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2nd International Conference on Cybernetics and Innovations, ICCI 2023

作者： Pramkeaw, Patiyuth Ganokratanaa, Thittaporn Pinthong, Thidarat Kitprasert, Napathsorn King Mongkut's University of Technology Thonburi Media Technology Program Bangkok Thailand King Mongkut's University of Technology Thonburi Applied Computer Science Program Bangkok Thailand Phetchaburi Rajabhat University Dep. of Business Information Management Phetchaburi Thailand

ISBN: (纸本)9798350336573

The purpose of this research was to design, produce and evaluate a stray child warning system. The device has been designed by using 2 ESP8266 boards to receive signals as specified. The system will notify the information via Line Notify to let users know how far the board that acts as a Client is from the board that acts as an Access Point. Expressed in meters (m) in the notification line notify process. The test results of the system in terms of signal transmission between the Access Point and the Client can work well. Buzzer speaker and LED notification light according to the specified distance every time. Notifications to LINE Notify to mobile phones can be done every time. © 2023 IEEE.

关键词： Internet of things

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Fictitious null spaces for improving the solution of injective inverse problems

arXiv

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arXiv 2024年

作者： Elvetun, Ole Løseth Knudsen, Kim Nielsen, Bjørn Fredrik Faculty of Science and Technology Norwegian University of Life Sciences Norway Department of Applied Mathematics and Computer Science Technical University of Denmark Denmark

For linear ill-posed problems with nontrivial null spaces, Tikhonov regularization and truncated singular value decomposition (TSVD) typically yield solutions that are close to the minimum norm solution. Such a bias is not always desirable, and we have therefore in a series of papers developed a weighting procedure which produces solutions with a different and controlled bias. This methodology can also conveniently be invoked when sparsity regularization is employed. The purpose of the present work is to study the potential use of this weighting applied to injective operators. The image under a compact operator of the singular vectors/functions associated with very small singular values will be almost zero. Consequently, one may regard these singular vectors/functions to constitute a basis for a fictitious null space that allows us to mimic the previous weighting procedure. It turns out that this regularization by weighting can improve the solution of injective inverse problems compared with more traditional approaches. We present some analysis of this methodology and exemplify it numerically, using sparsity regularization, for three PDE-driven inverse problems: the inverse heat conduction problem, the Cauchy problem for Laplace’s equation, and the (linearized) Electrical Impedance Tomography problem with experimental data. Copyright © 2024, The Authors. All rights reserved.

关键词： Inverse problems

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MassSpecGym: A benchmark for the discovery and identification of molecules 38

MassSpecGym: A benchmark for the discovery and identificatio...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Bushuiev, Roman Bushuiev, Anton de Jonge, Niek F. Young, Adamo Kretschmer, Fleming Samusevich, Raman Heirman, Janne Wang, Fei Zhang, Luke Dührkop, Kai Ludwig, Marcus Haupt, Nils A. Kalia, Apurva Brungs, Corinna Schmid, Robin Greiner, Russell Wang, Bo Wishart, David S. Liu, Li-Ping Rousu, Juho Bittremieux, Wout Rost, Hannes Mak, Tytus D. Hassoun, Soha Huber, Florian van der Hooft, Justin J.J. Stravs, Michael A. Böcker, Sebastian Sivic, Josef Pluskal, Tomáš Institute of Organic Chemistry and Biochemistry The Czech Academy of Sciences Czech Republic Czech Institute of Informatics Robotics and Cybernetics Czech Technical University Czech Republic Bioinformatics Group Wageningen University & Research Netherlands Department of Computer Science University of Toronto Canada Bioinformatics Institute for Computer Science Friedrich Schiller University Jena Germany Department of Computer Science University of Antwerp Belgium Department of computing science University of Alberta Canada Alberta Machine Intelligence Institute Canada Department of Molecular Genetics University of Toronto Canada Bright Giant GmbH Department of Computer Science Tufts University United States Department of Biological Sciences University of Alberta Canada Department of Computer Science Aalto University Finland Mass Spectrometry Data Center National Institute of Standards and Technology United States Department of Chemical and Biological Engineering Tufts University United States Centre for Digitalisation and Digitality University of Applied Sciences Düsseldorf Germany Department of Biochemistry University of Johannesburg South Africa Eawag Swiss Federal Institute of Aquatic Science and Technology Switzerland

The discovery and identification of molecules in biological and environmental samples is crucial for advancing biomedical and chemical sciences. Tandem mass spectrometry (MS/MS) is the leading technique for high-throughput elucidation of molecular structures. However, decoding a molecular structure from its mass spectrum is exceptionally challenging, even when performed by human experts. As a result, the vast majority of acquired MS/MS spectra remain uninterpreted, thereby limiting our understanding of the underlying (bio)chemical processes. Despite decades of progress in machine learning applications for predicting molecular structures from MS/MS spectra, the development of new methods is severely hindered by the lack of standard datasets and evaluation protocols. To address this problem, we propose MassSpecGym - the first comprehensive benchmark for the discovery and identification of molecules from MS/MS data. Our benchmark comprises the largest publicly available collection of high-quality labeled MS/MS spectra and defines three MS/MS annotation challenges: de novo molecular structure generation, molecule retrieval, and spectrum simulation. It includes new evaluation metrics and a generalization-demanding data split, therefore standardizing the MS/MS annotation tasks and rendering the problem accessible to the broad machine learning community. MassSpecGym is publicly available at https://***/pluskal-lab/MassSpecGym. © 2024 Neural information processing systems foundation. All rights reserved.

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Privacy Vulnerabilities in Marginals-based Synthetic Data

Privacy Vulnerabilities in Marginals-based Synthetic Data

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Secure and Trustworthy Machine Learning (SaTML), IEEE Conference on

作者： Steven Golob Sikha Pentyala Anuar Maratkhan Martine De Cock School of Engineering and Technology University of Washington Tacoma USA Department of Applied Mathematics Computer Science and Statistics Ghent University Belgium

ISBN: (数字)9798331517113

ISBN: (纸本)9798331517120

When acting as a privacy-enhancing technology, synthetic data generation (SDG) aims to maintain a resemblance to the real data while excluding personally-identifiable information. Many SDG algorithms provide robust differential privacy (DP) guarantees to this end. However, we show that the strongest class of SDG algorithms-those that preserve marginal probabilities, or similar statistics, from the underlying data-leak information about individuals that can be recovered more efficiently than previously understood. We demonstrate this by presenting a novel membership inference attack, MAMA-MIA, and evaluate it against three seminal DP SDG algorithms: MST, PrivBayes, and Private-GSD. MAMA-MIA leverages knowledge of which SDG algorithm was used, allowing it to learn information about the hidden data more accurately, and orders-of-magnitude faster, than other leading attacks. We use MAMA-MIA to lend insight into existing SDG vulnerabilities. Our approach went on to win the first SNAKE (SaNitization Algorithm under attacK … ε) competition.

关键词： Differential privacy Machine learning algorithms technology Machine learning Probability Inference algorithms Synthetic data Identification of persons

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Enhanced English Sentence Pair Classification Through KIRINLog Technique

Enhanced English Sentence Pair Classification Through KIRINL...

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Advances in Electrical, Electronics and Computing technology (EECT), International Conference on

作者： Patthanan Ounprasert Khantharat Anekboon Department of Computer and Information Sciences Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798331541545

ISBN: (纸本)9798331541552

Natural language processing is a rapidly evolving technology in an era of quick technological growth. Conventional rule-based methods are insufficient to meet the demands of today's complex challenges. One of the significant tasks in natural language processing is recognizing textual entailment. Recognizing textual entailment is vital in natural language processing as it enables systems to understand the relationship between texts, which is crucial for applications like text summarization, sentiment analysis, information verification, question answering, text classification, and machine translation. One challenge is reducing the size of the input vectors while maintaining good prediction results, F1 scores. To overcome this challenge, this research presents the novel encoding technique by reducing the encoding size for the sentences involving compositional knowledge dataset. This dataset is sentence pairs in English to classify textual entailments into three categories: entailment, neutral, and contradiction. This research proposes the KIRINLog technique, which consists of six main methods: preprocessing, removing the same word, max-length padding and truncation for sentence alignment, adding a flag value, word embedding by Word2Vec, and dimensionality re-duction with principal component analysis to reduce the size of input vector. All of six main processes are proposed to create feature vectors for recognizing textual entailment (the sentences involving compositional knowledge dataset). This study used an attention-based bidirectional LSTM as a classification model. This model combines bidirectional LSTM with an attention to make analyzing sequences in both forward and backward directions and to help the model focus on important information in the sentence. This proposed technique achieved an accuracy of 92.4%, with precision at 88.8%, recall at 88.6%, and an F1-score of 88.6% on the 2014 sentences involving compositional knowledge dataset.

关键词： Analytical models Sentiment analysis Text recognition Text categorization Bidirectional long short term memory Text summarization Vectors Encoding Question answering (information retrieval) Principal component analysis

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Towards Classifying File Segments in Memory Using Machine-Learning

Towards Classifying File Segments in Memory Using Machine-Le...

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HCT Information technology Trends (ITT)

作者： Mohammed I. Al-Saleh Akram Alkouz Abdulsalam Alarabeyyat Majed Bouchahma Computer Science Department Jordan University of Science and Technology Computer Information System Higher Colleges of Technology Jordan-Salt Software Engineering Department Al-Balqa’ Applied University

Digital media triage is a main challenge that faces a digital investigator. Knowing what might be useful during crime investigation could greatly save the investigator’s time and enhance outcomes. Memory investigation can get potential benefit from the triage process because of its scattered and diverse contents. The Operating System’s paging scheme might map the contents of a file into non-consecutive page frames in the physical memory, making the file-type classification and identification process harder. This paper tackles the memory content triage problem at the page level using a machine-learning methodology.

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Radiative peristaltic transport of Ree-Eyring fluid through porous medium in asymmetric channel subjected to combined effect of inclined MHD and convective conditions

Radiative peristaltic transport of Ree-Eyring fluid through ...

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2020 Ibn Al-Haitham International Conference for Pure and applied sciences, IHICPS 2020

作者： Ali, Hayat A. Department of Applied Science Mathematics and Application of Computer University of Technology Baghdad Iraq

This study emphasizes the flow phenomenon of trapped bolus traveling along the interior walls of asymmetric inclined channels contains a non-Newtonian Ree-Eyring. The flow was exposed to influenced by inclined MHD field, thermal heat radiative, and porous media. Further, no slip and convective thermal conditions are considered. Mathematical expression for governing equations are reformulated and in accordance with lubrication approximations, nonlinear partial differential equations of the flow reduced into a system of ordinary differential equations associated with boundary conditions an approximate solution is deduced by implementing perturbation strategy for tiny A Ree-Eyring fluid parameter. Finally, a graphical description is presented to figure out the elevation behavior of flow quantities i.e. velocity profile, temperature distribution, pressure rise, and streamlines formulation due to variation of emerging involved parameters. The study analyzed that the velocity profile reveals mixed behavior via increment of Ree-Eyring parameters η, A as well as Hartman number H and Darcy number Da. whereas the thermal radiative parameter Rn accelerates the temperature distribution profile. The study calculations are made by the "Mathematica 11.3"package. © Published under licence by IOP Publishing Ltd.

关键词： Temperature distribution

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Two Stages of Algebraic Matrix Decomposition in Optimal Image Steganographic Approach 5

Two Stages of Algebraic Matrix Decomposition in Optimal Imag...

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5th International Conference on Engineering technology and its Applications, IICETA 2022

作者： Waleed, Jumana Almaameri, Ihab Mahdi Abduldaim, Areej M. Al-Azzawi, Waleed University of Diyala College of Science Department of Computer Science Iraq The Islamic University College of Technical Engineering Department of Computer Technical Engineering Najaf54001 Iraq University of Technology Mathematics and Computer Applications Applied Sciences Department Baghdad Iraq Al-Farahidi University Medical Technical College Baghdad Iraq

ISBN: (纸本)9781665472159

With the evolution of social networks, the necessitate of providing information security methods becomes increasingly significant. Therefore, these methods should be studied and analyzed for presenting more secure and appropriate techniques. Since the images represent the most common and widely utilized visual formats, an optimized approach of image steganography based on algebraic matrix decomposition has been proposed in this paper. In particular, matrix decomposition methods represent a significant tool for increasing the security of the whole blent of digital steganographic approaches. In this paper, two stages of the Hessenberg matrix decomposition are implemented on the original image using modulo operation to show the impact of the matrix decomposition in the steganographic process. Additionally, a Chameleon Swarm optimization (CSO) algorithm is utilized to appoint the optimal positions in the embedding stage. The obtained results have been evaluated to show the difference between the utilization of decomposition or not. © 2022 IEEE.

关键词： Matrix algebra

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Combining Dynamic Programming and GPU to accelerate k-Distance Discord Detection in Time Series Data 23

Combining Dynamic Programming and GPU to accelerate k-Distan...

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Proceedings of the 6th International Conference on Machine Learning and Machine Intelligence

作者： Nguyen Trong Nhan Duong Tuan Anh Jiri Dvorsky Center for Applied Information Technology Ton Duc Thang University Vietnam and Department of Computer Science Faculty of Electrical Engineering and Computer Science VSB Technical University Czech Republic Department of IT Ho Chi Minh City University of Foreign Languages and Information Technology Vietnam Department of Computer Science Faculty of Electrical Engineering and Computer Science VSB Technical University Czech Republic

ISBN: (纸本)9798400709456

Detecting anomalous subsequences plays a vital role in time series analysis. Traditional approaches often rely on sliding windows to extract subsequences and identify anomalies. However, these methods encounter two difficulties: i) they are unable to address the "twin freak" problem and ii) they incur high computational costs when dealing with large time series datasets. We introduce a new method called DP_KBF_GPU to address the aforementioned challenge. Our method is designed for identifying K-Distance discords and effectively handle the "twin freak" problem commonly encountered. This method combines Dynamic Programming technique and Graphics Processing Units (GPUs) parallelization to enhance its performance. By employing Dynamic Programming with the Euclidean distance metric and leveraging the parallel processing capabilities of GPUs, our method achieves efficient computations. Experimental results on six benchmark time series datasets have demonstrated the high efficiency of our approach in timely detecting the most anomalous subsequences while maintaining high accuracy.

关键词： Discord detection Dynamic Programming Graphics Processing Unit (GPU) K-Distance discord Time Series

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Statistical Data Mining with Slime Mould Optimization for Intelligent Rainfall Classification

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computer Systems science & Engineering 2023年第10期47卷 919-935页

作者： Ramya Nemani G.Jose Moses Fayadh Alenezi K.Vijaya Kumar Seifedine Kadry Jungeun Kim Keejun Han Department of Mathematics Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science and Engineering University Institute of Engineering and Technology(UIET)Guru Nanak UniversityHyderabadIndia Department of Electrical Engineering College of EngineeringJouf UniversitySaudi Arabia Department of Computer Science and Engineering GITAM School of TechnologyVishakhapatnam CampusGITAM(Deemed to be a University)VishakhapatnamIndia Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea Division of Computer Engineering Hansung UniversitySeoul02876Korea

Statistics are most crucial than ever due to the accessibility of huge counts of data from several domains such as finance,medicine,science,engineering,and so *** data mining(SDM)is an interdisciplinary domain that examines huge existing databases to discover patterns and connections from the *** varies in classical statistics on the size of datasets and on the detail that the data could not primarily be gathered based on some experimental strategy but conversely for other ***,this paper introduces an effective statistical Data Mining for Intelligent Rainfall Prediction using Slime Mould Optimization with Deep Learning(SDMIRPSMODL)*** the presented SDMIRP-SMODL model,the feature subset selection process is performed by the SMO algorithm,which in turn minimizes the computation *** rainfall *** neural network with long short-term memory(CNN-LSTM)technique is *** last,this study involves the pelican optimization algorithm(POA)as a hyperparameter *** experimental evaluation of the SDMIRP-SMODL approach is tested utilizing a rainfall dataset comprising 23682 samples in the negative class and 1865 samples in the positive *** comparative outcomes reported the supremacy of the SDMIRP-SMODL model compared to existing techniques.

关键词： Statistical data mining predictive models deep learning rainfall prediction parameter tuning

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