Predicting translation behaviorsby using Hidden Markov Model

2020 ◽  
Vol 3 (1) ◽  
pp. 76-99 ◽  
Author(s):  
Sheng Lu ◽  
Michael Carl ◽  
Xinyue Yao ◽  
Wenchao Su
Keyword(s):  
Machine Learning ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Prediction Accuracy ◽  
Hidden Markov ◽  
Process Research ◽  
Target Language ◽  
Learning Technology ◽  
Translation Process ◽  
Activity Unit

Abstract The translation process can be studied as sequences of activity units. The application of machine learning technology offers researchers new possibilities in the study of the translation process. This research project developed a program, activity unit predictor, using the Hidden Markov Model. The program takes in duration, translation phase, target language and fixation as the input and produces an activity unit type as the output. The highest prediction accuracy reached is 61%. As one of the first endeavors, the program demonstrates strong potential of applying machine learning in translation process research.

scholarly journals Multi-Layer Hidden Markov Model Based Intrusion Detection System

2018 ◽  
Vol 1 (1) ◽  
pp. 265-286 ◽  
Author(s):  
Wondimu Zegeye ◽  
Richard Dean ◽  
Farzad Moazzami
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Detection System ◽  
Traffic Monitoring ◽  
Machine Learning Algorithms ◽  
Full Potential ◽  
Multi Phase

The all IP nature of the next generation (5G) networks is going to open a lot of doors for new vulnerabilities which are going to be challenging in preventing the risk associated with them. Majority of these vulnerabilities might be impossible to detect with simple networking traffic monitoring tools. Intrusion Detection Systems (IDS) which rely on machine learning and artificial intelligence can significantly improve network defense against intruders. This technology can be trained to learn and identify uncommon patterns in massive volume of traffic and notify, using such as alert flags, system administrators for additional investigation. This paper proposes an IDS design which makes use of machine learning algorithms such as Hidden Markov Model (HMM) using a multi-layer approach. This approach has been developed and verified to resolve the common flaws in the application of HMM to IDS commonly referred as the curse of dimensionality. It factors a huge problem of immense dimensionality to a discrete set of manageable and reliable elements. The multi-layer approach can be expanded beyond 2 layers to capture multi-phase attacks over longer spans of time. A pyramid of HMMs can resolve disparate digital events and signatures across protocols and platforms to actionable information where lower layers identify discrete events (such as network scan) and higher layers new states which are the result of multi-phase events of the lower layers. The concepts of this novel approach have been developed but the full potential has not been demonstrated.

Hybrid Physics-Based and Data-Driven Modeling for Improved Standpipe Pressure Prediction

2021 ◽  
Author(s):  
Oney Erge ◽  
Eric van Oort
Keyword(s):  
Machine Learning ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Hybrid Modeling ◽  
Data Driven ◽  
Learning Models ◽  
Drilling Data ◽  
Drilling Operations ◽  
Pump Pressure

Abstract During drilling operations, it is common to see pump pressure spikes when flow is initiated, including after a connection or after a prolonged break in drilling operations. It is important to be able to predict the magnitude of such pressure spikes to avoid compromising wellbore integrity. This study shows how a hybrid approach using data-driven machine learning coupled with physics-based modeling can be used to accurately predict the magnitude of pressure spikes. To model standpipe pressure behavior, machine learning techniques were combined with physics-based models via a rule-based, stochastic decision-making algorithm. To start, neural networks and deep learning models were trained using time-series drilling data. From there, physics-based equations that model the pressure required to break the mud's gel strength as well as the flow of non-Newtonian fluids through the entire circulation system were used to simulate standpipe pressure. Then, these two highly different methods for predicting/modeling standpipe pressure were combined by a hidden Markov model using a set of rules and transition probabilities. By combining machine learning and physics-based approaches, the best features of each model are leveraged by the hidden Markov model, yielding a more accurate and robust prediction of pressure. A similar result is not achievable with a purely data-driven black-box model, because it lacks a connection to the underlying physics. Our study highlights how drilling data analysis can be optimally leveraged. The overarching conclusion: hybrid modeling can more accurately predict pump pressure spikes and capture the transient events at flow initiation when compared to physics-based or machine learning models used in isolation. Moreover, the approach is not limited to pressure behavior but can be applied to a wide range of well construction operations. The proposed approach is easy to implement and the details of implementation are presented in this study. Being able to accurately model and manage the pressure response during drilling operations is essential, especially for wells drilled in narrow-margin environments. Pressure can be more accurately predicted through our proposed hybrid modeling, leading to safer, more optimized operations.

scholarly journals Research on Autoarrangement System of Accompaniment Chords Based on Hidden Markov Model with Machine Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Shuo Shi ◽  
Shuting Xi ◽  
Sang-Bing Tsai
Keyword(s):  
Machine Learning ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Template Matching ◽  
Hidden Markov ◽  
Data Set ◽  
Data Format ◽  
Automated Method ◽  
Machine Learning Model ◽  
And Training

Accompaniment production is one of the most important elements in music work, and chord arrangement is the key link of accompaniment production, which usually requires more musical talent and profound music theory knowledge to be competent. In this article, the machine learning model is used to replace manual accompaniment chords’ arrangement, and an automatic computer means is provided to complete and assist accompaniment chords’ arrangement. Also, through music feature extraction, automatic chord label construction, and model construction and training, the whole system finally has the ability of automatic accompaniment chord arrangement for the main melody. Based on the research of automatic chord label construction method and the characteristics of MIDI data format, a chord analysis method based on interval difference is proposed to construct chord labels of the whole track and realize the construction of automatic chord labels. In this study, the hidden Markov model is constructed according to the chord types, in which the input features are the improved theme PCP features proposed in this paper, and the input labels are the label data set constructed by the automated method proposed in this paper. After the training is completed, the PCP features of the theme to be predicted and improved are input to generate the accompaniment chords of the final arrangement. Through PCP features and template-matching model, the system designed in this paper improves the matching accuracy of the generated chords compared with that generated by the traditional method.

scholarly journals Identifying Intrusion Behaviour using Enhanced Hidden Markov Model

2020 ◽  
Vol 9 (4) ◽  
pp. 1618-1623
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Credit Card ◽  
Hidden Markov ◽  
Network Intrusion Detection ◽  
Detection Accuracy ◽  
Constant Attention ◽  
Network Intrusion

Data Mining is a method for detecting network intrusion detection in networks. It brings ideas from variety of areas including statistics, machine learning and database processes. Decreasing price of digital networking is now economically viable for network intrusion detection. This analysis chiefly examines the system intrusion detection with machine learning and DM methods. To improve the accuracy and efficiency of SHMM, we are collecting multiple observation in SHMM that will be called as Multiple Hidden Markov Model (MHMM). It is used to improve better Detection accuracy compare with SHMM. In the standard Hidden Markov Model, we have observed three fundamental problems are Evaluation and decoding another one is learning problem. The Evaluation problem can be used for word recognition. And the Decoding problem is related to constant attention and also the segmentation. In this Proposed Research, the primary purpose is to model the sequence of observation in Network log and credit card log transactions process using Enhanced Hidden Markov Model (EHMM). And show how it can be used for intrusion detection in Network. In this procedure, an EHMM is primarily trained with the conventional manners of a intruders. If the trained EHMM does not recognize an incoming Intruder transaction with adequately high probability, it is thought to be fraudulent.

scholarly journals Evaluation of Physical Education Teaching Quality in Colleges Based on the Hybrid Technology of Data Mining and Hidden Markov Model

2020 ◽  
Vol 15 (01) ◽  
pp. 4 ◽  
Author(s):  
Yuansheng Zeng
Keyword(s):  
Data Mining ◽  
Physical Education ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Evaluation System ◽  
Quality Evaluation ◽  
Hidden Markov ◽  
Teaching Quality ◽  
Learning Technology ◽  
Hybrid Technology

In order to further improve the teaching quality evaluation accuracy of physical education(abbreviated as PE) curriculum in colleges, this study conducts an in-deep research on the overall evaluation of PE teaching effect in colleges from the aspects of teachers’ teaching ability and students’ learning effect based on the hybrid technology of data mining and hidden Markov model. First of all, this study analyzes the development status of the teaching quality evaluation system of PE curriculum in colleges; Secondly, it analyzes the applicability of data mining technology and hidden Markov model to the evaluation of PE teaching quality in colleges, and proposes a mathematical model for evaluating the quality of PE teaching in colleges; Finally, this study carries out a series of experiments on the basis of mathematical models, and analyzes the experimental results in depth. The experimental analysis shows that the model proposed in this paper is helpful to improve the accuracy of PE teaching quality evaluation in colleges. The research results of this study provide a useful exploration for the integration of computing technology and language teaching. At the same time, it provides a reference path and implementation model for improving the teaching of PE for graduates in colleges through machine learning technology.

scholarly journals A Discrete Hidden Markov Model for SMS Spam Detection

2020 ◽  
Vol 10 (14) ◽  
pp. 5011 ◽  
Author(s):  
Tian Xia ◽  
Xuemin Chen
Keyword(s):  
Machine Learning ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Word Order ◽  
Short Term Memory ◽  
Hidden Markov ◽  
Support Vector ◽  
Spam Detection ◽  
Term Frequency ◽  
Length Limitation

Many machine learning methods have been applied for short messaging service (SMS) spam detection, including traditional methods such as naïve Bayes (NB), vector space model (VSM), and support vector machine (SVM), and novel methods such as long short-term memory (LSTM) and the convolutional neural network (CNN). These methods are based on the well-known bag of words (BoW) model, which assumes documents are unordered collection of words. This assumption overlooks an important piece of information, i.e., word order. Moreover, the term frequency, which counts the number of occurrences of each word in SMS, is unable to distinguish the importance of words, due to the length limitation of SMS. This paper proposes a new method based on the discrete hidden Markov model (HMM) to use the word order information and to solve the low term frequency issue in SMS spam detection. The popularly adopted SMS spam dataset from the UCI machine learning repository is used for performance analysis of the proposed HMM method. The overall performance is compatible with deep learning by employing CNN and LSTM models. A Chinese SMS spam dataset with 2000 messages is used for further performance evaluation. Experiments show that the proposed HMM method is not language-sensitive and can identify spam with high accuracy on both datasets.

scholarly journals Speech recognition for under-resourced languages: Data sharing in hidden Markov model systems

2017 ◽  
Vol Volume 113 (Number 1/2) ◽  
Author(s):  
Febe de Wet ◽  
Neil Kleynhans ◽  
Dirk van Compernolle ◽  
Reza Sahraeian ◽  
◽  
...  
Keyword(s):  
Speech Recognition ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Data Sharing ◽  
Hidden Markov ◽  
Model Development ◽  
Model Systems ◽  
Target Language ◽  
Acoustic Data ◽  
Acoustic Models

Abstract For purposes of automated speech recognition in under-resourced environments, techniques used to share acoustic data between closely related or similar languages become important. Donor languages with abundant resources can potentially be used to increase the recognition accuracy of speech systems developed in the resource poor target language. The assumption is that adding more data will increase the robustness of the statistical estimations captured by the acoustic models. In this study we investigated data sharing between Afrikaans and Flemish – an under-resourced and well-resourced language, respectively. Our approach was focused on the exploration of model adaptation and refinement techniques associated with hidden Markov model based speech recognition systems to improve the benefit of sharing data. Specifically, we focused on the use of currently available techniques, some possible combinations and the exact utilisation of the techniques during the acoustic model development process. Our findings show that simply using normal approaches to adaptation and refinement does not result in any benefits when adding Flemish data to the Afrikaans training pool. The only observed improvement was achieved when developing acoustic models on all available data but estimating model refinements and adaptations on the target data only.

HIDDEN MARKOV MODELS, GRAMMARS, AND BIOLOGY: A TUTORIAL

2005 ◽  
Vol 03 (02) ◽  
pp. 491-526 ◽  
Author(s):  
SHIBAJI MUKHERJEE ◽  
SUSHMITA MITRA
Keyword(s):  
Machine Learning ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Markov Models ◽  
Hidden Markov ◽  
Machine Learning Techniques ◽  
The Internet ◽  
Biological Sequences ◽  
Mathematical Concepts ◽  
Learning Techniques

Biological sequences and structures have been modelled using various machine learning techniques and abstract mathematical concepts. This article surveys methods using Hidden Markov Model and functional grammars for this purpose. We provide a formal introduction to Hidden Markov Model and grammars, stressing on a comprehensive mathematical description of the methods and their natural continuity. The basic algorithms and their application to analyzing biological sequences and modelling structures of bio-molecules like proteins and nucleic acids are discussed. A comparison of the different approaches is discussed, and possible areas of work and problems are highlighted. Related databases and softwares, available on the internet, are also mentioned.

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