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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Weiming Du Junmou Lin Binqing Du Uddin Md. Borhan Jianqiang Li Jie Chen College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

With the increasing use of unmanned aerial vehicles (UAVs) in a variety of applications, their safety has become a critical concern. UAVs face numerous emergencies during missions, in these situations, the UAVs need to autonomously find a suitable landing site, plan flight routes, and avoid obstacles in unstructured environments. However, due to the limitations of computing power and sensors, it is challenging to achieve this goal. This research aims to explore the monocular emergency autonomous landing algorithm. Specifically, this work focuses on extracting depth and vision information. A topology information extractor is designed to convert image to graph and assess the connectivity of terrain. Additionally, a depth information extractor is designed to calculate the slope and roughness of the ground. A 3D topology optimizer is designed to optimize the graph by depth information and evaluate the landing suitability by a heuristic strategy. To make the action decision, a hybrid decision network (DGN) based on 3D topology information is proposed. Finally, this work build a simulated scenario based on real scene to verify the efficiency of DGN. The results of the experiment show that DGN outperforms its counterparts in terms of action prediction accuracy and landing success rate.

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Improving QoS of Workloads with CPU Pinning: A Deep Reinforcement Learning Approach

Improving QoS of Workloads with CPU Pinning: A Deep Reinforc...

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International Workshop on Quality of Service

作者： Xi Tao Weipeng Cao Yinghui Pan Ye Liu Zhong Ming Guangdong Laboratory of Artificial Intelligence and Digital Economy (Shenzhen) Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9798350350128

ISBN: (纸本)9798350350135

In cloud computing, tenants tend to select predefined flavors with substantial resources that go beyond their needs, in order to avoid resource starvation. As a result, cloud service providers experience a high allocation ratio with a low utilization ratio of resources. To improve the resource utilization, cloud service providers offer lower-priced resources by building an overcommitted environment to reuse resources. However, the colocation interference of workloads in the overcommitted environment may cause performance anomalies and degrade quality of service (QoS). In this paper, we propose a real-time mechanism to pin a group of workloads to a certain subset of CPUs to increase resource utilization and minimize interference. To be specific, we leverage deep reinforcement learning (DRL) to automatically adjust the CPU pinning configuration in the overcommitted environment according to the performance of workloads.

关键词： Cloud computing Computational modeling Buildings Quality of service Interference Deep reinforcement learning Real-time systems

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Like an Ophthalmologist: Dynamic Selection Driven Multi-View Learning for Diabetic Retinopathy Grading 39

Like an Ophthalmologist: Dynamic Selection Driven Multi-View...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Luo, Xiaoling Xu, Qihao Wu, Huisi Liu, Chengliang Lai, Zhihui Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen Key Laboratory of Visual Object Detection and Recognition Harbin Institute of Technology Shenzhen China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

ISBN: (纸本)157735897X

Diabetic retinopathy (DR), with its large patient population, has become a formidable threat to human visual health. In the clinical diagnosis of DR, multi-view fundus images are considered to be more suitable for DR diagnosis because of the wide coverage of the field of view. Therefore, different from the previous single-view DR grading methods, we design a dynamic selection-driven multi-view DR grading method to fit clinical scenarios better. Since lesion information plays a key role in DR diagnosis, previous methods usually boost the model performance by enhancing the lesion feature. However, during the actual diagnosis, ophthalmologists not only focus on the crucial parts, but also exclude irrelevant features to ensure the accuracy of judgment. To this end, we introduce the idea of dynamic selection and design a series of selection mechanisms from fine granularity to coarse granularity. In this work, we first introduce an Ophthalmic Image Reader (OIR) agent to provide the model with pixel-level prompts of suspected lesion areas. Moreover, we design a Multi-View Token Selection Module (MVTSM) that prunes redundant feature tokens and dynamically selects key information. In the final decision stage, we dynamically fuse multi-view features through the novel Multi-View Mixture of Experts Module (MVMoEM), to enhance key views and reduce the impact of conflicting views. Extensive experiments on a large multi-view fundus image dataset with 34,452 images prove that our method performs favorably against state-of-the-art models. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Ophthalmology

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QoS Perception for Cloud Databases: Necessity, Trends, and Challenges

QoS Perception for Cloud Databases: Necessity, Trends, and C...

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International Workshop on Quality of Service

作者： Weipeng Cao Xi Tao Yinghui Pan Ye Liu Zhong Ming Guangdong Laboratory of Artificial Intelligence and Digital Economy (Shenzhen) Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9798350350128

ISBN: (纸本)9798350350135

The advantages of resource elasticity and proactive data backup in cloud databases have attracted a large number of users to consider deploying their IT systems in the cloud. Factors such as performance, reliability, security, and ease of use are the most important considerations for users when choosing to migrate to the cloud. This paper mainly discusses the necessity of Quality of Service (QoS) awareness for cloud applications in the current public cloud environment, as well as the technological trends and challenges associated with it.

关键词： Cloud computing Databases Quality of service Elasticity Market research Real-time systems Security

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GunitaHu: A VR serious game with montessori approach for dementia patients during COVID-19 1

GunitaHu: A VR serious game with montessori approach for dem...

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1st International Conference on Novelties in Intelligent Digital systems, NIDS 2021

作者： Ang, Aira Jazel Y. Principio, Roberto D. Juayong, Richelle Ann B. Caro, Jaime D.L. Service Science and Software Engineering Laboratory Department of Computer Science University of the Philippines Diliman Philippines

ISBN: (纸本)9781643682044

The study aims to explore VR Serious Games as a form of therapy for people with dementia. It seeks to establish the utility of VR-based interventions with the application of Montessori Method. This study also serves as a basis for researchers, healthcare professionals, and developers who plan to incorporate VR therapy with other therapeutic approaches and to create a system that may be replicated for other illnesses via telemedicine to address the most vulnerable sectors. The main beneficiaries of this study are people with dementia and those who directly interact with them such as their doctors, caregivers, and family members of the patient. © 2021 The authors and IOS Press.

关键词： Serious games

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Multi-fidelity Gaussian Process for Distribution system Voltage Probabilistic Estimation with PVs 6

Multi-fidelity Gaussian Process for Distribution System Volt...

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6th IEEE Conference on Energy Internet and Energy system Integration, EI2 2022

作者： Zhang, Jinxian Zhao, Junbo Ye, Ketian Ding, Fei University of Connecticut Department of Electrical and Computer Engineering StorrsCT06268 United States National Renewable Energy Laboratory Power System Engineering Center GoldenCO80401 United States

ISBN: (纸本)9798350347159

The increasing penetration of behind-the-meter PVs causes challenges to maintain voltage security due to the lack of distribution system visibility. This paper proposes a nonlinear autoregressive Gaussian process (NARGP) approach to fuse limited number of SCADA/AMI data together with historical pseudo measurements for distribution node voltage probabilistic estimation. The high-fidelity SCADA data are fused with the low-fidelity AMI and pseudo measurements by the autoregressive algorithm embedded in the Gaussian process. This allows us to use multi-fidelity data to achieve entire distribution system voltage visibility. Numerical results carried out on the IEEE 123node system demonstrate that the NARGP method is able to obtain high accuracy in estimating bus voltage and quantifying estimation uncertainties as compared to other approaches. © 2022 IEEE.

关键词： Visibility

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Enhancing Pulmonary Nodule Classification performance with TriCaps-RL: A Capsule Network Reinforcement Learning Approach

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第3期9卷 1441-1459页

作者： Zheng, Guang-Yuan Zhang, Fu-Quan Cheng, Chen Shan, Hong-Tao Soomro, Nouman Qadeer Liu, Yi College of Information Technology Shanghai Jian Qiao University Shanghai201306 China School of Mathematics and Computer Science Yanan University Shaanxi 716000 China School of Computer and Data Science Minjiang University No.200 Xiyuangong Road Fuzhou University Town Fuzhou350108 China Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music Chengdu610021 China Fuzhou Technology Innovation Center of intelligent Manufacturing information System Minjiang University Fuzhou350108 China Fujian Province University Fuzhou350300 China College of Business Shanghai Jian Qiao University Shanghai201306 China College of Electronic and Electrical Engineering Shanghai University of Engineering Science Shanghai201620 China Software Department Mehran University of Engineering & Technology Sindh 76062 Pakistan

Distinguishing the manifestations of pulmonary nodules poses a significant challenge in the medical field, demanding the expertise of experienced radiologists. This complexity results in the high cost and inadequacy of sample labeling. In response, this study introduces a reinforcement learning approach, denoted as TriCaps-RL, for the classification of pulmonary nodules based on CT signs. This approach initially employs the Q-value loss to train a single CapsNet. When the performance of the CapsNet plateaus and further enhancement becomes challenging, the loss function is progressively replaced by a triplet metric to augment performance. Consequently, a more nuanced differentiation among various types of pulmonary nodules is achieved. The proposed method can improve its performance through interactions with radiologists during their CT image reading process, thereby mitigating the shortage of radiologists and addressing the time-consuming issue of training sample labeling in medical image research. This paper delves into the fine-grained classification of pulmonary nodules using deep reinforcement learning based on CT imaging signs. In comparison to previous studies that primarily concentrate on the benign/malignant classification of lung nodules, this fine-grained classification of nodules founded on CT imaging signs proves to be more valuable for medical practitioners in making accurate diagnostic decisions. © 2024, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Lung cancer

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Fingerprint Presentation Attack Detector Using Global-Local Model

arXiv

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arXiv 2024年

作者： Liu, Haozhe Zhang, Wentian Liu, Feng Wu, Haoqian Shen, Linlin The Computer Vision Institute College of Computer Science and Software Engineering SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society The National Engineering Laboratory for Big Data System Computing Technology The Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China

The vulnerability of automated fingerprint recognition systems (AFRSs) to presentation attacks (PAs) promotes the vigorous development of PA detection (PAD) technology. However, PAD methods have been limited by information loss and poor generalization ability, resulting in new PA materials and fingerprint sensors. This paper thus proposes a global-local model-based PAD (RTK-PAD) method to overcome those limitations to some extent. The proposed method consists of three modules, called: 1) the global module;2) the local module;and 3) the rethinking module. By adopting the cut-out-based global module, a global spoofness score predicted from nonlocal features of the entire fingerprint images can be achieved. While by using the texture in-painting-based local module, a local spoofness score predicted from fingerprint patches is obtained. The two modules are not independent but connected through our proposed rethinking module by localizing two discriminative patches for the local module based on the global spoofness score. Finally, the fusion spoofness score by averaging the global and local spoofness scores is used for PAD. Our experimental results evaluated on LivDet 2017 show that the proposed RTK-PAD can achieve an average classification error (ACE) of 2.28% and a true detection rate (TDR) of 91.19% when the false detection rate (FDR) equals 1.0%, which significantly outperformed the state-of-the-art methods by ∼10% in terms of TDR (91.19% versus 80.74%). Copyright © 2024, The Authors. All rights reserved.

关键词： Supervised learning

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system for coronavirus classification based on a new textural descriptor 21

System for coronavirus classification based on a new textura...

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21st IEEE International Conference on Sciences and Techniques of Automatic Control and computer engineering, STA 2022

作者： Boubakri, Fathia Gargouri, Norhene Masmoudi, Alima Damak Sfax Tunisia Digital Research Center of Sfax Technopole of Sfax Computer Imaging and Electronic System Group Tunisia Sfax Engineering School University of Sfax Computer Imaging and Electronic System Group CEM Laboratory Department of Electrical Engineering P.O. Box 1169 Sfax3038 Tunisia

ISBN: (纸本)9781665482615

The combination of Chest X-Ray imaging and Artificial Intelligence (AI) has proven its efficiency in coronavirus disease (COVID-19) detection [1]. The present paper proposes an efficient COVID-19 detection system based on a new textural features descriptor: Monogenic Local Binary Pattern Variance (MLBPV). An Artificial Neural Network (ANN) model is used for Regions Of Interest (ROIs) classification. Evaluating MLBPV, it outperforms other tested models by achieving an Area Under Curve (A-{z}) of 0.96263 and an accuracy of 99.9805%. Comparing our method with previous ones proves that ours provides the best performance. This model may be implemented in digital X-Ray machine for radiography and help radiologists. © 2022 IEEE.

关键词： COVID-19

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Not All Fabrics Are Created Equal: Exploring eFPGA Parameters for IP Redaction

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IEEE Transactions on Very Large Scale Integration (VLSI) systems 2023年第10期31卷 1459-1471页

作者： Bhandari, Jitendra Moosa, Abdul Khader Thalakkattu Tan, Benjamin Pilato, Christian Gore, Ganesh Tang, Xifan Temple, Scott Gaillardon, Pierre-Emmanuel Karri, Ramesh New York University Center for Cybersecurity New YorkNY11201 United States University of Calgary Department of Electrical and Software Engineering CalgaryT2N 1N4 Canada Politecnico di Milano Informazione e Bioingegneria Dipartimento di Elettronica Milan20133 Italy The University of Utah Laboratory for NanoIntegrated Systems Electrical and Computer Engineering Department Salt Lake CityUT84112 United States

Semiconductor design houses rely on third-party foundries to manufacture their integrated circuits (ICs). While this trend allows them to tackle fabrication costs, it introduces security concerns as external (and potentially malicious) parties can access critical parts of the designs and steal or modify the intellectual property (IP). Embedded field-programmable gate array (eFPGA) redaction is a promising technique to protect critical IPs of an ASIC by redacting (i.e., removing) critical parts and mapping them onto a custom reconfigurable fabric. Only trusted parties will receive the correct bitstream to restore the redacted functionality. While previous studies imply that using an eFPGA is a sufficient condition to provide security against IP threats like reverse-engineering, whether this truly holds for all eFPGA architectures is unclear, thus motivating the study in this article. We examine the security of eFPGA fabrics generated by varying different FPGA design parameters. We characterize the power, performance, and area (PPA) characteristics and evaluate each fabric's resistance to Boolean satisfiability (SAT)-based bitstream recovery. Our results encourage designers to work with custom eFPGA fabrics rather than off-The-shelf commercial FPGAs and reveals that only considering a redaction fabric's bitstream size is inadequate for gauging security. © 1993-2012 IEEE.

关键词： Fabrication

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