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Secure and Private Execution Environment Design (SEED), International Symposium on

作者： Tong Liu Zhen Huang Jiaao Li Jianyu Niu Guoxing Chen Yinqian Zhang Southern University of Science and Technology The Department of Computer Science and Engineering The Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology (SUSTech) Shanghai Jiao Tong University Tsinghua University

ISBN: (数字)9798331505653

ISBN: (纸本)9798331505660

Multi-Party Computation (MPC) allows a set of parties to evaluate a joint function without revealing their private inputs to each other. However, existing MPC protocols suffer from high computation and communication complexity, making them incapable of practical privacy-preserving applications. Unlike MPC, which relies on expensive cryptography, Trusted Execution Environments (TEEs) aim to efficiently protect the integrity and confidentiality of run-time code and data from the hardware level. Despite this, TEE is not a panacea due to its vulnerability to various attacks to deteriorate their confidentiality. Therefore, integrating MPC protocols with TEE emerges as a promising approach to achieving both high performance and security. In this paper, we surveyed existing works of integrating TEE into MPC to build a privacy-preserving framework with optimized communication and computing overhead. These integrated MPC-TEE protocols vary from different trust assumptions of TEEs' confidentiality, by which they can be categorized into four classes: TEE with complete confidentiality, TEE with access pattern leakage, TEE with no confidentiality or integrity, and hybrid models of the above three cases. By analyzing these works, we summarized the focal points of integrating two approaches under different trust levels and provided paradigm cases for each level. We also figured out several challenges in integrating TEE and MPC protocols and suggested several future research directions that are worthy of further study.

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Hard Exudate Segmentation Supplemented by Super-Resolution with Multi-scale Attention Fusion Module

Hard Exudate Segmentation Supplemented by Super-Resolution w...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Zhang, Jiayi Chen, Xiaoshan Qiu, Zhongxi Yang, Mingming Hu, Yan Liu, Jiang Southern University of Science and Technology Department of Computer Science and Engineering Guangdong Shenzhen51805 China Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Guangdong Shenzhen51805 China Shenzhen People's Hospital Department of Ophthalmology Guangdong China

ISBN: (纸本)9781665468190

Hard exudates (HE) is the most specific biomarker for retina edema. Precise HE segmentation is vital for disease diagnosis and treatment, but automatic segmentation is challenged by its large variation of characteristics including size, shape and position, which makes it difficult to detect tiny lesions and lesion boundaries. Considering the complementary features between segmentation and super-resolution tasks, this paper proposes a novel hard exudates segmentation method named SSMAF with an auxiliary super-resolution task, which brings in helpful detailed features for tiny lesion and boundaries detection. Specifically, we propose a fusion module named Multi-scale Attention Fusion (MAF) module for our dual-stream framework to effectively integrate features of the two tasks. MAF first adopts split spatial convolutional (SSC) layer for multi-scale features extraction and then utilize attention mechanism for features fusion of the two tasks. Considering pixel dependency, we introduce region mutual information (RMI) loss to optimize MAF module for tiny lesions and boundary detection. We evaluate our method on two public lesion datasets, IDRiD and E-Ophtha. Our method shows competitive performance with low-resolution inputs, both quantitatively and qualitatively. On E-Ophtha dataset, the method can achieve ge 3% higher dice and recall compared with the state-of-the-art methods. © 2022 IEEE.

关键词： Semantic Segmentation

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CoLRIO: LiDAR-Ranging-Inertial Centralized State Estimation for Robotic Swarms

CoLRIO: LiDAR-Ranging-Inertial Centralized State Estimation ...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Shipeng Zhong Hongbo Chen Yuhua Qi Dapeng Feng Zhiqiang Chen Jin Wu Weisong Wen Ming Liu School of Systems Science and Engineering Sun Yat-sen University Guangzhou China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Department of Aeronautical and Aviation Engineering Hong Kong Polytechnic University Hong Kong

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Collaborative state estimation using different heterogeneous sensors is a fundamental prerequisite for robotic swarms operating in GPS-denied environments, posing a significant research challenge. In this paper, we introduce a centralized system to facilitate collaborative LiDAR-ranging-inertial state estimation, enabling robotic swarms to operate without the need for anchor deployment. The system efficiently distributes computationally intensive tasks to a central server, thereby reducing the computational burden on individual robots for local odometry calculations. The server back-end establishes a global reference by leveraging shared data and refining joint pose graph optimization through place recognition, global optimization techniques, and removal of outlier data to ensure precise and robust collaborative state estimation. Extensive evaluations of our system, utilizing both publicly available datasets and our custom datasets, demonstrate significant enhancements in the accuracy of collaborative SLAM estimates. Moreover, our system exhibits remarkable proficiency in large-scale missions, seamlessly enabling ten robots to collaborate effectively in performing SLAM tasks. In order to contribute to the research community, we will make our code open-source and accessible at https://***/PengYu-team/Co-LRIO.

关键词： Simultaneous localization and mapping Accuracy Scalability Collaboration Computational efficiency Sensors Servers

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Textual Emotion Recognition: Advancements and Insights

Textual Emotion Recognition: Advancements and Insights

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Novel Intelligent and Leading Emerging sciences Conference (NILES)

作者： Ahmed A. Esmail Sayed M. Abdallah Mohamed A. Elsayed Amr E. Ghaith Magy Elbanhawy Aya M. Al-Zoghby Ibrahim F. Moawad Faculty of Computer Science and Engineering Artificial Intelligence Science New Mansoura University Dakhlia Egypt Faculty of Computers and Information Mansoura University Dakhlia Egypt Faculty of Computer Science and Engineering New Mansoura University Dakhlia Egypt Faculty of Engineering Portsaid University Egypt Computer Science Department Faculty of Computers and Artificial Intelligence Damietta University Damietta Egypt Information Systems Faculty of Computer and Information Sciences Ain shams University Cairo Egypt Department of Artificial Intelligence College of Computer Science and Engineering Taibah University Medina Saudi Arabia

ISBN: (数字)9798350378511

ISBN: (纸本)9798350378528

In recent years, there has been a significant increase in attention toward emotion detection in text analysis, driven by its broad applications across marketing, political science, psychology, human-computer interaction, and artificial intelligence. This growing interest is primarily due to the critical role of textual expression as a repository of human emotions and sentiments. The development of sophisticated natural language processing (NLP) techniques has emphasized the importance of exploring emotion detection and recognition within textual data. By utilizing a wide range of sources, including social media content, microblogs, news articles, and customer feedback, text mining aims to reveal the underlying emotional currents within the text. However, existing models often struggle to capture the complicated emotional nuances woven into words. Addressing this challenge, the innovative semantic emotion neural network (SENN) architecture has been introduced. The SENN model marks a significant advancement, featuring two synergistic sub-networks: a bidirectional long short-term memory (BiLSTM) network that extracts contextual information and a convolutional neural network (CNN) that analyzes and extracts emotional features, highlighting the text's intrinsic emotional connections. The SENN model's performance has been thoroughly evaluated on widely used real-world datasets, benchmarked against Ekman's six fundamental emotions. Results demonstrated its superiority, showing that the SENN model excels in emotion recognition accuracy and quality in conjunction with additional techniques. It also holds potential for enhancement by incorporating more comprehensive emotional word embedding, suggesting a promising future for text-based emotion analysis. The proposed paper presents goals for detecting sentiment in text data and introduces a novel architecture that effectively captures the complexity of emotional nuances. We create an abstract model and compare three types of m

关键词： Training Emotion recognition Logistic regression Accuracy Text analysis Text recognition Machine learning Feature extraction Natural language processing Long short term memory

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Supervised Image Translation from Visible to Infrared Domain for Object Detection

arXiv

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arXiv 2024年

作者： Anand, Prahlad Saadiyean, Qiranul Sikdar, Aniruddh Nalini, N. Sundaram, Suresh The School of Computer Science and Engineering Vellore Institute of Technology Vellore India The Department of Aerospace Engineering Indian Institute of Science Bangalore India The Robert Bosch Centre for Cyber-Physical Systems Indian Institute of Science Bangalore India

This study aims to learn a translation from visible to infrared imagery, bridging the domain gap between the two modalities so as to improve accuracy on downstream tasks including object detection. Previous approaches attempt to perform bi-domain feature fusion through iterative optimization or end-to-end deep convolutional networks. However, we pose the problem as similar to that of image translation, adopting a two-stage training strategy with a Generative Adversarial Network and an object detection model. The translation model learns a conversion that preserves the structural detail of visible images while preserving the texture and other characteristics of infrared images. Images so generated are used to train standard object detection frameworks including Yolov5, Mask and Faster RCNN. We also investigate the usefulness of integrating a superresolution step into our pipeline to further improve model accuracy, and achieve an improvement of as high as 5.3% mAP. © 2024, CC BY-NC-ND.

关键词： Generative adversarial networks

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computer Vision-control-Based CNN-PID for Mobile Robot

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computers, Materials & Continua 2021年第7期68卷 1065-1079页

作者： Rihem Farkh Mohammad Tabrez Quasim Khaled Al jaloud Saad Alhuwaimel Shams Tabrez Siddiqui College of Engineering Muzahimiyah BranchKing Saud UniversityP.O.Box 2454Riyadh11451Saudi Arabia College of Computing and Information Technology University of BishaBisha67714Saudi Arabia King Abdulaziz City for Science and Technology Saudi Arabia Department of Computer Science College of Computer Science and Information TechnologyJazan UniversityJazanSaudi Arabia Department of Electrical Engineering Laboratory for AnalysisConception and Control of SystemsLR-11-ES20National Engineering School of TunisTunis El Manar UniversityTunisTunisia

With the development of artificial intelligence technology,various sectors of industry have *** them,the autonomous vehicle industry has developed considerably,and research on self-driving control systems using artificial intelligence has been extensively *** on the use of image-based deep learning to monitor autonomous driving systems have recently been *** this paper,we propose an advanced control for a serving robot.A serving robot acts as an autonomous line-follower vehicle that can detect and follow the line drawn on the floor and move in specified *** robot should be able to follow the trajectory with speed *** controllers were used simultaneously to achieve *** neural networks(CNNs)are used for target tracking and trajectory prediction,and a proportional-integral-derivative controller is designed for automatic steering and speed *** study makes use of a Raspberry PI,which is responsible for controlling the robot car and performing inference using CNN,based on its current image input.

关键词： Autonomous car pid control deep learning convolutional neural network differential drive system raspberry pi

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Drone-Enabled Connectivity: Advancements and Challenges in B5G / 6G Networks 8

Drone-Enabled Connectivity: Advancements and Challenges in B...

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8th IEEE International Conference on Image and Signal Processing and their Applications, ISPA 2024

作者： Telli, Khaled Kraa, Okba Himeur, Yassine Boumehraz, Mohamed Ouamane, Abdelmalik Benbraika, Kamel Atalla, Shadi Mansoor, Wathiq Mohamed Khider University Energy-Systems-Modelling Laboratory Biskra Algeria University of Dubai College of Engineering and Information Technology Dubai United Arab Emirates University of Biskra LI3C Laboratory Biskra Algeria University of El-Oued Department of Computer Science El-Oued Algeria

ISBN: (纸本)9798350309249

The rapid advancement of networking and broad-band communications has created a demand for efficient relaying methods, particularly in the context of high-fidelity and low-latency last-mile communications. This paper explores the utilization of cellular-enabled-drones as Aerial-Base-Station (Aerial-BS) in cellular networks next-generation, specifically focusing on their potential as flying base stations in Beyond 5G/6G networks. The study summarises various aspects, including energy availability, mobility, optimal positioning, and node localization, handover efficiency. Additionally, multi-objective optimizations for bandwidth allocation, accommodating highly mobile User Equipment (UE), mitigating interference, and ensuring quality of service (QoS) are discussed. The paper highlights the use of drone-base-stations (DBS) as extending terrestrial network capacity and coverage, assisting mobile ad-hoc-networks, and enabling beam-forming applications. The challenges associated with optimal positioning, energy availability, path planning mobility, security, and QoS are examined, providing insights into future research directions. The findings contribute to the enhancement of cellular connectivity and offer valuable insights into the utilization of drones as flying aerial base stations in B5G / 6G network. © 2024 IEEE.

关键词： Drones

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Robust control of Metric Discrete Event systems Against Bounded Disturbances

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IFAC-PapersOnLine 2024年第1期58卷 108-113页

作者： Yiding Ji Xiang Yin Robotics and Autonomous Systems Thrust The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong China Department of Automation Shanghai Jiao Tong University Shanghai China

This work investigates robust supervisory control problems of discrete event systems modeled as finite state automata equipped with metric functions to measure the distance between states. The system may deviate from its nominal behaviors and fail to achieve the specification under disturbances whose effects are considered to be bounded. Accordingly, the supervisor should be designed to ensure that the controlled system degrades gracefully against such adversary. We formally formulate two problems: robustness bound verification and optimal robust supervisor synthesis. For the special case of verification under constant disturbances, a control Lyapunov function approach is introduced. Then we develop a two player game framework for the general verification and synthesis problems. Specifically, a dynamic programming method is proposed on the game structure, which uniformly tackles both problems.

关键词： Metric discrete event systems supervisory control robustness two player games

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An Actor-Critic Reinforcement Learning Scheme for Reactive 3D Optimal Motion Planning Based on Fluid Dynamics

An Actor-Critic Reinforcement Learning Scheme for Reactive 3...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Marios Malliaropoulos Panagiotis Rousseas Charalampos P. Bechlioulis Kostas J. Kyriakopoulos School of Mechanical Engineering Control Systems Laboratory National Technical University of Athens Department of Electrical and Computer Engineering University of Patras Center of AI & Robotics (CAIR) New York University Abu Dhabi

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

This work proposes a novel and provably correct method for three-dimensional optimal motion planning in complex environments. Our approach models the 3D motion planning problem by solving streamlines of the potential fluid flow, filling a gap in traditional motion planning techniques by guaranteeing a closed-loop, smooth and natural-looking navigation solution. Special emphasis is given to an inherent challenge of artificial potential field (APF) methods, namely establishing proofs of safety and stability over the entire optimization process. A model-based actor-critic reinforcement learning algorithm is introduced to approximate the optimal solution to the Hamilton-Jacobi-Bellman equation and update the controller parameters in a deterministic manner. Through a series of ROS-Gazebo software-in-the-loop simulations the proposed methodology demonstrates robustness and outperforms widely used methods such as the RRT ∗ , highlighting its contribution to the field of 3D optimal motion planning.

关键词： Solid modeling Three-dimensional displays Navigation Reinforcement learning Mathematical models Stability analysis Robustness Planning Safety Optimization

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A Hybrid Ai Framework for Multi-Label Toxicity Category Detection

SSRN

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SSRN 2023年

作者： Jayaraman, Ashok Kumar Trueman, Tina Esther Murugappan, Abirami Cambria, Erik Mitra, Satanik Department of Information Science and Technology Anna University Chennai India Department of Computer Science University of the People United States School of Computer Science and Engineering Nanyang Technological University Singapore Department of Industrial and Systems Engineering Indian Institute of Technology Kharagpur India

Nowadays, online social media platforms enable individuals or groups to freely express their opinions. Sometimes such opinions lead to personal attacks, harassment, and abusive language. Detecting and classifying toxicity is a challenging task of natural language processing. Therefore, we first focus on subsymbolic artificial intelligence (AI) for multi-label toxicity identification, where an instance is mapped to a set of pre-defined label categories. Second, we propose a hybrid AI framework to analyze only negative toxic comments. The hybrid AI framework is the combination of symbolic AI and subsymbolic AI. First, symbolic AI defines sentiment polarities of the given text as positive, negative, and neutral. Second, subsymbolic AI uses only the negative sentiment polarity texts to map a set of pre-defined label categories. In particular, we use the BERT fine-tuning model as subsymbolic AI for multi-label toxicity category detection. The proposed model is evaluated on multi-label metrics such as label-based, example-based, and rank-based metrics. Results indicate that the proposed BERT model outperforms stateof-the-art results. © 2023, The Authors. All rights reserved.

关键词： Toxicity

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