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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yihang Bao Fei He Weixi Wang Han Wang Minglong Dong School of Information Science and Technology Northeast Normal University Changchun China School of Information Science and Technology Northeast Normal University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China School of Biological Sciences Nanyang Technological University Singapore Singapore

ISBN: (纸本)9781665429825

Transmembrane proteins (TMPs) serve as drug targets for more than half of the drugs currently available in the market. However, it had not been clearly explained how they realize their drug effects through multiple complex molecules bindings actions. Research into TMPs bindings and corresponding structural basis will provide key information for drug research and new drug development. In this study, we defined the binding domain of TMPs according to the binding region investigation of multiple conjugate types. A 3D deep learning model was architected to discover the structural universality inside those domains. The experimental results proved such binding domains existing on the surface of TMPs, and they are structural specific distinguishing to the surface regions without any binding activities. This work provides a new theoretical basis for TMPs binding research and can greatly boost the development of the drug industry.

关键词： Drugs Proteins Deep learning Industries Solid modeling Protein engineering Three-dimensional displays

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Multi-objective optimization for distributed collaborative beamforming in mobile wireless sensor networks

Multi-objective optimization for distributed collaborative b...

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IEEE Symposium on Computers and Communications

作者： Geng Sun Yanheng Liu Guojun Shen Aimin Wang Ying Zhang Victor C. M. Leung School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA 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 Electrical and Computer Engineering University of British Columbia Vancouver BC Canada

Mobile wireless sensor networks (MWSN) are resource constrained, and have limited energy and transmission range. Distributed collaborative beamforming (DCB) in MWSN based on a virtual node antenna array (VNAA) can increase the transmission distance and enhance energy efficiency of a single sensor node. To achieve a lower maximum sidelobe level (SLL), sensor nodes can move to optimal locations with optimal excitation currents for DCB. However, this leads to an extra motion energy consumption. In this paper, we construct a multi-objective optimization framework to jointly optimize the maximum SLL, the transmission power and the motion energy consumption of the DCB nodes in MWSN. Moreover, an improved non-dorminated sorting genetic algorithm-II (INSGA-II) is proposed for solving the optimization problem. Simulation results show that the maximum SLL, the transmission power and the motion energy consumption of the VNAA can be effectively optimized by the proposed algorithms.

关键词： Mobile wireless sensor networks Collaborative beamforming Antenna arrays Genetic algorithm

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Toward Moiré-Free and Detail-Preserving Demosaicking

arXiv

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arXiv 2023年

作者： Li, Xuanchen Niu, Yan Zhao, Bo Shi, Haoyuan An, Zitong State Key Laboratory of Symbol Computation and Knowledge Engineering College of Computer Science and Technology Ministry of Education Jilin University Changchun China The College of Software Jilin University Changchun130012 China

3D convolutions are commonly employed by demosaicking neural models, in the same way as solving other image restoration problems. Counter-intuitively, we show that 3D convolutions implicitly impede the RGB color spectra from exchanging complementary information, resulting in spectral-inconsistent inference of the local spatial high frequency components. As a consequence, shallow 3D convolution networks suffer the Moiré artifacts, but deep 3D convolutions cause over-smoothness. We analyze the fundamental difference between demosaicking and other problems that predict lost pixels between available ones (e.g., super-resolution reconstruction), and present the underlying reasons for the confliction between Moiré-free and detail-preserving. From the new perspective, our work decouples the common standard convolution procedure to spectral and spatial feature aggregations, which allow strengthening global communication in the spectral dimension while respecting local contrast in the spatial dimension. We apply our demosaicking model to two tasks: Joint Demosaicking-Denoising and Independently Demosaicking. In both applications, our model substantially alleviates artifacts such as Moiré and over-smoothness at similar or lower computational cost to currently top-performing models, as validated by diverse evaluations. Source code will be released along with paper publication. Copyright © 2023, The Authors. All rights reserved.

关键词： Convolution

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2D-3D Feature Co-Embedding Network with Sparse Annotation for 3D Medical Image Segmentation

2D-3D Feature Co-Embedding Network with Sparse Annotation fo...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Mingzhu Zhu Shuyu Guo Ziwei Wang Jianhang Jiao Tian Bai College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China College of Software Jilin University Changchun China Department of Orthopedics The Second Norman Bethune Hospital of Jilin University Changchun China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Supervised methods on 3D medical image segmentation need large amounts of annotated data, but annotating is time-consuming. Also, existing 3D segmentation methods capture more global structural information but overlook local detailed features, which negatively impacts the segmentation of small tissues. In this paper, we propose a novel weakly-supervised 2D-3D Feature Co-Embedding Network (2D-3D CoENet) that includes 2D and 3D encoding layers, simultaneously extracting 2D local detailed and 3D global structural features. To reduce annotation costs, we use fewer labeled slices as ground truth and pseudo-labels are generated by 2D-3D CoENet for other slices. Additionally, multi-view learning is introduced to capture more 2D local detailed information, and a Multi-view Semantic Consistency loss (MSC loss) is proposed to constrain features from multiple perspectives. To further enhance the local detailed texture features, we propose an Edge Enhancement Module (EEM) in the 3D segmentation network to enhance the edge detail features. Our experimental results on the SKI10 dataset and OAI ZIB dataset demonstrate that our method outperforms the SOTA weakly-supervised segmentation methods. Moreover, our approach achieves results that are comparable to the fully-supervised upper bound results.

关键词： Image segmentation Three-dimensional displays Costs Upper bound Annotations Image edge detection Semantics Feature extraction Encoding Biomedical imaging

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Escrow mechanism for personal security keys on IBE

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Tongxin Xuebao/Journal on Communications 2009年第11 A期30卷 75-80页

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

A new escrow mechanism for personal security keys on IBE was proposed. This mechanism constructed a security trust system composed of private key generator, key management center, and user security component to set up a personal security key escrow model, thus providing identity validation, confidentiality and integrality check for the application, backup, recovery, and renewal of the escrowed keys. The mechanism, taking advantage of the IBE, simplified the process of authentication and encryption, mede it possible for users to complete independently the escrow of personal security keys. Therefore, it is more practical than traditional key escrow schemes.

关键词： Authentication

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A verifiable multi-secret sharing scheme

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Dongbei Daxue Xuebao/Journal of Northeastern University 2011年第SUPPL.1期32卷 120-123页

作者： Wang, Yan-Xi Liu, Yan-Heng Wang, Jian Dong, Tian-Zhe College of Computer Science and Technology Jilin University Changchun 130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012 China

Analyzing two improved YCH schemes and a multi-secret sharing scheme based on homogeneous linear recursion, we propose and implement a new verifiable multi-secret sharing model based on Shamir secret sharing. The time complexity of this model in the phase of secrets recovery is O(k×t2), which is superior to other two improved YCH models (O(t3) (t>k) O(k3) (t≤k), O(k×(n+k)2)), and the time of secrets synthesis in the actual simulation is less than that of the other three models. Further, we compare the advantages and disadvantages of the four models on the time complexity, verifiability and open values. When n>k, the open values the new model needs are fewer than those of the other two improved YCH models. The experimental results show that the new model is better than the other three models on the time of secrets recovery.

关键词： Information theory

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Analysis on weighted AUC for imbalanced data learning through isometrics

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Journal of computational Information Systems 2012年第1期8卷 371-378页

作者： Dong, Yuanfang Li, Xiongfei Li, Jun Zhao, Haiying Key Laboratory of Symbolic Computation and Knowledge Engineering for Ministry of Education Jilin University Changchun 130012 China School of Economics and Management Changchun University of Science and Technology Changchun 130022 China Department of Mathematics Changchun University of Science and Technology Changchun 130022 China School of Computer Science and Technology Xinjiang Normal University Wulumuqi 830000 China

A performance evaluation model, weighted AUC (wAUC), is proposed to determine a better way to measure the imbalanced data learning classifiers. When computing the weighted area under the ROC curve, weights vary with the values of the true positive rate (TPrate) among regions in a bid to focus on the accuracy of minority class that is more important in common. As AUC is a special case of wAUC, wAUC is compared with other common performance evaluation measures by isometric analysis. The experimental results show that wAUC can distinguish the classifiers with the same AUC values. © 2012 Binary Information Press January 2012.

关键词： Learning systems

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Using SROIQB to model and compose semantic Web service

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Tongxin Xuebao/Journal on Communications 2010年第8 A期31卷 1-9页

作者： Liu, Da-You Liu, Si-Pei Qi, Hong College of Computer Science and Technology Jilin University Changchun 130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012 China

One kind of semantic Web service modeling and reasoning method based on description logic with Boolean role constructors (SROIQB) was introduced. By using DL to figure out the hyponymy relationship between concepts in domain ontology and model Web service functional semantic in ServiceProfile, and define Boolean role expressions to describe the composition process in ProcessProfile respectively. Furthermore, Boolean role expression is introduced to describe all possible composition structures of atomic services and then semantic Web service composition can be regarded as a process of consistence checking and class subsumption computing reasoning on DL knowledge base. Thus the comparison and example validation show that introduced one kind of new and reasonable semantic Web service modeling and composition method in the framework of semantic Web by using SROIQB, which with more strong expressive capability and decidable type inference.

关键词： Semantic Web

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Extracting Attributes from Deep Web Interface Using Instances

Extracting Attributes from Deep Web Interface Using Instance...

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

作者： Hao Liang Fei Ren Wanli Zuo Fengling He Junhua Wang College of Computer Science and Technology Jilin University Changchun China Department of Information Changchun Taxation College Changchun China Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education Changchun China

There is myriad high quality information in the Deep Web and the feasible method to access the Deep Web is through the query interface of the Deep Web. Itpsilas necessary to extract abundant attributes and semantic relation description from the query interface. Automatic extracting attributes from the query interface and automatically translating a query is a solvable way for addressing the current limitations in accessing Deep Web data sources. We design a framework to automatically extract the attributes and instances from the query interface using the WordNet as a kind of ontology technique to enrich the semantic description of the attributes. Each attribute is extended into a candidate attribute set in the form of a hierarchy tree. At the same time, the hierarchy tree generated by ontology describes the semantic relation of the attributes in the same query interface. We carry out our experiments in the real-world domain. The results of the experiments showed the validation of query translation framework.

关键词： Data mining Ontologies Computer science educational institutions Databases Programming profession HTML Laboratories Computer interfaces knowledge engineering

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Prompt Tuning Pushes Farther, Contrastive Learning Pulls Closer: A Two-Stage Approach to Mitigate Social Biases

arXiv

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arXiv 2023年

作者： Li, Yingji Du, Mengnan Wang, Xin Wang, Ying College of Computer Science and Technology Jilin University Changchun China Department of Data Science New Jersey Institute of Technology Newark United States School of Artificial Intelligence Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China

As the representation capability of Pre-trained Language Models (PLMs) improve, there is growing concern that they will inherit social biases from unprocessed corpora. Most previous debiasing techniques used Counterfactual Data Augmentation (CDA) to balance the training corpus. However, CDA slightly modifies the original corpus, limiting the representation distance between different demographic groups to a narrow range. As a result, the debiasing model easily fits the differences between counterfactual pairs, which affects its debiasing performance with limited text resources. In this paper, we propose an adversarial training-inspired two-stage debiasing model using Contrastive learning with Continuous Prompt Augmentation (named CCPA) to mitigate social biases in PLMs’ encoding. In the first stage, we propose a data augmentation method based on continuous prompt tuning to push farther the representation distance between sample pairs along different demographic groups. In the second stage, we utilize contrastive learning to pull closer the representation distance between the augmented sample pairs and then fine-tune PLMs’ parameters to get debiased encoding. Our approach guides the model to achieve stronger debiasing performance by adding difficulty to the training process. Extensive experiments show that CCPA outperforms baselines in terms of debiasing performance. Meanwhile, experimental results on the GLUE benchmark show that CCPA retains the language modeling capability of PLMs. Copyright © 2023, The Authors. All rights reserved.

关键词： Encoding (symbols)

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