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YouTube fashion videos
(2020)
YouTube is the most widely adopted and successful video sharing platform. It works as a marketing instrument and money-making tool for companies while reaching the target group. After considering the significant literature based on YouTube, it is striking that there is lack of information about YouTube’s benefits as a video marketing instrument for fashion brands. To establish this subject further, the purpose of this study is to enrich the existing findings on social video marketing on YouTube in the apparel industry. The findings indicate the importance of YouTube as a social network for fashion marketers. The second part conducts an empirical study, which makes the YouTube channel performance of nine fashion brands the subject of discussion. Thereby, three brands per lifestyle, sports and luxury sector are analyzed through comparative aspects. Accordingly, the differences and similarities within and between the sectors are analyzed and evaluated.
”I have never seen one who loves virtue as much as he loves beauty,” Confucius once said. If beauty is more important as goodness, it becomes clear why people invest so much effort in their first impression. The aesthetic of faces has many aspects and there is a strong correlation to all characteristics of humans, like age and gender. Often, research on aesthetics by social and ethic scientists lacks sufficient labelled data and the support of machine vision tools. In this position paper we propose the Aesthetic-Faces dataset, containing training data which is labelled by Chinese and German annotators. As a combination of three image subsets, the AF-dataset consists of European, Asian and African people. The research communities in machine learning, aesthetics and social ethics can benefit from our dataset and our toolbox. The toolbox provides many functions for machine learning with state-of-the-art CNNs and an Extreme-Gradient-Boosting regressor, but also 3D Morphable Model technolo gies for face shape evaluation and we discuss how to train an aesthetic estimator considering culture and ethics.
Context: Organizations are increasingly challenged by dynamic and technical market environments. Traditional product roadmapping practices such as detailed and fixed long-term planning typically fail in such environments. Therefore, companies are actively seeking ways to improve their product roadmapping approach.
Goal: This paper aims at identifying problems and challenges with respect to product roadmapping. In addition, it aims at understanding how companies succeed in improving their roadmapping practices in their respective company contexts.
Method: We conducted semi-structured expert interviews with 15 experts from 13 German companies and conducted athematic data analysis.
Results: The analysis showed that a significant number of companies is still struggling with traditional feature-based product-roadmapping and opinion-based prioritization of features. The most promising areas for improvement are stating the outcomes a company is trying to achieve and making them part of the roadmap, sharing or co-developing the roadmap with stakeholders, and establishing discovery activities.
Regardless of company size or industry sector, a majority of project teams and companies use customized processes that combine different development methods-so-called hybrid development methods. Even though such hybrid development methods are highly individualized, a common understanding of how to systematically construct synergetic practices is missing. Based on 1,467 data points from a large-scale online survey among practitioners, we study the current state of practice in process use to answer the question: What are hybrid development methods made of? Our findings reveal that only eight methods and few practices build the core of modern software development. This small set allows for statistically constructing hybrid development methods.
This study investigates how integrated reporting (IR) creates value for investors. It examines how providers of financial capital benefit from an improved firm information environment provided by IR. Specifically, this study investigates the effect of voluntary IR disclosure on analyst earnings forecast accuracy as well as on firm value. To do so, we use an international sample of 167 listed companies that voluntarily publish an integrated report. Our analysis shows no significant effect of a voluntary IR publication on analyst earnings forecast accuracy and no significant effect on firm value. We thus do not find evidence for the fulfillment of IR's promises regarding improved information environment and value creation of voluntary adopters. We conclude that such companies might already have a relatively high level of transparency leading to an absent additional effect of IR disclosure. Positive effects of IR appear to be more relevant in environments where IR is mandatory.
Formula One races provide a wealth of data worth investigating. Although the time-varying data has a clear structure, it is pretty challenging to analyze it for further properties. Here the focus is on a visual classification for events, drivers, as well as time periods. As a first step, the Formula One data is visually encoded based on a line plot visual metaphor reflecting the dynamic lap times, and finally, a classification of the races based on the visual outcomes gained from these line plots is presented. The visualization tool is web-based and provides several interactively linked views on the data; however, it starts with a calendar-based overview representation. To illustrate the usefulness of the approach, the provided Formula One data from several years is visually explored while the races took place in different locations. The chapter discusses algorithmic, visual, and perceptual limitations that might occur during the visual classification of time-series data such as Formula One races.
Artificial Intelligence enables innovative applications, and applications based on Artificial Intelligence are increasingly important for all aspects of the Digital Economy. However, the question of how AI resources such as tools and data can be linked to provide an AI-capability and create business value is still open. Therefore, this paper identifies the value-creating mechanisms of connectionist artificial intelligence using a capability-oriented view and points out the connections to different kinds of business value. The analysis supports an agenda that identifies areas that need further research to understand the mechanism of value creation in connectionist artificial intelligence.
Process quality has reached a high level on mass production, utilizing well known methods like the DoE. The drawback of the unterlying statistical methods is the need for tests under real production conditions, which cause high costs due to the lost output. Research over the last decade let to methods for correcting a process by using in-situ data to correct the process parameters, but still a lot of pre-production is necessary to get this working. This paper presents a new approach in improving the product quality in process chains by using context data - which in part are gathered by using Industry 4.0 devices - to reduce the necessary pre-production.
Going forward with the requirements of missions to the Moon and further into deep space, the European Space Agency is investigating new methods of astronaut training that can help accelerate learning, increase availability and reduce complexity and cost in comparison to currently used methods. To achieve this, technologies such as virtual reality may be utilized. In this paper, an investigation into the benefits of using virtual reality as a means for extravehicular activity training in comparison to conventional training methods, such as neutral buoyancy pools is given. To help determine the requirements and current uses of virtual reality for extravehicular activity training first hand tests of currently available software as well as expert interviews are utilized. With this knowledge a concept is developed that may be used to further advance training methods in virtual reality. The resulting concept is used as a basis for development of a prototype to showcase user interactions and locomotion in microgravity simulations.
Here, we study resin cure and network formation of solid melamine formaldehyde pre-polymer over a large temperature range viadynamic temperature curing profiles. Real-time infrared spectroscopy is used to analyze the chemical changes during network formation and network hardening. By applying chemometrics (multivariate curve resolution,MCR), the essential chemical functionalities that constitute the network at a given stage of curing are mathematically extracted and tracked over time. The three spectral components identified by MCR were methylol-rich, ether linkages-rich and methylene linkages-rich resin entities. Based on dynamic changes of their characteristic spectral patterns in dependence of temperature, curing is divided into five phases: (I) stationary phase with free methylols as main chemical feature, (II) formation of flexible network cross-linked by ether linkages, (III) formation of rigid, ether-cross-linked network, (IV) further hardening via transformation of methylols and ethers into methylene-cross-linkages, and (V) network consolidation via transformation of ether into methylene bridges. The presented spectroscopic/chemometric approach can be used as methodological basis for the functionality design of MF-based surface films at the stage of laminate pressing, i.e., for tailoring the technological property profile of cured MF films using a causal understanding of the underlying chemistry based on molecular markers and spectroscopic fingerprints.
Different types of raw cotton were investigated by a commercial ultraviolet-visible/near infrared (UV-Vis/NIR) spectrometer (210–2200 nm) as well as on a home-built setup for NIR hyperspectral imaging (NIR-HSI) in the range 1100–2200 nm. UV-Vis/NIR reflection spectroscopy reveals the dominant role proteins, hydrocarbons and hydroxyl groups play in the structure of cotton. NIR-HSI shows a similar result. Experimentally obtained data in combination with principal component analysis (PCA) provides a general differentiation of different cotton types. For UV-Vis/NIR spectroscopy, the first two principal components (PC) represent 82 % and 78 % of the total data variance for the UV-Vis and NIR regions, respectively. Whereas, for NIR-HSI, due to the large amount of data acquired, two methodologies for data processing were applied in low and high lateral resolution. In the first method, the average of the spectra from one sample was calculated and in the second method the spectra of each pixel were used. Both methods are able to explain ≥90 % of total variance by the first two PCs. The results show that it is possible to distinguish between different cotton types based on a few selected wavelength ranges. The combination of HSI and multivariate data analysis has a strong potential in industrial applications due to its short acquisition time and low-cost development. This study opens a novel possibility for a further development of this technique towards real large-scale processes.
Strong optical mode coupling between two adjacent λ/2 Fabry-Pérot microresonators consisting of three parallel silver mirrors is investigated experimentally and theoretically as a function of their detuning and coupling strength. Mode coupling can be precisely controlled by tuning the mirror spacing of one resonator with respect to the other by piezoelectric actuators. Mode splitting, anti-crossing and asymmetric modal damping are observed and theoretically discussed for the symmetric and antisymmetric supermodes of the coupled system. The spectral profile of the supermodes is obtained from the Fourier transform of the numerically calculated time evolution of the individual resonator modes, taking into account their resonance frequencies, damping and coupling constants, and is in excellent agreement with the experiments. Our microresonator design has potential applications for energy transfer between spatially separated quantum systems in micro optoelectronics and for the emerging field of polaritonic chemistry.
Customer orientation should be the core engine of every organisation while IT can be considered as the enabler to generate competitive advantages along customer processes in marketing, sales and service. Research shows that customer relationship management (CRM) enables organisations to perform better and experience indicates that organisations that focus on customer orientation are more successful. With marketplace organisations such as Amazon, Alibaba or Conrad shaping the future of customer centricity and information technology, German B2B organisations need to shift their value contribution from product-centric to customer-centric. While these organisations are currently attempting to implement CRM software and putting their customers more into focus, the question remains how organisations are approaching the implementation of CRM and whether these attempts are paying off in terms of business performance.
This paper aims at presenting a solution that enables end customers of the energy system to participate in new local micro-energy-markets by providing them with a distributed, decentralized, transparent and secure Peer to Peer (P2P) payment system, which functions automatically applying new concepts of Machine to Machine (M2M) communication technologies. This work was performed within the German project VK_2G, funded by the DBU. The key results were: Providing means to perform microtransactions in a P2P fashion between end consumers and prosumers in local communities at low cost in a transparent and secure manner; Developing a platform with pre-defined smart contracts able to be tailored to different end customers ‘needs in an easy way and; Integrating both the market platform as well as the local control of generation and loads. This solution has been developed, integrated and tested in a laboratory prototype. This paper discusses this solution and presents the results of the first test.
We investigate the toxicity of different types and sizes of microplastic particles (0.3–4 mm) under different conditions (new particles, aged particles with biofilm, and particles with adsorbed Tributyltin) on the freshwater amphipod Gammarus fossarum in 3-week exposures. All types of plastic particles, which were randomly taken up to a small extent, were mostly Polyphenylenoxide, Polybutylentherephthalate and Polypropylene, with particles < 1 mm in size. Plastic particles did not affect the feeding and locomotory behaviour of gammarids, and there was no strong difference between pristine plastic particles and aged particles with biofilm. Mortality tended to be higher compared with the control. Tributyltinhydride (TBTH) adsorbed to microplastic particles had no effect on uptake, survival, feeding and locomotory behaviour during the 3 weeks of exposure. Dissolved TBTH, however, was already very toxic after few days of exposure (LC50-96h < 1 ng l–1).
Hardly any software development process is used as prescribed by authors or standards. Regardless of company size or industry sector, a majority of project teams and companies use hybrid development methods (short: hybrid methods) that combine different development methods and practices. Even though such hybrid methods are highly individualized, a common understanding of how to systematically construct synergetic practices is missing. In this article, we make a first step towards a statistical construction procedure for hybrid methods. Grounded in 1467 data points from a large‐scale practitioner survey, we study the question: What are hybrid methods made of and how can they be systematically constructed? Our findings show that only eight methods and few practices build the core of modern software development. Using an 85% agreement level in the participants' selections, we provide examples illustrating how hybrid methods can be characterized by the practices they are made of. Furthermore, using this characterization, we develop an initial construction procedure, which allows for defining a method frame and enriching it incrementally to devise a hybrid method using ranked sets of practice.
Facial expressions play a dominant role in facilitating social interactions. We endeavor to develop tactile displays to reinstate facial expression modulated communication. The high spatial and temporal dimensionality of facial movements poses a unique challenge when designing tactile encodings of them. A further challenge is developing encodings that are at-tuned to the perceptual characteristics of our skin. A caveat of using vibrotactile displays is that tactile stimuli have been shown to induce perceptual tactile aftereffects when used on the fingers, arm and face. However, at present, despite the prevalence of waist-worn tactile displays, no such investigations of tactile aftereffects at the waist region exist in the literature, though they are warranted by the unique sensory and perceptual signalling characteristics of this area. Using an adaptation paradigm we investigated the presence of perceptual tactile aftereffects induced by continuous and burst vibrotactile stimuli delivered at the navel, side and spinal regions of the waist. We report evidence that the tactile perception topology of the waist is non-uniform, and specifically that the navel and spine regions are resistant to adaptive aftereffects while side regions are more prone to perceptual adaptations to continuous but not burst stimulations. Results of our current investigations highlight the unique set of challenges posed by designing waist-worn tactile displays. These and future perceptual studies can directly inform more realistic and effective implementations of complex high-dimensional spatiotemporal social cues.
AI technologies such as deep learning provide promising advances in many areas. Using these technologies, enterprises and organizations implement new business models and capabilities. In the beginning, AI-technologies have been deployed in an experimental environment. AI-based applications have been created in an ad-hoc manner and without methodological guidance or engineering approach. Due to the increasing importance of AI-technologies, however, a more structured approach is necessary that enable the methodological engineering of AI-based applications. Therefore, we develop in this paper first steps towards methodological engineering of AI-based applications. First, we identify some important differences between the technological foundations of AI- technologies, in particular deep learning, and traditional information technologies. Then we create a framework that enables to engineer AI-applications using four steps: identification of an AI-application type, sub-type identification, lifecycle phase, and definition of details. The introduced framework considers that AI-applications use an inductive approach to infer knowledge from huge collections and streams of data. It not only enables the rapid development of AI-application but also the efficient sharing of knowledge on AI-applications.
Intraoperative brain deformation, so called brain shift, affects the applicability of preoperative magnetic resonance imaging (MRI) data to assist the procedures of intraoperative ultrasound (iUS) guidance during neurosurgery. This paper proposes a deep learning-based approach for fast and accurate deformable registration of preoperative MRI to iUS images to correct brain shift. Based on the architecture of 3D convolutional neural networks, the proposed deep MRI-iUS registration method has been successfully tested and evaluated on the retrospective evaluation of cerebral tumors (RESECT) dataset. This study showed that our proposed method outperforms other registration methods in previous studies with an average mean squared error (MSE) of 85. Moreover, this method can register three 3D MRI-US pair in less than a second, improving the expected outcomes of brain surgery.