Hasil untuk "The performing arts. Show business"

Ding Yang, Ruixiang Qian, Zhao Wei et al.

REST APIs enable collaboration among microservices. A single fault in a REST API can bring down the entire microservice system and cause significant financial losses, underscoring the importance of REST API testing. Effectively testing REST APIs requires thoroughly exercising the functionalities behind them. To this end, existing techniques leverage REST specifications (e.g., Swagger or OpenAPI) to generate test cases. Using the resource constraints extracted from specifications, these techniques work well for testing simple, business-insensitive functionalities, such as resource creation, retrieval, update, and deletion. However, for complex, business-sensitive functionalities, these specification-based techniques often fall short, since exercising such functionalities requires additional business constraints that are typically absent from REST specifications. In this paper, we present LoBREST, a log-based, business-aware REST API testing technique that leverages historical request logs (HRLogs) to effectively exercise the business-sensitive functionalities behind REST APIs. To obtain compact operation sequences that preserve clean and complete business constraints, LoBREST first employs a locality-slicing strategy to partition HRLogs into smaller slices. Then, to ensure the effectiveness of the obtained slices, LoBREST enhances them in two steps: (1) adding slices for operations missing from HRLogs, and (2) completing missing resources within the slices. Finally, to improve test adequacy, LoBREST uses these enhanced slices as initial seeds to perform business-aware fuzzing. LoBREST outperformed eight tools (including Arat-rl, Morest, and Deeprest) across 17 real-world services. It achieved top operation coverage on 16 services and line coverage on 15, averaging 2.1x and 1.2x improvements over the runner-up. LoBREST detected 108 5XX bugs, including 38 found by no other tool.

Viet Trinh

Big data, both in its structured and unstructured formats, have brought in unforeseen challenges in economics and business. How to organize, classify, and then analyze such data to obtain meaningful insights are the ever-going research topics for business leaders and academic researchers. This paper studies recent applications of deep neural networks in decision making in economical business and investment; especially in risk management, portfolio optimization, and algorithmic trading. Set aside limitation in data privacy and cross-market analysis, the article establishes that deep neural networks have performed remarkably in financial classification and prediction. Moreover, the study suggests that by compositing multiple neural networks, spanning different data type modalities, a more robust, efficient, and scalable financial prediction framework can be constructed.

Joe Culpepper, Melanie I Stuckey

Gyan Wickremasinghe, Siofra Frost, Karen Rafferty et al.

Industry 4.0 and beyond will rely heavily on sustainable Business Decision Modelling (BDM) that can be accelerated by blockchain and Digital Twin (DT) solutions. BDM is built on models and frameworks refined by key identification factors, data analysis, and mathematical or computational aspects applicable to complex business scenarios. Gaining actionable intelligence from collected data for BDM requires a carefully considered infrastructure to ensure data transparency, security, accessibility and sustainability. Organisations should consider social, economic and environmental factors (based on the triple bottom line approach) to ensure sustainability when integrating such an infrastructure. These sustainability features directly impact BDM concerning resource optimisation, stakeholder engagement, regulatory compliance and environmental impacts. To further understand these segments, taxonomies are defined to evaluate blockchain and DT sustainability features based on an in-depth review of the current state-of-the-art research. Detailed comparative evaluations provide insight into the reachability of the sustainable solution in terms of ideologies, access control and performance overheads. Several research questions are put forward to motivate further research that significantly impacts BDM. Finally, a case study based on an exemplary supply chain management system is presented to show the interoperability of blockchain and DT with BDM.

Łukasz Lenart, Łukasz Kwiatkowski, Justyna Wróblewska

We design a novel, nonlinear single-source-of-error model for analysis of multiple business cycles. The model's specification is intended to capture key empirical characteristics of business cycle data by allowing for simultaneous cycles of different types and lengths, as well as time-variable amplitude and phase shift. The model is shown to feature relevant theoretical properties, including stationarity and pseudo-cyclical autocovariance function, and enables a decomposition of overall cyclic fluctuations into separate frequency-specific components. We develop a Bayesian framework for estimation and inference in the model, along with an MCMC procedure for posterior sampling, combining the Gibbs sampler and the Metropolis-Hastings algorithm, suitably adapted to address encountered numerical issues. Empirical results obtained from the model applied to the Polish GDP growth rates imply co-existence of two types of economic fluctuations: the investment and inventory cycles, and support the stochastic variability of the amplitude and phase shift, also capturing some business cycle asymmetries. Finally, the Bayesian framework enables a fully probabilistic inference on the business cycle clocks and dating, which seems the most relevant approach in view of economic uncertainties.

Ayelen Colosimo

Thomas C. Nicholas, Adam E. Stones, Adam Patel et al.

Amorphous calcium carbonate (ACC) is an important precursor for biomineralisation in marine organisms. Among the key outstanding problems regarding ACC are how best to understand its structure and how to rationalise its metastability as an amorphous phase. Here, we report high-quality atomistic models of ACC generated by using state-of-the-art interatomic potentials to help guide fits to X-ray total scattering data. Exploiting a recently-developed inversion approach, we extract from these models the effective Ca$\boldsymbol\cdots$Ca interaction potential governing ACC formation. This potential contains minima at two competing distances, corresponding to the two different ways in which carbonate ions bridge Ca$^{2+}$-ion pairs. We reveal an unexpected mapping to the Lennard-Jones--Gauss (LJG) model normally studied in the context of computational soft-matter, with the empirical LJG parameters for ACC taking values known to promote structural complexity. In this way we show that both the complex structure of ACC and its resilience to crystallisation are actually encoded in the geometrically-frustrated effective interactions between Ca$^{\boldsymbol 2+}$ ions.

Robin Hirt, Niklas Kühl, Dominik Martin et al.

Successful analytics solutions that provide valuable insights often hinge on the connection of various data sources. While it is often feasible to generate larger data pools within organizations, the application of analytics within (inter-organizational) business networks is still severely constrained. As data is distributed across several legal units, potentially even across countries, the fear of disclosing sensitive information as well as the sheer volume of the data that would need to be exchanged are key inhibitors for the creation of effective system-wide solutions -- all while still reaching superior prediction performance. In this work, we propose a meta machine learning method that deals with these obstacles to enable comprehensive analyses within a business network. We follow a design science research approach and evaluate our method with respect to feasibility and performance in an industrial use case. First, we show that it is feasible to perform network-wide analyses that preserve data confidentiality as well as limit data transfer volume. Second, we demonstrate that our method outperforms a conventional isolated analysis and even gets close to a (hypothetical) scenario where all data could be shared within the network. Thus, we provide a fundamental contribution for making business networks more effective, as we remove a key obstacle to tap the huge potential of learning from data that is scattered throughout the network.

Josefine Baark, Christian Laursen, Anne Troldtoft Hjorth

In The Mechanism, we follow Josefine Baark’s research journey as she seeks to unveil the global networks, games and friendships that resulted in a mechanical tableaux made in China in the 1730s and then brought to Denmark. The film serves two purposes. First to reveal the historical significance of putting objects centre stage in art historical research, even where written documents are lacking. Second, to advance current methodological discussions regarding the use of video techniques for generating research outcomes. The Mechanism argues for a reassessment of cinematic materiality and in correspondence with this, of the research process.

Maria De-Arteaga, Stefan Feuerriegel, Maytal Saar-Tsechansky

The extensive adoption of business analytics (BA) has brought financial gains and increased efficiencies. However, these advances have simultaneously drawn attention to rising legal and ethical challenges when BA inform decisions with fairness implications. As a response to these concerns, the emerging study of algorithmic fairness deals with algorithmic outputs that may result in disparate outcomes or other forms of injustices for subgroups of the population, especially those who have been historically marginalized. Fairness is relevant on the basis of legal compliance, social responsibility, and utility; if not adequately and systematically addressed, unfair BA systems may lead to societal harms and may also threaten an organization's own survival, its competitiveness, and overall performance. This paper offers a forward-looking, BA-focused review of algorithmic fairness. We first review the state-of-the-art research on sources and measures of bias, as well as bias mitigation algorithms. We then provide a detailed discussion of the utility-fairness relationship, emphasizing that the frequent assumption of a trade-off between these two constructs is often mistaken or short-sighted. Finally, we chart a path forward by identifying opportunities for business scholars to address impactful, open challenges that are key to the effective and responsible deployment of BA.

Hanan Shteingart, Gerben Oostra, Ohad Levinkron et al.

Data science has the potential to improve business in a variety of verticals. While the lion's share of data science projects uses a predictive approach, to drive improvements these predictions should become decisions. However, such a two-step approach is not only sub-optimal but might even degrade performance and fail the project. The alternative is to follow a prescriptive framing, where actions are "first citizens" so that the model produces a policy that prescribes an action to take, rather than predicting an outcome. In this paper, we explain why the prescriptive approach is important and provide a step-by-step methodology: the Prescriptive Canvas. The latter aims to improve framing and communication across the project stakeholders including project and data science managers towards a successful business impact.

Giuliano Andreoli

Este estudo tem o caráter teórico bibliográfico e analisa as implicações epistemológicas do conceito de danças negras, propondo-o como uma ferramenta de análise para a compreensão das dinâmicas sociais que articulam, a partir do racismo, dança, etnicidade e identidade negra. Utiliza como referencial teorias do campo da História, Sociologia, Antropologia e Estudos Culturais, com ênfase no conceito de Atlantico Negro (GILROY, 2011), na perspectiva decolonial (QUIJANO, 2000; GROSFOGUEL, 2007) e na noção de representatividade (DAVIS, 2014). O artigo aponta para duas possibilidades de uso do conceito: na atualização de práticas e saberes corporais afrodiaspóricos e na representatividade negra na dança. Por fim, discute as possibilidades e os limites das políticas anti-racistas na dança.

Katarzyna Bojarska

The point of departure for this article is the question of the possibility of a biography of a woman – an artist – a survivor of the Holocaust. This question is answered through the juxtaposition of two attempts to examine the history of the life and art of Pola Nireńska, which came into circulation more or less at the same time. One of them is an artistic project: the cooperation of a choreographer, dancer and visual artist - Druga natura Grzywnowicz / Siniarska / Wolinska, while the other is a written biography by Weronika Kostyrko entitled Tancerka i Zagłada. Historia Poli Nireńskiej. The author reconstructs the assumptions of both of these projects, the way they treat Nireńska and poses fundamental questions about the meaning of biography today, taking into account both the most recent diagnoses from the field of biographical writing, such as trauma theory and Holocaust research.

Jaleea Price

Perpetual Motion: Dance, Digital Cultures, and the Common takes the reader on a journey through a collection of digital dance works that cumulatively reveal a rich, and ongoing, interplay between dance and digital media.  Available for purchase as a book and as an open-access download, Perpetual Motion details an historical evolution of dance's engagement within shared digital media experiences, focusing on the period from 1996 to 2016.  As a reader, I quickly found within these pages a personal connectivity and, in these isolating times, a renewed membership into the global, online corporeal community.  With myriad works (re)discovered in each chapter, Perpetual Motion shows us the global impact dance and digital media have had upon each other through shared social relationships and interactions, both on- and off-screen.

Efrén Rama-Maneiro, Juan C. Vidal, Manuel Lama

Predictive monitoring of business processes is concerned with the prediction of ongoing cases on a business process. Lately, the popularity of deep learning techniques has propitiated an ever-growing set of approaches focused on predictive monitoring based on these techniques. However, the high disparity of process logs and experimental setups used to evaluate these approaches makes it especially difficult to make a fair comparison. Furthermore, it also difficults the selection of the most suitable approach to solve a specific problem. In this paper, we provide both a systematic literature review of approaches that use deep learning to tackle the predictive monitoring tasks. In addition, we performed an exhaustive experimental evaluation of 10 different approaches over 12 publicly available process logs.

Anastasija Nikiforova, Zane Bicevska

The research deals with application of the LEAN principles to business processes of a typical IT company. The paper discusses LEAN principles amplifying advantages and shortcomings of their application. The authors suggest use of the LEAN principles as a tool to identify improvement potential for IT company's business processes and work-flow efficiency. During a case study the implementation of LEAN principles has been exemplified in business processes of a particular Latvian IT company. The obtained results and conclusions can be used for meaningful and successful application of LEAN principles and methods in projects of other IT companies.

Takahiro Yabe, Yunchang Zhang, Satish Ukkusuri

In recent years, extreme shocks, such as natural disasters, are increasing in both frequency and intensity, causing significant economic loss to many cities around the world. Quantifying the economic cost of local businesses after extreme shocks is important for post-disaster assessment and pre-disaster planning. Conventionally, surveys have been the primary source of data used to quantify damages inflicted on businesses by disasters. However, surveys often suffer from high cost and long time for implementation, spatio-temporal sparsity in observations, and limitations in scalability. Recently, large scale human mobility data (e.g. mobile phone GPS) have been used to observe and analyze human mobility patterns in an unprecedented spatio-temporal granularity and scale. In this work, we use location data collected from mobile phones to estimate and analyze the causal impact of hurricanes on business performance. To quantify the causal impact of the disaster, we use a Bayesian structural time series model to predict the counterfactual performances of affected businesses (what if the disaster did not occur?), which may use performances of other businesses outside the disaster areas as covariates. The method is tested to quantify the resilience of 635 businesses across 9 categories in Puerto Rico after Hurricane Maria. Furthermore, hierarchical Bayesian models are used to reveal the effect of business characteristics such as location and category on the long-term resilience of businesses. The study presents a novel and more efficient method to quantify business resilience, which could assist policy makers in disaster preparation and relief processes.

Tathagata Chakraborti, Vatche Isahagian, Rania Khalaf et al.

In this survey, we study how recent advances in machine intelligence are disrupting the world of business processes. Over the last decade, there has been steady progress towards the automation of business processes under the umbrella of ``robotic process automation'' (RPA). However, we are currently at an inflection point in this evolution, as a new paradigm called ``Intelligent Process Automation'' (IPA) emerges, bringing machine learning (ML) and artificial intelligence (AI) technologies to bear in order to improve business process outcomes. The purpose of this paper is to provide a survey of this emerging theme and identify key open research challenges at the intersection of AI and business processes. We hope that this emerging theme will spark engaging conversations at the RPA Forum.

Ana María Cubeiro Rodríguez

En el presente artículo se propone una reflexión acerca de la capacidad poética del objeto en escenificaciones que no se encuadran en el denominado Teatro de objetos, como sucede con el teatro de Paco Giménez, cuya dramaturgia se alimenta fundamentalmente del trabajo actoral. En este sentido, se describen la configuración de metáforas y la emergencia de nuevos sentidos que los objetos cotidianos adquieren en el proceso creativo. Se observan también las cualidades sensibles que los objetos despliegan en escena, contemplando la influencia que ejercen sobre la corporalidad de los actores y el tipo de relaciones que se configuran entre ambos.

Revista Cena PPGAC