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12th International Conference on Learning Representations, ICLR 2024

作者： Tiboni, Gabriele Klink, Pascal Peters, Jan Tommasi, Tatiana D'Eramo, Carlo Chalvatzaki, Georgia Department of Control and Computer Engineering Politecnico di Torino Italy Department of Computer Science Technische Universität Darmstadt Germany Center for Artificial Intelligence and Data Science University of Würzburg Germany Darmstadt Germany Centre for Cognitive Science TU Darmstadt Germany Germany Center for Mind Brain and Behavior Uni. Marburg and JLU Giessen Germany

Varying dynamics parameters in simulation is a popular Domain Randomization (DR) approach for overcoming the reality gap in Reinforcement Learning (RL). Nevertheless, DR heavily hinges on the choice of the sampling distribution of the dynamics parameters, since high variability is crucial to regularize the agent's behavior but notoriously leads to overly conservative policies when randomizing excessively. In this paper, we propose a novel approach to address sim-to-real transfer, which automatically shapes dynamics distributions during training in simulation without requiring real-world data. We introduce DOmain RAndomization via Entropy MaximizatiON (DORAEMON), a constrained optimization problem that directly maximizes the entropy of the training distribution while retaining generalization capabilities. In achieving this, DORAEMON gradually increases the diversity of sampled dynamics parameters as long as the probability of success of the current policy is sufficiently high. We empirically validate the consistent benefits of DORAEMON in obtaining highly adaptive and generalizable policies, i.e. solving the task at hand across the widest range of dynamics parameters, as opposed to representative baselines from the DR literature. Notably, we also demonstrate the Sim2Real applicability of DORAEMON through its successful zero-shot transfer in a robotic manipulation setup under unknown real-world parameters. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Reinforcement learning

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Multi-omics graph convolutional networks for digestive system tumour classification and early-late stage diagnosis

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1572-1586页

作者： Lin Zhou Zhengzhi Zhu Hongbo Gao Chunyu Wang Muhammad Attique Khan Mati Ullah Siffat Ullah Khan School of Information Science and Technology University of Science and Technology of ChinaHefeiAnhuiChina Anhui Engineering Research Center on Information Fusion and Control of Intelligent Robot WuhuAnhuiChina Department of Breast Center West District of The Affiliated Hospital of University of Science and Technology of ChinaDivision of Life Sciences and MedicineUniversity of Science and Technology of ChinaHefeiAnhuiChina Institute of Advanced Technology University of Science and Technology of ChinaHefeiAnhuiChina School of Electrical and Electronic Engineering Nanyang Technological UniversitySingaporeSingapore School of Biological and Environmental Engineering Chaohu UniversityChaohu Regional Collaborative Technology Service Center for Rural RevitalizationHefeiChina Department of Artificial Intelligence College of Computer Engineering and SciencePrince Mohammad Bin Fahd UniversityAl-KhobarSaudi Arabia School of Automation Northwestern Polytechnical UniversityXi'anShaanxiChina Institute of Engineering and Computing Science University of Science and Technology of BannuKPKBannuPakistan

The prevalence of digestive system tumours(DST)poses a significant challenge in the global crusade against *** neoplasms constitute 20%of all documented cancer diagnoses and contribute to 22.5%of cancer-related *** accurate diagnosis of DST is paramount for vigilant patient monitoring and the judicious selection of optimal *** this challenge,the authors introduce a novel methodology,denominated as the Multi-omics Graph Transformer Convolutional Network(MGTCN).This innovative approach aims to discern various DST tumour types and proficiently discern between early-late stage tumours,ensuring a high degree of *** MGTCN model incorporates the Graph Transformer Layer framework to meticulously transform the multi-omics adjacency matrix,thereby illuminating potential associations among diverse samples.A rigorous experimental evaluation was undertaken on the DST dataset from The Cancer Genome Atlas to scrutinise the efficacy of the MGTCN *** outcomes unequivocally underscore the efficiency and precision of MGTCN in diagnosing diverse DST tumour types and successfully discriminating between early-late stage DST *** source code for this groundbreaking study is readily accessible for download at https://***/bigone1/MGTCN.

关键词： digestive system tumors early-late stage multi-omics tumor types

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Multifunctional control system to enhance the operational capability of the unmanned multirotor platform dedicated to the diagnostics of building structures

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Bulletin of the Polish Academy of sciences: Technical sciences 2025年第3期73卷

作者： Czyba, Roman Domin, Jaroslaw Górski, Marcin Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Gliwice Poland Silesian University of Technology Faculty of Electrical Engineering Gliwice Poland Silesian University of Technology Faculty of Civil Engineering Gliwice Poland

Drones have become common devices that are increasingly used, from toy mini drones to very advanced autonomous flying platforms used in various industries. The safety of using engineering structures and the need to shorten the time of carrying out diagnostic activities, and thus reduce costs, force the use of innovative tools to carry out an automated and unmanned process of assessing the technical condition of existing engineering structures, especially large-size ones, including bridges strengthened with external FRP (fibre reinforced polymer) composite overlays. There are conventional methods for assessing the technical condition of existing building structures conducted by inspectors and experts, involving the use of typical inspection and diagnostic tools. During the research, it was found that it is possible to conduct an automated and unmanned process of assessing the technical condition of engineering structures using an unmanned flying platform equipped with a thermal imaging diagnostic device. The uniqueness of the presented solution is that the drone is equipped with a docking module that allows the drone to be temporarily attached to the structure being diagnosed for the duration of diagnostic activities. To ensure appropriate conditions for structural diagnostics, a unique system of automated diagnostic processes and a multi-stage structure of the control system in all phases of operation were developed. The article presents a solution for the control system based on control consistent with the reference model. In particular, the focus was on the difficult and dangerous issue of altitude control during the docking phase. Simulation tests conducted in the MATLAB/Simulink environment, as well as laboratory and field tests on a real drone proved the correctness of the proposed solutions, which facilitated the undisturbed operation of the diagnostic module. © 2025 The Author(s).

关键词： Drones

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Remotely controllable Data Self-Destruction for Privacy-Preserving Sensitive Data Services 25

Remotely Controllable Data Self-Destruction for Privacy-Pres...

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25th IEEE International Conferences on High Performance Computing and Communications, 9th International Conference on Data science and Systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Zhou, Chengxin Xue, Xianbin Riad, Khaled Zhu, Yan Chu, William Cheng-Chung School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China North Navigation Control Technology Co. Ltd. Beijing China College of Computer Science and Information Technology King Faisal University Saudi Arabia Tunghai University Department of Computer Science Taichung Taiwan

ISBN: (纸本)9798350330014

This paper address a comprehensive solution to address data privacy and security challenges in data science, providing secure self-destruction of sensitive data, confidentiality through encryption, and flexible control over message destruction. Based on outsourcing cloud data services, we introduce a cloud retrograde storage (CRS) system for privacy-preserving self-destruction of sensitive data. The CRS is based on a frequently colliding hash table and recycle pool, and supports remote time, count, and range control. Hereby, we provide a remotely controllable mechanism for the prompt destruction of inactive messages against digital forensics without reducing the availability of services. The method used in the CRS involves storing messages in a recycle pool, where they are automatically erased at the end of their validity period or after being viewed by all recipients. The system also incorporates encryption and decryption processes to ensure confidentiality. The result of performance evaluation shows that the CRS effectively handles the destruction of messages and provides secure data removal from storage pools. The experiments demonstrate the efficiency and practicality of the CRS for large-scale data services. © 2023 IEEE.

关键词： Sensitive data

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Degradable Tracking System based on Hardware Multi-Model Estimators

Degradable Tracking System based on Hardware Multi-Model Est...

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2022 Resilience Week, RWS 2022

作者： Kiriakidis, Kiriakos Croteau, Brien Severson, Tracie Rodriguez-Seda, Erick Robucci, Ryan Islam, Riadul Rahman, Saad United States Naval Academy Department of Weapons Robotics and Control Engineering Department of Cyber Science United States University of Maryland Department of Computer Science and Electrical Engineering United States

ISBN: (数字)9781665488198

ISBN: (纸本)9781665488198

Sensing systems onboard unmanned vehicles operate in an environment of constrained computational resources. A cyber-Attack may primarily aim to degrade these computing devices and, ultimately, incapacitate the sensing system itself. To prepare a prototype tracking system for degradation, this paper proposes distributed hardware implementation of a Multiple Model estimator on two FPGA units and, after an attack, adaptation of the estimator by leveraging Dynamic Partial Reconfiguration of the single surviving FPGA. The method ensures that the most likely models of the estimator are loaded on to the fabric of the surviving FPGA with minimal interruption. © 2022 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Performing Effective Generative Learning from a Single Image Only 32

Performing Effective Generative Learning from a Single Image...

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32nd Wireless and Optical Communications Conference, WOCC 2023

作者： Xu, Qihui Chen, Jinshu Tang, Jiacheng Kang, Qi Zhou, MengChu Tongji University Department of Control Science and Engineering Shanghai201804 China New Jersey Institute of Technology The Helen and John C. Hartmann Department of Electrical and Computer Engineering NewarkNJ07102 United States

ISBN: (纸本)9798350337150

Generative adversarial networks (GANs) can be well used for image generation. Yet their training typically requires large amounts of data, which may not be available. This paper proposes a new algorithm for effective generative learning given a single image only. The proposed method involves building GAN models with a hierarchical pyramid structure and a parallel-branch design that enables independent learning of the foreground and background areas. This work conducts a set of well-designed experiments. The results well demonstrate that the proposed method produces the images of higher quality and better diversity than existing methods do. Thus, this work advances the field of generative learning for image generation. © 2023 IEEE.

关键词： Generative adversarial networks

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Advancing prognostic precision in gastric cancer with an immunoinflammatory index

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World Journal of Gastroenterology 2024年第44期30卷 4754-4758页

作者： Hong-Mei Zhang Guo-Hua Wang Shan-Wen Sun Lei Yuan College of Life Science Northeast Forestry UniversityHarbin 150040Heilongjiang ProvinceChina College of Computer and Control Engineering Northeast Forestry UniversityHarbin 150040Heilongjiang ProvinceChina Department of Hepatobiliary Surgery Quzhou People’s HospitalQuzhou 324000Zhejiang ProvinceChina

Gastric cancer remains a major global health challenge with high morbidity and mortality *** advancements in immunology and inflammation research have highlighted the crucial roles that these biological processes play in tumor progression and patient *** has sparked new interest in developing prognostic biomarkers that integrate these two key biological *** this letter,we discuss the recent study by Ba et al,which proposed a novel prognostic immunoinflammatory index for patients with gastric *** underscore the importance of this research,its potential impact on medical practice,and the prospective avenues for further investigation in this rapidly emerging area of study.

关键词： Gastric cancer Immunoinflammatory index Prognostic biomarkers Inflammation Immunology

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Routing with ART: Adaptive Routing for P4 Switches With In-Network Decision Trees

Routing with ART: Adaptive Routing for P4 Switches With In-N...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Antonino Angi Alessio Sacco Flavio Esposito Guido Marchetto Department of Control and Computer Engineering Politecnico di Torino Italy Computer Science Department Saint Louis University USA

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Recent advances in Machine Learning (ML) brought several advantages also within computer network management. For programmable data planes, however, it is more challenging to benefit from these advantages, given their limited resource capabilities colliding with the complexity of ML models. In this paper, we propose ART, an attempt to simplify ML-based solutions for routing, so that they can "fit", i.e., be executed, on P4 switches. To provide such model simplification, ART relies on efficient knowledge distillation techniques, converting, in particular, Deep Reinforcement Learning (DRL) models into a simpler Decision Tree (DT). Our evaluation results validate the accuracy of the extracted model and the application of the model logic directly into switches with little impact, paving the way for a more reactive data plane programmability via machine learning integration.

关键词： Adaptation models Accuracy Subspace constraints Packet loss Routing Deep reinforcement learning Throughput Data models Decision trees Load modeling

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Inferring Visibility of Internet Traffic Matrices Using eXplainable AI

Inferring Visibility of Internet Traffic Matrices Using eXpl...

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IEEE Symposium on Network Operations and Management

作者： Cristian Zilli Alessio Sacco Doriana Monaco Okwudilichukwu Okafor Flavio Esposito Guido Marchetto Department of Control and Computer Engineering Politecnico di Torino Italy Computer Science Department Saint Louis University USA

ISBN: (数字)9798350327939

ISBN: (纸本)9798350327946

A large fraction of recent network management tasks rely on Internet traffic matrices, ranging from planning and troubleshooting to routing and anomaly detection. Despite extensive research efforts over the years, acquiring a comprehensive overview of network traffic remains a difficult and error-prone task. While the literature has mostly proposed increasingly accurate and complex Machine Learning (ML) models to reconstruct missing information, in this paper we propose an alternative approach to further enhance this process: combining the ML model with eXplainable AI (XAI) to analyze the model behavior, detect most significant features, and limit the reconstruction process to such reduced input. With this methodology, not only we simplify the problem, but the entire solution finds greater deployability as the data acquisition phase is also simplified. Numerical results demonstrate that, with our solution on a Convolution Neural Network model, the error during completion can be lowered by 80% for a network telemetry traffic reduction of 75%.

关键词： Analytical models Accuracy Explainable AI Telecommunication traffic Routing Numerical models Internet

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Cancelable Speaker Identification System Based on Optical-Like Encryption Algorithms

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computer Systems science & engineering 2022年第10期43卷 87-102页

作者： Safaa El-Gazar Walid El-Shafai Ghada El-Banby Hesham F.A.Hamed Gerges M.Salama Mohammed Abd-Elnaby Fathi E.Abd El-Samie Department of Electrical Engineering Faculty of EngineeringEgyptian-Russian UniversityCairoEgypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Laboratory Department of Computer SciencePrince Sultan UniversityRiyadh11586Saudi Arabia Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Biometric authentication is a rapidly growing trend that is gaining increasing attention in the last *** achieves safe access to systems using biometrics instead of the traditional *** utilization of a biometric in its original format makes it usable only ***,a cancelable biometric template should be used,so that it can be replaced when it is *** biometrics aims to enhance the security and privacy of biometric *** encryption is an efficient technique to be used in order to generate cancelable biometric *** this paper,a highly-secure encryption algorithm is proposed to ensure secure biometric data in verification *** considered biometric in this paper is the speech *** speech signal is transformed into its ***,the spectrogram is encrypted using two cascaded optical encryption *** first algorithm is the Optical Scanning Holography(OSH)for its efficiency as an encryption *** OSH encrypted spectrogram is encrypted using Double Random Phase Encoding(DRPE)by implementing two Random Phase Masks(RPMs).After the two cascaded optical encryption algorithms,the cancelable template is *** verification is implemented through correlation estimation between enrolled and test templates in their encrypted *** the correlation value is larger than a threshold value,the user is *** threshold value can be determined from the genuine and imposter correlation distribution curves as the midpoint between the two *** implementation of optical encryption is adopted using its software rather than the optical *** efficiency of the proposed cancelable biometric algorithm is illustrated by the simulation *** can improve the biometric data security without deteriorating the recognition *** results give close-to-zero This values for the Equal Error Rate(EER)and close-to-one values for the Area under Receiver Operator Charact

关键词： Cancelable biometrics spectrogram OSH DRPE EER AROC

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