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Resumen: La sociedad de la información digital plantea nuevos retos a los investigadores. A mediada que la comunicación audiovisual se ha consolidado como disciplina, los estudios culturales se muestran como una perspectiva de análisis ventajosa para acercarse a las nuevas prácticas creativas y de consumo del medio audiovisual. Este artículo defiende el estudio de los productos culturales audiovisuales que esta sociedad digital produce por cuanto son un testimonio de los cambios sociales que se operan en ella. En concreto se propone el acercamiento a la publicidad social y a los objetos de creación colectiva en Internet como medio para conocer las circunstancias de nuestra sociedad. Abstract: The information society poses new challenges to researchers. While audiovisual communication has been consolidated as a discipline, cultural studies is an advantageous analytical perspective to approach the new creative practices and consumption of audiovisual media. This article defends the study of audiovisual cultural products produced by the digital society because they are a testimony of the social changes taking place in it. Specifically, it proposes an approach to social advertising and objects of collective creation on the Internet as a means to know the circumstances of our society.

This study investigates the issues raised by the creation of ‘new’ songs entirely from ‘old’ recordings, exemplified by mashups. The paper looks at the illegal status of the genre and the challenge to traditional ideas of authorship and originality posed by mashups. The repackaging of copyrighted material has serious legal implications; the inherent illegality of the mashup genre perpetuates its underground status inferring cultural kudos on the work’s producers. Peer-to-peer applications, home CD burners, and mp3s have speeded up the rate at which the musical past can be referenced. Recordings are ‘unmixed’ and song fragments are distributed freely on the internet often with no reference to the original artist or author. Mashup producers reassemble these fragments, arranging them sequentially (“Velcro text”) and layering them concurrently. The paper uses Baudrillard’s orders of signification to examine mashups. Baudrillard’s stated that “the age of simulation begins with a liquidation of all referentials” and that in ‘hyperreality’, a listener can no longer tell the difference between a ‘real’ piece of work or a simulation. Jameson identified “a new depthlessness” in a mediated self-referential culture obsessed with superficial detail, rendering the resulting product “meaningless” The question of how meaning is created and sustained in such a ‘meaningless’ post modern culture is discussed with reference to the writings of Barthes. A taxonomy of listener competence is proposed, whilst ideas of signifyin(g) and detournement are used to investigate the ironic use of musical quotation.

Situational language teaching (SLT) is an effective instruction paradigm for English teaching in terms of providing vocabularies and sentence patterns with their frequent situations through learning materials. With the growth of educational technology, we need powerful and suitable techniques to embody SLT’s features in ubiquitous learning (u-learning) and thus to benefit teachers and learners. Although researchers have proposed several innovative types of u-learning scenarios, the improved SLT paradigm in u-learning environment has been rarely investigated. This study indicated a framework of ubiquitous learning school to promote the concept of u-learning and employ SLT pedagogy in u-learning environment; it is called U-SLT. In order to support its innovation and provide situational learning services on demand, situational mashups was suggested to identify learners’ situation and learning requirement by means of integrating situation awareness with service mashups. The comparison between two u-learning modes, learning with situational mashups and learning without situation awareness support, were discussed. Experimental results showed that the students with the situational mashups support had a better learning performance and improved behaviours. Therefore, situational mashups was perceived to be a useful and desirable system for supporting U-SLT as well as the fundamental issue of a ubiquitous learning school.

In the last few years has begun the development of dynamic web applications, often called Web2.0. From this development wascreated a technology called Mashups. Mashups may easily combine huge amounts of data sources and functionalities of existing as wellas future web applications and services. Therefore they are used to develop a new device, which offers new possibilities of informationusage. This technology provides possibilities of developing basic as well as robust web applications not only for IT or GIS specialists,but also for common users. Software companies have developed web projects for building mashup application also called mashupeditors.

Crisis mapping has emerged as a method of connecting and empowering citizens during emergencies. This article explores the hyperbole behind crisis mapping as it extends into more long-term or ‘chronic’ community development practices. We critically examined developer issues and participant (i.e. community organization) usage within the context of local communities. We repurposed the predominant crisis mapping platform Crowdmap for three cases of community development in Canadian anglophone and francophone. Our case studies show mixed results about the actual cost of deployment, the results of disintermediation, and local context with the mapping application. Lastly, we discuss the relationship of hype, temporality, and community development as expressed in our cases. [ABSTRACT FROM AUTHOR]

A new type of sensor network called the demand-addressable sensor network (DASN) is proposed in this paper. The DASN actively acquires the desired information by addressing user demands and delivers the information to appropriate destinations. This is in contrast to the conventional sensor networks that simply send sensed data to users. The DASN is useful for finding the desired information in a short duration of time from a large amount of sensed data generated by a large-scale sensor network. The DASN is constructed with a demand-addressable network that integrates many on-demand reconfigurable wireless sensor networks (ODRWSN) and other existing information and communications technology systems or services, such as Google Maps and Twitter. In addition to the demand-addressing mechanism, the demand-addressable network has an in-network data combining or mashup mechanism. The mashed up data are displayed on the user terminal using an ordinary Web browser without any requirement to install a dedicated application program. The functions of the ODRWSN can be dynamically customized by injecting roles specified by the user. Thus, the user can actively get the desired information by customizing the sensor network function. The main application of the DASN is wide-area disaster site monitoring, for which the DASN features outlined above are suitable. In this paper, the concept underlying the DASN, its architecture and implementation, and experimental results are presented. [ABSTRACT FROM PUBLISHER]

Although the travel time is the most important information in road networks, many spatial queries, e.g., k-nearest-neighbor (k -NN) and range queries, for location-based services (LBS) are only based on the network distance. This is because it is costly for an LBS provider to collect real-time traffic data from vehicles or roadside sensors to compute the travel time between two locations. With the advance of web mapping services, e.g., Google Maps, Microsoft Bing Maps, and MapQuest Maps, there is an invaluable opportunity for using such services for processing spatial queries based on the travel time. In this paper, we propose a server-side Spatial Mashup Service (SMS) that enables the LBS provider to efficiently evaluate k-NN queries in road networks using the route information and travel time retrieved from an external web mapping service. Due to the high cost of retrieving such external information, the usage limits of web mapping services, and the large number of spatial queries, we optimize the SMS for a large number of k-NN queries. We first discuss how the SMS processes a single k-NN query using two optimizations, namely, direction sharing and parallel requesting. Then, we extend them to process multiple concurrent k -NN queries and design a performance tuning tool to provide a trade-off between the query response time and the number of external requests and more importantly, to prevent a starvation problem in the parallel requesting optimization for concurrent queries. We evaluate the performance of the proposed SMS using MapQuest Maps, a real road network, real and synthetic data sets. Experimental results show the efficiency and scalability of our optimizations designed for the SMS. [ABSTRACT FROM PUBLISHER]

Service mashups are applications created by combining single-functional services (or APIs) dispersed over the web. With the development of cloud computing and web technologies, service mashups are becoming more and more widely used and a large number of mashup platforms have been produced. However, due to the proliferation of services on the web, how to select component services to create mashups has become a challenging issue. Most developers pay more attention to the quality of service (QoS) and cost of services. Beside service selection, mashup deployment is another pivotal process, as the platform can significantly affect the quality of mashups. In this paper, we focus on creating service mashups from the perspective of developers. A genetic algorithm-based method, genetic algorithm for mashup creation (GA4MC), is proposed to select component services and deployment platforms in order to create service mashups with optimal cost performance. A series of experiments are conducted to evaluate the performance of GA4MC. The results show that the GA4MC method can achieve mashups whose cost performance is extremely close to the optimal. Moreover, the execution time of GA4MC is in a low order of magnitude and the algorithm performs good scalability as the experimental scale increases. [ABSTRACT FROM AUTHOR]

One of the central research challenges in the Internet of Things and Ubiquitous Computing domains is how users can be enabled to “program” their personal and industrial smart environments by combining services that are provided by devices around them. We present a service composition system that enables the goal-driven configuration of smart environments for end users by combining semantic metadata and reasoning with a visual modeling tool. In contrast to process-driven approaches where service mashups are statically defined, we make use of embedded semantic API descriptions to dynamically create mashups that fulfill the user’s goal. The main advantage of our system is its high degree of flexibility, as service mashups can adapt to dynamic environments and are fault-tolerant with respect to individual services becoming unavailable. To support users in expressing their goals, we integrated a visual programming tool with our system that allows to model the desired state of a smart environment graphically, thereby hiding the technicalities of the underlying semantics. Possible applications of the presented system include the management of smart homes to increase individual well-being, and reconfigurations of smart environments, for instance in the industrial automation or healthcare domains. [ABSTRACT FROM PUBLISHER]

The use of computational intelligence for leveraging social creativity is a relatively new approach that allows organizations to find creative solutions to complex problems in which the interaction between stakeholders is crucial. The creative solutions that come from joint thinking?from the combined knowledge and abilities of people with diverse perspectives?contrast with traditional views of creativity that focus primarily on the individual as the main contributor of creativity. In an effort to support social creativity in organizations, in this paper we present computational intelligence software tools for that aim and an architecture for creating software mashups based on the concept of affinity space. The affinity space defines a digital setting to facilitate specific scenarios in collaborative business environments. The solution presented includes a set of free and open source software tools ranging from newly developed brainstorming applications to an expertise recommender for enhancing social creativity in the enterprise. The current paper addresses software design issues and presents reflections on the research work undertaken in the COLLAGE project between 2012 and 2015. [ABSTRACT FROM PUBLISHER]

Pattern mining, that is, the automated discovery of patterns from data, is a mathematically complex and computationally demanding problem that is generally not manageable by humans. In this article, we focus on small datasets and study whether it is possible to mine patterns with the help of the crowd by means of a set of controlled experiments on a common crowdsourcing platform. We specifically concentrate on mining model patterns from a dataset of real mashup models taken from Yahoo! Pipes and cover the entire pattern mining process, including pattern identification and quality assessment. The results of our experiments show that a sensible design of crowdsourcing tasks indeed may enable the crowd to identify patterns from small datasets (40 models). The results, however, also show that the design of tasks for the assessment of the quality of patterns to decide which patterns to retain for further processing and use is much harder (our experiments fail to elicit assessments from the crowd that are similar to those by an expert). The problem is relevant in general to model-driven development (e.g., UML, business processes, scientific workflows), in that reusable model patterns encode valuable modeling and domain knowledge, such as best practices, organizational conventions, or technical choices, that modelers can benefit from when designing their own models. [ABSTRACT FROM AUTHOR]

A cloud mashup is composed of multiple services with shared datasets and integrated functionalities. For example, the elastic compute cloud (EC2) provided by Amazon Web Service (AWS), the authentication and authorization services provided by Facebook, and the Map service provided by Google can all be mashed up to deliver real-time, personalized driving route recommendation service. To discover qualified services and compose them with guaranteed quality of service (QoS), we propose an integrated skyline query processing method for building up cloud mashup applications. We use a similarity test to achieve optimal localized skyline. This mashup method scales well with the growing number of cloud sites involved in the mashup applications. Faster skyline selection, reduced composition time, dataset sharing, and resources integration assure the QoS over multiple clouds. We experiment with the quality of web service (QWS) benchmark over 10,000 web services along six QoS dimensions. By utilizing block-elimination, data-space partitioning, and service similarity pruning, the skyline process is shortened by three times, when compared with two state-of-the-art methods. [ABSTRACT FROM PUBLISHER]

With the proliferation of mobile devices, people are taking videos of the same events anytime and anywhere. Even though these crowdsourced videos are uploaded to the cloud and shared, the viewing experience is very limited due to monotonous viewing, visual redundancy, and bad audio–video quality. In this paper, we present a fully automatic mobile video mashup system that works in the cloud to combine recordings captured by multiple devices from different view angles and at different time slots into a single yet enriched and professional looking video–audio stream. We summarize a set of computational filming principles for multicamera settings from a formal focus study. Based on these principles, given a set of recordings of the same event, our system is able to synchronize these recordings with audio fingerprints, assess audio and video quality, detect video cut points, and generate video and audio mashups. The audio mashup is the maximization of audio quality under the less switching principle, while the video mashup is formalized as maximizing video quality and content diversity, constrained by the summarized filming principles. Our system is different from any existing work in this field in three ways: 1) our system is fully automatic; 2) the system incorporates a set of computational domain-specific filming principles summarized from a formal focus study; and 3) in addition to video, we also consider audio mashup that is a key factor of user experience (UX) yet often overlooked in existing research. Evaluations show that our system achieves performance results that are superior to state-of-the-art video mashup techniques, thus providing a better UX. [ABSTRACT FROM AUTHOR]

Support for component discovery has been identified as a key challenge in various forms of composite application development. In this paper, we describe a general method for component recommendation based on structural similarity of compositions. The method dynamically ranks and recommends components as a composition is incrementally developed. Recommendations are based on structural comparison of the partial composition begin developed with a database of previously completed compositions. Using this method, we define a probabilistic graph edit distance algorithm for component recommendation. We evaluate the accuracy, catalog coverage and response time of the presented algorithm and compare it to a neighborhood-based collaborative filtering approach and two simple statistical algorithms. The evaluation is performed on a Yahoo Pipes dataset and a synthetic dataset that models more complex composite applications. The results show that the proposed algorithm is competitive with the collaborative filtering algorithm in accuracy and outperforms it significantly in coverage. The results on the synthetic dataset suggest that the presented approach can be applied successfully to other composition environments where there is regularity in how components are connected. [ABSTRACT FROM AUTHOR]

Though the existing data service mashup tools are gaining acceptance, it is still challenging for developers with no or little programming skills to develop data service mashups for dealing with situational and ad-hoc business problems. The paper focuses on interactive recommendation in which assistance is provided in a context-sensitive manner when the mashup plan can't be determined in advance. The paper analyzes the problem with a motivating scenario of mashup building for criminal investigation. Inspired by the observation that there exist dataflow patterns for certain integration functionalities, a dataflow-pattern-based recommendation framework is proposed to solve the problems. The framework can not only recommend data services by discovering similar situations, but also recommend mashup patterns and target data services. We propose a method to analyze the relationships between data services and dataflow patterns through both mining history logs and matching the input/output parameters. Further, to recommend target data services, we propose a method to transform the data mashup plans into mixed graphs and apply the graph-based substructure pattern mining (gSpan) algorithm on them. Experiments show that the dataflow-pattern-based recommendation approach for data service mashup is effective and efficient. [ABSTRACT FROM PUBLISHER]

In this article, the authors offer a general overview of audio music concatenation, then deliberate on how to connect not-so-coherent music clips in response to the rising awareness of user-preference in music recomposition. In particular, they introduce sound-effect insertion into the proposed bridging process to make the transition natural and euphonious. To systematically verify the feasibility of the proposed music concatenation methods, they conducted specifically designed experiments to collect subjective opinions and reduce the cognitive loads of the participants. The results indicate that using suitable sound effects greatly enhances the listening experiences among various subjects. [ABSTRACT FROM PUBLISHER]

Drawing on sociolinguistic studies of language and place and on small stories insights and methods, I put forth rescripting as a social media-enabled practice of sharing that systematically exploits visual and/or verbal manipulations of the taleworld place of already circulated stories, so as to present the new tales as parody or satire of the originals. YouTube videos such as spoofs, memes, remixes, and mashups, form typical instances of rescripting, but other verbal activities should also be recognized as part of this practice, e.g. circulars of jokes about an incident on online blogs. Using as a case study a critical moment incident related to the Greek crisis that was repeatedly shared in social media, I examine rescripting as it occurs in the intersections between story making and social media affordances, arguing that it can productively open up the current sociolinguistic focus on resemiotizations of circulated activities. I show how changes in the place of the taleworld ultimately lead to changes in emplotment on the basis of the spatial semiotic repertoires associated with the new settings. Participation frameworks are decisively shaped by these changes, as commenters mainly engage with the current tale and telling, going along with the ostensibly 'fictional’ scenarios and engaging in active storytelling, that is, creating further plots on their basis. I discuss the implications of rescripting place for the study of sharing practices and vernacular participation as well as for the study of place as constitutive of narrative plots. [ABSTRACT FROM AUTHOR]

Mashup has emeraged as a promising way to allow developers to compose existed APIs (services) to create new or value-added services. With the rapid increasing number of services published on the Internet, service recommendation for automatic mashup creation gains a lot of momentum. Since mashup inherently requires services with different functions, the recommendation result should contain services from various categories. However, most existing recommendation approaches only rank all candidate services in a single list, which has two deficiencies. First, ranking services without considering to which categories they belong may lead to meaningless service ranking and affect the recommendation accuracy. Second, mashup developers are not always clear about which service categories they need and services in which categories cooperate better for mashup creation. Without explicitly recommending which service categories are relevant for mashup creation, it remains difficult for mashup developers to select proper services in a mixed ranking list, which lower the user friendliness of recommendation. To overcome these deficiencies, a novel category-aware service clustering and distributed recommending method is proposed for automatic mashup creation. First, a Kmeans variant (vKmeans) method based on topic model Latent Dirichlet Allocation is introduced for enhancing service categorization and providing a basis for recommendation. Second, on top of vKmeans, a service category relevance ranking (SCRR ) model, which combines machine learning and collaborative filtering, is developed to decompose mashup requirements and explicitly predict relevant service categories. Finally, a category-aware distributed service recommendation (CDSR) model, which is based on a distributed machine learning framework, is developed for predicting service ranking order within each category. Experiments on a real-world dataset have proved that the proposed approach not only gains significant improvement at precision rate but also enhances the diversity of recommendation results. [ABSTRACT FROM PUBLISHER]

In recent years, models, composition paradigms, and tools for mashup development have been proposed to support the integration of information sources, services and APIs available on the Web. The challenge is to provide a gate to a “programmable Web,” where end users are allowed to construct easily composite applications that merge content and functions so as to satisfy the long tail of their specific needs. The approaches proposed so far do not fully accommodate this vision. This article, therefore, proposes a mashup development framework that is oriented toward the End-User Development. Given the fundamental role of user interfaces (UIs) as a medium easily understandable by the end users, the proposed approach is characterized by UI-centric models able to support a WYSIWYG (What You See Is What You Get) specification of data integration and service orchestration. It, therefore, contributes to the definition of adequate abstractions that, by hiding the technology and implementation complexity, can be adopted by the end users in a kind of “democratic” paradigm for mashup development. This article also shows how model-to-code generative techniques translate models into application schemas, which in turn guide the dynamic instantiation of the composite applications at runtime. This is achieved through lightweight execution environments that can be deployed on the Web and on mobile devices to support the pervasive use of the created applications. [ABSTRACT FROM AUTHOR]

Web service recommendation has become a critical problem as services become increasingly prevalent on the Internet. Some existing methods focus on content matching techniques, while others are based on QoS measurement. However, service ecosystem is evolving over time with services publishing, prospering and perishing. Few existing methods consider or exploit the evolution of service ecosystem on service recommendation. This paper employs a probabilistic approach to predict the popularity of services to enhance the recommendation performance. A method is presented that extracts service evolution patterns by exploiting latent dirichlet allocation (LDA) and time series prediction. A time-aware service recommendation framework is established for mashup creation that conducts joint analysis of temporal information, content description and historical mashup-service usage in an evolving service ecosystem. Experiments on a real-world service repository, ProgrammableWeb.com, show that the proposed approach leads to a higher precision than traditional collaborative filtering and content matching methods, by taking into account temporal information. [ABSTRACT FROM PUBLISHER]

Service computing promotes a large number of web-delivered services, including web services, APIs and data feeds. Composing data, functionalities and even UI from these web-delivered services into a single web application, usually called service mashup, becomes a popular web development paradigm. The web-delivered services can be modeled as mashup components, while the development of mashup actually yields a set of inter-connected mashup components. The growing popularity of mashup components enriches functionality and user experiences, while the possible connections among components are complex and difficult to mashup developers, who might be non-professional programmers or even end-users, as actions over one component may have potential impacts on another. This paper proposes a novel approach for recommending developers in terms of navigation and completion of mashup components with a large-scale components repository. From data-driven perspective, we model the relationships between mashup components by a generic layered-graph model. Developers are allowed to select some initial components as starting point, while a graph-based algorithm recommends how to navigate to potentially relevant mashup components and complete the relevant mashup application. We experimentally demonstrate the efficiency and effectiveness of our approach for rapid mashup construction. [ABSTRACT FROM PUBLISHER]