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Different species of stored-grain pest prey alter the demographic parameters of the predator Cheyletus malaccensis (Acari: Cheyletidae)

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Journal of Stored Products research 2025年 112卷

作者： Han, Yinbiao Xue, Dingrong Xia, Liyuan Gao, Yongfu Lü, Jianhua Su, Xu Liu, Zhiqi Wu, Yi National Engineering Research Center of Grain Storage and Logistics Academy of National Food and Strategic Reserves Administration Beijing China Henan Collaborative Innovation Center for Grain Storage Security School of Food and Strategic Reserves Henan University of Technology Zhengzhou China Sinograin Dalian Depot Co. Ltd. Dalian China

Cheyletus malaccensis (Oudemans) is a predatory mite that can effectively control stored-grain pests and is a promising biological control agent in China. To promote the application of predatory mites to control stored-grain pests, the selection of stored-grain pest prey by C. malaccensis during large-scale feeding and population establishment needs to be studied. Therefore, we studied the life table parameters of C. malaccensis when fed five different prey species. Predator bodies were yellowish when consuming Aleuroglyphus ovatus and Carpoglyphus lactis, blackish when feeding on Lasioderma serricorne eggs, and brown spots appeared when feeding on Cryptolestes pusillus eggs. C. malaccensis completed its life cycle on each of the five types of prey. The effects of prey on C. malaccensis population reproduction were in the order, from greatest to least, C. lactis, A. ovatus, L. serricorne eggs, Rhyzopertha dominica eggs, and C. pusillus eggs. The highest fecundity (369.3 eggs per female) of C. malaccensis was recorded with the prey C. lactis, which was associated with an intrinsic rate of increase of 0.2417 day−1. According to nutrient analyses, high sugar and protein levels in prey might have restricted the growth of C. malaccensis. The study data support the conclusion that C. lactis can serve as alternative prey to support population increase of C. malaccensis. Effective populations of C. malaccensis can also established by feeding on the eggs of stored-grain pests. The information in this study will contribute to the development of effective biological control strategies for managing stored-grain pests. © 2025 Elsevier Ltd

关键词： Anthropogenic

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Effective Concurrency Testing for Go via Directional Primitive-Constrained Interleaving Exploration

Effective Concurrency Testing for Go via Directional Primiti...

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IEEE International Conference on Automated Software engineering (ASE)

作者： Zongze Jiang Ming Wen Yixin Yang Chao Peng Ping Yang Hai Jin School of Cyber Science and Engineering Huazhong University of Science and Technology China Hubei Key Laboratory of Distributed System Security Services Computing Technology and System Lab Cluster and Grid Computing Lab Hubei Engineering Research Center on Big Data Security National Engineering Research Center for Big Data Technology and System ByteDance Beijing China School of Computer Science and Technology Huazhong University of Science and Technology China

The Go language (Go/Golang) has been attracting increasing attention from the industry over recent years due to its strong concurrency support and ease of deployment. This programming language encourages developers to use channel-based concurrency, which simplifies the development of concurrent programs. Unfortunately, it also introduces new concurrency problems that differ from those caused by the mechanism of shared memory concurrency. However, there are only few works that aim to detect such Go-specific concurrency issues. Even state-of-the-art testing tools will miss critical concurrent bugs that require fine-grained and effective interleaving exploration. This paper presents GoPie, a novel testing approach for detecting Go concurrency bugs through primitive-constrained interleaving exploration. GoPie utilizes execution histories to identify new interleavings instead of relying on exhaustive exploration or random scheduling. To evaluate its performance, we applied GoPie to existing benchmarks and large-scale open-source projects. Results show that GoPie can effectively explore concurrent interleavings and detect significantly more bugs in the benchmark. Furthermore, it uncovered 11 unique previously unknown concurrent bugs, and 9 of which have been confirmed.

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PSRAKA: Physically Secure and Robust Authenticated Key Agreement for VANETs

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IEEE Transactions on Vehicular technology 2024年第5期74卷 7953-7968页

作者： Liu, Gao Li, Hao Liang, Yangfan Le, Junqing Wang, Ning Mu, Nankun Liu, Zhiquan Liu, Yining Xiang, Tao Chongqing University College of Computer Science Chongqing400044 China Jiaxing University Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems The Institute of Information Network and Artificial Intelligence The Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province The Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province Jiaxing314001 China Jinan University College of Cyber Security Guangzhou510632 China Wenzhou University of Technology School of Data Science and Artificial Intelligence Wenzhou325000 China

Authenticated key agreement (AKA) allows vehicles and road side units (RSUs) to mutually authenticate and establish shared session keys, which can be implemented to secure vehicular ad-hoc networks (VANETs). However, most of existing relative schemes fail to support physical security and tracing robustness, and lack a mechanism to balance pseudonym unlinkability and system efficiency. In this paper, we propose PSRAKA, a physically secure and robust authenticated key agreement scheme for VANETs, which enables anonymous authentication on vehicles through chameleon hash functions. In particular, physical security is guaranteed by integrating physical unclonable functions and fuzzy extractors to resist against physical capture attacks. Our scheme is the first to support robustness by allowing the vehicle and central authority to jointly sign or commit to the vehicle's real identity, chameleon hash value and public key for binding to ensure public verification and prevent malicious accusations during tracing. Meanwhile, it is the first to model the relationship of vehicle pseudonym unlinkability and system efficiency for balance by formulating the unlinkability based on the frequency of pseudonym updates. PSRAKA achieves anonymity, conditional privacy, robustness, key escrow freeness and physical security, and resists against most known attacks. Comprehensive performance analysis and evaluation suggest the potential usability of PSRAKA. © 1967-2012 IEEE.

关键词： Vehicular ad hoc networks

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Detector Collapse: Physical-World Backdooring Object Detection to Catastrophic Overload or Blindness in Autonomous Driving

arXiv

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arXiv 2024年

作者： Zhang, Hangtao Hu, Shengshan Wang, Yichen Zhang, Leo Yu Zhou, Ziqi Wang, Xianlong Zhang, Yanjun Chen, Chao School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China Cluster and Grid Computing Lab China School of Information and Communication Technology Griffith University Australia University of Technology Sydney Australia RMIT University Australia

Object detection tasks, crucial in safety-critical systems like autonomous driving, focus on pinpointing object locations. These detectors are known to be susceptible to backdoor attacks. However, existing backdoor techniques have primarily been adapted from classification tasks, overlooking deeper vulnerabilities specific to object detection. This paper is dedicated to bridging this gap by introducing Detector Collapse (DC), a brand-new backdoor attack paradigm tailored for object detection. DC is designed to instantly incapacitate detectors (i.e., severely impairing detector's performance and culminating in a denial-of-service). To this end, we develop two innovative attack schemes: SPONGE for triggering widespread misidentifications and BLINDING for rendering objects invisible. Remarkably, we introduce a novel poisoning strategy exploiting natural objects, enabling DC to act as a practical backdoor in real-world environments. Our experiments on different detectors across several benchmarks show a significant improvement (∼10%-60% absolute and ∼2-7× relative) in attack efficacy over state-of-the-art attacks. Copyright © 2024, The Authors. All rights reserved.

关键词： Denial-of-service attack

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EFFECTS OF IONOSPHERE ON LOWER-FREQUENCY SPACEBORNE SAR IMAGING

EFFECTS OF IONOSPHERE ON LOWER-FREQUENCY SPACEBORNE SAR IMAG...

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2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021

作者： Wang, Kuan Chen, Bingxu Li, Ning Guo, Zhengwei Fu, Zewen College of Computer and Information Engineering Henan University Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University Kaifeng China

The propagation of spaceborne synthetic aperture radar (SAR) signal is affected by the ionosphere, which will lead to the degradation of the imaging quality, especially in the conditions of low-frequency and wide-bandwidth. This paper analyzed the influence of ionosphere on SAR imaging under different carrier frequency, different bandwidth, different ionosphere total electron content (TEC), different azimuth resolution and different scintillation strength by time-domain simulation. The results show that the ionospheric dispersion effect leads to SAR image defocusing, which is closely related to the radar carrier frequency, signal bandwidth, azimuth resolution and TEC value on the propagation path. At the same time, due to the influence of time delay, the dispersion effect also leads to the range displacement of SAR image. The ionospheric scintillation effect reduces the azimuth resolution significantly and in extreme cases imaging is not possible. © 2021 IEEE

关键词： Synthetic aperture radar

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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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Self-Supervised Graph Representation with Proximity Matrix

SSRN

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SSRN 2023年

作者： Li, Huanhuan Luo, Haifeng Huang, Tianqiang Ding, Xuemei Huang, Liqing College of Computer and Cyber Security Fujian Normal University China Digital Fujian Big Data Security Technology Institute China Fujian Provincial Engineering Research Center for Public Service Big Data Mining and Application China

Graph representation learning(GRL) aims to extract valid graph information to generate informative representations for handling various tasks on graphs. Supervised graph representation learning (SGRL) has achieved remarkable success recently. However, when labels are missing, or task-free representations are expected, self-supervised graph representation learning (SSGRL) is a promising paradigm. Graph auto-encoders (GAEs) are one of the prevalent frameworks in SSGRL. There are two issues that hinder the effectiveness of existing GAEs: a) There is no appropriate strategy to fuse attributive and structural information in the reconstruction target;b) The decoder cannot fully retain valid information for classification tasks. In this work, we propose a self-supervised graph auto-encoders via proximity matrix reconstruction (PMGAE) to address the above issues. For the proper information fusion, a proximity matrix is designed as the reconstruction target. Then, we design a cosine decoder to enhance the decoder's ability to retain information. Moreover, to adapt PMGAE to the large-scale graph, a random sub-matrix reconstruction mechanism is introduced to reduce memory consumption and running time in the decoding stage. In the experiment, we used seven benchmarks to verify the effectiveness of PMGAE. The results show that the proposed PMGAE is significantly improved compared to the state-of-the-art methods. © 2023, The Authors. All rights reserved.

关键词： Decoding

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3DHacker: Spectrum-based Decision Boundary Generation for Hard-label 3D Point Cloud Attack

arXiv

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arXiv 2023年

作者： Tao, Yunbo Liu, Daizong Zhou, Pan Xie, Yulai Du, Wei Hu, Wei 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 China Wangxuan Institute of Computer Technology Peking University China

With the maturity of depth sensors, the vulnerability of 3D point cloud models has received increasing attention in various applications such as autonomous driving and robot navigation. Previous 3D adversarial attackers either follow the white-box setting to iteratively update the coordinate perturbations based on gradients, or utilize the output model logits to estimate noisy gradients in the black-box setting. However, these attack methods are hard to be deployed in real-world scenarios since realistic 3D applications will not share any model details to users. Therefore, we explore a more challenging yet practical 3D attack setting, i.e., attacking point clouds with black-box hard labels, in which the attacker can only have access to the prediction label of the input. To tackle this setting, we propose a novel 3D attack method, termed 3D Hard-label attacker (3DHacker), based on the developed decision boundary algorithm to generate adversarial samples solely with the knowledge of class labels. Specifically, to construct the class-aware model decision boundary, 3DHacker first randomly fuses two point clouds of different classes in the spectral domain to craft their intermediate sample with high imperceptibility, then projects it onto the decision boundary via binary search. To restrict the final perturbation size, 3DHacker further introduces an iterative optimization strategy to move the intermediate sample along the decision boundary for generating adversarial point clouds with smallest trivial perturbations. Extensive evaluations show that, even in the challenging hard-label setting, 3DHacker still competitively outperforms existing 3D attacks regarding the attack performance as well as adversary quality. Copyright © 2023, The Authors. All rights reserved.

关键词： Iterative methods

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Unsupervised Domain Adaptive Person Re-Identification with Adaptive Structure Learning

Unsupervised Domain Adaptive Person Re-Identification with A...

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IEEE International Conference on Image Processing

作者： Yang Peng Ping Liu Xiyuan Yang Pan Zhou Yawei Luo Jingen Liu The Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Center for Frontier AI Research A*STAR Singapore School of Software Technology ZheJiang University Disney Streaming USA

Unsupervised domain adaptive person re-identification has garnered considerable attention due to its practical significance. Previous research has focused on utilizing the teacher-student framework within the clustering and fine-tuning paradigm to reduce the domain gap between different person re-identification datasets. Building upon these recent advancements, we propose a novel approach to further explore the structural information of the teacher and student networks from multiple perspectives, including adaptive sample updating, global feature arrangement, and local discrepancy learning. By integrating these three components, we introduce the Adaptive Structure Learning Framework (ASL) for unsupervised domain adaptive person re-identification. Compared to the baseline method, our approach achieves a significant improvement of 7.3% in mean Average Precision (mAP) on the Market2MSMT task. Furthermore, our experimental results across three benchmark datasets provide further evidence of the effectiveness of our proposed method.

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CroPrompt: Cross-task Interactive Prompting for Zero-shot Spoken Language Understanding

CroPrompt: Cross-task Interactive Prompting for Zero-shot Sp...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Qin, Libo Wei, Fuxuan Chen, Qiguang Zhou, Jingxuan Huang, Shijue Si, Jiasheng Lu, Wenpeng Che, Wanxiang School of Computer Science and Engineering Central South University China Key Laboratory of Data Intelligence and Advanced Computing in Provincial Universities Soochow University China Research Center for Social Computing and Information Retrieval Harbin Institute of Technology China Harbin Institute of Technology Shenzhen China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology Shandong Academy of Sciences China

ISBN: (纸本)9798350368741

Slot filling and intent detection are two highly correlated tasks in spoken language understanding (SLU). Recent SLU research attempts to explore zero-shot prompting techniques in large language models to alleviate the data scarcity problem. Nevertheless, the existing prompting work ignores the cross-task interaction information for SLU, which leads to sub-optimal performance. To solve this problem, we present the pioneering work of Cross-task Interactive Prompting (CroPrompt) for SLU, which enables the model to interactively leverage the information exchange across the correlated tasks in SLU. Additionally, we further introduce a multi-task self-consistency mechanism to mitigate the error propagation caused by the intent information injection. We conduct extensive experiments on the standard SLU benchmark and the results reveal that CroPrompt consistently outperforms the existing prompting approaches. In addition, the multi-task self-consistency mechanism can effectively ease the error propagation issue, thereby enhancing the performance. We hope this work can inspire more research on cross-task prompting for SLU. © 2025 IEEE.

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