Formal Modeling of a Generic Middleware to Ensure Invariant Properties

Psychology endeavors to develop theories of human capacities and behaviors on the basis of a variety of methodologies and dependent measures. We argue that one of the most divisive factors in psychological science is whether researchers choose to use computational modeling of theories (over and above data) during the scientific-inference process. Modeling is undervalued yet holds promise for advancing psychological science. The inherent demands of computational modeling guide us toward better science by forcing us to conceptually analyze, specify, and formalize intuitions that otherwise remain unexamined—what we dub open theory. Constraining our inference process through modeling enables us to build explanatory and predictive theories. Here, we present scientific inference in psychology as a path function in which each step shapes the next. Computational modeling can constrain these steps, thus advancing scientific inference over and above the stewardship of experimental practice (e.g., preregistration). If psychology continues to eschew computational modeling, we predict more replicability crises and persistent failure at coherent theory building. This is because without formal modeling we lack open and transparent theorizing. We also explain how to formalize, specify, and implement a computational model, emphasizing that the advantages of modeling can be achieved by anyone with benefit to all.

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Formal Modeling IoT Systems on the Basis of BiAgents* and Maude

2020 International Conference on Advanced Aspects of Software Engineering (ICAASE) ◽

10.1109/icaase51408.2020.9380126 ◽

2020 ◽

Author(s):

Marir Souad ◽

Belala Faiza ◽

Hameurlain Nabil

Keyword(s):

Formal Modeling

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M2FOL: A Formal Modeling Language for Metamodels

Lecture Notes in Business Information Processing - The Practice of Enterprise Modeling ◽

10.1007/978-3-030-63479-7_8 ◽

2020 ◽

pp. 109-123

Author(s):

Victoria Döller

Keyword(s):

Formal Modeling ◽

Modeling Language

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Sparse pursuit and dictionary learning for blind source separation in polyphonic music recordings

EURASIP Journal on Audio Speech and Music Processing ◽

10.1186/s13636-020-00190-4 ◽

2021 ◽

Vol 2021 (1) ◽

Author(s):

Sören Schulze ◽

Emily J. King

Keyword(s):

Single Channel ◽

Spectral Characteristics ◽

Musical Instruments ◽

Model Parameters ◽

Blind Separation ◽

Audio Signals ◽

Frequency Spectra ◽

Acoustic Instruments ◽

Invariant Properties ◽

Music Recordings

AbstractWe propose an algorithm for the blind separation of single-channel audio signals. It is based on a parametric model that describes the spectral properties of the sounds of musical instruments independently of pitch. We develop a novel sparse pursuit algorithm that can match the discrete frequency spectra from the recorded signal with the continuous spectra delivered by the model. We first use this algorithm to convert an STFT spectrogram from the recording into a novel form of log-frequency spectrogram whose resolution exceeds that of the mel spectrogram. We then make use of the pitch-invariant properties of that representation in order to identify the sounds of the instruments via the same sparse pursuit method. As the model parameters which characterize the musical instruments are not known beforehand, we train a dictionary that contains them, using a modified version of Adam. Applying the algorithm on various audio samples, we find that it is capable of producing high-quality separation results when the model assumptions are satisfied and the instruments are clearly distinguishable, but combinations of instruments with similar spectral characteristics pose a conceptual difficulty. While a key feature of the model is that it explicitly models inharmonicity, its presence can also still impede performance of the sparse pursuit algorithm. In general, due to its pitch-invariance, our method is especially suitable for dealing with spectra from acoustic instruments, requiring only a minimal number of hyperparameters to be preset. Additionally, we demonstrate that the dictionary that is constructed for one recording can be applied to a different recording with similar instruments without additional training.

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