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Information science and Education (ICISE-IE), International Conference on

作者： Huan Chen Ying Liu Chan Shan Hequn Zhang School of Journalism and Communication Minjiang University Fuzhou China Xiamen Institute of Software Technology Xiamen China Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia College of Information Engineering Jimei University Xiamen China

ISBN: (纸本)9781665438308

In the era of rapid development of intelligent technology, the children’s digital publishing industry is facing a disruptive change, and this change may be realized through the reconstruction of the supply and demand chain. The value power of source content creation and the ability to gather users have been deconstructed. The increase in the amount of content requires platform institutions to have a more professional content operation system. The convenience of payment technology has greatly increased the digital content payment rate of users of children’s digital products. A development model of the supply and demand chain of children’s digital publishing with more innovative flexibility and enabling effects.

关键词： Electronic publishing Information science Supply and demand Statistical analysis Transfer learning Refining Tail

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Transformers in Medical Image Analysis: A Review

arXiv

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arXiv 2022年

作者： He, Kelei Gan, Chen Li, Zhuoyuan Rekik, Islem Yin, Zihao Ji, Wen Gao, Yang Wang, Qian Zhang, Junfeng Shen, Dinggang Medical School of Nanjing University Nanjing China National Institute of Healthcare Data Science Nanjing University Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China BASIRA Lab Faculty of Computer and Informatics Engineering Istanbul Technical University Istanbul Turkey School of Science and Engineering Computing University of Dundee United Kingdom School of Biomedical Engineering ShanghaiTech University Shanghai China Department of Research and Development Shanghai United Imaging Intelligence Co. Ltd. Shanghai China

Transformers have dominated the field of natural language processing, and recently impacted the computer vision area. In the field of medical image analysis, Transformers have also been successfully applied to full-stack clinical applications, including image synthesis/reconstruction, registration, segmentation, detection, and diagnosis. Our paper aims to promote awareness and application of Transformers in the field of medical image analysis. Specifically, we first overview the core concepts of the attention mechanism built into Transformers and other basic components. Second, we review various Transformer architectures tailored for medical image applications and discuss their limitations. Within this review, we investigate key challenges revolving around the use of Transformers in different learning paradigms, improving the model efficiency, and their coupling with other techniques. We hope this review can give a comprehensive picture of Transformers to the readers in the field of medical image analysis. © 2022, CC BY-SA.

关键词： Diagnosis

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Exploring the integration of multi criteria decision analysis in the clean energy biodiesels applications: A systematic review and gap analysis

引用

engineering Applications of Artificial Intelligence 2024年 133卷

作者： Alamoodi, A.H. Garfan, Salem Al-Zuhairi, Omar Zaidan, B.B. Zaidan, A.A. Albahri, O.S. Y.Y. Ahmaro, Ibraheem Albahri, A.S. Yussof, Salman Magableh, Aws A. Institute of Informatics and Computing in Energy Universiti Tenaga Nasional Malaysia Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Perak Malaysia Applied Science Research Center Applied Science Private University Amman Jordan Low Dimensional Materials Research Centre Department of Physics Faculty of Science Universiti Malaya Kuala Lumpur50603 Malaysia SP Jain School of Global Management Lidcombe SydneyNSW2141 Australia International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Douliu Taiwan Victorian Institute of Technology Australia Computer Techniques Engineering Department Mazaya University College Nasiriyah Iraq Computer Science Department College of Information Technology Hebron University Hebron Palestine Technical College Imam Ja'afar Al-Sadiq University Baghdad Iraq Department of Information Systems Yarmouk University Irbid Jordan Baghdad Iraq Department of Computing College of Computing and Informatics Universiti Tenaga Nasional Malaysia Departments of Software Engineering Prince Sultan University Riyad Saudi Arabia

Biodiesel has gained tremendous attention from researchers due to its environmentally friendly and renewable fuel manufactured domestically from vegetable oils or animal fats. Multiple factors, including emission and fuel efficiency, measure the performance of biodiesel. These characteristics make the process of evaluation and benchmarking a multi-criteria decision-making (MCDM) problem. This research attempts to survey the literature for multi-criteria decision-making applications on biodiesel. This systematic review was carried out on four scientific databases: PubMed, scienceDirect, IEEE Xplore, and WoS. The collected articles were summarized and mapped into a coherent taxonomy, while the main challenges, motivations, and recommendations were collected and categorized for better understanding and finding out essential characteristics. The final set of included articles was 33, and they were categorized into six different categories. Most articles (n=15/33) investigated the evaluation and selection of biodiesel alternatives using MCDM. Furthermore, a state-of-art bibliometric analysis is provided. The analysis demonstrates several challenges in this area, such as source selection, optimum fuel blending, and uncertainty. This article is expected to assist future researchers in addressing the current gaps and contribute to advancing this area. © 2024 Elsevier Ltd

关键词： Biodiesel

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A Time Effect based Collaborative Filtering Approach for User Preference Statistics and Recommendation 2

A Time Effect based Collaborative Filtering Approach for Use...

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2019 2nd International Conference on computer Information science and Artificial Intelligence, CISAI 2019

作者： Chen, Yuxi Zhang, Xiaotong Zhao, Qing Akhtar, Faheem School of Statistics and Mathematics Country Central University of Finance and Economics Beijing100081 China Harpur College of Arts and Sciences Binghamton University-SUNY New York13902 United States Faculty of Information Technology Beijing University of Technology Beijing100124 China Department of Computer Science Vrije Universiteit Amsterdam1081 HV Netherlands School of Software Engineering Beijing University of Technology Beijing Engineering Research Center for IoT Software and Systems Beijing100124 China Department of Computer Science Sukkur IBA University Sukkur65200 Pakistan

With the rapid development of the information technologies in the financial field, extracting meaningful information from a massive amount of data is hugely significant for efficient business decision making. The recommendation system is an intelligent system that applies historical knowledge of users to infer their preferences and make a personalized recommendation. However, it suffers from the problem of time effect of user's behaviour, which means a user's interests may change over time. To overcome this problem, we propose a time effect based collaborative filtering approach to adaptively statistics the change of user preferences. Firstly, Item-based collaborative filtering is used to calculate rating similarity between items. Since an Item-based collaborative filtering algorithm doesn't consider the time effect;next, the time decay function is proposed to statistics the change of user interests. Experimental results show that the proposed scheme retained higher accuracy compare to traditional collaborative filtering method. © Published under licence by IOP Publishing Ltd.

关键词： Collaborative filtering

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Wind Speed Time Series Prediction Using a Single Dendritic Neuron Model

Wind Speed Time Series Prediction Using a Single Dendritic N...

引用

International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI)

作者： Zhenyu Song Tianle Zhou Xuemei Yan Cheng Tang Junkai Ji College of Computer Science and Technology Taizhou University Taizhou China School of Computer Engineering Jiangsu University of Technology Changzhou China Faculty of Engineering University of Toyama Toyama Japan College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9781728196381

ISBN: (纸本)9781728196398

The chaotic and intrinsic complexity of wind speed time series calls for an appropriate model to accurately predict the moving tendency. In this study, we proposed a single dendritic neuron model (S-DNM) using a back-propagation algorithm to accomplish wind speed forecasting. First, based on mutual information method and false nearest neighbor method, the time delay and embedding dimension are calculated. Second, the phase space is reconstructed by time delay and embedding dimension, and the characteristics of wind speed time series are analyzed. Then, the maximum Lyapunov exponent is applied to confirm the chaotic properties of the wind speed time series. Finally, using wind speed data from Sotavento located in Galicia, Spain, the performance of the forecasting method is evaluated for short-term horizons (1 hour ahead). Experimental results show that the proposed S-DNM performed better than the traditional ELMAN model and classic multi-layered perceptron network. Thus, it is concluded that the proposed model is suitable for wind speed forecasting.

关键词： Wind speed Delay effects Time series analysis Neurons Predictive models Prediction algorithms Forecasting

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A Quantum System Control Method Based on Enhanced Reinforcement Learning

Research Square

引用

Research Square 2021年

作者： Liu, Wenjie Wang, Bosi Fan, Jihao Ge, Yebo Zidan, Mohammed School of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China Engineering Research Center of Digital Forensics Ministry of Education Nanjing210044 China School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China Hurghada Faculty of Computers and Artificial Intelligence South Valley University Egypt

The design of quantum system control is a key task to a powerful quantum information technology. In practical, traditional quantum system control methods often face different constraints, and are easy to cause both leakage and stochastic control errors under the condition of limited resources. Reinforcement learning has been proved as an efficient way to complete the quantum system control task. So a quantum system control method based on enhanced reinforcement learning (QSC-ERL) is proposed. A satisfactory control strategy is obtained through enhanced reinforcement learning so that the quantum system can be evolved accurately from the initial state to the target state. According to the number of candidate unitary operations, the three-switch control is used for simulation experiments. Compared with other methods, the QSC-ERL can achieve high fidelity learning control of quantum systems and improve the efficiency of quantum system control. © 2021, CC BY.

关键词： Reinforcement learning

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Lightweight image encryption using integer reversible discrete Dual-Hahn transform on embedded systems

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engineering science and Technology, an International Journal 2025年 69卷

作者： Mohamed Yamni Achraf Daoui Chakir El-Kasri May Almousa Ali Abdullah S. AlQahtani Ahmed A. Abd El-Latif Engineering Sciences Laboratory Polydisciplinary Faculty Sidi Mohamed Ben Abdellah University Oujda Road 1223 Taza Morocco Industrial Systems Engineering and Energy Conversion (ISEEC) Faculty of Science and Technology Abdelmalek Essaadi University Tangier Morocco Information Technology Department College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia Software Engineering Department Prince Sultan University Riyadh 11586 Saudi Arabia EIAS Data Science Lab College of Computer and Information Sciences and Center of Excellence in Quantum and Intelligent Computing Prince Sultan University Riyadh 11586 Saudi Arabia Department of Mathematics and Computer Science Faculty of Science Menoufia University 32511 Egypt

The security and performance of a cryptographic algorithm can be reduced when implemented on an embedded system, such as a 32-bit microcontroller, due to the limitation of hardware resources, such as computational precision, processing power, and memory. This paper introduces the Integer Reversible Discrete Dual-Hahn Transform (IRDDHT), a novel integer-based transform designed for secure image encryption in resource-constrained embedded systems. The IRDDHT ensures lossless encryption by eliminating rounding errors common in floating-point transforms, maintaining image fidelity during encryption and decryption. We then propose a lightweight encryption algorithm based on IRDDHT, which introduces implicit diffusion and relies on parameter sensitivity to enhance security. When implemented on the ESP32 microcontroller, the proposed algorithm occupies less than 4 % of SRAM and 8.5 % of Flash memory, achieving a throughput of 170.67 kbps at the device’s maximum clock frequency. It is energy-efficient, capable of encrypting up to 129,025 grayscale images of size 256 × 256 pixels on a standard 3300 mAh battery. The algorithm ensures perfect decryption with no loss of image fidelity and demonstrates strong resistance to statistical attacks with near-ideal correlation values, as well as brute-force attacks with a large key space of 2 199 . These results indicate that the IRDDHT-based encryption method is an effective solution for secure image encryption in embedded systems.

关键词： Embedded systems Integer reversible discrete Dual-Hahn transform Lightweight algorithm Lossless image encryption Security

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Performance Evaluation of PID Controller Parameters Gain Optimization for Wheel Mobile Robot Based on Bat Algorithm and Particle Swarm Optimization 10th

Performance Evaluation of PID Controller Parameters Gain Opt...

引用

10th National Technical Seminar on Underwater System Technology, NUSYS 2018

作者： Suarin, Nur Aisyah Syafinaz Pebrianti, Dwi Ann, Nurnajmin Qasrina Bayuaji, Luhur Syafrullah, Muhammad Riyanto, Indra Faculty of Electrical and Electronics Engineering Universiti Malaysia Pahang Pekan Malaysia Faculty of Computer Science and Software Engineering Universiti Malaysia Pahang Pekan Malaysia Magister of Computer Science Universitas Budi Luhur Jakarta12260 Indonesia Faculty of Engineering Universitas Budi Luhur Jakarta12260 Indonesia

ISBN: (纸本)9789811337079

Tuning Proportional Integral Differential (PID) controller to the best value of gains is essential to develop a reliable controller for wheel mobile robot (WMR). WMR is a nonlinear system that falls into category of underactuated system where the inputs number is less than output number. The selection of PID gains for such system is highly difficult. Optimization of PID controller using Bat Algorithm (BA) is presented in this paper. BA as a nature inspired algorithm is used to search the optimum PID gains for wheel mobile robot i.e. an off-the-shelf mobile robot called mBot so that the system will have good performance in term of steady state error and time response. Kinematic model of mBot robot is used to develop a simulation model to simulate the system. The result of tuning and optimizing PID gains using BA is compared with Particle Swarm Optimization (PSO). The tuning result by using BA outperformed PSO methods with faster processing time and best values of gain Kp and Kd to be applied in the WMR. The PID gain values obtained from the BA and PSO are then applied on the WMR model. The performance of BA shows better result compared to PSO. Settling time for BA is 10.62 s compared with PSO 11.1 s, rise time for BA is 3.24 s while PSO 2.68 s, percentage overshoot of BA 28.2% compared with PSO 28.4%. Thus, the result proven that BA is able to optimize gain of PID controller better than PSO. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Controllers

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Unified Instance and Knowledge Alignment Pretraining for Aspect-based Sentiment Analysis

arXiv

引用

arXiv 2021年

作者： Liu, Juhua Zhong, Qihuang Ding, Liang Jin, Hua Du, Bo Tao, Dacheng Research Center for Graphic Communication Printing and Packaging Institute of Artificial Intelligence Wuhan University Wuhan China National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan China JD Explore Academy at *** Beijing China Affiliated Hospital of Kunming University of Science and Technology Kunming China School of Computer Science Faculty of Engineering The University of Sydney Sydney Australia

The goal of aspect-based sentiment analysis (ABSA) is to determine the sentiment polarity towards an aspect. Because of the expensive and limited amounts of labelled data, the pretraining strategy has become the de facto standard for ABSA. However, there always exists a severe domain shift between the pretraining and downstream ABSA datasets, which hinders effective knowledge transfer when directly fine-tuning, making the downstream task suboptimal. To mitigate this domain shift, we introduce a unified alignment pretraining framework into the vanilla pretrain-finetune pipeline, that has both instance- and knowledge-level alignments. Specifically, we first devise a novel coarse-to-fine retrieval sampling approach to select target domain-related instances from the large-scale pretraining dataset, thus aligning the instances between pretraining and the target domains (First Stage). Then, we introduce a knowledge guidance-based strategy to further bridge the domain gap at the knowledge level. In practice, we formulate the model pretrained on the sampled instances into a knowledge guidance model and a learner model. On the target dataset, we design an on-the-fly teacher-student joint fine-tuning approach to progressively transfer the knowledge from the knowledge guidance model to the learner model (Second Stage). Therefore, the learner model can maintain more domain-invariant knowledge when learning new knowledge from the target dataset. In the Third Stage, the learner model is finetuned to better adapt its learned knowledge to the target dataset. Extensive experiments and analyses on several ABSA benchmarks demonstrate the effectiveness and universality of our proposed pretraining framework. Our source code and models are publicly available at https://***/WHU-ZQH/UIKA. Copyright © 2021, The Authors. All rights reserved.

关键词： Sentiment analysis

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Evaluation of the ARCore Indoor Localization Technology

Evaluation of the ARCore Indoor Localization Technology

引用

Roedunet International Conference (RoEduNet)

作者： Anca Morar Maria Anca Băluţoiu Alin Moldoveanu Florica Moldoveanu Alex Butean Victor Asavei University POLITEHNICA of Bucharest Faculty of Automatic Control and Computer Science Bucharest Romania Lucian Blaga University of Sibiu Faculty of Engineering Industrial Software Sibiu Romania

ISBN: (数字)9780738112657

ISBN: (纸本)9781665418850

Augmented reality has become a very powerful tool nowadays. With the recent technological advance, smartphones have the ability to display augmented content on the screen. What makes the difference is the naturalness with which the multimedia content is superimposed over the video stream acquired with the phone's camera. This perfect overlay depends on the accurate estimation of the position and orientation of the smartphone's camera relative to the 3D representation of the space. ARCore is an augmented reality framework that computes the position and orientation of the smartphone. In order to assess the possibility of integrating ARCore within a virtual and augmented reality platform, we evaluated the accuracy of the ARCore localization technology, using the HTC Vive system as a reference. We designed an evaluation methodology, along with visualization tools that enable a thorough comparison between these two technologies.

关键词： Location awareness Smart phones Augmented reality Cameras Trajectory Three-dimensional displays computer science

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