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Many online media platforms currently utilise algorithmically driven content moderation to prevent copyright infringement. This article explores content moderation's effect on mashup music – a form of remix which relies primarily on the unauthorised combining of pre-existing, recognisable recordings. Drawing on interviews (n = 30) and an online survey (n = 92) with mashup producers, we show that content moderation affects producers' creative decisions and distribution strategies, and has a strong negative effect on their overall motivation to create mashups. The objections that producers hold to this state of affairs often strongly resonate with current copyright exceptions. However, we argue that these exceptions, which form a legal 'grey zone', are currently unsatisfactorily accommodated for by platforms. Platforms' political-economic power allows them, in effect, to 'occupy' and control this zone. Consequently, the practical efficacy of copyright law's exceptions in this setting is significantly reduced. [ABSTRACT FROM AUTHOR]

Over the last decade, service selection and recommendation had been two strongly related service filtering steps. While service selection aims to filter the best available services according to QoS and contextual criteria, service recommendation refines the selection results by taking into account additional criteria, such as users feedbacks and ratings, similarities between users tastes, etc. However, the ever changing services environment, users tastes, as well as the perception and popularity of available services, rise a question regarding the appropriate means to capture and analyze such changes over time. Most service recommendation solutions are static and do not offer a multi-relational modeling of user-service interactions over time. Time is a contextual dimension that has, recently, received a lot of attention, leading to a new class of recommender systems, called time-aware recommender systems. In this work, we propose a service recommendation method that takes advantage of temporal knowledge graphs. As a de facto standard to model multiple and complex interactions between heterogeneous entities, knowledge graphs will serve as a historical knowledge base for our TASR system. We, first, model the user-service interactions over time, by constructing a temporal service knowledge graph (TSKG) that will be later enriched through a completion step. Second, to explore the TSKG and extract top-rated services, we use Convolutional Neural Networks (CNN) to embed the TSKG into a low-dimensional vector space, facilitating then its mining. Experimental studies have proven the effectiveness and accuracy of our approach, compared to traditional TASR methods and time-unaware KG-based recommendation. [ABSTRACT FROM AUTHOR]

With an growing number of web services available on the Internet, an increasing burden is imposed on the use and management of service repository. Service clustering has been employed to facilitate a wide range of service-oriented tasks, such as service discovery, selection, composition and recommendation. Conventional approaches have been proposed to cluster web services by using explicit features, including syntactic features contained in service descriptions or semantic features extracted by probabilistic topic models. However, service implicit features are ignored and have yet to be properly explored and leveraged. To this end, we propose a novel heuristics-based framework DeepWSC for web service clustering. It integrates deep semantic features extracted from service descriptions by an improved recurrent convolutional neural network and service composability features obtained from service invocation relationships by a signed graph convolutional network, to jointly generate integrated implicit features for web service clustering. Extensive experiments are conducted on 8,459 real-world web services. The experiment results demonstrate that DeepWSC outperforms state-of-the-art approaches for web service clustering in terms of multiple evaluation metrics. [ABSTRACT FROM AUTHOR]

Business process compliance ensures that the business processes of an organisation are designed and executed according to the rules that enforce the compliance controls that govern the company. We faced the challenge of building a Business Process Compliance Management System (BPCMS) for a process-aware organisation that had to provide support for several needs that, despite having been identified in the literature, were only partially satisfied by existing approaches. The variability in the types of rules and their interpretation generally restricts the existing support for compliance checking to specific types of rules (e.g., rules affecting the control flow of the process), a specific phase of the business process management (BPM) lifecycle (e.g., design time or run time), or certain information systems (ISs) for data retrieval (e.g., process event logs). Motivated by this, we designed a conceptual framework for design-time and run-time compliance checking that relies on the use of mashups for rule specification and checking. It presents the following advantages: (i) an open-ended set of types rules can be specified by designing and connecting mashup components; (ii) (parts of) the definitions of the rules can be reused as needed; and (iii) the mashup-based compliance checking (MCC) system can be integrated with ISs of the organisation, enabling the verification of actual facts on actions performed during the execution of a process (e.g., the existence of a specific document in a concrete location). Design-time and run-time implementations of the framework were conducted and tested in a real setting. [ABSTRACT FROM AUTHOR]

With the boom of Web services, there is a growing need for visualizing service ecosystems to help people browse services and understand their functionalities and positions in the systems. One foundational step of building a proper visualization is to ensure accurate representations for the comprising services. However, it is not a trivial task as service profiles may not be sufficient for two significant reasons. First, while the services themselves being used in various scenarios, their profiles may not always precisely reflect all of them. Second, service profiles usually comprise quite a few universal background terms that cannot distinguish services. To address these two issues, we apply machine learning techniques to incrementally learn service representations in a whole. A tailored topic model is developed, named Service Representation-Latent Dirichlet Allocation (SR-LDA). The core idea is to learn more comprehensive and up-to-date information about services from the profiles of the involved service compositions (i.e., mashup profiles), while introducing a global filter to identify and filter out background terms. Both quantitative and qualitative experiments on a real-world dataset demonstrate that the proposed SR-LDA builds higher-quality service representations comparing with baselines. We further generate a knowledge map to visualize a service ecosystem based on the learned service representations. Such a knowledge map directly leads to the detection of four typical functionality patterns of Web services and serves the purpose of mashup creation. [ABSTRACT FROM AUTHOR]

Driven by the widespread application of Service-Oriented Architecture (SOA), an increasing number of services and mashups have been developed and published onto the Internet in the past decades. With the number keeping on burgeoning, predicting the tendency of services invocation will provide various roles in service ecosystems with promising opportunities. However, services invocation bear three unique characteristics, which give rise to difficulties in predicting them. First, enormous services show different and complicated traits, like periodicity, nonlinearity and nonstationarity. Second, services providing similar or compensatory functions make up intricate relationship. Third, the combination dependencies between mashups and their comprising component services further amplify the difficulty. Given these factors, we have developed a tailored model Multi-Step Piecewise Recurrent Neural Network (MSP-RNN) to predict the tendency of services invocation. In MSP-RNN, Long Short Term Memory (LSTM) units are used to extract universal features. Based on these features, we have developed a piecewise regressive mechanism to make prediction discriminatingly. Besides, we have developed a multi-step prediction strategy to further enhance prediction accuracy and robustness. Extensive experiments in real-world data set with interpretable analysis show that MSP-RNN predicts the tendency of services invocation more accurately, i.e., by 3.7 percent in terms of symmetric mean absolute percentage error (SMAPE), than state-of-the-art baseline methods. [ABSTRACT FROM AUTHOR]

Stylistics is a branch of linguistics concerned with the systematic analysis of style in language, particularly literary style. Poetry has been a staple of stylistics. Creative performance of poems and stylistic analysis, however, have rarely been bedfellows. I showcase a stylistics pedagogy for creatively interpreting poetry in higher education where students make digitally multimodal storied interpretations of poems. The pedagogy reflects contemporary internet mashup culture, recognising that students inhabit a "postdigital" world where commonplace software and resources offer opportunities for DIY juxtaposition of audio and video for different purposes – artistic, comedic, etc. An advantage of such Postdigital Stylistics is that it integrates performance-based readers, marginalised in exegetical reading practices associated with print. I illustrate the pedagogy with a student video of Charles Bukowski's poem "the bluebird", accessible analysis of its foregrounded style, and explanation of how this analysis – crucially – motivates shot design. [ABSTRACT FROM AUTHOR]

The wide adoption of Service Oriented Architecture (SOA) has driven the creation of a massive amount of applications on the Internet, which includes the popular Mashups composed from multiple existing Web APIs. The availability of a large number of Web APIs with diverse functionalities on the Web makes it difficult for users to find APIs meeting their needs for Mashup development. To relieve this difficulty, recommending Web APIs for Mashup development has become an effective solution. A dozen of service recommendation approaches were proposed based on multi-dimensional features extracted from the service repository over the last couple of years, e.g., similarity based matching methods, matrix factorization based models, and factorization machine based models. Among these existing works, Factorization Machine (FM) based models, in particular the deep learning based FM models, have shown better performance compared with other conventional collaborative filtering techniques. Despite their superiority, the deep learning based FMs still have some strong model assumptions that can harm the recommendation accuracy. For example, it models factorized interactions with the same weight and ignores the non-linear and complex inherent structure in data. In a real-world service recommendation scenario, different predictor variables usually have different predictive power and not all features are predictable for estimating the target. Also, higher-order feature interactions are usually underlain in complex user-service environments. To address these deficiencies, this paper proposes a hybrid factorization machine model with a novel neural network architecture, named NAFM, which integrates a deep neural network to capture the non-linear and complex feature interactions and uses an attention mechanism to capture the varying importance of feature interactions. Comprehensive experiments are conducted on a real-world dataset from ProgrammableWeb. The experimental results show that the proposed approach outperforms the existing state-of-the-art models for service recommendation. [ABSTRACT FROM AUTHOR]

With the prevalence of web techniques and Internet-of-Things networks, an increasing number of developers build software by invoking existing application programming interfaces (APIs), especially in industrial systems. As the number of existing APIs in industrial systems is large, it is critical to recommend suitable APIs from big APIs data to developers in industrial software development. There have been some approaches proposed for APIs recommendation, but the existing approaches focus on the utilization of historical invocation records but ignore the exploitation of other information in the development process. We find that this ignored information can be mined as cognitive knowledge to learn the behavior rules of developers. In this article, we propose a holistic personalized recommendation framework that contains two individual models and one ensemble model, which are based on joint matrix factorization and cognitive knowledge mining. In the two individual models, we study the hidden relationships among users, which are mined from the APIs following records. We also study the hidden relationships among APIs, which are mined from the content information. We also propose an ensemble model. We crawled a large real-word dataset and conducted sufficient experiments, and compared our framework with well-known existing methods. The experimental results demonstrate that our framework achieves the best performance. [ABSTRACT FROM AUTHOR]

Sammendrag Mange av dagens internettplattformer bruker automatiske og algoritmiske verktøy for å gjenkjenne og moderere uønsket innhold, inkludert det som blir ansett for å krenke opphavsretten. Denne artikkelen undersøker hvordan slike gjenkjenningsprosedyrer påvirker mashup-musikk – en form for remiks som baserer seg på å kombinere (uautoriserte) utdrag av tidligere, gjenkjennelige musikkinnspillinger. På bakgrunn av intervjuer (n = 30) og en spørreundersøkelse (n = 92) med mashup-produsenter, viser vi at disse gjenkjenningsprosedyrene påvirker produsentenes kreative beslutninger og distribusjonsstrategier, og at de har en betydelig, negativ innvirkning på produsentenes generelle motivasjon for å lage mashup-musikk. Produsentenes innvendinger mot denne situasjonen vinner en viss gjenklang i de juridiske unntakene fra opphavsretten. Vi argumenterer imidlertid for at disse unntakene, som utgjør en juridisk «gråsone», ikke er lagt tilstrekkelig til rette for av plattformene. Plattformenes politisk-økonomiske makt gjør det dermed mulig for dem å «okkupere» og kontrollere denne gråsonen. Som en konsekvens er den praktiske anvendelsen av opphavsrettens unntak i denne settingen betydelig redusert.

Contribution: This article presents an experience report on the application of flipped classroom (FC) to the laboratory sessions (henceforth lab sessions) of an undergraduate computer science course. Background: Hands-on work in computer science lab sessions is typically preceded by technical instructions on how to install, configure, and use the software and hardware tools needed during the lab. In the course under study, this initial explanation took between 14% and 50% of the lab time, reducing drastically the time available for actual practice. It was also observed that students missing any of the labs had trouble catching up. Intended Outcomes: The application of FC is expected to increase the time for hands-on activities, and improve students’ performance and motivation. This improvement is expected to be more noticeable in those students who are unable to attend all lab sessions. Application Design: The study compares the application of FC and a traditional methodology. It encompasses two academic courses and involves 434 students and six lecturers. Findings: The FC is suitable for lab sessions in computer science. Among other results, flipping the labs resulted in 24 more minutes of practical and collaborative work on average at each lab session. It was observed a significant improvement in the motivation of students, with 9 out of every 10 students preferring it over traditional methodologies. Also, the FC made it much easier for students to catch up after missing a lab, making the final grades less dependent on lab attendance. [ABSTRACT FROM AUTHOR]

Mashups are a dominant approach for building data-centric applications, especially mobile applications, in recent years. Since mashups are predominantly based on public data sources and existing APIs, it requires no sophisticated programming knowledge of people to develop mashup applications. The recent prevalence of open APIs and open data sources in the Big Data era has provided new opportunities for mashup development, but at the same time increase the difficulty of selecting the right services for a given mashup task. The API recommendation for mashup differs from traditional service recommendation tasks in lacking the specific QoS information and formal semantic specification of the APIs, which limits the adoption of many existing methods. Although there are a significant number of service recommendation approaches, most of them focus on improving the recommendation accuracy and work pays attention to the diversity of the recommendation results. Another challenge comes from the existence of both explicit and implicit correlations among the different APIs, which are generally neglected by existing recommendation methods. In this paper, we address the above deficiencies of existing approaches by exploring API recommendation for mashups in the reusable composition context, with the goal of helping developers identify the most appropriate APIs for their composition tasks. In particular, we propose a probabilistic matrix factorization approach with implicit correlation regularization to solve the recommendation problem and enhance the recommendation diversity. We conjecture that the co-invocation of APIs in real-world mashups is driven by both the explicit textual similarity and implicit correlations of APIs such as the similarity or the complementary relationship of APIs. We develop a latent variable model to uncover the latent correlations between APIs by analyzing their co-invocation patterns. We further explore the relationships of topics/categories to the proposed approach. We demonstrate the effectiveness of our approach by conducting extensive experiments on a real dataset crawled from ProgrammableWeb. [ABSTRACT FROM AUTHOR]

In recent decades, cartography has experienced a number of paradigm changes seen in refreshed research agendas and renewed education programs. Yet cartography remains the science, art and technology of making and using maps. This paper addresses four persistent research questions in cartography: 1 ) What is a map? 2) What are maps made for? 3) How are maps made? and 4) Who is making maps? Based on a retrospective analysis of cartographic advances since the introduction of the Internet in the early 1990s, the author gives an overview of evolution with regard to map types, map affordances, mapmaking workflows and the roles of mapmakers and map users. While some cartographic principles used since ancient times will continue to serve as anchor points for future development, ever-changing technological potentials and user requirements force us to maintain vitality with more and more innovative maps and map-based services. The author also appeals for a sustainable map creation ecosystem supported by cloud computing platforms.

With increasing adoption of cloud services in the e-market, collaboration between stakeholders is easier than ever. Consumer stakeholders demand data from various sources to analyze trends and improve customer services. Data-as-a-service enables data integration to serve the demands of data consumers. However, the data must be of good quality and trustful for accurate analysis and effective decision making. In addition, a data custodian or provider must conform to privacy policies to avoid potential penalties for privacy breaches. To address these challenges, we propose a twofold solution: 1) we present the first information entropy-based trust computation algorithm, IEB_Trust, that allows a semitrusted arbitrator to detect the covert behavior of a dishonest data provider and chooses the qualified providers for a data mashup and 2) we incorporate the Vickrey–Clarke–Groves (VCG) auction mechanism for the valuation of data providers’ attributes into the data mashup process. Experiments on real-life data demonstrate the robustness of our approach in restricting dishonest providers from participation in the data mashup and improving the efficiency in comparison to provenance-based approaches. Furthermore, we derive the monetary shares for the chosen providers from their information utility and trust scores over the differentially private release of the integrated dataset under their joint privacy requirements. [ABSTRACT FROM AUTHOR]

As service-oriented computing (SOC) technologies gradually mature, developing service-based systems (such as mashups) has become increasingly popular in recent years. Faced with the rapidly increasing number of Web services, recommending appropriate component services for developers on demand is a vital issue in the development of mashups. In particular, since a new mashup to develop contains no component services, it is a new “user” to a service recommender system. To address this new “user” cold-start problem, we propose a multiplex interaction-oriented service recommendation approach, named MISR, which incorporates three types of interactions between services and mashups into a deep neural network. In this article, we utilize the powerful representation learning abilities provided by deep learning to extract hidden structures and features from various types of interactions between mashups and services. Experiments conducted on a real-world dataset from ProgrammableWeb show that MISR outperforms several state-of-the-art approaches regarding commonly used evaluation metrics. [ABSTRACT FROM AUTHOR]

The sensing and actuation as-a-service is an emerging business model to make sensors, actuators and data from the Internet of Things more attainable to everyday consumer. With the increase in the number of accessible Things, mashups can be created to combine services/data from one or multiple Things with services/data from virtual Web resources. These may involve complex tasks, with high computation requirements, and for this reason cloud infrastructures are envisaged as the most appropriate solution for storage and processing. This means that cloud-based services should be prepared to manage Thing mashups. Mashup management within the cloud allows not only the optimization of resources but also the reduction of the delay associated with data travel between client applications and the cloud. In this article, an optimization model is developed for the optimal allocation of resources in clouds under the sensing and actuation as-a-service paradigm. A heuristic algorithm is also proposed to quickly solve the problem. [ABSTRACT FROM AUTHOR]

The emergence of smarter and broader people-oriented IoT applications and services requires interoperability at both data and knowledge levels. However, although some semantic IoT architectures have been proposed, achieving a high degree of interoperability requires dealing with a sea of non-integrated data, scattered across vertical silos. Also, these architectures do not fit into the machine-to-machine requirements, as data annotation has no knowledge on object interactions behind arriving data. This paper presents a vision of how to overcome these issues. More specifically, the semantic profiling of objects, through CoRE related standards, is envisaged as the key for data integration, allowing more powerful data annotation, validation, and reasoning. These are the key blocks for the development of intelligent applications.

With the growing popularity of web services, more and more developers are composing multiple services into mashups. Developers show an increasing interest in non-popular services (i.e., long-tail ones), however, there are very scarce studies trying to address the long-tail web service recommendation problem. The major challenges for recommending long-tail services accurately include severe sparsity of historical usage data and unsatisfactory quality of description content. In this paper, we propose to build a deep learning framework to address these challenges and perform accurate long-tail recommendations. To tackle the problem of unsatisfactory quality of description content, we use stacked denoising autoencoders (SDAE) to perform feature extraction. Additionally, we impose the usage records in hot services as a regularization of the encoding output of SDAE, to provide feedback to content extraction. To address the sparsity of historical usage data, we learn the patterns of developers’ preference instead of modeling individual services. Our experimental results on a real-world dataset demonstrate that, with such joint autoencoder based feature representation and content-usage learning framework, the proposed algorithm outperforms the state-of-the-art baselines significantly. [ABSTRACT FROM AUTHOR]

Featured Application: Composing software services (mashups) enables the creation of diverse new applications, yet it is a daunting activity for users who are not programmers by profession. A confirmatory study of ordinary users' service compositional activities was undertaken, resulting in a set of recommendations to guide the creation of usable tools supporting service composition. For instance, service designers are advised to use natural metaphors to represent complex logic such as conditional statements, dataflow, and parallel events between services to aid user understanding. Ordinary web users can now create and publish online content. They even venture into "mashups," integrating information from different sources into a composite information-providing web service. This is a non-trivial design task, which falls into the area of end-user development when the ordinary users who perform it do not have programming education. In this article, we investigate the service design strategies of 12 such ordinary users and compare them against the baseline of 12 programmers. In our think-aloud study, users completed two contrasting types of tasks involved in developing service-based applications: (a) manual service composition and (b) parametric design using templates with a high degree of software support (or assisted composition). These service composition tasks were chosen to differ in respect to the level of user support provided by the tool. Our findings show that non-programmers liked, more than programmers, the template-based parametric design and did not find the tool assistance as constraining as the programmers did. The difficulty of design involved in manual service composition and the absence of user guidance hindered non-programmers in expressing and implementing accurate design solutions. The differences in the mental models and needs of non-programmers are established to be in stark contrast to those of programmers. We used the details of our findings to propose specialized design recommendations for service composition tools aligned with the profiles of their target users. [ABSTRACT FROM AUTHOR]

As society has progressed through periods of evolution and revolution, technology has played a key role as an enabler. In the same manner, mechanical machines of the 1800s drove the industrial revolution, now digitalized machines are driving another industrial revolution. Manufacturers are increasing the digital footprint on the factory floor. It is challenging to harness the vast amounts of data generated, stored, analyzed, archived, and returned. Data centralization has several well-known challenges, such as collection bottlenecks, secure retrieval, single point of failure, and data scheme fragility as data heterogeneity increases. This paper proposes a method of information distribution based on the principle of data at its source. It proposes that contextual data be used at runtime through the creation of dynamic queries that build compositions of different systems. Such system of systems (SoS) compositions handle the flow of data across its life cycle and present it as information to the initiating system. The proposal starts by creating a graph model of the Arrowhead framework. Then, building on the graph model, the query-based approach for specifying, validating, and forming the SoS is proposed. The proposed graph model allows for unambiguous description of systems and their interrelations, including security relations. The proposed composer operates on the edge computing hardware and gives the production floor the ability to extract information without impacting the overall operation of the factory. [ABSTRACT FROM AUTHOR]

Data visualizations take a variety of forms, from classic bar charts to three-dimensional presentations on globe maps. The purpose of this paper is a comparative analysis of selected techniques for visualizing statistical data on a background map. The exploratory tests were carried out to study the design possibilities of three software tools: Visualization: GeoChart, GIS and JavaScript and 3D Maps (MS Office), as well as the functionality and usability of applications created with them. In addition, selected technical attributes of the applications were measured, including the size and number of component files, loading time in the web browser window, as well as performance. It has been shown that the tested tools are predisposed to create data visualizations on administrative maps, that they have different design possibilities, that they differ in the degree of service advancement, and that they can also be useful in creating small visualizations, so-called "ad-hoc map". [ABSTRACT FROM AUTHOR]