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1021 research outputs found
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Saliency determines the integration of contextual information into stimulus–response episodes
No full textWhen humans perform a task, it has been shown that elements of this task, like stimulus (e.g., target and distractor)and response, are bound together into a common episodic representation called stimulus–response episode (or event file).Recently, the context, a completely task-irrelevant stimulus, was found to be integrated into an episode as well. However,instead of being bound directly with the response in a binary fashion, the context modulates the binary binding between thedistractor and response. This finding raises the questions of whether the context can also enter into a binary binding with theresponse, and if so, what determines the way of its integration. In order to resolve these questions, saliency of the contextwas manipulated in three experiments by changing the loudness (Experiment 1) and emotional valence (Experiment 2Aand 2B) of the context. All experiments implemented the four-alternative auditory negative priming paradigm introducedby Mayr and Buchner (2006, Journal of Experimental Psychology: Human Perception and Performance, 32[4], 932–943).Results showed that the integration of context changed as a function of its saliency level. Specifically, the context of lowsaliency was not bound at all, the context of moderate saliency modulated the binary binding between the distractor andresponse, whereas the context of high saliency entered into a binary binding with the response. The current results extend aprevious finding by Hommel (2004, Trends in Cognitive Sciences, 8[11], 494–500) that there is a saliency threshold whichdetermines whether a stimulus is bound or not, by suggesting that a second threshold determines the specific structure (i.e.,binary vs. configural) of the resulting binding
Uncovering the identity of Electronic Markets research through text mining techniques
No full textAs an established academic journal in the e-commerce and digital platforms fields, Electronic Markets (EM) features a diverse range of topics and occupies a significant role in the information systems field. The study investigates EM’s topic diversity over the time period 2009–2020 using a text mining analysis and a bibliometric analysis and identifies 28 cluster groups.The analysis reveals that the top three topics are 1) service quality, 2) blockchain and other shared trust building solutions,their impact and credibility, as well as 3) consumer buying behavior and interactions. EM's core identity lies in a balanced set of core themes that bring technological, business or human/ social perspectives to the research of networked business and digital economy. This includes research on digital and smart services, applications, consumer behavior and business models,as well as technology and e-commerce data. Ethical and sustainability related topics are however still less present in EM
Wave after wave: determining the temporal lag in Covid-19 infections and deaths using spatial panel data from Germany
No full textThe Covid-19 pandemic requires a continuous evaluation of whether current policies and measures taken are sufficient to protect vulnerable populations. One quantitative indicator of policy effectiveness and pandemic severity is the case fatality ratio, which relies on the lagged number of infections relative to current deaths. The appropriate length of the time lag to be used, however, is heavily debated. In this article, I contribute to this debate by determining the temporal lag between the number of infections and deaths using daily panel data from Germany’s 16 federal states. To account for the dynamic spatial spread of the virus, I rely on different spatial econometric models that allow not only to consider the infections in a given state but also spill over effects through infections in neighboring federal states. My results suggest that a wave of infections within a given state is followed by increasing death rates 12 days later. Yet, if the number of infections in other states rises, the number of death cases within that given state subsequently decreases. The results of this article contribute to the better understanding of the dynamic spatio-temporal spread of the virus in Germany, which is indispensable for the design of effective policy responses
Research data management systems and the organization of universities and research institutes: A systematic literature review
No full textNew technological developments, the availability of big data, and the creation of research platforms open a variety of opportunities to generate, store, and analyze research data. To ensure the sustainable handling of research data, the European Commission as well as scientific commissions have recently highlighted the importance of implementing a research data management system (RDMS) in higher education institutes (HEI) which combines technical as well as organizational solutions. A deep understanding of the requirements of research data management (RDM), as well as an overview of the different stakeholders, is a key prerequisite for the implementation of an RDMS. Based on a scientific literature review, the aim of this study is to answer the following research questions: “What organizational factors need to be considered when implementing an RDMS? How do these organizational factors interact with each other and how do they constrain or facilitate the implementation of an RDMS?” The structure of the analysis is built on the four components of Leavitt’s classical model of organizational change: task, structure, technology, and people. The findings reveal that the implementation of RDMS is strongly impacted by the organizational structure, infrastructure, labor culture as well as strategic considerations. Overall, this literature review summarizes different approaches for the implementation of an RDMS. It also identifies areas for future research
Applying Learning Analytics in Online Environments: Measuring Learners’ Engagement Unobtrusively
No full textPrior to the emergence of Big Data and technologies such as Learning Analytics (LA), classroom research focused mainly on measuring learning outcomes of a small sample through tests. Research on online environments shows that learners’ engagement is a critical precondition for successful learning and lack of engagement is associated with failure and dropout. LA helps instructors to track, measure and visualize students’ online behavior and use such digital traces to improve instruction and provide individualized support, i.e., feedback. This paper examines 1) metrics or indicators of learners’ engagement as extracted and displayed by LA, 2) their relationship with academic achievement and performance, and 3) some freely available LA tools for instructors and their usability. The paper concludes with making recommendations for practice and further research by considering challenges associated with using LA in classrooms
Four Essays on Digital Transformation Strategies from the Perspectives of Capital Markets, Incumbents and Start-ups
No full textThis dissertation uses four studies to examine the context-contingent strategic factors that are critical to the success of digital transformation strategies from the perspectives of capital markets, incumbents, and start-ups. It focuses on a better understanding of (1) digital innovations and their quantitative evaluation, (2) power disruptions in digitally servitized supply chains, (3) strategic measures and dynamics in digital B2B platform markets, and (4) strategizing by data-driven start-ups in digitalized business networks
Jahrbuch für Tod und Gesellschaft 2022
No full textAuseinandersetzungen mit Sterben, Tod und Trauer sind gesellschaftlich von permanenter Relevanz. Theoretische Zugänge und empirische Analysen zu diesem Themenfeld finden im Jahrbuch für Tod und Gesellschaft ein interdisziplinäres Forum. Neben der Vertiefung aktueller Debatten und der Besprechung von Neuerscheinungen dient das Periodikum der Weiterentwicklung der thanato(-sozio-)logischen Erkenntnislage sowie der (inter-)nationalen Vernetzung. Der thematische Horizont umfasst u.a. Hospizarbeit und Palliative Care, Sterbehilfe, Suizidalität, Tötung, Organspende, Bestattungs-, Erinnerungs- und Trauerkultur
Learning assessment in the age of big data: Learning analytics in higher education
No full textData-driven decision-making and data-intensive research are becoming prevalent in many sectors of modern society, i.e. healthcare, politics, business, and entertainment. During the COVID-19 pandemic, huge amounts of educational data and new types of evidence were generated through various online platforms, digital tools, and communication applications. Meanwhile, it is acknowledged that educa-tion lacks computational infrastructure and human capacity to fully exploit the potential of big data. This paper explores the use of Learning Analytics (LA) in higher education for measurement purposes. Four main LA functions in the assessment are outlined: (a) monitoring and analysis, (b) automated feedback, (c) prediction, prevention, and intervention, and (d) new forms of assessment. The paper con-cludes by discussing the challenges of adopting and upscaling LA as well as the implications for instructors in higher education
Gemeinsam
No full textKann die Gesellschaft die hermeneutische Ungerechtigkeit gegenüber Menschen mit Lernbehinderungen abmildern? Wie stellen sich die europäischen Theater dieser Herausforderung? Wie können Künstler mit Lernbehinderungen "eine Stimme haben"? Der vorliegende Band sucht in theoretischen Ansätzen und in Interviews mit Regisseuren nach Antworten auf diese Fragen.Can society mitigate hermeneutical injustice towards persons with learning disabilities? How do European theatres meet this challenge? How can artists with learning disabilities «have a voice»? The present volume searches for answers to these questions in theoretical approaches and in interviews with stage directors
User-centered intrusion detection using heterogeneous data
No full textWith the frequency and impact of data breaches raising, it has become essential for organizations to automate intrusion detection via machine learning solutions. This generally comes with numerous challenges, among others high class imbalance, changing target concepts and difficulties to conduct sound evaluation. In this thesis, we adopt a user-centered anomaly detection perspective to address selected challenges of intrusion detection, through a real-world use case in the identity and access management (IAM) domain. In addition to the previous challenges, salient properties of this particular problem are high relevance of categorical data, limited feature availability and total absence of ground truth.
First, we ask how to apply anomaly detection to IAM audit logs containing a restricted set of mixed (i.e. numeric and categorical) attributes. Then, we inquire how anomalous user behavior can be separated from normality, and this separation evaluated without ground truth. Finally, we examine how the lack of audit data can be alleviated in two complementary settings. On the one hand, we ask how to cope with users without relevant activity history ("cold start" problem). On the other hand, we seek how to extend audit data collection with heterogeneous attributes (i.e. categorical, graph and text) to improve insider threat detection.
After aggregating IAM audit data into sessions, we introduce and compare general anomaly detection methods for mixed data to a user identification approach, designed to learn the distinction between normal and malicious user behavior. We find that user identification outperforms general anomaly detection and is effective against masquerades. An additional clustering step allows to reduce false positives among similar users. However, user identification is not effective against insider threats. Furthermore, results suggest that the current scope of our audit data collection should be extended.
In order to tackle the "cold start" problem, we adopt a zero-shot learning approach. Focusing on the CERT insider threat use case, we extend an intrusion detection system by integrating user relations to organizational entities (like assignments to projects or teams) in order to better estimate user behavior and improve intrusion detection performance. Results show that this approach is effective in two realistic scenarios.
Finally, to support additional sources of audit data for insider threat detection, we propose a method representing audit events as graph edges with heterogeneous attributes. By performing detection at fine-grained level, this approach advantageously improves anomaly traceability while reducing the need for aggregation and feature engineering. Our results show that this method is effective to find intrusions in authentication and email logs.
Overall, our work suggests that masquerades and insider threats call for different detection methods. For masquerades, user identification is a promising approach. To find malicious insiders, graph features representing user context and relations to other entities can be informative. This opens the door for tighter coupling of intrusion detection with user identities, roles and privileges used in IAM solutions