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Improving Khmer-Vietnamese Machine Translation with data Augmentation methods 22

Improving Khmer-Vietnamese Machine Translation with Data Aug...

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11th international symposium on Information and Communication Technology, SoICT 2022

作者： Pham Van, Hanh Le thanh, Huong School of Information and Communication Technology Hanoi University of Science and Technology Viet Nam

ISBN: (纸本)9781450397254

Machine translation has achieved significant improvements with the development of neural models. However, such approaches require large-scale parallel data, which are hard to collect for low-resource language pairs. this paper solves this problem by applying a pretrained multilingual model and fine-Tuning it with a low-resource bilingual dataset. In addition, we propose two data-Augmentation strategies to receive new training data, including: (i) back-Translating with the dataset from the source language;(ii) translating sentences from the source language to the target one through a pivot language. the proposed approach is applied to the Khmer-Vietnamese machine translation. Experimental results show that our proposed approach gains 4.426% BLEU score higher than the Google translator model using a test set of 2000 Khmer-Vietnamese sentence pairs. © 2022 ACM.

关键词： Neural machine translation

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Encryption process of Blockchain based online course curriculum education system 11

Encryption process of Blockchain based online course curricu...

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11th international symposium on Digital Forensics and Security, ISDFS 2023

作者： Ahmmed, Md. Rayhan Mamun Sakib, Md. Rahman, Maruf Ur Bibi, Rabeya Hasan Talukder, Md. Maruf Johora Akter Polin, Ms. Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350336986

Blockchain security is a comprehensive risk management system that includes assurance services, cybersecurity frameworks, and best practices to reduce the risks of fraud and cyberattacks. In shortened, an inherent data structure with security features is produced by blockchain technology where it is founded on cryptographic, decentralized, and consensus principles that uphold transactional trust. As our technology continuously updates and changes, new techniques and methods are appearing to secularly keep user details. the "Blockchain-based system"is among the finest. In a blockchain-based system is nearly impossible to hack or leak someone's information without permission. this is a more secure and reliable system than most of the other systems. But as this is a new and growing technology few online courses industry is using the system but not with its full function. We suggested including blockchain technology in the system of "Online courses". As a security precaution, the course modules and details will be entered into it, two keys are used throughout the entire program. the first is the public key, which is necessary for our course title and description, and the second is the private key, which is necessary for each course module and the payment system. Multiple machine learning models like Linear regression, random forest that are used to detect fraudulent transactions in the application. Every block and user details were hashed (64 bits) to keep them unique from each other. Numerous applications and software projects have been secured by blockchain technology. During our research, we experimented in a novel way and developed a fresh methodology that can be more secure than previously used applications. In this instance, our machine learning models have performed well so far, and we have also gained high accuracy from our dataset while. © 2023 IEEE.

关键词： Cryptography

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Video Label Enhancing and Standardization through Transcription and WikiId Mapping Techniques 11

Video Label Enhancing and Standardization through Transcript...

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11th international symposium on Electronic Systems Devices and Computing, ESDC 2023

作者： thomas, Dinu Pratap, David Sudha, B.G. Pes University Data Science and Machine Learning Bangalore India

ISBN: (纸本)9781665455725

Volume of video content surpass all other content types in internet. As per the reports from different sources, video traffic had acquired 82% of internet usage in 2022. Video is going to be more important in the years to come for user engagement, advertisement & marketing, news, education etc. Video information retrieval becomes an important problem to solve in this context. An accurate and fast video tagging system can aid a good content recommendation to the end users. It helps to audit the content automatically thereby platforms can control the contents which are politically and morally harmful. there are not many faster or cost-effective mechanisms to tag user generated videos at this moment. Manual tagging is a costly and highly time taking task. A delay in indexing the videos like news, sports etc., shall reduce its freshness and relevancy. Deep learning techniques have reached its maturity in the contents like text and images, but it is not the case with videos. Deep learning models need more resources to deal with videos due to its multi-modality nature, and temporal behavior. Apart from that, there are not many large-scale video datasets available at this moment. Youtube-8M is the largest dataset which is publicly available as of now. Much research works happened over Youtube-8M dataset. from our study, all these have a potential limitation. For example, in Youtube-8M, Video labels are only around 3.8K which are not covering all real-world tags. It is not covering the new domains which are created along with the surge in the content traffic. this study aims to handle this problem of tag creation through different methods available thereby enhancing the labels to a much wider set. this work also aims to produce a scalable tagging pipeline which uses multiple retrieval mechanisms, combine their results. the work aims to standardize the retrieved tokens across languages. this work creates a dataset as an outcome from 'Wikidata', which can be used for any NLP

关键词： Standardization

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11th international symposium on Distributed Computing and Artificial Intelligence 2014, DCAI 2014

11th International Symposium on Distributed Computing and Ar...

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11th international symposium on Distributed Computing and Artificial Intelligence 2014, DCAI 2014

ISBN: (纸本)9783319075921

the proceedings contain 64 papers. the special focus in this conference is on Distributed Computing and Artificial Intelligence. the topics include: Reachability modeling for multimodal networks prototyping;hybrid solution framework for supply chain problems;scheduling of mobile robots with preemptive tasks;multimodal processes approach to supply chain modeling;behavioral macroeconomics and agent-based macroeconomics;designing a homo psychologicus more psychologicus;differences between entrepreneurs and managers in large organizations;the empirical microstructure of agent-based models;households debt behavior and financial instability;firm size distribution in oblivious equilibrium model with quality ladder;modeling uncertainty in banking networks;from design to production exploiting workflow patterns;distributed and guided genetic algorithm for humanitarian relief planning in disaster case;software fault prediction based on improved fuzzy clustering;SOA modeling based on MDA;intelligent lighting control system;multimodal conversational agents for E-learning;a multi-agent mechanism for human resources allocation in a distributed software development environment;multi-agent web recommendations;designing strategies for improving the performance of groups in collective environments;multiagent application in mobile environments to data collection in park zones;exploring the role of macroeconomic mechanisms in voluntary resource provisioning in community network clouds;mobile access to sensor network;building scalable view module of object-oriented database;modelling an orientation system based on speculative computation;stable learning for neural network tomography by using back projected image;security considerations for patient telemonitoring schemes through wireless networks;development of an ontology for supporting diagnosis in psychiatry;augmented reality sign language teaching model for deaf children;the case of physical activity and childhood obesity;the absorpti

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Calibration of strain gauges on a model runner blade combining numerical and experimental data 11

Calibration of strain gauges on a model runner blade combini...

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11th international symposium on Current Research in HydropowerTechnologies, CRHT 2023

作者： Kverno, Johannes Iliev, Igor Solemslie, Bjørn Winther Dahlhaug, Ole Gunnar Department of Energy and Process Engineering Norwegian University of Science and Technology Alfred Getz'vei 4 Trondheim7034 Norway SINTEF Energi AS Sem Sælands vei 11 Trondheim7034 Norway NINA Høgskoleringen 9 Trondheim7034 Norway

In this paper, we present the calibration setup, method, and results for a set of strain gauges installed on the trailing edge of the runner blades of a model Francis turbine. the calibration work is done as a step in the HydroFlex project, with the goal of taking experimental data to validate numerical models to better estimate the reduction in lifetime from more exible operation. Due to the complex geometry of the blade, an analytical solution for the stresses for a given load is not possible to obtain, so calibration is needed. A combination of strain measurement and numerical analysis is used to correlate the response from the strain gauges directly to the strains parallel to the trailing edge. the strain gauges are installed on the suction side of the blades, and close to the hub and shroud, and the calibration is done by applying known weights on the shroud using a tailor made blade fixture. the signal from the sensors is passed through a set of miniature amplifiers that fits in the hub of the model runner. the numerical setup is done in ANSYS Mechanical, and is set up to fully replicate the conditions of the physical setup. the results shows that the method is viable, however there is a challenge with the response, and thus required amplification, due to the stiffness of the runner blade material. © 2023 Institute of Physics Publishing. All rights reserved.

关键词： Strain gages

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Botnet dataset Overview Using Statistical Approach Based on Time Gap Activity Analysis

Botnet Dataset Overview Using Statistical Approach Based on ...

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international symposium on Digital Forensic and Security (ISDFS)

作者： Muhammad Aidiel Rachman Putra Tohari Ahmad Dandy Pramana Hostiadi Department of Informatics Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Informatics Institut Teknologi dan Bisnis STIKOM Bali Bali Indonesia

Botnet malware is a malicious activity that needs to be detected accurately. Several botnet detection models have been introduced using a mining-based method. the distribution analysis approach is often used to obtain the characteristics of the data before modeling it on a mining-based detection model. However, the distribution approach could be more optimal in describing the timeliness of botnet attacks. the botnet attack time analysis is needed to determine the right detection model for the botnet activity detection model. this paper proposes a new overview technique for botnet datasets using a statistical approach based on time gap analysis for each bot. the goal is to obtain a threshold value that can optimally separate botnet activity traffic from normal traffic. the experimental result is three different time gap threshold values to separate the botnet, normal, and background activity, ideally based on the highest time of 4,756 s, the lowest time of 28.69 s, and the average maximum time gap of 810.61 s.

关键词： Analytical models Botnet Computational modeling Digital forensics Telecommunication traffic data models Malware

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Multivariate and multi-output indoor air quality prediction using bidirectional LSTM

Multivariate and multi-output indoor air quality prediction ...

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international symposium on Digital Forensic and Security (ISDFS)

作者： Nuno Fernandes Joaquim Gonçalves 2Ai – School of Technology IPCA Barcelos Portugal LASI – Associate Laboratory of Intelligent Systems Guimarães Portugal

data from the World Health Organization show that air pollution is associated with seven million premature deaths annually and that 99% of the global population breathes air that exceeds guideline limits and contains high levels of pollutants. this problem gets worse when it comes to children, cancer patients and survivors, especially those who have received chemotherapy or radiotherapy. It then becomes important to monitor and predict air pollution patterns, particularly indoors, to improve quality of life. In this article, we propose a solution that uses a Long-Term Memory neural network to predict the levels of some pollutants in the air inside the home. the traditional model and the bidirectional model were analysed, the latter providing better results and therefore chosen for a deeper analysis. models were trained for forecasting at 1 minute and 1 hour. the 1-minute forecast model scored 8.703 for RMSE, 2.892 for MAE and 6% for MAPE, and the 1-hour forecast model scored 53.791 for RMSE, 16.193 for MAE and 18% for MAPE.

关键词： Analytical models Atmospheric modeling Soft sensors Sociology Predictive models Air pollution Security

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Driver Drowsiness Classification using Machine Learning and Heart Rate Variability

Driver Drowsiness Classification using Machine Learning and ...

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international symposium on Digital Forensic and Security (ISDFS)

作者： Miguel V. P. R. Meneses Ana Rita Antunes Joaquim Gonçalves Innovation and Knowledge Engineering Lab LITEC Optimizer Porto Portugal 2Ai – School of Technology LASI – Associate Laboratory of Intelligent Systems IPCA University of Minho 2Ai – School of Technology LASI – Associate Laboratory of Intelligent Systems IPCA Barcelos Portugal

Drowsiness at the wheel continues to be one of the causes of road accidents with higher fatality rates and current solutions fail to mitigate the problem from a preventive viewpoint. this work contributes towards the development of a non-intrusive and low-cost system for drowsiness detection and prediction, using a wearable device and artificial intelligence for continuous and real-time monitoring and alert of the driver’s state. In particular, Machine Learning (ML) algorithms were used to classify drowsiness levels using Heart Rate Variability (HRV) data extracted from physiological signals collected during driving simulations, following data labeling considering three different sets of drowsiness levels’ distribution. Obtained results showed promising potential, with the Extra Trees, Random Forest and K-Nearest Neighbors (KNN) classifiers achieving overall good performance, with a relative capability of distinguishing between drowsiness levels and between alert and drowsy states. However, further analysis should be made to improve the performance of the models and deal with the existing data imbalance, as well as identify the best subset of features capable of detecting drowsiness. Ultimately, it was shown that drowsiness detection using physiological data from a standalone wrist-worn wearable device in combination with ML techniques is feasible. Henceforth, prediction and forecasting of the drowsiness state should be addressed, to achieve the preventive component of the system.

关键词： Road accidents Wearable computers Wheels data models Real-time systems Security Heart rate variability

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Duo Satellite Based Surface Temperature Comparative Study of Jaipur City Using Soft Computing

Duo Satellite Based Surface Temperature Comparative Study of...

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Annual Information Technology, Electromechanical Engineering and Microelectronics Conference (IEMECON)

作者： Shivam Chauhan Ajay Singh Jethoo Umesh Kumar Das Department of Civil Engineering University of Engineering and Management Jaipur Malaviya National Institute of Technology Jaipur Jaipur India

In this paper, the remotely sensed land surface temperature distribution from two satellites i.e., Aqua and Terra of Jaipur city were used in back propagation neural network system to model and predict the thermal activity. As an input dependent variable TDLST and TNLST was fed to BPNN and showcased the regression of independent variable ADLST and ANLST by keeping constant initial weight, bias and test and training sets. In day-day LST model R 2 values for training and testing are 0.9827 and 0.9012 and in night-night LST model R 2 values for training and testing are 0.9812 and 0.9270 respectively. Model prediction results showed model ability to predict reliable results when compared with the collected data.

关键词： Training Temperature sensors Temperature distribution Computational modeling Urban areas Land surface Predictive models

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Encryption process of Blockchain based online course curriculum education system

Encryption process of Blockchain based online course curricu...

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international symposium on Digital Forensic and Security (ISDFS)

作者： Md. Rayhan Ahmmed Md. Mamun Sakib Maruf Ur Rahman Rabeya Bibi Md. Maruf Hasan Talukder Ms. Johora Akter Polin Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh

Blockchain security is a comprehensive risk management system that includes assurance services, cybersecurity frameworks, and best practices to reduce the risks of fraud and cyberattacks. In shortened, an inherent data structure with security features is produced by blockchain technology where it is founded on cryptographic, decentralized, and consensus principles that uphold transactional trust. As our technology continuously updates and changes, new techniques and methods are appearing to secularly keep user details. the "Blockchain-based system" is among the finest. In a blockchain-based system is nearly impossible to hack or leak someone's information without permission. this is a more secure and reliable system than most of the other systems. But as this is a new and growing technology few online courses industry is using the system but not with its full function. We suggested including blockchain technology in the system of "Online courses". As a security precaution, the course modules and details will be entered into it, two keys are used throughout the entire program. the first is the public key, which is necessary for our course title and description, and the second is the private key, which is necessary for each course module and the payment system. Multiple machine learning models like Linear regression, random forest that are used to detect fraudulent transactions in the application. Every block and user details were hashed (64 bits) to keep them unique from each other. Numerous applications and software projects have been secured by blockchain technology. During our research, we experimented in a novel way and developed a fresh methodology that can be more secure than previously used applications. In this instance, our machine learning models have performed well so far, and we have also gained high accuracy from our dataset while.

关键词： data privacy Education Linear regression Public key Software Blockchains Security

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