Miracle-dz/UniFAR-FTUs huggingface dataset:
UAV Formation Shape Control via Decentralized Markov Decision Processes

In this paper, we present a decentralized unmanned aerial vehicle (UAV) swarm formation control approach based on a decision theoretic approach. Specifically, we pose the UAV swarm motion control problem as a decentralized Markov decision process (Dec-MDP). Here, the goal is to drive the UAV swarm from an initial geographical region to another geographical region where the swarm must form a three-dimensional shape (e.g., surface of a sphere). As most decision-theoretic formulations suffer from the curse of dimensionality, we adapt an existing fast approximate dynamic programming method called nominal belief-state optimization (NBO) to approximately solve the formation control problem. We perform numerical studies in MATLAB to validate the performance of the above control algorithms.
From data Processing to Knowledge Processing: Working with Operational Schemas by Autopoietic Machines

Knowledge processing is an important feature of intelligence in general and artificial intelligence in particular. To develop computing systems working with knowledge, it is necessary to elaborate the means of working with knowledge representations (as opposed to data), because knowledge is an abstract structure. There are different forms of knowledge representations derived from data. One of the basic forms is called a schema, which can belong to one of three classes: operational, descriptive, and representation schemas. The goal of this paper is the development of theoretical and practical tools for processing operational schemas. To achieve this goal, we use schema representations elaborated in the mathematical theory of schemas and use structural machines as a powerful theoretical tool for modeling parallel and concurrent computational processes. We describe the schema of autopoietic machines as physical realizations of structural machines. An autopoietic machine is a technical system capable of regenerating, reproducing, and maintaining itself by production, transformation, and destruction of its components and the networks of processes downstream contained in them. We present the theory and practice of designing and implementing autopoietic machines as information processing structures integrating both symbolic computing and neural networks. Autopoietic machines use knowledge structures containing the behavioral evolution of the system and its interactions with the environment to maintain stability by counteracting fluctuations.
Micro-environment Recognition in the context of Environmental Crowdsensing

With the rapid advancements of sensor technologies and mobile computing, Mobile Crowd-Sensing (MCS) has emerged as a new paradigm to collect massive-scale rich trajectory data. Nomadic sensors empower people and objects with the capability of reporting and sharing observations on their state, their behavior and/or their surrounding environments. Processing and mining multi-source sensor data in MCS raise several challenges due to their multi-dimensional nature where the measured parameters (i.e., dimensions) may differ in terms of quality, variabilty, and time scale. We consider the context of air quality MCS, and focus on the task of mining the context from the MCS data. Re-lating the measures to their context is crucial to interpret them and analyse the participant’s exposure. This paper investigates the feasibility of recognizing the human’s context (called herein micro-environment) in an environmental MCS scenario. We put forward a multi-view learning approach, that we adapt to our context, and implement it along with other time series classification approaches. The experimental results, applied to real MCS data, not only confirm the power of MCS data in characterizing the micro-environment, but also show a moderate impact of the integration of mobility data in this recognition. Furthermore, multi-view learning shows similar performance as the reference deep learning algorithm, without requiring specific
Evaluating the Design of Service-Dominant Business Models: A Qualitative Method

Evaluating the Design of Service-Dominant Business Models: A Qualitative Method Rick Gilsing, Eindhoven University of TechnologyFollow Oktay Turetken, Eindhoven University of TechnologyFollow Baris Ozkan, Eindhoven University of TechnologyFollow Paul Grefen, Eindhoven University of TechnologyFollow Onat Ege Adali, Eindhoven University of TechnologyFollow Anna Wilbik, Maastricht UniversityFollow Frank Berkers, TNOFollow Abstract Background: Driven by factors such as digitization and rapid technological change, many contemporary organizations adopt a service orientation to sustain competitiveness and to improve their value propositions to customers. In doing so, organizations typically engage in collaborative service ecosystems to co-create value and exchange services, and conceptualize such collaborations using business models. The resulting models should be evaluated to support the development of service ecosystems and their long- term viability. Despite academic efforts on the evaluation of traditional, organization- centric business models, limited research is present supporting the evaluation of service- dominant business models, taking into account their key characteristics, such as service exchange and value co-creation in business networks. Method: Following a design science research methodology, we have iteratively designed a method addressing the qualitative evaluation of service-dominant business models, building on and integrating the theory on service-dominant logic, business model design and business model evaluation. To structure the steps of the design process, we leverage a situational method engineering approach, following a paradigm-based strategy. To evaluate the validity and utility our method, we have applied it to a real-life business case in the mobility domain, involving eight industry stakeholders in the process. Results: The method constitutes a set of guiding questions and a procedural description of their use, addressing the evaluation concerns of feasibility, viability, structural validity and robustness with respect to the service-dominant business model. The results of the evaluation demonstrate that the use of the method facilitates users to reflect qualitatively on design decision with respect the business model design and offers insights on its expected performance. Conclusions: This work contributes to extant research on service systems engineering and the instantiation of service-dominant logic, clarifying how service ecosystems can be evaluated through the business model concept and explicating how business models are impacted through service-dominant logic.
Visual Analysis of Player Interactions in Soccer Games

Recently, visualization of sport data in general, and soccer data in particular, has received much research attention. Visual sport data exploration helps to understand behavior and performance of athletes and teams, identify possible influence factors, and changes over time, among other important tasks. In soccer match data, much of the play is determined by direct interactions between players spatially close to each other, competing for influence. We introduce a novel visual analytics system for exploring pairwise player interactions using a trajectory-based data representation in a highly interactive multiple view approach. Our notion of player interaction is based on proximity of pairs of players, and respective motion patterns represented as trajectories. Our approach segments player interactions from soccer match data, as the basis for linked analytical views. A matrix view allows to explore interaction frequencies between players, group of player roles, and assess overall game dominance between teams. An appropriately defined interaction glyph allows to compare interactions based on player motion, ball possession, and pitch position. We further investigate the design of a descriptor encoding the geometric configuration of interaction trajectory pairs, enabling common analytical tasks like clustering or searching for similar interactions. We demonstrate the applicability of our approach by use cases on real soccer match data, detailing the analytical tasks supported by our system
Determinization and Minimization of Automata for Nested Words Revisited

We consider the problem of determinizing and minimizing automata for nested words in practice. For this we compile the nested regular expressions (NREs) from the usual XPath benchmark to nested word automata (NWAs). The determinization of these NWAs, however, fails to produce reasonably small automata. In the best case, huge deterministic NWAs are produced after few hours, even for relatively small NREs of the benchmark. We propose a different approach to the determinization of automata for nested words. For this, we introduce stepwise hedge automata (SHAs) that generalize naturally on both (stepwise) tree automata and on finite word automata. We then show how to determinize SHAs, yielding reasonably small deterministic automata for the NREs from the XPath benchmark. The size of deterministic SHAs automata can be reduced further by a novel minimization algorithm for a subclass of SHAs. In order to understand why the new approach to determinization and minimization works so nicely, we investigate the relationship between NWAs and SHAs further. Clearly, deterministic SHAs can be compiled to deterministic NWAs in linear time, and conversely NWAs can be compiled to nondeterministic SHAs in polynomial time. Therefore, we can use SHAs as intermediates for determinizing NWAs, while avoiding the huge size increase with the usual determinization algorithm for NWAs. Notably, the NWAs obtained from the SHAs perform bottom-up and left-to-right computations only, but no top-down computations. This NWA behavior can be distinguished syntactically by the (weak) single-entry property, suggesting a close relationship between SHAs and single-entry NWAs. In particular, it turns out that the usual determinization algorithm for NWAs behaves well for single-entry NWAs, while it quickly explodes without the single-entry property. Furthermore, it is known that the class of deterministic multi-module single-entry NWAs enjoys unique minimization. The subclass of deterministic SHAs to which our novel minimization algorithm applies is different though, in that we do not impose multiple modules. As further optimizations for reducing the sizes of the constructed SHAs, we propose schema-based cleaning and symbolic representations based on apply-else rules that can be maintained by determinization. We implemented the optimizations and report the experimental results for the automata constructed for the XPathMark benchmark.
ParlTech: Transformation Framework for the Digital Parliament

Societies are entering the age of technological disruption, which also impacts governance institutions such as parliamentary organizations. Thus, parliaments need to adjust swiftly by incorporating innovative methods into their organizational culture and novel technologies into their working procedures. Inter-Parliamentary Union World e-Parliament Reports capture digital transformation trends towards open data production, standardized and knowledge-driven business processes, and the implementation of inclusive and participatory schemes. Nevertheless, there is still a limited consensus on how these trends will materialize into specific tools, products, and services, with added value for parliamentary and societal stakeholders. This article outlines the rapid evolution of the digital parliament from the user perspective. In doing so, it describes a transformational framework based on the evaluation of empirical data by an expert survey of parliamentarians and parliamentary administrators. Basic sets of tools and technologies that are perceived as vital for future parliamentary use by intra-parliamentary stakeholders, such as systems and processes for information and knowledge sharing, are analyzed. Moreover, boundary conditions for development and implementation of parliamentary technologies are set and highlighted. Concluding recommendations regarding the expected investments, interdisciplinary research, and cross-sector collaboration within the defined framework are presented.
Feature and Language Selection in Temporal Symbolic Regression for Interpretable Air Quality Modelling

: Air quality modelling that relates meteorological, car trafﬁc, and pollution data is a fundamental problem, approached in several different ways in the recent literature. In particular, a set of such data sampled at a speciﬁc location and during a speciﬁc period of time can be seen as a multivariate time series, and modelling the values of the pollutant concentrations can be seen as a multivariate temporal regression problem. In this paper, we propose a new method for symbolic multivariate temporal regression, and we apply it to several data sets that contain real air quality data from the city of Wrocław (Poland). Our experiments show that our approach is superior to classical, especially symbolic, ones, both in statistical performances and the interpretability of the results.
The Potential of the SP System in Machine Learning and Data Analysis for Image Processing

This paper aims to describe how pattern recognition and scene analysis may with advantage be viewed from the perspective of the SP system (meaning the SP theory of intelligence and its realisation in the SP computer model (SPCM), both described in an appendix), and the strengths and potential of the system in those areas. In keeping with evidence for the importance of information compression (IC) in human learning, perception, and cognition, IC is central in the structure and workings of the SPCM. Most of that IC is achieved via the powerful concept of SP-multiple-alignment, which is largely responsible for the AI-related versatility of the system. With examples from the SPCM, the paper describes: how syntactic parsing and pattern recognition may be achieved, with corresponding potential for visual parsing and scene analysis; how those processes are robust in the face of errors in input data; how in keeping with what people do, the SP system can “see” things in its data that are not objectively present; the system can recognise things at multiple levels of abstraction and via part-whole hierarchies, and via an integration of the two; the system also has potential for the creation of a 3D construct from pictures of a 3D object from different viewpoints, and for the recognition of 3D entities.
Managing Data in a Big Financial Institution: Conclusions from a R&D Project

Financial institutions (FIs) use the state-of-the art data management and data engineering solutions to support their day-to-day businesses. They follow strict data governance policies as well as country and international regulations. In spite of these facts, the quality of their data is not perfect. Experts in the field estimate that from 1% to about 5% of data owned by financial institutions are dirty. Typically, FIs include in their IT architectures from dozens to a few hundreds of data sources that are being integrated in multiple data warehouses. Such complex architectures generate substantial monetary costs and they are difficult to manage. FIs compete in the financial services market. One way to gain a competitive advantage is to apply the latest technologies for the purpose of data management and shortening a software development cycle. A promising direction is to migrate on-premise infrastructures into private, public, or private-public cloud architectures. In this paper we present our experience from preparing and running a project for a big financial institution in Poland. The project is run in two stages: (1) building a central repository of customers data and (2) developing a data lake architecture in a private-public cloud.
Categorizing Young Facebook Users Based On Their Differential Preference of Social Media Heuristics: A Q-Methodology Approach

Abstract Background: Social media have become an integral part of our modern society by providing platforms for users to create and exchange news, ideas, and information. The increasing use of social media has raised concerns about the reliability of the shared information, particularly information that is generated from anonymous users. Though prior studies have confirmed the important roles of heuristics and cues in the users’ evaluation of trustworthy information, there has been no research–to our knowledge–that categorized Facebook users based on their approaches to evaluating information credibility. Method: We employed Q-methodology to extract insights from 55 young Vietnamese users and to categorize them into different groups based on the distinct sets of heuristics that they used to evaluate the trustworthiness of online information on Facebook. Results: We identified four distinct types of young Facebook user groups that emerged based on their evaluation of online information trustworthiness. When evaluating online information trustworthiness on Facebook, these user groups assigned priorities differently to the characteristics of the online content, its original source, and the sharers or aggregators. We named these groups: (1) the balanced analyst, (2) the critical analyst, (3) the source analyst, and (4) the social network analyst. Conclusion: The findings offer insights that contribute to information processing literature. Moreover, marketing practitioners who aim to disseminate information effectively on social networks should take these user groups’ perspectives into consideration.
Local Data Debiasing for Fairness Based on Generative Adversarial Training

The widespread use of automated decision processes in many areas of our society raises serious ethical issues with respect to the fairness of the process and the possible resulting discrimination. To solve this issue, we propose a novel adversarial training approach called GANSan for learning a sanitizer whose objective is to prevent the possibility of any discrimination (i.e., direct and indirect) based on a sensitive attribute by removing the attribute itself as well as the existing correlations with the remaining attributes. Our method GANSan is partially inspired by the powerful framework of generative adversarial networks (in particular Cycle-GANs), which offers a flexible way to learn a distribution empirically or to translate between two different distributions. In contrast to prior work, one of the strengths of our approach is that the sanitization is performed in the same space as the original data by only modifying the other attributes as little as possible, thus preserving the interpretability of the sanitized data. Consequently, once the sanitizer is trained, it can be applied to new data locally by an individual on their profile before releasing it. Finally, experiments on real datasets demonstrate the effectiveness of the approach as well as the achievable trade-off between fairness and utility.
A Select and Rewrite Approach to the Generation of Related Work Reports

A related work report is a text which integrates key information from a list of related scientific papers providing context to the work being presented. In this paper we study the automatic generation of related work reports using extractive and abstractive text summarization approaches. Our extractive approach scores the sentences of the scientific papers based on their citations, selecting top scored sentences from each scientific paper to be mentioned in the related work report. The sentences are then organized in the report according to the topic they belong to. In additional experiments we use top scored sentences from our extractive methods and rephrase them using pre-trained abstractive models that generate citation sentences. We discuss automatic and manual evaluation of the generated related work reports showing the viability of the proposed approaches.
Too Much or Too Little Messaging? Situational Determinants of Guilt About Mobile Messaging

Mobile messaging has been associated with guilt. Guilt about too much messaging may result from self-control failures during goal conflicts. Conversely, guilt about too little messaging may result from violating the salient norm to be available. This research considers both boundary conditions of guilt about mobile communication—goal conflicts and availability norm salience—simultaneously for the first time. We conducted two preregistered experiments to investigate their interplay. Results from a vignette experiment, but not from a laboratory experiment, support the hypotheses that goal conflicts trigger guilt about using messengers and that guilt about not using messengers arises if the availability norm is salient. In both studies, using messengers elicited more guilt than not using messengers. The boundary conditions did not interact in influencing guilt. Overall, this research emphasizes the importance of self-control, norms, and usage contexts when studying effects of mobile media use on emotional well-being.
Contributions à l'amélioration des communications dans les réseaux sans fil multi-sauts

Nos travaux se sont interesses en premier lieu au probleme de collecte de donnees sous contrainte d'economie d'energie dans les reseaux de capteurs sans l large echelle. Nous avons d'abord propose diverses strategies de deploiement et de re-deploiement tolerant aux pannes qui visent a assurer une connectivite totale et une couverture optimale et assurer une dissemination efficace des donnees. Nous nous sommes penches ensuite sur la proposition de methodes d'organisation hierarchique des communications et nous avons montre l'interet d'une gestion efficace de la redondance spatiale des capteurs dans le cadre d'un mecanisme de clustering. Nous avons initialement propose des strategies de routage economes en energie en combinant les approches d'organisation hierarchique avec des methodes de routage intelligentes. Dans un second volet, nous avons considere l'adoption de la radio cognitive dans les reseaux de capteurs sans l. An de gerer efficacement l'acces opportuniste au spectre, nous avons formalise d'abord le probleme d'ordonnancement spatio-temporel des communications dans les reseaux de capteurs sans l radio cognitifs. Ensuite, nous avons propose des mecanismes d'ordonnancement spatio-temporels, centralises puis hierarchiques, permettant de reduire la duree totale d'ordonnancement des capteurs tout en reduisant leur consommation energetique. Nos propositions tiennent compte des retours imprevus des utilisateurs primaires et de la mobilite des capteurs sans fil. Dans un troisieme volet, nous avons considere le probleme de routage oriente qualite de service dans les reseaux sans fil Mesh metropolitains. Dans le cadre du standard IEEE 802.11n, nous nous interessons au deploiement d'un reseau Mesh et a la proposition de nouvelles approches de routage maximisant la bande passante offerte aux utilisateurs. Nous avons propose, dans cet axe, de nouvelles metriques de routage orientees qualite de service. Notre contribution majeure a ete la proposition d'une metrique qui tient compte des interferences inter et intra flux ainsi qu'un protocole de routage proactif qui tient compte de cette metrique pour la recherche des meilleurs chemins en terme de bande passante disponible.