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arXiv 2023年

作者： Liu, Wenjie Xu, Yinsong Zhang, Maojun Chen, Junxiu Yang, Ching-Nung Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing210044 China School of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China School of Mathematics and Computing Science Guilin University of Electronic Technology Guilin541004 China Division Department of Computer Science and Information Engineering National Dong Hwa University Taiwan97441 Taiwan

Inspired by Naor et al.’s visual secret sharing (VSS) scheme, a novel n out of n quantum visual secret sharing (QVSS) scheme is proposed, which consists of two phases: sharing process and recovering process. In the first process, the color information of each pixel from the original secret image is encoded into an n-qubit superposition state by using the strategy of quantum expansion instead of classical pixel expansion, and then these n qubits are distributed as shares to n participants, respectively. During the recovering process, all participants cooperate to collect these n shares of each pixel together, then perform the corresponding measurement on them, and execute the n-qubit XOR operation to recover each pixel of the secret image. The proposed scheme has the advantage of single-pixel parallel processing that is not available in the existing analogous quantum schemes, and perfectly solves the problem that in the classic VSS schemes the recovered image has the loss in resolution. Moreover, its experiment implementation with IBM Q is conducted to demonstrate the practical feasibility. © 2023, CC BY.

关键词： Pixels

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Efficient Batch Verification and Privacy-Preserving Data Aggregation Scheme in V2G Networks

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IEEE Transactions on Vehicular Technology 2025年

作者： Wang, Congcong Wang, Chen Shen, Jian Vasilakos, Athanasios V. Wang, Bin Wang, Weizheng Department of Software Engineering Nanjing University of Information Science and Technology Nanjing210044 China the Zhejiang Key Laboratory of Digital Fashion and Data Governance the Zhejiang Provincial International Cooperation Base for Science and Technology on Cloud Computing Security and Data Aggregation Zhejiang Sci-Tech University Hangzhou310018 China Zhejiang Provincial Innovation Center of Advanced Textile Technology Shaoxing312000 China College of Computer Science and Information Technology IAU Saudi Arabia Grimstad4879 Norway Network and Data Security Hangzhou310053 China Computer Science Department City University of Hong Kong Hong Kong the Division of Computer Science University of Aizu Fukushima965–8580 Japan

In recent years, against the backdrop of smart grids, vehicle-to-grid (V2G) interaction has become a hot topic. With the increasing number of new energy vehicles, handling massive amounts of electricity transaction data has become an urgent problem in need of resolution. Ensuring the security of the vast quantities of electricity transaction data has emerged as a significant challenge. Therefore, two data aggregation schemes are proposed in this paper, both supporting batch verification and privacy protection in centralized and distributed scenarios, respectively. Firstly, an improved Paillier cryptosystem is utilized to achieve lightweight encryption in the proposed schemes. In addition, a novel distributed data aggregation structure that combines a large number of geographically distributed fog nodes with centralized cloud servers is introduced. This structure not only meets the requirements of lightweight encryption and resistance against single point failures but also reduces the communication complexity of distributed data aggregation to O(n). According to the security proofs and experimental analysis, it can be demonstrated that both proposed schemes are effective and secure, meeting the lightweight requirements of modern smart grid systems. © 2025 IEEE. All rights reserved,

关键词： Data aggregation

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Employing Multi-Layer, Sensorised Kirigami Grippers for Single-Grasp Based Identification of Objects and Force Exertion Estimation

Employing Multi-Layer, Sensorised Kirigami Grippers for Sing...

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

作者： Junbang Liang Joao Buzzatto Bryan Busby Ricardo V. Godoy Saori Matsunaga Rintaro Haraguchi Toshisada Mariyama Bruce A. MacDonald Minas Liarokapis Department of Mechanical and Mechatronics Engineering The New Dexterity research group The University of Auckland New Zealand The Information Technology R&D Center Mitsubishi Electric Corporation Japan The Advanced Technology R&D Center Mitsubishi Electric Corporation Japan Department of Electrical Computer and Soft-ware Engineering The Centre for Automation and Robotic Engineering Science The University of Auckland New Zealand

soft robotic devices have been popular in handling intricate grasping and dexterous manipulation tasks, serving as an alternative to conventional, rigid end-effectors. These devices are relatively simple, lightweight, and cost-effective. Recently, kirigami based structures have been used to create low-cost and disposable soft robotic grippers and hands. These grippers undergo a complex post-contact reconfiguration and conform to an object's shape and size during grasping. In this paper, we explore this new class of soft robotic grippers by utilising them for single-grasp object classification and grasping force estimation. We install simplistic sensors on both the gripper and the actuation system to estimate the state of the kirigami gripper, and the collected data features are employed to train Random Forest models for identifying the grasped object. The classifier trained exhibits a high accuracy of 98 % in discriminating objects of various shapes. When handling food items, the classifier achieves an accuracy of 94 %, while in classifying transparent objects, the classifier obtained again a high accuracy of 97 %. Finally, object-specific force estimation models are triggered based on the classification decision of the Random Forest model to estimate the grasping force exerted by the gripper. These positive outcomes demonstrate the kirigami based robotic gripper's potential for object classification in a variety of circumstances, particularly where vision systems are not available or not reliable.

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Numerical Stability of DeepGOPlus Inference

arXiv

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arXiv 2022年

作者： Pepe, Inés Gonzalez Chatelain, Yohan Kiar, Gregory Glatard, Tristan Department of Computer Science and Software Engineering Concordia University Montreal Canada Center for the Developing Brain Child Mind Institute New YorkNY United States

Convolutional neural networks (CNNs) are currently among the most widely-used deep neural network (DNN) architectures available and achieve state-of-the-art performance for many problems. Originally applied to computer vision tasks, CNNs work well with any data with a spatial relationship, besides images, and have been applied to different fields. However, recent works have highlighted numerical stability challenges in DNNs, which also relates to their known sensitivity to noise injection. These challenges can jeopardise their performance and reliability. This paper investigates DeepGOPlus, a CNN that predicts protein function. DeepGOPlus has achieved state-of-the-art performance and can successfully take advantage and annotate the abounding protein sequences emerging in proteomics. We determine the numerical stability of the model's inference stage by quantifying the numerical uncertainty resulting from perturbations of the underlying floating-point data. In addition, we explore the opportunity to use reduced-precision floating point formats for DeepGOPlus inference, to reduce memory consumption and latency. This is achieved by instrumenting DeepGOPlus' execution using Monte Carlo Arithmetic, a technique that experimentally quantifies floating point operation errors and VPREC, a tool that emulates results with customizable floating point precision formats. Focus is placed on the inference stage as it is the primary deliverable of the DeepGOPlus model, widely applicable across different environments. All in all, our results show that although the DeepGOPlus CNN is very stable numerically, it can only be selectively implemented with lower-precision floating-point formats. We conclude that predictions obtained from the pre-trained DeepGOPlus model are very reliable numerically, and use existing floating-point formats efficiently. © 2022, CC BY.

关键词： Proteins

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Modeling and simulation of the“IL-36 cytokine”and CAR-T cells interplay in cancer onset

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International Journal of Modeling, Simulation, and Scientific Computing 2022年第3期13卷 207-222页

作者： Khaled A.A-Utaibi Alessandro Nutini Ayesha Sohail Robia Arif Sumeyye Tunc Sadiq M.Sait Computer Science and Software Engineering Department University of Ha’il Ha’ilSaudi Arabia Biology and Biomechanics Department Centro Studi Attivita Motorie via di Tiglio 94 LuccaItaly Department of Mathematics Comsats University Islamabad Lahore 54000Pakistan Vocational School of Sciences Physiotherapy Programme Medipol University UnkapanıAtat¨urk BulvarıNo.27Halic Campus34083 FatihIstanbulTurkey Center for Communications and IT Research Research Institute King Fahd University of Petroleum&Minerals Dhahran 31261Saudi Arabia

Background:CAR-T cells are chimeric antigen receptor(CAR)-T cells;they are targetspecific engineered cells on tumor cells and produce T cell-mediated antitumor ***-T cell therapy is the“first-line”therapy in immunotherapy for the treatment of highly clonal neoplasms such as lymphoma and *** adoptive therapy is currently being studied and tested even in the case of solid tumors such as osteosarcoma since,precisely for this type of tumor,the use of immune checkpoint inhibitors remained *** CAR-T is a promising therapeutic technique,there are therapeutic limits linked to the persistence of these cells and to the tumor’s immune ***-T cell engineering techniques are allowed to express interleukin IL-36,and seem to be much more efficient in antitumoral ***-36 is involved in the long-term antitumor action,allowing CAR-T cells to be more efficient in their antitumor action due to a“cross-talk”action between the“IL-36/dendritic cells”axis and the adaptive ***:This analysis makes the model useful for evaluating cell dynamics in the case of tumor relapses or specific understanding of the action of CAR-T cells in certain types of *** model proposed here seeks to quantify the action and interaction between the three fundamental elements of this antitumor activity induced by this type of adoptive immunotherapy:IL-36,“armored”CAR-T cells(i.e.,engineered to produce IL-36)and the tumor cell population,focusing exclusively on the action of this interleukin and on the antitumor consequences of the so modified CAR-T *** model was developed and numerical simulations were carried out during this *** development of the model with stability analysis by conditions of Routh–Hurwitz shows how IL-36 makes CAR-T cells more efficient and persistent over time and more effective in the antitumoral treatment,making therapy more effective against the“solid tumor”.Findings:Primary malignant bone tumors are

关键词： IL-36 bone remodeling mathematical model BMUs CAR-T cells

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Channel Cycle Time: A New Measure of Short-Term Fairness

Channel Cycle Time: A New Measure of Short-Term Fairness

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IEEE Conference on Wireless Communications and Networking

作者： Pengfei Shen Yulin Shao Haoyuan Pan Lu Lu Yonina C. Eldar Key Laboratory of Space Utilization Technology and Engineering Center for Space Utilization CAS Beijing China University of Chinese Academy of Sciences Beijing China State Key Laboratory of Internet of Things for Smart City University of Macau Macau S.A.R Department of Electrical and Computer Engineering University of Macau Macau S.A.R. College of Computer Science and Software Engineering Shenzhen University China Weizmann Institute of Science Rehovot Israel

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

This paper puts forth a new metric, dubbed channel cycle time (CCT), to measure the short-term fairness of Communication networks. CCT characterizes the average duration between two consecutive successful transmissions of a user, during which all other users successfully accessed the channel at least once. In contrast to existing short-term fairness measures, CCT provides more comprehensive insight into the transient dynamics of communication networks, with a particular focus on users' delays and jitter. To validate the efficacy of our approach, we analytically characterize the CCTs for two classical commu-nication protocols: slotted Aloha and CSMA/CA. The analysis demonstrates that CSMA/CA exhibits superior short-term fairness over slotted Aloha. Beyond its role as a measurement metric, CCT has broader implications as a guiding principle for the design of future communication networks by emphasizing factors like fairness, delay, and jitter in short-term behaviors.

关键词： Jitter Media Access Protocol Time measurement Real-time systems Heterogeneous networks Delays Communication networks

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Machine Learning in E-health: A Comprehensive Survey of Anxiety

Machine Learning in E-health: A Comprehensive Survey of Anxi...

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2021 International Conference on Data Analytics for Business and Industry, ICDABI 2021

作者： Teelhawod, Bibi Nushrina Akhtar, Faijan Heyat, Md Belal Bin Tripathi, Pragati Mehrotra, Rajat Asfaw, Ashamo Betelihem Shorman, Omar Al Masadeh, Mahmoud University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu Sichuan China Shenzhen University IoT Research Center College of Computer Science and Software Engineering Guangdong Shenzhen China Gautam Buddha University Department of Electrical Engineering Greater Noida India Gl Bajaj Institute of Technology and Management Aktu UP India Northwestern Polytechnical University School of Software Shaanxi Xian China Najran University College of Engineering and A1 Shrouk Trading Company Najran Saudi Arabia Yarmouk University Computer Engineering Department Irbid Jordan

ISBN: (纸本)9781665416566

Anxiety is the sixth major disorder that arises due to sleeplessness, inspiration, energy, hunger loss and desperate ideas. This study aims to define the extent of the research carried on anxiety with machine learning (ML) and to generate a database. We highlight serious problems for additional research obligations and communications. This paper designs a perception-based image of ML We have investigated deep insights of material to re-claim the current understanding of anxiety with ML from engineering detection to clinical treatment. We found 18 research articles and 28 closest keywords in the Web of science (WoS) database. In addition, we extracted the information including objective, subject, major, scoring technique, classifier, and performance measure from previously published 18 articles. This study has discovered the research gap and future direction, so far focused on anxiety with ML. In addition, it also delivers objectives for the upcoming work to be competent in a significant and scientific manner. © 2021 IEEE.

关键词： Machine learning

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STDEN: Towards Physics-Guided Neural Networks for Traffic Flow Prediction

arXiv

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arXiv 2022年

作者： Ji, Jiahao Wang, Jingyuan Jiang, Zhe Jiang, Jiawei Zhang, Hu State Key Laboratory of Software Development Environment School of Computer Science & Engineering Beihang University Beijing China Peng Cheng Laboratory Shenzhen China Department of Computer & Information Science & Engineering The University of Florida United States MOE Engineering Research Center of Advanced Computer Application Technology School of Computer Science & Engineering Beihang University Beijing China

High-performance traffic flow prediction model designing, a core technology of Intelligent Transportation System, is a long-standing but still challenging task for industrial and academic communities. The lack of integration between physical principles and data-driven models is an important reason for limiting the development of this field. In the literature, physics-based methods can usually provide a clear interpretation of the dynamic process of traffic flow systems but are with limited accuracy, while data-driven methods, especially deep learning with black-box structures, can achieve improved performance but can not be fully trusted due to lack of a reasonable physical basis. To bridge the gap between purely data-driven and physics-driven approaches, we propose a physics-guided deep learning model named Spatio- Temporal Differential Equation Network (STDEN), which casts the physical mechanism of traffic flow dynamics into a deep neural network framework. Specifically, we assume the traffic flow on road networks is driven by a latent potential energy field (like water flows are driven by the gravity field), and model the spatio-temporal dynamic process of the potential energy field as a differential equation network. STDEN absorbs both the performance advantage of data-driven models and the interpretability of physics-based models, so is named a physics-guided prediction model. Experiments on three real-world traffic datasets in Beijing show that our model outperforms state-of-the-art baselines by a significant margin. A case study further verifies that STDEN can capture the mechanism of urban traffic and generate accurate predictions with physical meaning. The proposed framework of differential equation network modeling may also cast light on other similar applications. Copyright © 2022, The Authors. All rights reserved.

关键词： Differential equations

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Dear-DIA^(XMBD): Deep Autoencoder Enables Deconvolution of Data-Independent Acquisition Proteomics

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Research 2024年第1期6卷 707-720页

作者： Qingzu He Chuan-Qi Zhong Xiang Li Huan Guo Yiming Li Mingxuan Gao Rongshan Yu Xianming Liu Fangfei Zhang Donghui Guo Fangfu Ye Tiannan Guo Jianwei Shuai Jiahuai Han Department of Physics and Fujian Provincial Key Laboratory for Soft Functional Materials ResearchXiamen UniversityXiamen 361005China Oujiang Laboratory(Zhejiang Lab for Regenerative Medicine Vision and Brain Health)and Wenzhou InstituteUniversity of Chinese Academy of SciencesWenzhouZhejiang 325001China School of Life Sciences Xiamen UniversityXiamen 361102China State Key Laboratory of Cellular Stress Biology Innovation Center for Cell Signaling NetworkXiamen 361102China Department of Computer Science Xiamen UniversityXiamen 361005China National Institute for Data Science in Health and Medicine School of MedicineXiamen UniversityXiamen 361102China Bruker(Beijing)Scientific Technology Co.Ltd. BeijingChina Westlake Laboratory of Life Sciences and Biomedicine Key Laboratory of Structural Biology of Zhejiang ProvinceSchool of Life SciencesWestlake University18 Shilongshan RoadHangzhou 310024China Institute of Basic Medical Sciences Westlake Institute for Advanced Study18 Shilongshan RoadHangzhou 310024China Westlake Omics Ltd. Yunmeng Road 1HangzhouChina Department of Electronic Engineering Xiamen UniversityXiamen 361005China

Data-independent acquisition(DIA)technology for protein identification from mass spectrometry and related algorithms is developing *** spectrum-centric analysis of DIA data without the use of spectra library from data-dependent acquisition data represents a promising *** this paper,we proposed an untargeted analysis method,Dear-DIA^(XMBD),for direct analysis of DIA ***-DIA^(XMBD) first integrates the deep variational autoencoder and triplet loss to learn the representations of the extracted fragment ion chromatograms,then uses the k-means clustering algorithm to aggregate fragments with similar representations into the same classes,and finally establishes the inverted index tables to determine the precursors of fragment clusters between precursors and peptides and between fragments and *** show that Dear-DIA^(XMBD) performs superiorly with the highly complicated DIA data of different species obtained by different instrument platforms.

关键词： Deep Auto Independent

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Role of masks in mitigating viral spread on networks

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Physical Review E 2023年第1期108卷 014306-014306页

作者： Yurun Tian Anirudh Sridhar Chai Wah Wu Simon A. Levin Kathleen M. Carley H. Vincent Poor Osman Yağan Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh Pennsylvania 15213 USA Department of Electrical and Computer Engineering Princeton University Princeton New Jersey 08544 USA Thomas J. Watson Research Center IBM Yorktown Heights New York 10598 USA Department of Ecology & Evolutionary Biology Princeton University Princeton New Jersey 08544 USA Software and Societal Systems School of Computer Science Carnegie Mellon University Pittsburgh Pennsylvania 15213 USA

Masks have remained an important mitigation strategy in the fight against COVID-19 due to their ability to prevent the transmission of respiratory droplets between individuals. In this work, we provide a comprehensive quantitative analysis of the impact of mask-wearing. To this end, we propose a novel agent-based model of viral spread on networks where agents may either wear no mask or wear one of several types of masks with different properties (e.g., cloth or surgical). We derive analytical expressions for three key epidemiological quantities: The probability of emergence, the epidemic threshold, and the expected epidemic size. In particular, we show how the aforementioned quantities depend on the structure of the contact network, viral transmission dynamics, and the distribution of the different types of masks within the population. Through extensive simulations, we then investigate the impact of different allocations of masks within the population and tradeoffs between the outward efficiency and inward efficiency of the masks. Interestingly, we find that masks with high outward efficiency and low inward efficiency are most useful for controlling the spread in the early stages of an epidemic, while masks with high inward efficiency but low outward efficiency are most useful in reducing the size of an already large spread. Last, we study whether degree-based mask allocation is more effective in reducing the probability of epidemic as well as epidemic size compared to random allocation. The result echoes the previous findings that mitigation strategies should differ based on the stage of the spreading process, focusing on source control before the epidemic emerges and on self-protection after the emergence.

关键词： Cascades Epidemic Percolation phase transition Spreading Multilayer & multiplex networks Poisson degree distribution networks Random graphs Social networks SIR model Spreading models Uncorrelated percolation

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