Improving Human Happiness Analysis based on Transfer Learning：Algorithm Development and Validation (Preprint)

BACKGROUND Happiness refers to the joyful and pleasant emotions that humans produce subjectively. It is the positive part of emotions, and it affects the quality of human life. Therefore, understanding human happiness is a meaningful task in sentiment analysis. We mainly discuss two facets (Agency/Sociality) of happiness in this study. Through analysis and research on happiness, we can expand on new concepts that define happiness and enrich our understanding of emotions. OBJECTIVE In this paper, we treated each happy moment as a sequence of short sentences, then proposed a short happiness detection model based on transfer learning to analyze the Agency and Sociality aspects of happiness. METHODS Happiness analysis is a novel and challenging research task. However, the current dataset in the field of happiness is small. To solve this problem，we utilized the unlabeled training set and transfer learning to train a semantically enhanced language model in the target domain. Then, the trained language model with domain characteristics was further combined with other deep learning models to obtain various models. Finally, we used the improved voting strategy to further improve the experimental results. RESULTS The proposed approach was evaluated on the public dataset. Experimental results showed that our approach significantly outperforms the baselines. When predicting the Agency aspect of happiness, our approach achieved an accuracy of 0.8574 and an F1 score of 0.90, repectively. When predicting Sociality, our approach achieved an accuracy of 0.928 and an F1 score of 0.9360, respectively. CONCLUSIONS Through the evaluation of the dataset, the comparison results demonstrated the effectiveness of our approach for happiness analysis. Experimental results confirmed that our method achieved state-of-the-art performance and transfer learning effectively improved happiness analysis.

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Weapon Detection Using YOLO V3 for Smart Surveillance System

Mathematical Problems in Engineering ◽

10.1155/2021/9975700 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Sanam Narejo ◽

Bishwajeet Pandey ◽

Doris Esenarro vargas ◽

Ciro Rodriguez ◽

M. Rizwan Anjum

Keyword(s):

Object Detection ◽

Transfer Learning ◽

Surveillance System ◽

Human Life ◽

Automated System ◽

Detection Model ◽

Smart Surveillance ◽

Computer Based ◽

Smart Surveillance System ◽

Detection And Recognition

Every year, a large amount of population reconciles gun-related violence all over the world. In this work, we develop a computer-based fully automated system to identify basic armaments, particularly handguns and rifles. Recent work in the field of deep learning and transfer learning has demonstrated significant progress in the areas of object detection and recognition. We have implemented YOLO V3 “You Only Look Once” object detection model by training it on our customized dataset. The training results confirm that YOLO V3 outperforms YOLO V2 and traditional convolutional neural network (CNN). Additionally, intensive GPUs or high computation resources were not required in our approach as we used transfer learning for training our model. Applying this model in our surveillance system, we can attempt to save human life and accomplish reduction in the rate of manslaughter or mass killing. Additionally, our proposed system can also be implemented in high-end surveillance and security robots to detect a weapon or unsafe assets to avoid any kind of assault or risk to human life.

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A clustering and selection based transfer ensemble model for customer credit scoring

Filomat ◽

10.2298/fil1615015x ◽

2016 ◽

Vol 30 (15) ◽

pp. 4015-4026

Author(s):

Jin Xiao ◽

Ling Xie ◽

Dunhu Liu ◽

Yi Xiao ◽

Yi Hu

Keyword(s):

Transfer Learning ◽

Ensemble Learning ◽

Test Data ◽

Credit Scoring ◽

Experimental Results ◽

Ensemble Model ◽

Learning Models ◽

Target Domain ◽

Satisfactory Performance ◽

Important Concern

Customer credit scoring is an important concern for numerous domestic and global industries. It is difficult to achieve satisfactory performance by traditional models constructed on the assumption that the training and test data are subject to the same distribution, because the customers usually come from different districts and may be subject to different distributions in reality. This study combines ensemble learning with transfer learning, and proposes a clustering and selection based transfer ensemble (CSTS) model to transfer the instances from related source domains to target domain for assisting in modeling. The experimental results in two customer credit scoring datasets show that CSTE model outperforms two traditional credit scoring models, as well as three existing transfer learning models.

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Transfer Learning for Collaborative Filtering Using a Psychometrics Model

Mathematical Problems in Engineering ◽

10.1155/2016/8063962 ◽

2016 ◽

Vol 2016 ◽

pp. 1-9

Author(s):

Haijun Zhang ◽

Bo Zhang ◽

Zhoujun Li ◽

Guicheng Shen ◽

Liping Tian

Keyword(s):

Collaborative Filtering ◽

Transfer Learning ◽

Learning Algorithm ◽

Experimental Results ◽

Extended Model ◽

Target Domain ◽

Rating Data ◽

Source Domain ◽

One Dimensional ◽

Recommendation Algorithms

In a real e-commerce website, usually only a small number of users will give ratings to the items they purchased, and this can lead to the very sparse user-item rating data. The data sparsity issue will greatly limit the recommendation performance of most recommendation algorithms. However, a user may register accounts in many e-commerce websites. If such users’ historical purchasing data on these websites can be integrated, the recommendation performance could be improved. But it is difficult to align the users and items between these websites, and thus how to effectively borrow the users’ rating data of one website (source domain) to help improve the recommendation performance of another website (target domain) is very challenging. To this end, this paper extended the traditional one-dimensional psychometrics model to multidimension. The extended model can effectively capture users’ multiple interests. Based on this multidimensional psychometrics model, we further propose a novel transfer learning algorithm. It can effectively transfer users’ rating preferences from the source domain to the target domain. Experimental results show that the proposed method can significantly improve the recommendation performance.

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A Two-Phase Fashion Apparel Detection Method Based on YOLOv4

Applied Sciences ◽

10.3390/app11093782 ◽

2021 ◽

Vol 11 (9) ◽

pp. 3782

Author(s):

Chu-Hui Lee ◽

Chen-Wei Lin

Keyword(s):

Object Detection ◽

Transfer Learning ◽

Detection Method ◽

Phase Transfer ◽

Recognition Task ◽

Phase Detection ◽

Target Domain ◽

Two Phase ◽

Detection Technology ◽

Fashion Apparel

Object detection is one of the important technologies in the field of computer vision. In the area of fashion apparel, object detection technology has various applications, such as apparel recognition, apparel detection, fashion recommendation, and online search. The recognition task is difficult for a computer because fashion apparel images have different characteristics of clothing appearance and material. Currently, fast and accurate object detection is the most important goal in this field. In this study, we proposed a two-phase fashion apparel detection method named YOLOv4-TPD (YOLOv4 Two-Phase Detection), based on the YOLOv4 algorithm, to address this challenge. The target categories for model detection were divided into the jacket, top, pants, skirt, and bag. According to the definition of inductive transfer learning, the purpose was to transfer the knowledge from the source domain to the target domain that could improve the effect of tasks in the target domain. Therefore, we used the two-phase training method to implement the transfer learning. Finally, the experimental results showed that the mAP of our model was better than the original YOLOv4 model through the two-phase transfer learning. The proposed model has multiple potential applications, such as an automatic labeling system, style retrieval, and similarity detection.

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Phonetic Variation Modeling and a Language Model Adaptation for Korean English Code-Switching Speech Recognition

Applied Sciences ◽

10.3390/app11062866 ◽

2021 ◽

Vol 11 (6) ◽

pp. 2866

Author(s):

Damheo Lee ◽

Donghyun Kim ◽

Seung Yun ◽

Sanghun Kim

Keyword(s):

Speech Recognition ◽

Language Model ◽

Reduction Rate ◽

Code Switching ◽

Training Data ◽

Target Domain ◽

Phonetic Variation ◽

Language Model Adaptation ◽

Imbalanced Training Data ◽

Lm Adaptation

In this paper, we propose a new method for code-switching (CS) automatic speech recognition (ASR) in Korean. First, the phonetic variations in English pronunciation spoken by Korean speakers should be considered. Thus, we tried to find a unified pronunciation model based on phonetic knowledge and deep learning. Second, we extracted the CS sentences semantically similar to the target domain and then applied the language model (LM) adaptation to solve the biased modeling toward Korean due to the imbalanced training data. In this experiment, training data were AI Hub (1033 h) in Korean and Librispeech (960 h) in English. As a result, when compared to the baseline, the proposed method improved the error reduction rate (ERR) by up to 11.6% with phonetic variant modeling and by 17.3% when semantically similar sentences were applied to the LM adaptation. If we considered only English words, the word correction rate improved up to 24.2% compared to that of the baseline. The proposed method seems to be very effective in CS speech recognition.

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Multiscale Object Detection from Drone Imagery Using Ensemble Transfer Learning

Drones ◽

10.3390/drones5030066 ◽

2021 ◽

Vol 5 (3) ◽

pp. 66

Author(s):

Rahee Walambe ◽

Aboli Marathe ◽

Ketan Kotecha

Keyword(s):

Object Detection ◽

Transfer Learning ◽

Data Augmentation ◽

Test Time ◽

Complex Task ◽

Open Domain ◽

End User ◽

Aerial Vehicle ◽

Uav Images ◽

Voting Strategy

Object detection in uncrewed aerial vehicle (UAV) images has been a longstanding challenge in the field of computer vision. Specifically, object detection in drone images is a complex task due to objects of various scales such as humans, buildings, water bodies, and hills. In this paper, we present an implementation of ensemble transfer learning to enhance the performance of the base models for multiscale object detection in drone imagery. Combined with a test-time augmentation pipeline, the algorithm combines different models and applies voting strategies to detect objects of various scales in UAV images. The data augmentation also presents a solution to the deficiency of drone image datasets. We experimented with two specific datasets in the open domain: the VisDrone dataset and the AU-AIR Dataset. Our approach is more practical and efficient due to the use of transfer learning and two-level voting strategy ensemble instead of training custom models on entire datasets. The experimentation shows significant improvement in the mAP for both VisDrone and AU-AIR datasets by employing the ensemble transfer learning method. Furthermore, the utilization of voting strategies further increases the 3reliability of the ensemble as the end-user can select and trace the effects of the mechanism for bounding box predictions.

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Augmenting Crop Detection for Precision Agriculture with Deep Visual Transfer Learning—A Case Study of Bale Detection

Remote Sensing ◽

10.3390/rs13010023 ◽

2020 ◽

Vol 13 (1) ◽

pp. 23

Author(s):

Wei Zhao ◽

William Yamada ◽

Tianxin Li ◽

Matthew Digman ◽

Troy Runge

Keyword(s):

Object Detection ◽

Transfer Learning ◽

Precision Agriculture ◽

Crop Production ◽

Domain Adaptation ◽

Training Data ◽

Detection Accuracy ◽

Detection Model ◽

Agriculture Products

In recent years, precision agriculture has been researched to increase crop production with less inputs, as a promising means to meet the growing demand of agriculture products. Computer vision-based crop detection with unmanned aerial vehicle (UAV)-acquired images is a critical tool for precision agriculture. However, object detection using deep learning algorithms rely on a significant amount of manually prelabeled training datasets as ground truths. Field object detection, such as bales, is especially difficult because of (1) long-period image acquisitions under different illumination conditions and seasons; (2) limited existing prelabeled data; and (3) few pretrained models and research as references. This work increases the bale detection accuracy based on limited data collection and labeling, by building an innovative algorithms pipeline. First, an object detection model is trained using 243 images captured with good illimitation conditions in fall from the crop lands. In addition, domain adaptation (DA), a kind of transfer learning, is applied for synthesizing the training data under diverse environmental conditions with automatic labels. Finally, the object detection model is optimized with the synthesized datasets. The case study shows the proposed method improves the bale detecting performance, including the recall, mean average precision (mAP), and F measure (F1 score), from averages of 0.59, 0.7, and 0.7 (the object detection) to averages of 0.93, 0.94, and 0.89 (the object detection + DA), respectively. This approach could be easily scaled to many other crop field objects and will significantly contribute to precision agriculture.

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Aircraft Cable Fault Location Based on Detection Model

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.55-57.332 ◽

2011 ◽

Vol 55-57 ◽

pp. 332-336 ◽

Cited By ~ 1

Author(s):

Xiao Lin Liu ◽

Zhi Quan Li

Keyword(s):

Fault Location ◽

Reference Signal ◽

Experimental Results ◽

Civil Aviation ◽

Location Method ◽

Aviation Maintenance ◽

Location System ◽

Detection Model ◽

Experimental Installation

An aircraft cable fault location method based on detection model is proposed to solve the problem of being difficult to inspect the fault for the civil aviation maintenance. In response to the condition of the experimental installation, the reference signal is designed. The fault of the cable can be located according to the reflected waveform. An aircraft cable fault location system is designed and the experimental results show that the method is rational and effective.

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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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