Interval probability propagation

AbstractBelief networks are tried as a method for propagation of singleton interval probabilities. A convex polytope representation of the interval probabilities is shown to make the problem intractable even for small parameters. A solution to this is to use the interval bounds directly in computations of the propagation algorithm. The algorithm presented leads to approximative results but has the advantage of being polynomial in time. It is shown that the method gives fairly good results

What causes positive customer satisfaction in an ineffectual software development project? A mechanism from a process tracing case study

The customer role is crucial in agile information systems development (ISD). There is, however, a scarceness in research on how this role is enacted, and how its practice influences project outcome. In this longitudinal case study, an agile ISD project is followed with a particular focus on the customer organization’s participation, aiming to contribute to the understanding of how customers influence agile ISD projects. The data analysis follows a process tracing approach, a case study method where one aims to identify the causes and outcomes of any kind of process through the rigorous analysis of qualitative data. The analysis of the case shows that the low completion of the initial project requirements was caused by over-scoping and by an immature customer. Further, the customer’s acceptance of the outcome was caused by the agile practices introduced in the project. These helped to create a high customer’s sense of responsibility for the outcome, which worked as a mediator towards a positive acceptance of the delivery. The study contributes a mechanism for why agile projects may still be successful in light of low delivery. It is also a first case study in the information systems field explicitly using a process tracing approach

Towards Ontological Support for Journalistic Angles

Journalism relies more and more on information and communication technology (ICT). New journalistic ICT platforms continuously harvest potentially news-related information from the internet and try to make it useful for journalists. Because the information sources and formats vary widely, knowledge graphs are emerging as a preferred technology for integrating, enriching, and preparing journalistic information. The paper explores how journalistic knowledge graphs can be augmented with support for news angles, in order to help journalists detect news-worthy events and present them in ways that will interest the intended audience. We argue that finding newsworthy angles on news-related information is important as an example of a more general problem in information science: that of finding the most interesting events and situations in big data sets and presenting those events and situations in the most interesting ways.acceptedVersio

Construction of a relevance knowledge graph with application to the LOCAL news angle

News angles are approaches to journalism content often used to provide a way to present a new report from an event. One particular type of news angle is the LOCAL news angle where a local news outlet focuses on an event by emphasising a local connection. Knowledge graphs are most often used to represent knowledge about a particular entity in the form of relationships to other entities. In this paper we see how we can extract a knowledge sub graph containing entities and relevant relationships that are connected to the locality of a news outlet. The purpose of this graph is to use it for automated journalism or as an aid for the journalist to find local connections to an event, as well as how the local connection relate to the event. We call such a graph a relevance knowledge graph. An algorithm for extracting such a graph from a linked data source like DBpedia is presented and examples of the use of a relevance graph in a LOCAL news angle context are provided.publishedVersio

Towards an automated journalism framework for social data monitoring

Presented at: Nordic AI young researcher symposium, Oslo, 14.11. - 15.11.22News and information dissemination have long been a vital human practice. Concurrent with the traditional media channels such as radio and television, online social networks (OSNs), are regarded as the new generation of media that seem to have the ability to compete with traditional media. Millions of individuals around the world can communicate breaking news on social media platforms during the hours after midnight. The spread of misinformation and disinformation aside, the process of publishing news on OSNs, to a very good extent, happens more openly and unbiasedly. Automated journalism or according to [1] “the auto generation of journalistic stories through software and algorithms, without any human input”, can be used in newsrooms to supplement or replace traditional journalism in a variety of ways, such as providing real-time reporting of events or generating stories from data that would be otherwise difficult to mine. Due to their real-time and open nature, OSNs, particularly Twitter, are among the greatest candidate data sources to be explored in this context. MediaFutures, Centre for Research-Based Innovation (SFI), is a research centre in Bergen, Norway, which is a consortium of the most important media players in Norway and beyond. The centre is hosted and lead by the University of Bergen’s Department of Information Science and Media Studies. In this research, in collaboration with MediaFuture SFI, we are developing a platform that can assist journalists in newsrooms in real time and enables them to easily obtain and monitor their desired newsworthy content from the mass volume of unverified content from Twitter platform. AI techniques have been applied for analysing social media data but many of them do not function in real time. In MediaFuture SFI we are involved in developing innovative tools which could be used by the journalists in the newsroom daily, secondly, most of prior works, either focus on collecting, filtering, and analysing tweets using predefined metrics [2] (such as number of replies, likes, etc.) or are only focused on analysing tweets’ content [3][4]. Considering the lack of a comprehensive framework suited to the needs of journalists, we present our own visual analytical framework that is not only based on information retrieval from Twitter but also enriched by machine learning and network science. In this work, we intend to use state of the art techniques such as community detection, influential node identification and monitoring, fake news, deepfake and cheapfake detection, etc

Realistic face manipulation by morphing with average faces

Face manipulation has become a standard feature of many social media services.. Most of these applications use the feature for entertainment purposes.. However,, such manipulation techniques could also have potential in a journalistic setting.. For instance,, one could create realis tic,, anonymized faces,, as an aesthetic alternative to the coarse techniques of blurring or pixelation normally used today.. In this paper,, we describe how we can use algorithms for face manipulation from computer vision to anonymize faces in journalism . The technique described uses morphing with average faces from a selection of faces that is similar to the original face,, and alters the faces in the original pictures into realistic - looking face manipulations . However,, it struggle s with sufficient anonymizati on due to identifiable non - facial features of persons in an image.

Analysis and Design of Computational News Angles

A key skill for a journalist is the ability to assess the newsworthiness of an event or situation. To this purpose journalists often rely on news angles, conceptual criteria that are used both i) to assess whether something is newsworthy and also ii) to shape the structure of the resulting news item. As journalism becomes increasingly computer-supported, and more and more sources of potentially newsworthy data become available in real time, it makes sense to try and equip journalistic software tools with operational versions of news angles, so that, when searching this vast data space, these tools can both identify effectively the events most relevant to the target audience, and also link them to appropriate news angles. In this paper we analyse the notion of news angle and, in particular, we i) introduce a formal framework and data schema for representing news angles and related concepts and ii) carry out a preliminary analysis and characterization of a number of commonly used news angles, both in terms of our formal model and also in terms of the computational reasoning capabilities that are needed to apply them effectively to real-world scenarios. This study provides a stepping stone towards our ultimate goal of realizing a solution capable of exploiting a library of news angles to identify potentially newsworthy events in a large journalistic data space

The News Angler Project: Exploring the Next Generation of Journalistic Knowledge Platforms

The News Angler project aims to support journalists in finding new and unexpected connections and angles in the news. The project therefore explores how recent artificial intelligence (AI) techniques — such as knowledge graphs, natural-language processing (NLP) and machine learning (ML) — can support high-quality journalism that exploits big and open data sources. A central contribution is News Hunter, a series of prototype journalistic knowledge platforms (JKPs)

Towards a Big Data Platform for News Angles

Finding good angles on news events is a central journalistic and editorial skill. As news work becomes increasingly computer-assisted and big-data based, journalistic tools therefore need to become better able to support news angles too. This paper outlines a big-data platform that is able to suggest appropriate angles on news events to journalists. We first clarify and discuss the central characteristics of news angles. We then proceed to outline a big-data architecture that can propose news angles. Important areas for further work include: representing news angles formally; identifying interesting and unexpected angles on unfolding events; and designing a big-data architecture that works on a global scale.publishedVersio

Semantic Knowledge Graphs for the News: A Review

ICT platforms for news production, distribution, and consumption must exploit the ever-growing availability of digital data. These data originate from different sources and in different formats; they arrive at different velocities and in different volumes. Semantic knowledge graphs (KGs) is an established technique for integrating such heterogeneous information. It is therefore well-aligned with the needs of news producers and distributors, and it is likely to become increasingly important for the news industry. This article reviews the research on using semantic knowledge graphs for production, distribution, and consumption of news. The purpose is to present an overview of the field; to investigate what it means; and to suggest opportunities and needs for further research and development.publishedVersio