Decentralized Machine Autonomy for Manufacturing Servitization

Blockchain ecosystems are rapidly maturing and meeting the needs of business environments (e.g., industry, manufacturing, and robotics). The decentralized approaches in industries enable novel business concepts, such as machine autonomy and servitization of manufacturing environments. Introducing the distributed ledger technology principles into the machine sharing and servitization economy faces several challenges, and the integration opens new interesting research questions. Our research focuses on data and event models and secure upgradeable smart contract platforms for machine servitization. Our research indicates that with the proposed approaches, we can efficiently separate on- and off-chain data and assure scalability of the DApp without compromising the trust. We demonstrate that the secure upgradeable smart contract platform, which was adapted for machine servitization, supports the business workflow and, at the same time, assures common identification and authorization of all the participants in the system, including people, devices, and legal entities. We present a hybrid decentralized application (DApp) for the servitization of 3D printing. The solution can be used for or easily adapted to other manufacturing domains. It comprises a modular, upgradeable smart contract platform and off-chain machine, customer and web management, and monitoring interfaces. We pay special attention to the data and event models during the design, which are fundamental for the hybrid data storage and DApp architecture and the responsiveness of off-chain interfaces. The smart contract platform uses a proxy contract to control the access of smart contracts and role-based access control in function calls for blockchain users. We deploy and evaluate the DApp in a consortium blockchain network for performance and privacy. All the actors in the solution, including the machines, are identified by their blockchain accounts and are compeers. Our solution thus facilitates integration with the traditional information-communication systems in terms of the hybrid architectures and security standards for smart contract design comparable to those in traditional software engineering.

Multisensory integration of contextual boundaries affects temporal order memory, but not encoding or recognition

We memorize our daily life experiences, which are often multisensory in nature, by segmenting them into distinct event models, in accordance with perceived contextual or situational changes. However, very little is known about how multisensory integration affects segmentation, as most studies have focused on unisensory (visual or audio) segmentation. In three experiments, we investigated the effect of multisensory integration on segmentation in memory and perception. In Experiment 1, participants encoded lists of visual objects while audio and visual contexts changed synchronously or asynchronously. After each list, we tested recognition and temporal associative memory for pictures that were encoded in the same audio-visual context or that crossed a synchronous or an asynchronous multisensory change. We found no effect of multisensory integration for recognition memory: Synchronous and asynchronous changes similarly impaired recognition for pictures encoded at those changes, compared to pictures encoded further away from those changes. Multisensory integration did affect temporal associative memory, which was worse for pictures encoded at synchronous than at asynchronous changes. Follow up experiments showed that this effect was not due to the higher complexity of multisensory over unisensory contexts (Experiment 2), nor that it was due to the temporal unpredictability of contextual changes inherent to Experiment 1 (Experiment 3). We argue that participants formed situational expectations through multisensory integration, such that synchronous multisensory changes deviated more strongly from those expectations than asynchronous changes. We discuss our findings in light of supportive and conflicting findings of uni- and multisensory segmentation.

Emergent impoliteness and persuasive emotionality in Polish media discourses

The focus of the paper is to identify and discuss cases of what we call emergent impoliteness and persuasive emotionality based on selected types of discourse strategies in Polish media which contribute to increasingly high negative emotionality in audiences and to the radicalization of language and attitudes when addressing political opponents. The role and function of emotional discourse are particularly foregrounded to identify its persuasive role in media discourses and beyond. Examples discussed are derived from current Polish media texts. The materials are collected from the large Polish monitor media corpus monco.frazeo.pl (Pęzik 2020). The analysis is conducted in terms of quantitative corpus tools (Pęzik 2012, 2014), concerning emotive and media discourse approaches (Lewandowska-Tomaszczyk and Wilson 2013, Lewandowska-Tomaszczyk 2015, 2017a, 2017b). The analysis includes a presentation of the ways mass media construe events (Langacker 1987/1991) in terms of their ideological framing, understood as particular imposed/constructed event models and structures (cf. Gans 1979). Special attention is paid to the negative axiological evaluation of people and events in terms of mostly implicitly persuasive and offensive discourse, including the role emotion clusters of harm, hurt and offence, anger and contempt play in the media persuasive tactics. The research outcomes provide a research basis and categorization of types of emergent impoliteness and persuasive emotionality, which involve implicit persuasion directed at negative emotionality raising with the media public, as identifiedin the analyzed media texts.

An Introspective Overview of the Dynamics of Recurrent Events Data Analysis

A recurrent event remains the outcome variable of interest in many biometric studies. Recurrent events can be explained as events of defined interest that can occur to same person more than once during the study period. This study presents an overview of different pertinent recurrent models for analyzing recurrent events. Aims: To introduce, compare, evaluate and discuss pros and cons of four models in analyzing recurrent events, so as to validate previous findings in respect of the superiority or appropriateness of these models. Study Design: A comparative studies based on simulation of recurrent event models applied to a tertiary data on cancer studies. Methodology: Codes in R were implemented for simulating four recurrent event models, namely; The Andersen and Gill model; Prentice, Williams and Peterson models; Wei, Lin and Weissferd; and Cox frailty model. Finally, these models were applied to analyze the first forty subjects from a study of Bladder Cancer Tumors. The data set contained the first four repetitions of the tumor for each patient, and each recurrence time was recorded from the entry time of the patient into the study. An isolated risk interval is defined by each time to an event or censoring. Results: The choice and usage of any of the models lead to different conclusions, but the choice depends on: risk intervals; baseline hazard; risk set; and correlation adjustment or simplistically, type of data and research question. The PWP-GT model could be used if the research question is focused on whether treatment was effective for the event since the previous event happened. However, if the research question is designed to find out whether treatment was effective for the event since the start of treatment, then we could use the PWP- TT. The AG model will be adequate if a common baseline hazard could be assumed, but the model lacks the details and versatility of the event-specific models. The WLW model is very suitable for data with diverse events for the same person, which underscores a potentially different baseline hazard for each type. Conclusion: PWP-GT has proven to be the most useful model for analyzing recurrent event data.

spaceNtime: an R package for estimating abundance of unmarked animals using camera-trap photographs

The space to event (STE), time to event (TTE), and instantaneous sampling (IS) methods were developed to estimate abundance of unmarked animals from camera trap images (Moeller et al. in Ecosphere 9(8):e02331, 2018). The space and time to event models use camera data in a different way than other abundance estimation methods do. Instead of using counts of animals over independent events, STE uses a measure of sampled space before the first detection of the target species, and TTE uses the time until the first detection. We introduce , a free and open-source R package designed to assist in the implementation of the STE and TTE models, along with the IS estimator. This package takes the user through the steps of transforming data, defining sampling effort, selecting sampling occasions, building encounter histories, and estimating abundance from camera data using these three methods. The package is designed for users with a baseline level of knowledge of R and statistics, without requiring expertise in either.

Representing and Reasoning with Event Models for Epistemic Planning

The standard representation formalism for multi-agent epistemic planning has one central disadvantage: When you use event models in dynamic epistemic logic (DEL) to describe the action of one agent, the model must specify not only the actual change and the change of that agent's knowledge. Also required is the epistemic change of any agents that may be observing the first agent performing the action, plus the epistemic change for any further agents that failed to observe that anything had taken place. To overcome the gap between this complex DEL notion of events and a more commonsense notion of actions, we propose a simple high-level action description language for multi-agent epistemic planning domains with just one type of effect laws: a causes x if y. Effect x can either be a physical effect, or an observation from an independent set that is specific to individual agents. We formally prove that any DEL event model can be described in this way. We show how this language provides a framework for expressing a variety of executability and action models; such as describing actions that are both ontic and epistemic, partially observable, or nondeterministic. We further combine our representation of event models with a description language for finitary initial epistemic theories, and we show how this allows us to reason about the effects of a sequence of actions in a multi-agent epistemic domain by updating a single multi-pointed epistemic model.