Computer-Supported Collaborative Production

This paper proposes the concept of collaborative production as a focus of concern within the general area of collaborative work. We position the concept with respect to McGrath's framework for small group dynamics and the more familiar collaboration processes of awareness, coordination, and communication (McGrath 1991). After reviewing research issues and computer-based support for these interacting aspects of collaboration, we turn to a discussion of implications for how to design improved support for collaborative production. We illustrate both the challenges of collaborative production and our design implications with a collaborative map-updating scenario drawn from the work domain of geographical information systems

knowlEdge Project –Concept, methodology and innovations for artificial intelligence in industry 4.0

AI is one of the biggest megatrends towards the 4th industrial revolution. Although these technologies promise business sustainability as well as product and process quality, it seems that the ever-changing market demands, the complexity of technologies and fair concerns about privacy, impede broad application and reuse of Artificial Intelligence (AI) models across the industry. To break the entry barriers for these technologies and unleash its full potential, the knowlEdge project will develop a new generation of AI methods, systems, and data management infrastructure. Subsequently, as part of the knowlEdge project we propose several major innovations in the areas of data management, data analytics and knowledge management including (i) a set of AI services that allows the usage of edge deployments as computational and live data infrastructure as well as a continuous learning execution pipeline on the edge, (ii) a digital twin of the shop-floor able to test AI models, (iii) a data management framework deployed along the edge-to-cloud continuum ensuring data quality, privacy and confidentiality, (iv) Human-AI Collaboration and Domain Knowledge Fusion tools for domain experts to inject their experience into the system, (v) a set of standardisation mechanisms for the exchange of trained AI models from one context to another, and (vi) a knowledge marketplace platform to distribute and interchange trained AI models. In this paper, we present a short overview of the EU Project knowlEdge –Towards Artificial Intelligence powered manufacturing services, processes, and products in an edge-to-cloud-knowledge continuum for humans [in-the-loop], which is funded by the Horizon 2020 (H2020) Framework Programme of the European Commission under Grant Agreement 957331. Our overview includes a description of the project’s main concept and methodology as well as the envisioned innovations.The research leading to these results has received funding from the Horizon 2020 Programme of the European Commission under Grant Agreement No. 957331 for EU Project knowlEdge –Towards Artificial Intelligence powered manufacturing services, processes, and products in an edge-to-cloud-knowledge continuum for humans [in-the-loop].Peer ReviewedTreball signat per 21 autors/autores: Sergio Alvarez-Napagao, Barcelona Supercomputing Center, Spain; Boki Ashmore, ICE, United Kingdom; Marta Barroso, Barcelona Supercomputing Center, Spain; Cristian Barrué, Barcelona Supercomputing Center, Spain; Christian Beecks, University of Münster, Germany; Fabian Berns, University of Münster, Germany; Ilaria Bosi, LINKS Foundation, Italy; Sisay Adugna Chala, Fraunhofer FIT, Germany; Nicola Ciulli, Nextworks, Italy; Marta Garcia-Gasulla, Barcelona Supercomputing Center, Spain; Alexander Grass, Fraunhofer FIT, Germany; Dimosthenis Ioannidis, CERTH/ITI, Greece; Natalia Jakubiak, Universitat Politècnica de Catalunya, Spain; Karl Köpke, Kautex Textron, Germany; Ville Lämsä, VTT Technical Research Centre, Finland; Pedro Megias, Barcelona Supercomputing Center, Spain; Alexandros Nizamis, CERTH/ITI, Greece; Claudio Pastrone, LINKS Foundation, Italy; Rosaria Rossini, LINKS Foundation, Italy; Miquel Sànchez-Marrè, Universitat Politècnica de Catalunya, Spain; Luca Ziliotti, Parmalat, ItalyPostprint (author's final draft

Cyber Attack Surface Mapping For Offensive Security Testing

Security testing consists of automated processes, like Dynamic Application Security Testing (DAST) and Static Application Security Testing (SAST), as well as manual offensive security testing, like Penetration Testing and Red Teaming. This nonautomated testing is frequently time-constrained and difficult to scale. Previous literature suggests that most research is spent in support of improving fully automated processes or in finding specific vulnerabilities, with little time spent improving the interpretation of the scanned attack surface critical to nonautomated testing. In this work, agglomerative hierarchical clustering is used to compress the Internet-facing hosts of 13 representative companies as collected by the Shodan search engine, resulting in an average 89% reduction in attack surface complexity. The work is then extended to map network services and also analyze the characteristics of the Log4Shell security vulnerability and its impact on attack surface mapping. The results highlighted outliers indicative of possible anti-patterns as well as opportunities to improve how testers and tools map the web attack surface. Ultimately the work is extended to compress web attack surfaces based on security relevant features, demonstrating via accuracy measurements not only that this compression is feasible but can also be automated. In the process a framework is created which could be extended in future work to compress other attack surfaces, including physical structures/campuses for physical security testing and even humans for social engineering tests

The Role of Regulatory Foci and Information Seeking Behavior on Self-Disclosure on Social Media

This paper aims to explore the role of Regulatory Foci and Information Seeking Behavior on Self-Disclosure on social media platforms. This research used quantitative research methodology, a self -administrated survey was conducted among generation Y college students. Participants answers were recorded using Google forms and the results were analyzed using structural equation modeling. The Results showed that there was a significant relationship between the Regularity Foci and the Self disclosure. Moreover, the results showed that there was a mediating effect of the Information seeking behavior between Regularity Foci and Self disclosure. The limitation of this study is the sample size (150) and generalizability of the results

Exploring the Effects of Experience on Drone Piloting

abstract: The current study aims to explore factors affecting trust in human-drone collaboration. A current gap exists in research surrounding civilian drone use and the role of trust in human-drone interaction and collaboration. Specifically, existing research lacks an explanation of the relationship between drone pilot experience, trust, and trust-related behaviors as well as other factors. Using two dimensions of trust in human-automation team—purpose and performance—the effects of experience on drone design and trust is studied to explore factors that may contribute to such a model. An online survey was conducted to examine civilian drone operators’ experience, familiarity, expertise, and trust in commercially available drones. It was predicted that factors of prior experience (familiarity, self-reported expertise) would have a significant effect on trust in drones. The choice to use or exclude the drone propellers in a search-and-identify scenario, paired with the pilots’ experience with drones, would further confirm the relevance of the trust dimensions of purpose versus performance in the human-drone relationship. If the pilot has a positive sense of purpose and benevolence with the drone, the pilot trusts the drone has a positive intent towards them and the task. If the pilot has trust in the performance of the drone, they ascertain that the drone has the skill to do the task. The researcher found no significant differences between mean trust scores across levels of familiarity, but did find some interaction between self-report expertise, familiarity, and trust. Future research should further explore more concrete measures of situational participant factors such as self-confidence and expertise to understand their role in civilian pilots’ trust in their drone.Dissertation/ThesisMasters Thesis Human Systems Engineering 201

Analytic Provenance for Software Reverse Engineers

Reverse engineering is a time-consuming process essential to software-security tasks such as malware analysis and vulnerability discovery. During the process, an engineer will follow multiple leads to determine how the software functions. The combination of time and possible explanations makes it difficult for the engineers to maintain a context of their findings within the overall task. Analytic provenance tools have demonstrated value in similarly complex fields that require open-ended exploration and hypothesis vetting. However, they have not been explored in the reverse engineering domain. This dissertation presents SensorRE, the first analytic provenance tool designed to support software reverse engineers. A semi-structured interview with experts led to the design and implementation of the system. We describe the visual interfaces and their integration within an existing software analysis tool. SensorRE automatically captures user\u27s sense making actions and provides a graph and storyboard view to support further analysis. User study results with both experts and graduate students demonstrate that SensorRE is easy to use and that it improved the participants\u27 exploration process

TOWARDS INSTITUTIONAL INFRASTRUCTURES FOR E-SCIENCE: The Scope of the Challenge

The three-fold purpose of this Report to the Joint Information Systems Committee (JISC) of the Research Councils (UK) is to: • articulate the nature and significance of the non-technological issues that will bear on the practical effectiveness of the hardware and software infrastructures that are being created to enable collaborations in e- Science; • characterise succinctly the fundamental sources of the organisational and institutional challenges that need to be addressed in regard to defining terms, rights and responsibilities of the collaborating parties, and to illustrate these by reference to the limited experience gained to date in regard to intellectual property, liability, privacy, and security and competition policy issues affecting scientific research organisations; and • propose approaches for arriving at institutional mechanisms whose establishment would generate workable, specific arrangements facilitating collaboration in e-Science; and, that also might serve to meet similar needs in other spheres such as e- Learning, e-Government, e-Commerce, e-Healthcare. In carrying out these tasks, the report examines developments in enhanced computer-mediated telecommunication networks and digital information technologies, and recent advances in technologies of collaboration. It considers the economic and legal aspects of scientific collaboration, with attention to interactions between formal contracting and 'private ordering' arrangements that rest upon research community norms. It offers definitions of e-Science, virtual laboratories, collaboratories, and develops a taxonomy of collaborative e-Science activities which is implemented to classify British e-Science pilot projects and contrast these with US collaboratory projects funded during the 1990s. The approach to facilitating inter-organizational participation in collaborative projects rests upon the development of a modular structure of contractual clauses that permit flexibility and experience-based learning.

Expectations to data : Perspectives of service providers and users of future health and wellness services

The healthcare and wellness sector currently attempts to provide more proactive service models with data-driven solutions. This study examines the expectations and values related to personal data i.e. data valences from the perspective of service providers and individual users. The study is based on the analysis of extensive empirical material collected through interviews and a collaborative workshop. The data was collected in one cultural context, Finland. The results suggest that the potential service providers and users have similar expectations regarding self-evidence of data while the main differences concern the expectations of transparency. The results of the study propose some basic requirements for the development of personalised data-driven services in future. The study suggests that basic requirements for the development of future data driven services concern expectations to usable data visualisations, data as a motivator, data accuracy and data transparency. Even though there are varying expectations to personal health data and even some concerns, it can be seen that here different ecosystem actors primarily perceived the wider use of personal health and wellness data as a positive trend. It can be concluded that collaborative personal data-driven service ecosystems are an integral part of development towards proactive service models in healthcare.Peer reviewe