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Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA), International Conference on

作者： Yojna Arora Ajeet Singh Abhay Sharma Nitin Aggarwal Nidhi Bansal Department of Computer Science & Engineering Sharda University Greater Noida India Department of Computer Science & Engineering Moradabad Institute of Technology Moradabad Uttar Pradesh India Department of IoT & IS School of Computing and Intelligent System Manipal University Jaipur Jaipur Rajasthan Supervisor Oracle Development Database Admin Data Science PA USA Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798331517953

ISBN: (纸本)9798331517960

A chatbot is an AI-powered software or application designed to communicate with people. This technology can perform a variety of tasks, including providing instant responses and answers to users, delivering information, assisting with purchasing products, and enhancing customer service. In my paper, I present the fundamental principles and essential concepts of computing related to chatbots and its applications various sectors like Education, E commerce, Entertainment, Healthcare, Telecommunication, Customer Service etc. The paper also mentions detailed literature explain the contribution by various researchers in this fields. Architecture and step wise approach of creating a chatbot is presented lately.

关键词： Quantum computing Customer services Education Medical services Computer architecture Chatbots User experience Software Artificial intelligence systematic literature review

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Large Scale Model Enabled Semantic Communications Based on Robust Knowledge Distillation

Large Scale Model Enabled Semantic Communications Based on R...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Kuiyuan Ding Fangfang Liu Yang Yang Mingzhe Chen Caili Guo Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering and the Institute for Data Science and Computing University of Miami Coral Gables FL USA Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Large scale artificial intelligence (AI) models possess excellent capabilities in semantic representation and understanding, making them particularly well-suited for semantic encoding and decoding. However, the substantial scale of these AI models imposes unacceptable computational resources and communication delays. To address this issue, we propose a semantic communication scheme based on robust knowledge distillation (RKD-SC) for large scale model enabled semantic communications. In the considered system, a transmitter extracts the features of the source image for robust transmission and accurate image classification at the receiver. To effectively utilize the superior capability of large scale model while make the cost affordable, we first transfer knowledge from a large scale model to a smaller scale model to serve as the semantic encoder. Then, to enhance the robustness of the system against channel noise, we propose a channel-aware autoencoder (CAA) based on the Transformer architecture. Experimental results show that the encoder of proposed RKD-SC system can achieve over 93.3% of the performance of a large scale model while compressing 96.67% number of parameters. Code: https://***/echojayne/RKD-SC.

关键词： Transmitters Computational modeling Noise Autoencoders Receivers Semantic communication Transformers Feature extraction Robustness Decoding

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TCSloT: Text Guided 3D Context and Slope Aware Triple Network for Dental Implant Position Prediction

TCSloT: Text Guided 3D Context and Slope Aware Triple Networ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xinquan Yang Jinheng Xie Xuechen Li Xuguang Li Linlin Shen Yongqiang Deng College of Computer Science and Software Engineering Shenzhen University Shenzhen China Show Lab National University of Singapore Singapore National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Stomatology Shenzhen University General Hospital Shenzhen China

In implant prosthesis treatment, the surgical guide of implant is used to ensure accurate implantation. However, such design heavily relies on the manual location of the implant position. When deep neural network has been proposed to assist the dentist in locating the implant position, most of them take a single slice as input, which do not fully explore 3D contextual information and ignores the influence of implant slope. In this paper, we design a Text Guided 3D Context and Slope Aware Triple Network (TCSloT) to integrate the perception of contextual information from multiple adjacent slices and awareness of variation of implant slopes. A Texture Variation Perception (TVP) module is correspondingly design to process the multiple slices and capture the texture variation among slices and a Slope-Aware Loss (SAL) is proposed to dynamically assign adaptive weights for the regression head. Additionally, we design a conditional text guidance (CTG) module to integrate the text condition (i.e., left, middle and right) from the CLIP to assist the implant position prediction. Extensive experiments on a dental implant dataset through five-fold cross-validation, demonstrated that the proposed TCSloT achieves superior performance than existing methods.

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Integrated Multi-Omics Analysis Identities a Machine Learning-Derived Signature for Predicting Prognosis and Therapeutic Vulnerability in Clear Cell Renal Cell Carcinoma

SSRN

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SSRN 2024年

作者： Chi, Shengqiang Ma, Jing Ding, Yiming Lu, Zeyi Zhou, Zhenwei Wang, Mingchao Li, Gonghui Chen, Yuanlei Research Center for Data Hub and Security Zhejiang Laboratory Hangzhou311121 China Department of Urology Sir Run Run Shaw Hospital Zhejiang University School of Medicine Hangzhou310016 China The Engineering Research Center of EMR and Intelligent Expert System Ministry of Education College of Biomedical Engineering and Instrument Science Zhejiang University Hangzhou310027 China

Background: Clear cell renal cell carcinoma (ccRCC) shows considerable variation within and between tumors, presents varying treatment responses among patients, possibly due to molecular distinctions. Methods: To address this complexity, we constructed an integrative multi-omics analysis using 10 clustering algorithms on ccRCC patient data. Afterwards, we applied bootstrapping in univariate Cox regression and the Boruta algorithm to pinpoint clinically relevant genes. Based on this, we developed a robust Prognosis and therapeutic vulnerability signature (PTVS) using seven machine learning algorithms, capable of predicting patient prognosis and drug ***: Our analysis revealed two distinct ccRCC subtypes with differential prognostic implications, notably identifying subtype 2 with poorer outcomes. Patients in the low PTVS group exhibited superior prognosis statistics and an augmented sensitivity to immunotherapy, features consistent with a 'hot tumor' phenotype. Conversely, individuals within the high PTVS group exhibited diminished prognosis statistic and restricted advantages from immunotherapy. Importantly, the PTVS holds future potential as a notable biomarker for guiding personalized treatment strategies, with four prospective targets (CTSK, XDH, PKMYT1, and EGLN2) indicating therapeutic promise in patients scoring high on PTVS. Conclusions: The integrative analysis of multi-omics data profoundly enhances the molecular stratification of ccRCC, underscoring far-reaching impact of such comprehensive profiling on its therapeutic strategies. © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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LOPO: An Out-of-order Layer Pulling Orchestration Strategy for Fast Microservice Startup

LOPO: An Out-of-order Layer Pulling Orchestration Strategy f...

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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Lin Gu Junhao Huang Shaoxing Huang Deze Zeng Bo Li Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Computer Science China University of Geosciences Wuhan China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

Container based microservices have been widely applied to promote the cloud elasticity. The mainstream Docker containers are structured in layers, which are organized in stack with bottom-up dependency. To start a microservice, the required layers are pulled from a remote registry and stored on its host server, following the layer dependency order. This incurs long microservice startup time and hinders the performance efficiency. In this paper, we discover that, for the first time, the layer pulling order can be adjusted to accelerate the microservice startup. Specifically, we address the problem on microservice layer pulling orchestration for startup time minimization and prove it as NP-hard. We propose a Longest-chain based Out-of-order layer Pulling Orchestration (LOPO) strategy with low computational complexity and guaranteed approximation ratio. Through extensive real-world trace driven experiments, we verify the efficiency of our LOPO and demonstrate that it reduces the microservice startup time by 22.71% on average in comparison with state-of-the-art solutions.

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Spatial public goods games on any population structure

arXiv

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arXiv 2024年

作者： Wang, Chaoqian Su, Qi Department of Computational and Data Sciences George Mason University FairfaxVA22030 United States Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

Understanding the emergence of cooperation in spatially structured populations has advanced significantly in the context of pairwise games, but the fundamental theory of group-based public goods games (PGGs) remains less explored. Here, we provide theoretical conditions under which cooperation thrive in spatial PGGs on any population structure, which are accurate under weak selection. We find that PGGs can support cooperation across all kinds of model details and on almost all network structures in contrast to pairwise games. For example, a class of networks that would otherwise fail to produce cooperation, such as star graphs, are particularly conducive to cooperation in spatial PGGs. This fundamental advantage of spatial PGGs derives from reciprocity through second-order interactions, allowing local structures such as the clustering coefficient to play positive roles. We also verify the robustness of spatial PGGs on empirical networks where pairwise games cannot support cooperation, which implies that PGGs could be a universal interaction mode in real-world systems. © 2024, CC BY.

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Delay-Induced Watermarking for Detection of Replay Attacks in Linear systems

Delay-Induced Watermarking for Detection of Replay Attacks i...

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American Control Conference (ACC)

作者： Christoforos Somarakis Raman Goyal Erfaun Noorani Shantanu Rane Data Science and Applied Mathematics Group Merck & Co West Point PA USA Palo Alto Research Center - An SRI Company Palo Alto CA USA Department of Electrical and Computer Engineering the Institute for System Research (ISR) at the University of Maryland College Park College Park MD USA A. James Clark School of Engineering

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

A state-feedback watermarking signal design for the detection of replay attacks in linear systems is proposed. The control input is augmented with a random time-delayed term of the system state estimate, in order to secure the system against attacks of replay type. We outline the basic analysis of the closed-loop response of the state-feedback watermarking in a LQG controlled system. Our theoretical results are applied on a temperature process control example. While the proposed secure control scheme requires very involved analysis, it, nevertheless, holds promise of being superior to conventional, feed-forward, watermarking schemes, in both its ability to detect attacks as well as the secured system performance.

关键词： Linear systems system performance Process control Watermarking Control systems Temperature control Signal design

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Physics-informed Neural Network for system identification of rotors ⁎

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IFAC-PapersOnLine 2024年第15期58卷 307-312页

作者： Xue Liu Wei Cheng Ji Xing Xuefeng Chen Zhibin Zhao Rongyong Zhang Qian Huang Jinqi Lu Hongpeng Zhou Wei Xing Zheng Wei Pan The National Key Laboratory of Aerospace Power System and Plasma Technology and School of Mechanical Engineering Xi’an Jiaotong University Xi’an China China Nuclear Power Engineering Co. Ltd. Beijing China Shanghai Apollo Machinery Co. Ltd. Shanghai China Department of Computer Science University of Manchester Manchester United Kingdom School of Computer Data and Mathematical Sciences Western Sydney University Sydney Australia

The condition of the rotor system remains difficult to assess due to system nonlinearity and nosiy measurements. To deal with the problem, we proposed a hierarchical physics-informed neural network (HPINN) to discover the ordinary differential equation (ODE) of a healthy/faulty rotor system from noise measurements and then assess the machine condition based on the discovered ODE. Specifically, the ODE of a healthy rotor system is first stably identified from noise measurement through HPINN guided by rotor dynamics. Based on the identified healthy ODE, the extra fault terms in the ODE of the faulty rotor system are then sparsely regressed from the predefined library embedded in HPINN. Moreover, with the mathematical terms of discovered fault, the potential fault and the health indicator (HI) are diagnosed and constructed to assess the condition of the rotor system, respectively. Finally, the effectiveness of the proposed method is verified by the data set collected on the circulating water test bench, showing the potential for practical applications.

关键词： rotor system fault diagnosis health indicator physics-informed neural network

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Automated identification and quantification of metastatic brain tumors and perilesional edema based on a deep learning neural network

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Journal of Neuro-Oncology 2024年第1期166卷 167-174页

作者： Chou, Chi-Jen Yang, Huai-Che Chang, Po-Yao Chen, Ching-Jen Wu, Hsiu-Mei Lin, Chun-Fu Lai, I-Chun Peng, Syu-Jyun Division of Neurosurgery Department of Surgery Kaohsiung Veterans General Hospital Kaohsiung Taiwan School of Medicine College of Medicine National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Neurosurgery Neurological Institute Taipei Veterans General Hospital Taipei Taiwan Department of Electrical Engineering National Central University Taoyuan Taiwan Department of Neurological Surgery University of Virginia Health System Charlottesville 22903 VA United States Department of Radiology Taipei Veterans General Hospital Taipei Taiwan Department of Heavy Particles & Radiation Oncology Taipei Veterans General Hospital Taipei Taiwan Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University No.250 Wuxing St. Xinyi Dist. Taipei City 110 Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei Medical University Taipei Taiwan

Purpose: This paper presents a deep learning model for use in the automated segmentation of metastatic brain tumors and associated perilesional edema. Methods: The model was trained using Gamma Knife surgical data (90 MRI sets from 46 patients), including the initial treatment plan and follow-up images (T1-weighted contrast-enhanced (T1cWI) and T2-weighted images (T2WI)) manually annotated by neurosurgeons to indicate the target tumor and edema regions. A mask region-based convolutional neural network was used to extract brain parenchyma, after which the DeepMedic 3D convolutional neural network was in the segmentation of tumors and edemas. Results: Five-fold cross-validation demonstrated the efficacy of the brain parenchyma extraction model, achieving a Dice similarity coefficient of 96.4%. The segmentation models used for metastatic tumors and brain edema achieved Dice similarity coefficients of 71.6% and 85.1%, respectively. This study also presents an intuitive graphical user interface to facilitate the use of these models in clinical analysis. Conclusion: This paper introduces a deep learning model for the automated segmentation and quantification of brain metastatic tumors and perilesional edema trained using only T1cWI and T2WI. This technique could facilitate further research on metastatic tumors and perilesional edema as well as other intracranial lesions. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Deep learning neural network Identification Metastatic brain tumors Perilesional edema Quantification

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PrintListener: Uncovering the Vulnerability of Fingerprint Authentication via the Finger Friction Sound

arXiv

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arXiv 2024年

作者： Zhou, Man Su, Shuao Wang, Qian Li, Qi Zhou, Yuting Ma, Xiaojing Li, Zhengxiong School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Cyber Science and Engineering Wuhan University China Institute for Network Sciences and Cyberspace Tsinghua University China Department of Computer Science and Engineering University of Colorado Denver United States Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology China

Fingerprint authentication has been extensively employed in contemporary identity verification systems owing to its rapidity and cost-effectiveness. Due to its widespread use, fingerprint leakage may cause sensitive information theft, enormous economic and personnel losses, and even a potential compromise of national security. As a fingerprint that can coincidentally match a specific proportion of the overall fingerprint population, MasterPrint rings the alarm bells for the security of fingerprint authentication. In this paper, we propose a new side-channel attack on the minutiae-based Automatic Fingerprint Identification system (AFIS), called PrintListener, which leverages users’ fingertip swiping actions on the screen to extract fingerprint pattern features (the first-level features) and synthesizes a stronger targeted PatternMasterPrint with potential second-level features. The attack scenario of PrintListener is extensive and covert. It only needs to record users’ fingertip friction sound and can be launched by leveraging a large number of social media platforms. Extensive experimental results in real-world scenarios show that Printlistener can significantly improve the attack potency of MasterPrint. © 2024, CC0.

关键词： Friction

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