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作者： Lu, Jing Ya Zuo, Wan Li Zhu, Liang College of Computer Science and Technology Jilin University Changchun 130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education of China China

ISBN: (纸本)9783038351399

Mining newsworthy events from a large number of microblogging information is not only the primary problem that several big microblogging websites need to solve, but also a new research field in micro-information age. For now, a lot of study about even recognizing has been made at home and abroad, but relatively rarely contrapose short text(microblogging message). The paper considers newsworthy event recognizing in short text as classification problem, utilizes the decision tree classification algorithm in data mining, sufficiently mines features of event in short text, and then recognizes the newsworthy event in microblogging. In the last, we verify the effect of the model. © (2014) Trans Tech Publications, Switzerland.

关键词： Data mining

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A Novel Method for Prediction of Protein Domain Using Distance-Based Maximal Entropy

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Journal of Bionic engineering 2008年第3期5卷 215-223页

作者： Shu-xue Zou Yan-xin Huang Yan Wang Chun-guang Zhou Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun 130012 P. R. China

Detecting the boundaries of protein domains is an important and challenging task in both experimental and computational structural biology. In this paper, a promising method for detecting the domain structure of a protein from sequence information alone is presented. The method is based on analyzing multiple sequence alignments derived from a database search. Multiple measures are defined to quantify the domain information content of each position along the sequence. Then they are combined into a single predictor using support vector machine. What is more important, the domain detection is first taken as an imbal- anced data learning problem. A novel undersampling method is proposed on distance-based maximal entropy in the feature space of Support Vector Machine （SVM）. The overall precision is about 80%. Simulation results demonstrate that the method can help not only in predicting the complete 3D structure of a protein but also in the machine learning system on general im- balanced datasets.

关键词： protein domain boundary SVM imbalanced data learning distance-based maximal entropy

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Superpixels Based Interactive Image Segmentation Algorithm

Superpixels Based Interactive Image Segmentation Algorithm

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2013 International Conference on Information,Business and Education Technology(ICIBET 2013)

作者： LONG Jian-wu SHEN Xuan-jing ZANG Hui CHEN Hai-peng College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University

ISBN: (纸本)9781629932101

A superpixels based interactive image segmentation algorithm is proposed in this paper. Firstly the initial segmentation is obtained by MeanShift algorithm, and then a graph is built using pre-segmented regions as nodes, finally min-cut/maxflow algorithm is implemented for global solution. In this process, each region is represented by a color histogram and Bhattacharyya coefficient is chosen to calculate the similarity between any two regions. Extensive experiments are performed and the results show that the presented algorithm obtains much more satisfactory segmentation results with less user interaction and less comsuming time than MSRM algorithm.

关键词： interactive image segmentation graph cut mean shift

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An unmanned aerial vehicle pose estimation system guided by desired shot 2

An unmanned aerial vehicle pose estimation system guided by ...

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2nd IEEE International Conference on Automation, Electronics and Electrical engineering, AUTEEE 2019

作者： Dou, Xinglei Ding, Qijian Du, Qiao Yang, Min Huo, Yuzhou Wang, Xin College of Software Engineering Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineer Ministry of Education Jilin University Changchun China

ISBN: (纸本)9781728150291

Unmanned aerial vehicles (UAVs) are widely used in aerial photography nowadays for their strong maneuverability, good image quality and high cost performance, while they have limited battery capacity and difficulty in operation. It is difficult to reach the ideal pose and get the desired shot by manual manipulation in a short time. To solve these problems, an UAV pose estimation system guided by desired shot is constructed. Either a sequence of images or a video is taken as input. The user interacts with the system to express the desired shot. The 3D-reconstruction is done by Structure from Motion (SfM). Then the Perspective-n-Point (PnP) problem is solved to estimate the ideal pose of the UAV. The experimental results proves that the system is effective and valid. © 2019 IEEE.

关键词： Aerial photography

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Adaptive Image Thresholding by Background Subtraction

Adaptive Image Thresholding by Background Subtraction

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2013 International Conference on Information,Business and Education Technology(ICIBET 2013)

ISBN: (纸本)9781629932101

In this paper, an adaptive image thresholding algorithm is proposed for thresholding images with uneven illumination. Firstly, a Gaussian scale space, which is produced from the convolution of a two-dimensional Gaussian function with an input image, is used to estimate the background image. Followed by background subtraction, the objective image can be easily obtained to eliminate interference of uneven illumination. Thirdly, to highlight those darker objects, gamma correction is employed to enhance the objective image. Finally, the thresholding result is extracted easily using the global valley-emphasis Otsu method. The experimental results show that the introduced method yields satisfactory visual quality.

关键词： thresholding background estimation background subtraction

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K-means on commodity GPUs with CUDA

K-means on commodity GPUs with CUDA

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2009 WRI World Congress on Computer Science and Information engineering, CSIE 2009

作者： Bai, Hong-Tao He, Li-Li Ouyang, Dan-Tong Li, Zhan-Shan Li, He College of Computer Science and Technology Jilin University 130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University 130012 China

ISBN: (纸本)9780769535074

K-means algorithm is one of the most famous unsupervised clustering algorithms. Many theoretical improvements for the performance of original algorithms have been put forward, while almost all of them are based on Single Instruction Single Data (SISD) architecture processors (CPUs), which partly ignored the inherent paralleled characteristic of the algorithms. In this paper, a novel Single Instruction Multiple Data (SIMD) architecture processors (GPUs) based k-means algorithm is proposed. In this algorithm, in order to accelerate compute-intensive portions of traditional k-means, both data objects assignment and k centroids recalculation are off-loaded to the GPU in parallel. We have implemented this GPU-based k-means on the newest generation GPU with Compute Unified Device Architecture (CUDA). The numerical experiments demonstrated that the speed of GPU-based k-means could reach as high as 40 times of the CPU-based k-means. © 2008 IEEE.

关键词： Graphics processing unit

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SMRI: A New Method for siRNA Design for COVID-19 Therapy

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Journal of Computer Science & Technology 2022年第4期37卷 991-1002页

作者： Meng-Xin Chen Xiao-Dong Zhu Hao Zhang Zhen Liu Yuan-Ning Liu College of Software Jilin UniversityChangchun 130012China Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education Jilin UniversityChangchun 130012China College of Computer Science and Technology Jilin UniversityChangchun 130012China Graduate School of Engineering Nagasaki Institute of Applied ScienceNagasaki 851-0193Japan

First discovered in Wuhan, China, SARS-CoV-2 is a highly pathogenic novel coronavirus, which rapidly spreads globally and becomes a pandemic with no vaccine and limited distinctive clinical drugs available till March 13th, 2020. Ribonucleic Acid interference (RNAi) technology, a gene-silencing technology that targets mRNA, can cause damage to RNA viruses effectively. Here, we report a new efficient small interfering RNA (siRNA) design method named Simple Multiple Rules Intelligent Method (SMRI) to propose a new solution of the treatment of COVID-19. To be specific, this study proposes a new model named Base Preference and Thermodynamic Characteristic model (BPTC model) indicating the siRNA silencing efficiency and a new index named siRNA Extended Rules index (SER index) based on the BPTC model to screen high-efficiency siRNAs and filter out the siRNAs that are difficult to take effect or synthesize as a part of the SMRI method, which is more robust and efficient than the traditional statistical indicators under the same circumstances. Besides, to silence the spike protein of SARS-CoV-2 to invade cells, this study further puts forward the SMRI method to search candidate high-efficiency siRNAs on SARS-CoV-2's S gene. This study is one of the early studies applying RNAi therapy to the COVID-19 treatment. According to the analysis, the average value of predicted interference efficiency of the candidate siRNAs designed by the SMRI method is comparable to that of the mainstream siRNA design algorithms. Moreover, the SMRI method ensures that the designed siRNAs have more than three base mismatches with human genes, thus avoiding silencing normal human genes. This is not considered by other mainstream methods, thereby the five candidate high-efficiency siRNAs which are easy to take effect or synthesize and much safer for human body are obtained by our SMRI method, which provide a new safer, small dosage and long efficacy solution for the treatment of COVID-19.

关键词： COVID-19 SARS-CoV-2 RibonucleicAcid interference

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Caption-Aware Multimodal Relation Extraction with Mutual Information Maximization 24

Caption-Aware Multimodal Relation Extraction with Mutual Inf...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhang, Zefan Zhang, Weiqi Li, Yanhui Bai, Tian College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun China

ISBN: (纸本)9798400706868

Multimodal Relation Extraction (MRE) has achieved great improvements. However, modern MRE models are easily affected by irrelevant objects during multimodal alignment which are called error sensitivity issues. The main reason is that visual features are not fully aligned with textual features and the reasoning process may suppress redundant and noisy information at the risk of losing critical information. In light of this, we propose a Caption-Aware Multimodal Relation Extraction Network with Mutual Information Maximization (CAMIM). Specifically, we first generate detailed image captions through the Large Language Model (LLM). Then, the Caption-Aware Module (CAM) hierarchically aligns the fine-grained visual entities and textual entities for reasoning. In addition, for preserving crucial information within different modalities, we leverage a Mutual Information Maximization method to regulate the multimodal reasoning module. Experiments show that our model outperforms the state-of-the-art MRE models on the benchmark dataset MNRE. Further ablation studies prove the pluggable and effective performance of our Caption-Aware Module and Mutual Information Maximization method. Our code is available at https://***/zefanZhang-cn/CAMIM. © 2024 ACM.

关键词： Modeling languages

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A novel approach for modeling Direction Relations between Uncertain Regions

A novel approach for modeling Direction Relations between Un...

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2010 International Conference on Machine Vision and Human-Machine Interface, MVHI 2010

作者： Dong, Yiqun Liu, Dayou Wang, Fang Tian, Ye College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun China

ISBN: (纸本)9780769540092

Conventional models based on crisp regions can not deal with the Direction Relations between Uncertain Regions (DRUR). Using broad boundary to represent the uncertain boundary, a novel approach is proposed based on model SK for modeling DRUR in this paper. DRUR are described as the combinations of basic cardinal direction relations, then we study the composition of DRUR, and a method is put forward for calculating this composition. © 2010 IEEE.

关键词： Board boundary Composition Direction relations Model SK Uncertain regions Plant uncertain Uncertainty Modeling Boundary

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Harmfully Manipulated Images Matter in Multimodal Misinformation Detection 24

Harmfully Manipulated Images Matter in Multimodal Misinforma...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Wang, Bing Wang, Shengsheng Li, Changchun Guan, Renchu Li, Ximing College of Computer Science and Technology Jilin University Jilin Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education Jilin University China

ISBN: (纸本)9798400706868

Nowadays, misinformation is widely spreading over various social media platforms and causes extremely negative impacts on society. To combat this issue, automatically identifying misinformation, especially those containing multimodal content, has attracted growing attention from the academic and industrial communities, and induced an active research topic named Multimodal Misinformation Detection (MMD). Typically, existing MMD methods capture the semantic correlation and inconsistency between multiple modalities, but neglect some potential clues in multimodal content. Recent studies suggest that manipulated traces of the images in articles are non-trivial clues for detecting misinformation. Meanwhile, we find that the underlying intentions behind the manipulation, e.g., harmful and harmless, also matter in MMD. Accordingly, in this work, we propose to detect misinformation by learning manipulation features that indicate whether the image has been manipulated, as well as intention features regarding the harmful and harmless intentions of the manipulation. Unfortunately, the manipulation and intention labels that make these features discriminative are unknown. To overcome the problem, we propose two weakly supervised signals as alternatives by introducing additional datasets on image manipulation detection and formulating two classification tasks as positive and unlabeled learning problems. Based on these ideas, we propose a novel MMD method, namely Harmfully Manipulated Images Matter in MMD (Hami-m3d). Extensive experiments across three benchmark datasets can demonstrate that Hami-m3d can consistently improve the performance of any MMD baselines. © 2024 ACM.

关键词： image manipulation

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