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End-to-end differentiability and tensor processing unit computing to accelerate materials’ inverse design

npj Computational Materials 2023年第1期9卷 1072-1083页

作者： Han Liu Yuhan Liu Kevin Li Zhangji Zhao Samuel S.Schoenholz Ekin D.Cubuk Puneet Gupta Mathieu Bauchy SOlids inFormaTics AI-Laboratory(SOFT-AI-Lab) College of Polymer Science and EngineeringSichuan UniversityChengdu 610065China AIMSOLID Research Wuhan 430223China Physics of AmoRphous and Inorganic Solids Laboratory(PARISlab) Department of Civil and Environmental EngineeringUniversity of CaliforniaLos AngelesCA 90095USA Department of Computer Science University of CaliforniaLos AngelesCA 90095USA Google Research Brain TeamMountain ViewCAUSA Department of Electrical and Computer Engineering University of CaliforniaLos AngelesCA 90095USA

Numerical simulations have revolutionized material ***,although simulations excel at mapping an input material to its output property,their direct application to inverse design has traditionally been limited by their high computing cost and lack of ***,taking the example of the inverse design of a porous matrix featuring targeted sorption isotherm,we introduce a computational inverse design framework that addresses these challenges,by programming differentiable simulation on TensorFlow platform that leverages automated end-to-end *** to its differentiability,the simulation is used to directly train a deep generative model,which outputs an optimal porous matrix based on an arbitrary input sorption isotherm ***,this inverse design pipeline leverages the power of tensor processing units(TPU)—an emerging family of dedicated chips,which,although they are specialized in deep learning,are flexible enough for intensive scientific *** approach holds promise to accelerate inverse materials design.

关键词： enough inverse isotherm

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Narrow Band Internet of Things (NB-IoT) for Wireless Communication and Network Optimization

Narrow Band Internet of Things (NB-IoT) for Wireless Communi...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Khond, Smita Chythanya, Kanegonda Ravi Munawar, Shaik Mandava, Manjusha Kruthika, G.C. Malla Reddy Engineering College for Women It Department Hyderabad India Sr University School of Computer Science & Artificial Intelligence Warangal India Kakatiya Institute of Technology and Science Warangal India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaadeswaram India Nitte Meenakshi Institute of Technology Department of Ai and Ml Bengaluru India

ISBN: (纸本)9798350313413

This research presents an all-encompassing method for improving Narrow Band Internet of Things (NB-IoT) network performance by including network design, interference management, power control techniques, and security measures. Planning your network ahead of time is essential, and will involve decisions like network design, base station placement, and channel allocation. The second stage, interference control, seeks to reduce background noise emanating from various sources. The third and last stage is to use transmission power management algorithms for NB-IoT devices. In real-time, these algorithms optimize power levels for the channel and the task at hand. In the last stage, we apply security measures including authentication, encryption, and other types of network access control. According to the data, the suggested approach would have cost a total of $500,000, but more conventional approaches would have cost between $700,000 and $900,000. Also, the suggested method had an average delay of 1.5 seconds. © 2023 IEEE.

关键词： Interference management NB-IoT networks Network design Network Optimization Power control Security

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SSL4Q: Semi-Supervised Learning of Quantum Data with Application to Quantum State Classification 41

SSL4Q: Semi-Supervised Learning of Quantum Data with Applica...

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41st International Conference on Machine Learning, ICml 2024

作者： Tang, Yehui Yang, Nianzu Long, Mabiao Yan, Junchi School of Artificial Intelligence Department of Computer Science and Engineering MoE Lab of AI Shanghai Jiao Tong University Shanghai China

The accurate classification of quantum states is crucial for advancing quantum computing, as it allows for the effective analysis and correct functioning of quantum devices by analyzing the statistics of the data from quantum measurements. Traditional supervised methods, which rely on extensive labeled measurement outcomes, are used to categorize unknown quantum states with different properties. However, the labeling process demands computational and memory resources that increase exponentially with the number of qubits. We propose SSL4Q, manage to achieve (for the first time) semi-supervised learning specifically designed for quantum state classification. SSL4Q’s architecture is tailored to ensure permutation invariance for unordered quantum measurements and maintain robustness in the face of measurement uncertainties. Our empirical studies encompass simulations on two types of quantum systems: the Heisenberg Model and the Variational Quantum Circuit (VQC) Model, with system size reaching up to 50 qubits. The numerical results demonstrate SSL4Q’s superiority over traditional supervised models in scenarios with limited labels, highlighting its potential in efficiently classifying quantum states with reduced computational and resource overhead. Copyright 2024 by the author(s)

关键词： Qubits

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Ensemble IDPS With Tripartite Firewall Solution for Enhancing Healthcare Data Security

Ensemble IDPS With Tripartite Firewall Solution for Enhancin...

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IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT)

作者： Vishnu Kurnala Aniketh Reddy Adireddy Department of Computer Science and Engineering(AI&ML) Vardhaman College of Engineering Department of Artificial Intelligence and Machine Learning Vardhaman College of Engineering

ISBN: (数字)9798350385922

ISBN: (纸本)9798350385939

An innovative security mechanism to thwart growing cyberattacks aimed at healthcare data is presented in this research. By use of sophisticated cryptographic techniques, the system combines a three-layer firewall with an ensemble Intrusion Detection and Protection System (IDPS). To minimize false positives and identify and counteract a range of cyberattacks, including advanced persistent threats and ransomware, it integrates signature-based, anomaly-based, and behavior-based detection techniques. Key healthcare data is better protected by the dynamic three-layer firewall, which adjusts to new threats. Tested extensively in simulated healthcare settings, the technology has shown to be highly effective in preserving data security and integrity. This framework is easily deployable across different healthcare IT infrastructures and offers a revolutionary solution to healthcare cybersecurity that tackles present and future security issues.

关键词： Firewalls (computing) Prevention and mitigation Intrusion detection Medical services Communications technology Ransomware Cryptography

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Temporal-Aware Deep Reinforcement Learning for Energy Storage Bidding in Energy and Contingency Reserve Markets

IEEE Transactions on Energy Markets, Policy and Regulation

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IEEE Transactions on Energy Markets, Policy and Regulation 2024年第3期2卷 392-406页

作者： Li, Jinhao Wang, Changlong Zhang, Yanru Wang, Hao Monash University Department of Data Science and AI Faculty of IT and Monash Energy Institute MelbourneVIC3800 Australia Monash University Department of Civil Engineering MelbourneVIC3800 Australia University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China

The battery energy storage system (BESS) has immense potential for enhancing grid reliability and security through its participation in the electricity market. BESS often seeks various revenue streams by taking part in multiple markets to unlock its full potential, but effective algorithms for joint-market participation under price uncertainties are insufficiently explored in the existing research. To bridge this gap, we develop a novel BESS joint bidding strategy that utilizes deep reinforcement learning (DRL) to bid in the spot and contingency frequency control ancillary services (FCAS) markets. Our approach leverages a transformer-based temporal feature extractor to effectively respond to price fluctuations in seven markets simultaneously and helps DRL learn the best BESS bidding strategy in joint-market participation. Additionally, unlike conventional 'black-box' DRL model, our approach is more interpretable and provides valuable insights into the temporal bidding behavior of BESS in the dynamic electricity market. We validate our method using realistic market prices from the Australian National Electricity Market. The results show that our strategy outperforms benchmarks, including both optimization-based and other DRL-based strategies, by substantial margins. Our findings further suggest that effective temporal-aware bidding can significantly increase profits in the spot and contingency FCAS markets compared to individual market participation. © 2023 IEEE.

关键词： Real time systems

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Deployment Schemes of In-Band Management Mode Based on Industrial SDN Network 7

Deployment Schemes of In-Band Management Mode Based on Indus...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Mittal, Mudit Al-Jawahry, Hassan M. Varshney, Neeraj Kumar, Sunil Prashanth Michaelson, Jacob Reddy, R. Akhilesh Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India The Islamic university College of technical engineering Department of computers Techniques engineering Najaf Iraq Gla University Department of Computer Engineering and Applications Mathura India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Chennai India Mlr Institute of Technology Department of CSE-AI&ML Telangana Hyderabad India

ISBN: (纸本)9798350350067

In order to properly manage and monitor their networks in industrial environments, industrial Software-Defined Networking (SDN) networks need to have an in-band administration mode. This research investigates a wide variety of alternative deployment options for in-band management mode in industrial SDN networks in order to increase the performance of the network, as well as its dependability and level of network security. We study how the performance of the network differs depending on whatever coupling of in-band management and industrial SDN architecture we choose. We analyse a range of potential pairings of in-band management with industrial SDN design. We also discuss the advantages and disadvantages of each deployment technique, providing insights into the creation and management of robust Industrial SDN networks. It provides useful recommendations for increasing the efficacy and security of particular network infrastructures, which might be valuable to both corporate groups and network administrators. The adoption of SDN technology and the installation of tailored management modes are becoming more and more significant as industrial settings continue to expand. This is of the utmost significance in order to guarantee the highest possible standards of both operational excellence and safety. © 2024 IEEE.

关键词： Deployment Schemes In-band Management Industrial SDN Network Network Control Network Monitoring Network Security

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Specifying Goals to Deep Neural Networks with Answer Set Programming 34

Specifying Goals to Deep Neural Networks with Answer Set Pro...

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34th International Conference on Automated Planning and Scheduling, ICAPS 2024

作者： Agostinelli, Forest Panta, Rojina Khandelwal, Vedant AI Institute University of South Carolina United States Department of Computer Science and Engineering University of South Carolina United States

ISBN: (纸本)9781577358893

Recently, methods such as DeepCubeA have used deep reinforcement learning to learn domain-specific heuristic functions in a largely domain-independent fashion. However, such methods either assume a predetermined goal or assume that goals will be given as fully-specified states. Therefore, specifying a set of goal states to these learned heuristic functions is often impractical. To address this issue, we introduce a method of training a heuristic function that estimates the distance between a given state and a set of goal states represented as a set of ground atoms in first-order logic. Furthermore, to allow for more expressive goal specification, we introduce techniques for specifying goals as answer set programs and using answer set solvers to discover sets of ground atoms that meet the specified goals. In our experiments with the Rubik's cube, sliding tile puzzles, and Sokoban, we show that we can specify and reach different goals without any need to re-train the heuristic function. Our code is publicly available at https://***/forestagostinelli/SpecGoal. Copyright © 2024, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Deep neural networks

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Failure Analysis of IoT-based Smart Agriculture System: Towards Sustainable Food Security 5

Failure Analysis of IoT-based Smart Agriculture System: Towa...

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5th International Conference on Sustainable Technologies for Industry 5.0, STI 2023

作者： Rahman, Md Mokhlesur Abdulhamid, Alhassan Kabir, Sohag Gope, Prosanta Military Institute of Science and Technology Department of Computer Science and Engineering Bangladesh School of Computer Science Ai and Electronics University of Bradford United Kingdom University of Sheffield Department of Computer Science United Kingdom

ISBN: (纸本)9798350394290

Internet of Things (IoT)-based smart agriculture systems are increasingly being used to improve agricultural yield. IoT devices used for agricultural monitoring are often deployed in outdoor environments in remote areas. Due to the exposure to harsh environments and the nature of deployment, sensors and other devices are susceptible to an increased rate of failure, which can take a system to unsafe and dangerous states. Failure of a smart agriculture system can cause significant harm to nature and people and reduce agricultural production. To address the concerns associated with the failure of the system, it is necessary to understand how the failures of the components of a system can contribute to causing the overall system failure. This paper adopts Fault Tree Analysis, a widely used framework for failure behaviour analysis in other safety-critical domains, to demonstrate the qualitative failure analysis of smart irrigation systems based on the components' failure. © 2023 IEEE.

关键词： Internet of things

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Human Action Recognition Using Difference of Gaussian and Difference of Wavelet

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Big Data Mining and Analytics 2023年第3期6卷 336-346页

作者： Gopampallikar Vinoda Reddy Kongara Deepika Lakshmanan Malliga Duraivelu Hemanand Chinnadurai Senthilkumar Subburayalu Gopalakrishnan Yousef Farhaoui Department of Computer Science and Engineering(AI&ML) CMR Technical CampusHyderabad 501401India. Department of Information Technology Kakatiya Institute of Technology and ScienceWarangal 506015India. Department of Electronics and Communication Engineering Malla Reddy Engineering College for Women(Autonomous)Hyderabad 500100India. Department of Computer Science and Engineering S.A.Engineering College(Autonomous)Thiruverkadu 600077India. Department of Electronics and Communication Engineering Saveetha Institute of Medical and Technical Sciences(SIMATS)Chennai 602105India. Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai 600062India. IDMS Team STI LaboratoryFaculty of Sciences and TechniquesMoulay Ismail University of MeknèsErrachidia 25003Morocco.

Human Action Recognition(HAR)attempts to recognize the human action from images and *** major challenge in HAR is the design of an action descriptor that makes the HAR system robust for different environments.A novel action descriptor is proposed in this study,based on two independent spatial and spectral *** proposed descriptor uses a Difference of Gaussian(DoG)filter to extract scale-invariant features and a Difference of Wavelet(DoW)filter to extract spectral *** create a composite feature vector for a particular test action picture,the Discriminant of Guassian(DoG)and Difference of Wavelet(DoW)features are *** Discriminant Analysis(LDA),a widely used dimensionality reduction technique,is also used to eliminate duplicate ***,a closest neighbor method is used to classify the *** and UCF 11 datasets were used to run extensive simulations of the suggested strategy,and the accuracy assessed after the simulations were run on Weizmann datasets for five-fold cross validation is shown to perform *** average accuracy of DoG+DoW is observed as 83.6635%while the average accuracy of Discrinanat of Guassian(DoG)and Difference of Wavelet(DoW)is observed as 80.2312%and 77.4215%,*** average accuracy measured after the simulation of proposed methods over UCF 11 action dataset for five-fold cross validation DoG+DoW is observed as 62.5231%while the average accuracy of Difference of Guassian(DoG)and Difference of Wavelet(DoW)is observed as 60.3214%and 58.1247%,*** the above accuracy observations,the accuracy of Weizmann is high compared to the accuracy of UCF 11,hence verifying the effectiveness in the improvisation of recognition accuracy.

关键词： human action recognition difference of Gaussian difference of wavelet linear discriminant analysis Weizmann UCF 11 accuracy

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Improving Support Vector Machine Performance with Advanced Kernel Methods 7

Improving Support Vector Machine Performance with Advanced K...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

ISBN: (纸本)9798350350067

In many different fields, Support Vector Machines (SVMs) have shown to be an effective tool for regression and classification problems. When using support vector machines (SVMs), the kernel function - which specifies the feature space where the data is linearly separable - has a significant impact on the performance of the model. In this research, we look at how to improve SVM performance by creating and using new kernel approaches. We contrast the effectiveness of conventional kernels like the linear, polynomial, and radial basis function (RBF) kernels with new kernel functions designed to capture complicated data structures. Extensive studies on benchmark datasets show that classification accuracy, convergence speed, and generalisability are significantly improved. Applications in image recognition, bioinformatics, and financial forecasting are well-suited to the suggested kernels because to their exceptional performance in dealing with high-dimensional and non-linearly separable data. Research and practitioners in the area may benefit greatly from this study's thorough examination of the computational complexity and actual implementation of these sophisticated kernel approaches. It offers useful insights for optimising SVM performance. © 2024 IEEE.

关键词： Support vector regression

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