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第32届中国控制与决策会议

作者： Yitu Fu Qing Lan School of Information Science and Engineering Northeastern University BGRIMM Technology Group Co. Ltd.

Malware detection play different roles in a computer system and exhibit high degrees of importance with respect to system *** detection is the process of attempting to infer the reputation score of the files via the ***,malware detection approaches are challenged by the large,dynamic,and heterogeneous space of benign binaries that they must *** this research,we use deep generative models to develop two a semi-supervised Bayesian models for malware detection,in which we model the data generating process to be dependent on a Gaussian ***,we propose the efficient stochastic gradient optimization technique used in deep generative models makes our model suitable for large data *** experimental results on one real-world dataset demonstrate that our model is the ***,the semi-supervised deep generative scheme achieves comparable or even better results in malware detection when compared with classic and alternative machine learning *** demonstrates the feasibility of deep generative model and presents a promising new approach to malware detection.

关键词： deep generative model Malware detection Bayesian model Semi-supervised learning

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Iris:a multi-constraint graphic layout generation system

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Frontiers of Information Technology & Electronic Engineering 2024年第7期25卷 968-987页

作者： Liuqing CHEN Qianzhi JING Yixin TSANG Tingting ZHOU College of Computer Science and Technology Zhejiang UniversityHangzhou 310030China Zhejiang–Singapore Innovation and AI Joint Research Lab Hangzhou 310058China Alibaba Group Hangzhou 310034China

In graphic design,layout is a result of the interaction between the design elements in the foreground and background ***,prevalent research focuses on enhancing the quality of layout generation algorithms,overlooking the interaction and controllability that are essential for designers when applying these methods in realworld *** paper proposes a user-centered layout design system,Iris,which provides designers with an interactive environment to expedite the workflow,and this environment encompasses the features of user-constraint specification,layout generation,custom editing,and final *** satisfy the multiple constraints specified by designers,we introduce a novel generation model,multi-constraint LayoutVQ-VAE,for advancing layout generation under intra-and inter-domain *** and quantitative experiments on our proposed model indicate that it outperforms or is comparable to prevalent state-of-the-art models in multiple *** studies on Iris further demonstrate that the system significantly enhances design efficiency while achieving human-like layout designs.

关键词： Graphic layout generation deep generative model Layout design system

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