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1st IEEE Industrial Electronics Society Annual On-Line Conference, ONCON 2022

作者： Jahangiri, Mojtaba Sberna, Paolo Sammak, Amir Nihtianov, Stoyan Mathematics and Computer Science Delft University of Technology Faculty of Electrical Engineering Department of Microelectronics Delft Netherlands Delft Netherlands

ISBN: (纸本)9798350398069

Most of the studies on narrow-band near-infrared detection reported so far are related to the 1.3μm and 1.55μm spectral windows. There is insufficient research work done on radiation detection in the narrow band around 1 μm wavelength, which is just outside the Si (0.95μ m) and GaAs (0.85μ m) effective cut-off spectral sensitivity. This paper presents a p+n Ge-on-Si detector with a customized large active window, employing the PureGaB technology, to detect radiation in a very narrow band around 1μ m. The advantages of the proposed detector are: (1) CMOS-compatibility and micro-spectroscopic capability;(2) low dark current and high photoresponsivity, compared to similar devices reported in the literature;(3) enhanced sensitivity to weak radiation by realizing an ultra-shallow and very thin depletion region. These detectors can be good candidates for measuring the YAG laser radiation and measuring stray radiation in photolithography. © 2022 IEEE.

关键词： III-V semiconductors

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Investigation of adequate closed form travelling wave solution to the space-time fractional non-linear evolution equations

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Journal of Ocean engineering and science 2022年第3期7卷 292-303页

作者： Mohammad Asif Arefin M.Ayesha Khatun M.Hafiz Uddin Mustafa Inc Department of Mathematics Jashore University of Science and TechnologyJashore 7408Bangladesh Department of Computer Engineering Biruni UniversityIstanbulTurkey Department of Mathematics Science FacultyFirat UniversityElazigTurkey Department of Medical Research China Medical University HospitalChina Medical UniversityTaichungTaiwan

This work aims to construct exact solutions for the space-time fractional(2+1)-dimensional dispersive longwave(DLW)equation and approximate long water wave equation(ALW)utilizing the twovariable(G′/G,1/G)-expansion method and the modified Riemann-Liouville fractional *** recommended equations play a significant role to describe the travel of the shallow water *** fractional complex transform is used to convert fractional differential equations into ordinary differential *** wave solutions have been successfully achieved using the proposed approach and the symbolic computer Maple *** Maple package program was used to set up and validate all of the computations in this *** choosing particular values of the embedded parameters,we pro-duce multiple periodic solutions,periodic wave solutions,single soliton solutions,kink wave solutions,and more forms of soliton *** achieved solutions might be useful to comprehend nonlinear *** is worth noting that the implemented method for solving nonlinear fractional partial dif-ferential equations(NLFPDEs)is efficient,and simple to find further and new-fangled solutions in the arena of mathematical physics and coastal engineering.

关键词： Riemann-Liouville fractional derivative Space-time fractional(2+1)-dimensional dispersive long wave equation Approximate long water wave equation Wave transformation The two-variable(G′/G,1/G)-expansion method

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Contrastive Learning at the Relation and Event Level for Rumor Detection 48

Contrastive Learning at the Relation and Event Level for Rum...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Xu, Yingrui Hu, Jingyuan Ge, Jingguo Wu, Yulei Li, Tong Li, Hui Chinese Academy of Sciences Institute of Information Engineering Beijing China University of Chinese Academy of Sciences School of Cyber Security Beijing China University of Exeter Faculty of Environment Science and Economy Department of Computer Science United Kingdom

ISBN: (纸本)9781728163277

Existing studies for rumor detection rely heavily on a large number of labeled data to operate in a fully-supervised manner. However, manual data annotation in realistic cases is very expensive and time-consuming. In this paper, we propose a novel self-supervised Relation-Event based Contrastive Learning (RECL) framework for rumor detection to address the above issue. Specifically, we present both the relation-level and event-level augmentation strategies to generate contrastive samples, which capture both the semantics revealed by repost relations and the structural features of rumor events. Moreover, contrastive learning tasks are devised to generate informative graph representations by utilizing self-supervision signals of unlabeled data. Extensive experimental results on real-world datasets demonstrate the effectiveness of our model, especially with limited labeled data. © 2023 IEEE.

关键词： Graph neural networks

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MSConv: Multiplicative and Subtractive Convolution for Face Recognition

arXiv

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arXiv 2025年

作者： Zhou, Si Si, Yain-Whar Yuan, Xiaochen Li, Xiaofan Liu, Xiaoxiang Zhang, Xinyuan Lin, Cong Gong, Xueyuan School of Intelligent Systems Science and Engineering Jinan University Guangdong China Department of Computer and Information Science University of Macau China Faculty of Applied Sciences Macau Polytechnic University China

In Neural Networks, there are various methods of feature fusion. Different strategies can significantly affect the effectiveness of feature representation, consequently influencing the model’s ability to extract representative and discriminative features. In the field of face recognition, traditional feature fusion methods include feature concatenation and feature addition. Recently, various attention mechanism-based fusion strategies have emerged. However, we found that these methods primarily focus on the important features in the image, referred to as salient features in this paper, while neglecting another equally important set of features for image recognition tasks, which we term differential features. This may cause the model to overlook critical local differences when dealing with complex facial samples. Therefore, in this paper, we propose an efficient convolution module called MSConv (Multiplicative and Subtractive Convolution), designed to balance the model’s learning of salient and differential features. Specifically, we employ multi-scale mixed convolution to capture both local and broader contextual information from face images, and then utilize Multiplication Operation (MO) and Subtraction Operation (SO) to extract salient and differential features, respectively. Experimental results demonstrate that by integrating both salient and differential features, MSConv outperforms models that only focus on salient features. Copyright © 2025, The Authors. All rights reserved.

关键词： Convolution

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An investigation on catalytic performance and reaction mechanisms of Fe/OMS-2 for the oxidation of carbon monoxide, ethyl acetate, and toluene

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Journal of Environmental sciences 2022年第2期34卷 258-268页

作者： Ning Dong Mengyue Chen Qing Ye Dan Zhang Hongxing Dai Key Laboratory of Beijing on Regional Air Pollution Control Department of Environmental ScienceSchool of Environmental and Chemical EngineeringFaculty of Environment and LifeBeijing University of TechnologyBeijing 100124China Beijing Key Laboratory for Green Catalysis and Separation Key Laboratory of Beijing on Regional Air Pollution ControlKey Laboratory of Advanced Functional MaterialsEducation Ministry of Chinaand Laboratory of Catalysis Chemistry and NanoscienceDepartment of Environmental Chemical EngineeringSchool of Environmental and Chemical EngineeringFaculty of Environment and LifeBeijing University of TechnologyBeijing 100124China

The octahedral molecular sieve(OMS-2)-supported Fe( xFe/OMS-2: x = 1, 3, 5, and 10) catalysts were prepared using the pre-incorporation method. Physicochemical properties of the as-synthesized materials were characterized by means of various techniques, and their catalytic activities for CO, ethyl acetate, and toluene oxidation were evaluated. Among all of the samples, performed the best, with the reaction temperature required to achieve 90% conversion( T 90%) being 160 ℃ for CO oxidation, 210 ℃ for ethyl acetate oxidation, and 285 ℃ for toluene oxidation. Such a good catalytic performance of 5Fe/OMS-2 was associated with its high(Mn^(3+) + Mn^(2+)) content and adsorbed oxygen species concentration, and good lowtemperature reducibility and lattice oxygen mobility as well as strong interaction between Fe and OMS-2. In addition, catalytic mechanisms of the oxidation of three pollutants over the 5Fe/OMS-2 catalyst were also studied. It was found that CO, ethyl acetate or toluene was first adsorbed, then the related intermediates were formed, and finally the formed intermediates were completely converted into CO_(2) and H_(2)O.

关键词： Cryptomelane-type OMS-2 Supported iron catalyst CO oxidation Volatile organic compound oxidation Toluene oxidation

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An Improved Lightweight Framework for Handwritten Chinese Text Recognition Based on CRNN

An Improved Lightweight Framework for Handwritten Chinese Te...

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2022 International Symposium on Intelligent Robotics and systems, ISoIRS 2022

作者： Shen, Lu Tang, Su-Kit Mirri, Silvia Macao Polytechnic University Faculty of Applied Sciences China Department of Computer Science and Engineering University of Bologna Bologna Italy

ISBN: (纸本)9781665455213

Robots with computer vision and text recognition functions are widely used in industrial production, especially in highly automated factories. However, most robots have an excellent ability to recognize printed characters and show low accuracy in recognition of handwritten characters. Therefore, this paper considers recognizing handwritten text in the intelligent processing of handwritten documents. Its high accuracy prediction results are closely related to the effectiveness of manuscript input, intelligent translation, and intelligent scoring. Handwritten text is more difficult to recognize because it contains sequential information, and the images are more complex than single-character images. This paper proposes a new handwritten Chinese text recognition (HCTR) framework based on existing classical convolutional neural network (CNN) and recurrent neural network (RNN) algorithms. We use a handwritten Chinese text dataset from CASIA-HWDB containing numbers and symbols close to real application scenarios to train the model and compare the performance of various models, such as MobileNetV1 and MobileNetV2, with the proposed model. From the analysis of experimental results, it can be found that the proposed method can achieve higher performance with fewer parameters. In addition, we optimize the dropout rates of input blocks and obtain the best CER of our method is 6.11%. © 2022 IEEE.

关键词： Convolution

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Enhanced Gorilla Troops Optimizer with Deep Learning Enabled Cybersecurity Threat Detection

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computer systems science & engineering 2023年第6期45卷 3037-3052页

作者： Fatma S.Alrayes Najm Alotaibi Jaber S.Alzahrani Sana Alazwari Areej Alhogail Ali M.Al-Sharafi Mahmoud Othman Manar Ahmed Hamza Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Prince Saud Al Faisal Institute for Diplomatic Studies RiyadhSaudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Information Systems College of Computer and Information SciencesKing Saud UniversitySaudi Arabia Department of Computer Science College of Computers and Information TechnologyUniversity of BishaSaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Recent developments in computer networks and Internet of Things(IoT)have enabled easy access to *** the government and business sectors face several difficulties in resolving cybersecurity network issues,like novel attacks,hackers,internet criminals,and so ***,malware attacks and software piracy pose serious risks in compromising the security of *** can steal confidential data which results infinancial and reputational *** advent of machine learning(ML)and deep learning(DL)models has been employed to accomplish security in the IoT cloud *** article pre-sents an Enhanced Artificial Gorilla Troops Optimizer with Deep Learning Enabled Cybersecurity Threat Detection(EAGTODL-CTD)in IoT Cloud *** presented EAGTODL-CTD model encompasses the identification of the threats in the IoT cloud *** proposed EAGTODL-CTD mod-el mainly focuses on the conversion of input binaryfiles to color images,where the malware can be detected using an image classification *** EAG-TODL-CTD model pre-processes the input data to transform to a compatible *** threat detection and classification,cascaded gated recurrent unit(CGRU)model is exploited to determine class ***,EAGTO approach is employed as a hyperparameter optimizer to tune the CGRU parameters,showing the novelty of our *** performance evaluation of the EAGTODL-CTD model is assessed on a dataset comprising two class labels namely malignant and *** experimental values reported the supremacy of the EAG-TODL-CTD model with increased accuracy of 99.47%.

关键词： Cybersecurity computer networks threat detection internet of things cloud computing deep learning

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PACTDet - An Artificially Intelligent Approach to Detect Pulmonary Illnesses: Pneumonia, Asthma, COVID-19, and Tuberculosis 1

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2nd International Conference on Information Technologies and Intelligent Decision Making systems, ITIDMS 2022

作者： Tasmi, Sidratul Tanzila Raihan, Md. Mohsin Sarker Nasif, Atahar Imtiaz Shams, Abdullah Bin Department of Computer Science and Engineering Islamic University of Technology Dhaka1704 Bangladesh Chattogram Bangladesh Department of Computer Science Faculty of Science University of British Columbia 3333 University Way KelownaBCV1V 1V7 Canada The Edward S. Rogers Sr. Department of Electrical Computer Engineering University of Toronto TorontoONM5S 3G4 Canada

ISBN: (数字)9783031313530

ISBN: (纸本)9783031313523

Pulmonary Illness has become very common due to different emerging viruses, bacteria, pollution, and lifestyle. If these diseases are not diagnosed in a patient, they may have a severe impact and fatal conditions in a patient’s life. In this paper, we propose a methodology for the diagnosis of common pulmonary diseases: Pneumonia, Bronchial Asthma, COVID-19, and Tuberculosis based on symptoms. Our research paper functions as a technological doctor, which can detect the probability of a person having a certain category of pulmonary disease. The patient can then take necessary precautions and treatment for an early cure and minimize the severity of the disease. In this research, common classification-based machine learning algorithms: Random Forest, Decision Tree, Support Vector Machine, and Gradient Boosting algorithms were used after preprocessing the dataset from 2 sources. We have developed an Artificial Neural Network (ANN) model that can successfully detect the type of pulmonary disease with an accuracy of 99%. This application can help medical workers in their decision-making process regarding a patient, and the widespread, inexpensive application of this can help save people’s lives globally. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： COVID-19

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Hadiyyissa Automatic Speech Recognition Using Deep Learning Approach 5

Hadiyyissa Automatic Speech Recognition Using Deep Learning ...

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5th International Conference on Information and Communication Technology for Development for Africa, ICT4DA 2023

作者： Shamore, Selamu Gure, Amin Tuni Yimer, Mohammed Abebe Wachemo University Department of Computer Science Hossana Ethiopia Arsi University Department of Information System Arsi Ethiopia Arba Minch University Faculty of Computing and Software Engineering Arba Minch Ethiopia

ISBN: (纸本)9798350328486

Speech Recognition focuses on developing advanced technologies that can recognize and translate spoken language into text. Automatic Speech Recognition (ASR) is an Artificial Intelligence (AI) technology that captures human voice from a mi-crophone and analyzes it based on spoken words to comprehend natural language. In Ethiopia, ASR models have been developed for specific local languages such as Amharic, Tigrigna, Wolaytta, and Afaan Oromo, but there has been a lack of research on ASR models for the Hadiyyisa language, which is also spoken in Ethiopia. The research study designed and developed an ASR model for the Hadiyyisa language. The researchers collected data from native speakers of the Hadiyyisa language and used Python programming to design and develop the deep learning model for the Hadiyissa ASR model. Different methods and techniques were used in the study to achieve the research objectives and complete the entire research process. The proposed model utilized deep learning algorithms including RNN, LSTM, BiLSTM, and CNN, along with suitable feature extraction techniques such as Mel Frequency Cepstral Coefficients (MFCC). The accuracy of the proposed ASR model was evaluated through experimentation, which demonstrated that the BiLSTM model outperformed the other deep learning models with an overall accuracy of 90.95 % Based on the experimental evidence, the proposed model was found to be better suited for ASR for the Hadiyyisa language. © 2023 IEEE.

关键词： Speech recognition

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Challenges of Maturity Models in Public-Private Partnerships (PPPs) in the Post-COVID-19 Era

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Journal of engineering, Project, and Production Management 2022年第2期12卷 149-165页

作者： Datti, Yusuf Umar Abdullah Zawawi, Noor Amila Wan Umar, Abdullahi Ahmed Sutanto, Muslich Hartadi Danyaro, Kamaluddeen Usman Department of Civil Engineering and Quantity Surveying Military Technological College Muscat Oman Department of Computer and Information Sciences Faculty of Science and Information Technology Universiti Teknologi PETRONAS Seri Iskandar Perak32610 Malaysia

Maturity models (MMs) have witnessed exponential increase due to their successful application in several domains. However, there is an absence of review that guides researchers in developing, applying and validating Public-Private Partnership maturity models (PPPMMs). This study examines PPPMMs, provides guidance on the topic and highlights gaps in the literature. A literature search on selected electronic databases was conducted, and the study adopted the widely accepted Preferred Reporting Items for Systematic Review and Meta-analysis statement (PRISMA). The study identified a total of four thousand six hundred and eighteen (4,618) studies, and twenty-one studies (21Nr) were rigorously selected. The results revealed PPPMMs as an emerging area of research with a low number – 21 publications since its deployment for about two (2) decades. Similarly, the findings unveiled a lack of uniformity in conceptualising the terms, dimensions used, and methodology adopted. This finding is attributed mainly to the limited use of the theoretical lens, which considerably weakens the model’s theoretical foundation and limits its potential to guide improvement. Additionally, there are more efforts in developing MMs than applying and validating them. Furthermore, there is an unbalanced focus on descriptive models over prescriptive and comparative models, which inhibit the model’s potential to guide improvement. Future work should provide a solid ground to the field using a theoretical lens and focus on prescriptive models with a strong emphasis on application and validation. This research is the first of its kind that synthesises and brings together available PPPMMs literature into one place. It also contributes to the body of knowledge by highlighting areas of research that require immediate attention to enhance the much-needed success of PPP in the post-COVID-19 era. Copyright © Journal of engineering, Project, and Production Management (EPPM-Journal).

关键词： Mergers and acquisitions

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