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6th IEEE Novel Intelligent and Leading Emerging sciences Conference, NILES 2024

作者： Gaafar, Ahmed Ali Fouad, Khaled M. Sadek, Mohamed A. Nile University Faculty of Information Technology and Computer Science Cairo Egypt Benha University Faculty of Computers and Artificial Intelligence Egypt New Mansoura University Faculty of Computer Science and Engineering Egypt Nile University Egypt

ISBN: (纸本)9798350378511

Despite software startups often not handlingsensitive data, the implementation of robust security measures is crucial to mitigate significant financial and reputational risks. This study investigates the cost-benefit analysis of implementing best practices in penetration testing (Pentest) versus notimplementing them, using Roboost as a case study. It emphasizes that proactive security investments not only protect current assets but also prepare organizations for future growthThe research employs a mixed-methods approach, combining quantitative analysis of financial data with qualitative insights from stakeholder interviews. The findings indicate that while the initial costs of hiring penetration testers and implementing security protocols are substantial, the benefits far outweigh these expenses. Securing new clients and retaining existing ones, which constitute a significant portion of Roboost' s revenue, underscores the financial prudence of these investments. Our analysis demonstrates that the costs of not implementing security practices, such as potential losses from security breaches, are substantially higher than the investments in proactive measures. This underscores the critical importance of embedding security best practices within organizational frameworks to ensure long-term sustainability and success. © 2024 IEEE.

关键词： Decentralized finance

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Source Number Enumeration Approach Based on CEEMD 12th

Source Number Enumeration Approach Based on CEEMD

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12th International Conference on Frontier Computing, FC 2022

作者： Ge, Shengguo Rum, Siti Nurulain Mohd Ibrahim, Hamidah Marsilah, Erzam Perumal, Thinagaran Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang43400 Malaysia

ISBN: (纸本)9789819914272

Empirical mode decomposition (EMD) can be used to decompose complex signals into a limited number of intrinsic mode functions (IMFs), and each decomposed IMF component contains local characteristic signals of different time scales of the original signal. Thus, EMD can be used to transform the problem of source number enumeration into a pattern recognition problem, thereby improving the accuracy of source number enumeration. However, EMD is prone to mode mixing, which also affects the accuracy of source number enumeration. To overcome this issue, this research work proposes the replacement of EMD decomposition with the Complementary Ensemble Empirical Mode Decomposition (CEEMD) to suppress the mode mixing phenomenon. The result of the experimental simulation in this proved that CEEMD achieved a better accuracy of source number enumerations compared to three other approaches namely based on EMD, SORTE and GED. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Empirical mode decomposition

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Real-Time Deepfake Detection for AI-Generated Arabic Speech 6

Real-Time Deepfake Detection for AI-Generated Arabic Speech

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6th IEEE Novel Intelligent and Leading Emerging sciences Conference, NILES 2024

作者： Awaad, Rewaa Hedya, Abdelrahman Fadel, Mariam Zaky, Ahmed B. Minia University Department of Computer Science and Information Systems Minya61111 Egypt Egypt-Japan University of Science and Technology Department of Computer Science and Information Technology Alexandria21934 Egypt Benha University Faculty of Engineering at Shoubra Benha13511 Egypt

ISBN: (纸本)9798350378511

This study investigates the effectiveness of Retrieval-based Voice Conversion (RVC) in detecting AI-generated Arabic speech across diverse linguistic contexts. The primary research questions address whether the RVC model can accurately differentiate between real and synthetic speech samples in various languages and its generalization capability across different linguistic contexts. Experimental evaluations show that the model achieves accuracies of 92% using Ensemble Learning (XGBoost and LightGBM), 93% with Meta Learning, and 90% with Neural Networks. Assessments encompass scenarios with real Arabic and English speech not included in training datasets, as well as different speakers across languages. The study contributes insights into the robustness and practical application of RVC for speech authentication. © 2024 IEEE.

关键词： Translation (languages)

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Evaluating LightGBM Classifier for Knowledge Tracing on EdNet Dataset 1

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1st International Conference on Digital Transformation in Education and Artificial Intelligence Applications, MoStart 2023

作者： Habijan, Marija Galić, Irena Faculty of Electrical Engineering Computer Science and Information Technology Osijek Osijek31000 Croatia

ISBN: (数字)9783031368332

ISBN: (纸本)9783031368325

Knowledge tracing is critical in educational data mining to accurately predict student performance and knowledge in future interactions. In this study, we utilized the EdNet dataset to implement different classifiers and evaluate their performance in knowledge tracing. We found that the LightGBM classifier outperformed other classifiers, achieving the highest accuracy of 0.723 on the test set. Furthermore, we applied three ensemble methods, including boosting, bagging, and stacking, to investigate if they could further improve the performance of the LightGBM classifier. Our experimental results showed that the stacking method provided the most significant improvement, while the bagging and boosting methods did not yield significant improvements. These findings suggest that the LightGBM classifier, combined with the boosting method, can be an effective approach for knowledge tracing and have potential practical applications in education. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Data mining

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Lightweight Brain Tumor Diagnosis via Knowledge Distillation 7

Lightweight Brain Tumor Diagnosis via Knowledge Distillation

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7th International Conference on Multimedia Analysis and Pattern Recognition, MAPR 2024

作者： Anantathanavit, Rungpilin Raswa, Farchan Hakim Thaipisutikul, Tipajin Wang, Jia-Ching Mahidol University Faculty of Information and Communication Technology Nakhon Pathom Thailand National Central University Department of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9798350368437

Brain tumors pose a significant medical challenge, necessitating precise and rapid diagnosis for effective treatment and improved patient outcomes. This paper introduces knowledge distillation, which has the potential to revolutionize brain tumor diagnosis by enabling early identification from medical imaging data. Using a sophisticated teacher' model to capture intricate patterns, we distill this knowledge into a more efficient "student' model, aiming for comparable accuracy with reduced memory usage and improved inference times. Our method, based on a dataset of 357 MRI scans, demonstrated the potential of knowledge distillation in brain tumor diagnosis, offering a promising avenue for advancing patient care. The proposed model serves as a vital tool for healthcare practitioners, providing accurate and efficient support in detecting brain tumors and contributing to advancements in healthcare technology. The evaluation results indicate the effectiveness of our technique, achieving an impressive accuracy of 98.10 © 2024 IEEE.

关键词： Patient treatment

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Shortest Cover After Edit 35

Shortest Cover After Edit

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35th Annual Symposium on Combinatorial Pattern Matching, CPM 2024

作者： Mitani, Kazuki Mieno, Takuya Seto, Kazuhisa Horiyama, Takashi Graduate School of Information Science and Technology Hokkaido University Japan Department of Computer and Network Engineering University of Electro-Communications Tokyo Japan Faculty of Information Science and Technology Hokkaido University Japan

ISBN: (纸本)9783959773263

This paper investigates the (quasi-)periodicity of a string when the string is edited. A string C is called a cover (as known as a quasi-period) of a string T if each character of T lies within some occurrence of C. By definition, a cover of T must be a border of T;that is, it occurs both as a prefix and as a suffix of T. In this paper, we focus on the changes in the longest border and the shortest cover of a string when the string is edited only once. We propose a data structure of size O(n) that computes the longest border and the shortest cover of the string in O(log n) time after an edit operation (either insertion, deletion, or substitution of some string) is applied to the input string T of length n, where is the length of the string being inserted or substituted. The data structure can be constructed in O(n) time given string T. © Kazuki Mitani, Takuya Mieno, Kazuhisa Seto, and Takashi Horiyama.

关键词： Data structures

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SLP-LMNMT: Source Language Prediction in Bilingual and Many-to-One Neural Machine Translation Tasks 24

SLP-LMNMT: Source Language Prediction in Bilingual and Many-...

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3rd Asia Conference on Algorithms, Computing and Machine Learning, CACML 2024

作者： Li, Dongxing Guo, Dongdong Faculty of Geographical Science in Beijing Normal University Beijing China Computer School in Beijing Information Science and Technology University Beijing China

ISBN: (纸本)9798400716416

This paper proposes a novel approach, Source Language Prediction-Language Model for Neural Machine Translation (SLP-LMNMT), based on the UNILM's sequence-to-sequence (seq2seq) model. This model is specifically designed for bilingual and many-to-one translation tasks, with a focus on predicting the source language type. It reframes NMT as a natural language understanding task by using a unified encoder and decoder structure, departing from the conventional encoder2decoder setup. Similar to BERT, SLP-LMNMT combines a restricted masked language model (R-MLM) to determine whether the masked tokens are from the source and target sequences, along with a Source Language Prediction (SLP) task to identify the source language type. Experiments demonstrate the efficiency of SLP-LMNMT, show significant improvements in translation performance compared to unidirectional models. © 2024 ACM.

关键词： Decoding

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Data secure transmission intelligent prediction algorithm for mobile industrial IoT networks

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Digital Communications and Networks 2023年第2期9卷 400-410页

作者： Lingwei Xu Hao Yin Hong Jia Wenzhong Lin Xinpeng Zhou Yong Fu Xu Yu Department of Information Science and Technology Qingdao University of Science and TechnologyQingdao266061China Fujian Key Laboratory of Sensing and Computing for Smart Cities Xiamen UniversityXiamen361005China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control(Minjiang University) Fuzhou350121China Faculty of Information Science and Engineering Ocean University of ChinaQingdao266100China Shandong Provincial Key Laboratory of Computer Networks Shandong Computer Science Center(National Supercomputer Center in Jinan)JinanChina

Mobile Industrial Internet of Things(IIoT)applications have achieved the explosive growth in recent *** mobile IIoT has flourished and become the backbone of the industry,laying a solid foundation for the interconnection of all *** variety of application scenarios has brought serious challenges to mobile IIoT networks,which face complex and changeable communication *** data secure transmission is critical for mobile IIoT *** paper investigates the data secure transmission performance prediction of mobile IIoT *** cut down computational complexity,we propose a data secure transmission scheme employing Transmit Antenna Selection(TAS).The novel secrecy performance expressions are first ***,to realize real-time secrecy analysis,we design an improved Convolutional Neural Network(CNN)model,and propose an intelligent data secure transmission performance prediction *** mobile signals,the important features may be removed by the pooling *** will lead to negative effects on the secrecy performance prediction.A novel nine-layer improved CNN model is *** of the input and output layers,it removes the pooling layer and contains six convolution ***,Back-Propagation(BP)and LeNet methods are employed to compare with the proposed *** simulation analysis,good prediction accuracy is achieved by the CNN *** prediction accuracy obtains a 59%increase.

关键词： Mobile IIoT networks Data secure transmission Performance analysis Intelligent prediction Improved CNN

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Breaking demodulation limitations:AWG and Deep Learning in Dynamic Spectral Reconstruction for Differential FBG Accelerometers

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IEEE Sensors Journal 2025年第11期25卷 19210-19222页

作者： Yang, Qian Liu, Lei Niu, Ning Quan, ChenYi Ren, SuFen Chen, ShengChao Zhang, JiaLin Yan, YuMeng Xiang, LongTeng Wang, YiPing Liao, ChangRui He, Jun Wang, GuanJun School of Information and Communication Engineering Hainan University College of Electronic Science and Technology China Henan Mechanical and Electrical Vocational College School of Electrical Engineering Henan451100 China Hainan University College of Electronic Science and Technology School of Information and Communication Engineering Haikou570228 China University of Technology Sydney School of Computer Science Faculty of Engineering and Information Technology SydneyNSW Australia Hainan University College of Electronic Science and Technology Haikou570228 China Shenzhen University School of Physics and Optoelectronic Engineering Shenzhen518060 China

Conventional fiber Bragg grating (FBG) accelerometer demodulation often suffers from high environmental sensitivity, complexity, and cost. To address these issues, this paper presents two arrayed waveguide grating (AWG)-based dynamic spectral reconstruction models. First, a novel differential FBG accelerometer with an equal-intensity triangular cantilever beam and a symmetric string-like design enhances sensitivity and mitigates temperature interference. A power ratio-nonlinear logarithmic regression (PR-NLR) model derived from static calibration then enables spectral reconstruction and wavelength demodulation across varying accelerations, achieving 4-82pm accuracy, slightly below that of high-speed demodulators (HSD). Next, by integrating convolutional neural networks (CNN), bidirectional long short-term memory (BiLSTM), attention mechanisms, and a generative adversarial network (GAN) for data augmentation, prediction errors drop to 1-26pm at 20Hz and 0.2-21pm at 40Hz-a reduction of at least 60% over PR-NLR. This outcome underscores the advantages of deep learning (DL) in managing complex spectral shifts. By overcoming frequency-only demodulation limits, the proposed AWG approach achieves precise wavelength demodulation through dynamic spectral reconstruction. Its compact design, low manufacturing cost, and robust performance facilitate scalable deployment in structural health monitoring, particularly for bridge vibration analysis and industrial machinery diagnostics. © 2001-2012 IEEE.

关键词： Arrayed waveguide gratings

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A machine learning approach for accelerated design of magnesium alloys. Part A:Alloy data and property space

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Journal of Magnesium and Alloys 2023年第11期11卷 3620-3633页

作者： M.Ghorbani M.Boley P.N.H.Nakashima N.Birbilis Department of Materials Science and Engineering Monash UniversityVIC3800Australia Faculty of Information Technology Monash UniversityVIC3800Australia College of Engineering and Computer Science Australian National UniversityACT2601Australia Faculty of Engineering Science and the Built EnvironmentDeakin UniversityVIC3125Australia

Typically, magnesium alloys have been designed using a so-called hill-climbing approach, with rather incremental advances over the past century. Iterative and incremental alloy design is slow and expensive, but more importantly it does not harness all the data that exists in the field. In this work, a new approach is proposed that utilises data science and provides a detailed understanding of the data that exists in the field of Mg-alloy design to date. In this approach, first a consolidated alloy database that incorporates 916 datapoints was developed from the literature and experimental work. To analyse the characteristics of the database, alloying and thermomechanical processing effects on mechanical properties were explored via composition-process-property matrices. An unsupervised machine learning(ML) method of clustering was also implemented, using unlabelled data, with the aim of revealing potentially useful information for an alloy representation space of low dimensionality. In addition, the alloy database was correlated to thermodynamically stable secondary phases to further understand the relationships between microstructure and mechanical properties. This work not only introduces an invaluable open-source database, but it also provides, for the first-time data, insights that enable future accelerated digital Mg-alloy design.

关键词： Magnesium Alloy design Mg-alloy database Data analysis Data visualisation Unsupervised machine learning

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