scholarly journals Do DMOs Promote the Right Aspects of the Destination? A Study of Instagram Photography with a Visual Classifier

2022 ◽  
pp. 174-186
Author(s):  
Lyndon Nixon
Keyword(s):  
Deep Learning ◽  
Target Group ◽  
Destination Image ◽  
Destination Marketing ◽  
Image Measurement ◽  
Marketing Intelligence ◽  
Visual Projection ◽  
The Right ◽  
Global Travel

AbstractAs global travel emerges from the pandemic, pent up interest in travel will lead to consumers making their choice between global destinations. Instagram is a key source of destination inspiration. DMO marketing success on this channel relies on projecting a destination image that resonates with this target group. However, usual text-based marketing intelligence on this channel does not work as content is consumed first and foremost as a visual projection. The author has built a deep learning based visual classifier for destination image measurement from photos. In this paper, we compare projected and perceived destination images in Instagram photography for four of the most Instagrammed destinations worldwide. We find that whereas the projected destination image aligns well to the perceived image, there are specific aspects of the destinations that are of more interest to Instagrammers than reflected in the current destination marketing.

Surrounding Vehicles’ Contribution to Car-Following Models: Deep-Learning-Based Analysis

2021 ◽  
pp. 036119812110186
Author(s):  
Saeed Vasebi ◽  
Yeganeh M. Hayeri ◽  
Peter J. Jin
Keyword(s):  
Deep Learning ◽  
Traffic Flow ◽  
Short Term Memory ◽  
Data Availability ◽  
Car Following ◽  
Preceding Vehicle ◽  
Long Short Term Memory ◽  
The Right ◽  
Mean Square Errors ◽  
Deep Learning Model

Relatively recent increased computational power and extensive traffic data availability have provided a unique opportunity to re-investigate drivers’ car-following (CF) behavior. Classic CF models assume drivers’ behavior is only influenced by their preceding vehicle. Recent studies have indicated that considering surrounding vehicles’ information (e.g., multiple preceding vehicles) could affect CF models’ performance. An in-depth investigation of surrounding vehicles’ contribution to CF modeling performance has not been reported in the literature. This study uses a deep-learning model with long short-term memory (LSTM) to investigate to what extent considering surrounding vehicles could improve CF models’ performance. This investigation helps to select the right inputs for traffic flow modeling. Five CF models are compared in this study (i.e., classic, multi-anticipative, adjacent-lanes, following-vehicle, and all-surrounding-vehicles CF models). Performance of the CF models is compared in relation to accuracy, stability, and smoothness of traffic flow. The CF models are trained, validated, and tested by a large publicly available dataset. The average mean square errors (MSEs) for the classic, multi-anticipative, adjacent-lanes, following-vehicle, and all-surrounding-vehicles CF models are 1.58 × 10−3, 1.54 × 10−3, 1.56 × 10−3, 1.61 × 10−3, and 1.73 × 10−3, respectively. However, the results show insignificant performance differences between the classic CF model and multi-anticipative model or adjacent-lanes model in relation to accuracy, stability, or smoothness. The following-vehicle CF model shows similar performance to the multi-anticipative model. The all-surrounding-vehicles CF model has underperformed all the other models.

Specification of retinotectal connexions during development of the toad Xenopus laevis

Development ◽  
1980 ◽  
Vol 55 (1) ◽  
pp. 77-92
Author(s):  
S. C. Sharma ◽  
J. G. Hollyfield
Keyword(s):  
Xenopus Laevis ◽  
Ganglion Cell ◽  
Retinal Ganglion Cells ◽  
Optic Tectum ◽  
Ganglion Cells ◽  
The Other ◽  
Retinal Ganglion ◽  
Visual Projection ◽  
Series Of Experiments ◽  
The Right

The specification of central connexions of retinal ganglion cells was studied in Xenopus laevis. In one series of experiments, the right eye primordium was rotated 180° at embryonic stages 24–32. In the other series, the left eye was transplanted into the right orbit, and vice versa, with either 0° or 180° rotation. After metamorphosis the visual projections from the operated eye to the contralateral optic tectum were mapped electrophysiologically and compared with the normal retinotectal map. In all cases the visual projection map was rotated through the same angle as was indicated by the position of the choroidal fissure. The left eye exchanged into the right orbit retained its original axes and projected to the contralateral tectum. These results suggest that retinal ganglion cell connexions are specified before stage 24.

scholarly journals Iss2Image: A Novel Signal-Encoding Technique for CNN-Based Human Activity Recognition

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3910 ◽  
Author(s):  
Taeho Hur ◽  
Jaehun Bang ◽  
Thien Huynh-The ◽  
Jongwon Lee ◽  
Jee-In Kim ◽  
...  
Keyword(s):  
Deep Learning ◽  
Activity Recognition ◽  
Human Activity ◽  
Expert Knowledge ◽  
Inertial Sensor ◽  
Human Activity Recognition ◽  
Learning Technology ◽  
Sensor Signal ◽  
Signal Encoding ◽  
The Right

The most significant barrier to success in human activity recognition is extracting and selecting the right features. In traditional methods, the features are chosen by humans, which requires the user to have expert knowledge or to do a large amount of empirical study. Newly developed deep learning technology can automatically extract and select features. Among the various deep learning methods, convolutional neural networks (CNNs) have the advantages of local dependency and scale invariance and are suitable for temporal data such as accelerometer (ACC) signals. In this paper, we propose an efficient human activity recognition method, namely Iss2Image (Inertial sensor signal to Image), a novel encoding technique for transforming an inertial sensor signal into an image with minimum distortion and a CNN model for image-based activity classification. Iss2Image converts real number values from the X, Y, and Z axes into three color channels to precisely infer correlations among successive sensor signal values in three different dimensions. We experimentally evaluated our method using several well-known datasets and our own dataset collected from a smartphone and smartwatch. The proposed method shows higher accuracy than other state-of-the-art approaches on the tested datasets.

scholarly journals Design of Personalized Recommendation System for Swimming Teaching Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Zhan Shi ◽  
Wei Wang
Keyword(s):  
Deep Learning ◽  
Recommendation System ◽  
The Body ◽  
Test Method ◽  
Personalized Recommendation ◽  
Survey Method ◽  
Sporting Event ◽  
Motivation For Learning ◽  
Great Confidence ◽  
The Right

Swimming is not only an entertaining hobby but also a sporting event. It is a sport for strengthening the body. Although there are many swimming coaches, there are different swimming teaching courses. However, choosing the right swimming instructor or course is the motivation for learning swimming activities. To this end, this paper conducts related research on the personalized recommendation system for swimming teaching based on deep learning with the purpose of improving the accuracy of the recommendation system to meet the needs of the users and promote the development of swimming events. This article mainly uses the experimental test method, the system construction method, and the questionnaire survey method to analyze and study the personalized swimming teaching system and the students’ attitude to it and draw a conclusion finally. The data results show that the accuracy of the system designed in this paper can meet the basic requirements. Hence, it can bring an excellent experience to the users. According to the questionnaire data, 85%–95% of people have great confidence in the personalized recommendation system.

Experiencing the Story

2022 ◽  
pp. 240-256
Author(s):  
Eleni Michopoulou ◽  
Aleksandra Siurnicka ◽  
Delia Gabriela Moisa
Keyword(s):  
Literature Review ◽  
Case Studies ◽  
Destination Image ◽  
Destination Marketing ◽  
Tourism Destination ◽  
Research Papers ◽  
Factors Influencing ◽  
Marketing Activities ◽  
Specific Factors ◽  
Practical Implications

The importance of destination image in film tourism has been recognized by scholars and practitioners. However, despite a large number of research papers related to the destination image within the field of film tourism, several issues remain unclear. This chapter provides insights into how movies influence the featured destination's image by focusing on specific film tourists' perceptions, their motivations, and emotional relation to the movies. The chapter begins by offering a film tourism definition followed by film tourist typology with the context of film fans. Then, factors influencing film tourism destination image are examined, in particular destination marketing activities, film-specific factors, and destination attributes. Two case studies will also be provided to better showcase the findings from the literature review. Theoretical and practical implications are also presented.

scholarly journals Mastcam Image Resolution Enhancement with Application to Disparity Map Generation for Stereo Images with Different Resolutions

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3526 ◽  
Author(s):  
Ayhan ◽  
Kwan
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Image Enhancement ◽  
Image Resolution ◽  
Map Estimation ◽  
Stereo Images ◽  
Disparity Map ◽  
Camera Image ◽  
The Right ◽  
The Impact

In this paper, we introduce an in-depth application of high-resolution disparity map estimation using stereo images from Mars Curiosity rover’s Mastcams, which have two imagers with different resolutions. The left Mastcam has three times lower resolution as that of the right. The left Mastcam image’s resolution is first enhanced with three methods: Bicubic interpolation, pansharpening-based method, and a deep learning super resolution method. The enhanced left camera image and the right camera image are then used to estimate the disparity map. The impact of the left camera image enhancement is examined. The comparative performance analyses showed that the left camera enhancement results in getting more accurate disparity maps in comparison to using the original left Mastcam images for disparity map estimation. The deep learning-based method provided the best performance among the three for both image enhancement and disparity map estimation accuracy. A high-resolution disparity map, which is the result of the left camera image enhancement, is anticipated to improve the conducted science products in the Mastcam imagery such as 3D scene reconstructions, depth maps, and anaglyph images.

scholarly journals Development of a Mushroom Growth Measurement System Applying Deep Learning for Image Recognition

Agronomy ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 32 ◽  
Author(s):  
Chuan-Pin Lu ◽  
Jiun-Jian Liaw ◽  
Tzu-Ching Wu ◽  
Tsung-Fu Hung
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Economic Benefit ◽  
Nutritional Value ◽  
Production Management ◽  
Correction Method ◽  
Agricultural Product ◽  
Image Measurement ◽  
Growth Measurement ◽  
Affected Plant

In Taiwan, mushrooms are an agricultural product with high nutritional value and economic benefit. However, global warming and climate change have affected plant quality. As a result, technological greenhouses are replacing traditional tin houses as locations for mushroom planting. These greenhouses feature several complex parameters. If we can reduce the complexity such greenhouses and improve the efficiency of their production management using intelligent schemes, technological greenhouses could become the expert assistants of farmers. In this paper, the main goal of the developed system is to measure the mushroom size and to count the amount of mushrooms. According to the results of each measurement, the growth rate of the mushrooms can be estimated. The proposed system also records the data of the mushrooms and broadcasts them to the mobile phone of the farmer. This improves the effectiveness of the production management. The proposed system is based on the convolutional neural network of deep learning, which is used to localize the mushrooms in the image. A positioning correction method is also proposed to modify the localization result. The experiments show that the proposed system has a good performance concerning the image measurement of mushrooms.

scholarly journals Genetic Analysis of Right Heart Structure and Function in 40,000 People

2021 ◽  
Author(s):  
James P. Pirruccello ◽  
Paolo Di Achille ◽  
Victor Nauffal ◽  
Mahan Nekoui ◽  
Samuel N. Friedman ◽  
...  
Keyword(s):  
Pulmonary Hypertension ◽  
Deep Learning ◽  
Right Ventricular ◽  
Congenital Heart ◽  
Left Ventricular ◽  
Structure And Function ◽  
Right Heart ◽  
Heart Chamber ◽  
The Right ◽  
And Function

The heart evolved hundreds of millions of years ago. During mammalian evolution, the cardiovascular system developed with complete separation between pulmonary and systemic circulations incorporated into a single pump with chambers dedicated to each circulation. A lower pressure right heart chamber supplies deoxygenated blood to the lungs, while a high pressure left heart chamber supplies oxygenated blood to the rest of the body. Due to the complexity of morphogenic cardiac looping and septation required to form these two chambers, congenital heart diseases often involve maldevelopment of the evolutionarily recent right heart chamber. Additionally, some diseases predominantly affect structures of the right heart, including arrhythmogenic right ventricular cardiomyopathy (ARVC) and pulmonary hypertension. To gain insight into right heart structure and function, we fine-tuned deep learning models to recognize the right atrium, the right ventricle, and the pulmonary artery, and then used those models to measure right heart structures in over 40,000 individuals from the UK Biobank with magnetic resonance imaging. We found associations between these measurements and clinical disease including pulmonary hypertension and dilated cardiomyopathy. We then conducted genome-wide association studies, identifying 104 distinct loci associated with at least one right heart measurement. Several of these loci were found near genes previously linked with congenital heart disease, such as NKX2-5, TBX3, WNT9B, and GATA4. We also observed interesting commonalities and differences in association patterns at genetic loci linked with both right and left ventricular measurements. Finally, we found that a polygenic predictor of right ventricular end systolic volume was associated with incident dilated cardiomyopathy (HR 1.28 per standard deviation; P = 2.4E-10), and remained a significant predictor of disease even after accounting for a left ventricular polygenic score. Harnessing deep learning to perform large-scale cardiac phenotyping, our results yield insights into the genetic and clinical determinants of right heart structure and function.

scholarly journals Beyond Influencer Credibility: The Power of Content and Parasocial Relationship on Processing Social Media Influencer Destination Marketing Campaigns

2022 ◽  
pp. 110-122
Author(s):  
Yi Xuan Ong ◽  
Tao Sun ◽  
Naoya Ito
Keyword(s):  
Social Media ◽  
Source Credibility ◽  
Destination Image ◽  
Destination Marketing ◽  
Elaboration Likelihood ◽  
Argument Quality ◽  
Parasocial Relationship ◽  
Likelihood Model ◽  
Shed Light

AbstractThe power of social media influencers (SMIs) as effective endorsers for destinations and tourism products have been widely acknowledged. Despite being characterised as content generators by prior research, little has been done to examine how consumers perceive content produced by SMI, a key component of destination marketing campaigns. Moreover, parasocial relationship between SMI and the follower has been proven to enhance the persuasive impact of SMIs. Hence, this study aims to shed light on how consumers would assess the SMI and the content the SMI produced, as well as the effect of parasocial relationship on processing SMI destination marketing campaigns. Findings (N = 501) have highlighted that argument quality of SMI content has a stronger direct impact on campaign attitude, destination image and travel intention, as compared to source credibility. With the application of the Elaboration Likelihood Model (ELM) as a framework, this study illuminates consumers’ interaction with the SMI destination marketing campaign and extends prior studies in understanding the importance of SMI content and parasocial relationship as a significant tool for future destination marketing.

scholarly journals The Patchwork Text as Assessment Tool for Postgraduate Law Teaching in South Africa

Obiter ◽  
2020 ◽  
Vol 41 (2) ◽  
pp. 292-308
Author(s):  
JG Horn ◽  
L Van Niekerk
Keyword(s):  
Deep Learning ◽  
Alternative Assessment ◽  
Assessment Tool ◽  
Assessment Tools ◽  
Postgraduate Students ◽  
Traditional Assessment ◽  
Academic Knowledge ◽  
Assessment Approach ◽  
Problem Solvers ◽  
The Right

In the increasingly competitive higher education sphere, delivering graduates with a sound academic grounding in their discipline is no longer enough. Institutions of higher learning must yield lifelong learners who are employable and equipped with the practical skills required by the profession. To ensure this, the right assessment approach is key. While assessment has always been a crucial component of instruction, traditional assessment tools run the risk of being mere tools for certification, facilitating surface learning instead of deep learning. Assessment approaches need to be re-evaluated to strike a balance between encouraging deep learning and instilling proper academic knowledge in graduates. To contribute to such a re-evaluation of traditional assessment methods, this article reports on the introduction of the patchwork text (PWT) as an alternative assessment tool in postgraduate law teaching at the University of the Free State (UFS). After making the case for the move towards more authentic, alternative assessment techniques, the authors embark on a discussion of the main features of the PWT, as well as guidelines for drafting a PWT assessment. The focus then shifts to an overview of PWT implementation in other postgraduate modules, ending with a discussion of the authors’ experience introducing the PWT in their own teaching. Useful information about the authors’ approach is shared, including examples of formative assessment exercises used as part of the PWT, specifics regarding the portfolio of evidence of learning to be handed in, and an outline of the four “patches” making up the assessment. It is concluded that the PWT has proven to be a viable tool for assessing postgraduate students in certain law modules at the UFS. It has managed to promote deep learning, develop students into critical thinkers and problem-solvers, and compel them to continuously engage with the study material – all while achieving the intended learning outcomes. The PWT is therefore recommended to lecturers who seek to equip students with a macro-vision of their field of study, the ability to integrate and contextualise different areas of the discipline, and the skill to reflect critically on new, emerging developments in the field.

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