The Structure Transfer Machine Theory and Applications

Representation learning is a fundamental but challenging problem, especially when the distribution of data is unknown. We propose a new representation learning method, termed Structure Transfer Machine (STM), which enables feature learning process to converge at the representation expectation in a probabilistic way. We theoretically show that such an expected value of the representation (mean) is achievable if the manifold structure can be transferred from the data space to the feature space. The resulting structure regularization term, named manifold loss, is incorporated into the loss function of the typical deep learning pipeline. The STM architecture is constructed to enforce the learned deep representation to satisfy the intrinsic manifold structure from the data, which results in robust features that suit various application scenarios, such as digit recognition, image classification and object tracking. Compared to state-of-the-art CNN architectures, we achieve the better results on several commonly used benchmarks\footnote{The source code is available. https://github.com/stmstmstm/stm }

The structure transfer machine theory and applications

Representation learning is a fundamental but challenging problem, especially when the distribution of data is unknown. In this paper, we propose a new representation learning method, named Structure Transfer Machine (STM), which enables feature learning process to converge at the representation expectation in a probabilistic way. We theoretically show that such an expected value of the representation (mean) is achievable if the manifold structure can be transferred from the data space to the feature space. The resulting structure regularization term, named manifold loss, is incorporated into the loss function of the typical deep learning pipeline. The STM architecture is constructed to enforce the learned deep representation to satisfy the intrinsic manifold structure from the data, which results in robust features that suit various application scenarios, such as digit recognition, image classification and object tracking. Compared with state-of-the-art CNN architectures, we achieve better results on several commonly used public benchmarks

Dismantling the Climate Denial Machine: Theory and Methods

Many Americans do not believe in the existence of climate change, and even those who believe climate change exists often seriously underestimate its potential harms as predicted by the world\u27s best scientific organizations. Most political scholars agree that much higher consensus among American citizens is necessary to create necessary policy reform to mitigate climate change, both in the US and at large. However, there are also organizations who actively wish to deter and decrease belief in climate change among US citizens, not for the sake of scientific skepticism, but for personal benefit from preventing policy reform. This text examines what these institutions are, how they manipulate psychological variables among climate deniers to maximize the salience of their message, and how we may best reduce (and even reverse) these messages\u27 impacts

A bibliometric overview of Mechanism and Machine Theory journal: publication trends from 1990 to 2020

This work reports a bibliometric overview of Mechanism and Machine Theory journal in the timespan 1990-2020. This desideratum is achieved by considering the most relevant features associated with the life of this scientific journal, namely in terms of publications, citations, regions of origin of publications, authors, institutions, etc. In the present study, the Scopus database was chosen as the platform to identify and extract information on those aspects. Thus, based on the data collected, a comprehensive bibliometric analysis of Mechanism and Machine Theory is performed, which permits to reveal the overall picture of the journal trends in evolution, as well as its impact and influence in the mechanism and machine science community. Overall, the outcomes presented in this study allow to observe that Mechanism and Machine Theory journal has been attracting more and more interest year after year.FCT -Fundação para a Ciência e a Tecnologia(UIDB/04436/2020

State machines for large scale computer software and systems

A method for specifying the behavior and architecture of discrete state systems such as digital electronic devices and software using deterministic state machines and automata products. The state machines are represented by sequence maps $f:A^*\to X$ where $f(s)=x$ indicates that the output of the system is $x$ in the state reached by following the sequence of events $s$ from the initial state. Examples provided include counters, networks, reliable message delivery, real-time analysis of gates and latches, and producer/consumer. Techniques for defining, parameterizing, characterizing abstract properties, and connecting sequence functions are developed. Sequence functions are shown to represent (possibly non-finite) Moore type state machines and general products of state machines. The method draws on state machine theory, automata products, and recursive functions and is ordinary working mathematics, not involving formal methods or any foundational or meta-mathematical techniques. Systems in which there are levels of components that may operate in parallel or concurrently are specified in terms of function composition

A role-based software architecture to support mobile service computing in IoT scenarios

The interaction among components of an IoT-based system usually requires using low latency or real time for message delivery, depending on the application needs and the quality of the communication links among the components. Moreover, in some cases, this interaction should consider the use of communication links with poor or uncertain Quality of Service (QoS). Research efforts in communication support for IoT scenarios have overlooked the challenge of providing real-time interaction support in unstable links, making these systems use dedicated networks that are expensive and usually limited in terms of physical coverage and robustness. This paper presents an alternative to address such a communication challenge, through the use of a model that allows soft real-time interaction among components of an IoT-based system. The behavior of the proposed model was validated using state machine theory, opening an opportunity to explore a whole new branch of smart distributed solutions and to extend the state-of-the-art and the-state-of-the-practice in this particular IoT study scenario.Peer ReviewedPostprint (published version