Transfer Learning Performance for Remote Pastureland Trait Estimation in Real-Time Farm Monitoring

The Controller Area Network (CAN) bus works as an important protocol in the real-time In-Vehicle Network (IVN) systems for its simple, suitable, and robust architecture. The risk of IVN devices has still been insecure and vulnerable due to the complex data-intensive architectures which greatly increase the accessibility to unauthorized networks and the possibility of various types of cyberattacks. Therefore, the detection of cyberattacks in IVN devices has become a growing interest. With the rapid development of IVNs and evolving threat types, the traditional machine learning-based IDS has to update to cope with the security requirements of the current environment. Nowadays, the progression of deep learning, deep transfer learning, and its impactful outcome in several areas has guided as an effective solution for network intrusion detection. This manuscript proposes a deep transfer learning-based IDS model for IVN along with improved performance in comparison to several other existing models. The unique contributions include effective attribute selection which is best suited to identify malicious CAN messages and accurately detect the normal and abnormal activities, designing a deep transfer learning-based LeNet model, and evaluating considering real-world data. To this end, an extensive experimental performance evaluation has been conducted. The architecture along with empirical analyses shows that the proposed IDS greatly improves the detection accuracy over the mainstream machine learning, deep learning, and benchmark deep transfer learning models and has demonstrated better performance for real-time IVN security.

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Gaze awareness and metacognitive suggestions by a pedagogical conversational agent: an experimental investigation on interventions to support collaborative learning process and performance

International Journal of Computer-Supported Collaborative Learning ◽

10.1007/s11412-020-09333-3 ◽

2020 ◽

Vol 15 (4) ◽

pp. 469-498 ◽

Cited By ~ 1

Author(s):

Yugo Hayashi

Keyword(s):

Collaborative Learning ◽

Real Time ◽

Learning Process ◽

Common Ground ◽

Learning Performance ◽

Conversational Agent ◽

Learning Gains ◽

Computer Supported Collaborative Learning ◽

Collaborative Process ◽

And Performance

AbstractResearch on collaborative learning has revealed that peer-collaboration explanation activities facilitate reflection and metacognition and that establishing common ground and successful coordination are keys to realizing effective knowledge-sharing in collaborative learning tasks. Studies on computer-supported collaborative learning have investigated how awareness tools can facilitate coordination within a group and how the use of external facilitation scripts can elicit elaborated knowledge during collaboration. However, the separate and joint effects of these tools on the nature of the collaborative process and performance have rarely been investigated. This study investigates how two facilitation methods—coordination support via learner gaze-awareness feedback and metacognitive suggestion provision via a pedagogical conversational agent (PCA)—are able to enhance the learning process and learning gains. Eighty participants, organized into dyads, were enrolled in a 2 × 2 between-subject study. The first and second factors were the presence of real-time gaze feedback (no vs. visible gaze) and that of a suggestion-providing PCA (no vs. visible agent), respectively. Two evaluation methods were used: namely, dialog analysis of the collaborative process and evaluation of learning gains. The real-time gaze feedback and PCA suggestions facilitated the coordination process, while gaze was relatively more effective in improving the learning gains. Learners in the Gaze-feedback condition achieved superior learning gains upon receiving PCA suggestions. A successful coordination/high learning performance correlation was noted solely for learners receiving visible gaze feedback and PCA suggestions simultaneously (visible gaze/visible agent). This finding has the potential to yield improved collaborative processes and learning gains through integration of these two methods as well as contributing towards design principles for collaborative-learning support systems more generally.

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Intuitive real-time control strategy for high-density myoelectric hand prosthesis using deep and transfer learning

Scientific Reports ◽

10.1038/s41598-021-90688-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Simon Tam ◽

Mounir Boukadoum ◽

Alexandre Campeau-Lecours ◽

Benoit Gosselin

Keyword(s):

Real Time ◽

Transfer Learning ◽

Control Strategy ◽

High Density ◽

Fine Tuning ◽

Mode Switching ◽

Correct Prediction ◽

Learning Approach ◽

Predictive Values ◽

Hand Prosthesis

AbstractMyoelectric hand prostheses offer a way for upper-limb amputees to recover gesture and prehensile abilities to ease rehabilitation and daily life activities. However, studies with prosthesis users found that a lack of intuitiveness and ease-of-use in the human-machine control interface are among the main driving factors in the low user acceptance of these devices. This paper proposes a highly intuitive, responsive and reliable real-time myoelectric hand prosthesis control strategy with an emphasis on the demonstration and report of real-time evaluation metrics. The presented solution leverages surface high-density electromyography (HD-EMG) and a convolutional neural network (CNN) to adapt itself to each unique user and his/her specific voluntary muscle contraction patterns. Furthermore, a transfer learning approach is presented to drastically reduce the training time and allow for easy installation and calibration processes. The CNN-based gesture recognition system was evaluated in real-time with a group of 12 able-bodied users. A real-time test for 6 classes/grip modes resulted in mean and median positive predictive values (PPV) of 93.43% and 100%, respectively. Each gesture state is instantly accessible from any other state, with no mode switching required for increased responsiveness and natural seamless control. The system is able to output a correct prediction within less than 116 ms latency. 100% PPV has been attained in many trials and is realistically achievable consistently with user practice and/or employing a thresholded majority vote inference. Using transfer learning, these results are achievable after a sensor installation, data recording and network training/fine-tuning routine taking less than 10 min to complete, a reduction of 89.4% in the setup time of the traditional, non-transfer learning approach.

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Real-time robot path planning from simple to complex obstacle patterns via transfer learning of options

Autonomous Robots ◽

10.1007/s10514-019-09852-5 ◽

2019 ◽

Vol 43 (8) ◽

pp. 2071-2093 ◽

Cited By ~ 2

Author(s):

Olimpiya Saha ◽

Prithviraj Dasgupta ◽

Bradley Woosley

Keyword(s):

Path Planning ◽

Real Time ◽

Transfer Learning ◽

Robot Path Planning ◽

Robot Path

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Real-time electricity price forecasting of wind farms with deep neural network transfer learning and hybrid datasets

Applied Energy ◽

10.1016/j.apenergy.2021.117242 ◽

2021 ◽

Vol 299 ◽

pp. 117242

Author(s):

Haolin Yang ◽

Kristen R. Schell

Keyword(s):

Neural Network ◽

Real Time ◽

Transfer Learning ◽

Deep Neural Network ◽

Wind Farms ◽

Electricity Price ◽

Price Forecasting ◽

Electricity Price Forecasting

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Computer-Aided Colon Polyp Detection on High Resolution Colonoscopy Using Transfer Learning Techniques

Sensors ◽

10.3390/s21165315 ◽

2021 ◽

Vol 21 (16) ◽

pp. 5315

Author(s):

Chia-Pei Tang ◽

Kai-Hong Chen ◽

Tu-Liang Lin

Keyword(s):

Real Time ◽

Transfer Learning ◽

Early Recognition ◽

Detection System ◽

Colon Polyp ◽

Colon Polyps ◽

Polyp Detection ◽

Detection Model ◽

Real Time Detection ◽

Polyp Type

Colonoscopies reduce the incidence of colorectal cancer through early recognition and resecting of the colon polyps. However, the colon polyp miss detection rate is as high as 26% in conventional colonoscopy. The search for methods to decrease the polyp miss rate is nowadays a paramount task. A number of algorithms or systems have been developed to enhance polyp detection, but few are suitable for real-time detection or classification due to their limited computational ability. Recent studies indicate that the automated colon polyp detection system is developing at an astonishing speed. Real-time detection with classification is still a yet to be explored field. Newer image pattern recognition algorithms with convolutional neuro-network (CNN) transfer learning has shed light on this topic. We proposed a study using real-time colonoscopies with the CNN transfer learning approach. Several multi-class classifiers were trained and mAP ranged from 38% to 49%. Based on an Inception v2 model, a detector adopting a Faster R-CNN was trained. The mAP of the detector was 77%, which was an improvement of 35% compared to the same type of multi-class classifier. Therefore, our results indicated that the polyp detection model could attain a high accuracy, but the polyp type classification still leaves room for improvement.

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Real-Time Text Steganalysis Based on Multi-Stage Transfer Learning

IEEE Signal Processing Letters ◽

10.1109/lsp.2021.3097241 ◽

2021 ◽

Vol 28 ◽

pp. 1510-1514

Author(s):

Wanli Peng ◽

Jinyu Zhang ◽

Yiming Xue ◽

Zhenghong Yang

Keyword(s):

Real Time ◽

Transfer Learning ◽

Multi Stage

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Machine Learning as a Silent Observer of Advanced Geoscience Interpretation

10.2118/207939-ms ◽

2021 ◽

Author(s):

Muhammad Sajid

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Transfer Learning ◽

Learning Performance ◽

Learning Approach ◽

Seismic Fault ◽

Facies Classification ◽

The World ◽

Learning Machine ◽

Fault Interpretation

Abstract Machine learning is proving its successes in all fields of life including medical, automotive, planning, engineering, etc. In the world of geoscience, ML showed impressive results in seismic fault interpretation, advance seismic attributes analysis, facies classification, and geobodies extraction such as channels, carbonates, and salt, etc. One of the challenges faced in geoscience is the availability of label data which is one of the most time-consuming requirements in supervised deep learning. In this paper, an advanced learning approach is proposed for geoscience where the machine observes the seismic interpretation activities and learns simultaneously as the interpretation progresses. Initial testing showed that through the proposed method along with transfer learning, machine learning performance is highly effective, and the machine accurately predicts features requiring minor post prediction filtering to be accepted as the optimal interpretation.

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