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IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

作者： Yannan Chen Licheng Zhao Kaiming Shen School of Science and Engineering The Chinese University of Hong Kong (Shenzhen) China Shenzhen Research Institute of Big Data China

There is considerable interest in the use of fractional programming (FP) for the communication system design because many problems in this area are fractionally structured. Notably, max-FP and min-FP are not interchangeable in general if there are multiple ratios, so the two types of FP are often dealt with separately in the existing literature. As a result, an FP method for maximizing the signal-to-interference-plus-noise ratios (SINRs) typically cannot be used for minimizing the Cramér-Rao bounds (CRBs). In contrast, this work proposes a unified approach that bridges the gap between max-FP and min-FP. Particularly, we examine the theoretical basis of this unified approach from a minorization-maximization (MM) perspective, and in return obtain a matrix extension of this new FP technique. Moreover, this work presents two application cases: (i) joint radar sensing and (ii) multi-cell secure transmission, neither of which can be efficiently addressed by the existing FP tools.

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Overview of SMP-CAIL2020-Argmine:The Interactive Argument-Pair Extraction in Judgement Document Challenge

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data Intelligence 2021年第2期3卷 287-307页

作者： Jian Yuan Zhongyu Wei Yixu Gao Wei Chen Yun Song Donghua Zhao Jinglei Ma Zhen Hu Shaokun Zou Donghai Li Xuanjing Huang School of Data Science Fudan UniversityShanghai 200433China Heilongjiang University Heilongjiang 150080China School of Mathematical Sciences Fudan UniversityShanghai 200433China China Judicial Big Data Institute Beijing 100043China THUNISOFT Co. Ltd.Beijing 100084China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China School of Computer Science Fudan UniversityShanghai 200433China

In this paper we present the results of the Interactive Argument-Pair Extraction in Judgement Document Challenge held by both the Chinese AI and Law Challenge(CAIL)and the Chinese National Social Media Processing Conference(SMP),and introduce the related data *** task challenged participants to choose the correct argument among five candidates proposed by the defense to refute or acknowledge the given argument made by the plaintiff,providing the full context recorded in the judgement documents of both *** received entries from 63 competing teams,38 of which scored higher than the provided baseline model(BERT)in the first phase and entered the second *** best performing system in the two phases achieved accuracy of 0.856 and 0.905,*** this paper,we will present the results of the competition and a summary of the systems,highlighting commonalities and innovations among participating *** SMP-CAIL2020-Argmine data set and baseline modelshave been already released.

关键词： Argumentation mining Judgement documents Natural language understanding Pretrained language model

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MP-GAN: Cyber-Attack Detection and Localization for Cyber-Physical Systems with Multi-Process Generative Adversarial Networks*

MP-GAN: Cyber-Attack Detection and Localization for Cyber-Ph...

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Artificial Intelligence of Things and Systems (AIoTSys), International Conference on

作者： Yikui Zhou Jie Wang Junnan Tang Chao Gou Zigui Jiang Dan Li See-Kiong Ng School of Software Engineering Sun-Yat Sen University Zhuhai China School of Intelligent Systems Engineering Sun-Yat Sen University Shenzhen China Institute of Data Science/School of Computing National University of Singapore

Cyber-Physical System (CPS) integrates sensing, computation, cybernetics, and networking to control a hybrid physical system consisting of different functional subsystems, making the production process more intelligent and controllable. However, cyber-attacks during its operation will lead to abnormal system behaviors or even system breakdowns. In recent years, data-driven anomaly detection methods have been adopted to judge whether the CPS system is under cyber-attacks based on rich sensor measurements to avoid further economic losses or safety issues. However, the multi-process essence of CPS has not been adequately addressed in existing works to locate the processes or points being attacked for in-time actions. In this work, we proposed a Multi-Process Generative Adversarial Network (MP-GAN) framework to detect anomalous CPS statuses and locate cyber-attacks. Specifically, the Long-Short-Term-Memory Recurrent Neural Networks (LSTM-RNN) was adopted as the base model to capture the temporal correlation of time series distributions, and the data was transferred between latent space and data space in a bi-directional manner, which regulated the generator to generate more realistic samples and thus better grasping the underline principles of the system. Moreover, parallel generators were employed to capture system performances at different physical processes, thus localizing the attacked CPS processes. Experiments on three CPS datasets, two collected from the Secure Water Treatment (SWaT) system and one collected from the water Distribution (WADI) system, showed that the proposed MP-GAN framework effectively reported anomalies caused by various cyber-attacks inserted in these complex multiprocess CPSs, which outperforms the state-of-the-art methods and can also locate most of the detected irregularities at the corresponding stages where the attacks were inserted.

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OPTIMIZATION MODELS TO MEET THE CONDITIONS OF ORDER PRESERVATION IN THE ANALYTIC HIERARCHY PROCESS

arXiv

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arXiv 2024年

作者： Tu, Jiancheng Zhibin, Wu Li, Yueyuan XIang, Chuankai Department of Computing Hong Kong Polytechnic University Hong Kong Business School Sichuan University Chengdu610065 China City University of Hong Kong School of Data Science Kowloon Tong Hong Kong

Deriving a priority vector from a pairwise comparison matrix (PCM) is a crucial step in the Analytical Hierarchy Process (AHP). Although there exists a priority vector that satisfies the conditions of order preservation (COP), the priority vectors obtained through existing prioritization methods frequently violate these conditions, resulting in numerous COP violations. To address this issue, this paper introduces a novel procedure to manage COP violations in AHP. Firstly, we prove that the index-exchangeability condition is both a necessary and sufficient condition for determining whether a priority vector satisfies COP. This enables the direct detection of COP violations, relying solely on the pairwise comparison preferences of decision-makers, rather than the prioritization methods utilized. Subsequently, we propose the Minimal Number of Violations and Deviations Method (MNVDM) model, which aims to derive a priority vector with the minimal number of COP violations. In particular, the MNVDM can obtain a violation-free priority vector when the PCM meets the index exchangeability conditions. Furthermore, an optimization model based on minimizing information loss is designed to ensure the COP by revising the preferences when the index-exchangeability conditions are violated. Finally, the feasibility and efficiency of the proposed models are validated through numerical examples and Monte Carlo simulation experiments. Our implementation is available at: https://***/Tommytutu/COP. © 2024, CC BY.

关键词： Vectors

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Partial Homoscedasticity in Causal Discovery with Linear Models

arXiv

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arXiv 2023年

作者： Wu, Jun Drton, Mathias Technical University of Munich GermanyTUM School of Computation Information and Technology Department of Mathematics and Munich Data Science Institute

Recursive linear structural equation models and the associated directed acyclic graphs (DAGs) play an important role in causal discovery. The classic identifiability result for this class of models states that when only observational data is available, each DAG can be identified only up to a Markov equivalence class. In contrast, recent work has shown that the DAG can be uniquely identified if the errors in the model are homoscedastic, i.e., all have the same variance. This equal variance assumption yields methods that, if appropriate, are highly scalable and also sheds light on fundamental information-theoretic limits and optimality in causal discovery. In this paper, we fill the gap that exists between the two previously considered cases, which assume the error variances to be either arbitrary or all equal. Specifically, we formulate a framework of partial homoscedasticity, in which the variables are partitioned into blocks and each block shares the same error variance. For any such groupwise equal variances assumption, we characterize when two DAGs give rise to identical Gaussian linear structural equation models. Furthermore, we show how the resulting distributional equivalence classes may be represented using a completed partially directed acyclic graph (CPDAG), and we give an algorithm to efficiently construct this CPDAG. In a simulation study, we demonstrate that greedy search provides an effective way to learn the CPDAG and exploit partial knowledge about homoscedasticity of errors in structural equation models. © 2023, CC BY.

关键词： Errors

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

SAM Advanced Management Journal

引用

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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Target Threat Assessment Method based on RCS Feature Classifier and TOPSIS

Target Threat Assessment Method based on RCS Feature Classif...

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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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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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