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13th Innovations in Theoretical computer Science Conference, ITCS 2022

作者： Chia, Nai-Hui Chou, Chi-Ning Zhang, Jiayu Zhang, Ruizhe Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States School of Engineering and Applied Sciences Harvard University BostonMA United States Department of Computer Science Boston University MA United States California Institute and Technology PasadenaCA United States Department of Computer Science The University of Texas AustinTX United States

ISBN: (纸本)9783959772174

In this work, we initiate the study of the Minimum Circuit Size Problem (MCSP) in the quantum setting. MCSP is a problem to compute the circuit complexity of Boolean functions. It is a fascinating problem in complexity theory - its hardness is mysterious, and a better understanding of its hardness can have surprising implications to many fields in computer science. We first define and investigate the basic complexity-theoretic properties of minimum quantum circuit size problems for three natural objects: Boolean functions, unitaries, and quantum states. We show that these problems are not trivially in NP but in QCMA (or have QCMA protocols). Next, we explore the relations between the three quantum MCSPs and their variants. We discover that some reductions that are not known for classical MCSP exist for quantum MCSPs for unitaries and states, e.g., search-to-decision reductions and self-reductions. Finally, we systematically generalize results known for classical MCSP to the quantum setting (including quantum cryptography, quantum learning theory, quantum circuit lower bounds, and quantum fine-grained complexity) and also find new connections to tomography and quantum gravity. Due to the fundamental differences between classical and quantum circuits, most of our results require extra care and reveal properties and phenomena unique to the quantum setting. Our findings could be of interest for future studies, and we post several open problems for further exploration along this direction. © Nai-Hui Chia, Chi-Ning Chou, Jiayu Zhang, and Ruizhe Zhang;licensed under Creative Commons License CC-BY 4.0

关键词： Boolean functions

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Mobile Game Development Using Unity Engine 13th

Mobile Game Development Using Unity Engine

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13th International Conference on Methodologies and Intelligent Systems for Technology Enhanced Learning, mis4TEL 2023

作者： Sapundzhi, Fatima Kitanov, Anton Lazarova, Meglena Georgiev, Slavi Department of Communication and Computer Engineering Faculty of Engineering South-West University "Neofit Rilski" 66 Ivan Mihailov Street Blagoevgrad2700 Bulgaria Faculty of Applied Mathematics and Informatics Technical University of Sofia 8 Kliment Ohridski Blvd. Sofia1000 Bulgaria Institute of Mathematics and Infromatics Bulgarian Academy of Sciences 8 Acad. Georgi Bonchev Street Sofia1113 Bulgaria Department of Applied Mathematics and Statistics University of Ruse "Angel Kanchev" 8 Studentska Street Ruse7004 Bulgaria

ISBN: (纸本)9783031421334

Unity is a popular game development platform. Various industries are inspired by it and this can be a positive impact on the learning motivation, career growth and job opportunities. The aim of this paper is to develop and implement a 3D game application for education promoting through innovation and digital skills. The game consists of two parts. The first part of the developed application is a maze that the player must overcome. In this labyrinth the player has to collect a certain number of specific bags that contain pieces of the needed puzzle. The second part is a puzzle that the player begins to arrange. If the puzzle is arranged correctly, then the player can read an interesting fact about what is seen on the picture. The theme given on the picture is an educational theme and it main aim is to develop the educational and mental abilities of the playersp who may also be students. The game is suitable for a wide range of potential players as it can be interesting and educational for children, teenagers and even adults. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Image processing

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Prediction of Heat Energy Release Rate for Ammonia Combustion in a Constant Volume Combustion Chamber: A Machine Learning Approach 15

Prediction of Heat Energy Release Rate for Ammonia Combustio...

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15th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2023

作者： Fang, Lijia Singh, Hardeep Ichiyanagi, Mitsuhisa Suzuki, Takashi Raboaca, Maria Simona Filote, Constantin Verma, Chaman Sophia University Department of Engineering and Applied Sciences Tokyo Japan University of Windsor Turbulence and Energy Laboratory Windsor Canada Institute for Cryogenics and Isotopic Technologies Icsi Energy Department National Research and Development Ramnicu Valcea Romania Stefan Cel Mare University of Suceava Faculty of Electrical Engineering and Computer Science Suceava Romania Eötvös Loránd University Faculty of Informatics Budapest Hungary

ISBN: (纸本)9798350321388

Addressing global warming requires immediate and concerted action. Transitioning to non-conventional energy sources, such as solar and Aeolian power, reducing emissions from transportation and industry, and implementing sustainable land-use practices are crucial steps. Therefore, Ammonia attracts researchers when used as a fuel, and offers several potential benefits such as higher energy density. However, due to poor combustion speed of Ammonia offers slower energy release. To enhance the average flame velocity of Ammonia, the current investigation employed the exhaust gas expelled from the secondary chamber to compress the mixture within the primary chamber and facilitate its HCCI combustion. The main goal of research is the optimization on air-fuel ER to contribute to the development of more efficient and sustainable constant volume combustion. However, the time required for constructing physical prototypes and conducting experiments can be considerable, thus, to mitigate these challenges, the authors turned to machine learning tools as an alternative approach. In the proposed research, results were evaluated through Support Vector Regression. However, due to poor prediction accuracy, authors tried KNeighborsRegressor model for predicting and validating the results. Finally, it is revealed from the KNN model that data sets are highly accurate. © 2023 IEEE.

关键词： Ammonia

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Transformative effects of ChatGPT on modern education: Emerging Era of AI Chatbots

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Internet of Things and Cyber-Physical Systems 2024年第1期4卷 19-23页

作者： Gill, Sukhpal Singh Xu, Minxian Patros, Panos Wu, Huaming Kaur, Rupinder Kaur, Kamalpreet Fuller, Stephanie Singh, Manmeet Arora, Priyansh Parlikad, Ajith Kumar Stankovski, Vlado Abraham, Ajith Ghosh, Soumya K. Lutfiyya, Hanan Kanhere, Salil S. Bahsoon, Rami Rana, Omer Dustdar, Schahram Sakellariou, Rizos Uhlig, Steve Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Raygun Performance Monitoring Wellington New Zealand Center for Applied Mathematics Tianjin University Tianjin China Department of Science Kings Education London United Kingdom Cymax Group Technologies BC Canada QM Academy Queen Mary University of London London United Kingdom Jackson School of Geosciences University of Texas at Austin AustinTX United States Centre for Climate Change Research Indian Institute of Tropical Meteorology Pune India Microsoft Hyderabad India Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Machine Intelligence Research Labs AuburnWA United States Faculty of Computing and Data Science FLAME University Maharashtra Pune India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Department of Computer Science University of Western Ontario London Canada Sydney Australia School of Computer Science University of Birmingham United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom Laboratory School of Computing and Information Systems The University of Melbourne Australia

ChatGPT, an AI-based chatbot, offers coherent and useful replies based on analysis of large volumes of data. In this article, leading academics, scientists, distinguish researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research discusses ChatGPT capabilities and its use in the education sector, identifies potential concerns and challenges. Our preliminary evaluation shows that ChatGPT perform differently in different subject areas including finance, coding, maths, and general public queries. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions, transforming education through smartphones and IoT gadgets, and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported "hallucinations" within GenerativeAI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial. © 2023 The Authors

关键词： Machine learning

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Patient Monitoring and Disease Analysis Based on IoT Wearable Sensors and Cloud Computing

Patient Monitoring and Disease Analysis Based on IoT Wearabl...

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2022 International Conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2022

作者： Rosa, Sri Listia Kadir, Evizal Abdul Abbasi, Qammer Hussain Almansour, Amal Abdullah Othman, Mahmod Siswanto, Apri Department of Informatics Engineering Universitas Islam Riau Pekanbaru28284 Indonesia Department of Computer Science King Abdulaziz University Jeddah22254 Saudi Arabia Department of Applied Mathematics Universiti Teknologi Petronas Perak86400 Malaysia Department Electronics Engineering University of Glasgow GlasgowG12 8QQ United Kingdom

ISBN: (数字)9781665470957

ISBN: (纸本)9781665470957

The number of patients to be treated in healthcare facilities is increasing over time due to the growing awareness and importance of formal healthcare. Most healthcare centers lacked modern automation systems, such as continuous patient monitoring, which of schedule the doctor or nurse's visits with the patient. This research is designed to implement a new method of patient monitoring system in a treatment room, using wearable sensors enabled by the Internet of Things (IoT) technology and patient data analysis in cloud computing. The proposed system consists of several sensors to retrieve patient information, such as body temperature, heart rate, blood pressure, Electrocardiogram (ECG), and motion sensor. Those parameters are used to analyze patient disease and healthcare during treatment with real-time monitoring to ensure medical professionals obtain the latest update on patient health. The system is designed in an embedded module that is applicable for mobile phones and connected through a Wireless Fidelity (Wi- Fi) system in healthcare facilities. All the patient data retrieved by IoT sensors is delivered to cloud computing to store the data and then analyzed using Long Short-Term Memory (LSTM) Algorithm to examine data related to the patient health and illness. Results show the performance of the IoT sensing system working well and are able to detect and send the data in real-time to healthcare centers globally through a mobile device. Based on real case scenario testing performance, the system accuracy ability to send data is more than 95% while any abnormality is readily detected. Overall, the system has enormous potential for further development and widespread use in the healthcare industry for efficient operations. © 2022 IEEE.

关键词： Internet of things

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Input Image Pixel Interval method for Classification Using Transfer Learning 16

Input Image Pixel Interval method for Classification Using T...

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16th International Conference on INnovations in Intelligent SysTems and Applications, INISTA 2022

作者： Anorboev, Abdulaziz Javokhir, Musaev Hong, Jeongkyu Nguyen, Ngoc Thanh Hwang, Dosam Yeungnam University Department of Computer Engineering Gyeongsan Korea Republic of Wroclaw University of Science and Technology Department of Applied Informatics Wroclaw50-570 Poland Nguyen Tat Thanh University Faculty of Information Technology Ho Chi Minh City70000 Viet Nam

ISBN: (数字)9781665498104

ISBN: (纸本)9781665498104

Deep learning has been used in many applications where patterns from past-trained data can be extracted to predict future outcomes. Deep learning is characterized by training and testing data with the identical input feature space and same data distribution. However, whereas the data distribution is same between the training and testing data, the results might be different. This study introduces input image preprocessing, an enhanced neural network optimization method, and prediction probability ensemble to minimize the number of trainable parameters but maintain the outcome accuracy. In the suggested methodology, input images are separated into pixel interval and the fully connected layer jointly used with saved weights. Outcome results of separated input images are ensembled to the corresponding class probabilities of the original image. The results of the proposed method were compared with those of other previous methods in the image classification task and achieved successful performance accuracies in several datasets. © 2022 IEEE.

关键词： Classification (of information)

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Observation of Su-Schrieffer-Heeger Topological Model Band Structure in Integrated LNOI Coupled Ring Cavities 16

Observation of Su-Schrieffer-Heeger Topological Model Band S...

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16th Pacific Rim Conference on Lasers and Electro-Optics, CLEO-PR 2024

作者： Dinh, Hiep X. Balcytis, Armandas Ozawa, Tomoki Ota, Yasutomo Baba, Toshihiko Iwamoto, Satoshi Mitchell, Arnan Nguyen, Thach G. Rmit University Integrated Photonics and Applications Centre MelbourneVIC3000 Australia Advanced Institute for Materials Research Tohoku University Sendai980-8577 Japan Keio University Department of Applied Physics and Physico-Informatics Yokohama223-8522 Japan Yokohama National University Department of Electrical and Computer Engineering Yokohama240-8501 Japan The University of Tokyo Research Center for Advanced Science and Technology Tokyo153-8904 Japan

We report synthetic frequency dimension Su-Schrieffer-Heeger model and its band structure observation using coupled ring cavities on an integrated photonic chip. Intra-cell and inter-cell couplings between hybridized ... 详细信息

ISBN: (纸本)9798350372076

关键词： Photonic integrated circuits

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Towards Spiking Control for Dielectric Elastomer Actuators

Towards Spiking Control for Dielectric Elastomer Actuators

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International Conference on Control, Mechatronics and Automation (ICCMA)

作者： Lukas Sohlbach René Harmann Fernando M. Quintana Heng Wei Zhang Fernando Perez-Peña Karsten Schmidt Department of Computer Science and Engineering Frankfurt University of Applied Sciences Frankfurt/Main Germany Department of Informatics and Technology Engineering School of Engineering University of Cá diz Cádiz Spain Department of Automation Electronics and Computer Architecture and Networks School of Engineering University of Cádiz Cádiz Spain

Dielectric Elastomer Actuators (DEAs) can be seen as flexible capacitors, where an incompressible elastomer is sandwiched between two compliant electrodes. From the elastomer non-linear and time-dependent phenomena arise during the activation of the actuator. This results in the need for appropriate control methods, especially when they are used in the field of soft robots. Therefore, this paper presents a feedforward controller which is based on Spiking Neural Networks (SNNs). To the best of our knowledge this is the first time DEAs and SNNs are combined to make a step towards true soft robots. It was demonstrated that it is necessary to include feedback into the control scheme. Also, it was found that in the context of a SpiNN-3 development board as a neuromorphic hardware platform, neuron encoding is the most promising encoding method. This also implies that a classification approach is preferable to a regression approach.

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Recent Advances of Chimp Optimization Algorithm:Variants and Applications

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Journal of Bionic engineering 2023年第6期20卷 2840-2862页

作者： Mohammad Sh.Daoud Mohammad Shehab Laith Abualigah Mohammad Alshinwan Mohamed Abd Elaziz Mohd Khaled Yousef Shambour Diego Oliva Mohammad AAlia Raed Abu Zitar College of Engineering Al Ain University112612Abu DhabiUnited Arab Emirates College of Computer Sciences and Informatics Amman Arab UniversityAmman11953Jordan Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl Al-Bayt UniversityMafraq25113Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan MEU Research Unit Middle East UniversityAmman11831Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan School of Computer Sciences Universiti Sains Malaysia11800Pulau PinangMalaysia Faculty of Information Technology Applied Science Private UniversityAmman 11931Jordan Department of Mathematics Faculty of ScienceZagazig UniversityZagazig 7120001Egypt The Custodian of the Two Holy Mosques Institute for Haj and Umrah Research Umm Al-Qura UniversityMakkah 24382Saudi Arabia Depto.de Innovacio'n Basada en la Informacio'n y el Conocimiento Universidad de GuadalajaraCUCEI45129 GuadalajaraMexico Cyber Security Department Faculty of Science and Information TechnologyAl Zaytoonah University of JordanAmman 11733Jordan Sorbonne Center of Artificial Intelligence Sorbonne University-Abu Dhabi38044 Abu DhabiUnited Arab Emirates School of Engineering and Technology Sunway University Malaysia27500Petaling JayaMalaysia

Chimp Optimization Algorithm(ChOA)is one of the recent metaheuristics swarm intelligence *** has been widely tailored for a wide variety of optimization problems due to its impressive characteristics over other swarm intelligence methods:it has very few parameters,and no derivation information is required in the initial ***,it is simple,easy to use,flexible,scalable,and has a special capability to strike the right balance between exploration and exploitation during the search which leads to favorable ***,the ChOA has recently gained a very big research interest with tremendous audiences from several domains in a very short ***,in this review paper,several research publications using ChOA have been overviewed and ***,introductory information about ChOA is provided which illustrates the natural foundation context and its related optimization conceptual *** main operations of ChOA are procedurally discussed,and the theoretical foundation is ***,the recent versions of ChOA are discussed in detail which are categorized into modified,hybridized,and paralleled *** main applications of ChOA are also thoroughly *** applications belong to the domains of economics,image processing,engineering,neural network,power and energy,networks,*** of ChOA is also *** review paper will be helpful for the researchers and practitioners of ChOA belonging to a wide range of audiences from the domains of optimization,engineering,medical,data mining,and *** well,it is wealthy in research on health,environment,and public ***,it will aid those who are interested by providing them with potential future research.

关键词： Artificial intelligence Nature-inspired optimization algorithms Chimp optimization algorithm Optimization problems

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The Development of the Optical-Electronic Images Segmentation Method Based on the Canny Edge Detector 18

The Development of the Optical-Electronic Images Segmentatio...

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18th IEEE International Conference on computer Science and Information Technologies, CSIT 2023

作者： Khudov, Hennadii Makoveichuk, Oleksandr Khudov, Vladyslav Khizhnyak, Irina Kalimulin, Temir Khudov, Rostyslav Ivan Kozhedub Kharkiv National Air Force University Department of Radar Troops Tactic Kharkiv Ukraine Academician Y. Bugay International Scientific and Technical University Department of Computer Science and Software Engineering Kyiv Ukraine Kharkiv National University of Radio Electronics Department of Electronic Computers Kharkiv Ukraine V.N.Karazin Kharkiv National University Department of Theoretical and Applied Informatics Kharkiv Ukraine

ISBN: (纸本)9798350360462

The article deals with the problem associated with the segmentation of color images. The main groups of color image segmentation methods operation, are considered. The main advantages and disadvantages of each group of methods are shown. The paper suggests the use of the Canny edge detector for optical-electronic images obtained from space observation systems. It is proposed to regulate the parameters of the Canny edge detector in order to improve the segmentation result. It's determined that the value of the lower threshold should be no more than 10-20 unit, and the value of the upper threshold should be within 60-100 unit. © 2023 IEEE.

关键词： Image segmentation

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