检索结果-内蒙古大学图书馆

A Fast Authentication Scheme for Cross-Network-Slicing Based on Multiple Operators in 5G Environments 1

学校读者我要写书评

暂无评论

8th International Symposium on Security in Computing and Communications, SSCC 2020

作者： Huang, Jheng-Jia Fan, Chun-I Hsu, Yu-Chen Karati, Arijit Department of Information Management National Taiwan University of Science and Technology Taipei Taiwan Department of Computer Science and Engineering National Sun Yat-sen University Kaohsiung Taiwan Information Security Research Center National Sun Yat-sen University Kaohsiung Taiwan Intelligent Electronic Commerce Research Center National Sun Yat-sen University Kaohsiung Taiwan

ISBN: (数字)9789811604225

ISBN: (纸本)9789811604218

With 5G Network Slicing, the 5G telecommunication operators can achieve the goal of supporting users with a variety of different services and can also create a slice with certain unique characteristics. For example: Enhanced Mobile Broadband slicing, Ultra-reliable and Low Latency Communications slicing, etc. However, the traditional authentication mechanism does not address any concrete strategy for network slicing handover in 5G, so that the computational process must be still calculated by the core network. Hence, we propose a network slicing handover authentication scheme that not only satisfies the standards defined by 3rd Generation Partnership Project but also achieves low time latency through delegating computation overhead to the edge clouds. In addition, we incorporate the concepts of the proxy re-signature and certificateless signature in the scheme. As a result, when users need to use the network slicing services across the telecommunications operators, they can still meet the requirements of reducing the time latency in the process of the authentication flows. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Authentication

Pay Self-Attention to Audio-Visual Navigation

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Yu, Yinfeng Cao, Lele Sun, Fuchun Liu, Xiaohong Wang, Liejun Department of Computer Science and Technology State Key Lab on Intelligent Technology and Systems Tsinghua University Beijing China Motherbrain EQT Stockholm Sweden College of Information Science and Engineering Xinjiang University Urumqi China

Audio-visual embodied navigation, as a hot research topic, aims training a robot to reach an audio target using egocentric visual (from the sensors mounted on the robot) and audio (emitted from the target) input. The audio-visual information fusion strategy is naturally important to the navigation performance, but the state-of-the-art methods still simply concatenate the visual and audio features, potentially ignoring the direct impact of context. Moreover, the existing approaches requires either phase-wise training or additional aid (e.g. topology graph and sound semantics). Up till this date, the work that deals with the more challenging setup with moving target(s) is still rare. As a result, we propose an end-to-end framework FSAAVN (feature self-attention audio-visual navigation) to learn chasing after a moving audio target using a context-aware audio-visual fusion strategy implemented as a self-attention module. Our thorough experiments validate the superior performance (both quantitatively and qualitatively) of FSAAVN in comparison with the state-of-the-arts, and also provide unique insights about the choice of visual modalities, visual/audio encoder backbones and fusion patterns. © 2022, CC BY-NC-ND.

关键词： Topology

Measuring Acoustics with Collaborative Multiple Agents

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Yu, Yinfeng Chen, Changan Cao, Lele Yang, Fangkai Sun, Fuchun Beijing National Research Center for Information Science and Technology State Key Lab on Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University China University of Texas Austin United States Motherbrain EQT Group Microsoft Research United States THU-Bosch JCML Center China College of Information Science and Engineering Xinjiang University China

As humans, we hear sound every second of our life. The sound we hear is often affected by the acoustics of the environment surrounding us. For example, a spacious hall leads to more reverberation. Room Impulse Responses (RIR) are commonly used to characterize environment acoustics as a function of the scene geometry, materials, and source/receiver locations. Traditionally, RIRs are measured by setting up a loudspeaker and microphone in the environment for all source/receiver locations, which is time-consuming and inefficient. We propose to let two robots measure the environment's acoustics by actively moving and emitting/receiving sweep signals. We also devise a collaborative multi-agent policy where these two robots are trained to explore the environment's acoustics while being rewarded for wide exploration and accurate prediction. We show that the robots learn to collaborate and move to explore environment acoustics while minimizing the prediction error. To the best of our knowledge, we present the very first problem formulation and solution to the task of collaborative environment acoustics measurements with multiple agents. © 2023, CC BY-NC-ND.

关键词： Robots

New In-Air Signature Datasets

学校读者我要写书评

暂无评论

New In-Air Signature Datasets

International Symposium on Networks, computers and Communications (ISNCC)

作者： AbdulAzeez R. Alobaidi Thameur Dhieb Zeina N. Nuimi Tarek M. Hamdani Ali Wali Adel M. Alimi REGIM-Lab.: REsearch Groups in Intelligent Machines Enis University of Sfax Tunisia Department of Computer Techniques Engineering Faculty of Information Technology Imam Ja'afar Al-Sadiq University Iraq Higher Institute of Business Administration University of Gafsa Tunisia Department of Electrical Engineering College of Engineering University of Misan Iraq Higher Institute of Computer Science Mahdia University of Monastir Tunisia Higher Institute of Computer Science and Multimedia of Sfax University of Sfax Tunisia Department of Electrical and Electronic Engineering Science Faculty of Engineering and the Built Environment University of Johannesburg South Africa

Compared to traditional biometric systems, in-air signatures are considered more robust and secure than classical pen paper. A few datasets capturing in-air signatures have been introduced, utilizing various devices such as the Leap Motion and the Microsoft Kinect sensor camera. However, these devices are not exempt from shortcomings and exhibit certain limitations. The expenses associated with their implementation and the requirement for technical proficiency in operating them present notable challenges for in-air signature analysis. Additionally, users may encounter difficulties in adapting their finger movements to fit within the device's limited field of view, particularly if they lack familiarity with these devices. To address these concerns, this paper proposes the creation of three in-air signature datasets using solely the camera of a laptop or a smartphone, eliminating the need for any additional specialized equipment. Our datasets were collected in three ways. The first is the In-Air Signature dataset (IAS dataset) and the second is the In-Air Signature dataset using a transparent Glass Plate (IASGP dataset) while the third is the In-Air Signature dataset using Smart Phone (IASSP dataset). Forty volunteers participated in the construction of these datasets. Their ages ranged from 21 to 40 years. Each volunteer signs in the air five signatures and imitates five signatures of five other volunteers. Our in-air signatures datasets are publicly available and can be used for various research tasks like in-air signature verification and identification.

关键词：

LOG-LIO2: A LiDAR-Inertial Odometry with Efficient Uncertainty Analysis

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Huang, Kai Zhao, Junqiao Lin, Jiaye Zhu, Zhongyang Song, Shuangfu Ye, Chen Feng, Tiantian The School of Surveying and Geo-Informatics Tongji University Shanghai China Department of Computer Science and Technology School of Electronics and Information Engineering Tongji University Shanghai China The MOE Key Lab of Embedded System and Service Computing Tongji University Shanghai China Institute of Intelligent Vehicles Tongji University Shanghai China

Uncertainty in LiDAR measurements, stemming from factors such as range sensing, is crucial for LIO (LiDAR-Inertial Odometry) systems as it affects the accurate weighting in the loss function. While recent LIO systems address uncertainty related to range sensing, the impact of incident angle on uncertainty is often overlooked by the community. Moreover, the existing uncertainty propagation methods suffer from computational inefficiency. This paper proposes a comprehensive point uncertainty model that accounts for both the uncertainties from LiDAR measurements and surface characteristics, along with an efficient local uncertainty analytical method for LiDAR-based state estimation problem. We employ a projection operator that separates the uncertainty into the ray direction and its orthogonal plane. Then, we derive incremental Jacobian matrices of eigenvalues and eigenvectors w.r.t. points, which enables a fast approximation of uncertainty propagation. This approach eliminates the requirement for redundant traversal of points, significantly reducing the time complexity of uncertainty propagation from O(n) to O(1) when a new point is added. Simulations and experiments on public datasets are conducted to validate the accuracy and efficiency of our formulations. The proposed methods have been integrated into a LIO system, which is available at https://***/tiev-tongji/LOG-LIO2. Copyright © 2024, The Authors. All rights reserved.

关键词： Optical radar

Attention-effective multiple instance learning on weakly stem cell colony segmentation

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Yudistira, Novanto Kavitha, Muthu Subash Rajan, Jeny Kurita, Takio Intelligent System Lab Faculty of Computer Science Brawijaya University Indonesia School of Information and Data Sciences Nagasaki University Nagasaki Japan Department of Computer Science and Engineering National Institute of Technology Karnataka Surathkal India Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima Japan

The detection of induced pluripotent stem cell (iPSC) colonies often needs the precise extraction of the colony features. However, existing computerized systems relied on segmentation of contours by preprocessing for classifying the colony conditions were task-extensive. To maximize the efficiency in categorizing colony conditions, we propose a multiple instance learning (MIL) in weakly supervised settings. It is designed in a single model to produce weak segmentation and classification of colonies without using finely labeled samples. As a single model, we employ a U-net-like convolution neural network (CNN) to train on binary image-level labels for MIL colonies classification. Furthermore, to specify the object of interest we used a simple post-processing method. The proposed approach is compared over conventional methods using five-fold cross-validation and receiver operating characteristic (ROC) curve. The maximum accuracy of the MIL-net is 95%, which is 15% higher than the conventional methods. Furthermore, the ability to interpret the location of the iPSC colonies based on the image level label without using a pixel-wise ground truth image is more appealing and cost-effective in colony condition recognition. © 2022, CC BY.

关键词： Learning systems

A Three-Fingered Adaptive Gripper with Multiple Grasping Modes

学校读者我要写书评

暂无评论

A Three-Fingered Adaptive Gripper with Multiple Grasping Mod...

2021 IEEE/RSJ International Conference on intelligent Robots and Systems, IROS 2021

作者： Kang, Long Yang, Yang Yang, Jian Yi, Byung-Ju Nanjing University of Science and Technology PCA Lab Key Lab. of Intelligent Percept. and Syst. for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing210094 China School of Automation Nanjing University of Information Science and Technology Nanjing210044 China Department of the Electronic Systems Engineering Hanyang University Ansan426-791 Korea Republic of

ISBN: (纸本)9781665417143

This paper presents an underactuated robotic gripper that consists of three fingers. This gripper is driven by seven actuators and capable of grasping a wide range of objects in different working scenarios. A combination of a four-bar mechanism and parallelograms ensures that each finger can provide the basic pinch grasp and power grasp. Detailed fingertip grasping force analysis shows the large payload of this gripper. To fulfill multiple challenging grasping tasks, the fingertip orientation of each finger was designed to be decoupled from the finger flexion motion. Particular emphasis is placed on the environmental contact-based grasp and active transition from the pinch grasp to the power grasp. Detailed analysis shows that the contact-based fingertip grasp could be used to grasp thin objects lying on a flat surface with widths less than 168 mm safely and stably. To prevent over-squeezing of the grasped object in the active transition, the fingertip orientation and finger flexion motion were controlled coordinately. Moreover, a combination of the contact-based grasp and the direct power grasp was also allowed to grasp objects lying on a flat surface and having smooth surfaces robustly. Experimental results demonstrate the effectiveness of the proposed gripper in real-world applications. © 2021 IEEE.

关键词： Grippers

Health Care and management based on Block chain and Machine Learning

学校读者我要写书评

暂无评论

Health Care and Management based on Block chain and Machine ...

Artificial Intelligence and knowledge Discovery in Concurrent engineering (ICECONF), International Conference on

作者： Ruhi Bakh VeenaNair Sarkar Jerlin Priya Lovelin Auguskani Galiveeti Poornima Monali N. Shetty Meerjumla Govind Raj Dr. Ambedkar Institute of Management Studies and Research IIHMR UNIVERSITY. Department of Nursing College Aldrab Jazan University Kingdom of Saudi Arabia. High Performance Computing Lab School of Computer Science and Information Science Presidency University Bengaluru Computer Science Engineering Fr. CRCE St. Martin's Engineering College Secunderabad Telangana

With the coming of technology, we currently have an immense measure of information accessible in each field, making it conceivable to offer responses to many issues. We will utilize machine learning and block chain technology to resolve issues with healthcare information the executives in this review. With the guide of machine learning, separating just the relevant information from the data is plausible. Using prepared calculations, this is finished. The reliability of information trade turns into a test after this information has been put away. This is where block chain technology is valuable. Block chain technology's agreement guarantees that information is credible and exchanges are secure. By putting the patient at the focal point of the healthcare framework and upgrading the security and interoperability of health information, block chain technology can possibly further develop health care organization. This article fundamentally centres around settling issues with healthcare information the board using Block chain technology and including a few urgent highlights utilizing Machine Learning. The freshest technology, block chain, offers a strong and secure groundwork. With these offices, block chain has gotten some momentum in an assortment of use areas, including the food business, store network the board, energy, government support offices, and healthcare.

关键词： Telemedicine Government Medical treatment Machine learning Medical services knowledge discovery Blockchains

A Novel Sparse Adaptive Filter for Suppressing Impulsive Disturbance in Audio Signals

学校读者我要写书评

暂无评论

SSRN

SSRN 2024年

作者： Zhou, Lei Liu, Hongqing Gan, Lu Zhou, Yi Niedźwiecki, Maciej Truong, Trieu-Kien School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing Key Lab of Mobile Communications Technology and Intelligent Speech and Audio Research Lab Chongqing University of Posts and Telecommunications Chongqing400065 China College of Engineering Design and Physical Science Brunel University LondonUB8 3PH United Kingdom Department of Automatic Control Faculty of Electronics Telecommunications and Computer Science Gdansk University of Technology Gdansk80-233 Poland Department of Information Engineering I-Shou University 84001 Taiwan

This work studies the sparse adaptive filter designs in the presence of impulsive disturbance for audio signal recovery. By using the sparse representation of desired signal and compressibility of impulsive disturbance, a joint sparse least mean p-norm (JSLMP) optimization, in which p-norm (1 ≤ p ≤ 2) measures the data fidelity and q-norm (0 ≤ q ≤ 1) enforces sparse solutions, is developed, termed as q-JSLMP. The filter weights update is derived based on the gradient descent, and the Adam and variable step size (VSS) are integrated to accelerate convergence and avoid potential local minima. In the special case of q = 1, namely 1-JSLMP, its convergence condition and mean square deviation (MSD) analysis are derived. Finally, the frame of application to corrupted audio signals is designed. The results from experiments demonstrate that the proposed approach outperforms other approaches in terms of objective measures on numerical simulation and speech datasets. © 2024, The Authors. All rights reserved.

关键词： Adaptive filters