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How does Manufacturing Strategy Impact the Goals of a Firm? A Relational Framework Characterizing the Related Business Models’ Components

Boffa Eleonora, Maffei Antonio

Management and Production Engineering Review

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2023

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Vol. 14, No. 2

18--36

EN

The fourth industrial revolution has resulted in technology advancements in the manufacturing industry. However, the innovation potential embedded in these technologies should be unlocked by a viable application, i.e., the business model (BM). The BM as a holistic concept featuring different interacting elements is thus emerging as a promising vehicle for innovation. Current BM research describes the entire domain but lacks depth in the characterization of its individual components. This paper investigates the available manufacturing literature through the lens of the BM concept performing a scientometric analysis. The results are presented in a relational framework that provides an in-depth characterization of the manufacturing element of the BM and highlights identified connections that link the BM components. This is the basis for tools that will support firms in developing manufacturing portfolios aligned with their strategic goals.

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Empirical Study of the Evolution of Python Questions on Stack Overflow

Syam Gopika, Lal Sangeeta, Chen Tao

e-Informatica Software Engineering Journal

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2023

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Vol. 17, nr 1

art. no. 230107

EN

Background: Python is a popular and easy-to-use programming language. It is constantly expanding, with new features and libraries being introduced daily for a broad range of applications. This dynamic expansion needs a robust support structure for developers to effectively utilise the language. Aim: In this study we conduct an in-depth analysis focusing on several research topics to understand the theme of Python questions and identify the challenges that developers encounter, using the questions posted on Stack Overflow. Method:We perform a quantitative and qualitative analysis of Python questions in Stack Overflow. Topic Modelling is also used to determine the most popular and difficult topics among developers. Results: The findings of this study revealed a recent surge in questions about scientific computing libraries pandas and TensorFlow. Also, we observed that the discussion of Data Structures and Formats is more popular in the Python community, whereas areas such as Installation, Deployment, and IDE are still challenging. Conclusion: This study can direct the research and development community to put more emphasis on tackling the actual issues that Python programmers are facing.

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Ensemble Methods for Improving Classification of Data Produced by Latent Dirichlet Allocation

Jankowski M.

Computer Science and Mathematical Modelling

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2018

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No. 8

17--28

EN

Topic models are very popular methods of text analysis. The most popular algorithm for topic modelling is LDA (Latent Dirichlet Allocation). Recently, many new methods were proposed, that enable the usage of this model in large scale processing. One of the problem is, that a data scientist has to choose the number of topics manually. This step, requires some previous analysis. A few methods were proposed to automatize this step, but none of them works very well if LDA is used as a preprocessing for further classification. In this paper, we propose an ensemble approach which allows us to use more than one model at prediction phase, at the same time, reducing the need of finding a single best number of topics. We have also analyzed a few methods of estimating topic number.

PL

Modelowanie tematyczne, jest popularną metodą analizy tekstów. Jednym z najbardziej popularnych algorytmów modelowania tematycznego jest LDA (Latent Dirichlet Allocation) [14]. W ostatnim czasie zostało zaproponowanych wiele nowych rozszerzeń tego modelu, które pozwalają na przetwarzanie dużych ilości danych. Jednym z problemów podczas użycia algorytmu LDA jest to, że liczba tematów musi zostać wybrana przed uruchomieniem algorytmu. Ten krok, wymaga wcześniejszej analizy i zaangażowania analityka danych. Powstało kilka metod, które pozwalają automatyzować ten krok, ale żadna z nich, nie działa dobrze, gdy LDA jest użyte do redukcji wymiarów przed klasyfikacją danych. W tej pracy, proponujemy podejście oparte o ensemble wielu modeli. Taki model, unika problemu wybrania jednego, najlepszego modelu LDA. Pokażemy, że takie podejście pozwala uzyskać niższy błąd klasyfikacji. Zaproponujemy również, dwie nowe metody wyboru liczby tematów, gdy chcemy użyć tylko pojedynczego modelu.