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Knowledge Transfer Learning via Dual Density Sampling for Resource-Limited Domain Adaptation

IEEE/CAA Journal of Automatica Sinica 2023年第12期10卷 2269-2291页

作者： Zefeng Zheng Luyao Teng Wei Zhang Naiqi Wu Shaohua Teng the School of Computer Science and Technology Guangdong University of TechnologyGuangzhou 510006China the School of Information Engineering Guangzhou Panyu PolytechicGuangzhou 511483China the Faculty of Information Technology Monash University20 Exhibition Walk ClaytonVIC 3800Australia IEEE the Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and Systems Macao University of Science and TechnologyMacao 999078China

Most existing domain adaptation(DA) methods aim to explore favorable performance under complicated environments by ***,there are three unsolved problems that limit their efficiencies:ⅰ) they adopt global sampling but neglect to exploit global and local sampling simultaneously;ⅱ)they either transfer knowledge from a global perspective or a local perspective,while overlooking transmission of confident knowledge from both perspectives;and ⅲ) they apply repeated sampling during iteration,which takes a lot of *** address these problems,knowledge transfer learning via dual density sampling(KTL-DDS) is proposed in this study,which consists of three parts:ⅰ) Dual density sampling(DDS) that jointly leverages two sampling methods associated with different views,i.e.,global density sampling that extracts representative samples with the most common features and local density sampling that selects representative samples with critical boundary information;ⅱ)Consistent maximum mean discrepancy(CMMD) that reduces intra-and cross-domain risks and guarantees high consistency of knowledge by shortening the distances of every two subsets among the four subsets collected by DDS;and ⅲ) Knowledge dissemination(KD) that transmits confident and consistent knowledge from the representative target samples with global and local properties to the whole target domain by preserving the neighboring relationships of the target *** analyses show that DDS avoids repeated sampling during the *** the above three actions,confident knowledge with both global and local properties is transferred,and the memory and running time are greatly *** addition,a general framework named dual density sampling approximation(DDSA) is extended,which can be easily applied to other DA *** experiments on five datasets in clean,label corruption(LC),feature missing(FM),and LC&FM environments demonstrate the encouraging performance of KTL-DDS.

关键词： Cross-domain risk dual density sampling intra-domain risk maximum mean discrepancy knowledge transfer learning resource-limited domain adaptation

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No-Reference Image Quality Assessment via Multibranch Convolutional Neural Networks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第1期4卷 148-160页

作者： Pan, Zhaoqing Yuan, Feng Wang, Xu Xu, Long Shao, Xiao Kwong, Sam the School of Electrical and Information Engineering Tianjin University Tianjin300072 China the College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China the Key Laboratory of Solar Activity National Astronomical Observatories Chinese Academy of Sciences Beijing100012 China the School of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China the Department of Computer Science City University of Hong Kong Hong Kong

No-reference image quality assessment (NR-IQA) aims to evaluate image quality without using the original reference images. Since the early NR-IQA methods based on distortion types were only applicable to specific distortion scenarios, and lack of practicality, it is challenging to designing a universal NR-IQA method. In this article, a multibranch convolutional neural network (MB-CNN) based NR-IQA method is proposed, which includes a spatial-domain feature extractor, a gradient-domain feature extractor, and a weight mechanism. The spatial-domain feature extractor aims to extract the distortion features from the spatial domain. The gradient-domain feature extractor is used to guide the spatial-domain feature extractor to pay more attention to the distortions of the structure information. Particularly, the spatial-domain feature extractor uses the hierarchical feature merge module to realize multiscale feature representation, and the gradient-domain feature extractor uses pyramidal convolution to extract the multiscale structure information of the distorted image. In addition, a position vector is proposed to build the weight mechanism by considering the position relationships between patches and its entire image for improving the final prediction performance. We conduct the experiments on five representative databases: LIVE, TID2013, CSIQ, LIVE MD and Waterloo Exploration Database, and the experimental results show that the proposed NR-IQA method achieves the state-of-the-art performance, which demonstrate the effectiveness of our proposed NR-IQA method. The code ofthe proposed MB-CNN will be released at https://***/NUIST-Videocoding/MB-CNN. © 2020 IEEE.

关键词： Neural networks

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Baby Bump: A Monitoring System for Pregnant Mothers and Babies 9

Baby Bump: A Monitoring System for Pregnant Mothers and Babi...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Balasooriya, Dhanuka Perera, Sasini Wijendra, Dinuka R. Rathnayaka, Sahani Kishan, Rajendra Rajendran, Karthiga Thirukumar, Markandu Sri Lankan Institute of Information Technology Department of Computer Systems And Software Engineering Malabe Sri Lanka Teaching Hospital Department of Obstetrics & Gynaecology Batticaloa Sri Lanka

ISBN: (纸本)9798350394474

In contemporary times, nations like Sri Lanka are actively enhancing their efforts to improve the life expectancy of their citizens, with a strong focus on public health. The relationship between health and life expectancy is pivotal. Poor health conditions tend to diminish life expectancy, while robust health measures tend to extend it. Within the context of health, birth and death events are of paramount importance. Birth represents the genesis of life, enabling individuals to contribute to future generations and the overall population's expansion. It's the cornerstone of life on our planet. However, modern times have witnessed challenges in pregnancy and childbirth, with some infants not surviving the prenatal period or being born with health complications. Consequently, there is a pressing need to present a solution that can contribute to the betterment of maternal and infant health, ultimately augmenting Sri Lanka's life expectancy. The proposed solution is the development of a mobile application, encompassing features such as a Nutrition Predictor, Medicine Effect Predictor, AI chatbot, and Baby Status Predictor. This innovative application aims to address the complex issues surrounding pregnancy and childbirth, with the overarching goal of improving the nation's life expectancy. © 2024 IEEE.

关键词： Nutrition

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Comp-RPKI: A Decentralized Protocol for Full Route Origin Validation 9

Comp-RPKI: A Decentralized Protocol for Full Route Origin Va...

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9th International Conference on Big Data Computing and Communications, BigCom 2023

作者： Wu, Yanwei Wang, Yun Dejulius, Jack Abboud, Riad Woitel, Melvin Osadchyy, Bohdan Richardson, Kyree University of Wisconsin Computer Science and Software Engineering PlattevilleWI United States Bradley University Computer Science and Information Systems PeoriaIL United States

ISBN: (纸本)9798350331240

The Resource Public Key Infrastructure (RPKI) is an essential technology for securing the Border Gateway Protocol (BGP) routing on the Internet. Although the global RPKI-ROV (Route Origin Validation) rate of unique Prefix-Origin Pairs(IPv4) has been improved from 17% in June 2019 (pre-pandemic) to 41.12% in March 2023 (post-pandemic) [1] - [3], the overall RPKI-ROV rate remains below 50%. This indicates a significant insufficiency in RPKI-ROV deployment for ensuring secure BGP routing and persistent security challenges continue to persist in the Internet. To address this, we first develop software to analyze and visualize the relationships among Autonomous systems (ASes), degree distribution, and their RPKI validation status, given a region in IL. Next, we investigate the RPKI validation rates along real-time routing paths from Peoria, IL to all 50 states in the USA. Based on the analysis, we propose a novel lightweight, decentralized, easy-to-deploy, offline protocol called Comp-RPKI, which aims to complement and address the partial deployment of RPKI. Comp-RPKI aims to ensure every segment of a routing path is validated either through online RPKI or secured by offline Comp-RPKI, thereby achieving full RKPI-ROV coverage and enhancing BGP security. © 2023 IEEE.

关键词： Border Gateway Protocol

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MLPFormer: MLP-integrated transformer for colorectal histopathology whole slide image segmentation

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Neural Computing and Applications 2025年第6期37卷 4651-4661页

作者： Wang, Yuxuan Li, Dan Li, Xuechen Guo, Yan Zuo, Yanfei Shen, Linlin Department of Computer Science Wenzhou-Kean University Wenzhou China School of Electronics and Information Engineering Wuyi University Jiangmen China Department of Histo Pathology Shanghai Histo Pathology Diagnostic Center Shanghai China College of Computer Sicence and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

Colorectal intraepithelial neoplasia is a precancerous lesion of colorectal cancer, which is mainly diagnosed using pathological images. According to the characteristics of lesions, precancerous lesions can be classified into four different grades, i.e., background, normal, low and high level. However, due to the continuity of lesion development, tissue in different stages has high similarity. Recently, visual transformers have achieved impressive results in different visual tasks, due to their capacity of capturing long-range dependencies. However, due to computational cost, transformer cannot well extract detailed feature, which is important for lesion grading. To accurately segment pathological tissues with high similarity, in this work, we embed the multi-head MLP mixer module in the transformer network to extract cell and edge information. Furthermore, we propose a hierarchical MLP decoder to facilitate multi-scale feature fusion. The proposed transformer, namely MLPFormer, achieves remarkable results against competitive baselines on the Histo-CRC Biopsy dataset. The experimental results demonstrate that MLPFormer significantly outperforms the competitive baselines, i.e., a 3% dice improvement is achieved over the SegFormer series. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Image segmentation

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Convergence Track Based Adaptive Differential Evolution Algorithm(CTbADE)

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computers, Materials & Continua 2022年第7期72卷 1229-1250页

作者： Qamar Abbas Khalid Mahmood Malik Abdul Khader Jilani Saudagar Muhammad Badruddin Khan Mozaherul Hoque Abul Hasanat Abdullah AlTameem Mohammed AlKhathami Department of Computer Science and Software Engineering International Islamic UniversityIslamabad44000Pakistan Department of Computer Science and Engineering Oakland UniversityRochesterMIUSA Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)RiyadhSaudi Arabia

One of the challenging problems with evolutionary computing algorithms is to maintain the balance between exploration and exploitation capability in order to search global optima.A novel convergence track based adaptive differential evolution(CTbADE)algorithm is presented in this research *** crossover rate and mutation probability parameters in a differential evolution algorithm have a significant role in searching global optima.A more diverse population improves the global searching capability and helps to escape from the local optima *** the convergence path over time helps enhance the searching speed of a differential evolution algorithm for varying *** adaptive powerful parameter-controlled sequences utilized learning period-based memory and following convergence track over time are introduced in this *** proposed algorithm will be helpful in maintaining the equilibrium between an algorithm’s exploration and exploitation capability.A comprehensive test suite of standard benchmark problems with different natures,i.e.,unimodal/multimodal and separable/non-separable,was used to test the convergence power of the proposed CTbADE *** results show the significant performance of the CTbADE algorithm in terms of average fitness,solution quality,and convergence speed when compared with standard differential evolution algorithms and a few other commonly used state-of-the-art algorithms,such as jDE,CoDE,and EPSDE *** algorithm will prove to be a significant addition to the literature in order to solve real time problems and to optimize computationalmodels with a high number of parameters to adjust during the problem-solving process.

关键词： Differential evolution function optimization convergence track parameter sequence adaptive control parameters

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Injections Attacks Efficient and Secure Techniques Based on Bidirectional Long Short Time Memory Model

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computers, Materials & Continua 2023年第9期76卷 3605-3622页

作者： Abdulgbar A.R.Farea Gehad Abdullah Amran Ebraheem Farea Amerah Alabrah Ahmed A.Abdulraheem Muhammad Mursil Mohammed A.A.Al-qaness School of Big Data&Software Engineering Chongqing UniversityChongqing401331China Department of Management Science Engineering Dalian University of TechnologyDalian116024China Software College Northeastern UniversityShenyang110169China Department of Information Systems College of Computer and Information ScienceKing Saud UniversityRiyadh11543Saudi Arabia Department of Management Science and Engineering South China University of TechnologyGuangzhou510641China Department of Computer Engineering and Mathematics University of Rovira i VirgiliTarragonaSpain College of Physics and Electronic Information Engineering Zhejiang Normal UniversityJinhua321004China

E-commerce,online ticketing,online banking,and other web-based applications that handle sensitive data,such as passwords,payment information,and financial information,are widely *** web developers may have varying levels of understanding when it comes to securing an online *** Query language SQL injection and cross-site scripting are the two vulnerabilities defined by the OpenWeb Application Security Project(OWASP)for its 2017 Top Ten List Cross Site Scripting(XSS).An attacker can exploit these two flaws and launch malicious web-based actions as a result of these *** published articles focused on these attacks’binary *** article described a novel deep-learning approach for detecting SQL injection and XSS *** datasets for SQL injection and XSS payloads are combined into a single *** dataset is labeledmanually into three labels,each representing a kind of *** work implements some pre-processing algorithms,including Porter stemming,one-hot encoding,and the word-embedding method to convert a word’s text into a *** model used bidirectional long short-term memory(BiLSTM)to extract features automatically,train,and test the payload *** payloads were classified into three types by BiLSTM:XSS,SQL injection attacks,and *** outcomes demonstrated excellent performance in classifying payloads into XSS attacks,injection attacks,and non-malicious ***’s high performance was demonstrated by its accuracy of 99.26%.

关键词： Web security SQL injection XSS deep learning RNN LSTM BiLSTM

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Rollick Games: A Formal Language Based Platform for Location-Based Pervasive Games 1

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15th International Conference on Interactive Mobile Communication, Technologies, and Learning, IMCL 2023

作者： Anestis, George Gioldasis, Nektarios Karasavvidis, Stefanos Palioudakis, Tzanis Moumoutzis, Nektarios Christodoulakis, Stavros Laboratory of Distributed Multimedia Information Systems and Applications School of Electrical and Computer Engineering Technical University of Crete Chania Greece School of Electrical and Computer Engineering Technical University of Crete Chania Greece

ISBN: (数字)9783031560750

ISBN: (纸本)9783031560743

In this paper, we present the Rollick Games platform and its Pervasive Game Modeling Language that enables the description of location-based pervasive games using a GUI provided by the Game Studio web app. The runtime environment (Pervasive Game Execution Environment) is responsible for generating the Pervasive Game Execution Language from a game model and loading it to an instance of the game's Execution Engine. The Execution Environment includes a set of agents responsible for monitoring the players’ context and interaction with other game elements and among each other. The Engine follows an event-driven paradigm and uses an event loop waiting for events to be dispatched from the agents to carry out its computations based on its current state, context, and event data. The proposed approach allows for both sharing and reusing game models, significantly reducing time to market and development costs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Modeling languages

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A Transformer-Based Language Model for Sentiment Classification and Cross-Linguistic Generalization: Empowering Low-Resource African Languages

A Transformer-Based Language Model for Sentiment Classificat...

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2023 IEEE International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2023

作者： Raychawdhary, Nilanjana Hughes, Nathaniel Bhattacharya, Sutanu Dozier, Gerry Seals, Cheryl D. Auburn University Computer Science and Software Engineering AuburnAL United States Auburn University at Montgomery Computer Science and Computer Information Systems MontgomeryAL United States

ISBN: (纸本)9798350322347

Sentiment analysis is a widely studied area of natural language processing (NLP), however, a lot of attention has been given to languages with higher resources, like English. By concentrating on low-resource African languages including Hausa, Nigerian Pidgin, Igbo, Yoruba, Swahili, and Kinyarwanda for which there is a lack of curated datasets for sophisticated AI applications. This research intends to close the gap by examining the viability of current NLP strategies and creating new ones to meet this challenge. Sentiment analysis enables us to comprehend the feelings and viewpoints expressed in textual information. A transformer-based language model is presented in this research for low-resource African languages in order to classify sentiment and generalize across languages. We fine-tune the XLM-RoBERTa model on Hausa, Nigerian Pidgin, Igbo, and Yoruba, and then assess its performance in these languages as well as Swahili and Kinyarwanda, which the model had not previously seen. Our findings show that, even in languages with few linguistic resources, the model is effective at performing sentiment analysis tasks for known and unknown languages. This research helps close the digital language gap by extending natural language processing capabilities to under-resourced African languages. © 2023 IEEE.

关键词： Sentiment analysis

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Deep Learning-Based Audio Splicing Forgery Detection Using PVTv2 Transformer

Deep Learning-Based Audio Splicing Forgery Detection Using P...

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2024 Innovations in Intelligent systems and Applications Conference, ASYU 2024

作者： Kanca, Elif Gulsoy, Tolgahan Ustubioglu, Arda Ustubioglu, Beste Ayas, Selen Baykal Kablan, Elif Ulutas, Guzin Department of Software Engineering Karadeniz Technical University Trabzon Turkey Department of Management Information Systems Trabzon University Trabzon Turkey Department of Computer Engineering Karadeniz Technical University Trabzon Turkey

ISBN: (纸本)9798350379433

Attacks on multimedia files by malicious users have become quite common, especially with the increase in the number of editing tools and their ease of use. Considering that such files can now be used both as evidence and for social visibility in all kinds of environments, it has become important to prove their authenticity. With the proposed method, the detection of merging forgeries in audio files has been carried out. For this purpose, the audio files received from the input are converted into cochleagram images. The PVTv2 based deep network architecture is trained with the generated cochleagram images. As a result of the training, the suspicious audio file given as input is labelled as original/fake. The proposed method gives 96.11% accuracy for 2s database, 94.63% accuracy for 3s database and 95.19% accuracy for 2s-3s database. © 2024 IEEE.

关键词： Deep learning

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