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Dynamic graph software watermark based on the Chinese Remainder Theorem

Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (engineering and Technology Edition) 2008年第SUPPL. 2期38卷 198-201页

作者： Zhu, Jian-Qi Liu, Yan-Heng Yin, Ke-Xin Song, Shuang Key Laboratory of Symbol Computation and Knowledge Engineer College of Computer Science and Technology Jilin University Changchun 130012 China College of Computer Science and Engineering Changchun University of Technology Changchun 130012 China

The watermark is split into a set of shares and represented as a set of graphs, which are embedded into the program dynamically. Code obfuscating technique in the embedding phase was used. A novel scheme of dynamic data structure software watermarking is presented. This scheme can restore the watermark according to partial watermark shares and is very robust and stealthy.

关键词： Application programs

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Contrastive Fusion of Generative and Discriminative Lesion Maps for Weakly Supervised Medical Image Segmentation

SSRN

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SSRN 2023年

作者： Du, Wei Huo, Yongkang Tang, Shiyi Chen, Liang Li, Ying Li, Gaoyang Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun130012 China College of Engineering Shantou University Shantou515063 China Translational Medical Center for Stem Cell Therapy Institute for Regenerative Medicine Shanghai East Hospital Bioinformatics Department School of Life Sciences and Technology Tongji University Shanghai200092 China

Medical image segmentation is a crucial computer-aided diagnosis (CAD) procedure in various clinical applications. However, most existing methods rely on pixel-level annotations, which are costly and time-consuming. We propose a novel contrastive fusion model of generative and discriminative lesion maps for weakly supervised medical image segmentation to address this limitation. The generative lesion map is generated by a lesion region-guided cycle-consistent adversarial network (CCANet), and the discriminative lesion map is produced by a class-aware-based discriminative lesion localization network (DLLNet). CCANet and DLLNet share weights at the encoder layer, which contains skip connections and channel attention modules to enhance feature embedding. In order to fully utilize and integrate the generative and discriminative lesion maps, and improve the segmentation accuracy, we design a fusion method based on contrastive loss to jointly train CCANet and DLLNet. The final lesion segmentation result is obtained by applying dense conditional random fields (Dense CRF) to the fused lesion map. Extensive experiments on three benchmark medical image datasets show that our model outperforms the state-of-the-art methods on weakly supervised medical image segmentation, especially in datasets with few samples, validating the proposed model's good generalization ability and robustness in various medical image segmentation scenarios. The source code and experiment datasets are available on GitHub: https://***/mlcb-jlu/GDC4MedSeg. © 2023, The Authors. All rights reserved.

关键词： Image segmentation

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Multimodal Hypothetical Summary for Retrieval-based Multi-image Question Answering

arXiv

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arXiv 2024年

作者： Li, Peize Si, Qingyi Fu, Peng Lin, Zheng Wang, Yan School of Artificial Intelligence Jilin University Changchun China Institute of Information Engineering Chinese Academy of Sciences Beijing China Huawei Technologies Co. Ltd. Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education College of Computer Science and Technology Jilin University Changchun China

Retrieval-based multi-image question answering (QA) task involves retrieving multiple question-related images and synthesizing these images to generate an answer. Conventional "retrieve-then-answer" pipelines often suffer from cascading errors because the training objective of QA fails to optimize the retrieval stage. To address this issue, we propose a novel method to effectively introduce and reference retrieved information into the QA. Given the image set to be retrieved, we employ a multimodal large language model (visual perspective) and a large language model (textual perspective) to obtain multimodal hypothetical summary in question-form and description-form. By combining visual and textual perspectives, MHyS captures image content more specifically and replaces real images in retrieval, which eliminates the modality gap by transforming into text-to-text retrieval and helps improve retrieval. To more advantageously introduce retrieval with QA, we employ contrastive learning to align queries (questions) with MHyS. Moreover, we propose a coarse-to-fine strategy for calculating both sentence-level and word-level similarity scores, to further enhance retrieval and filter out irrelevant details. Our approach achieves a 3.7% absolute improvement over state-of-the-art methods on RETVQA and a 14.5% improvement over CLIP. Comprehensive experiments and detailed ablation studies demonstrate the superiority of our method. Copyright © 2024, The Authors. All rights reserved.

关键词： Question answering

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Design of a Daily Brief Business Report Generator based on Web Scraping with KNN Algorithm

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Journal of Physics: Conference Series 2019年第5期1345卷

作者： Gang Lin Yanchun Liang Xiaoyang Fu Guangshun Chen Shaocan Cai Zhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Department of Computer Science and Technology School of Computer Science Zhuhai 519041 Guangdong China Department of Computer Science and Technology School of Computer Science Zhuhai College of Jilin University Zhuhai 519041 Guangdong China

In order to generate a report for an enterprise where there is neither the API supporting from their existing website systems nor the granted database access rights approval, a daily business report generator system based on web scraping with k nearest neighbor (kNN) classification algorithm is proposed in this paper. It covers the web crawler technology that is to access existing website system and extract business data. The kNN algorithm is applied to identify the verification code on the login page, and the brief daily report generating in a spread-sheet style grid. Compared with some OCR engine for image recognition, the system in Python can automatically generate the brief daily business reports by the kNN algorithm, which is better than some library with default training set on validating the verification code.

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The Response of Lake to Glacier in Akesu River-Kaidu River Small Watershed during 30 Years

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IOP Conference Series: Earth and Environmental Science 2017年第1期51卷

作者： Hanwen Cui Qigang Jiang Department of Computer Science and Technology Zhuhai College of Jilin University Zhuhai 519041 China Zhuhai Laboratory of Key Laboratory of Education Ministry's Symbol Computation and Knowledge Engineer Zhuhai 519041 China College of geo-exploration science and technology Jilin University Changchun 130026 China

Glacier is a sensitive indicator of the climate change, and also closely related to human beings. RS and GIS supply effective means to research the change of glacier. Those are important methods for compensating the shortage of previous ones. Small watershed is the basic unit of water circulation. Thus, the more reliable conclusion, which is the response of lake to glacier, will be obtained by the small watershed. In this paper, dividing the watershed into the smaller one, the Akesu River-Kaidu River small watershed was chosen as study area. Based on RS and GIS, under the climate change from cold-dry to warm-wet since 1977, 2000 and 2007, the variety of glacier was shown as decreasing firstly and then increasing a little. Meanwhile, the variety of lake was presented as increasing firstly and then decreasing greatly. However, the change tendency of glacier and lake was decreased. The decreased areas were 823.3 km2 and 239.5km2 separately. According to the spatial bivariate auto-correlation analysis map, the negative correlation between glacier and lake was more distinct. In Tianshan Mountain, owing to the change mainly influenced by elevation, glacier was decreased and lake was increased. In Bosten Lake, glacier was increased and lake was decreased due to the change mainly affected by climate.

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Multi-Classification of Cancer Samples Based on Co-Expression Analyses

Multi-Classification of Cancer Samples Based on Co-Expressio...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Hongyang Jiang Qiang Huang Liang Chen Zhi Li Ying Xu Huiyan Sun Yi Chang Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun China College of Engineering Key Laboratory of Intelligent ManufacturingTechnology of Ministry of Education Shantou University Shantou China Key Laboratory of Anticancer Drugs and Biotherapy of Liaoning Province The First Hospital of China Medical University Shenyang China The China-Japan Union Hospital Cancer Systems Biology Center Jilin University Changchun China The China-Japan Union Hospital School of Artificial Intelligence Cancer Systems Biology Center Jilin University Changchun China School of Artificial Intelligence Jilin University Changchun China

Cancer staging, grading and subtyping all represent important problems for precision diagnosis, treatment and mechanistic studies of cancer. The majority of the existing computational methods solve this problem via multi-classification of differential gene-expressions of cancer samples of specific classes (Stages, Grades and subtypes) vs. controls. However, the performance of such classification techniques is generally not satisfactory since the discerning power of differential expression patterns in such classifications is limited. We present here a multi-classification technique, based on co-expression patterns specific to individual subclasses in provided training data as co-expression patterns tend to be more conserved than differential expressions within each subclass. A challenge in implementing this strategy lies in how to effectively derive co-expression patterns in individual samples, which is solved through comparing co-expression patterns within a subclass and those in the subclass plus a new sample. Compared with the state-of-the-art gene expression-based classification methods, our method outperforms them in cancer staging, grading and subtyping of cancer samples from TCGA in almost all the measures used. In addition, the co-expressed genes computationally selected for classifications are biologically meaningful, which will prove important for diagnostic biomarker design, treatment plan selection and possibly mechanistic studies of cancer.

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Effective pruning algorithm for least squares support vector machine classifier

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Jisuanji Yanjiu yu Fazhan/Computer Research and Development 2007年第7期44卷 1128-1136页

作者： Yang, Xiaowei Lu, Jie Zhang, Guangquan School of Mathematical Sciences South China University of Technology Guangzhou 510641 China Faculty of Information Technology Sydney University of Technology NSW 2007 Australia Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Changchun 130012 China

A well-known drawback in the least squares support vector machine (LS-SVM) is that the sparseness is lost. In this study, an effective pruning algorithm is developed to deal with this problem. To avoid solving the primal set of linear equations, the bottom to the top strategy is adopted in the proposed algorithm. During the training process of the algorithm, the chunking incremental and decremental learning procedures are used alternately. A small support vector set, which can cover most of the information in the training set, can be formed adaptively. Using the support vector set, one can construct the final classifier. In order to test the validation of the proposed algorithm, it has been applied to five benchmarking UCI datasets. In order to show the relationships among the chunking size, the number of support vector machine, the training time, and the testing accuracy, different chunking sizes are tested. The experimental results show that the proposed algorithm can adaptively obtain the sparse solutions without almost losing generalization performance when the chunking size is equal to 2, and also its training speed is much faster than that of the sequential minimal optimization (SMO) algorithm. The proposed algorithm can also be applied to the least squares support vector regression machine as well as LS-SVM classifier.

关键词： Support vector machines

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Multilocus association analysis into susceptibility genes of schizophrenia

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Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (engineering and Technology Edition) 2008年第SUPPL. 2期38卷 205-212页

作者： Zhang, Hong-Li Liu, Yuan-Ning Zhang, Hao Yu, Ya-Qin Yu, Qiong Kou, Chang-Gui Shi, Jie-Ping Meng, Xiang-Fei Tao, Lu Qu, Ting College of Computer Science and Technology Jilin University Changchun 130012 China Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Changchun 130012 China Research Center for Genomic Medicine School of Public Health Jilin University Changchun 130021 China

A model based on multilocus interaction of schizophrenia was designed. Using the program of MATLAB through a C-language interface, combined with Fisher's exact test, a total of 18 SNPs that came from 9 susceptibility genes of the cPLA2 and PNPLA8 family were analyzed to detect positive sites of schizophrenia in multilocus association analysis. Results show that 4 positive sites at rs3816533, rsl668589, rsl356410 and rs40848 were detected in 13 SNPs, which did not deviate from the Hardy-Weinberg equilibrium (P>0.05). The haplotype significance test of these positive sites indicates that 6 haplotypes passing through rs3816533, rsl668589, rsl356410 and 2 haplotypes passing through rs40848 are associated with schizophrenia.

关键词： MATLAB

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Drive: An Adjustable Parallel Architecture Based on Evidence Awareness for Fake News Detection

SSRN

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SSRN 2024年

作者： Cong, Mou Liu, Lu Han, Jiayu Zhu, Beibei Yuan, Xiaosong Peng, Tao College of Computer Science and Technology Jilin University 2699 Qianjin Street Jilin Changchun130012 China College of Software Jilin University 2699 Qianjin Street Jilin Changchun130012 China Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education Jilin University 2699 Qianjin Street Jilin Changchun130012 China Department of Linguistics University of Washington SeattleWA98195 United States

Fake news detection becomes a critical challenge in NLP. Batch processing with attention-derivative models is a popular solution for fake news detection. Models with attention mechanisms could mitigate the long-distance dependence problem. However, we find that extracting local dependencies using convolutional networks is as important as extracting global dependencies using attention mechanisms. Besides, fake news datasets often suffer from problems such as imbalance and are hard to understand by current models. To address these problems, we propose DRIVE, which is short for an adjustable parallel architecture based on evidence awareness for fake news detection to alleviate the above problems, in which we introduce Claim as a supporting clue to enhance the model’s comprehension ability. Specifically, we propose a novel method for unbalanced datasets and design adjustable parallel branches, using Claim Additives Network (ClaimAddNet) and the MLP with convolutional gating mechanism (CPMLP) to capture global and local information in distinct branches, respectively. Experiments conducted on 5 public datasets demonstrate that DRIVE outperforms all baselines, improving accuracy by approximately 1.7 %. Furthermore, we implement an anti-noise interference test and evaluate the weighted average of the two branches along with ablation studies. © 2024, The Authors. All rights reserved.

关键词： Parallel architectures

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Efficient Self-Learning Evolutionary Neural Architecture Search

SSRN

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SSRN 2023年

作者： Qiu, Zhengzhong Bi, Wei Xu, Dong Guo, Hua Ge, Hongwei Liang, Yanchun Lee, Heow Pueh Wu, Chunguo Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Jilin Changchun130012 China YGSOFT INC Guangdong Zhuhai519085 China Department of Electrical Engineering and Computer Science Informatics Institute Christopher S. Bond Life Sciences Center University of Missouri ColumbiaMO65211 United States College of Computer Science and Technology Dalian University of Technology Liaoning Dalian116081 China School of Computer Science Zhuhai College of Science and Technology Guangdong Zhuhai519041 China Department of Mechanical Engineering National University of Singapore 9 Engineering Drive 1 117575 Singapore

The evolutionary algorithm has become a major method for neural architecture search recently. However, the fixed probability distribution adopted by the traditional evolutionary algorithm makes the algorithm unable to control the increase or decrease of the individual architecture size and thus cannot guarantee the lightweight and inference efficiency of the candidate architecture. In addition, the existing approaches cannot learn the optimal sampling probability distribution relevant to the specific problem from the empirical information accumulated through the search process. What’s more, the algorithm needs to evaluate the performance of all the individual architectures, which requires huge computing resources and time overhead. To overcome these problems and challenges, an Efficient Self-learning Evolutionary Neural Architecture Search method, called ESE-NAS, is presented in this paper. Firstly, we propose a Model Size Control module, which generates the probability distribution for sampling the type of mutation operators according to the current model size, so as to control both the node number of network architecture and the sparsity of links, therefore ensuring that neural architectures can remain compact and efficient as they evolve. Thereafter, a Mutation Candidate Credit Assignment method is proposed, which enables the algorithm to dynamically adjust the probability distribution for available types of node operations and directed links according to the empirical information from individuals’ performance evaluation, which can guide the evolutionary direction of neural architecture and shorten the first hitting time of the optimal architecture. Finally, a neural architecture performance predictor is formulated, and the accuracy of each individual architecture is predicted by using the simplified input features, to further improve the efficiency of neural architecture search. Experiments show that the ESE-NAS method proposed in this paper can learn a highly i

关键词： Evolutionary algorithms

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