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Generation of Chinese classical poetry based on pretrained model

Generation of Chinese classical poetry based on pretrained m...

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Big Data, information and computer network (BDICN), International Conference on

作者： Ziyao Wang Lujin Guan Guanyu Liu Jun Ma dept. School of Information Science and Engineering Lanzhou University Lanzhou Gansu China College Of Life Science Technology Huazhong University line Of Science And Technology Wuhan Hubei China School of Computer and Cyberspace Security Communication University of China Beijing China

Chinese traditional poetry is an important art form, and it is a meaningful task to use artificial intelligence algorithm to generate poetry. In the past, researchers have proposed different generation algorithms, but these algorithms have some *** paper mainly tries to use BART and other pretrained models, proposes FS2TEXT and RR2TEXT to generate metrical poetry text and even specific style poetry text, and solves the problem that the user’s writing intention gradually reduces the relevance of the generated poetry text. Finally, in order to test the effect of the algorithm, this paper imitates Turing test and finds more than 600 testers to distinguish the works generated by the algorithm from the works of real human poets, and the results show that they can’t tell whether the work is from an algorithm or a *** shows that the algorithm proposed in this paper works well.

关键词： Correlation Art Computational modeling Writing Big Data Data models computer networks

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Understanding the Robustness of 3D Object Detection with Bird'View Representations in Autonomous Driving

Understanding the Robustness of 3D Object Detection with Bir...

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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition, CVPR 2023

作者： Zhu, Zijian Zhang, Yichi Chen, Hai Dong, Yinpeng Zhao, Shu Ding, Wenbo Zhong, Jiachen Zheng, Shibao Institute of Image Communication and Network Engineering Shanghai Jiao Tong University China Institute for Ai Tsinghua University BNRist Center Thbi Lab Dept. of Comp. Sci. and Tech. China School of Computer Science and Technology Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Information Materials and Intelligent Sensing Laboratory of Anhui Province China Saic Motor Ai Lab Zhongguancun Laboratory China

ISBN: (纸本)9798350301298

3D object detection is an essential perception task in autonomous driving to understand the environments. The Bird's-Eye-View (BEV) representations have significantly improved the performance of 3D detectors with camera inputs on popular benchmarks. However, there still lacks a systematic understanding of the robustness of these vision-dependent BEV models, which is closely related to the safety of autonomous driving systems. In this paper, we evaluate the natural and adversarial robustness of various representative models under extensive settings, to fully understand their behaviors influenced by explicit BEV features compared with those without BEV. In addition to the classic settings, we propose a 3D consistent patch attack by applying adversarial patches in the 3D space to guarantee the spatiotemporal consistency, which is more realistic for the scenario of autonomous driving. With substantial experiments, we draw several findings: 1) BEV models tend to be more stable than previous methods under different natural conditions and common corruptions due to the expressive spatial representations;2) BEV models are more vulnerable to adversarial noises, mainly caused by the redundant BEV features;3) Camera-LiDARfusion models have superior performance under different settings with multi-modal inputs, but BEV fusion model is still vulnerable to adversarial noises of both point cloud and image. These findings alert the safety issue in the applications of BEV detectors and could facilitate the development of more robust models. © 2023 IEEE.

关键词： Autonomous driving

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Improving the Efficiency of a Multistage Evaporator Station for Sugar Production Using Neural networks

Improving the Efficiency of a Multistage Evaporator Station ...

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International Conference on Modern Electrical and Energy systems (MEES)

作者： Nataliia Zaiets Nataliia Lutska Lidiia Vlasenko det. of Automation and Robotic Systems National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine dept. of Automation and Computer Technologies of Control Systems National University of Food Technologies Kyiv Ukraine dept. of Software Engineering and Cyber Security State University of Trade and Economics Kyiv Ukraine

ISBN: (纸本)9798350346848

The paper shows the process of developing a model of multistage evaporator station of a sugar factory based on a neural network. The neural network predicts the main performance indicators of the multistage evaporator station, such as the concentration of syrup at the output of the evaporator and the amount of evaporated water at the station. Unlike existing solutions, forecasting is performed in real time based on a set of indicators of the automation system (temperatures, flow rates, levels, pressure, density in the apparatus) and industrial laboratory (concentration of solids at the inlet to the evaporation station). The proposed neural network model has an MLP structure with 27 inputs, 2 outputs and one hidden layer with 20 neurons and predicts these indicators with an error of less than 5%. This allows to increase the informative support of the operator-technologist on the quality indicators of the enterprise without additional load on the industrial laboratory and without additional cost automatic quality devices, as well as to perform timely adjustment of the technological mode of the department.

关键词： Productivity Soft sensors Predictive models Solids Sugar industry Data models Real-time systems

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Modular Structure of the Intelligent Oil Production Equipment Control Station 2

Modular Structure of the Intelligent Oil Production Equipmen...

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2nd International Conference on Control systems, Mathematical Modeling, Automation and Energy Efficiency, SUMMA 2020

作者： Tagirova, Clara Gladkih, Alexey Bakiev, Damir Vulfin, Alexey Ufa State Aviation Technical University Dept. of Technical Cynernetics Ufa Russia Ufa State Aviation Technical University Dept. of Computer Engineering and Information Security Ufa Russia

ISBN: (纸本)9781728188409

A promising approach to the construction of an intelligent control station for a bore-hole sucker rod pump unit is presented. The approach is based on the decomposition of each specific control station of various manufacturers into permanent and variable parts in order to further expand the functionality by adding new modules: monitoring and diagnosing the state of ground and underground equipment, controlling operating modes based on the flow rate estimation and the module for determining the equilibrium part of the aerial part rocking machine. As an example, in addition to the previously implemented modules, the task of global optimization based on evolutionary calculations is posed and solved, which allows one to choose the balancing parameters of the aerial part of the rocking machine. The proposed modular principle of building an open extensible architecture of an intelligent control station provides both rational design and simplification of field operation. © 2020 IEEE.

关键词： Balancing

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Enhancing Cyber-Physical systems Resilience: Adaptive Self-Healing security Using Long Short-Term Memory networks

Enhancing Cyber-Physical Systems Resilience: Adaptive Self-H...

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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： Franciskus Antonius Alijoyo Chamandeep Kaur Afsana Anjum Veera Ankalu Vuyyuru B Kiran Bala School of Business and Information Technology STMIK LIKMI Bandung Indonesia Department of Computer Science & Information Technology Jazan University Jizan Kingdom of Saudi Arabia Dept. Of Information technology Security Jazan University Jazan Saudi Arabia Department of CSE Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of AI & DS K. Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

Cyber-Physical systems (CPS) form the backbone of critical infrastructures, integrating computational and physical processes to enhance efficiency and automation. However, the increasing interconnectivity exposes these systems to diverse cyber threats, necessitating proactive security measures. This research seeks to advance the security of Cyber-Physical systems (CPS) through the implementation of a self-healing mechanism driven by neural networks. CPS, pivotal in critical infrastructures, have become increasingly susceptible to a myriad of cyber threats owing to their intricate interconnectivity. The paramount significance of this research is rooted in the creation of a dynamic and intelligent defense system capable of autonomously identifying, responding to, and recuperating from cyber-physical attacks. The traditional CPS security landscape has grappled with static and rule-based approaches, struggling to keep pace with the dynamic nature of contemporary cyber threats. Moreover, the recovery processes in place have been predominantly manual and time-consuming. This research addresses these longstanding issues by introducing LSTM into the CPS security framework. This incorporation represents a paradigm shift, ushering in an era of adaptive resilience. The novelty of the research lies in the seamless integration of neural networks, enabling the system to learn from past incidents and adapt to emerging threats. The proposed self-healing mechanism emphasizes real-time threat detection, allowing for swift responses and the automation of the recovery phase, ultimately reducing downtime associated with security incidents. the integration of self-healing mechanisms using Long Short-Term Memory (LSTM) networks proves to be a promising approach for advancing cybersecurity in Cyber-Physical systems (CPS), with the proposed model achieving an impressive accuracy of 99%. The research not only tackles existing vulnerabilities but also pioneers a transformative approach to CPS

关键词： Adaptation models Computational modeling Neural networks Cyber-physical systems Real-time systems Critical infrastructure Computational efficiency

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Blockchain Integration for Robust Intellectual Property Protection in New Product Development (NPD)

Blockchain Integration for Robust Intellectual Property Prot...

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Electronics and Renewable systems (ICEARS), International Conference on

作者： P. Chinnasamy Subashini. B Nijanthan Nijanthan Madasamy Raja. G Devulapally Swetha Sreenivasulu Reddy L Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamilnadu India Department of Data Science and Business Systems School of Computing SRMIST India Computer Science and Engineering Paavai Engineering College Namakkal Tamilnadu India Information Technology Paavai Engineering College Namakkal Tamilnadu India Dept. of Computer Science and Engineering Vaagdevi Engineering College Telangana India Department of Mathematics School of Advanced Sciences Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

The rapid advancement of Blockchain technology has led to its widespread application in several domains of digital activities, such as e-government concerns and monetary security. This article presents a smart contract-based Blockchain solution for protecting intellectual property rights in new product development. The proposed solution effectively tackles the challenges associated with the confidentiality of personally identifiable information, efficiency in storing transaction-related information, as well as cooperation and monitoring in IPR. Initially, we create a mathematical model based on smart contract enabled Blockchain and intelligent contracts. Subsequently, we develop algorithms for Blockchain generation, and intelligent contracts. The system is suitable for protecting IPR in a blockchain network, by implementation of distributed identity authentication and real-time extensible storage. These features enhance the security and expandability of circuit copyright protection based on blockchain technology. The experimental results demonstrate that the suggested algorithm has decreased the cost for communication and enhanced the effectiveness of storing information and administration. Furthermore, it demonstrates resilience against many common attacks such as double-spending assaults, while maintaining low cost and overhead. Additionally, it exhibits a superior level of security in comparison to three other competing algorithms.

关键词： Patents Technological innovation Costs Smart contracts Intellectual property Product development Blockchains security Protection Resilience

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Machine Learning for Climate Change Impact Assessment and Adaptation Planning

Machine Learning for Climate Change Impact Assessment and Ad...

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Trends in Quantum Computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： T. Suresh Balakrishnan Prabhakar Krishnan U. Samson Ebenezar M. Mohammed Nizarudeen N. Kamal Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Center for Cyber Security Systems and Networks Amrita Vishwa Vidyapeetham Amritapuri-Campus India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Computer Science and Engineering CARE College of Engineering Trichy Tamil Nadu Dept. of Computer Science and Engineering GRT Institute of Engineering and Technology Tiruttani Tamil Nadu India

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

The presented research introduces a new advanced data analytics methodology for climate change impact assessment and adaptive planning that utilizes machine learning and deep-learning techniques. Quantitative analysis was performed over several databases such as GHCN, ERA5, National Centers for Environmental Prediction (NCEP) and JRA-55 in addition to CRU for a thorough assessment of the proposed methodology. In climate modeling, the Convolutional Neural networks (CNN) showed better accuracy than ERA5 and JRA-55 which outperformed Global Historical Climatology network (GHCN) with lower Mean Absolute Error (MAE), Root Mean Squared Error values. The Early Warning System, which was built using the LSTM network algorithm, had a higher accuracy of 0.90 and recall rate of 0.88 especially with JRA-55 proving its effectiveness in early detection for extreme weather events before they occur. The RL-based adaptive planning component performed well across all datasets, with ERA5 reaching the best feedback loop performance score of 94%. The precision agriculture optimization, which was enabled by Random Forest revealed different accuracy and AUC-ROC values across various datasets showing flexibility. Against international climate agreements, the overall alignment score was 0.78 with ERA5 being slightly better at 0.80 using Support Vector Machines (SVM). These quantitative assessments accentuate the practicality, efficacy, and applicability of this methodology in various datasets making it an apt tool for evidence-based decisions on climate resilience strategies at the global level.

关键词： Climate change Machine learning Planning Data analysis Quality assessment Meteorology Support vector machines Convolutional neural networks Statistical analysis Random forests Long short term memory Feedback loop Evidence theory

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IB-RPL: Embedding Isolation and Blacklisting of Malicious Nodes in RPL for Securing IoT-LLNs

IB-RPL: Embedding Isolation and Blacklisting of Malicious No...

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International Symposium on Advanced networks and Telecommunication systems (ANTS)

作者： Rashmi Sahay G Geethakumari Barsha Mitra Dept. of Computer Science and Engineering IcfaiTech The Icfai Foundation for Higher Education Hyderabad India Dept. of Computer Science and Information Systems BITS-Pilani Hyderabad India

ISBN: (数字)9781665448932

ISBN: (纸本)9781665448949

The constrained characteristics of devices in the Internet of Things (IoT) environment deters the implementation of robust security solutions. Consequently, intrusion of malicious nodes in IoT environment is a frequent phenomenon. Such malicious nodes often exploit the vulnerabilities of the underlying routing protocol to instigate several DDoS attacks. The IPv6 Routing Protocol for Low Power and Lossy networks (RPL), popularly used in numerous IoT applications, is susceptible to several routing attacks. Various researchers have proposed mechanisms for detecting routing attacks and identifying malicious nodes in RPL-based IoT environments. However, the benefit of such mechanisms can only be harness when identified malicious nodes are quickly isolated from the IoT environment. Therefore, the removal of identified malicious nodes outweighs any other security measure. Malicious nodes, often located in remote locations, are difficult to dislodge at the earliest. This paper proposes to embed the isolation and blacklisting mechanism of suspected malicious nodes in the routing process itself. We propose upgrades in the existing RPL routing process to enable fair nodes to collaboratively weed out malicious nodes and restrict the spread of malicious activities in the IoT environment. We call the modified RPL with self mechanized isolation and blacklisting of malicious nodes as IB-RPL.

关键词： Process control Inspection Routing Denial-of-service attack Routing protocols Blocklists Telecommunications

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Digital Map Generation Workflow Demonstrated on ZalaZONE Automotive Proving Ground Elements

Digital Map Generation Workflow Demonstrated on ZalaZONE Aut...

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IEEE Symposium on network Operations and Management

作者： Árpád Somogyi Tamás Tettamanti Pál Varga Zsolt Szalay Dániel Baranyai Tamás Lovas Dept. of Photogrammetry and Geoinformatics Fac. of Civil Engineering Budapest University of Technology and Economics Budapest Hungary Systems and Control Laboratory Institute for Computer Science and Control of the Eötvös Loránd Research Network Budapest Hungary Dept. of Telecommunications and Media Informatics Fac. of Electrical Engineering and Informatics Budapest University of Technology and Economics Budapest Hungary Dept. of Automotive Technologies Fac. of Transportation Eng. and Vehicle Eng. Budapest University of Technology and Economics Budapest Hungary

One major goal of digital twin technology applied in the Architecture, engineering, and Construction (AEC) Industry is the mapping of roads and road environments with their associated information. Such digital twins can support cost-effective testing, simulations, and the production of mapping backgrounds for automated vehicles and generally for Cooperative, Connected and Automated Mobility (CCAM) developments. The efficient workflow that leads from data acquisition to finished products depends on a number of requirements and knowledge sets. The main contribution of the paper is a general methodology of creating HD-map from point cloud and image data set. Accordingly, the most widely used survey techniques are presented and the requirements for the objects to be derived are summarized. The paper then outlines a general implementation procedure as an expert knowledge through a real-life example, where the results obtained for the ZalaZONE Automotive Proving Ground are demonstrated.

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TPM, a pattern for an architecture for trusted computing 20

TPM, a pattern for an architecture for trusted computing

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2020 European Conference on Pattern Languages of Programs, EuroPLoP 2020

作者： Muñoz, Antonio Fernandez, Eduardo B. Network Information and Computer Security Lab University of Malaga Malaga Spain Dept. of Computer and Electrical Eng. and Computer Science Florida Atlantic University Boca RatonFL United States

ISBN: (纸本)9781450377690

Trusted Computing (TC) is a valuable technology to use when we need to process highly sensitive data and several variants of it are now available. We present here a pattern that describes the architecture of a standard for TC, the Trusted Platform Module (TPM), which is an architecture for which there are several possible implementations. Standards are effectively patterns because of the freedom to implement them in a variety of ways. The TPM architecture contains cryptographic functions to attest the use of legitimate hardware and software platforms and defines operations to perform related trusted operations. Some of its variants are intended to only store private keys in a secure way. Although promising, this technology often has not been properly used. This motivated us for defining a comprehensive methodology that facilitates the design and development of secure TC-equipped systems that can fully exploit the functionalities that this technology provides. This paper provides the first step of this methodology in the form of a security pattern. We have tried to provide a reasonable amount of information in our pattern description, aimed at the needs of application designers. This pattern is described in an abstract form independent of implementation details, but expressed with enough detail and precision for the use of designers. © 2020 ACM.

关键词： Trusted computing

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