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International Conference on Microwave and Millimeter Wave Technology Proceedings

作者： Yujie Shan Jinhuan Zhang Jian Dong Jun Long School of Computer Science and Engineering Central South University Big Data Institute Central South University Changsha China

Target threat assessment involves many uncertainties and is a tactical decision assessment problem with incomplete and uncertain information. In traditional target threat assessment, only the influence of the target's state attributes on its threat level is considered, while the target's type is ignored. To address this issue, radar target recognition is incorporated into the threat assessment process, and a method based on radar target recognition is proposed to improve the accuracy of threat assessment by incorporating Radar Cross Section (RCS) feature classifier into a Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Using simulations, the practicability and viability of the above method are examined.

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JSRevealer: A Robust Malicious JavaScript Detector against Obfuscation

JSRevealer: A Robust Malicious JavaScript Detector against O...

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International Conference on Dependable Systems and Networks (DSN)

作者： Kunlun Ren Weizhong Qiang Yueming Wu Yi Zhou Deqing Zou Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Jinyinhu Laboratory Wuhan China Nanyang Technological University Singapore National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Due to the convenience and popularity of Web applications, they have become a prime target for attackers. As the main programming language for Web applications, many methods have been proposed for detecting malicious JavaScript, among which static analysis-based methods play an important role because of their high effectiveness and efficiency. However, obfuscation techniques are commonly used in JavaScript, which makes the features extracted by static analysis contain many useless and disguised features, leading to many false positives and false negatives in detection results. In this paper, we propose a novel method to find out the essential features related to the semantics of JavaScript code. Specifically, we develop JS-Revealer, a robust, effective, scalable, and interpretable detector for malicious JavaScript. To test the capabilities of JSRevealer, we conduct comparative experiments with four other state-of-the-art malicious JavaScript detection tools. The experimental results show that JSRevealer has an average F1 of 84.8% on the data obfuscated by different obfuscators, which is 21.6%, 22.3%, 18.7%, and 22.9% higher than the tools CUJO, ZOZZLE, JAST, and JSTAP, respectively. Moreover, the detection results of JSRevealer can be interpreted, which can provide meaningful insights for further security research.

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Examining the impact of mobile money on financial inclusion in Sub-Saharan Africa: the role of institutions and governance

SAM Advanced Management Journal

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SAM Advanced Management Journal 2024年第4期89卷 315-339页

作者： Kwaku Kyei Gyamerah Francis Kamewor Tetteh Department of Accounting and Finance Data Link Institute of Business and Technology Tema Ghana Department of Supply Chain and Information Systems KNUST School of Business Kwame Nkrumah University of Science and Technology Kumasi Ghana

Purpose This paper aims to investigate the role of institutional quality in the relationship between mobile money and financial inclusion among Sub-Saharan Africa (SSA) from 2002 to 2022. Design/methodology/approach The paper uses annual data from SSA on a bundle of four financial inclusion variables, six institutional quality indicators (i.e. rule of law, government effectiveness, control of corruption, voice and accountability, regulatory quality and political stability) and total volume of mobile money transaction in a year. The two-stage least squares regression was used to validate the hypotheses. Also, the random effects model was also used to account for potential unobserved heterogeneity across countries in SSA. Findings The empirical results reveal that institutional quality and mobile money have direct impact on financial inclusion. Also, institutional quality plays a positive and significant contingency role in the relationship between mobile money and financial inclusion. Originality/value The study contributes to financial inclusion theory by providing multi-country empirical evidence to validate the theory in explaining mobile money’s role in expanding financial access. It also highlights the key insight from financial inclusion theory regarding the need for strong governance institutions for technology-enabled inclusion. By examining interactions between mobile money, institutions and financial inclusion across 15 African SSA economies, the study allows for more generalizable conclusions about contextual dependencies.

关键词： Mobile money Financial inclusion Institutional quality Sub-Saharan Africa

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MT-PCR: Leveraging Modality Transformation for Large-Scale Point Cloud Registration with Limited Overlap

arXiv

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arXiv 2025年

作者： Wu, Yilong Duan, Yifan Chen, Yuxi Zhang, Xinran Shen, Yedong Ji, Jianmin Zhang, Yanyong Zhang, Lu School of Computer Science and Technology University of Science and Technology of China Hefei230026 China Department of Statistics and Finance School of Management University of Science and Technology of China Hefei230026 China School of Artificial Intelligence and Data Science University of Science and Technology of China Hefei230026 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230088 China

Large-scale scene point cloud registration with limited overlap is a challenging task due to computational load and constrained data acquisition. To tackle these issues, we propose a point cloud registration method, MT-PCR, based on Modality Transformation. MT-PCR leverages a Bird’s Eye View (BEV) capturing the maximal overlap information to improve the accuracy and utilizes images to provide complementary spatial features. Specifically, MT-PCR converts 3D point clouds to BEV images and estimates correspondence by 2D image keypoints extraction and matching. Subsequently, the 2D correspondence estimates are then transformed back to 3D point clouds using inverse mapping. We have applied MT-PCR to Terrestrial Laser Scanning (TLS) and Aerial Laser Scanning (ALS) point cloud registration on the GrAco dataset, involving 8 low-overlap, square-kilometer scale registration scenarios. Experiments and comparisons with commonly used methods demonstrate that MT-PCR can achieve superior accuracy and robustness in large-scale scenes with limited overlap. Copyright © 2025, The Authors. All rights reserved.

关键词： Laser applications

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PSP-GEN: Stochastic Inversion of the Process-Structure-Property Chain in Materials Design Through Deep, Generative Probabilistic Modeling

SSRN

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SSRN 2024年

作者： Zang, Yaohua Koutsourelakis, Phaedon-Stelios Technical University of Munich School of Engineering and Design Boltzmannstr. 15 Garching85748 Germany Munich Data Science Institute-MDSI Garching Germany

Inverse material design is a cornerstone challenge in materials science, withsignificant applications across many industries. Traditional approaches thatinvert the structure-property (SP) linkage to identify microstructures withtargeted properties often overlook the feasibility of production processes,leading to microstructures that may not be manufacturable. Achieving bothdesired properties and a realizable manufacturing procedure necessitates in-verting the entire Process-Structure-Property (PSP) chain. However, thistask is fraught with challenges, including stochasticity along the whole mod-eling chain, the high dimensionality of microstructures and process param-eters, and the inherent ill-posedness of the inverse problem. This paperproposes a novel framework, named PSP-GEN, for the goal-oriented mate-rial design that effectively addresses these challenges by modeling the entirePSP chain with a deep generative model. It employs two sets of continuous,microstructure- and property-aware, latent variables, the first of which pro-vides a lower-dimensional representation that captures the stochastic aspectsof microstructure generation, while the second is a direct link to processingparameters. This structured, low-dimensional embedding not only simplifiesthe handling of high-dimensional microstructure data but also facilitates theapplication of gradient-based optimization techniques. The effectiveness andefficiency of this method are demonstrated in the inverse design of two-phasematerials, where the objective is to design microstructures with target effective permeability. We compare state-of-the-art alternatives in challenging settings involving limited training data, target property regions for which no training data is available, and design tasks where the process parameters and microstructures have high-dimensional representations. © 2024, The Authors. All rights reserved.

关键词： Materials properties

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Control Design of Nonlinear ODE/Transport PDE Cascaded Systems with Varying Propagation Speed 42

Control Design of Nonlinear ODE/Transport PDE Cascaded Syste...

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42nd Chinese Control Conference, CCC 2023

作者： Cai, Xiushan Lin, Yuhang Wang, Ping Ke, Changwu School of Data Science and Intelligent Engineering Xiamen Institute of Technology Xiamen361021 China College of Engineering North Carolina State University RaleighNC27695 United States College of Electrical Engineering and Automation Hubei Normal University Huangshi435002 China

ISBN: (纸本)9789887581543

We consider control design for a nonlinear ordinary differential equation (ODE) and transport partial differential equation (PDE) cascaded system whose propagation speed is spatially-varying. The ODE state is driven by the uncontrolled boundary of the transport PDE. Our nonlinear delay-compensated control law enables global asymptotic stability of the closed-loop system. The controller design is deduced using a backstepping transformation and constructing a Lyapunov functional. We offer global stability guaranteed by the assumption of the propagation speed is continuously differentiable and positive. The validity of the proposed controller is illustrated by a simulation result. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Backstepping

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MSR: A Multifaceted Self-Retrieval Framework for Microscopic Cascade Prediction 39

MSR: A Multifaceted Self-Retrieval Framework for Microscopic...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Hong, Dongsheng Chen, Chao Li, Xujia Wang, Shuhui Lin, Wen Liao, Xiangwen College of Computer and Data Science Fuzhou University China School of Computer Science and Technology Harbin Institute of Technology Shenzhen China Hong Kong University of Science and Technology Hong Kong College of Computer and Control Engineering Minjiang University China

ISBN: (纸本)157735897X

The microscopic cascade prediction task has wide applications in downstream areas like "rumor detection". Its goal is to forecast the diffusion routines of information cascade within networks. Existing works typically formulate it as a classification task, which fails to well align with the Social Homophily assumption, as it just use the features of "infected" users while neglecting those of "uninfected" users in representation learning. Moreover, these methods focus primarily on social relationships, thereby dismissing other vital dimensions like users' historical behavior and the underlying preferences behind it. To address these challenges, we introduce the MSR (Multifaceted Self-Retrieval) framework. During encoding, in addition to the existing social graph, we construct a preference graph to represent "behavioral preferences" and further propose a modified multi-channel GRAU for multi-view analysis of cascade phenomenon. For decoding, our approach diverges from classification-based methods by reformulating the task as an information retrieval problem that predicts the target user with similarity measures. Empirical evaluations on public datasets demonstrate that this framework significantly outperforms baselines on Hits@κ and MAP@κ, affirming its enhanced ability. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Taxonomies

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Keeping Pace with Ever-Increasing data: Towards Continual Learning of Code Intelligence Models

arXiv

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arXiv 2023年

作者： Gao, Shuzheng Zhang, Hongyu Gao, Cuiyun Wang, Chaozheng School of Computer Science and Technology Harbin Institute of Technology Shenzhen China School of Big Data and Software Engineering Chongqing University China

—Previous research on code intelligence usually trains a deep learning model on a fixed dataset in an offline manner. However, in real-world scenarios, new code repositories emerge incessantly, and the carried new knowledge is beneficial for providing up-to-date code intelligence services to developers. In this paper, we aim at the following problem: How to enable code intelligence models to continually learn from ever-increasing data? One major challenge here is catastrophic forgetting, meaning that the model can easily forget knowledge learned from previous datasets when learning from the new dataset. To tackle this challenge, we propose REPEAT, a novel method for continual learning of code intelligence models. Specifically, REPEAT addresses the catastrophic forgetting problem with representative exemplars replay and adaptive parameter regularization. The representative exemplars replay component selects informative and diverse exemplars in each dataset and uses them to retrain model periodically. The adaptive parameter regularization component recognizes important parameters in the model and adaptively penalizes their changes to preserve the knowledge learned before. We evaluate the proposed approach on three code intelligence tasks including code summarization, software vulnerability detection, and code clone detection. Extensive experiments demonstrate that REPEAT consistently outperforms baseline methods on all tasks. For example, REPEAT improves the conventional fine-tuning method by 1.22, 5.61, and 1.72 on code summarization, vulnerability detection and clone detection, respectively. Copyright © 2023, The Authors. All rights reserved.

关键词： Cloning

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Stacking-Based Attention Temporal Convolutional Network for Action Segmentation 48

Stacking-Based Attention Temporal Convolutional Network for ...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Yang, Liu Jiang, Yu Hong, Junkun Wu, Zhenjie Yang, Zhan Long, Jun Central South University School of Computer Science and Engineering China Central South University Big Data Institute Central South University China Central South University Network Resources Management and Trust Evaluation Key Laboratory of Hunan Province Changsha China

ISBN: (纸本)9781728163277

Action segmentation plays an important role in video understanding, which is implemented by frame-wise action classification. Recent works on action segmentation capture long-term dependencies by increasing temporal convolution layers in Temporal Convolution Networks (TCNs). However, high layers in TCNs are more coarse access to video features, resulting in the loss of fine-grained information for frame-wise action classification. To address the above issues, we propose a novel Attention-based Temporal Convolution (ATC) block to capture fine-grained information of temporal dependencies for frame-wise action classification by self-attention mechanism. Via stacking ATC blocks, we design a Stacking-based Attention Temporal Convolutional Network (SATC) to adaptively capture long-term and short-term dependencies, according to the semantic similarity of features on different temporal receptive fields simultaneously. The experimental results demonstrate that our SATC outperforms other baselines on all three challenging datasets: GTEA, 50Salads and Breakfast. © 2023 IEEE.

关键词： Convolution

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Downstream-agnostic Adversarial Examples

Downstream-agnostic Adversarial Examples

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International Conference on Computer Vision (ICCV)

作者： Ziqi Zhou Shengshan Hu Ruizhi Zhao Qian Wang Leo Yu Zhang Junhui Hou Hai Jin School of Cyber Science and Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan University School of Information and Communication Technology Griffith University Department of Computer Science City University of Hong Kong School of Computer Science and Technology Huazhong University of Science and Technology Cluster and Grid Computing Lab

Self-supervised learning usually uses a large amount of unlabeled data to pre-train an encoder which can be used as a general-purpose feature extractor, such that downstream users only need to perform fine-tuning operations to enjoy the benefit of "large model". Despite this promising prospect, the security of pre-trained encoder has not been thoroughly investigated yet, especially when the pre-trained encoder is publicly available for commercial *** this paper, we propose AdvEncoder, the first framework for generating downstream-agnostic universal adversarial examples based on the pre-trained encoder. AdvEncoder aims to construct a universal adversarial perturbation or patch for a set of natural images that can fool all the downstream tasks inheriting the victim pre-trained encoder. Unlike traditional adversarial example works, the pre-trained encoder only outputs feature vectors rather than classification labels. Therefore, we first exploit the high frequency component information of the image to guide the generation of adversarial examples. Then we design a generative attack framework to construct adversarial perturbations/patches by learning the distribution of the attack surrogate dataset to improve their attack success rates and transferability. Our results show that an attacker can successfully attack downstream tasks without knowing either the pre-training dataset or the downstream dataset. We also tailor four defenses for pre-trained encoders, the results of which further prove the attack ability of AdvEncoder. Our codes are available at: https://***/CGCL-codes/AdvEncoder.

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