关键词： Genetic algorithms

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A Surrogate Model Based Genetic Algorithm for Complex Problem Solving

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Journal of Physics: Conference Series 2020年第1期1646卷

作者： Ying Pei Hao Gao Xiaosong Han The College of Information Engineering Changchun University of Finance and Economics Changchun 130122 China Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Computer Science and Technology College of Software Jilin University Changchun 130012 China

It is well known that when the fitness function is relatively complex, the optimization time cost of the genetic algorithm will be extremely huge. To address this issue, the surrogate model was employed to predict the fitness value of the optimization problem, to reduce the number of actual calculated fitness values. In this paper, BP neural network, the least square method and support vector machine were fused in the genetic algorithm to evaluate partial individuals' fitness. Sufficient benchmark numerical experiments were conducted, and the results proved that the strategy could reduce the calculating counts of fitness function on similar accuracy basis compared with simple genetic algorithm.

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Friend Recommendation System Based on Geolocation and Contents of Users

Friend Recommendation System Based on Geolocation and Conten...

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2018 International Conference on Computer Modeling, Simulation and Algorithm（CMSA2018）

作者： Mingyue Duan Jing Huang Jiaxuan Zhang Yunjie Tian Dongwei Li Bin Wang College of Computer Science and Technology Jilin university Key Laboratory of Symbol Computation and Knowledge Engineering (Jilin university) Ministry of Education

ISBN: (纸本)9781510864696

In recent years, with the rapid development of wireless mobile network and smart phone operating systems, various social software based on wireless Internet has emerged one after another. Current popular social software such as QQ and We Chat have become important tools for people to meet new friends. At present, existing social software can recommend other users in the vicinity according to the geographical location of the user. However, this method does not consider the user's interests, hobbies, etc. So that the effectiveness of such a friend recommmendation system is often unsatisfactory. In order to solve the above problems, a personalized friend recommendation system based on geolocation information and user content is designed and developed. In this system, not only the geolocation information of the user is considered, but also the features of the user's published statuses are extracted, aiming to recommend more similar other users to the user. After testing, the effectiveness of the proposed method is verified.

关键词： instant message friend recommendation geolocation information context information

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A multi-level neural network for implicit causality detection in Web texts

arXiv

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arXiv 2019年

作者： Liang, Shining Zuo, Wanli Shi, Zhenkun Wang, Sen Wang, Junhu Zuo, Xianglin Key Laboratory of Symbol Computation Knowledge Engineering of Ministry of Education Changchun China College of Computer Science and Technology Jilin University Jilin China Tianjin Institute of Industrial Biotechnology Chinese Academy of Sciences School of Information Technology and Electrical Engineering The University of Queensland Brisbane Australia School of Information and Communication Technology Griffith University QLD Australia

Mining causality from text is a complex and crucial natural language understanding task corresponding to the human cognition. Existing studies at its solution can be grouped into two primary categories: feature engineering based and neural model based methods. In this paper, we find that the former has incomplete coverage and inherent errors but provide prior knowledge;while the latter leverages context information but causal inference of which is insufficiency. To handle the limitations, we propose a novel causality detection model named MCDN to explicitly model causal reasoning process, and furthermore, to exploit the advantages of both methods. Specifically, we adopt multi-head self-attention to acquire semantic feature at word level and develop the SCRN to infer causality at segment level. To the best of our knowledge, with regards to the causality tasks, this is the first time that the Relation Network is applied. The experimental results show that: i) the proposed approach performs prominent performance on causality detection;ii) further analysis manifests the effectiveness and robustness of MCDN. © 2019, CC BY-NC-SA.

关键词： Semantics

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Design of vehicle automatic braking systems considering drivers' braking characteristics

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International Journal of Performability engineering 2019年第9期15卷 2338-2345页

作者： Gao, Zhenhai Sun, Tianjun Hassan, MuHammad Wang, Liupu College of Automotive Engineering Jilin University Changchun130022 China College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbol Computation Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China Cancer Hospital Attached Xinjiang Medical University Urumqi830000 China Department of Computer Science and Information Technology University of Malakand KP Pakistan

With the rapid development of advanced driver assistance systems (ADAS), an automatic braking system has become increasingly important when faced with complicated traffic. The conventional decision-making method for the braking system is focused on security but lacks consideration of drivers' characteristics, which generates feelings of fear and frustration. We propose an anthropomorphic braking method based on vehicle kinematic characteristics and dynamic theories to improve the original system and eliminate the sense of jerking. Furthermore, on the basis of the traditional time to collision (TTC) algorithm, we consider the random motion of the target vehicle and develop a dynamic time to collision (DTTC) algorithm to meet different drivers' braking requirements. Therefore, in this study, the braking model is optimized by a series of driving simulator and real-vehicle braking tests. Finally, the validity and feasibility of the method are verified through the simulations of different braking modes. © 2019 Totem Publisher, Inc. All rights reserved.

关键词： Advanced driver assistance systems

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Low cost edge sensing for high quality demosaicking

arXiv

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arXiv 2018年

作者： Niu, Yan State Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education College of Computer Science and Technology Jilin University Changchun130012 China

Digital cameras that use Color Filter Arrays (CFA) entail a demosaicking procedure to form full RGB images. As today's camera users generally require images to be viewed instantly, demosaicking algorithms for real applications must be fast. Moreover, the associated cost should be lower than the cost saved by using CFA. For this purpose, we revisit the classical Hamilton-Adams (HA) algorithm, which outperforms many sophisticated techniques in both speed and accuracy. Based on a close look at HA's strength and weakness, we design a very low cost edge sensing scheme. Briefly, it guides demosaicking by a logistic functional of the difference between directional variations. We extensively compare our algorithm with 28 demosaicking algorithms by running their open source codes on benchmark datasets. Compared to methods of similar computational cost, our method achieves substantially higher accuracy;Whereas compared to methods of similar accuracy, our method has significantly lower cost. Moreover, on test images of currently popular resolution, the quality of our algorithm is comparable to top performers, whereas its speed is tens of times faster. Copyright © 2018, The Authors. All rights reserved.

关键词： Costs

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Detection of Glue Spray on Bottom of Vehicle Based on Machine Vision

Detection of Glue Spray on Bottom of Vehicle Based on Machin...

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2018 International Conference on Network, Communication, Computer engineering (NCCE 2018)

作者： Dongyang Liu Jindong Zhang Xiaofeng Zhang Xiaoyu Ma Yuxin Kou College of Computer Science and Technology Jilin University Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University College of Software Jilin University

ISBN: (纸本)9781510871076

With the continuous development of machine vision,intelligent detection of bottom spray glue has gradually become a *** this paper,we proposed an algorithm of detection the glue spray on bottom of vehicle based on machine *** sampling and detecting the spray interest area,the algorithm can obtain its features which were used for intelligent identification of the spray interest area,and then carry out the pseudo target and detect whether the detection area is complete or *** can test the vehicles on pipeline in real *** the average,each frame's processing time is less than 150 *** there is an unqualified area,it will alarm and inform the operators of the wrong *** algorithm can improve the quality of the vehicle and save human resources and identify the spray interest area with high *** research directions in the future is to accelerate the speed to meet the practical application requirements,and to solve the intelligent identification of the spray interest area which has the same color with the vehicle body.

关键词： Glue spray Machine vision intelligent detection

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The vehicle audio encryption system based on scrambling thought

The vehicle audio encryption system based on scrambling thou...

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2018 International Conference on Network, Communication, Computer engineering (NCCE 2018)

作者： Peibin Wu Jindong Zhang Yang Liu Dongqi Han Yiding Sun College of Computer Science and Technology Jilin University Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University State Key Laboratory of Automobile Simulation and Control Jilin University

ISBN: (纸本)9781510871076

In this paper,the vehicle audio encryption system based on scrambling thought and applied to vehicle-mounted media is *** pseudo random number is generated each time it is encrypted,and it is substituted into the Fibonacci transform to generate a key with variable *** audio data is divided by the number of pseudo random numbers into the Fibonacci sequence,and is encrypted in permutation *** Fibonacci transform has symmetry and creates an audio file that cannot be parsed and played after the permutation *** the security analysis of encryption algorithm,the audio files of different length are *** key space,anti-attack ability,key sensitivity and other indicators are tested to achieve good *** addition,this algorithm is simple in calculation and can realize the ideal real-time performance in practical application.

关键词： vehicle media audio encryption scrambling thought security analysis

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Live Video Streaming Authentication for Vehicle Multimedia based on Merkle Tree

Live Video Streaming Authentication for Vehicle Multimedia b...

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2018 International Conference on Network, Communication, Computer engineering (NCCE 2018)

作者： Dongqi Han Jindong Zhang Yang Liu Peibin Wu Yiding Sun College of Computer Science and Technology Jilin University Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University State Key Laboratory of Automobile Simulation and Control Jilin University

ISBN: (纸本)9781510871076

Merkle tree has been widely applied in the field of P2 P *** this paper,an efficient authentication scheme is proposed in the integrity check module for vehicle live video streaming system by optimizing the construction and verification of the Merkle *** can create and check nodes at the same time in a shorter time without trusted *** the same time,in order to prevent the oversensitivity of hash algorithm,this paper uses the perceptual hash algorithm to generate the hash value of nodes in the Merkle *** performance evaluation and analysis,our algorithm can build a Merkle tree with 2 N-1 nodes only after the N+log2 N times hash *** has better time performance than the traditional tree,whether receiving attacks or *** it is proved that the scheme is secure under the condition that the vehicle live video streaming system is safe.

关键词： vehicle streaming media system Merkle tree P2P-streaming integrity authentication perceptual hash algorithm

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