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arXiv 2024年

作者： Singh, Alakhsimar Verma, Nischay Goyal, Kanav Singh, Amritpal Kumar, Puneet Li, Xiaobai Computer Science and Engineering Department National Institute of Technology Jalandhar India Center for Machine Vision and Signal Analysis University of Oulu Finland The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China

This paper presents VisioPhysioENet, a novel multimodal system that leverages visual and physiological signals to detect learner engagement. It employs a two-level approach for extracting both visual and physiological features. For visual feature extraction, Dlib is used to detect facial landmarks, while OpenCV provides additional estimations. The face_recognition library, built on Dlib, is used to identify the facial region of interest specifically for physiological signal extraction. Physiological signals are then extracted using the plane-orthogonal-to-skin method to assess cardiovascular activity. These features are integrated using advanced machine learning classifiers, enhancing the detection of various levels of engagement. We thoroughly tested VisioPhysioENet on the DAiSEE dataset. It achieved an accuracy of 63.09%. This shows it can better identify different levels of engagement compared to many existing methods. It performed 8.6% better than the only other model that uses both physiological and visual features. Copyright © 2024, The Authors. All rights reserved.

关键词： Physiological models

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Detecting Exploit Primitives Automatically for Heap Vulnerabilities on Binary Programs

arXiv

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arXiv 2022年

作者： Liu, Jie An, Hang Li, Jin Liang, Hongliang School of Computer ScienceBeijing University of Posts and TelecommunicationsBeijing Beijing China Codesafe Team of LegendsecQi’anxin Group Beijing China Nation Key Laboratory of Science and Technology on Information System Security Beijing China

Automated Exploit Generation (AEG) is a well-known difficult task, especially for heap vulnerabilities. Previous works first detected heap vulnerabilities and then searched for exploitable states by using symbolic execution and fuzzing techniques on binary programs. However, it is not always easy to discovery bugs using fuzzing or symbolic technologies and solvable for internal overflow of heap objects. In this paper, we present a solution DEPA to detect exploit primitives based on primitive-crucial-behavior model for heap vulnerabilities. The core of DEPA contains two novel techniques, 1) primitive-crucial-behavior identification through pointer dependence analysis, and 2) exploit primitive determination method which includes triggering both vulnerabilities and exploit primitives. We evaluate DEPA on eleven real-world CTF(capture the flag) programs with heap vulnerabilities and DEPA can discovery arbitrary write and arbitrary jump exploit primitives for ten programs except for program multiheap. Results showed that primitive-crucial-behavior identification and determining exploit primitives are accurate and effective by using our approach. In addition, DEPA is superior to the state-of-the-art tools in determining exploit primitives for the heap object internal overflow vulnerability. Copyright © 2022, The Authors. All rights reserved.

关键词： Program debugging

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Retinal Vascular Segmentation Network with Connectivity Guidance 9

Retinal Vascular Segmentation Network with Connectivity Guid...

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9th IEEE Smart World Congress, SWC 2023

作者： Wu, Ronghua Tao, Chen Chen, Hongyu Xu, Wenjing Yan, Xiao Liu, HongZhe Zhang, XiuMing Jian, Muwei Linyi University School of Information Science and Engineering Linyi China The First Affiliated Hospital of Ningbo University Ningbo Clinical Research Center for Hematologic Malignancies Department of Haematology Ningybo China Beijing Union University Beijing Key Laboratory of Information Service Engineering Beijing China The First Affiliated Hospital of Zhejiang University College of Medicine Department of Pathology Hangzhou China Shandong University of Finance and Economics School of Computer Science and Technology Jinan China

ISBN: (纸本)9798350319804

The morphology of the retinal vasculature is closely related to a variety of ophthalmic diseases. Despite significant progresses have been achieved in retinal vessel segmentation with the development of deep learning, several challenging problems still persist. Specifically, some components present in the retina (such as optic discs or lesions) may interfere with or cover the vessels, and current segmentation models rarely focus on the connectivity of the vessels. In this paper, we propose an efficient network to conquer this issue. Firstly, we propose a semantic enhancement module, to insert into the bottom layer of the UNet encoder section for guiding the network to enhance features representation. Secondly, we introduce a skeleton fitting module that maximizes the preservation of the vascular topology using skeleton prior and contrast loss. Finally, we perform a morphological complementation operation on the final output of the decoder by means of a voting mechanism. The designed segmentation network has been evaluated on publicly available datasets and experimental results demonstrate the effectiveness of the method. © 2023 IEEE.

关键词： Convolutional neural network medical image processing retinal vessel segmentation

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RedAgent: Red Teaming Large Language Models with Context-aware Autonomous Language Agent

arXiv

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arXiv 2024年

作者： Xu, Huiyu Zhang, Wenhui Wang, Zhibo Xiao, Feng Zheng, Rui Feng, Yunhe Ba, Zhongjie Ren, Kui The State Key Laboratory of Blockchain and Data Security Zhejiang University China School of Cyber Science and Technology Zhejiang University China Palo Alto Networks United States Department of Computer Science and Engineering University of North Texas United States

Recently, advanced Large Language Models (LLMs) such as GPT-4 have been integrated into many real-world applications like Code Copilot. These applications have significantly expanded the attack surface of LLMs, exposing them to a variety of threats. Among them, jailbreak attacks that induce toxic responses through carefully constructed jailbreak prompts have raised critical safety concerns. To effectively identify these threats, a growing number of red team approaches simulate potential adversarial scenarios by crafting jailbreak prompts to test the target LLM. However, existing red teaming methods do not consider the unique vulnerabilities of LLM in different scenarios (e.g., code-related tasks), making it difficult to adjust the jailbreak prompts to find context-specific vulnerabilities, thereby lacking efficiency. Meanwhile, these methods are limited to refining handcrafted jailbreak templates using a few mutation operations (such as synonym replacement), lacking the automation and scalability to continuously adapt to different scenarios. To enable context-aware and efficient red teaming, we abstract and model existing attacks into a coherent concept called "jailbreak strategy" and propose a multi-agent LLM system named RedAgent that leverages these strategies to generate context-aware jailbreak prompts. By self-reflecting on contextual feedback and red teaming trials in an additional memory buffer, RedAgent continuously learns how to leverage these strategies to achieve more effective jailbreaks in specific contexts. Extensive experiments demonstrate that our system can jailbreak most black-box LLMs within just five queries, improving the efficiency of existing red teaming methods by two times. Additionally, RedAgent can jailbreak customized LLM applications more efficiently. By generating context-aware jailbreak prompts towards trending applications on GPTs, we discover 60 severe vulnerabilities of these real-world applications with only two queries per vulnera

关键词： Autonomous agents

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Experimental Results of Federated Learning in Unmanned Aerial Vehicle Swarm

Experimental Results of Federated Learning in Unmanned Aeria...

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2021 IEEE International Conference on Space-Air-Ground Computing, SAGC 2021

作者： Shen, Yun Dong, Chao Qu, Yuben Ren, Weiqing Zhu, Xiaojun Ministry of Industry and Information Technology Nanjing University of Aeronautics and Astronautics The Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space China Shanghai Jiao Tong University Department of Computer Science and Engineering China

ISBN: (纸本)9781665400114

Unmanned aerial vehicles (UAVs) need to deploy machine learning (ML) models to execute complex tasks such as target tracking. For traditional centralized ML, UAVs need to transmit the raw data to the ground central server for model training. However, the transmission of raw data not only causes a large delay but also may incur sensitive information leakage. Federated learning (FL) as a distributed machine learning enables multiple UAVs to form a swarm for distributed model training. Nevertheless, most of current researches on FL of UAV swarm are based on theoretical architecture and simulations. In this paper, we focus on verifying the feasibility of deploying FL in a UAV swarm through practical experiments. Moreover, we further explore the impact of the distance change between the training node UAVs and the parameter server UAV on the training accuracy. The experimental results show that the UAV swarm can perform 15 rounds of global training in 5 minutes, with a training accuracy of 84.28%. © 2021 IEEE.

关键词： Antennas

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Ranking on Heterogeneous Manifold for Multimodal information Retrieval

Ranking on Heterogeneous Manifold for Multimodal Information...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Zhenkun Jin Xingshi Wan Xin Nie Xinlei Zhou Yuanyuan Yi Gefei Zhou Wuhan Business University Wuhan Research Institute of Posts and Telecommunications Wuhan China Wuhan Research Institute of Posts and Telecommunications Wuhan China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Huazhong University of Science and Technology Wuhan China School of Journalism and Information Communication Huazhong University of Science and Technology Wuhan China School of Computer Science & Technology Huazhong University of Science and Technology Wuhan China

In light of recent advancements in Internet of Multimedia Things (IoMT) and 5G technology, both the variety and quantity of data have been rapidly increasing. Consequently, handling zero-shot cross-modal retrieval (ZSCMR) of unlabeled multi-modal data has emerged as a prominent research topic. Early models utilized generative adversarial networks and class embeddings to assist in building a common feature space. However, this approach resulted in a loss of reliability in the feature distributions of individual modalities. In this paper, we propose a method called Ranking on Heterogeneous Manifold (RHM) to address the aforementioned issue. Our model comprises three feature extractors, each dedicated to extracting features from text, images and multimodal data. Subsequently, the relationship structures derived from these feature extractors are employed to construct a heterogeneous manifold. A diffusion process is then conducted on this heterogeneous manifold, ultimately yielding the final ranking scores. Comprehensive experiments demonstrate that the proposed modal has the capability to acquire more effective knowledge representation, thus enhancing the effectiveness of ZSCMR.

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Mesenchymal stem cells loaded on 3D-printed gradient poly(e-caprolactone)/methacrylated alginate composite scaffolds for cartilage tissue engineering

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Regenerative Biomaterials 2021年第3期8卷 70-83页

作者： Yanyan Cao Peng Cheng Shengbo Sang Chuan Xiang Yang An Xiaochun Wei Zhizhong Shen Yixia Zhang Pengcui Li Key Lab of Advanced Transducers and Intelligent Control System of the Ministry of Education MicroNano System Research CenterCollege of Information and ComputerTaiyuan University of TechnologyTaiyuan 030024China College of Information Science and Engineering Hebei North UniversityZhangjiakou 075000China Shanxi Key Laboratory of Bone and Soft Tissue Injury Repair Department of OrthopedicsThe Second Hospital of Shanxi Medical UniversityTaiyuan 030001China Department of Plastic Surgery Peking University Third HospitalBeijing 100191China Department of Biomedical Engineering College of Biomedical EngineeringTaiyuan University of TechnologyTaiyuan 030024China

Cartilage has limited self-repair ability due to its avascular,alymphatic and aneural *** combination of three-dimensional(3D)printing and tissue engineering provides an up-and-coming approach to address this ***,we designed and fabricated a tri-layered(superficial layer(SL),middle layer(ML)and deep layer(DL))stratified scaffold,inspired by the architecture of collagen fibers in native cartilage *** scaffold was composed of 3D printed depth-dependent gradient poly(e-caprolactone)(PCL)impregnated with methacrylated alginate(ALMA),and its morphological analysis and mechanical properties were *** prove the feasibility of the composite scaffolds for cartilage regeneration,the viability,proliferation,collagen deposition and chondrogenic differentiation of embedded rat bone marrow mesenchymal stem cells(BMSCs)in the scaffolds were assessed by Live/dead assay,CCK-8,DNA content,cell morphology,immunofluorescence and real-time reverse transcription polymerase chain ***-loaded gradient PCL/ALMA scaffolds showed excellent cell survival,cell proliferation,cell morphology,collagen II deposition and hopeful chondrogenic differentiation compared with three individual-layer ***,our study demonstrates the potential use of the gradient PCL/ALMA construct for enhanced cartilage tissue engineering.

关键词： three-dimensional printing cartilage tissue engineering PCL methacrylated alginate bone marrow mesenchymal stem cells

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EventTrojan: Manipulating Non-Intrusive Speech Quality Assessment via Imperceptible Events

arXiv

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arXiv 2023年

作者： Ren, Ying Shen, Kailai Ye, Zhe Yan, Diqun Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology Shandong Academy of Sciences Shandong China Juphoon System Software Co. Ltd Ningbo China

Non-Intrusive speech quality assessment (NISQA) has gained significant attention for predicting speech’s mean opinion score (MOS) without requiring the reference speech. Researchers have gradually started to apply NISQA to various practical scenarios. However, little attention has been paid to the security of NISQA models. Backdoor attacks represent the most serious threat to deep neural networks (DNNs) due to the fact that backdoors possess a very high attack success rate once embedded. However, existing backdoor attacks assume that the attacker actively feeds samples containing triggers into the model during the inference phase. This is not adapted to the specific scenario of NISQA. And current backdoor attacks on regression tasks lack an objective metric to measure the attack performance. To address these issues, we propose a novel backdoor triggering approach (EventTrojan) that utilizes an event during the usage of the NISQA model as a trigger. Moreover, we innovatively provide an objective metric for backdoor attacks on regression tasks. Extensive experiments on four benchmark datasets demonstrate the effectiveness of the EventTrojan attack. Besides, it also has good resistance to several defense methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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Air-Ground Integrated Federated Learning: An Experimental Implementation

Air-Ground Integrated Federated Learning: An Experimental Im...

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2021 IEEE International Conference on Space-Air-Ground Computing, SAGC 2021

作者： Jing, Yuqian Dong, Chao Qu, Yuben Zhou, Fuhui Ministry of Industry and Information Technology Nanjing University of Aeronautics and Astronautics The Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space China Shanghai Jiao Tong University Department of Computer Science and Engineering China

ISBN: (纸本)9781665400114

With the envision of sixth generation (6G) networks technology, diverse artificial intelligence (AI) services are gradually developed from the network center to the edge, which enables unmanned aerial vehicle (UAV) as a hot spot to provide auxiliary services of machine learning (ML). Due to the sensitive privacy and limited resources, traditional centralized ML may not be used directly in such networks. As a promising distributed collaborative ML, federated learning (FL) is more suitable. However, the current research on air-ground integrated federal learning (AGIFL) mostly focuses on architecture design and network simulation. In this paper, we mainly verify the feasibility of deploying FL between UAV and terrestrial users through practical platform. Extensive experiments demonstrate that the training accuracy is higher when the training nodes is close to the server, and the accuracy is improved from 69.48% to 83.52% when distance is reduced from 20 meters to 5 meters. © 2021 IEEE.

关键词： Antennas

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Design of optimal trajectory transition controller for thrust-vectored V/STOL aircraft

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science China(information sciences) 2021年第3期64卷 235-237页

作者： Zhiqiang CHENG Jihong ZHU Xiaming YUAN Xiangyang WANG Department of Computer Science and Technology Tsinghua University Key Laboratory of Complex Ship System Simulation

Dear editor,In recent years, vertical and/or short take-off and landing(V/STOL) aircraft have attracted much attention because they combine the efficiency of fixed-wing vehicles with the flexibility of traditional ver... 详细信息

关键词： backward propagation V/STOL transition control thrust-vector optimal trajectory

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