检索结果-南通市图书馆

Memory-enhanced text style transfer with dynamic style learning and calibration

在线全文

学校读者我要写书评

暂无评论

science China(Information sciences) 2024年第4期67卷 181-196页

作者： Fuqiang LIN Yiping SONG Zhiliang TIAN Wangqun CHEN Diwen DONG Bo LIU College of computer science and Technology National University of Defense Technology Department of computer science and Engineering The Hong Kong University of Science and Technology Strategic Assessments and Consultation Institute Academy of Military Sciences

Text style transfer aims to rephrase a sentence to match the desired style while retaining the original content. As a controllable text generation task, mainstream approaches use content-independent style embedding as control variables to guide stylistic generation. Nonetheless, stylistic properties are contextsensitive even under the same style. For example, “delicious” and “helpful” convey positive sentiments,although they are more likely to describe food and people, respectively. Therefore, desired style signals must vary with the content. To this end, we propose a memory-enhanced transfer method, which learns fine-grained style representation concerning content to assist transfer. Rather than employing static style embedding or latent variables, our method abstracts linguistic characteristics from training corpora and memorizes subdivided content with the corresponding style representations. The style signal is dynamically retrieved from memory using the content as a query, providing a more expressive and flexible latent style space. To address the imbalance between quantity and quality in different content, we further introduce a calibration method to augment memory construction by modeling the relationship between candidate *** results obtained using three benchmark datasets confirm the superior performance of our model compared to competitive approaches. The evaluation metrics and case study also indicate that our model can generate diverse stylistic phrases matching context.

关键词： style transfer memory-enhanced method text generation deep learning text representation

Drug-Target Interactions Prediction Based on Signed Heterogeneous Graph Neural Networks

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Chinese Journal of Electronics 2024年第1期33卷 231-244页

作者： Ming CHEN Yajian JIANG Xiujuan LEI Yi PAN Chunyan JI Wei JIANG College of Information science and Engineering Hunan Normal University School of computer science Shaanxi Normal University Faculty of computer science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences computer science Department BNU-HKBU United International College

Drug-target interactions(DTIs) prediction plays an important role in the process of drug *** computational methods treat it as a binary prediction problem, determining whether there are connections between drugs and targets while ignoring relational types information. Considering the positive or negative effects of DTIs will facilitate the study on comprehensive mechanisms of multiple drugs on a common target, in this work, we model DTIs on signed heterogeneous networks, through categorizing interaction patterns of DTIs and additionally extracting interactions within drug pairs and target protein pairs. We propose signed heterogeneous graph neural networks(SHGNNs), further put forward an end-to-end framework for signed DTIs prediction, called SHGNN-DTI,which not only adapts to signed bipartite networks, but also could naturally incorporate auxiliary information from drug-drug interactions(DDIs) and protein-protein interactions(PPIs). For the framework, we solve the message passing and aggregation problem on signed DTI networks, and consider different training modes on the whole networks consisting of DTIs, DDIs and PPIs. Experiments are conducted on two datasets extracted from Drug Bank and related databases, under different settings of initial inputs, embedding dimensions and training modes. The prediction results show excellent performance in terms of metric indicators, and the feasibility is further verified by the case study with two drugs on breast cancer.

关键词： Drugs Training Proteins Measurement Message passing Prediction methods Graph neural networks

Identifying malicious traffic under concept drift based on intraclass consistency enhanced variational autoencoder

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

science China(Information sciences) 2024年第8期67卷 238-252页

作者： Xiang LUO Chang LIU Gaopeng GOU Gang XIONG Zhen LI Binxing FANG Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security University of Chinese Academy of Sciences School of computer science and Technology Harbin Institute of Technology (Shenzhen)

Accurate identification of malicious traffic is crucial for implementing effective defense countermeasures and has led to extensive research efforts. However, the continuously evolving techniques employed by adversaries have introduced the issues of concept drift, which significantly affects the performance of existing methods. To tackle this challenge, some researchers have focused on improving the separability of malicious traffic representation and designing drift detectors to reduce the number of false ***, these methods often overlook the importance of enhancing the generalization and intraclass consistency in the representation. Additionally, the detectors are not sufficiently sensitive to the variations among different malicious traffic classes, which results in poor performance and limited robustness. In this paper, we propose intraclass consistency enhanced variational autoencoder with Class-Perception detector(ICE-CP) to identify malicious traffic under concept drift. It comprises two key modules during training:intraclass consistency enhanced(ICE) representation learning and Class-Perception(CP) detector construction. In the first module, we employ a variational autoencoder(VAE) in conjunction with Kullback-Leibler(KL)-divergence and cross-entropy loss to model the distribution of each input malicious traffic flow. This approach simultaneously enhances the generalization, interclass consistency, and intraclass differences in the learned representation. Consequently, we obtain a compact representation and a trained classifier for nondrifting malicious traffic. In the second module, we design the CP detector, which generates a centroid and threshold for each malicious traffic class separately based on the learned representation, depicting the boundaries between drifting and non-drifting malicious traffic. During testing, we utilize the trained classifier to predict malicious traffic classes for the testing samples. Then, we use the CP det

关键词： concept drift cyberspace security intrusion detection malicious traffic identification variational autoencoder

Quantum search with prior knowledge

在线全文

学校读者我要写书评

暂无评论

science China(Information sciences) 2024年第09期67卷 321-329页

作者： Xiaoyu HE Xiaoming SUN Jialing ZHANG State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences School of computer science and Technology University of Chinese Academy of Sciences

The combination of contextual side information and search is a powerful paradigm in the scope of artificial intelligence. The prior knowledge enables the identification of possible solutions but may be imperfect. Contextual information can arise naturally, for example in game AI where prior knowledge is used to bias move decisions. In this work we investigate the problem of taking quantum advantage of contextual information, especially searching with prior knowledge. We propose a new generalization of Grover's search algorithm that achieves the optimal expected success probability of finding the solution if the number of queries is fixed. Experiments on small-scale quantum circuits verify the advantage of our algorithm. Since contextual information exists widely, our method has wide applications. We take game tree search as an example.

关键词： quantum computing quantum search quantum query algorithm prior knowledge

EvolveKG:a general framework to learn evolving knowledge graphs

在线全文

学校读者我要写书评

暂无评论

Frontiers of computer science 2024年第3期18卷 43-59页

作者： Jiaqi LIU Zhiwen YU Bin GUO Cheng DENG Luoyi FU Xinbing WANG Chenghu ZHOU School of computer science Northwestern Polytechnical UniversityXi’an 710129China Department of computer science Shanghai Jiao Tong UniversityShanghai 200240China Institute of Geographical science and Natural Resources Research Chinese Academy of SciencesBejjing 100864China

A great many practical applications have observed knowledge evolution,i.e.,continuous born of new knowledge,with its formation influenced by the structure of historical *** observation gives rise to evolving knowledge graphs whose structure temporally grows over ***,both the modal characterization and the algorithmic implementation of evolving knowledge graphs remain *** this end,we propose EvolveKG–a general framework that enables algorithms in the static knowledge graphs to learn the evolving *** quantifies the influence of a historical fact on a current one,called the effectiveness of the fact,and makes knowledge prediction by leveraging all the cross-time knowledge *** novelty of EvolveKG lies in Derivative Graph–a weighted snapshot of evolution at a certain ***,each weight quantifies knowledge effectiveness through a temporarily decaying function of consistency and attenuation,two proposed factors depicting whether or not the effectiveness of a fact fades away with ***,considering both knowledge creation and loss,we obtain higher prediction accuracy when the effectiveness of all the facts increases with time or remains *** four real datasets,the superiority of EvolveKG is confirmed in prediction accuracy.

关键词： knowledge graph evolution modal characterization algorithmic implementation

DepthGAN: GAN-based depth generation from semantic layouts

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computational Visual Media 2024年第3期10卷 505-522页

作者： Yidi Li Jun Xiao Yiqun Wang Zhengda Lu School of Artificial Intelligence University of Chinese Academy of SciencesBeijingChina College of computer science Chongqing UniversityChongqingChina

Existing GAN-based generative methodsare typically used for semantic image synthesis. Wepose the question of whether GAN-based architecturescan generate plausible depth maps and find thatexisting methods have difficulty in generating depthmaps which reasonably represent 3D scene structuredue to the lack of global geometric ***, we propose DepthGAN, a novel method ofgenerating a depth map using a semantic layout asinput to aid construction, and manipulation of wellstructured 3D scene point clouds. Specifically, wefirst build a feature generation model with a cascadeof semantically-aware transformer blocks to obtaindepth features with global structural *** our semantically aware transformer block, wepropose a mixed attention module and a semanticallyaware layer normalization module to better exploitsemantic consistency for depth features ***, we present a novel semantically weighteddepth synthesis module, which generates adaptivedepth intervals for the current scene. We generate thefinal depth map by using a weighted combination ofsemantically aware depth weights for different depthranges. In this manner, we obtain a more accuratedepth map. Extensive experiments on indoor andoutdoor datasets demonstrate that DepthGAN achievessuperior results both quantitatively and visually for thedepth generation task.

关键词： depth map generation generative model transformer scene generation

Molecular Generation and Optimization of Molecular Properties Using a Transformer Model

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Big Data Mining and Analytics 2024年第1期7卷 142-155页

作者： Zhongyin Xu Xiujuan Lei Mei Ma Yi Pan School of computer science Shaanxi Normal UniversityXi'an 710119China Faculty of computer science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

Generating novel molecules to satisfy specific properties is a challenging task in modern drug discovery,which requires the optimization of a specific objective based on satisfying chemical ***,we aim to optimize the properties of a specific molecule to satisfy the specific properties of the generated *** Matched Molecular Pairs(MMPs),which contain the source and target molecules,are used herein,and logD and solubility are selected as the optimization *** main innovative work lies in the calculation related to a specific transformer from the perspective of a matrix *** intervals and state changes are then used to encode logD and solubility for subsequent *** the experiments,we screen the data based on the proportion of heavy atoms to all atoms in the groups and select 12365,1503,and 1570 MMPs as the training,validation,and test sets,*** models are compared with the baseline models with respect to their abilities to generate molecules with specific *** show that the transformer model can accurately optimize the source molecules to satisfy specific properties.

关键词： molecular optimization transformer Matched Molecular Pairs(MMPs) logD solubility

An efficient schedulability analysis based on worst-case interference time for real-time systems

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

science China(Information sciences) 2024年第09期67卷 88-104页

作者： Hongbiao LIU Mengfei YANG Lei QIAO Xi CHEN Jian GONG School of computer science and Technology Xidian University China academy of Space Technology Beijing Institute of Control Engineering

Real-time systems are widely implemented in the Internet of Things(IoT) and safety-critical systems, both of which have generated enormous social value. Aiming at the classic schedulability analysis problem in real-time systems, we proposed an exact Boolean analysis based on interference(EBAI) for schedulability analysis in real-time systems. EBAI is based on worst-case interference time(WCIT), which considers both the release jitter and blocking time of the task. We improved the efficiency of the three existing tests and provided a comprehensive summary of related research results in the field. Abundant experiments were conducted to compare EBAI with other related results. Our evaluation showed that in certain cases, the runtime gain achieved using our analysis method may exceed 73% compared to the stateof-the-art schedulability test. Furthermore, the benefits obtained from our tests grew with the number of tasks, reaching a level suitable for practical application. EBAI is oriented to the five-tuple real-time task model with stronger expression ability and possesses a low runtime overhead. These characteristics make it applicable in various real-time systems such as spacecraft, autonomous vehicles, industrial robots, and traffic command systems.

关键词： five-tuple real-time task model real-time system spacecraft Internet of Things exact schedulability analysis worst-case interference time

Fiducial Approach for the Storage Reliability Assessment of Complex Repairable Systems

在线全文

学校读者我要写书评

暂无评论

Journal of Systems science & Complexity 2024年第4期37卷 1653-1671页

作者： YANG Yang ZHAO Letian CHEN Siyi YU Dan University of Chinese academy of sciences Beijing 100190China computer Application Institute of Nuclear Industry Beijing 100142China academy of Mathematics and Systems science Chinese Academy of SciencesBeijing 100190China

In recent years,there has been a growing focus on high-reliability products in industrial and military *** plays a crucial role in these products,and as a result,research on storage reliability has gained more *** paper specifically targets a class of products with complex structures that require long-term storage,regular testing,and ***,an expression method for system availability is provided based on the reliability structure of the system and the maintenance situation of the constituent equipment in *** the expression of system availability is typically complex and difficult to compute,and the storage life of the system cannot be represented as an explicit function of the reliability indicators of the constituent equipment,it is challenging to evaluate the availability and storage life of repairable *** address this,the paper proposes a comprehensive evaluation methodology for assessing the storage reliability of complex repairable systems based on fiducial *** approach is employed to derive point estimates and determine the lower fiducial limits for both system availability and storage ***,simulation results demonstrate that this comprehensive evaluation method for storage reliability of complex repairable systems is not only convenient but also highly effective.

关键词： Availability fiducial method repairable system storage life

维普期刊数据库