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IEEE International Conference on Control System, computing and Engineering (ICCSCE)

作者： Samuel-Soma M. Ajibade Bayan Issa Muhammed Basheer Jasser Farrukh Hassan Ghassan Saleh ALDharhani Ismail Ahmed Al-Qasem Al-Hadi Kayode Akinlekan Akintoye Almighty Cortezo Tabuena Department of Computing and Information Systems School of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Darul Ehsan Malaysia Department of Computer Engineering Istanbul Ticaret University Istanbul Turkiye Faculty of Informatics Engineering University of Aleppo Syria Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur Malaysia Faculty of Information Science and Technology UKM University Bangi Selangor Malaysia Department of Computer Science The Federal Polytechnic Ado Ekiti Ekiti State Nigeria College of Arts and Sciences University of the City of Valenzuela Philippines

ISBN: (数字)9798350364507

ISBN: (纸本)9798350364514

This study examines the academic field of Artificial Intelligence in Education (AI-ED) by examining the publications and indexed documents in Scopus from 2010 to 2023. Consequently, we examined the publishing trends, exemplary articles, and the most engaged participants and funding institutions in the domain of AI-ED research. A bibliometric analysis was performed to examine the network of co-authorships, keywords, and citations in the domain of AIED research. The Scopus search resulted in the discovery of 10,474 published documents on AI-ED. Additionally, trends analysis indicated a significant increase of over 3,000% in publications between 2010 and 2023. Conference papers are the most often utilized document type, including 6757 publications or $64.5 \%$ of the overall amount. The ACM International Conference Proceeding Series is the primary source for published materials, accounting for 675 publications or $6.44 \%$ of the total publications on the topic. The most prolific researchers in the field of AI-ED are Breazeal, C and Chai C.S, each having published 18 documents. Additionally, the two institutions that have produced the highest number of published documents are Carnegie Mellon University (CMU) and the University of NC State University (NCSU). The output of the affiliations active in the topic is primarily attributed to the funders, specifically the National Science Foundation, which is the leading funding institution based in the United States and has contributed to 354 publications. An analysis of keyword co-occurrence revealed 6 main study areas that cover the essential tools, theories, methodologies, and socioeconomic and financial aspects of artificial intelligence in education (AIED). Future research in the field of artificial intelligence in education will focus on employing sophisticated deep learning, machine learning, and neural network algorithms to precisely predict students’ learning patterns.

关键词： Deep learning Education Bibliometrics Neural networks Market research Prediction algorithms Stakeholders Artificial intelligence Socioeconomics Meteorology

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Modeling and analysis of COVID-19 new deaths using tree-based ensemble

TechRxiv

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TechRxiv 2021年

作者： Hashem, Ibrahim Abaker Targio Usmani, Raja Sher Afgun Shah, Asad Ali Almazroi, Abdulwahab Ali Bilal, Muhammad College of Computing and Informatics Department of Computer Science University of Sharjah Sharjah27272 United Arab Emirates Department of Software Engineering Faculty of Computing and Information Technology University of Sialkot Pakistan Department of Computing School of Electrical Engineering & Computer Science National University of Sciences and Technology Pakistan Department of Information Technology College of Computing and Information Technology at Khulais University of Jeddah Jeddah Saudi Arabia Department of Computer Science FAST National University of Computer and Emerging Sciences Islamabad44000 Pakistan

The COVID-19 pandemic has emerged as the world's most serious health crisis, affecting millions of people all over the world. The majority of nations have imposed nationwide curfews and reduced economic activity to combat the spread of this infectious disease. Governments are monitoring the situation and making critical decisions based on the daily number of new cases and deaths reported. Therefore, this study aims to predict the daily new deaths using four tree-based ensemble models i.e., Gradient Tree Boosting (GB), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Voting Regressor (VR) for the three most affected countries, which are the United States, Brazil, and India. The results showed that VR outperformed other models in predicting daily new deaths for all three countries. The predictions of daily new deaths made using VR for Brazil and India are very close to the actual new deaths, whereas the prediction of daily new deaths for the United States still needs to be improved. © 2021, CC BY.

关键词： COVID-19

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The Advanced Encryption Standard with a Dynamic S-Box Using the RC4 Key Schedule

The Advanced Encryption Standard with a Dynamic S-Box Using ...

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Electrical, Computer and Energy Technologies (ICECET), International Conference on

作者： Abdelrahman Altigani Muhammad Rukunuddin Ghalib Huwaida T. Elshoush Muawia A. Elsadig Nashat Alrefai Shiraz Naserelden Computer Information Science Higher Colleges of Technology Al Ain UAE School of Engineering & Computing De Monfort University Dubai UAE Computer Science Department Faculty of Mathematical Sciences and Informatics University of Khartoum Khartoum Sudan College of Computer Science and Information Technology Imam Abdulrahman bin Faisal University Dammam KSA Department of Mathematics College of Science Imam Abdulrahman Bin Faisal University Basic and Applied Scientific Research Center Imam Abdulrahman Bin Faisal University Dammam KSA Department of Mathematical Sciences Faculty of Science Universiti Teknologi Malaysia Johor Malaysia

ISBN: (数字)9798350395914

ISBN: (纸本)9798350395921

Since the inception of secure communication, safeguarding data confidentiality has been paramount. With the increased reliance on technology, the need for robust security mechanisms is more critical than ever. This paper explores confidentiality in cybersecurity, focusing on symmetric encryption algorithms. While symmetric encryption, particularly the Advanced Encryption Standard (AES), is in wide use, concerns arise regarding its static nature. Therefore, the paper introduces a dynamic cipher, augmenting AES security by introducing a key-dependent S-Box. By leveraging the RC4 key schedule, the proposed cipher generates unique S-Boxes for each encryption key, significantly increasing security against non-generic attacks. Theoretical analysis demonstrates the proposed cipher is capable of resisting brute force, linear cryptanalysis, and differential cryptanalysis. Furthermore, its adaptability to existing AES implementations ensures practicality and ease of adoption.

关键词： Ciphers Schedules Heuristic algorithms Dynamics Force Dynamic scheduling Encryption Computer security Time complexity Standards

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Opinion Mining using Machine Learning Approaches: A Critical Study

Opinion Mining using Machine Learning Approaches: A Critical...

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2020 International Conference on computing and Information technology, ICCIT 2020

作者： Ramakrishnan, Jayabrabu Mavaluru, DInesh Srinivasan, Karthik Mubarakali, Azath Narmatha, C. Malathi, G. Jazan University College of Computer Science and Information Technology Department of Information Technology and Security Saudi Arabia Saudi Electronic University College of Computing and Informatics Department of Information Technology Saudi Arabia King Khalid University College of Computer Science Department of CNE Saudi Arabia University of Tabuk Faculty of Computer and Information Technology Department of Computer Science Tabuk Saudi Arabia Vellore Institute of Technology School of Computing Science and Engineering Chennai India

ISBN: (纸本)9781728126807

With the advancement of internet technology, there is a large volume of information exists on the web for users of the internet. These users not just utilize the accessible resources on the web yet, in addition, give their feedback, consequently creating more useful data. Opinion mining (OM) is contemplated as a subfield of NLP, information retrieval, and data mining. It is the way toward extricating people's thoughts and observations from unformed content, which with respect to the development of online social media and the huge amount of user feedback, has gotten an effective, attractive and furthermore complex issue. The fundamental in OM is classifying the polarity of text as far as positive (good), neutral (surprise) or negative (bad). This paper is proposed to review the methods and applications of opinion mining. This paper presents a review of OM and related strategies and additionally discusses the applications and difficulties for OM with knowledge into previous researchers. © 2020 IEEE.

关键词： Sentiment analysis

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Spectrograms Fusion-based End-to-end Robust Automatic Speech Recognition

Spectrograms Fusion-based End-to-end Robust Automatic Speech...

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Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Hao Shi Longbiao Wang Sheng Li Cunhang Fan Jianwu Dang Tatsuya Kawahara Graduate School of Informatics Kyoto University Kyoto Japan Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China National Institute of Information and Communications Technology (NICT) Kyoto Japan Anhui Province Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Japan Advanced Institute of Science and Technology Ishikawa Japan

ISBN: (纸本)9781665441629

To improve the robustness of automatic speech recognition (ASR), speech enhancement (SE) is often used as a front-end noise-removal process. Although there is complemen-tarity between the mapping-based and the mask-based SE system, one of the SE systems has been conventionally used as the front-end of ASR. We propose a spectrogram fusion (SF)-based end-to-end (E2E) robust ASR system, in which the mapping-based and masking-based SE are used as the front-end simultaneously. We adopt SF to combine the advantages of mapping-based and masking-based SE systems. SF and ASR modules are connected in an E2E manner, and joint training is conducted to finetune the front-end and the back-end. We compared the performance of different front-ends after joint training. From the experiments using Aishell and PNL 100 Nonspeech Sounds datasets, we found that the fusion of two SEs are beneficial for ASR, especially under low signal-to-noise ratio, where a relative improvement of more than 7% is achieved.

关键词： Training Information processing Speech enhancement Robustness Spectrogram Automatic speech recognition Signal to noise ratio

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Distributed Energy Management Analysis for Microgrids

Distributed Energy Management Analysis for Microgrids

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International Universities Power Engineering Conference (UPEC)

作者： Muhammad Majid Hussain Waqas Javed Rizwan Akram Tasmiyah Javed Abdul Razaq Muhammad Siddique Faculty of Computing Engineering & Science University of South Wales Cardiff UK School of Computing Engineering and Built Enviornment Glasgow Caledonian University Glasgow UK College of Engineering Qassim Univeristy Buraydah Saudi Arabia Univeristy of Engineering and Technology Lahore Pakistan School of Design & Informatics Abertay University Dundee United Kingdom NFC Institute of Engineering & Technology Multan Pakistan

A key method of reducing energy usage and bills is collaborative demand management of buildings, and smart distribution networks using local renewable energy integrated sources. Emerging technologies such as the IOT (internet of things) can be integrated with grids, allowing bidirectional communication between customers and suppliers, while supporting remote management and monitoring of live energy usage. These systems create opportunities to deal with the distributed aggregation and control of distributed energy resources (DERs). Investigating how DERs are used at residential level in buildings such as smart houses reveal new possibilities relating to how the demand side can be made more efficient. In the long term, reducing energy demand during onpeak hours is a step towards creating a clean energy future. The primary goal of this paper is to observe and investigate how specific DSM strategies minimize energy consumption while maximizing efficiency with the use of new emerging technologies. A software called ETAP is used to analyse the integration of distributed generation such as renewable energy sources (RES) to utilize local power storage. In short, this is useful in providing flexibility to consumers. A PV system was also tested with an individual domestic load using an energy management system. This test examined how appliance energy usage can be reduced and shows why a management system is required to optimally utilize a PV system.

关键词： Renewable energy sources Schedules Energy consumption Buildings Software Wind turbines Smart grids

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Imputation of Rainfall Data Using Improved Neural Network Algorithm 25th

Imputation of Rainfall Data Using Improved Neural Network Al...

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25th International Conference on Pattern Recognition Workshops, ICPR 2020

作者： Chiu, Po Chan Selamat, Ali Krejcar, Ondrej Kuok, King Kuok School of Computing Faculty of Engineering Universiti Teknologi Malaysia Johor Bahru Johor81310 Malaysia Universiti Teknologi Malaysia Johor Bahru Johor81310 Malaysia Faculty of Computer Science and Information Technology Universiti Malaysia Sarawak Kota Samarahan Sarawak94300 Malaysia Universiti Teknologi Malaysia Kuala Lumpur Jalan Sultan Yahya Petra Kuala Lumpur54100 Malaysia Faculty of Informatics and Management University of Hradec Kralove Rokitanského 62 Hradec Kralove500 03 Czech Republic Faculty of Engineering Computing and Science Swinburne University of Technology Sarawak Campus Kuching Sarawak93350 Malaysia

ISBN: (纸本)9783030687984

Missing rainfall data have reduced the quality of hydrological data analysis because they are the essential input for hydrological modeling. Much research has focused on rainfall data imputation. However, the compatibility of precipitation (rainfall) and non-precipitation (meteorology) as input data has received less attention. First, we propose a novel input structure for the missing data imputation method. Principal component analysis (PCA) is used to extract the most relevant features from the meteorological data. This paper introduces the combined input of the significant principal components (PCs) and rainfall data from nearest neighbor gauging stations as the input to the estimation of the missing values. Second, the effects of the combination input for infilling the missing rainfall data series were compared using the sine cosine algorithm neural network (SCANN) and feedforward neural network (FFNN). The results showed that SCANN outperformed FFNN imputation in terms of mean absolute error (MAE), root means square error (RMSE) and correlation coefficient (R), with an average accuracy of more than 90%. This study revealed that as the percentage of missingness increased, the precision of both imputation methods reduced. © 2021, Springer Nature Switzerland AG.

关键词： Principal component analysis

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Physical Human-UAV Interaction with Commercial Drones using Admittance Control

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IFAC-PapersOnLine 2021年第20期54卷 258-264页

作者： Christopher Banks Antonio Bono Samuel Coogan School of Interactive Computing Georgia Institute of Technology North Ave NW Atlanta GA 30332 USA Department of Informatics Modeling Electronics and System Engineering University of Calabria Via P. Bucci 42-C 87036 Rende (CS) Italy School of Electrical and Computer Engineering and School of Civil and Environmental Engineering Georgia Institute of Technology North Ave NW Atlanta GA 30332 USA

In this paper, we propose a control scheme that allows generic commercial multirotor Unmanned Aerial Vehicles (UAVs) to infer human intent from physical interaction. Humans apply forces by touching it or pulling a nylon string attached to the body frame of the vehicle. The estimations of these forces, obtained via a square root unscented Kalman filter, are used by an admittance controller to generate a reference trajectory such that the vehicle reacts as a desired mass-damper system. To track this trajectory, the differential flatness property of the multirotor dynamic model is exploited. This, in fact, allows the generation of any possible kind of input required by the on-board controller supplied by the manufacturer. We validate our results experimentally on a Parrot Bebop 2 quadrotor, a commercially available UAV.

关键词： Physical Human-Robot Interaction Aerial Robots Force Estimation Admittance Control Differentially Flat Systems

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Persistent Hierarchy in Contemporary International Collaboration

arXiv

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arXiv 2024年

作者： Miao, Lili Larivière, Vincent Lee, Byungkyu Ahn, Yong-Yeol Sugimoto, Cassidy R. Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University Bloomington BloomingtonIN United States École de bibliothéconomie et des sciences de l’information Université de Montréal MontréalQC Canada Observatoire des sciences et des technologies Université du Québec à Montréal MontréalQC Canada Department of Sociology New York University New YorkNY United States School of Public Policy Georgia Institute of Technology AtlantaGA United States

Science is increasingly global, with international collaboration playing a crucial role in advancing scientific development and knowledge exchange across borders. However, the processes that regulate how scientific labor is distributed among countries remain underexplored, leading to challenges in ensuring both effective collaboration and equitable participation across diverse scientific communities. Here, we leverage three million internationally coauthored publications produced by countries worldwide to examine the division of scientific labor in international collaboration, identify the factors that shape this distribution, and assess its broader consequences. Our findings uncover a persistent hierarchical structure in international collaboration, with researchers from scientifically advanced countries occupy dominant roles, while those from less-developed countries are relegated to supportive roles, even after controlling for other influential factors. This hierarchy is also reflected in the research content, as countries with lower scientific capacity tend to participate in international collaborations that deviate from their domestic science. By analyzing the labor division within international collaborations, we demonstrate that researchers from less-developed countries face systematic disadvantages, which not only limit their contributions to the global scientific community but also prevent them from fully benefiting from international collaborations. © 2024, CC BY.

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An improved Otsu threshold segmentation algorithm

An improved Otsu threshold segmentation algorithm

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作者： Yang, Pei Song, Wei Zhao, Xiaobing Zheng, Rui Qingge, Letu Department of Computer Technology and Application Qinghai University Xining810016 China School of Information Engineering Minzu University of China Beijing100081 China College of Computing and Informatics University of North Carolina CharlotteNC28223 United States

Image segmentation is widely used as a fundamental step for various image processing applications. This paper focuses on improving the famous image thresholding method named Otsu's algorithm. Based on the fact that threshold acquired by Otsu's algorithm tends to be closer to the class with larger intraclass variance when the foreground and background have large intraclass variance difference, an improved strategy is proposed to adjust the threshold bias. We analysed the relationship between pixel greyscale value and the change of cumulative pixel number, and selected the ratio of pixel grey level value to a certain cumulative pixel number as the adjusted threshold. Experiments using typical testing images were set up to verify the proposed method both quantitatively and qualitatively. Two widely used metrics named misclassification error (ME) and dice similarity coefficient (DSC) were adopted for quantitative evaluation, and both quantitative and qualitative results indicated that the proposed algorithm could better segment the testing images and get competitive misclassification error and DSC values compared with Otsu's method and its improved versions proposed by Hu and Gong (2009) and Xu et al. (2011), and the time consumption of our method can be significantly reduced. © 2020 Inderscience Enterprises Ltd.

关键词： Pixels

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