Academic literature on the topic 'YouTube Application'

La presente tesis está enfocada en el estudio de los efectos de la publicidad emergente generada por Youtube, en la actitud de los Millennials. La investigación pretende rescatar información útil de la situación actual de la publicidad emergente y su comportamiento frente a los Millennials. Es importante resaltar que se abacaran temas como los antecendetes de la publicidad digital, tipos de publicidad emergente, las redes sociales, las características principales de la generación millennial y el concepto de la red social en cuestión, Youtube y su evolución a nivel mundial. Por otro lado, la presente investigación mostrará estadísticas y resultados de empresas que han invertido en publicidad emergente y que tan eficaz ha sido el efecto en las actitudes de los Millennials. Hoy en día, muchas empresas se encuentran invirtiendo en publicidad emergente debido al bajo costo que implica para éstas, y la frecuencia de repetición de la publicidad para el usuario de Youtube. Sin embargo ¿Cómo podemos saber qué tan eficaz es este tipo de publicidad y cómo influye en las actitudes de los Millennials? En la presente tesis se analizará en profundidad el tema en mención, mediante la teoría, análisis cualitativo y cuantitativo. Por consecuencia, las respuestas a los objetivos planteados en el presente estudio, contribuirá a las empresas para que puedan invertir eficazmente en Youtube y ser mucho más rentables.<br>This thesis is focused on the study of the effects of the emerging publicity generated by Youtube, in the attitude of the Millennials. The research aims to rescue useful information from the current situation of emerging advertising and its behavior against the Millennials. It is important to highlight that it will cover topics such as the antecedents of digital advertising, types of emerging advertising, social networks, the main characteristics of the Millennials generation and the concept of the social network in question, Youtube and its evolution worldwide. On the other hand, this research will show statistics and results of companies that have invested in emerging advertising and how effective the effect on Millennials' attitudes has been. Nowadays, many companies are investing in emerging advertising due to the low costs it means for them, and the frequency of repetition of advertising for the Youtube user. However, how can we know how effective this type of advertising is and how it influences the attitudes of Millennials? In the present thesis will be analyzed in depth the subject in question, through theory, qualitative and quantitative analysis. Consequently, the answers to the objectives set out in this study, will contribute to the companies so that they can invest effectively in Youtube and be much more profitable.<br>Tesis

Bien qu’ils soient largement utilisés dans le domaine, les mesures vibratoires classiques présentent plusieurs limites. A la base, l’analyse vibratoire ne peut identifier qu’environ 60% des défauts qui peuvent survenir dans les machines. Cependant, les principaux inconvénients des mesures de la vibration sont l’accès difficile au système de transmission afin d’y placer le capteur ainsi que le coût conséquent de la mise en œuvre. Ceci résulte en des problèmes de sensibilité relatifs à la position de l’installation et ceux de difficulté pour distinguer la source de vibration à cause de la diversité des excitations mécaniques qui existent dans l’environnement industriel.Par conséquent, l’analyse des signatures du courant électrique des moteurs s’impose comme une alternative prometteuse à l’analyse vibratoire et a donc fait l’objet d’une attention grandissante au cours des dernières années. En effet, l’analyse des signatures électriques a l’avantage d’être une méthode techniquement accessible, non-intrusive au système et peu coûteuse. Les techniques basées sur le courant et la tension ne requièrent que les mesures électriques du moteur qui sont souvent déjà surveillées pour le contrôle et la protection des machines électriques. Ce processus a été principalement utilisé pour la détection des défauts de moteur tels que la rupture de barres du rotor et les défauts d’excentricité ainsi que les défauts de roulements. En revanche, très peu de recherches concernent la détection des défauts en utilisant l’analyse du courant. En outre, les signaux électriques triphasés sont caractérisés par des représentations géométriques particulières liées à leur forme d’onde qui peuvent servir en tant qu’indicateurs différents offrant des informations supplémentaires. Parmi ces indicateurs géométriques, les transformées de Park et de Concordia modélisent les composantes électriques dans un repère bidimensionnel et toute déviation par rapport à la représentation d’origine indique l’apparition d’un dysfonctionnement. Aussi, les équations différentielles de Frenet-Serret représentent la trajectoire du signal dans un espace euclidien tridimensionnel et indiquent ainsi tout changement dans l’état du système. Bien qu’ils aient été utilisés pour les défauts de roulements, ces indicateurs n’ont pas été appliqués dans la détection des défauts d’engrenages en utilisant l’analyse des signatures des courants électriques. D’où l’idée novatrice de combiner ces indicateurs avec des techniques de traitement de signal, ainsi que des techniques de classification pour le diagnostic des engrenages en utilisant l’analyse des signatures de courant et de tension du moteur électrique.Ainsi, dans ce travail, on propose une nouvelle approche pour le diagnostic des défauts d’engrenages en utilisant l’analyse des courants et des tensions électriques du stator de la machine et ceci en se basant sur un ensemble d’indicateurs géométriques (Transformées de Park et de Concordia ainsi que les propriétés du repère Frenet-Serret). Ces indicateurs font partie d’une bibliothèque de signatures de défauts qui a été construite et qui comprend également les indicateurs classiques utilisés pour un large éventail de défauts. Ainsi, un algorithme combine les acquisitions expérimentales des signaux électriques à des méthodes de traitement de signal avancées (décomposition modale empirique,…). Ensuite, celui-ci sélectionne les indicateurs les plus pertinents au sein de la bibliothèque en se basant sur les algorithmes de sélection de paramètres (sélection séquentielle rétrograde et analyse des composantes principales). Enfin, cette sélection est utilisée pour la classification non-supervisée (K-moyennes) pour la distinction entre l’état sain et l’état défaillant<br>Although they are widely used, classical vibration measurements have several limitations. Vibration analysis can only identify about 60% of the defects that may occur in mechanical systems. However, the main drawbacks of vibration measurements are the difficult access to the transmission system in order to place the sensor as well as the consequent cost of implementation. This results in sensitivity problems relative to the position of the installation and the difficulty to distinguish the source of vibration because of the diversity of mechanical excitations that exist in the industrial environment.Hence, the Motor Current Signatures Analysis (M.C.S.A.) represents a promising alternative to the vibration analysis and has therefore been the subject of increasing attention in recent years. Indeed, the analysis of electrical signatures has the advantage of being a technically accessible method as well as inexpensive and non-intrusive to the system. Techniques based on currents and voltages only require the motor’s electrical measurements which are often already supervised for the purposes of the control and the protection of the electrical machines. This process was mainly used for the detection of motors faults such as rotor bars breakage and eccentricity faults as well as bearings defects. On the other hand, very little research has been focused on gear faults detection using the current analysis. In addition, three-phase electrical signals are characterized by specific geometric representations related to their waveforms and they can serve as different indicators providing additional information. Among these geometric indicators, the Park and Concordia transforms model the electrical components in a two-dimensional coordinate system and any deviation from the original representation indicates the apparition of a malfunction. Moreover, the differential equations of Frenet-Serret represent the trajectory of the signal in a three-dimensional euclidean space and thus indicate any changes in the state of the system. Although they have been previously used for bearing defects, these indicators have not been applied in the detection of gear defects using the analysis of electrical current signatures. Hence, the innovative idea of combining these indicators with signal processing techniques, as well as classification techniques for gears diagnosis using the three-phase motor’s electrical current signatures analysis is established.Hence, in this work, a new approach is proposed for gear faults diagnosis using the motor currents analysis, based on a set of geometric indicators (Park and Concordia transforms as well as the properties of the Frenet-Serret frame). These indicators are part of a specifically built fault signatures library and which also includes the classical indicators used for a wide range of faults. Thus, a proposed estimation algorithm combines experimental measurements of electrical signals with advanced signal processing methods (Empirical Mode Decomposition, ...). Next, it selects the most relevant indicators within the library based on feature selection algorithms (Sequential Backward Selection and Principal Component Analysis). Finally, this selection is combined with non-supervised classification (K-means) for the distinction between the healthy state and faulty states. It was finally validated with a an additional experimental configuration in different cases with gear faults, bearing faults and combined faults with various load levels

碩士<br>國立體育大學<br>休閒產業經營學系<br>103<br>The purpose of this study was to explore the effects of applying new media YouTube to educational curation in English curriculum of elementary school 2nd graders. Through the process of action research, researcher analyzed the students and teaching materials, designed the teaching activities, developed curatorial resources, implemented for English teaching and kept evaluating and modifying. The study brought up the findings and suggestions of educational curation which could be reference to further studies. Action research was adopted as the approach of the study. 142 students, 2nd graders of 5 classes, were selected as the sample in the research. Based on prior research, the researcher concluded English Competence Indicators from Grade 1-9 Curriculum Guidelines and the criteria of video selections. According to the above, the researcher designed the instructional activities. The research instruments included “English learning background Questionnaire”, “pre-test and post-test”, “classroom observation record”, “worksheet”, “semi-structure interview guide” and “reflective teaching diary” as the research tools. All the materials obtained from the research were described under qualitative records and quantitative statistics. The quantitative data were analyzed with descriptive statistics and t-test in computer statistical software “SPSS 20.0”. After analyzing the research results, the conclusions of this study are summarized as follows: 1. Educational Curation through the criteria of video selection, the thematic content educational curation is helpful to English learning. 2. Teaching through thematic content educational curation can enhance students’ English learning effectiveness. 3. Teaching through thematic content educational curation can enhance students’ English learning interests. 4. Most students have a good evaluation and hold a positive view toward curatorial teaching, but some of them give negative feedback. Finally, according to the results of this study, some suggestions for English teachers and further studies are provided.

碩士<br>國立中山大學<br>資訊管理學系研究所<br>100<br>This thesis address the study of the recently popular Apple iOS platform, and its online application marketplace, called the “App Store”. The thesis examines the relation in Youtube videos and raking of the Apps. According to the research result, over 25% people use mobile device to watch the Apps videos. If marketer can manage the result of the searching and embedded the video in high traffic website, it can get more higher Apps ranking and Youtube video views. In addition, people/App player upload the video and share it with friends. It is just like the word and mouth marketing. Whenever people upload a lot of videos and get more video views, it can get higher App ranking. Furthermore, the video type is belonged to game trailer and walkthrough will has more views. In the other hand, the KUSO video will spread quickly in social network website and it brings more video views and Apps downloads potentially.

碩士<br>世新大學<br>傳播管理學研究所(含碩專班)<br>107<br>The advertisement of industries and marketing activities are expanded to internet domain for huge virtual market nowadays. Besides, the mobile devices which is cell phone or tablet computer are universal. And network system evolve for more convenient. Therefore, there are lots of scales confluence connecting for many industries, due to digital media changing for internet markets. Currently, YouTube is a media platform which is used by most users in Taiwan.Within YouTube developing nowadays, there are lots of creator those who are called “Youtuber” in this media platform. Under the YouTube system, this group of YouTuber can get real reward when their videos had been watch by certain number of hours and click rate, which is also stimulate them to create better film. Additionally, it attracted a large number of audiences and generated business activities. Owners are looking for cooperation, but they only make decisions based on experience and data. Is there an objective and measurable standard for selection as an option? Therefore, this study offer owner different way to choose YouTuber who could join owner’s placement video. The establishment of this mode is divided into two parts.First of all, through the literature survey and the Modified Delphi Method could derive the best placement YouTuber. Next, the research model applies the Analytic Hierarchy Process (AHP) to determine the weight and order of the criteria. Additionally, there takes an example which is a food company in Taiwan to adopt this study model.Besides, it is used to demonstrate the process of industry selected YouTuber’s placement video.Therefore, the results of this study discovered owners choose YouTubers could follow some emphasis order as below. First the personal traits. Second, the marketing ability. Third, the community management ability, and last the production capacity.

碩士<br>大同大學<br>資訊經營學系(所)<br>107<br>This thesis studies the method of machine learning to analyze YouTube viewers’ comments in Chinese. It has been a common practice to use multimedia to promote merchandise because netizens’ comments are the first response from the audience. From their comments, we can not only know the demand of customers, but find out their opinion on the products and services we offered and know the satisfaction of them for future improvement. The Chinese language has a complicated structure and diverse expressions, and consequently. it is very difficult for computer to process. In English language processing, the word-oriented approach has been effectively applied in great depth and satisfactory result has been reached. However, there are few successful analyses in Chinese. One of the biggest differences between the two languages is the unique Chinese text structure. To identify "word boundaries", most Chinese natural language processors rely on word-breaking. In addition, Chinese language processing tools are not so applicable as English. Therefore, this paper attempts to extract possible word-oriented terms by means of machine learning. Using Python to collect comments on YouTuber&apos;s marketing merchandise, we got 107,421 comments. After first filtering, there were 266,947 sentences left for machine learning. The filter and screening rules used in this study were applied to YouTuber&apos;s comments. The precision for judging term groups, FMBVAT, is 57.97%. The end-word feature of the phrase has a considerable influence on the YouTuber marketing films. In terms of words, the scorer without word frequency feature (F) predicts a larger number of word-oriented items, while the best combination of features is FBVA, which has a precision of 45.54%. From this research, the results can be effectively used to analyze the word-oriented terms in Chinese. It can effectively find the facets of the commentator&apos;s intention, and improve the product quality by the commentary&apos;s intention. This will effectively reduce the cost of marketing, and lower the possibility of negative comments.

The emergence of new Internet applications and technologies have resulted in an increased complexity as well as a need for lower latency, higher bandwidth, and increased reliability. This ultimately results in an increased complexity of network operation and management. Manual management is not sufficient to meet these new requirements. There is a need for data driven techniques to advance from manual management to autonomous management of network systems. One such technique, Machine Learning (ML), can use data to create models from hidden patterns in the data and make autonomous modifications. This approach has shown significant improvements in other domains (e.g., image recognition and natural language processing). The use of ML, along with advances in programmable control of Software- Defined Networks (SDNs), will alleviate manual network intervention and ultimately aid in autonomous network operations. However, realizing a data driven system that can not only understand what is happening in the network but also operate autonomously requires advances in the networking domain, as well as in ML algorithms. In this thesis, we focus on developing ML-based network architectures and data driven net- working algorithms whose objective is to improve the performance and management of future networks and network applications. We focus on problems spanning across the network protocol stack from the application layer to the physical layer. We design algorithms and architectures that are motivated by measurements and observations in real world or experimental testbeds. In Part I we focus on the challenge of monitoring and estimating user video quality of experience (QoE) of encrypted video traffic for network operators. We develop a system for REal-time QUality of experience metric detection for Encrypted Traffic, Requet. Requet uses a detection algorithm to identify video and audio chunks from the IP headers of encrypted traffic. Features extracted from the chunk statistics are used as input to a random forest ML model to predict QoE metrics. We evaluate Requet on a YouTube dataset we collected, consisting of diverse video assets delivered over various WiFi and LTE network conditions. We then extend Requet, and present a study on YouTube TV live streaming traffic behavior over WiFi and cellular networks covering a 9-month period. We observed pipelined chunk requests, a reduced buffer capacity, and a more stable chunk duration across various video resolutions compared to prior studies of on-demand streaming services. We develop a YouTube TV analysis tool using chunks statistics detected from the extracted data as input to a ML model to infer user QoE metrics. In Part II we consider allocating end-to-end resources in cellular networks. Future cellular networks will utilize SDN and Network Function Virtualization (NFV) to offer increased flexibility for network infrastructure operators to utilize network resources. Combining these technologies with real-time network load prediction will enable efficient use of network resources. Specifically, we leverage a type of recurrent neural network, Long Short-Term Memory (LSTM) neural networks, for (i) service specific traffic load prediction for network slicing, and (ii) Baseband Unit (BBU) pool traffic load prediction in a 5G cloud Radio Access Network (RAN). We show that leveraging a system with better accuracy to predict service requirements results in a reduction of operation costs. We focus on addressing the optical physical layer in Part III. Greater network flexibility through SDN and the growth of high bandwidth services are motivating faster service provisioning and capacity management in the optical layer. These functionalities require increased capacity along with rapid reconfiguration of network resources. Recent advances in optical hardware can enable a dramatic reduction in wavelength provisioning times in optical circuit switched networks. To support such operations, it is imperative to reconfigure the network without causing a drop in service quality to existing users. Therefore, we present a ML system that uses feedforward neural networks to predict the dynamic response of an optically circuit-switched 90-channel multi-hop Reconfigurable Optical Add-Drop Multiplexer (ROADM) network. We show that the trained deep neural network can recommend wavelength assignments for wavelength switching with minimal power excursions. We extend the performance of the ML system by implementing and testing a Hybrid Machine Learning (HML) model, which combines an analytical model with a neural network machine learning model to achieve higher prediction accuracy. In Part IV, we use a data-driven approach to address the challenge of wireless content delivery in crowded areas. We present the Adaptive Multicast Services (AMuSe) system, whose objective is to enable scalable and adaptive WiFi multicast. Specifically, we develop an algorithm for dynamic selection of a subset of the multicast receivers as feedback nodes. Further, we describe the Multicast Dynamic Rate Adaptation (MuDRA) algorithm that utilizes AMuSe’s feedback to optimally tune the physical layer multicast rate. Our experimental evaluation of MuDRA on the ORBIT testbed shows that MuDRA outperforms other schemes and supports high throughput multicast flows to hundreds of nodes while meeting quality requirements. We leverage the lessons learned from AMuSe for WiFi and use order statistics to address the performance issues with LTE evolved Multimedia Broadcast/Multicast Service (eMBMS). We present the Dynamic Monitoring (DyMo) system which provides low-overhead and real-time feedback about eMBMS performance to be used for network optimization. We focus on the Quality of Service (QoS) Evaluation module and develop a Two-step estimation algorithm which can efficiently identify the SNR Threshold as a one time estimation. DyMo significantly outperforms alternative schemes based on the Order-Statistics estimation method which relies on random or periodic sampling.