004 Datenverarbeitung; Informatik
Refine
Year of publication
Document Type
- Article (21)
- Conference Proceeding (7)
- Report (5)
- Part of Periodical (4)
- Contribution to a Periodical (2)
- Doctoral Thesis (2)
- Working Paper (2)
- Part of a Book (1)
Has Fulltext
- yes (44)
Is part of the Bibliography
- no (44)
Keywords
- Data protection (3)
- GDPR (3)
- Privacy (3)
- Datenschutz (2)
- Machine learning (2)
- Software Engineering (2)
- machine learning (2)
- (mobile) Internet (1)
- AI fairness (1)
- Abrechnung (1)
Institute
- Wirtschaftswissenschaften (44) (remove)
Optimal investment decisions by institutional investors require accurate predictions with respect to the development of stock markets. Motivated by previous research that revealed the unsatisfactory performance of existing stock market prediction models, this study proposes a novel prediction approach. Our proposed system combines Artificial Intelligence (AI) with data from Virtual Investment Communities (VICs) and leverages VICs’ ability to support the process of predicting stock markets. An empirical study with two different models using real data shows the potential of the AI-based system with VICs information as an instrument for stock market predictions. VICs can be a valuable addition but our results indicate that this type of data is only helpful in certain market phases.
This article discusses the counterpart of interactive machine learning, i.e., human learning while being in the loop in a human-machine collaboration. For such cases we propose the use of a Contradiction Matrix to assess the overlap and the contradictions of human and machine predictions. We show in a small-scaled user study with experts in the area of pneumology (1) that machine-learning based systems can classify X-rays with respect to diseases with a meaningful accuracy, (2) humans partly use contradictions to reconsider their initial diagnosis, and (3) that this leads to a higher overlap between human and machine diagnoses at the end of the collaboration situation. We argue that disclosure of information on diagnosis uncertainty can be beneficial to make the human expert reconsider her or his initial assessment which may ultimately result in a deliberate agreement. In the light of the observations from our project, it becomes apparent that collaborative learning in such a human-in-the-loop scenario could lead to mutual benefits for both human learning and interactive machine learning. Bearing the differences in reasoning and learning processes of humans and intelligent systems in mind, we argue that interdisciplinary research teams have the best chances at tackling this undertaking and generating valuable insights.
In the upcoming years, the internet of things (IoT)will enrich daily life. The combination of artificial intelligence(AI) and highly interoperable systems will bring context-sensitive multi-domain services to reality. This paper describesa concept for an AI-based smart living platform with open-HAB, a smart home middleware, and Web of Things (WoT) askey components of our approach. The platform concept con-siders different stakeholders, i.e. the housing industry, serviceproviders, and tenants. These activities are part of the Fore-Sight project, an AI-driven, context-sensitive smart living plat-form.
Effort estimates are of utmost economic importance in software development projects. Estimates bridge the gap between managers and the invisible and almost artistic domain of developers. They give a means to managers to track and control projects. Consequently, numerous estimation approaches have been developed over the past decades, starting with Allan Albrecht's Function Point Analysis in the late 1970s. However, this work neither tries to develop just another estimation approach, nor focuses on improving accuracy of existing techniques. Instead of characterizing software development as a technological problem, this work understands software development as a sociological challenge. Consequently, this work focuses on the question, what happens when developers are confronted with estimates representing the major instrument of management control? Do estimates influence developers, or are they unaffected? Is it irrational to expect that developers start to communicate and discuss estimates, conform to them, work strategically, hide progress or delay? This study shows that it is inappropriate to assume an independency of estimated and actual development effort. A theory is developed and tested, that explains how developers and managers influence the relationship between estimated and actual development effort. The theory therefore elaborates the phenomenon of estimation fulfillment.
The recent COVID-19 pandemic represents an unprecedented worldwide event to study the influence of related news on the financial markets, especially during the early stage of the pandemic when information on the new threat came rapidly and was complex for investors to process. In this paper, we investigate whether the flow of news on COVID-19 had an impact on forming market expectations. We analyze 203,886 online articles dealing with COVID-19 and published on three news platforms (MarketWatch.com, NYTimes.com, and Reuters.com) in the period from January to June 2020. Using machine learning techniques, we extract the news sentiment through a financial market-adapted BERT model that enables recognizing the context of each word in a given item. Our results show that there is a statistically significant and positive relationship between sentiment scores and S&P 500 market. Furthermore, we provide evidence that sentiment components and news categories on NYTimes.com were differently related to market returns.
The Dagstuhl Perspectives Workshop "Online Privacy: Towards Informational Self-Determination on the Internet" (11061) has been held in February 6-11, 2011 at Schloss Dagstuhl. 30 participants from academia, public sector, and industry have identified the current status-of-the-art of and challenges for online privacy as well as derived recommendations for improving online privacy. Whereas the Dagstuhl Manifesto of this workshop concludes the results of the working groups and panel discussions, this article presents the talks of this workshop by their abstracts.
With ubiquitous use of digital camera devices, especially in mobile phones, privacy is no longer threatened by governments and companies only. The new technology creates a new threat by ordinary people, who now have the means to take and distribute pictures of one’s face at no risk and little cost in any situation in public and private spaces. Fast distribution via web based photo albums, online communities and web pages expose an individual’s private life to the public in unpreceeded ways. Social and legal measures are increasingly taken to deal with this problem. In practice however, they lack efficiency, as they are hard to enforce in practice. In this paper, we discuss a supportive infrastructure aiming for the distribution channel; as soon as the picture is publicly available, the exposed individual has a chance to find it and take proper action.
Augmented reality (AR) gained much public attention since the success of Pok´emon Go in 2016. Technology companies like Apple or Google are currently focusing primarily on mobile AR (MAR) technologies, i.e. applications on mobile devices, like smartphones or tablets. Associated privacy issues have to be investigated early to foster market adoption. This is especially relevant since past research found several threats associated with the use of smartphone applications. Thus, we investigate two of the main privacy risks for MAR application users based on a sample of 19 of the most downloaded MAR applications for Android. First, we assess threats arising from bad privacy policies based on a machine-learning approach. Second, we investigate which smartphone data resources are accessed by the MAR applications. Third, we combine both approaches to evaluate whether privacy policies cover certain data accesses or not. We provide theoretical and practical implications and recommendations based on our results.