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Domain Adaptation in Visual Reinforcement Learning via Self-Expert Imitation with Purifying Latent Feature

Domain Adaptation in Visual Reinforcement Learning via Self-...

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2024 International conference on Intelligent Robots and systems

作者： Chen, Lin Huang, Jianan Zhou, Zhen Wang, Yaonan Mo, Yang Miao, Zhiqiang Zeng, Kai Feng, Mingtao Wang, Danwei Hunan Univ Sch Elect & Informat Engn Changsha 410082 Peoples R China Natl Engn Res Ctr Robot Visual Percept & Control Changsha 410082 Peoples R China Xidian Univ Sch Comp Sci & Technol Xian 710126 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Nanyang Ave Singapore 639798 Singapore

ISBN: (纸本)9798350377712;9798350377705

Generalizing visual reinforcement learning is fundamental to robot visual navigation, involving the acquisition of a policy from interactions with source environments to facilitate adaptation to analogous, yet unfamiliar target environments. Recent advancements capitalize on data augmentation techniques, self-supervised learning methods, and the generative adversarial network framework to train policy neural networks with enhanced generalizability. However, current methods, upon extracting domain-general latent features, further utilize these features to train the reinforcement learning policy, resulting in a decline in the performance of the learned policy guiding the agent to accomplish tasks. To tackle these challenges, a framework of self-expert imitation with purifying latent features was devised, empowering the policy to achieve robust and stable zero-shot generalization performance in visually similar domains previously unseen, without diminishing the performance of guiding the agent to accomplish tasks. The extraction method of domain-general latent features is proposed to enhance their quality based on the variational autoencoder. Extensive experiments have shown that our policy, compared with state-of-the-art counterparts, does not diminish the performance of the policy guiding the agent to accomplish tasks after generalization.

关键词： Adversarial machine learning

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Optimizing Lighting Efficiency Through Automated Smart Lighting systems: A Study on network-Based performance 6

Optimizing Lighting Efficiency Through Automated Smart Light...

引用

6th International conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Sari, Lestari Ningratna Anwariningsih, Sri Huning Khusnuliawati, Hardika Susilo, Dahlan Informatics Departement Universitas Sahid Surakarta Surakarta Indonesia

ISBN: (纸本)9798331539603

Electrical lighting systems contribute to approximately 20% of global electricity consumption and 6% of carbon dioxide (CO2) emissions, underlining the urgent need for more efficient and adaptable lighting solutions. Addressing the inefficiency of traditional manual lighting controls is crucial for reducing energy waste, especially in the global energy crisis and increasing carbon emission reduction targets. Despite advances in lighting systems, existing solutions often lack integration of intelligent control and real-time adaptability across diverse networks. This research addresses these gaps by developing a novel automated smart lighting system to optimize energy usage through intelligent control across multiple network environments. The proposed prototype integrates Raspberry Pi modules, relay modules, JavaScript Object Notation (JSON), Thonny IDE, and a web-based Application Programming Interface (API) to enable seamless automation and remote operation. The proposed system was tested under various conditions: local area network (LAN), wide area network (WAN), offline functionality, and across multiple web browsers to evaluate latency and operational reliability. The research results demonstrate that the system performs efficiently online and offline within the same or different networks. For online operations within the same network, delay times for light A ranged between 27 to 39 seconds, light B from 37 to 39 seconds, and light C at 33 seconds. The fastest delay on different networks was 5 seconds, with the longest at 39 seconds. Chrome browser exhibited the fastest performance with a delay of 6 seconds, while Samsung Internet had the longest delay at 39 seconds. These findings highlight the system's adaptability and potential for energy savings, though further work is required to optimize latency and extend network coverage. © 2024 IEEE.

关键词： Web browsers

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network performance Analysis Of SCTP Protocol In Multihoming Transmission Through 5G network

Network Performance Analysis Of SCTP Protocol In Multihoming...

引用

2024 International Visualization, Informatics and Technology conference, IVIT 2024

作者： Baharudin, Shahidatul Arfah Taufik, Nurul Iman Muhammad Munisamy, Sri Banu Lajis, Adidah Universiti Kuala Lumpur Miit Computer Engineering Technology Section Kuala Lumpur Malaysia

ISBN: (纸本)9798350354447

The research presents that the Stream control Transmission Protocol (SCTP) provides a very interesting alternative to classical TCP and UDP, especially in multihoming settings. We therefore specifically sought to test SCTP's performance in multihoming scenarios within 5G networks and compare its reliability to TCP and UDP under various network Address Translation (NAT) settings through complete simulations. We took care to focus on the important performance metrics of throughput, latency, packet loss, and connection stability. The ability of multihoming with SCTP, where a single SCTP endpoint can support several IP addresses, was also tested. In the context that devices will be able to have more than one network interface, this feature is welcomed in 5G networks. An empirical investigation revealed that in NAT settings, while TCP and UDP see higher packet loss and more delay, SCTP maintains a more reliable connection. The as much as needed ultra-reliable and low-latency communications transport protocol for 5G must be very stable. For example, applications such as online gaming, video conferencing, and major IoT operations will be relying on this new network. This paper has specifically emphasized the potential that SCTP's multihoming feature and improved performance metrics hold for 5G networks. This means that SCTP should be recommended to increase network stability and enhance performance, based on the results presented here that have indicated its great potential. Further research will be done on enhancing SCTP for specific 5G use cases under different deployment scenarios and investigating its integration with promising new technologies like edge computing and network slicing. © 2024 IEEE.

关键词： 5G mobile communication systems

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Hierarchy-Aware Bilateral-Branch network for Imbalanced Hierarchical Text Classification 34th

Hierarchy-Aware Bilateral-Branch Network for Imbalanced Hier...

引用

34th International conference on Database and Expert systems Applications (DEXA)

作者： Zhao, Jiangjiang Lie, Jiyi Fukumoto, Fumiyo Hangzhou Dianzi Univ Hangzhou Peoples R China Univ Yamanashi Kofu Japan

ISBN: (纸本)9783031398209;9783031398216

Hierarchical text classification is an essential task in natural language processing. Existing studies focus only on label hierarchy structure, such as building classifiers for each level of labels or employing the label taxonomic hierarchy to improve the hierarchy classification performance. However, these methods ignore issues with imbalanced datasets, which present tremendous challenges to text classification performance, especially for the tail categories. To this end, we propose Hierarchyaware-Bilateral-Branch-network (HiBBN) to address this problem, where we introduce the bilateral-branch network and apply a hierarchy-aware encoder to model text representation with label dependencies. In addition, HiBBN has two network branches that cooperate with the uniform sampler and reversed sampler, which can deal with the data imbalance problem sufficiently. Therefore, our-model handles both hierarchical structural information and modeling of tail data simultaneously, and extensive experiments on benchmark datasets indicate that our model achieves better performance, especially for fine-grained categories.

关键词： Hierarchical Text Classification Imbalanced Data

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A study on Anomaly-based Intrusion Detection systems Employing Supervised Deep Learning Techniques 8

A study on Anomaly-based Intrusion Detection Systems Employi...

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8th International conference on Inventive systems and control, ICISC 202

作者： Shaffi, Abubucker.S. Chacko, John Velloreuzhathil Eliyan, Greeshma Balaji, S. Gulf College Department of Computing Sciences Oman Al Zahra College for Women Department of Information Technology Ghala Muscat Oman

ISBN: (纸本)9798350386578

The rise of smart cities, driverless automobiles, smart watches, and mobile banking has led to increased reliance on the Internet. Although technology has enormous advantages for people and society, it also introduces threats. Cyber attacks are more common in this digital world and the intruders are working hard to enter into the business websites or an organization data server. Hence, integrating an intrusion detection system (IDS) is essential in the security environment because it enables IT infrastructure to resist threats. Conventional IDS are limited to detect only sophisticated attacks and fails to detect the hidden and other anomalies that occur in the network systems. An accurate and strong approach for IDS must be created to solve this difficulty for the successful functioning of businesses. The present study explores the use of supervised Deep Learning (DL) techniques and recommends an effective model for anomaly detection. The performance evaluation of the model is performed using NSL-KDD dataset and KDDcup99 and the explored DL models in this study are compared in terms of accuracy and precision. © 2024 IEEE.

关键词： network intrusion

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Advanced Persistent Threats Detection based on Deep Learning Approach 6

Advanced Persistent Threats Detection based on Deep Learning...

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IEEE 6th International conference on Industrial Cyber-Physical systems (ICPS)

作者： Eke, Hope Nkiruka Petrovski, Andrei Robert Gordon Univ Sch Comp Aberdeen Scotland Natl Subsea Ctr Sch Comp Aberdeen Scotland

ISBN: (纸本)9798350311259

Advanced Persistent Threats (APTs) have been a major challenge in securing both Information Technology (IT) and Operational Technology (OT) systems. APT is a sophisticated attack that masquerade their actions to navigates around defenses, breach networks, often, over multiple network hosts and evades detection. It also uses "low-and-slow" approach over a long period of time. Resource availability, integrity, and confidentiality of the operational cyber-physical systems (CPS) state and control is highly impacted by the safety and security measures in place. A framework multi-stage detection approach termed "APT(DASAC)" to detect different tactics, techniques, and procedures (TTPs) used during various APT steps is proposed. Implementation was carried out in three stages: (i) Data input and probing layer - this involves data gathering and pre-processing, (ii) Data analysis layer;applies the core process of "APT(DASAC)" to learn the behaviour of attack steps from the sequence data, correlate and link the related output and, (iii) Decision layer;the ensemble probability approach is utilized to integrate the output and make attack prediction. The framework was validated with three different datasets and three case studies. The proposed approach achieved a significant attacks detection capability of 86.36% with loss as 0.32%, demonstrating that attack detection techniques applied that performed well in one domain may not yield the same good result in another domain. This suggests that robustness and resilience of operational systems state to withstand attack and maintain system performance are regulated by the safety and security measures in place, which is specific to the system in question.

关键词： Advanced Persistent Threats Cyber-Physical systems Critical Infrastructures Deep Learning Industrial control systems Supervisory control and Data Acquisition

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Tariff Versus Sanction Under Bounded Rationality 11th

Tariff Versus Sanction Under Bounded Rationality

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11th International conference on network Games, control and Optimization

作者： Pokou, Fredy Fochesato, Marta Le Cadre, Helene Univ Lille Inria CNRS Cent LilleUMR 9189 CRIStAL F-59000 Lille France Swiss Fed Inst Technol Automat Control Lab Dept Elect Engn & Informat Technol Phys Str CH-38092 Zurich Switzerland

ISBN: (纸本)9783031785993;9783031786006

We formulate the design of a taxation mechanism as a Stackelberg game assuming: a) perfect competition, with exogenous prices;b) imperfect competition, captured through a variational inequality approach, with endogenous prices. Three settings of the mechanism are considered: (i) benchmark involving no taxation, (ii) optimum tariff, (iii) optimum sanction. The expected utility maximization formulation of the game is extended further by relying on cumulative prospect theory to account for the bounded rationality of the stakeholders. We derive closed-form mappings linking the outcomes of the three settings. Additionally, we assess the impact of bounded rationality through a new performance metric, the Price of Irrationality. Numerical results are derived on a randomized instance of a gas trading game between Europe, Asia, and Russia.

关键词： Game Theory Sanction Design Cumulative Prospect Theory

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Dynamic Bidirectional-controlled Inverter-Based Grid Optimized by Neural networks

Dynamic Bidirectional-Controlled Inverter-Based Grid Optimiz...

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2024 Vehicle Power and Propulsion conference

作者： Alzayed, Mohamad Lemaire, Michel Chaoui, Hicham Massicotte, Daniel Carleton Univ DoE Ottawa ON Canada UQTR DoEC Trois Rivieres PQ Canada Univ Quebec Trois Rivieres Dept Elect & Comp Engn Trois Rivieres PQ Canada Carleton Univ Dept Elect Ottawa ON Canada

ISBN: (纸本)9798331541613;9798331541606

Grid-based voltage source inverters frequently utilize the droop control technique combined with inner/outer voltage and current regulation mechanisms to ensure a dependable electricity supply. This study seeks to introduce a Cascade-Forward Neural network (CFNN) control approach designed to lead inverter-based grids when operating in grid-connected or islanded modes, focusing on improving the transient state performance of the CFNN technique. The suggested approach involves utilizing the inverter in a bidirectional manner, suitable for a diverse array of battery energy storage systems and distributed generation setups. The proposed strategy leverages CFNN to grasp the inverter's non-linear model, enabling precise monitoring of demand and reference power across various operational scenarios within smart grid applications. Furthermore, the approach redefines the grid control concept, guiding the inverter according to optimal parameters that encompass power demand, reference power, equipment dimensions, and external disturbances. Notably, this method circumvents the need for any manual tuning procedures. Furthermore, incorporating dynamic elements into the approach improves the protection system's responsiveness. This ensures continuous power supply during faults, enabling a more effective and rapid response, enhancing system resilience, and reducing downtime. This dual advantage of better power supply and heightened protection system sensitivity highlights the proposed method's significance in fortifying the reliability of power systems.. To assess the efficacy of the suggested CFNN controller, its power tracking, operational capabilities, and dynamic response are assessed via multiple experimental trials employing the hardware-in-the-loop (HIL) approach across various scenarios. The outcomes of these tests are meticulously compared to a well-established conventional strategy, affirming the effectiveness of the proposed method.

关键词： dynamic control inverter-based grid neural network

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Learning state observers for recurrent neural network models 63

Learning state observers for recurrent neural network models

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63rd IEEE conference on Decision and control, CDC 2024

作者： Bonassi, Fabio Andersson, Carl Mattsson, Per Schon, Thomas B. Uppsala University Department of Information Technology Lägerhyddsvägen 1 Uppsala751 05 Sweden

ISBN: (纸本)9798350316339

In this paper, we discuss the problem of learning state observers for Recurrent Neural network (RNN) black-box models of dynamical systems. State observers are indeed key to designing state-feedback control laws, such as nonlinear Model Predictive control, with satisfactory closed-loop performance. Besides, they can also improve the training procedure of RNN models themselves. Then, we summarize recent developments aimed at jointly learning RNN models and neural network-based state observers, and we propose a new structure based on the recent S 5 architecture. We finally test various observer structures on a p H neutralization process benchmark system, showing the advantages and shortcomings of each architecture. © 2024 IEEE.

关键词： Neural network models

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Advanced Intrusion Detection systems Leveraging Knowledge Graph-Based Techniques 12

Advanced Intrusion Detection Systems Leveraging Knowledge Gr...

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2024 12th International conference on systems and control, ICSC 2024

作者： Daoud, Mohamed Amine Mostefaoui, Sid Ahmed Mokhtar Meghazi, Hadj Madani Labbadi, Ourida Zenina, Chaima Bouguessa, Abdelkader Ibn-Khaldoun University of Tiaret LRIAS Lab Dept. of Computer Sciences Algeria Ibn-Khaldoun University of Tiaret Dept. of Computer Sciences Algeria

ISBN: (纸本)9798331531812

The integration of intrusion detection systems (IDS) is crucial for strengthening network security. Improving IDS performance requires advanced techniques for handling intrusion detection data, with machine learning playing a key role. However, machine learning methods often face difficulties in detecting complex attack patterns and are prone to generating a high number of false positives, especially when dealing with unknown attacks. To overcome these issues, enhancing IDS capabilities with knowledge graphs is essential. Knowledge graphs have proven to be valuable tools for modeling and analyzing complex interactions within security data. This article proposes an approach that combines knowledge graphs with machine learning and deep learning. By harnessing the semantic modeling, querying, and reasoning capabilities of knowledge graphs, this integration aims to address the challenges of intelligent detection and decision-making in cybersecurity. © 2024 IEEE.

关键词： network intrusion

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