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Correction to: MRI enhancement based on visual-attention by adaptive contrast adjustment and image fusion

Multimedia Tools and Applications 2021年第9期80卷 13019-13019页

作者： Rui Zhu Xiongfei Li Xiaoli Zhang Xiaowei Xu Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China College of Computer Science and Technology Jilin University Changchun China Department of Information Sciences University of Arkansas at Little Rock Little Rock USA

A Correction to this paper has been published: https://***/10.1007/s11042-021-10782-7

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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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Black-box expectation propagation for Bayesian models

Black-box expectation propagation for Bayesian models

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2018 SIAM International Conference on Data Mining, SDM 2018

作者： Li, Ximing Li, Changchun Chi, Jinjin Ouyang, Jihong Wang, Wenting College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University China College of Mathematics Jilin University China

In this paper, we develop a generic black-box expectation propagation (BBEP) algorithm that can be directly applied to Bayesian models without model-specific derivations. BBEP is built on the spirit of using Monte Carlo estimates, where the moment matching step in EP is replaced with Monte Carlo approximations. To avoid high variance, we employ importance sampling for variance reduction and analyze how to find an optimal proposal distribution. We compare BBEP against the state-of-the-art black-box algorithms on both synthetic and real-world data sets. The experimental results indicate that BBEP can reach better predictive performance than baseline algorithms, and even can be on a par with analytical solutions in some settings. © 2018 by SIAM.

关键词： Importance sampling

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A Fuzzy Clustering Based Collaborative Filtering Algorithm for Time-aware POI Recommendation

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Journal of Physics: Conference Series 2021年第1期1746卷

作者： Minghao Yin Yanheng Liu Xu Zhou Geng Sun College of Computer Science and Technology Jilin University Changchun Jilin China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun Jilin China Center for Computer Fundamental Education Jilin University Changchun Jilin China

Location based social network develops and gets widely concern along with the population and widespread use of mobile. Point of interest(POI) recommendation become one of the most widely application among location-based service. To get better POI recommendation performance, a fuzzy clustering based collaborative filtering algorithm (FCCF) for time-aware POI recommendation is proposed in this paper. It first constructs the user feature vector from users' check-in behaviours. Individual's check-in behaviour can be under the influence of location region and time slots, so user's feature consists of two parts. One is the vising frequency of each user in different location regions, and the other is the vising frequency of each user in different time slots. Next fuzzy c-means is adopted due to its simplicity to group users according to user feature vector. Then the user similarity computation can be limited in the similar small user groups. In the end, a collaborative filtering algorithm is applied to recommend a number of top-N POIs at a given time for the target user. Some experiments are conducted and the comparative results on Foursquare and Gowalla show that FCCF has higher precision and recall value than the comparative algorithms.

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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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A Registration Method for Augmented Reality System Based on Visual SLAM

A Registration Method for Augmented Reality System Based on ...

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International Conference on Electronic engineering and Informatics (EEI)

作者： Zuoming Zhang Man Shu Zixuan Wang Hanwen Wang Xin Wang College of Software Engineering Jilin University Changchun China College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineer of Ministry of Education Jilin University Changchun China

ISBN: (纸本)9781728140773

In augmented reality, smart devices need to sense their own position in real physical space and complex scene structure to achieve good virtual reality interaction and three-dimensional registration. This paper proposes a technical framework of visual SLAM, which uses VO and BA methods based on feature points to obtain pose, and applies it to the augmented reality system to realize pose estimation, registration, tracking, collision and other effects of intelligent equipment independent of artificial or natural landmarks.

关键词： augmented reality image registration pose estimation robot vision SLAM (robots) augmented reality pose estimation robot vision image registration supersonic low altitude missile Registration

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SNB-PPB: Social-network-based-privacy-preserving Broadcast for Vehicular Communications 4

SNB-PPB: Social-network-based-privacy-preserving Broadcast f...

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4th International Conference on Vehicle Technology and Intelligent Transport Systems, VEHITS 2018

作者： Liu, Yanheng Zhu, Haifeng Wang, Jian College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China

ISBN: (纸本)9789897582936

Internet key Exchange (IKE) can be used in vehicular communications in vehicular ad hoc networks (VANETs), but it requires a stable communication procedure, which is usually difficult to achieve because of the highly dynamic context of a VANET. In this paper, we propose a new method named social-network-based-privacy-preserving broadcast (SNB-PPB) for secure vehicular communications, which can omit the key exchange procedure. It transfers the information of online social networks to offline vehicular communications, dividing messages into different privacy levels and vehicles into different trust levels based on trust relations on social networks. The sender uses attributes on social networks and the message’s privacy level to encrypt messages;only those receivers who satisfy the corresponding trust level can decrypt the broadcast *** the best of our knowledge, this is the first time that social networks have been applied in VANETs to achieve secure vehicular communications and privacy preservation. Our simulation results show that our method enables the sender to decide the amount and percentage of vehicles that can decrypt the received broadcast. Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

关键词： Vehicular ad hoc networks

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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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Deep Bayesian optimization on attributed graphs

arXiv

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arXiv 2019年

作者： Cui, Jiaxu Yang, Bo Hu, Xia College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation Knowledge Engineering of Ministry of Education China Department of Computer Science and Engineering Texas A&M University College Station United States

Attributed graphs, which contain rich contextual features beyond just network structure, are ubiquitous and have been observed to benefit various network analytics applications. Graph structure optimization, aiming to find the optimal graphs in terms of some specific measures, has become an effective computational tool in complex network analysis. However, traditional model-free methods suffer from the expensive computational cost of evaluating graphs;existing vectorial Bayesian optimization methods cannot be directly applied to attributed graphs and have the scalability issue due to the use of Gaussian processes (GPs). To bridge the gap, in this paper, we propose a novel scalable Deep Graph Bayesian Optimization (DGBO) method on attributed graphs. The proposed DGBO prevents the cubical complexity of the GPs by adopting a deep graph neural network to surrogate black-box functions, and can scale linearly with the number of observations. Intensive experiments are conducted on both artificial and real-world problems, including molecular discovery and urban road network design, and demonstrate the effectiveness of the DGBO compared with the state-of-the-art. Copyright © 2019, The Authors. All rights reserved.

关键词： Graphic methods

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