Jesse James\u27 Hideout or Civil War Midden?

Whether the infamous outlaw Jesse James (1847-1882) ever lived in Iron County Missouri during his post-Civil War crime spree is a highly debated issue shrouded in legend and myth. A plot of land called “The Hideout” in Southern Iron County is a prime source for these legends to be tested. Archaeologists Benjamin Ebert, Steven Meyer, and Tim Evers will attempt to answer the question “Could Jesse James have stayed at the Hideout?” Iron County is steeped in rich history dating back to the Civil War, and other historic landmarks add credence to the legends and help push tourism and preservation efforts. With constant urbanization and potential erosion as a looming threat to destroying the site, this research becomes more crucial with every passing day. A collection of approximately 100 artifacts previously recovered from the site will be cataloged and examined. Photography and oral interviews with local experts will create a timeframe for the site. Google Earth and old maps will be researched to better understand the geography and historical context of the site during the late 19th century. Digital research and contemporary literature sources will create a living roadmap for the path and timeframe that Jesse James went through in Southern Missouri during his crime spree. Using these methods, a timeline will be established for both Jesse James and the “Hideout” to give credence to any possible link

THE DATA COLLABORATION CANVAS: A VISUAL FRAMEWORK FOR SYSTEMATICALLY IDENTIFYING AND EVALUATING ORGANIZATIONAL DATA COLLABORATION OPPORTUNITIES

For organizations, the use of Big Data and data analytics provides the opportunity to gain competitive advantages and foster innovation. In most of these data analytics initiatives, it is possible that data from external stakeholders could enrich the internal data assets and lead to enhanced outcomes. Currently, no framework is available that systematically guides practitioners in identifying and evaluating suitable inter-organizational data collaborations at an early stage. This paper closes the gap by following an action design research approach to develop the Data Collaboration Canvas (DCC). The DCC was rigorously evaluated by practitioners from Swiss organizations in six different industries, instantiated in four workshops, and used to guide innovative data collaboration projects. This artifact gives practitioners a guideline for identifying data collaboration opportunities and an insight into the main factors that must be addressed before further pursuing a collaborative partnership

Data collaboration canvas : facilitating data innovation between organizations

The Data Collaboration Canvas (DCC) is a visual framework that allows users to generate ideas for sharing data across (or within) organizations. It can be used by organizations that want to explore the potential of data innovation with other organizations at an early stage of the collaboration to create mutual added value. This simple, visual structuring aid can, among other things, be employed in workshops to identify common potential and hurdles of collaboration. The DCC helps to identify opportunities for data collaboration between companies or within an organization (e.g., between different divisions or departments)

Trusted execution environments : applications and organizational challenges

A lack of trust in the providers is still a major barrier to cloud computing adoption – especially when sensitive data is involved. While current privacy-enhancing technologies, such as homomorphic encryption, can increase security, they come with a considerable performance overhead. As an alternative Trusted Executing Environment (TEE) provides trust guarantees for code execution in the cloud similar to transport layer security for data transport or advanced encryption standard algorithms for data storage. Cloud infrastructure providers like Amazon, Google, and Microsoft introduced TEEs as part of their infrastructure offerings. This review will shed light on the different technological options of TEEs, as well as give insight into organizational issues regarding their usage

QButterfly : lightweight survey extension for online user interaction studies for non-tech-savvy researchers

We provide a user-friendly, flexible, and lightweight open-source HCI toolkit (github.com/QButterfly) that allows non-tech-savvy researchers to conduct online user interaction studies using the widespread Qualtrics and LimeSurvey platforms. These platforms already provide rich functionality (e.g., for experiments or usability tests) and therefore lend themselves to an extension to display stimulus web pages and record clickstreams. The toolkit consists of a survey template with embedded JavaScript, a JavaScript library embedded in the HTML web pages, and scripts to analyze the collected data. No special programming skills are required to set up a study or match survey data and user interaction data after data collection. We empirically validated the software in a laboratory and a field study. We conclude that this extension, even in its preliminary version, has the potential to make online user interaction studies (e.g., with crowdsourced participants) accessible to a broader range of researchers

Creative beyond TikToks : investigating adolescents' social privacy management on TikTok

TikTok has been criticized for its low privacy standards, but little is known about how its adolescent users protect their privacy. Based on interviews with 54 adolescents in Switzerland, this study provides a comprehensive understanding of young TikTok users' privacy management practices related to the creation of videos. The data were explored using the COM-B model, an established behavioral analysis framework adapted for sociotechnical privacy research. Our overall findings are in line with previous research on other social networks: adolescents are aware of privacy related to their online social connections (social privacy) and perform conscious privacy management. However, we also identified new patterns related to the central role of algorithmic recommendations potentially relevant for other social networks. Adolescents are aware that TikTok's special algorithm, combined with the app's high prevalence among their peers, could easily put them in the spotlight. Some adolescents also reduce TikTok, which was originally conceived as a social network, to its extensive audio-visual capabilities and share TikToks via more private channels (e.g., Snapchat) to manage audiences and avoid identification by peers. Young users also find other creative ways to protect their privacy such as identifying stalkers or maintaining multiple user accounts with different privacy settings to establish granular audience management. Based on our findings, we propose various concrete measures to develop interventions that protect the privacy of adolescents on TikTok