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Composing Synergistic Macro Actions for Reinforcement Learning Agents

IEEE Transactions on Neural Networks and Learning systems 2024年第5期35卷 7251-7258页

作者： Chen, Yu-Ming Chang, Kaun-Yu Liu, Chien Hsiao, Tsu-Ching Hong, Zhang-Wei Lee, Chun-Yi Hsinchu300 Taiwan Avery Design Systems Inc. Taipei100 Taiwan University of Rostock Faculty of Computer Science and Electrical Engineering Rostock18051 Germany National Tsing Hua University Department of Computer Science Hsinchu300 Taiwan Massachusetts Institute of Technology CambridgeMA02139 United States

Macro actions have been demonstrated to be beneficial for the learning processes of an agent and have encouraged a variety of techniques to be developed for constructing more effective ones. However, previous techniques usually do not further consider combining macro actions to form a synergistic macro action ensemble, in which synergism exhibits when the constituent macro actions are favorable to be jointly used by an agent during evaluation. Such a synergistic macro action ensemble may potentially allow an agent to perform even better than the individual macro actions within it. Motivated by the recent advances of neural architecture search (NAS), in this brief, we formulate the construction of a synergistic macro action ensemble as a Markov decision process (MDP) and evaluate the constructed macro action ensemble as a whole. Such a problem formulation enables synergism to be taken into account by the proposed evaluation procedure. Our experimental results demonstrate that the proposed framework is able to discover the synergistic macro action ensembles. Furthermore, we also highlight the benefits of these macro action ensembles through a set of analytical cases. © 2012 IEEE.

关键词： Reinforcement learning

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Pager Explosion:Cybersecurity Insights and Afterthoughts

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IEEE/CAA Journal of Automatica Sinica 2024年第12期11卷 2359-2362页

作者： Chuan Sheng Wanlun Ma Qing-Long Han Wei Zhou Xiaogang Zhu Sheng Wen Yang Xiang Fei-Yue Wang Swinburne University of Technology Hawthorn VIC 3122Australia IEEE the School of Computer and Mathematical Sciences the University of AdelaideAdelaideSouth Australia 5005Australia the State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 the Department of Engineering Science Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao 999078China

PAGER EXPLOSION:THE *** 2024 Lebanon pager explosions represent one of the most unexpected and devastating technological incidents in recent *** September 17 and 18,2024,thousands of pagers and walkie-talkies exploded simultaneously across Lebanon and parts of Syria,resulting in 42 deaths and more than 3500 *** handheld communication devices,previously regarded as secure and low-profile,were rigged with concealed explosives and remotely triggered by attackers.

关键词： injuries thoughts explosion

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Improving VANET Localization Performance with Data Verification: Challenges and Solutions

Improving VANET Localization Performance with Data Verificat...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Kadhim, Aqeel Kamil Najim, Ali Hamza Ali Alheeti, Khattab M. Mohd Satar, Nurhizam Safie Abdallah Hashim, Aisa Hassan Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq Department of Computer Technical Engineering Baghdad Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia International Islamic University Malaysia Faculty of Engineering Department of Electrical and Computer Engineering Selangor Malaysia

ISBN: (纸本)9798350369106

In VANETs, the important and effective applications of vehicle localization include safety and communication applications. Thus, it is difficult to get very accurate localization in the dynamic and often very fluctuating environment of VANET. This research responds to this challenge with a keen thrust on improving vehicle localization using the Mobility Data Verification process. The primary goal of this work is to achieve high accuracy of vehicle positioning within VANET by checking the coherence of mobility data of moving vehicles. Hence, we present a concept that builds upon Mobility Data Verification to provide real-time location details from vehicles. Both GPS and onboard sensors feed this method, which uses a multi-level verification to flag out or eliminate the errors of deliberately spoofed data points. We use simulations to evaluate and validate the effectiveness of the proposed verification approach. As the proposed system, our previously developed and fine-tuned weighted localization (OWL) strategy does so. The performance evaluation results suggest that our proposed solution is feasible to distinguish automobiles that are not trustworthy and improve the performance of the localization algorithm. © 2024 IEEE.

关键词： Vane pumps

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Divide-and-Conquer Strategy for Large-Scale Dynamic Bayesian Network Structure Learning 13th

Divide-and-Conquer Strategy for Large-Scale Dynamic Bayesia...

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13th IFIP TC 12 International Conference on Intelligent Information Processing, IIP 2024

作者： Ouyang, Hui Chen, Cheng Tang, Ke Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen518055 China

ISBN: (纸本)9783031578076

Dynamic Bayesian Networks (DBNs), renowned for their interpretability, have become increasingly vital in representing complex stochastic processes in various domains such as gene expression analysis, healthcare, and traffic prediction. Structure learning of DBNs from data is a challenging endeavor, particularly for datasets with thousands of variables. Most current algorithms for DBN structure learning are adaptations from those used in static Bayesian Networks (BNs), and are typically focused on smaller-scale problems. In order to solve large-scale problems while taking full advantage of existing algorithms, this paper introduces a novel divide-and-conquer strategy, originally developed for static BNs, and adapts it for large-scale DBN structure learning. Additionally, we leverage the prior knowledge of 2 Time-sliced BNs (2-TBNs), a special class of DBNs, to enhance the performance of this strategy. Our approach significantly improves the scalability and accuracy of 2-TBN structure learning. Designed experiments demonstrate the effectiveness of our method, showing substantial improvements over existing algorithms in both computational efficiency and structure learning accuracy. In problem instances with more than 1,000 variables, our proposed approach on average improves two accuracy metrics by 74.45% and 110.94%, respectively, while reducing runtime by an average of 93.65%. Moreover, in problem instances with more than 10,000 variables, our proposed approach successfully completed the task in a matter of hours, whereas the baseline algorithm failed to produce a reasonable result within a one-day runtime limit. © IFIP International Federation for Information Processing 2024.

关键词： Bayesian networks

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Automated Surface Classification System Using Vibration Patterns - A Case Study With Wheelchairs

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第4期4卷 884-895页

作者： Yu, Haoxiang Raychoudhury, Vaskar Saha, Snehanshu Edinger, Janick Smith, Roger O. Gani, Md Osman The University of Texas at Austin Department of Electrical and Computer Engineering AustinTX78712 United States Miami University Department of Computer Science & Software Engineering OxfordOH45056 United States Bits Pilani K.K. Birla Goa Campus Department of Computer Science & Information Systems Appcair Zuarinagar403726 India University of Hamburg Department of Informatics & Distributed Operating Systems Hamburg20146 Germany University of Wisconsin-Milwaukee Department of Rehabilitation Sciences & Technology MilwaukeeWI53706 United States University of Maryland Baltimore County Department of Information Systems BaltimoreMD21250 United States

Finding a barrier-free accessible path through the built environment is necessary for wheelchair users. Researchers have identified the effect of surface vibration on the health of wheelchair users and proposed various solutions to identify and annotate mobility barriers. However, the effects of surface-induced vibration on accessibility are yet to be investigated. In order to address this problem, we present the WheelShare system, which uses machine learning to identify surfaces and curbs from vibration captured by accelerometer and gyroscope sensors (both smartphone and wearable device). WheelShare allows users to share their collected data via crowdsourcing. Based on this surface-specific vibration, we recognize 32 different surfaces found across the built environment (in Austria, China, France, Germany, India, and the USA for 32 human subjects) with an accuracy of 97.5%. The recognized surfaces are then grouped into different categories based on their characteristics. Our novel contributions in this article include the following. 1) We have developed a machine-learning-enabled surface recognition system that uses sensor data to capture surface-induced vibration patterns. 2) We have tested the robustness of the system for devices with only accelerometer data (such as low-end smartphones) and achieved an accuracy of 91.6%. 3) We have deployed our prototype WheelShare system for real-time surface classification in new sites that are unseen in training and testing and successfully recognized the surface characteristics. © 2020 IEEE.

关键词： Accelerometers

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Smart Water-Powered Renewable Energy System with IoT Integration

Smart Water-Powered Renewable Energy System with IoT Integra...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Najim, Ali Hamza Ibraheem, Ahmed Salman Ali Alheeti, Khattab M. Mohd Satar, Nurhizam Safie Abdallah Hashim, Aisha Hassan Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq Department of Computer Technical Engineering Baghdad Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia International Islamic University Malaysia Faculty of Engineering Department of Electrical and Computer Engineering Selangor Malaysia

ISBN: (纸本)9798350369106

An advanced hybrid renewable energy collecting and storage system prototype that utilizes water flow as its primary power source. Additionally, the system incorporates an energy monitoring system based on the Internet of Things (IoT) technology. The device effectively harnesses energy from the movement of water, storing it for future use in low-voltage applications. It demonstrates a maximum daily energy capture capacity of about 100Wh. The energy monitoring system, based on the Internet of Things (IoT), enables the real-time and remote monitoring of energy harvesting and usage. We thoroughly assessed the energy harvesting component across different situations to identify the crucial parameters that impact energy output. The results of our study demonstrate that several elements, including the spatial separation 'between the energy harvester and the water source, Water flow' speed, sensor orientation, and sensor density, all play significant roles in determining the quantity of energy collected. This research shows that it is possible to integrate IoT technology with hydropower renewable energy systems. It provides significant perspectives on enhancing energy extraction efficiency in practical scenarios. © 2024 IEEE.

关键词： harvesting renewable energy IoT storage energy water flow energy

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A Study of Cyber Bullying Classification Using Social Media and Texual Analysis Based on Machine Learning Approches 14

A Study of Cyber Bullying Classification Using Social Media ...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Rahman Aronno, Md. Shafiur Zumma, Md.Thoufiq Prodhan, Rashed Zohora, Fatema Tuz Sakib, Nazmus Tahmiduzzaman, K.B.M. Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh Wilmington University Department of Information Systems Technologies DE United States Bangladesh University of Business &technology Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350335095

In today's world, cyberbullying is a problem that is becoming more and more common, especially among teenagers and young people. The prevalence of social networking sites and other digital communication tools has made it simpler for offenders to harass their victims in secret and without repercussions. Natural language processing (NLP) methods have been used in recent years to categorize instances of cyberbullying and assist identify them. The language used in online conversations is examined using these approaches to look for trends and signs of cyberbullying behavior. The purpose of this study is to investigate how well NLP approaches can be used to recognize and categorize cyberbullying behavior. To provide a thorough knowledge of the many types of cyberbullying, the study will use a variety of data sources, including social media posts, chat logs, and other online conversations. Overall, this research will further our knowledge of the intricate nature of cyberbullying and shed light on the potential applications of NLP approaches to lessen its negative impacts. © 2023 IEEE.

关键词： Digital communication systems

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Smart-Fragile Authentication Scheme for Robust Detecting of Tampering Attacks on English Text

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computers, Materials & Continua 2022年第5期71卷 2497-2513页

作者： Mohammad Alamgeer Fahd N.Al-Wesabi Huda G.Iskandar Imran Khan Nadhem Nemri Mohammad Medani Mohammed Abdullah Al-Hagery Ali Mohammed Al-Sharafi Department of Information Systems King Khalid UniversityMuhayel AseerKingdom of Saudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerKingdom of Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen School of Computing Faculty of EngineeringUniversiti Teknologi MalaysiaMalaysia Department of Electrical Engineering University of Engineering and Technology PeshawarPakistan Department of Computer Science College of ComputerQassim UniversitySaudi Arabia Department of Computer Science College of Computers and Information TechnologyUniversity of BishaKSA

Content authentication,integrity verification,and tampering detection of digital content exchanged via the internet have been used to address a major concern in information and communication *** this paper,a text zero-watermarking approach known as Smart-Fragile Approach based on Soft Computing and Digital Watermarking(SFASCDW)is proposed for content authentication and tampering detection of English text.A first-level order of alphanumeric mechanism,based on hidden Markov model,is integrated with digital zero-watermarking techniques to improve the watermark robustness of the proposed *** researcher uses the first-level order and alphanumeric mechanism of Markov model as a soft computing technique to analyze English ***,he extracts the features of the interrelationship among the contexts of the text,utilizes the extracted features as watermark information,and validates it later with the studied English text to detect any *** has been implemented using PHP with VS code *** robustness,effectiveness,and applicability of SFASCDW are proved with experiments involving four datasets of various lengths in random locations using the three common attacks,namely insertion,reorder,and *** SFASCDW was found to be effective and could be applicable in detecting any possible tampering.

关键词： Watermarking soft computing text analysis hidden Markov model content authentication

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An Improved Structure for Academic Information Services through AI Chatbots

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Journal of engineering science and Technology Review 2023年第5期16卷 164-173页

作者： Ula, Mutammimul Hardi, Richki Hipiny, Irwandi Department of Information Systems Faculty of Engineering Universitas Malikussaleh Aceh Indonesia Department of informatics Universitas Mulia Balikpapan Indonesia Faculty of Computer Science and Information Technology Universiti Malaysia Sarawak Sarawak Malaysia

Academic information service is the most critical factor that must be considered in a university. Service quality is an important indicator affecting all academics’ satisfaction and loyalty. Improvement of information services on campus has been a lot of research focusing on academics. Meanwhile, the software aspect has received less attention. Researchers have previously discussed information service frameworks but have not accommodated future needs. Software that must be considered can quickly respond to the user, one of which is a chatbot. Chatbots can be used to solve problems related to question-and-answer services, especially for academic institutions that do not have specific resources to handle this work. With chatbots, redundant user questions can be handled automatically. Chatbots are increasingly being offered as an alternative source of customer service. For a user to use a chatbot for this purpose, the user must trust the chatbot to provide the necessary support. To overcome this knowledge gap, a framework can integrate software, stakeholders, and academics in higher education. Therefore it is necessary to conduct new research to help answer future needs. © 2023 School of science, IHU. All rights reserved.

关键词： Information services

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IOT Based Smart Parking System Using Ensemble Learning

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Intelligent Automation & Soft Computing 2023年第6期36卷 3637-3656页

作者： Walaa H.Elashmawi Ahmad Akram Mohammed Yasser Menna Hisham Manar Mohammed Noha Ihab Ahmed Ali Department of Computer Science Faculty of Computers and InformaticsSuez Canal University4.5 Km the Ring RoadIsmailiaIsmailia41522Egypt Department of Computer Engineering and Software Systems Faculty of EngineeringAin Shams UniversityAl-Abbaseya4543070Egypt Department of Computer Science Faculty of Computer ScienceMisr International University28 KM Cairo–Ismailia RoadCairo44971Egypt College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Higher Future Institute for Specialized Technological Studies Cairo3044Egypt

Parking space is usually very limited in major cities,especially Cairo,leading to traffic congestion,air pollution,and driver *** car parking systems tend to tackle parking issues in a non-digitized *** systems require the drivers to search for an empty parking space with no guaran-tee of finding any wasting time,resources,and causing unnecessary *** address these issues,this paper proposes a digitized parking system with a proof-of-concept implementation that combines multiple technological concepts into one solution with the advantages of using IoT for real-time tracking of park-ing *** authentication and automated payments are handled using a quick response(QR)code on entry and *** experiments were done on real data collected for six different locations in Cairo via a live popular times *** machine learning models were investigated in order to estimate the occu-pancy rate of certain ***,a clear analysis of the differences in per-formance is illustrated with the final model deployed being *** has achieved the most efficient results with a R^(2) score of 85.7%.

关键词： IoT XGBoost linear regression random forest ensemble learning isolation forest

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